Chemically intuited, large-scale screening of MOFs by machine learning techniques

Science.gov (United States)

Borboudakis, Giorgos; Stergiannakos, Taxiarchis; Frysali, Maria; Klontzas, Emmanuel; Tsamardinos, Ioannis; Froudakis, George E.

2017-10-01

A novel computational methodology for large-scale screening of MOFs is applied to gas storage with the use of machine learning technologies. This approach is a promising trade-off between the accuracy of ab initio methods and the speed of classical approaches, strategically combined with chemical intuition. The results demonstrate that the chemical properties of MOFs are indeed predictable (stochastically, not deterministically) using machine learning methods and automated analysis protocols, with the accuracy of predictions increasing with sample size. Our initial results indicate that this methodology is promising to apply not only to gas storage in MOFs but in many other material science projects.

Large-scale transportation network congestion evolution prediction using deep learning theory.

Directory of Open Access Journals (Sweden)

Xiaolei Ma

Full Text Available Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS and Internet of Things (IoT, transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.

A Large-Scale Multi-Hop Localization Algorithm Based on Regularized Extreme Learning for Wireless Networks.

Science.gov (United States)

Zheng, Wei; Yan, Xiaoyong; Zhao, Wei; Qian, Chengshan

2017-12-20

A novel large-scale multi-hop localization algorithm based on regularized extreme learning is proposed in this paper. The large-scale multi-hop localization problem is formulated as a learning problem. Unlike other similar localization algorithms, the proposed algorithm overcomes the shortcoming of the traditional algorithms which are only applicable to an isotropic network, therefore has a strong adaptability to the complex deployment environment. The proposed algorithm is composed of three stages: data acquisition, modeling and location estimation. In data acquisition stage, the training information between nodes of the given network is collected. In modeling stage, the model among the hop-counts and the physical distances between nodes is constructed using regularized extreme learning. In location estimation stage, each node finds its specific location in a distributed manner. Theoretical analysis and several experiments show that the proposed algorithm can adapt to the different topological environments with low computational cost. Furthermore, high accuracy can be achieved by this method without setting complex parameters.

A Large Scale Problem Based Learning inter-European Student Satellite Construction Project

DEFF Research Database (Denmark)

Nielsen, Jens Frederik Dalsgaard; Alminde, Lars; Bisgaard, Morten

2006-01-01

that electronic communication technology was vital within the project. Additionally the SSETI EXPRESS project implied the following problems it didn’t fit to a standard semester - 18 months for the satellite project compared to 5/6 months for a “normal” semester project. difficulties in integrating the tasks......A LARGE SCALE PROBLEM BASED LEARNING INTER-EUROPEAN STUDENT SATELLITE CONSTRUCTION PROJECT This paper describes the pedagogical outcome of a large scale PBL experiment. ESA (European Space Agency) Education Office launched January 2004 an ambitious project: Let students from all over Europe build....... The satellite was successfully launched on October 27th 2005 (http://www.express.space.aau.dk). The project was a student driven project with student project responsibility adding at lot of international experiences and project management skills to the outcome of more traditional one semester, single group...

Large-scale assessment of olfactory preferences and learning in Drosophila melanogaster: behavioral and genetic components

Directory of Open Access Journals (Sweden)

Elisabetta Versace

2015-09-01

Full Text Available In the Evolve and Resequence method (E&R, experimental evolution and genomics are combined to investigate evolutionary dynamics and the genotype-phenotype link. As other genomic approaches, this methods requires many replicates with large population sizes, which imposes severe restrictions on the analysis of behavioral phenotypes. Aiming to use E&R for investigating the evolution of behavior in Drosophila, we have developed a simple and effective method to assess spontaneous olfactory preferences and learning in large samples of fruit flies using a T-maze. We tested this procedure on (a a large wild-caught population and (b 11 isofemale lines of Drosophila melanogaster. Compared to previous methods, this procedure reduces the environmental noise and allows for the analysis of large population samples. Consistent with previous results, we show that flies have a preference for orange vs. apple odor. With our procedure wild-derived flies exhibit olfactory learning in the absence of previous laboratory selection. Furthermore, we find genetic differences in the olfactory learning with relatively high heritability. We propose this large-scale method as an effective tool for E&R and genome-wide association studies on olfactory preferences and learning.

Higher Education Teachers' Descriptions of Their Own Learning: A Large-Scale Study of Finnish Universities of Applied Sciences

Science.gov (United States)

Töytäri, Aija; Piirainen, Arja; Tynjälä, Päivi; Vanhanen-Nuutinen, Liisa; Mäki, Kimmo; Ilves, Vesa

2016-01-01

In this large-scale study, higher education teachers' descriptions of their own learning were examined with qualitative analysis involving application of principles of phenomenographic research. This study is unique: it is unusual to use large-scale data in qualitative studies. The data were collected through an e-mail survey sent to 5960 teachers…

An industrial perspective on bioreactor scale-down: what we can learn from combined large-scale bioprocess and model fluid studies.

Science.gov (United States)

Noorman, Henk

2011-08-01

For industrial bioreactor design, operation, control and optimization, the scale-down approach is often advocated to efficiently generate data on a small scale, and effectively apply suggested improvements to the industrial scale. In all cases it is important to ensure that the scale-down conditions are representative of the real large-scale bioprocess. Progress is hampered by limited detailed and local information from large-scale bioprocesses. Complementary to real fermentation studies, physical aspects of model fluids such as air-water in large bioreactors provide useful information with limited effort and cost. Still, in industrial practice, investments of time, capital and resources often prohibit systematic work, although, in the end, savings obtained in this way are trivial compared to the expenses that result from real process disturbances, batch failures, and non-flyers with loss of business opportunity. Here we try to highlight what can be learned from real large-scale bioprocess in combination with model fluid studies, and to provide suitable computation tools to overcome data restrictions. Focus is on a specific well-documented case for a 30-m(3) bioreactor. Areas for further research from an industrial perspective are also indicated. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

Science.gov (United States)

Fan, Yu; Guo, Huiming

2017-06-01

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

Balancing Tensions in Educational Policy Reforms: Large-Scale Implementation of Assessment for Learning in Norway

Science.gov (United States)

Hopfenbeck, Therese N.; Flórez Petour, María Teresa; Tolo, Astrid

2015-01-01

This study investigates how different stakeholders in Norway experienced a government-initiated, large-scale policy implementation programme on "Assessment for Learning" ("AfL"). Data were collected through 58 interviews with stakeholders in charge of the policy; Ministers of Education and members of the Directorate of…

Gene prediction in metagenomic fragments: A large scale machine learning approach

Directory of Open Access Journals (Sweden)

Morgenstern Burkhard

2008-04-01

Full Text Available Abstract Background Metagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. The amount of metagenomic sequence data is growing fast while computational methods for metagenome analysis are still in their infancy. In contrast to genomic sequences of single species, which can usually be assembled and analyzed by many available methods, a large proportion of metagenome data remains as unassembled anonymous sequencing reads. One of the aims of all metagenomic sequencing projects is the identification of novel genes. Short length, for example, Sanger sequencing yields on average 700 bp fragments, and unknown phylogenetic origin of most fragments require approaches to gene prediction that are different from the currently available methods for genomes of single species. In particular, the large size of metagenomic samples requires fast and accurate methods with small numbers of false positive predictions. Results We introduce a novel gene prediction algorithm for metagenomic fragments based on a two-stage machine learning approach. In the first stage, we use linear discriminants for monocodon usage, dicodon usage and translation initiation sites to extract features from DNA sequences. In the second stage, an artificial neural network combines these features with open reading frame length and fragment GC-content to compute the probability that this open reading frame encodes a protein. This probability is used for the classification and scoring of gene candidates. With large scale training, our method provides fast single fragment predictions with good sensitivity and specificity on artificially fragmented genomic DNA. Additionally, this method is able to predict translation initiation sites accurately and distinguishes complete from incomplete genes with high reliability. Conclusion Large scale machine learning methods are well-suited for gene

When the globe is your classroom: teaching and learning about large-scale environmental change online

Science.gov (United States)

Howard, E. A.; Coleman, K. J.; Barford, C. L.; Kucharik, C.; Foley, J. A.

2005-12-01

Understanding environmental problems that cross physical and disciplinary boundaries requires a more holistic view of the world - a "systems" approach. Yet it is a challenge for many learners to start thinking this way, particularly when the problems are large in scale and not easily visible. We will describe our online university course, "Humans and the Changing Biosphere," which takes a whole-systems perspective for teaching regional to global-scale environmental science concepts, including climate, hydrology, ecology, and human demographics. We will share our syllabus and learning objectives and summarize our efforts to incorporate "best" practices for online teaching. We will describe challenges we have faced, and our efforts to reach different learner types. Our goals for this presentation are: (1) to communicate how a systems approach ties together environmental sciences (including climate, hydrology, ecology, biogeochemistry, and demography) that are often taught as separate disciplines; (2) to generate discussion about challenges of teaching large-scale environmental processes; (3) to share our experiences in teaching these topics online; (4) to receive ideas and feedback on future teaching strategies. We will explain why we developed this course online, and share our experiences about benefits and challenges of teaching over the web - including some suggestions about how to use technology to supplement face-to-face learning experiences (and vice versa). We will summarize assessment data about what students learned during the course, and discuss key misconceptions and barriers to learning. We will highlight the role of an online discussion board in creating classroom community, identifying misconceptions, and engaging different types of learners.

Learning from large scale neural simulations

DEFF Research Database (Denmark)

Serban, Maria

2017-01-01

Large-scale neural simulations have the marks of a distinct methodology which can be fruitfully deployed to advance scientific understanding of the human brain. Computer simulation studies can be used to produce surrogate observational data for better conceptual models and new how...

Large-scale multimedia modeling applications

International Nuclear Information System (INIS)

Droppo, J.G. Jr.; Buck, J.W.; Whelan, G.; Strenge, D.L.; Castleton, K.J.; Gelston, G.M.

1995-08-01

Over the past decade, the US Department of Energy (DOE) and other agencies have faced increasing scrutiny for a wide range of environmental issues related to past and current practices. A number of large-scale applications have been undertaken that required analysis of large numbers of potential environmental issues over a wide range of environmental conditions and contaminants. Several of these applications, referred to here as large-scale applications, have addressed long-term public health risks using a holistic approach for assessing impacts from potential waterborne and airborne transport pathways. Multimedia models such as the Multimedia Environmental Pollutant Assessment System (MEPAS) were designed for use in such applications. MEPAS integrates radioactive and hazardous contaminants impact computations for major exposure routes via air, surface water, ground water, and overland flow transport. A number of large-scale applications of MEPAS have been conducted to assess various endpoints for environmental and human health impacts. These applications are described in terms of lessons learned in the development of an effective approach for large-scale applications

Setting Learning Analytics in Context: Overcoming the Barriers to Large-Scale Adoption

Science.gov (United States)

Ferguson, Rebecca; Macfadyen, Leah P.; Clow, Doug; Tynan, Belinda; Alexander, Shirley; Dawson, Shane

2014-01-01

A core goal for most learning analytic projects is to move from small-scale research towards broader institutional implementation, but this introduces a new set of challenges because institutions are stable systems, resistant to change. To avoid failure and maximize success, implementation of learning analytics at scale requires explicit and…

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.

Science.gov (United States)

Ma, Xiaolei; Dai, Zhuang; He, Zhengbing; Ma, Jihui; Wang, Yong; Wang, Yunpeng

2017-04-10

This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

Large Scale Processes and Extreme Floods in Brazil

Science.gov (United States)

Ribeiro Lima, C. H.; AghaKouchak, A.; Lall, U.

2016-12-01

Persistent large scale anomalies in the atmospheric circulation and ocean state have been associated with heavy rainfall and extreme floods in water basins of different sizes across the world. Such studies have emerged in the last years as a new tool to improve the traditional, stationary based approach in flood frequency analysis and flood prediction. Here we seek to advance previous studies by evaluating the dominance of large scale processes (e.g. atmospheric rivers/moisture transport) over local processes (e.g. local convection) in producing floods. We consider flood-prone regions in Brazil as case studies and the role of large scale climate processes in generating extreme floods in such regions is explored by means of observed streamflow, reanalysis data and machine learning methods. The dynamics of the large scale atmospheric circulation in the days prior to the flood events are evaluated based on the vertically integrated moisture flux and its divergence field, which are interpreted in a low-dimensional space as obtained by machine learning techniques, particularly supervised kernel principal component analysis. In such reduced dimensional space, clusters are obtained in order to better understand the role of regional moisture recycling or teleconnected moisture in producing floods of a given magnitude. The convective available potential energy (CAPE) is also used as a measure of local convection activities. We investigate for individual sites the exceedance probability in which large scale atmospheric fluxes dominate the flood process. Finally, we analyze regional patterns of floods and how the scaling law of floods with drainage area responds to changes in the climate forcing mechanisms (e.g. local vs large scale).

Working memory training mostly engages general-purpose large-scale networks for learning.

Science.gov (United States)

Salmi, Juha; Nyberg, Lars; Laine, Matti

2018-03-21

The present meta-analytic study examined brain activation changes following working memory (WM) training, a form of cognitive training that has attracted considerable interest. Comparisons with perceptual-motor (PM) learning revealed that WM training engages domain-general large-scale networks for learning encompassing the dorsal attention and salience networks, sensory areas, and striatum. Also the dynamics of the training-induced brain activation changes within these networks showed a high overlap between WM and PM training. The distinguishing feature for WM training was the consistent modulation of the dorso- and ventrolateral prefrontal cortex (DLPFC/VLPFC) activity. The strongest candidate for mediating transfer to similar untrained WM tasks was the frontostriatal system, showing higher striatal and VLPFC activations, and lower DLPFC activations after training. Modulation of transfer-related areas occurred mostly with longer training periods. Overall, our findings place WM training effects into a general perception-action cycle, where some modulations may depend on the specific cognitive demands of a training task. Copyright © 2018 Elsevier Ltd. All rights reserved.

Accelerating Relevance Vector Machine for Large-Scale Data on Spark

Directory of Open Access Journals (Sweden)

Liu Fang

2017-01-01

Full Text Available Relevance vector machine (RVM is a machine learning algorithm based on a sparse Bayesian framework, which performs well when running classification and regression tasks on small-scale datasets. However, RVM also has certain drawbacks which restricts its practical applications such as (1 slow training process, (2 poor performance on training large-scale datasets. In order to solve these problem, we propose Discrete AdaBoost RVM (DAB-RVM which incorporate ensemble learning in RVM at first. This method performs well with large-scale low-dimensional datasets. However, as the number of features increases, the training time of DAB-RVM increases as well. To avoid this phenomenon, we utilize the sufficient training samples of large-scale datasets and propose all features boosting RVM (AFB-RVM, which modifies the way of obtaining weak classifiers. In our experiments we study the differences between various boosting techniques with RVM, demonstrating the performance of the proposed approaches on Spark. As a result of this paper, two proposed approaches on Spark for different types of large-scale datasets are available.

How much is too much assessment? Insight into assessment-driven student learning gains in large-scale undergraduate microbiology courses.

Science.gov (United States)

Wang, Jack T H; Schembri, Mark A; Hall, Roy A

2013-01-01

Designing and implementing assessment tasks in large-scale undergraduate science courses is a labor-intensive process subject to increasing scrutiny from students and quality assurance authorities alike. Recent pedagogical research has provided conceptual frameworks for teaching introductory undergraduate microbiology, but has yet to define best-practice assessment guidelines. This study assessed the applicability of Biggs' theory of constructive alignment in designing consistent learning objectives, activities, and assessment items that aligned with the American Society for Microbiology's concept-based microbiology curriculum in MICR2000, an introductory microbiology course offered at the University of Queensland, Australia. By improving the internal consistency in assessment criteria and increasing the number of assessment items explicitly aligned to the course learning objectives, the teaching team was able to efficiently provide adequate feedback on numerous assessment tasks throughout the semester, which contributed to improved student performance and learning gains. When comparing the constructively aligned 2011 offering of MICR2000 with its 2010 counterpart, students obtained higher marks in both coursework assignments and examinations as the semester progressed. Students also valued the additional feedback provided, as student rankings for course feedback provision increased in 2011 and assessment and feedback was identified as a key strength of MICR2000. By designing MICR2000 using constructive alignment and iterative assessment tasks that followed a common set of learning outcomes, the teaching team was able to effectively deliver detailed and timely feedback in a large introductory microbiology course. This study serves as a case study for how constructive alignment can be integrated into modern teaching practices for large-scale courses.

Large scale electrolysers

International Nuclear Information System (INIS)

B Bello; M Junker

2006-01-01

Hydrogen production by water electrolysis represents nearly 4 % of the world hydrogen production. Future development of hydrogen vehicles will require large quantities of hydrogen. Installation of large scale hydrogen production plants will be needed. In this context, development of low cost large scale electrolysers that could use 'clean power' seems necessary. ALPHEA HYDROGEN, an European network and center of expertise on hydrogen and fuel cells, has performed for its members a study in 2005 to evaluate the potential of large scale electrolysers to produce hydrogen in the future. The different electrolysis technologies were compared. Then, a state of art of the electrolysis modules currently available was made. A review of the large scale electrolysis plants that have been installed in the world was also realized. The main projects related to large scale electrolysis were also listed. Economy of large scale electrolysers has been discussed. The influence of energy prices on the hydrogen production cost by large scale electrolysis was evaluated. (authors)

On the use of Cloud Computing and Machine Learning for Large-Scale SAR Science Data Processing and Quality Assessment Analysi

Science.gov (United States)

Hua, H.

2016-12-01

Geodetic imaging is revolutionizing geophysics, but the scope of discovery has been limited by labor-intensive technological implementation of the analyses. The Advanced Rapid Imaging and Analysis (ARIA) project has proven capability to automate SAR data processing and analysis. Existing and upcoming SAR missions such as Sentinel-1A/B and NISAR are also expected to generate massive amounts of SAR data. This has brought to the forefront the need for analytical tools for SAR quality assessment (QA) on the large volumes of SAR data-a critical step before higher-level time series and velocity products can be reliably generated. Initially leveraging an advanced hybrid-cloud computing science data system for performing large-scale processing, machine learning approaches were augmented for automated analysis of various quality metrics. Machine learning-based user-training of features, cross-validation, prediction models were integrated into our cloud-based science data processing flow to enable large-scale and high-throughput QA analytics for enabling improvements to the production quality of geodetic data products.

Scale-Up: Improving Large Enrollment Physics Courses

Science.gov (United States)

Beichner, Robert

1999-11-01

The Student-Centered Activities for Large Enrollment University Physics (SCALE-UP) project is working to establish a learning environment that will promote increased conceptual understanding, improved problem-solving performance, and greater student satisfaction, while still maintaining class sizes of approximately 100. We are also addressing the new ABET engineering accreditation requirements for inquiry-based learning along with communication and team-oriented skills development. Results of studies of our latest classroom design, plans for future classroom space, and the current iteration of instructional materials will be discussed.

BILGO: Bilateral greedy optimization for large scale semidefinite programming

KAUST Repository

Hao, Zhifeng; Yuan, Ganzhao; Ghanem, Bernard

2013-01-01

Many machine learning tasks (e.g. metric and manifold learning problems) can be formulated as convex semidefinite programs. To enable the application of these tasks on a large-scale, scalability and computational efficiency are considered as desirable properties for a practical semidefinite programming algorithm. In this paper, we theoretically analyze a new bilateral greedy optimization (denoted BILGO) strategy in solving general semidefinite programs on large-scale datasets. As compared to existing methods, BILGO employs a bilateral search strategy during each optimization iteration. In such an iteration, the current semidefinite matrix solution is updated as a bilateral linear combination of the previous solution and a suitable rank-1 matrix, which can be efficiently computed from the leading eigenvector of the descent direction at this iteration. By optimizing for the coefficients of the bilateral combination, BILGO reduces the cost function in every iteration until the KKT conditions are fully satisfied, thus, it tends to converge to a global optimum. In fact, we prove that BILGO converges to the global optimal solution at a rate of O(1/k), where k is the iteration counter. The algorithm thus successfully combines the efficiency of conventional rank-1 update algorithms and the effectiveness of gradient descent. Moreover, BILGO can be easily extended to handle low rank constraints. To validate the effectiveness and efficiency of BILGO, we apply it to two important machine learning tasks, namely Mahalanobis metric learning and maximum variance unfolding. Extensive experimental results clearly demonstrate that BILGO can solve large-scale semidefinite programs efficiently.

BILGO: Bilateral greedy optimization for large scale semidefinite programming

KAUST Repository

Hao, Zhifeng

2013-10-03

Many machine learning tasks (e.g. metric and manifold learning problems) can be formulated as convex semidefinite programs. To enable the application of these tasks on a large-scale, scalability and computational efficiency are considered as desirable properties for a practical semidefinite programming algorithm. In this paper, we theoretically analyze a new bilateral greedy optimization (denoted BILGO) strategy in solving general semidefinite programs on large-scale datasets. As compared to existing methods, BILGO employs a bilateral search strategy during each optimization iteration. In such an iteration, the current semidefinite matrix solution is updated as a bilateral linear combination of the previous solution and a suitable rank-1 matrix, which can be efficiently computed from the leading eigenvector of the descent direction at this iteration. By optimizing for the coefficients of the bilateral combination, BILGO reduces the cost function in every iteration until the KKT conditions are fully satisfied, thus, it tends to converge to a global optimum. In fact, we prove that BILGO converges to the global optimal solution at a rate of O(1/k), where k is the iteration counter. The algorithm thus successfully combines the efficiency of conventional rank-1 update algorithms and the effectiveness of gradient descent. Moreover, BILGO can be easily extended to handle low rank constraints. To validate the effectiveness and efficiency of BILGO, we apply it to two important machine learning tasks, namely Mahalanobis metric learning and maximum variance unfolding. Extensive experimental results clearly demonstrate that BILGO can solve large-scale semidefinite programs efficiently.

Large-Scale Transit Signal Priority Implementation

OpenAIRE

Lee, Kevin S.; Lozner, Bailey

2018-01-01

In 2016, the District Department of Transportation (DDOT) deployed Transit Signal Priority (TSP) at 195 intersections in highly urbanized areas of Washington, DC. In collaboration with a broader regional implementation, and in partnership with the Washington Metropolitan Area Transit Authority (WMATA), DDOT set out to apply a systems engineering–driven process to identify, design, test, and accept a large-scale TSP system. This presentation will highlight project successes and lessons learned.

Large-Scale Cooperative Task Distribution on Peer-to-Peer Networks

Science.gov (United States)

2012-01-01

SUBTITLE Large-scale cooperative task distribution on peer-to-peer networks 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6...disadvantages of ML- Chord are its fixed size (two layers), and limited scala - bility for large-scale systems. RC-Chord extends ML- D. Karrels et al...configurable before runtime. This can be improved by incorporating a distributed learning algorithm to tune the number and range of the DLoE tracking

Large-Scale Sentinel-1 Processing for Solid Earth Science and Urgent Response using Cloud Computing and Machine Learning

Science.gov (United States)

Hua, H.; Owen, S. E.; Yun, S. H.; Agram, P. S.; Manipon, G.; Starch, M.; Sacco, G. F.; Bue, B. D.; Dang, L. B.; Linick, J. P.; Malarout, N.; Rosen, P. A.; Fielding, E. J.; Lundgren, P.; Moore, A. W.; Liu, Z.; Farr, T.; Webb, F.; Simons, M.; Gurrola, E. M.

2017-12-01

With the increased availability of open SAR data (e.g. Sentinel-1 A/B), new challenges are being faced with processing and analyzing the voluminous SAR datasets to make geodetic measurements. Upcoming SAR missions such as NISAR are expected to generate close to 100TB per day. The Advanced Rapid Imaging and Analysis (ARIA) project can now generate geocoded unwrapped phase and coherence products from Sentinel-1 TOPS mode data in an automated fashion, using the ISCE software. This capability is currently being exercised on various study sites across the United States and around the globe, including Hawaii, Central California, Iceland and South America. The automated and large-scale SAR data processing and analysis capabilities use cloud computing techniques to speed the computations and provide scalable processing power and storage. Aspects such as how to processing these voluminous SLCs and interferograms at global scales, keeping up with the large daily SAR data volumes, and how to handle the voluminous data rates are being explored. Scene-partitioning approaches in the processing pipeline help in handling global-scale processing up to unwrapped interferograms with stitching done at a late stage. We have built an advanced science data system with rapid search functions to enable access to the derived data products. Rapid image processing of Sentinel-1 data to interferograms and time series is already being applied to natural hazards including earthquakes, floods, volcanic eruptions, and land subsidence due to fluid withdrawal. We will present the status of the ARIA science data system for generating science-ready data products and challenges that arise from being able to process SAR datasets to derived time series data products at large scales. For example, how do we perform large-scale data quality screening on interferograms? What approaches can be used to minimize compute, storage, and data movement costs for time series analysis in the cloud? We will also

Plans for Embedding ICTs into Teaching and Learning through a Large-Scale Secondary Education Reform in the Country of Georgia

Science.gov (United States)

Richardson, Jayson W.; Sales, Gregory; Sentocnik, Sonja

2015-01-01

Integrating ICTs into international development projects is common. However, focusing on how ICTs support leading, teaching, and learning is often overlooked. This article describes a team's approach to technology integration into the design of a large-scale, five year, teacher and leader professional development project in the country of Georgia.…

Self-Report Measures of the Home Learning Environment in Large Scale Research: Measurement Properties and Associations with Key Developmental Outcomes

Science.gov (United States)

Niklas, Frank; Nguyen, Cuc; Cloney, Daniel S.; Tayler, Collette; Adams, Raymond

2016-01-01

Favourable home learning environments (HLEs) support children's literacy, numeracy and social development. In large-scale research, HLE is typically measured by self-report survey, but there is little consistency between studies and many different items and latent constructs are observed. Little is known about the stability of these items and…

Machine learning for large-scale wearable sensor data in Parkinson's disease: Concepts, promises, pitfalls, and futures.

Science.gov (United States)

Kubota, Ken J; Chen, Jason A; Little, Max A

2016-09-01

For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, "wearable," sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that "learn" from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice. © 2016 International Parkinson and Movement Disorder Society. © 2016 International Parkinson and Movement Disorder Society.

Eight attention points when evaluating large-scale public sector reforms

DEFF Research Database (Denmark)

Hansen, Morten Balle; Breidahl, Karen Nielsen; Furubo, Jan-Eric

2017-01-01

This chapter analyses the challenges related to evaluations of large-scale public sector reforms. It is based on a meta-evaluation of the evaluation of the reform of the Norwegian Labour Market and Welfare Administration (the NAV-reform) in Norway, which entailed both a significant reorganization...... sector reforms. Based on the analysis, eight crucial points of attention when evaluating large-scale public sector reforms are elaborated. We discuss their reasons and argue that other countries will face the same challenges and thus can learn from the experiences of Norway....

Large-Scale Science Observatories: Building on What We Have Learned from USArray

Science.gov (United States)

Woodward, R.; Busby, R.; Detrick, R. S.; Frassetto, A.

2015-12-01

With the NSF-sponsored EarthScope USArray observatory, the Earth science community has built the operational capability and experience to tackle scientific challenges at the largest scales, such as a Subduction Zone Observatory. In the first ten years of USArray, geophysical instruments were deployed across roughly 2% of the Earth's surface. The USArray operated a rolling deployment of seismic stations that occupied ~1,700 sites across the USA, made co-located atmospheric observations, occupied hundreds of sites with magnetotelluric sensors, expanded a backbone reference network of seismic stations, and provided instruments to PI-led teams that deployed thousands of additional seismic stations. USArray included a comprehensive outreach component that directly engaged hundreds of students at over 50 colleges and universities to locate station sites and provided Earth science exposure to roughly 1,000 landowners who hosted stations. The project also included a comprehensive data management capability that received, archived and distributed data, metadata, and data products; data were acquired and distributed in real time. The USArray project was completed on time and under budget and developed a number of best practices that can inform other large-scale science initiatives that the Earth science community is contemplating. Key strategies employed by USArray included: using a survey, rather than hypothesis-driven, mode of observation to generate comprehensive, high quality data on a large-scale for exploration and discovery; making data freely and openly available to any investigator from the very onset of the project; and using proven, commercial, off-the-shelf systems to ensure a fast start and avoid delays due to over-reliance on unproven technology or concepts. Scope was set ambitiously, but managed carefully to avoid overextending. Configuration was controlled to ensure efficient operations while providing consistent, uniform observations. Finally, community

Blackthorn: Large-Scale Interactive Multimodal Learning

DEFF Research Database (Denmark)

Zahálka, Jan; Rudinac, Stevan; Jónsson, Björn Thór

2018-01-01

learning process. The Ratio-64 data representation introduced in this work only costs tens of bytes per item yet preserves most of the visual and textual semantic information with good accuracy. The optimized interactive learning model scores the Ratio-64- compressed data directly, greatly reducing...... outperforming the baseline with respect to the relevance of results: it vastly outperforms the baseline on recall over time and reaches up to 108% of its precision. Compared to the product quantization variant, Blackthorn is just as fast, while producing more relevant results. On the full YFCC100M dataset...

Large-scale retrieval for medical image analytics: A comprehensive review.

Science.gov (United States)

Li, Zhongyu; Zhang, Xiaofan; Müller, Henning; Zhang, Shaoting

2018-01-01

Over the past decades, medical image analytics was greatly facilitated by the explosion of digital imaging techniques, where huge amounts of medical images were produced with ever-increasing quality and diversity. However, conventional methods for analyzing medical images have achieved limited success, as they are not capable to tackle the huge amount of image data. In this paper, we review state-of-the-art approaches for large-scale medical image analysis, which are mainly based on recent advances in computer vision, machine learning and information retrieval. Specifically, we first present the general pipeline of large-scale retrieval, summarize the challenges/opportunities of medical image analytics on a large-scale. Then, we provide a comprehensive review of algorithms and techniques relevant to major processes in the pipeline, including feature representation, feature indexing, searching, etc. On the basis of existing work, we introduce the evaluation protocols and multiple applications of large-scale medical image retrieval, with a variety of exploratory and diagnostic scenarios. Finally, we discuss future directions of large-scale retrieval, which can further improve the performance of medical image analysis. Copyright © 2017 Elsevier B.V. All rights reserved.

Large-scale simulations of plastic neural networks on neuromorphic hardware

Directory of Open Access Journals (Sweden)

James Courtney Knight

2016-04-01

Full Text Available SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Rather than using bespoke analog or digital hardware, the basic computational unit of a SpiNNaker system is a general-purpose ARM processor, allowing it to be programmed to simulate a wide variety of neuron and synapse models. This flexibility is particularly valuable in the study of biological plasticity phenomena. A recently proposed learning rule based on the Bayesian Confidence Propagation Neural Network (BCPNN paradigm offers a generic framework for modeling the interaction of different plasticity mechanisms using spiking neurons. However, it can be computationally expensive to simulate large networks with BCPNN learning since it requires multiple state variables for each synapse, each of which needs to be updated every simulation time-step. We discuss the trade-offs in efficiency and accuracy involved in developing an event-based BCPNN implementation for SpiNNaker based on an analytical solution to the BCPNN equations, and detail the steps taken to fit this within the limited computational and memory resources of the SpiNNaker architecture. We demonstrate this learning rule by learning temporal sequences of neural activity within a recurrent attractor network which we simulate at scales of up to 20000 neurons and 51200000 plastic synapses: the largest plastic neural network ever to be simulated on neuromorphic hardware. We also run a comparable simulation on a Cray XC-30 supercomputer system and find that, if it is to match the run-time of our SpiNNaker simulation, the super computer system uses approximately more power. This suggests that cheaper, more power efficient neuromorphic systems are becoming useful discovery tools in the study of plasticity in large-scale brain models.

Experience of Google's latest deep learning library, TensorFlow, in a large-scale WLCG cluster

Energy Technology Data Exchange (ETDEWEB)

Kawamura, Gen; Smith, Joshua Wyatt; Quadt, Arnulf [II. Physikalisches Institut, Georg-August-Universitaet Goettingen (Germany)

2016-07-01

The researchers at the Google Brain team released their second generation's Deep Learning library, TensorFlow, as an open-source package under the Apache 2.0 license in November, 2015. Google has already deployed the first generation's library using DistBlief in various systems such as Google Search, advertising systems, speech recognition systems, Google Images, Google Maps, Street View, Google Translate and many other latest products. In addition, many researchers in high energy physics have recently started to understand and use Deep Learning algorithms in their own research and analysis. We conceive a first use-case scenario of TensorFlow to create the Deep Learning models from high-dimensional inputs like physics analysis data in a large-scale WLCG computing cluster. TensorFlow carries out computations using a dataflow model and graph structure onto a wide variety of different hardware platforms and systems, such as many CPU architectures, GPUs and smartphone platforms. Having a single library that can distribute the computations to create a model to the various platforms and systems would significantly simplify the use of Deep Learning algorithms in high energy physics. We deploy TensorFlow with the Docker container environments and present the first use in our grid system.

Creating Large Scale Database Servers

International Nuclear Information System (INIS)

Becla, Jacek

2001-01-01

The BaBar experiment at the Stanford Linear Accelerator Center (SLAC) is designed to perform a high precision investigation of the decays of the B-meson produced from electron-positron interactions. The experiment, started in May 1999, will generate approximately 300TB/year of data for 10 years. All of the data will reside in Objectivity databases accessible via the Advanced Multi-threaded Server (AMS). To date, over 70TB of data have been placed in Objectivity/DB, making it one of the largest databases in the world. Providing access to such a large quantity of data through a database server is a daunting task. A full-scale testbed environment had to be developed to tune various software parameters and a fundamental change had to occur in the AMS architecture to allow it to scale past several hundred terabytes of data. Additionally, several protocol extensions had to be implemented to provide practical access to large quantities of data. This paper will describe the design of the database and the changes that we needed to make in the AMS for scalability reasons and how the lessons we learned would be applicable to virtually any kind of database server seeking to operate in the Petabyte region

Creating Large Scale Database Servers

Energy Technology Data Exchange (ETDEWEB)

Becla, Jacek

2001-12-14

The BaBar experiment at the Stanford Linear Accelerator Center (SLAC) is designed to perform a high precision investigation of the decays of the B-meson produced from electron-positron interactions. The experiment, started in May 1999, will generate approximately 300TB/year of data for 10 years. All of the data will reside in Objectivity databases accessible via the Advanced Multi-threaded Server (AMS). To date, over 70TB of data have been placed in Objectivity/DB, making it one of the largest databases in the world. Providing access to such a large quantity of data through a database server is a daunting task. A full-scale testbed environment had to be developed to tune various software parameters and a fundamental change had to occur in the AMS architecture to allow it to scale past several hundred terabytes of data. Additionally, several protocol extensions had to be implemented to provide practical access to large quantities of data. This paper will describe the design of the database and the changes that we needed to make in the AMS for scalability reasons and how the lessons we learned would be applicable to virtually any kind of database server seeking to operate in the Petabyte region.

Large-scale solar purchasing

International Nuclear Information System (INIS)

1999-01-01

The principal objective of the project was to participate in the definition of a new IEA task concerning solar procurement (''the Task'') and to assess whether involvement in the task would be in the interest of the UK active solar heating industry. The project also aimed to assess the importance of large scale solar purchasing to UK active solar heating market development and to evaluate the level of interest in large scale solar purchasing amongst potential large scale purchasers (in particular housing associations and housing developers). A further aim of the project was to consider means of stimulating large scale active solar heating purchasing activity within the UK. (author)

Integration and segregation of large-scale brain networks during short-term task automatization.

Science.gov (United States)

Mohr, Holger; Wolfensteller, Uta; Betzel, Richard F; Mišić, Bratislav; Sporns, Olaf; Richiardi, Jonas; Ruge, Hannes

2016-11-03

The human brain is organized into large-scale functional networks that can flexibly reconfigure their connectivity patterns, supporting both rapid adaptive control and long-term learning processes. However, it has remained unclear how short-term network dynamics support the rapid transformation of instructions into fluent behaviour. Comparing fMRI data of a learning sample (N=70) with a control sample (N=67), we find that increasingly efficient task processing during short-term practice is associated with a reorganization of large-scale network interactions. Practice-related efficiency gains are facilitated by enhanced coupling between the cingulo-opercular network and the dorsal attention network. Simultaneously, short-term task automatization is accompanied by decreasing activation of the fronto-parietal network, indicating a release of high-level cognitive control, and a segregation of the default mode network from task-related networks. These findings suggest that short-term task automatization is enabled by the brain's ability to rapidly reconfigure its large-scale network organization involving complementary integration and segregation processes.

Optimizing Distributed Machine Learning for Large Scale EEG Data Set

Directory of Open Access Journals (Sweden)

M Bilal Shaikh

2017-06-01

Full Text Available Distributed Machine Learning (DML has gained its importance more than ever in this era of Big Data. There are a lot of challenges to scale machine learning techniques on distributed platforms. When it comes to scalability, improving the processor technology for high level computation of data is at its limit, however increasing machine nodes and distributing data along with computation looks as a viable solution. Different frameworks   and platforms are available to solve DML problems. These platforms provide automated random data distribution of datasets which miss the power of user defined intelligent data partitioning based on domain knowledge. We have conducted an empirical study which uses an EEG Data Set collected through P300 Speller component of an ERP (Event Related Potential which is widely used in BCI problems; it helps in translating the intention of subject w h i l e performing any cognitive task. EEG data contains noise due to waves generated by other activities in the brain which contaminates true P300Speller. Use of Machine Learning techniques could help in detecting errors made by P300 Speller. We are solving this classification problem by partitioning data into different chunks and preparing distributed models using Elastic CV Classifier. To present a case of optimizing distributed machine learning, we propose an intelligent user defined data partitioning approach that could impact on the accuracy of distributed machine learners on average. Our results show better average AUC as compared to average AUC obtained after applying random data partitioning which gives no control to user over data partitioning. It improves the average accuracy of distributed learner due to the domain specific intelligent partitioning by the user. Our customized approach achieves 0.66 AUC on individual sessions and 0.75 AUC on mixed sessions, whereas random / uncontrolled data distribution records 0.63 AUC.

CONSORT to community: translation of an RCT to a large-scale community intervention and learnings from evaluation of the upscaled program.

Science.gov (United States)

Moores, Carly Jane; Miller, Jacqueline; Perry, Rebecca Anne; Chan, Lily Lai Hang; Daniels, Lynne Allison; Vidgen, Helen Anna; Magarey, Anthea Margaret

2017-11-29

Translation encompasses the continuum from clinical efficacy to widespread adoption within the healthcare service and ultimately routine clinical practice. The Parenting, Eating and Activity for Child Health (PEACH™) program has previously demonstrated clinical effectiveness in the management of child obesity, and has been recently implemented as a large-scale community intervention in Queensland, Australia. This paper aims to describe the translation of the evaluation framework from a randomised controlled trial (RCT) to large-scale community intervention (PEACH™ QLD). Tensions between RCT paradigm and implementation research will be discussed along with lived evaluation challenges, responses to overcome these, and key learnings for future evaluation conducted at scale. The translation of evaluation from PEACH™ RCT to the large-scale community intervention PEACH™ QLD is described. While the CONSORT Statement was used to report findings from two previous RCTs, the REAIM framework was more suitable for the evaluation of upscaled delivery of the PEACH™ program. Evaluation of PEACH™ QLD was undertaken during the project delivery period from 2013 to 2016. Experiential learnings from conducting the evaluation of PEACH™ QLD to the described evaluation framework are presented for the purposes of informing the future evaluation of upscaled programs. Evaluation changes in response to real-time changes in the delivery of the PEACH™ QLD Project were necessary at stages during the project term. Key evaluation challenges encountered included the collection of complete evaluation data from a diverse and geographically dispersed workforce and the systematic collection of process evaluation data in real time to support program changes during the project. Evaluation of large-scale community interventions in the real world is challenging and divergent from RCTs which are rigourously evaluated within a more tightly-controlled clinical research setting. Constructs

Large Scale Metric Learning for Distance-Based Image Classification on Open Ended Data Sets

NARCIS (Netherlands)

Mensink, T.; Verbeek, J.; Perronnin, F.; Csurka, G.; Farinella, G.M.; Battiato, S.; Cipolla, R,

2013-01-01

Many real-life large-scale datasets are open-ended and dynamic: new images are continuously added to existing classes, new classes appear over time, and the semantics of existing classes might evolve too. Therefore, we study large-scale image classification methods that can incorporate new classes

Advancing the large-scale CCS database for metabolomics and lipidomics at the machine-learning era.

Science.gov (United States)

Zhou, Zhiwei; Tu, Jia; Zhu, Zheng-Jiang

2018-02-01

Metabolomics and lipidomics aim to comprehensively measure the dynamic changes of all metabolites and lipids that are present in biological systems. The use of ion mobility-mass spectrometry (IM-MS) for metabolomics and lipidomics has facilitated the separation and the identification of metabolites and lipids in complex biological samples. The collision cross-section (CCS) value derived from IM-MS is a valuable physiochemical property for the unambiguous identification of metabolites and lipids. However, CCS values obtained from experimental measurement and computational modeling are limited available, which significantly restricts the application of IM-MS. In this review, we will discuss the recently developed machine-learning based prediction approach, which could efficiently generate precise CCS databases in a large scale. We will also highlight the applications of CCS databases to support metabolomics and lipidomics. Copyright © 2017 Elsevier Ltd. All rights reserved.

Quantitative Missense Variant Effect Prediction Using Large-Scale Mutagenesis Data.

Science.gov (United States)

Gray, Vanessa E; Hause, Ronald J; Luebeck, Jens; Shendure, Jay; Fowler, Douglas M

2018-01-24

Large datasets describing the quantitative effects of mutations on protein function are becoming increasingly available. Here, we leverage these datasets to develop Envision, which predicts the magnitude of a missense variant's molecular effect. Envision combines 21,026 variant effect measurements from nine large-scale experimental mutagenesis datasets, a hitherto untapped training resource, with a supervised, stochastic gradient boosting learning algorithm. Envision outperforms other missense variant effect predictors both on large-scale mutagenesis data and on an independent test dataset comprising 2,312 TP53 variants whose effects were measured using a low-throughput approach. This dataset was never used for hyperparameter tuning or model training and thus serves as an independent validation set. Envision prediction accuracy is also more consistent across amino acids than other predictors. Finally, we demonstrate that Envision's performance improves as more large-scale mutagenesis data are incorporated. We precompute Envision predictions for every possible single amino acid variant in human, mouse, frog, zebrafish, fruit fly, worm, and yeast proteomes (https://envision.gs.washington.edu/). Copyright © 2017 Elsevier Inc. All rights reserved.

Energy transfers in large-scale and small-scale dynamos

Science.gov (United States)

Samtaney, Ravi; Kumar, Rohit; Verma, Mahendra

2015-11-01

We present the energy transfers, mainly energy fluxes and shell-to-shell energy transfers in small-scale dynamo (SSD) and large-scale dynamo (LSD) using numerical simulations of MHD turbulence for Pm = 20 (SSD) and for Pm = 0.2 on 10243 grid. For SSD, we demonstrate that the magnetic energy growth is caused by nonlocal energy transfers from the large-scale or forcing-scale velocity field to small-scale magnetic field. The peak of these energy transfers move towards lower wavenumbers as dynamo evolves, which is the reason for the growth of the magnetic fields at the large scales. The energy transfers U2U (velocity to velocity) and B2B (magnetic to magnetic) are forward and local. For LSD, we show that the magnetic energy growth takes place via energy transfers from large-scale velocity field to large-scale magnetic field. We observe forward U2U and B2B energy flux, similar to SSD.

GPU-based large-scale visualization

KAUST Repository

Hadwiger, Markus

2013-11-19

Recent advances in image and volume acquisition as well as computational advances in simulation have led to an explosion of the amount of data that must be visualized and analyzed. Modern techniques combine the parallel processing power of GPUs with out-of-core methods and data streaming to enable the interactive visualization of giga- and terabytes of image and volume data. A major enabler for interactivity is making both the computational and the visualization effort proportional to the amount of data that is actually visible on screen, decoupling it from the full data size. This leads to powerful display-aware multi-resolution techniques that enable the visualization of data of almost arbitrary size. The course consists of two major parts: An introductory part that progresses from fundamentals to modern techniques, and a more advanced part that discusses details of ray-guided volume rendering, novel data structures for display-aware visualization and processing, and the remote visualization of large online data collections. You will learn how to develop efficient GPU data structures and large-scale visualizations, implement out-of-core strategies and concepts such as virtual texturing that have only been employed recently, as well as how to use modern multi-resolution representations. These approaches reduce the GPU memory requirements of extremely large data to a working set size that fits into current GPUs. You will learn how to perform ray-casting of volume data of almost arbitrary size and how to render and process gigapixel images using scalable, display-aware techniques. We will describe custom virtual texturing architectures as well as recent hardware developments in this area. We will also describe client/server systems for distributed visualization, on-demand data processing and streaming, and remote visualization. We will describe implementations using OpenGL as well as CUDA, exploiting parallelism on GPUs combined with additional asynchronous

Large-scale Machine Learning in High-dimensional Datasets

DEFF Research Database (Denmark)

Hansen, Toke Jansen

Over the last few decades computers have gotten to play an essential role in our daily life, and data is now being collected in various domains at a faster pace than ever before. This dissertation presents research advances in four machine learning fields that all relate to the challenges imposed...... are better at modeling local heterogeneities. In the field of machine learning for neuroimaging, we introduce learning protocols for real-time functional Magnetic Resonance Imaging (fMRI) that allow for dynamic intervention in the human decision process. Specifically, the model exploits the structure of f...

Large-scale data analytics

CERN Document Server

Gkoulalas-Divanis, Aris

2014-01-01

Provides cutting-edge research in large-scale data analytics from diverse scientific areas Surveys varied subject areas and reports on individual results of research in the field Shares many tips and insights into large-scale data analytics from authors and editors with long-term experience and specialization in the field

Large Scale Composite Manufacturing for Heavy Lift Launch Vehicles

Science.gov (United States)

Stavana, Jacob; Cohen, Leslie J.; Houseal, Keth; Pelham, Larry; Lort, Richard; Zimmerman, Thomas; Sutter, James; Western, Mike; Harper, Robert; Stuart, Michael

2012-01-01

Risk reduction for the large scale composite manufacturing is an important goal to produce light weight components for heavy lift launch vehicles. NASA and an industry team successfully employed a building block approach using low-cost Automated Tape Layup (ATL) of autoclave and Out-of-Autoclave (OoA) prepregs. Several large, curved sandwich panels were fabricated at HITCO Carbon Composites. The aluminum honeycomb core sandwich panels are segments of a 1/16th arc from a 10 meter cylindrical barrel. Lessons learned highlight the manufacturing challenges required to produce light weight composite structures such as fairings for heavy lift launch vehicles.

Large-scale grid management

International Nuclear Information System (INIS)

Langdal, Bjoern Inge; Eggen, Arnt Ove

2003-01-01

The network companies in the Norwegian electricity industry now have to establish a large-scale network management, a concept essentially characterized by (1) broader focus (Broad Band, Multi Utility,...) and (2) bigger units with large networks and more customers. Research done by SINTEF Energy Research shows so far that the approaches within large-scale network management may be structured according to three main challenges: centralization, decentralization and out sourcing. The article is part of a planned series

Evaluation of the clinical implementation of a large-scale online e-learning program on venous blood specimen collection guideline practices.

Science.gov (United States)

Willman, Britta; Grankvist, Kjell; Bölenius, Karin

2018-05-11

When performed erroneously, the venous blood specimen collection (VBSC) practice steps patient identification, test request management and test tube labeling are at high risk to jeopardize patient safety. VBSC educational programs with the intention to minimize risk of harm to patients are therefore needed. In this study, we evaluate the efficiency of a large-scale online e-learning program on personnel's adherence to VBSC practices and their experience of the e-learning program. An interprofessional team transformed an implemented traditional VBSC education program to an online e-learning program developed to stimulate reflection with focus on the high-risk practice steps. We used questionnaires to evaluate the effect of the e-learning program on personnel's self-reported adherence to VBSC practices compared to questionnaire surveys before and after introduction of the traditional education program. We used content analysis to evaluate the participants free text experience of the VBSC e-learning program. Adherence to the VBSC guideline high-risk practice steps generally increased following the implementation of a traditional educational program followed by an e-learning program. We however found a negative trend over years regarding participation rates and the practice to always send/sign the request form following the introduction of an electronic request system. The participants were in general content with the VBSC e-learning program. Properly designed e-learning programs on VBSC practices supersedes traditional educational programs in usefulness and functionality. Inclusion of questionnaires in the e-learning program is necessary for follow-up of VBSC participant's practices and educational program efficiency.

Ethics of large-scale change

OpenAIRE

Arler, Finn

2006-01-01

  The subject of this paper is long-term large-scale changes in human society. Some very significant examples of large-scale change are presented: human population growth, human appropriation of land and primary production, the human use of fossil fuels, and climate change. The question is posed, which kind of attitude is appropriate when dealing with large-scale changes like these from an ethical point of view. Three kinds of approaches are discussed: Aldo Leopold's mountain thinking, th...

Large-scale urban point cloud labeling and reconstruction

Science.gov (United States)

Zhang, Liqiang; Li, Zhuqiang; Li, Anjian; Liu, Fangyu

2018-04-01

The large number of object categories and many overlapping or closely neighboring objects in large-scale urban scenes pose great challenges in point cloud classification. In this paper, a novel framework is proposed for classification and reconstruction of airborne laser scanning point cloud data. To label point clouds, we present a rectified linear units neural network named ReLu-NN where the rectified linear units (ReLu) instead of the traditional sigmoid are taken as the activation function in order to speed up the convergence. Since the features of the point cloud are sparse, we reduce the number of neurons by the dropout to avoid over-fitting of the training process. The set of feature descriptors for each 3D point is encoded through self-taught learning, and forms a discriminative feature representation which is taken as the input of the ReLu-NN. The segmented building points are consolidated through an edge-aware point set resampling algorithm, and then they are reconstructed into 3D lightweight models using the 2.5D contouring method (Zhou and Neumann, 2010). Compared with deep learning approaches, the ReLu-NN introduced can easily classify unorganized point clouds without rasterizing the data, and it does not need a large number of training samples. Most of the parameters in the network are learned, and thus the intensive parameter tuning cost is significantly reduced. Experimental results on various datasets demonstrate that the proposed framework achieves better performance than other related algorithms in terms of classification accuracy and reconstruction quality.

Learning directed acyclic graphs from large-scale genomics data.

Science.gov (United States)

Nikolay, Fabio; Pesavento, Marius; Kritikos, George; Typas, Nassos

2017-09-20

In this paper, we consider the problem of learning the genetic interaction map, i.e., the topology of a directed acyclic graph (DAG) of genetic interactions from noisy double-knockout (DK) data. Based on a set of well-established biological interaction models, we detect and classify the interactions between genes. We propose a novel linear integer optimization program called the Genetic-Interactions-Detector (GENIE) to identify the complex biological dependencies among genes and to compute the DAG topology that matches the DK measurements best. Furthermore, we extend the GENIE program by incorporating genetic interaction profile (GI-profile) data to further enhance the detection performance. In addition, we propose a sequential scalability technique for large sets of genes under study, in order to provide statistically significant results for real measurement data. Finally, we show via numeric simulations that the GENIE program and the GI-profile data extended GENIE (GI-GENIE) program clearly outperform the conventional techniques and present real data results for our proposed sequential scalability technique.

Neighborhood Discriminant Hashing for Large-Scale Image Retrieval.

Science.gov (United States)

Tang, Jinhui; Li, Zechao; Wang, Meng; Zhao, Ruizhen

2015-09-01

With the proliferation of large-scale community-contributed images, hashing-based approximate nearest neighbor search in huge databases has aroused considerable interest from the fields of computer vision and multimedia in recent years because of its computational and memory efficiency. In this paper, we propose a novel hashing method named neighborhood discriminant hashing (NDH) (for short) to implement approximate similarity search. Different from the previous work, we propose to learn a discriminant hashing function by exploiting local discriminative information, i.e., the labels of a sample can be inherited from the neighbor samples it selects. The hashing function is expected to be orthogonal to avoid redundancy in the learned hashing bits as much as possible, while an information theoretic regularization is jointly exploited using maximum entropy principle. As a consequence, the learned hashing function is compact and nonredundant among bits, while each bit is highly informative. Extensive experiments are carried out on four publicly available data sets and the comparison results demonstrate the outperforming performance of the proposed NDH method over state-of-the-art hashing techniques.

Building Participation in Large-scale Conservation: Lessons from Belize and Panama

Directory of Open Access Journals (Sweden)

Jesse Guite Hastings

2015-01-01

Full Text Available Motivated by biogeography and a desire for alignment with the funding priorities of donors, the twenty-first century has seen big international NGOs shifting towards a large-scale conservation approach. This shift has meant that even before stakeholders at the national and local scale are involved, conservation programmes often have their objectives defined and funding allocated. This paper uses the experiences of Conservation International′s Marine Management Area Science (MMAS programme in Belize and Panama to explore how to build participation at the national and local scale while working within the bounds of the current conservation paradigm. Qualitative data about MMAS was gathered through a multi-sited ethnographic research process, utilising document review, direct observation, and semi-structured interviews with 82 informants in Belize, Panama, and the United States of America. Results indicate that while a large-scale approach to conservation disadvantages early national and local stakeholder participation, this effect can be mediated through focusing engagement efforts, paying attention to context, building horizontal and vertical partnerships, and using deliberative processes that promote learning. While explicit consideration of geopolitics and local complexity alongside biogeography in the planning phase of a large-scale conservation programme is ideal, actions taken by programme managers during implementation can still have a substantial impact on conservation outcomes.

Political consultation and large-scale research

International Nuclear Information System (INIS)

Bechmann, G.; Folkers, H.

1977-01-01

Large-scale research and policy consulting have an intermediary position between sociological sub-systems. While large-scale research coordinates science, policy, and production, policy consulting coordinates science, policy and political spheres. In this very position, large-scale research and policy consulting lack of institutional guarantees and rational back-ground guarantee which are characteristic for their sociological environment. This large-scale research can neither deal with the production of innovative goods under consideration of rentability, nor can it hope for full recognition by the basis-oriented scientific community. Policy consulting knows neither the competence assignment of the political system to make decisions nor can it judge succesfully by the critical standards of the established social science, at least as far as the present situation is concerned. This intermediary position of large-scale research and policy consulting has, in three points, a consequence supporting the thesis which states that this is a new form of institutionalization of science: These are: 1) external control, 2) the organization form, 3) the theoretical conception of large-scale research and policy consulting. (orig.) [de

Parallel multiple instance learning for extremely large histopathology image analysis.

Science.gov (United States)

Xu, Yan; Li, Yeshu; Shen, Zhengyang; Wu, Ziwei; Gao, Teng; Fan, Yubo; Lai, Maode; Chang, Eric I-Chao

2017-08-03

Histopathology images are critical for medical diagnosis, e.g., cancer and its treatment. A standard histopathology slice can be easily scanned at a high resolution of, say, 200,000×200,000 pixels. These high resolution images can make most existing imaging processing tools infeasible or less effective when operated on a single machine with limited memory, disk space and computing power. In this paper, we propose an algorithm tackling this new emerging "big data" problem utilizing parallel computing on High-Performance-Computing (HPC) clusters. Experimental results on a large-scale data set (1318 images at a scale of 10 billion pixels each) demonstrate the efficiency and effectiveness of the proposed algorithm for low-latency real-time applications. The framework proposed an effective and efficient system for extremely large histopathology image analysis. It is based on the multiple instance learning formulation for weakly-supervised learning for image classification, segmentation and clustering. When a max-margin concept is adopted for different clusters, we obtain further improvement in clustering performance.

A Fast SVD-Hidden-nodes based Extreme Learning Machine for Large-Scale Data Analytics.

Science.gov (United States)

Deng, Wan-Yu; Bai, Zuo; Huang, Guang-Bin; Zheng, Qing-Hua

2016-05-01

Big dimensional data is a growing trend that is emerging in many real world contexts, extending from web mining, gene expression analysis, protein-protein interaction to high-frequency financial data. Nowadays, there is a growing consensus that the increasing dimensionality poses impeding effects on the performances of classifiers, which is termed as the "peaking phenomenon" in the field of machine intelligence. To address the issue, dimensionality reduction is commonly employed as a preprocessing step on the Big dimensional data before building the classifiers. In this paper, we propose an Extreme Learning Machine (ELM) approach for large-scale data analytic. In contrast to existing approaches, we embed hidden nodes that are designed using singular value decomposition (SVD) into the classical ELM. These SVD nodes in the hidden layer are shown to capture the underlying characteristics of the Big dimensional data well, exhibiting excellent generalization performances. The drawback of using SVD on the entire dataset, however, is the high computational complexity involved. To address this, a fast divide and conquer approximation scheme is introduced to maintain computational tractability on high volume data. The resultant algorithm proposed is labeled here as Fast Singular Value Decomposition-Hidden-nodes based Extreme Learning Machine or FSVD-H-ELM in short. In FSVD-H-ELM, instead of identifying the SVD hidden nodes directly from the entire dataset, SVD hidden nodes are derived from multiple random subsets of data sampled from the original dataset. Comprehensive experiments and comparisons are conducted to assess the FSVD-H-ELM against other state-of-the-art algorithms. The results obtained demonstrated the superior generalization performance and efficiency of the FSVD-H-ELM. Copyright © 2016 Elsevier Ltd. All rights reserved.

Learning to Voice? The Evolving Roles of Family Farmers in the Coordination of Large-Scale Irrigation Schemes in Morocco

Directory of Open Access Journals (Sweden)

Nicolas Faysse

2010-02-01

Full Text Available In Morocco, large-scale irrigation schemes have evolved over the past twenty years from the centralised management of irrigation and agricultural production into more complex multi-actor systems. This study analysed whether, and how, in the context of state withdrawal, increased farmer autonomy and political liberalisation, family farmers currently participate in the coordination and negotiation of issues that affect them and involve scheme-level organisations. Issues related to water management, the sugar industry and the dairy sector were analysed in five large-scale irrigation schemes. Farmer organisations that were set up to intervene in water management and sugar production were seen to be either inactive or to have weak links with their constituency; hence, the irrigation administration and the sugar industry continue to interact directly with farmers in a centralised way. Given their inability to voice their interests, when farmers have the opportunity, many choose exit strategies, for instance by resorting to the use of groundwater. In contrast, many community-based milk collection cooperatives were seen to function as accountable intermediaries between smallholders and dairy firms. While, as in the past, family farmers are still generally not involved in decision making at scheme level, in the milk collection cooperatives studied, farmers learn to coordinate and negotiate for the development of their communities.

Decentralized Large-Scale Power Balancing

DEFF Research Database (Denmark)

Halvgaard, Rasmus; Jørgensen, John Bagterp; Poulsen, Niels Kjølstad

2013-01-01

problem is formulated as a centralized large-scale optimization problem but is then decomposed into smaller subproblems that are solved locally by each unit connected to an aggregator. For large-scale systems the method is faster than solving the full problem and can be distributed to include an arbitrary...

Large scale PV plants - also in Denmark. Project report

Energy Technology Data Exchange (ETDEWEB)

Ahm, P [PA Energy, Malling (Denmark); Vedde, J [SiCon. Silicon and PV consulting, Birkeroed (Denmark)

2011-04-15

Large scale PV (LPV) plants, plants with a capacity of more than 200 kW, has since 2007 constituted an increasing share of the global PV installations. In 2009 large scale PV plants with cumulative power more that 1,3 GWp were connected to the grid. The necessary design data for LPV plants in Denmark are available or can be found, although irradiance data could be improved. There seems to be very few institutional barriers for LPV projects, but as so far no real LPV projects have been processed, these findings have to be regarded as preliminary. The fast growing number of very large scale solar thermal plants for district heating applications supports these findings. It has further been investigated, how to optimize the lay-out of LPV plants. Under the Danish irradiance conditions with several winter months with very low solar height PV installations on flat surfaces will have to balance the requirements of physical space - and cost, and the loss of electricity production due to shadowing effects. The potential for LPV plants in Denmark are found in three main categories: PV installations on flat roof of large commercial buildings, PV installations on other large scale infrastructure such as noise barriers and ground mounted PV installations. The technical potential for all three categories is found to be significant and in the range of 50 - 250 km2. In terms of energy harvest PV plants will under Danish conditions exhibit an overall efficiency of about 10 % in conversion of the energy content of the light compared to about 0,3 % for biomass. The theoretical ground area needed to produce the present annual electricity consumption of Denmark at 33-35 TWh is about 300 km2 The Danish grid codes and the electricity safety regulations mention very little about PV and nothing about LPV plants. It is expected that LPV plants will be treated similarly to big wind turbines. A number of LPV plant scenarios have been investigated in detail based on real commercial offers and

Automating large-scale reactor systems

International Nuclear Information System (INIS)

Kisner, R.A.

1985-01-01

This paper conveys a philosophy for developing automated large-scale control systems that behave in an integrated, intelligent, flexible manner. Methods for operating large-scale systems under varying degrees of equipment degradation are discussed, and a design approach that separates the effort into phases is suggested. 5 refs., 1 fig

Large-Scale Unsupervised Hashing with Shared Structure Learning.

Science.gov (United States)

Liu, Xianglong; Mu, Yadong; Zhang, Danchen; Lang, Bo; Li, Xuelong

2015-09-01

Hashing methods are effective in generating compact binary signatures for images and videos. This paper addresses an important open issue in the literature, i.e., how to learn compact hash codes by enhancing the complementarity among different hash functions. Most of prior studies solve this problem either by adopting time-consuming sequential learning algorithms or by generating the hash functions which are subject to some deliberately-designed constraints (e.g., enforcing hash functions orthogonal to one another). We analyze the drawbacks of past works and propose a new solution to this problem. Our idea is to decompose the feature space into a subspace shared by all hash functions and its complementary subspace. On one hand, the shared subspace, corresponding to the common structure across different hash functions, conveys most relevant information for the hashing task. Similar to data de-noising, irrelevant information is explicitly suppressed during hash function generation. On the other hand, in case that the complementary subspace also contains useful information for specific hash functions, the final form of our proposed hashing scheme is a compromise between these two kinds of subspaces. To make hash functions not only preserve the local neighborhood structure but also capture the global cluster distribution of the whole data, an objective function incorporating spectral embedding loss, binary quantization loss, and shared subspace contribution is introduced to guide the hash function learning. We propose an efficient alternating optimization method to simultaneously learn both the shared structure and the hash functions. Experimental results on three well-known benchmarks CIFAR-10, NUS-WIDE, and a-TRECVID demonstrate that our approach significantly outperforms state-of-the-art hashing methods.

The Software Reliability of Large Scale Integration Circuit and Very Large Scale Integration Circuit

OpenAIRE

Artem Ganiyev; Jan Vitasek

2010-01-01

This article describes evaluation method of faultless function of large scale integration circuits (LSI) and very large scale integration circuits (VLSI). In the article there is a comparative analysis of factors which determine faultless of integrated circuits, analysis of already existing methods and model of faultless function evaluation of LSI and VLSI. The main part describes a proposed algorithm and program for analysis of fault rate in LSI and VLSI circuits.

Phylogenetic distribution of large-scale genome patchiness

Directory of Open Access Journals (Sweden)

Hackenberg Michael

2008-04-01

Full Text Available Abstract Background The phylogenetic distribution of large-scale genome structure (i.e. mosaic compositional patchiness has been explored mainly by analytical ultracentrifugation of bulk DNA. However, with the availability of large, good-quality chromosome sequences, and the recently developed computational methods to directly analyze patchiness on the genome sequence, an evolutionary comparative analysis can be carried out at the sequence level. Results The local variations in the scaling exponent of the Detrended Fluctuation Analysis are used here to analyze large-scale genome structure and directly uncover the characteristic scales present in genome sequences. Furthermore, through shuffling experiments of selected genome regions, computationally-identified, isochore-like regions were identified as the biological source for the uncovered large-scale genome structure. The phylogenetic distribution of short- and large-scale patchiness was determined in the best-sequenced genome assemblies from eleven eukaryotic genomes: mammals (Homo sapiens, Pan troglodytes, Mus musculus, Rattus norvegicus, and Canis familiaris, birds (Gallus gallus, fishes (Danio rerio, invertebrates (Drosophila melanogaster and Caenorhabditis elegans, plants (Arabidopsis thaliana and yeasts (Saccharomyces cerevisiae. We found large-scale patchiness of genome structure, associated with in silico determined, isochore-like regions, throughout this wide phylogenetic range. Conclusion Large-scale genome structure is detected by directly analyzing DNA sequences in a wide range of eukaryotic chromosome sequences, from human to yeast. In all these genomes, large-scale patchiness can be associated with the isochore-like regions, as directly detected in silico at the sequence level.

Managing large-scale models: DBS

International Nuclear Information System (INIS)

1981-05-01

A set of fundamental management tools for developing and operating a large scale model and data base system is presented. Based on experience in operating and developing a large scale computerized system, the only reasonable way to gain strong management control of such a system is to implement appropriate controls and procedures. Chapter I discusses the purpose of the book. Chapter II classifies a broad range of generic management problems into three groups: documentation, operations, and maintenance. First, system problems are identified then solutions for gaining management control are disucssed. Chapters III, IV, and V present practical methods for dealing with these problems. These methods were developed for managing SEAS but have general application for large scale models and data bases

Large Scale Self-Organizing Information Distribution System

National Research Council Canada - National Science Library

Low, Steven

2005-01-01

This project investigates issues in "large-scale" networks. Here "large-scale" refers to networks with large number of high capacity nodes and transmission links, and shared by a large number of users...

What Will the Neighbors Think? Building Large-Scale Science Projects Around the World

International Nuclear Information System (INIS)

Jones, Craig; Mrotzek, Christian; Toge, Nobu; Sarno, Doug

2007-01-01

Public participation is an essential ingredient for turning the International Linear Collider into a reality. Wherever the proposed particle accelerator is sited in the world, its neighbors -- in any country -- will have something to say about hosting a 35-kilometer-long collider in their backyards. When it comes to building large-scale physics projects, almost every laboratory has a story to tell. Three case studies from Japan, Germany and the US will be presented to examine how community relations are handled in different parts of the world. How do particle physics laboratories interact with their local communities? How do neighbors react to building large-scale projects in each region? How can the lessons learned from past experiences help in building the next big project? These and other questions will be discussed to engage the audience in an active dialogue about how a large-scale project like the ILC can be a good neighbor.

Large scale structure and baryogenesis

International Nuclear Information System (INIS)

Kirilova, D.P.; Chizhov, M.V.

2001-08-01

We discuss a possible connection between the large scale structure formation and the baryogenesis in the universe. An update review of the observational indications for the presence of a very large scale 120h -1 Mpc in the distribution of the visible matter of the universe is provided. The possibility to generate a periodic distribution with the characteristic scale 120h -1 Mpc through a mechanism producing quasi-periodic baryon density perturbations during inflationary stage, is discussed. The evolution of the baryon charge density distribution is explored in the framework of a low temperature boson condensate baryogenesis scenario. Both the observed very large scale of a the visible matter distribution in the universe and the observed baryon asymmetry value could naturally appear as a result of the evolution of a complex scalar field condensate, formed at the inflationary stage. Moreover, for some model's parameters a natural separation of matter superclusters from antimatter ones can be achieved. (author)

PubChemQC Project: A Large-Scale First-Principles Electronic Structure Database for Data-Driven Chemistry.

Science.gov (United States)

Nakata, Maho; Shimazaki, Tomomi

2017-06-26

Large-scale molecular databases play an essential role in the investigation of various subjects such as the development of organic materials, in silico drug design, and data-driven studies with machine learning. We have developed a large-scale quantum chemistry database based on first-principles methods. Our database currently contains the ground-state electronic structures of 3 million molecules based on density functional theory (DFT) at the B3LYP/6-31G* level, and we successively calculated 10 low-lying excited states of over 2 million molecules via time-dependent DFT with the B3LYP functional and the 6-31+G* basis set. To select the molecules calculated in our project, we referred to the PubChem Project, which was used as the source of the molecular structures in short strings using the InChI and SMILES representations. Accordingly, we have named our quantum chemistry database project "PubChemQC" ( http://pubchemqc.riken.jp/ ) and placed it in the public domain. In this paper, we show the fundamental features of the PubChemQC database and discuss the techniques used to construct the data set for large-scale quantum chemistry calculations. We also present a machine learning approach to predict the electronic structure of molecules as an example to demonstrate the suitability of the large-scale quantum chemistry database.

Automatic management software for large-scale cluster system

International Nuclear Information System (INIS)

Weng Yunjian; Chinese Academy of Sciences, Beijing; Sun Gongxing

2007-01-01

At present, the large-scale cluster system faces to the difficult management. For example the manager has large work load. It needs to cost much time on the management and the maintenance of large-scale cluster system. The nodes in large-scale cluster system are very easy to be chaotic. Thousands of nodes are put in big rooms so that some managers are very easy to make the confusion with machines. How do effectively carry on accurate management under the large-scale cluster system? The article introduces ELFms in the large-scale cluster system. Furthermore, it is proposed to realize the large-scale cluster system automatic management. (authors)

The Large Scale Machine Learning in an Artificial Society: Prediction of the Ebola Outbreak in Beijing

Directory of Open Access Journals (Sweden)

Peng Zhang

2015-01-01

Full Text Available Ebola virus disease (EVD distinguishes its feature as high infectivity and mortality. Thus, it is urgent for governments to draw up emergency plans against Ebola. However, it is hard to predict the possible epidemic situations in practice. Luckily, in recent years, computational experiments based on artificial society appeared, providing a new approach to study the propagation of EVD and analyze the corresponding interventions. Therefore, the rationality of artificial society is the key to the accuracy and reliability of experiment results. Individuals’ behaviors along with travel mode directly affect the propagation among individuals. Firstly, artificial Beijing is reconstructed based on geodemographics and machine learning is involved to optimize individuals’ behaviors. Meanwhile, Ebola course model and propagation model are built, according to the parameters in West Africa. Subsequently, propagation mechanism of EVD is analyzed, epidemic scenario is predicted, and corresponding interventions are presented. Finally, by simulating the emergency responses of Chinese government, the conclusion is finally drawn that Ebola is impossible to outbreak in large scale in the city of Beijing.

Large scale network-centric distributed systems

CERN Document Server

Sarbazi-Azad, Hamid

2014-01-01

A highly accessible reference offering a broad range of topics and insights on large scale network-centric distributed systems Evolving from the fields of high-performance computing and networking, large scale network-centric distributed systems continues to grow as one of the most important topics in computing and communication and many interdisciplinary areas. Dealing with both wired and wireless networks, this book focuses on the design and performance issues of such systems. Large Scale Network-Centric Distributed Systems provides in-depth coverage ranging from ground-level hardware issu

Large-Scale Outflows in Seyfert Galaxies

Science.gov (United States)

Colbert, E. J. M.; Baum, S. A.

1995-12-01

\\catcode`\\@=11 \\ialign{m @th#1hfil ##hfil \\crcr#2\\crcr\\sim\\crcr}}} \\catcode`\\@=12 Highly collimated outflows extend out to Mpc scales in many radio-loud active galaxies. In Seyfert galaxies, which are radio-quiet, the outflows extend out to kpc scales and do not appear to be as highly collimated. In order to study the nature of large-scale (>~1 kpc) outflows in Seyferts, we have conducted optical, radio and X-ray surveys of a distance-limited sample of 22 edge-on Seyfert galaxies. Results of the optical emission-line imaging and spectroscopic survey imply that large-scale outflows are present in >~{{1} /{4}} of all Seyferts. The radio (VLA) and X-ray (ROSAT) surveys show that large-scale radio and X-ray emission is present at about the same frequency. Kinetic luminosities of the outflows in Seyferts are comparable to those in starburst-driven superwinds. Large-scale radio sources in Seyferts appear diffuse, but do not resemble radio halos found in some edge-on starburst galaxies (e.g. M82). We discuss the feasibility of the outflows being powered by the active nucleus (e.g. a jet) or a circumnuclear starburst.

Discriminative Hierarchical K-Means Tree for Large-Scale Image Classification.

Science.gov (United States)

Chen, Shizhi; Yang, Xiaodong; Tian, Yingli

2015-09-01

A key challenge in large-scale image classification is how to achieve efficiency in terms of both computation and memory without compromising classification accuracy. The learning-based classifiers achieve the state-of-the-art accuracies, but have been criticized for the computational complexity that grows linearly with the number of classes. The nonparametric nearest neighbor (NN)-based classifiers naturally handle large numbers of categories, but incur prohibitively expensive computation and memory costs. In this brief, we present a novel classification scheme, i.e., discriminative hierarchical K-means tree (D-HKTree), which combines the advantages of both learning-based and NN-based classifiers. The complexity of the D-HKTree only grows sublinearly with the number of categories, which is much better than the recent hierarchical support vector machines-based methods. The memory requirement is the order of magnitude less than the recent Naïve Bayesian NN-based approaches. The proposed D-HKTree classification scheme is evaluated on several challenging benchmark databases and achieves the state-of-the-art accuracies, while with significantly lower computation cost and memory requirement.

SCALE INTERACTION IN A MIXING LAYER. THE ROLE OF THE LARGE-SCALE GRADIENTS

KAUST Repository

Fiscaletti, Daniele

2015-08-23

The interaction between scales is investigated in a turbulent mixing layer. The large-scale amplitude modulation of the small scales already observed in other works depends on the crosswise location. Large-scale positive fluctuations correlate with a stronger activity of the small scales on the low speed-side of the mixing layer, and a reduced activity on the high speed-side. However, from physical considerations we would expect the scales to interact in a qualitatively similar way within the flow and across different turbulent flows. Therefore, instead of the large-scale fluctuations, the large-scale gradients modulation of the small scales has been additionally investigated.

Scaling up graph-based semisupervised learning via prototype vector machines.

Science.gov (United States)

Zhang, Kai; Lan, Liang; Kwok, James T; Vucetic, Slobodan; Parvin, Bahram

2015-03-01

When the amount of labeled data are limited, semisupervised learning can improve the learner's performance by also using the often easily available unlabeled data. In particular, a popular approach requires the learned function to be smooth on the underlying data manifold. By approximating this manifold as a weighted graph, such graph-based techniques can often achieve state-of-the-art performance. However, their high time and space complexities make them less attractive on large data sets. In this paper, we propose to scale up graph-based semisupervised learning using a set of sparse prototypes derived from the data. These prototypes serve as a small set of data representatives, which can be used to approximate the graph-based regularizer and to control model complexity. Consequently, both training and testing become much more efficient. Moreover, when the Gaussian kernel is used to define the graph affinity, a simple and principled method to select the prototypes can be obtained. Experiments on a number of real-world data sets demonstrate encouraging performance and scaling properties of the proposed approach. It also compares favorably with models learned via l1 -regularization at the same level of model sparsity. These results demonstrate the efficacy of the proposed approach in producing highly parsimonious and accurate models for semisupervised learning.

Dissecting the large-scale galactic conformity

Science.gov (United States)

Seo, Seongu

2018-01-01

Galactic conformity is an observed phenomenon that galaxies located in the same region have similar properties such as star formation rate, color, gas fraction, and so on. The conformity was first observed among galaxies within in the same halos (“one-halo conformity”). The one-halo conformity can be readily explained by mutual interactions among galaxies within a halo. Recent observations however further witnessed a puzzling connection among galaxies with no direct interaction. In particular, galaxies located within a sphere of ~5 Mpc radius tend to show similarities, even though the galaxies do not share common halos with each other ("two-halo conformity" or “large-scale conformity”). Using a cosmological hydrodynamic simulation, Illustris, we investigate the physical origin of the two-halo conformity and put forward two scenarios. First, back-splash galaxies are likely responsible for the large-scale conformity. They have evolved into red galaxies due to ram-pressure stripping in a given galaxy cluster and happen to reside now within a ~5 Mpc sphere. Second, galaxies in strong tidal field induced by large-scale structure also seem to give rise to the large-scale conformity. The strong tides suppress star formation in the galaxies. We discuss the importance of the large-scale conformity in the context of galaxy evolution.

A Ranking Approach on Large-Scale Graph With Multidimensional Heterogeneous Information.

Science.gov (United States)

Wei, Wei; Gao, Bin; Liu, Tie-Yan; Wang, Taifeng; Li, Guohui; Li, Hang

2016-04-01

Graph-based ranking has been extensively studied and frequently applied in many applications, such as webpage ranking. It aims at mining potentially valuable information from the raw graph-structured data. Recently, with the proliferation of rich heterogeneous information (e.g., node/edge features and prior knowledge) available in many real-world graphs, how to effectively and efficiently leverage all information to improve the ranking performance becomes a new challenging problem. Previous methods only utilize part of such information and attempt to rank graph nodes according to link-based methods, of which the ranking performances are severely affected by several well-known issues, e.g., over-fitting or high computational complexity, especially when the scale of graph is very large. In this paper, we address the large-scale graph-based ranking problem and focus on how to effectively exploit rich heterogeneous information of the graph to improve the ranking performance. Specifically, we propose an innovative and effective semi-supervised PageRank (SSP) approach to parameterize the derived information within a unified semi-supervised learning framework (SSLF-GR), then simultaneously optimize the parameters and the ranking scores of graph nodes. Experiments on the real-world large-scale graphs demonstrate that our method significantly outperforms the algorithms that consider such graph information only partially.

Measuring strategies for learning regulation in medical education: scale reliability and dimensionality in a Swedish sample.

Science.gov (United States)

Edelbring, Samuel

2012-08-15

The degree of learners' self-regulated learning and dependence on external regulation influence learning processes in higher education. These regulation strategies are commonly measured by questionnaires developed in other settings than in which they are being used, thereby requiring renewed validation. The aim of this study was to psychometrically evaluate the learning regulation strategy scales from the Inventory of Learning Styles with Swedish medical students (N = 206). The regulation scales were evaluated regarding their reliability, scale dimensionality and interrelations. The primary evaluation focused on dimensionality and was performed with Mokken scale analysis. To assist future scale refinement, additional item analysis, such as item-to-scale correlations, was performed. Scale scores in the Swedish sample displayed good reliability in relation to published results: Cronbach's alpha: 0.82, 0.72, and 0.65 for self-regulation, external regulation and lack of regulation scales respectively. The dimensionalities in scales were adequate for self-regulation and its subscales, whereas external regulation and lack of regulation displayed less unidimensionality. The established theoretical scales were largely replicated in the exploratory analysis. The item analysis identified two items that contributed little to their respective scales. The results indicate that these scales have an adequate capacity for detecting the three theoretically proposed learning regulation strategies in the medical education sample. Further construct validity should be sought by interpreting scale scores in relation to specific learning activities. Using established scales for measuring students' regulation strategies enables a broad empirical base for increasing knowledge on regulation strategies in relation to different disciplinary settings and contributes to theoretical development.

Large-scale perspective as a challenge

NARCIS (Netherlands)

Plomp, M.G.A.

2012-01-01

1. Scale forms a challenge for chain researchers: when exactly is something ‘large-scale’? What are the underlying factors (e.g. number of parties, data, objects in the chain, complexity) that determine this? It appears to be a continuum between small- and large-scale, where positioning on that

Algorithm 896: LSA: Algorithms for Large-Scale Optimization

Czech Academy of Sciences Publication Activity Database

Lukšan, Ladislav; Matonoha, Ctirad; Vlček, Jan

2009-01-01

Roč. 36, č. 3 (2009), 16-1-16-29 ISSN 0098-3500 R&D Pro jects: GA AV ČR IAA1030405; GA ČR GP201/06/P397 Institutional research plan: CEZ:AV0Z10300504 Keywords : algorithms * design * large-scale optimization * large-scale nonsmooth optimization * large-scale nonlinear least squares * large-scale nonlinear minimax * large-scale systems of nonlinear equations * sparse pro blems * partially separable pro blems * limited-memory methods * discrete Newton methods * quasi-Newton methods * primal interior-point methods Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.904, year: 2009

Scale interactions in a mixing layer – the role of the large-scale gradients

KAUST Repository

Fiscaletti, D.

2016-02-15

© 2016 Cambridge University Press. The interaction between the large and the small scales of turbulence is investigated in a mixing layer, at a Reynolds number based on the Taylor microscale of , via direct numerical simulations. The analysis is performed in physical space, and the local vorticity root-mean-square (r.m.s.) is taken as a measure of the small-scale activity. It is found that positive large-scale velocity fluctuations correspond to large vorticity r.m.s. on the low-speed side of the mixing layer, whereas, they correspond to low vorticity r.m.s. on the high-speed side. The relationship between large and small scales thus depends on position if the vorticity r.m.s. is correlated with the large-scale velocity fluctuations. On the contrary, the correlation coefficient is nearly constant throughout the mixing layer and close to unity if the vorticity r.m.s. is correlated with the large-scale velocity gradients. Therefore, the small-scale activity appears closely related to large-scale gradients, while the correlation between the small-scale activity and the large-scale velocity fluctuations is shown to reflect a property of the large scales. Furthermore, the vorticity from unfiltered (small scales) and from low pass filtered (large scales) velocity fields tend to be aligned when examined within vortical tubes. These results provide evidence for the so-called \\'scale invariance\\' (Meneveau & Katz, Annu. Rev. Fluid Mech., vol. 32, 2000, pp. 1-32), and suggest that some of the large-scale characteristics are not lost at the small scales, at least at the Reynolds number achieved in the present simulation.

Learning mathematics through inquiry; a large scale evaluation

NARCIS (Netherlands)

de Jong, Anthonius J.M.; Hendrikse, Petra; van der Meij, Hans; Jacobson, M.J.; Reiman, P.

2010-01-01

Mathematics education is changing from a procedure-oriented approach to one in which concepts and their relations take a central place. Inquiry environments offer students the opportunity to investigate a domain and to focus on conceptual aspects. In this chapter, we describe a learning arrangement

Large-scale matrix-handling subroutines 'ATLAS'

International Nuclear Information System (INIS)

Tsunematsu, Toshihide; Takeda, Tatsuoki; Fujita, Keiichi; Matsuura, Toshihiko; Tahara, Nobuo

1978-03-01

Subroutine package ''ATLAS'' has been developed for handling large-scale matrices. The package is composed of four kinds of subroutines, i.e., basic arithmetic routines, routines for solving linear simultaneous equations and for solving general eigenvalue problems and utility routines. The subroutines are useful in large scale plasma-fluid simulations. (auth.)

Large-scale solar heat

Energy Technology Data Exchange (ETDEWEB)

Tolonen, J.; Konttinen, P.; Lund, P. [Helsinki Univ. of Technology, Otaniemi (Finland). Dept. of Engineering Physics and Mathematics

1998-12-31

In this project a large domestic solar heating system was built and a solar district heating system was modelled and simulated. Objectives were to improve the performance and reduce costs of a large-scale solar heating system. As a result of the project the benefit/cost ratio can be increased by 40 % through dimensioning and optimising the system at the designing stage. (orig.)

Probes of large-scale structure in the Universe

International Nuclear Information System (INIS)

Suto, Yasushi; Gorski, K.; Juszkiewicz, R.; Silk, J.

1988-01-01

Recent progress in observational techniques has made it possible to confront quantitatively various models for the large-scale structure of the Universe with detailed observational data. We develop a general formalism to show that the gravitational instability theory for the origin of large-scale structure is now capable of critically confronting observational results on cosmic microwave background radiation angular anisotropies, large-scale bulk motions and large-scale clumpiness in the galaxy counts. (author)

Large-scale grid management; Storskala Nettforvaltning

Energy Technology Data Exchange (ETDEWEB)

Langdal, Bjoern Inge; Eggen, Arnt Ove

2003-07-01

The network companies in the Norwegian electricity industry now have to establish a large-scale network management, a concept essentially characterized by (1) broader focus (Broad Band, Multi Utility,...) and (2) bigger units with large networks and more customers. Research done by SINTEF Energy Research shows so far that the approaches within large-scale network management may be structured according to three main challenges: centralization, decentralization and out sourcing. The article is part of a planned series.

Large-Scale Urban Decontamination; Developments, Historical Examples and Lessons Learned

Energy Technology Data Exchange (ETDEWEB)

Rick Demmer

2007-02-01

Recent terrorist threats and actual events have lead to a renewed interest in the technical field of large scale, urban environment decontamination. One of the driving forces for this interest is the real potential for the cleanup and removal of radioactive dispersal device (RDD or “dirty bomb”) residues. In response the U. S. Government has spent many millions of dollars investigating RDD contamination and novel decontamination methodologies. Interest in chemical and biological (CB) cleanup has also peaked with the threat of terrorist action like the anthrax attack at the Hart Senate Office Building and with catastrophic natural events such as Hurricane Katrina. The efficiency of cleanup response will be improved with these new developments and a better understanding of the “old reliable” methodologies. Perhaps the most interesting area of investigation for large area decontamination is that of the RDD. While primarily an economic and psychological weapon, the need to cleanup and return valuable or culturally significant resources to the public is nonetheless valid. Several private companies, universities and National Laboratories are currently developing novel RDD cleanup technologies. Because of its longstanding association with radioactive facilities, the U. S. Department of Energy National Laboratories are at the forefront in developing and testing new RDD decontamination methods. However, such cleanup technologies are likely to be fairly task specific; while many different contamination mechanisms, substrate and environmental conditions will make actual application more complicated. Some major efforts have also been made to model potential contamination, to evaluate both old and new decontamination techniques and to assess their readiness for use. Non-radioactive, CB threats each have unique decontamination challenges and recent events have provided some examples. The U. S. Environmental Protection Agency (EPA), as lead agency for these emergency

Measuring strategies for learning regulation in medical education: Scale reliability and dimensionality in a Swedish sample

Directory of Open Access Journals (Sweden)

Edelbring Samuel

2012-08-01

Full Text Available Abstract Background The degree of learners’ self-regulated learning and dependence on external regulation influence learning processes in higher education. These regulation strategies are commonly measured by questionnaires developed in other settings than in which they are being used, thereby requiring renewed validation. The aim of this study was to psychometrically evaluate the learning regulation strategy scales from the Inventory of Learning Styles with Swedish medical students (N = 206. Methods The regulation scales were evaluated regarding their reliability, scale dimensionality and interrelations. The primary evaluation focused on dimensionality and was performed with Mokken scale analysis. To assist future scale refinement, additional item analysis, such as item-to-scale correlations, was performed. Results Scale scores in the Swedish sample displayed good reliability in relation to published results: Cronbach’s alpha: 0.82, 0.72, and 0.65 for self-regulation, external regulation and lack of regulation scales respectively. The dimensionalities in scales were adequate for self-regulation and its subscales, whereas external regulation and lack of regulation displayed less unidimensionality. The established theoretical scales were largely replicated in the exploratory analysis. The item analysis identified two items that contributed little to their respective scales. Discussion The results indicate that these scales have an adequate capacity for detecting the three theoretically proposed learning regulation strategies in the medical education sample. Further construct validity should be sought by interpreting scale scores in relation to specific learning activities. Using established scales for measuring students’ regulation strategies enables a broad empirical base for increasing knowledge on regulation strategies in relation to different disciplinary settings and contributes to theoretical development.

Japanese large-scale interferometers

CERN Document Server

Kuroda, K; Miyoki, S; Ishizuka, H; Taylor, C T; Yamamoto, K; Miyakawa, O; Fujimoto, M K; Kawamura, S; Takahashi, R; Yamazaki, T; Arai, K; Tatsumi, D; Ueda, A; Fukushima, M; Sato, S; Shintomi, T; Yamamoto, A; Suzuki, T; Saitô, Y; Haruyama, T; Sato, N; Higashi, Y; Uchiyama, T; Tomaru, T; Tsubono, K; Ando, M; Takamori, A; Numata, K; Ueda, K I; Yoneda, H; Nakagawa, K; Musha, M; Mio, N; Moriwaki, S; Somiya, K; Araya, A; Kanda, N; Telada, S; Sasaki, M; Tagoshi, H; Nakamura, T; Tanaka, T; Ohara, K

2002-01-01

The objective of the TAMA 300 interferometer was to develop advanced technologies for kilometre scale interferometers and to observe gravitational wave events in nearby galaxies. It was designed as a power-recycled Fabry-Perot-Michelson interferometer and was intended as a step towards a final interferometer in Japan. The present successful status of TAMA is presented. TAMA forms a basis for LCGT (large-scale cryogenic gravitational wave telescope), a 3 km scale cryogenic interferometer to be built in the Kamioka mine in Japan, implementing cryogenic mirror techniques. The plan of LCGT is schematically described along with its associated R and D.

Deep Hashing Based Fusing Index Method for Large-Scale Image Retrieval

Directory of Open Access Journals (Sweden)

Lijuan Duan

2017-01-01

Full Text Available Hashing has been widely deployed to perform the Approximate Nearest Neighbor (ANN search for the large-scale image retrieval to solve the problem of storage and retrieval efficiency. Recently, deep hashing methods have been proposed to perform the simultaneous feature learning and the hash code learning with deep neural networks. Even though deep hashing has shown the better performance than traditional hashing methods with handcrafted features, the learned compact hash code from one deep hashing network may not provide the full representation of an image. In this paper, we propose a novel hashing indexing method, called the Deep Hashing based Fusing Index (DHFI, to generate a more compact hash code which has stronger expression ability and distinction capability. In our method, we train two different architecture’s deep hashing subnetworks and fuse the hash codes generated by the two subnetworks together to unify images. Experiments on two real datasets show that our method can outperform state-of-the-art image retrieval applications.

Large scale model testing

International Nuclear Information System (INIS)

Brumovsky, M.; Filip, R.; Polachova, H.; Stepanek, S.

1989-01-01

Fracture mechanics and fatigue calculations for WWER reactor pressure vessels were checked by large scale model testing performed using large testing machine ZZ 8000 (with a maximum load of 80 MN) at the SKODA WORKS. The results are described from testing the material resistance to fracture (non-ductile). The testing included the base materials and welded joints. The rated specimen thickness was 150 mm with defects of a depth between 15 and 100 mm. The results are also presented of nozzles of 850 mm inner diameter in a scale of 1:3; static, cyclic, and dynamic tests were performed without and with surface defects (15, 30 and 45 mm deep). During cyclic tests the crack growth rate in the elastic-plastic region was also determined. (author). 6 figs., 2 tabs., 5 refs

Why small-scale cannabis growers stay small: five mechanisms that prevent small-scale growers from going large scale.

Science.gov (United States)

Hammersvik, Eirik; Sandberg, Sveinung; Pedersen, Willy

2012-11-01

Over the past 15-20 years, domestic cultivation of cannabis has been established in a number of European countries. New techniques have made such cultivation easier; however, the bulk of growers remain small-scale. In this study, we explore the factors that prevent small-scale growers from increasing their production. The study is based on 1 year of ethnographic fieldwork and qualitative interviews conducted with 45 Norwegian cannabis growers, 10 of whom were growing on a large-scale and 35 on a small-scale. The study identifies five mechanisms that prevent small-scale indoor growers from going large-scale. First, large-scale operations involve a number of people, large sums of money, a high work-load and a high risk of detection, and thus demand a higher level of organizational skills than for small growing operations. Second, financial assets are needed to start a large 'grow-site'. Housing rent, electricity, equipment and nutrients are expensive. Third, to be able to sell large quantities of cannabis, growers need access to an illegal distribution network and knowledge of how to act according to black market norms and structures. Fourth, large-scale operations require advanced horticultural skills to maximize yield and quality, which demands greater skills and knowledge than does small-scale cultivation. Fifth, small-scale growers are often embedded in the 'cannabis culture', which emphasizes anti-commercialism, anti-violence and ecological and community values. Hence, starting up large-scale production will imply having to renegotiate or abandon these values. Going from small- to large-scale cannabis production is a demanding task-ideologically, technically, economically and personally. The many obstacles that small-scale growers face and the lack of interest and motivation for going large-scale suggest that the risk of a 'slippery slope' from small-scale to large-scale growing is limited. Possible political implications of the findings are discussed. Copyright

Distributed large-scale dimensional metrology new insights

CERN Document Server

Franceschini, Fiorenzo; Maisano, Domenico

2011-01-01

Focuses on the latest insights into and challenges of distributed large scale dimensional metrology Enables practitioners to study distributed large scale dimensional metrology independently Includes specific examples of the development of new system prototypes

SCALE INTERACTION IN A MIXING LAYER. THE ROLE OF THE LARGE-SCALE GRADIENTS

KAUST Repository

Fiscaletti, Daniele; Attili, Antonio; Bisetti, Fabrizio; Elsinga, Gerrit E.

2015-01-01

from physical considerations we would expect the scales to interact in a qualitatively similar way within the flow and across different turbulent flows. Therefore, instead of the large-scale fluctuations, the large-scale gradients modulation of the small scales has been additionally investigated.

Unraveling the photovoltaic technology learning curve by incorporation of input price changes and scale effects

International Nuclear Information System (INIS)

Yu, C.F.; van Sark, W.G.J.H.M.; Alsema, E.A.

2011-01-01

In a large number of energy models, the use of learning curves for estimating technological improvements has become popular. This is based on the assumption that technological development can be monitored by following cost development as a function of market size. However, recent data show that in some stages of photovoltaic technology (PV) production, the market price of PV modules stabilizes even though the cumulative capacity increases. This implies that no technological improvement takes place in these periods: the cost predicted by the learning curve in the PV study is lower than the market one. We propose that this bias results from ignoring the effects of input prices and scale effects, and that incorporating the input prices and scale effects into the learning curve theory is an important issue in making cost predictions more reliable. In this paper, a methodology is described to incorporate the scale and input-prices effect as the additional variables into the one factor learning curve, which leads to the definition of the multi-factor learning curve. This multi-factor learning curve is not only derived from economic theories, but also supported by an empirical study. The results clearly show that input prices and scale effects are to be included, and that, although market prices are stabilizing, learning is still taking place. (author)

Trends in large-scale testing of reactor structures

International Nuclear Information System (INIS)

Blejwas, T.E.

2003-01-01

Large-scale tests of reactor structures have been conducted at Sandia National Laboratories since the late 1970s. This paper describes a number of different large-scale impact tests, pressurization tests of models of containment structures, and thermal-pressure tests of models of reactor pressure vessels. The advantages of large-scale testing are evident, but cost, in particular limits its use. As computer models have grown in size, such as number of degrees of freedom, the advent of computer graphics has made possible very realistic representation of results - results that may not accurately represent reality. A necessary condition to avoiding this pitfall is the validation of the analytical methods and underlying physical representations. Ironically, the immensely larger computer models sometimes increase the need for large-scale testing, because the modeling is applied to increasing more complex structural systems and/or more complex physical phenomena. Unfortunately, the cost of large-scale tests is a disadvantage that will likely severely limit similar testing in the future. International collaborations may provide the best mechanism for funding future programs with large-scale tests. (author)

Tile-Based Semisupervised Classification of Large-Scale VHR Remote Sensing Images

Directory of Open Access Journals (Sweden)

Haikel Alhichri

2018-01-01

Full Text Available This paper deals with the problem of the classification of large-scale very high-resolution (VHR remote sensing (RS images in a semisupervised scenario, where we have a limited training set (less than ten training samples per class. Typical pixel-based classification methods are unfeasible for large-scale VHR images. Thus, as a practical and efficient solution, we propose to subdivide the large image into a grid of tiles and then classify the tiles instead of classifying pixels. Our proposed method uses the power of a pretrained convolutional neural network (CNN to first extract descriptive features from each tile. Next, a neural network classifier (composed of 2 fully connected layers is trained in a semisupervised fashion and used to classify all remaining tiles in the image. This basically presents a coarse classification of the image, which is sufficient for many RS application. The second contribution deals with the employment of the semisupervised learning to improve the classification accuracy. We present a novel semisupervised approach which exploits both the spectral and spatial relationships embedded in the remaining unlabelled tiles. In particular, we embed a spectral graph Laplacian in the hidden layer of the neural network. In addition, we apply regularization of the output labels using a spatial graph Laplacian and the random Walker algorithm. Experimental results obtained by testing the method on two large-scale images acquired by the IKONOS2 sensor reveal promising capabilities of this method in terms of classification accuracy even with less than ten training samples per class.

Large Scale Computations in Air Pollution Modelling

DEFF Research Database (Denmark)

Zlatev, Z.; Brandt, J.; Builtjes, P. J. H.

Proceedings of the NATO Advanced Research Workshop on Large Scale Computations in Air Pollution Modelling, Sofia, Bulgaria, 6-10 July 1998......Proceedings of the NATO Advanced Research Workshop on Large Scale Computations in Air Pollution Modelling, Sofia, Bulgaria, 6-10 July 1998...

Multidimensionality of Teachers' Graded Responses for Preschoolers' Stylistic Learning Behavior: The Learning-to-Learn Scales

Science.gov (United States)

McDermott, Paul A.; Fantuzzo, John W.; Warley, Heather P.; Waterman, Clare; Angelo, Lauren E.; Gadsden, Vivian L.; Sekino, Yumiko

2011-01-01

Assessment of preschool learning behavior has become very popular as a mechanism to inform cognitive development and promote successful interventions. The most widely used measures offer sound predictions but distinguish only a few types of stylistic learning and lack sensitive growth detection. The Learning-to-Learn Scales was designed to…

Large-Scale 3D Printing: The Way Forward

Science.gov (United States)

Jassmi, Hamad Al; Najjar, Fady Al; Ismail Mourad, Abdel-Hamid

2018-03-01

Research on small-scale 3D printing has rapidly evolved, where numerous industrial products have been tested and successfully applied. Nonetheless, research on large-scale 3D printing, directed to large-scale applications such as construction and automotive manufacturing, yet demands a great a great deal of efforts. Large-scale 3D printing is considered an interdisciplinary topic and requires establishing a blended knowledge base from numerous research fields including structural engineering, materials science, mechatronics, software engineering, artificial intelligence and architectural engineering. This review article summarizes key topics of relevance to new research trends on large-scale 3D printing, particularly pertaining (1) technological solutions of additive construction (i.e. the 3D printers themselves), (2) materials science challenges, and (3) new design opportunities.

On-line transient stability assessment of large-scale power systems by using ball vector machines

International Nuclear Information System (INIS)

Mohammadi, M.; Gharehpetian, G.B.

2010-01-01

In this paper ball vector machine (BVM) has been used for on-line transient stability assessment of large-scale power systems. To classify the system transient security status, a BVM has been trained for all contingencies. The proposed BVM based security assessment algorithm has very small training time and space in comparison with artificial neural networks (ANN), support vector machines (SVM) and other machine learning based algorithms. In addition, the proposed algorithm has less support vectors (SV) and therefore is faster than existing algorithms for on-line applications. One of the main points, to apply a machine learning method is feature selection. In this paper, a new Decision Tree (DT) based feature selection technique has been presented. The proposed BVM based algorithm has been applied to New England 39-bus power system. The simulation results show the effectiveness and the stability of the proposed method for on-line transient stability assessment procedure of large-scale power system. The proposed feature selection algorithm has been compared with different feature selection algorithms. The simulation results demonstrate the effectiveness of the proposed feature algorithm.

Growth Limits in Large Scale Networks

DEFF Research Database (Denmark)

Knudsen, Thomas Phillip

limitations. The rising complexity of network management with the convergence of communications platforms is shown as problematic for both automatic management feasibility and for manpower resource management. In the fourth step the scope is extended to include the present society with the DDN project as its......The Subject of large scale networks is approached from the perspective of the network planner. An analysis of the long term planning problems is presented with the main focus on the changing requirements for large scale networks and the potential problems in meeting these requirements. The problems...... the fundamental technological resources in network technologies are analysed for scalability. Here several technological limits to continued growth are presented. The third step involves a survey of major problems in managing large scale networks given the growth of user requirements and the technological...

Accelerating sustainability in large-scale facilities

CERN Multimedia

Marina Giampietro

2011-01-01

Scientific research centres and large-scale facilities are intrinsically energy intensive, but how can big science improve its energy management and eventually contribute to the environmental cause with new cleantech? CERN’s commitment to providing tangible answers to these questions was sealed in the first workshop on energy management for large scale scientific infrastructures held in Lund, Sweden, on the 13-14 October.   Participants at the energy management for large scale scientific infrastructures workshop. The workshop, co-organised with the European Spallation Source (ESS) and  the European Association of National Research Facilities (ERF), tackled a recognised need for addressing energy issues in relation with science and technology policies. It brought together more than 150 representatives of Research Infrastrutures (RIs) and energy experts from Europe and North America. “Without compromising our scientific projects, we can ...

Large scale reflood test

International Nuclear Information System (INIS)

Hirano, Kemmei; Murao, Yoshio

1980-01-01

The large-scale reflood test with a view to ensuring the safety of light water reactors was started in fiscal 1976 based on the special account act for power source development promotion measures by the entrustment from the Science and Technology Agency. Thereafter, to establish the safety of PWRs in loss-of-coolant accidents by joint international efforts, the Japan-West Germany-U.S. research cooperation program was started in April, 1980. Thereupon, the large-scale reflood test is now included in this program. It consists of two tests using a cylindrical core testing apparatus for examining the overall system effect and a plate core testing apparatus for testing individual effects. Each apparatus is composed of the mock-ups of pressure vessel, primary loop, containment vessel and ECCS. The testing method, the test results and the research cooperation program are described. (J.P.N.)

Millions Learning: Scaling up Quality Education in Developing Countries

Science.gov (United States)

Robinson, Jenny Perlman; Winthrop, Rebecca

2016-01-01

"Millions Learning: Scaling up Quality Education in Developing Countries" tells the story of where and how quality education has scaled in low- and middle-income countries. The story emerges from wide-ranging research on scaling and learning, including 14 in-depth case studies from around the globe. Ultimately, "Millions…

Large Scale Cosmological Anomalies and Inhomogeneous Dark Energy

Directory of Open Access Journals (Sweden)

Leandros Perivolaropoulos

2014-01-01

Full Text Available A wide range of large scale observations hint towards possible modifications on the standard cosmological model which is based on a homogeneous and isotropic universe with a small cosmological constant and matter. These observations, also known as “cosmic anomalies” include unexpected Cosmic Microwave Background perturbations on large angular scales, large dipolar peculiar velocity flows of galaxies (“bulk flows”, the measurement of inhomogenous values of the fine structure constant on cosmological scales (“alpha dipole” and other effects. The presence of the observational anomalies could either be a large statistical fluctuation in the context of ΛCDM or it could indicate a non-trivial departure from the cosmological principle on Hubble scales. Such a departure is very much constrained by cosmological observations for matter. For dark energy however there are no significant observational constraints for Hubble scale inhomogeneities. In this brief review I discuss some of the theoretical models that can naturally lead to inhomogeneous dark energy, their observational constraints and their potential to explain the large scale cosmic anomalies.

Large-scale patterns in Rayleigh-Benard convection

International Nuclear Information System (INIS)

Hardenberg, J. von; Parodi, A.; Passoni, G.; Provenzale, A.; Spiegel, E.A.

2008-01-01

Rayleigh-Benard convection at large Rayleigh number is characterized by the presence of intense, vertically moving plumes. Both laboratory and numerical experiments reveal that the rising and descending plumes aggregate into separate clusters so as to produce large-scale updrafts and downdrafts. The horizontal scales of the aggregates reported so far have been comparable to the horizontal extent of the containers, but it has not been clear whether that represents a limitation imposed by domain size. In this work, we present numerical simulations of convection at sufficiently large aspect ratio to ascertain whether there is an intrinsic saturation scale for the clustering process when that ratio is large enough. From a series of simulations of Rayleigh-Benard convection with Rayleigh numbers between 10 5 and 10 8 and with aspect ratios up to 12π, we conclude that the clustering process has a finite horizontal saturation scale with at most a weak dependence on Rayleigh number in the range studied

Knowledge Guided Disambiguation for Large-Scale Scene Classification With Multi-Resolution CNNs

Science.gov (United States)

Wang, Limin; Guo, Sheng; Huang, Weilin; Xiong, Yuanjun; Qiao, Yu

2017-04-01

Convolutional Neural Networks (CNNs) have made remarkable progress on scene recognition, partially due to these recent large-scale scene datasets, such as the Places and Places2. Scene categories are often defined by multi-level information, including local objects, global layout, and background environment, thus leading to large intra-class variations. In addition, with the increasing number of scene categories, label ambiguity has become another crucial issue in large-scale classification. This paper focuses on large-scale scene recognition and makes two major contributions to tackle these issues. First, we propose a multi-resolution CNN architecture that captures visual content and structure at multiple levels. The multi-resolution CNNs are composed of coarse resolution CNNs and fine resolution CNNs, which are complementary to each other. Second, we design two knowledge guided disambiguation techniques to deal with the problem of label ambiguity. (i) We exploit the knowledge from the confusion matrix computed on validation data to merge ambiguous classes into a super category. (ii) We utilize the knowledge of extra networks to produce a soft label for each image. Then the super categories or soft labels are employed to guide CNN training on the Places2. We conduct extensive experiments on three large-scale image datasets (ImageNet, Places, and Places2), demonstrating the effectiveness of our approach. Furthermore, our method takes part in two major scene recognition challenges, and achieves the second place at the Places2 challenge in ILSVRC 2015, and the first place at the LSUN challenge in CVPR 2016. Finally, we directly test the learned representations on other scene benchmarks, and obtain the new state-of-the-art results on the MIT Indoor67 (86.7\\%) and SUN397 (72.0\\%). We release the code and models at~\\url{https://github.com/wanglimin/MRCNN-Scene-Recognition}.

Manufacturing test of large scale hollow capsule and long length cladding in the large scale oxide dispersion strengthened (ODS) martensitic steel

International Nuclear Information System (INIS)

Narita, Takeshi; Ukai, Shigeharu; Kaito, Takeji; Ohtsuka, Satoshi; Fujiwara, Masayuki

2004-04-01

Mass production capability of oxide dispersion strengthened (ODS) martensitic steel cladding (9Cr) has being evaluated in the Phase II of the Feasibility Studies on Commercialized Fast Reactor Cycle System. The cost for manufacturing mother tube (raw materials powder production, mechanical alloying (MA) by ball mill, canning, hot extrusion, and machining) is a dominant factor in the total cost for manufacturing ODS ferritic steel cladding. In this study, the large-sale 9Cr-ODS martensitic steel mother tube which is made with a large-scale hollow capsule, and long length claddings were manufactured, and the applicability of these processes was evaluated. Following results were obtained in this study. (1) Manufacturing the large scale mother tube in the dimension of 32 mm OD, 21 mm ID, and 2 m length has been successfully carried out using large scale hollow capsule. This mother tube has a high degree of accuracy in size. (2) The chemical composition and the micro structure of the manufactured mother tube are similar to the existing mother tube manufactured by a small scale can. And the remarkable difference between the bottom and top sides in the manufactured mother tube has not been observed. (3) The long length cladding has been successfully manufactured from the large scale mother tube which was made using a large scale hollow capsule. (4) For reducing the manufacturing cost of the ODS steel claddings, manufacturing process of the mother tubes using a large scale hollow capsules is promising. (author)

Amplification of large-scale magnetic field in nonhelical magnetohydrodynamics

KAUST Repository

Kumar, Rohit

2017-08-11

It is typically assumed that the kinetic and magnetic helicities play a crucial role in the growth of large-scale dynamo. In this paper, we demonstrate that helicity is not essential for the amplification of large-scale magnetic field. For this purpose, we perform nonhelical magnetohydrodynamic (MHD) simulation, and show that the large-scale magnetic field can grow in nonhelical MHD when random external forcing is employed at scale 1/10 the box size. The energy fluxes and shell-to-shell transfer rates computed using the numerical data show that the large-scale magnetic energy grows due to the energy transfers from the velocity field at the forcing scales.

Hydrometeorological variability on a large french catchment and its relation to large-scale circulation across temporal scales

Science.gov (United States)

Massei, Nicolas; Dieppois, Bastien; Fritier, Nicolas; Laignel, Benoit; Debret, Maxime; Lavers, David; Hannah, David

2015-04-01

In the present context of global changes, considerable efforts have been deployed by the hydrological scientific community to improve our understanding of the impacts of climate fluctuations on water resources. Both observational and modeling studies have been extensively employed to characterize hydrological changes and trends, assess the impact of climate variability or provide future scenarios of water resources. In the aim of a better understanding of hydrological changes, it is of crucial importance to determine how and to what extent trends and long-term oscillations detectable in hydrological variables are linked to global climate oscillations. In this work, we develop an approach associating large-scale/local-scale correlation, enmpirical statistical downscaling and wavelet multiresolution decomposition of monthly precipitation and streamflow over the Seine river watershed, and the North Atlantic sea level pressure (SLP) in order to gain additional insights on the atmospheric patterns associated with the regional hydrology. We hypothesized that: i) atmospheric patterns may change according to the different temporal wavelengths defining the variability of the signals; and ii) definition of those hydrological/circulation relationships for each temporal wavelength may improve the determination of large-scale predictors of local variations. The results showed that the large-scale/local-scale links were not necessarily constant according to time-scale (i.e. for the different frequencies characterizing the signals), resulting in changing spatial patterns across scales. This was then taken into account by developing an empirical statistical downscaling (ESD) modeling approach which integrated discrete wavelet multiresolution analysis for reconstructing local hydrometeorological processes (predictand : precipitation and streamflow on the Seine river catchment) based on a large-scale predictor (SLP over the Euro-Atlantic sector) on a monthly time-step. This approach

Superconducting materials for large scale applications

International Nuclear Information System (INIS)

Dew-Hughes, D.

1975-01-01

Applications of superconductors capable of carrying large current densities in large-scale electrical devices are examined. Discussions are included on critical current density, superconducting materials available, and future prospects for improved superconducting materials. (JRD)

Large-scale influences in near-wall turbulence.

Science.gov (United States)

Hutchins, Nicholas; Marusic, Ivan

2007-03-15

Hot-wire data acquired in a high Reynolds number facility are used to illustrate the need for adequate scale separation when considering the coherent structure in wall-bounded turbulence. It is found that a large-scale motion in the log region becomes increasingly comparable in energy to the near-wall cycle as the Reynolds number increases. Through decomposition of fluctuating velocity signals, it is shown that this large-scale motion has a distinct modulating influence on the small-scale energy (akin to amplitude modulation). Reassessment of DNS data, in light of these results, shows similar trends, with the rate and intensity of production due to the near-wall cycle subject to a modulating influence from the largest-scale motions.

Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures.

Science.gov (United States)

Arana-Daniel, Nancy; Gallegos, Alberto A; López-Franco, Carlos; Alanís, Alma Y; Morales, Jacob; López-Franco, Adriana

2016-01-01

With the increasing power of computers, the amount of data that can be processed in small periods of time has grown exponentially, as has the importance of classifying large-scale data efficiently. Support vector machines have shown good results classifying large amounts of high-dimensional data, such as data generated by protein structure prediction, spam recognition, medical diagnosis, optical character recognition and text classification, etc. Most state of the art approaches for large-scale learning use traditional optimization methods, such as quadratic programming or gradient descent, which makes the use of evolutionary algorithms for training support vector machines an area to be explored. The present paper proposes an approach that is simple to implement based on evolutionary algorithms and Kernel-Adatron for solving large-scale classification problems, focusing on protein structure prediction. The functional properties of proteins depend upon their three-dimensional structures. Knowing the structures of proteins is crucial for biology and can lead to improvements in areas such as medicine, agriculture and biofuels.

Large Scale eHealth Deployment in Europe: Insights from Concurrent Use of Standards.

Science.gov (United States)

Eichelberg, Marco; Chronaki, Catherine

2016-01-01

Large-scale eHealth deployment projects face a major challenge when called to select the right set of standards and tools to achieve sustainable interoperability in an ecosystem including both legacy systems and new systems reflecting technological trends and progress. There is not a single standard that would cover all needs of an eHealth project, and there is a multitude of overlapping and perhaps competing standards that can be employed to define document formats, terminology, communication protocols mirroring alternative technical approaches and schools of thought. eHealth projects need to respond to the important question of how alternative or inconsistently implemented standards and specifications can be used to ensure practical interoperability and long-term sustainability in large scale eHealth deployment. In the eStandards project, 19 European case studies reporting from R&D and large-scale eHealth deployment and policy projects were analyzed. Although this study is not exhaustive, reflecting on the concepts, standards, and tools for concurrent use and the successes, failures, and lessons learned, this paper offers practical insights on how eHealth deployment projects can make the most of the available eHealth standards and tools and how standards and profile developing organizations can serve the users embracing sustainability and technical innovation.

A Proactive Complex Event Processing Method for Large-Scale Transportation Internet of Things

OpenAIRE

Wang, Yongheng; Cao, Kening

2014-01-01

The Internet of Things (IoT) provides a new way to improve the transportation system. The key issue is how to process the numerous events generated by IoT. In this paper, a proactive complex event processing method is proposed for large-scale transportation IoT. Based on a multilayered adaptive dynamic Bayesian model, a Bayesian network structure learning algorithm using search-and-score is proposed to support accurate predictive analytics. A parallel Markov decision processes model is design...

PKI security in large-scale healthcare networks.

Science.gov (United States)

Mantas, Georgios; Lymberopoulos, Dimitrios; Komninos, Nikos

2012-06-01

During the past few years a lot of PKI (Public Key Infrastructures) infrastructures have been proposed for healthcare networks in order to ensure secure communication services and exchange of data among healthcare professionals. However, there is a plethora of challenges in these healthcare PKI infrastructures. Especially, there are a lot of challenges for PKI infrastructures deployed over large-scale healthcare networks. In this paper, we propose a PKI infrastructure to ensure security in a large-scale Internet-based healthcare network connecting a wide spectrum of healthcare units geographically distributed within a wide region. Furthermore, the proposed PKI infrastructure facilitates the trust issues that arise in a large-scale healthcare network including multi-domain PKI infrastructures.

Emerging large-scale solar heating applications

International Nuclear Information System (INIS)

Wong, W.P.; McClung, J.L.

2009-01-01

Currently the market for solar heating applications in Canada is dominated by outdoor swimming pool heating, make-up air pre-heating and domestic water heating in homes, commercial and institutional buildings. All of these involve relatively small systems, except for a few air pre-heating systems on very large buildings. Together these applications make up well over 90% of the solar thermal collectors installed in Canada during 2007. These three applications, along with the recent re-emergence of large-scale concentrated solar thermal for generating electricity, also dominate the world markets. This paper examines some emerging markets for large scale solar heating applications, with a focus on the Canadian climate and market. (author)

Emerging large-scale solar heating applications

Energy Technology Data Exchange (ETDEWEB)

Wong, W.P.; McClung, J.L. [Science Applications International Corporation (SAIC Canada), Ottawa, Ontario (Canada)

2009-07-01

Currently the market for solar heating applications in Canada is dominated by outdoor swimming pool heating, make-up air pre-heating and domestic water heating in homes, commercial and institutional buildings. All of these involve relatively small systems, except for a few air pre-heating systems on very large buildings. Together these applications make up well over 90% of the solar thermal collectors installed in Canada during 2007. These three applications, along with the recent re-emergence of large-scale concentrated solar thermal for generating electricity, also dominate the world markets. This paper examines some emerging markets for large scale solar heating applications, with a focus on the Canadian climate and market. (author)

Large-scale regions of antimatter

International Nuclear Information System (INIS)

Grobov, A. V.; Rubin, S. G.

2015-01-01

Amodified mechanism of the formation of large-scale antimatter regions is proposed. Antimatter appears owing to fluctuations of a complex scalar field that carries a baryon charge in the inflation era

Large-scale regions of antimatter

Energy Technology Data Exchange (ETDEWEB)

Grobov, A. V., E-mail: alexey.grobov@gmail.com; Rubin, S. G., E-mail: sgrubin@mephi.ru [National Research Nuclear University MEPhI (Russian Federation)

2015-07-15

Amodified mechanism of the formation of large-scale antimatter regions is proposed. Antimatter appears owing to fluctuations of a complex scalar field that carries a baryon charge in the inflation era.

Large-Scale Analysis of Art Proportions

DEFF Research Database (Denmark)

Jensen, Karl Kristoffer

2014-01-01

While literature often tries to impute mathematical constants into art, this large-scale study (11 databases of paintings and photos, around 200.000 items) shows a different truth. The analysis, consisting of the width/height proportions, shows a value of rarely if ever one (square) and with majo......While literature often tries to impute mathematical constants into art, this large-scale study (11 databases of paintings and photos, around 200.000 items) shows a different truth. The analysis, consisting of the width/height proportions, shows a value of rarely if ever one (square...

The Expanded Large Scale Gap Test

Science.gov (United States)

1987-03-01

NSWC TR 86-32 DTIC THE EXPANDED LARGE SCALE GAP TEST BY T. P. LIDDIARD D. PRICE RESEARCH AND TECHNOLOGY DEPARTMENT ’ ~MARCH 1987 Ap~proved for public...arises, to reduce the spread in the LSGT 50% gap value.) The worst charges, such as those with the highest or lowest densities, the largest re-pressed...Arlington, VA 22217 PE 62314N INS3A 1 RJ14E31 7R4TBK 11 TITLE (Include Security CIlmsilficatiorn The Expanded Large Scale Gap Test . 12. PEIRSONAL AUTHOR() T

Large scale and big data processing and management

CERN Document Server

Sakr, Sherif

2014-01-01

Large Scale and Big Data: Processing and Management provides readers with a central source of reference on the data management techniques currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses the fundamental challenges associated with Big Data processing tools and techniques across a range of computing environments.The book begins by discussing the basic concepts and tools of large-scale Big Data processing and cloud computing. It also provides an overview of different programming models and cloud-bas

Large scale cluster computing workshop

International Nuclear Information System (INIS)

Dane Skow; Alan Silverman

2002-01-01

Recent revolutions in computer hardware and software technologies have paved the way for the large-scale deployment of clusters of commodity computers to address problems heretofore the domain of tightly coupled SMP processors. Near term projects within High Energy Physics and other computing communities will deploy clusters of scale 1000s of processors and be used by 100s to 1000s of independent users. This will expand the reach in both dimensions by an order of magnitude from the current successful production facilities. The goals of this workshop were: (1) to determine what tools exist which can scale up to the cluster sizes foreseen for the next generation of HENP experiments (several thousand nodes) and by implication to identify areas where some investment of money or effort is likely to be needed. (2) To compare and record experimences gained with such tools. (3) To produce a practical guide to all stages of planning, installing, building and operating a large computing cluster in HENP. (4) To identify and connect groups with similar interest within HENP and the larger clustering community

Large-Scale Agriculture and Outgrower Schemes in Ethiopia

DEFF Research Database (Denmark)

Wendimu, Mengistu Assefa

, the impact of large-scale agriculture and outgrower schemes on productivity, household welfare and wages in developing countries is highly contentious. Chapter 1 of this thesis provides an introduction to the study, while also reviewing the key debate in the contemporary land ‘grabbing’ and historical large...... sugarcane outgrower scheme on household income and asset stocks. Chapter 5 examines the wages and working conditions in ‘formal’ large-scale and ‘informal’ small-scale irrigated agriculture. The results in Chapter 2 show that moisture stress, the use of untested planting materials, and conflict over land...... commands a higher wage than ‘formal’ large-scale agriculture, while rather different wage determination mechanisms exist in the two sectors. Human capital characteristics (education and experience) partly explain the differences in wages within the formal sector, but play no significant role...

Economically viable large-scale hydrogen liquefaction

Science.gov (United States)

Cardella, U.; Decker, L.; Klein, H.

2017-02-01

The liquid hydrogen demand, particularly driven by clean energy applications, will rise in the near future. As industrial large scale liquefiers will play a major role within the hydrogen supply chain, production capacity will have to increase by a multiple of today’s typical sizes. The main goal is to reduce the total cost of ownership for these plants by increasing energy efficiency with innovative and simple process designs, optimized in capital expenditure. New concepts must ensure a manageable plant complexity and flexible operability. In the phase of process development and selection, a dimensioning of key equipment for large scale liquefiers, such as turbines and compressors as well as heat exchangers, must be performed iteratively to ensure technological feasibility and maturity. Further critical aspects related to hydrogen liquefaction, e.g. fluid properties, ortho-para hydrogen conversion, and coldbox configuration, must be analysed in detail. This paper provides an overview on the approach, challenges and preliminary results in the development of efficient as well as economically viable concepts for large-scale hydrogen liquefaction.

Large scale chromatographic separations using continuous displacement chromatography (CDC)

International Nuclear Information System (INIS)

Taniguchi, V.T.; Doty, A.W.; Byers, C.H.

1988-01-01

A process for large scale chromatographic separations using a continuous chromatography technique is described. The process combines the advantages of large scale batch fixed column displacement chromatography with conventional analytical or elution continuous annular chromatography (CAC) to enable large scale displacement chromatography to be performed on a continuous basis (CDC). Such large scale, continuous displacement chromatography separations have not been reported in the literature. The process is demonstrated with the ion exchange separation of a binary lanthanide (Nd/Pr) mixture. The process is, however, applicable to any displacement chromatography separation that can be performed using conventional batch, fixed column chromatography

Improving Learning in a Traditional, Large-Scale Science Module with a Simple and Efficient Learning Design

DEFF Research Database (Denmark)

Godsk, Mikkel

2014-01-01

the impact on teaching and learning in terms of how the teacher and the students used the materials and the impact on the students’ performance and satisfaction. The article concludes that replacing face-to-face lectures with webcasts and online activities has the potential to improve learning in terms...... of a better student performance, higher student satisfaction, and a higher degree of flexibility for the students. In addition, the article discusses implications of using learning design for educational development, how learning design may help breaking with the perception that facilitating blended learning...... is a daunting process, and, ultimately, its potential for addressing some of the grand challenges in science education and the political agenda of today....

Computing in Large-Scale Dynamic Systems

NARCIS (Netherlands)

Pruteanu, A.S.

2013-01-01

Software applications developed for large-scale systems have always been difficult to de- velop due to problems caused by the large number of computing devices involved. Above a certain network size (roughly one hundred), necessary services such as code updating, topol- ogy discovery and data

Fires in large scale ventilation systems

International Nuclear Information System (INIS)

Gregory, W.S.; Martin, R.A.; White, B.W.; Nichols, B.D.; Smith, P.R.; Leslie, I.H.; Fenton, D.L.; Gunaji, M.V.; Blythe, J.P.

1991-01-01

This paper summarizes the experience gained simulating fires in large scale ventilation systems patterned after ventilation systems found in nuclear fuel cycle facilities. The series of experiments discussed included: (1) combustion aerosol loading of 0.61x0.61 m HEPA filters with the combustion products of two organic fuels, polystyrene and polymethylemethacrylate; (2) gas dynamic and heat transport through a large scale ventilation system consisting of a 0.61x0.61 m duct 90 m in length, with dampers, HEPA filters, blowers, etc.; (3) gas dynamic and simultaneous transport of heat and solid particulate (consisting of glass beads with a mean aerodynamic diameter of 10μ) through the large scale ventilation system; and (4) the transport of heat and soot, generated by kerosene pool fires, through the large scale ventilation system. The FIRAC computer code, designed to predict fire-induced transients in nuclear fuel cycle facility ventilation systems, was used to predict the results of experiments (2) through (4). In general, the results of the predictions were satisfactory. The code predictions for the gas dynamics, heat transport, and particulate transport and deposition were within 10% of the experimentally measured values. However, the code was less successful in predicting the amount of soot generation from kerosene pool fires, probably due to the fire module of the code being a one-dimensional zone model. The experiments revealed a complicated three-dimensional combustion pattern within the fire room of the ventilation system. Further refinement of the fire module within FIRAC is needed. (orig.)

Large scale deep learning for computer aided detection of mammographic lesions.

Science.gov (United States)

Kooi, Thijs; Litjens, Geert; van Ginneken, Bram; Gubern-Mérida, Albert; Sánchez, Clara I; Mann, Ritse; den Heeten, Ard; Karssemeijer, Nico

2017-01-01

Recent advances in machine learning yielded new techniques to train deep neural networks, which resulted in highly successful applications in many pattern recognition tasks such as object detection and speech recognition. In this paper we provide a head-to-head comparison between a state-of-the art in mammography CAD system, relying on a manually designed feature set and a Convolutional Neural Network (CNN), aiming for a system that can ultimately read mammograms independently. Both systems are trained on a large data set of around 45,000 images and results show the CNN outperforms the traditional CAD system at low sensitivity and performs comparable at high sensitivity. We subsequently investigate to what extent features such as location and patient information and commonly used manual features can still complement the network and see improvements at high specificity over the CNN especially with location and context features, which contain information not available to the CNN. Additionally, a reader study was performed, where the network was compared to certified screening radiologists on a patch level and we found no significant difference between the network and the readers. Copyright © 2016 Elsevier B.V. All rights reserved.

Large-scale Complex IT Systems

OpenAIRE

Sommerville, Ian; Cliff, Dave; Calinescu, Radu; Keen, Justin; Kelly, Tim; Kwiatkowska, Marta; McDermid, John; Paige, Richard

2011-01-01

This paper explores the issues around the construction of large-scale complex systems which are built as 'systems of systems' and suggests that there are fundamental reasons, derived from the inherent complexity in these systems, why our current software engineering methods and techniques cannot be scaled up to cope with the engineering challenges of constructing such systems. It then goes on to propose a research and education agenda for software engineering that identifies the major challen...

Large-scale complex IT systems

OpenAIRE

Sommerville, Ian; Cliff, Dave; Calinescu, Radu; Keen, Justin; Kelly, Tim; Kwiatkowska, Marta; McDermid, John; Paige, Richard

2012-01-01

12 pages, 2 figures This paper explores the issues around the construction of large-scale complex systems which are built as 'systems of systems' and suggests that there are fundamental reasons, derived from the inherent complexity in these systems, why our current software engineering methods and techniques cannot be scaled up to cope with the engineering challenges of constructing such systems. It then goes on to propose a research and education agenda for software engineering that ident...

First Mile Challenges for Large-Scale IoT

KAUST Repository

Bader, Ahmed; Elsawy, Hesham; Gharbieh, Mohammad; Alouini, Mohamed-Slim; Adinoyi, Abdulkareem; Alshaalan, Furaih

2017-01-01

The Internet of Things is large-scale by nature. This is not only manifested by the large number of connected devices, but also by the sheer scale of spatial traffic intensity that must be accommodated, primarily in the uplink direction. To that end

Prospects for large scale electricity storage in Denmark

DEFF Research Database (Denmark)

Krog Ekman, Claus; Jensen, Søren Højgaard

2010-01-01

In a future power systems with additional wind power capacity there will be an increased need for large scale power management as well as reliable balancing and reserve capabilities. Different technologies for large scale electricity storage provide solutions to the different challenges arising w...

FutureGen 2.0 Oxy-combustion Large Scale Test – Final Report

Energy Technology Data Exchange (ETDEWEB)

Kenison, LaVesta [URS, Pittsburgh, PA (United States); Flanigan, Thomas [URS, Pittsburgh, PA (United States); Hagerty, Gregg [URS, Pittsburgh, PA (United States); Gorrie, James [Air Liquide, Kennesaw, GA (United States); Leclerc, Mathieu [Air Liquide, Kennesaw, GA (United States); Lockwood, Frederick [Air Liquide, Kennesaw, GA (United States); Falla, Lyle [Babcock & Wilcox and Burns McDonnell, Kansas City, MO (United States); Macinnis, Jim [Babcock & Wilcox and Burns McDonnell, Kansas City, MO (United States); Fedak, Mathew [Babcock & Wilcox and Burns McDonnell, Kansas City, MO (United States); Yakle, Jeff [Babcock & Wilcox and Burns McDonnell, Kansas City, MO (United States); Williford, Mark [Futuregen Industrial Alliance, Inc., Morgan County, IL (United States); Wood, Paul [Futuregen Industrial Alliance, Inc., Morgan County, IL (United States)

2016-04-01

The primary objectives of the FutureGen 2.0 CO₂ Oxy-Combustion Large Scale Test Project were to site, permit, design, construct, and commission, an oxy-combustion boiler, gas quality control system, air separation unit, and CO₂ compression and purification unit, together with the necessary supporting and interconnection utilities. The project was to demonstrate at commercial scale (168MWe gross) the capability to cleanly produce electricity through coal combustion at a retrofitted, existing coal-fired power plant; thereby, resulting in near-zeroemissions of all commonly regulated air emissions, as well as 90% CO₂ capture in steady-state operations. The project was to be fully integrated in terms of project management, capacity, capabilities, technical scope, cost, and schedule with the companion FutureGen 2.0 CO₂ Pipeline and Storage Project, a separate but complementary project whose objective was to safely transport, permanently store and monitor the CO₂ captured by the Oxy-combustion Power Plant Project. The FutureGen 2.0 Oxy-Combustion Large Scale Test Project successfully achieved all technical objectives inclusive of front-end-engineering and design, and advanced design required to accurately estimate and contract for the construction, commissioning, and start-up of a commercial-scale "ready to build" power plant using oxy-combustion technology, including full integration with the companion CO₂ Pipeline and Storage project. Ultimately the project did not proceed to construction due to insufficient time to complete necessary EPC contract negotiations and commercial financing prior to expiration of federal co-funding, which triggered a DOE decision to closeout its participation in the project. Through the work that was completed, valuable technical, commercial, and programmatic lessons were learned. This project has significantly advanced the development of near-zero emission technology and will

Active Learning of Classification Models with Likert-Scale Feedback.

Science.gov (United States)

Xue, Yanbing; Hauskrecht, Milos

2017-01-01

Annotation of classification data by humans can be a time-consuming and tedious process. Finding ways of reducing the annotation effort is critical for building the classification models in practice and for applying them to a variety of classification tasks. In this paper, we develop a new active learning framework that combines two strategies to reduce the annotation effort. First, it relies on label uncertainty information obtained from the human in terms of the Likert-scale feedback. Second, it uses active learning to annotate examples with the greatest expected change. We propose a Bayesian approach to calculate the expectation and an incremental SVM solver to reduce the time complexity of the solvers. We show the combination of our active learning strategy and the Likert-scale feedback can learn classification models more rapidly and with a smaller number of labeled instances than methods that rely on either Likert-scale labels or active learning alone.

A guided note taking strategy supports student learning in the large lecture classes

Science.gov (United States)

Tanamatayarat, J.; Sujarittham, T.; Wuttiprom, S.; Hefer, E.

2017-09-01

In higher education, lecturing has been found to be the most prevalent teaching format for large classes. Generally, this format tends not to result in effective learning outcomes. Therefore, to support student learning in these large lecture classes, we developed guided notes containing quotations, blank spaces, pictures, and problems. A guided note taking strategy was selected and has been used in our introductory physics course for many years. In this study, we investigated the results of implementing the guided note taking strategy to promote student learning on electrostatics. The samples were three groups of first-year students from two universities: 163 and 224 science students and 147 engineering students. All of the students were enrolled in the introductory physics course in the second semester. To assess the students’ understanding, we administered pre- and post-tests to the students by using the electrostatics test. The questions were selected from the conceptual survey of electricity and magnetism (CSEM) and some leading physics textbooks. The results of the students’ understanding were analyzed by the average normalized gains (). The value of each group was 0.61, 0.55, and 0.54, respectively. Furthermore, the students’ views on learning with the guided note taking strategy were explored by using the five-point rating scale survey. Most students perceived that the strategy helped support their active learning and engagement in the lectures.

Evolution of scaling emergence in large-scale spatial epidemic spreading.

Science.gov (United States)

Wang, Lin; Li, Xiang; Zhang, Yi-Qing; Zhang, Yan; Zhang, Kan

2011-01-01

Zipf's law and Heaps' law are two representatives of the scaling concepts, which play a significant role in the study of complexity science. The coexistence of the Zipf's law and the Heaps' law motivates different understandings on the dependence between these two scalings, which has still hardly been clarified. In this article, we observe an evolution process of the scalings: the Zipf's law and the Heaps' law are naturally shaped to coexist at the initial time, while the crossover comes with the emergence of their inconsistency at the larger time before reaching a stable state, where the Heaps' law still exists with the disappearance of strict Zipf's law. Such findings are illustrated with a scenario of large-scale spatial epidemic spreading, and the empirical results of pandemic disease support a universal analysis of the relation between the two laws regardless of the biological details of disease. Employing the United States domestic air transportation and demographic data to construct a metapopulation model for simulating the pandemic spread at the U.S. country level, we uncover that the broad heterogeneity of the infrastructure plays a key role in the evolution of scaling emergence. The analyses of large-scale spatial epidemic spreading help understand the temporal evolution of scalings, indicating the coexistence of the Zipf's law and the Heaps' law depends on the collective dynamics of epidemic processes, and the heterogeneity of epidemic spread indicates the significance of performing targeted containment strategies at the early time of a pandemic disease.

Large-Scale Structure and Hyperuniformity of Amorphous Ices

Science.gov (United States)

Martelli, Fausto; Torquato, Salvatore; Giovambattista, Nicolas; Car, Roberto

2017-09-01

We investigate the large-scale structure of amorphous ices and transitions between their different forms by quantifying their large-scale density fluctuations. Specifically, we simulate the isothermal compression of low-density amorphous ice (LDA) and hexagonal ice to produce high-density amorphous ice (HDA). Both HDA and LDA are nearly hyperuniform; i.e., they are characterized by an anomalous suppression of large-scale density fluctuations. By contrast, in correspondence with the nonequilibrium phase transitions to HDA, the presence of structural heterogeneities strongly suppresses the hyperuniformity and the system becomes hyposurficial (devoid of "surface-area fluctuations"). Our investigation challenges the largely accepted "frozen-liquid" picture, which views glasses as structurally arrested liquids. Beyond implications for water, our findings enrich our understanding of pressure-induced structural transformations in glasses.

Learning design and feedback processes at scale

DEFF Research Database (Denmark)

Ringtved, Ulla L.; Miligan, Sandra; Corrin, Linda

2016-01-01

Design for teaching in scaled courses is shifting away from replication of the traditional on-campus or online teaching-learning relationship towards exploiting the distinctive characteristic and potentials of that environment to transform both teaching and learning. This involves consideration...... design and would benefit from learning analytics support? What is the character of analytics that can be deployed to help deliver good design of online learning platforms? What are the theoretical and pedagogical bases inherent in different analytics designs? These and other questions will be examined...

Approximate kernel competitive learning.

Science.gov (United States)

Wu, Jian-Sheng; Zheng, Wei-Shi; Lai, Jian-Huang

2015-03-01

Kernel competitive learning has been successfully used to achieve robust clustering. However, kernel competitive learning (KCL) is not scalable for large scale data processing, because (1) it has to calculate and store the full kernel matrix that is too large to be calculated and kept in the memory and (2) it cannot be computed in parallel. In this paper we develop a framework of approximate kernel competitive learning for processing large scale dataset. The proposed framework consists of two parts. First, it derives an approximate kernel competitive learning (AKCL), which learns kernel competitive learning in a subspace via sampling. We provide solid theoretical analysis on why the proposed approximation modelling would work for kernel competitive learning, and furthermore, we show that the computational complexity of AKCL is largely reduced. Second, we propose a pseudo-parallelled approximate kernel competitive learning (PAKCL) based on a set-based kernel competitive learning strategy, which overcomes the obstacle of using parallel programming in kernel competitive learning and significantly accelerates the approximate kernel competitive learning for large scale clustering. The empirical evaluation on publicly available datasets shows that the proposed AKCL and PAKCL can perform comparably as KCL, with a large reduction on computational cost. Also, the proposed methods achieve more effective clustering performance in terms of clustering precision against related approximate clustering approaches. Copyright © 2014 Elsevier Ltd. All rights reserved.

High-Performance Monitoring Architecture for Large-Scale Distributed Systems Using Event Filtering

Science.gov (United States)

Maly, K.

1998-01-01

Monitoring is an essential process to observe and improve the reliability and the performance of large-scale distributed (LSD) systems. In an LSD environment, a large number of events is generated by the system components during its execution or interaction with external objects (e.g. users or processes). Monitoring such events is necessary for observing the run-time behavior of LSD systems and providing status information required for debugging, tuning and managing such applications. However, correlated events are generated concurrently and could be distributed in various locations in the applications environment which complicates the management decisions process and thereby makes monitoring LSD systems an intricate task. We propose a scalable high-performance monitoring architecture for LSD systems to detect and classify interesting local and global events and disseminate the monitoring information to the corresponding end- points management applications such as debugging and reactive control tools to improve the application performance and reliability. A large volume of events may be generated due to the extensive demands of the monitoring applications and the high interaction of LSD systems. The monitoring architecture employs a high-performance event filtering mechanism to efficiently process the large volume of event traffic generated by LSD systems and minimize the intrusiveness of the monitoring process by reducing the event traffic flow in the system and distributing the monitoring computation. Our architecture also supports dynamic and flexible reconfiguration of the monitoring mechanism via its Instrumentation and subscription components. As a case study, we show how our monitoring architecture can be utilized to improve the reliability and the performance of the Interactive Remote Instruction (IRI) system which is a large-scale distributed system for collaborative distance learning. The filtering mechanism represents an Intrinsic component integrated

Double inflation: A possible resolution of the large-scale structure problem

International Nuclear Information System (INIS)

Turner, M.S.; Villumsen, J.V.; Vittorio, N.; Silk, J.; Juszkiewicz, R.

1986-11-01

A model is presented for the large-scale structure of the universe in which two successive inflationary phases resulted in large small-scale and small large-scale density fluctuations. This bimodal density fluctuation spectrum in an Ω = 1 universe dominated by hot dark matter leads to large-scale structure of the galaxy distribution that is consistent with recent observational results. In particular, large, nearly empty voids and significant large-scale peculiar velocity fields are produced over scales of ∼100 Mpc, while the small-scale structure over ≤ 10 Mpc resembles that in a low density universe, as observed. Detailed analytical calculations and numerical simulations are given of the spatial and velocity correlations. 38 refs., 6 figs

Large-scale fracture mechancis testing -- requirements and possibilities

International Nuclear Information System (INIS)

Brumovsky, M.

1993-01-01

Application of fracture mechanics to very important and/or complicated structures, like reactor pressure vessels, brings also some questions about the reliability and precision of such calculations. These problems become more pronounced in cases of elastic-plastic conditions of loading and/or in parts with non-homogeneous materials (base metal and austenitic cladding, property gradient changes through material thickness) or with non-homogeneous stress fields (nozzles, bolt threads, residual stresses etc.). For such special cases some verification by large-scale testing is necessary and valuable. This paper discusses problems connected with planning of such experiments with respect to their limitations, requirements to a good transfer of received results to an actual vessel. At the same time, an analysis of possibilities of small-scale model experiments is also shown, mostly in connection with application of results between standard, small-scale and large-scale experiments. Experience from 30 years of large-scale testing in SKODA is used as an example to support this analysis. 1 fig

Ethics of large-scale change

DEFF Research Database (Denmark)

Arler, Finn

2006-01-01

, which kind of attitude is appropriate when dealing with large-scale changes like these from an ethical point of view. Three kinds of approaches are discussed: Aldo Leopold's mountain thinking, the neoclassical economists' approach, and finally the so-called Concentric Circle Theories approach...

Learning Local Components to Understand Large Bayesian Networks

DEFF Research Database (Denmark)

Zeng, Yifeng; Xiang, Yanping; Cordero, Jorge

2009-01-01

(domain experts) to extract accurate information from a large Bayesian network due to dimensional difficulty. We define a formulation of local components and propose a clustering algorithm to learn such local components given complete data. The algorithm groups together most inter-relevant attributes......Bayesian networks are known for providing an intuitive and compact representation of probabilistic information and allowing the creation of models over a large and complex domain. Bayesian learning and reasoning are nontrivial for a large Bayesian network. In parallel, it is a tough job for users...... in a domain. We evaluate its performance on three benchmark Bayesian networks and provide results in support. We further show that the learned components may represent local knowledge more precisely in comparison to the full Bayesian networks when working with a small amount of data....

Comparison Between Overtopping Discharge in Small and Large Scale Models

DEFF Research Database (Denmark)

Helgason, Einar; Burcharth, Hans F.

2006-01-01

The present paper presents overtopping measurements from small scale model test performed at the Haudraulic & Coastal Engineering Laboratory, Aalborg University, Denmark and large scale model tests performed at the Largde Wave Channel,Hannover, Germany. Comparison between results obtained from...... small and large scale model tests show no clear evidence of scale effects for overtopping above a threshold value. In the large scale model no overtopping was measured for waveheights below Hs = 0.5m as the water sunk into the voids between the stones on the crest. For low overtopping scale effects...

Scaling prediction errors to reward variability benefits error-driven learning in humans.

Science.gov (United States)

Diederen, Kelly M J; Schultz, Wolfram

2015-09-01

Effective error-driven learning requires individuals to adapt learning to environmental reward variability. The adaptive mechanism may involve decays in learning rate across subsequent trials, as shown previously, and rescaling of reward prediction errors. The present study investigated the influence of prediction error scaling and, in particular, the consequences for learning performance. Participants explicitly predicted reward magnitudes that were drawn from different probability distributions with specific standard deviations. By fitting the data with reinforcement learning models, we found scaling of prediction errors, in addition to the learning rate decay shown previously. Importantly, the prediction error scaling was closely related to learning performance, defined as accuracy in predicting the mean of reward distributions, across individual participants. In addition, participants who scaled prediction errors relative to standard deviation also presented with more similar performance for different standard deviations, indicating that increases in standard deviation did not substantially decrease "adapters'" accuracy in predicting the means of reward distributions. However, exaggerated scaling beyond the standard deviation resulted in impaired performance. Thus efficient adaptation makes learning more robust to changing variability. Copyright © 2015 the American Physiological Society.

Machine learning techniques to examine large patient databases.

Science.gov (United States)

Meyfroidt, Geert; Güiza, Fabian; Ramon, Jan; Bruynooghe, Maurice

2009-03-01

Computerization in healthcare in general, and in the operating room (OR) and intensive care unit (ICU) in particular, is on the rise. This leads to large patient databases, with specific properties. Machine learning techniques are able to examine and to extract knowledge from large databases in an automatic way. Although the number of potential applications for these techniques in medicine is large, few medical doctors are familiar with their methodology, advantages and pitfalls. A general overview of machine learning techniques, with a more detailed discussion of some of these algorithms, is presented in this review.

Learning by bidding: evidence from a large-scale natural experiment

Czech Academy of Sciences Publication Activity Database

Hanousek, Jan; Kočenda, Evžen

-, č. 247 (2005), s. 1-33 ISSN 1211-3298 Institutional research plan: CEZ:AV0Z70850503 Keywords : learning * natural experiment * auction Subject RIV: AH - Economics http://www.cerge-ei.cz/pdf/wp/Wp247.pdf

Needs, opportunities, and options for large scale systems research

Energy Technology Data Exchange (ETDEWEB)

Thompson, G.L.

1984-10-01

The Office of Energy Research was recently asked to perform a study of Large Scale Systems in order to facilitate the development of a true large systems theory. It was decided to ask experts in the fields of electrical engineering, chemical engineering and manufacturing/operations research for their ideas concerning large scale systems research. The author was asked to distribute a questionnaire among these experts to find out their opinions concerning recent accomplishments and future research directions in large scale systems research. He was also requested to convene a conference which included three experts in each area as panel members to discuss the general area of large scale systems research. The conference was held on March 26--27, 1984 in Pittsburgh with nine panel members, and 15 other attendees. The present report is a summary of the ideas presented and the recommendations proposed by the attendees.

Large-scale modelling of neuronal systems

International Nuclear Information System (INIS)

Castellani, G.; Verondini, E.; Giampieri, E.; Bersani, F.; Remondini, D.; Milanesi, L.; Zironi, I.

2009-01-01

The brain is, without any doubt, the most, complex system of the human body. Its complexity is also due to the extremely high number of neurons, as well as the huge number of synapses connecting them. Each neuron is capable to perform complex tasks, like learning and memorizing a large class of patterns. The simulation of large neuronal systems is challenging for both technological and computational reasons, and can open new perspectives for the comprehension of brain functioning. A well-known and widely accepted model of bidirectional synaptic plasticity, the BCM model, is stated by a differential equation approach based on bistability and selectivity properties. We have modified the BCM model extending it from a single-neuron to a whole-network model. This new model is capable to generate interesting network topologies starting from a small number of local parameters, describing the interaction between incoming and outgoing links from each neuron. We have characterized this model in terms of complex network theory, showing how this, learning rule can be a support For network generation.

Large-scale structure of the Universe

International Nuclear Information System (INIS)

Doroshkevich, A.G.

1978-01-01

The problems, discussed at the ''Large-scale Structure of the Universe'' symposium are considered on a popular level. Described are the cell structure of galaxy distribution in the Universe, principles of mathematical galaxy distribution modelling. The images of cell structures, obtained after reprocessing with the computer are given. Discussed are three hypothesis - vortical, entropic, adiabatic, suggesting various processes of galaxy and galaxy clusters origin. A considerable advantage of the adiabatic hypothesis is recognized. The relict radiation, as a method of direct studying the processes taking place in the Universe is considered. The large-scale peculiarities and small-scale fluctuations of the relict radiation temperature enable one to estimate the turbance properties at the pre-galaxy stage. The discussion of problems, pertaining to studying the hot gas, contained in galaxy clusters, the interactions within galaxy clusters and with the inter-galaxy medium, is recognized to be a notable contribution into the development of theoretical and observational cosmology

Seismic safety in conducting large-scale blasts

Science.gov (United States)

Mashukov, I. V.; Chaplygin, V. V.; Domanov, V. P.; Semin, A. A.; Klimkin, M. A.

2017-09-01

In mining enterprises to prepare hard rocks for excavation a drilling and blasting method is used. With the approach of mining operations to settlements the negative effect of large-scale blasts increases. To assess the level of seismic impact of large-scale blasts the scientific staff of Siberian State Industrial University carried out expertise for coal mines and iron ore enterprises. Determination of the magnitude of surface seismic vibrations caused by mass explosions was performed using seismic receivers, an analog-digital converter with recording on a laptop. The registration results of surface seismic vibrations during production of more than 280 large-scale blasts at 17 mining enterprises in 22 settlements are presented. The maximum velocity values of the Earth’s surface vibrations are determined. The safety evaluation of seismic effect was carried out according to the permissible value of vibration velocity. For cases with exceedance of permissible values recommendations were developed to reduce the level of seismic impact.

Image-based Exploration of Large-Scale Pathline Fields

KAUST Repository

Nagoor, Omniah H.

2014-05-27

While real-time applications are nowadays routinely used in visualizing large nu- merical simulations and volumes, handling these large-scale datasets requires high-end graphics clusters or supercomputers to process and visualize them. However, not all users have access to powerful clusters. Therefore, it is challenging to come up with a visualization approach that provides insight to large-scale datasets on a single com- puter. Explorable images (EI) is one of the methods that allows users to handle large data on a single workstation. Although it is a view-dependent method, it combines both exploration and modification of visual aspects without re-accessing the original huge data. In this thesis, we propose a novel image-based method that applies the concept of EI in visualizing large flow-field pathlines data. The goal of our work is to provide an optimized image-based method, which scales well with the dataset size. Our approach is based on constructing a per-pixel linked list data structure in which each pixel contains a list of pathlines segments. With this view-dependent method it is possible to filter, color-code and explore large-scale flow data in real-time. In addition, optimization techniques such as early-ray termination and deferred shading are applied, which further improves the performance and scalability of our approach.

Universal Design for Learning in Teaching Large Lecture Classes

Science.gov (United States)

Dean, Tereza; Lee-Post, Anita; Hapke, Holly

2017-01-01

To augment traditional lecture with instructional tools that provide options for content representation, learner engagement, and learning expression, we followed the Universal Design for Learning (UDL) principles to design and implement a learning environment for teaching and learning in large lecture classes. To this end, we incorporated four…

Homogenization of Large-Scale Movement Models in Ecology

Science.gov (United States)

Garlick, M.J.; Powell, J.A.; Hooten, M.B.; McFarlane, L.R.

2011-01-01

A difficulty in using diffusion models to predict large scale animal population dispersal is that individuals move differently based on local information (as opposed to gradients) in differing habitat types. This can be accommodated by using ecological diffusion. However, real environments are often spatially complex, limiting application of a direct approach. Homogenization for partial differential equations has long been applied to Fickian diffusion (in which average individual movement is organized along gradients of habitat and population density). We derive a homogenization procedure for ecological diffusion and apply it to a simple model for chronic wasting disease in mule deer. Homogenization allows us to determine the impact of small scale (10-100 m) habitat variability on large scale (10-100 km) movement. The procedure generates asymptotic equations for solutions on the large scale with parameters defined by small-scale variation. The simplicity of this homogenization procedure is striking when compared to the multi-dimensional homogenization procedure for Fickian diffusion,and the method will be equally straightforward for more complex models. ?? 2010 Society for Mathematical Biology.

The role of large-scale, extratropical dynamics in climate change

Energy Technology Data Exchange (ETDEWEB)

Shepherd, T.G. [ed.

1994-02-01

The climate modeling community has focused recently on improving our understanding of certain processes, such as cloud feedbacks and ocean circulation, that are deemed critical to climate-change prediction. Although attention to such processes is warranted, emphasis on these areas has diminished a general appreciation of the role played by the large-scale dynamics of the extratropical atmosphere. Lack of interest in extratropical dynamics may reflect the assumption that these dynamical processes are a non-problem as far as climate modeling is concerned, since general circulation models (GCMs) calculate motions on this scale from first principles. Nevertheless, serious shortcomings in our ability to understand and simulate large-scale dynamics exist. Partly due to a paucity of standard GCM diagnostic calculations of large-scale motions and their transports of heat, momentum, potential vorticity, and moisture, a comprehensive understanding of the role of large-scale dynamics in GCM climate simulations has not been developed. Uncertainties remain in our understanding and simulation of large-scale extratropical dynamics and their interaction with other climatic processes, such as cloud feedbacks, large-scale ocean circulation, moist convection, air-sea interaction and land-surface processes. To address some of these issues, the 17th Stanstead Seminar was convened at Bishop`s University in Lennoxville, Quebec. The purpose of the Seminar was to promote discussion of the role of large-scale extratropical dynamics in global climate change. Abstracts of the talks are included in this volume. On the basis of these talks, several key issues emerged concerning large-scale extratropical dynamics and their climatic role. Individual records are indexed separately for the database.

The role of large-scale, extratropical dynamics in climate change

International Nuclear Information System (INIS)

Shepherd, T.G.

1994-02-01

The climate modeling community has focused recently on improving our understanding of certain processes, such as cloud feedbacks and ocean circulation, that are deemed critical to climate-change prediction. Although attention to such processes is warranted, emphasis on these areas has diminished a general appreciation of the role played by the large-scale dynamics of the extratropical atmosphere. Lack of interest in extratropical dynamics may reflect the assumption that these dynamical processes are a non-problem as far as climate modeling is concerned, since general circulation models (GCMs) calculate motions on this scale from first principles. Nevertheless, serious shortcomings in our ability to understand and simulate large-scale dynamics exist. Partly due to a paucity of standard GCM diagnostic calculations of large-scale motions and their transports of heat, momentum, potential vorticity, and moisture, a comprehensive understanding of the role of large-scale dynamics in GCM climate simulations has not been developed. Uncertainties remain in our understanding and simulation of large-scale extratropical dynamics and their interaction with other climatic processes, such as cloud feedbacks, large-scale ocean circulation, moist convection, air-sea interaction and land-surface processes. To address some of these issues, the 17th Stanstead Seminar was convened at Bishop's University in Lennoxville, Quebec. The purpose of the Seminar was to promote discussion of the role of large-scale extratropical dynamics in global climate change. Abstracts of the talks are included in this volume. On the basis of these talks, several key issues emerged concerning large-scale extratropical dynamics and their climatic role. Individual records are indexed separately for the database

Status: Large-scale subatmospheric cryogenic systems

International Nuclear Information System (INIS)

Peterson, T.

1989-01-01

In the late 1960's and early 1970's an interest in testing and operating RF cavities at 1.8K motivated the development and construction of four large (300 Watt) 1.8K refrigeration systems. in the past decade, development of successful superconducting RF cavities and interest in obtaining higher magnetic fields with the improved Niobium-Titanium superconductors has once again created interest in large-scale 1.8K refrigeration systems. The L'Air Liquide plant for Tore Supra is a recently commissioned 300 Watt 1.8K system which incorporates new technology, cold compressors, to obtain the low vapor pressure for low temperature cooling. CEBAF proposes to use cold compressors to obtain 5KW at 2.0K. Magnetic refrigerators of 10 Watt capacity or higher at 1.8K are now being developed. The state of the art of large-scale refrigeration in the range under 4K will be reviewed. 28 refs., 4 figs., 7 tabs

Large-scale networks in engineering and life sciences

CERN Document Server

Findeisen, Rolf; Flockerzi, Dietrich; Reichl, Udo; Sundmacher, Kai

2014-01-01

This edited volume provides insights into and tools for the modeling, analysis, optimization, and control of large-scale networks in the life sciences and in engineering. Large-scale systems are often the result of networked interactions between a large number of subsystems, and their analysis and control are becoming increasingly important. The chapters of this book present the basic concepts and theoretical foundations of network theory and discuss its applications in different scientific areas such as biochemical reactions, chemical production processes, systems biology, electrical circuits, and mobile agents. The aim is to identify common concepts, to understand the underlying mathematical ideas, and to inspire discussions across the borders of the various disciplines.  The book originates from the interdisciplinary summer school “Large Scale Networks in Engineering and Life Sciences” hosted by the International Max Planck Research School Magdeburg, September 26-30, 2011, and will therefore be of int...

Learning and clean-up in a large scale music database

DEFF Research Database (Denmark)

Hansen, Lars Kai; Lehn-Schiøler, Tue; Petersen, Kaare Brandt

2007-01-01

We have collected a database of musical features from radio broadcasts (N > 100.000). The database poses a number of hard modeling challenges including: Segmentation problems and missing metadata. We describe our efforts towards cleaning the database using signal processing and machine learning...

TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild

KAUST Repository

Mü ller, Matthias; Bibi, Adel Aamer; Giancola, Silvio; Al-Subaihi, Salman; Ghanem, Bernard

2018-01-01

Despite the numerous developments in object tracking, further development of current tracking algorithms is limited by small and mostly saturated datasets. As a matter of fact, data-hungry trackers based on deep-learning currently rely on object detection datasets due to the scarcity of dedicated large-scale tracking datasets. In this work, we present TrackingNet, the first large-scale dataset and benchmark for object tracking in the wild. We provide more than 30K videos with more than 14 million dense bounding box annotations. Our dataset covers a wide selection of object classes in broad and diverse context. By releasing such a large-scale dataset, we expect deep trackers to further improve and generalize. In addition, we introduce a new benchmark composed of 500 novel videos, modeled with a distribution similar to our training dataset. By sequestering the annotation of the test set and providing an online evaluation server, we provide a fair benchmark for future development of object trackers. Deep trackers fine-tuned on a fraction of our dataset improve their performance by up to 1.6% on OTB100 and up to 1.7% on TrackingNet Test. We provide an extensive benchmark on TrackingNet by evaluating more than 20 trackers. Our results suggest that object tracking in the wild is far from being solved.

TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild

KAUST Repository

Müller, Matthias

2018-03-28

Despite the numerous developments in object tracking, further development of current tracking algorithms is limited by small and mostly saturated datasets. As a matter of fact, data-hungry trackers based on deep-learning currently rely on object detection datasets due to the scarcity of dedicated large-scale tracking datasets. In this work, we present TrackingNet, the first large-scale dataset and benchmark for object tracking in the wild. We provide more than 30K videos with more than 14 million dense bounding box annotations. Our dataset covers a wide selection of object classes in broad and diverse context. By releasing such a large-scale dataset, we expect deep trackers to further improve and generalize. In addition, we introduce a new benchmark composed of 500 novel videos, modeled with a distribution similar to our training dataset. By sequestering the annotation of the test set and providing an online evaluation server, we provide a fair benchmark for future development of object trackers. Deep trackers fine-tuned on a fraction of our dataset improve their performance by up to 1.6% on OTB100 and up to 1.7% on TrackingNet Test. We provide an extensive benchmark on TrackingNet by evaluating more than 20 trackers. Our results suggest that object tracking in the wild is far from being solved.

Preparing laboratory and real-world EEG data for large-scale analysis: A containerized approach

Directory of Open Access Journals (Sweden)

Nima eBigdely-Shamlo

2016-03-01

Full Text Available Large-scale analysis of EEG and other physiological measures promises new insights into brain processes and more accurate and robust brain-computer interface (BCI models.. However, the absence of standard-ized vocabularies for annotating events in a machine understandable manner, the welter of collection-specific data organizations, the diffi-culty in moving data across processing platforms, and the unavailability of agreed-upon standards for preprocessing have prevented large-scale analyses of EEG. Here we describe a containerized approach and freely available tools we have developed to facilitate the process of an-notating, packaging, and preprocessing EEG data collections to enable data sharing, archiving, large-scale machine learning/data mining and (meta-analysis. The EEG Study Schema (ESS comprises three data Levels, each with its own XML-document schema and file/folder convention, plus a standardized (PREP pipeline to move raw (Data Level 1 data to a basic preprocessed state (Data Level 2 suitable for application of a large class of EEG analysis methods. Researchers can ship a study as a single unit and operate on its data using a standardized interface. ESS does not require a central database and provides all the metadata data necessary to execute a wide variety of EEG processing pipelines. The primary focus of ESS is automated in-depth analysis and meta-analysis EEG studies. However, ESS can also encapsulate meta-information for the other modalities such as eye tracking, that are in-creasingly used in both laboratory and real-world neuroimaging. ESS schema and tools are freely available at eegstudy.org, and a central cata-log of over 850 GB of existing data in ESS format is available at study-catalog.org. These tools and resources are part of a larger effort to ena-ble data sharing at sufficient scale for researchers to engage in truly large-scale EEG analysis and data mining (BigEEG.org.

An Novel Architecture of Large-scale Communication in IOT

Science.gov (United States)

Ma, Wubin; Deng, Su; Huang, Hongbin

2018-03-01

In recent years, many scholars have done a great deal of research on the development of Internet of Things and networked physical systems. However, few people have made the detailed visualization of the large-scale communications architecture in the IOT. In fact, the non-uniform technology between IPv6 and access points has led to a lack of broad principles of large-scale communications architectures. Therefore, this paper presents the Uni-IPv6 Access and Information Exchange Method (UAIEM), a new architecture and algorithm that addresses large-scale communications in the IOT.

Benefits of transactive memory systems in large-scale development

OpenAIRE

Aivars, Sablis

2016-01-01

Context. Large-scale software development projects are those consisting of a large number of teams, maybe even spread across multiple locations, and working on large and complex software tasks. That means that neither a team member individually nor an entire team holds all the knowledge about the software being developed and teams have to communicate and coordinate their knowledge. Therefore, teams and team members in large-scale software development projects must acquire and manage expertise...

Study of a large scale neutron measurement channel

International Nuclear Information System (INIS)

Amarouayache, Anissa; Ben Hadid, Hayet.

1982-12-01

A large scale measurement channel allows the processing of the signal coming from an unique neutronic sensor, during three different running modes: impulses, fluctuations and current. The study described in this note includes three parts: - A theoretical study of the large scale channel and its brief description are given. The results obtained till now in that domain are presented. - The fluctuation mode is thoroughly studied and the improvements to be done are defined. The study of a fluctuation linear channel with an automatic commutation of scales is described and the results of the tests are given. In this large scale channel, the method of data processing is analogical. - To become independent of the problems generated by the use of a an analogical processing of the fluctuation signal, a digital method of data processing is tested. The validity of that method is improved. The results obtained on a test system realized according to this method are given and a preliminary plan for further research is defined [fr

Development and validation of the Simulation Learning Effectiveness Scale for nursing students.

Science.gov (United States)

Pai, Hsiang-Chu

2016-11-01

To develop and validate the Simulation Learning Effectiveness Scale, which is based on Bandura's social cognitive theory. A simulation programme is a significant teaching strategy for nursing students. Nevertheless, there are few evidence-based instruments that validate the effectiveness of simulation learning in Taiwan. This is a quantitative descriptive design. In Study 1, a nonprobability convenience sample of 151 student nurses completed the Simulation Learning Effectiveness Scale. Exploratory factor analysis was used to examine the factor structure of the instrument. In Study 2, which involved 365 student nurses, confirmatory factor analysis and structural equation modelling were used to analyse the construct validity of the Simulation Learning Effectiveness Scale. In Study 1, exploratory factor analysis yielded three components: self-regulation, self-efficacy and self-motivation. The three factors explained 29·09, 27·74 and 19·32% of the variance, respectively. The final 12-item instrument with the three factors explained 76·15% of variance. Cronbach's alpha was 0·94. In Study 2, confirmatory factor analysis identified a second-order factor termed Simulation Learning Effectiveness Scale. Goodness-of-fit indices showed an acceptable fit overall with the full model (χ 2 /df (51) = 3·54, comparative fit index = 0·96, Tucker-Lewis index = 0·95 and standardised root-mean-square residual = 0·035). In addition, teacher's competence was found to encourage learning, and self-reflection and insight were significantly and positively associated with Simulation Learning Effectiveness Scale. Teacher's competence in encouraging learning also was significantly and positively associated with self-reflection and insight. Overall, theses variable explained 21·9% of the variance in the student's learning effectiveness. The Simulation Learning Effectiveness Scale is a reliable and valid means to assess simulation learning effectiveness for nursing students

Capabilities of the Large-Scale Sediment Transport Facility

Science.gov (United States)

2016-04-01

pump flow meters, sediment trap weigh tanks , and beach profiling lidar. A detailed discussion of the original LSTF features and capabilities can be...ERDC/CHL CHETN-I-88 April 2016 Approved for public release; distribution is unlimited. Capabilities of the Large-Scale Sediment Transport...describes the Large-Scale Sediment Transport Facility (LSTF) and recent upgrades to the measurement systems. The purpose of these upgrades was to increase

Spatiotemporal property and predictability of large-scale human mobility

Science.gov (United States)

Zhang, Hai-Tao; Zhu, Tao; Fu, Dongfei; Xu, Bowen; Han, Xiao-Pu; Chen, Duxin

2018-04-01

Spatiotemporal characteristics of human mobility emerging from complexity on individual scale have been extensively studied due to the application potential on human behavior prediction and recommendation, and control of epidemic spreading. We collect and investigate a comprehensive data set of human activities on large geographical scales, including both websites browse and mobile towers visit. Numerical results show that the degree of activity decays as a power law, indicating that human behaviors are reminiscent of scale-free random walks known as Lévy flight. More significantly, this study suggests that human activities on large geographical scales have specific non-Markovian characteristics, such as a two-segment power-law distribution of dwelling time and a high possibility for prediction. Furthermore, a scale-free featured mobility model with two essential ingredients, i.e., preferential return and exploration, and a Gaussian distribution assumption on the exploration tendency parameter is proposed, which outperforms existing human mobility models under scenarios of large geographical scales.

Problems of large-scale vertically-integrated aquaculture

Energy Technology Data Exchange (ETDEWEB)

Webber, H H; Riordan, P F

1976-01-01

The problems of vertically-integrated aquaculture are outlined; they are concerned with: species limitations (in the market, biological and technological); site selection, feed, manpower needs, and legal, institutional and financial requirements. The gaps in understanding of, and the constraints limiting, large-scale aquaculture are listed. Future action is recommended with respect to: types and diversity of species to be cultivated, marketing, biotechnology (seed supply, disease control, water quality and concerted effort), siting, feed, manpower, legal and institutional aids (granting of water rights, grants, tax breaks, duty-free imports, etc.), and adequate financing. The last of hard data based on experience suggests that large-scale vertically-integrated aquaculture is a high risk enterprise, and with the high capital investment required, banks and funding institutions are wary of supporting it. Investment in pilot projects is suggested to demonstrate that large-scale aquaculture can be a fully functional and successful business. Construction and operation of such pilot farms is judged to be in the interests of both the public and private sector.

Large-scale computing with Quantum Espresso

International Nuclear Information System (INIS)

Giannozzi, P.; Cavazzoni, C.

2009-01-01

This paper gives a short introduction to Quantum Espresso: a distribution of software for atomistic simulations in condensed-matter physics, chemical physics, materials science, and to its usage in large-scale parallel computing.

Developing Scale for Assimilate the Integration between Learning Theories and E-learning.

Directory of Open Access Journals (Sweden)

George Maher Iskander

2014-03-01

Full Text Available As e-learning tend to get more and more significant for all kind of universities, researchers and consultants are becoming aware of the fact that a high technology approach and Blackboard do not guarantee successful teaching and learning. Thus, a move to pedagogy-based theories can be observed within the field of e-learning. This study describes the procedure of the development of an empirically-based psychometrically-sound instrument to measure instructional model for e-learning system at Middle East universities. In order to accelerate the acceptance of e-learning and implementation of institution-wide adoption of e-learning, it is important to understand students' perceptions with instructional model for e- learning. The 19-item scale developed shows a high probability of differentiating between positive and negative perceptions and the methods which can be used for embedding the traditional learning theories into e-learning.

The Learning Behaviors Scale: National Standardization in Trinidad and Tobago

Science.gov (United States)

Chao, Jessica L.; McDermott, Paul A.; Watkins, Marley W.; Drogalis, Anna Rhoad; Worrell, Frank C.; Hall, Tracey E.

2018-01-01

This study reports on the national standardization and validation of the Learning Behaviors Scale (LBS) for use in Trinidad and Tobago. The LBS is a teacher rating scale centering on observable behaviors relevant to identifying childhood approaches to classroom learning. Teachers observed a stratified sample of 900 students across the islands'…

VESPA: Very large-scale Evolutionary and Selective Pressure Analyses

Directory of Open Access Journals (Sweden)

Andrew E. Webb

2017-06-01

Full Text Available Background Large-scale molecular evolutionary analyses of protein coding sequences requires a number of preparatory inter-related steps from finding gene families, to generating alignments and phylogenetic trees and assessing selective pressure variation. Each phase of these analyses can represent significant challenges, particularly when working with entire proteomes (all protein coding sequences in a genome from a large number of species. Methods We present VESPA, software capable of automating a selective pressure analysis using codeML in addition to the preparatory analyses and summary statistics. VESPA is written in python and Perl and is designed to run within a UNIX environment. Results We have benchmarked VESPA and our results show that the method is consistent, performs well on both large scale and smaller scale datasets, and produces results in line with previously published datasets. Discussion Large-scale gene family identification, sequence alignment, and phylogeny reconstruction are all important aspects of large-scale molecular evolutionary analyses. VESPA provides flexible software for simplifying these processes along with downstream selective pressure variation analyses. The software automatically interprets results from codeML and produces simplified summary files to assist the user in better understanding the results. VESPA may be found at the following website: http://www.mol-evol.org/VESPA.

Large scale identification and categorization of protein sequences using structured logistic regression.

Directory of Open Access Journals (Sweden)

Bjørn P Pedersen

Full Text Available BACKGROUND: Structured Logistic Regression (SLR is a newly developed machine learning tool first proposed in the context of text categorization. Current availability of extensive protein sequence databases calls for an automated method to reliably classify sequences and SLR seems well-suited for this task. The classification of P-type ATPases, a large family of ATP-driven membrane pumps transporting essential cations, was selected as a test-case that would generate important biological information as well as provide a proof-of-concept for the application of SLR to a large scale bioinformatics problem. RESULTS: Using SLR, we have built classifiers to identify and automatically categorize P-type ATPases into one of 11 pre-defined classes. The SLR-classifiers are compared to a Hidden Markov Model approach and shown to be highly accurate and scalable. Representing the bulk of currently known sequences, we analysed 9.3 million sequences in the UniProtKB and attempted to classify a large number of P-type ATPases. To examine the distribution of pumps on organisms, we also applied SLR to 1,123 complete genomes from the Entrez genome database. Finally, we analysed the predicted membrane topology of the identified P-type ATPases. CONCLUSIONS: Using the SLR-based classification tool we are able to run a large scale study of P-type ATPases. This study provides proof-of-concept for the application of SLR to a bioinformatics problem and the analysis of P-type ATPases pinpoints new and interesting targets for further biochemical characterization and structural analysis.

RESTRUCTURING OF THE LARGE-SCALE SPRINKLERS

Directory of Open Access Journals (Sweden)

Paweł Kozaczyk

2016-09-01

Full Text Available One of the best ways for agriculture to become independent from shortages of precipitation is irrigation. In the seventies and eighties of the last century a number of large-scale sprinklers in Wielkopolska was built. At the end of 1970’s in the Poznan province 67 sprinklers with a total area of 6400 ha were installed. The average size of the sprinkler reached 95 ha. In 1989 there were 98 sprinklers, and the area which was armed with them was more than 10 130 ha. The study was conducted on 7 large sprinklers with the area ranging from 230 to 520 hectares in 1986÷1998. After the introduction of the market economy in the early 90’s and ownership changes in agriculture, large-scale sprinklers have gone under a significant or total devastation. Land on the State Farms of the State Agricultural Property Agency has leased or sold and the new owners used the existing sprinklers to a very small extent. This involved a change in crop structure, demand structure and an increase in operating costs. There has also been a threefold increase in electricity prices. Operation of large-scale irrigation encountered all kinds of barriers in practice and limitations of system solutions, supply difficulties, high levels of equipment failure which is not inclined to rational use of available sprinklers. An effect of a vision of the local area was to show the current status of the remaining irrigation infrastructure. The adopted scheme for the restructuring of Polish agriculture was not the best solution, causing massive destruction of assets previously invested in the sprinkler system.

Large-scale synthesis of YSZ nanopowder by Pechini method

Indian Academy of Sciences (India)

Administrator

structure and chemical purity of 99⋅1% by inductively coupled plasma optical emission spectroscopy on a large scale. Keywords. Sol–gel; yttria-stabilized zirconia; large scale; nanopowder; Pechini method. 1. Introduction. Zirconia has attracted the attention of many scientists because of its tremendous thermal, mechanical ...

The Phoenix series large scale LNG pool fire experiments.

Energy Technology Data Exchange (ETDEWEB)

Simpson, Richard B.; Jensen, Richard Pearson; Demosthenous, Byron; Luketa, Anay Josephine; Ricks, Allen Joseph; Hightower, Marion Michael; Blanchat, Thomas K.; Helmick, Paul H.; Tieszen, Sheldon Robert; Deola, Regina Anne; Mercier, Jeffrey Alan; Suo-Anttila, Jill Marie; Miller, Timothy J.

2010-12-01

The increasing demand for natural gas could increase the number and frequency of Liquefied Natural Gas (LNG) tanker deliveries to ports across the United States. Because of the increasing number of shipments and the number of possible new facilities, concerns about the potential safety of the public and property from an accidental, and even more importantly intentional spills, have increased. While improvements have been made over the past decade in assessing hazards from LNG spills, the existing experimental data is much smaller in size and scale than many postulated large accidental and intentional spills. Since the physics and hazards from a fire change with fire size, there are concerns about the adequacy of current hazard prediction techniques for large LNG spills and fires. To address these concerns, Congress funded the Department of Energy (DOE) in 2008 to conduct a series of laboratory and large-scale LNG pool fire experiments at Sandia National Laboratories (Sandia) in Albuquerque, New Mexico. This report presents the test data and results of both sets of fire experiments. A series of five reduced-scale (gas burner) tests (yielding 27 sets of data) were conducted in 2007 and 2008 at Sandia's Thermal Test Complex (TTC) to assess flame height to fire diameter ratios as a function of nondimensional heat release rates for extrapolation to large-scale LNG fires. The large-scale LNG pool fire experiments were conducted in a 120 m diameter pond specially designed and constructed in Sandia's Area III large-scale test complex. Two fire tests of LNG spills of 21 and 81 m in diameter were conducted in 2009 to improve the understanding of flame height, smoke production, and burn rate and therefore the physics and hazards of large LNG spills and fires.

Large-scale image-based profiling of single-cell phenotypes in arrayed CRISPR-Cas9 gene perturbation screens.

Science.gov (United States)

de Groot, Reinoud; Lüthi, Joel; Lindsay, Helen; Holtackers, René; Pelkmans, Lucas

2018-01-23

High-content imaging using automated microscopy and computer vision allows multivariate profiling of single-cell phenotypes. Here, we present methods for the application of the CISPR-Cas9 system in large-scale, image-based, gene perturbation experiments. We show that CRISPR-Cas9-mediated gene perturbation can be achieved in human tissue culture cells in a timeframe that is compatible with image-based phenotyping. We developed a pipeline to construct a large-scale arrayed library of 2,281 sequence-verified CRISPR-Cas9 targeting plasmids and profiled this library for genes affecting cellular morphology and the subcellular localization of components of the nuclear pore complex (NPC). We conceived a machine-learning method that harnesses genetic heterogeneity to score gene perturbations and identify phenotypically perturbed cells for in-depth characterization of gene perturbation effects. This approach enables genome-scale image-based multivariate gene perturbation profiling using CRISPR-Cas9. © 2018 The Authors. Published under the terms of the CC BY 4.0 license.

Automated Detection of Microaneurysms Using Scale-Adapted Blob Analysis and Semi-Supervised Learning

Energy Technology Data Exchange (ETDEWEB)

Adal, Kedir M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Sidebe, Desire [Univ. of Burgundy, Dijon (France); Ali, Sharib [Univ. of Burgundy, Dijon (France); Chaum, Edward [Univ. of Tennessee, Knoxville, TN (United States); Karnowski, Thomas Paul [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Meriaudeau, Fabrice [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2014-01-07

Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are then introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier to detect true MAs. The developed system is built using only few manually labeled and a large number of unlabeled retinal color fundus images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. A competition performance measure (CPM) of 0.364 shows the competitiveness of the proposed system against state-of-the art techniques as well as the applicability of the proposed features to analyze fundus images.

Geospatial Optimization of Siting Large-Scale Solar Projects

Energy Technology Data Exchange (ETDEWEB)

Macknick, Jordan [National Renewable Energy Lab. (NREL), Golden, CO (United States); Quinby, Ted [National Renewable Energy Lab. (NREL), Golden, CO (United States); Caulfield, Emmet [Stanford Univ., CA (United States); Gerritsen, Margot [Stanford Univ., CA (United States); Diffendorfer, Jay [U.S. Geological Survey, Boulder, CO (United States); Haines, Seth [U.S. Geological Survey, Boulder, CO (United States)

2014-03-01

Recent policy and economic conditions have encouraged a renewed interest in developing large-scale solar projects in the U.S. Southwest. However, siting large-scale solar projects is complex. In addition to the quality of the solar resource, solar developers must take into consideration many environmental, social, and economic factors when evaluating a potential site. This report describes a proof-of-concept, Web-based Geographical Information Systems (GIS) tool that evaluates multiple user-defined criteria in an optimization algorithm to inform discussions and decisions regarding the locations of utility-scale solar projects. Existing siting recommendations for large-scale solar projects from governmental and non-governmental organizations are not consistent with each other, are often not transparent in methods, and do not take into consideration the differing priorities of stakeholders. The siting assistance GIS tool we have developed improves upon the existing siting guidelines by being user-driven, transparent, interactive, capable of incorporating multiple criteria, and flexible. This work provides the foundation for a dynamic siting assistance tool that can greatly facilitate siting decisions among multiple stakeholders.

Large-scale Agricultural Land Acquisitions in West Africa | IDRC ...

International Development Research Centre (IDRC) Digital Library (Canada)

This project will examine large-scale agricultural land acquisitions in nine West African countries -Burkina Faso, Guinea-Bissau, Guinea, Benin, Mali, Togo, Senegal, Niger, and Côte d'Ivoire. ... They will use the results to increase public awareness and knowledge about the consequences of large-scale land acquisitions.

FFTLasso: Large-Scale LASSO in the Fourier Domain

KAUST Repository

Bibi, Adel Aamer

2017-11-09

In this paper, we revisit the LASSO sparse representation problem, which has been studied and used in a variety of different areas, ranging from signal processing and information theory to computer vision and machine learning. In the vision community, it found its way into many important applications, including face recognition, tracking, super resolution, image denoising, to name a few. Despite advances in efficient sparse algorithms, solving large-scale LASSO problems remains a challenge. To circumvent this difficulty, people tend to downsample and subsample the problem (e.g. via dimensionality reduction) to maintain a manageable sized LASSO, which usually comes at the cost of losing solution accuracy. This paper proposes a novel circulant reformulation of the LASSO that lifts the problem to a higher dimension, where ADMM can be efficiently applied to its dual form. Because of this lifting, all optimization variables are updated using only basic element-wise operations, the most computationally expensive of which is a 1D FFT. In this way, there is no need for a linear system solver nor matrix-vector multiplication. Since all operations in our FFTLasso method are element-wise, the subproblems are completely independent and can be trivially parallelized (e.g. on a GPU). The attractive computational properties of FFTLasso are verified by extensive experiments on synthetic and real data and on the face recognition task. They demonstrate that FFTLasso scales much more effectively than a state-of-the-art solver.

FFTLasso: Large-Scale LASSO in the Fourier Domain

KAUST Repository

Bibi, Adel Aamer; Itani, Hani; Ghanem, Bernard

2017-01-01

In this paper, we revisit the LASSO sparse representation problem, which has been studied and used in a variety of different areas, ranging from signal processing and information theory to computer vision and machine learning. In the vision community, it found its way into many important applications, including face recognition, tracking, super resolution, image denoising, to name a few. Despite advances in efficient sparse algorithms, solving large-scale LASSO problems remains a challenge. To circumvent this difficulty, people tend to downsample and subsample the problem (e.g. via dimensionality reduction) to maintain a manageable sized LASSO, which usually comes at the cost of losing solution accuracy. This paper proposes a novel circulant reformulation of the LASSO that lifts the problem to a higher dimension, where ADMM can be efficiently applied to its dual form. Because of this lifting, all optimization variables are updated using only basic element-wise operations, the most computationally expensive of which is a 1D FFT. In this way, there is no need for a linear system solver nor matrix-vector multiplication. Since all operations in our FFTLasso method are element-wise, the subproblems are completely independent and can be trivially parallelized (e.g. on a GPU). The attractive computational properties of FFTLasso are verified by extensive experiments on synthetic and real data and on the face recognition task. They demonstrate that FFTLasso scales much more effectively than a state-of-the-art solver.

Large-scale motions in the universe: a review

International Nuclear Information System (INIS)

Burstein, D.

1990-01-01

The expansion of the universe can be retarded in localised regions within the universe both by the presence of gravity and by non-gravitational motions generated in the post-recombination universe. The motions of galaxies thus generated are called 'peculiar motions', and the amplitudes, size scales and coherence of these peculiar motions are among the most direct records of the structure of the universe. As such, measurements of these properties of the present-day universe provide some of the severest tests of cosmological theories. This is a review of the current evidence for large-scale motions of galaxies out to a distance of ∼5000 km s -1 (in an expanding universe, distance is proportional to radial velocity). 'Large-scale' in this context refers to motions that are correlated over size scales larger than the typical sizes of groups of galaxies, up to and including the size of the volume surveyed. To orient the reader into this relatively new field of study, a short modern history is given together with an explanation of the terminology. Careful consideration is given to the data used to measure the distances, and hence the peculiar motions, of galaxies. The evidence for large-scale motions is presented in a graphical fashion, using only the most reliable data for galaxies spanning a wide range in optical properties and over the complete range of galactic environments. The kinds of systematic errors that can affect this analysis are discussed, and the reliability of these motions is assessed. The predictions of two models of large-scale motion are compared to the observations, and special emphasis is placed on those motions in which our own Galaxy directly partakes. (author)

State of the Art in Large-Scale Soil Moisture Monitoring

Science.gov (United States)

Ochsner, Tyson E.; Cosh, Michael Harold; Cuenca, Richard H.; Dorigo, Wouter; Draper, Clara S.; Hagimoto, Yutaka; Kerr, Yan H.; Larson, Kristine M.; Njoku, Eni Gerald; Small, Eric E.;

A route to explosive large-scale magnetic reconnection in a super-ion-scale current sheet

Directory of Open Access Journals (Sweden)

K. G. Tanaka

2009-01-01

Full Text Available How to trigger magnetic reconnection is one of the most interesting and important problems in space plasma physics. Recently, electron temperature anisotropy (αeo=Te⊥/Te|| at the center of a current sheet and non-local effect of the lower-hybrid drift instability (LHDI that develops at the current sheet edges have attracted attention in this context. In addition to these effects, here we also study the effects of ion temperature anisotropy (αio=Ti⊥/Ti||. Electron anisotropy effects are known to be helpless in a current sheet whose thickness is of ion-scale. In this range of current sheet thickness, the LHDI effects are shown to weaken substantially with a small increase in thickness and the obtained saturation level is too low for a large-scale reconnection to be achieved. Then we investigate whether introduction of electron and ion temperature anisotropies in the initial stage would couple with the LHDI effects to revive quick triggering of large-scale reconnection in a super-ion-scale current sheet. The results are as follows. (1 The initial electron temperature anisotropy is consumed very quickly when a number of minuscule magnetic islands (each lateral length is 1.5~3 times the ion inertial length form. These minuscule islands do not coalesce into a large-scale island to enable large-scale reconnection. (2 The subsequent LHDI effects disturb the current sheet filled with the small islands. This makes the triggering time scale to be accelerated substantially but does not enhance the saturation level of reconnected flux. (3 When the ion temperature anisotropy is added, it survives through the small island formation stage and makes even quicker triggering to happen when the LHDI effects set-in. Furthermore the saturation level is seen to be elevated by a factor of ~2 and large-scale reconnection is achieved only in this case. Comparison with two-dimensional simulations that exclude the LHDI effects confirms that the saturation level

The Validation of the Active Learning in Health Professions Scale

Science.gov (United States)

Kammer, Rebecca; Schreiner, Laurie; Kim, Young K.; Denial, Aurora

2015-01-01

There is a need for an assessment tool for evaluating the effectiveness of active learning strategies such as problem-based learning in promoting deep learning and clinical reasoning skills within the dual environments of didactic and clinical settings in health professions education. The Active Learning in Health Professions Scale (ALPHS)…

Large-scale structure observables in general relativity

International Nuclear Information System (INIS)

Jeong, Donghui; Schmidt, Fabian

2015-01-01

We review recent studies that rigorously define several key observables of the large-scale structure of the Universe in a general relativistic context. Specifically, we consider (i) redshift perturbation of cosmic clock events; (ii) distortion of cosmic rulers, including weak lensing shear and magnification; and (iii) observed number density of tracers of the large-scale structure. We provide covariant and gauge-invariant expressions of these observables. Our expressions are given for a linearly perturbed flat Friedmann–Robertson–Walker metric including scalar, vector, and tensor metric perturbations. While we restrict ourselves to linear order in perturbation theory, the approach can be straightforwardly generalized to higher order. (paper)

Fatigue Analysis of Large-scale Wind turbine

Directory of Open Access Journals (Sweden)

Zhu Yongli

2017-01-01

Full Text Available The paper does research on top flange fatigue damage of large-scale wind turbine generator. It establishes finite element model of top flange connection system with finite element analysis software MSC. Marc/Mentat, analyzes its fatigue strain, implements load simulation of flange fatigue working condition with Bladed software, acquires flange fatigue load spectrum with rain-flow counting method, finally, it realizes fatigue analysis of top flange with fatigue analysis software MSC. Fatigue and Palmgren-Miner linear cumulative damage theory. The analysis result indicates that its result provides new thinking for flange fatigue analysis of large-scale wind turbine generator, and possesses some practical engineering value.

Real-time simulation of large-scale floods

Science.gov (United States)

Liu, Q.; Qin, Y.; Li, G. D.; Liu, Z.; Cheng, D. J.; Zhao, Y. H.

2016-08-01

According to the complex real-time water situation, the real-time simulation of large-scale floods is very important for flood prevention practice. Model robustness and running efficiency are two critical factors in successful real-time flood simulation. This paper proposed a robust, two-dimensional, shallow water model based on the unstructured Godunov- type finite volume method. A robust wet/dry front method is used to enhance the numerical stability. An adaptive method is proposed to improve the running efficiency. The proposed model is used for large-scale flood simulation on real topography. Results compared to those of MIKE21 show the strong performance of the proposed model.

Large-scale numerical simulations of plasmas

International Nuclear Information System (INIS)

Hamaguchi, Satoshi

2004-01-01

The recent trend of large scales simulations of fusion plasma and processing plasmas is briefly summarized. Many advanced simulation techniques have been developed for fusion plasmas and some of these techniques are now applied to analyses of processing plasmas. (author)

Nearly incompressible fluids: Hydrodynamics and large scale inhomogeneity

International Nuclear Information System (INIS)

Hunana, P.; Zank, G. P.; Shaikh, D.

2006-01-01

A system of hydrodynamic equations in the presence of large-scale inhomogeneities for a high plasma beta solar wind is derived. The theory is derived under the assumption of low turbulent Mach number and is developed for the flows where the usual incompressible description is not satisfactory and a full compressible treatment is too complex for any analytical studies. When the effects of compressibility are incorporated only weakly, a new description, referred to as 'nearly incompressible hydrodynamics', is obtained. The nearly incompressible theory, was originally applied to homogeneous flows. However, large-scale gradients in density, pressure, temperature, etc., are typical in the solar wind and it was unclear how inhomogeneities would affect the usual incompressible and nearly incompressible descriptions. In the homogeneous case, the lowest order expansion of the fully compressible equations leads to the usual incompressible equations, followed at higher orders by the nearly incompressible equations, as introduced by Zank and Matthaeus. With this work we show that the inclusion of large-scale inhomogeneities (in this case time-independent and radially symmetric background solar wind) modifies the leading-order incompressible description of solar wind flow. We find, for example, that the divergence of velocity fluctuations is nonsolenoidal and that density fluctuations can be described to leading order as a passive scalar. Locally (for small lengthscales), this system of equations converges to the usual incompressible equations and we therefore use the term 'locally incompressible' to describe the equations. This term should be distinguished from the term 'nearly incompressible', which is reserved for higher-order corrections. Furthermore, we find that density fluctuations scale with Mach number linearly, in contrast to the original homogeneous nearly incompressible theory, in which density fluctuations scale with the square of Mach number. Inhomogeneous nearly

Performance Health Monitoring of Large-Scale Systems

Energy Technology Data Exchange (ETDEWEB)

Rajamony, Ram [IBM Research, Austin, TX (United States)

2014-11-20

This report details the progress made on the ASCR funded project Performance Health Monitoring for Large Scale Systems. A large-­scale application may not achieve its full performance potential due to degraded performance of even a single subsystem. Detecting performance faults, isolating them, and taking remedial action is critical for the scale of systems on the horizon. PHM aims to develop techniques and tools that can be used to identify and mitigate such performance problems. We accomplish this through two main aspects. The PHM framework encompasses diagnostics, system monitoring, fault isolation, and performance evaluation capabilities that indicates when a performance fault has been detected, either due to an anomaly present in the system itself or due to contention for shared resources between concurrently executing jobs. Software components called the PHM Control system then build upon the capabilities provided by the PHM framework to mitigate degradation caused by performance problems.

Very-large-scale production of antibodies in plants: The biologization of manufacturing.

Science.gov (United States)

Buyel, J F; Twyman, R M; Fischer, R

2017-07-01

Gene technology has facilitated the biologization of manufacturing, i.e. the use and production of complex biological molecules and systems at an industrial scale. Monoclonal antibodies (mAbs) are currently the major class of biopharmaceutical products, but they are typically used to treat specific diseases which individually have comparably low incidences. The therapeutic potential of mAbs could also be used for more prevalent diseases, but this would require a massive increase in production capacity that could not be met by traditional fermenter systems. Here we outline the potential of plants to be used for the very-large-scale (VLS) production of biopharmaceutical proteins such as mAbs. We discuss the potential market sizes and their corresponding production capacities. We then consider available process technologies and scale-down models and how these can be used to develop VLS processes. Finally, we discuss which adaptations will likely be required for VLS production, lessons learned from existing cell culture-based processes and the food industry, and practical requirements for the implementation of a VLS process. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

An augmented Lagrangian multi-scale dictionary learning algorithm

Directory of Open Access Journals (Sweden)

Ye Meng

2011-01-01

Full Text Available Abstract Learning overcomplete dictionaries for sparse signal representation has become a hot topic fascinated by many researchers in the recent years, while most of the existing approaches have a serious problem that they always lead to local minima. In this article, we present a novel augmented Lagrangian multi-scale dictionary learning algorithm (ALM-DL, which is achieved by first recasting the constrained dictionary learning problem into an AL scheme, and then updating the dictionary after each inner iteration of the scheme during which majorization-minimization technique is employed for solving the inner subproblem. Refining the dictionary from low scale to high makes the proposed method less dependent on the initial dictionary hence avoiding local optima. Numerical tests for synthetic data and denoising applications on real images demonstrate the superior performance of the proposed approach.

Analyzing the cosmic variance limit of remote dipole measurements of the cosmic microwave background using the large-scale kinetic Sunyaev Zel'dovich effect

Energy Technology Data Exchange (ETDEWEB)

Terrana, Alexandra; Johnson, Matthew C. [Department of Physics and Astronomy, York University, Toronto, Ontario, M3J 1P3 (Canada); Harris, Mary-Jean, E-mail: aterrana@perimeterinstitute.ca, E-mail: mharris8@perimeterinstitute.ca, E-mail: mjohnson@perimeterinstitute.ca [Perimeter Institute for Theoretical Physics, Waterloo, Ontario N2L 2Y5 (Canada)

2017-02-01

Due to cosmic variance we cannot learn any more about large-scale inhomogeneities from the primary cosmic microwave background (CMB) alone. More information on large scales is essential for resolving large angular scale anomalies in the CMB. Here we consider cross correlating the large-scale kinetic Sunyaev Zel'dovich (kSZ) effect and probes of large-scale structure, a technique known as kSZ tomography. The statistically anisotropic component of the cross correlation encodes the CMB dipole as seen by free electrons throughout the observable Universe, providing information about long wavelength inhomogeneities. We compute the large angular scale power asymmetry, constructing the appropriate transfer functions, and estimate the cosmic variance limited signal to noise for a variety of redshift bin configurations. The signal to noise is significant over a large range of power multipoles and numbers of bins. We present a simple mode counting argument indicating that kSZ tomography can be used to estimate more modes than the primary CMB on comparable scales. A basic forecast indicates that a first detection could be made with next-generation CMB experiments and galaxy surveys. This paper motivates a more systematic investigation of how close to the cosmic variance limit it will be possible to get with future observations.

Development of the Self-Directed Learning Skills Scale

Science.gov (United States)

Ayyildiz, Yildizay; Tarhan, Leman

2015-01-01

The purpose of this study was to develop a valid and reliable scale for assessing high school students' self-directed learning skills. Based on a literature review and data obtained from similar instruments, all skills related to self-directed learning were identified. Next, an item pool was prepared and administered to 255 students from various…

Large-scale functional MRI analysis to accumulate knowledge on brain functions

International Nuclear Information System (INIS)

Schwartz, Yannick

2015-01-01

How can we accumulate knowledge on brain functions? How can we leverage years of research in functional MRI to analyse finer-grained psychological constructs, and build a comprehensive model of the brain? Researchers usually rely on single studies to delineate brain regions recruited by mental processes. They relate their findings to previous works in an informal way by defining regions of interest from the literature. Meta-analysis approaches provide a more principled way to build upon the literature. This thesis investigates three ways to assemble knowledge using activation maps from a large amount of studies. First, we present an approach that uses jointly two similar fMRI experiments, to better condition an analysis from a statistical standpoint. We show that it is a valuable data-driven alternative to traditional regions of interest analyses, but fails to provide a systematic way to relate studies, and thus does not permit to integrate knowledge on a large scale. Because of the difficulty to associate multiple studies, we resort to using a single dataset sampling a large number of stimuli for our second contribution. This method estimates functional networks associated with functional profiles, where the functional networks are interacting brain regions and the functional profiles are a weighted set of cognitive descriptors. This work successfully yields known brain networks and automatically associates meaningful descriptions. Its limitations lie in the unsupervised nature of this method, which is more difficult to validate, and the use of a single dataset. It however brings the notion of cognitive labels, which is central to our last contribution. Our last contribution presents a method that learns functional atlases by combining several datasets. [Henson 2006] shows that forward inference, i.e. the probability of an activation given a cognitive process, is often not sufficient to conclude on the engagement of brain regions for a cognitive process

Phenomenology of two-dimensional stably stratified turbulence under large-scale forcing

KAUST Repository

Kumar, Abhishek; Verma, Mahendra K.; Sukhatme, Jai

2017-01-01

In this paper, we characterise the scaling of energy spectra, and the interscale transfer of energy and enstrophy, for strongly, moderately and weakly stably stratified two-dimensional (2D) turbulence, restricted in a vertical plane, under large-scale random forcing. In the strongly stratified case, a large-scale vertically sheared horizontal flow (VSHF) coexists with small scale turbulence. The VSHF consists of internal gravity waves and the turbulent flow has a kinetic energy (KE) spectrum that follows an approximate k−3 scaling with zero KE flux and a robust positive enstrophy flux. The spectrum of the turbulent potential energy (PE) also approximately follows a k−3 power-law and its flux is directed to small scales. For moderate stratification, there is no VSHF and the KE of the turbulent flow exhibits Bolgiano–Obukhov scaling that transitions from a shallow k−11/5 form at large scales, to a steeper approximate k−3 scaling at small scales. The entire range of scales shows a strong forward enstrophy flux, and interestingly, large (small) scales show an inverse (forward) KE flux. The PE flux in this regime is directed to small scales, and the PE spectrum is characterised by an approximate k−1.64 scaling. Finally, for weak stratification, KE is transferred upscale and its spectrum closely follows a k−2.5 scaling, while PE exhibits a forward transfer and its spectrum shows an approximate k−1.6 power-law. For all stratification strengths, the total energy always flows from large to small scales and almost all the spectral indicies are well explained by accounting for the scale-dependent nature of the corresponding flux.

Phenomenology of two-dimensional stably stratified turbulence under large-scale forcing

KAUST Repository

Kumar, Abhishek

2017-01-11

In this paper, we characterise the scaling of energy spectra, and the interscale transfer of energy and enstrophy, for strongly, moderately and weakly stably stratified two-dimensional (2D) turbulence, restricted in a vertical plane, under large-scale random forcing. In the strongly stratified case, a large-scale vertically sheared horizontal flow (VSHF) coexists with small scale turbulence. The VSHF consists of internal gravity waves and the turbulent flow has a kinetic energy (KE) spectrum that follows an approximate k−3 scaling with zero KE flux and a robust positive enstrophy flux. The spectrum of the turbulent potential energy (PE) also approximately follows a k−3 power-law and its flux is directed to small scales. For moderate stratification, there is no VSHF and the KE of the turbulent flow exhibits Bolgiano–Obukhov scaling that transitions from a shallow k−11/5 form at large scales, to a steeper approximate k−3 scaling at small scales. The entire range of scales shows a strong forward enstrophy flux, and interestingly, large (small) scales show an inverse (forward) KE flux. The PE flux in this regime is directed to small scales, and the PE spectrum is characterised by an approximate k−1.64 scaling. Finally, for weak stratification, KE is transferred upscale and its spectrum closely follows a k−2.5 scaling, while PE exhibits a forward transfer and its spectrum shows an approximate k−1.6 power-law. For all stratification strengths, the total energy always flows from large to small scales and almost all the spectral indicies are well explained by accounting for the scale-dependent nature of the corresponding flux.

Exploring the large-scale structure of Taylor–Couette turbulence through Large-Eddy Simulations

Science.gov (United States)

Ostilla-Mónico, Rodolfo; Zhu, Xiaojue; Verzicco, Roberto

2018-04-01

Large eddy simulations (LES) of Taylor-Couette (TC) flow, the flow between two co-axial and independently rotating cylinders are performed in an attempt to explore the large-scale axially-pinned structures seen in experiments and simulations. Both static and dynamic LES models are used. The Reynolds number is kept fixed at Re = 3.4 · 104, and the radius ratio η = ri /ro is set to η = 0.909, limiting the effects of curvature and resulting in frictional Reynolds numbers of around Re τ ≈ 500. Four rotation ratios from Rot = ‑0.0909 to Rot = 0.3 are simulated. First, the LES of TC is benchmarked for different rotation ratios. Both the Smagorinsky model with a constant of cs = 0.1 and the dynamic model are found to produce reasonable results for no mean rotation and cyclonic rotation, but deviations increase for increasing rotation. This is attributed to the increasing anisotropic character of the fluctuations. Second, “over-damped” LES, i.e. LES with a large Smagorinsky constant is performed and is shown to reproduce some features of the large-scale structures, even when the near-wall region is not adequately modeled. This shows the potential for using over-damped LES for fast explorations of the parameter space where large-scale structures are found.

Large-scale preparation of hollow graphitic carbon nanospheres

International Nuclear Information System (INIS)

Feng, Jun; Li, Fu; Bai, Yu-Jun; Han, Fu-Dong; Qi, Yong-Xin; Lun, Ning; Lu, Xi-Feng

2013-01-01

Hollow graphitic carbon nanospheres (HGCNSs) were synthesized on large scale by a simple reaction between glucose and Mg at 550 °C in an autoclave. Characterization by X-ray diffraction, Raman spectroscopy and transmission electron microscopy demonstrates the formation of HGCNSs with an average diameter of 10 nm or so and a wall thickness of a few graphenes. The HGCNSs exhibit a reversible capacity of 391 mAh g −1 after 60 cycles when used as anode materials for Li-ion batteries. -- Graphical abstract: Hollow graphitic carbon nanospheres could be prepared on large scale by the simple reaction between glucose and Mg at 550 °C, which exhibit superior electrochemical performance to graphite. Highlights: ► Hollow graphitic carbon nanospheres (HGCNSs) were prepared on large scale at 550 °C ► The preparation is simple, effective and eco-friendly. ► The in situ yielded MgO nanocrystals promote the graphitization. ► The HGCNSs exhibit superior electrochemical performance to graphite.

Accelerating large-scale phase-field simulations with GPU

Directory of Open Access Journals (Sweden)

Xiaoming Shi

2017-10-01

Full Text Available A new package for accelerating large-scale phase-field simulations was developed by using GPU based on the semi-implicit Fourier method. The package can solve a variety of equilibrium equations with different inhomogeneity including long-range elastic, magnetostatic, and electrostatic interactions. Through using specific algorithm in Compute Unified Device Architecture (CUDA, Fourier spectral iterative perturbation method was integrated in GPU package. The Allen-Cahn equation, Cahn-Hilliard equation, and phase-field model with long-range interaction were solved based on the algorithm running on GPU respectively to test the performance of the package. From the comparison of the calculation results between the solver executed in single CPU and the one on GPU, it was found that the speed on GPU is enormously elevated to 50 times faster. The present study therefore contributes to the acceleration of large-scale phase-field simulations and provides guidance for experiments to design large-scale functional devices.

First Mile Challenges for Large-Scale IoT

KAUST Repository

Bader, Ahmed

2017-03-16

The Internet of Things is large-scale by nature. This is not only manifested by the large number of connected devices, but also by the sheer scale of spatial traffic intensity that must be accommodated, primarily in the uplink direction. To that end, cellular networks are indeed a strong first mile candidate to accommodate the data tsunami to be generated by the IoT. However, IoT devices are required in the cellular paradigm to undergo random access procedures as a precursor to resource allocation. Such procedures impose a major bottleneck that hinders cellular networks\\' ability to support large-scale IoT. In this article, we shed light on the random access dilemma and present a case study based on experimental data as well as system-level simulations. Accordingly, a case is built for the latent need to revisit random access procedures. A call for action is motivated by listing a few potential remedies and recommendations.

Thermal power generation projects ``Large Scale Solar Heating``; EU-Thermie-Projekte ``Large Scale Solar Heating``

Energy Technology Data Exchange (ETDEWEB)

Kuebler, R.; Fisch, M.N. [Steinbeis-Transferzentrum Energie-, Gebaeude- und Solartechnik, Stuttgart (Germany)

1998-12-31

The aim of this project is the preparation of the ``Large-Scale Solar Heating`` programme for an Europe-wide development of subject technology. The following demonstration programme was judged well by the experts but was not immediately (1996) accepted for financial subsidies. In November 1997 the EU-commission provided 1,5 million ECU which allowed the realisation of an updated project proposal. By mid 1997 a small project was approved, that had been requested under the lead of Chalmes Industriteteknik (CIT) in Sweden and is mainly carried out for the transfer of technology. (orig.) [Deutsch] Ziel dieses Vorhabens ist die Vorbereitung eines Schwerpunktprogramms `Large Scale Solar Heating`, mit dem die Technologie europaweit weiterentwickelt werden sollte. Das daraus entwickelte Demonstrationsprogramm wurde von den Gutachtern positiv bewertet, konnte jedoch nicht auf Anhieb (1996) in die Foerderung aufgenommen werden. Im November 1997 wurden von der EU-Kommission dann kurzfristig noch 1,5 Mio ECU an Foerderung bewilligt, mit denen ein aktualisierter Projektvorschlag realisiert werden kann. Bereits Mitte 1997 wurde ein kleineres Vorhaben bewilligt, das unter Federfuehrung von Chalmers Industriteknik (CIT) in Schweden beantragt worden war und das vor allem dem Technologietransfer dient. (orig.)

Photorealistic large-scale urban city model reconstruction.

Science.gov (United States)

Poullis, Charalambos; You, Suya

2009-01-01

The rapid and efficient creation of virtual environments has become a crucial part of virtual reality applications. In particular, civil and defense applications often require and employ detailed models of operations areas for training, simulations of different scenarios, planning for natural or man-made events, monitoring, surveillance, games, and films. A realistic representation of the large-scale environments is therefore imperative for the success of such applications since it increases the immersive experience of its users and helps reduce the difference between physical and virtual reality. However, the task of creating such large-scale virtual environments still remains a time-consuming and manual work. In this work, we propose a novel method for the rapid reconstruction of photorealistic large-scale virtual environments. First, a novel, extendible, parameterized geometric primitive is presented for the automatic building identification and reconstruction of building structures. In addition, buildings with complex roofs containing complex linear and nonlinear surfaces are reconstructed interactively using a linear polygonal and a nonlinear primitive, respectively. Second, we present a rendering pipeline for the composition of photorealistic textures, which unlike existing techniques, can recover missing or occluded texture information by integrating multiple information captured from different optical sensors (ground, aerial, and satellite).

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.

Enhancing students' learning in problem based learning: validation of a self-assessment scale for active learning and critical thinking.

Science.gov (United States)

Khoiriyah, Umatul; Roberts, Chris; Jorm, Christine; Van der Vleuten, C P M

2015-08-26

Problem based learning (PBL) is a powerful learning activity but fidelity to intended models may slip and student engagement wane, negatively impacting learning processes, and outcomes. One potential solution to solve this degradation is by encouraging self-assessment in the PBL tutorial. Self-assessment is a central component of the self-regulation of student learning behaviours. There are few measures to investigate self-assessment relevant to PBL processes. We developed a Self-assessment Scale on Active Learning and Critical Thinking (SSACT) to address this gap. We wished to demonstrated evidence of its validity in the context of PBL by exploring its internal structure. We used a mixed methods approach to scale development. We developed scale items from a qualitative investigation, literature review, and consideration of previous existing tools used for study of the PBL process. Expert review panels evaluated its content; a process of validation subsequently reduced the pool of items. We used structural equation modelling to undertake a confirmatory factor analysis (CFA) of the SSACT and coefficient alpha. The 14 item SSACT consisted of two domains "active learning" and "critical thinking." The factorial validity of SSACT was evidenced by all items loading significantly on their expected factors, a good model fit for the data, and good stability across two independent samples. Each subscale had good internal reliability (>0.8) and strongly correlated with each other. The SSACT has sufficient evidence of its validity to support its use in the PBL process to encourage students to self-assess. The implementation of the SSACT may assist students to improve the quality of their learning in achieving PBL goals such as critical thinking and self-directed learning.

Development and examination of the psychometric properties of the Learning Experience Scale in nursing.

Science.gov (United States)

Takase, Miyuki; Imai, Takiko; Uemura, Chizuru

2016-06-01

This paper examines the psychometric properties of the Learning Experience Scale. A survey method was used to collect data from a total of 502 nurses. Data were analyzed by factor analysis and the known-groups technique to examine the construct validity of the scale. In addition, internal consistency was evaluated by Cronbach's alpha, and stability was examined by test-retest correlation. Factor analysis showed that the Learning Experience Scale consisted of five factors: learning from practice, others, training, feedback, and reflection. The scale also had the power to discriminate between nurses with high and low levels of nursing competence. The internal consistency and the stability of the scale were also acceptable. The Learning Experience Scale is a valid and reliable instrument, and helps organizations to effectively design learning interventions for nurses. © 2015 Wiley Publishing Asia Pty Ltd.

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.

A SCALE-UP Mock-Up: Comparison of Student Learning Gains in High- and Low-Tech Active-Learning Environments

Science.gov (United States)

Soneral, Paula A. G.; Wyse, Sara A.

2017-01-01

Student-centered learning environments with upside-down pedagogies (SCALE-UP) are widely implemented at institutions across the country, and learning gains from these classrooms have been well documented. This study investigates the specific design feature(s) of the SCALE-UP classroom most conducive to teaching and learning. Using pilot survey data from instructors and students to prioritize the most salient SCALE-UP classroom features, we created a low-tech “Mock-up” version of this classroom and tested the impact of these features on student learning, attitudes, and satisfaction using a quasi-­experimental setup. The same instructor taught two sections of an introductory biology course in the SCALE-UP and Mock-up rooms. Although students in both sections were equivalent in terms of gender, grade point average, incoming ACT, and drop/fail/withdraw rate, the Mock-up classroom enrolled significantly more freshmen. Controlling for class standing, multiple regression modeling revealed no significant differences in exam, in-class, preclass, and Introduction to Molecular and Cellular Biology Concept Inventory scores between the SCALE-UP and Mock-up classrooms. Thematic analysis of student comments highlighted that collaboration and whiteboards enhanced the learning experience, but technology was not important. Student satisfaction and attitudes were comparable. These results suggest that the benefits of a SCALE-UP experience can be achieved at lower cost without technology features. PMID:28213582

Studies on learning by detecting impasse and by resulting it for building large scale knowledge base for autonomous plant

International Nuclear Information System (INIS)

Sawaragi, Tetsuo

1997-03-01

The acquisition of knowledge from human experts in an exhaustive way is extremely difficult, and even if it were possible, the maintenance of such a large knowledge base for realtime operation is not an easy task. The autonomous system having just incomplete knowledge would face with so many problems that contradicts with the system's current beliefs and/or are novel or unknown to the system. Experienced humans can manage to do with such novelty due to their generalizing ability and analogical inference based on the repertoire of precedents, even if they with new problems. Moreover, through experiencing such breakdowns and impasse, they can acquire some novel knowledge by their proactive attempts to interpret a provided problem as well as by updating their beliefs and contents and organization of their prior knowledge. We call such a style of learning as impasse-driven learning, meaning that learning dose occur being motivated by facing with contradiction and impasse. The related studies concerning with such a style of leaning have been studied within a field of machine learning of artificial intelligence so far as well as within a cognitive science field. In this paper, we at first summarize an outline of machine learning methodologies, and then, we detail about the impasse-driven learning. We discuss that from two different perspective of learning, one is from deductive and analogical learning and the other one is from inductive conceptual learning (i.e., concept formation or generalization-based memory). The former mainly discuss about how the learning system updates its prior beliefs and knowledge so that it can explain away the current contradiction using some meta-cognition heuristics. The latter attempts to assimilate a contradicting problem into its prior memory structure by dynamically reorganizing a collection of the precedents. We present those methodologies, and finally we introduce a case study of concept formation for plant anomalies and its usage for

On Modeling Large-Scale Multi-Agent Systems with Parallel, Sequential and Genuinely Asynchronous Cellular Automata

International Nuclear Information System (INIS)

Tosic, P.T.

2011-01-01

We study certain types of Cellular Automata (CA) viewed as an abstraction of large-scale Multi-Agent Systems (MAS). We argue that the classical CA model needs to be modified in several important respects, in order to become a relevant and sufficiently general model for the large-scale MAS, and so that thus generalized model can capture many important MAS properties at the level of agent ensembles and their long-term collective behavior patterns. We specifically focus on the issue of inter-agent communication in CA, and propose sequential cellular automata (SCA) as the first step, and genuinely Asynchronous Cellular Automata (ACA) as the ultimate deterministic CA-based abstract models for large-scale MAS made of simple reactive agents. We first formulate deterministic and nondeterministic versions of sequential CA, and then summarize some interesting configuration space properties (i.e., possible behaviors) of a restricted class of sequential CA. In particular, we compare and contrast those properties of sequential CA with the corresponding properties of the classical (that is, parallel and perfectly synchronous) CA with the same restricted class of update rules. We analytically demonstrate failure of the studied sequential CA models to simulate all possible behaviors of perfectly synchronous parallel CA, even for a very restricted class of non-linear totalistic node update rules. The lesson learned is that the interleaving semantics of concurrency, when applied to sequential CA, is not refined enough to adequately capture the perfect synchrony of parallel CA updates. Last but not least, we outline what would be an appropriate CA-like abstraction for large-scale distributed computing insofar as the inter-agent communication model is concerned, and in that context we propose genuinely asynchronous CA. (author)

Bayesian hierarchical model for large-scale covariance matrix estimation.

Science.gov (United States)

Zhu, Dongxiao; Hero, Alfred O

2007-12-01

Many bioinformatics problems implicitly depend on estimating large-scale covariance matrix. The traditional approaches tend to give rise to high variance and low accuracy due to "overfitting." We cast the large-scale covariance matrix estimation problem into the Bayesian hierarchical model framework, and introduce dependency between covariance parameters. We demonstrate the advantages of our approaches over the traditional approaches using simulations and OMICS data analysis.

Selective visual scaling of time-scale processes facilitates broadband learning of isometric force frequency tracking.

Science.gov (United States)

King, Adam C; Newell, Karl M

2015-10-01

The experiment investigated the effect of selectively augmenting faster time scales of visual feedback information on the learning and transfer of continuous isometric force tracking tasks to test the generality of the self-organization of 1/f properties of force output. Three experimental groups tracked an irregular target pattern either under a standard fixed gain condition or with selectively enhancement in the visual feedback display of intermediate (4-8 Hz) or high (8-12 Hz) frequency components of the force output. All groups reduced tracking error over practice, with the error lowest in the intermediate scaling condition followed by the high scaling and fixed gain conditions, respectively. Selective visual scaling induced persistent changes across the frequency spectrum, with the strongest effect in the intermediate scaling condition and positive transfer to novel feedback displays. The findings reveal an interdependence of the timescales in the learning and transfer of isometric force output frequency structures consistent with 1/f process models of the time scales of motor output variability.

Large-scale pool fires

Directory of Open Access Journals (Sweden)

Steinhaus Thomas

2007-01-01

Full Text Available A review of research into the burning behavior of large pool fires and fuel spill fires is presented. The features which distinguish such fires from smaller pool fires are mainly associated with the fire dynamics at low source Froude numbers and the radiative interaction with the fire source. In hydrocarbon fires, higher soot levels at increased diameters result in radiation blockage effects around the perimeter of large fire plumes; this yields lower emissive powers and a drastic reduction in the radiative loss fraction; whilst there are simplifying factors with these phenomena, arising from the fact that soot yield can saturate, there are other complications deriving from the intermittency of the behavior, with luminous regions of efficient combustion appearing randomly in the outer surface of the fire according the turbulent fluctuations in the fire plume. Knowledge of the fluid flow instabilities, which lead to the formation of large eddies, is also key to understanding the behavior of large-scale fires. Here modeling tools can be effectively exploited in order to investigate the fluid flow phenomena, including RANS- and LES-based computational fluid dynamics codes. The latter are well-suited to representation of the turbulent motions, but a number of challenges remain with their practical application. Massively-parallel computational resources are likely to be necessary in order to be able to adequately address the complex coupled phenomena to the level of detail that is necessary.

Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning.

Science.gov (United States)

Adal, Kedir M; Sidibé, Désiré; Ali, Sharib; Chaum, Edward; Karnowski, Thomas P; Mériaudeau, Fabrice

2014-04-01

Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier which can detect true MAs. The developed system is built using only few manually labeled and a large number of unlabeled retinal color fundus images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. A competition performance measure (CPM) of 0.364 shows the competitiveness of the proposed system against state-of-the art techniques as well as the applicability of the proposed features to analyze fundus images. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Decentralised stabilising controllers for a class of large-scale linear ...

Indian Academy of Sciences (India)

subsystems resulting from a new aggregation-decomposition technique. The method has been illustrated through a numerical example of a large-scale linear system consisting of three subsystems each of the fourth order. Keywords. Decentralised stabilisation; large-scale linear systems; optimal feedback control; algebraic ...

Large Scale Survey Data in Career Development Research

Science.gov (United States)

Diemer, Matthew A.

2008-01-01

Large scale survey datasets have been underutilized but offer numerous advantages for career development scholars, as they contain numerous career development constructs with large and diverse samples that are followed longitudinally. Constructs such as work salience, vocational expectations, educational expectations, work satisfaction, and…

Developing a "Social Presence Scale" for E-Learning Environments

Science.gov (United States)

Kilic Cakmak, Ebru; Cebi, Ayça; Kan, Adnan

2014-01-01

The purpose of the current study is to develop a "social presence scale" for e-learning environments. A systematic approach was followed for developing the scale. The scale was applied to 461 students registered in seven different programs at Gazi University. The sample was split into two subsamples on a random basis (n1 = 261; n2 =…

Similitude and scaling of large structural elements: Case study

Directory of Open Access Journals (Sweden)

M. Shehadeh

2015-06-01

Full Text Available Scaled down models are widely used for experimental investigations of large structures due to the limitation in the capacities of testing facilities along with the expenses of the experimentation. The modeling accuracy depends upon the model material properties, fabrication accuracy and loading techniques. In the present work the Buckingham π theorem is used to develop the relations (i.e. geometry, loading and properties between the model and a large structural element as that is present in the huge existing petroleum oil drilling rigs. The model is to be designed, loaded and treated according to a set of similitude requirements that relate the model to the large structural element. Three independent scale factors which represent three fundamental dimensions, namely mass, length and time need to be selected for designing the scaled down model. Numerical prediction of the stress distribution within the model and its elastic deformation under steady loading is to be made. The results are compared with those obtained from the full scale structure numerical computations. The effect of scaled down model size and material on the accuracy of the modeling technique is thoroughly examined.

Virtual neutron scattering experiments - Training and preparing students for large-scale facility experiments

Directory of Open Access Journals (Sweden)

Julie Hougaard Overgaard

2016-11-01

Full Text Available Dansk Vi beskriver, hvordan virtuelle eksperimenter kan udnyttes i et læringsdesign ved at forberede de studerende til hands-on-eksperimenter ved storskalafaciliteter. Vi illustrerer designet ved at vise, hvordan virtuelle eksperimenter bruges på Niels Bohr Institutets kandidatkursus om neutronspredning. I den sidste uge af kurset, rejser studerende til et storskala neutronspredningsfacilitet for at udføre neutronspredningseksperimenter. Vi bruger studerendes udsagn om deres oplevelser til at argumentere for, at arbejdet med virtuelle experimenter forbereder de studerende til at engagere sig mere frugtbart med eksperimenter ved at lade dem fokusere på fysikken og relevante data i stedet for instrumenternes funktion. Vi hævder, at det er, fordi de kan overføre deres erfaringer med virtuelle eksperimenter til rigtige eksperimenter. Vi finder dog, at læring stadig er situeret i den forstand, at kun kendskab til bestemte eksperimenter overføres. Vi afslutter med at diskutere de muligheder, som virtuelle eksperimenter giver. English We describe how virtual experiments can be utilized in a learning design that prepares students for hands-on experiments at large-scale facilities. We illustrate the design by showing how virtual experiments are used at the Niels Bohr Institute in a master level course on neutron scattering. In the last week of the course, students travel to a large-scale neutron scattering facility to perform real neutron scattering experiments. Through student interviews and survey answers, we argue, that the virtual training prepares the students to engage more fruitfully with experiments by letting them focus on physics and data rather than the overwhelming instrumentation. We argue that this is because they can transfer their virtual experimental experience to the real-life situation. However, we also find that learning is still situated in the sense that only knowledge of particular experiments is transferred. We proceed to

Large-scale preparation of hollow graphitic carbon nanospheres

Energy Technology Data Exchange (ETDEWEB)

Feng, Jun; Li, Fu [Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials, Ministry of Education, Shandong University, Jinan 250061 (China); Bai, Yu-Jun, E-mail: byj97@126.com [Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials, Ministry of Education, Shandong University, Jinan 250061 (China); State Key laboratory of Crystal Materials, Shandong University, Jinan 250100 (China); Han, Fu-Dong; Qi, Yong-Xin; Lun, Ning [Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials, Ministry of Education, Shandong University, Jinan 250061 (China); Lu, Xi-Feng [Lunan Institute of Coal Chemical Engineering, Jining 272000 (China)

2013-01-15

Hollow graphitic carbon nanospheres (HGCNSs) were synthesized on large scale by a simple reaction between glucose and Mg at 550 Degree-Sign C in an autoclave. Characterization by X-ray diffraction, Raman spectroscopy and transmission electron microscopy demonstrates the formation of HGCNSs with an average diameter of 10 nm or so and a wall thickness of a few graphenes. The HGCNSs exhibit a reversible capacity of 391 mAh g{sup -1} after 60 cycles when used as anode materials for Li-ion batteries. -- Graphical abstract: Hollow graphitic carbon nanospheres could be prepared on large scale by the simple reaction between glucose and Mg at 550 Degree-Sign C, which exhibit superior electrochemical performance to graphite. Highlights: Black-Right-Pointing-Pointer Hollow graphitic carbon nanospheres (HGCNSs) were prepared on large scale at 550 Degree-Sign C Black-Right-Pointing-Pointer The preparation is simple, effective and eco-friendly. Black-Right-Pointing-Pointer The in situ yielded MgO nanocrystals promote the graphitization. Black-Right-Pointing-Pointer The HGCNSs exhibit superior electrochemical performance to graphite.

Large-scale impact cratering on the terrestrial planets

International Nuclear Information System (INIS)

Grieve, R.A.F.

1982-01-01

The crater densities on the earth and moon form the basis for a standard flux-time curve that can be used in dating unsampled planetary surfaces and constraining the temporal history of endogenic geologic processes. Abundant evidence is seen not only that impact cratering was an important surface process in planetary history but also that large imapact events produced effects that were crucial in scale. By way of example, it is noted that the formation of multiring basins on the early moon was as important in defining the planetary tectonic framework as plate tectonics is on the earth. Evidence from several planets suggests that the effects of very-large-scale impacts go beyond the simple formation of an impact structure and serve to localize increased endogenic activity over an extended period of geologic time. Even though no longer occurring with the frequency and magnitude of early solar system history, it is noted that large scale impact events continue to affect the local geology of the planets. 92 references

A scale of lifelong learning attitudes of teachers: The development of LLLAS

Directory of Open Access Journals (Sweden)

Cigdem Hursen

2016-03-01

Full Text Available Knowledge, which is the most significant characteristic of today’s knowledge society, has been changing and improving very rapidly. Particularly, the developments in science and technology have been influencing social, economical and cultural life; thus professions and descriptions of professions have been continuously renewed. In addition, the needed profile of man power relevant to the changing professions has been changing continuously and the learnt knowledge has not been prevalent. For this reason, there is a need for the individuals to update their knowledge and skills continuously in order to adopt themselves to the technological changes and new work conditions. Lifelong learning approach can provide societies and individuals with opportunities to catch up with these changes and developments. In raising individuals as lifelong learners, teachers play a big role. In order to establish lifelong learning societies, first of all teachers should have all the characteristics of lifelong learning. This is why it is extremely important to determine what the attitudes of the teachers are towards lifelong learning approach. However, there has not been developed any scale measuring teachers’ attitudes towards lifelong learning approach so far. Therefore, in this current study, it is aimed to develop a scale to determine what the attitudes of the teachers are towards lifelong learning approach. The subject group is consisted of 300 teachers, working in the schools of the Northern Cyprus. The findings on the validity of the structure of the scale are measured by the factor analysis. As a result of the analysis, a lifelong learning attitude scale is developed with 19 items in three sub-dimensions (LLLAS. The sub-dimensions of the scale are formed in the following expressions: “reluctance to learn”, “belief in the benefits of learning activities for professional development”, “awareness of personal learning skills”. As the result of the

Optical interconnect for large-scale systems

Science.gov (United States)

Dress, William

2013-02-01

This paper presents a switchless, optical interconnect module that serves as a node in a network of identical distribution modules for large-scale systems. Thousands to millions of hosts or endpoints may be interconnected by a network of such modules, avoiding the need for multi-level switches. Several common network topologies are reviewed and their scaling properties assessed. The concept of message-flow routing is discussed in conjunction with the unique properties enabled by the optical distribution module where it is shown how top-down software control (global routing tables, spanning-tree algorithms) may be avoided.

[A large-scale accident in Alpine terrain].

Science.gov (United States)

Wildner, M; Paal, P

2015-02-01

Due to the geographical conditions, large-scale accidents amounting to mass casualty incidents (MCI) in Alpine terrain regularly present rescue teams with huge challenges. Using an example incident, specific conditions and typical problems associated with such a situation are presented. The first rescue team members to arrive have the elementary tasks of qualified triage and communication to the control room, which is required to dispatch the necessary additional support. Only with a clear "concept", to which all have to adhere, can the subsequent chaos phase be limited. In this respect, a time factor confounded by adverse weather conditions or darkness represents enormous pressure. Additional hazards are frostbite and hypothermia. If priorities can be established in terms of urgency, then treatment and procedure algorithms have proven successful. For evacuation of causalities, a helicopter should be strived for. Due to the low density of hospitals in Alpine regions, it is often necessary to distribute the patients over a wide area. Rescue operations in Alpine terrain have to be performed according to the particular conditions and require rescue teams to have specific knowledge and expertise. The possibility of a large-scale accident should be considered when planning events. With respect to optimization of rescue measures, regular training and exercises are rational, as is the analysis of previous large-scale Alpine accidents.

Hierarchical Cantor set in the large scale structure with torus geometry

Energy Technology Data Exchange (ETDEWEB)

Murdzek, R. [Physics Department, ' Al. I. Cuza' University, Blvd. Carol I, Nr. 11, Iassy 700506 (Romania)], E-mail: rmurdzek@yahoo.com

2008-12-15

The formation of large scale structures is considered within a model with string on toroidal space-time. Firstly, the space-time geometry is presented. In this geometry, the Universe is represented by a string describing a torus surface. Thereafter, the large scale structure of the Universe is derived from the string oscillations. The results are in agreement with the cellular structure of the large scale distribution and with the theory of a Cantorian space-time.

A large-scale peer teaching programme - acceptance and benefit.

Science.gov (United States)

Schuetz, Elisabeth; Obirei, Barbara; Salat, Daniela; Scholz, Julia; Hann, Dagmar; Dethleffsen, Kathrin

2017-08-01

The involvement of students in the embodiment of university teaching through peer-assisted learning formats is commonly applied. Publications on this topic exclusively focus on strictly defined situations within the curriculum and selected target groups. This study, in contrast, presents and evaluates a large-scale structured and quality-assured peer teaching programme, which offers diverse and targeted courses throughout the preclinical part of the medical curriculum. The large-scale peer teaching programme consists of subject specific and interdisciplinary tutorials that address all scientific, physiological and anatomic subjects of the preclinical curriculum as well as tutorials with contents exceeding the formal curriculum. In the study year 2013/14 a total of 1,420 lessons were offered as part of the programme. Paper-based evaluations were conducted over the full range of courses. Acceptance and benefit of this peer teaching programme were evaluated in a retrospective study covering the period 2012 to 2014. Usage of tutorials by students who commenced their studies in 2012/13 (n=959) was analysed from 2012 till 2014. Based on the results of 13 first assessments in the preclinical subjects anatomy, biochemistry and physiology, the students were assigned to one of five groups. These groups were compared according to participation in the tutorials. To investigate the benefit of tutorials of the peer teaching programme, the results of biochemistry re-assessments of participants and non-participants of tutorials in the years 2012 till 2014 (n=188, 172 and 204, respectively) were compared using Kolmogorov-Smirnov- and Chi-square tests as well as the effect size Cohen's d. Almost 70 % of the students attended the voluntary additional programme during their preclinical studies. The students participating in the tutorials had achieved different levels of proficiency in first assessments. The acceptance of different kinds of tutorials appears to correlate with their

Large-scale Motion of Solar Filaments

Indian Academy of Sciences (India)

tribpo

Large-scale Motion of Solar Filaments. Pavel Ambrož, Astronomical Institute of the Acad. Sci. of the Czech Republic, CZ-25165. Ondrejov, The Czech Republic. e-mail: pambroz@asu.cas.cz. Alfred Schroll, Kanzelhöehe Solar Observatory of the University of Graz, A-9521 Treffen,. Austria. e-mail: schroll@solobskh.ac.at.

Sensitivity analysis for large-scale problems

Science.gov (United States)

Noor, Ahmed K.; Whitworth, Sandra L.

1987-01-01

The development of efficient techniques for calculating sensitivity derivatives is studied. The objective is to present a computational procedure for calculating sensitivity derivatives as part of performing structural reanalysis for large-scale problems. The scope is limited to framed type structures. Both linear static analysis and free-vibration eigenvalue problems are considered.

Topology Optimization of Large Scale Stokes Flow Problems

DEFF Research Database (Denmark)

Aage, Niels; Poulsen, Thomas Harpsøe; Gersborg-Hansen, Allan

2008-01-01

This note considers topology optimization of large scale 2D and 3D Stokes flow problems using parallel computations. We solve problems with up to 1.125.000 elements in 2D and 128.000 elements in 3D on a shared memory computer consisting of Sun UltraSparc IV CPUs.......This note considers topology optimization of large scale 2D and 3D Stokes flow problems using parallel computations. We solve problems with up to 1.125.000 elements in 2D and 128.000 elements in 3D on a shared memory computer consisting of Sun UltraSparc IV CPUs....

The Cosmology Large Angular Scale Surveyor

Science.gov (United States)

Harrington, Kathleen; Marriage, Tobias; Ali, Aamir; Appel, John; Bennett, Charles; Boone, Fletcher; Brewer, Michael; Chan, Manwei; Chuss, David T.; Colazo, Felipe;

Prehospital Acute Stroke Severity Scale to Predict Large Artery Occlusion: Design and Comparison With Other Scales.

Science.gov (United States)

Hastrup, Sidsel; Damgaard, Dorte; Johnsen, Søren Paaske; Andersen, Grethe

2016-07-01

We designed and validated a simple prehospital stroke scale to identify emergent large vessel occlusion (ELVO) in patients with acute ischemic stroke and compared the scale to other published scales for prediction of ELVO. A national historical test cohort of 3127 patients with information on intracranial vessel status (angiography) before reperfusion therapy was identified. National Institutes of Health Stroke Scale (NIHSS) items with the highest predictive value of occlusion of a large intracranial artery were identified, and the most optimal combination meeting predefined criteria to ensure usefulness in the prehospital phase was determined. The predictive performance of Prehospital Acute Stroke Severity (PASS) scale was compared with other published scales for ELVO. The PASS scale was composed of 3 NIHSS scores: level of consciousness (month/age), gaze palsy/deviation, and arm weakness. In derivation of PASS 2/3 of the test cohort was used and showed accuracy (area under the curve) of 0.76 for detecting large arterial occlusion. Optimal cut point ≥2 abnormal scores showed: sensitivity=0.66 (95% CI, 0.62-0.69), specificity=0.83 (0.81-0.85), and area under the curve=0.74 (0.72-0.76). Validation on 1/3 of the test cohort showed similar performance. Patients with a large artery occlusion on angiography with PASS ≥2 had a median NIHSS score of 17 (interquartile range=6) as opposed to PASS <2 with a median NIHSS score of 6 (interquartile range=5). The PASS scale showed equal performance although more simple when compared with other scales predicting ELVO. The PASS scale is simple and has promising accuracy for prediction of ELVO in the field. © 2016 American Heart Association, Inc.

Adaptive Scaling of Cluster Boundaries for Large-Scale Social Media Data Clustering.

Science.gov (United States)

Meng, Lei; Tan, Ah-Hwee; Wunsch, Donald C

2016-12-01

The large scale and complex nature of social media data raises the need to scale clustering techniques to big data and make them capable of automatically identifying data clusters with few empirical settings. In this paper, we present our investigation and three algorithms based on the fuzzy adaptive resonance theory (Fuzzy ART) that have linear computational complexity, use a single parameter, i.e., the vigilance parameter to identify data clusters, and are robust to modest parameter settings. The contribution of this paper lies in two aspects. First, we theoretically demonstrate how complement coding, commonly known as a normalization method, changes the clustering mechanism of Fuzzy ART, and discover the vigilance region (VR) that essentially determines how a cluster in the Fuzzy ART system recognizes similar patterns in the feature space. The VR gives an intrinsic interpretation of the clustering mechanism and limitations of Fuzzy ART. Second, we introduce the idea of allowing different clusters in the Fuzzy ART system to have different vigilance levels in order to meet the diverse nature of the pattern distribution of social media data. To this end, we propose three vigilance adaptation methods, namely, the activation maximization (AM) rule, the confliction minimization (CM) rule, and the hybrid integration (HI) rule. With an initial vigilance value, the resulting clustering algorithms, namely, the AM-ART, CM-ART, and HI-ART, can automatically adapt the vigilance values of all clusters during the learning epochs in order to produce better cluster boundaries. Experiments on four social media data sets show that AM-ART, CM-ART, and HI-ART are more robust than Fuzzy ART to the initial vigilance value, and they usually achieve better or comparable performance and much faster speed than the state-of-the-art clustering algorithms that also do not require a predefined number of clusters.

Analysis using large-scale ringing data

Directory of Open Access Journals (Sweden)

Baillie, S. R.

2004-06-01

Full Text Available Birds are highly mobile organisms and there is increasing evidence that studies at large spatial scales are needed if we are to properly understand their population dynamics. While classical metapopulation models have rarely proved useful for birds, more general metapopulation ideas involving collections of populations interacting within spatially structured landscapes are highly relevant (Harrison, 1994. There is increasing interest in understanding patterns of synchrony, or lack of synchrony, between populations and the environmental and dispersal mechanisms that bring about these patterns (Paradis et al., 2000. To investigate these processes we need to measure abundance, demographic rates and dispersal at large spatial scales, in addition to gathering data on relevant environmental variables. There is an increasing realisation that conservation needs to address rapid declines of common and widespread species (they will not remain so if such trends continue as well as the management of small populations that are at risk of extinction. While the knowledge needed to support the management of small populations can often be obtained from intensive studies in a few restricted areas, conservation of widespread species often requires information on population trends and processes measured at regional, national and continental scales (Baillie, 2001. While management prescriptions for widespread populations may initially be developed from a small number of local studies or experiments, there is an increasing need to understand how such results will scale up when applied across wider areas. There is also a vital role for monitoring at large spatial scales both in identifying such population declines and in assessing population recovery. Gathering data on avian abundance and demography at large spatial scales usually relies on the efforts of large numbers of skilled volunteers. Volunteer studies based on ringing (for example Constant Effort Sites [CES

Kernel methods for large-scale genomic data analysis

Science.gov (United States)

Xing, Eric P.; Schaid, Daniel J.

2015-01-01

Machine learning, particularly kernel methods, has been demonstrated as a promising new tool to tackle the challenges imposed by today’s explosive data growth in genomics. They provide a practical and principled approach to learning how a large number of genetic variants are associated with complex phenotypes, to help reveal the complexity in the relationship between the genetic markers and the outcome of interest. In this review, we highlight the potential key role it will have in modern genomic data processing, especially with regard to integration with classical methods for gene prioritizing, prediction and data fusion. PMID:25053743

Fast Simulation of Large-Scale Floods Based on GPU Parallel Computing

OpenAIRE

Qiang Liu; Yi Qin; Guodong Li

2018-01-01

Computing speed is a significant issue of large-scale flood simulations for real-time response to disaster prevention and mitigation. Even today, most of the large-scale flood simulations are generally run on supercomputers due to the massive amounts of data and computations necessary. In this work, a two-dimensional shallow water model based on an unstructured Godunov-type finite volume scheme was proposed for flood simulation. To realize a fast simulation of large-scale floods on a personal...

Managing Risk and Uncertainty in Large-Scale University Research Projects

Science.gov (United States)

Moore, Sharlissa; Shangraw, R. F., Jr.

2011-01-01

Both publicly and privately funded research projects managed by universities are growing in size and scope. Complex, large-scale projects (over $50 million) pose new management challenges and risks for universities. This paper explores the relationship between project success and a variety of factors in large-scale university projects. First, we…

Parallel clustering algorithm for large-scale biological data sets.

Science.gov (United States)

Wang, Minchao; Zhang, Wu; Ding, Wang; Dai, Dongbo; Zhang, Huiran; Xie, Hao; Chen, Luonan; Guo, Yike; Xie, Jiang

2014-01-01

Recent explosion of biological data brings a great challenge for the traditional clustering algorithms. With increasing scale of data sets, much larger memory and longer runtime are required for the cluster identification problems. The affinity propagation algorithm outperforms many other classical clustering algorithms and is widely applied into the biological researches. However, the time and space complexity become a great bottleneck when handling the large-scale data sets. Moreover, the similarity matrix, whose constructing procedure takes long runtime, is required before running the affinity propagation algorithm, since the algorithm clusters data sets based on the similarities between data pairs. Two types of parallel architectures are proposed in this paper to accelerate the similarity matrix constructing procedure and the affinity propagation algorithm. The memory-shared architecture is used to construct the similarity matrix, and the distributed system is taken for the affinity propagation algorithm, because of its large memory size and great computing capacity. An appropriate way of data partition and reduction is designed in our method, in order to minimize the global communication cost among processes. A speedup of 100 is gained with 128 cores. The runtime is reduced from serval hours to a few seconds, which indicates that parallel algorithm is capable of handling large-scale data sets effectively. The parallel affinity propagation also achieves a good performance when clustering large-scale gene data (microarray) and detecting families in large protein superfamilies.

Profiling medical school learning environments in Malaysia: a validation study of the Johns Hopkins Learning Environment Scale

Directory of Open Access Journals (Sweden)

Sean Tackett

2015-07-01

Full Text Available Purpose: While a strong learning environment is critical to medical student education, the assessment of medical school learning environments has confounded researchers. Our goal was to assess the validity and utility of the Johns Hopkins Learning Environment Scale (JHLES for preclinical students at three Malaysian medical schools with distinct educational and institutional models. Two schools were new international partnerships, and the third was school leaver program established without international partnership. Methods: First- and second-year students responded anonymously to surveys at the end of the academic year. The surveys included the JHLES, a 28-item survey using five-point Likert scale response options, the Dundee Ready Educational Environment Measure (DREEM, the most widely used method to assess learning environments internationally, a personal growth scale, and single-item global learning environment assessment variables. Results: The overall response rate was 369/429 (86%. After adjusting for the medical school year, gender, and ethnicity of the respondents, the JHLES detected differences across institutions in four out of seven domains (57%, with each school having a unique domain profile. The DREEM detected differences in one out of five categories (20%. The JHLES was more strongly correlated than the DREEM to two thirds of the single-item variables and the personal growth scale. The JHLES showed high internal reliability for the total score (α=0.92 and the seven domains (α, 0.56-0.85. Conclusion: The JHLES detected variation between learning environment domains across three educational settings, thereby creating unique learning environment profiles. Interpretation of these profiles may allow schools to understand how they are currently supporting trainees and identify areas needing attention.

Adaptive visualization for large-scale graph

International Nuclear Information System (INIS)

Nakamura, Hiroko; Shinano, Yuji; Ohzahata, Satoshi

2010-01-01

We propose an adoptive visualization technique for representing a large-scale hierarchical dataset within limited display space. A hierarchical dataset has nodes and links showing the parent-child relationship between the nodes. These nodes and links are described using graphics primitives. When the number of these primitives is large, it is difficult to recognize the structure of the hierarchical data because many primitives are overlapped within a limited region. To overcome this difficulty, we propose an adaptive visualization technique for hierarchical datasets. The proposed technique selects an appropriate graph style according to the nodal density in each area. (author)

Stabilization Algorithms for Large-Scale Problems

DEFF Research Database (Denmark)

Jensen, Toke Koldborg

2006-01-01

The focus of the project is on stabilization of large-scale inverse problems where structured models and iterative algorithms are necessary for computing approximate solutions. For this purpose, we study various iterative Krylov methods and their abilities to produce regularized solutions. Some......-curve. This heuristic is implemented as a part of a larger algorithm which is developed in collaboration with G. Rodriguez and P. C. Hansen. Last, but not least, a large part of the project has, in different ways, revolved around the object-oriented Matlab toolbox MOORe Tools developed by PhD Michael Jacobsen. New...

Active Learning in a Large General Physics Classroom.

Science.gov (United States)

Trousil, Rebecca

2008-04-01

In 2004, we launched a new calculus-based, introductory physics sequence at Washington University. Designed as an alternative to our traditional lecture-based sequence, the primary objectives for this new course were to actively engage students in the learning process, to significantly strengthen students' conceptual reasoning skills, to help students develop higher level quantitative problem solving skills necessary for analyzing ``real world'' problems, and to integrate modern physics into the curriculum. This talk will describe our approach, using The Six Ideas That Shaped Physics text by Thomas Moore, to creating an active learning environment in large classes as well as share our perspective on key elements for success and challenges that we face in the large class environment.

Stochastically Estimating Modular Criticality in Large-Scale Logic Circuits Using Sparsity Regularization and Compressive Sensing

Directory of Open Access Journals (Sweden)

Mohammed Alawad

2015-03-01

Full Text Available This paper considers the problem of how to efficiently measure a large and complex information field with optimally few observations. Specifically, we investigate how to stochastically estimate modular criticality values in a large-scale digital circuit with a very limited number of measurements in order to minimize the total measurement efforts and time. We prove that, through sparsity-promoting transform domain regularization and by strategically integrating compressive sensing with Bayesian learning, more than 98% of the overall measurement accuracy can be achieved with fewer than 10% of measurements as required in a conventional approach that uses exhaustive measurements. Furthermore, we illustrate that the obtained criticality results can be utilized to selectively fortify large-scale digital circuits for operation with narrow voltage headrooms and in the presence of soft-errors rising at near threshold voltage levels, without excessive hardware overheads. Our numerical simulation results have shown that, by optimally allocating only 10% circuit redundancy, for some large-scale benchmark circuits, we can achieve more than a three-times reduction in its overall error probability, whereas if randomly distributing such 10% hardware resource, less than 2% improvements in the target circuit’s overall robustness will be observed. Finally, we conjecture that our proposed approach can be readily applied to estimate other essential properties of digital circuits that are critical to designing and analyzing them, such as the observability measure in reliability analysis and the path delay estimation in stochastic timing analysis. The only key requirement of our proposed methodology is that these global information fields exhibit a certain degree of smoothness, which is universally true for almost any physical phenomenon.

Design study on sodium cooled large-scale reactor

International Nuclear Information System (INIS)

Murakami, Tsutomu; Hishida, Masahiko; Kisohara, Naoyuki

2004-07-01

In Phase 1 of the 'Feasibility Studies on Commercialized Fast Reactor Cycle Systems (F/S)', an advanced loop type reactor has been selected as a promising concept of sodium-cooled large-scale reactor, which has a possibility to fulfill the design requirements of the F/S. In Phase 2, design improvement for further cost reduction of establishment of the plant concept has been performed. This report summarizes the results of the design study on the sodium-cooled large-scale reactor performed in JFY2003, which is the third year of Phase 2. In the JFY2003 design study, critical subjects related to safety, structural integrity and thermal hydraulics which found in the last fiscal year has been examined and the plant concept has been modified. Furthermore, fundamental specifications of main systems and components have been set and economy has been evaluated. In addition, as the interim evaluation of the candidate concept of the FBR fuel cycle is to be conducted, cost effectiveness and achievability for the development goal were evaluated and the data of the three large-scale reactor candidate concepts were prepared. As a results of this study, the plant concept of the sodium-cooled large-scale reactor has been constructed, which has a prospect to satisfy the economic goal (construction cost: less than 200,000 yens/kWe, etc.) and has a prospect to solve the critical subjects. From now on, reflecting the results of elemental experiments, the preliminary conceptual design of this plant will be preceded toward the selection for narrowing down candidate concepts at the end of Phase 2. (author)

Design study on sodium-cooled large-scale reactor

International Nuclear Information System (INIS)

Shimakawa, Yoshio; Nibe, Nobuaki; Hori, Toru

2002-05-01

In Phase 1 of the 'Feasibility Study on Commercialized Fast Reactor Cycle Systems (F/S)', an advanced loop type reactor has been selected as a promising concept of sodium-cooled large-scale reactor, which has a possibility to fulfill the design requirements of the F/S. In Phase 2 of the F/S, it is planed to precede a preliminary conceptual design of a sodium-cooled large-scale reactor based on the design of the advanced loop type reactor. Through the design study, it is intended to construct such a plant concept that can show its attraction and competitiveness as a commercialized reactor. This report summarizes the results of the design study on the sodium-cooled large-scale reactor performed in JFY2001, which is the first year of Phase 2. In the JFY2001 design study, a plant concept has been constructed based on the design of the advanced loop type reactor, and fundamental specifications of main systems and components have been set. Furthermore, critical subjects related to safety, structural integrity, thermal hydraulics, operability, maintainability and economy have been examined and evaluated. As a result of this study, the plant concept of the sodium-cooled large-scale reactor has been constructed, which has a prospect to satisfy the economic goal (construction cost: less than 200,000yens/kWe, etc.) and has a prospect to solve the critical subjects. From now on, reflecting the results of elemental experiments, the preliminary conceptual design of this plant will be preceded toward the selection for narrowing down candidate concepts at the end of Phase 2. (author)

Large scale CMB anomalies from thawing cosmic strings

Energy Technology Data Exchange (ETDEWEB)

Ringeval, Christophe [Centre for Cosmology, Particle Physics and Phenomenology, Institute of Mathematics and Physics, Louvain University, 2 Chemin du Cyclotron, 1348 Louvain-la-Neuve (Belgium); Yamauchi, Daisuke; Yokoyama, Jun' ichi [Research Center for the Early Universe (RESCEU), Graduate School of Science, The University of Tokyo, Tokyo 113-0033 (Japan); Bouchet, François R., E-mail: christophe.ringeval@uclouvain.be, E-mail: yamauchi@resceu.s.u-tokyo.ac.jp, E-mail: yokoyama@resceu.s.u-tokyo.ac.jp, E-mail: bouchet@iap.fr [Institut d' Astrophysique de Paris, UMR 7095-CNRS, Université Pierre et Marie Curie, 98bis boulevard Arago, 75014 Paris (France)

2016-02-01

Cosmic strings formed during inflation are expected to be either diluted over super-Hubble distances, i.e., invisible today, or to have crossed our past light cone very recently. We discuss the latter situation in which a few strings imprint their signature in the Cosmic Microwave Background (CMB) Anisotropies after recombination. Being almost frozen in the Hubble flow, these strings are quasi static and evade almost all of the previously derived constraints on their tension while being able to source large scale anisotropies in the CMB sky. Using a local variance estimator on thousand of numerically simulated Nambu-Goto all sky maps, we compute the expected signal and show that it can mimic a dipole modulation at large angular scales while being negligible at small angles. Interestingly, such a scenario generically produces one cold spot from the thawing of a cosmic string loop. Mixed with anisotropies of inflationary origin, we find that a few strings of tension GU = O(1) × 10{sup −6} match the amplitude of the dipole modulation reported in the Planck satellite measurements and could be at the origin of other large scale anomalies.

Exploiting multi-scale parallelism for large scale numerical modelling of laser wakefield accelerators

International Nuclear Information System (INIS)

Fonseca, R A; Vieira, J; Silva, L O; Fiuza, F; Davidson, A; Tsung, F S; Mori, W B

2013-01-01

A new generation of laser wakefield accelerators (LWFA), supported by the extreme accelerating fields generated in the interaction of PW-Class lasers and underdense targets, promises the production of high quality electron beams in short distances for multiple applications. Achieving this goal will rely heavily on numerical modelling to further understand the underlying physics and identify optimal regimes, but large scale modelling of these scenarios is computationally heavy and requires the efficient use of state-of-the-art petascale supercomputing systems. We discuss the main difficulties involved in running these simulations and the new developments implemented in the OSIRIS framework to address these issues, ranging from multi-dimensional dynamic load balancing and hybrid distributed/shared memory parallelism to the vectorization of the PIC algorithm. We present the results of the OASCR Joule Metric program on the issue of large scale modelling of LWFA, demonstrating speedups of over 1 order of magnitude on the same hardware. Finally, scalability to over ∼10 6 cores and sustained performance over ∼2 P Flops is demonstrated, opening the way for large scale modelling of LWFA scenarios. (paper)

Multi-scale learning based segmentation of glands in digital colonrectal pathology images.

Science.gov (United States)

Gao, Yi; Liu, William; Arjun, Shipra; Zhu, Liangjia; Ratner, Vadim; Kurc, Tahsin; Saltz, Joel; Tannenbaum, Allen

2016-02-01

Digital histopathological images provide detailed spatial information of the tissue at micrometer resolution. Among the available contents in the pathology images, meso-scale information, such as the gland morphology, texture, and distribution, are useful diagnostic features. In this work, focusing on the colon-rectal cancer tissue samples, we propose a multi-scale learning based segmentation scheme for the glands in the colon-rectal digital pathology slides. The algorithm learns the gland and non-gland textures from a set of training images in various scales through a sparse dictionary representation. After the learning step, the dictionaries are used collectively to perform the classification and segmentation for the new image.

Balancing modern Power System with large scale of wind power

DEFF Research Database (Denmark)

Basit, Abdul; Altin, Müfit; Hansen, Anca Daniela

2014-01-01

Power system operators must ensure robust, secure and reliable power system operation even with a large scale integration of wind power. Electricity generated from the intermittent wind in large propor-tion may impact on the control of power system balance and thus deviations in the power system...... frequency in small or islanded power systems or tie line power flows in interconnected power systems. Therefore, the large scale integration of wind power into the power system strongly concerns the secure and stable grid operation. To ensure the stable power system operation, the evolving power system has...... to be analysed with improved analytical tools and techniques. This paper proposes techniques for the active power balance control in future power systems with the large scale wind power integration, where power balancing model provides the hour-ahead dispatch plan with reduced planning horizon and the real time...

Towards large-scale FAME-based bacterial species identification using machine learning techniques.

Science.gov (United States)

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

2009-05-01

In the last decade, bacterial taxonomy witnessed a huge expansion. The swift pace of bacterial species (re-)definitions has a serious impact on the accuracy and completeness of first-line identification methods. Consequently, back-end identification libraries need to be synchronized with the List of Prokaryotic names with Standing in Nomenclature. In this study, we focus on bacterial fatty acid methyl ester (FAME) profiling as a broadly used first-line identification method. From the BAME@LMG database, we have selected FAME profiles of individual strains belonging to the genera Bacillus, Paenibacillus and Pseudomonas. Only those profiles resulting from standard growth conditions have been retained. The corresponding data set covers 74, 44 and 95 validly published bacterial species, respectively, represented by 961, 378 and 1673 standard FAME profiles. Through the application of machine learning techniques in a supervised strategy, different computational models have been built for genus and species identification. Three techniques have been considered: artificial neural networks, random forests and support vector machines. Nearly perfect identification has been achieved at genus level. Notwithstanding the known limited discriminative power of FAME analysis for species identification, the computational models have resulted in good species identification results for the three genera. For Bacillus, Paenibacillus and Pseudomonas, random forests have resulted in sensitivity values, respectively, 0.847, 0.901 and 0.708. The random forests models outperform those of the other machine learning techniques. Moreover, our machine learning approach also outperformed the Sherlock MIS (MIDI Inc., Newark, DE, USA). These results show that machine learning proves very useful for FAME-based bacterial species identification. Besides good bacterial identification at species level, speed and ease of taxonomic synchronization are major advantages of this computational species

Development of Learning to Learn Skills in Primary School

Science.gov (United States)

Vainikainen, Mari-Pauliina; Wüstenberg, Sascha; Kupiainen, Sirkku; Hotulainen, Risto; Hautamäki, Jarkko

2015-01-01

In Finland, schools' effectiveness in fostering the development of transversal skills is evaluated through large-scale learning to learn (LTL) assessments. This article presents how LTL skills--general cognitive competences and learning-related motivational beliefs--develop during primary school and how they predict pupils' CPS skills at the end…

Large-Scale Graph Processing Using Apache Giraph

KAUST Repository

Sakr, Sherif

2017-01-07

This book takes its reader on a journey through Apache Giraph, a popular distributed graph processing platform designed to bring the power of big data processing to graph data. Designed as a step-by-step self-study guide for everyone interested in large-scale graph processing, it describes the fundamental abstractions of the system, its programming models and various techniques for using the system to process graph data at scale, including the implementation of several popular and advanced graph analytics algorithms.

Large-Scale Graph Processing Using Apache Giraph

KAUST Repository

Sakr, Sherif; Orakzai, Faisal Moeen; Abdelaziz, Ibrahim; Khayyat, Zuhair

2017-01-01

This book takes its reader on a journey through Apache Giraph, a popular distributed graph processing platform designed to bring the power of big data processing to graph data. Designed as a step-by-step self-study guide for everyone interested in large-scale graph processing, it describes the fundamental abstractions of the system, its programming models and various techniques for using the system to process graph data at scale, including the implementation of several popular and advanced graph analytics algorithms.

An interactive display system for large-scale 3D models

Science.gov (United States)

Liu, Zijian; Sun, Kun; Tao, Wenbing; Liu, Liman

2018-04-01

With the improvement of 3D reconstruction theory and the rapid development of computer hardware technology, the reconstructed 3D models are enlarging in scale and increasing in complexity. Models with tens of thousands of 3D points or triangular meshes are common in practical applications. Due to storage and computing power limitation, it is difficult to achieve real-time display and interaction with large scale 3D models for some common 3D display software, such as MeshLab. In this paper, we propose a display system for large-scale 3D scene models. We construct the LOD (Levels of Detail) model of the reconstructed 3D scene in advance, and then use an out-of-core view-dependent multi-resolution rendering scheme to realize the real-time display of the large-scale 3D model. With the proposed method, our display system is able to render in real time while roaming in the reconstructed scene and 3D camera poses can also be displayed. Furthermore, the memory consumption can be significantly decreased via internal and external memory exchange mechanism, so that it is possible to display a large scale reconstructed scene with over millions of 3D points or triangular meshes in a regular PC with only 4GB RAM.

RISK MANAGEMENT IN A LARGE-SCALE NEW RAILWAY TRANSPORT SYSTEM PROJECT

Directory of Open Access Journals (Sweden)

Sunduck D. SUH, Ph.D., P.E.

2000-01-01

Full Text Available Risk management experiences of the Korean Seoul-Pusan high-speed railway (KTX project since the planning stage are evaluated. One can clearly see the interplay of engineering and construction risks, financial risks and political risks in the development of the KTX project, which is the peculiarity of large-scale new railway system projects. A brief description on evaluation methodology and overview of the project is followed by detailed evaluations on key differences in risks between conventional railway system and high-speed railway system, social and political risks, engineering and construction risks, and financial risks. Risks involved in system procurement process, such as proposal solicitation, evaluation, selection, and scope of solicitation are separated out and evaluated in depth. Detailed events resulting from these issues are discussed along with their possible impact on system risk. Lessons learned and further possible refinements are also discussed.

Large-scale hydrology in Europe : observed patterns and model performance

Energy Technology Data Exchange (ETDEWEB)

Gudmundsson, Lukas

2011-06-15

In a changing climate, terrestrial water storages are of great interest as water availability impacts key aspects of ecosystem functioning. Thus, a better understanding of the variations of wet and dry periods will contribute to fully grasp processes of the earth system such as nutrient cycling and vegetation dynamics. Currently, river runoff from small, nearly natural, catchments is one of the few variables of the terrestrial water balance that is regularly monitored with detailed spatial and temporal coverage on large scales. River runoff, therefore, provides a foundation to approach European hydrology with respect to observed patterns on large scales, with regard to the ability of models to capture these.The analysis of observed river flow from small catchments, focused on the identification and description of spatial patterns of simultaneous temporal variations of runoff. These are dominated by large-scale variations of climatic variables but also altered by catchment processes. It was shown that time series of annual low, mean and high flows follow the same atmospheric drivers. The observation that high flows are more closely coupled to large scale atmospheric drivers than low flows, indicates the increasing influence of catchment properties on runoff under dry conditions. Further, it was shown that the low-frequency variability of European runoff is dominated by two opposing centres of simultaneous variations, such that dry years in the north are accompanied by wet years in the south.Large-scale hydrological models are simplified representations of our current perception of the terrestrial water balance on large scales. Quantification of the models strengths and weaknesses is the prerequisite for a reliable interpretation of simulation results. Model evaluations may also enable to detect shortcomings with model assumptions and thus enable a refinement of the current perception of hydrological systems. The ability of a multi model ensemble of nine large-scale

Large-scale perturbations from the waterfall field in hybrid inflation

International Nuclear Information System (INIS)

Fonseca, José; Wands, David; Sasaki, Misao

2010-01-01

We estimate large-scale curvature perturbations from isocurvature fluctuations in the waterfall field during hybrid inflation, in addition to the usual inflaton field perturbations. The tachyonic instability at the end of inflation leads to an explosive growth of super-Hubble scale perturbations, but they retain the steep blue spectrum characteristic of vacuum fluctuations in a massive field during inflation. The power spectrum thus peaks around the Hubble-horizon scale at the end of inflation. We extend the usual δN formalism to include the essential role of these small fluctuations when estimating the large-scale curvature perturbation. The resulting curvature perturbation due to fluctuations in the waterfall field is second-order and the spectrum is expected to be of order 10 −54 on cosmological scales

Decoupling local mechanics from large-scale structure in modular metamaterials

Science.gov (United States)

Yang, Nan; Silverberg, Jesse L.

2017-04-01

A defining feature of mechanical metamaterials is that their properties are determined by the organization of internal structure instead of the raw fabrication materials. This shift of attention to engineering internal degrees of freedom has coaxed relatively simple materials into exhibiting a wide range of remarkable mechanical properties. For practical applications to be realized, however, this nascent understanding of metamaterial design must be translated into a capacity for engineering large-scale structures with prescribed mechanical functionality. Thus, the challenge is to systematically map desired functionality of large-scale structures backward into a design scheme while using finite parameter domains. Such “inverse design” is often complicated by the deep coupling between large-scale structure and local mechanical function, which limits the available design space. Here, we introduce a design strategy for constructing 1D, 2D, and 3D mechanical metamaterials inspired by modular origami and kirigami. Our approach is to assemble a number of modules into a voxelized large-scale structure, where the module’s design has a greater number of mechanical design parameters than the number of constraints imposed by bulk assembly. This inequality allows each voxel in the bulk structure to be uniquely assigned mechanical properties independent from its ability to connect and deform with its neighbors. In studying specific examples of large-scale metamaterial structures we show that a decoupling of global structure from local mechanical function allows for a variety of mechanically and topologically complex designs.

The origin of large scale cosmic structure

International Nuclear Information System (INIS)

Jones, B.J.T.; Palmer, P.L.

1985-01-01

The paper concerns the origin of large scale cosmic structure. The evolution of density perturbations, the nonlinear regime (Zel'dovich's solution and others), the Gott and Rees clustering hierarchy, the spectrum of condensations, and biassed galaxy formation, are all discussed. (UK)

Supervised Outlier Detection in Large-Scale Mvs Point Clouds for 3d City Modeling Applications

Science.gov (United States)

Stucker, C.; Richard, A.; Wegner, J. D.; Schindler, K.

2018-05-01

We propose to use a discriminative classifier for outlier detection in large-scale point clouds of cities generated via multi-view stereo (MVS) from densely acquired images. What makes outlier removal hard are varying distributions of inliers and outliers across a scene. Heuristic outlier removal using a specific feature that encodes point distribution often delivers unsatisfying results. Although most outliers can be identified correctly (high recall), many inliers are erroneously removed (low precision), too. This aggravates object 3D reconstruction due to missing data. We thus propose to discriminatively learn class-specific distributions directly from the data to achieve high precision. We apply a standard Random Forest classifier that infers a binary label (inlier or outlier) for each 3D point in the raw, unfiltered point cloud and test two approaches for training. In the first, non-semantic approach, features are extracted without considering the semantic interpretation of the 3D points. The trained model approximates the average distribution of inliers and outliers across all semantic classes. Second, semantic interpretation is incorporated into the learning process, i.e. we train separate inlieroutlier classifiers per semantic class (building facades, roof, ground, vegetation, fields, and water). Performance of learned filtering is evaluated on several large SfM point clouds of cities. We find that results confirm our underlying assumption that discriminatively learning inlier-outlier distributions does improve precision over global heuristics by up to ≍ 12 percent points. Moreover, semantically informed filtering that models class-specific distributions further improves precision by up to ≍ 10 percent points, being able to remove very isolated building, roof, and water points while preserving inliers on building facades and vegetation.

A practical process for light-water detritiation at large scales

Energy Technology Data Exchange (ETDEWEB)

Boniface, H.A. [Atomic Energy of Canada Limited, Chalk River, ON (Canada); Robinson, J., E-mail: jr@tyne-engineering.com [Tyne Engineering, Burlington, ON (Canada); Gnanapragasam, N.V.; Castillo, I.; Suppiah, S. [Atomic Energy of Canada Limited, Chalk River, ON (Canada)

2014-07-01

AECL and Tyne Engineering have recently completed a preliminary engineering design for a modest-scale tritium removal plant for light water, intended for installation at AECL's Chalk River Laboratories (CRL). This plant design was based on the Combined Electrolysis and Catalytic Exchange (CECE) technology developed at CRL over many years and demonstrated there and elsewhere. The general features and capabilities of this design have been reported as well as the versatility of the design for separating any pair of the three hydrogen isotopes. The same CECE technology could be applied directly to very large-scale wastewater detritiation, such as the case at Fukushima Daiichi Nuclear Power Station. However, since the CECE process scales linearly with throughput, the required capital and operating costs are substantial for such large-scale applications. This paper discusses some options for reducing the costs of very large-scale detritiation. Options include: Reducing tritium removal effectiveness; Energy recovery; Improving the tolerance of impurities; Use of less expensive or more efficient equipment. A brief comparison with alternative processes is also presented. (author)

Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining

Science.gov (United States)

Hero, Alfred O.; Rajaratnam, Bala

2015-01-01

When can reliable inference be drawn in fue “Big Data” context? This paper presents a framework for answering this fundamental question in the context of correlation mining, wifu implications for general large scale inference. In large scale data applications like genomics, connectomics, and eco-informatics fue dataset is often variable-rich but sample-starved: a regime where the number n of acquired samples (statistical replicates) is far fewer than fue number p of observed variables (genes, neurons, voxels, or chemical constituents). Much of recent work has focused on understanding the computational complexity of proposed methods for “Big Data”. Sample complexity however has received relatively less attention, especially in the setting when the sample size n is fixed, and the dimension p grows without bound. To address fuis gap, we develop a unified statistical framework that explicitly quantifies the sample complexity of various inferential tasks. Sampling regimes can be divided into several categories: 1) the classical asymptotic regime where fue variable dimension is fixed and fue sample size goes to infinity; 2) the mixed asymptotic regime where both variable dimension and sample size go to infinity at comparable rates; 3) the purely high dimensional asymptotic regime where the variable dimension goes to infinity and the sample size is fixed. Each regime has its niche but only the latter regime applies to exa cale data dimension. We illustrate this high dimensional framework for the problem of correlation mining, where it is the matrix of pairwise and partial correlations among the variables fua t are of interest. Correlation mining arises in numerous applications and subsumes the regression context as a special case. we demonstrate various regimes of correlation mining based on the unifying perspective of high dimensional learning rates and sample complexity for different structured covariance models and different inference tasks. PMID:27087700

Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining.

Science.gov (United States)

Hero, Alfred O; Rajaratnam, Bala

2016-01-01

When can reliable inference be drawn in fue "Big Data" context? This paper presents a framework for answering this fundamental question in the context of correlation mining, wifu implications for general large scale inference. In large scale data applications like genomics, connectomics, and eco-informatics fue dataset is often variable-rich but sample-starved: a regime where the number n of acquired samples (statistical replicates) is far fewer than fue number p of observed variables (genes, neurons, voxels, or chemical constituents). Much of recent work has focused on understanding the computational complexity of proposed methods for "Big Data". Sample complexity however has received relatively less attention, especially in the setting when the sample size n is fixed, and the dimension p grows without bound. To address fuis gap, we develop a unified statistical framework that explicitly quantifies the sample complexity of various inferential tasks. Sampling regimes can be divided into several categories: 1) the classical asymptotic regime where fue variable dimension is fixed and fue sample size goes to infinity; 2) the mixed asymptotic regime where both variable dimension and sample size go to infinity at comparable rates; 3) the purely high dimensional asymptotic regime where the variable dimension goes to infinity and the sample size is fixed. Each regime has its niche but only the latter regime applies to exa cale data dimension. We illustrate this high dimensional framework for the problem of correlation mining, where it is the matrix of pairwise and partial correlations among the variables fua t are of interest. Correlation mining arises in numerous applications and subsumes the regression context as a special case. we demonstrate various regimes of correlation mining based on the unifying perspective of high dimensional learning rates and sample complexity for different structured covariance models and different inference tasks.

OffshoreDC DC grids for integration of large scale wind power

DEFF Research Database (Denmark)

Zeni, Lorenzo; Endegnanew, Atsede Gualu; Stamatiou, Georgios

The present report summarizes the main findings of the Nordic Energy Research project “DC grids for large scale integration of offshore wind power – OffshoreDC”. The project is been funded by Nordic Energy Research through the TFI programme and was active between 2011 and 2016. The overall...... objective of the project was to drive the development of the VSC based HVDC technology for future large scale offshore grids, supporting a standardised and commercial development of the technology, and improving the opportunities for the technology to support power system integration of large scale offshore...

Low-Complexity Transmit Antenna Selection and Beamforming for Large-Scale MIMO Communications

Directory of Open Access Journals (Sweden)

Kun Qian

2014-01-01

Full Text Available Transmit antenna selection plays an important role in large-scale multiple-input multiple-output (MIMO communications, but optimal large-scale MIMO antenna selection is a technical challenge. Exhaustive search is often employed in antenna selection, but it cannot be efficiently implemented in large-scale MIMO communication systems due to its prohibitive high computation complexity. This paper proposes a low-complexity interactive multiple-parameter optimization method for joint transmit antenna selection and beamforming in large-scale MIMO communication systems. The objective is to jointly maximize the channel outrage capacity and signal-to-noise (SNR performance and minimize the mean square error in transmit antenna selection and minimum variance distortionless response (MVDR beamforming without exhaustive search. The effectiveness of all the proposed methods is verified by extensive simulation results. It is shown that the required antenna selection processing time of the proposed method does not increase along with the increase of selected antennas, but the computation complexity of conventional exhaustive search method will significantly increase when large-scale antennas are employed in the system. This is particularly useful in antenna selection for large-scale MIMO communication systems.

e-Learning quality: Scale development and validation in Indian context

Directory of Open Access Journals (Sweden)

Arun Kumar Agariya

2012-12-01

Full Text Available The aim of this paper is to develop a reliable and valid e-learning quality measurement scales from the learner as well as faculty perspectives in Indian context. Exploratory factor analysis followed by confirmatory factor analysis was done which is presented in two forms; covariance model and the structural model. The covariance model shows that the factors namely collaboration, industry acceptance and value addition are important from the learner’s point of view whereas the factors namely transparency in assessment, technical know-how and engagement (from students are important from faculty point of view. Factors namely course content and design structures (technology/website design are found equally important for learner’s as well as faculty’s perspective. The structural models validate the previously extracted factors along with their indicators. The findings of this study validate the long held belief that e-learning quality is a multidimensional construct and serves as a critical success factor. The proposed scale will help in identifying issues that contribute towards e-learning quality in Indian context and thereby formulating strategies accordingly, resulting in efficient (in terms of cost and effective (outcomes e-learning practices, which is the necessity of the hour for the economic development of the country. A fair amount of literature on e-learning dealt with identifying factors explaining the constructs of quality, perceived value and satisfaction. But there is paucity of research pertaining to e-learning quality scale development and validation from the learner as well as faculty perspective. This study is an attempt to bridge this gap in the existing literature.

The effective field theory of cosmological large scale structures

Energy Technology Data Exchange (ETDEWEB)

Carrasco, John Joseph M. [Stanford Univ., Stanford, CA (United States); Hertzberg, Mark P. [Stanford Univ., Stanford, CA (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States); Senatore, Leonardo [Stanford Univ., Stanford, CA (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States)

2012-09-20

Large scale structure surveys will likely become the next leading cosmological probe. In our universe, matter perturbations are large on short distances and small at long scales, i.e. strongly coupled in the UV and weakly coupled in the IR. To make precise analytical predictions on large scales, we develop an effective field theory formulated in terms of an IR effective fluid characterized by several parameters, such as speed of sound and viscosity. These parameters, determined by the UV physics described by the Boltzmann equation, are measured from N-body simulations. We find that the speed of sound of the effective fluid is c²_s ≈ 10^–6c² and that the viscosity contributions are of the same order. The fluid describes all the relevant physics at long scales k and permits a manifestly convergent perturbative expansion in the size of the matter perturbations δ(k) for all the observables. As an example, we calculate the correction to the power spectrum at order δ(k)⁴. As a result, the predictions of the effective field theory are found to be in much better agreement with observation than standard cosmological perturbation theory, already reaching percent precision at this order up to a relatively short scale k ≃ 0.24h Mpc^–1.

Temporal flexibility and careers: The role of large-scale organizations for physicians

OpenAIRE

Forrest Briscoe

2006-01-01

Temporal flexibility and careers: The role of large-scale organizations for physicians. Forrest Briscoe Briscoe This study investigates how employment in large-scale organizations affects the work lives of practicing physicians. Well-established theory associates larger organizations with bureaucratic constraint, loss of workplace control, and dissatisfaction, but this author finds that large scale is also associated with greater schedule and career flexibility. Ironically, the bureaucratic p...

The role of large scale motions on passive scalar transport

Science.gov (United States)

Dharmarathne, Suranga; Araya, Guillermo; Tutkun, Murat; Leonardi, Stefano; Castillo, Luciano

2014-11-01

We study direct numerical simulation (DNS) of turbulent channel flow at Reτ = 394 to investigate effect of large scale motions on fluctuating temperature field which forms a passive scalar field. Statistical description of the large scale features of the turbulent channel flow is obtained using two-point correlations of velocity components. Two-point correlations of fluctuating temperature field is also examined in order to identify possible similarities between velocity and temperature fields. The two-point cross-correlations betwen the velocity and temperature fluctuations are further analyzed to establish connections between these two fields. In addition, we use proper orhtogonal decompotion (POD) to extract most dominant modes of the fields and discuss the coupling of large scale features of turbulence and the temperature field.

Signatures of non-universal large scales in conditional structure functions from various turbulent flows

International Nuclear Information System (INIS)

Blum, Daniel B; Voth, Greg A; Bewley, Gregory P; Bodenschatz, Eberhard; Gibert, Mathieu; Xu Haitao; Gylfason, Ármann; Mydlarski, Laurent; Yeung, P K

2011-01-01

We present a systematic comparison of conditional structure functions in nine turbulent flows. The flows studied include forced isotropic turbulence simulated on a periodic domain, passive grid wind tunnel turbulence in air and in pressurized SF 6 , active grid wind tunnel turbulence (in both synchronous and random driving modes), the flow between counter-rotating discs, oscillating grid turbulence and the flow in the Lagrangian exploration module (in both constant and random driving modes). We compare longitudinal Eulerian second-order structure functions conditioned on the instantaneous large-scale velocity in each flow to assess the ways in which the large scales affect the small scales in a variety of turbulent flows. Structure functions are shown to have larger values when the large-scale velocity significantly deviates from the mean in most flows, suggesting that dependence on the large scales is typical in many turbulent flows. The effects of the large-scale velocity on the structure functions can be quite strong, with the structure function varying by up to a factor of 2 when the large-scale velocity deviates from the mean by ±2 standard deviations. In several flows, the effects of the large-scale velocity are similar at all the length scales we measured, indicating that the large-scale effects are scale independent. In a few flows, the effects of the large-scale velocity are larger on the smallest length scales. (paper)

Cytology of DNA Replication Reveals Dynamic Plasticity of Large-Scale Chromatin Fibers.

Science.gov (United States)

Deng, Xiang; Zhironkina, Oxana A; Cherepanynets, Varvara D; Strelkova, Olga S; Kireev, Igor I; Belmont, Andrew S

2016-09-26

In higher eukaryotic interphase nuclei, the 100- to >1,000-fold linear compaction of chromatin is difficult to reconcile with its function as a template for transcription, replication, and repair. It is challenging to imagine how DNA and RNA polymerases with their associated molecular machinery would move along the DNA template without transient decondensation of observed large-scale chromatin "chromonema" fibers [1]. Transcription or "replication factory" models [2], in which polymerases remain fixed while DNA is reeled through, are similarly difficult to conceptualize without transient decondensation of these chromonema fibers. Here, we show how a dynamic plasticity of chromatin folding within large-scale chromatin fibers allows DNA replication to take place without significant changes in the global large-scale chromatin compaction or shape of these large-scale chromatin fibers. Time-lapse imaging of lac-operator-tagged chromosome regions shows no major change in the overall compaction of these chromosome regions during their DNA replication. Improved pulse-chase labeling of endogenous interphase chromosomes yields a model in which the global compaction and shape of large-Mbp chromatin domains remains largely invariant during DNA replication, with DNA within these domains undergoing significant movements and redistribution as they move into and then out of adjacent replication foci. In contrast to hierarchical folding models, this dynamic plasticity of large-scale chromatin organization explains how localized changes in DNA topology allow DNA replication to take place without an accompanying global unfolding of large-scale chromatin fibers while suggesting a possible mechanism for maintaining epigenetic programming of large-scale chromatin domains throughout DNA replication. Copyright © 2016 Elsevier Ltd. All rights reserved.

Evaluation of drought propagation in an ensemble mean of large-scale hydrological models

NARCIS (Netherlands)

Loon, van A.F.; Huijgevoort, van M.H.J.; Lanen, van H.A.J.

2012-01-01

Hydrological drought is increasingly studied using large-scale models. It is, however, not sure whether large-scale models reproduce the development of hydrological drought correctly. The pressing question is how well do large-scale models simulate the propagation from meteorological to hydrological

Configuration management in large scale infrastructure development

NARCIS (Netherlands)

Rijn, T.P.J. van; Belt, H. van de; Los, R.H.

2000-01-01

Large Scale Infrastructure (LSI) development projects such as the construction of roads, rail-ways and other civil engineering (water)works is tendered differently today than a decade ago. Traditional workflow requested quotes from construction companies for construction works where the works to be

Dual Decomposition for Large-Scale Power Balancing

DEFF Research Database (Denmark)

Halvgaard, Rasmus; Jørgensen, John Bagterp; Vandenberghe, Lieven

2013-01-01

Dual decomposition is applied to power balancing of exible thermal storage units. The centralized large-scale problem is decomposed into smaller subproblems and solved locallyby each unit in the Smart Grid. Convergence is achieved by coordinating the units consumption through a negotiation...

Generation of large-scale vortives in compressible helical turbulence

International Nuclear Information System (INIS)

Chkhetiani, O.G.; Gvaramadze, V.V.

1989-01-01

We consider generation of large-scale vortices in compressible self-gravitating turbulent medium. The closed equation describing evolution of the large-scale vortices in helical turbulence with finite correlation time is obtained. This equation has the form similar to the hydromagnetic dynamo equation, which allows us to call the vortx genertation effect the vortex dynamo. It is possible that principally the same mechanism is responsible both for amplification and maintenance of density waves and magnetic fields in gaseous disks of spiral galaxies. (author). 29 refs

Dipolar modulation of Large-Scale Structure

Science.gov (United States)

Yoon, Mijin

For the last two decades, we have seen a drastic development of modern cosmology based on various observations such as the cosmic microwave background (CMB), type Ia supernovae, and baryonic acoustic oscillations (BAO). These observational evidences have led us to a great deal of consensus on the cosmological model so-called LambdaCDM and tight constraints on cosmological parameters consisting the model. On the other hand, the advancement in cosmology relies on the cosmological principle: the universe is isotropic and homogeneous on large scales. Testing these fundamental assumptions is crucial and will soon become possible given the planned observations ahead. Dipolar modulation is the largest angular anisotropy of the sky, which is quantified by its direction and amplitude. We measured a huge dipolar modulation in CMB, which mainly originated from our solar system's motion relative to CMB rest frame. However, we have not yet acquired consistent measurements of dipolar modulations in large-scale structure (LSS), as they require large sky coverage and a number of well-identified objects. In this thesis, we explore measurement of dipolar modulation in number counts of LSS objects as a test of statistical isotropy. This thesis is based on two papers that were published in peer-reviewed journals. In Chapter 2 [Yoon et al., 2014], we measured a dipolar modulation in number counts of WISE matched with 2MASS sources. In Chapter 3 [Yoon & Huterer, 2015], we investigated requirements for detection of kinematic dipole in future surveys.

Impact of large-scale tides on cosmological distortions via redshift-space power spectrum

Science.gov (United States)

Akitsu, Kazuyuki; Takada, Masahiro

2018-03-01

Although large-scale perturbations beyond a finite-volume survey region are not direct observables, these affect measurements of clustering statistics of small-scale (subsurvey) perturbations in large-scale structure, compared with the ensemble average, via the mode-coupling effect. In this paper we show that a large-scale tide induced by scalar perturbations causes apparent anisotropic distortions in the redshift-space power spectrum of galaxies in a way depending on an alignment between the tide, wave vector of small-scale modes and line-of-sight direction. Using the perturbation theory of structure formation, we derive a response function of the redshift-space power spectrum to large-scale tide. We then investigate the impact of large-scale tide on estimation of cosmological distances and the redshift-space distortion parameter via the measured redshift-space power spectrum for a hypothetical large-volume survey, based on the Fisher matrix formalism. To do this, we treat the large-scale tide as a signal, rather than an additional source of the statistical errors, and show that a degradation in the parameter is restored if we can employ the prior on the rms amplitude expected for the standard cold dark matter (CDM) model. We also discuss whether the large-scale tide can be constrained at an accuracy better than the CDM prediction, if the effects up to a larger wave number in the nonlinear regime can be included.

Large-scale Intelligent Transporation Systems simulation

Energy Technology Data Exchange (ETDEWEB)

Ewing, T.; Canfield, T.; Hannebutte, U.; Levine, D.; Tentner, A.

1995-06-01

A prototype computer system has been developed which defines a high-level architecture for a large-scale, comprehensive, scalable simulation of an Intelligent Transportation System (ITS) capable of running on massively parallel computers and distributed (networked) computer systems. The prototype includes the modelling of instrumented ``smart`` vehicles with in-vehicle navigation units capable of optimal route planning and Traffic Management Centers (TMC). The TMC has probe vehicle tracking capabilities (display position and attributes of instrumented vehicles), and can provide 2-way interaction with traffic to provide advisories and link times. Both the in-vehicle navigation module and the TMC feature detailed graphical user interfaces to support human-factors studies. The prototype has been developed on a distributed system of networked UNIX computers but is designed to run on ANL`s IBM SP-X parallel computer system for large scale problems. A novel feature of our design is that vehicles will be represented by autonomus computer processes, each with a behavior model which performs independent route selection and reacts to external traffic events much like real vehicles. With this approach, one will be able to take advantage of emerging massively parallel processor (MPP) systems.

The Hamburg large scale geostrophic ocean general circulation model. Cycle 1

International Nuclear Information System (INIS)

Maier-Reimer, E.; Mikolajewicz, U.

1992-02-01

The rationale for the Large Scale Geostrophic ocean circulation model (LSG-OGCM) is based on the observations that for a large scale ocean circulation model designed for climate studies, the relevant characteristic spatial scales are large compared with the internal Rossby radius throughout most of the ocean, while the characteristic time scales are large compared with the periods of gravity modes and barotropic Rossby wave modes. In the present version of the model, the fast modes have been filtered out by a conventional technique of integrating the full primitive equations, including all terms except the nonlinear advection of momentum, by an implicit time integration method. The free surface is also treated prognostically, without invoking a rigid lid approximation. The numerical scheme is unconditionally stable and has the additional advantage that it can be applied uniformly to the entire globe, including the equatorial and coastal current regions. (orig.)

Soft X-ray Emission from Large-Scale Galactic Outflows in Seyfert Galaxies

Science.gov (United States)

Colbert, E. J. M.; Baum, S.; O'Dea, C.; Veilleux, S.

1998-01-01

Kiloparsec-scale soft X-ray nebulae extend along the galaxy minor axes in several Seyfert galaxies, including NGC 2992, NGC 4388 and NGC 5506. In these three galaxies, the extended X-ray emission observed in ROSAT HRI images has 0.2-2.4 keV X-ray luminosities of 0.4-3.5 x 10(40) erg s(-1) . The X-ray nebulae are roughly co-spatial with the large-scale radio emission, suggesting that both are produced by large-scale galactic outflows. Assuming pressure balance between the radio and X-ray plasmas, the X-ray filling factor is >~ 10(4) times as large as the radio plasma filling factor, suggesting that large-scale outflows in Seyfert galaxies are predominantly winds of thermal X-ray emitting gas. We favor an interpretation in which large-scale outflows originate as AGN-driven jets that entrain and heat gas on kpc scales as they make their way out of the galaxy. AGN- and starburst-driven winds are also possible explanations if the winds are oriented along the rotation axis of the galaxy disk. Since large-scale outflows are present in at least 50 percent of Seyfert galaxies, the soft X-ray emission from the outflowing gas may, in many cases, explain the ``soft excess" X-ray feature observed below 2 keV in X-ray spectra of many Seyfert 2 galaxies.

Pro website development and operations streamlining DevOps for large-scale websites

CERN Document Server

Sacks, Matthew

2012-01-01

Pro Website Development and Operations gives you the experience you need to create and operate a large-scale production website. Large-scale websites have their own unique set of problems regarding their design-problems that can get worse when agile methodologies are adopted for rapid results. Managing large-scale websites, deploying applications, and ensuring they are performing well often requires a full scale team involving the development and operations sides of the company-two departments that don't always see eye to eye. When departments struggle with each other, it adds unnecessary comp

Neutrinos and large-scale structure

International Nuclear Information System (INIS)

Eisenstein, Daniel J.

2015-01-01

I review the use of cosmological large-scale structure to measure properties of neutrinos and other relic populations of light relativistic particles. With experiments to measure the anisotropies of the cosmic microwave anisotropies and the clustering of matter at low redshift, we now have securely measured a relativistic background with density appropriate to the cosmic neutrino background. Our limits on the mass of the neutrino continue to shrink. Experiments coming in the next decade will greatly improve the available precision on searches for the energy density of novel relativistic backgrounds and the mass of neutrinos

Neutrinos and large-scale structure

Energy Technology Data Exchange (ETDEWEB)

Eisenstein, Daniel J. [Daniel J. Eisenstein, Harvard-Smithsonian Center for Astrophysics, 60 Garden St., MS #20, Cambridge, MA 02138 (United States)

2015-07-15

I review the use of cosmological large-scale structure to measure properties of neutrinos and other relic populations of light relativistic particles. With experiments to measure the anisotropies of the cosmic microwave anisotropies and the clustering of matter at low redshift, we now have securely measured a relativistic background with density appropriate to the cosmic neutrino background. Our limits on the mass of the neutrino continue to shrink. Experiments coming in the next decade will greatly improve the available precision on searches for the energy density of novel relativistic backgrounds and the mass of neutrinos.

Evaluation of Large-scale Public Sector Reforms

DEFF Research Database (Denmark)

Breidahl, Karen Nielsen; Gjelstrup, Gunnar; Hansen, Hanne Foss

2017-01-01

and more delimited policy areas take place. In our analysis we apply four governance perspectives (rational-instrumental, rational-interest based, institutional-cultural and a chaos perspective) in a comparative analysis of the evaluations of two large-scale public sector reforms in Denmark and Norway. We...

Highly Scalable Trip Grouping for Large Scale Collective Transportation Systems

DEFF Research Database (Denmark)

Gidofalvi, Gyozo; Pedersen, Torben Bach; Risch, Tore

2008-01-01

Transportation-related problems, like road congestion, parking, and pollution, are increasing in most cities. In order to reduce traffic, recent work has proposed methods for vehicle sharing, for example for sharing cabs by grouping "closeby" cab requests and thus minimizing transportation cost...... and utilizing cab space. However, the methods published so far do not scale to large data volumes, which is necessary to facilitate large-scale collective transportation systems, e.g., ride-sharing systems for large cities. This paper presents highly scalable trip grouping algorithms, which generalize previous...

Penalized Estimation in Large-Scale Generalized Linear Array Models

DEFF Research Database (Denmark)

Lund, Adam; Vincent, Martin; Hansen, Niels Richard

2017-01-01

Large-scale generalized linear array models (GLAMs) can be challenging to fit. Computation and storage of its tensor product design matrix can be impossible due to time and memory constraints, and previously considered design matrix free algorithms do not scale well with the dimension...

Large-scale coastal impact induced by a catastrophic storm

DEFF Research Database (Denmark)

Fruergaard, Mikkel; Andersen, Thorbjørn Joest; Johannessen, Peter N

breaching. Our results demonstrate that violent, millennial-scale storms can trigger significant large-scale and long-term changes on barrier coasts, and that coastal changes assumed to take place over centuries or even millennia may occur in association with a single extreme storm event....

Large-eddy simulation with accurate implicit subgrid-scale diffusion

NARCIS (Netherlands)

B. Koren (Barry); C. Beets

1996-01-01

textabstractA method for large-eddy simulation is presented that does not use an explicit subgrid-scale diffusion term. Subgrid-scale effects are modelled implicitly through an appropriate monotone (in the sense of Spekreijse 1987) discretization method for the advective terms. Special attention is

Challenges for Large Scale Structure Theory

CERN Multimedia

CERN. Geneva

2018-01-01

I will describe some of the outstanding questions in Cosmology where answers could be provided by observations of the Large Scale Structure of the Universe at late times.I will discuss some of the theoretical challenges which will have to be overcome to extract this information from the observations. I will describe some of the theoretical tools that might be useful to achieve this goal. 

Macroecological factors explain large-scale spatial population patterns of ancient agriculturalists

NARCIS (Netherlands)

Xu, C.; Chen, B.; Abades, S.; Reino, L.; Teng, S.; Ljungqvist, F.C.; Huang, Z.Y.X.; Liu, X.

2015-01-01

Aim: It has been well demonstrated that the large-scale distribution patterns of numerous species are driven by similar macroecological factors. However, understanding of this topic remains limited when applied to our own species. Here we take a large-scale look at ancient agriculturalist

Large Scale Investments in Infrastructure : Competing Policy regimes to Control Connections

NARCIS (Netherlands)

Otsuki, K.; Read, M.L.; Zoomers, E.B.

2016-01-01

This paper proposes to analyse implications of large-scale investments in physical infrastructure for social and environmental justice. While case studies on the global land rush and climate change have advanced our understanding of how large-scale investments in land, forests and water affect

Rotation invariant fast features for large-scale recognition

Science.gov (United States)

Takacs, Gabriel; Chandrasekhar, Vijay; Tsai, Sam; Chen, David; Grzeszczuk, Radek; Girod, Bernd

2012-10-01

We present an end-to-end feature description pipeline which uses a novel interest point detector and Rotation- Invariant Fast Feature (RIFF) descriptors. The proposed RIFF algorithm is 15× faster than SURF1 while producing large-scale retrieval results that are comparable to SIFT.2 Such high-speed features benefit a range of applications from Mobile Augmented Reality (MAR) to web-scale image retrieval and analysis.

Large-scale bioenergy production: how to resolve sustainability trade-offs?

Science.gov (United States)

Humpenöder, Florian; Popp, Alexander; Bodirsky, Benjamin Leon; Weindl, Isabelle; Biewald, Anne; Lotze-Campen, Hermann; Dietrich, Jan Philipp; Klein, David; Kreidenweis, Ulrich; Müller, Christoph; Rolinski, Susanne; Stevanovic, Miodrag

2018-02-01

Large-scale 2nd generation bioenergy deployment is a key element of 1.5 °C and 2 °C transformation pathways. However, large-scale bioenergy production might have negative sustainability implications and thus may conflict with the Sustainable Development Goal (SDG) agenda. Here, we carry out a multi-criteria sustainability assessment of large-scale bioenergy crop production throughout the 21st century (300 EJ in 2100) using a global land-use model. Our analysis indicates that large-scale bioenergy production without complementary measures results in negative effects on the following sustainability indicators: deforestation, CO2 emissions from land-use change, nitrogen losses, unsustainable water withdrawals and food prices. One of our main findings is that single-sector environmental protection measures next to large-scale bioenergy production are prone to involve trade-offs among these sustainability indicators—at least in the absence of more efficient land or water resource use. For instance, if bioenergy production is accompanied by forest protection, deforestation and associated emissions (SDGs 13 and 15) decline substantially whereas food prices (SDG 2) increase. However, our study also shows that this trade-off strongly depends on the development of future food demand. In contrast to environmental protection measures, we find that agricultural intensification lowers some side-effects of bioenergy production substantially (SDGs 13 and 15) without generating new trade-offs—at least among the sustainability indicators considered here. Moreover, our results indicate that a combination of forest and water protection schemes, improved fertilization efficiency, and agricultural intensification would reduce the side-effects of bioenergy production most comprehensively. However, although our study includes more sustainability indicators than previous studies on bioenergy side-effects, our study represents only a small subset of all indicators relevant for the

Large-scale structure in the universe: Theory vs observations

International Nuclear Information System (INIS)

Kashlinsky, A.; Jones, B.J.T.

1990-01-01

A variety of observations constrain models of the origin of large scale cosmic structures. We review here the elements of current theories and comment in detail on which of the current observational data provide the principal constraints. We point out that enough observational data have accumulated to constrain (and perhaps determine) the power spectrum of primordial density fluctuations over a very large range of scales. We discuss the theories in the light of observational data and focus on the potential of future observations in providing even (and ever) tighter constraints. (orig.)

Evaluation of drought propagation in an ensemble mean of large-scale hydrological models

Directory of Open Access Journals (Sweden)

A. F. Van Loon

2012-11-01

Full Text Available Hydrological drought is increasingly studied using large-scale models. It is, however, not sure whether large-scale models reproduce the development of hydrological drought correctly. The pressing question is how well do large-scale models simulate the propagation from meteorological to hydrological drought? To answer this question, we evaluated the simulation of drought propagation in an ensemble mean of ten large-scale models, both land-surface models and global hydrological models, that participated in the model intercomparison project of WATCH (WaterMIP. For a selection of case study areas, we studied drought characteristics (number of droughts, duration, severity, drought propagation features (pooling, attenuation, lag, lengthening, and hydrological drought typology (classical rainfall deficit drought, rain-to-snow-season drought, wet-to-dry-season drought, cold snow season drought, warm snow season drought, composite drought.

Drought characteristics simulated by large-scale models clearly reflected drought propagation; i.e. drought events became fewer and longer when moving through the hydrological cycle. However, more differentiation was expected between fast and slowly responding systems, with slowly responding systems having fewer and longer droughts in runoff than fast responding systems. This was not found using large-scale models. Drought propagation features were poorly reproduced by the large-scale models, because runoff reacted immediately to precipitation, in all case study areas. This fast reaction to precipitation, even in cold climates in winter and in semi-arid climates in summer, also greatly influenced the hydrological drought typology as identified by the large-scale models. In general, the large-scale models had the correct representation of drought types, but the percentages of occurrence had some important mismatches, e.g. an overestimation of classical rainfall deficit droughts, and an

Multiresolution comparison of precipitation datasets for large-scale models

Science.gov (United States)

Chun, K. P.; Sapriza Azuri, G.; Davison, B.; DeBeer, C. M.; Wheater, H. S.

2014-12-01

Gridded precipitation datasets are crucial for driving large-scale models which are related to weather forecast and climate research. However, the quality of precipitation products is usually validated individually. Comparisons between gridded precipitation products along with ground observations provide another avenue for investigating how the precipitation uncertainty would affect the performance of large-scale models. In this study, using data from a set of precipitation gauges over British Columbia and Alberta, we evaluate several widely used North America gridded products including the Canadian Gridded Precipitation Anomalies (CANGRD), the National Center for Environmental Prediction (NCEP) reanalysis, the Water and Global Change (WATCH) project, the thin plate spline smoothing algorithms (ANUSPLIN) and Canadian Precipitation Analysis (CaPA). Based on verification criteria for various temporal and spatial scales, results provide an assessment of possible applications for various precipitation datasets. For long-term climate variation studies (~100 years), CANGRD, NCEP, WATCH and ANUSPLIN have different comparative advantages in terms of their resolution and accuracy. For synoptic and mesoscale precipitation patterns, CaPA provides appealing performance of spatial coherence. In addition to the products comparison, various downscaling methods are also surveyed to explore new verification and bias-reduction methods for improving gridded precipitation outputs for large-scale models.

Toward Instructional Leadership: Principals' Perceptions of Large-Scale Assessment in Schools

Science.gov (United States)

Prytula, Michelle; Noonan, Brian; Hellsten, Laurie

2013-01-01

This paper describes a study of the perceptions that Saskatchewan school principals have regarding large-scale assessment reform and their perceptions of how assessment reform has affected their roles as principals. The findings revealed that large-scale assessments, especially provincial assessments, have affected the principal in Saskatchewan…

A large scale field experiment in the Amazon basin (LAMBADA/BATERISTA)

NARCIS (Netherlands)

Dolman, A.J.; Kabat, P.; Gash, J.H.C.; Noilhan, J.; Jochum, A.M.; Nobre, C.

1995-01-01

A description is given of a large-scale field experiment planned in the Amazon basin, aimed at assessing the large-scale balances of energy, water and carbon dioxide. The embedding of this experiment in global change programmes is described, viz. the Biospheric Aspects of the Hydrological Cycle

Large-scale derived flood frequency analysis based on continuous simulation

Science.gov (United States)

Dung Nguyen, Viet; Hundecha, Yeshewatesfa; Guse, Björn; Vorogushyn, Sergiy; Merz, Bruno

2016-04-01

There is an increasing need for spatially consistent flood risk assessments at the regional scale (several 100.000 km2), in particular in the insurance industry and for national risk reduction strategies. However, most large-scale flood risk assessments are composed of smaller-scale assessments and show spatial inconsistencies. To overcome this deficit, a large-scale flood model composed of a weather generator and catchments models was developed reflecting the spatially inherent heterogeneity. The weather generator is a multisite and multivariate stochastic model capable of generating synthetic meteorological fields (precipitation, temperature, etc.) at daily resolution for the regional scale. These fields respect the observed autocorrelation, spatial correlation and co-variance between the variables. They are used as input into catchment models. A long-term simulation of this combined system enables to derive very long discharge series at many catchment locations serving as a basic for spatially consistent flood risk estimates at the regional scale. This combined model was set up and validated for major river catchments in Germany. The weather generator was trained by 53-year observation data at 528 stations covering not only the complete Germany but also parts of France, Switzerland, Czech Republic and Australia with the aggregated spatial scale of 443,931 km2. 10.000 years of daily meteorological fields for the study area were generated. Likewise, rainfall-runoff simulations with SWIM were performed for the entire Elbe, Rhine, Weser, Donau and Ems catchments. The validation results illustrate a good performance of the combined system, as the simulated flood magnitudes and frequencies agree well with the observed flood data. Based on continuous simulation this model chain is then used to estimate flood quantiles for the whole Germany including upstream headwater catchments in neighbouring countries. This continuous large scale approach overcomes the several

GAIA: A WINDOW TO LARGE-SCALE MOTIONS

Energy Technology Data Exchange (ETDEWEB)

Nusser, Adi [Physics Department and the Asher Space Science Institute-Technion, Haifa 32000 (Israel); Branchini, Enzo [Department of Physics, Universita Roma Tre, Via della Vasca Navale 84, 00146 Rome (Italy); Davis, Marc, E-mail: adi@physics.technion.ac.il, E-mail: branchin@fis.uniroma3.it, E-mail: mdavis@berkeley.edu [Departments of Astronomy and Physics, University of California, Berkeley, CA 94720 (United States)

2012-08-10

Using redshifts as a proxy for galaxy distances, estimates of the two-dimensional (2D) transverse peculiar velocities of distant galaxies could be obtained from future measurements of proper motions. We provide the mathematical framework for analyzing 2D transverse motions and show that they offer several advantages over traditional probes of large-scale motions. They are completely independent of any intrinsic relations between galaxy properties; hence, they are essentially free of selection biases. They are free from homogeneous and inhomogeneous Malmquist biases that typically plague distance indicator catalogs. They provide additional information to traditional probes that yield line-of-sight peculiar velocities only. Further, because of their 2D nature, fundamental questions regarding vorticity of large-scale flows can be addressed. Gaia, for example, is expected to provide proper motions of at least bright galaxies with high central surface brightness, making proper motions a likely contender for traditional probes based on current and future distance indicator measurements.

Large-scale hydrogen production using nuclear reactors

Energy Technology Data Exchange (ETDEWEB)

Ryland, D.; Stolberg, L.; Kettner, A.; Gnanapragasam, N.; Suppiah, S. [Atomic Energy of Canada Limited, Chalk River, ON (Canada)

2014-07-01

For many years, Atomic Energy of Canada Limited (AECL) has been studying the feasibility of using nuclear reactors, such as the Supercritical Water-cooled Reactor, as an energy source for large scale hydrogen production processes such as High Temperature Steam Electrolysis and the Copper-Chlorine thermochemical cycle. Recent progress includes the augmentation of AECL's experimental capabilities by the construction of experimental systems to test high temperature steam electrolysis button cells at ambient pressure and temperatures up to 850{sup o}C and CuCl/HCl electrolysis cells at pressures up to 7 bar and temperatures up to 100{sup o}C. In parallel, detailed models of solid oxide electrolysis cells and the CuCl/HCl electrolysis cell are being refined and validated using experimental data. Process models are also under development to assess options for economic integration of these hydrogen production processes with nuclear reactors. Options for large-scale energy storage, including hydrogen storage, are also under study. (author)

Planck intermediate results XLII. Large-scale Galactic magnetic fields

DEFF Research Database (Denmark)

Adam, R.; Ade, P. A. R.; Alves, M. I. R.

2016-01-01

Recent models for the large-scale Galactic magnetic fields in the literature have been largely constrained by synchrotron emission and Faraday rotation measures. We use three different but representative models to compare their predicted polarized synchrotron and dust emission with that measured ...

A Topology Visualization Early Warning Distribution Algorithm for Large-Scale Network Security Incidents

Directory of Open Access Journals (Sweden)

Hui He

2013-01-01

Full Text Available It is of great significance to research the early warning system for large-scale network security incidents. It can improve the network system’s emergency response capabilities, alleviate the cyber attacks’ damage, and strengthen the system’s counterattack ability. A comprehensive early warning system is presented in this paper, which combines active measurement and anomaly detection. The key visualization algorithm and technology of the system are mainly discussed. The large-scale network system’s plane visualization is realized based on the divide and conquer thought. First, the topology of the large-scale network is divided into some small-scale networks by the MLkP/CR algorithm. Second, the sub graph plane visualization algorithm is applied to each small-scale network. Finally, the small-scale networks’ topologies are combined into a topology based on the automatic distribution algorithm of force analysis. As the algorithm transforms the large-scale network topology plane visualization problem into a series of small-scale network topology plane visualization and distribution problems, it has higher parallelism and is able to handle the display of ultra-large-scale network topology.

Medical students perceive better group learning processes when large classes are made to seem small.

Science.gov (United States)

Hommes, Juliette; Arah, Onyebuchi A; de Grave, Willem; Schuwirth, Lambert W T; Scherpbier, Albert J J A; Bos, Gerard M J

2014-01-01

Medical schools struggle with large classes, which might interfere with the effectiveness of learning within small groups due to students being unfamiliar to fellow students. The aim of this study was to assess the effects of making a large class seem small on the students' collaborative learning processes. A randomised controlled intervention study was undertaken to make a large class seem small, without the need to reduce the number of students enrolling in the medical programme. The class was divided into subsets: two small subsets (n=50) as the intervention groups; a control group (n=102) was mixed with the remaining students (the non-randomised group n∼100) to create one large subset. The undergraduate curriculum of the Maastricht Medical School, applying the Problem-Based Learning principles. In this learning context, students learn mainly in tutorial groups, composed randomly from a large class every 6-10 weeks. The formal group learning activities were organised within the subsets. Students from the intervention groups met frequently within the formal groups, in contrast to the students from the large subset who hardly enrolled with the same students in formal activities. Three outcome measures assessed students' group learning processes over time: learning within formally organised small groups, learning with other students in the informal context and perceptions of the intervention. Formal group learning processes were perceived more positive in the intervention groups from the second study year on, with a mean increase of β=0.48. Informal group learning activities occurred almost exclusively within the subsets as defined by the intervention from the first week involved in the medical curriculum (E-I indexes>-0.69). Interviews tapped mainly positive effects and negligible negative side effects of the intervention. Better group learning processes can be achieved in large medical schools by making large classes seem small.

Globalizing Lessons Learned from Regional-scale Observatories

Science.gov (United States)

Glenn, S. M.

2016-02-01

The Mid Atlantic Regional Association Coastal Ocean Observing System (MARACOOS) has accumulated a decade of experience designing, building and operating a Regional Coastal Ocean Observing System for the U.S. Integrated Ocean Observing System (IOOS). MARACOOS serves societal goals and supports scientific discovery at the scale of a Large Marine Ecosystem (LME). Societal themes include maritime safety, ecosystem decision support, coastal inundation, water quality and offshore energy. Scientific results that feed back on societal goals with better products include improved understanding of seasonal transport pathways and their impact on phytoplankton blooms and hypoxia, seasonal evolution of the subsurface Mid Atlantic Cold Pool and its impact on fisheries, biogeochemical transformations in coastal plumes, coastal ocean evolution and impact on hurricane intensities, and storm sediment transport pathways. As the global ocean observing requirements grow to support additional societal needs for information on fisheries and aquaculture, ocean acidification and deoxygenation, water quality and offshore development, global observing will necessarily evolve to include more coastal observations and forecast models at the scale of the world's many LMEs. Here we describe our efforts to share lessons learned between the observatory operators at the regional-scale of the LMEs. Current collaborators are spread across Europe, and also include Korea, Indonesia, Australia, Brazil and South Africa. Specific examples include the development of a world standard QA/QC approach for HF Radar data that will foster the sharing of data between countries, basin-scale underwater glider missions between internationally-distributed glider ports to developed a shared understanding of operations and an ongoing evaluation of the global ocean models in which the regional models for the LME will be nested, and joint training programs to develop the distributed teams of scientists and technicians

Validity & reliability of the Persian version of Grasha-Richmann student learning styles scale

Directory of Open Access Journals (Sweden)

ALI REZA BANESHI

2013-10-01

Full Text Available Introduction: The present study aimed to investigate the psychometric properties of Grasha-Riechmann Student Learning Styles Scale. Method: The participants included 1039 students (421 students in human and 618 students in technical sciences, selected through the stratified sampling method from Tehran University. They answered the Grasha-Riechmann student learning style scale and the data was analyzed with exploratory and confirmatory factor analyses. Results: The findings acquired from exploratory factor analysis (n=561, using principal components analysis with varimax rotation showed that Grasha- Riechmann Student Learning Styles Scale includes six factors: Avoidant, Collaborative, Participative, Dependent, Competitive, and Independent. The factors acquired from confirmatory factor analysis (n=478, as model fit indices indicated, was confirmed by indices in exploratory factor analysis. The internal consistency of each subscale, ranging from 0.58 to 0.80, was at an acceptable level. Conclusion: According to the findings, it seems that Participative Styles Scale to be an instrument qualifying validity and reliability for measuring learning interactive styles.

No Large Scale Curvature Perturbations during Waterfall of Hybrid Inflation

OpenAIRE

Abolhasani, Ali Akbar; Firouzjahi, Hassan

2010-01-01

In this paper the possibility of generating large scale curvature perturbations induced from the entropic perturbations during the waterfall phase transition of standard hybrid inflation model is studied. We show that whether or not appreciable amounts of large scale curvature perturbations are produced during the waterfall phase transition depend crucially on the competition between the classical and the quantum mechanical back-reactions to terminate inflation. If one considers only the clas...

Large Scale Emerging Properties from Non Hamiltonian Complex Systems

Directory of Open Access Journals (Sweden)

Marco Bianucci

2017-06-01

Full Text Available The concept of “large scale” depends obviously on the phenomenon we are interested in. For example, in the field of foundation of Thermodynamics from microscopic dynamics, the spatial and time large scales are order of fraction of millimetres and microseconds, respectively, or lesser, and are defined in relation to the spatial and time scales of the microscopic systems. In large scale oceanography or global climate dynamics problems the time scales of interest are order of thousands of kilometres, for space, and many years for time, and are compared to the local and daily/monthly times scales of atmosphere and ocean dynamics. In all the cases a Zwanzig projection approach is, at least in principle, an effective tool to obtain class of universal smooth “large scale” dynamics for few degrees of freedom of interest, starting from the complex dynamics of the whole (usually many degrees of freedom system. The projection approach leads to a very complex calculus with differential operators, that is drastically simplified when the basic dynamics of the system of interest is Hamiltonian, as it happens in Foundation of Thermodynamics problems. However, in geophysical Fluid Dynamics, Biology, and in most of the physical problems the building block fundamental equations of motions have a non Hamiltonian structure. Thus, to continue to apply the useful projection approach also in these cases, we exploit the generalization of the Hamiltonian formalism given by the Lie algebra of dissipative differential operators. In this way, we are able to analytically deal with the series of the differential operators stemming from the projection approach applied to these general cases. Then we shall apply this formalism to obtain some relevant results concerning the statistical properties of the El Niño Southern Oscillation (ENSO.

A new system of labour management in African large-scale agriculture?

DEFF Research Database (Denmark)

Gibbon, Peter; Riisgaard, Lone

2014-01-01

This paper applies a convention theory (CT) approach to the analysis of labour management systems in African large-scale farming. The reconstruction of previous analyses of high-value crop production on large-scale farms in Africa in terms of CT suggests that, since 1980–95, labour management has...

Pseudoscalar-photon mixing and the large scale alignment of QsO ...

Indian Academy of Sciences (India)

physics pp. 679-682. Pseudoscalar-photon mixing and the large scale alignment of QsO optical polarizations. PANKAJ JAIN, sUKANTA PANDA and s sARALA. Physics Department, Indian Institute of Technology, Kanpur 208 016, India. Abstract. We review the observation of large scale alignment of QSO optical polariza-.

A SCALE-UP Mock-Up: Comparison of Student Learning Gains in High- and Low-Tech Active-Learning Environments.

Science.gov (United States)

Soneral, Paula A G; Wyse, Sara A

2017-01-01

Student-centered learning environments with upside-down pedagogies (SCALE-UP) are widely implemented at institutions across the country, and learning gains from these classrooms have been well documented. This study investigates the specific design feature(s) of the SCALE-UP classroom most conducive to teaching and learning. Using pilot survey data from instructors and students to prioritize the most salient SCALE-UP classroom features, we created a low-tech "Mock-up" version of this classroom and tested the impact of these features on student learning, attitudes, and satisfaction using a quasi--experimental setup. The same instructor taught two sections of an introductory biology course in the SCALE-UP and Mock-up rooms. Although students in both sections were equivalent in terms of gender, grade point average, incoming ACT, and drop/fail/withdraw rate, the Mock-up classroom enrolled significantly more freshmen. Controlling for class standing, multiple regression modeling revealed no significant differences in exam, in-class, preclass, and Introduction to Molecular and Cellular Biology Concept Inventory scores between the SCALE-UP and Mock-up classrooms. Thematic analysis of student comments highlighted that collaboration and whiteboards enhanced the learning experience, but technology was not important. Student satisfaction and attitudes were comparable. These results suggest that the benefits of a SCALE-UP experience can be achieved at lower cost without technology features. © 2017 P. A. G. Soneral and S. A. Wyse. CBE—Life Sciences Education © 2017 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

On the universal character of the large scale structure of the universe

International Nuclear Information System (INIS)

Demianski, M.; International Center for Relativistic Astrophysics; Rome Univ.; Doroshkevich, A.G.

1991-01-01

We review different theories of formation of the large scale structure of the Universe. Special emphasis is put on the theory of inertial instability. We show that for a large class of initial spectra the resulting two point correlation functions are similar. We discuss also the adhesion theory which uses the Burgers equation, Navier-Stokes equation or coagulation process. We review the Zeldovich theory of gravitational instability and discuss the internal structure of pancakes. Finally we discuss the role of the velocity potential in determining the global characteristics of large scale structures (distribution of caustics, scale of voids, etc.). In the last chapter we list the main unsolved problems and main successes of the theory of formation of large scale structure. (orig.)

LAVA: Large scale Automated Vulnerability Addition

Science.gov (United States)

2016-05-23

LAVA: Large-scale Automated Vulnerability Addition Brendan Dolan -Gavitt∗, Patrick Hulin†, Tim Leek†, Fredrich Ulrich†, Ryan Whelan† (Authors listed...released, and thus rapidly become stale. We can expect tools to have been trained to detect bugs that have been released. Given the commercial price tag...low TCN) and dead (low liveness) program data is a powerful one for vulnera- bility injection. The DUAs it identifies are internal program quantities

Probing cosmology with the homogeneity scale of the Universe through large scale structure surveys

International Nuclear Information System (INIS)

Ntelis, Pierros

2017-01-01

This thesis exposes my contribution to the measurement of homogeneity scale using galaxies, with the cosmological interpretation of results. In physics, any model is characterized by a set of principles. Most models in cosmology are based on the Cosmological Principle, which states that the universe is statistically homogeneous and isotropic on a large scales. Today, this principle is considered to be true since it is respected by those cosmological models that accurately describe the observations. However, while the isotropy of the universe is now confirmed by many experiments, it is not the case for the homogeneity. To study cosmic homogeneity, we propose to not only test a model but to test directly one of the postulates of modern cosmology. Since 1998 the measurements of cosmic distances using type Ia supernovae, we know that the universe is now in a phase of accelerated expansion. This phenomenon can be explained by the addition of an unknown energy component, which is called dark energy. Since dark energy is responsible for the expansion of the universe, we can study this mysterious fluid by measuring the rate of expansion of the universe. The universe has imprinted in its matter distribution a standard ruler, the Baryon Acoustic Oscillation (BAO) scale. By measuring this scale at different times during the evolution of our universe, it is then possible to measure the rate of expansion of the universe and thus characterize this dark energy. Alternatively, we can use the homogeneity scale to study this dark energy. Studying the homogeneity and the BAO scale requires the statistical study of the matter distribution of the universe at large scales, superior to tens of Mega-parsecs. Galaxies and quasars are formed in the vast over densities of matter and they are very luminous: these sources trace the distribution of matter. By measuring the emission spectra of these sources using large spectroscopic surveys, such as BOSS and eBOSS, we can measure their positions

CLAss-Specific Subspace Kernel Representations and Adaptive Margin Slack Minimization for Large Scale Classification.

Science.gov (United States)

Yu, Yinan; Diamantaras, Konstantinos I; McKelvey, Tomas; Kung, Sun-Yuan

2018-02-01

In kernel-based classification models, given limited computational power and storage capacity, operations over the full kernel matrix becomes prohibitive. In this paper, we propose a new supervised learning framework using kernel models for sequential data processing. The framework is based on two components that both aim at enhancing the classification capability with a subset selection scheme. The first part is a subspace projection technique in the reproducing kernel Hilbert space using a CLAss-specific Subspace Kernel representation for kernel approximation. In the second part, we propose a novel structural risk minimization algorithm called the adaptive margin slack minimization to iteratively improve the classification accuracy by an adaptive data selection. We motivate each part separately, and then integrate them into learning frameworks for large scale data. We propose two such frameworks: the memory efficient sequential processing for sequential data processing and the parallelized sequential processing for distributed computing with sequential data acquisition. We test our methods on several benchmark data sets and compared with the state-of-the-art techniques to verify the validity of the proposed techniques.

Large-Scale Optimization for Bayesian Inference in Complex Systems

Energy Technology Data Exchange (ETDEWEB)

Willcox, Karen [MIT; Marzouk, Youssef [MIT

2013-11-12

The SAGUARO (Scalable Algorithms for Groundwater Uncertainty Analysis and Robust Optimization) Project focused on the development of scalable numerical algorithms for large-scale Bayesian inversion in complex systems that capitalize on advances in large-scale simulation-based optimization and inversion methods. The project was a collaborative effort among MIT, the University of Texas at Austin, Georgia Institute of Technology, and Sandia National Laboratories. The research was directed in three complementary areas: efficient approximations of the Hessian operator, reductions in complexity of forward simulations via stochastic spectral approximations and model reduction, and employing large-scale optimization concepts to accelerate sampling. The MIT--Sandia component of the SAGUARO Project addressed the intractability of conventional sampling methods for large-scale statistical inverse problems by devising reduced-order models that are faithful to the full-order model over a wide range of parameter values; sampling then employs the reduced model rather than the full model, resulting in very large computational savings. Results indicate little effect on the computed posterior distribution. On the other hand, in the Texas--Georgia Tech component of the project, we retain the full-order model, but exploit inverse problem structure (adjoint-based gradients and partial Hessian information of the parameter-to-observation map) to implicitly extract lower dimensional information on the posterior distribution; this greatly speeds up sampling methods, so that fewer sampling points are needed. We can think of these two approaches as ``reduce then sample'' and ``sample then reduce.'' In fact, these two approaches are complementary, and can be used in conjunction with each other. Moreover, they both exploit deterministic inverse problem structure, in the form of adjoint-based gradient and Hessian information of the underlying parameter-to-observation map, to

Response of deep and shallow tropical maritime cumuli to large-scale processes

Science.gov (United States)

Yanai, M.; Chu, J.-H.; Stark, T. E.; Nitta, T.

1976-01-01

The bulk diagnostic method of Yanai et al. (1973) and a simplified version of the spectral diagnostic method of Nitta (1975) are used for a more quantitative evaluation of the response of various types of cumuliform clouds to large-scale processes, using the same data set in the Marshall Islands area for a 100-day period in 1956. The dependence of the cloud mass flux distribution on radiative cooling, large-scale vertical motion, and evaporation from the sea is examined. It is shown that typical radiative cooling rates in the tropics tend to produce a bimodal distribution of mass spectrum exhibiting deep and shallow clouds. The bimodal distribution is further enhanced when the large-scale vertical motion is upward, and a nearly unimodal distribution of shallow clouds prevails when the relative cooling is compensated by the heating due to the large-scale subsidence. Both deep and shallow clouds are modulated by large-scale disturbances. The primary role of surface evaporation is to maintain the moisture flux at the cloud base.

Accuracy assessment of planimetric large-scale map data for decision-making

Directory of Open Access Journals (Sweden)

Doskocz Adam

2016-06-01

Full Text Available This paper presents decision-making risk estimation based on planimetric large-scale map data, which are data sets or databases which are useful for creating planimetric maps on scales of 1:5,000 or larger. The studies were conducted on four data sets of large-scale map data. Errors of map data were used for a risk assessment of decision-making about the localization of objects, e.g. for land-use planning in realization of investments. An analysis was performed for a large statistical sample set of shift vectors of control points, which were identified with the position errors of these points (errors of map data.

Reviving large-scale projects

International Nuclear Information System (INIS)

Desiront, A.

2003-01-01

For the past decade, most large-scale hydro development projects in northern Quebec have been put on hold due to land disputes with First Nations. Hydroelectric projects have recently been revived following an agreement signed with Aboriginal communities in the province who recognized the need to find new sources of revenue for future generations. Many Cree are working on the project to harness the waters of the Eastmain River located in the middle of their territory. The work involves building an 890 foot long dam, 30 dikes enclosing a 603 square-km reservoir, a spillway, and a power house with 3 generating units with a total capacity of 480 MW of power for start-up in 2007. The project will require the use of 2,400 workers in total. The Cree Construction and Development Company is working on relations between Quebec's 14,000 Crees and the James Bay Energy Corporation, the subsidiary of Hydro-Quebec which is developing the project. Approximately 10 per cent of the $735-million project has been designated for the environmental component. Inspectors ensure that the project complies fully with environmental protection guidelines. Total development costs for Eastmain-1 are in the order of $2 billion of which $735 million will cover work on site and the remainder will cover generating units, transportation and financial charges. Under the treaty known as the Peace of the Braves, signed in February 2002, the Quebec government and Hydro-Quebec will pay the Cree $70 million annually for 50 years for the right to exploit hydro, mining and forest resources within their territory. The project comes at a time when electricity export volumes to the New England states are down due to growth in Quebec's domestic demand. Hydropower is a renewable and non-polluting source of energy that is one of the most acceptable forms of energy where the Kyoto Protocol is concerned. It was emphasized that large-scale hydro-electric projects are needed to provide sufficient energy to meet both

Large-scale Flow and Transport of Magnetic Flux in the Solar ...

Indian Academy of Sciences (India)

tribpo

Abstract. Horizontal large-scale velocity field describes horizontal displacement of the photospheric magnetic flux in zonal and meridian directions. The flow systems of solar plasma, constructed according to the velocity field, create the large-scale cellular-like patterns with up-flow in the center and the down-flow on the ...

Utilization of Large Scale Surface Models for Detailed Visibility Analyses

Science.gov (United States)

Caha, J.; Kačmařík, M.

2017-11-01

This article demonstrates utilization of large scale surface models with small spatial resolution and high accuracy, acquired from Unmanned Aerial Vehicle scanning, for visibility analyses. The importance of large scale data for visibility analyses on the local scale, where the detail of the surface model is the most defining factor, is described. The focus is not only the classic Boolean visibility, that is usually determined within GIS, but also on so called extended viewsheds that aims to provide more information about visibility. The case study with examples of visibility analyses was performed on river Opava, near the Ostrava city (Czech Republic). The multiple Boolean viewshed analysis and global horizon viewshed were calculated to determine most prominent features and visibility barriers of the surface. Besides that, the extended viewshed showing angle difference above the local horizon, which describes angular height of the target area above the barrier, is shown. The case study proved that large scale models are appropriate data source for visibility analyses on local level. The discussion summarizes possible future applications and further development directions of visibility analyses.

Characterizing Listener Engagement with Popular Songs Using Large-Scale Music Discovery Data.

Science.gov (United States)

Kaneshiro, Blair; Ruan, Feng; Baker, Casey W; Berger, Jonathan

2017-01-01

Music discovery in everyday situations has been facilitated in recent years by audio content recognition services such as Shazam. The widespread use of such services has produced a wealth of user data, specifying where and when a global audience takes action to learn more about music playing around them. Here, we analyze a large collection of Shazam queries of popular songs to study the relationship between the timing of queries and corresponding musical content. Our results reveal that the distribution of queries varies over the course of a song, and that salient musical events drive an increase in queries during a song. Furthermore, we find that the distribution of queries at the time of a song's release differs from the distribution following a song's peak and subsequent decline in popularity, possibly reflecting an evolution of user intent over the "life cycle" of a song. Finally, we derive insights into the data size needed to achieve consistent query distributions for individual songs. The combined findings of this study suggest that music discovery behavior, and other facets of the human experience of music, can be studied quantitatively using large-scale industrial data.

Characterizing Listener Engagement with Popular Songs Using Large-Scale Music Discovery Data

Science.gov (United States)

Kaneshiro, Blair; Ruan, Feng; Baker, Casey W.; Berger, Jonathan

2017-01-01

Music discovery in everyday situations has been facilitated in recent years by audio content recognition services such as Shazam. The widespread use of such services has produced a wealth of user data, specifying where and when a global audience takes action to learn more about music playing around them. Here, we analyze a large collection of Shazam queries of popular songs to study the relationship between the timing of queries and corresponding musical content. Our results reveal that the distribution of queries varies over the course of a song, and that salient musical events drive an increase in queries during a song. Furthermore, we find that the distribution of queries at the time of a song's release differs from the distribution following a song's peak and subsequent decline in popularity, possibly reflecting an evolution of user intent over the “life cycle” of a song. Finally, we derive insights into the data size needed to achieve consistent query distributions for individual songs. The combined findings of this study suggest that music discovery behavior, and other facets of the human experience of music, can be studied quantitatively using large-scale industrial data. PMID:28386241

Large-scale modeling of rain fields from a rain cell deterministic model

Science.gov (United States)

FéRal, Laurent; Sauvageot, Henri; Castanet, Laurent; Lemorton, JoëL.; Cornet, FréDéRic; Leconte, Katia

2006-04-01

A methodology to simulate two-dimensional rain rate fields at large scale (1000 × 1000 km2, the scale of a satellite telecommunication beam or a terrestrial fixed broadband wireless access network) is proposed. It relies on a rain rate field cellular decomposition. At small scale (˜20 × 20 km2), the rain field is split up into its macroscopic components, the rain cells, described by the Hybrid Cell (HYCELL) cellular model. At midscale (˜150 × 150 km2), the rain field results from the conglomeration of rain cells modeled by HYCELL. To account for the rain cell spatial distribution at midscale, the latter is modeled by a doubly aggregative isotropic random walk, the optimal parameterization of which is derived from radar observations at midscale. The extension of the simulation area from the midscale to the large scale (1000 × 1000 km2) requires the modeling of the weather frontal area. The latter is first modeled by a Gaussian field with anisotropic covariance function. The Gaussian field is then turned into a binary field, giving the large-scale locations over which it is raining. This transformation requires the definition of the rain occupation rate over large-scale areas. Its probability distribution is determined from observations by the French operational radar network ARAMIS. The coupling with the rain field modeling at midscale is immediate whenever the large-scale field is split up into midscale subareas. The rain field thus generated accounts for the local CDF at each point, defining a structure spatially correlated at small scale, midscale, and large scale. It is then suggested that this approach be used by system designers to evaluate diversity gain, terrestrial path attenuation, or slant path attenuation for different azimuth and elevation angle directions.

Facile Large-scale synthesis of stable CuO nanoparticles

Science.gov (United States)

Nazari, P.; Abdollahi-Nejand, B.; Eskandari, M.; Kohnehpoushi, S.

2018-04-01

In this work, a novel approach in synthesizing the CuO nanoparticles was introduced. A sequential corrosion and detaching was proposed in the growth and dispersion of CuO nanoparticles in the optimum pH value of eight. The produced CuO nanoparticles showed six nm (±2 nm) in diameter and spherical feather with a high crystallinity and uniformity in size. In this method, a large-scale production of CuO nanoparticles (120 grams in an experimental batch) from Cu micro-particles was achieved which may met the market criteria for large-scale production of CuO nanoparticles.

Comparative Analysis of Different Protocols to Manage Large Scale Networks

OpenAIRE

Anil Rao Pimplapure; Dr Jayant Dubey; Prashant Sen

2013-01-01

In recent year the numbers, complexity and size is increased in Large Scale Network. The best example of Large Scale Network is Internet, and recently once are Data-centers in Cloud Environment. In this process, involvement of several management tasks such as traffic monitoring, security and performance optimization is big task for Network Administrator. This research reports study the different protocols i.e. conventional protocols like Simple Network Management Protocol and newly Gossip bas...

The Student-Centered Active Learning Environment for Undergraduate Programs (SCALE-UP) Project

Science.gov (United States)

Beichner, Robert J.

2011-04-01

How do you keep a classroom of 100 undergraduates actively learning? Can students practice communication and teamwork skills in a large class? How do you boost the performance of underrepresented groups? The Student-Centered Active Learning Environment for Undergraduate Programs (SCALE-UP) Project has addressed these concerns. Because of their inclusion in a leading introductory physics textbook, project materials are used by more than 1/3 of all science, math, and engineering majors nationwide. The room design and pedagogy have been adopted at more than 100 leading institutions across the country. Physics, chemistry, math, astronomy, biology, engineering, earth sciences, and even literature classes are currently being taught this way. Educational research indicates that students should collaborate on interesting tasks and be deeply involved with the material they are studying. We promote active learning in a redesigned classroom for 100 students or more. (Of course, smaller classes can also benefit.) Class time is spent primarily on "tangibles" and "ponderables"--hands-on activities, simulations, and interesting questions. Nine students sit in three teams at round tables. Instructors circulate and engage in Socratic dialogues. The setting looks like a banquet hall, with lively interactions nearly all the time. Hundreds of hours of classroom video and audio recordings, transcripts of numerous interviews and focus groups, data from conceptual learning assessments (using widely-recognized instruments in a pretest/posttest protocol), and collected portfolios of student work are part of our rigorous assessment effort. Our findings (based on data from over 16,000 students collected over five years as well as replications at adopting sites) can be summarized as the following: 1) Female failure rate is 1/5 of previous levels, even though more is demanded of students. 2) Minority failure rate is 1/4 that seen in traditionally taught courses. 3) At-risk students are more

From Swimming Pool to Collaborative Learning Studio: Pedagogy, Space, and Technology in a Large Active Learning Classroom

Science.gov (United States)

Lee, Dabae; Morrone, Anastasia S.; Siering, Greg

2018-01-01

To promote student learning and bolster student success, higher education institutions are increasingly creating large active learning classrooms to replace traditional lecture halls. Although there have been many efforts to examine the effects of those classrooms on learning outcomes, there is paucity of research that can inform the design and…

Puzzles of large scale structure and gravitation

International Nuclear Information System (INIS)

Sidharth, B.G.

2006-01-01

We consider the puzzle of cosmic voids bounded by two-dimensional structures of galactic clusters as also a puzzle pointed out by Weinberg: How can the mass of a typical elementary particle depend on a cosmic parameter like the Hubble constant? An answer to the first puzzle is proposed in terms of 'Scaled' Quantum Mechanical like behaviour which appears at large scales. The second puzzle can be answered by showing that the gravitational mass of an elementary particle has a Machian character (see Ahmed N. Cantorian small worked, Mach's principle and the universal mass network. Chaos, Solitons and Fractals 2004;21(4))

Personalized Opportunistic Computing for CMS at Large Scale

CERN Multimedia

CERN. Geneva

2015-01-01

**Douglas Thain** is an Associate Professor of Computer Science and Engineering at the University of Notre Dame, where he designs large scale distributed computing systems to power the needs of advanced science and...

Stability of large scale interconnected dynamical systems

International Nuclear Information System (INIS)

Akpan, E.P.

1993-07-01

Large scale systems modelled by a system of ordinary differential equations are considered and necessary and sufficient conditions are obtained for the uniform asymptotic connective stability of the systems using the method of cone-valued Lyapunov functions. It is shown that this model significantly improves the existing models. (author). 9 refs

Large scale cross hole testing

International Nuclear Information System (INIS)

Ball, J.K.; Black, J.H.; Doe, T.

1991-05-01

As part of the Site Characterisation and Validation programme the results of the large scale cross hole testing have been used to document hydraulic connections across the SCV block, to test conceptual models of fracture zones and obtain hydrogeological properties of the major hydrogeological features. The SCV block is highly heterogeneous. This heterogeneity is not smoothed out even over scales of hundreds of meters. Results of the interpretation validate the hypothesis of the major fracture zones, A, B and H; not much evidence of minor fracture zones is found. The uncertainty in the flow path, through the fractured rock, causes sever problems in interpretation. Derived values of hydraulic conductivity were found to be in a narrow range of two to three orders of magnitude. Test design did not allow fracture zones to be tested individually. This could be improved by testing the high hydraulic conductivity regions specifically. The Piezomac and single hole equipment worked well. Few, if any, of the tests ran long enough to approach equilibrium. Many observation boreholes showed no response. This could either be because there is no hydraulic connection, or there is a connection but a response is not seen within the time scale of the pumping test. The fractional dimension analysis yielded credible results, and the sinusoidal testing procedure provided an effective means of identifying the dominant hydraulic connections. (10 refs.) (au)

Large transverse momentum processes in a non-scaling parton model

International Nuclear Information System (INIS)

Stirling, W.J.

1977-01-01

The production of large transverse momentum mesons in hadronic collisions by the quark fusion mechanism is discussed in a parton model which gives logarithmic corrections to Bjorken scaling. It is found that the moments of the large transverse momentum structure function exhibit a simple scale breaking behaviour similar to the behaviour of the Drell-Yan and deep inelastic structure functions of the model. An estimate of corresponding experimental consequences is made and the extent to which analogous results can be expected in an asymptotically free gauge theory is discussed. A simple set of rules is presented for incorporating the logarithmic corrections to scaling into all covariant parton model calculations. (Auth.)

Mathematical methods in material science and large scale optimization workshops: Final report, June 1, 1995-November 30, 1996

Energy Technology Data Exchange (ETDEWEB)

Friedman, A. [Minnesota Univ., Minneapolis, MN (United States). Inst. for Mathematics and Its Applications

1996-12-01

The summer program in Large Scale Optimization concentrated largely on process engineering, aerospace engineering, inverse problems and optimal design, and molecular structure and protein folding. The program brought together application people, optimizers, and mathematicians with interest in learning about these topics. Three proceedings volumes are being prepared. The year in Materials Sciences deals with disordered media and percolation, phase transformations, composite materials, microstructure; topological and geometric methods as well as statistical mechanics approach to polymers (included were Monte Carlo simulation for polymers); miscellaneous other topics such as nonlinear optical material, particulate flow, and thin film. All these activities saw strong interaction among material scientists, mathematicians, physicists, and engineers. About 8 proceedings volumes are being prepared.

Automatic Generation of Connectivity for Large-Scale Neuronal Network Models through Structural Plasticity.

Science.gov (United States)

Diaz-Pier, Sandra; Naveau, Mikaël; Butz-Ostendorf, Markus; Morrison, Abigail

2016-01-01

With the emergence of new high performance computation technology in the last decade, the simulation of large scale neural networks which are able to reproduce the behavior and structure of the brain has finally become an achievable target of neuroscience. Due to the number of synaptic connections between neurons and the complexity of biological networks, most contemporary models have manually defined or static connectivity. However, it is expected that modeling the dynamic generation and deletion of the links among neurons, locally and between different regions of the brain, is crucial to unravel important mechanisms associated with learning, memory and healing. Moreover, for many neural circuits that could potentially be modeled, activity data is more readily and reliably available than connectivity data. Thus, a framework that enables networks to wire themselves on the basis of specified activity targets can be of great value in specifying network models where connectivity data is incomplete or has large error margins. To address these issues, in the present work we present an implementation of a model of structural plasticity in the neural network simulator NEST. In this model, synapses consist of two parts, a pre- and a post-synaptic element. Synapses are created and deleted during the execution of the simulation following local homeostatic rules until a mean level of electrical activity is reached in the network. We assess the scalability of the implementation in order to evaluate its potential usage in the self generation of connectivity of large scale networks. We show and discuss the results of simulations on simple two population networks and more complex models of the cortical microcircuit involving 8 populations and 4 layers using the new framework.

On the Renormalization of the Effective Field Theory of Large Scale Structures

OpenAIRE

Pajer, Enrico; Zaldarriaga, Matias

2013-01-01

Standard perturbation theory (SPT) for large-scale matter inhomogeneities is unsatisfactory for at least three reasons: there is no clear expansion parameter since the density contrast is not small on all scales; it does not fully account for deviations at large scales from a perfect pressureless fluid induced by short-scale non-linearities; for generic initial conditions, loop corrections are UV-divergent, making predictions cutoff dependent and hence unphysical. The Effective Field Theory o...

Methods for Large-Scale Nonlinear Optimization.

Science.gov (United States)

1980-05-01

STANFORD, CALIFORNIA 94305 METHODS FOR LARGE-SCALE NONLINEAR OPTIMIZATION by Philip E. Gill, Waiter Murray, I Michael A. Saunden, and Masgaret H. Wright...typical iteration can be partitioned so that where B is an m X m basise matrix. This partition effectively divides the vari- ables into three classes... attention is given to the standard of the coding or the documentation. A much better way of obtaining mathematical software is from a software library

Generation of large-scale vorticity in rotating stratified turbulence with inhomogeneous helicity: mean-field theory

Science.gov (United States)

Kleeorin, N.

2018-06-01

We discuss a mean-field theory of the generation of large-scale vorticity in a rotating density stratified developed turbulence with inhomogeneous kinetic helicity. We show that the large-scale non-uniform flow is produced due to either a combined action of a density stratified rotating turbulence and uniform kinetic helicity or a combined effect of a rotating incompressible turbulence and inhomogeneous kinetic helicity. These effects result in the formation of a large-scale shear, and in turn its interaction with the small-scale turbulence causes an excitation of the large-scale instability (known as a vorticity dynamo) due to a combined effect of the large-scale shear and Reynolds stress-induced generation of the mean vorticity. The latter is due to the effect of large-scale shear on the Reynolds stress. A fast rotation suppresses this large-scale instability.

Recent Advances in Understanding Large Scale Vapour Explosions

International Nuclear Information System (INIS)

Board, S.J.; Hall, R.W.

1976-01-01

In foundries, violent explosions occur occasionally when molten metal comes into contact with water. If similar explosions can occur with other materials, hazardous situations may arise for example in LNG marine transportation accidents, or in liquid cooled reactor incidents when molten UO 2 contacts water or sodium coolant. Over the last 10 years a large body of experimental data has been obtained on the behaviour of small quantities of hot material in contact with a vaporisable coolant. Such experiments generally give low energy yields, despite producing fine fragmentation of the molten material. These events have been interpreted in terms of a wide range of phenomena such as violent boiling, liquid entrainment, bubble collapse, superheat, surface cracking and many others. Many of these studies have been aimed at understanding the small scale behaviour of the particular materials of interest. However, understanding the nature of the energetic events which were the original cause for concern may also be necessary to give confidence that violent events cannot occur for these materials in large scale situations. More recently, there has been a trend towards larger experiments and some of these have produced explosions of moderately high efficiency. Although occurrence of such large scale explosions can depend rather critically on initial conditions in a way which is not fully understood, there are signs that the interpretation of these events may be more straightforward than that of the single drop experiments. In the last two years several theoretical models for large scale explosions have appeared which attempt a self contained explanation of at least some stages of such high yield events: these have as their common feature a description of how a propagating breakdown of an initially quasi-stable distribution of materials is induced by the pressure and flow field caused by the energy release in adjacent regions. These models have led to the idea that for a full

Robust large-scale parallel nonlinear solvers for simulations.

Energy Technology Data Exchange (ETDEWEB)

Bader, Brett William; Pawlowski, Roger Patrick; Kolda, Tamara Gibson (Sandia National Laboratories, Livermore, CA)

2005-11-01

This report documents research to develop robust and efficient solution techniques for solving large-scale systems of nonlinear equations. The most widely used method for solving systems of nonlinear equations is Newton's method. While much research has been devoted to augmenting Newton-based solvers (usually with globalization techniques), little has been devoted to exploring the application of different models. Our research has been directed at evaluating techniques using different models than Newton's method: a lower order model, Broyden's method, and a higher order model, the tensor method. We have developed large-scale versions of each of these models and have demonstrated their use in important applications at Sandia. Broyden's method replaces the Jacobian with an approximation, allowing codes that cannot evaluate a Jacobian or have an inaccurate Jacobian to converge to a solution. Limited-memory methods, which have been successful in optimization, allow us to extend this approach to large-scale problems. We compare the robustness and efficiency of Newton's method, modified Newton's method, Jacobian-free Newton-Krylov method, and our limited-memory Broyden method. Comparisons are carried out for large-scale applications of fluid flow simulations and electronic circuit simulations. Results show that, in cases where the Jacobian was inaccurate or could not be computed, Broyden's method converged in some cases where Newton's method failed to converge. We identify conditions where Broyden's method can be more efficient than Newton's method. We also present modifications to a large-scale tensor method, originally proposed by Bouaricha, for greater efficiency, better robustness, and wider applicability. Tensor methods are an alternative to Newton-based methods and are based on computing a step based on a local quadratic model rather than a linear model. The advantage of Bouaricha's method is that it can use any

Large Scale GW Calculations on the Cori System

Science.gov (United States)

Deslippe, Jack; Del Ben, Mauro; da Jornada, Felipe; Canning, Andrew; Louie, Steven

The NERSC Cori system, powered by 9000+ Intel Xeon-Phi processors, represents one of the largest HPC systems for open-science in the United States and the world. We discuss the optimization of the GW methodology for this system, including both node level and system-scale optimizations. We highlight multiple large scale (thousands of atoms) case studies and discuss both absolute application performance and comparison to calculations on more traditional HPC architectures. We find that the GW method is particularly well suited for many-core architectures due to the ability to exploit a large amount of parallelism across many layers of the system. This work was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, as part of the Computational Materials Sciences Program.

Cosmic ray acceleration by large scale galactic shocks

International Nuclear Information System (INIS)

Cesarsky, C.J.; Lagage, P.O.

1987-01-01

The mechanism of diffusive shock acceleration may account for the existence of galactic cosmic rays detailed application to stellar wind shocks and especially to supernova shocks have been developed. Existing models can usually deal with the energetics or the spectral slope, but the observed energy range of cosmic rays is not explained. Therefore it seems worthwhile to examine the effect that large scale, long-lived galactic shocks may have on galactic cosmic rays, in the frame of the diffusive shock acceleration mechanism. Large scale fast shocks can only be expected to exist in the galactic halo. We consider three situations where they may arise: expansion of a supernova shock in the halo, galactic wind, galactic infall; and discuss the possible existence of these shocks and their role in accelerating cosmic rays

Lagrangian space consistency relation for large scale structure

International Nuclear Information System (INIS)

Horn, Bart; Hui, Lam; Xiao, Xiao

2015-01-01

Consistency relations, which relate the squeezed limit of an (N+1)-point correlation function to an N-point function, are non-perturbative symmetry statements that hold even if the associated high momentum modes are deep in the nonlinear regime and astrophysically complex. Recently, Kehagias and Riotto and Peloso and Pietroni discovered a consistency relation applicable to large scale structure. We show that this can be recast into a simple physical statement in Lagrangian space: that the squeezed correlation function (suitably normalized) vanishes. This holds regardless of whether the correlation observables are at the same time or not, and regardless of whether multiple-streaming is present. The simplicity of this statement suggests that an analytic understanding of large scale structure in the nonlinear regime may be particularly promising in Lagrangian space

Electron drift in a large scale solid xenon

International Nuclear Information System (INIS)

Yoo, J.; Jaskierny, W.F.

2015-01-01

A study of charge drift in a large scale optically transparent solid xenon is reported. A pulsed high power xenon light source is used to liberate electrons from a photocathode. The drift speeds of the electrons are measured using a 8.7 cm long electrode in both the liquid and solid phase of xenon. In the liquid phase (163 K), the drift speed is 0.193 ± 0.003 cm/μs while the drift speed in the solid phase (157 K) is 0.397 ± 0.006 cm/μs at 900 V/cm over 8.0 cm of uniform electric fields. Therefore, it is demonstrated that a factor two faster electron drift speed in solid phase xenon compared to that in liquid in a large scale solid xenon

Wind and Photovoltaic Large-Scale Regional Models for hourly production evaluation

DEFF Research Database (Denmark)

Marinelli, Mattia; Maule, Petr; Hahmann, Andrea N.

2015-01-01

This work presents two large-scale regional models used for the evaluation of normalized power output from wind turbines and photovoltaic power plants on a European regional scale. The models give an estimate of renewable production on a regional scale with 1 h resolution, starting from a mesosca...... of the transmission system, especially regarding the cross-border power flows. The tuning of these regional models is done using historical meteorological data acquired on a per-country basis and using publicly available data of installed capacity.......This work presents two large-scale regional models used for the evaluation of normalized power output from wind turbines and photovoltaic power plants on a European regional scale. The models give an estimate of renewable production on a regional scale with 1 h resolution, starting from a mesoscale...

Quality assurance of the clinical learning environment in Austria: Construct validity of the Clinical Learning Environment, Supervision and Nurse Teacher Scale (CLES+T scale).

Science.gov (United States)

Mueller, Gerhard; Mylonas, Demetrius; Schumacher, Petra

2018-04-21

Within nursing education, the clinical learning environment is of a high importance in regards to the development of competencies and abilities. The organization, atmosphere, and supervision in the clinical learning environment are only a few factors that influence this development. In Austria there is currently no valid instrument available for the evaluation of influencing factors. The aim of the study was to test the construct validity with principal component analysis as well as the internal consistency of the German Clinical Learning Environment, Supervision and Teacher Scale (CLES+T scale) in Austria. The present validation study has a descriptive-quantitative cross-sectional design. The sample consisted of 385 nursing students from thirteen training institutions in Austria. The data collection was carried out online between March and April 2016. Starting with a polychoric correlation matrix, a parallel analysis with principal component extraction and promax rotation was carried out due to the ordinal data. The exploratory ordinal factor analysis supported a four-component solution and explained 73% of the total variance. The internal consistency of all 25 items reached a Cronbach's α of 0.95 and the four components ranged between 0.83 and 0.95. The German version of the CLES+T scale seems to be a useful instrument for identifying potential areas of improvement in clinical practice in order to derive specific quality measures for the practical learning environment. Copyright © 2018 Elsevier Ltd. All rights reserved.

A Study of Developing an Attitude Scale towards Authentic Learning Environments and Evaluation

Science.gov (United States)

Çetinkaya, Murat

2018-01-01

The aim of the research is to improve a valid and reliable attributing scale which identifies authentic learning environments and evaluation attributes of the science teacher candidates. The study has been designed on the base of validity and reliability of the scale developed to evaluate the authentic learning environments. The research group is…

Energy efficiency supervision strategy selection of Chinese large-scale public buildings

International Nuclear Information System (INIS)

Jin Zhenxing; Wu Yong; Li Baizhan; Gao Yafeng

2009-01-01

This paper discusses energy consumption, building development and building energy consumption in China, and points that energy efficiency management and maintenance of large-scale public buildings is the breakthrough point of building energy saving in China. Three obstacles are lack of basic statistics data, lack of service market for building energy saving, and lack of effective management measures account for the necessity of energy efficiency supervision for large-scale public buildings. And then the paper introduces the supervision aims, the supervision system and the five basic systems' role in the supervision system, and analyzes the working mechanism of the five basic systems. The energy efficiency supervision system of large-scale public buildings takes energy consumption statistics as a data basis, Energy auditing as a technical support, energy consumption ration as a benchmark of energy saving and price increase beyond ration as a price lever, and energy efficiency public-noticing as an amplifier. The supervision system promotes energy efficiency operation and maintenance of large-scale public building, and drives a comprehensive building energy saving in China.

Energy efficiency supervision strategy selection of Chinese large-scale public buildings

Energy Technology Data Exchange (ETDEWEB)

Jin, Zhenxing; Li, Baizhan; Gao, Yafeng [The Faculty of Urban Construction and Environmental Engineering, Chongqing University, Chongqing (China); Key Laboratory of the Three Gorges Reservoir Region' s Eco-Environment, Ministry of Education, Chongqing 400045 (China); Wu, Yong [The Department of Science and Technology, Ministry of Construction, Beijing 100835 (China)

2009-06-15

This paper discusses energy consumption, building development and building energy consumption in China, and points that energy efficiency management and maintenance of large-scale public buildings is the breakthrough point of building energy saving in China. Three obstacles are lack of basic statistics data, lack of service market for building energy saving, and lack of effective management measures account for the necessity of energy efficiency supervision for large-scale public buildings. And then the paper introduces the supervision aims, the supervision system and the five basic systems' role in the supervision system, and analyzes the working mechanism of the five basic systems. The energy efficiency supervision system of large-scale public buildings takes energy consumption statistics as a data basis, Energy auditing as a technical support, energy consumption ration as a benchmark of energy saving and price increase beyond ration as a price lever, and energy efficiency public-noticing as an amplifier. The supervision system promotes energy efficiency operation and maintenance of large-scale public building, and drives a comprehensive building energy saving in China. (author)

Energy efficiency supervision strategy selection of Chinese large-scale public buildings

Energy Technology Data Exchange (ETDEWEB)

Jin Zhenxing [Faculty of Urban Construction and Environmental Engineering, Chongqing University, Chongqing (China); Key Laboratory of the Three Gorges Reservoir Region' s Eco-Environment, Ministry of Education, Chongqing 400045 (China)], E-mail: jinzhenxing33@sina.com; Wu Yong [Department of Science and Technology, Ministry of Construction, Beijing 100835 (China); Li Baizhan; Gao Yafeng [Faculty of Urban Construction and Environmental Engineering, Chongqing University, Chongqing (China); Key Laboratory of the Three Gorges Reservoir Region' s Eco-Environment, Ministry of Education, Chongqing 400045 (China)

2009-06-15

This paper discusses energy consumption, building development and building energy consumption in China, and points that energy efficiency management and maintenance of large-scale public buildings is the breakthrough point of building energy saving in China. Three obstacles are lack of basic statistics data, lack of service market for building energy saving, and lack of effective management measures account for the necessity of energy efficiency supervision for large-scale public buildings. And then the paper introduces the supervision aims, the supervision system and the five basic systems' role in the supervision system, and analyzes the working mechanism of the five basic systems. The energy efficiency supervision system of large-scale public buildings takes energy consumption statistics as a data basis, Energy auditing as a technical support, energy consumption ration as a benchmark of energy saving and price increase beyond ration as a price lever, and energy efficiency public-noticing as an amplifier. The supervision system promotes energy efficiency operation and maintenance of large-scale public building, and drives a comprehensive building energy saving in China.

Mirror dark matter and large scale structure

International Nuclear Information System (INIS)

Ignatiev, A.Yu.; Volkas, R.R.

2003-01-01

Mirror matter is a dark matter candidate. In this paper, we reexamine the linear regime of density perturbation growth in a universe containing mirror dark matter. Taking adiabatic scale-invariant perturbations as the input, we confirm that the resulting processed power spectrum is richer than for the more familiar cases of cold, warm and hot dark matter. The new features include a maximum at a certain scale λ max , collisional damping below a smaller characteristic scale λ S ' , with oscillatory perturbations between the two. These scales are functions of the fundamental parameters of the theory. In particular, they decrease for decreasing x, the ratio of the mirror plasma temperature to that of the ordinary. For x∼0.2, the scale λ max becomes galactic. Mirror dark matter therefore leads to bottom-up large scale structure formation, similar to conventional cold dark matter, for x(less-or-similar sign)0.2. Indeed, the smaller the value of x, the closer mirror dark matter resembles standard cold dark matter during the linear regime. The differences pertain to scales smaller than λ S ' in the linear regime, and generally in the nonlinear regime because mirror dark matter is chemically complex and to some extent dissipative. Lyman-α forest data and the early reionization epoch established by WMAP may hold the key to distinguishing mirror dark matter from WIMP-style cold dark matter

The Large-Scale Structure of Scientific Method

Science.gov (United States)

Kosso, Peter

2009-01-01

The standard textbook description of the nature of science describes the proposal, testing, and acceptance of a theoretical idea almost entirely in isolation from other theories. The resulting model of science is a kind of piecemeal empiricism that misses the important network structure of scientific knowledge. Only the large-scale description of…

Detection of large-scale concentric gravity waves from a Chinese airglow imager network

Science.gov (United States)

Lai, Chang; Yue, Jia; Xu, Jiyao; Yuan, Wei; Li, Qinzeng; Liu, Xiao

2018-06-01

Concentric gravity waves (CGWs) contain a broad spectrum of horizontal wavelengths and periods due to their instantaneous localized sources (e.g., deep convection, volcanic eruptions, or earthquake, etc.). However, it is difficult to observe large-scale gravity waves of >100 km wavelength from the ground for the limited field of view of a single camera and local bad weather. Previously, complete large-scale CGW imagery could only be captured by satellite observations. In the present study, we developed a novel method that uses assembling separate images and applying low-pass filtering to obtain temporal and spatial information about complete large-scale CGWs from a network of all-sky airglow imagers. Coordinated observations from five all-sky airglow imagers in Northern China were assembled and processed to study large-scale CGWs over a wide area (1800 km × 1 400 km), focusing on the same two CGW events as Xu et al. (2015). Our algorithms yielded images of large-scale CGWs by filtering out the small-scale CGWs. The wavelengths, wave speeds, and periods of CGWs were measured from a sequence of consecutive assembled images. Overall, the assembling and low-pass filtering algorithms can expand the airglow imager network to its full capacity regarding the detection of large-scale gravity waves.

Bottom-Up Accountability Initiatives and Large-Scale Land ...

International Development Research Centre (IDRC) Digital Library (Canada)

... Security can help increase accountability for large-scale land acquisitions in ... to build decent economic livelihoods and participate meaningfully in decisions ... its 2017 call for proposals to establish Cyber Policy Centres in the Global South.

The Cosmology Large Angular Scale Surveyor (CLASS)

Science.gov (United States)

Harrington, Kathleen; Marriange, Tobias; Aamir, Ali; Appel, John W.; Bennett, Charles L.; Boone, Fletcher; Brewer, Michael; Chan, Manwei; Chuss, David T.; Colazo, Felipe;

Accelerating Deep Learning with Shrinkage and Recall

OpenAIRE

Zheng, Shuai; Vishnu, Abhinav; Ding, Chris

2016-01-01

Deep Learning is a very powerful machine learning model. Deep Learning trains a large number of parameters for multiple layers and is very slow when data is in large scale and the architecture size is large. Inspired from the shrinking technique used in accelerating computation of Support Vector Machines (SVM) algorithm and screening technique used in LASSO, we propose a shrinking Deep Learning with recall (sDLr) approach to speed up deep learning computation. We experiment shrinking Deep Lea...

Measuring the topology of large-scale structure in the universe

Science.gov (United States)

Gott, J. Richard, III

1988-11-01

An algorithm for quantitatively measuring the topology of large-scale structure has now been applied to a large number of observational data sets. The present paper summarizes and provides an overview of some of these observational results. On scales significantly larger than the correlation length, larger than about 1200 km/s, the cluster and galaxy data are fully consistent with a sponge-like random phase topology. At a smoothing length of about 600 km/s, however, the observed genus curves show a small shift in the direction of a meatball topology. Cold dark matter (CDM) models show similar shifts at these scales but not generally as large as those seen in the data. Bubble models, with voids completely surrounded on all sides by wall of galaxies, show shifts in the opposite direction. The CDM model is overall the most successful in explaining the data.

Measuring the topology of large-scale structure in the universe

International Nuclear Information System (INIS)

Gott, J.R. III

1988-01-01

An algorithm for quantitatively measuring the topology of large-scale structure has now been applied to a large number of observational data sets. The present paper summarizes and provides an overview of some of these observational results. On scales significantly larger than the correlation length, larger than about 1200 km/s, the cluster and galaxy data are fully consistent with a sponge-like random phase topology. At a smoothing length of about 600 km/s, however, the observed genus curves show a small shift in the direction of a meatball topology. Cold dark matter (CDM) models show similar shifts at these scales but not generally as large as those seen in the data. Bubble models, with voids completely surrounded on all sides by wall of galaxies, show shifts in the opposite direction. The CDM model is overall the most successful in explaining the data. 45 references

How the Internet Will Help Large-Scale Assessment Reinvent Itself

Directory of Open Access Journals (Sweden)

Randy Elliot Bennett

2001-02-01

Full Text Available Large-scale assessment in the United States is undergoing enormous pressure to change. That pressure stems from many causes. Depending upon the type of test, the issues precipitating change include an outmoded cognitive-scientific basis for test design; a mismatch with curriculum; the differential performance of population groups; a lack of information to help individuals improve; and inefficiency. These issues provide a strong motivation to reconceptualize both the substance and the business of large-scale assessment. At the same time, advances in technology, measurement, and cognitive science are providing the means to make that reconceptualization a reality. The thesis of this paper is that the largest facilitating factor will be technological, in particular the Internet. In the same way that it is already helping to revolutionize commerce, education, and even social interaction, the Internet will help revolutionize the business and substance of large-scale assessment.

Engaging Students in Large Health Classes with Active Learning Strategies

Science.gov (United States)

Elliott, Steven; Combs, Sue; Huelskamp, Amelia; Hritz, Nancy

2017-01-01

Creative K-12 health teachers can engage students in large classes by utilizing active learning strategies. Active learning involves engaging students in higher-order tasks, such as analysis and synthesis, which is a crucial element of the movement toward what is commonly called "learner-centered" teaching. Health education teachers who…

Contribution of large scale coherence to wind turbine power: A large eddy simulation study in periodic wind farms

Science.gov (United States)

Chatterjee, Tanmoy; Peet, Yulia T.

2018-03-01

Length scales of eddies involved in the power generation of infinite wind farms are studied by analyzing the spectra of the turbulent flux of mean kinetic energy (MKE) from large eddy simulations (LES). Large-scale structures with an order of magnitude bigger than the turbine rotor diameter (D ) are shown to have substantial contribution to wind power. Varying dynamics in the intermediate scales (D -10 D ) are also observed from a parametric study involving interturbine distances and hub height of the turbines. Further insight about the eddies responsible for the power generation have been provided from the scaling analysis of two-dimensional premultiplied spectra of MKE flux. The LES code is developed in a high Reynolds number near-wall modeling framework, using an open-source spectral element code Nek5000, and the wind turbines have been modelled using a state-of-the-art actuator line model. The LES of infinite wind farms have been validated against the statistical results from the previous literature. The study is expected to improve our understanding of the complex multiscale dynamics in the domain of large wind farms and identify the length scales that contribute to the power. This information can be useful for design of wind farm layout and turbine placement that take advantage of the large-scale structures contributing to wind turbine power.

Medical Students Perceive Better Group Learning Processes when Large Classes Are Made to Seem Small

Science.gov (United States)

Hommes, Juliette; Arah, Onyebuchi A.; de Grave, Willem; Schuwirth, Lambert W. T.; Scherpbier, Albert J. J. A.; Bos, Gerard M. J.

2014-01-01

Objective Medical schools struggle with large classes, which might interfere with the effectiveness of learning within small groups due to students being unfamiliar to fellow students. The aim of this study was to assess the effects of making a large class seem small on the students' collaborative learning processes. Design A randomised controlled intervention study was undertaken to make a large class seem small, without the need to reduce the number of students enrolling in the medical programme. The class was divided into subsets: two small subsets (n = 50) as the intervention groups; a control group (n = 102) was mixed with the remaining students (the non-randomised group n∼100) to create one large subset. Setting The undergraduate curriculum of the Maastricht Medical School, applying the Problem-Based Learning principles. In this learning context, students learn mainly in tutorial groups, composed randomly from a large class every 6–10 weeks. Intervention The formal group learning activities were organised within the subsets. Students from the intervention groups met frequently within the formal groups, in contrast to the students from the large subset who hardly enrolled with the same students in formal activities. Main Outcome Measures Three outcome measures assessed students' group learning processes over time: learning within formally organised small groups, learning with other students in the informal context and perceptions of the intervention. Results Formal group learning processes were perceived more positive in the intervention groups from the second study year on, with a mean increase of β = 0.48. Informal group learning activities occurred almost exclusively within the subsets as defined by the intervention from the first week involved in the medical curriculum (E-I indexes>−0.69). Interviews tapped mainly positive effects and negligible negative side effects of the intervention. Conclusion Better group learning processes can be

Bottom-Up Accountability Initiatives and Large-Scale Land ...

International Development Research Centre (IDRC) Digital Library (Canada)

Corey Piccioni

fuel/energy, climate, and finance has occurred and one of the most ... this wave of large-scale land acquisitions. In fact, esti- ... Environmental Rights Action/Friends of the Earth,. Nigeria ... map the differentiated impacts (gender, ethnicity,.

Subgrid-scale models for large-eddy simulation of rotating turbulent channel flows

Science.gov (United States)

Silvis, Maurits H.; Bae, Hyunji Jane; Trias, F. Xavier; Abkar, Mahdi; Moin, Parviz; Verstappen, Roel

2017-11-01

We aim to design subgrid-scale models for large-eddy simulation of rotating turbulent flows. Rotating turbulent flows form a challenging test case for large-eddy simulation due to the presence of the Coriolis force. The Coriolis force conserves the total kinetic energy while transporting it from small to large scales of motion, leading to the formation of large-scale anisotropic flow structures. The Coriolis force may also cause partial flow laminarization and the occurrence of turbulent bursts. Many subgrid-scale models for large-eddy simulation are, however, primarily designed to parametrize the dissipative nature of turbulent flows, ignoring the specific characteristics of transport processes. We, therefore, propose a new subgrid-scale model that, in addition to the usual dissipative eddy viscosity term, contains a nondissipative nonlinear model term designed to capture transport processes, such as those due to rotation. We show that the addition of this nonlinear model term leads to improved predictions of the energy spectra of rotating homogeneous isotropic turbulence as well as of the Reynolds stress anisotropy in spanwise-rotating plane-channel flows. This work is financed by the Netherlands Organisation for Scientific Research (NWO) under Project Number 613.001.212.

Origin of the large scale structures of the universe

International Nuclear Information System (INIS)

Oaknin, David H.

2004-01-01

We revise the statistical properties of the primordial cosmological density anisotropies that, at the time of matter-radiation equality, seeded the gravitational development of large scale structures in the otherwise homogeneous and isotropic Friedmann-Robertson-Walker flat universe. Our analysis shows that random fluctuations of the density field at the same instant of equality and with comoving wavelength shorter than the causal horizon at that time can naturally account, when globally constrained to conserve the total mass (energy) of the system, for the observed scale invariance of the anisotropies over cosmologically large comoving volumes. Statistical systems with similar features are generically known as glasslike or latticelike. Obviously, these conclusions conflict with the widely accepted understanding of the primordial structures reported in the literature, which requires an epoch of inflationary cosmology to precede the standard expansion of the universe. The origin of the conflict must be found in the widespread, but unjustified, claim that scale invariant mass (energy) anisotropies at the instant of equality over comoving volumes of cosmological size, larger than the causal horizon at the time, must be generated by fluctuations in the density field with comparably large comoving wavelength

Technologies and challenges in large-scale phosphoproteomics

DEFF Research Database (Denmark)

Engholm-Keller, Kasper; Larsen, Martin Røssel

2013-01-01

become the main technique for discovery and characterization of phosphoproteins in a nonhypothesis driven fashion. In this review, we describe methods for state-of-the-art MS-based analysis of protein phosphorylation as well as the strategies employed in large-scale phosphoproteomic experiments...... with focus on the various challenges and limitations this field currently faces....

Some ecological guidelines for large-scale biomass plantations

Energy Technology Data Exchange (ETDEWEB)

Hoffman, W.; Cook, J.H.; Beyea, J. [National Audubon Society, Tavernier, FL (United States)

1993-12-31

The National Audubon Society sees biomass as an appropriate and necessary source of energy to help replace fossil fuels in the near future, but is concerned that large-scale biomass plantations could displace significant natural vegetation and wildlife habitat, and reduce national and global biodiversity. We support the development of an industry large enough to provide significant portions of our energy budget, but we see a critical need to ensure that plantations are designed and sited in ways that minimize ecological disruption, or even provide environmental benefits. We have been studying the habitat value of intensively managed short-rotation tree plantations. Our results show that these plantations support large populations of some birds, but not all of the species using the surrounding landscape, and indicate that their value as habitat can be increased greatly by including small areas of mature trees within them. We believe short-rotation plantations can benefit regional biodiversity if they can be deployed as buffers for natural forests, or as corridors connecting forest tracts. To realize these benefits, and to avoid habitat degradation, regional biomass plantation complexes (e.g., the plantations supplying all the fuel for a powerplant) need to be planned, sited, and developed as large-scale units in the context of the regional landscape mosaic.

Large-scale building energy efficiency retrofit: Concept, model and control

International Nuclear Information System (INIS)

Wu, Zhou; Wang, Bo; Xia, Xiaohua

2016-01-01

BEER (Building energy efficiency retrofit) projects are initiated in many nations and regions over the world. Existing studies of BEER focus on modeling and planning based on one building and one year period of retrofitting, which cannot be applied to certain large BEER projects with multiple buildings and multi-year retrofit. In this paper, the large-scale BEER problem is defined in a general TBT (time-building-technology) framework, which fits essential requirements of real-world projects. The large-scale BEER is newly studied in the control approach rather than the optimization approach commonly used before. Optimal control is proposed to design optimal retrofitting strategy in terms of maximal energy savings and maximal NPV (net present value). The designed strategy is dynamically changing on dimensions of time, building and technology. The TBT framework and the optimal control approach are verified in a large BEER project, and results indicate that promising performance of energy and cost savings can be achieved in the general TBT framework. - Highlights: • Energy efficiency retrofit of many buildings is studied. • A TBT (time-building-technology) framework is proposed. • The control system of the large-scale BEER is modeled. • The optimal retrofitting strategy is obtained.

Worldwide large-scale fluctuations of sardine and anchovy ...

African Journals Online (AJOL)

Worldwide large-scale fluctuations of sardine and anchovy populations. ... African Journal of Marine Science. Journal Home · ABOUT THIS JOURNAL · Advanced ... Fullscreen Fullscreen Off. http://dx.doi.org/10.2989/AJMS.2008.30.1.13.463.

Large-scale linear programs in planning and prediction.

Science.gov (United States)

2017-06-01

Large-scale linear programs are at the core of many traffic-related optimization problems in both planning and prediction. Moreover, many of these involve significant uncertainty, and hence are modeled using either chance constraints, or robust optim...

Large Scale Solar Heating

DEFF Research Database (Denmark)

Heller, Alfred

2001-01-01

The main objective of the research was to evaluate large-scale solar heating connected to district heating (CSDHP), to build up a simulation tool and to demonstrate the application of the simulation tool for design studies and on a local energy planning case. The evaluation was mainly carried out...... model is designed and validated on the Marstal case. Applying the Danish Reference Year, a design tool is presented. The simulation tool is used for proposals for application of alternative designs, including high-performance solar collector types (trough solar collectors, vaccum pipe collectors......). Simulation programs are proposed as control supporting tool for daily operation and performance prediction of central solar heating plants. Finaly the CSHP technolgy is put into persepctive with respect to alternatives and a short discussion on the barries and breakthrough of the technology are given....

Large-scale exact diagonalizations reveal low-momentum scales of nuclei

Science.gov (United States)

Forssén, C.; Carlsson, B. D.; Johansson, H. T.; Sääf, D.; Bansal, A.; Hagen, G.; Papenbrock, T.

2018-03-01

Ab initio methods aim to solve the nuclear many-body problem with controlled approximations. Virtually exact numerical solutions for realistic interactions can only be obtained for certain special cases such as few-nucleon systems. Here we extend the reach of exact diagonalization methods to handle model spaces with dimension exceeding 1010 on a single compute node. This allows us to perform no-core shell model (NCSM) calculations for 6Li in model spaces up to Nmax=22 and to reveal the 4He+d halo structure of this nucleus. Still, the use of a finite harmonic-oscillator basis implies truncations in both infrared (IR) and ultraviolet (UV) length scales. These truncations impose finite-size corrections on observables computed in this basis. We perform IR extrapolations of energies and radii computed in the NCSM and with the coupled-cluster method at several fixed UV cutoffs. It is shown that this strategy enables information gain also from data that is not fully UV converged. IR extrapolations improve the accuracy of relevant bound-state observables for a range of UV cutoffs, thus making them profitable tools. We relate the momentum scale that governs the exponential IR convergence to the threshold energy for the first open decay channel. Using large-scale NCSM calculations we numerically verify this small-momentum scale of finite nuclei.

Large Scale Visual Recommendations From Street Fashion Images

OpenAIRE

Jagadeesh, Vignesh; Piramuthu, Robinson; Bhardwaj, Anurag; Di, Wei; Sundaresan, Neel

2014-01-01

We describe a completely automated large scale visual recommendation system for fashion. Our focus is to efficiently harness the availability of large quantities of online fashion images and their rich meta-data. Specifically, we propose four data driven models in the form of Complementary Nearest Neighbor Consensus, Gaussian Mixture Models, Texture Agnostic Retrieval and Markov Chain LDA for solving this problem. We analyze relative merits and pitfalls of these algorithms through extensive e...

``Large''- vs Small-scale friction control in turbulent channel flow

Science.gov (United States)

Canton, Jacopo; Örlü, Ramis; Chin, Cheng; Schlatter, Philipp

2017-11-01

We reconsider the ``large-scale'' control scheme proposed by Hussain and co-workers (Phys. Fluids 10, 1049-1051 1998 and Phys. Rev. Fluids, 2, 62601 2017), using new direct numerical simulations (DNS). The DNS are performed in a turbulent channel at friction Reynolds number Reτ of up to 550 in order to eliminate low-Reynolds-number effects. The purpose of the present contribution is to re-assess this control method in the light of more modern developments in the field, in particular also related to the discovery of (very) large-scale motions. The goals of the paper are as follows: First, we want to better characterise the physics of the control, and assess what external contribution (vortices, forcing, wall motion) are actually needed. Then, we investigate the optimal parameters and, finally, determine which aspects of this control technique actually scale in outer units and can therefore be of use in practical applications. In addition to discussing the mentioned drag-reduction effects, the present contribution will also address the potential effect of the naturally occurring large-scale motions on frictional drag, and give indications on the physical processes for potential drag reduction possible at all Reynolds numbers.

Power suppression at large scales in string inflation

Energy Technology Data Exchange (ETDEWEB)

Cicoli, Michele [Dipartimento di Fisica ed Astronomia, Università di Bologna, via Irnerio 46, Bologna, 40126 (Italy); Downes, Sean; Dutta, Bhaskar, E-mail: mcicoli@ictp.it, E-mail: sddownes@physics.tamu.edu, E-mail: dutta@physics.tamu.edu [Mitchell Institute for Fundamental Physics and Astronomy, Department of Physics and Astronomy, Texas A and M University, College Station, TX, 77843-4242 (United States)

2013-12-01

We study a possible origin of the anomalous suppression of the power spectrum at large angular scales in the cosmic microwave background within the framework of explicit string inflationary models where inflation is driven by a closed string modulus parameterizing the size of the extra dimensions. In this class of models the apparent power loss at large scales is caused by the background dynamics which involves a sharp transition from a fast-roll power law phase to a period of Starobinsky-like slow-roll inflation. An interesting feature of this class of string inflationary models is that the number of e-foldings of inflation is inversely proportional to the string coupling to a positive power. Therefore once the string coupling is tuned to small values in order to trust string perturbation theory, enough e-foldings of inflation are automatically obtained without the need of extra tuning. Moreover, in the less tuned cases the sharp transition responsible for the power loss takes place just before the last 50-60 e-foldings of inflation. We illustrate these general claims in the case of Fibre Inflation where we study the strength of this transition in terms of the attractor dynamics, finding that it induces a pivot from a blue to a redshifted power spectrum which can explain the apparent large scale power loss. We compute the effects of this pivot for example cases and demonstrate how magnitude and duration of this effect depend on model parameters.

Imprint of thawing scalar fields on the large scale galaxy overdensity

Science.gov (United States)

Dinda, Bikash R.; Sen, Anjan A.

2018-04-01

We investigate the observed galaxy power spectrum for the thawing class of scalar field models taking into account various general relativistic corrections that occur on very large scales. We consider the full general relativistic perturbation equations for the matter as well as the dark energy fluid. We form a single autonomous system of equations containing both the background and the perturbed equations of motion which we subsequently solve for different scalar field potentials. First we study the percentage deviation from the Λ CDM model for different cosmological parameters as well as in the observed galaxy power spectra on different scales in scalar field models for various choices of scalar field potentials. Interestingly the difference in background expansion results from the enhancement of power from Λ CDM on small scales, whereas the inclusion of general relativistic (GR) corrections results in the suppression of power from Λ CDM on large scales. This can be useful to distinguish scalar field models from Λ CDM with future optical/radio surveys. We also compare the observed galaxy power spectra for tracking and thawing types of scalar field using some particular choices for the scalar field potentials. We show that thawing and tracking models can have large differences in observed galaxy power spectra on large scales and for smaller redshifts due to different GR effects. But on smaller scales and for larger redshifts, the difference is small and is mainly due to the difference in background expansion.

Optimization of large-scale heterogeneous system-of-systems models.

Energy Technology Data Exchange (ETDEWEB)

Parekh, Ojas; Watson, Jean-Paul; Phillips, Cynthia Ann; Siirola, John; Swiler, Laura Painton; Hough, Patricia Diane (Sandia National Laboratories, Livermore, CA); Lee, Herbert K. H. (University of California, Santa Cruz, Santa Cruz, CA); Hart, William Eugene; Gray, Genetha Anne (Sandia National Laboratories, Livermore, CA); Woodruff, David L. (University of California, Davis, Davis, CA)

2012-01-01

Decision makers increasingly rely on large-scale computational models to simulate and analyze complex man-made systems. For example, computational models of national infrastructures are being used to inform government policy, assess economic and national security risks, evaluate infrastructure interdependencies, and plan for the growth and evolution of infrastructure capabilities. A major challenge for decision makers is the analysis of national-scale models that are composed of interacting systems: effective integration of system models is difficult, there are many parameters to analyze in these systems, and fundamental modeling uncertainties complicate analysis. This project is developing optimization methods to effectively represent and analyze large-scale heterogeneous system of systems (HSoS) models, which have emerged as a promising approach for describing such complex man-made systems. These optimization methods enable decision makers to predict future system behavior, manage system risk, assess tradeoffs between system criteria, and identify critical modeling uncertainties.

Mining Together : Large-Scale Mining Meets Artisanal Mining, A Guide for Action

OpenAIRE

World Bank

2009-01-01

The present guide mining together-when large-scale mining meets artisanal mining is an important step to better understanding the conflict dynamics and underlying issues between large-scale and small-scale mining. This guide for action not only points to some of the challenges that both parties need to deal with in order to build a more constructive relationship, but most importantly it sh...

TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning

OpenAIRE

Tang, Yuan

2016-01-01

TF.Learn is a high-level Python module for distributed machine learning inside TensorFlow. It provides an easy-to-use Scikit-learn style interface to simplify the process of creating, configuring, training, evaluating, and experimenting a machine learning model. TF.Learn integrates a wide range of state-of-art machine learning algorithms built on top of TensorFlow's low level APIs for small to large-scale supervised and unsupervised problems. This module focuses on bringing machine learning t...

Large transverse momenta in inclusive hadronic reactions and asymptotic scale invariance

International Nuclear Information System (INIS)

Miralles, F.; Sala, C.

1976-01-01

The inclusive reaction among scalar particles in considered, assuming that in the large-transverse momentum limit, scale invariance becomes important. Predictions are made of the asymptotic scale invariance for large four transverse momentum in hadron-hadron interactions, and they are compared with previous predictions. Photoproduction is also studied and the predictions that follow from different assumptions about the compositeness of hadrons are compared

Chirping for large-scale maritime archaeological survey

DEFF Research Database (Denmark)

Grøn, Ole; Boldreel, Lars Ole

2014-01-01

Archaeological wrecks exposed on the sea floor are mapped using side-scan and multibeam techniques, whereas the detection of submerged archaeological sites, such as Stone Age settlements, and wrecks, partially or wholly embedded in sea-floor sediments, requires the application of high-resolution ...... the present state of this technology, it appears well suited to large-scale maritime archaeological mapping....

LARGE-SCALE COMMERCIAL INVESTMENTS IN LAND: SEEKING ...

African Journals Online (AJOL)

extent of large-scale investment in land or to assess its impact on the people in recipient countries. .... favorable lease terms, apparently based on a belief that this is necessary to .... Harm to the rights of local occupiers of land can result from a dearth. 24. ..... applies to a self-identified group based on the group's traditions.

Active power reserves evaluation in large scale PVPPs

DEFF Research Database (Denmark)

Crăciun, Bogdan-Ionut; Kerekes, Tamas; Sera, Dezso

2013-01-01

The present trend on investing in renewable ways of producing electricity in the detriment of conventional fossil fuel-based plants will lead to a certain point where these plants have to provide ancillary services and contribute to overall grid stability. Photovoltaic (PV) power has the fastest...... growth among all renewable energies and managed to reach high penetration levels creating instabilities which at the moment are corrected by the conventional generation. This paradigm will change in the future scenarios where most of the power is supplied by large scale renewable plants and parts...... of the ancillary services have to be shared by the renewable plants. The main focus of the proposed paper is to technically and economically analyze the possibility of having active power reserves in large scale PV power plants (PVPPs) without any auxiliary storage equipment. The provided reserves should...

Analysis for Large Scale Integration of Electric Vehicles into Power Grids

DEFF Research Database (Denmark)

Hu, Weihao; Chen, Zhe; Wang, Xiaoru

2011-01-01

Electric Vehicles (EVs) provide a significant opportunity for reducing the consumption of fossil energies and the emission of carbon dioxide. With more and more electric vehicles integrated in the power systems, it becomes important to study the effects of EV integration on the power systems......, especially the low and middle voltage level networks. In the paper, the basic structure and characteristics of the electric vehicles are introduced. The possible impacts of large scale integration of electric vehicles on the power systems especially the advantage to the integration of the renewable energies...... are discussed. Finally, the research projects related to the large scale integration of electric vehicles into the power systems are introduced, it will provide reference for large scale integration of Electric Vehicles into power grids....

Synthesizing large-scale pyroclastic flows: Experimental design, scaling, and first results from PELE

Science.gov (United States)

Lube, G.; Breard, E. C. P.; Cronin, S. J.; Jones, J.

2015-03-01

Pyroclastic flow eruption large-scale experiment (PELE) is a large-scale facility for experimental studies of pyroclastic density currents (PDCs). It is used to generate high-energy currents involving 500-6500 m3 natural volcanic material and air that achieve velocities of 7-30 m s-1, flow thicknesses of 2-4.5 m, and runouts of >35 m. The experimental PDCs are synthesized by a controlled "eruption column collapse" of ash-lapilli suspensions onto an instrumented channel. The first set of experiments are documented here and used to elucidate the main flow regimes that influence PDC dynamic structure. Four phases are identified: (1) mixture acceleration during eruption column collapse, (2) column-slope impact, (3) PDC generation, and (4) ash cloud diffusion. The currents produced are fully turbulent flows and scale well to natural PDCs including small to large scales of turbulent transport. PELE is capable of generating short, pulsed, and sustained currents over periods of several tens of seconds, and dilute surge-like PDCs through to highly concentrated pyroclastic flow-like currents. The surge-like variants develop a basal <0.05 m thick regime of saltating/rolling particles and shifting sand waves, capped by a 2.5-4.5 m thick, turbulent suspension that grades upward to lower particle concentrations. Resulting deposits include stratified dunes, wavy and planar laminated beds, and thin ash cloud fall layers. Concentrated currents segregate into a dense basal underflow of <0.6 m thickness that remains aerated. This is capped by an upper ash cloud surge (1.5-3 m thick) with 100 to 10-4 vol % particles. Their deposits include stratified, massive, normally and reversely graded beds, lobate fronts, and laterally extensive veneer facies beyond channel margins.

Novel algorithm of large-scale simultaneous linear equations

International Nuclear Information System (INIS)

Fujiwara, T; Hoshi, T; Yamamoto, S; Sogabe, T; Zhang, S-L

2010-01-01

We review our recently developed methods of solving large-scale simultaneous linear equations and applications to electronic structure calculations both in one-electron theory and many-electron theory. This is the shifted COCG (conjugate orthogonal conjugate gradient) method based on the Krylov subspace, and the most important issue for applications is the shift equation and the seed switching method, which greatly reduce the computational cost. The applications to nano-scale Si crystals and the double orbital extended Hubbard model are presented.

Worldwide large-scale fluctuations of sardine and anchovy ...

African Journals Online (AJOL)

Worldwide large-scale fluctuations of sardine and anchovy populations. ... African Journal of Marine Science. Journal Home · ABOUT THIS JOURNAL · Advanced ... http://dx.doi.org/10.2989/AJMS.2008.30.1.13.463 · AJOL African Journals ...

Large-Scale Systems Control Design via LMI Optimization

Czech Academy of Sciences Publication Activity Database

Rehák, Branislav

2015-01-01

Roč. 44, č. 3 (2015), s. 247-253 ISSN 1392-124X Institutional support: RVO:67985556 Keywords : Combinatorial linear matrix inequalities * large-scale system * decentralized control Subject RIV: BC - Control Systems Theory Impact factor: 0.633, year: 2015

Unraveling The Connectome: Visualizing and Abstracting Large-Scale Connectomics Data

KAUST Repository

Al-Awami, Ali K.

2017-01-01

-user system seamlessly integrates a diverse set of tools. Our system provides support for the management, provenance, accountability, and auditing of large-scale segmentations. Finally, we present a novel architecture to render very large volumes interactively

Report of the Workshop on Petascale Systems Integration for LargeScale Facilities

Energy Technology Data Exchange (ETDEWEB)

Kramer, William T.C.; Walter, Howard; New, Gary; Engle, Tom; Pennington, Rob; Comes, Brad; Bland, Buddy; Tomlison, Bob; Kasdorf, Jim; Skinner, David; Regimbal, Kevin

2007-10-01

There are significant issues regarding Large Scale System integration that are not being addressed in other forums such as current research portfolios or vendor user groups. Unfortunately, the issues in the area of large-scale system integration often fall into a netherworld; not research, not facilities, not procurement, not operations, not user services. Taken together, these issues along with the impact of sub-optimal integration technology means the time required to deploy, integrate and stabilize large scale system may consume up to 20 percent of the useful life of such systems. Improving the state of the art for large scale systems integration has potential to increase the scientific productivity of these systems. Sites have significant expertise, but there are no easy ways to leverage this expertise among them . Many issues inhibit the sharing of information, including available time and effort, as well as issues with sharing proprietary information. Vendors also benefit in the long run from the solutions to issues detected during site testing and integration. There is a great deal of enthusiasm for making large scale system integration a full-fledged partner along with the other major thrusts supported by funding agencies in the definition, design, and use of a petascale systems. Integration technology and issues should have a full 'seat at the table' as petascale and exascale initiatives and programs are planned. The workshop attendees identified a wide range of issues and suggested paths forward. Pursuing these with funding opportunities and innovation offers the opportunity to dramatically improve the state of large scale system integration.

Nuclear-pumped lasers for large-scale applications

International Nuclear Information System (INIS)

Anderson, R.E.; Leonard, E.M.; Shea, R.F.; Berggren, R.R.

1989-05-01

Efficient initiation of large-volume chemical lasers may be achieved by neutron induced reactions which produce charged particles in the final state. When a burst mode nuclear reactor is used as the neutron source, both a sufficiently intense neutron flux and a sufficiently short initiation pulse may be possible. Proof-of-principle experiments are planned to demonstrate lasing in a direct nuclear-pumped large-volume system; to study the effects of various neutron absorbing materials on laser performance; to study the effects of long initiation pulse lengths; to demonstrate the performance of large-scale optics and the beam quality that may be obtained; and to assess the performance of alternative designs of burst systems that increase the neutron output and burst repetition rate. 21 refs., 8 figs., 5 tabs

Large scale sodium-water reaction tests for Monju steam generators

International Nuclear Information System (INIS)

Sato, M.; Hiroi, H.; Hori, M.

1976-01-01

To demonstrate the safe design of the steam generator system of the prototype fast reactor Monju against the postulated large leak sodium-water reaction, a large scale test facility SWAT-3 was constructed. SWAT-3 is a 1/2.5 scale model of the Monju secondary loop on the basis of the iso-velocity modeling. Two tests have been conducted in SWAT-3 since its construction. The test items using SWAT-3 are discussed, and the description of the facility and the test results are presented

Qualitative Analysis of Collaborative Learning Groups in Large Enrollment Introductory Astronomy

Science.gov (United States)

Skala, Chija; Slater, Timothy F.; Adams, Jeffrey P.

2000-08-01

Large-lecture introductory astronomy courses for undergraduate, non-science majors present numerous problems for faculty. As part of a systematic effort to improve the course learning environment, a series of small-group, collaborative learning activities were implemented in an otherwise conventional lecture astronomy survey course. These activities were used once each week during the regularly scheduled lecture period. After eight weeks, ten focus group interviews were conducted to qualitatively assess the impact and dynamics of these small group learning activities. Overall, the data strongly suggest that students enjoy participating in the in-class learning activities in learning teams of three to four students. These students firmly believe that they are learning more than they would from lectures alone. Inductive analysis of the transcripts revealed five major themes prevalent among the students' perspectives: (1) self-formed, cooperative group composition and formation should be more regulated by the instructor; (2) team members' assigned rolls should be less formally structured by the instructors; (3) cooperative groups helped in learning the course content; (4) time constraints on lectures and activities need to be more carefully aligned; and (5) gender issues can exist within the groups. These themes serve as a guide for instructors who are developing instructional interventions for large lecture courses.

A large-scale mass casualty simulation to develop the non-technical skills medical students require for collaborative teamwork.

Science.gov (United States)

Jorm, Christine; Roberts, Chris; Lim, Renee; Roper, Josephine; Skinner, Clare; Robertson, Jeremy; Gentilcore, Stacey; Osomanski, Adam

2016-03-08

There is little research on large-scale complex health care simulations designed to facilitate student learning of non-technical skills in a team-working environment. We evaluated the acceptability and effectiveness of a novel natural disaster simulation that enabled medical students to demonstrate their achievement of the non-technical skills of collaboration, negotiation and communication. In a mixed methods approach, survey data were available from 117 students and a thematic analysis undertaken of both student qualitative comments and tutor observer participation data. Ninety three per cent of students found the activity engaging for their learning. Three themes emerged from the qualitative data: the impact of fidelity on student learning, reflexivity on the importance of non-technical skills in clinical care, and opportunities for collaborative teamwork. Physical fidelity was sufficient for good levels of student engagement, as was sociological fidelity. We demonstrated the effectiveness of the simulation in allowing students to reflect upon and evidence their acquisition of skills in collaboration, negotiation and communication, as well as situational awareness and attending to their emotions. Students readily identified emerging learning opportunities though critical reflection. The scenarios challenged students to work together collaboratively to solve clinical problems, using a range of resources including interacting with clinical experts. A large class teaching activity, framed as a simulation of a natural disaster is an acceptable and effective activity for medical students to develop the non-technical skills of collaboration, negotiation and communication, which are essential to team working. The design could be of value in medical schools in disaster prone areas, including within low resource countries, and as a feasible intervention for learning the non-technical skills that are needed for patient safety.

A Chain Perspective on Large-scale Number Systems

NARCIS (Netherlands)

Grijpink, J.H.A.M.

2012-01-01

As large-scale number systems gain significance in social and economic life (electronic communication, remote electronic authentication), the correct functioning and the integrity of public number systems take on crucial importance. They are needed to uniquely indicate people, objects or phenomena

Image-based Exploration of Large-Scale Pathline Fields

KAUST Repository

Nagoor, Omniah H.

2014-01-01

structure in which each pixel contains a list of pathlines segments. With this view-dependent method it is possible to filter, color-code and explore large-scale flow data in real-time. In addition, optimization techniques such as early-ray termination

Research on the Construction Management and Sustainable Development of Large-Scale Scientific Facilities in China

Science.gov (United States)

Guiquan, Xi; Lin, Cong; Xuehui, Jin

2018-05-01

As an important platform for scientific and technological development, large -scale scientific facilities are the cornerstone of technological innovation and a guarantee for economic and social development. Researching management of large-scale scientific facilities can play a key role in scientific research, sociology and key national strategy. This paper reviews the characteristics of large-scale scientific facilities, and summarizes development status of China's large-scale scientific facilities. At last, the construction, management, operation and evaluation of large-scale scientific facilities is analyzed from the perspective of sustainable development.

Development and analysis of prognostic equations for mesoscale kinetic energy and mesoscale (subgrid scale) fluxes for large-scale atmospheric models

Science.gov (United States)

Avissar, Roni; Chen, Fei

1993-01-01

Generated by landscape discontinuities (e.g., sea breezes) mesoscale circulation processes are not represented in large-scale atmospheric models (e.g., general circulation models), which have an inappropiate grid-scale resolution. With the assumption that atmospheric variables can be separated into large scale, mesoscale, and turbulent scale, a set of prognostic equations applicable in large-scale atmospheric models for momentum, temperature, moisture, and any other gaseous or aerosol material, which includes both mesoscale and turbulent fluxes is developed. Prognostic equations are also developed for these mesoscale fluxes, which indicate a closure problem and, therefore, require a parameterization. For this purpose, the mean mesoscale kinetic energy (MKE) per unit of mass is used, defined as E-tilde = 0.5 (the mean value of u'(sub i exp 2), where u'(sub i) represents the three Cartesian components of a mesoscale circulation (the angle bracket symbol is the grid-scale, horizontal averaging operator in the large-scale model, and a tilde indicates a corresponding large-scale mean value). A prognostic equation is developed for E-tilde, and an analysis of the different terms of this equation indicates that the mesoscale vertical heat flux, the mesoscale pressure correlation, and the interaction between turbulence and mesoscale perturbations are the major terms that affect the time tendency of E-tilde. A-state-of-the-art mesoscale atmospheric model is used to investigate the relationship between MKE, landscape discontinuities (as characterized by the spatial distribution of heat fluxes at the earth's surface), and mesoscale sensible and latent heat fluxes in the atmosphere. MKE is compared with turbulence kinetic energy to illustrate the importance of mesoscale processes as compared to turbulent processes. This analysis emphasizes the potential use of MKE to bridge between landscape discontinuities and mesoscale fluxes and, therefore, to parameterize mesoscale fluxes

Solving large scale structure in ten easy steps with COLA

Energy Technology Data Exchange (ETDEWEB)

Tassev, Svetlin [Department of Astrophysical Sciences, Princeton University, 4 Ivy Lane, Princeton, NJ 08544 (United States); Zaldarriaga, Matias [School of Natural Sciences, Institute for Advanced Study, Olden Lane, Princeton, NJ 08540 (United States); Eisenstein, Daniel J., E-mail: stassev@cfa.harvard.edu, E-mail: matiasz@ias.edu, E-mail: deisenstein@cfa.harvard.edu [Center for Astrophysics, Harvard University, 60 Garden Street, Cambridge, MA 02138 (United States)

2013-06-01

We present the COmoving Lagrangian Acceleration (COLA) method: an N-body method for solving for Large Scale Structure (LSS) in a frame that is comoving with observers following trajectories calculated in Lagrangian Perturbation Theory (LPT). Unlike standard N-body methods, the COLA method can straightforwardly trade accuracy at small-scales in order to gain computational speed without sacrificing accuracy at large scales. This is especially useful for cheaply generating large ensembles of accurate mock halo catalogs required to study galaxy clustering and weak lensing, as those catalogs are essential for performing detailed error analysis for ongoing and future surveys of LSS. As an illustration, we ran a COLA-based N-body code on a box of size 100 Mpc/h with particles of mass ≈ 5 × 10{sup 9}M{sub s}un/h. Running the code with only 10 timesteps was sufficient to obtain an accurate description of halo statistics down to halo masses of at least 10{sup 11}M{sub s}un/h. This is only at a modest speed penalty when compared to mocks obtained with LPT. A standard detailed N-body run is orders of magnitude slower than our COLA-based code. The speed-up we obtain with COLA is due to the fact that we calculate the large-scale dynamics exactly using LPT, while letting the N-body code solve for the small scales, without requiring it to capture exactly the internal dynamics of halos. Achieving a similar level of accuracy in halo statistics without the COLA method requires at least 3 times more timesteps than when COLA is employed.

Concepts for Large Scale Hydrogen Production

OpenAIRE

Jakobsen, Daniel; Åtland, Vegar

2016-01-01

The objective of this thesis is to perform a techno-economic analysis of large-scale, carbon-lean hydrogen production in Norway, in order to evaluate various production methods and estimate a breakeven price level. Norway possesses vast energy resources and the export of oil and gas is vital to the country s economy. The results of this thesis indicate that hydrogen represents a viable, carbon-lean opportunity to utilize these resources, which can prove key in the future of Norwegian energy e...

Measuring Cosmic Expansion and Large Scale Structure with Destiny

Science.gov (United States)

Benford, Dominic J.; Lauer, Tod R.

2007-01-01

Destiny is a simple, direct, low cost mission to determine the properties of dark energy by obtaining a cosmologically deep supernova (SN) type Ia Hubble diagram and by measuring the large-scale mass power spectrum over time. Its science instrument is a 1.65m space telescope, featuring a near-infrared survey camera/spectrometer with a large field of view. During its first two years, Destiny will detect, observe, and characterize 23000 SN Ia events over the redshift interval 0.4Destiny will be used in its third year as a high resolution, wide-field imager to conduct a weak lensing survey covering >lo00 square degrees to measure the large-scale mass power spectrum. The combination of surveys is much more powerful than either technique on its own, and will have over an order of magnitude greater sensitivity than will be provided by ongoing ground-based projects.

Testing, development and demonstration of large scale solar district heating systems

DEFF Research Database (Denmark)

Furbo, Simon; Fan, Jianhua; Perers, Bengt

2015-01-01

In 2013-2014 the project “Testing, development and demonstration of large scale solar district heating systems” was carried out within the Sino-Danish Renewable Energy Development Programme, the so called RED programme jointly developed by the Chinese and Danish governments. In the project Danish...... know how on solar heating plants and solar heating test technology have been transferred from Denmark to China, large solar heating systems have been promoted in China, test capabilities on solar collectors and large scale solar heating systems have been improved in China and Danish-Chinese cooperation...

Optimal Selection of AC Cables for Large Scale Offshore Wind Farms

DEFF Research Database (Denmark)

Hou, Peng; Hu, Weihao; Chen, Zhe

2014-01-01

The investment of large scale offshore wind farms is high in which the electrical system has a significant contribution to the total cost. As one of the key components, the cost of the connection cables affects the initial investment a lot. The development of cable manufacturing provides a vast...... and systematical way for the optimal selection of cables in large scale offshore wind farms....

Cooperative learning benefits scale: construction and validation studies

Directory of Open Access Journals (Sweden)

José Lopes

2014-07-01

Full Text Available The aim of this study was to develop and validate a scale of benefits of the Cooperative Learning (SBCL given the exiguity of instruments that evaluate these outputs of the method. The study resorted to a convenience sample comprised of 162 students, males and females, aged between 11 and 18 years. The final instrument has 23 items in a two-dimensional factor structure: psychological and academic benefits and social benefits. The results indicate that the SBCL present good psychometric properties (construct and discriminant validity and reliability. The results are discussed in light of the model of cooperative learning.

Large-scale river regulation

International Nuclear Information System (INIS)

Petts, G.

1994-01-01

Recent concern over human impacts on the environment has tended to focus on climatic change, desertification, destruction of tropical rain forests, and pollution. Yet large-scale water projects such as dams, reservoirs, and inter-basin transfers are among the most dramatic and extensive ways in which our environment has been, and continues to be, transformed by human action. Water running to the sea is perceived as a lost resource, floods are viewed as major hazards, and wetlands are seen as wastelands. River regulation, involving the redistribution of water in time and space, is a key concept in socio-economic development. To achieve water and food security, to develop drylands, and to prevent desertification and drought are primary aims for many countries. A second key concept is ecological sustainability. Yet the ecology of rivers and their floodplains is dependent on the natural hydrological regime, and its related biochemical and geomorphological dynamics. (Author)

The Large-scale Effect of Environment on Galactic Conformity

Science.gov (United States)

Sun, Shuangpeng; Guo, Qi; Wang, Lan; Wang, Jie; Gao, Liang; Lacey, Cedric G.; Pan, Jun

2018-04-01

We use a volume-limited galaxy sample from the SDSS Data Release 7 to explore the dependence of galactic conformity on the large-scale environment, measured on ˜ 4 Mpc scales. We find that the star formation activity of neighbour galaxies depends more strongly on the environment than on the activity of their primary galaxies. In under-dense regions most neighbour galaxies tend to be active, while in over-dense regions neighbour galaxies are mostly passive, regardless of the activity of their primary galaxies. At a given stellar mass, passive primary galaxies reside in higher density regions than active primary galaxies, leading to the apparently strong conformity signal. The dependence of the activity of neighbour galaxies on environment can be explained by the corresponding dependence of the fraction of satellite galaxies. Similar results are found for galaxies in a semi-analytical model, suggesting that no new physics is required to explain the observed large-scale conformity.

Investigating the dependence of SCM simulated precipitation and clouds on the spatial scale of large-scale forcing at SGP

Science.gov (United States)

Tang, Shuaiqi; Zhang, Minghua; Xie, Shaocheng

2017-08-01

Large-scale forcing data, such as vertical velocity and advective tendencies, are required to drive single-column models (SCMs), cloud-resolving models, and large-eddy simulations. Previous studies suggest that some errors of these model simulations could be attributed to the lack of spatial variability in the specified domain-mean large-scale forcing. This study investigates the spatial variability of the forcing and explores its impact on SCM simulated precipitation and clouds. A gridded large-scale forcing data during the March 2000 Cloud Intensive Operational Period at the Atmospheric Radiation Measurement program's Southern Great Plains site is used for analysis and to drive the single-column version of the Community Atmospheric Model Version 5 (SCAM5). When the gridded forcing data show large spatial variability, such as during a frontal passage, SCAM5 with the domain-mean forcing is not able to capture the convective systems that are partly located in the domain or that only occupy part of the domain. This problem has been largely reduced by using the gridded forcing data, which allows running SCAM5 in each subcolumn and then averaging the results within the domain. This is because the subcolumns have a better chance to capture the timing of the frontal propagation and the small-scale systems. Other potential uses of the gridded forcing data, such as understanding and testing scale-aware parameterizations, are also discussed.

Distance education student accompaniment: IPGN course, an experience in large scale capacitation

Directory of Open Access Journals (Sweden)

Sônia Inez Grüdtner Floriano

2005-06-01

Full Text Available One of the most difficulties found by the providersinstitution of courses on the distance education modality, since its beginning until nowadays, is to accompany the development of its students. Today there are too much possibilities brought up by the new technologies of communication and information. Although it is known that those one are only a thru, once the difference is exactly in the pedagogical proposal of the course. For this, this paper intens to present the pedagogical proposal, the methodology and the technological resourles utilized to accompany, orient and support on de systematic way, permanent and proactive the students of a e-learning course of large scale, free with two months lenght, and 30 hours/class charge. It is a result of a partnership between SEBRAE (Serviço Brasileiro de Micro e Pequenas Empresas and IEA(Instituto de Estudos Avançados. This course has begun in 2001 and til the present moment has capacited 216.648 students.

Dynamic Reactive Power Compensation of Large Scale Wind Integrated Power System

DEFF Research Database (Denmark)

Rather, Zakir Hussain; Chen, Zhe; Thøgersen, Paul

2015-01-01

wind turbines especially wind farms with additional grid support functionalities like dynamic support (e,g dynamic reactive power support etc.) and ii) refurbishment of existing conventional central power plants to synchronous condensers could be one of the efficient, reliable and cost effective option......Due to progressive displacement of conventional power plants by wind turbines, dynamic security of large scale wind integrated power systems gets significantly compromised. In this paper we first highlight the importance of dynamic reactive power support/voltage security in large scale wind...... integrated power systems with least presence of conventional power plants. Then we propose a mixed integer dynamic optimization based method for optimal dynamic reactive power allocation in large scale wind integrated power systems. One of the important aspects of the proposed methodology is that unlike...

Constructing sites on a large scale

DEFF Research Database (Denmark)

Braae, Ellen Marie; Tietjen, Anne

2011-01-01

Since the 1990s, the regional scale has regained importance in urban and landscape design. In parallel, the focus in design tasks has shifted from master plans for urban extension to strategic urban transformation projects. A prominent example of a contemporary spatial development approach...... for setting the design brief in a large scale urban landscape in Norway, the Jaeren region around the city of Stavanger. In this paper, we first outline the methodological challenges and then present and discuss the proposed method based on our teaching experiences. On this basis, we discuss aspects...... is the IBA Emscher Park in the Ruhr area in Germany. Over a 10 years period (1988-1998), more than a 100 local transformation projects contributed to the transformation from an industrial to a post-industrial region. The current paradigm of planning by projects reinforces the role of the design disciplines...

Learning in an estimated medium-scale DSGE model

Czech Academy of Sciences Publication Activity Database

Slobodyan, Sergey; Wouters, R.

2012-01-01

Roč. 36, č. 1 (2012), s. 26-46 ISSN 0165-1889 R&D Projects: GA ČR(CZ) GCP402/11/J018 Institutional support: PRVOUK-P23 Keywords : constant-gain adaptive learning * medium-scale DSGE model * DSGE- VAR Subject RIV: AH - Economics Impact factor: 0.807, year: 2012

Report of the LASCAR forum: Large scale reprocessing plant safeguards

International Nuclear Information System (INIS)

1992-01-01

This report has been prepared to provide information on the studies which were carried out from 1988 to 1992 under the auspices of the multinational forum known as Large Scale Reprocessing Plant Safeguards (LASCAR) on safeguards for four large scale reprocessing plants operated or planned to be operated in the 1990s. The report summarizes all of the essential results of these studies. The participants in LASCAR were from France, Germany, Japan, the United Kingdom, the United States of America, the Commission of the European Communities - Euratom, and the International Atomic Energy Agency

Large-scale melting and impact mixing on early-formed asteroids

DEFF Research Database (Denmark)

Greenwood, Richard; Barrat, J.-A.; Scott, Edward Robert Dalton

Large-scale melting of asteroids and planetesimals is now known to have taken place ex-tremely early in solar system history [1]. The first-generation bodies produced by this process would have been subject to rapid collisional reprocessing, leading in most cases to fragmentation and/or accretion...... the relationship between the different groups of achondrites [3, 4]. Here we present new oxygen isotope evidence con-cerning the role of large-scale melting and subsequent impact mixing in the evolution of three important achondrite groups: the main-group pallasites, meso-siderites and HEDs....

Incorporating Direct Rapid Immunohistochemical Testing into Large-Scale Wildlife Rabies Surveillance

Directory of Open Access Journals (Sweden)

Kevin Middel

2017-06-01

Full Text Available Following an incursion of the mid-Atlantic raccoon variant of the rabies virus into southern Ontario, Canada, in late 2015, the direct rapid immunohistochemical test for rabies (dRIT was employed on a large scale to establish the outbreak perimeter and to diagnose specific cases to inform rabies control management actions. In a 17-month period, 5800 wildlife carcasses were tested using the dRIT, of which 307 were identified as rabid. When compared with the gold standard fluorescent antibody test (FAT, the dRIT was found to have a sensitivity of 100% and a specificity of 98.2%. Positive and negative test agreement was shown to be 98.3% and 99.1%, respectively, with an overall test agreement of 98.8%. The average cost to test a sample was $3.13 CAD for materials, and hands-on technical time to complete the test is estimated at 0.55 h. The dRIT procedure was found to be accurate, fast, inexpensive, easy to learn and perform, and an excellent tool for monitoring the progression of a wildlife rabies incursion.

EGameFlow: A Scale to Measure Learners' Enjoyment of E-Learning Games

Science.gov (United States)

Fu, Fong-Ling; Su, Rong-Chang; Yu, Sheng-Chin

2009-01-01

In an effective e-learning game, the learner's enjoyment acts as a catalyst to encourage his/her learning initiative. Therefore, the availability of a scale that effectively measures the enjoyment offered by e-learning games assist the game designer to understanding the strength and flaw of the game efficiently from the learner's points of view.…

Updating Geospatial Data from Large Scale Data Sources