WorldWideScience

Sample records for network combines fun

  1. FunPred-1: protein function prediction from a protein interaction network using neighborhood analysis.

    Science.gov (United States)

    Saha, Sovan; Chatterjee, Piyali; Basu, Subhadip; Kundu, Mahantapas; Nasipuri, Mita

    2014-12-01

    Proteins are responsible for all biological activities in living organisms. Thanks to genome sequencing projects, large amounts of DNA and protein sequence data are now available, but the biological functions of many proteins are still not annotated in most cases. The unknown function of such non-annotated proteins may be inferred or deduced from their neighbors in a protein interaction network. In this paper, we propose two new methods to predict protein functions based on network neighborhood properties. FunPred 1.1 uses a combination of three simple-yet-effective scoring techniques: the neighborhood ratio, the protein path connectivity and the relative functional similarity. FunPred 1.2 applies a heuristic approach using the edge clustering coefficient to reduce the search space by identifying densely connected neighborhood regions. The overall accuracy achieved in FunPred 1.2 over 8 functional groups involving hetero-interactions in 650 yeast proteins is around 87%, which is higher than the accuracy with FunPred 1.1. It is also higher than the accuracy of many of the state-of-the-art protein function prediction methods described in the literature. The test datasets and the complete source code of the developed software are now freely available at http://code.google.com/p/cmaterbioinfo/ .

  2. FunMod: A Cytoscape Plugin for Identifying Functional Modules in Undirected Protein–Protein Networks

    Directory of Open Access Journals (Sweden)

    Massimo Natale

    2014-08-01

    Full Text Available The characterization of the interacting behaviors of complex biological systems is a primary objective in protein–protein network analysis and computational biology. In this paper we present FunMod, an innovative Cytoscape version 2.8 plugin that is able to mine undirected protein–protein networks and to infer sub-networks of interacting proteins intimately correlated with relevant biological pathways. This plugin may enable the discovery of new pathways involved in diseases. In order to describe the role of each protein within the relevant biological pathways, FunMod computes and scores three topological features of the identified sub-networks. By integrating the results from biological pathway clustering and topological network analysis, FunMod proved to be useful for the data interpretation and the generation of new hypotheses in two case studies.

  3. EFFICIENCY METRICS COMPUTING IN COMBINED SENSOR NETWORKS

    OpenAIRE

    Luntovskyy, Andriy; Vasyutynskyy, Volodymyr

    2014-01-01

    This paper discusses the computer-aided design of combined networks for offices and building automation systems based on diverse wired and wireless standards. The design requirements for these networks are often contradictive and have to consider performance, energy and cost efficiency together. For usual office communication, quality of service is more important. In the wireless sensor networks, the energy efficiency is a critical requirement to ensure their long life, to reduce maintenance ...

  4. Combined Heuristic Attack Strategy on Complex Networks

    Directory of Open Access Journals (Sweden)

    Marek Šimon

    2017-01-01

    Full Text Available Usually, the existence of a complex network is considered an advantage feature and efforts are made to increase its robustness against an attack. However, there exist also harmful and/or malicious networks, from social ones like spreading hoax, corruption, phishing, extremist ideology, and terrorist support up to computer networks spreading computer viruses or DDoS attack software or even biological networks of carriers or transport centers spreading disease among the population. New attack strategy can be therefore used against malicious networks, as well as in a worst-case scenario test for robustness of a useful network. A common measure of robustness of networks is their disintegration level after removal of a fraction of nodes. This robustness can be calculated as a ratio of the number of nodes of the greatest remaining network component against the number of nodes in the original network. Our paper presents a combination of heuristics optimized for an attack on a complex network to achieve its greatest disintegration. Nodes are deleted sequentially based on a heuristic criterion. Efficiency of classical attack approaches is compared to the proposed approach on Barabási-Albert, scale-free with tunable power-law exponent, and Erdős-Rényi models of complex networks and on real-world networks. Our attack strategy results in a faster disintegration, which is counterbalanced by its slightly increased computational demands.

  5. 47 CFR 51.315 - Combination of unbundled network elements.

    Science.gov (United States)

    2010-10-01

    ... 47 Telecommunication 3 2010-10-01 2010-10-01 false Combination of unbundled network elements. 51... Combination of unbundled network elements. (a) An incumbent LEC shall provide unbundled network elements in a manner that allows requesting telecommunications carriers to combine such network elements in order to...

  6. Together we have fun: native-place networks and sexual risk behaviours among Chinese male rural-urban migrants.

    Science.gov (United States)

    Yang, Xiaozhao Yousef; Kelly, Brian C; Yang, Tingzhong

    2016-05-01

    Some scholars argue that the maintenance of social networks contributes to the lower prevalence of deviant behaviours and fewer adverse health effects among migrants. But others suggest that if migrants are embedded in homogeneous networks, such networks may enable the formation of a deviant subculture that promotes risk taking. Facing this dilemma, the present study investigates how native-place networks influence sexual risk behaviours (SRBs), specifically the pursuit of commercial sex and condomless sex with sex workers, for male rural-urban migrants. Using a multi-stage sample of 1,591 male rural-urban migrants from two major migrant-influx cities within China, we assessed migrants' general friend network ties and native place networks (townsmen in migrants' local networks) and tested their associations with SRBs. Multiple logistic regression analyses indicate that native-place network ties are associated with paying for sex (OR = 1.33, p platform for the emergence of a deviant subculture that promotes risk behaviours. A Virtual Abstract of this paper is available at: https://www.youtube.com/watch?v=3Wg20I6j8XQ. © 2015 Foundation for the Sociology of Health & Illness.

  7. "I Don't Know What Fun Is": Examining the Intersection of Social Capital, Social Networks, and Social Recovery.

    Science.gov (United States)

    Boeri, Miriam; Gardner, Megan; Gerken, Erin; Ross, Melissa; Wheeler, Jack

    The purpose of this paper is to understand how people with problematic drug use access positive social capital. Social capital is defined as relations that provide valuable resources to individuals through participation in social networks. People with low socioeconomic status remain at a disadvantage for acquiring positive social capital, a component of recovery capital. The concept of social recovery emphasises the relational processes of recovery. In-depth life history data were collected from 29 individuals who used heroin, cocaine, crack, or methamphetamine for at least five years, have less than a high school education, and unstable employment and housing. Qualitative data were coded for social networks accessed throughout the life course, distinguished by bonding, bridging and linking social capital. Social networks included drug treatment programs; non-drug-using family and friends; religious/spiritual groups; workplace networks, and social clubs/activities. Bonding and/or bridging social capital were acquired through treatment, family and friends, religious/spiritual groups, workplaces, and social clubs. Linking social capital was not acquired through any social networks available, and many barriers to accessing mainstream social networks were found. This is a small study conducted in the US. A greater focus on social recovery is needed to achieve sustained recovery for individuals lacking access to and engagement in mainstream social networks. Social recovery is proposed as an analytical tool as well as for developing prevention, intervention, and treatment strategies.

  8. Efficient combined security system for wireless sensor network

    Directory of Open Access Journals (Sweden)

    N.S. Fayed

    2012-11-01

    Full Text Available Wireless Sensor Networks (WSNs need effective security mechanisms because these networks deployed in hostel unattended environments. There are many parameters affect selecting the security mechanism as its speed and energy consumption. This paper presents a combined security system for WSN that enhance the speed of the network and it is energy consumption. This system combines two strong protocols, Lightweight Kerberos and Elliptic Curve Menezes–Qu–Vanstone (ECMQV. The simulation results demonstrate that the combined system can enlarge the life time for wireless sensor networks, enhance its security, and increase its speed.

  9. Combining neural networks for protein secondary structure prediction

    DEFF Research Database (Denmark)

    Riis, Søren Kamaric

    1995-01-01

    In this paper structured neural networks are applied to the problem of predicting the secondary structure of proteins. A hierarchical approach is used where specialized neural networks are designed for each structural class and then combined using another neural network. The submodels are designed...... by using a priori knowledge of the mapping between protein building blocks and the secondary structure and by using weight sharing. Since none of the individual networks have more than 600 adjustable weights over-fitting is avoided. When ensembles of specialized experts are combined the performance...

  10. Bayesian networks: a combined tuning heuristic

    NARCIS (Netherlands)

    Bolt, J.H.

    2016-01-01

    One of the issues in tuning an output probability of a Bayesian network by changing multiple parameters is the relative amount of the individual parameter changes. In an existing heuristic parameters are tied such that their changes induce locally a maximal change of the tuned probability. This

  11. Combining morphological analysis and Bayesian networks for ...

    African Journals Online (AJOL)

    Morphological analysis (MA) and Bayesian networks (BN) are two closely related modelling methods, each of which has its advantages and disadvantages for strategic decision support modelling. MA is a method for defining, linking and evaluating problem spaces. BNs are graphical models which consist of a qualitative ...

  12. Family Fitness Fun

    Science.gov (United States)

    Being active with your family can be a fun way to get everybody moving. All of you will get the health benefits that come from being active. Plus, you’ll be a positive role model, helping your children develop good habits for an active lifetime.

  13. Fun with Optical Fibres

    Science.gov (United States)

    Alti, Kamlesh

    2017-01-01

    Optical fibres play a very crucial role in today's technologies. Academic courses in optical fibres start at the undergraduate level. Nevertheless, student's curiosity towards optical fibres starts from the school level. In this paper, some fun experiments have been designed for both school and college students, which have some concrete…

  14. A simple network agreement-based approach for combining evidences in a heterogeneous sensor network

    Directory of Open Access Journals (Sweden)

    Raúl Eusebio-Grande

    2015-12-01

    Full Text Available In this research we investigate how the evidences provided by both static and mobile nodes that are part of a heterogenous sensor network can be combined to have trustworthy results. A solution relying on a network agreement-based approach was implemented and tested.

  15. Cool Cats: Feline Fun with Abstract Art.

    Science.gov (United States)

    Lambert, Phyllis Gilchrist

    2002-01-01

    Presents a lesson that teaches students about abstract art in a fun way. Explains that students draw cats, learn about the work of Pablo Picasso, and, in the style of Picasso, combine the parts of the cats (tail, legs, head, body) together in unconventional ways. (CMK)

  16. QSAR modelling using combined simple competitive learning networks and RBF neural networks.

    Science.gov (United States)

    Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E

    2018-04-01

    The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.

  17. Django fun for Girls!

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    Nowadays there is a gender gap in the IT world. Django Girls is a non-profit organization and a community that empowers and helps women to organize free, one-day programming workshops by providing tools, resources and support. Through Django Girls, we want to show our motivation in the IT world, how much fun is to code, and the things they are able to create coding. How is CERN getting involved?

  18. Combination methods for identifying influential nodes in networks

    Science.gov (United States)

    Gao, Chao; Zhong, Lu; Li, Xianghua; Zhang, Zili; Shi, Ning

    2015-11-01

    Identifying influential nodes is of theoretical significance in many domains. Although lots of methods have been proposed to solve this problem, their evaluations are under single-source attack in scale-free networks. Meanwhile, some researches have speculated that the combinations of some methods may achieve more optimal results. In order to evaluate this speculation and design a universal strategy suitable for different types of networks under the consideration of multi-source attacks, this paper proposes an attribute fusion method with two independent strategies to reveal the correlation of existing ranking methods and indicators. One is based on feature union (FU) and the other is based on feature ranking (FR). Two different propagation models in the fields of recommendation system and network immunization are used to simulate the efficiency of our proposed method. Experimental results show that our method can enlarge information spreading and restrain virus propagation in the application of recommendation system and network immunization in different types of networks under the condition of multi-source attacks.

  19. Fun in the Kitchen

    CERN Multimedia

    2004-01-01

    You may be familiar with Microcosm's "Fun with Physics" hands-on activities programme, which is the delight of young and old alike. In order to demonstrate the different states of matter, the "Fun with Physics"1 guides use liquid nitrogen to freeze various substances. Yoghurts, for example, are instantaneously turned into mouth-watering ice-creams! But, did you know that a distinguished chef also uses this technique? Ettore Bocchia, chef at the Grand Hotel Villa Serbelloni in Italy, uses liquid nitrogen at -196°C to freeze the dishes he prepares. Mix some yoghurts, add a soupçon of balsamic vinegar, pour in some liquid nitrogen at -196°C and the result is some delicious ice-cream. This was the recipe that Ettore Bocchia (in the centre of the picture on the left), the distinguished Italian chef, presented at the Gourmet Festival. He gave a demonstration of his skills at the St. Moritz Gourmet Festival, during the week beginning 2 February, in which the best chefs from around the world took part. CERN, whic...

  20. Serious Simulations (for fun)

    DEFF Research Database (Denmark)

    Andersen, Christian Ulrik

    2006-01-01

    ’Serious Simulations (for fun)’ deals with a dramatic change in the area of computer games. Computer games have throughout the past decades given us the opportunity to experience, tell stories and play in virtual, computer generated worlds. Today, however, the narratives of the computer games......, their laws of physics and their rule structure not only belong to the game world. Incessantly and innovatively, they reach far beyond the game universe and into reality. The computer game today is the place where we not only escape reality, but also relate to reality – similar to the role of the movie...... in the 20th century. They have become an important part of marketing, teaching, political activism, communication and information to the public. It is the language of the future, the language for and about the reality we are living in. The game simulations are still compelling and entertaining...

  1. “I Don’t Know What Fun Is”: Examining the Intersection of Social Capital, Social Networks, and Social Recovery

    Science.gov (United States)

    Boeri, Miriam; Gardner, Megan; Gerken, Erin; Ross, Melissa; Wheeler, Jack

    2016-01-01

    Purpose The purpose of this paper is to understand how people with problematic drug use access positive social capital. Social capital is defined as relations that provide valuable resources to individuals through participation in social networks. People with low socioeconomic status remain at a disadvantage for acquiring positive social capital, a component of recovery capital. The concept of social recovery emphasises the relational processes of recovery. Design/methodology/approach In-depth life history data were collected from 29 individuals who used heroin, cocaine, crack, or methamphetamine for at least five years, have less than a high school education, and unstable employment and housing. Qualitative data were coded for social networks accessed throughout the life course, distinguished by bonding, bridging and linking social capital. Findings Social networks included drug treatment programs; non-drug-using family and friends; religious/spiritual groups; workplace networks, and social clubs/activities. Bonding and/or bridging social capital were acquired through treatment, family and friends, religious/spiritual groups, workplaces, and social clubs. Linking social capital was not acquired through any social networks available, and many barriers to accessing mainstream social networks were found. Limitations This is a small study conducted in the US. Social implications A greater focus on social recovery is needed to achieve sustained recovery for individuals lacking access to and engagement in mainstream social networks. Practical implications Social recovery is proposed as an analytical tool as well as for developing prevention, intervention, and treatment strategies. PMID:27668008

  2. Combining Unsupervised Anomaly Detection and Neural Networks for Driver Identification

    Directory of Open Access Journals (Sweden)

    Thitaree Tanprasert

    2017-01-01

    Full Text Available This paper proposes an algorithm for real-time driver identification using the combination of unsupervised anomaly detection and neural networks. The proposed algorithm uses nonphysiological signals as input, namely, driving behavior signals from inertial sensors (e.g., accelerometers and geolocation signals from GPS sensors. First anomaly detection is performed to assess if the current driver is whom he/she claims to be. If an anomaly is detected, the algorithm proceeds to find relevant features in the input signals and use neural networks to identify drivers. To assess the proposed algorithm, real-world data are collected from ten drivers who drive different vehicles on several routes in real-world traffic conditions. Driver identification is performed on each of the seven-second-long driving behavior signals and geolocation signals in a streaming manner. It is shown that the proposed algorithm can achieve relatively high accuracy and identify drivers within 13 seconds. The proposed algorithm also outperforms the previously proposed driver identification algorithms. Furthermore, to demonstrate how the proposed algorithm can be deployed in real-world applications, results from real-world data associated with each operation of the proposed algorithm are shown step-by-step.

  3. Xplora: making science fun!

    CERN Multimedia

    2006-01-01

    Remember those humdrum lectures in science class? Static textbook lessons have not done much to ignite excitement and interest in young children. Now the tables are turned and it is the teachers who are learning, but this time it is all about how to make science classes fun and spark the imaginations of the next generation. Xplora conference participants observing a working cloud experiment. The Xplora Conference, held at CERN from 15 to 18 June, was attended by more than 80 teachers and educators from across Europe ready to share and acquire some creative ways of teaching science. Xplora is an online reference project providing inventive techniques for teaching science in the classroom and beyond. Xplora is part of the Permanent European Resource Centre for Informal Learning (PENCIL) sponsored by the European Commission. PENCIL is comprised of 13 science centres, museums and aquariums, is partners with the University of Naples, Italy and King's College London, UK and is involved with 14 pilot projects thro...

  4. FunSAV: predicting the functional effect of single amino acid variants using a two-stage random forest model.

    Directory of Open Access Journals (Sweden)

    Mingjun Wang

    Full Text Available Single amino acid variants (SAVs are the most abundant form of known genetic variations associated with human disease. Successful prediction of the functional impact of SAVs from sequences can thus lead to an improved understanding of the underlying mechanisms of why a SAV may be associated with certain disease. In this work, we constructed a high-quality structural dataset that contained 679 high-quality protein structures with 2,048 SAVs by collecting the human genetic variant data from multiple resources and dividing them into two categories, i.e., disease-associated and neutral variants. We built a two-stage random forest (RF model, termed as FunSAV, to predict the functional effect of SAVs by combining sequence, structure and residue-contact network features with other additional features that were not explored in previous studies. Importantly, a two-step feature selection procedure was proposed to select the most important and informative features that contribute to the prediction of disease association of SAVs. In cross-validation experiments on the benchmark dataset, FunSAV achieved a good prediction performance with the area under the curve (AUC of 0.882, which is competitive with and in some cases better than other existing tools including SIFT, SNAP, Polyphen2, PANTHER, nsSNPAnalyzer and PhD-SNP. The sourcecodes of FunSAV and the datasets can be downloaded at http://sunflower.kuicr.kyoto-u.ac.jp/sjn/FunSAV.

  5. Serious computing, big fun

    CERN Multimedia

    Lohr, S

    2002-01-01

    Butterfly.net Inc company plans to announce today its Butterfly Grid, software development tools and a network for so-called massively multiplayer video games, which can be played by thousands of people at a time. One of the first applications of Grid technology outside of scientific data analysis and research (1/2 page).

  6. FACERAPE- CYBERBULLYING OR INNOCENT FUN

    OpenAIRE

    Brovig, Marit

    2013-01-01

    In this master thesis I look at a phenomenon known as Facerape, Facerape is to take over somebody else s profile online on a social media without the owners knowledge or consent. My main research question was whether Facerape is seen as cyberbullying or innocent fun. I performed a survey amongst 4 different classes in Upper secondary school. I found out that amongst the participants in this survey this was seen as innocent fun, since no one said that they had Faceraped in a hurtful or nasty ...

  7. Having Fun with Error Analysis

    Science.gov (United States)

    Siegel, Peter

    2007-01-01

    We present a fun activity that can be used to introduce students to error analysis: the M&M game. Students are told to estimate the number of individual candies plus uncertainty in a bag of M&M's. The winner is the group whose estimate brackets the actual number with the smallest uncertainty. The exercise produces enthusiastic discussions and…

  8. Produção de mudas de alface em função de diferentes combinações de substratos Production of lettuce seedlings for different combinations of substrata

    Directory of Open Access Journals (Sweden)

    Gilson Araújo de Freitas

    2013-03-01

    Full Text Available O substrato utilizado na produção de mudas exerce papel primordial, no desenvolvimento inicial da planta. Neste sentido, com o presente trabalho objetivou-se avaliar a produção de mudas de alface em função de diferentes combinações de substratos. O experimento foi implantado seguindo um delineamento inteiramente casualizado, com quatro repetições. Os 20 tratamentos foram dispostos em esquema fatorial 4x5; sendo o primeiro fator constituído por quatro substratos (PlantHort I, PlantHort II, PlantHort III e substrato comercial Plantmax® e o segundo constituído de cinco níveis de casca de arroz carbonizada (0; 25; 50; 75; 100%. Foi utilizada a cultivar de alface Elba (Lactuca sativa L.. As sementes foram semeadas nos diferentes substratos contidos em bandejas de poliestireno expandido, com 128 células, na profundidade 0,5 cm. O aumento da porcentagem de casca de arroz carbonizada nos substratos avaliados apresentou comportamentos semelhantes em todas as características avaliadas. Os substratos PlantHort I, PlantHort II, PlantHort III considerados alternativos, independentemente da proporção adicionada de casca de arroz carbonizada proporcionaram maiores crescimentos em relação ao substrato comercial Plantmax®.The substratum used in seedling production has a key role in the initial development of the plant. To this effect, the present work aimed to evaluate the production of lettuce seedlings for different combinations of substrata. The experiment was set up following a completely randomized design with four replications. The 20 treatments were arranged in a 4x5 factorial, the first factor consisting of four substrata (PlantHort I, PlantHort II, PlantHort III and the commercial substratum Plantmax® and the second consisting of five levels of burnt rice husks (0, 25, 50 , 75, 100%. The lettuce cultivar used was Elba (Lactuca sativa L.. The seeds were sown in the different substrates contained in polystyrene trays of 128 cells, at

  9. Using Fun to Teach Rigorous Content

    Directory of Open Access Journals (Sweden)

    Mary Francis

    2013-02-01

    Full Text Available This paper will offer a position on the place of fun within education and learning. It will place fun as an important component of learning. The intent is not to espouse the belief that it is the duty of teachers and instructors to entertain students. Unlike a movie or TV show that provides passive entertainment, fun in this context relates to actions and techniques that aid students in learning new material. So rather than fun being associated with ease, fun is associated with rigor. In drawing together research on the successful impact of fun in education, this paper hopes to be an impetus for librarians to consider fun within their pedagogical approach to instruction and to spur conversation on how information literacy instruction is formatted.

  10. A Combined Network Architecture Using Art2 and Back Propagation for Adaptive Estimation of Dynamic Processes

    Directory of Open Access Journals (Sweden)

    Einar Sørheim

    1990-10-01

    Full Text Available A neural network architecture called ART2/BP is proposed. Thc goal has been to construct an artificial neural network that learns incrementally an unknown mapping, and is motivated by the instability found in back propagation (BP networks: after first learning pattern A and then pattern B, a BP network often has completely 'forgotten' pattern A. A network using both supervised and unsupervised training is proposed, consisting of a combination of ART2 and BP. ART2 is used to build and focus a supervised backpropagation network consisting of many small subnetworks each specialized on a particular domain of the input space. The ART2/BP network has the advantage of being able to dynamically expand itself in response to input patterns containing new information. Simulation results show that the ART2/BP network outperforms a classical maximum likelihood method for the estimation of a discrete dynamic and nonlinear transfer function.

  11. Fun and Games and Boredom.

    Science.gov (United States)

    Buday, Richard; Baranowski, Tom; Thompson, Debbe

    2012-08-01

    Serious videogames use entertainment to teach, train, or change behavior. What began in the 1970s as tentative attempts to create learning software is now a recognized videogame genre and an emerging health science. Although more research is needed, a growing body of literature suggests serious videogames can be effective. Support for serious videogames, however, is not universal. An informal Web search reveals numerous skeptics. Critics question serious videogames' entertainment value and, thus, their viability. "How can serious videogames attract and maintain players," the argument goes, "if they aren't as much fun as commercial titles, or even any fun at all?" This article examines the argument that, to be effective, serious videogames should be overtly fun and comparable to commercial off-the-shelf videogames. It explores differences between game developer- and researcher-led projects and discusses ways serious videogames can avoid boring and alienating players. It concludes that direct comparisons between serious and commercial game entertainment values may be misdirected.

  12. Combining complex networks and data mining: why and how

    CERN Document Server

    Zanin, M; Sousa, P A; Menasalvas, E; Nicchi, A; Kubik, E; Boccaletti, S

    2016-01-01

    The increasing power of computer technology does not dispense with the need to extract meaningful information out of data sets of ever growing size, and indeed typically exacerbates the complexity of this task. To tackle this general problem, two methods have emerged, at chronologically different times, that are now commonly used in the scientific community: data mining and complex network theory. Not only do complex network analysis and data mining share the same general goal, that of extracting information from complex systems to ultimately create a new compact quantifiable representation, but they also often address similar problems too. In the face of that, a surprisingly low number of researchers turn out to resort to both methodologies. One may then be tempted to conclude that these two fields are either largely redundant or totally antithetic. The starting point of this review is that this state of affairs should be put down to contingent rather than conceptual differences, and that these two fields ca...

  13. Targeting Neuronal Networks with Combined Drug and Stimulation Paradigms Guided by Neuroimaging to Treat Brain Disorders.

    Science.gov (United States)

    Faingold, Carl L; Blumenfeld, Hal

    2015-10-01

    Improved therapy of brain disorders can be achieved by focusing on neuronal networks, utilizing combined pharmacological and stimulation paradigms guided by neuroimaging. Neuronal networks that mediate normal brain functions, such as hearing, interact with other networks, which is important but commonly neglected. Network interaction changes often underlie brain disorders, including epilepsy. "Conditional multireceptive" (CMR) brain areas (e.g., brainstem reticular formation and amygdala) are critical in mediating neuroplastic changes that facilitate network interactions. CMR neurons receive multiple inputs but exhibit extensive response variability due to milieu and behavioral state changes and are exquisitely sensitive to agents that increase or inhibit GABA-mediated inhibition. Enhanced CMR neuronal responsiveness leads to expression of emergent properties--nonlinear events--resulting from network self-organization. Determining brain disorder mechanisms requires animals that model behaviors and neuroanatomical substrates of human disorders identified by neuroimaging. However, not all sites activated during network operation are requisite for that operation. Other active sites are ancillary, because their blockade does not alter network function. Requisite network sites exhibit emergent properties that are critical targets for pharmacological and stimulation therapies. Improved treatment of brain disorders should involve combined pharmacological and stimulation therapies, guided by neuroimaging, to correct network malfunctions by targeting specific network neurons. © The Author(s) 2015.

  14. Single and combined fault diagnosis of reciprocating compressor valves using a hybrid deep belief network

    NARCIS (Netherlands)

    Tran, Van Tung; Thobiani, Faisal Al; Tinga, Tiedo; Ball, Andrew David; Niu, Gang

    2017-01-01

    In this paper, a hybrid deep belief network is proposed to diagnose single and combined faults of suction and discharge valves in a reciprocating compressor. This hybrid integrates the deep belief network structured by multiple stacked restricted Boltzmann machines for pre-training and simplified

  15. On the Potential of Interference Rejection Combining in B4G Networks

    DEFF Research Database (Denmark)

    Tavares, Fernando Menezes Leitão; Berardinelli, Gilberto; Mahmood, Nurul Huda

    2013-01-01

    Beyond 4th Generation (B4G) local area networks will be characterized by the dense uncoordinated deployment of small cells. This paper shows that inter-cell interference, which is a main limiting factor in such networks, can be effectively contained using Interference Rejection Combining (IRC...

  16. Combining neural networks and genetic algorithms for hydrological flow forecasting

    Science.gov (United States)

    Neruda, Roman; Srejber, Jan; Neruda, Martin; Pascenko, Petr

    2010-05-01

    We present a neural network approach to rainfall-runoff modeling for small size river basins based on several time series of hourly measured data. Different neural networks are considered for short time runoff predictions (from one to six hours lead time) based on runoff and rainfall data observed in previous time steps. Correlation analysis shows that runoff data, short time rainfall history, and aggregated API values are the most significant data for the prediction. Neural models of multilayer perceptron and radial basis function networks with different numbers of units are used and compared with more traditional linear time series predictors. Out of possible 48 hours of relevant history of all the input variables, the most important ones are selected by means of input filters created by a genetic algorithm. The genetic algorithm works with population of binary encoded vectors defining input selection patterns. Standard genetic operators of two-point crossover, random bit-flipping mutation, and tournament selection were used. The evaluation of objective function of each individual consists of several rounds of building and testing a particular neural network model. The whole procedure is rather computational exacting (taking hours to days on a desktop PC), thus a high-performance mainframe computer has been used for our experiments. Results based on two years worth data from the Ploucnice river in Northern Bohemia suggest that main problems connected with this approach to modeling are ovetraining that can lead to poor generalization, and relatively small number of extreme events which makes it difficult for a model to predict the amplitude of the event. Thus, experiments with both absolute and relative runoff predictions were carried out. In general it can be concluded that the neural models show about 5 per cent improvement in terms of efficiency coefficient over liner models. Multilayer perceptrons with one hidden layer trained by back propagation algorithm and

  17. Combining epidemiological and genetic networks signifies the importance of early treatment in HIV-1 transmission.

    Directory of Open Access Journals (Sweden)

    Narges Zarrabi

    Full Text Available Inferring disease transmission networks is important in epidemiology in order to understand and prevent the spread of infectious diseases. Reconstruction of the infection transmission networks requires insight into viral genome data as well as social interactions. For the HIV-1 epidemic, current research either uses genetic information of patients' virus to infer the past infection events or uses statistics of sexual interactions to model the network structure of viral spreading. Methods for a reliable reconstruction of HIV-1 transmission dynamics, taking into account both molecular and societal data are still lacking. The aim of this study is to combine information from both genetic and epidemiological scales to characterize and analyse a transmission network of the HIV-1 epidemic in central Italy.We introduce a novel filter-reduction method to build a network of HIV infected patients based on their social and treatment information. The network is then combined with a genetic network, to infer a hypothetical infection transmission network. We apply this method to a cohort study of HIV-1 infected patients in central Italy and find that patients who are highly connected in the network have longer untreated infection periods. We also find that the network structures for homosexual males and heterosexual populations are heterogeneous, consisting of a majority of 'peripheral nodes' that have only a few sexual interactions and a minority of 'hub nodes' that have many sexual interactions. Inferring HIV-1 transmission networks using this novel combined approach reveals remarkable correlations between high out-degree individuals and longer untreated infection periods. These findings signify the importance of early treatment and support the potential benefit of wide population screening, management of early diagnoses and anticipated antiretroviral treatment to prevent viral transmission and spread. The approach presented here for reconstructing HIV-1

  18. Meeting report from the fourth meeting of computational modeling in biology network (COMBINE).

    NARCIS (Netherlands)

    Waltemath, D.; Bergmann, F.T.; Chaouiya, C.; Czauderna, T.; Gleeson, P.; Goble, C.A.; Golebiewski, M.; Hucka, M.; Juty, N.; Krebs, O.; Le Novere, N.; Mi, H.; Moraru, I.I.; Myers, C.J.; Nickerson, D.; Olivier, B.G.; Rodriguez, N.; Schreiber, F.; Smith, L.; Zhang, F.; Bonnet, E.

    2014-01-01

    The Computational Modeling in Biology Network (COMBINE) is an initiative to coordinate the development of community standards and formats in computational systems biology and related fields. This report summarizes the topics and activities of the fourth edition of the annual COMBINE meeting, held in

  19. The Fun Culture in Seniors' Online Communities

    Science.gov (United States)

    Nimrod, Galit

    2011-01-01

    Purpose of the study: Previous research found that "fun on line" is the most dominant content in seniors' online communities. The present study aimed to further explore the "fun culture" in these communities and to discover its unique qualities. Design and Methods: The study applied an online ethnography (netnography) approach, utilizing a full…

  20. Network target for screening synergistic drug combinations with application to traditional Chinese medicine

    Directory of Open Access Journals (Sweden)

    Zhang Ningbo

    2011-06-01

    Full Text Available Abstract Background Multicomponent therapeutics offer bright prospects for the control of complex diseases in a synergistic manner. However, finding ways to screen the synergistic combinations from numerous pharmacological agents is still an ongoing challenge. Results In this work, we proposed for the first time a “network target”-based paradigm instead of the traditional "single target"-based paradigm for virtual screening and established an algorithm termed NIMS (Network target-based Identification of Multicomponent Synergy to prioritize synergistic agent combinations in a high throughput way. NIMS treats a disease-specific biological network as a therapeutic target and assumes that the relationship among agents can be transferred to network interactions among the molecular level entities (targets or responsive gene products of agents. Then, two parameters in NIMS, Topology Score and Agent Score, are created to evaluate the synergistic relationship between each given agent combinations. Taking the empirical multicomponent system traditional Chinese medicine (TCM as an illustrative case, we applied NIMS to prioritize synergistic agent pairs from 63 agents on a pathological process instanced by angiogenesis. The NIMS outputs can not only recover five known synergistic agent pairs, but also obtain experimental verification for synergistic candidates combined with, for example, a herbal ingredient Sinomenine, which outperforms the meet/min method. The robustness of NIMS was also showed regarding the background networks, agent genes and topological parameters, respectively. Finally, we characterized the potential mechanisms of multicomponent synergy from a network target perspective. Conclusions NIMS is a first-step computational approach towards identification of synergistic drug combinations at the molecular level. The network target-based approaches may adjust current virtual screen mode and provide a systematic paradigm for facilitating the

  1. Research on Large-Scale Road Network Partition and Route Search Method Combined with Traveler Preferences

    Directory of Open Access Journals (Sweden)

    De-Xin Yu

    2013-01-01

    Full Text Available Combined with improved Pallottino parallel algorithm, this paper proposes a large-scale route search method, which considers travelers’ route choice preferences. And urban road network is decomposed into multilayers effectively. Utilizing generalized travel time as road impedance function, the method builds a new multilayer and multitasking road network data storage structure with object-oriented class definition. Then, the proposed path search algorithm is verified by using the real road network of Guangzhou city as an example. By the sensitive experiments, we make a comparative analysis of the proposed path search method with the current advanced optimal path algorithms. The results demonstrate that the proposed method can increase the road network search efficiency by more than 16% under different search proportion requests, node numbers, and computing process numbers, respectively. Therefore, this method is a great breakthrough in the guidance field of urban road network.

  2. On the Combination of Multi-Layer Source Coding and Network Coding for Wireless Networks

    DEFF Research Database (Denmark)

    Krigslund, Jeppe; Fitzek, Frank; Pedersen, Morten Videbæk

    2013-01-01

    This paper introduces a mutually beneficial interplay between network coding and scalable video source coding in order to propose an energy-efficient video streaming approach accommodating multiple heterogeneous receivers, for which current solutions are either inefficient or insufficient. State...... support of multi-resolution video streaming....

  3. Learning has to be fun

    DEFF Research Database (Denmark)

    Langebæk, Rikke

    2007-01-01

    Video on demand and live role play in clinical skills and surgery training The teaching of Clinical skills at the Department of Small Animal Clinical Sciences, KU has traditionally been dominated by teaching of concepts and theories through lectures and books followed by practice on our ‘in house...... on the available patients at any given time (case presentation). By using new techniques such as video, e-learning and live role play it is possible to overcome many of these obstacles. As a bonus, teaching and learning become much more fun! As from September 2006 the lectures in Clinical Skills were 'digitalized......', i.e. the students was given access to narrated PowerPoint presentations through a virtual, on-line learning system. In the same manner narrated videos of clinical procedures were made available to the students. Thus the students were given the possibility to prepare by going through lectures...

  4. Modeling the future evolution of the virtual water trade network: A combination of network and gravity models

    Science.gov (United States)

    Sartori, Martina; Schiavo, Stefano; Fracasso, Andrea; Riccaboni, Massimo

    2017-12-01

    The paper investigates how the topological features of the virtual water (VW) network and the size of the associated VW flows are likely to change over time, under different socio-economic and climate scenarios. We combine two alternative models of network formation -a stochastic and a fitness model, used to describe the structure of VW flows- with a gravity model of trade to predict the intensity of each bilateral flow. This combined approach is superior to existing methodologies in its ability to replicate the observed features of VW trade. The insights from the models are used to forecast future VW flows in 2020 and 2050, under different climatic scenarios, and compare them with future water availability. Results suggest that the current trend of VW exports is not sustainable for all countries. Moreover, our approach highlights that some VW importers might be exposed to "imported water stress" as they rely heavily on imports from countries whose water use is unsustainable.

  5. A soft-hard combination-based cooperative spectrum sensing scheme for cognitive radio networks.

    Science.gov (United States)

    Do, Nhu Tri; An, Beongku

    2015-02-13

    In this paper we propose a soft-hard combination scheme, called SHC scheme, for cooperative spectrum sensing in cognitive radio networks. The SHC scheme deploys a cluster based network in which Likelihood Ratio Test (LRT)-based soft combination is applied at each cluster, and weighted decision fusion rule-based hard combination is utilized at the fusion center. The novelties of the SHC scheme are as follows: the structure of the SHC scheme reduces the complexity of cooperative detection which is an inherent limitation of soft combination schemes. By using the LRT, we can detect primary signals in a low signal-to-noise ratio regime (around an average of -15 dB). In addition, the computational complexity of the LRT is reduced since we derive the closed-form expression of the probability density function of LRT value. The SHC scheme also takes into account the different effects of large scale fading on different users in the wide area network. The simulation results show that the SHC scheme not only provides the better sensing performance compared to the conventional hard combination schemes, but also reduces sensing overhead in terms of reporting time compared to the conventional soft combination scheme using the LRT.

  6. Customising the therapeutic response of signalling networks to promote antitumor responses by drug combinations

    Directory of Open Access Journals (Sweden)

    Alexey eGoltsov

    2014-02-01

    Full Text Available Drug resistance, de novo and acquired, pervades cellular signalling networks from one signalling motif to another as a result of cancer progression and/or drug intervention. This resistance is one of the key determinants of efficacy in targeted anticancer drug therapy. Although poorly understood, drug resistance is already being addressed in combination therapy by selecting drug targets where sensitivity increases due to combination components or as a result of de novo or acquired mutations. Additionally, successive drug combinations have shown low resistance potency. To promote a rational, systematic development of combination therapies, it is necessary to establish the underlying mechanisms that drive the advantages of drug combinations and design methods to determine advanced targets for drug combination therapy. Based on a joint systems analysis of cellular signalling network (SN response and its sensitivity to drug action and oncogenic mutations, we describe an in silico method to analyse the targets of drug combinations. The method explores mechanisms of sensitizing the SN through combination of two drugs targeting vertical signalling pathways. We propose a paradigm of SN response customization by one drug to both maximize the effect of another drug in combination and promote a robust therapeutic response against oncogenic mutations. The method was applied to the customization of the response of the ErbB/PI3K/PTEN/AKT pathway by combination of drugs targeting HER2 receptors and proteins in the downstream pathway. The results of a computational experiment showed that the modification of the SN response from hyperbolic to smooth sigmoid response by manipulation of two drugs in combination leads to greater robustness in therapeutic response against oncogenic mutations determining cancer heterogeneity. The application of this method in drug combination co-development suggests a combined evaluation of inhibition effects along with the

  7. Material basis of Chinese herbal formulas explored by combining pharmacokinetics with network pharmacology.

    Directory of Open Access Journals (Sweden)

    Lixia Pei

    Full Text Available The clinical application of Traditional Chinese medicine (TCM, using several herbs in combination (called formulas, has a history of more than one thousand years. However, the bioactive compounds that account for their therapeutic effects remain unclear. We hypothesized that the material basis of a formula are those compounds with a high content in the decoction that are maintained at a certain level in the system circulation. Network pharmacology provides new methodological insights for complicated system studies. In this study, we propose combining pharmacokinetic (PK analysis with network pharmacology to explore the material basis of TCM formulas as exemplified by the Bushen Zhuanggu formula (BZ composed of Psoralea corylifolia L., Aconitum carmichaeli Debx., and Cnidium monnieri (L. Cuss. A sensitive and credible liquid chromatography tandem mass spectrometry (LC-MS/MS method was established for the simultaneous determination of 15 compounds present in the three herbs. The concentrations of these compounds in the BZ decoction and in rat plasma after oral BZ administration were determined. Up to 12 compounds were detected in the BZ decoction, but only 5 could be analyzed using PK parameters. Combined PK results, network pharmacology analysis revealed that 4 compounds might serve as the material basis for BZ. We concluded that a sensitive, reliable, and suitable LC-MS/MS method for both the composition and pharmacokinetic study of BZ has been established. The combination of PK with network pharmacology might be a potent method for exploring the material basis of TCM formulas.

  8. Material Basis of Chinese Herbal Formulas Explored by Combining Pharmacokinetics with Network Pharmacology

    Science.gov (United States)

    Liu, Sheng; Zheng, Jin; Chen, Xiuping

    2013-01-01

    The clinical application of Traditional Chinese medicine (TCM), using several herbs in combination (called formulas), has a history of more than one thousand years. However, the bioactive compounds that account for their therapeutic effects remain unclear. We hypothesized that the material basis of a formula are those compounds with a high content in the decoction that are maintained at a certain level in the system circulation. Network pharmacology provides new methodological insights for complicated system studies. In this study, we propose combining pharmacokinetic (PK) analysis with network pharmacology to explore the material basis of TCM formulas as exemplified by the Bushen Zhuanggu formula (BZ) composed of Psoralea corylifolia L., Aconitum carmichaeli Debx., and Cnidium monnieri (L.) Cuss. A sensitive and credible liquid chromatography tandem mass spectrometry (LC-MS/MS) method was established for the simultaneous determination of 15 compounds present in the three herbs. The concentrations of these compounds in the BZ decoction and in rat plasma after oral BZ administration were determined. Up to 12 compounds were detected in the BZ decoction, but only 5 could be analyzed using PK parameters. Combined PK results, network pharmacology analysis revealed that 4 compounds might serve as the material basis for BZ. We concluded that a sensitive, reliable, and suitable LC-MS/MS method for both the composition and pharmacokinetic study of BZ has been established. The combination of PK with network pharmacology might be a potent method for exploring the material basis of TCM formulas. PMID:23468985

  9. Combining Cloud Networks and Course Management Systems for Enhanced Analysis in Teaching Laboratories

    Science.gov (United States)

    Abrams, Neal M.

    2012-01-01

    A cloud network system is combined with standard computing applications and a course management system to provide a robust method for sharing data among students. This system provides a unique method to improve data analysis by easily increasing the amount of sampled data available for analysis. The data can be shared within one course as well as…

  10. Making Physics Fascinating and Fun

    Science.gov (United States)

    Ramdass, Vede; White, Amanda; Harris, Wendy; Mlack, Jerome; Ramos, Roberto

    2010-02-01

    Since it's inception the Society of Physics Students at Drexel University established a goal of promoting and encouraging the pursuit of Physics or science among middle school students. As a result, we have established an outreach project that partners with a local Philadelphia middle school in order for us to expose the students to Physics concepts and theories that surpasses their curriculum in a way that is exciting. The program with the school involves bi-weekly meetings with students interested in science from both 7th and 8th grade classes. At each meeting we first talk about a general topic in physics, such as thermodynamics or kinetics, and then perform demonstrations that are designed to encourage their participation and hence enthusiasm towards the material. The program lasts for school year and culminates in a final project which tries to incorporate all the things they have learned. The final project and by extension the entire outreach has proven great success in motivating these students to becoming more involved in science and most importantly, proving to them that science is fascinating and fun. )

  11. "Are We Gonna Do Anything Fun?"

    Science.gov (United States)

    Mitchell-Dwyer, Barbi

    1981-01-01

    Reports on ways of having fun with the classics of literature. Describes classroom uses of parody and satire to emphasize the themes and characterizations found in Shakespeare, J.D. Salinger, Ernest Hemingway, and other noted authors. (RL)

  12. Synergistic target combination prediction from curated signaling networks: Machine learning meets systems biology and pharmacology.

    Science.gov (United States)

    Chua, Huey Eng; Bhowmick, Sourav S; Tucker-Kellogg, Lisa

    2017-10-01

    Given a signaling network, the target combination prediction problem aims to predict efficacious and safe target combinations for combination therapy. State-of-the-art in silico methods use Monte Carlo simulated annealing (mcsa) to modify a candidate solution stochastically, and use the Metropolis criterion to accept or reject the proposed modifications. However, such stochastic modifications ignore the impact of the choice of targets and their activities on the combination's therapeutic effect and off-target effects, which directly affect the solution quality. In this paper, we present mascot, a method that addresses this limitation by leveraging two additional heuristic criteria to minimize off-target effects and achieve synergy for candidate modification. Specifically, off-target effects measure the unintended response of a signaling network to the target combination and is often associated with toxicity. Synergy occurs when a pair of targets exerts effects that are greater than the sum of their individual effects, and is generally a beneficial strategy for maximizing effect while minimizing toxicity. mascot leverages on a machine learning-based target prioritization method which prioritizes potential targets in a given disease-associated network to select more effective targets (better therapeutic effect and/or lower off-target effects); and on Loewe additivity theory from pharmacology which assesses the non-additive effects in a combination drug treatment to select synergistic target activities. Our experimental study on two disease-related signaling networks demonstrates the superiority of mascot in comparison to existing approaches. Copyright © 2017 Elsevier Inc. All rights reserved.

  13. FunCoup 4: new species, data, and visualization.

    Science.gov (United States)

    Ogris, Christoph; Guala, Dimitri; Sonnhammer, Erik L L

    2018-01-04

    This release of the FunCoup database (http://funcoup.sbc.su.se) is the fourth generation of one of the most comprehensive databases for genome-wide functional association networks. These functional associations are inferred via integrating various data types using a naive Bayesian algorithm and orthology based information transfer across different species. This approach provides high coverage of the included genomes as well as high quality of inferred interactions. In this update of FunCoup we introduce four new eukaryotic species: Schizosaccharomyces pombe, Plasmodium falciparum, Bos taurus, Oryza sativa and open the database to the prokaryotic domain by including networks for Escherichia coli and Bacillus subtilis. The latter allows us to also introduce a new class of functional association between genes - co-occurrence in the same operon. We also supplemented the existing classes of functional association: metabolic, signaling, complex and physical protein interaction with up-to-date information. In this release we switched to InParanoid v8 as the source of orthology and base for calculation of phylogenetic profiles. While populating all other evidence types with new data we introduce a new evidence type based on quantitative mass spectrometry data. Finally, the new JavaScript based network viewer provides the user an intuitive and responsive platform to further evaluate the results. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  14. Two critical brain networks for generation and combination of remote associations.

    Science.gov (United States)

    Bendetowicz, David; Urbanski, Marika; Garcin, Béatrice; Foulon, Chris; Levy, Richard; Bréchemier, Marie-Laure; Rosso, Charlotte; Thiebaut de Schotten, Michel; Volle, Emmanuelle

    2018-01-01

    Recent functional imaging findings in humans indicate that creativity relies on spontaneous and controlled processes, possibly supported by the default mode and the fronto-parietal control networks, respectively. Here, we examined the ability to generate and combine remote semantic associations, in relation to creative abilities, in patients with focal frontal lesions. Voxel-based lesion-deficit mapping, disconnection-deficit mapping and network-based lesion-deficit approaches revealed critical prefrontal nodes and connections for distinct mechanisms related to creative cognition. Damage to the right medial prefrontal region, or its potential disrupting effect on the default mode network, affected the ability to generate remote ideas, likely by altering the organization of semantic associations. Damage to the left rostrolateral prefrontal region and its connections, or its potential disrupting effect on the left fronto-parietal control network, spared the ability to generate remote ideas but impaired the ability to appropriately combine remote ideas. Hence, the current findings suggest that damage to specific nodes within the default mode and fronto-parietal control networks led to a critical loss of verbal creative abilities by altering distinct cognitive mechanisms. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  15. DREAM4: Combining genetic and dynamic information to identify biological networks and dynamical models.

    Directory of Open Access Journals (Sweden)

    Alex Greenfield

    2010-10-01

    Full Text Available Current technologies have lead to the availability of multiple genomic data types in sufficient quantity and quality to serve as a basis for automatic global network inference. Accordingly, there are currently a large variety of network inference methods that learn regulatory networks to varying degrees of detail. These methods have different strengths and weaknesses and thus can be complementary. However, combining different methods in a mutually reinforcing manner remains a challenge.We investigate how three scalable methods can be combined into a useful network inference pipeline. The first is a novel t-test-based method that relies on a comprehensive steady-state knock-out dataset to rank regulatory interactions. The remaining two are previously published mutual information and ordinary differential equation based methods (tlCLR and Inferelator 1.0, respectively that use both time-series and steady-state data to rank regulatory interactions; the latter has the added advantage of also inferring dynamic models of gene regulation which can be used to predict the system's response to new perturbations.Our t-test based method proved powerful at ranking regulatory interactions, tying for first out of methods in the DREAM4 100-gene in-silico network inference challenge. We demonstrate complementarity between this method and the two methods that take advantage of time-series data by combining the three into a pipeline whose ability to rank regulatory interactions is markedly improved compared to either method alone. Moreover, the pipeline is able to accurately predict the response of the system to new conditions (in this case new double knock-out genetic perturbations. Our evaluation of the performance of multiple methods for network inference suggests avenues for future methods development and provides simple considerations for genomic experimental design. Our code is publicly available at http://err.bio.nyu.edu/inferelator/.

  16. Combined principal component preprocessing and n-tuple neural networks for improved classification

    DEFF Research Database (Denmark)

    Høskuldsson, Agnar; Linneberg, Christian

    2000-01-01

    We present a combined principal component analysis/neural network scheme for classification. The data used to illustrate the method consist of spectral fluorescence recordings from seven different production facilities, and the task is to relate an unknown sample to one of these seven factories. ...... system non-linear capabilities, and all derived two-class models are combined to facilitate multiclass classification. Validation results show that the combined scheme is superior to the individual methods. Copyright (C) 2000 John Wiley & Sons, Ltd.......We present a combined principal component analysis/neural network scheme for classification. The data used to illustrate the method consist of spectral fluorescence recordings from seven different production facilities, and the task is to relate an unknown sample to one of these seven factories...... of two-class problems. For each possible pair of classes we further apply a transformation to the calculated principal components in order to increase the separation between the classes. Finally we apply the so-called n-tuple neural network to the transformed data in order to give the classification...

  17. Combining core drop policy and edge determinant threshold in TCP over OBS networks with retransmission

    Science.gov (United States)

    Peng, Shuping; Li, Zhengbin; He, Yongqi; Xu, Anshi

    2007-11-01

    We proposed a novel drop policy in the core nodes which is combined with the determinant strategy in the ingress edge nodes. The proposed drop policy is based on the field of Hop Number (HN) taken by the burst control packets, which is introduced to determine which burst should be dropped when the contention happened in the core nodes. In the drop policy, the long-hop traffic is given the high priority, and most of the retransmitted traffic is left to be short-hop traffic. Therefore, there is a trade-off between the short-hop traffic and the long-hop traffic. The determinant strategy in the edge nodes is an initialized threshold, Retransmission Number Threshold (RNT), which is introduced to determine whether to start a retransmission operation when NAK is received. The unnecessary retransmissions in the network are limited, and the burst loss rate is reduced. The mechanism also takes the upper layer, TCP layer, into account. When the network has already been in the state of real congestion, the retransmission will only deteriorate the network performance. In the case, the combined mechanism leaves the retransmission process to the TCP layer. It can improve the network performance cost-effectively.

  18. Enhanced Stochastic Methodology for Combined Architecture of E-Commerce and Security Networks

    OpenAIRE

    Song-Kyoo Kim

    2009-01-01

    This paper deals with network architecture which is a combination of electronic commerce and security systems in the typical Internet ecosystems. The e-commerce model that is typically known as online shopping can be considered as a multichannel queueing system. In the other hand, stochastic security system is designed for improving the reliability and availability of the e-commerce system. The security system in this paper deals with a complex system that consists of main unreliable servers,...

  19. Efficient synthesis of heat exchanger networks combining heuristic approaches with a genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Brandt, Christopher; Fieg, Georg [Hamburg University of Technology, Institute of Process and Plant Engineering, Hamburg (Germany); Luo, Xing [Helmut Schmidt University, Institute of Thermodynamics, Hamburg (Germany); University of Shanghai for Science and Technology, Institute of Thermal Engineering, Shanghai (China)

    2011-08-15

    In this work an innovative method for heat exchanger network (HEN) synthesis is introduced and examined. It combines a genetic algorithm (GA) with a heuristic based optimization procedure. The novel algorithm removes appearing heat load loops from the HEN structures when profitable, throughout the evolution. Two examples were examined with the new HEN synthesis method and for both better results were obtained. Thus, a positive effect of heuristic based optimization methods on the HEN synthesis with GA could be located. (orig.)

  20. Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast.

    Science.gov (United States)

    Wang, Zhuo; Danziger, Samuel A; Heavner, Benjamin D; Ma, Shuyi; Smith, Jennifer J; Li, Song; Herricks, Thurston; Simeonidis, Evangelos; Baliga, Nitin S; Aitchison, John D; Price, Nathan D

    2017-05-01

    Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM's enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.

  1. Analysis and evolution of air quality monitoring networks using combined statistical information indexes

    Directory of Open Access Journals (Sweden)

    Axel Osses

    2013-10-01

    Full Text Available In this work, we present combined statistical indexes for evaluating air quality monitoring networks based on concepts derived from the information theory and Kullback–Liebler divergence. More precisely, we introduce: (1 the standard measure of complementary mutual information or ‘specificity’ index; (2 a new measure of information gain or ‘representativity’ index; (3 the information gaps associated with the evolution of a network and (4 the normalised information distance used in clustering analysis. All these information concepts are illustrated by applying them to 14 yr of data collected by the air quality monitoring network in Santiago de Chile (33.5 S, 70.5 W, 500 m a.s.l.. We find that downtown stations, located in a relatively flat area of the Santiago basin, generally show high ‘representativity’ and low ‘specificity’, whereas the contrary is found for a station located in a canyon to the east of the basin, consistently with known emission and circulation patterns of Santiago. We also show interesting applications of information gain to the analysis of the evolution of a network, where the choice of background information is also discussed, and of mutual information distance to the classifications of stations. Our analyses show that information as those presented here should of course be used in a complementary way when addressing the analysis of an air quality network for planning and evaluation purposes.

  2. Optimal Operation of Network-Connected Combined Heat and Powers for Customer Profit Maximization

    Directory of Open Access Journals (Sweden)

    Da Xie

    2016-06-01

    Full Text Available Network-connected combined heat and powers (CHPs, owned by a community, can export surplus heat and electricity to corresponding heat and electric networks after community loads are satisfied. This paper proposes a new optimization model for network-connected CHP operation. Both CHPs’ overall efficiency and heat to electricity ratio (HTER are assumed to vary with loading levels. Based on different energy flow scenarios where heat and electricity are exported to the network from the community or imported, four profit models are established accordingly. They reflect the different relationships between CHP energy supply and community load demand across time. A discrete optimization model is then developed to maximize the profit for the community. The models are derived from the intervals determined by the daily operation modes of CHP and real-time buying and selling prices of heat, electricity and natural gas. By demonstrating the proposed models on a 1 MW network-connected CHP, results show that the community profits are maximized in energy markets. Thus, the proposed optimization approach can help customers to devise optimal CHP operating strategies for maximizing benefits.

  3. Combining graph and flux-based structures to decipher phenotypic essential metabolites within metabolic networks

    Science.gov (United States)

    Frioux, Clémence; Nicolas, Jacques; Baroukh, Caroline; Cortes, Maria-Paz; Got, Jeanne; Trottier, Camille; Eveillard, Damien

    2017-01-01

    Background The emergence of functions in biological systems is a long-standing issue that can now be addressed at the cell level with the emergence of high throughput technologies for genome sequencing and phenotyping. The reconstruction of complete metabolic networks for various organisms is a key outcome of the analysis of these data, giving access to a global view of cell functioning. The analysis of metabolic networks may be carried out by simply considering the architecture of the reaction network or by taking into account the stoichiometry of reactions. In both approaches, this analysis is generally centered on the outcome of the network and considers all metabolic compounds to be equivalent in this respect. As in the case of genes and reactions, about which the concept of essentiality has been developed, it seems, however, that some metabolites play crucial roles in system responses, due to the cell structure or the internal wiring of the metabolic network. Results We propose a classification of metabolic compounds according to their capacity to influence the activation of targeted functions (generally the growth phenotype) in a cell. We generalize the concept of essentiality to metabolites and introduce the concept of the phenotypic essential metabolite (PEM) which influences the growth phenotype according to sustainability, producibility or optimal-efficiency criteria. We have developed and made available a tool, Conquests, which implements a method combining graph-based and flux-based analysis, two approaches that are usually considered separately. The identification of PEMs is made effective by using a logical programming approach. Conclusion The exhaustive study of phenotypic essential metabolites in six genome-scale metabolic models suggests that the combination and the comparison of graph, stoichiometry and optimal flux-based criteria allows some features of the metabolic network functionality to be deciphered by focusing on a small number of

  4. Combining graph and flux-based structures to decipher phenotypic essential metabolites within metabolic networks.

    Science.gov (United States)

    Laniau, Julie; Frioux, Clémence; Nicolas, Jacques; Baroukh, Caroline; Cortes, Maria-Paz; Got, Jeanne; Trottier, Camille; Eveillard, Damien; Siegel, Anne

    2017-01-01

    The emergence of functions in biological systems is a long-standing issue that can now be addressed at the cell level with the emergence of high throughput technologies for genome sequencing and phenotyping. The reconstruction of complete metabolic networks for various organisms is a key outcome of the analysis of these data, giving access to a global view of cell functioning. The analysis of metabolic networks may be carried out by simply considering the architecture of the reaction network or by taking into account the stoichiometry of reactions. In both approaches, this analysis is generally centered on the outcome of the network and considers all metabolic compounds to be equivalent in this respect. As in the case of genes and reactions, about which the concept of essentiality has been developed, it seems, however, that some metabolites play crucial roles in system responses, due to the cell structure or the internal wiring of the metabolic network. We propose a classification of metabolic compounds according to their capacity to influence the activation of targeted functions (generally the growth phenotype) in a cell. We generalize the concept of essentiality to metabolites and introduce the concept of the phenotypic essential metabolite (PEM) which influences the growth phenotype according to sustainability, producibility or optimal-efficiency criteria. We have developed and made available a tool, Conquests, which implements a method combining graph-based and flux-based analysis, two approaches that are usually considered separately. The identification of PEMs is made effective by using a logical programming approach. The exhaustive study of phenotypic essential metabolites in six genome-scale metabolic models suggests that the combination and the comparison of graph, stoichiometry and optimal flux-based criteria allows some features of the metabolic network functionality to be deciphered by focusing on a small number of compounds. By considering the best

  5. Target inhibition networks: predicting selective combinations of druggable targets to block cancer survival pathways.

    Directory of Open Access Journals (Sweden)

    Jing Tang

    Full Text Available A recent trend in drug development is to identify drug combinations or multi-target agents that effectively modify multiple nodes of disease-associated networks. Such polypharmacological effects may reduce the risk of emerging drug resistance by means of attacking the disease networks through synergistic and synthetic lethal interactions. However, due to the exponentially increasing number of potential drug and target combinations, systematic approaches are needed for prioritizing the most potent multi-target alternatives on a global network level. We took a functional systems pharmacology approach toward the identification of selective target combinations for specific cancer cells by combining large-scale screening data on drug treatment efficacies and drug-target binding affinities. Our model-based prediction approach, named TIMMA, takes advantage of the polypharmacological effects of drugs and infers combinatorial drug efficacies through system-level target inhibition networks. Case studies in MCF-7 and MDA-MB-231 breast cancer and BxPC-3 pancreatic cancer cells demonstrated how the target inhibition modeling allows systematic exploration of functional interactions between drugs and their targets to maximally inhibit multiple survival pathways in a given cancer type. The TIMMA prediction results were experimentally validated by means of systematic siRNA-mediated silencing of the selected targets and their pairwise combinations, showing increased ability to identify not only such druggable kinase targets that are essential for cancer survival either individually or in combination, but also synergistic interactions indicative of non-additive drug efficacies. These system-level analyses were enabled by a novel model construction method utilizing maximization and minimization rules, as well as a model selection algorithm based on sequential forward floating search. Compared with an existing computational solution, TIMMA showed both enhanced

  6. Application of artificial neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer.

    Science.gov (United States)

    Duan, Xiaoran; Yang, Yongli; Tan, Shanjuan; Wang, Sihua; Feng, Xiaolei; Cui, Liuxin; Feng, Feifei; Yu, Songcheng; Wang, Wei; Wu, Yongjun

    2017-08-01

    The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length. Real-time quantitative methylation-specific PCR was used to detect the levels of three-gene promoter methylation, and real-time PCR method was applied to determine the relative telomere length. BP neural network and Fisher discrimination analysis were used to establish the discrimination diagnosis model. The levels of three-gene promoter methylation in patients with lung cancer were significantly higher than those of the normal controls. The values of Z(P) in two groups were 2.641 (0.008), 2.075 (0.038) and 3.044 (0.002), respectively. The relative telomere lengths of patients with lung cancer (0.93 ± 0.32) were significantly lower than those of the normal controls (1.16 ± 0.57), t = 4.072, P neural network were 0.670 (0.569-0.761) and 0.760 (0.664-0.840). The AUC of BP neural network was higher than that of Fisher discrimination analysis, and Z(P) was 0.76. Four biomarkers are associated with lung cancer. BP neural network model for the prediction of lung cancer is better than Fisher discrimination analysis, and it can provide an excellent and intelligent diagnosis tool for lung cancer.

  7. Use of uniform designs in combination with neural networks for viral infection process development.

    Science.gov (United States)

    Buenno, Laís Hara; Rocha, José Celso; Leme, Jaci; Caricati, Celso Pereira; Tonso, Aldo; Fernández Núñez, Eutimio Gustavo

    2015-01-01

    This work aimed to compare the predictive capacity of empirical models, based on the uniform design utilization combined to artificial neural networks with respect to classical factorial designs in bioprocess, using as example the rabies virus replication in BHK-21 cells. The viral infection process parameters under study were temperature (34°C, 37°C), multiplicity of infection (0.04, 0.07, 0.1), times of infection, and harvest (24, 48, 72 hours) and the monitored output parameter was viral production. A multilevel factorial experimental design was performed for the study of this system. Fractions of this experimental approach (18, 24, 30, 36 and 42 runs), defined according uniform designs, were used as alternative for modelling through artificial neural network and thereafter an output variable optimization was carried out by means of genetic algorithm methodology. Model prediction capacities for all uniform design approaches under study were better than that found for classical factorial design approach. It was demonstrated that uniform design in combination with artificial neural network could be an efficient experimental approach for modelling complex bioprocess like viral production. For the present study case, 67% of experimental resources were saved when compared to a classical factorial design approach. In the near future, this strategy could replace the established factorial designs used in the bioprocess development activities performed within biopharmaceutical organizations because of the improvements gained in the economics of experimentation that do not sacrifice the quality of decisions. © 2015 American Institute of Chemical Engineers.

  8. Exploring the Combination of Dempster-Shafer Theory and Neural Network for Predicting Trust and Distrust.

    Science.gov (United States)

    Wang, Xin; Wang, Ying; Sun, Hongbin

    2016-01-01

    In social media, trust and distrust among users are important factors in helping users make decisions, dissect information, and receive recommendations. However, the sparsity and imbalance of social relations bring great difficulties and challenges in predicting trust and distrust. Meanwhile, there are numerous inducing factors to determine trust and distrust relations. The relationship among inducing factors may be dependency, independence, and conflicting. Dempster-Shafer theory and neural network are effective and efficient strategies to deal with these difficulties and challenges. In this paper, we study trust and distrust prediction based on the combination of Dempster-Shafer theory and neural network. We firstly analyze the inducing factors about trust and distrust, namely, homophily, status theory, and emotion tendency. Then, we quantify inducing factors of trust and distrust, take these features as evidences, and construct evidence prototype as input nodes of multilayer neural network. Finally, we propose a framework of predicting trust and distrust which uses multilayer neural network to model the implementing process of Dempster-Shafer theory in different hidden layers, aiming to overcome the disadvantage of Dempster-Shafer theory without optimization method. Experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework.

  9. Exploring the Combination of Dempster-Shafer Theory and Neural Network for Predicting Trust and Distrust

    Directory of Open Access Journals (Sweden)

    Xin Wang

    2016-01-01

    Full Text Available In social media, trust and distrust among users are important factors in helping users make decisions, dissect information, and receive recommendations. However, the sparsity and imbalance of social relations bring great difficulties and challenges in predicting trust and distrust. Meanwhile, there are numerous inducing factors to determine trust and distrust relations. The relationship among inducing factors may be dependency, independence, and conflicting. Dempster-Shafer theory and neural network are effective and efficient strategies to deal with these difficulties and challenges. In this paper, we study trust and distrust prediction based on the combination of Dempster-Shafer theory and neural network. We firstly analyze the inducing factors about trust and distrust, namely, homophily, status theory, and emotion tendency. Then, we quantify inducing factors of trust and distrust, take these features as evidences, and construct evidence prototype as input nodes of multilayer neural network. Finally, we propose a framework of predicting trust and distrust which uses multilayer neural network to model the implementing process of Dempster-Shafer theory in different hidden layers, aiming to overcome the disadvantage of Dempster-Shafer theory without optimization method. Experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework.

  10. Combining Topological Hardware and Topological Software: Color-Code Quantum Computing with Topological Superconductor Networks

    Directory of Open Access Journals (Sweden)

    Daniel Litinski

    2017-09-01

    Full Text Available We present a scalable architecture for fault-tolerant topological quantum computation using networks of voltage-controlled Majorana Cooper pair boxes and topological color codes for error correction. Color codes have a set of transversal gates which coincides with the set of topologically protected gates in Majorana-based systems, namely, the Clifford gates. In this way, we establish color codes as providing a natural setting in which advantages offered by topological hardware can be combined with those arising from topological error-correcting software for full-fledged fault-tolerant quantum computing. We provide a complete description of our architecture, including the underlying physical ingredients. We start by showing that in topological superconductor networks, hexagonal cells can be employed to serve as physical qubits for universal quantum computation, and we present protocols for realizing topologically protected Clifford gates. These hexagonal-cell qubits allow for a direct implementation of open-boundary color codes with ancilla-free syndrome read-out and logical T gates via magic-state distillation. For concreteness, we describe how the necessary operations can be implemented using networks of Majorana Cooper pair boxes, and we give a feasibility estimate for error correction in this architecture. Our approach is motivated by nanowire-based networks of topological superconductors, but it could also be realized in alternative settings such as quantum-Hall–superconductor hybrids.

  11. COMBINING PCA ANALYSIS AND ARTIFICIAL NEURAL NETWORKS IN MODELLING ENTREPRENEURIAL INTENTIONS OF STUDENTS

    Directory of Open Access Journals (Sweden)

    Marijana Zekić-Sušac

    2013-02-01

    Full Text Available Despite increased interest in the entrepreneurial intentions and career choices of young adults, reliable prediction models are yet to be developed. Two nonparametric methods were used in this paper to model entrepreneurial intentions: principal component analysis (PCA and artificial neural networks (ANNs. PCA was used to perform feature extraction in the first stage of modelling, while artificial neural networks were used to classify students according to their entrepreneurial intentions in the second stage. Four modelling strategies were tested in order to find the most efficient model. Dataset was collected in an international survey on entrepreneurship self-efficacy and identity. Variables describe students’ demographics, education, attitudes, social and cultural norms, self-efficacy and other characteristics. The research reveals benefits from the combination of the PCA and ANNs in modeling entrepreneurial intentions, and provides some ideas for further research.

  12. Combining network analysis with Cognitive Work Analysis: insights into social organisational and cooperation analysis.

    Science.gov (United States)

    Houghton, Robert J; Baber, Chris; Stanton, Neville A; Jenkins, Daniel P; Revell, Kirsten

    2015-01-01

    Cognitive Work Analysis (CWA) allows complex, sociotechnical systems to be explored in terms of their potential configurations. However, CWA does not explicitly analyse the manner in which person-to-person communication is performed in these configurations. Consequently, the combination of CWA with Social Network Analysis provides a means by which CWA output can be analysed to consider communication structure. The approach is illustrated through a case study of a military planning team. The case study shows how actor-to-actor and actor-to-function mapping can be analysed, in terms of centrality, to produce metrics of system structure under different operating conditions. In this paper, a technique for building social network diagrams from CWA is demonstrated.The approach allows analysts to appreciate the potential impact of organisational structure on a command system.

  13. Research on image retrieval using deep convolutional neural network combining L1 regularization and PRelu activation function

    Science.gov (United States)

    QingJie, Wei; WenBin, Wang

    2017-06-01

    In this paper, the image retrieval using deep convolutional neural network combined with regularization and PRelu activation function is studied, and improves image retrieval accuracy. Deep convolutional neural network can not only simulate the process of human brain to receive and transmit information, but also contains a convolution operation, which is very suitable for processing images. Using deep convolutional neural network is better than direct extraction of image visual features for image retrieval. However, the structure of deep convolutional neural network is complex, and it is easy to over-fitting and reduces the accuracy of image retrieval. In this paper, we combine L1 regularization and PRelu activation function to construct a deep convolutional neural network to prevent over-fitting of the network and improve the accuracy of image retrieval

  14. Valuable Social Learning from Halloween Fun.

    Science.gov (United States)

    Hoge, John Douglas

    1988-01-01

    Presents five activities for grades 3-6 which show how to harness the fun of Halloween to achieve social studies goals such as values clarification, critical thinking, personal decision making, and inquiry. States that Halloween's rich history, contemporary customs, ancient rituals, and myths provide a high-interest background of common experience…

  15. Putting the Fun Back into Fluency Instruction

    Science.gov (United States)

    Cahill, Mary Ann; Gregory, Anne E.

    2011-01-01

    Based on recent research in fluency instruction, the authors present a scenario in which a teacher focuses her fluency instruction on authentic fluency tasks based in performance. Beginning with establishing a student-friendly definition of fluency and culminating with student engagement in fun fluency activities, this article explores the…

  16. Choice of implant combinations in total hip replacement: systematic review and network meta-analysis.

    Science.gov (United States)

    López-López, José A; Humphriss, Rachel L; Beswick, Andrew D; Thom, Howard H Z; Hunt, Linda P; Burston, Amanda; Fawsitt, Christopher G; Hollingworth, William; Higgins, Julian P T; Welton, Nicky J; Blom, Ashley W; Marques, Elsa M R

    2017-11-02

    Objective To compare the survival of different implant combinations for primary total hip replacement (THR). Design Systematic review and network meta-analysis. Data sources Medline, Embase, The Cochrane Library, ClinicalTrials.gov, WHO International Clinical Trials Registry Platform, and the EU Clinical Trials Register.Review methods Published randomised controlled trials comparing different implant combinations. Implant combinations were defined by bearing surface materials (metal-on-polyethylene, ceramic-on-polyethylene, ceramic-on-ceramic, or metal-on-metal), head size (large ≥36 mm or small meta-analysis for revision. There was no evidence that the risk of revision surgery was reduced by other implant combinations compared with the reference implant combination. Although estimates are imprecise, metal-on-metal, small head, cemented implants (hazard ratio 4.4, 95% credible interval 1.6 to 16.6) and resurfacing (12.1, 2.1 to 120.3) increase the risk of revision at 0-2 years after primary THR compared with the reference implant combination. Similar results were observed for the 2-10 years period. 31 studies (2888 patients) were included in the analysis of Harris hip score. No implant combination had a better score than the reference implant combination.Conclusions Newer implant combinations were not found to be better than the reference implant combination (metal-on-polyethylene (not highly cross linked), small head, cemented) in terms of risk of revision surgery or Harris hip score. Metal-on-metal, small head, cemented implants and resurfacing increased the risk of revision surgery compared with the reference implant combination. The results were consistent with observational evidence and were replicated in sensitivity analysis but were limited by poor reporting across studies.Systematic review registration PROSPERO CRD42015019435. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go

  17. A Network Inversion Filter combining GNSS and InSAR for tectonic slip modeling

    Science.gov (United States)

    Bekaert, D. P.; Segall, P.; Wright, T. J.; Hooper, A. J.

    2016-12-01

    Time-dependent slip modeling can be a powerful tool to improve our understanding of the interaction of earthquake cycle processes such as interseismic, coseismic, postseismic, and aseismic slip. Interferometric Synthetic Aperture Radar (InSAR) observations allow us to model slip at depth with a higher spatial resolution than when using GNSS alone. Typically the temporal resolution of InSAR has been limited. However, the recent generation of SAR satellites including Sentinel-1, COSMO-SkyMED, and RADARSAT-2 permits the use of InSAR for time-dependent slip modeling, at intervals of a few days when combined. The increasing amount of SAR data makes a simultaneous data inversion of all epochs challenging. Here, we expanded the original Network Inversion Filter (Segall and Matthews, 1997) to include InSAR observations of surface displacements in addition to GNSS. In the NIF framework, geodetic observations are limited to those of a given epoch, where a physical model describes the slip evolution over time. The combination of the Kalman forward filtering and backward smoothing allows all geodetic observations to constrain the complete observation period. Combining GNSS and InSAR allows us to model time-dependent slip at an unprecedented spatial resolution. We validate the approach with a simulation of the 2006 Guerrero slow slip event. In our study, we emphasize the importance of including the InSAR covariance information, and demonstrate that InSAR provides an additional constraint on the spatial extent of the slow slip. References: Segall, P., and M. Matthews (1997), Time dependent inversion of geodetic data, J. Geophys. Res., 102 (B10), 22,391 - 22,409, doi:10.1029/97JB01795. Bekaert, D., P. Segall, T.J. Wright, and A. Hooper (2016), A Network Inversion Filter combining GNSS and InSAR for tectonic slip modeling, JGR, doi:10.1002/2015JB012638 (open access).

  18. Combined Metabolomic and Correlation Networks Analyses Reveal Fumarase Insufficiency Altered Amino Acids Metabolism.

    Science.gov (United States)

    Hou, Entai; Li, Xian; Liu, Zerong; Zhang, Fuchang; Tian, Zhongmin

    2017-11-11

    Fumarase catalyzes the interconversion of fumarate and L-malate in the tricarboxylic acid cycle. Fumarase insufficiencies were associated with increased level of fumarate and decreased level of malate and exacerbated salt-induced hypertension. To gain insights into the metabolism profiles that induced by fumarase insufficiency and identify key regulatory metabolites, we applied a GC-MS based metabolomics platform coupled with a network approach to analyze fumarase insufficient HUVEC cells and negative controls. A total of 24 altered metabolites involved in 7 metabolic pathways were identified as significantly altered, and enriched for the biological module of amino acids metabolism. In addition, Pearson correlation network analysis revealed that fumaric acid, L-malic acid, L-aspartic acid, glycine and L-glutamic acid were hub metabolites according to Pagerank based on their three centrality indices. ALT and GDH activities increased significantly in fumarase deficiency HUVEC cells. These results confirmed that fumarase insufficiency altered amino acid metabolism. The combination of metabolomics and network methods would provide another perspective on expounding the molecular mechanism at metabolomics level. This article is protected by copyright. All rights reserved.

  19. SHORT-TERM SOLAR RADIATION FORECASTING BY USING AN ITERATIVE COMBINATION OF WAVELET ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Julio Cesar Royer

    2016-03-01

    Full Text Available The information provided by accurate forecasts of solar energy time series are considered essential for performing an appropriate prediction of the electrical power that will be available in an electric system, as pointed out in Zhou et al. (2011. However, since the underlying data are highly non-stationary, it follows that to produce their accurate predictions is a very difficult assignment. In order to accomplish it, this paper proposes an iterative Combination of Wavelet Artificial Neural Networks (CWANN which is aimed to produce short-term solar radiation time series forecasting. Basically, the CWANN method can be split into three stages: at first one, a decomposition of level p, defined in terms of a wavelet basis, of a given solar radiation time series is performed, generating r+1 Wavelet Components (WC; at second one, these r+1 WCs are individually modeled by the k different ANNs, where k>5, and the 5 best forecasts of each WC are combined by means of another ANN, producing the combined forecasts of WC; and, at third one, the combined forecasts WC are simply added, generating the forecasts of the underlying solar radiation data. An iterative algorithm is proposed for iteratively searching for the optimal values for the CWANN parameters, as we will see. In order to evaluate it, ten real solar radiation time series of Brazilian system were modeled here. In all statistical results, the CWANN method has achieved remarkable greater forecasting performances when compared with a traditional ANN (described in Section 2.1.

  20. Combined Ozone Retrieval From METOP Sensors Using META-Training Of Deep Neural Networks

    Science.gov (United States)

    Felder, Martin; Sehnke, Frank; Kaifel, Anton

    2013-12-01

    The newest installment of our well-proven Neural Net- work Ozone Retrieval System (NNORSY) combines the METOP sensors GOME-2 and IASI with cloud information from AVHRR. Through the use of advanced meta- learning techniques like automatic feature selection and automatic architecture search applied to a set of deep neural networks, having at least two or three hidden layers, we have been able to avoid many technical issues normally encountered during the construction of such a joint retrieval system. This has been made possible by harnessing the processing power of modern consumer graphics cards with high performance graphic processors (GPU), which decreases training times by about two orders of magnitude. The system was trained on data from 2009 and 2010, including target ozone profiles from ozone sondes, ACE- FTS and MLS-AURA. To make maximum use of tropospheric information in the spectra, the data were partitioned into several sets of different cloud fraction ranges with the GOME-2 FOV, on which specialized retrieval networks are being trained. For the final ozone retrieval processing the different specialized networks are combined. The resulting retrieval system is very stable and does not show any systematic dependence on solar zenith angle, scan angle or sensor degradation. We present several sensitivity studies with regard to cloud fraction and target sensor type, as well as the performance in several latitude bands and with respect to independent validation stations. A visual cross-comparison against high-resolution ozone profiles from the KNMI EUMETSAT Ozone SAF product has also been performed and shows some distinctive features which we will briefly discuss. Overall, we demonstrate that a complex retrieval system can now be constructed with a minimum of ma- chine learning knowledge, using automated algorithms for many design decisions previously requiring expert knowledge. Provided sufficient training data and computation power of GPUs is available, the

  1. Noise reduction in urban LRT networks by combining track based solutions.

    Science.gov (United States)

    Vogiatzis, Konstantinos; Vanhonacker, Patrick

    2016-10-15

    The overall objective of the Quiet-Track project is to provide step-changing track based noise mitigation and maintenance schemes for railway rolling noise in LRT (Light Rail Transit) networks. WP 4 in particular focuses on the combination of existing track based solutions to yield a global performance of at least 6dB(A). The validation was carried out using a track section in the network of Athens Metro Line 1 with an existing outside concrete slab track (RHEDA track) where high airborne rolling noise was observed. The procedure for the selection of mitigation measures is based on numerical simulations, combining WRNOISE and IMMI software tools for noise prediction with experimental determination of the required track and vehicle parameters (e.g., rail and wheel roughness). The availability of a detailed rolling noise calculation procedure allows for detailed designing of measures and of ranking individual measures. It achieves this by including the modelling of the wheel/rail source intensity and of the noise propagation with the ability to evaluate the effect of modifications at source level (e.g., grinding, rail dampers, wheel dampers, change in resiliency of wheels and/or rail fixation) and of modifications in the propagation path (absorption at the track base, noise barriers, screening). A relevant combination of existing solutions was selected in the function of the simulation results. Three distinct existing solutions were designed in detail aiming at a high rolling noise attenuation and not affecting the normal operation of the metro system: Action 1: implementation of sound absorbing precast elements (panel type) on the track bed, Action 2: implementation of an absorbing noise barrier with a height of 1.10-1.20m above rail level, and Action 3: installation of rail dampers. The selected solutions were implemented on site and the global performance was measured step by step for comparison with simulations. Copyright © 2015 Elsevier B.V. All rights reserved.

  2. Combining neural network models to predict spatial patterns of airborne pollutant accumulation in soils around an industrial point emission source.

    Science.gov (United States)

    Dimopoulos, Ioannis F; Tsiros, Ioannis X; Serelis, Konstantinos; Chronopoulou, Aikaterini

    2004-12-01

    Neural networks (NNs) have the ability to model a wide range of complex nonlinearities. A major disadvantage of NNs, however, is their instability, especially under conditions of sparse, noisy, and limited data sets. In this paper, different combining network methods are used to benefit from the existence of local minima and from the instabilities of NNs. A nonlinear k-fold cross-validation method is used to test the performance of the various networks and also to develop and select a set of networks that exhibits a low correlation of errors. The various NN models are applied to estimate the spatial patterns of atmospherically transported and deposited lead (Pb) in soils around an historical industrial air emission point source. It is shown that the resulting ensemble networks consistently give superior predictions compared with the individual networks because, for the ensemble networks, R2 values were found to be higher than 0.9 while, for the contributing individual networks, values for R2 ranged between 0.35 and 0.85. It is concluded that combining networks can be adopted as an important component in the application of artificial NN techniques in applied air quality studies.

  3. A signal combining technique based on channel shortening for cooperative sensor networks

    KAUST Repository

    Hussain, Syed Imtiaz

    2010-06-01

    The cooperative relaying process needs proper coordination among the communicating and the relaying nodes. This coordination and the required capabilities may not be available in some wireless systems, e.g. wireless sensor networks where the nodes are equipped with very basic communication hardware. In this paper, we consider a scenario where the source node transmits its signal to the destination through multiple relays in an uncoordinated fashion. The destination can capture the multiple copies of the transmitted signal through a Rake receiver. We analyze a situation where the number of Rake fingers N is less than that of the relaying nodes L. In this case, the receiver can combine N strongest signals out of L. The remaining signals will be lost and act as interference to the desired signal components. To tackle this problem, we develop a novel signal combining technique based on channel shortening. This technique proposes a processing block before the Rake reception which compresses the energy of L signal components over N branches while keeping the noise level at its minimum. The proposed scheme saves the system resources and makes the received signal compatible to the available hardware. Simulation results show that it outperforms the selection combining scheme. ©2010 IEEE.

  4. Feature Selection Combined with Neural Network Structure Optimization for HIV-1 Protease Cleavage Site Prediction

    Directory of Open Access Journals (Sweden)

    Hui Liu

    2015-01-01

    Full Text Available It is crucial to understand the specificity of HIV-1 protease for designing HIV-1 protease inhibitors. In this paper, a new feature selection method combined with neural network structure optimization is proposed to analyze the specificity of HIV-1 protease and find the important positions in an octapeptide that determined its cleavability. Two kinds of newly proposed features based on Amino Acid Index database plus traditional orthogonal encoding features are used in this paper, taking both physiochemical and sequence information into consideration. Results of feature selection prove that p2, p1, p1′, and p2′ are the most important positions. Two feature fusion methods are used in this paper: combination fusion and decision fusion aiming to get comprehensive feature representation and improve prediction performance. Decision fusion of subsets that getting after feature selection obtains excellent prediction performance, which proves feature selection combined with decision fusion is an effective and useful method for the task of HIV-1 protease cleavage site prediction. The results and analysis in this paper can provide useful instruction and help designing HIV-1 protease inhibitor in the future.

  5. Artificial neural network versus case-based approaches to lexical combination

    Science.gov (United States)

    Dunbar, George L.

    1994-03-01

    Lexical combination presents a number of intriguing problems for cognitive science. By studying the empirical phenomena of combination we can derive constraints on models of the representation of individual lexical items. One particular phenomenon that symbolic models have been unable to accommodate is `semantic interaction'. Medin & Shoben (1988) have shown that properties associated with nouns by subjects vary with the choice of adjective. For example, wooden spoons are not just made of a different material: the phrase is interpreted as denoting a `larger' object. However, the adjective wooden is not generally held to carry implications as to size. We report experimental results showing similar effects across a range of properties for a single adjective in combination with different nouns from a single semantic field. It is this more radical dependence of interpretative features on lexical partners that we term `semantic interaction'. The phenomenon described by Medin and Shoben cannot be accounted for by the Selective Modification model, the most complete model hitherto. We show that a case-based reasoning system could account for earlier data because of the particular examples chosen, but that such a model could not handle semantic interaction. A neural network system is presented that does handle semantic interaction.

  6. Feature Selection Combined with Neural Network Structure Optimization for HIV-1 Protease Cleavage Site Prediction.

    Science.gov (United States)

    Liu, Hui; Shi, Xiaomiao; Guo, Dongmei; Zhao, Zuowei; Yimin

    2015-01-01

    It is crucial to understand the specificity of HIV-1 protease for designing HIV-1 protease inhibitors. In this paper, a new feature selection method combined with neural network structure optimization is proposed to analyze the specificity of HIV-1 protease and find the important positions in an octapeptide that determined its cleavability. Two kinds of newly proposed features based on Amino Acid Index database plus traditional orthogonal encoding features are used in this paper, taking both physiochemical and sequence information into consideration. Results of feature selection prove that p2, p1, p1', and p2' are the most important positions. Two feature fusion methods are used in this paper: combination fusion and decision fusion aiming to get comprehensive feature representation and improve prediction performance. Decision fusion of subsets that getting after feature selection obtains excellent prediction performance, which proves feature selection combined with decision fusion is an effective and useful method for the task of HIV-1 protease cleavage site prediction. The results and analysis in this paper can provide useful instruction and help designing HIV-1 protease inhibitor in the future.

  7. Equal gain combining for cooperative spectrum sensing in cognitive radio networks

    KAUST Repository

    Hamza, Doha R.

    2014-08-01

    Sensing with equal gain combining (SEGC), a novel cooperative spectrum sensing technique for cognitive radio networks, is proposed. Cognitive radios simultaneously transmit their sensing results to the fusion center (FC) over multipath fading reporting channels. The cognitive radios estimate the phases of the reporting channels and use those estimates for coherent combining of the sensing results at the FC. A global decision is made at the FC by comparing the received signal with a threshold. We obtain the global detection probabilities and secondary throughput exactly through a moment generating function approach. We verify our solution via system simulation and demonstrate that the Chernoff bound and central limit theory approximation are not tight. The cases of hard sensing and soft sensing are considered and we provide examples in which hard sensing is advantageous to soft sensing. We contrast the performance of SEGC with maximum ratio combining of the sensors\\' results and provide examples where the former is superior. Furthermore, we evaluate the performance of SEGC against existing orthogonal reporting techniques such as time division multiple access (TDMA). SEGC performance always dominates that of TDMA in terms of secondary throughput. We also study the impact of phase and synchronization errors and demonstrate the robustness of the SEGC technique against such imperfections. © 2002-2012 IEEE.

  8. Astronomy Fun with Mobile Devices

    Science.gov (United States)

    Pilachowski, Catherine A.; Morris, Frank

    2016-01-01

    Those mobile devices your students bring to class can do more that tweet and text. Engage your students with these web-based astronomy learning tools that allow students to manipulate astronomical data to learn important concepts. The tools are HTML5, CSS3, Javascript-based applications that provide access to the content on iPad and Android tablets. With "Three Color" students can combine monochrome astronomical images taken through different color filters or in different wavelength regions into a single color image. "Star Clusters" allows students to compare images of clusters with a pre-defined template of colors and sizes to compare clusters of different ages. An adaptation of Travis Rector's "NovaSearch" allows students to examine images of the central regions of the Andromeda Galaxy to find novae and to measure the time over which the nova fades away. New additions to our suite of applications allow students to estimate the surface temperatures of exoplanets and the probability of life elsewhere in the Universe. Further information and access to these web-based tools are available at www.astro.indiana.edu/ala/.

  9. Combined D-optimal design and generalized regression neural network for modeling of plasma etching rate

    Directory of Open Access Journals (Sweden)

    You Hailong

    2014-01-01

    Full Text Available Plasma etching process plays a critical role in semiconductor manufacturing. Because physical and chemical mechanisms involved in plasma etching are extremely complicated, models supporting process control are difficult to construct. This paper uses a 35-run D-optimal design to efficiently collect data under well planned conditions for important controllable variables such as power, pressure, electrode gap and gas flows of Cl2 and He and the response, etching rate, for building an empirical underlying model. Since the relationship between the control and response variables could be highly nonlinear, a generalized regression neural network is used to select important model variables and their combination effects and to fit the model. Compared with the response surface methodology, the proposed method has better prediction performance in training and testing samples. A success application of the model to control the plasma etching process demonstrates the effectiveness of the methods.

  10. Novel Auto-Reclosing Blocking Method for Combined Overhead-Cable Lines in Power Networks

    Directory of Open Access Journals (Sweden)

    Ricardo Granizo Arrabé

    2016-11-01

    Full Text Available This paper presents a novel auto-reclosing blocking method for combined overhead-cable lines in power distribution networks that are solidly or impedance grounded, with distribution transformers in a delta connection in their high-voltage sides. The main contribution of this new technique is that it can detect whether a ground fault has been produced at the overhead line side or at the cable line side, thus improving the performance of the auto-reclosing functionality. This localization technique is based on the measurements and analysis of the argument differences between the load currents in the active conductors of the cable and the currents in the shields at the cable end where the transformers in delta connection are installed, including a wavelet analysis. This technique has been verified through computer simulations and experimental laboratory tests.

  11. The fun culture in seniors' online communities.

    Science.gov (United States)

    Nimrod, Galit

    2011-04-01

    Previous research found that "fun on line" is the most dominant content in seniors' online communities. The present study aimed to further explore the fun culture in these communities and to discover its unique qualities. The study applied an online ethnography (netnography) approach, utilizing a full year's data from 6 leading seniors' online communities. The final database included about 50,000 posts. The majority of posts were part of online social games, including cognitive, associative, and creative games. The main subjects in all contents were sex, gender differences, aging, grandparenting, politics, faith, and alcohol. Main participatory behaviors were selective timing, using expressive style, and personalization of the online character. Although most participants were "lurkers," the active participants nurtured community norms and relationships, as reflected in the written dialogues. In a reality of limited alternatives for digital games that meet older adults' needs and interests, seniors found an independent system to satisfy their need for play. Seniors' online communities provided a unique form of casual leisure, whose nature varied among different groups of participants. The fun culture seemed to offer participants many desired benefits, including meaningful play, liminality and communitas, opportunity to practice and demonstrate their abilities, and means for coping with aging. Therefore, it may have positive impact on seniors' well-being and successful aging.

  12. Combined Resource Allocation System for Device-to-Device Communication towards LTE Networks

    Directory of Open Access Journals (Sweden)

    Abbas Fakhar

    2016-01-01

    Full Text Available The LTE networks are being developed to grant mobile broadband services in the fourth generation (4G systems and allow operators to use spectrum more efficiently.D2D communication is a promising technique to provide wireless services and enhance spectrum exploitation in the LTE Heterogeneous Networks (HetNets.D2D communication in HetNets allows users to communicate with each other directly by reusing the resources when communicating via the base stations. But during the downlink period, both the D2D receiver and the Heterogeneous Users equipment’s (HUE experience interference caused by resource allocation. In this article, we identify and analyze the interference problem of HetNets caused by D2D transmitter during download. We propose a combined resource allocation and resource reuse method for LTE HetNets, where resource allocation to HUEs is employed on the basis of comparative fair algorithm and resource reuse to D2D users is employed on acquisitive empirical algorithm. This approach evaluates whether D2D mode is suitable or not by path loss evaluation, after that decreases the interference to HUE by selection of the minimum channel gain between HUE and D2D transmitter each time to mitigate interference. Our simulation results show that the efficiency and throughput of HetNets is improved by using the proposed method.

  13. Transcriptional regulatory networks in Arabidopsis thaliana during single and combined stresses

    Science.gov (United States)

    Barah, Pankaj; B N, Mahantesha Naika; Jayavelu, Naresh Doni; Sowdhamini, Ramanathan; Shameer, Khader; Bones, Atle M.

    2016-01-01

    Differentially evolved responses to various stress conditions in plants are controlled by complex regulatory circuits of transcriptional activators, and repressors, such as transcription factors (TFs). To understand the general and condition-specific activities of the TFs and their regulatory relationships with the target genes (TGs), we have used a homogeneous stress gene expression dataset generated on ten natural ecotypes of the model plant Arabidopsis thaliana, during five single and six combined stress conditions. Knowledge-based profiles of binding sites for 25 stress-responsive TF families (187 TFs) were generated and tested for their enrichment in the regulatory regions of the associated TGs. Condition-dependent regulatory sub-networks have shed light on the differential utilization of the underlying network topology, by stress-specific regulators and multifunctional regulators. The multifunctional regulators maintain the core stress response processes while the transient regulators confer the specificity to certain conditions. Clustering patterns of transcription factor binding sites (TFBS) have reflected the combinatorial nature of transcriptional regulation, and suggested the putative role of the homotypic clusters of TFBS towards maintaining transcriptional robustness against cis-regulatory mutations to facilitate the preservation of stress response processes. The Gene Ontology enrichment analysis of the TGs reflected sequential regulation of stress response mechanisms in plants. PMID:26681689

  14. Uncertainty assessment in geodetic network adjustment by combining GUM and Monte-Carlo-simulations

    Science.gov (United States)

    Niemeier, Wolfgang; Tengen, Dieter

    2017-06-01

    In this article first ideas are presented to extend the classical concept of geodetic network adjustment by introducing a new method for uncertainty assessment as two-step analysis. In the first step the raw data and possible influencing factors are analyzed using uncertainty modeling according to GUM (Guidelines to the Expression of Uncertainty in Measurements). This approach is well established in metrology, but rarely adapted within Geodesy. The second step consists of Monte-Carlo-Simulations (MC-simulations) for the complete processing chain from raw input data and pre-processing to adjustment computations and quality assessment. To perform these simulations, possible realizations of raw data and the influencing factors are generated, using probability distributions for all variables and the established concept of pseudo-random number generators. Final result is a point cloud which represents the uncertainty of the estimated coordinates; a confidence region can be assigned to these point clouds, as well. This concept may replace the common concept of variance propagation and the quality assessment of adjustment parameters by using their covariance matrix. It allows a new way for uncertainty assessment in accordance with the GUM concept for uncertainty modelling and propagation. As practical example the local tie network in "Metsähovi Fundamental Station", Finland is used, where classical geodetic observations are combined with GNSS data.

  15. Who runs public health? A mixed-methods study combining qualitative and network analyses.

    Science.gov (United States)

    Oliver, Kathryn; de Vocht, Frank; Money, Annemarie; Everett, Martin

    2013-09-01

    Persistent health inequalities encourage researchers to identify new ways of understanding the policy process. Informal relationships are implicated in finding evidence and making decisions for public health policy (PHP), but few studies use specialized methods to identify key actors in the policy process. We combined network and qualitative data to identify the most influential individuals in PHP in a UK conurbation and describe their strategies to influence policy. Network data were collected by asking for nominations of powerful and influential people in PHP (n = 152, response rate 80%), and 23 semi-structured interviews were analysed using a framework approach. The most influential PHP makers in this conurbation were mid-level managers in the National Health Service and local government, characterized by managerial skills: controlling policy processes through gate keeping key organizations, providing policy content and managing selected experts and executives to lead on policies. Public health professionals and academics are indirectly connected to policy via managers. The most powerful individuals in public health are managers, not usually considered targets for research. As we show, they are highly influential through all stages of the policy process. This study shows the importance of understanding the daily activities of influential policy individuals.

  16. Combinations of specilaized conceptual and neural network rainfall-runoff models: comparison of performance

    Science.gov (United States)

    Kayastha, Nagendra; Solomatine, Dimitri

    2013-04-01

    A single hydrological model (process-based or data driven) might not equally well describe the characteristic of a complex rainfall-runoff relationship. One possibility here is building several specialized (local) models which can be specifically oriented at a particular process in the same model structure and combining them using weighting scheme the result can be called a multi-model, or a committee model. In this approach first we build the individual specialized models which are mainly calibrated on various regimes corresponding to hydrological sub-processes for example, low flow and high flow, and combining their outputs using the ideas of a fuzzy membership with various parameterisations. This experiment explores the several committee models of specialized hydrological models [1, 2] which are employed for rainfall-runoff model prediction. Comparison of three committee models are demonstrated which constructed from specialized models: (1) processes-based conceptual HBV rainfall-runoff model (CRRM) (2) rainfall-runoff model based on artificial neural networks (ANN) and (3) combination of CRRM and ANN. The weights assigned to each specialized model's output are based on fuzzy membership functions which are different at every time step depending on the current value of flow. Comparison results indicated that committee model CRRM-ANN built from the high flow HBV model and low flow ANN model outperformed other models. Bagmati catchment in Nepal and Leaf catchment in USA are considered as case studies. [1] Fenicia, F., Solomatine, D. P., Savenije, H. H. G. and Matgen, P. Soft combination of local models in a multi-objective framework. Hydrol. Earth Syst. Sci., 11, 1797-1809, Special Issue "Data-driven approaches, optimization and model integration: hydrological applications", R. Abrahart, L. See, D. Solomatine, and E. Toth (eds.), 2007. [2] Kayastha N., J. Ye, Fenicia, F., Solomatine, D. P. Fuzzy committees of specialized rainfall-runoff models: further enhancements

  17. Reliability Evaluation of a Distribution Network with Microgrid Based on a Combined Power Generation System

    Directory of Open Access Journals (Sweden)

    Hao Bai

    2015-02-01

    Full Text Available Distributed generation (DG, battery storage (BS and electric vehicles (EVs in a microgrid constitute the combined power generation system (CPGS. A CPGS can be applied to achieve a reliable evaluation of a distribution network with microgrids. To model charging load and discharging capacity, respectively, the EVs in a CPGS can be divided into regular EVs and ruleless EVs, according to their driving behavior. Based on statistical data of gasoline-fueled vehicles and the probability distribution of charging start instant and charging time, a statistical model can be built to describe the charging load and discharging capacity of ruleless EVs. The charge and discharge curves of regular EVs can also be drawn on the basis of a daily dispatch table. The CPGS takes the charge and discharge curves of EVs, daily load and DG power generation into consideration to calculate its power supply time during islanding. Combined with fault duration, the power supply time during islanding will be used to analyze and determine the interruption times and interruption duration of loads in islands. Then the Sequential Monte Carlo method is applied to complete the reliability evaluation of the distribution system. The RBTS Bus 4 test system is utilized to illustrate the proposed technique. The effects on the system reliability of BS capacity and V2G technology, driving behavior, recharging mode and penetration of EVs are all investigated.

  18. Combined application of mixture experimental design and artificial neural networks in the solid dispersion development.

    Science.gov (United States)

    Medarević, Djordje P; Kleinebudde, Peter; Djuriš, Jelena; Djurić, Zorica; Ibrić, Svetlana

    2016-01-01

    This study for the first time demonstrates combined application of mixture experimental design and artificial neural networks (ANNs) in the solid dispersions (SDs) development. Ternary carbamazepine-Soluplus®-poloxamer 188 SDs were prepared by solvent casting method to improve carbamazepine dissolution rate. The influence of the composition of prepared SDs on carbamazepine dissolution rate was evaluated using d-optimal mixture experimental design and multilayer perceptron ANNs. Physicochemical characterization proved the presence of the most stable carbamazepine polymorph III within the SD matrix. Ternary carbamazepine-Soluplus®-poloxamer 188 SDs significantly improved carbamazepine dissolution rate compared to pure drug. Models developed by ANNs and mixture experimental design well described the relationship between proportions of SD components and percentage of carbamazepine released after 10 (Q10) and 20 (Q20) min, wherein ANN model exhibit better predictability on test data set. Proportions of carbamazepine and poloxamer 188 exhibited the highest influence on carbamazepine release rate. The highest carbamazepine release rate was observed for SDs with the lowest proportions of carbamazepine and the highest proportions of poloxamer 188. ANNs and mixture experimental design can be used as powerful data modeling tools in the systematic development of SDs. Taking into account advantages and disadvantages of both techniques, their combined application should be encouraged.

  19. Combining Bayesian Networks and Agent Based Modeling to develop a decision-support model in Vietnam

    Science.gov (United States)

    Nong, Bao Anh; Ertsen, Maurits; Schoups, Gerrit

    2016-04-01

    Complexity and uncertainty in natural resources management have been focus themes in recent years. Within these debates, with the aim to define an approach feasible for water management practice, we are developing an integrated conceptual modeling framework for simulating decision-making processes of citizens, in our case in the Day river area, Vietnam. The model combines Bayesian Networks (BNs) and Agent-Based Modeling (ABM). BNs are able to combine both qualitative data from consultants / experts / stakeholders, and quantitative data from observations on different phenomena or outcomes from other models. Further strengths of BNs are that the relationship between variables in the system is presented in a graphical interface, and that components of uncertainty are explicitly related to their probabilistic dependencies. A disadvantage is that BNs cannot easily identify the feedback of agents in the system once changes appear. Hence, ABM was adopted to represent the reaction among stakeholders under changes. The modeling framework is developed as an attempt to gain better understanding about citizen's behavior and factors influencing their decisions in order to reduce uncertainty in the implementation of water management policy.

  20. INGA: protein function prediction combining interaction networks, domain assignments and sequence similarity.

    Science.gov (United States)

    Piovesan, Damiano; Giollo, Manuel; Leonardi, Emanuela; Ferrari, Carlo; Tosatto, Silvio C E

    2015-07-01

    Identifying protein functions can be useful for numerous applications in biology. The prediction of gene ontology (GO) functional terms from sequence remains however a challenging task, as shown by the recent CAFA experiments. Here we present INGA, a web server developed to predict protein function from a combination of three orthogonal approaches. Sequence similarity and domain architecture searches are combined with protein-protein interaction network data to derive consensus predictions for GO terms using functional enrichment. The INGA server can be queried both programmatically through RESTful services and through a web interface designed for usability. The latter provides output supporting the GO term predictions with the annotating sequences. INGA is validated on the CAFA-1 data set and was recently shown to perform consistently well in the CAFA-2 blind test. The INGA web server is available from URL: http://protein.bio.unipd.it/inga. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

  1. Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques.

    Science.gov (United States)

    Illias, Hazlee Azil; Chai, Xin Rui; Abu Bakar, Ab Halim; Mokhlis, Hazlie

    2015-01-01

    It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.

  2. Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques.

    Directory of Open Access Journals (Sweden)

    Hazlee Azil Illias

    Full Text Available It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN and particle swarm optimisation (PSO techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.

  3. Combination Adaptive Traffic Algorithm and Coordinated Sleeping in Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    M. Udin Harun Al Rasyid

    2014-12-01

    Full Text Available Wireless sensor network (WSN uses a battery as its primary power source, so that WSN will be limited to battery power for long operations. The WSN should be able to save the energy consumption in order to operate in a long time.WSN has the potential to be the future of wireless communications solutions. WSN are small but has a variety of functions that can help human life. WSN has the wide variety of sensors and can communicate quickly making it easier for people to obtain information accurately and quickly. In this study, we combine adaptive traffic algorithms and coordinated sleeping as power‐efficient WSN solution. We compared the performance of our proposed ideas combination adaptive traffic and coordinated sleeping algorithm with non‐adaptive scheme. From the simulation results, our proposed idea has good‐quality data transmission and more efficient in energy consumption, but it has higher delay than that of non‐adaptive scheme. Keywords:WSN,adaptive traffic,coordinated sleeping,beacon order,superframe order.

  4. Adaptive predictions of the euro/złoty currency exchange rate using state space wavelet networks and forecast combinations

    Directory of Open Access Journals (Sweden)

    Brdyś Mietek A.

    2016-03-01

    Full Text Available The paper considers the forecasting of the euro/Polish złoty (EUR/PLN spot exchange rate by applying state space wavelet network and econometric forecast combination models. Both prediction methods are applied to produce one-trading-day-ahead forecasts of the EUR/PLN exchange rate. The paper presents the general state space wavelet network and forecast combination models as well as their underlying principles. The state space wavelet network model is, in contrast to econometric forecast combinations, a non-parametric prediction technique which does not make any distributional assumptions regarding the underlying input variables. Both methods can be used as forecasting tools in portfolio investment management, asset valuation, IT security and integrated business risk intelligence in volatile market conditions.

  5. Network bursting dynamics in excitatory cortical neuron cultures results from the combination of different adaptive mechanisms.

    Directory of Open Access Journals (Sweden)

    Timothée Masquelier

    Full Text Available In the brain, synchronization among cells of an assembly is a common phenomenon, and thought to be functionally relevant. Here we used an in vitro experimental model of cell assemblies, cortical cultures, combined with numerical simulations of a spiking neural network (SNN to investigate how and why spontaneous synchronization occurs. In order to deal with excitation only, we pharmacologically blocked GABAAergic transmission using bicuculline. Synchronous events in cortical cultures tend to involve almost every cell and to display relatively constant durations. We have thus named these "network spikes" (NS. The inter-NS-intervals (INSIs proved to be a more interesting phenomenon. In most cortical cultures NSs typically come in series or bursts ("bursts of NSs", BNS, with short (~1 s INSIs and separated by long silent intervals (tens of s, which leads to bimodal INSI distributions. This suggests that a facilitating mechanism is at work, presumably short-term synaptic facilitation, as well as two fatigue mechanisms: one with a short timescale, presumably short-term synaptic depression, and another one with a longer timescale, presumably cellular adaptation. We thus incorporated these three mechanisms into the SNN, which, indeed, produced realistic BNSs. Next, we systematically varied the recurrent excitation for various adaptation timescales. Strong excitability led to frequent, quasi-periodic BNSs (CV~0, and weak excitability led to rare BNSs, approaching a Poisson process (CV~1. Experimental cultures appear to operate within an intermediate weakly-synchronized regime (CV~0.5, with an adaptation timescale in the 2-8 s range, and well described by a Poisson-with-refractory-period model. Taken together, our results demonstrate that the INSI statistics are indeed informative: they allowed us to infer the mechanisms at work, and many parameters that we cannot access experimentally.

  6. Facial Sketch Synthesis Using 2D Direct Combined Model-Based Face-Specific Markov Network.

    Science.gov (United States)

    Tu, Ching-Ting; Chan, Yu-Hsien; Chen, Yi-Chung

    2016-08-01

    A facial sketch synthesis system is proposed, featuring a 2D direct combined model (2DDCM)-based face-specific Markov network. In contrast to the existing facial sketch synthesis systems, the proposed scheme aims to synthesize sketches, which reproduce the unique drawing style of a particular artist, where this drawing style is learned from a data set consisting of a large number of image/sketch pairwise training samples. The synthesis system comprises three modules, namely, a global module, a local module, and an enhancement module. The global module applies a 2DDCM approach to synthesize the global facial geometry and texture of the input image. The detailed texture is then added to the synthesized sketch in a local patch-based manner using a parametric 2DDCM model and a non-parametric Markov random field (MRF) network. Notably, the MRF approach gives the synthesized results an appearance more consistent with the drawing style of the training samples, while the 2DDCM approach enables the synthesis of outcomes with a more derivative style. As a result, the similarity between the synthesized sketches and the input images is greatly improved. Finally, a post-processing operation is performed to enhance the shadowed regions of the synthesized image by adding strong lines or curves to emphasize the lighting conditions. The experimental results confirm that the synthesized facial images are in good qualitative and quantitative agreement with the input images as well as the ground-truth sketches provided by the same artist. The representing power of the proposed framework is demonstrated by synthesizing facial sketches from input images with a wide variety of facial poses, lighting conditions, and races even when such images are not included in the training data set. Moreover, the practical applicability of the proposed framework is demonstrated by means of automatic facial recognition tests.

  7. ANALYSIS RESOURCE AWARE FRAMEWORK BY COMBINING SUNSPOT AND IMOTE2 PLATFORM WIRELESS SENSOR NETWORKS USING DISTANCE VECTOR ALGORITHM

    Directory of Open Access Journals (Sweden)

    Muhammad Ilyas Syarif

    2012-07-01

    Full Text Available Efficiency energy and stream data mining on Wireless Sensor Networks (WSNs are a very interesting issue to be discussed. Routing protocols technology and resource-aware can be done to improve energy efficiency. In this paper we try to merge routing protocol technology using routing Distance Vector and Resource-Aware (RA framework on heterogeneity wireless sensor networks by combining sun-SPOT and Imote2 platform wireless sensor networks. RA perform resource monitoring process of the battery, memory and CPU load more optimally and efficiently. The process uses Light-Weight Clustering (LWC and Light Weight Frequent Item (LWF. The results obtained that by adapting Resource-Aware in wireless sensor networks, the lifetime of wireless sensor improve up to ± 16.62%. Efisiensi energi dan stream data mining pada Wireless Sensor Networks (WSN adalah masalah yang sangat menarik untuk dibahas. Teknologi Routing Protocol dan Resource-Aware dapat dilakukan untuk meningkatkan efisiensi energi. Dalam penelitian ini peneliti mencoba untuk menggabungkan teknologi Routing Protocol menggunakan routing Distance Vector dan Resource-Aware (RA framework pada Wireless Sensor Networks heterogen dengan menggabungkan sun-SPOT dan platform Imote2 Wireless Sensor Networks. RA melakukan proses pemantauan sumber daya dari memori, baterai, dan beban CPU lebih optimal dan efisien. Proses ini menggunakan Light-Weight Clustering (LWC dan Light Weight Frequent Item (LWF. Hasil yang diperoleh bahwa dengan mengadaptasi Resource-Aware dalam Wireless Sensor Networks, masa pakai wireless sensor meningkatkan sampai ± 16,62%.

  8. Fun and software exploring pleasure, paradox and pain in computing

    CERN Document Server

    Goriunova, Olga

    2014-01-01

    Fun and Software offers the untold story of fun as constitutive of the culture and aesthetics of computing. Fun in computing is a mode of thinking, making and experiencing. It invokes and convolutes the question of rationalism and logical reason, addresses the sensibilities and experience of computation and attests to its creative drives. By exploring topics as diverse as the pleasure and pain of the programmer, geek wit, affects of play and coding as a bodily pursuit of the unique in recursive structures, Fun and Software helps construct a different point of entry to the understanding of soft

  9. Combining data and meta-analysis to build Bayesian networks for clinical decision support.

    Science.gov (United States)

    Yet, Barbaros; Perkins, Zane B; Rasmussen, Todd E; Tai, Nigel R M; Marsh, D William R

    2014-12-01

    Complex clinical decisions require the decision maker to evaluate multiple factors that may interact with each other. Many clinical studies, however, report 'univariate' relations between a single factor and outcome. Such univariate statistics are often insufficient to provide useful support for complex clinical decisions even when they are pooled using meta-analysis. More useful decision support could be provided by evidence-based models that take the interaction between factors into account. In this paper, we propose a method of integrating the univariate results of a meta-analysis with a clinical dataset and expert knowledge to construct multivariate Bayesian network (BN) models. The technique reduces the size of the dataset needed to learn the parameters of a model of a given complexity. Supplementing the data with the meta-analysis results avoids the need to either simplify the model - ignoring some complexities of the problem - or to gather more data. The method is illustrated by a clinical case study into the prediction of the viability of severely injured lower extremities. The case study illustrates the advantages of integrating combined evidence into BN development: the BN developed using our method outperformed four different data-driven structure learning methods, and a well-known scoring model (MESS) in this domain. Copyright © 2014 Elsevier Inc. All rights reserved.

  10. Short-Term Wind Speed Forecasting Using Decomposition-Based Neural Networks Combining Abnormal Detection Method

    Directory of Open Access Journals (Sweden)

    Xuejun Chen

    2014-01-01

    Full Text Available As one of the most promising renewable resources in electricity generation, wind energy is acknowledged for its significant environmental contributions and economic competitiveness. Because wind fluctuates with strong variation, it is quite difficult to describe the characteristics of wind or to estimate the power output that will be injected into the grid. In particular, short-term wind speed forecasting, an essential support for the regulatory actions and short-term load dispatching planning during the operation of wind farms, is currently regarded as one of the most difficult problems to be solved. This paper contributes to short-term wind speed forecasting by developing two three-stage hybrid approaches; both are combinations of the five-three-Hanning (53H weighted average smoothing method, ensemble empirical mode decomposition (EEMD algorithm, and nonlinear autoregressive (NAR neural networks. The chosen datasets are ten-minute wind speed observations, including twelve samples, and our simulation indicates that the proposed methods perform much better than the traditional ones when addressing short-term wind speed forecasting problems.

  11. An OFDM Receiver with Frequency Domain Diversity Combined Impulsive Noise Canceller for Underwater Network.

    Science.gov (United States)

    Saotome, Rie; Hai, Tran Minh; Matsuda, Yasuto; Suzuki, Taisaku; Wada, Tomohisa

    2015-01-01

    In order to explore marine natural resources using remote robotic sensor or to enable rapid information exchange between ROV (remotely operated vehicles), AUV (autonomous underwater vehicle), divers, and ships, ultrasonic underwater communication systems are used. However, if the communication system is applied to rich living creature marine environment such as shallow sea, it suffers from generated Impulsive Noise so-called Shrimp Noise, which is randomly generated in time domain and seriously degrades communication performance in underwater acoustic network. With the purpose of supporting high performance underwater communication, a robust digital communication method for Impulsive Noise environments is necessary. In this paper, we propose OFDM ultrasonic communication system with diversity receiver. The main feature of the receiver is a newly proposed Frequency Domain Diversity Combined Impulsive Noise Canceller. The OFDM receiver utilizes 20-28 KHz ultrasonic channel and subcarrier spacing of 46.875 Hz (MODE3) and 93.750 Hz (MODE2) OFDM modulations. In addition, the paper shows Impulsive Noise distribution data measured at a fishing port in Okinawa and at a barge in Shizuoka prefectures and then proposed diversity OFDM transceivers architecture and experimental results are described. By the proposed Impulsive Noise Canceller, frame bit error rate has been decreased by 20-30%.

  12. An OFDM Receiver with Frequency Domain Diversity Combined Impulsive Noise Canceller for Underwater Network

    Directory of Open Access Journals (Sweden)

    Rie Saotome

    2015-01-01

    Full Text Available In order to explore marine natural resources using remote robotic sensor or to enable rapid information exchange between ROV (remotely operated vehicles, AUV (autonomous underwater vehicle, divers, and ships, ultrasonic underwater communication systems are used. However, if the communication system is applied to rich living creature marine environment such as shallow sea, it suffers from generated Impulsive Noise so-called Shrimp Noise, which is randomly generated in time domain and seriously degrades communication performance in underwater acoustic network. With the purpose of supporting high performance underwater communication, a robust digital communication method for Impulsive Noise environments is necessary. In this paper, we propose OFDM ultrasonic communication system with diversity receiver. The main feature of the receiver is a newly proposed Frequency Domain Diversity Combined Impulsive Noise Canceller. The OFDM receiver utilizes 20–28 KHz ultrasonic channel and subcarrier spacing of 46.875 Hz (MODE3 and 93.750 Hz (MODE2 OFDM modulations. In addition, the paper shows Impulsive Noise distribution data measured at a fishing port in Okinawa and at a barge in Shizuoka prefectures and then proposed diversity OFDM transceivers architecture and experimental results are described. By the proposed Impulsive Noise Canceller, frame bit error rate has been decreased by 20–30%.

  13. Disease named entity recognition by combining conditional random fields and bidirectional recurrent neural networks

    Science.gov (United States)

    Wei, Qikang; Chen, Tao; Xu, Ruifeng; He, Yulan; Gui, Lin

    2016-01-01

    The recognition of disease and chemical named entities in scientific articles is a very important subtask in information extraction in the biomedical domain. Due to the diversity and complexity of disease names, the recognition of named entities of diseases is rather tougher than those of chemical names. Although there are some remarkable chemical named entity recognition systems available online such as ChemSpot and tmChem, the publicly available recognition systems of disease named entities are rare. This article presents a system for disease named entity recognition (DNER) and normalization. First, two separate DNER models are developed. One is based on conditional random fields model with a rule-based post-processing module. The other one is based on the bidirectional recurrent neural networks. Then the named entities recognized by each of the DNER model are fed into a support vector machine classifier for combining results. Finally, each recognized disease named entity is normalized to a medical subject heading disease name by using a vector space model based method. Experimental results show that using 1000 PubMed abstracts for training, our proposed system achieves an F1-measure of 0.8428 at the mention level and 0.7804 at the concept level, respectively, on the testing data of the chemical-disease relation task in BioCreative V. Database URL: http://219.223.252.210:8080/SS/cdr.html PMID:27777244

  14. Voice Quality Estimation in Combined Radio-VoIP Networks for Dispatching Systems

    Directory of Open Access Journals (Sweden)

    Jiri Vodrazka

    2016-01-01

    Full Text Available The voice quality modelling assessment and planning field is deeply and widely theoretically and practically mastered for common voice communication systems, especially for the public fixed and mobile telephone networks including Next Generation Networks (NGN - internet protocol based networks. This article seeks to contribute voice quality modelling assessment and planning for dispatching communication systems based on Internet Protocol (IP and private radio networks. The network plan, correction in E-model calculation and default values for the model are presented and discussed.

  15. KeyPathwayMiner - De-novo network enrichment by combining multiple OMICS data and biological networks

    DEFF Research Database (Denmark)

    Baumbach, Jan; Alcaraz, Nicolas; Pauling, Josch K.

    We tackle the problem of de-novo pathway extraction. Given a biological network and a set of case-control studies, KeyPathwayMiner efficiently extracts and visualizes all maximal connected sub-networks that contain mainly genes that are dysregulated, e.g., differentially expressed, in most cases...... problems and designed a set of algorithms to tackle the combinatorial explosion of the search space. During the presentation we will demonstrate how to: Import and process the data, set the parameters for the two models, compute and visualize the key pathways, judge and statistically evaluate the results...

  16. Combined neural network/Phillips-Tikhonov approach to aerosol retrievals over land from the NASA Research Scanning Polarimeter

    Science.gov (United States)

    Di Noia, Antonio; Hasekamp, Otto P.; Wu, Lianghai; van Diedenhoven, Bastiaan; Cairns, Brian; Yorks, John E.

    2017-11-01

    In this paper, an algorithm for the retrieval of aerosol and land surface properties from airborne spectropolarimetric measurements - combining neural networks and an iterative scheme based on Phillips-Tikhonov regularization - is described. The algorithm - which is an extension of a scheme previously designed for ground-based retrievals - is applied to measurements from the Research Scanning Polarimeter (RSP) on board the NASA ER-2 aircraft. A neural network, trained on a large data set of synthetic measurements, is applied to perform aerosol retrievals from real RSP data, and the neural network retrievals are subsequently used as a first guess for the Phillips-Tikhonov retrieval. The resulting algorithm appears capable of accurately retrieving aerosol optical thickness, fine-mode effective radius and aerosol layer height from RSP data. Among the advantages of using a neural network as initial guess for an iterative algorithm are a decrease in processing time and an increase in the number of converging retrievals.

  17. Combining MLP and Using Decision Tree in Order to Detect the Intrusion into Computer Networks

    OpenAIRE

    Saba Sedigh Rad; Alireza Zebarjad

    2013-01-01

    The security of computer networks has an important role in computer systems. The increasing use of computer networks results in penetration and destruction of systems by system operations. So, in order to keep the systems away from these hazards, it is essential to use the intrusion detection system (IDS). This intrusion detection is done in order to detect the illicit use and misuse and to avoid damages to the systems and computer networks by both the external and internal intruders. Intrusi...

  18. Altered temporal features of intrinsic connectivity networks in boys with combined type of attention deficit hyperactivity disorder

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Xun-Heng, E-mail: xhwang@hdu.edu.cn [College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou 310018 (China); School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096 (China); Li, Lihua [College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou 310018 (China)

    2015-05-15

    Highlights: • Temporal patterns within ICNs provide new way to investigate ADHD brains. • ADHD exhibits enhanced temporal activities within and between ICNs. • Network-wise ALFF influences functional connectivity between ICNs. • Univariate patterns within ICNs are correlated to behavior scores. - Abstract: Purpose: Investigating the altered temporal features within and between intrinsic connectivity networks (ICNs) for boys with attention-deficit/hyperactivity disorder (ADHD); and analyzing the relationships between altered temporal features within ICNs and behavior scores. Materials and methods: A cohort of boys with combined type of ADHD and a cohort of age-matched healthy boys were recruited from ADHD-200 Consortium. All resting-state fMRI datasets were preprocessed and normalized into standard brain space. Using general linear regression, 20 ICNs were taken as spatial templates to analyze the time-courses of ICNs for each subject. Amplitude of low frequency fluctuations (ALFFs) were computed as univariate temporal features within ICNs. Pearson correlation coefficients and node strengths were computed as bivariate temporal features between ICNs. Additional correlation analysis was performed between temporal features of ICNs and behavior scores. Results: ADHD exhibited more activated network-wise ALFF than normal controls in attention and default mode-related network. Enhanced functional connectivities between ICNs were found in ADHD. The network-wise ALFF within ICNs might influence the functional connectivity between ICNs. The temporal pattern within posterior default mode network (pDMN) was positively correlated to inattentive scores. The subcortical network, fusiform-related DMN and attention-related networks were negatively correlated to Intelligence Quotient (IQ) scores. Conclusion: The temporal low frequency oscillations of ICNs in boys with ADHD were more activated than normal controls during resting state; the temporal features within ICNs could

  19. Comparative efficacy of combination bronchodilator therapies in COPD: a network meta-analysis

    Directory of Open Access Journals (Sweden)

    Huisman EL

    2015-09-01

    Full Text Available Eline L Huisman,1 Sarah M Cockle,2 Afisi S Ismaila,3,4 Andreas Karabis,1 Yogesh Suresh Punekar2 1Mapi Group, Real World Strategy and Analytics and Strategic Market Access, Houten, the Netherlands; 2Value Evidence and Outcomes, GlaxoSmithKline, Uxbridge, UK; 3Value Evidence and Outcomes, GlaxoSmithKline R&D, Research Triangle Park, NC, USA; 4Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada Background: Several new fixed-dose combination bronchodilators have been recently launched, and assessing their efficacy relative to each other, and with open dual combinations is desirable. This network meta-analysis (NMA assessed the efficacy of umeclidinium and vilanterol (UMEC/VI with that of available dual bronchodilators in single/separate inhalers. Methods: A systematic literature review identified randomized controlled trials of ≥10 weeks among chronic obstructive pulmonary disease patients (≥40 years, assessing the efficacy of combination bronchodilators in single or separate inhalers. Comparative assessment was conducted on change from baseline in trough forced expiratory volume in 1 second (FEV1, St George’s Respiratory Questionnaire (SGRQ total scores, transitional dyspnea index (TDI focal scores, and rescue medication use at 12 weeks and 24 weeks using an NMA within a Bayesian framework. Results: A systematic literature review identified 77 articles of 26 trials comparing UMEC/VI, indacaterol/glycopyrronium (QVA149, formoterol plus tiotropium (TIO 18 µg, salmeterol plus TIO, or indacaterol plus TIO, with TIO and placebo as common comparators at 12 weeks and approximately 24 weeks. The NMA showed that at 24 weeks, efficacy of UMEC/VI was not significantly different compared with QVA149 on trough FEV1 (14.1 mL [95% credible interval: -14.2, 42.3], SGRQ total score (0.18 [-1.28, 1.63], TDI focal score (-0.30 [-0.73, 0.13], and rescue medication use (0.02 [-0.27, 0.32]; compared with salmeterol plus

  20. Fun-Sort—or the chaos of unordered binary search

    National Research Council Canada - National Science Library

    Biedl, Therese; Chan, Timothy; Demaine, Erik D; Fleischer, Rudolf; Golin, Mordecai; King, James A; Munro, J.Ian

    2004-01-01

    ... by using linear Insertion-Sort . We study exchange sorting algorithms , i.e., algorithms that compare pairs of array elements and swap them if they are out of order. In particular, we consider the algorithm Fun-Sort 2 2 After the conference series Fun with Algorithms . that repeatedly moves values from current to “more likely” locations by perfo...

  1. Kick, Glide, Pole! Cross-Country Skiing Fun (Part II)

    Science.gov (United States)

    Duoos, Bridget A.

    2012-01-01

    Part I of Kick, Glide, Pole! Cross-Country Skiing Fun, which was published in last issue, discussed how to select cross-country ski equipment, dress for the activity and the biomechanics of the diagonal stride. Part II focuses on teaching the diagonal stride technique and begins with a progression of indoor activities. Incorporating this fun,…

  2. Halloween: Have Fun and Stay Safe and Healthy!

    Centers for Disease Control (CDC) Podcasts

    2010-10-25

    Halloween is a fun time for kids, but it's no fun if you get sick or hurt. In this podcast for kids, the Kidtastics offer some simple ways to stay safe and healthy on Halloween.  Created: 10/25/2010 by CDC Office of Women’s Health.   Date Released: 10/25/2010.

  3. "Ten Things" to Enhance Learning and Fun in the Classroom

    Science.gov (United States)

    Mermelstein, Aaron David

    2016-01-01

    This Teaching Technique introduces a fun, versatile game that gets students thinking, talking, and working together in the English as a second language (ESL) or English as a foreign language (EFL) classroom. It is easy to prepare, and it is a fun and efficient way to enhance learning. The game can be adapted to almost any grade level or ESL/EFL…

  4. Novel amphiphilic poly(dimethylsiloxane) based polyurethane networks tethered with carboxybetaine and their combined antibacterial and anti-adhesive property

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Jingxian; Fu, Yuchen; Zhang, Qinghua, E-mail: qhzhang@zju.edu.cn; Zhan, Xiaoli; Chen, Fengqiu

    2017-08-01

    Highlights: • An amphiphilic poly(dimethylsiloxane) (PDMS) based polyurethane (PU) network tethered with carboxybetaine is prepared. • The surface distribution of PDMS and zwitterionic segments produces an obvious amphiphilic heterogeneous surface. • This designed PDMS-based amphiphilic PU network exhibits combined antibacterial and anti-adhesive properties. - Abstract: The traditional nonfouling materials are powerless against bacterial cells attachment, while the hydrophobic bactericidal surfaces always suffer from nonspecific protein adsorption and dead bacterial cells accumulation. Here, amphiphilic polyurethane (PU) networks modified with poly(dimethylsiloxane) (PDMS) and cationic carboxybetaine diol through simple crosslinking reaction were developed, which had an antibacterial efficiency of 97.7%. Thereafter, the hydrolysis of carboxybetaine ester into zwitterionic groups brought about anti-adhesive properties against bacteria and proteins. The surface chemical composition and wettability performance of the PU network surfaces were investigated by attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), X-ray photoelectron spectroscopy (XPS) and contact angle analysis. The surface distribution of PDMS and zwitterionic segments produced an obvious amphiphilic heterogeneous surface, which was demonstrated by atomic force microscopy (AFM). Enzyme-linked immunosorbent assays (ELISA) were used to test the nonspecific protein adsorption behaviors. With the advantages of the transition from excellent bactericidal performance to anti-adhesion and the combination of fouling resistance and fouling release property, the designed PDMS-based amphiphilic PU network shows great application potential in biomedical devices and marine facilities.

  5. Segmentation of magnetic resonance images using a combination of neural networks and active contour models.

    Science.gov (United States)

    Middleton, Ian; Damper, Robert I

    2004-01-01

    Segmentation of medical images is very important for clinical research and diagnosis, leading to a requirement for robust automatic methods. This paper reports on the combined use of a neural network (a multilayer perceptron, MLP) and active contour model ('snake') to segment structures in magnetic resonance (MR) images. The perceptron is trained to produce a binary classification of each pixel as either a boundary or a non-boundary point. Subsequently, the resulting binary (edge-point) image forms the external energy function for a snake, used to link the candidate boundary points into a continuous, closed contour. We report here on the segmentation of the lungs from multiple MR slices of the torso; lung-specific constraints have been avoided to keep the technique as general as possible. In initial investigations, the inputs to the MLP were limited to normalised intensity values of the pixels from an (7 x 7) window scanned across the image. The use of spatial coordinates as additional inputs to the MLP is then shown to provide an improvement in segmentation performance as quantified using the effectiveness measure (a weighted product of precision and recall). Training sets were first developed using a lengthy iterative process. Thereafter, a novel cost function based on effectiveness is proposed for training that allows us to achieve dramatic improvements in segmentation performance, as well as faster, non-iterative selection of training examples. The classifications produced using this cost function were sufficiently good that the binary image produced by the MLP could be post-processed using an active contour model to provide an accurate segmentation of the lungs from the multiple slices in almost all cases, including unseen slices and subjects.

  6. A new and accurate fault location algorithm for combined transmission lines using Adaptive Network-Based Fuzzy Inference System

    Energy Technology Data Exchange (ETDEWEB)

    Sadeh, Javad; Afradi, Hamid [Electrical Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, P.O. Box: 91775-1111, Mashhad (Iran)

    2009-11-15

    This paper presents a new and accurate algorithm for locating faults in a combined overhead transmission line with underground power cable using Adaptive Network-Based Fuzzy Inference System (ANFIS). The proposed method uses 10 ANFIS networks and consists of 3 stages, including fault type classification, faulty section detection and exact fault location. In the first part, an ANFIS is used to determine the fault type, applying four inputs, i.e., fundamental component of three phase currents and zero sequence current. Another ANFIS network is used to detect the faulty section, whether the fault is on the overhead line or on the underground cable. Other eight ANFIS networks are utilized to pinpoint the faults (two for each fault type). Four inputs, i.e., the dc component of the current, fundamental frequency of the voltage and current and the angle between them, are used to train the neuro-fuzzy inference systems in order to accurately locate the faults on each part of the combined line. The proposed method is evaluated under different fault conditions such as different fault locations, different fault inception angles and different fault resistances. Simulation results confirm that the proposed method can be used as an efficient means for accurate fault location on the combined transmission lines. (author)

  7. Combining epidemiological and genetic networks signifies the importance of early treatment in HIV-1 transmission

    NARCIS (Netherlands)

    Zarrabi, N.; Prosperi, M.; Belleman, R.G.; Colafigli, M.; De Luca, A.; Sloot, P.M.A.

    2012-01-01

    Inferring disease transmission networks is important in epidemiology in order to understand and prevent the spread of infectious diseases. Reconstruction of the infection transmission networks requires insight into viral genome data as well as social interactions. For the HIV-1 epidemic, current

  8. Combining social and genetic networks to study HIV transmission in mixing risk groups

    NARCIS (Netherlands)

    Zarrabi, N.; Prosperi, M.C.F.; Belleman, R.G.; Di Giambenedetto, S.; Fabbiani, M.; De Luca, A.; Sloot, P.M.A.

    2013-01-01

    Reconstruction of HIV transmission networks is important for understanding and preventing the spread of the virus and drug resistant variants. Mixing risk groups is important in network analysis of HIV in order to assess the role of transmission between risk groups in the HIV epidemic. Most of the

  9. Combining Host-based and network-based intrusion detection system

    African Journals Online (AJOL)

    These attacks were simulated using hping. The proposed system is implemented in Java. The results show that the proposed system is able to detect attacks both from within (host-based) and outside sources (network-based). Key Words: Intrusion Detection System (IDS), Host-based, Network-based, Signature, Security log.

  10. Doing is understanding: science fun in India

    CERN Document Server

    Singh, Anand Pratap; Gulvady, Ranjit; Mhamane, Amit; Saunders, Timothy Edward

    2015-01-01

    In India, as in many countries, the main focus in science classrooms is on exams rather than musing on the fascinating concepts and understanding of the world that science offers. This can mean that students lose interest in studying science -a problem that is further hampered where there is a lack of facilities, expertise or mentors. We started the 'Science is fun' outreach programme to address these problems. The 15-person team, led by undergraduate and research scientists, conducted four workshops with underprivileged children in Indian primary and secondary schools during December 2014 and January 2015. The workshops explored basic science concepts, reinforced by hands-on experiments using readily available materials. They were generally successful, with students keen to participate and motivated to learn more after the workshops. We were also pleasantly surprised to see students engaging with new concepts and not hesitating to participate in the discussions. We tried to ensure teachers were central to th...

  11. Combining Partial Directed Coherence and Graph Theory to Analyse Effective Brain Networks of Different Mental Tasks.

    Science.gov (United States)

    Huang, Dengfeng; Ren, Aifeng; Shang, Jing; Lei, Qiao; Zhang, Yun; Yin, Zhongliang; Li, Jun; von Deneen, Karen M; Huang, Liyu

    2016-01-01

    The aim of this study is to qualify the network properties of the brain networks between two different mental tasks (play task or rest task) in a healthy population. EEG signals were recorded from 19 healthy subjects when performing different mental tasks. Partial directed coherence (PDC) analysis, based on Granger causality (GC), was used to assess the effective brain networks during the different mental tasks. Moreover, the network measures, including degree, degree distribution, local and global efficiency in delta, theta, alpha, and beta rhythms were calculated and analyzed. The local efficiency is higher in the beta frequency and lower in the theta frequency during play task whereas the global efficiency is higher in the theta frequency and lower in the beta frequency in the rest task. This study reveals the network measures during different mental states and efficiency measures may be used as characteristic quantities for improvement in attentional performance.

  12. Having Fun and Accepting Challenges Are Natural Instincts: Jigsaw Puzzles to Challenge Students and Test Their Abilities While Having Fun!

    Science.gov (United States)

    Rodenbaugh, Hanna R.; Lujan, Heidi L.; Rodenbaugh, David W.; DiCarlo, Stephen E.

    2014-01-01

    Because jigsaw puzzles are fun, and challenging, students will endure and discover that persistence and grit are rewarded. Importantly, play and fun have a biological place just like sleep and dreams. Students also feel a sense of accomplishment when they have completed a puzzle. Importantly, the reward of mastering a challenge builds confidence…

  13. Economics made fun, and made fun of: How ‘Fun’ redefines the Domain and Identity of the Economics Profession

    NARCIS (Netherlands)

    Dekker, E.; Teule, P.

    2012-01-01

    This paper compares two aspects of the use of ‘fun’ within the economics profession. It analyzes the way in which a recently emerged genre of economics-made-fun uses fun and surprising insights to reach new audiences. And it also analyzes the way in which humor is used within and from outside the

  14. The Combined Effect of Connectivity and Dependency Links on Percolation of Networks

    Science.gov (United States)

    Bashan, Amir; Havlin, Shlomo

    2011-11-01

    Percolation theory is extensively studied in statistical physics and mathematics with applications in diverse fields. However, the research is focused on systems with only one type of links, connectivity links. We review a recently developed mathematical framework for analyzing percolation properties of realistic scenarios of networks having links of two types, connectivity and dependency links. This formalism was applied to study Erdős-Rényi (ER) networks that include also dependency links. For an ER network with average degree bar{k} that is composed of dependency clusters of size s, the fraction of nodes that belong to the giant component, P ∞, is given by P_{infty}=p^{s-1}[1-exp{(-bar{k}pP_{infty})} ]s where 1- p is the initial fraction of randomly removed nodes. Here, we apply the formalism to the study of random-regular (RR) networks and find a formula for the size of the giant component in the percolation process: P ∞= p s-1(1- r k ) s where r is the solution of r= p s ( r k-1-1)(1- r k )+1, and k is the degree of the nodes. These general results coincide, for s=1, with the known equations for percolation in ER and RR networks respectively without dependency links. In contrast to s=1, where the percolation transition is second order, for s>1 it is of first order. Comparing the percolation behavior of ER and RR networks we find a remarkable difference regarding their resilience. We show, analytically and numerically, that in ER networks with low connectivity degree or large dependency clusters, removal of even a finite number (zero fraction) of the infinite network nodes will trigger a cascade of failures that fragments the whole network. Specifically, for any given s there exists a critical degree value, bar{k}_{min}, such that an ER network with bar{k}≤ bar{k}_{min} is unstable and collapse when removing even a single node. This result is in contrast to RR networks where such cascades and full fragmentation can be triggered only by removal of a

  15. Combined flatland ST radar and digital-barometer network observations of mesoscale processes

    Science.gov (United States)

    Clark, W. L.; Vanzandt, T. E.; Gage, K. S.; Einaudi, F. E.; Rottman, J. W.; Hollinger, S. E.

    1991-01-01

    The paper describes a six-station digital-barometer network centered on the Flatland ST radar to support observational studies of gravity waves and other mesoscale features at the Flatland Atmospheric Observatory in central Illinois. The network's current mode of operation is examined, and a preliminary example of an apparent group of waves evident throughout the network as well as throughout the troposphere is presented. Preliminary results demonstrate the capabilities of the current operational system to study wave convection, wave-front, and other coherent mesoscale interactions and processes throughout the troposphere. Unfiltered traces for the pressure and horizontal zonal wind, for days 351 to 353 UT, 1990, are illustrated.

  16. [Rapid Identification of Epicarpium Citri Grandis via Infrared Spectroscopy and Fluorescence Spectrum Imaging Technology Combined with Neural Network].

    Science.gov (United States)

    Pan, Sha-sha; Huang, Fu-rong; Xiao, Chi; Xian, Rui-yi; Ma, Zhi-guo

    2015-10-01

    To explore rapid reliable methods for detection of Epicarpium citri grandis (ECG), the experiment using Fourier Transform Attenuated Total Reflection Infrared Spectroscopy (FTIR/ATR) and Fluorescence Spectrum Imaging Technology combined with Multilayer Perceptron (MLP) Neural Network pattern recognition, for the identification of ECG, and the two methods are compared. Infrared spectra and fluorescence spectral images of 118 samples, 81 ECG and 37 other kinds of ECG, are collected. According to the differences in tspectrum, the spectra data in the 550-1 800 cm(-1) wavenumber range and 400-720 nm wavelength are regarded as the study objects of discriminant analysis. Then principal component analysis (PCA) is applied to reduce the dimension of spectroscopic data of ECG and MLP Neural Network is used in combination to classify them. During the experiment were compared the effects of different methods of data preprocessing on the model: multiplicative scatter correction (MSC), standard normal variable correction (SNV), first-order derivative(FD), second-order derivative(SD) and Savitzky-Golay (SG). The results showed that: after the infrared spectra data via the Savitzky-Golay (SG) pretreatment through the MLP Neural Network with the hidden layer function as sigmoid, we can get the best discrimination of ECG, the correct percent of training set and testing set are both 100%. Using fluorescence spectral imaging technology, corrected by the multiple scattering (MSC) results in the pretreatment is the most ideal. After data preprocessing, the three layers of the MLP Neural Network of the hidden layer function as sigmoid function can get 100% correct percent of training set and 96.7% correct percent of testing set. It was shown that the FTIR/ATR and fluorescent spectral imaging technology combined with MLP Neural Network can be used for the identification study of ECG and has the advantages of rapid, reliable effect.

  17. Network meta-analysis of Chinese herbal injections combined with the chemotherapy for the treatment of pancreatic cancer.

    Science.gov (United States)

    Zhang, Dan; Wu, Jiarui; Liu, Shi; Zhang, Xiaomeng; Zhang, Bing

    2017-05-01

    This study sought to use a network meta-analysis to assess the effectiveness and safety of Chinese herbal injections (CHIs) combined with the chemotherapy for the treatment of pancreatic cancer. Randomized controlled trials (RCTs) regarding CHIs to treat pancreatic cancer were searched in PubMed, the Cochrane library, Embase, the China National Knowledge Infrastructure Database (CNKI), the Wan-Fang Database, the Chinese Scientific Journals Full-text Database (VIP), and the Chinese Biomedical Literature Database (SinoMed) up to November 2016. The quality assessment was conducted by the Cochrane risk of bias tool and network meta-analysis was performed to compare the effectiveness and safety of different CHIs combined with the chemotherapy. Data were analyzed using STATA 12.0 and Win-BUGS 1.4 software. A total of 278 records were searched, and 22 eligible RCTs involving 1329 patients and 9 CHIs were included. The results of the network meta-analysis demonstrated that compared with the chemotherapy alone, Compound Kushen, Kangai or Kanglaite injection combined with chemotherapy yielded significantly higher probability of improving performance status. Aidi injection combined with chemotherapy was more effective in relieving leucopenia than using chemotherapy single. And these between-group differences were statistically significant. However, CHIs combined with chemotherapy could not achieve a better effect in the total clinical effect, nausea and vomiting. As for the cluster analysis for the adverse reactions (ADRs), the chemotherapy alone and Huachansu injection combined with the chemotherapy were inferior to relieve ADRs than the other CHIs plus chemotherapy for patients with pancreatic cancer. The current evidence showed that using CHIs on the basis of the chemotherapy could be beneficial for patients with pancreatic cancer in improving performance status and reducing the ADRs.

  18. Combining network modeling and gene expression microarray analysis to explore the dynamics of Th1 and Th2 cell regulation.

    Science.gov (United States)

    Pedicini, Marco; Barrenäs, Fredrik; Clancy, Trevor; Castiglione, Filippo; Hovig, Eivind; Kanduri, Kartiek; Santoni, Daniele; Benson, Mikael

    2010-12-16

    Two T helper (Th) cell subsets, namely Th1 and Th2 cells, play an important role in inflammatory diseases. The two subsets are thought to counter-regulate each other, and alterations in their balance result in different diseases. This paradigm has been challenged by recent clinical and experimental data. Because of the large number of genes involved in regulating Th1 and Th2 cells, assessment of this paradigm by modeling or experiments is difficult. Novel algorithms based on formal methods now permit the analysis of large gene regulatory networks. By combining these algorithms with in silico knockouts and gene expression microarray data from human T cells, we examined if the results were compatible with a counter-regulatory role of Th1 and Th2 cells. We constructed a directed network model of genes regulating Th1 and Th2 cells through text mining and manual curation. We identified four attractors in the network, three of which included genes that corresponded to Th0, Th1 and Th2 cells. The fourth attractor contained a mixture of Th1 and Th2 genes. We found that neither in silico knockouts of the Th1 and Th2 attractor genes nor gene expression microarray data from patients with immunological disorders and healthy subjects supported a counter-regulatory role of Th1 and Th2 cells. By combining network modeling with transcriptomic data analysis and in silico knockouts, we have devised a practical way to help unravel complex regulatory network topology and to increase our understanding of how network actions may differ in health and disease.

  19. Fun cube based brain gym cognitive function assessment system.

    Science.gov (United States)

    Zhang, Tao; Lin, Chung-Chih; Yu, Tsang-Chu; Sun, Jing; Hsu, Wen-Chuin; Wong, Alice May-Kuen

    2017-05-01

    The aim of this study is to design and develop a fun cube (FC) based brain gym (BG) cognitive function assessment system using the wireless sensor network and multimedia technologies. The system comprised (1) interaction devices, FCs and a workstation used as interactive tools for collecting and transferring data to the server, (2) a BG information management system responsible for managing the cognitive games and storing test results, and (3) a feedback system used for conducting the analysis of cognitive functions to assist caregivers in screening high risk groups with mild cognitive impairment. Three kinds of experiments were performed to evaluate the developed FC-based BG cognitive function assessment system. The experimental results showed that the Pearson correlation coefficient between the system's evaluation outcomes and the traditional Montreal Cognitive Assessment scores was 0.83. The average Technology Acceptance Model 2 score was close to six for 31 elderly subjects. Most subjects considered that the brain games are interesting and the FC human-machine interface is easy to learn and operate. The control group and the cognitive impairment group had statistically significant difference with respect to the accuracy of and the time taken for the brain cognitive function assessment games, including Animal Naming, Color Search, Trail Making Test, Change Blindness, and Forward / Backward Digit Span. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Combining Volcano Monitoring Timeseries Analyses with Bayesian Belief Networks to Update Hazard Forecast Estimates

    Science.gov (United States)

    Odbert, Henry; Hincks, Thea; Aspinall, Willy

    2015-04-01

    Volcanic hazard assessments must combine information about the physical processes of hazardous phenomena with observations that indicate the current state of a volcano. Incorporating both these lines of evidence can inform our belief about the likelihood (probability) and consequences (impact) of possible hazardous scenarios, forming a basis for formal quantitative hazard assessment. However, such evidence is often uncertain, indirect or incomplete. Approaches to volcano monitoring have advanced substantially in recent decades, increasing the variety and resolution of multi-parameter timeseries data recorded at volcanoes. Interpreting these multiple strands of parallel, partial evidence thus becomes increasingly complex. In practice, interpreting many timeseries requires an individual to be familiar with the idiosyncrasies of the volcano, monitoring techniques, configuration of recording instruments, observations from other datasets, and so on. In making such interpretations, an individual must consider how different volcanic processes may manifest as measureable observations, and then infer from the available data what can or cannot be deduced about those processes. We examine how parts of this process may be synthesised algorithmically using Bayesian inference. Bayesian Belief Networks (BBNs) use probability theory to treat and evaluate uncertainties in a rational and auditable scientific manner, but only to the extent warranted by the strength of the available evidence. The concept is a suitable framework for marshalling multiple strands of evidence (e.g. observations, model results and interpretations) and their associated uncertainties in a methodical manner. BBNs are usually implemented in graphical form and could be developed as a tool for near real-time, ongoing use in a volcano observatory, for example. We explore the application of BBNs in analysing volcanic data from the long-lived eruption at Soufriere Hills Volcano, Montserrat. We show how our method

  1. Potentials and Limitations of CDMA Networks for Combined Inter-Satellite Communication and Relative Navigation

    NARCIS (Netherlands)

    Sun, R.; Guo, J.; Gill, E.K.A.; Maessen, D.C.

    2012-01-01

    Precision formation flying missions require formation acquisition and maintenance through the interactions among spacecraft by the inter-satellite communication and relative navigation. This paper analyses the dedicated system constraints of the network architecture for precision formation flying

  2. FunBlocks. A Modular Framework for AmI System Development

    Directory of Open Access Journals (Sweden)

    Alfredo Piero Mateos Papis

    2012-07-01

    Full Text Available The last decade has seen explosive growth in the technologies required to implement Ambient Intelligence (AmI systems. Technologies such as facial and speech recognition, home networks, household cleaning robots, to name a few, have become commonplace. However, due to the multidisciplinary nature of AmI systems and the distinct requirements of different user groups, integrating these developments into full-scale systems is not an easy task. In this paper we propose FunBlocks, a minimalist modular framework for the development of AmI systems based on the function module abstraction used in the IEC 61499 standard for distributed control systems. FunBlocks provides a framework for the development of AmI systems through the integration of modules loosely joined by means of an event-driven middleware and a module and sensor/actuator catalog. The modular design of the FunBlocks framework allows the development of AmI systems which can be customized to a wide variety of usage scenarios.

  3. A Hybrid Combination Scheme for Cooperative Spectrum Sensing in Cognitive Radio Networks

    Directory of Open Access Journals (Sweden)

    Changhua Yao

    2014-01-01

    Full Text Available We propose a novel hybrid combination scheme in cooperative spectrum sensing (CSS, which utilizes the diversity of reporting channels to achieve better throughput performance. Secondary users (SUs with good reporting channel quality transmit quantized local observation statistics to fusion center (FC, while others report their local decisions. FC makes the final decision by carrying out hybrid combination. We derive the closed-form expressions of throughput and detection performance as a function of the number of SUs which report local observation statistics. The simulation and numerical results show that the hybrid combination scheme can achieve better throughput performance than hard combination scheme and soft combination scheme.

  4. Machine Learning Classification Combining Multiple Features of A Hyper-Network of fMRI Data in Alzheimer's Disease

    Directory of Open Access Journals (Sweden)

    Hao Guo

    2017-11-01

    Full Text Available Exploring functional interactions among various brain regions is helpful for understanding the pathological underpinnings of neurological disorders. Brain networks provide an important representation of those functional interactions, and thus are widely applied in the diagnosis and classification of neurodegenerative diseases. Many mental disorders involve a sharp decline in cognitive ability as a major symptom, which can be caused by abnormal connectivity patterns among several brain regions. However, conventional functional connectivity networks are usually constructed based on pairwise correlations among different brain regions. This approach ignores higher-order relationships, and cannot effectively characterize the high-order interactions of many brain regions working together. Recent neuroscience research suggests that higher-order relationships between brain regions are important for brain network analysis. Hyper-networks have been proposed that can effectively represent the interactions among brain regions. However, this method extracts the local properties of brain regions as features, but ignores the global topology information, which affects the evaluation of network topology and reduces the performance of the classifier. This problem can be compensated by a subgraph feature-based method, but it is not sensitive to change in a single brain region. Considering that both of these feature extraction methods result in the loss of information, we propose a novel machine learning classification method that combines multiple features of a hyper-network based on functional magnetic resonance imaging in Alzheimer's disease. The method combines the brain region features and subgraph features, and then uses a multi-kernel SVM for classification. This retains not only the global topological information, but also the sensitivity to change in a single brain region. To certify the proposed method, 28 normal control subjects and 38 Alzheimer

  5. Machine Learning Classification Combining Multiple Features of A Hyper-Network of fMRI Data in Alzheimer's Disease.

    Science.gov (United States)

    Guo, Hao; Zhang, Fan; Chen, Junjie; Xu, Yong; Xiang, Jie

    2017-01-01

    Exploring functional interactions among various brain regions is helpful for understanding the pathological underpinnings of neurological disorders. Brain networks provide an important representation of those functional interactions, and thus are widely applied in the diagnosis and classification of neurodegenerative diseases. Many mental disorders involve a sharp decline in cognitive ability as a major symptom, which can be caused by abnormal connectivity patterns among several brain regions. However, conventional functional connectivity networks are usually constructed based on pairwise correlations among different brain regions. This approach ignores higher-order relationships, and cannot effectively characterize the high-order interactions of many brain regions working together. Recent neuroscience research suggests that higher-order relationships between brain regions are important for brain network analysis. Hyper-networks have been proposed that can effectively represent the interactions among brain regions. However, this method extracts the local properties of brain regions as features, but ignores the global topology information, which affects the evaluation of network topology and reduces the performance of the classifier. This problem can be compensated by a subgraph feature-based method, but it is not sensitive to change in a single brain region. Considering that both of these feature extraction methods result in the loss of information, we propose a novel machine learning classification method that combines multiple features of a hyper-network based on functional magnetic resonance imaging in Alzheimer's disease. The method combines the brain region features and subgraph features, and then uses a multi-kernel SVM for classification. This retains not only the global topological information, but also the sensitivity to change in a single brain region. To certify the proposed method, 28 normal control subjects and 38 Alzheimer's disease patients were

  6. Is Congenital Amusia a Disconnection Syndrome? A Study Combining Tract- and Network-Based Analysis.

    Science.gov (United States)

    Wang, Jieqiong; Zhang, Caicai; Wan, Shibiao; Peng, Gang

    2017-01-01

    Previous studies on congenital amusia mainly focused on the impaired fronto-temporal pathway. It is possible that neural pathways of amusia patients on a larger scale are affected. In this study, we investigated changes in structural connections by applying both tract-based and network-based analysis to DTI data of 12 subjects with congenital amusia and 20 demographic-matched normal controls. TBSS (tract-based spatial statistics) was used to detect microstructural changes. The results showed that amusics had higher diffusivity indices in the corpus callosum, the right inferior/superior longitudinal fasciculus, and the right inferior frontal-occipital fasciculus (IFOF). The axial diffusivity values of the right IFOF were negatively correlated with musical scores in the amusia group. Network-based analysis showed that the efficiency of the brain network was reduced in amusics. The impairments of WM tracts were also found to be correlated with reduced network efficiency in amusics. This suggests that impaired WM tracts may lead to the reduced network efficiency seen in amusics. Our findings suggest that congenital amusia is a disconnection syndrome.

  7. Is Congenital Amusia a Disconnection Syndrome? A Study Combining Tract- and Network-Based Analysis

    Directory of Open Access Journals (Sweden)

    Jieqiong Wang

    2017-09-01

    Full Text Available Previous studies on congenital amusia mainly focused on the impaired fronto-temporal pathway. It is possible that neural pathways of amusia patients on a larger scale are affected. In this study, we investigated changes in structural connections by applying both tract-based and network-based analysis to DTI data of 12 subjects with congenital amusia and 20 demographic-matched normal controls. TBSS (tract-based spatial statistics was used to detect microstructural changes. The results showed that amusics had higher diffusivity indices in the corpus callosum, the right inferior/superior longitudinal fasciculus, and the right inferior frontal-occipital fasciculus (IFOF. The axial diffusivity values of the right IFOF were negatively correlated with musical scores in the amusia group. Network-based analysis showed that the efficiency of the brain network was reduced in amusics. The impairments of WM tracts were also found to be correlated with reduced network efficiency in amusics. This suggests that impaired WM tracts may lead to the reduced network efficiency seen in amusics. Our findings suggest that congenital amusia is a disconnection syndrome.

  8. A combined geostatistical-optimization model for the optimal design of a groundwater quality monitoring network

    Science.gov (United States)

    Kolosionis, Konstantinos; Papadopoulou, Maria P.

    2017-04-01

    Monitoring networks provide essential information for water resources management especially in areas with significant groundwater exploitation due to extensive agricultural activities. In this work, a simulation-optimization framework is developed based on heuristic optimization methodologies and geostatistical modeling approaches to obtain an optimal design for a groundwater quality monitoring network. Groundwater quantity and quality data obtained from 43 existing observation locations at 3 different hydrological periods in Mires basin in Crete, Greece will be used in the proposed framework in terms of Regression Kriging to develop the spatial distribution of nitrates concentration in the aquifer of interest. Based on the existing groundwater quality mapping, the proposed optimization tool will determine a cost-effective observation wells network that contributes significant information to water managers and authorities. The elimination of observation wells that add little or no beneficial information to groundwater level and quality mapping of the area can be obtain using estimations uncertainty and statistical error metrics without effecting the assessment of the groundwater quality. Given the high maintenance cost of groundwater monitoring networks, the proposed tool could used by water regulators in the decision-making process to obtain a efficient network design that is essential.

  9. Combining SDM-Based Circuit Switching with Packet Switching in a Router for On-Chip Networks

    Directory of Open Access Journals (Sweden)

    Angelo Kuti Lusala

    2012-01-01

    Full Text Available A Hybrid router architecture for Networks-on-Chip “NoC” is presented, it combines Spatial Division Multiplexing “SDM” based circuit switching and packet switching in order to efficiently and separately handle both streaming and best-effort traffic generated in real-time applications. Furthermore the SDM technique is combined with Time Division Multiplexing “TDM” technique in the circuit switching part in order to increase path diversity, thus improving throughput while sharing communication resources among multiple connections. Combining these two techniques allows mitigating the poor resource usage inherent to circuit switching. In this way Quality of Service “QoS” is easily provided for the streaming traffic through the circuit-switched sub-router while the packet-switched sub-router handles best-effort traffic. The proposed hybrid router architectures were synthesized, placed and routed on an FPGA. Results show that a practicable Network-on-Chip “NoC” can be built using the proposed router architectures. 7 × 7 mesh NoCs were simulated in SystemC. Simulation results show that the probability of establishing paths through the NoC increases with the number of sub-channels and has its highest value when combining SDM with TDM, thereby significantly reducing contention in the NoC.

  10. USING FUN ACTIVITIES TO IMPROVE LISTENING SKILL

    Directory of Open Access Journals (Sweden)

    Hanna Andyani

    2015-12-01

    Full Text Available Based on the researcher’s experience in teaching English at MTsN Mojokerto, there are three problems dealing with the teaching of listening especially for the third year students: 1 most of the students’ scores on listening test are still under the minimum passing criterion (KKM, which is 60; 2 most students are not very enthusiastic in listening activities; 3 it is difficult for students to understand native speech in a tape recorder. Based on the problems, the main purpose of the study is to improve the ninth grade students’ listening skill using Fun Activity in the form of Games at MTsN Mojokerto. The design of this study was Classroom Action Research. The instruments were the listening tests, observation checklist and questionnaires. With the implementation of the games, the criteria of success were successfully achieved in Cycle 2. 74% of the total number of the students could get the scores more than 60 and 90% have positive responses on the implementation of games. Keywords: tic tac toe game, running dictation game, whispering game, listening skill

  11. Stabilized Finite Elements in FUN3D

    Science.gov (United States)

    Anderson, W. Kyle; Newman, James C.; Karman, Steve L.

    2017-01-01

    A Streamlined Upwind Petrov-Galerkin (SUPG) stabilized finite-element discretization has been implemented as a library into the FUN3D unstructured-grid flow solver. Motivation for the selection of this methodology is given, details of the implementation are provided, and the discretization for the interior scheme is verified for linear and quadratic elements by using the method of manufactured solutions. A methodology is also described for capturing shocks, and simulation results are compared to the finite-volume formulation that is currently the primary method employed for routine engineering applications. The finite-element methodology is demonstrated to be more accurate than the finite-volume technology, particularly on tetrahedral meshes where the solutions obtained using the finite-volume scheme can suffer from adverse effects caused by bias in the grid. Although no effort has been made to date to optimize computational efficiency, the finite-element scheme is competitive with the finite-volume scheme in terms of computer time to reach convergence.

  12. CTF: Computer security competitions for learning and fun

    CERN Multimedia

    CERN. Geneva

    2015-01-01

    CTF hacking competitions condense practical security knowledge in short and measurable challenges, in short: education, fun, prizes and fame! This talk is an introduction to these type of competitions from a player perspective over the years.

  13. Geo-casting of queries combined with coverage area reporting for wireless sensor networks

    NARCIS (Netherlands)

    van Hoesel, L.F.W.; Erman-Tüysüz, A.; Dilo, Arta; Havinga, Paul J.M.

    In order to efficiently deal with queries or other location dependent information, it is key that the wireless sensor network informs gateways what geographical area is serviced by which gateway. The gateways are then able to e.g. efficiently route queries which are only valid in particular regions

  14. Identification of Linkages between EDCs in Personal Care Products and Breast Cancer through Data Integration Combined with Gene Network Analysis.

    Science.gov (United States)

    Jeong, Hyeri; Kim, Jongwoon; Kim, Youngjun

    2017-09-30

    Approximately 1000 chemicals have been reported to possibly have endocrine disrupting effects, some of which are used in consumer products, such as personal care products (PCPs) and cosmetics. We conducted data integration combined with gene network analysis to: (i) identify causal molecular mechanisms between endocrine disrupting chemicals (EDCs) used in PCPs and breast cancer; and (ii) screen candidate EDCs associated with breast cancer. Among EDCs used in PCPs, four EDCs having correlation with breast cancer were selected, and we curated 27 common interacting genes between those EDCs and breast cancer to perform the gene network analysis. Based on the gene network analysis, ESR1, TP53, NCOA1, AKT1, and BCL6 were found to be key genes to demonstrate the molecular mechanisms of EDCs in the development of breast cancer. Using GeneMANIA, we additionally predicted 20 genes which could interact with the 27 common genes. In total, 47 genes combining the common and predicted genes were functionally grouped with the gene ontology and KEGG pathway terms. With those genes, we finally screened candidate EDCs for their potential to increase breast cancer risk. This study highlights that our approach can provide insights to understand mechanisms of breast cancer and identify potential EDCs which are in association with breast cancer.

  15. Combined IR imaging-neural network method for the estimation of internal temperature in cooked chicken meat

    Science.gov (United States)

    Ibarra, Juan G.; Tao, Yang; Xin, Hongwei

    2000-11-01

    A noninvasive method for the estimation of internal temperature in chicken meat immediately following cooking is proposed. The external temperature from IR images was correlated with measured internal temperature through a multilayer neural network. To provide inputs for the network, time series experiments were conducted to obtain simultaneous observations of internal and external temperatures immediately after cooking during the cooling process. An IR camera working at the spectral band of 3.4 to 5.0 micrometers registered external temperature distributions without the interference of close-to-oven environment, while conventional thermocouples registered internal temperatures. For an internal temperature at a given time, simultaneous and lagged external temperature observations were used as the input of the neural network. Based on practical and statistical considerations, a criterion is established to reduce the nodes in the neural network input. The combined method was able to estimate internal temperature for times between 0 and 540 s within a standard error of +/- 1.01 degree(s)C, and within an error of +/- 1.07 degree(s)C for short times after cooking (3 min), with two thermograms at times t and t+30s. The method has great potential for monitoring of doneness of chicken meat in conveyor belt type cooking and can be used as a platform for similar studies in other food products.

  16. Energy Efficiency of Ultra-Low-Power Bicycle Wireless Sensor Networks Based on a Combination of Power Reduction Techniques

    Directory of Open Access Journals (Sweden)

    Sadik Kamel Gharghan

    2016-01-01

    Full Text Available In most wireless sensor network (WSN applications, the sensor nodes (SNs are battery powered and the amount of energy consumed by the nodes in the network determines the network lifespan. For future Internet of Things (IoT applications, reducing energy consumption of SNs has become mandatory. In this paper, an ultra-low-power nRF24L01 wireless protocol is considered for a bicycle WSN. The power consumption of the mobile node on the cycle track was modified by combining adjustable data rate, sleep/wake, and transmission power control (TPC based on two algorithms. The first algorithm was a TPC-based distance estimation, which adopted a novel hybrid particle swarm optimization-artificial neural network (PSO-ANN using the received signal strength indicator (RSSI, while the second algorithm was a novel TPC-based accelerometer using inclination angle of the bicycle on the cycle track. Based on the second algorithm, the power consumption of the mobile and master nodes can be improved compared with the first algorithm and constant transmitted power level. In addition, an analytical model is derived to correlate the power consumption and data rate of the mobile node. The results indicate that the power savings based on the two algorithms outperformed the conventional operation (i.e., without power reduction algorithm by 78%.

  17. Deep Manifold Learning Combined With Convolutional Neural Networks for Action Recognition.

    Science.gov (United States)

    Chen, Xin; Weng, Jian; Lu, Wei; Xu, Jiaming; Weng, Jiasi

    2017-09-15

    Learning deep representations have been applied in action recognition widely. However, there have been a few investigations on how to utilize the structural manifold information among different action videos to enhance the recognition accuracy and efficiency. In this paper, we propose to incorporate the manifold of training samples into deep learning, which is defined as deep manifold learning (DML). The proposed DML framework can be adapted to most existing deep networks to learn more discriminative features for action recognition. When applied to a convolutional neural network, DML embeds the previous convolutional layer's manifold into the next convolutional layer; thus, the discriminative capacity of the next layer can be promoted. We also apply the DML on a restricted Boltzmann machine, which can alleviate the overfitting problem. Experimental results on four standard action databases (i.e., UCF101, HMDB51, KTH, and UCF sports) show that the proposed method outperforms the state-of-the-art methods.

  18. Healthy habits are no fun: How Dutch youth negotiate discourses about food, fit, fat, and fun.

    Science.gov (United States)

    van Amsterdam, Noortje; Knoppers, Annelies

    2017-01-01

    In this article, we use the notion of "biopedagogical practices" to explore how Dutch youth respond to health messages that focus on body weight. Previous studies suggest that such health messages encourage body dissatisfaction in youth. Few studies, however, focus on the local/cultural specificity of youth's responses to these biopedagogical practices. In this article, we address questions about the re-interpretation of and resistance to health messages that Dutch youth engage in and how these can be understood in their local context. The data were drawn from two previously conducted studies in which a total of 64 Dutch teenagers (aged 12-18 years) took part. We employed a variety of qualitative data collection methods and a feminist poststructuralist perspective to analyze how Dutch youth negotiate biopedagogical practices about health. The results show that our participants constructed health in terms of appearance and reproduced negative constructions regarding fat embodiment. Yet they also often circumvented "healthy" lifestyle behaviors prescribed by biopedagogies of health. They did so first by avoiding physical activities because they were afraid of displaying fat embodiment in the settings of sport and physical education where surveillance is omnipresent. Second, they disregarded advice about healthy eating by drawing on having fun as an alternative discursive resource. We argue that having fun is both part of youth culture and characteristic of the discourse about sociability ( gezelligheid) that is a central element of Dutch culture.

  19. Optical alignment procedure utilizing neural networks combined with Shack-Hartmann wavefront sensor

    Science.gov (United States)

    Adil, Fatime Zehra; Konukseven, Erhan İlhan; Balkan, Tuna; Adil, Ömer Faruk

    2017-05-01

    In the design of pilot helmets with night vision capability, to not limit or block the sight of the pilot, a transparent visor is used. The reflected image from the coated part of the visor must coincide with the physical human sight image seen through the nonreflecting regions of the visor. This makes the alignment of the visor halves critical. In essence, this is an alignment problem of two optical parts that are assembled together during the manufacturing process. Shack-Hartmann wavefront sensor is commonly used for the determination of the misalignments through wavefront measurements, which are quantified in terms of the Zernike polynomials. Although the Zernike polynomials provide very useful feedback about the misalignments, the corrective actions are basically ad hoc. This stems from the fact that there exists no easy inverse relation between the misalignment measurements and the physical causes of the misalignments. This study aims to construct this inverse relation by making use of the expressive power of the neural networks in such complex relations. For this purpose, a neural network is designed and trained in MATLAB® regarding which types of misalignments result in which wavefront measurements, quantitatively given by Zernike polynomials. This way, manual and iterative alignment processes relying on trial and error will be replaced by the trained guesses of a neural network, so the alignment process is reduced to applying the counter actions based on the misalignment causes. Such a training requires data containing misalignment and measurement sets in fine detail, which is hard to obtain manually on a physical setup. For that reason, the optical setup is completely modeled in Zemax® software, and Zernike polynomials are generated for misalignments applied in small steps. The performance of the neural network is experimented and found promising in the actual physical setup.

  20. Combining LiDAR Space Clustering and Convolutional Neural Networks for Pedestrian Detection

    OpenAIRE

    Matti, Damien; Ekenel, Hazim Kemal; Thiran, Jean-Philippe

    2017-01-01

    Pedestrian detection is an important component for safety of autonomous vehicles, as well as for traffic and street surveillance. There are extensive benchmarks on this topic and it has been shown to be a challenging problem when applied on real use-case scenarios. In purely image-based pedestrian detection approaches, the state-of-the-art results have been achieved with convolutional neural networks (CNN) and surprisingly few detection frameworks have been built upon multi-cue approaches. In...

  1. Combining fuzzy logic and eigenvector centrality measure in social network analysis

    Science.gov (United States)

    Parand, Fereshteh-Azadi; Rahimi, Hossein; Gorzin, Mohsen

    2016-10-01

    The rapid growth of social networks use has made a great platform to present different services, increasing beneficiary of services and business profit. Therefore considering different levels of member activities in these networks, finding highly active members who can have the influence on the choice and the role of other members of the community is one the most important and challenging issues in recent years. These nodes that usually have a high number of relations with a lot of quality interactions are called influential nodes. There are various types of methods and measures presented to find these nodes. Among all the measures, centrality is the one that identifies various types of influential nodes in a network. Here we define four different factors which affect the strength of a relationship. A fuzzy inference system calculates the strength of each relation, creates a crisp matrix in which the corresponding elements identify the strength of each relation, and using this matrix eigenvector measure calculates the most influential node. Applying our suggested method resulted in choosing a more realistic central node with consideration of the strength of all friendships.

  2. Combined Saliency with Multi-Convolutional Neural Network for High Resolution Remote Sensing Scene Classification

    Directory of Open Access Journals (Sweden)

    HE Xiaofei

    2016-09-01

    Full Text Available The scene information existing in high resolution remote sensing images is important for image interpretation and understanding of the real world. Traditional scene classification methods often use middle and low-level artificial features, but high resolution images have rich information and complex scene configuration, which need high-level feature to express. A joint saliency and multi-convolutional neural network method is proposed in this paper. Firstly, we obtain meaningful patches that include dominant image information by saliency sampling. Secondly, these patches will be set as a sample input to the convolutional neural network for training, obtain feature expression on different levels. Finally, we embed the multi-layer features into the support vector machine (SVM for image classification. Experiments using two high resolution image scene data show that saliency sampling can effectively get the main target, weaken the impact of other unrelated targets, and reduce data redundancy; convolutional neural network can automatically learn the high-level feature, compared to existing methods, the proposed method can effectively improve the classification accuracy.

  3. An expert-based approach to forest road network planning by combining Delphi and spatial multi-criteria evaluation.

    Science.gov (United States)

    Hayati, Elyas; Majnounian, Baris; Abdi, Ehsan; Sessions, John; Makhdoum, Majid

    2013-02-01

    Changes in forest landscapes resulting from road construction have increased remarkably in the last few years. On the other hand, the sustainable management of forest resources can only be achieved through a well-organized road network. In order to minimize the environmental impacts of forest roads, forest road managers must design the road network efficiently and environmentally as well. Efficient planning methodologies can assist forest road managers in considering the technical, economic, and environmental factors that affect forest road planning. This paper describes a three-stage methodology using the Delphi method for selecting the important criteria, the Analytic Hierarchy Process for obtaining the relative importance of the criteria, and finally, a spatial multi-criteria evaluation in a geographic information system (GIS) environment for identifying the lowest-impact road network alternative. Results of the Delphi method revealed that ground slope, lithology, distance from stream network, distance from faults, landslide susceptibility, erosion susceptibility, geology, and soil texture are the most important criteria for forest road planning in the study area. The suitability map for road planning was then obtained by combining the fuzzy map layers of these criteria with respect to their weights. Nine road network alternatives were designed using PEGGER, an ArcView GIS extension, and finally, their values were extracted from the suitability map. Results showed that the methodology was useful for identifying road that met environmental and cost considerations. Based on this work, we suggest future work in forest road planning using multi-criteria evaluation and decision making be considered in other regions and that the road planning criteria identified in this study may be useful.

  4. Providing full point-to-point communications among compute nodes of an operational group in a global combining network of a parallel computer

    Energy Technology Data Exchange (ETDEWEB)

    Archer, Charles J.; Faraj, Daniel A.; Inglett, Todd A.; Ratterman, Joseph D.

    2018-01-30

    Methods, apparatus, and products are disclosed for providing full point-to-point communications among compute nodes of an operational group in a global combining network of a parallel computer, each compute node connected to each adjacent compute node in the global combining network through a link, that include: receiving a network packet in a compute node, the network packet specifying a destination compute node; selecting, in dependence upon the destination compute node, at least one of the links for the compute node along which to forward the network packet toward the destination compute node; and forwarding the network packet along the selected link to the adjacent compute node connected to the compute node through the selected link.

  5. Comparison of strategies for combining dynamic linear models with artificial neural networks for detecting diarrhea in slaughter pigs

    DEFF Research Database (Denmark)

    Jensen, Dan Børge; Kristensen, Anders Ringgaard

    2016-01-01

    , can provide early and automatic detection of diarrhea. To determine the best approach to achieve this goal, we compared 36 different strategies for combining a multivariate dynamic linear model (DLM) with an artificial neural network (ANN). We used data collected in 16 pens between November 2013...... (SP), and the sensitivity (SE). The best performance was seen when using a training window with a total of 42 hours for the numerical forecast errors, which produced an error rate=0.16, a specificity=0.88, and a sensitivity=0.80. For the other tested strategies, the ranges of error rates...

  6. Combined Use of Tissue Morphology, Neural Network Analysis of Chromatin Texture and Clinical Variables to Predict Prostate Cancer Agressiveness from Biopsy Water

    National Research Council Canada - National Science Library

    Partin, Alan

    2000-01-01

    Purpose: To combine clinical, serum, pathologic and computer derived information into an artificial neural network to develop/validate a model to predict prostate cancer tumor aggressiveness in both a...

  7. Combined Use of Tissue Morphology, Neural Network Analysis of Chromatin Texture & Clinical Variables to Predict Prostate Cancer Agressiveness from Biopsy Material

    National Research Council Canada - National Science Library

    Partin, Alan

    1999-01-01

    the purpose of this report is to combine clinical, serum, pathological and computer derived information into an artificial neural network to develop/validate a model to predict prostate cancer tumor...

  8. Correction: An integrated anti-arrhythmic target network of compound Chinese medicine Wenxin Keli revealed by combined machine learning and molecular pathway analysis.

    Science.gov (United States)

    Wang, Taiyi; Lu, Ming; Du, Qunqun; Yao, Xi; Zhang, Peng; Chen, Xiaonan; Xie, Weiwei; Li, Zheng; Ma, Yuling; Zhu, Yan

    2017-09-26

    Correction for 'An integrated anti-arrhythmic target network of a Chinese medicine compound, Wenxin Keli, revealed by combined machine learning and molecular pathway analysis' by Taiyi Wang et al., Mol. BioSyst., 2017, 13, 1018-1030.

  9. Downregulation of GNA13-ERK network in prefrontal cortex of schizophrenia brain identified by combined focused and targeted quantitative proteomics.

    Science.gov (United States)

    Hirayama-Kurogi, Mio; Takizawa, Yohei; Kunii, Yasuto; Matsumoto, Junya; Wada, Akira; Hino, Mizuki; Akatsu, Hiroyasu; Hashizume, Yoshio; Yamamoto, Sakon; Kondo, Takeshi; Ito, Shingo; Tachikawa, Masanori; Niwa, Shin-Ichi; Yabe, Hirooki; Terasaki, Tetsuya; Setou, Mitsutoshi; Ohtsuki, Sumio

    2017-03-31

    Schizophrenia is a disabling mental illness associated with dysfunction of the prefrontal cortex, which affects cognition and emotion. The purpose of the present study was to identify altered molecular networks in the prefrontal cortex of schizophrenia patients by comparing protein expression levels in autopsied brains of patients and controls, using a combination of targeted and focused quantitative proteomics. We selected 125 molecules possibly related to schizophrenia for quantification by knowledge-based targeted proteomics. Among the quantified molecules, GRIK4 and MAO-B were significantly decreased in plasma membrane and cytosolic fractions, respectively, of prefrontal cortex. Focused quantitative proteomics identified 15 increased and 39 decreased proteins. Network analysis identified "GNA13-ERK1-eIF4G2 signaling" as a downregulated network, and proteins involved in this network were significantly decreased. Furthermore, searching downstream of eIF4G2 revealed that eIF4A1/2 and CYFIP1 were decreased, suggesting that downregulation of the network suppresses expression of CYFIP1, which regulates actin remodeling and is involved in axon outgrowth and spine formation. Downregulation of this signaling seems likely to impair axon formation and synapse plasticity of neuronal cells, and could be associated with development of cognitive impairment in the pathology of schizophrenia. The present study compared the proteome of the prefrontal cortex between schizophrenia patients and healthy controls by means of targeted proteomics and global quantitative proteomics. Targeted proteomics revealed that GRIK4 and MAOB were significantly decreased among 125 putatively schizophrenia-related proteins in prefrontal cortex of schizophrenia patients. Global quantitative proteomics identified 54 differentially expressed proteins in schizophrenia brains. The protein profile indicates attenuation of "GNA13-ERK signaling" in schizophrenia brain. In particular, EIF4G2 and CYFIP1

  10. Towards a proper assignment of systemic risk: the combined roles of network topology and shock characteristics.

    Directory of Open Access Journals (Sweden)

    Lasse Loepfe

    Full Text Available The 2007-2008 financial crisis solidified the consensus among policymakers that a macro-prudential approach to regulation and supervision should be adopted. The currently preferred policy option is the regulation of capital requirements, with the main focus on combating procyclicality and on identifying the banks that have a high systemic importance, those that are "too big to fail". Here we argue that the concept of systemic risk should include the analysis of the system as a whole and we explore systematically the most important properties for policy purposes of networks topology on resistance to shocks. In a thorough study going from analytical models to empirical data, we show two sharp transitions from safe to risky regimes: 1 diversification becomes harmful with just a small fraction (~2% of the shocks sampled from a fat tailed shock distributions and 2 when large shocks are present a critical link density exists where an effective giant cluster forms and most firms become vulnerable. This threshold depends on the network topology, especially on modularity. Firm size heterogeneity has important but diverse effects that are heavily dependent on shock characteristics. Similarly, degree heterogeneity increases vulnerability only when shocks are directed at the most connected firms. Furthermore, by studying the structure of the core of the transnational corporation network from real data, we show that its stability could be clearly increased by removing some of the links with highest centrality betweenness. Our results provide a novel insight and arguments for policy makers to focus surveillance on the connections between firms, in addition to capital requirements directed at the nodes.

  11. Towards a proper assignment of systemic risk: the combined roles of network topology and shock characteristics.

    Science.gov (United States)

    Loepfe, Lasse; Cabrales, Antonio; Sánchez, Angel

    2013-01-01

    The 2007-2008 financial crisis solidified the consensus among policymakers that a macro-prudential approach to regulation and supervision should be adopted. The currently preferred policy option is the regulation of capital requirements, with the main focus on combating procyclicality and on identifying the banks that have a high systemic importance, those that are "too big to fail". Here we argue that the concept of systemic risk should include the analysis of the system as a whole and we explore systematically the most important properties for policy purposes of networks topology on resistance to shocks. In a thorough study going from analytical models to empirical data, we show two sharp transitions from safe to risky regimes: 1) diversification becomes harmful with just a small fraction (~2%) of the shocks sampled from a fat tailed shock distributions and 2) when large shocks are present a critical link density exists where an effective giant cluster forms and most firms become vulnerable. This threshold depends on the network topology, especially on modularity. Firm size heterogeneity has important but diverse effects that are heavily dependent on shock characteristics. Similarly, degree heterogeneity increases vulnerability only when shocks are directed at the most connected firms. Furthermore, by studying the structure of the core of the transnational corporation network from real data, we show that its stability could be clearly increased by removing some of the links with highest centrality betweenness. Our results provide a novel insight and arguments for policy makers to focus surveillance on the connections between firms, in addition to capital requirements directed at the nodes.

  12. The spatial decision-supporting system combination of RBR & CBR based on artificial neural network and association rules

    Science.gov (United States)

    Tian, Yangge; Bian, Fuling

    2007-06-01

    The technology of artificial intelligence should be imported on the basis of the geographic information system to bring up the spatial decision-supporting system (SDSS). The paper discusses the structure of SDSS, after comparing the characteristics of RBR and CBR, the paper brings up the frame of a spatial decisional system that combines RBR and CBR, which has combined the advantages of them both. And the paper discusses the CBR in agriculture spatial decisions, the application of ANN (Artificial Neural Network) in CBR, and enriching the inference rule base based on association rules, etc. And the paper tests and verifies the design of this system with the examples of the evaluation of the crops' adaptability.

  13. Combined Geometric and Neural Network Approach to Generic Fault Diagnosis in Satellite Actuators and Sensors

    DEFF Research Database (Denmark)

    Baldi, P.; Blanke, Mogens; Castaldi, P.

    2016-01-01

    This paper presents a novel scheme for diagnosis of faults affecting the sensors measuring the satellite attitude, body angular velocity and flywheel spin rates as well as defects related to the control torques provided by satellite reaction wheels. A nonlinear geometric design is used to avoid...... that aerodynamic disturbance torques have unwanted influence on the residuals exploited for fault detection and isolation. Radial basis function neural networks are used to obtain fault estimation filters that do not need a priori information about the fault internal models. Simulation results are based...... on a detailed nonlinear satellite model with embedded disturbance description. The results document the efficacy of the proposed diagnosis scheme....

  14. Predicting targeted drug combinations based on Pareto optimal patterns of coexpression network connectivity.

    Science.gov (United States)

    Penrod, Nadia M; Greene, Casey S; Moore, Jason H

    2014-01-01

    Molecularly targeted drugs promise a safer and more effective treatment modality than conventional chemotherapy for cancer patients. However, tumors are dynamic systems that readily adapt to these agents activating alternative survival pathways as they evolve resistant phenotypes. Combination therapies can overcome resistance but finding the optimal combinations efficiently presents a formidable challenge. Here we introduce a new paradigm for the design of combination therapy treatment strategies that exploits the tumor adaptive process to identify context-dependent essential genes as druggable targets. We have developed a framework to mine high-throughput transcriptomic data, based on differential coexpression and Pareto optimization, to investigate drug-induced tumor adaptation. We use this approach to identify tumor-essential genes as druggable candidates. We apply our method to a set of ER(+) breast tumor samples, collected before (n = 58) and after (n = 60) neoadjuvant treatment with the aromatase inhibitor letrozole, to prioritize genes as targets for combination therapy with letrozole treatment. We validate letrozole-induced tumor adaptation through coexpression and pathway analyses in an independent data set (n = 18). We find pervasive differential coexpression between the untreated and letrozole-treated tumor samples as evidence of letrozole-induced tumor adaptation. Based on patterns of coexpression, we identify ten genes as potential candidates for combination therapy with letrozole including EPCAM, a letrozole-induced essential gene and a target to which drugs have already been developed as cancer therapeutics. Through replication, we validate six letrozole-induced coexpression relationships and confirm the epithelial-to-mesenchymal transition as a process that is upregulated in the residual tumor samples following letrozole treatment. To derive the greatest benefit from molecularly targeted drugs it is critical to design combination

  15. Acoustic measurement and morphological features of organic sediment deposits in combined sewer networks.

    Science.gov (United States)

    Carnacina, Iacopo; Larrarte, Frédérique; Leonardi, Nicoletta

    2017-04-01

    The performance of sewer networks has important consequences from an environmental and social point of view. Poor functioning can result in flood risk and pollution at a large scale. Sediment deposits forming in sewer trunks might severely compromise the sewer line by affecting the flow field, reducing cross-sectional areas, and increasing roughness coefficients. In spite of numerous efforts, the morphological features of these depositional environments remain poorly understood. The interface between water and sediment remains inefficiently identified and the estimation of the stock of deposit is frequently inaccurate. In part, this is due to technical issues connected to difficulties in collecting accurate field measurements without disrupting existing morphologies. In this paper, results from an extensive field campaign are presented; during the campaign a new survey methodology based on acoustic techniques has been tested. Furthermore, a new algorithm for the detection of the soil-water interface, and therefore for the correct esteem of sediment stocks is proposed. Finally, results in regard to bed topography, and morphological features at two different field sites are presented and reveal that a large variability in bed forms is present along sewer networks. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. Fast Simulation of Membrane Filtration by Combining Particle Retention Mechanisms and Network Models

    Science.gov (United States)

    Krupp, Armin; Griffiths, Ian; Please, Colin

    2016-11-01

    Porous membranes are used for their particle retention capabilities in a wide range of industrial filtration processes. The underlying mechanisms for particle retention are complex and often change during the filtration process, making it hard to predict the change in permeability of the membrane during the process. Recently, stochastic network models have been shown to predict the change in permeability based on retention mechanisms, but remain computationally intensive. We show that the averaged behaviour of such a stochastic network model can efficiently be computed using a simple partial differential equation. Moreover, we also show that the geometric structure of the underlying membrane and particle-size distribution can be represented in our model, making it suitable for modelling particle retention in interconnected membranes as well. We conclude by demonstrating the particular application to microfluidic filtration, where the model can be used to efficiently compute a probability density for flux measurements based on the geometry of the pores and particles. A. U. K. is grateful for funding from Pall Corporation and the Mathematical Institute, University of Oxford. I.M.G. gratefully acknowledges support from the Royal Society through a University Research Fellowship.

  17. Combining Wireless Sensor Networks and Semantic Middleware for an Internet of Things-Based Sportsman/Woman Monitoring Application

    Directory of Open Access Journals (Sweden)

    Lourdes López

    2013-01-01

    Full Text Available Wireless Sensor Networks (WSNs are spearheading the efforts taken to build and deploy systems aiming to accomplish the ultimate objectives of the Internet of Things. Due to the sensors WSNs nodes are provided with, and to their ubiquity and pervasive capabilities, these networks become extremely suitable for many applications that so-called conventional cabled or wireless networks are unable to handle. One of these still underdeveloped applications is monitoring physical parameters on a person. This is an especially interesting application regarding their age or activity, for any detected hazardous parameter can be notified not only to the monitored person as a warning, but also to any third party that may be helpful under critical circumstances, such as relatives or healthcare centers. We propose a system built to monitor a sportsman/woman during a workout session or performing a sport-related indoor activity. Sensors have been deployed by means of several nodes acting as the nodes of a WSN, along with a semantic middleware development used for hardware complexity abstraction purposes. The data extracted from the environment, combined with the information obtained from the user, will compose the basis of the services that can be obtained.

  18. Novel amphiphilic poly(dimethylsiloxane) based polyurethane networks tethered with carboxybetaine and their combined antibacterial and anti-adhesive property

    Science.gov (United States)

    Jiang, Jingxian; Fu, Yuchen; Zhang, Qinghua; Zhan, Xiaoli; Chen, Fengqiu

    2017-08-01

    The traditional nonfouling materials are powerless against bacterial cells attachment, while the hydrophobic bactericidal surfaces always suffer from nonspecific protein adsorption and dead bacterial cells accumulation. Here, amphiphilic polyurethane (PU) networks modified with poly(dimethylsiloxane) (PDMS) and cationic carboxybetaine diol through simple crosslinking reaction were developed, which had an antibacterial efficiency of 97.7%. Thereafter, the hydrolysis of carboxybetaine ester into zwitterionic groups brought about anti-adhesive properties against bacteria and proteins. The surface chemical composition and wettability performance of the PU network surfaces were investigated by attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), X-ray photoelectron spectroscopy (XPS) and contact angle analysis. The surface distribution of PDMS and zwitterionic segments produced an obvious amphiphilic heterogeneous surface, which was demonstrated by atomic force microscopy (AFM). Enzyme-linked immunosorbent assays (ELISA) were used to test the nonspecific protein adsorption behaviors. With the advantages of the transition from excellent bactericidal performance to anti-adhesion and the combination of fouling resistance and fouling release property, the designed PDMS-based amphiphilic PU network shows great application potential in biomedical devices and marine facilities.

  19. Combining wireless sensor networks and semantic middleware for an Internet of Things-based sportsman/woman monitoring application.

    Science.gov (United States)

    Rodríguez-Molina, Jesús; Martínez, José-Fernán; Castillejo, Pedro; López, Lourdes

    2013-01-31

    Wireless Sensor Networks (WSNs) are spearheading the efforts taken to build and deploy systems aiming to accomplish the ultimate objectives of the Internet of Things. Due to the sensors WSNs nodes are provided with, and to their ubiquity and pervasive capabilities, these networks become extremely suitable for many applications that so-called conventional cabled or wireless networks are unable to handle. One of these still underdeveloped applications is monitoring physical parameters on a person. This is an especially interesting application regarding their age or activity, for any detected hazardous parameter can be notified not only to the monitored person as a warning, but also to any third party that may be helpful under critical circumstances, such as relatives or healthcare centers. We propose a system built to monitor a sportsman/woman during a workout session or performing a sport-related indoor activity. Sensors have been deployed by means of several nodes acting as the nodes of a WSN, along with a semantic middleware development used for hardware complexity abstraction purposes. The data extracted from the environment, combined with the information obtained from the user, will compose the basis of the services that can be obtained.

  20. Combining Wireless Sensor Networks and Semantic Middleware for an Internet of Things-Based Sportsman/Woman Monitoring Application

    Science.gov (United States)

    Rodríguez-Molina, Jesús; Martínez, José-Fernán; Castillejo, Pedro; López, Lourdes

    2013-01-01

    Wireless Sensor Networks (WSNs) are spearheading the efforts taken to build and deploy systems aiming to accomplish the ultimate objectives of the Internet of Things. Due to the sensors WSNs nodes are provided with, and to their ubiquity and pervasive capabilities, these networks become extremely suitable for many applications that so-called conventional cabled or wireless networks are unable to handle. One of these still underdeveloped applications is monitoring physical parameters on a person. This is an especially interesting application regarding their age or activity, for any detected hazardous parameter can be notified not only to the monitored person as a warning, but also to any third party that may be helpful under critical circumstances, such as relatives or healthcare centers. We propose a system built to monitor a sportsman/woman during a workout session or performing a sport-related indoor activity. Sensors have been deployed by means of several nodes acting as the nodes of a WSN, along with a semantic middleware development used for hardware complexity abstraction purposes. The data extracted from the environment, combined with the information obtained from the user, will compose the basis of the services that can be obtained. PMID:23385405

  1. Exploring the evolution of post-acupuncture resting-state networks combining ICA and multivariate Granger causality.

    Science.gov (United States)

    Zhong, Chongguang; Bai, Lijun; Dai, Ruwei; Xue, Ting; Feng, Yuanyuan; Wang, Hu; Liu, Zhenyu; You, Youbo; Tian, Jie

    2011-01-01

    The sustained effects of acupuncture have been widely applied to clinical treatment, thus it can be assumed that the relatively functional specificity of acupoints may evolve as the function of time. In this study, we originally combined ICA and multivariate Granger causality analysis to explore the causal interactions within and among the post-acupuncture resting-state networks (RSNs) at a hearing-related acupoint GB40, with the cognition-related acupoint KI3 as a control. Following acupuncture at GB40, the superior temporal gyrus (STG) and anterior insula (AI) within auditory network appeared persistent bidirectional connection with maximal strength, and the interactions between the auditory network and others became more complex as time passed. For KI3, both the superior parietal lobule (SPL) and dorsolateral prefrontal cortex (DLPFC), as vital nuclei of cognitive function, emerged increased causal outflows and inflows as time went on. We concluded that acupuncture at different acupoints may exert different evolutive effects on causal interactions within and across the RSNs during segmented post-stimulus resting states.

  2. The development of artificial neural networks to predict virological response to combination HIV therapy

    NARCIS (Netherlands)

    Larder, Brendan; Wang, Dechao; Revell, Andrew; Montaner, Julio; Harrigan, Richard; de Wolf, Frank; Lange, Joep; Wegner, Scott; Ruiz, Lidia; Pérez-Elías, Maria Jésus; Emery, Sean; Gatell, Jose; D'Arminio Monforte, Antonella; Torti, Carlo; Zazzi, Maurizio; Lane, Clifford

    2007-01-01

    When used in combination, antiretroviral drugs are highly effective for suppressing HIV replication. Nevertheless, treatment failure commonly occurs and is generally associated with viral drug resistance. The choice of an alternative regimen may be guided by a drug-resistance test. However,

  3. Risk Analysis for Cascade Reservoirs Collapse Based on Bayesian Networks under the Combined Action of Flood and Landslide Surge

    Directory of Open Access Journals (Sweden)

    Ping Li

    2016-01-01

    Full Text Available A method based on a Bayesian network (BN combined with stochastic Monte Carlo (MC simulation is used in this research to calculate the probability and analyze the risk of a single reservoir dam overtopping and two reservoirs collapsing under the combined action of flood and landslide surge. Two adjacent cascade reservoirs on the Dadu River are selected for risk calculation and analysis. The results show that the conditional probability of a dam overtopping due to flooding in a single reservoir is relatively small; the conditional probability of a dam overtopping due to landslide surge in a single reservoir is relatively large; a combination of flooding and landslide surge greatly increases the risk of the dam overtopping. The conditional probability that the dam in (downstream Changheba reservoir overtops as a result of a dam-break flood from (upstream Houziyan reservoir is greater than 0.8 when the water in Changheba reservoir is at its normal level. Under the combined action of flooding and landslide surges, the joint probability that the two cascade reservoirs collapse in a variety of typical situations is very small.

  4. Multiscale River Hydraulic and Water Quality Observations Combining Stationary and Mobile Sensor Network Nodes

    Science.gov (United States)

    Harmon, T. C.; Fisher, J. C.; Kaiser, W. J.

    2006-05-01

    Increasing demands on water supplies, non-point source pollution, and water quality-based ecological concerns all point to the need for observing stream flow perturbations and pollutant discharges at higher resolution than was practical in the past. This work presents the results from a test of a rapidly deployable river observational approach consisting of four components: (1) existing geospatial data and federal, state, and private river gauging infrastructure for identifying key river reaches and critical sampling times, (2) human- actuated sensor deployments for broad spatial characterization of the targeted river reach, (3) stationary sensors embedded in the river and its sediments for longer term spatiotemporal observations within the targeted reach, and (4) the robotic Networked Infomechanical System (NIMS RD) for high resolution scanning of spatiotemporal hydraulic and chemical properties at specific points along the reach. The approach is demonstrated for a test bed deployment at the confluence of the Merced and San Joaquin Rivers in Central California. After identifying a suitable reach for the test deployment, a network of on-line gauging stations, accessed through the California Data Exchange Center (CDEC), is used to coordinate the timing of the subsequent three deployment aspects based on flow and river stage forecasts. Kayak-mounted sonar and water quality sensors are used to rapidly survey the test zone bathymetry and basic water quality parameters (temperature, salinity). Results from the rapid survey are then used to guide locations of the sediment- anchored sensor arrays (javelins) which include temperature, water pressure (depth) and water quality sensors distributed vertically at screened intervals. The NIMS RD is comprised of two supporting towers and a suspension cable delivering power and Internet connectivity for controlling and actuating the tram-like NIMS unit. The NIMS unit is capable of raising and lowering a payload of sensors

  5. Application of 1 D Finite Element Method in Combination with Laminar Solution Method for Pipe Network Analysis

    Science.gov (United States)

    Dudar, O. I.; Dudar, E. S.

    2017-11-01

    The features of application of the 1D dimensional finite element method (FEM) in combination with the laminar solutions method (LSM) for the calculation of underground ventilating networks are considered. In this case the processes of heat and mass transfer change the properties of a fluid (binary vapour-air mix). Under the action of gravitational forces it leads to such phenomena as natural draft, local circulation, etc. The FEM relations considering the action of gravity, the mass conservation law, the dependence of vapour-air mix properties on the thermodynamic parameters are derived so that it allows one to model the mentioned phenomena. The analogy of the elastic and plastic rod deformation processes to the processes of laminar and turbulent flow in a pipe is described. Owing to this analogy, the guaranteed convergence of the elastic solutions method for the materials of plastic type means the guaranteed convergence of the LSM for any regime of a turbulent flow in a rough pipe. By means of numerical experiments the convergence rate of the FEM - LSM is investigated. This convergence rate appeared much higher than the convergence rate of the Cross – Andriyashev method. Data of other authors on the convergence rate comparison for the finite element method, the Newton method and the method of gradient are provided. These data allow one to conclude that the FEM in combination with the LSM is one of the most effective methods of calculation of hydraulic and ventilating networks. The FEM - LSM has been used for creation of the research application programme package “MineClimate” allowing to calculate the microclimate parameters in the underground ventilating networks.

  6. The combined geodetic network adjusted on the reference ellipsoid – a comparison of three functional models for GNSS observations

    Directory of Open Access Journals (Sweden)

    Kadaj Roman

    2016-12-01

    Full Text Available The adjustment problem of the so-called combined (hybrid, integrated network created with GNSS vectors and terrestrial observations has been the subject of many theoretical and applied works. The network adjustment in various mathematical spaces was considered: in the Cartesian geocentric system on a reference ellipsoid and on a mapping plane. For practical reasons, it often takes a geodetic coordinate system associated with the reference ellipsoid. In this case, the Cartesian GNSS vectors are converted, for example, into geodesic parameters (azimuth and length on the ellipsoid, but the simple form of converted pseudo-observations are the direct differences of the geodetic coordinates. Unfortunately, such an approach may be essentially distorted by a systematic error resulting from the position error of the GNSS vector, before its projection on the ellipsoid surface. In this paper, an analysis of the impact of this error on the determined measures of geometric ellipsoid elements, including the differences of geodetic coordinates or geodesic parameters is presented. Assuming that the adjustment of a combined network on the ellipsoid shows that the optimal functional approach in relation to the satellite observation, is to create the observational equations directly for the original GNSS Cartesian vector components, writing them directly as a function of the geodetic coordinates (in numerical applications, we use the linearized forms of observational equations with explicitly specified coefficients. While retaining the original character of the Cartesian vector, one avoids any systematic errors that may occur in the conversion of the original GNSS vectors to ellipsoid elements, for example the vector of the geodesic parameters. The problem is theoretically developed and numerically tested. An example of the adjustment of a subnet loaded from the database of reference stations of the ASG-EUPOS system was considered for the preferred functional

  7. Combining convolutional neural networks and Hough Transform for classification of images containing lines

    Science.gov (United States)

    Sheshkus, Alexander; Limonova, Elena; Nikolaev, Dmitry; Krivtsov, Valeriy

    2017-03-01

    In this paper, we propose an expansion of convolutional neural network (CNN) input features based on Hough Transform. We perform morphological contrasting of source image followed by Hough Transform, and then use it as input for some convolutional filters. Thus, CNNs computational complexity and the number of units are not affected. Morphological contrasting and Hough Transform are the only additional computational expenses of introduced CNN input features expansion. Proposed approach was demonstrated on the example of CNN with very simple structure. We considered two image recognition problems, that were object classification on CIFAR-10 and printed character recognition on private dataset with symbols taken from Russian passports. Our approach allowed to reach noticeable accuracy improvement without taking much computational effort, which can be extremely important in industrial recognition systems or difficult problems utilising CNNs, like pressure ridge analysis and classification.

  8. Combining Social Networks and Semantic Web Technologies for Personalizing Web Access

    Science.gov (United States)

    Carminati, Barbara; Ferrari, Elena; Perego, Andrea

    The original purpose of Web metadata was to protect end-users from possible harmful content and to simplify search and retrieval. However they can also be also exploited in more enhanced applications, such as Web access personalization on the basis of end-users’ preferences. In order to achieve this, it is however necessary to address several issues. One of the most relevant is how to assess the trustworthiness of Web metadata. In this paper, we discuss how such issue can be addressed through the use of collaborative and Semantic Web technologies. The system we propose is based on a Web-based Social Network, where members are able not only to specify labels, but also to rate existing labels. Both labels and ratings are then used to assess the trustworthiness of resources’ descriptions and to enforce Web access personalization.

  9. Growing adaptive machines combining development and learning in artificial neural networks

    CERN Document Server

    Bredeche, Nicolas; Doursat, René

    2014-01-01

    The pursuit of artificial intelligence has been a highly active domain of research for decades, yielding exciting scientific insights and productive new technologies. In terms of generating intelligence, however, this pursuit has yielded only limited success. This book explores the hypothesis that adaptive growth is a means of moving forward. By emulating the biological process of development, we can incorporate desirable characteristics of natural neural systems into engineered designs, and thus move closer towards the creation of brain-like systems. The particular focus is on how to design artificial neural networks for engineering tasks. The book consists of contributions from 18 researchers, ranging from detailed reviews of recent domains by senior scientists, to exciting new contributions representing the state of the art in machine learning research. The book begins with broad overviews of artificial neurogenesis and bio-inspired machine learning, suitable both as an introduction to the domains and as a...

  10. Combined application of Artificial Neural Networks and life cycle assessment in lentil farming in Iran

    Directory of Open Access Journals (Sweden)

    Behzad Elhami

    2017-03-01

    Full Text Available In this study, an Artificial Neural Network (ANN was applied to model yield and environmental emissions from lentil cultivation in Esfahan province of Iran. Data was gathered from lentil farmers using face to face questionnaire method during 2014–2015 cropping season. Life cycle assessment (LCA was applied to investigate the environmental impact categories associated with lentil production. Based on the results, total energy input, energy output to input ratio and energy productivity were determined to be 32,970.10 MJ ha−1, 0.902 and 0.06 kg MJ−1, respectively. The greatest amount of energy consumption was attributed to chemical fertilizer (42.76%. Environmental analysis indicated that the acidification potential was higher than other environmental impact categories in lentil production system. Also results showed that the production of agricultural machinery was the main hotspot in abiotic depletion, eutrophication, global warming, human toxicity, fresh water aquatic ecotoxicity, marine aquatic ecotoxicity and terrestrial ecotoxicity impact categories, while direct emissions associated with lentil cultivation was the main hotspot in acidification potential and photochemical oxidation potential. In addition, diesel fuel was the main hotspot only in ozone layer depletion. The ANN model with 9-10-6-11 structure was identified as the most appropriate network for predicting yield and related environmental impact categories of lentil cultivation. Overall, the results of sensitivity analysis revealed that farmyard manure had the greatest effect on the most of the environmental impacts, while machinery was the most affecting parameter on the yield of the crop.

  11. Design and Performance Investigation for the Optical Combinational Networks at High Data Rate

    Science.gov (United States)

    Tripathi, Devendra Kr.

    2017-05-01

    This article explores performance study for optical combinational designs based on nonlinear characteristics with semiconductor optical amplifier (SOA). Two configurations for optical half-adder with non-return-to-zero modulation pattern altogether with Mach-Zehnder modulator, interferometer at 50-Gbps data rate have been successfully realized. Accordingly, SUM and CARRY outputs have been concurrently executed and verified for their output waveforms. Numerical simulations for variation of data rate and key design parameters have been effectively executed outcome with optimum performance. Investigations depict overall good performance of the design in terms of the extinction factor. It also inferred that all-optical realization based on SOA is competent scheme, as it circumvents costly optoelectronic translation. This could be well supportive to erect larger complex optical combinational circuits.

  12. Different combined oral contraceptives and the risk of venous thrombosis: systematic review and network meta-analysis

    Science.gov (United States)

    Stegeman, Bernardine H; de Bastos, Marcos; Rosendaal, Frits R; van Hylckama Vlieg, A; Helmerhorst, Frans M; Stijnen, Theo

    2013-01-01

    Objective To provide a comprehensive overview of the risk of venous thrombosis in women using different combined oral contraceptives. Design Systematic review and network meta-analysis. Data sources PubMed, Embase, Web of Science, Cochrane, Cumulative Index to Nursing and Allied Health Literature, Academic Search Premier, and ScienceDirect up to 22 April 2013. Review methods Observational studies that assessed the effect of combined oral contraceptives on venous thrombosis in healthy women. The primary outcome of interest was a fatal or non-fatal first event of venous thrombosis with the main focus on deep venous thrombosis or pulmonary embolism. Publications with at least 10 events in total were eligible. The network meta-analysis was performed using an extension of frequentist random effects models for mixed multiple treatment comparisons. Unadjusted relative risks with 95% confidence intervals were reported. The requirement for crude numbers did not allow adjustment for potential confounding variables. Results 3110 publications were retrieved through a search strategy; 25 publications reporting on 26 studies were included. Incidence of venous thrombosis in non-users from two included cohorts was 1.9 and 3.7 per 10 000 woman years, in line with previously reported incidences of 1-6 per 10 000 woman years. Use of combined oral contraceptives increased the risk of venous thrombosis compared with non-use (relative risk 3.5, 95% confidence interval 2.9 to 4.3). The relative risk of venous thrombosis for combined oral contraceptives with 30-35 µg ethinylestradiol and gestodene, desogestrel, cyproterone acetate, or drospirenone were similar and about 50-80% higher than for combined oral contraceptives with levonorgestrel. A dose related effect of ethinylestradiol was observed for gestodene, desogestrel, and levonorgestrel, with higher doses being associated with higher thrombosis risk. Conclusion All combined oral contraceptives investigated in this analysis were

  13. Regional brain network organization distinguishes the combined and inattentive subtypes of Attention Deficit Hyperactivity Disorder

    OpenAIRE

    Jacqueline F. Saad; Kristi R. Griffiths; Michael R. Kohn; Simon Clarke; Leanne M. Williams; Mayuresh S. Korgaonkar

    2017-01-01

    Attention Deficit Hyperactivity Disorder (ADHD) is characterized clinically by hyperactive/impulsive and/or inattentive symptoms which determine diagnostic subtypes as Predominantly Hyperactive-Impulsive (ADHD-HI), Predominantly Inattentive (ADHD-I), and Combined (ADHD-C). Neuroanatomically though we do not yet know if these clinical subtypes reflect distinct aberrations in underlying brain organization. We imaged 34 ADHD participants defined using DSM-IV criteria as ADHD-I (n?=?16) or as ADH...

  14. A diversity compression and combining technique based on channel shortening for cooperative networks

    KAUST Repository

    Hussain, Syed Imtiaz

    2012-02-01

    The cooperative relaying process with multiple relays needs proper coordination among the communicating and the relaying nodes. This coordination and the required capabilities may not be available in some wireless systems where the nodes are equipped with very basic communication hardware. We consider a scenario where the source node transmits its signal to the destination through multiple relays in an uncoordinated fashion. The destination captures the multiple copies of the transmitted signal through a Rake receiver. We analyze a situation where the number of Rake fingers N is less than that of the relaying nodes L. In this case, the receiver can combine N strongest signals out of L. The remaining signals will be lost and act as interference to the desired signal components. To tackle this problem, we develop a novel signal combining technique based on channel shortening principles. This technique proposes a processing block before the Rake reception which compresses the energy of L signal components over N branches while keeping the noise level at its minimum. The proposed scheme saves the system resources and makes the received signal compatible to the available hardware. Simulation results show that it outperforms the selection combining scheme. © 2012 IEEE.

  15. THE EFFECT OF FUN ATHLETICS EXERCISES ON PSYCHOMOTOR DEVELOPMENT

    Directory of Open Access Journals (Sweden)

    Mustafa Güler

    2017-04-01

    Full Text Available The purpose of this research was to investigate the effect of fun athletics exercises on the psychomotor development. The research group consisted of 9 boys and 27 girls with a total of 36 students between 11-14 ages in Kocaeli. In the study, the fun athletics exercises (featured somersault, obstacles to jump, slip under the barrier, slalom, training ladder, double jump, medicine ball handling applied for 90 minutes a day and 3 days a week over 8 weeks. The data was analyzed with Wilcoxon Matched Pairs Test and significance level was determined as α=0.05. According to findings of this study significant differences were found between pre and post tests results. As a result this study showed that fun athletic exercises have a positive effect on 11-14 aged children’s psychomotor development.

  16. Compression and Combining Based on Channel Shortening and Rank Reduction Technique for Cooperative Wireless Sensor Networks

    KAUST Repository

    Ahmed, Qasim Zeeshan

    2013-12-18

    This paper investigates and compares the performance of wireless sensor networks where sensors operate on the principles of cooperative communications. We consider a scenario where the source transmits signals to the destination with the help of L sensors. As the destination has the capacity of processing only U out of these L signals, the strongest U signals are selected while the remaining (L?U) signals are suppressed. A preprocessing block similar to channel-shortening is proposed in this contribution. However, this preprocessing block employs a rank-reduction technique instead of channel-shortening. By employing this preprocessing, we are able to decrease the computational complexity of the system without affecting the bit error rate (BER) performance. From our simulations, it can be shown that these schemes outperform the channel-shortening schemes in terms of computational complexity. In addition, the proposed schemes have a superior BER performance as compared to channel-shortening schemes when sensors employ fixed gain amplification. However, for sensors which employ variable gain amplification, a tradeoff exists in terms of BER performance between the channel-shortening and these schemes. These schemes outperform channel-shortening scheme for lower signal-to-noise ratio.

  17. Combined expert system/neural networks method for process fault diagnosis

    Science.gov (United States)

    Reifman, Jaques; Wei, Thomas Y. C.

    1995-01-01

    A two-level hierarchical approach for process fault diagnosis is an operating system employs a function-oriented approach at a first level and a component characteristic-oriented approach at a second level, where the decision-making procedure is structured in order of decreasing intelligence with increasing precision. At the first level, the diagnostic method is general and has knowledge of the overall process including a wide variety of plant transients and the functional behavior of the process components. An expert system classifies malfunctions by function to narrow the diagnostic focus to a particular set of possible faulty components that could be responsible for the detected functional misbehavior of the operating system. At the second level, the diagnostic method limits its scope to component malfunctions, using more detailed knowledge of component characteristics. Trained artificial neural networks are used to further narrow the diagnosis and to uniquely identify the faulty component by classifying the abnormal condition data as a failure of one of the hypothesized components through component characteristics. Once an anomaly is detected, the hierarchical structure is used to successively narrow the diagnostic focus from a function misbehavior, i.e., a function oriented approach, until the fault can be determined, i.e., a component characteristic-oriented approach.

  18. Combined expert system/neural networks method for process fault diagnosis

    Science.gov (United States)

    Reifman, J.; Wei, T.Y.C.

    1995-08-15

    A two-level hierarchical approach for process fault diagnosis of an operating system employs a function-oriented approach at a first level and a component characteristic-oriented approach at a second level, where the decision-making procedure is structured in order of decreasing intelligence with increasing precision. At the first level, the diagnostic method is general and has knowledge of the overall process including a wide variety of plant transients and the functional behavior of the process components. An expert system classifies malfunctions by function to narrow the diagnostic focus to a particular set of possible faulty components that could be responsible for the detected functional misbehavior of the operating system. At the second level, the diagnostic method limits its scope to component malfunctions, using more detailed knowledge of component characteristics. Trained artificial neural networks are used to further narrow the diagnosis and to uniquely identify the faulty component by classifying the abnormal condition data as a failure of one of the hypothesized components through component characteristics. Once an anomaly is detected, the hierarchical structure is used to successively narrow the diagnostic focus from a function misbehavior, i.e., a function oriented approach, until the fault can be determined, i.e., a component characteristic-oriented approach. 9 figs.

  19. Adaptive Steganalysis Based on Selection Region and Combined Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Donghui Hu

    2017-01-01

    Full Text Available Digital image steganalysis is the art of detecting the presence of information hiding in carrier images. When detecting recently developed adaptive image steganography methods, state-of-art steganalysis methods cannot achieve satisfactory detection accuracy, because the adaptive steganography methods can adaptively embed information into regions with rich textures via the guidance of distortion function and thus make the effective steganalysis features hard to be extracted. Inspired by the promising success which convolutional neural network (CNN has achieved in the fields of digital image analysis, increasing researchers are devoted to designing CNN based steganalysis methods. But as for detecting adaptive steganography methods, the results achieved by CNN based methods are still far from expected. In this paper, we propose a hybrid approach by designing a region selection method and a new CNN framework. In order to make the CNN focus on the regions with complex textures, we design a region selection method by finding a region with the maximal sum of the embedding probabilities. To evolve more diverse and effective steganalysis features, we design a new CNN framework consisting of three separate subnets with independent structure and configuration parameters and then merge and split the three subnets repeatedly. Experimental results indicate that our approach can lead to performance improvement in detecting adaptive steganography.

  20. Combining multiple algorithms for road network tracking from multiple source remotely sensed imagery: a practical system and performance evaluation.

    Science.gov (United States)

    Lin, Xiangguo; Liu, Zhengjun; Zhang, Jixian; Shen, Jing

    2009-01-01

    In light of the increasing availability of commercial high-resolution imaging sensors, automatic interpretation tools are needed to extract road features. Currently, many approaches for road extraction are available, but it is acknowledged that there is no single method that would be successful in extracting all types of roads from any remotely sensed imagery. In this paper, a novel classification of roads is proposed, based on both the roads' geometrical, radiometric properties and the characteristics of the sensors. Subsequently, a general road tracking framework is proposed, and one or more suitable road trackers are designed or combined for each type of roads. Extensive experiments are performed to extract roads from aerial/satellite imagery, and the results show that a combination strategy can automatically extract more than 60% of the total roads from very high resolution imagery such as QuickBird and DMC images, with a time-saving of approximately 20%, and acceptable spatial accuracy. It is proven that a combination of multiple algorithms is more reliable, more efficient and more robust for extracting road networks from multiple-source remotely sensed imagery than the individual algorithms.

  1. Development and validation of a new PCR optimization method by combining experimental design and artificial neural network.

    Science.gov (United States)

    Li, Ye; Du, Xueling; Yuan, Qipeng; Lv, Xinhua

    2010-01-01

    Polymerase chain reaction (PCR) is one of the most powerful techniques in a variety of clinical and biological research fields. In this paper, a chemometrics approach, combining experimental design (ED) and artificial neural network (ANN), was proposed for optimization of PCR amplification of lycopene cyclase gene carRA in Blakeslea Trispora. Five-level star design was carried out to obtain experimental information and provide data source for ANN modeling. Nine variables were used as inputs in ANN, including the added amount of template, primer, dNTP, polymerase and magnesium ion, the temperature of denaturating, annealing and extension, and the number of cycles. The output variable was the efficiency (yield) of the PCR. Based on the developed model, the effects of each parameter on PCR efficiency were predicted and the most suitable operation condition for present system was determined. At last, the validation experiment was performed under the optimized condition, and the expectant results were produced. The results obtained in this paper showed that the combination of ANN and ED provided a satisfactory optimization model with good descriptive and predictive abilities, indicating that the method of combining ANN and ED can be a useful tool in PCR optimization and other biological applications.

  2. Combining CFD simulations with blockoriented heatflow-network model for prediction of photovoltaic energy-production

    Science.gov (United States)

    Haber, I. E.; Farkas, I.

    2011-01-01

    The exterior factors which influencing the working circumstances of photovoltaic modules are the irradiation, the optical air layer (Air Mass - AM), the irradiation angle, the environmental temperature and the cooling effect of the wind. The efficiency of photovoltaic (PV) devices is inversely proportional to the cell temperature and therefore the mounting of the PV modules can have a big affect on the cooling, due to wind flow-around and naturally convection. The construction of the modules could be described by a heatflow-network model, and that can define the equation which determines the cells temperature. An equation like this can be solved as a block oriented model with hybrid-analogue simulator such as Matlab-Simulink. In view of the flow field and the heat transfer, witch was calculated numerically, the heat transfer coefficients can be determined. Five inflow rates were set up for both pitched and flat roof cases, to let the trend of the heat transfer coefficient know, while these functions can be used for the Matlab/Simulink model. To model the free convection flows, the Boussinesq-approximation were used, integrated into the Navier-Stokes equations and the energy equation. It has been found that under a constant solar heat gain, the air velocity around the modules and behind the pitched-roof mounted module is increasing, proportionately to the wind velocities, and as result the heat transfer coefficient increases linearly, and can be described by a function in both cases. To the block based model the meteorological parameters and the results of the CFD simulations as single functions were attached. The final aim was to make a model that could be used for planning photovoltaic systems, and define their accurate performance for better sizing of an array of modules.

  3. Combining network and array waveform coherence for automatic location: examples from induced seismicity monitoring

    Science.gov (United States)

    Sick, Benjamin; Joswig, Manfred

    2017-03-01

    Events from induced seismicity suffer from low signal-to-noise ratios and noise spikes due to the industrial setting. Low magnitude thresholds are needed for traffic light warning systems. Conventional automatic location methods rely on independent picking of first arrivals from seismic wave onsets at recordings of single stations. Picking is done separately and without feedback from the actual location algorithm. If the recording network is small or only few phases can be associated, single wrong associations can lead to large errors in hypocentre locations and magnitude. Event location by source scanning which was established in the last two decades can provide more robust results. This study investigates how source-scanning can be extended and improved by integrating information from seismic arrays, that is, waveform stacking and Fisher ratio. These array methods rely on the coherency of the raw filtered waveforms while traditional source scanning uses a characteristic function to obtain coherency from otherwise incoherent waveforms between distant stations. Short-term/long-term average (STA/LTA) serves as the characteristic function and single station vertical-component traces for P-phases and radial and transverse components for S-phases are used. For array stations, the STA/LTA of the stacked vertical seismogram which is furthermore weighted by the STA/LTA of the Fisher ratio, dependent on backazimuth and slowness, is utilized for P-phases. The new method is tested on two diverse data sets from induced seismicity monitoring. In the chosen examples, the extension by array-processing techniques can reduce mean hypocentre errors up to a factor of 2.9, resolve ambiguities and further restrain the location.

  4. Combining affinity propagation clustering and mutual information network to investigate key genes in fibroid.

    Science.gov (United States)

    Chen, Qian-Song; Wang, Dan; Liu, Bao-Lian; Gao, Shu-Feng; Gao, Dan-Li; Li, Gui-Rong

    2017-07-01

    The aim of the present study was to investigate key genes in fibroids based on the multiple affinity propogation-Krzanowski and Lai (mAP-KL) method, which included the maxT multiple hypothesis, Krzanowski and Lai (KL) cluster quality index, affinity propagation (AP) clustering algorithm and mutual information network (MIN) constructed by the context likelihood of relatedness (CLR) algorithm. In order to achieve this goal, mAP-KL was initially implemented to investigate exemplars in fibroid, and the maxT function was employed to rank the genes of training and test sets, and the top 200 genes were obtained for further study. In addition, the KL cluster index was applied to determine the quantity of clusters and the AP clustering algorithm was conducted to identify the clusters and their exemplars. Subsequently, the support vector machine (SVM) model was selected to evaluate the classification performance of mAP-KL. Finally, topological properties (degree, closeness, betweenness and transitivity) of exemplars in MIN constructed according to the CLR algorithm were assessed to investigate key genes in fibroid. The SVM model validated that the classification between normal controls and fibroid patients by mAP-KL had a good performance. A total of 9 clusters and exemplars were identified based on mAP-KL, which were comprised of CALCOCO2, COL4A2, COPS8, SNCG, PA2G4, C17orf70, MARK3, BTNL3 and TBC1D13. By accessing the topological analysis for exemplars in MIN, SNCG and COL4A2 were identified as the two most significant genes of four types of methods, and they were denoted as key genes in the progress of fibroid. In conclusion, two key genes (SNCG and COL4A2) and 9 exemplars were successfully investigated, and these may be potential biomarkers for the detection and treatment of fibroid.

  5. Optimal Seamline Detection for Orthoimage Mosaicking by Combining Deep Convolutional Neural Network and Graph Cuts

    Directory of Open Access Journals (Sweden)

    Li Li

    2017-07-01

    Full Text Available When mosaicking orthoimages, especially in urban areas with various obvious ground objects like buildings, roads, cars or trees, the detection of optimal seamlines is one of the key technologies for creating seamless and pleasant image mosaics. In this paper, we propose a new approach to detect optimal seamlines for orthoimage mosaicking with the use of deep convolutional neural network (CNN and graph cuts. Deep CNNs have been widely used in many fields of computer vision and photogrammetry in recent years, and graph cuts is one of the most widely used energy optimization frameworks. We first propose a deep CNN for land cover semantic segmentation in overlap regions between two adjacent images. Then, the energy cost of each pixel in the overlap regions is defined based on the classification probabilities of belonging to each of the specified classes. To find the optimal seamlines globally, we fuse the CNN-classified energy costs of all pixels into the graph cuts energy minimization framework. The main advantage of our proposed method is that the pixel similarity energy costs between two images are defined using the classification results of the CNN based semantic segmentation instead of using the image informations of color, gradient or texture as traditional methods do. Another advantage of our proposed method is that the semantic informations are fully used to guide the process of optimal seamline detection, which is more reasonable than only using the hand designed features defined to represent the image differences. Finally, the experimental results on several groups of challenging orthoimages show that the proposed method is capable of finding high-quality seamlines among urban and non-urban orthoimages, and outperforms the state-of-the-art algorithms and the commercial software based on the visual comparison, statistical evaluation and quantitative evaluation based on the structural similarity (SSIM index.

  6. GANN: Genetic algorithm neural networks for the detection of conserved combinations of features in DNA

    Directory of Open Access Journals (Sweden)

    Beiko Robert G

    2005-02-01

    Full Text Available Abstract Background The multitude of motif detection algorithms developed to date have largely focused on the detection of patterns in primary sequence. Since sequence-dependent DNA structure and flexibility may also play a role in protein-DNA interactions, the simultaneous exploration of sequence- and structure-based hypotheses about the composition of binding sites and the ordering of features in a regulatory region should be considered as well. The consideration of structural features requires the development of new detection tools that can deal with data types other than primary sequence. Results GANN (available at http://bioinformatics.org.au/gann is a machine learning tool for the detection of conserved features in DNA. The software suite contains programs to extract different regions of genomic DNA from flat files and convert these sequences to indices that reflect sequence and structural composition or the presence of specific protein binding sites. The machine learning component allows the classification of different types of sequences based on subsamples of these indices, and can identify the best combinations of indices and machine learning architecture for sequence discrimination. Another key feature of GANN is the replicated splitting of data into training and test sets, and the implementation of negative controls. In validation experiments, GANN successfully merged important sequence and structural features to yield good predictive models for synthetic and real regulatory regions. Conclusion GANN is a flexible tool that can search through large sets of sequence and structural feature combinations to identify those that best characterize a set of sequences.

  7. Identification of T1D susceptibility genes within the MHC region by combining protein interaction networks and SNP genotyping data

    DEFF Research Database (Denmark)

    Brorsson, C.; Hansen, Niclas Tue; Hansen, Kasper Lage

    2009-01-01

    region were analysed in 1000 affected offspring trios generated by the Type 1 Diabetes Genetics Consortium (T1DGC). The most associated SNP in each gene was chosen and genes were mapped to ppi networks for identification of interaction partners. The association testing and resulting interacting protein...... are well known in the pathogenesis of T1D, but the modules also contain additional candidates that have been implicated in beta-cell development and diabetic complications. The extensive LD within the MHC region makes it important to develop new methods for analysing genotyping data for identification...... of additional risk genes for T1D. Combining genetic data with knowledge about functional pathways provides new insight into mechanisms underlying T1D....

  8. Combining Image and Non-Image Data for Automatic Detection of Retina Disease in a Telemedicine Network

    Energy Technology Data Exchange (ETDEWEB)

    Aykac, Deniz [ORNL; Chaum, Edward [University of Tennessee, Knoxville (UTK); Fox, Karen [Delta Health Alliance; Garg, Seema [University of North Carolina; Giancardo, Luca [ORNL; Karnowski, Thomas Paul [ORNL; Li, Yaquin [University of Tennessee, Knoxville (UTK); Nichols, Trent L [ORNL; Tobin Jr, Kenneth William [ORNL

    2011-01-01

    A telemedicine network with retina cameras and automated quality control, physiological feature location, and lesion/anomaly detection is a low-cost way of achieving broad-based screening for diabetic retinopathy (DR) and other eye diseases. In the process of a routine eye-screening examination, other non-image data is often available which may be useful in automated diagnosis of disease. In this work, we report on the results of combining this non-image data with image data, using the protocol and processing steps of a prototype system for automated disease diagnosis of retina examinations from a telemedicine network. The system includes quality assessments, automated physiology detection, and automated lesion detection to create an archive of known cases. Non-image data such as diabetes onset date and hemoglobin A1c (HgA1c) for each patient examination are included as well, and the system is used to create a content-based image retrieval engine capable of automated diagnosis of disease into 'normal' and 'abnormal' categories. The system achieves a sensitivity and specificity of 91.2% and 71.6% using hold-one-out validation testing.

  9. A Combination of Central Pattern Generator-based and Reflex-based Neural Networks for Dynamic, Adaptive, Robust Bipedal Locomotion

    DEFF Research Database (Denmark)

    Di Canio, Giuliano; Larsen, Jørgen Christian; Wörgötter, Florentin

    2016-01-01

    Robotic systems inspired from humans have always been lightening up the curiosity of engineers and scientists. Of many challenges, human locomotion is a very difficult one where a number of different systems needs to interact in order to generate a correct and balanced pattern. To simulate the in...... network to generate basic walking patterns of a dynamic bipedal walking robot (DACBOT) and then a CPG-based neural network to ensure robust walking behavior......Robotic systems inspired from humans have always been lightening up the curiosity of engineers and scientists. Of many challenges, human locomotion is a very difficult one where a number of different systems needs to interact in order to generate a correct and balanced pattern. To simulate...... the interaction of these systems, implementations with reflexbased or central pattern generator (CPG)-based controllers have been tested on bipedal robot systems. In this paper we will combine the two controller types, into a controller that works with both reflex and CPG signals. We use a reflex-based neural...

  10. Combining a Complex Network Approach and a SEIR Compartmental Model to link Fast Spreading of Infectious Diseases with Climate Change

    Science.gov (United States)

    Brenner, F.; Hoffmann, P.; Marwan, N.

    2016-12-01

    Infectious diseases are a major threat to human health. The spreading of airborne diseases has become fast and hard to predict. Global air travelling created a network which allows a pathogen to migrate worldwide in only a few days. Pandemics of SARS (2002/03) and H1N1 (2009) have impressively shown the epidemiological danger in a strongly connected world. In this study we simulate the outbreak of an airborne infectious disease that is directly transmitted from human to human. We use a regular Susceptible-Infected-Recovered (SIR) model and a modified Susceptible-Exposed-Infected-Recovered (SEIR) compartmental approach with the basis of a complex network built by global air traffic data (from openflights.org). Local Disease propagation is modeled with a global population dataset (from SEDAC and MaxMind) and parameterizations of human behavior regarding mobility, contacts and awareness. As a final component we combine the worldwide outbreak simulation with daily averaged climate data from WATCH-Forcing-Data-ERA-Interim (WFDEI) and Coupled Model Intercomparison Project Phase 5 (CMIP5). Here we focus on Influenza-like illnesses (ILI), whose transmission rate has a dependency on relative humidity and temperature. Even small changes in relative humidity are sufficient to trigger significant differences in the global outbreak behavior. Apart from the direct effect of climate change on the transmission of airborne diseases, there are indirect ramifications that alter spreading patterns. For example seasonal changing human mobility is influenced by climate settings.

  11. A Practical Application Combining Wireless Sensor Networks and Internet of Things: Safety Management System for Tower Crane Groups

    Directory of Open Access Journals (Sweden)

    Dexing Zhong

    2014-07-01

    Full Text Available The so-called Internet of Things (IoT has attracted increasing attention in the field of computer and information science. In this paper, a specific application of IoT, named Safety Management System for Tower Crane Groups (SMS-TC, is proposed for use in the construction industry field. The operating status of each tower crane was detected by a set of customized sensors, including horizontal and vertical position sensors for the trolley, angle sensors for the jib and load, tilt and wind speed sensors for the tower body. The sensor data is collected and processed by the Tower Crane Safety Terminal Equipment (TC-STE installed in the driver’s operating room. Wireless communication between each TC-STE and the Local Monitoring Terminal (LMT at the ground worksite were fulfilled through a Zigbee wireless network. LMT can share the status information of the whole group with each TC-STE, while the LMT records the real-time data and reports it to the Remote Supervision Platform (RSP through General Packet Radio Service (GPRS. Based on the global status data of the whole group, an anti-collision algorithm was executed in each TC-STE to ensure the safety of each tower crane during construction. Remote supervision can be fulfilled using our client software installed on a personal computer (PC or smartphone. SMS-TC could be considered as a promising practical application that combines a Wireless Sensor Network with the Internet of Things.

  12. A practical application combining wireless sensor networks and Internet of Things: Safety Management System for Tower Crane Groups.

    Science.gov (United States)

    Zhong, Dexing; Lv, Hongqiang; Han, Jiuqiang; Wei, Quanrui

    2014-07-30

    The so-called Internet of Things (IoT) has attracted increasing attention in the field of computer and information science. In this paper, a specific application of IoT, named Safety Management System for Tower Crane Groups (SMS-TC), is proposed for use in the construction industry field. The operating status of each tower crane was detected by a set of customized sensors, including horizontal and vertical position sensors for the trolley, angle sensors for the jib and load, tilt and wind speed sensors for the tower body. The sensor data is collected and processed by the Tower Crane Safety Terminal Equipment (TC-STE) installed in the driver's operating room. Wireless communication between each TC-STE and the Local Monitoring Terminal (LMT) at the ground worksite were fulfilled through a Zigbee wireless network. LMT can share the status information of the whole group with each TC-STE, while the LMT records the real-time data and reports it to the Remote Supervision Platform (RSP) through General Packet Radio Service (GPRS). Based on the global status data of the whole group, an anti-collision algorithm was executed in each TC-STE to ensure the safety of each tower crane during construction. Remote supervision can be fulfilled using our client software installed on a personal computer (PC) or smartphone. SMS-TC could be considered as a promising practical application that combines a Wireless Sensor Network with the Internet of Things.

  13. Distributed Least-Squares Estimation of a Remote Chemical Source via Convex Combination in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Meng-Li Cao

    2014-06-01

    Full Text Available This paper investigates the problem of locating a continuous chemical source using the concentration measurements provided by a wireless sensor network (WSN. Such a problem exists in various applications: eliminating explosives or drugs, detecting the leakage of noxious chemicals, etc. The limited power and bandwidth of WSNs have motivated collaborative in-network processing which is the focus of this paper. We propose a novel distributed least-squares estimation (DLSE method to solve the chemical source localization (CSL problem using a WSN. The DLSE method is realized by iteratively conducting convex combination of the locally estimated chemical source locations in a distributed manner. Performance assessments of our method are conducted using both simulations and real experiments. In the experiments, we propose a fitting method to identify both the release rate and the eddy diffusivity. The results show that the proposed DLSE method can overcome the negative interference of local minima and saddle points of the objective function, which would hinder the convergence of local search methods, especially in the case of locating a remote chemical source.

  14. On Singularities and Black Holes in Combination-Driven Models of Technological Innovation Networks.

    Science.gov (United States)

    Solé, Ricard; Amor, Daniel R; Valverde, Sergi

    2016-01-01

    It has been suggested that innovations occur mainly by combination: the more inventions accumulate, the higher the probability that new inventions are obtained from previous designs. Additionally, it has been conjectured that the combinatorial nature of innovations naturally leads to a singularity: at some finite time, the number of innovations should diverge. Although these ideas are certainly appealing, no general models have been yet developed to test the conditions under which combinatorial technology should become explosive. Here we present a generalised model of technological evolution that takes into account two major properties: the number of previous technologies needed to create a novel one and how rapidly technology ages. Two different models of combinatorial growth are considered, involving different forms of ageing. When long-range memory is used and thus old inventions are available for novel innovations, singularities can emerge under some conditions with two phases separated by a critical boundary. If the ageing has a characteristic time scale, it is shown that no singularities will be observed. Instead, a "black hole" of old innovations appears and expands in time, making the rate of invention creation slow down into a linear regime.

  15. Placement of Combined Heat, Power and Hydrogen Production Fuel Cell Power Plants in a Distribution Network

    Directory of Open Access Journals (Sweden)

    Bahman Bahmanifirouzi

    2012-03-01

    Full Text Available This paper presents a new Fuzzy Adaptive Modified Particle Swarm Optimization algorithm (FAMPSO for the placement of Fuel Cell Power Plants (FCPPs in distribution systems. FCPPs, as Distributed Generation (DG units, can be considered as Combined sources of Heat, Power, and Hydrogen (CHPH. CHPH operation of FCPPs can improve overall system efficiency, as well as produce hydrogen which can be stored for the future use of FCPPs or can be sold for profit. The objective functions investigated are minimizing the operating costs of electrical energy generation of distribution substations and FCPPs, minimizing the voltage deviation and minimizing the total emission. In this regard, this paper just considers the placement of CHPH FCPPs while investment cost of devices is not considered. Considering the fact that the objectives are different, non-commensurable and nonlinear, it is difficult to solve the problem using conventional approaches that may optimize a single objective. Moreover, the placement of FCPPs in distribution systems is a mixed integer problem. Therefore, this paper uses the FAMPSO algorithm to overcome these problems. For solving the proposed multi-objective problem, this paper utilizes the Pareto Optimality idea to obtain a set of solution in the multi-objective problem instead of only one. Also, a fuzzy system is used to tune parameters of FAMPSO algorithm such as inertia weight. The efficacy of the proposed approach is validated on a 69-bus distribution system.

  16. Linearized FUN3D for Rapid Aeroelastic and Aeroservoelastic Design and Analysis Project

    Data.gov (United States)

    National Aeronautics and Space Administration — The overall objective of this Phase I project is to develop a hybrid approach in FUN3D, referred herein to as the Linearized FUN3D, for rapid aeroelastic and...

  17. Prediction of Increasing Production Activities using Combination of Query Aggregation on Complex Events Processing and Neural Network

    Directory of Open Access Journals (Sweden)

    Achmad Arwan

    2016-07-01

    Full Text Available AbstrakProduksi, order, penjualan, dan pengiriman adalah serangkaian event yang saling terkait dalam industri manufaktur. Selanjutnya hasil dari event tersebut dicatat dalam event log. Complex Event Processing adalah metode yang digunakan untuk menganalisis apakah terdapat pola kombinasi peristiwa tertentu (peluang/ancaman yang terjadi pada sebuah sistem, sehingga dapat ditangani secara cepat dan tepat. Jaringan saraf tiruan adalah metode yang digunakan untuk mengklasifikasi data peningkatan proses produksi. Hasil pencatatan rangkaian proses yang menyebabkan peningkatan produksi digunakan sebagai data latih untuk mendapatkan fungsi aktivasi dari jaringan saraf tiruan. Penjumlahan hasil catatan event log dimasukkan ke input jaringan saraf tiruan untuk perhitungan nilai aktivasi. Ketika nilai aktivasi lebih dari batas yang ditentukan, maka sistem mengeluarkan sinyal untuk meningkatkan produksi, jika tidak, sistem tetap memantau kejadian. Hasil percobaan menunjukkan bahwa akurasi dari metode ini adalah 77% dari 39 rangkaian aliran event.Kata kunci: complex event processing, event, jaringan saraf tiruan, prediksi peningkatan produksi, proses. AbstractProductions, orders, sales, and shipments are series of interrelated events within manufacturing industry. Further these events were recorded in the event log. Complex event processing is a method that used to analyze whether there are patterns of combinations of certain events (opportunities / threats that occur in a system, so it can be addressed quickly and appropriately. Artificial neural network is a method that we used to classify production increase activities. The series of events that cause the increase of the production used as a dataset to train the weight of neural network which result activation value. An aggregate stream of events inserted into the neural network input to compute the value of activation. When the value is over a certain threshold (the activation value results

  18. Functional Fun in Statistics Teaching: Resources, Research and Recommendations

    Science.gov (United States)

    Lesser, Lawrence M.; Pearl, Dennis K.

    2009-01-01

    This paper presents an overview of modalities that can be used to make learning statistics fun. Representative examples or points of departure in the literature are provided for no less than 20 modalities. Empirical evidence of effectiveness specific to statistics education is starting to emerge for some of these modalities--namely, humor, song,…

  19. Serious Fun: Using Toys to Demonstrate Fluid Mechanics Principles

    Science.gov (United States)

    Saviz, Camilla M.; Shakerin, Said

    2014-01-01

    Many students have owned or seen fluids toys in which two immiscible fluids within a closed container can be tilted to generate waves. These types of inexpensive and readily available toys are fun to play with, but they are also useful for provoking student learning about fluid properties or complex fluid behavior, including drop formation and…

  20. Fun with Hands-on Science Activities for Elementary Teachers.

    Science.gov (United States)

    Barry, Dana M.

    This document contains hands-on activities in science that make use of balloons and are fun and stimulating as well as challenging. By actively participating in these activities, students can develop science process and critical thinking skills as well as technical and measuring skills. Topics include Air as Matter, Pressure, Chemical Change,…

  1. Keeping PCs up to Date Can Be Fun

    Science.gov (United States)

    Goldsborough, Reid

    2004-01-01

    The "joy" of computer maintenance takes many forms. These days, automation is the byword. Operating systems such as Microsoft Windows and utility suites such as Symantec's Norton Internet Security let you automatically keep crucial parts of your computer system up to date. It's fun to watch the technology keep tabs on itself. This document offers…

  2. Bike Skills Training in PE Is Fun, Keeps Kids Safe

    Science.gov (United States)

    Wallace, Judi Lawson; Sutton, Nancy P.

    2015-01-01

    Incorporating bike skills into the elementary- and middle-school physical education curriculum encourages students to be physically active in a fun way while also learning bike safety skills. Winston-Salem's (NC) Safe Routes to School program demonstrates how collaboration with the public schools' health and physical education program can…

  3. Balancing Fun and Learning in a Serious Game Design

    Science.gov (United States)

    Franzwa, Christopher; Tang, Ying; Johnson, Aaron; Bielefeldt, Talbot

    2014-01-01

    This article presents the underlying philosophy of Sustain City, an educational serious game system that engages students, particularly prospective and beginning science and engineering students, in a series of engineering design challenges. Various strategies implemented in Sustain City for achieving a balance of fun and learning are discussed,…

  4. High-performance combination method of electric network frequency and phase for audio forgery detection in battery-powered devices.

    Science.gov (United States)

    Savari, Maryam; Abdul Wahab, Ainuddin Wahid; Anuar, Nor Badrul

    2016-09-01

    Audio forgery is any act of tampering, illegal copy and fake quality in the audio in a criminal way. In the last decade, there has been increasing attention to the audio forgery detection due to a significant increase in the number of forge in different type of audio. There are a number of methods for forgery detection, which electric network frequency (ENF) is one of the powerful methods in this area for forgery detection in terms of accuracy. In spite of suitable accuracy of ENF in a majority of plug-in powered devices, the weak accuracy of ENF in audio forgery detection for battery-powered devices, especially in laptop and mobile phone, can be consider as one of the main obstacles of the ENF. To solve the ENF problem in terms of accuracy in battery-powered devices, a combination method of ENF and phase feature is proposed. From experiment conducted, ENF alone give 50% and 60% accuracy for forgery detection in mobile phone and laptop respectively, while the proposed method shows 88% and 92% accuracy respectively, for forgery detection in battery-powered devices. The results lead to higher accuracy for forgery detection with the combination of ENF and phase feature. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  5. Embedding global barrier and collective in torus network with each node combining input from receivers according to class map for output to senders

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Dong; Coteus, Paul W; Eisley, Noel A; Gara, Alan; Heidelberger, Philip; Senger, Robert M; Salapura, Valentina; Steinmacher-Burow, Burkhard; Sugawara, Yutaka; Takken, Todd E

    2013-08-27

    Embodiments of the invention provide a method, system and computer program product for embedding a global barrier and global interrupt network in a parallel computer system organized as a torus network. The computer system includes a multitude of nodes. In one embodiment, the method comprises taking inputs from a set of receivers of the nodes, dividing the inputs from the receivers into a plurality of classes, combining the inputs of each of the classes to obtain a result, and sending said result to a set of senders of the nodes. Embodiments of the invention provide a method, system and computer program product for embedding a collective network in a parallel computer system organized as a torus network. In one embodiment, the method comprises adding to a torus network a central collective logic to route messages among at least a group of nodes in a tree structure.

  6. Synchronous colorectal liver metastasis: a network meta-analysis review comparing classical, combined, and liver-first surgical strategies.

    Science.gov (United States)

    Kelly, M E; Spolverato, G; Lê, G N; Mavros, M N; Doyle, F; Pawlik, T M; Winter, D C

    2015-03-01

    In recent years, the management of synchronous colorectal liver metastasis has changed significantly. Alternative surgical strategies to the classical colorectal-first approach have been proposed. These include the liver-first and combined resections approaches. The objectives of this review were to compare the short- and long-term outcomes for all three approaches. A systematic review of comparative studies was performed. Evaluated endpoints included surgical outcomes (5-year overall survival, 30-day mortality, and post-operative complications). Pair-wise and network meta-analysis (NMA) were performed to compare survival outcomes. Eighteen studies were included in this review, reporting on 3,605 patients. NMA and pair-wise meta-analysis of the 5-year overall survival did not show significant difference between the three surgical approaches: combined versus colorectal-first, mean odds ratio (OR) 1.02 (95% CI 0.8-1.28, P = 0.93); liver-first versus colorectal-first, mean OR 0.81 (95% CI 0.53-1.26, P = 0.37); liver-first versus combined, mean OR 0.80 (95% CI 0.52-1.24, P = 0.41). In addition NMA of the 30-day mortality among the three approaches also did not observe statistical difference. Analysis of variance showed that mean post-operative complications of all approaches were comparable (P = 0.51). There are considerable differences in the peri-operative management of synchronous CLM patients. This meta-analysis demonstrated no clear statistical surgical outcome or survival advantage towards any of the three approaches. © 2014 Wiley Periodicals, Inc.

  7. Glooveth: healthy living, fun and serious gaming.

    Science.gov (United States)

    Macías, Enric; García, Oscar; Moreno, Pau; Presno, Maria Montserrat; Forrest, Tallulah

    2012-01-01

    Serious Games and Gamification deliver powerful and truthful experiences by providing the user with goals, challenges, problem solving and rules, besides a clear internal value and an interactive experience. In fact, Serious Games can be considered memorable experiences that deliver intense moments with the support of different platforms and social networks while ensuring high degrees of motivation, efficiency and performance. Here, we describe Glooveth, an educational game for children ages 6 to 12 years, which was the winner of the Silver Award in the Global eHealth Challenge 2010. Glooveth is a platform computer game that teaches healthy living. We developed a game to be used by three different peripherals: a mouse and two special gloves. These peripherals provide the user with a more intense gameplaying and learning experience. The paper explains the project, from concept to application to usability testing.

  8. Combining Observations of a Digital Camera Network, Satellite Remote Sensing, and Micrometeorology for Improved Understanding of Forest Phenology

    Science.gov (United States)

    Braswell, B. H.; Richardson, A. D.; Ollinger, S. V.; Friedl, M. A.; Hollinger, D. Y.

    2009-04-01

    The observed phenological behavior of terrestrial ecosystems is a result of the seasonality of climatic forcing superposed with physical and biological responses of the plant-soil system. Biogeochemical models that represent rapid time scale phenomena well tend to simulate interannual variability and trends in productivity more accurately when phenology is prescribed, suggesting a gap in our understanding of the underlying processes or a generic means to represent their emergent behavior. Specifically, questions surround environmental triggers of leaf turnover, the relative importance of internal nutrient cycling, and the potential for generalization across broadly defined biome types. Satellite observations provide a spatially comprehensive record of the seasonality of land vegetation characteristics, but are most valuable when combined with direct measurements of ecosystem state. Time series of meteorology and fluxes (e.g. from eddy covariance tower sites) are one such data source, providing a valuable means to estimate productivity, but not a view of the state of the vegetation canopy. We have begun to assemble a network of digital cameras ('webcams') by deploying camera systems at existing research sites, and by harvesting imagery from collaborating sites and institutions. There are currently 80 cameras in the network, 17 of which are 'core' locations that are located at flux towers or field stations. We process and analyze the camera imagery as remote sensing data, utilizing the red, green, and blue, channels as a means to stratify the scenes and quantify relative vegetation 'greenness'. Our initial analyses have shown that these images do yield hourly-to-daily information about the seasonal cycle of vegetation state as compared both to fluxes and satellite indices. This presentation will summarize the current findings of the project, specifically focusing on (a) insights into controls on interannual variability at sites with long records (2000-present), and

  9. Facial Sketch Synthesis Using Two-dimensional Direct Combined Model-based Face-Specific Markov Network.

    Science.gov (United States)

    Tu, Ching-Ting; Chan, Yu-Hsien; Chen, Yi-Chung

    2016-05-20

    A facial sketch synthesis system is proposed featuring a two-dimensional direct combined model (2DDCM)-based facespecific Markov network. In contrast to existing facial sketch synthesis systems, the proposed scheme aims to synthesize sketches which reproduce the unique drawing style of a particular artist, where this drawing style is learned from a dataset consisting of a large number of image/sketch pairwise training samples. The synthesis system comprises three modules, namely a global module, a local module, and an enhancement module. The global module applies a 2DDCM approach to synthesize the global facial geometry and texture of the input image. The detailed texture is then added to the synthesized sketch in a local patch-based manner using a parametric 2DDCM model and a non-parametric Markov random field (MRF) network. Notably, the MRF approach gives the synthesized results an appearance more consistent with the drawing style of the training samples, while the 2DDCM approach enables the synthesis of outcomes with a more derivative style. As a result, the similarity between the synthesized sketches and the input images is greatly improved. Finally, a post-processing operation is performed to enhance the shadowed regions of the synthesized image by adding strong lines or curves to emphasize the lighting conditions. The experimental results confirm that the synthesized facial images are in good qualitative and quantitative agreement with the input images as well as the ground-truth sketches provided by the same artist. The representing power of the proposed framework is demonstrated by synthesizing facial sketches from input images with a wide variety of facial poses, lighting conditions, and races even when such images are not included in the training dataset. Moreover, the practical applicability of the proposed framework is demonstrated by means of automatic facial recognition tests.

  10. Hallucination- and speech-specific hypercoupling in frontotemporal auditory and language networks in schizophrenia using combined task-based fMRI data: An fBIRN study.

    Science.gov (United States)

    Lavigne, Katie M; Woodward, Todd S

    2017-12-21

    Hypercoupling of activity in speech-perception-specific brain networks has been proposed to play a role in the generation of auditory-verbal hallucinations (AVHs) in schizophrenia; however, it is unclear whether this hypercoupling extends to nonverbal auditory perception. We investigated this by comparing schizophrenia patients with and without AVHs, and healthy controls, on task-based functional magnetic resonance imaging (fMRI) data combining verbal speech perception (SP), inner verbal thought generation (VTG), and nonverbal auditory oddball detection (AO). Data from two previously published fMRI studies were simultaneously analyzed using group constrained principal component analysis for fMRI (group fMRI-CPCA), which allowed for comparison of task-related functional brain networks across groups and tasks while holding the brain networks under study constant, leading to determination of the degree to which networks are common to verbal and nonverbal perception conditions, and which show coordinated hyperactivity in hallucinations. Three functional brain networks emerged: (a) auditory-motor, (b) language processing, and (c) default-mode (DMN) networks. Combining the AO and sentence tasks allowed the auditory-motor and language networks to separately emerge, whereas they were aggregated when individual tasks were analyzed. AVH patients showed greater coordinated activity (deactivity for DMN regions) than non-AVH patients during SP in all networks, but this did not extend to VTG or AO. This suggests that the hypercoupling in AVH patients in speech-perception-related brain networks is specific to perceived speech, and does not extend to perceived nonspeech or inner verbal thought generation. © 2017 Wiley Periodicals, Inc.

  11. Making your presentation fun: creative presentation techniques

    Energy Technology Data Exchange (ETDEWEB)

    KEENEN,MARTHA JANE

    2000-05-18

    What possesses someone to volunteer and go through hoops and red tape to make a presentation at a conference? For that matter, why does anyone ever present anything to anyone? Actually, presentations are a fact of life and there are many reasons for doing a presentation and doing it well. New and existing staff need training and orientation to the way things are done here. Handing all of them a manual and hoping they read it is pretty much a waste of paper. On the other hand, an effective, entertaining and upbeat presentation on the relevant topics is more likely to stick with those people. They will even have a name and face to remember and seek out when they have an issue on or with that topic. This can be a very effective beginning for networking with new peers. The presenter is seen as knowledgeable, as a source of information on company topics and possibly evaluated as a potential mentor or future manager. Project staff and/or peers benefit from clear, concise, presentations of topical knowledge. This is one way that a group working on various aspects of the same project or program can stay in touch and in step with each other. Most importantly, presentations may be the best or only door into the minds (and budgets) of management and customers. These presentations are a wonderful opportunity to address legal and compliance issues, budget, staffing, and services. Here is a chance, maybe the only one, to demonstrate and explain the wonderfulness of a program and the benefit they get by using the services offered most effectively. An interactive presentation on legal and compliance issues can be an effective tool in helping customers and/or management make good risk management decisions.

  12. The fun integration theory: toward sustaining children and adolescents sport participation.

    Science.gov (United States)

    Visek, Amanda J; Achrati, Sara M; Mannix, Heather; McDonnell, Karen; Harris, Brandonn S; DiPietro, Loretta

    2015-03-01

    Children cite "fun" as the primary reason for participation in organized sport and its absence as the number-one reason for youth sport attrition. Therefore, the purpose of this study was to develop a theoretical framework of fun using a novel mixed-method assessment of participants in sport (FUN MAPS) via concept mapping. Youth soccer players (n = 142), coaches (n = 37), and parents (n = 57) were stratified by age, sex, and competition level and contributed their ideas through (a) qualitative brainstorming, identifying all of the things that make playing sports fun for players; (b) sorting of ideas; and (c) rating each idea on its importance, frequency, and feasibility. The FUN MAPS identify the 4 fundamental tenets of fun in youth sport within 11 fun-dimensions composed of 81 specific fun-determinants, while also establishing the youth sport ethos. The FUN MAPS provide pictorial evidence-based blueprints for the fun integration theory (FIT), which is a multitheoretical, multidimensional, and stakeholder derived framework that can be used to maximize fun for children and adolescents to promote and sustain an active and healthy lifestyle through sport.

  13. Sharing network resources

    CERN Document Server

    Parekh, Abhay

    2014-01-01

    Resource Allocation lies at the heart of network control. In the early days of the Internet the scarcest resource was bandwidth, but as the network has evolved to become an essential utility in the lives of billions, the nature of the resource allocation problem has changed. This book attempts to describe the facets of resource allocation that are most relevant to modern networks. It is targeted at graduate students and researchers who have an introductory background in networking and who desire to internalize core concepts before designing new protocols and applications. We start from the fun

  14. A robust observer based on H∞ filtering with parameter uncertainties combined with Neural Networks for estimation of vehicle roll angle

    Science.gov (United States)

    Boada, Beatriz L.; Boada, Maria Jesus L.; Vargas-Melendez, Leandro; Diaz, Vicente

    2018-01-01

    Nowadays, one of the main objectives in road transport is to decrease the number of accident victims. Rollover accidents caused nearly 33% of all deaths from passenger vehicle crashes. Roll Stability Control (RSC) systems prevent vehicles from untripped rollover accidents. The lateral load transfer is the main parameter which is taken into account in the RSC systems. This parameter is related to the roll angle, which can be directly measured from a dual-antenna GPS. Nevertheless, this is a costly technique. For this reason, roll angle has to be estimated. In this paper, a novel observer based on H∞ filtering in combination with a neural network (NN) for the vehicle roll angle estimation is proposed. The design of this observer is based on four main criteria: to use a simplified vehicle model, to use signals of sensors which are installed onboard in current vehicles, to consider the inaccuracy in the system model and to attenuate the effect of the external disturbances. Experimental results show the effectiveness of the proposed observer.

  15. Forecasting of UV-Vis absorbance time series using artificial neural networks combined with principal component analysis.

    Science.gov (United States)

    Plazas-Nossa, Leonardo; Hofer, Thomas; Gruber, Günter; Torres, Andres

    2017-02-01

    This work proposes a methodology for the forecasting of online water quality data provided by UV-Vis spectrometry. Therefore, a combination of principal component analysis (PCA) to reduce the dimensionality of a data set and artificial neural networks (ANNs) for forecasting purposes was used. The results obtained were compared with those obtained by using discrete Fourier transform (DFT). The proposed methodology was applied to four absorbance time series data sets composed by a total number of 5705 UV-Vis spectra. Absolute percentage errors obtained by applying the proposed PCA/ANN methodology vary between 10% and 13% for all four study sites. In general terms, the results obtained were hardly generalizable, as they appeared to be highly dependent on specific dynamics of the water system; however, some trends can be outlined. PCA/ANN methodology gives better results than PCA/DFT forecasting procedure by using a specific spectra range for the following conditions: (i) for Salitre wastewater treatment plant (WWTP) (first hour) and Graz West R05 (first 18 min), from the last part of UV range to all visible range; (ii) for Gibraltar pumping station (first 6 min) for all UV-Vis absorbance spectra; and (iii) for San Fernando WWTP (first 24 min) for all of UV range to middle part of visible range.

  16. Economic competitiveness of underground coal gasification combined with carbon capture and storage in the Bulgarian energy network

    Energy Technology Data Exchange (ETDEWEB)

    Nakaten, Natalie Christine

    2014-11-15

    Underground coal gasification (UCG) allows for exploitation of deep-seated coal seams not economically exploitable by conventional coal mining. Aim of the present study is to examine UCG economics based on coal conversion into a synthesis gas to fuel a combined cycle gas turbine power plant (CCGT) with CO2 capture and storage (CCS). Thereto, a techno-economic model is developed for UCG-CCGT-CCS costs of electricity (COE) determination which, considering sitespecific data of a selected target area in Bulgaria, sum up to 72 Euro/MWh in total. To quantify the impact of model constraints on COE, sensitivity analyses are undertaken revealing that varying geological model constraints impact COE with 0.4% to 4%, chemical with 13%, technical with 8% to 17% and market-dependent with 2% to 25%. Besides site-specific boundary conditions, UCG-CCGT-CCS economics depend on resources availability and infrastructural characteristics of the overall energy system. Assessing a model based implementation of UCG-CCGT-CCS and CCS power plants into the Bulgarian energy network revealed that both technologies provide essential and economically competitive options to achieve the EU environmental targets and a complete substitution of gas imports by UCG synthesis gas production.

  17. Age-related reorganization of functional networks for successful conflict resolution: a combined functional and structural MRI study.

    Science.gov (United States)

    Schulte, Tilman; Müller-Oehring, Eva M; Chanraud, Sandra; Rosenbloom, Margaret J; Pfefferbaum, Adolf; Sullivan, Edith V

    2011-11-01

    Aging has readily observable effects on the ability to resolve conflict between competing stimulus attributes that are likely related to selective structural and functional brain changes. To identify age-related differences in neural circuits subserving conflict processing, we combined structural and functional MRI and a Stroop Match-to-Sample task involving perceptual cueing and repetition to modulate resources in healthy young and older adults. In our Stroop Match-to-Sample task, older adults handled conflict by activating a frontoparietal attention system more than young adults and engaged a visuomotor network more than young adults when processing repetitive conflict and when processing conflict following valid perceptual cueing. By contrast, young adults activated frontal regions more than older adults when processing conflict with perceptual cueing. These differential activation patterns were not correlated with regional gray matter volume despite smaller volumes in older than young adults. Given comparable performance in speed and accuracy of responding between both groups, these data suggest that successful aging is associated with functional reorganization of neural systems to accommodate functionally increasing task demands on perceptual and attentional operations. Copyright © 2009 Elsevier Inc. All rights reserved.

  18. Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks

    Science.gov (United States)

    Sbarufatti, Claudio; Corbetta, Matteo; Giglio, Marco; Cadini, Francesco

    2017-03-01

    Lithium-Ion rechargeable batteries are widespread power sources with applications to consumer electronics, electrical vehicles, unmanned aerial and spatial vehicles, etc. The failure to supply the required power levels may lead to severe safety and economical consequences. Thus, in view of the implementation of adequate maintenance strategies, the development of diagnostic and prognostic tools for monitoring the state of health of the batteries and predicting their remaining useful life is becoming a crucial task. Here, we propose a method for predicting the end of discharge of Li-Ion batteries, which stems from the combination of particle filters with radial basis function neural networks. The major innovation lies in the fact that the radial basis function model is adaptively trained on-line, i.e., its parameters are identified in real time by the particle filter as new observations of the battery terminal voltage become available. By doing so, the prognostic algorithm achieves the flexibility needed to provide sound end-of-discharge time predictions as the charge-discharge cycles progress, even in presence of anomalous behaviors due to failures or unforeseen operating conditions. The method is demonstrated with reference to actual Li-Ion battery discharge data contained in the prognostics data repository of the NASA Ames Research Center database.

  19. Breaking with fun, educational and realistic learning games

    DEFF Research Database (Denmark)

    Duus Henriksen, Thomas

    2009-01-01

    between the game and other didactic activities that formed the learning process; and, the game might have been intended to be realistic, but it was in the gaps where this realism was critically assessed that learned understanding was forged. While thinking learning games as fun, educative and realistic......This paper addresses the game conceptions and values that learning games inherit from regular gaming, as well as how they affect the use and development of learning games. Its key points concern the issues of thinking learning games as fun, educative and realistic, which is how learning games...... are commonly conceived as means for staging learning processes, and that thinking learning games so has an inhibiting effect in regard to creating learning processes. The paper draws upon a qualitative study of participants' experiences with ‘the EIS Simulation', which is a computer-based learning game...

  20. The Architect as a Social Designer: The Fun Palace Case

    Directory of Open Access Journals (Sweden)

    Lubomir Savov Popov

    2015-12-01

    Full Text Available The goal of this paper is to discuss how the architectural profession and its work, through development of physical structures, relate to the idea of social design. Toward this end, we explore a number of issues that emerge from this concept—the social role of the architect, the emerging engagements in social design, and the need for corresponding design ethics. Through an applied sociological approach that focuses on interaction, emphasizing collaborative and transformative work within situated contexts, we conduct a case study on a project known as The Fun Palace. Rather than providing a detailed examination of the Fun Palace or its architect, Cedric Price, this paper uses this case to explore and discuss the potential for architecture in social design. Consequently, the study contributes to the ongoing debate on the social role of the architect, the scope of the architectural profession, and involvement with social design.

  1. Função mastigatoria em indios Ianomami

    OpenAIRE

    Thomas Van Der Laan

    1998-01-01

    Resumo: Este trabalho é baseado nas observações da dentição, oclusão e função mastigatória em duas aldeias de indígenas lanomami: uma (rio Maturacá) com o processo de aculturação mais avançado que a outra (rio Maiá). Tem o mesmo o objetivo de questionar atuais conceitos de função e tratamento oclusais. Quatro milhões de anos de evidências naturais, representados por fósseis pré-históricos e crânios antigos, são escavados e estudados anualmente apresentando adequado desenvolvimento das arcadas...

  2. Molecular Determinants Underlying Binding Specificities of the ABL Kinase Inhibitors: Combining Alanine Scanning of Binding Hot Spots with Network Analysis of Residue Interactions and Coevolution.

    Directory of Open Access Journals (Sweden)

    Amanda Tse

    Full Text Available Quantifying binding specificity and drug resistance of protein kinase inhibitors is of fundamental importance and remains highly challenging due to complex interplay of structural and thermodynamic factors. In this work, molecular simulations and computational alanine scanning are combined with the network-based approaches to characterize molecular determinants underlying binding specificities of the ABL kinase inhibitors. The proposed theoretical framework unveiled a relationship between ligand binding and inhibitor-mediated changes in the residue interaction networks. By using topological parameters, we have described the organization of the residue interaction networks and networks of coevolving residues in the ABL kinase structures. This analysis has shown that functionally critical regulatory residues can simultaneously embody strong coevolutionary signal and high network centrality with a propensity to be energetic hot spots for drug binding. We have found that selective (Nilotinib and promiscuous (Bosutinib, Dasatinib kinase inhibitors can use their energetic hot spots to differentially modulate stability of the residue interaction networks, thus inhibiting or promoting conformational equilibrium between inactive and active states. According to our results, Nilotinib binding may induce a significant network-bridging effect and enhance centrality of the hot spot residues that stabilize structural environment favored by the specific kinase form. In contrast, Bosutinib and Dasatinib can incur modest changes in the residue interaction network in which ligand binding is primarily coupled only with the identity of the gate-keeper residue. These factors may promote structural adaptability of the active kinase states in binding with these promiscuous inhibitors. Our results have related ligand-induced changes in the residue interaction networks with drug resistance effects, showing that network robustness may be compromised by targeted mutations

  3. Molecular Determinants Underlying Binding Specificities of the ABL Kinase Inhibitors: Combining Alanine Scanning of Binding Hot Spots with Network Analysis of Residue Interactions and Coevolution

    Science.gov (United States)

    Tse, Amanda; Verkhivker, Gennady M.

    2015-01-01

    Quantifying binding specificity and drug resistance of protein kinase inhibitors is of fundamental importance and remains highly challenging due to complex interplay of structural and thermodynamic factors. In this work, molecular simulations and computational alanine scanning are combined with the network-based approaches to characterize molecular determinants underlying binding specificities of the ABL kinase inhibitors. The proposed theoretical framework unveiled a relationship between ligand binding and inhibitor-mediated changes in the residue interaction networks. By using topological parameters, we have described the organization of the residue interaction networks and networks of coevolving residues in the ABL kinase structures. This analysis has shown that functionally critical regulatory residues can simultaneously embody strong coevolutionary signal and high network centrality with a propensity to be energetic hot spots for drug binding. We have found that selective (Nilotinib) and promiscuous (Bosutinib, Dasatinib) kinase inhibitors can use their energetic hot spots to differentially modulate stability of the residue interaction networks, thus inhibiting or promoting conformational equilibrium between inactive and active states. According to our results, Nilotinib binding may induce a significant network-bridging effect and enhance centrality of the hot spot residues that stabilize structural environment favored by the specific kinase form. In contrast, Bosutinib and Dasatinib can incur modest changes in the residue interaction network in which ligand binding is primarily coupled only with the identity of the gate-keeper residue. These factors may promote structural adaptability of the active kinase states in binding with these promiscuous inhibitors. Our results have related ligand-induced changes in the residue interaction networks with drug resistance effects, showing that network robustness may be compromised by targeted mutations of key mediating

  4. FunTAL: Reasonably Mixing a Functional Language with Assembly

    OpenAIRE

    Patterson, Daniel; Perconti, Jamie; Dimoulas, Christos; Ahmed, Amal

    2017-01-01

    We present FunTAL, the first multi-language system to formalize safe interoperability between a high-level functional language and low-level assembly code while supporting compositional reasoning about the mix. A central challenge in developing such a multi-language is bridging the gap between assembly, which is staged into jumps to continuations, and high-level code, where subterms return a result. We present a compositional stack-based typed assembly language that supports components, compr...

  5. Fun&Co: identification of key functional differences in transcriptomes.

    Science.gov (United States)

    Gamberoni, Giacomo; Lamma, Evelina; Lodo, Gianluca; Marchesini, Jlenia; Mascellani, Nicoletta; Rossi, Simona; Storari, Sergio; Tagliavini, Luca; Volinia, Stefano

    2007-10-15

    Microarray and other genome-wide technologies allow a global view of gene expression that can be used in several ways and whose potential has not been yet fully discovered. Functional insight into expression profiles is routinely obtained by using gene ontology terms associated to the cellular genes. In this article, we deal with functional data mining from expression profiles, proposing a novel approach that studies the correlations between genes and their relations to Gene Ontology (GO). We implemented this approach in a public web-based application named Fun&Co. By using Fun&Co, the user dissects in a pair-wise manner gene expression patterns and links correlated pairs to gene ontology terms. The proof of principle for our study was accomplished by dissecting molecular pathways in muscles. In particular, we identified specific cellular pathways by comparing the three different types of muscle in a pairwise fashion. In fact, we were interested in the specific molecular mechanisms regulating the cardiovascular system (cardiomyocytes and smooth muscle cells). We applied here Fun&Co to the molecular study of cardiovascular system and the identification of the specific molecular pathways in heart, skeletal and smooth muscles (using 317 microarrays) and to reveal functional differences between the three different kinds of muscle cells. Application is online at http://tommy.unife.it. Supplementary data are available at Bioinformatics online.

  6. Optimizing Observation Networks Combining Ships of Opportunity, Gliders, Moored Buoys and FerryBox in the Bay of Biscay and English Channel

    Science.gov (United States)

    Charria, G.; Lamouroux, J.; De Mey, P. J.; Raynaud, S.; Heyraud, C.; Craneguy, P.; Dumas, F.; Le Henaff, M.

    2016-02-01

    Designing optimal observation networks in coastal oceans remains one of the major challenges towards the implementation of future Integrated Ocean Observing Systems to monitor the coastal environment. In the Bay of Biscay and the English Channel, the diversity of involved processes requires to adapt observing systems to the specific targeted environments. Also important is the requirement for those systems to sustain coastal applications. An efficient way to measure the hydrological content of the water column over the continental shelf is to consider ships of opportunity. In the French observation strategy, the RECOPESCA program, as a component of the High frequency Observation network for the environment in coastal SEAs (HOSEA), aims to collect environmental observations from sensors attached to fishing nets. In the present study, we assess that network performances using the ArM method (Le Hénaff et al., 2009). A reference network, based on fishing vessels observations in 2008, is assessed using that method. Moreover, three scenarios, based on the reference network, a denser network in 2010 and a fictive network aggregated from a pluri-annual collection of profiles, are also analyzed. Two other observational network design experiments have been implemented for the spring season in two regions: 1) the Loire River plume (northern part of the Bay of Biscay) to explore different possible glider endurance lines combined with a fixed mooring to monitor temperature and salinity and 2) the Western English Channel using a glider below FerryBox measurements. These experiments combining existing and future observing systems, as well as numerical ensemble simulations, highlight the key issue of monitoring the whole water column in and close to river plumes (e.g. using gliders), the efficiency of the surface high frequency sampling from FerryBoxes in macrotidal regions and the importance of sampling key regions instead of increasing the number of Voluntary Observing Ships.

  7. Uncertainty analysis of a combined Artificial Neural Network - Fuzzy logic - Kriging system for spatial and temporal simulation of Hydraulic Head.

    Science.gov (United States)

    Tapoglou, Evdokia; Karatzas, George P.; Trichakis, Ioannis C.; Varouchakis, Emmanouil A.

    2015-04-01

    The purpose of this study is to evaluate the uncertainty, using various methodologies, in a combined Artificial Neural Network (ANN) - Fuzzy logic - Kriging system, which can simulate spatially and temporally the hydraulic head in an aquifer. This system uses ANNs for the temporal prediction of hydraulic head in various locations, one ANN for every location with available data, and Kriging for the spatial interpolation of ANN's results. A fuzzy logic is used for the interconnection of these two methodologies. The full description of the initial system and its functionality can be found in Tapoglou et al. (2014). Two methodologies were used for the calculation of uncertainty for the implementation of the algorithm in a study area. First, the uncertainty of Kriging parameters was examined using a Bayesian bootstrap methodology. In this case the variogram is calculated first using the traditional methodology of Ordinary Kriging. Using the parameters derived and the covariance function of the model, the covariance matrix is constructed. A common method for testing a statistical model is the use of artificial data. Normal random numbers generation is the first step in this procedure and by multiplying them by the decomposed covariance matrix, correlated random numbers (sample set) can be calculated. These random values are then fitted into a variogram and the value in an unknown location is estimated using Kriging. The distribution of the simulated values using the Kriging of different correlated random values can be used in order to derive the prediction intervals of the process. In this study 500 variograms were constructed for every time step and prediction point, using the method described above, and their results are presented as the 95th and 5th percentile of the predictions. The second methodology involved the uncertainty of ANNs training. In this case, for all the data points 300 different trainings were implemented having different training datasets each time

  8. The effect of artificial neural network model combined with six tumor markers in auxiliary diagnosis of lung cancer.

    Science.gov (United States)

    Feng, Feifei; Wu, Yiming; Wu, Yongjun; Nie, Guangjin; Ni, Ran

    2012-10-01

    To evaluate the diagnosis potential of artificial neural network (ANN) model combined with six tumor markers in auxiliary diagnosis of lung cancer, to differentiate lung cancer from lung benign disease, normal control, and gastrointestinal cancers. Serum carcino-embryonic antigen (CEA), gastrin, neurone specific enolase (NSE), sialic acid (SA), Cu/Zn, Ca were measured through different experimental procedures in 117 lung cancer patients, 93 lung benign disease patients, 111 normal control, 47 gastric cancer patients, 50 patients with colon cancer and 50 esophagus cancer patients, 19 parameters of basic information were surveyed among lung cancer, lung benign disease and normal control, then developed and evaluated ANN models to distinguish lung cancer. Using the ANN model with the six serum tumor markers and 19 parameters to distinguish lung cancer from benign lung disease and healthy people, the sensitivity was 98.3%, the specificity was 99.5% and the accuracy was 96.9%. Another three ANN models with the six serum tumor markers were employed to differentiate lung cancer from three gastrointestinal cancers, the sensitivity, specificity and accuracy of distinguishing lung cancer from gastric cancer by the ANN model of lung cancer-gastric cancer were 100%, 83.3% and 93.5%, respectively; The sensitivity, specificity and accuracy of discriminating lung cancer by lung cancer-colon cancer ANN model were 90.0%, 90.0%, and 90.0%; And which were 86.7%, 84.6%, and 86.0%, respectively, by lung cancer-esophagus cancer ANN model. ANN model built with the six serum tumor markers could distinguish lung cancer, not only from lung benign disease and normal people, but also from three common gastrointestinal cancers. And our evidence indicates the ANN model maybe is an excellent and intelligent system to discriminate lung cancer.

  9. Structural and Functional Abnormalities of Default Mode Network in Minimal Hepatic Encephalopathy: A Study Combining DTI and fMRI

    Science.gov (United States)

    Zhong, Jianhui; Zheng, Gang; Wu, Shengyong; Zhang, Zhiqiang; Liao, Wei; Zhong, Yuan; Ni, Ling; Jiao, Qing; Zhang, Zongjun; Liu, Yijun; Lu, Guangming

    2012-01-01

    Background and Purpose Live failure can cause brain edema and aberrant brain function in cirrhotic patients. In particular, decreased functional connectivity within the brain default-mode network (DMN) has been recently reported in overt hepatic encephalopathy (HE) patients. However, so far, little is known about the connectivity among the DMN in the minimal HE (MHE), the mildest form of HE. Here, we combined diffusion tensor imaging (DTI) and resting-state functional MRI (rs-fMRI) to test our hypothesis that both structural and functional connectivity within the DMN were disturbed in MHE. Materials and Methods Twenty MHE patients and 20 healthy controls participated in the study. We explored the changes of structural (path length, tracts count, fractional anisotropy [FA] and mean diffusivity [MD] derived from DTI tractography) and functional (temporal correlation coefficient derived from rs-fMRI) connectivity of the DMN in MHE patients. Pearson correlation analysis was performed between the structural/functional indices and venous blood ammonia levels/neuropsychological tests scores of patients. All thresholds were set at P<0.05, Bonferroni corrected. Results Compared to the healthy controls, MHE patients showed both decreased FA and increased MD in the tract connecting the posterior cingulate cortex/precuneus (PCC/PCUN) to left parahippocampal gyrus (PHG), and decreased functional connectivity between the PCC/PCUN and left PHG, and medial prefrontal cortex (MPFC). MD values of the tract connecting PCC/PCUN to the left PHG positively correlated to the ammonia levels, the temporal correlation coefficients between the PCC/PCUN and the MPFC showed positive correlation to the digital symbol tests scores of patients. Conclusion MHE patients have both disturbed structural and functional connectivity within the DMN. The decreased functional connectivity was also detected between some regions without abnormal structural connectivity, suggesting that the former may be more

  10. The Fun Integration Theory: Towards Sustaining Children and Adolescents Sport Participation

    Science.gov (United States)

    Visek, Amanda J.; Achrati, Sara M.; Manning, Heather; McDonnell, Karen; Harris, Brandonn S.; DiPietro, Loretta

    2014-01-01

    BACKGROUND Children cite ‘fun’ as the primary reason for participation in organized sport and its absence as the number one reason for youth sport attrition. Therefore, the purpose of this study was to develop a theoretical framework of fun using a novel mixed-method assessment of participants in sport (FUN MAPS) via concept mapping. METHODS Youth soccer players (n = 142), coaches (n = 37), and parents (n = 57) were stratified by age, sex, and competition level and contributed their “fun” ideas through: (a) qualitative brainstorming, identifying all of the things that make playing sports fun for players; (b) sorting of ideas; and (c) rating each idea on its importance, frequency, and feasibility. RESULTS The FUN MAPS identify the four fundamental tenets of fun in youth sport within 11 fun-dimensions composed of 81 specific fun-determinants, while also establishing the youth sport ethos. CONCLUSION The FUN MAPS provide pictorial evidence-based blueprints for the fun integration theory (FIT), which is a multi-theoretical, multidimensional, and stakeholder derived framework that can be used to maximize fun for children and adolescents in order to promote and sustain an active and healthy lifestyle through sport. PMID:24770788

  11. Combining Self-Organizing Mapping and Supervised Affinity Propagation Clustering Approach to Investigate Functional Brain Networks Involved in Motor Imagery and Execution with fMRI Measurements

    Directory of Open Access Journals (Sweden)

    Jiang eZhang

    2015-07-01

    Full Text Available AbstractClustering analysis methods have been widely applied to identifying the functional brain networks of a multitask paradigm. However, the previously used clustering analysis techniques are computationally expensive and thus impractical for clinical applications. In this study a novel method, called SOM-SAPC that combines self-organizing mapping (SOM and supervised affinity propagation clustering (SAPC, is proposed and implemented to identify the motor execution (ME and motor imagery (MI networks. In SOM-SAPC, SOM was first performed to process fMRI data and SAPC is further utilized for clustering the patterns of functional networks. As a result, SOM-SAPC is able to significantly reduce the computational cost for brain network analysis. Simulation and clinical tests involving ME and MI were conducted based on SOM-SAPC, and the analysis results indicated that functional brain networks were clearly identified with different response patterns and reduced computational cost. In particular, three activation clusters were clearly revealed, which include parts of the visual, ME and MI functional networks. These findings validated that SOM-SAPC is an effective and robust method to analyze the fMRI data with multitasks.

  12. Combining self-organizing mapping and supervised affinity propagation clustering approach to investigate functional brain networks involved in motor imagery and execution with fMRI measurements.

    Science.gov (United States)

    Zhang, Jiang; Liu, Qi; Chen, Huafu; Yuan, Zhen; Huang, Jin; Deng, Lihua; Lu, Fengmei; Zhang, Junpeng; Wang, Yuqing; Wang, Mingwen; Chen, Liangyin

    2015-01-01

    Clustering analysis methods have been widely applied to identifying the functional brain networks of a multitask paradigm. However, the previously used clustering analysis techniques are computationally expensive and thus impractical for clinical applications. In this study a novel method, called SOM-SAPC that combines self-organizing mapping (SOM) and supervised affinity propagation clustering (SAPC), is proposed and implemented to identify the motor execution (ME) and motor imagery (MI) networks. In SOM-SAPC, SOM was first performed to process fMRI data and SAPC is further utilized for clustering the patterns of functional networks. As a result, SOM-SAPC is able to significantly reduce the computational cost for brain network analysis. Simulation and clinical tests involving ME and MI were conducted based on SOM-SAPC, and the analysis results indicated that functional brain networks were clearly identified with different response patterns and reduced computational cost. In particular, three activation clusters were clearly revealed, which include parts of the visual, ME and MI functional networks. These findings validated that SOM-SAPC is an effective and robust method to analyze the fMRI data with multitasks.

  13. Comparative efficacy and acceptability of combined antipsychotics and mood stabilizers versus individual drug classes for acute mania: Network meta-analysis.

    Science.gov (United States)

    Glue, Paul; Herbison, Peter

    2015-12-01

    Recent network meta-analyses of drug treatments for acute mania have only evaluated the efficacy and acceptability of individual drug treatments. The relative efficacy and acceptability of combined drug treatment has not been assessed. Double-blind drug trials in acute mania were identified using a systematic search strategy. We recorded numbers of patients enrolled, endpoints for efficacy (changes in mania rating scales, numbers of responders) and acceptability (numbers of dropouts) and treatment administered (categorized as antipsychotic, mood stabilizer, combined antipsychotic/mood stabilizer or placebo). Data were analyzed using a random effects frequentist network meta-analysis. All three drug categories were more effective than placebo. Antipsychotics and combined antipsychotic/mood stabilizer were significantly more effective than mood stabilizers for changes in mania rating scales. Combined antipsychotic/mood stabilizer was significantly more effective than mood stabilizers and antipsychotics for responder rate. Dropout rates were significantly lower for antipsychotics compared with placebo and mood stabilizers. Combined antipsychotic/mood stabilizer had the highest probability of being the best treatment based on change in mania rating scales (96.1% for all mania scales; 85.5% for Young Mania Rating Scale), and 99.3% for being the best treatment for responders. Antipsychotics had 82.0% probability as the best treatment to minimize dropouts. Combined antipsychotic/mood stabilizer appears to have efficacy advantages over antipsychotic or mood stabilizer monotherapy in acute mania, and should be considered as first line therapy. © The Royal Australian and New Zealand College of Psychiatrists 2015.

  14. Fault detection on a sewer network by a combination of a Kalman filter and a binary sequential probability ratio test

    Science.gov (United States)

    Piatyszek, E.; Voignier, P.; Graillot, D.

    2000-05-01

    One of the aims of sewer networks is the protection of population against floods and the reduction of pollution rejected to the receiving water during rainy events. To meet these goals, managers have to equip the sewer networks with and to set up real-time control systems. Unfortunately, a component fault (leading to intolerable behaviour of the system) or sensor fault (deteriorating the process view and disturbing the local automatism) makes the sewer network supervision delicate. In order to ensure an adequate flow management during rainy events it is essential to set up procedures capable of detecting and diagnosing these anomalies. This article introduces a real-time fault detection method, applicable to sewer networks, for the follow-up of rainy events. This method consists in comparing the sensor response with a forecast of this response. This forecast is provided by a model and more precisely by a state estimator: a Kalman filter. This Kalman filter provides not only a flow estimate but also an entity called 'innovation'. In order to detect abnormal operations within the network, this innovation is analysed with the binary sequential probability ratio test of Wald. Moreover, by crossing available information on several nodes of the network, a diagnosis of the detected anomalies is carried out. This method provided encouraging results during the analysis of several rains, on the sewer network of Seine-Saint-Denis County, France.

  15. Simultaneous non-destructive determination of two components of combined paracetamol and amantadine hydrochloride in tablets and powder by NIR spectroscopy and artificial neural networks.

    Science.gov (United States)

    Dou, Ying; Sun, Ying; Ren, Yuqiu; Ju, Ping; Ren, Yulin

    2005-03-09

    The two components (paracetamol and amantadine hydrochloride) were simultaneously determined in combined paracetamol and amantadine hydrochloride tablets and powder by using near-infrared (NIR) spectroscopy and artificial neural networks (ANNs). The ANN models of three pretreated spectra (first-derivative, second-derivative and standard normal variate (SNV), respectively) were established. The mathematical corrected models of tablets were compared with those of the powder. In the models, the concentrations of paracetamol and amantadine hydrochloride as the active components were determined simultaneously and compared with the results of their separate determination. The parameters that affected the network were studied and the concentrations of the test set samples were predicted. The degree of approximation, a new evaluation criterion of the network was employed to prove the accuracy of the predicted results.

  16. Identification of GRB2 and GAB1 coexpression as an unfavorable prognostic factor for hepatocellular carcinoma by a combination of expression profile and network analysis.

    Directory of Open Access Journals (Sweden)

    Yanqiong Zhang

    Full Text Available AIM: To screen novel markers for hepatocellular carcinoma (HCC by a combination of expression profile, interaction network analysis and clinical validation. METHODS: HCC significant molecules which are differentially expressed or had genetic variations in HCC tissues were obtained from five existing HCC related databases (OncoDB.HCC, HCC.net, dbHCCvar, EHCO and Liverome. Then, the protein-protein interaction (PPI network of these molecules was constructed. Three topological features of the network ('Degree', 'Betweenness', and 'Closeness' and the k-core algorithm were used to screen candidate HCC markers which play crucial roles in tumorigenesis of HCC. Furthermore, the clinical significance of two candidate HCC markers growth factor receptor-bound 2 (GRB2 and GRB2-associated-binding protein 1 (GAB1 was validated. RESULTS: In total, 6179 HCC significant genes and 977 HCC significant proteins were collected from existing HCC related databases. After network analysis, 331 candidate HCC markers were identified. Especially, GAB1 has the highest k-coreness suggesting its central localization in HCC related network, and the interaction between GRB2 and GAB1 has the largest edge-betweenness implying it may be biologically important to the function of HCC related network. As the results of clinical validation, the expression levels of both GRB2 and GAB1 proteins were significantly higher in HCC tissues than those in their adjacent nonneoplastic tissues. More importantly, the combined GRB2 and GAB1 protein expression was significantly associated with aggressive tumor progression and poor prognosis in patients with HCC. CONCLUSION: This study provided an integrative analysis by combining expression profile and interaction network analysis to identify a list of biologically significant HCC related markers and pathways. Further experimental validation indicated that the aberrant expression of GRB2 and GAB1 proteins may be strongly related to tumor

  17. Advanced Ring-Shaped Microelectrode Assay Combined with Small Rectangular Electrode for Quasi-In vivo Measurement of Cell-to-Cell Conductance in Cardiomyocyte Network

    Science.gov (United States)

    Nomura, Fumimasa; Kaneko, Tomoyuki; Hamada, Tomoyo; Hattori, Akihiro; Yasuda, Kenji

    2013-06-01

    To predict the risk of fatal arrhythmia induced by cardiotoxicity in the highly complex human heart system, we have developed a novel quasi-in vivo electrophysiological measurement assay, which combines a ring-shaped human cardiomyocyte network and a set of two electrodes that form a large single ring-shaped electrode for the direct measurement of irregular cell-to-cell conductance occurrence in a cardiomyocyte network, and a small rectangular microelectrode for forced pacing of cardiomyocyte beating and for acquiring the field potential waveforms of cardiomyocytes. The advantages of this assay are as follows. The electrophysiological signals of cardiomyocytes in the ring-shaped network are superimposed directly on a single loop-shaped electrode, in which the information of asynchronous behavior of cell-to-cell conductance are included, without requiring a set of huge numbers of microelectrode arrays, a set of fast data conversion circuits, or a complex analysis in a computer. Another advantage is that the small rectangular electrode can control the position and timing of forced beating in a ring-shaped human induced pluripotent stem cell (hiPS)-derived cardiomyocyte network and can also acquire the field potentials of cardiomyocytes. First, we constructed the human iPS-derived cardiomyocyte ring-shaped network on the set of two electrodes, and acquired the field potential signals of particular cardiomyocytes in the ring-shaped cardiomyocyte network during simultaneous acquisition of the superimposed signals of whole-cardiomyocyte networks representing cell-to-cell conduction. Using the small rectangular electrode, we have also evaluated the response of the cell network to electrical stimulation. The mean and SD of the minimum stimulation voltage required for pacing (VMin) at the small rectangular electrode was 166+/-74 mV, which is the same as the magnitude of amplitude for the pacing using the ring-shaped electrode (179+/-33 mV). The results showed that the

  18. Fun with maths and physics: brain teasers tricks illusions

    CERN Document Server

    Perelman, Yakov

    2013-01-01

    Fun with Maths and Physics details a large number of intriguing physics experiments, entertaining mathematics problems, and amazing optical illusions.The book’s main objective is to arouse the reader’s scientific imagination, teach him to think in a scientific manner, and create in his mind a variety of associations between physical knowledge and a large number of real daily life observations.Immensely instructive and entertaining, it has been one of the best sellers in Russia during the first part of last century.

  19. Combination of Deep Recurrent Neural Networks and Conditional Random Fields for Extracting Adverse Drug Reactions from User Reviews

    National Research Council Canada - National Science Library

    Elena Tutubalina; Sergey Nikolenko

    2017-01-01

    .... Text reviews, either on specialized web sites or in general-purpose social networks, may lead to a data source of unprecedented size, but identifying ADRs in free-form text is a challenging natural...

  20. Frequency of victimization experiences and well-being among online, offline and combined victims on social online network sites of German children and adolescents

    Directory of Open Access Journals (Sweden)

    Michael eGlüer

    2015-12-01

    Full Text Available Victimization is associated with negative developmental outcomes in childhood and adolescence. However, previous studies have provided mixed results regarding the association between offline and online victimization and indicators of social, psychological, and somatic well-being. In this study, we investigated 1,906 German children and adolescents (grades 5 to 10, mean age = 13.9; SD = 2.1 with and without offline or online victimization experiences who participated in a social online network (SNS. Online questionnaires were used to assess previous victimization (offline, online, combined, and without, somatic and psychological symptoms, self-esteem, and social self-concept (social competence, resistance to peer influence, esteem by others. In total, 1,362 (71.4% children and adolescents reported being a member of at least one social online network, and 377 students (28.8% reported previous victimization. Most children and adolescents had offline victimization experiences (17.5%, whereas 2.7% reported online victimization, and 8.6% reported combined experiences. Girls reported more online and combined victimization, and boys reported more offline victimization. The type of victimization (offline, online, combined was associated with increased reports of psychological and somatic symptoms, lower self-esteem and esteem by others, and lower resistance to peer influences. The effects were comparable for the groups with offline and online victimization. They were, however, increased in the combined group in comparison to victims with offline experiences alone.

  1. Improved Diagnostic Accuracy of Alzheimer's Disease by Combining Regional Cortical Thickness and Default Mode Network Functional Connectivity: Validated in the Alzheimer's Disease Neuroimaging Initiative Set.

    Science.gov (United States)

    Park, Ji Eun; Park, Bumwoo; Kim, Sang Joon; Kim, Ho Sung; Choi, Choong Gon; Jung, Seung Chai; Oh, Joo Young; Lee, Jae-Hong; Roh, Jee Hoon; Shim, Woo Hyun

    2017-01-01

    To identify potential imaging biomarkers of Alzheimer's disease by combining brain cortical thickness (CThk) and functional connectivity and to validate this model's diagnostic accuracy in a validation set. Data from 98 subjects was retrospectively reviewed, including a study set (n = 63) and a validation set from the Alzheimer's Disease Neuroimaging Initiative (n = 35). From each subject, data for CThk and functional connectivity of the default mode network was extracted from structural T1-weighted and resting-state functional magnetic resonance imaging. Cortical regions with significant differences between patients and healthy controls in the correlation of CThk and functional connectivity were identified in the study set. The diagnostic accuracy of functional connectivity measures combined with CThk in the identified regions was evaluated against that in the medial temporal lobes using the validation set and application of a support vector machine. Group-wise differences in the correlation of CThk and default mode network functional connectivity were identified in the superior temporal (p functional connectivity combined with the CThk of those two regions were more accurate than that combined with the CThk of both medial temporal lobes (91.7% vs. 75%). Combining functional information with CThk of the superior temporal and supramarginal gyri in the left cerebral hemisphere improves diagnostic accuracy, making it a potential imaging biomarker for Alzheimer's disease.

  2. Precise deformation measurement of prestressed concrete beam during a strain test using the combination of intersection photogrammetry and micro-network measurement

    Science.gov (United States)

    Urban, Rudolf; Braun, Jaroslav; Štroner, Martin

    2015-05-01

    The prestressed thin-walled concrete elements enable the bridge a relatively large span. These structures are advantageous in economic and environmental way due to their thickness and lower consumption of materials. The bending moments can be effectively influenced by using the pre-stress. The experiment was done to monitor deformation of the under load. During the experiment the discrete points were monitored. To determine a large number of points, the intersection photogrammetry combined with precise micro-network were chosen. Keywords:

  3. Exploring patterns of alteration in Alzheimer’s disease brain networks: a combined structural and functional connectomics analysis

    Directory of Open Access Journals (Sweden)

    Fulvia Palesi

    2016-09-01

    Full Text Available Alzheimer’s disease (AD is a neurodegenerative disorder characterized by a severe derangement of cognitive functions, primarily memory, in elderly subjects. As far as the functional impairment is concerned, growing evidence supports the disconnection syndrome hypothesis. Recent investigations using fMRI have revealed a generalized alteration of resting state networks in patients affected by AD and mild cognitive impairment (MCI. However, it was unclear whether the changes in functional connectivity were accompanied by corresponding structural network changes. In this work, we have developed a novel structural/functional connectomic approach: resting state fMRI was used to identify the functional cortical network nodes and diffusion MRI to reconstruct the fiber tracts to give a weight to internodal subcortical connections. Then, local and global efficiency were determined for different networks, exploring specific alterations of integration and segregation patterns in AD and MCI patients compared to healthy controls (HC. In the default mode network (DMN, that was the most affected, axonal loss and reduced axonal integrity appeared to compromise both local and global efficiency along posterior-anterior connections. In the basal ganglia network (BGN, disruption of white matter integrity implied that main alterations occurred in local microstructure. In the anterior insular network (AIN, neuronal loss probably subtended a compromised communication with the insular cortex. Cognitive performance, evaluated by neuropsychological examinations, revealed a dependency on integration and segregation of brain networks. These findings are indicative of the fact that cognitive deficits in AD could be associated not only with cortical alterations (revealed by fMRI but also with subcortical alterations (revealed by diffusion MRI that extend beyond the areas primarily damaged by neurodegeneration, towards the support of an emerging concept of AD as a

  4. Quantitative analysis of cefalexin based on artificial neural networks combined with modified genetic algorithm using short near-infrared spectroscopy.

    Science.gov (United States)

    Huan, Yanfu; Feng, Guodong; Wang, Bin; Ren, Yulin; Fei, Qiang

    2013-05-15

    In this paper, a novel chemometric method was developed for rapid, accurate, and quantitative analysis of cefalexin in samples. The experiments were carried out by using the short near-infrared spectroscopy coupled with artificial neural networks. In order to enhancing the predictive ability of artificial neural networks model, a modified genetic algorithm was used to select fixed number of wavelength. Copyright © 2013 Elsevier B.V. All rights reserved.

  5. Efficacy and safety of a combination of HER2-targeted agents as first-line treatment for metastatic HER2-positive breast cancer: a network meta-analysis.

    Science.gov (United States)

    Leung, Henry W C; Leung, John-Hang; Chan, Agnes L F

    2018-01-01

    Using network meta-analysis, we assessed the efficacy and safety of a combination regimen of HER2-targeted agents as first-line treatment for metastatic HER2-positive breast cancer. We searched the Medline, Embase, and Cochrane Library electronic databases (through December 2016) for phase II/III randomized controlled trials that compared regimens of one or two HER2-targeted agents combined with trastuzumab or chemotherapy. A network meta-analysis including direct and indirect analyses was conducted in WinBUGS using fixed and random effects. Study quality was assessed following the Grading of Recommendations, Assessment, Development and Evaluations method. The primary outcome was overall survival. The network meta-analysis incorporated nine HER2-targeted regimens with 9 direct comparisons and 28 indirect comparisons for the main outcomes (8 studies; n = 3976). Combining direct and indirect effects showed significant increased efficacy of trastuzumab and docetaxel plus pertuzumab (TDP) over other regimens as first-line treatment. With indirect comparison of overall safety, TDP, TDM-1, and TDM-1 plus pertuzumab demonstrated a lower risk of grade 3-4 adverse events compared to other regimens. TDPs are a preferred first-line treatment for HER2-positive metastatic breast cancer compared with other target agent regimens.

  6. Modified feed-forward neural network structures and combined-function-derivative approximations incorporating exchange symmetry for potential energy surface fitting.

    Science.gov (United States)

    Nguyen, Hieu T T; Le, Hung M

    2012-05-10

    The classical interchange (permutation) of atoms of similar identity does not have an effect on the overall potential energy. In this study, we present feed-forward neural network structures that provide permutation symmetry to the potential energy surfaces of molecules. The new feed-forward neural network structures are employed to fit the potential energy surfaces for two illustrative molecules, which are H(2)O and ClOOCl. Modifications are made to describe the symmetric interchange (permutation) of atoms of similar identity (or mathematically, the permutation of symmetric input parameters). The combined-function-derivative approximation algorithm (J. Chem. Phys. 2009, 130, 134101) is also implemented to fit the neural-network potential energy surfaces accurately. The combination of our symmetric neural networks and the function-derivative fitting effectively produces PES fits using fewer numbers of training data points. For H(2)O, only 282 configurations are employed as the training set; the testing root-mean-squared and mean-absolute energy errors are respectively reported as 0.0103 eV (0.236 kcal/mol) and 0.0078 eV (0.179 kcal/mol). In the ClOOCl case, 1693 configurations are required to construct the training set; the root-mean-squared and mean-absolute energy errors for the ClOOCl testing set are 0.0409 eV (0.943 kcal/mol) and 0.0269 eV (0.620 kcal/mol), respectively. Overall, we find good agreements between ab initio and NN prediction in term of energy and gradient errors, and conclude that the new feed-forward neural-network models advantageously describe the molecules with excellent accuracy.

  7. Combined bio-inspired/evolutionary computational methods in cross-layer protocol optimization for wireless ad hoc sensor networks

    Science.gov (United States)

    Hortos, William S.

    2011-06-01

    Published studies have focused on the application of one bio-inspired or evolutionary computational method to the functions of a single protocol layer in a wireless ad hoc sensor network (WSN). For example, swarm intelligence in the form of ant colony optimization (ACO), has been repeatedly considered for the routing of data/information among nodes, a network-layer function, while genetic algorithms (GAs) have been used to select transmission frequencies and power levels, physical-layer functions. Similarly, artificial immune systems (AISs) as well as trust models of quantized data reputation have been invoked for detection of network intrusions that cause anomalies in data and information; these act on the application and presentation layers. Most recently, a self-organizing scheduling scheme inspired by frog-calling behavior for reliable data transmission in wireless sensor networks, termed anti-phase synchronization, has been applied to realize collision-free transmissions between neighboring nodes, a function of the MAC layer. In a novel departure from previous work, the cross-layer approach to WSN protocol design suggests applying more than one evolutionary computational method to the functions of the appropriate layers to improve the QoS performance of the cross-layer design beyond that of one method applied to a single layer's functions. A baseline WSN protocol design, embedding GAs, anti-phase synchronization, ACO, and a trust model based on quantized data reputation at the physical, MAC, network, and application layers, respectively, is constructed. Simulation results demonstrate the synergies among the bioinspired/ evolutionary methods of the proposed baseline design improve the overall QoS performance of networks over that of a single computational method.

  8. Effects of the distribution density of a biomass combined heat and power plant network on heat utilisation efficiency in village-town systems.

    Science.gov (United States)

    Zhang, Yifei; Kang, Jian

    2017-11-01

    The building of biomass combined heat and power (CHP) plants is an effective means of developing biomass energy because they can satisfy demands for winter heating and electricity consumption. The purpose of this study was to analyse the effect of the distribution density of a biomass CHP plant network on heat utilisation efficiency in a village-town system. The distribution density is determined based on the heat transmission threshold, and the heat utilisation efficiency is determined based on the heat demand distribution, heat output efficiency, and heat transmission loss. The objective of this study was to ascertain the optimal value for the heat transmission threshold using a multi-scheme comparison based on an analysis of these factors. To this end, a model of a biomass CHP plant network was built using geographic information system tools to simulate and generate three planning schemes with different heat transmission thresholds (6, 8, and 10 km) according to the heat demand distribution. The heat utilisation efficiencies of these planning schemes were then compared by calculating the gross power, heat output efficiency, and heat transmission loss of the biomass CHP plant for each scenario. This multi-scheme comparison yielded the following results: when the heat transmission threshold was low, the distribution density of the biomass CHP plant network was high and the biomass CHP plants tended to be relatively small. In contrast, when the heat transmission threshold was high, the distribution density of the network was low and the biomass CHP plants tended to be relatively large. When the heat transmission threshold was 8 km, the distribution density of the biomass CHP plant network was optimised for efficient heat utilisation. To promote the development of renewable energy sources, a planning scheme for a biomass CHP plant network that maximises heat utilisation efficiency can be obtained using the optimal heat transmission threshold and the nonlinearity

  9. Combining Amplitude Spectrum Area with Previous Shock Information Using Neural Networks Improves Prediction Performance of Defibrillation Outcome for Subsequent Shocks in Out-Of-Hospital Cardiac Arrest Patients.

    Directory of Open Access Journals (Sweden)

    Mi He

    Full Text Available Quantitative ventricular fibrillation (VF waveform analysis is a potentially powerful tool to optimize defibrillation. However, whether combining VF features with additional attributes that related to the previous shock could enhance the prediction performance for subsequent shocks is still uncertain.A total of 528 defibrillation shocks from 199 patients experienced out-of-hospital cardiac arrest were analyzed in this study. VF waveform was quantified using amplitude spectrum area (AMSA from defibrillator's ECG recordings prior to each shock. Combinations of AMSA with previous shock index (PSI or/and change of AMSA (ΔAMSA between successive shocks were exercised through a training dataset including 255shocks from 99patientswith neural networks. Performance of the combination methods were compared with AMSA based single feature prediction by area under receiver operating characteristic curve(AUC, sensitivity, positive predictive value (PPV, negative predictive value (NPV and prediction accuracy (PA through a validation dataset that was consisted of 273 shocks from 100patients.A total of61 (61.0% patients required subsequent shocks (N = 173 in the validation dataset. Combining AMSA with PSI and ΔAMSA obtained highest AUC (0.904 vs. 0.819, p<0.001 among different combination approaches for subsequent shocks. Sensitivity (76.5% vs. 35.3%, p<0.001, NPV (90.2% vs. 76.9%, p = 0.007 and PA (86.1% vs. 74.0%, p = 0.005were greatly improved compared with AMSA based single feature prediction with a threshold of 90% specificity.In this retrospective study, combining AMSA with previous shock information using neural networks greatly improves prediction performance of defibrillation outcome for subsequent shocks.

  10. Combining ground-based and airborne EM through Artificial Neural Networks for modelling glacial till under saline groundwater conditions

    DEFF Research Database (Denmark)

    Gunnink, J.L.; Bosch, A.; Siemon, B.

    2012-01-01

    Airborne electromagnetic (AEM) methods supply data over large areas in a cost-effective way. We used ArtificialNeural Networks (ANN) to classify the geophysical signal into a meaningful geological parameter. By using examples of known relations between ground-based geophysical data (in this case...

  11. Resilience to climate change in a cross-scale tourism governance context: a combined quantitative-qualitative network analysis

    Directory of Open Access Journals (Sweden)

    Tobias Luthe

    2016-03-01

    Full Text Available Social systems in mountain regions are exposed to a number of disturbances, such as climate change. Calls for conceptual and practical approaches on how to address climate change have been taken up in the literature. The resilience concept as a comprehensive theory-driven approach to address climate change has only recently increased in importance. Limited research has been undertaken concerning tourism and resilience from a network governance point of view. We analyze tourism supply chain networks with regard to resilience to climate change at the municipal governance scale of three Alpine villages. We compare these with a planned destination management organization (DMO as a governance entity of the same three municipalities on the regional scale. Network measures are analyzed via a quantitative social network analysis (SNA focusing on resilience from a tourism governance point of view. Results indicate higher resilience of the regional DMO because of a more flexible and diverse governance structure, more centralized steering of fast collective action, and improved innovative capacity, because of higher modularity and better core-periphery integration. Interpretations of quantitative results have been qualitatively validated by interviews and a workshop. We conclude that adaptation of tourism-dependent municipalities to gradual climate change should be dealt with at a regional governance scale and adaptation to sudden changes at a municipal scale. Overall, DMO building at a regional scale may enhance the resilience of tourism destinations, if the municipalities are well integrated.

  12. Combined Coverage Area Reporting and Geographical Routing in Wireless Sensor-Actuator Networks for Cooperating with Unmanned Aerial Vehicles

    NARCIS (Netherlands)

    van Hoesel, L.F.W.; Erman-Tüysüz, A.; Havinga, Paul J.M.; Brogle, Marc; Heijenk, Gerhard J.; Braun, Torsten; Konstantas, D.

    In wireless sensor network (WSN) applications with multiple gateways, it is key to route location dependent subscriptions efficiently at two levels in the system. At the gateway level, data sinks must not waste the energy of the WSN by injecting subscriptions that are not relevant for the nodes in

  13. Techniques for Using Humor and Fun in the Language Arts Classroom

    Science.gov (United States)

    Minchew, Sue S.; Hopper, Peggy F.

    2008-01-01

    The authors, former middle and high school English teachers, review the rationale for using humor and fun in the classroom and provide detailed descriptions for teaching practices and activities that confer enjoyment and learning for language arts students. Although fun activities, these methods foster vocabulary development, grammar instruction,…

  14. Variational Monte Carlo method for fermionic models combined with tensor networks and applications to the hole-doped two-dimensional Hubbard model

    Science.gov (United States)

    Zhao, Hui-Hai; Ido, Kota; Morita, Satoshi; Imada, Masatoshi

    2017-08-01

    The conventional tensor-network states employ real-space product states as reference wave functions. Here, we propose a many-variable variational Monte Carlo (mVMC) method combined with tensor networks by taking advantages of both to study fermionic models. The variational wave function is composed of a pair product wave function operated by real-space correlation factors and tensor networks. Moreover, we can apply quantum number projections, such as spin, momentum, and lattice symmetry projections, to recover the symmetry of the wave function to further improve the accuracy. We benchmark our method for one- and two-dimensional Hubbard models, which show significant improvement over the results obtained individually either by mVMC or by tensor network. We have applied the present method to a hole-doped Hubbard model on the square lattice, which indicates the stripe charge/spin order coexisting with a weak d -wave superconducting order in the ground state for the doping concentration of less than 0.3, where the stripe oscillation period gets longer with increasing hole concentration. The charge homogeneous and highly superconducting state also exists as a metastable excited state for the doping concentration less than 0.25.

  15. Diagnosis of Breast Cancer using a Combination of Genetic Algorithm and Artificial Neural Network in Medical Infrared Thermal Imaging

    Directory of Open Access Journals (Sweden)

    Hossein Ghayoumi zadeh

    2013-03-01

    Full Text Available Introduction This study is an effort to diagnose breast cancer by processing the quantitative and qualitative information obtained from medical infrared imaging. The medical infrared imaging is free from any harmful radiation and it is one of the best advantages of the proposed method. By analyzing this information, the best diagnostic parameters among the available parameters are selected and its sensitivity and precision in cancer diagnosis is improved by utilizing genetic algorithm and artificial neural network. Materials and Methods In this research, the necessary information is obtained from thermal imaging of 200 people, and 8 diagnostic parameters are extracted from these images by the research team. Then these 8 parameters are used as input of our proposed combinatorial model which is formed using artificial neural network and genetic algorithm. Results Our results have revealed that comparison of the breast areas; thermal pattern and kurtosis are the most important parameters in breast cancer diagnosis from proposed medical infrared imaging. The proposed combinatorial model with a 50% sensitivity, 75% specificity and, 70% accuracy shows good precision in cancer diagnosis. Conclusion The main goal of this article is to describe the capability of infrared imaging in preliminary diagnosis of breast cancer. This method is beneficial to patients with and without symptoms. The results indicate that the proposed combinatorial model produces optimum and efficacious parameters in comparison to other parameters and can improve the capability and power of globalizing the artificial neural network. This will help physicians in more accurate diagnosis of this type of cancer.

  16. Classification of protein fold classes by knot theory and prediction of folds by neural networks: A combined theoretical and experimental approach

    DEFF Research Database (Denmark)

    Ramnarayan, K.; Bohr, Henrik; Jalkanen, Karl J.

    2008-01-01

    classifications, we utilize standard neural network methods for predicting protein fold classes from amino acid sequences. We also make an analysis of the redundancy of the structural classifications in relation to function and ligand binding. Finally we advocate the use of combining the measurement of the VA......We present different means of classifying protein structure. One is made rigorous by mathematical knot invariants that coincide reasonably well with ordinary graphical fold classification and another classification is by packing analysis. Furthermore when constructing our mathematical fold......, VCD, Raman, ROA, EA and ECD spectra with the primary sequence as a way to improve both the accuracy and reliability of fold class prediction schemes....

  17. Assessing Fun Items' Effectiveness in Increasing Learning of College Introductory Statistics Students: Results of a Randomized Experiment

    Science.gov (United States)

    Lesser, Lawrence M.; Pearl, Dennis K.; Weber, John J., III

    2016-01-01

    There has been a recent emergence of scholarship on the use of fun in the college statistics classroom, with at least 20 modalities identified. While there have been randomized experiments that suggest that fun can enhance student achievement or attitudes in statistics, these studies have generally been limited to one particular fun modality or…

  18. A network of networks.

    Science.gov (United States)

    Iedema, Rick; Verma, Raj; Wutzke, Sonia; Lyons, Nigel; McCaughan, Brian

    2017-04-10

    Purpose To further our insight into the role of networks in health system reform, the purpose of this paper is to investigate how one agency, the NSW Agency for Clinical Innovation (ACI), and the multiple networks and enabling resources that it encompasses, govern, manage and extend the potential of networks for healthcare practice improvement. Design/methodology/approach This is a case study investigation which took place over ten months through the first author's participation in network activities and discussions with the agency's staff about their main objectives, challenges and achievements, and with selected services around the state of New South Wales to understand the agency's implementation and large system transformation activities. Findings The paper demonstrates that ACI accommodates multiple networks whose oversight structures, self-organisation and systems change approaches combined in dynamic ways, effectively yield a diversity of network governances. Further, ACI bears out a paradox of "centralised decentralisation", co-locating agents of innovation with networks of implementation and evaluation expertise. This arrangement strengthens and legitimates the role of the strategic hybrid - the healthcare professional in pursuit of change and improvement, and enhances their influence and impact on the wider system. Research limitations/implications While focussing the case study on one agency only, this study is unique as it highlights inter-network connections. Contributing to the literature on network governance, this paper identifies ACI as a "network of networks" through which resources, expectations and stakeholder dynamics are dynamically and flexibly mediated and enhanced. Practical implications The co-location of and dynamic interaction among clinical networks may create synergies among networks, nurture "strategic hybrids", and enhance the impact of network activities on health system reform. Social implications Network governance requires more

  19. Next Day Price Forecasting in Deregulated Market by Combination of Artificial Neural Network and ARIMA Time Series Models

    Science.gov (United States)

    Areekul, Phatchakorn; Senjyu, Tomonobu; Urasaki, Naomitsu; Yona, Atsushi

    Electricity price forecasting is becoming increasingly relevant to power producers and consumers in the new competitive electric power markets, when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper proposed a method to predict hourly electricity prices for next-day electricity markets by combination methodology of ARIMA and ANN models. The proposed method is examined on the Australian National Electricity Market (NEM), New South Wales regional in year 2006. Comparison of forecasting performance with the proposed ARIMA, ANN and combination (ARIMA-ANN) models are presented. Empirical results indicate that an ARIMA-ANN model can improve the price forecasting accuracy.

  20. Advanced Wind Speed Prediction Model Based on a Combination of Weibull Distribution and an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Athraa Ali Kadhem

    2017-10-01

    Full Text Available One of the most crucial prerequisites for effective wind power planning and operation in power systems is precise wind speed forecasting. Highly random fluctuations of wind influenced by the conditions of the atmosphere, weather and terrain result in difficulties of forecasting regardless of whether it is short-term or long-term. The current study has developed a method to model wind speed data predictions with dependence on seasonal wind variations over a particular time frame, usually a year, in the form of a Weibull distribution model with an artificial neural network (ANN. As a result, the essential dependencies between the wind speed and seasonal weather variation are exploited. The proposed model utilizes the ANN to predict the wind speed data, which has similar chronological and seasonal characteristics to the actual wind data. This model was applied to wind speed databases from selected sites in Malaysia, namely Mersing, Kudat, and Kuala Terengganu, to validate the proposed model. The results indicate that the proposed hybrid artificial neural network (HANN model is capable of depicting the fluctuating wind speed during different seasons of the year at different locations.

  1. A Fault Diagnosis Method for Oil Well Pump Using Radial Basis Function Neural Network Combined with Modified Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Deliang Yu

    2017-01-01

    Full Text Available This paper presents a new method to diagnose oil well pump faults using a modified radial basis function neural network. With the development of submersible linear motor technology, rodless pumping units have been widely used in oil exploration. However, the ground indicator diagram method cannot be used to diagnose the working conditions of rodless pumping units because it is based on the load change of the polished rod suspension point and its displacement. To solve this problem, this paper presents a new method that is applicable to rodless oil pumps. The advantage of this new method is its use of a simple feature extraction method and advanced genetic algorithm to optimize the threshold and weight of the RBF neural network. In this paper, we extract the characteristic value from the operation parameters of the submersible linear motor and oil wellhead as the input vector of the fault diagnosis model. Through experimental analysis, the proposed method is proven to have good convergence performance, high accuracy, and high reliability.

  2. Distribution and Network of Basal Temporal Language Areas: A Study of the Combination of Electric Cortical Stimulation and Diffusion Tensor Imaging.

    Science.gov (United States)

    Enatsu, Rei; Kanno, Aya; Ookawa, Satoshi; Ochi, Satoko; Ishiai, Sumio; Nagamine, Takashi; Mikuni, Nobuhiro

    2017-10-01

    The basal temporal language area (BTLA) is considered to have several functions in language processing; however, its brain network is still unknown. This study investigated the distribution and networks of the BTLA using a combination of electric cortical stimulation and diffusion tensor imaging (DTI). 10 patients with intractable focal epilepsy who underwent presurgical evaluation with subdural electrodes were enrolled in this study (language dominant side: 6 patients, language nondominant side: 4 patients). Electric stimulation at 50 Hz was applied to the electrodes during Japanese sentence reading, morphograms (kanji) reading, and syllabograms (kana) reading tasks to identify the BTLA. DTI was used to identify the subcortical fibers originating from the BTLA found by electric stimulation. The BTLA was found in 6 patients who underwent implantation of the subdural electrodes in the dominant hemisphere. The BTLA was located anywhere between 20 mm and 56 mm posterior to the temporal tips. In 3 patients, electric stimulation of some or all areas within the BTLA induced disturbance in reading of kanji words only. DTI detected the inferior longitudinal fasciculus (ILF) in all patients and the uncinate fasciculus (UF) in 1 patient, originating from the BTLA. ILF was detected from both kanji-specific areas and kanji-nonspecific areas. This study indicates that the network of the BTLA is a part of a ventral stream and is mainly composed of the ILF, which acts as a critical structure for lexical retrieval. ILF is also associated with the specific processing of kanji words. Copyright © 2017 Elsevier Inc. All rights reserved.

  3. Application of Chitosan-Zinc Oxide Nanoparticles for Lead Extraction From Water Samples by Combining Ant Colony Optimization with Artificial Neural Network

    Science.gov (United States)

    Khajeh, M.; Pourkarami, A.; Arefnejad, E.; Bohlooli, M.; Khatibi, A.; Ghaffari-Moghaddam, M.; Zareian-Jahromi, S.

    2017-09-01

    Chitosan-zinc oxide nanoparticles (CZPs) were developed for solid-phase extraction. Combined artificial neural network-ant colony optimization (ANN-ACO) was used for the simultaneous preconcentration and determination of lead (Pb2+) ions in water samples prior to graphite furnace atomic absorption spectrometry (GF AAS). The solution pH, mass of adsorbent CZPs, amount of 1-(2-pyridylazo)-2-naphthol (PAN), which was used as a complexing agent, eluent volume, eluent concentration, and flow rates of sample and eluent were used as input parameters of the ANN model, and the percentage of extracted Pb2+ ions was used as the output variable of the model. A multilayer perception network with a back-propagation learning algorithm was used to fit the experimental data. The optimum conditions were obtained based on the ACO. Under the optimized conditions, the limit of detection for Pb2+ ions was found to be 0.078 μg/L. This procedure was also successfully used to determine the amounts of Pb2+ ions in various natural water samples.

  4. Combining an Elastic Network With a Coarse-Grained Molecular Force Field : Structure, Dynamics, and Intermolecular Recognition

    NARCIS (Netherlands)

    Periole, Xavier; Cavalli, Marco; Marrink, Siewert-Jan; Ceruso, Marco A.

    Structure-based and physics-based coarse-grained molecular force fields have become attractive approaches to gain mechanistic insight into the function of large biomolecular assemblies. Here, we study how both approaches can be combined into a single representation, that we term ELNEDIN. In this

  5. Integrative analysis of kinase networks in TRAIL-induced apoptosis provides a source of potential targets for combination therapy

    DEFF Research Database (Denmark)

    So, Jonathan; Pasculescu, Adrian; Dai, Anna Y.

    2015-01-01

    /threonine kinase (PXK) and AP2-associated kinase 1 (AAK1), which promote receptor endocytosis and may enable cells to resist TRAIL-induced apoptosis by enhancing endocytosis of the TRAIL receptors. We assembled protein interaction maps using mass spectrometry-based protein interaction analysis and quantitative...... combination therapies to selectively kill cancer cells....

  6. Network meta-analysis combining individual patient and aggregate data from a mixture of study designs with an application to pulmonary arterial hypertension.

    Science.gov (United States)

    Thom, Howard H Z; Capkun, Gorana; Cerulli, Annamaria; Nixon, Richard M; Howard, Luke S

    2015-04-12

    Network meta-analysis (NMA) is a methodology for indirectly comparing, and strengthening direct comparisons of two or more treatments for the management of disease by combining evidence from multiple studies. It is sometimes not possible to perform treatment comparisons as evidence networks restricted to randomized controlled trials (RCTs) may be disconnected. We propose a Bayesian NMA model that allows to include single-arm, before-and-after, observational studies to complete these disconnected networks. We illustrate the method with an indirect comparison of treatments for pulmonary arterial hypertension (PAH). Our method uses a random effects model for placebo improvements to include single-arm observational studies into a general NMA. Building on recent research for binary outcomes, we develop a covariate-adjusted continuous-outcome NMA model that combines individual patient data (IPD) and aggregate data from two-arm RCTs with the single-arm observational studies. We apply this model to a complex comparison of therapies for PAH combining IPD from a phase-III RCT of imatinib as add-on therapy for PAH and aggregate data from RCTs and single-arm observational studies, both identified by a systematic review. Through the inclusion of observational studies, our method allowed the comparison of imatinib as add-on therapy for PAH with other treatments. This comparison had not been previously possible due to the limited RCT evidence available. However, the credible intervals of our posterior estimates were wide so the overall results were inconclusive. The comparison should be treated as exploratory and should not be used to guide clinical practice. Our method for the inclusion of single-arm observational studies allows the performance of indirect comparisons that had previously not been possible due to incomplete networks composed solely of available RCTs. We also built on many recent innovations to enable researchers to use both aggregate data and IPD. This method

  7. [Complex network analysis on Shenxiong glucose injection in combined use with Chinese and Western medicine for cerebral infarction in real world study].

    Science.gov (United States)

    Liu, Huan; Xie, Yan-Ming; Zhang, Yin; Jia, Ping-Ping; Zhuang, Yan

    2017-08-01

    In order to analyze Shenxiong glucose injection in combined use with other medicines for cerebral infarction in real world, the basic information, Chinese and western medicine diagnosis information, doctors'advice information, and laboratory checking information for the patients with Shenxiong glucose injection in treatment of cerebral infarction were extracted from the hospital information system (HIS) of sixteen 3A hospitals. Apriori Algorithm was used to establish models, and Clementine 12.0 was used for correlation analysis. Then complex network was established to analyze the combined drug use and visualize the results. A total of 635 patients were included in the study, among which 599 patients (94.33%) showed superior effect. Shenxiong glucose injection was often used with platelet suppressant drug, neuroprotective agent, lipid regulating agents, free radical scavenger, vitamins and Chinese medicine blood activating and stasis eliminating agent in the treatment of cerebral infarction. In the patients with superior effect, neuroprotective agent and free radical scavengers were also used based on the combined use with Aspirin, hypolipidemic drugs and blood activating and stasis removing agents, highlighting the rain protection strategies. Shenxiong glucose injection in combined use with Chinese and western medicines for cerebral infarction complied with the latest clinical practice guideline on the treatment of cerebral infarction, and the application of neuroprotective agent was propitious to improve the therapeutic effect. Copyright© by the Chinese Pharmaceutical Association.

  8. Performance evaluation and modeling of a submerged membrane bioreactor treating combined municipal and industrial wastewater using radial basis function artificial neural networks.

    Science.gov (United States)

    Mirbagheri, Seyed Ahmad; Bagheri, Majid; Boudaghpour, Siamak; Ehteshami, Majid; Bagheri, Zahra

    2015-01-01

    Treatment process models are efficient tools to assure proper operation and better control of wastewater treatment systems. The current research was an effort to evaluate performance of a submerged membrane bioreactor (SMBR) treating combined municipal and industrial wastewater and to simulate effluent quality parameters of the SMBR using a radial basis function artificial neural network (RBFANN). The results showed that the treatment efficiencies increase and hydraulic retention time (HRT) decreases for combined wastewater compared with municipal and industrial wastewaters. The BOD, COD, [Formula: see text] and total phosphorous (TP) removal efficiencies for combined wastewater at HRT of 7 hours were 96.9%, 96%, 96.7% and 92%, respectively. As desirable criteria for treating wastewater, the TBOD/TP ratio increased, the BOD and COD concentrations decreased to 700 and 1000 mg/L, respectively and the BOD/COD ratio was about 0.5 for combined wastewater. The training procedures of the RBFANN models were successful for all predicted components. The train and test models showed an almost perfect match between the experimental and predicted values of effluent BOD, COD, [Formula: see text] and TP. The coefficient of determination (R(2)) values were higher than 0.98 and root mean squared error (RMSE) values did not exceed 7% for train and test models.

  9. Combining Amplitude Spectrum Area with Previous Shock Information Using Neural Networks Improves Prediction Performance of Defibrillation Outcome for Subsequent Shocks in Out-Of-Hospital Cardiac Arrest Patients.

    Science.gov (United States)

    He, Mi; Lu, Yubao; Zhang, Lei; Zhang, Hehua; Gong, Yushun; Li, Yongqin

    2016-01-01

    Quantitative ventricular fibrillation (VF) waveform analysis is a potentially powerful tool to optimize defibrillation. However, whether combining VF features with additional attributes that related to the previous shock could enhance the prediction performance for subsequent shocks is still uncertain. A total of 528 defibrillation shocks from 199 patients experienced out-of-hospital cardiac arrest were analyzed in this study. VF waveform was quantified using amplitude spectrum area (AMSA) from defibrillator's ECG recordings prior to each shock. Combinations of AMSA with previous shock index (PSI) or/and change of AMSA (ΔAMSA) between successive shocks were exercised through a training dataset including 255shocks from 99patientswith neural networks. Performance of the combination methods were compared with AMSA based single feature prediction by area under receiver operating characteristic curve(AUC), sensitivity, positive predictive value (PPV), negative predictive value (NPV) and prediction accuracy (PA) through a validation dataset that was consisted of 273 shocks from 100patients. A total of61 (61.0%) patients required subsequent shocks (N = 173) in the validation dataset. Combining AMSA with PSI and ΔAMSA obtained highest AUC (0.904 vs. 0.819, pdefibrillation outcome for subsequent shocks.

  10. Interview with Alison Bechdel about her presentation of Fun Home in Paris and Tours

    Directory of Open Access Journals (Sweden)

    Anne Crémieux

    2007-09-01

    Full Text Available “Self-portrait by Alison Bechdel,” Courtesy of the authorTransat: How has Fun Home’s reception been different in nature from the reactions to Dykes to Watch Out For? Alison Bechdel: Fun Home has had a very different reception than Dykes to Watch Out For. It’s a very different type of book. I don’t want to downplay DTWOF—I’m very proud of the series, and I think it’s been a worthy contribution to queer culture as well as to the comics genre. But Fun Home was a real creative leap for me. Creati...

  11. Perceptions of service quality: what's fun got to do with it?

    Science.gov (United States)

    Karl, Katherine A; Harland, Lynn K; Peluchette, Joy V; Rodie, Amy R

    2010-04-01

    While incorporating fun into healthcare work environments to improve productivity, employee satisfaction, and patient satisfaction has been gaining attention since the release of the popular Fish! books (e.g., Lundin, Christensen, Paul, & Strand, 2002), no empirical research has been conducted examining customer/client reactions to witnessing the use of such fun activities. Using a 2 x 2 x 2 experimental scenario-based study, this research evaluated the impact of 3 independent variables (attentiveness to the customer, customer waiting time, and level of fun) on the dependent variables of perceived service quality and intent to return, refer, and complain.

  12. A network meta-analysis on the efficacy of targeted agents in combination with chemotherapy for treatment of advanced/metastatic triple-negative breast cancer.

    Science.gov (United States)

    Ge, Long; Tang, Yan; Zhang, Qiu-Ning; Tian, Jin-Hui; Wang, Xiao-Hu; Pieper, Dawid; Pan, Bei; Li, Lun; Ling, Juan; Bing, Zhi-Tong; Yang, Ke-Hu

    2017-08-29

    Our network meta-analysis aimed to determine the assistant efficacy of targeted therapy in combined with chemotherapy for advanced/metastatic triple-negative breast cancer (TNBC). A total of 15 randomized controlled trials (RCTs), involving 2,410 patients, met our inclusion criteria. Eight targeted agents involving 11 treatment arms were included. The methodological quality of included RCTs was acceptable. The results of direct comparisons showed that progression-free survival (PFS) was significantly longer with bevacizumab+chemotherapy when compared to chemotherapy alone (hazard ratio [HR] = 0.62, 95% credible intervals [CrI]: 0.41-0.87). However, there were no statistically significant differences for all other direct comparison groups. The results of indirect comparison of different targeted agents revealed no significant differences regarding all outcomes of interest. According to ranking probabilities, all outcomes favored bevacizumab+chemotherapy and veliparib+chemotherapy. Bayesian and Frequentist network meta-analysis showed similar results, and the probability of bias of small-study effects was small. A comprehensive literature search in PubMed, EMBASE, the Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science (via ISI Web of Knowledge), BIOSIS Previews (via ISI Web of Knowledge), and Chemical Abstracts (CA) was conducted to identify RCTs involving targeted agents in the treatment of advanced/metastatic TNBC. Two reviewers independently extracted related data and assessed the risk of bias of included studies. Bayesian network meta-analysis was conducted using R-3.3.2 software. Limited evidence showed that targeted agents combined with chemotherapy for advanced/metastatic TNBC were slightly effective. Further investigation of targeted therapies for TNBC is required to improve patient outcomes. The registration number was CRD42014014299.

  13. IN MY OPINION: Physics is fun - for whom?

    Science.gov (United States)

    Allday, Jonathan

    1998-09-01

    (modified) Punch cartoon. Marcie and Linus are discussing life: Marcie `Thank you for the Chocolate Sundae, Linus' Linus `You're welcome... Maybe we can do it again sometime?' Marcie `I don't think so... I don't find you very interesting.' Linus, wistfully and leaning against a tree - `Physics....' This cartoon the students find funny. The fact that the study of physics should be a block to a meaningful relationship seems to strike a chord with them. It takes them by surprise that physicists have a real life. Now I realize that any teacher having a real life often takes students by surprise. I remember visiting my mother's infants school when I was a sixth-former and the kids there found it unbelievable that Mrs Allday should have a son. Teachers, you see, cease to exist the minute the students walk out of the classroom. However, physics and its practitioners seem to be especially branded in this way. From time to time I show videos to my students. Well chosen ones (so I think) that reflect modern developments and show physicists enjoying what they are doing. One such tape shows the reaction to the first pictures coming down from the newly repaired Hubble Space Telescope. The control room is filled with whoops, delighted cheering and general bouncing up and down with enthusiasm. This always raises a laugh. However, one can tell that they are being laughed at, not with. Another tape is about the comet strike on Jupiter. The narrator spends some time talking with Gene and Carolyn Shoemaker (two of the discoverers of the comet) - a married team of astronomers. You can see that their married life in part revolves round their joint love of astronomy and physics. I have overheard comments like `can you imagine the breakfast conversations?' Why do students find it so difficult to imagine that physicists in real life have fun doing physics? Pondering this I have come to the conclusion that they do not see enough teachers having fun teaching the subject. Perhaps with all the time we

  14. Ultraperformance liquid chromatography-mass spectrometry based comprehensive metabolomics combined with pattern recognition and network analysis methods for characterization of metabolites and metabolic pathways from biological data sets.

    Science.gov (United States)

    Zhang, Ai-hua; Sun, Hui; Han, Ying; Yan, Guang-li; Yuan, Ye; Song, Gao-chen; Yuan, Xiao-xia; Xie, Ning; Wang, Xi-jun

    2013-08-06

    Metabolomics is the study of metabolic changes in biological systems and provides the small molecule fingerprints related to the disease. Extracting biomedical information from large metabolomics data sets by multivariate data analysis is of considerable complexity. Therefore, more efficient and optimizing metabolomics data processing technologies are needed to improve mass spectrometry applications in biomarker discovery. Here, we report the findings of urine metabolomic investigation of hepatitis C virus (HCV) patients by high-throughput ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) coupled with pattern recognition methods (principal component analysis, partial least-squares, and OPLS-DA) and network pharmacology. A total of 20 urinary differential metabolites (13 upregulated and 7 downregulated) were identified and contributed to HCV progress, involve several key metabolic pathways such as taurine and hypotaurine metabolism, glycine, serine and threonine metabolism, histidine metabolism, arginine and proline metabolism, and so forth. Metabolites identified through metabolic profiling may facilitate the development of more accurate marker algorithms to better monitor disease progression. Network analysis validated close contact between these metabolites and implied the importance of the metabolic pathways. Mapping altered metabolites to KEGG pathways identified alterations in a variety of biological processes mediated through complex networks. These findings may be promising to yield a valuable and noninvasive tool that insights into the pathophysiology of HCV and to advance the early diagnosis and monitor the progression of disease. Overall, this investigation illustrates the power of the UPLC-MS platform combined with the pattern recognition and network analysis methods that can engender new insights into HCV pathobiology.

  15. Combination of Deep Recurrent Neural Networks and Conditional Random Fields for Extracting Adverse Drug Reactions from User Reviews

    Directory of Open Access Journals (Sweden)

    Elena Tutubalina

    2017-01-01

    Full Text Available Adverse drug reactions (ADRs are an essential part of the analysis of drug use, measuring drug use benefits, and making policy decisions. Traditional channels for identifying ADRs are reliable but very slow and only produce a small amount of data. Text reviews, either on specialized web sites or in general-purpose social networks, may lead to a data source of unprecedented size, but identifying ADRs in free-form text is a challenging natural language processing problem. In this work, we propose a novel model for this problem, uniting recurrent neural architectures and conditional random fields. We evaluate our model with a comprehensive experimental study, showing improvements over state-of-the-art methods of ADR extraction.

  16. Good oral absorption prediction on non-nucleoside benzothiadiazine dioxide human cytomegalovirus inhibitors using combined chromatographic and neuronal network techniques.

    Science.gov (United States)

    Gil, Carmen; Dorronsoro, Isabel; Castro, Ana; Martinez, Ana

    2005-04-01

    The current drugs available against human cytomegalovirus (HCMV) suffer from a number of shortcomings such as toxic side effect, poor bioavailability and/or risk for emergence of drug-resistance virus strains. Due to these limitations, the development of new drugs against HCMV is of great interest. Taking into account the therapeutic potential of benzothiadiazines dioxides (BTD) derivatives, it is most important to know their oral bioavailability because all the current clinical drugs are poorly absorbed. In this work, the utility of CODES neural networks and biopartitioning micellar chromatography (BMC) in predicting pharmacokinetic properties has been used to estimate the oral absorption of BTD derivatives and their efficacy has been verified. The results indicate higher values for BTD derivatives than the currently licensed anti-viral agents.

  17. Learning to Generate Sequences with Combination of Hebbian and Non-hebbian Plasticity in Recurrent Spiking Neural Networks.

    Science.gov (United States)

    Panda, Priyadarshini; Roy, Kaushik

    2017-01-01

    Synaptic Plasticity, the foundation for learning and memory formation in the human brain, manifests in various forms. Here, we combine the standard spike timing correlation based Hebbian plasticity with a non-Hebbian synaptic decay mechanism for training a recurrent spiking neural model to generate sequences. We show that inclusion of the adaptive decay of synaptic weights with standard STDP helps learn stable contextual dependencies between temporal sequences, while reducing the strong attractor states that emerge in recurrent models due to feedback loops. Furthermore, we show that the combined learning scheme suppresses the chaotic activity in the recurrent model substantially, thereby enhancing its' ability to generate sequences consistently even in the presence of perturbations.

  18. Technologies for all-optical wavelength conversion in DWDM networks

    DEFF Research Database (Denmark)

    Wolfson, David; Fjelde, Tina; Kloch, Allan

    2001-01-01

    Different techniques for all-optical wavelength conversion are reviewed and the advantages and disadvantages seen from a system perspective are highlighted. All-optical wavelength conversion will play a major role in making cost-effective network nodes in future high-speed WDM networks, where fun...

  19. A Serious Game for Romanic Bathhouse: the Combination of Learning and fun for Cultural Heritage

    NARCIS (Netherlands)

    Qi, Wen

    2014-01-01

    Current cultural heritages often make use of images, sounds and video together, aiming to complement existing presentations and to create a memora-ble exhibition. In addition to this, modern culture heritages’ identities have shift-ed from simple holders of cultural objects to an educational

  20. Is talking to an automated teller machine natural and fun?

    Science.gov (United States)

    Chan, F Y; Khalid, H M

    Usability and affective issues of using automatic speech recognition technology to interact with an automated teller machine (ATM) are investigated in two experiments. The first uncovered dialogue patterns of ATM users for the purpose of designing the user interface for a simulated speech ATM system. Applying the Wizard-of-Oz methodology, multiple mapping and word spotting techniques, the speech driven ATM accommodates bilingual users of Bahasa Melayu and English. The second experiment evaluates the usability of a hybrid speech ATM, comparing it with a simulated manual ATM. The aim is to investigate how natural and fun can talking to a speech ATM be for these first-time users. Subjects performed the withdrawal and balance enquiry tasks. The ANOVA was performed on the usability and affective data. The results showed significant differences between systems in the ability to complete the tasks as well as in transaction errors. Performance was measured on the time taken by subjects to complete the task and the number of speech recognition errors that occurred. On the basis of user emotions, it can be said that the hybrid speech system enabled pleasurable interaction. Despite the limitations of speech recognition technology, users are set to talk to the ATM when it becomes available for public use.

  1. Work, school and fun. The utopia of Pinocchio

    Directory of Open Access Journals (Sweden)

    Joaquim MACHADO DE ARAUJO

    2013-07-01

    Full Text Available The Adventures of Pinocchio tell the Story of a Toy in a transition from the state of nature to the state of culture, of someone who develops himself morally from heteronomy to autonomy. In his development, he experiences the disenchantment of the miracle of the money multiplication without work and the utopia of a fun land, the dream of any child. He reduces himself to the condition of an animal. The metaphor of the monkey to which he is faced when he is not succeeded in school is brought to the life of the animated toy. The cultural perspective on the consequences of the delay in maintaining a state of nature becomes a reality to the one who has run away from school at first. Then the one who has idleness has a goal turns to be a serious concern. At least the one who has work and self-directed learning as main aims is a relevant turning point to this tale.

  2. Six to Ten Digits Multiplication Fun Learning Using Puppet Prototype

    Science.gov (United States)

    Islamiah Rosli, D.'oria; Ali, Azita; Peng, Lim Soo; Sujardi, Imam; Usodo, Budi; Adie Perdana, Fengky

    2017-01-01

    Logic and technical subjects require students to understand basic knowledge in mathematic. For instance, addition, minus, division and multiplication operations need to be mastered by students due to mathematic complexity as the learning mathematic grows higher. Weak foundation in mathematic also contribute to high failure rate in mathematic subjects in schools. In fact, students in primary schools are struggling to learn mathematic because they need to memorize formulas, multiplication or division operations. To date, this study will develop a puppet prototyping for learning mathematic for six to ten digits multiplication. Ten participants involved in the process of developing the prototype in this study. Students involved in the study were those from the intermediate class students whilst teachers were selected based on their vast knowledge and experiences and have more than five years of experience in teaching mathematic. Close participatory analysis will be used in the prototyping process as to fulfil the requirements of the students and teachers whom will use the puppet in learning six to ten digit multiplication in mathematic. Findings showed that, the students had a great time and fun learning experience in learning multiplication and they able to understand the concept of multiplication using puppet. Colour and materials of the puppet also help to attract student attention during learning. Additionally, students able to visualized and able to calculate accurate multiplication value and the puppet help them to recall in multiplying and adding the digits accordingly.

  3. Make Celebrations Fun, Healthy, and Active: 10 Tips to Creating Healthy, Active Events

    Science.gov (United States)

    ... have when tasty, healthy foods from all the food groups are offered in a fun, active environment. Above all, focus on enjoying friends and family. 1 Make healthy habits part of your celebrations ...

  4. “Pick-up Lines”: A Fun Way to Facilitate Learning Microbiological Concepts

    Directory of Open Access Journals (Sweden)

    Thomas Edison E. dela Cruz

    2014-05-01

    Full Text Available Learning microbiology can be made fun by writing funny lines related to microbiology. Students were tasked to create their own pick-up lines and explain these based on their understanding of the basic concepts in microbiology.

  5. A Limited Evaluation of Full Scale Control Surface Deflection Drag (Have FUN)

    National Research Council Canada - National Science Library

    Reinhardt, R. B; Celi, Sean A; Geraghty, Jeffrey T; Stahl, James W; Glover, Victor J; Bowman, Geoffrey G

    2007-01-01

    The Have FUN (FUll Scale Numbers) Test Management Project was conducted at the request of the USAF TPS as an investigation into the drag caused by control surface deflection during dynamic soaring techniques...

  6. Wireless home networking for dummies

    CERN Document Server

    Briere, Danny; Ferris, Edward

    2010-01-01

    The perennial bestseller shows you how share your files and Internet connection across a wireless network. Fully updated for Windows 7 and Mac OS X Snow Leopard, this new edition of this bestseller returns with all the latest in wireless standards and security. This fun and friendly guide shows you how to integrate your iPhone, iPod touch, smartphone, or gaming system into your home network. Veteran authors escort you through the various financial and logisitical considerations that you need to take into account before building a wireless network at home.: Covers the basics of planning, instal

  7. Combining ground-based and airborne EM through Artificial Neural Networks for modelling glacial till under saline groundwater conditions

    Directory of Open Access Journals (Sweden)

    J. L. Gunnink

    2012-08-01

    Full Text Available Airborne electromagnetic (AEM methods supply data over large areas in a cost-effective way. We used Artificial Neural Networks (ANN to classify the geophysical signal into a meaningful geological parameter. By using examples of known relations between ground-based geophysical data (in this case electrical conductivity, EC, from electrical cone penetration tests and geological parameters (presence of glacial till, we extracted learning rules that could be applied to map the presence of a glacial till using the EC profiles from the airborne EM data. The saline groundwater in the area was obscuring the EC signal from the till but by using ANN we were able to extract subtle and often non-linear, relations in EC that were representative of the presence of the till. The ANN results were interpreted as the probability of having till and showed a good agreement with drilling data. The glacial till is acting as a layer that inhibits groundwater flow, due to its high clay-content, and is therefore an important layer in hydrogeological modelling and for predicting the effects of climate change on groundwater quantity and quality.

  8. DT-Web: a web-based application for drug-target interaction and drug combination prediction through domain-tuned network-based inference.

    Science.gov (United States)

    Alaimo, Salvatore; Bonnici, Vincenzo; Cancemi, Damiano; Ferro, Alfredo; Giugno, Rosalba; Pulvirenti, Alfredo

    2015-01-01

    The identification of drug-target interactions (DTI) is a costly and time-consuming step in drug discovery and design. Computational methods capable of predicting reliable DTI play an important role in the field. Algorithms may aim to design new therapies based on a single approved drug or a combination of them. Recently, recommendation methods relying on network-based inference in connection with knowledge coming from the specific domain have been proposed. Here we propose a web-based interface to the DT-Hybrid algorithm, which applies a recommendation technique based on bipartite network projection implementing resources transfer within the network. This technique combined with domain-specific knowledge expressing drugs and targets similarity is used to compute recommendations for each drug. Our web interface allows the users: (i) to browse all the predictions inferred by the algorithm; (ii) to upload their custom data on which they wish to obtain a prediction through a DT-Hybrid based pipeline; (iii) to help in the early stages of drug combinations, repositioning, substitution, or resistance studies by finding drugs that can act simultaneously on multiple targets in a multi-pathway environment. Our system is periodically synchronized with DrugBank and updated accordingly. The website is free, open to all users, and available at http://alpha.dmi.unict.it/dtweb/. Our web interface allows users to search and visualize information on drugs and targets eventually providing their own data to compute a list of predictions. The user can visualize information about the characteristics of each drug, a list of predicted and validated targets, associated enzymes and transporters. A table containing key information and GO classification allows the users to perform their own analysis on our data. A special interface for data submission allows the execution of a pipeline, based on DT-Hybrid, predicting new targets with the corresponding p-values expressing the reliability of

  9. FunImageJ: a Lisp framework for scientific image processing.

    Science.gov (United States)

    Harrington, Kyle I S; Rueden, Curtis T; Eliceiri, Kevin W

    2017-11-02

    FunImageJ is a Lisp framework for scientific image processing built upon the ImageJ software ecosystem. The framework provides a natural functional-style for programming, while accounting for the performance requirements necessary in big data processing commonly encountered in biological image analysis. Freely available plugin to Fiji (http://fiji.sc/#download). Installation and use instructions available at (http://imagej.net/FunImageJ). kharrington@uidaho.edu. Supplementary data are available at Bioinformatics online.

  10. Fun During Knee Rehabilitation: Feasibility and Acceptability Testing of a New Android-Based Training Device.

    Science.gov (United States)

    Weber-Spickschen, Thomas Sanjay; Colcuc, Christian; Hanke, Alexander; Clausen, Jan-Dierk; James, Paul Abraham; Horstmann, Hauke

    2017-01-01

    The initial goals of rehabilitation after knee injuries and operations are to achieve full knee extension and to activate quadriceps muscle. In addition to regular physiotherapy, an android-based knee training device is designed to help patients achieve these goals and improve compliance in the early rehabilitation period. This knee training device combines fun in a computer game with muscular training or rehabilitation. Our aim was to test the feasibility and acceptability of this new device. 50 volunteered subjects enrolled to test out the computer game aided device. The first game was the high-striker game, which recorded maximum knee extension power. The second game involved controlling quadriceps muscular power to simulate flying an aeroplane in order to record accuracy of muscle activation. The subjects evaluated this game by completing a simple questionnaire. No technical problem was encountered during the usage of this device. No subjects complained of any discomfort after using this device. Measurements including maximum knee extension power, knee muscle activation and control were recorded successfully. Subjects rated their experience with the device as either excellent or very good and agreed that the device can motivate and monitor the progress of knee rehabilitation training. To the best of our knowledge, this is the first android-based tool available to fast track knee rehabilitation training. All subjects gave very positive feedback to this computer game aided knee device.

  11. Reconstruction of multidimensional carbon hosts with combined 0D, 1D and 2D networks for enhanced lithium-sulfur batteries

    Science.gov (United States)

    Li, S. H.; Xia, X. H.; Wang, Y. D.; Wang, X. L.; Tu, J. P.

    2017-02-01

    It is a core task to find solutions to suppress the "shuttle effect" of polysulfides and improve high rate capability at the sulfur cathode of lithium sulfur batteries. Herein we first time propose a concept of multileveled blocking "dams" to suppress the diffusion of polysulfides. We report a facile and effective strategy to construct multidimensional conductive carbon hosts for accommodation of active sulfur. Multidimensional ternary carbon networks (MTCNs) with 0D nanospheres, 1D nanotubes and 2D nanoflakes are organically combined together to provide multileveled conductive channels to reserve active sulfur and promote stable sustained reactions. In the light of enhanced conductivity and multileveled blocking "dams" for polysulfides, the designed MTCNs/S cathode has been demonstrated with noticeable improvement in discharge capacity (1472 mAh g-1 at 0.l C) and long-term cycling stability (65% retention at 5.0 C after 500 cycles). Our research may provide a new insight in the gradient blocking of polysulfides with the help of multidimensional carbon networks.

  12. The combination of circle topology and leaky integrator neurons remarkably improves the performance of echo state network on time series prediction.

    Science.gov (United States)

    Xue, Fangzheng; Li, Qian; Li, Xiumin

    2017-01-01

    Recently, echo state network (ESN) has attracted a great deal of attention due to its high accuracy and efficient learning performance. Compared with the traditional random structure and classical sigmoid units, simple circle topology and leaky integrator neurons have more advantages on reservoir computing of ESN. In this paper, we propose a new model of ESN with both circle reservoir structure and leaky integrator units. By comparing the prediction capability on Mackey-Glass chaotic time series of four ESN models: classical ESN, circle ESN, traditional leaky integrator ESN, circle leaky integrator ESN, we find that our circle leaky integrator ESN shows significantly better performance than other ESNs with roughly 2 orders of magnitude reduction of the predictive error. Moreover, this model has stronger ability to approximate nonlinear dynamics and resist noise than conventional ESN and ESN with only simple circle structure or leaky integrator neurons. Our results show that the combination of circle topology and leaky integrator neurons can remarkably increase dynamical diversity and meanwhile decrease the correlation of reservoir states, which contribute to the significant improvement of computational performance of Echo state network on time series prediction.

  13. PDA-phone-based instant transmission of radiological images over a CDMA network by combining the PACS screen with a Bluetooth-interfaced local wireless link.

    Science.gov (United States)

    Kim, Dong Keun; Yoo, Sun K; Park, Jeong Jin; Kim, Sun Ho

    2007-06-01

    Remote teleconsultation by specialists is important for timely, correct, and specialized emergency surgical and medical decision making. In this paper, we designed a new personal digital assistant (PDA)-phone-based emergency teleradiology system by combining cellular communication with Bluetooth-interfaced local wireless links. The mobility and portability resulting from the use of PDAs and wireless communication can provide a more effective means of emergency teleconsultation without requiring the user to be limited to a fixed location. Moreover, it enables synchronized radiological image sharing between the attending physician in the emergency room and the remote specialist on picture archiving and communication system terminals without distorted image acquisition. To enable rapid and fine-quality radiological image transmission over a cellular network in a secure manner, progressive compression and security mechanisms have been incorporated. The proposed system is tested over a code division Multiple Access 1x-Evolution Data-Only network to evaluate the performance and to demonstrate the feasibility of this system in a real-world setting.

  14. The combination of circle topology and leaky integrator neurons remarkably improves the performance of echo state network on time series prediction.

    Directory of Open Access Journals (Sweden)

    Fangzheng Xue

    Full Text Available Recently, echo state network (ESN has attracted a great deal of attention due to its high accuracy and efficient learning performance. Compared with the traditional random structure and classical sigmoid units, simple circle topology and leaky integrator neurons have more advantages on reservoir computing of ESN. In this paper, we propose a new model of ESN with both circle reservoir structure and leaky integrator units. By comparing the prediction capability on Mackey-Glass chaotic time series of four ESN models: classical ESN, circle ESN, traditional leaky integrator ESN, circle leaky integrator ESN, we find that our circle leaky integrator ESN shows significantly better performance than other ESNs with roughly 2 orders of magnitude reduction of the predictive error. Moreover, this model has stronger ability to approximate nonlinear dynamics and resist noise than conventional ESN and ESN with only simple circle structure or leaky integrator neurons. Our results show that the combination of circle topology and leaky integrator neurons can remarkably increase dynamical diversity and meanwhile decrease the correlation of reservoir states, which contribute to the significant improvement of computational performance of Echo state network on time series prediction.

  15. A Web 2.0 and Epidemiology Mash-Up: Using Respondent-Driven Sampling in Combination with Social Network Site Recruitment to Reach Young Transwomen.

    Science.gov (United States)

    Arayasirikul, Sean; Chen, Yea-Hung; Jin, Harry; Wilson, Erin

    2016-06-01

    Respondent-driven sampling (RDS) peer referral has been proven to be an effective recruitment method for hard-to-reach populations; however, its application in diverse populations is limited. Recruitment occurred in two phases: RDS-only followed by development and implementation of an online social network strategy in combination with RDS peer referral (RDS + SNS). Compared to RDS-only, RDS + SNS reached a sample that was younger (χ(2) = 9.19, P = .03), more likely to identify with a non-binary gender identity (χ(2) = 10.4247, P = .03), with less housing instability (50.5 vs. 68.6 %, χ(2) = 9.0038, P = .002) and less sex work (19.7 vs. 31.4 %, χ(2) = 5.0798, P = .02). Additionally, we describe lessons learned as a result of implementing our online social network strategy. Our findings underscore the importance of integrating Internet-driven strategies to meet challenges in sample diversity and recruitment of young transwomen.

  16. Properties of grain boundary networks in the NEEM ice core analyzed by combined transmission and reflection optical microscopy

    Science.gov (United States)

    Binder, Tobias; Weikusat, Ilka; Garbe, Christoph; Svensson, Anders; Kipfstuhl, Sepp

    2014-05-01

    Microstructure analysis of ice cores is vital to understand the processes controlling the flow of ice on the microscale. To quantify the microstructural variability (and thus occurring processes) on centimeter, meter and kilometer scale along deep polar ice cores, a large number of sections has to be analyzed. In the last decade, two different methods have been applied: On the one hand, transmission optical microscopy of thin sections between crossed polarizers yields information on the distribution of crystal c-axes. On the other hand, reflection optical microscopy of polished and controlled sublimated section surfaces allows to characterize the high resolution properties of a single grain boundary, e.g. its length, shape or curvature (further developed by [1]). Along the entire NEEM ice core (North-West Greenland, 2537 m length) drilled in 2008-2011 we applied both methods to the same set of vertical sections. The data set comprises series of six consecutive 6 x 9 cm2 sections in steps of 20 m - in total about 800 images. A dedicated method for automatic processing and matching both image types has recently been developed [2]. The high resolution properties of the grain boundary network are analyzed. Furthermore, the automatic assignment of c-axis misorientations to visible sublimation grooves enables us to quantify the degree of similarity between the microstructure revealed by both analysis techniques. The reliability to extract grain boundaries from both image types as well as the appearance of sublimation groove patterns exhibiting low misorientations is investigated. X-ray Laue diffraction measurements (yielding full crystallographic orientation) have validated the sensitivity of the surface sublimation method for sub-grain boundaries [3]. We introduce an approach for automatic extraction of sub-grain structures from sublimation grooves. A systematic analysis of sub-grain boundary densities indicates a possible influence of high impurity contents (amongst

  17. Reverberant speech recognition combining deep neural networks and deep autoencoders augmented with a phone-class feature

    Science.gov (United States)

    Mimura, Masato; Sakai, Shinsuke; Kawahara, Tatsuya

    2015-12-01

    We propose an approach to reverberant speech recognition adopting deep learning in the front-end as well as b a c k-e n d o f a r e v e r b e r a n t s p e e c h r e c o g n i t i o n s y s t e m, a n d a n o v e l m e t h o d t o i m p r o v e t h e d e r e v e r b e r a t i o n p e r f o r m a n c e of the front-end network using phone-class information. At the front-end, we adopt a deep autoencoder (DAE) for enhancing the speech feature parameters, and speech recognition is performed in the back-end using DNN-HMM acoustic models trained on multi-condition data. The system was evaluated through the ASR task in the Reverb Challenge 2014. The DNN-HMM system trained on the multi-condition training set achieved a conspicuously higher word accuracy compared to the MLLR-adapted GMM-HMM system trained on the same data. Furthermore, feature enhancement with the deep autoencoder contributed to the improvement of recognition accuracy especially in the more adverse conditions. While the mapping between reverberant and clean speech in DAE-based dereverberation is conventionally conducted only with the acoustic information, we presume the mapping is also dependent on the phone information. Therefore, we propose a new scheme (pDAE), which augments a phone-class feature to the standard acoustic features as input. Two types of the phone-class feature are investigated. One is the hard recognition result of monophones, and the other is a soft representation derived from the posterior outputs of monophone DNN. The augmented feature in either type results in a significant improvement (7-8 % relative) from the standard DAE.

  18. Combined diffusion-weighted and functional magnetic resonance imaging reveals a temporal-occipital network involved in auditory-visual object processing

    Directory of Open Access Journals (Sweden)

    Anton Ludwig Beer

    2013-02-01

    Full Text Available Functional magnetic resonance imaging (MRI showed that the superior temporal and occipital cortex are involved in multisensory integration. Probabilistic fiber tracking based on diffusion-weighted MRI suggests that multisensory processing is supported by white matter connections between auditory cortex and the temporal and occipital lobe. Here, we present a combined functional MRI and probabilistic fiber tracking study that reveals multisensory processing mechanisms that remained undetected by either technique alone. Ten healthy participants passively observed visually presented lip or body movements, heard speech or body action sounds, or were exposed to a combination of both. Bimodal stimulation engaged a temporal-occipital brain network including the multisensory superior temporal sulcus (msSTS, the lateral superior temporal gyrus (lSTG, and the extrastriate body area (EBA. A region-of-interest analysis showed multisensory interactions (e.g., subadditive responses to bimodal compared to unimodal stimuli in the msSTS, the lSTG, and the EBA region. Moreover, sounds elicited responses in the medial occipital cortex. Probabilistic tracking revealed white matter tracts between the auditory cortex and the medial occipital, the inferior-occipital cortex, and the superior temporal sulcus (STS. However, STS terminations of auditory cortex tracts showed limited overlap with the msSTS region. Instead, msSTS was connected to primary sensory regions via intermediate nodes in the temporal and occipital cortex. Similarly, the lSTG and EBA regions showed limited direct white matter connections but instead were connected via intermediate nodes. Our results suggest that multisensory processing in the STS is mediated by separate brain areas that form a distinct network in the lateral temporal and inferior occipital cortex.

  19. Network meta-analysis on the log-hazard scale, combining count and hazard ratio statistics accounting for multi-arm trials: A tutorial

    Directory of Open Access Journals (Sweden)

    Hawkins Neil

    2010-06-01

    Full Text Available Abstract Background Data on survival endpoints are usually summarised using either hazard ratio, cumulative number of events, or median survival statistics. Network meta-analysis, an extension of traditional pairwise meta-analysis, is typically based on a single statistic. In this case, studies which do not report the chosen statistic are excluded from the analysis which may introduce bias. Methods In this paper we present a tutorial illustrating how network meta-analyses of survival endpoints can combine count and hazard ratio statistics in a single analysis on the hazard ratio scale. We also describe methods for accounting for the correlations in relative treatment effects (such as hazard ratios that arise in trials with more than two arms. Combination of count and hazard ratio data in a single analysis is achieved by estimating the cumulative hazard for each trial arm reporting count data. Correlation in relative treatment effects in multi-arm trials is preserved by converting the relative treatment effect estimates (the hazard ratios to arm-specific outcomes (hazards. Results A worked example of an analysis of mortality data in chronic obstructive pulmonary disease (COPD is used to illustrate the methods. The data set and WinBUGS code for fixed and random effects models are provided. Conclusions By incorporating all data presentations in a single analysis, we avoid the potential selection bias associated with conducting an analysis for a single statistic and the potential difficulties of interpretation, misleading results and loss of available treatment comparisons associated with conducting separate analyses for different summary statistics.

  20. Development of a spatial decision support system for flood risk management in Brazil that combines volunteered geographic information with wireless sensor networks

    Science.gov (United States)

    Horita, Flávio E. A.; Albuquerque, João Porto de; Degrossi, Lívia C.; Mendiondo, Eduardo M.; Ueyama, Jó

    2015-07-01

    Effective flood risk management requires updated information to ensure that the correct decisions can be made. This can be provided by Wireless Sensor Networks (WSN) which are a low-cost means of collecting updated information about rivers. Another valuable resource is Volunteered Geographic Information (VGI) which is a comparatively new means of improving the coverage of monitored areas because it is able to supply supplementary information to the WSN and thus support decision-making in flood risk management. However, there still remains the problem of how to combine WSN data with VGI. In this paper, an attempt is made to investigate AGORA-DS, which is a Spatial Decision Support System (SDSS) that is able to make flood risk management more effective by combining these data sources, i.e. WSN with VGI. This approach is built over a conceptual model that complies with the interoperable standards laid down by the Open Geospatial Consortium (OGC) - e.g. Sensor Observation Service (SOS) and Web Feature Service (WFS) - and seeks to combine and present unified information in a web-based decision support tool. This work was deployed in a real scenario of flood risk management in the town of São Carlos in Brazil. The evidence obtained from this deployment confirmed that interoperable standards can support the integration of data from distinct data sources. In addition, they also show that VGI is able to provide information about areas of the river basin which lack data since there is no appropriate station in the area. Hence it provides a valuable support for the WSN data. It can thus be concluded that AGORA-DS is able to combine information provided by WSN and VGI, and provide useful information for supporting flood risk management.

  1. Combined transcriptome and metabolome analyses of metformin effects reveal novel links between metabolic networks in steroidogenic systems.

    Science.gov (United States)

    Udhane, Sameer S; Legeza, Balazs; Marti, Nesa; Hertig, Damian; Diserens, Gaëlle; Nuoffer, Jean-Marc; Vermathen, Peter; Flück, Christa E

    2017-08-17

    Metformin is an antidiabetic drug, which inhibits mitochondrial respiratory-chain-complex I and thereby seems to affect the cellular metabolism in many ways. It is also used for the treatment of the polycystic ovary syndrome (PCOS), the most common endocrine disorder in women. In addition, metformin possesses antineoplastic properties. Although metformin promotes insulin-sensitivity and ameliorates reproductive abnormalities in PCOS, its exact mechanisms of action remain elusive. Therefore, we studied the transcriptome and the metabolome of metformin in human adrenal H295R cells. Microarray analysis revealed changes in 693 genes after metformin treatment. Using high resolution magic angle spinning nuclear magnetic resonance spectroscopy (HR-MAS-NMR), we determined 38 intracellular metabolites. With bioinformatic tools we created an integrated pathway analysis to understand different intracellular processes targeted by metformin. Combined metabolomics and transcriptomics data analysis showed that metformin affects a broad range of cellular processes centered on the mitochondrium. Data confirmed several known effects of metformin on glucose and androgen metabolism, which had been identified in clinical and basic studies previously. But more importantly, novel links between the energy metabolism, sex steroid biosynthesis, the cell cycle and the immune system were identified. These omics studies shed light on a complex interplay between metabolic pathways in steroidogenic systems.

  2. Fun Teaching: The Key to the Future Climatology

    Science.gov (United States)

    Mulvey, G.

    2016-12-01

    In general meteorology is a science of immediate impact. What will the weather be tomorrow or next week? Climatology and climate change is the science of our long range past and future. Decisions made in the past, now, and in the future on climate change issues did and will continue to impact the global climate. It is essential that current and future generations understand the causes of climate change to make informed decisions regarding individual and government actions needed to mitigate human impacts on the future climate. The university challenge is make climatology an exciting and dynamic adventure into the past, present and future. Instructor and supporting organizations have stepped outside the "old yellow notes" approach to enable students to progress beyond remember, understand, and apply; to analyze, evaluate and create. Responding to this instructional challenge by shifting instructional techniques and tools to a new paradigm does not happen overnight. The instructional strategies to make this jump are known in general, but not in specific. This paper deals with examples of how to translate the instructional strategies into practice in ways that are fun for students and instructors. Techniques to be described include interactive discussions, debates and team challenges, such as: - Describing continental climates during past geological periods - In-class teams debates on legislature to control/modify human CO2 releases Low or no cost teaching aids such as video clips, demonstrations, specimens, and experiments will be described with outcomes and resources interest. Some examples to be discussed are - Tree cookies, cross sections - Ocean core smear slide samples of diatoms, foraminifera, etc. - Ice pack/glacial melt experiments - Glacial flow and interpreting glacial ice cores experiment - Field trips to observe geological strata and geological samples - Storytelling - the shared experiences of each instructor

  3. Mathematical Modeling and Optimizing of in Vitro Hormonal Combination for G × N15 Vegetative Rootstock Proliferation Using Artificial Neural Network-Genetic Algorithm (ANN-GA).

    Science.gov (United States)

    Arab, Mohammad M; Yadollahi, Abbas; Ahmadi, Hamed; Eftekhari, Maliheh; Maleki, Masoud

    2017-01-01

    The efficiency of a hybrid systems method which combined artificial neural networks (ANNs) as a modeling tool and genetic algorithms (GAs) as an optimizing method for input variables used in ANN modeling was assessed. Hence, as a new technique, it was applied for the prediction and optimization of the plant hormones concentrations and combinations for in vitro proliferation of Garnem (G × N15) rootstock as a case study. Optimizing hormones combination was surveyed by modeling the effects of various concentrations of cytokinin-auxin, i.e., BAP, KIN, TDZ, IBA, and NAA combinations (inputs) on four growth parameters (outputs), i.e., micro-shoots number per explant, length of micro-shoots, developed callus weight (CW) and the quality index (QI) of plantlets. Calculation of statistical values such as R2 (coefficient of determination) related to the accuracy of ANN-GA models showed a considerably higher prediction accuracy for ANN models, i.e., micro-shoots number: R2 = 0.81, length of micro-shoots: R2 = 0.87, CW: R2 = 0.88, QI: R2 = 0.87. According to the results, among the input variables, BAP (19.3), KIN (9.64), and IBA (2.63) showed the highest values of variable sensitivity ratio for proliferation rate. The GA showed that media containing 1.02 mg/l BAP in combination with 0.098 mg/l IBA could lead to the optimal proliferation rate (10.53) for G × N15 rootstock. Another objective of the present study was to compare the performance of predicted and optimized cytokinin-auxin combination with the best optimized obtained concentrations of our other experiments. Considering three growth parameters (length of micro-shoots, micro-shoots number, and proliferation rate), the last treatment was found to be superior to the rest of treatments for G × N15 rootstock in vitro multiplication. Very little difference between the ANN predicted and experimental data confirmed high capability of ANN-GA method in predicting new optimized protocols for plant in vitro propagation.

  4. Mathematical Modeling and Optimizing of in Vitro Hormonal Combination for G × N15 Vegetative Rootstock Proliferation Using Artificial Neural Network-Genetic Algorithm (ANN-GA

    Directory of Open Access Journals (Sweden)

    Mohammad M. Arab

    2017-11-01

    Full Text Available The efficiency of a hybrid systems method which combined artificial neural networks (ANNs as a modeling tool and genetic algorithms (GAs as an optimizing method for input variables used in ANN modeling was assessed. Hence, as a new technique, it was applied for the prediction and optimization of the plant hormones concentrations and combinations for in vitro proliferation of Garnem (G × N15 rootstock as a case study. Optimizing hormones combination was surveyed by modeling the effects of various concentrations of cytokinin–auxin, i.e., BAP, KIN, TDZ, IBA, and NAA combinations (inputs on four growth parameters (outputs, i.e., micro-shoots number per explant, length of micro-shoots, developed callus weight (CW and the quality index (QI of plantlets. Calculation of statistical values such as R2 (coefficient of determination related to the accuracy of ANN-GA models showed a considerably higher prediction accuracy for ANN models, i.e., micro-shoots number: R2 = 0.81, length of micro-shoots: R2 = 0.87, CW: R2 = 0.88, QI: R2 = 0.87. According to the results, among the input variables, BAP (19.3, KIN (9.64, and IBA (2.63 showed the highest values of variable sensitivity ratio for proliferation rate. The GA showed that media containing 1.02 mg/l BAP in combination with 0.098 mg/l IBA could lead to the optimal proliferation rate (10.53 for G × N15 rootstock. Another objective of the present study was to compare the performance of predicted and optimized cytokinin–auxin combination with the best optimized obtained concentrations of our other experiments. Considering three growth parameters (length of micro-shoots, micro-shoots number, and proliferation rate, the last treatment was found to be superior to the rest of treatments for G × N15 rootstock in vitro multiplication. Very little difference between the ANN predicted and experimental data confirmed high capability of ANN-GA method in predicting new optimized protocols for plant in vitro

  5. Application of smart spectrophotometric methods and artificial neural network for the simultaneous quantitation of olmesartan medoxamil, amlodipine besylate and hydrochlorothiazide in their combined pharmaceutical dosage form

    Science.gov (United States)

    2013-01-01

    Background New, simple and specific spectrophotometric methods and artificial neural network (ANN) were developed and validated in accordance with ICH guidelines for the simultaneous estimation of Olmesartan (OLM), Amlodipine (AML), and Hydrochlorothiazide (HCT) in commercial tablets. Results For spectrophotometric methods: First, Amlodipine (AML) was determined by direct spectrophotometry at 359 nm and by application of the ratio subtraction, the AML spectrum was removed from the mixture spectra. Then Hydrochlorothiazide (HCT) was determined directly at 315 nm without interference from Olmesartan medoxamil (OLM) which could be determined using the isoabsorptive method. The calibration curve is linear over the concentration range of 5–40, 2.5-40 and 2–40 μg mL-1 for AML, OLM and HCT, respectively. ANN (as a multivariate calibration method) was also applied for the simultaneous determination of the three analytes in their combined pharmaceutical dosage form using spectral region from 230–340 nm. Conclusions The proposed methods were successfully applied for the assay of the three analytes in laboratory prepared mixtures and combined pharmaceutical tablets with excellent recoveries. No interference was observed from common pharmaceutical additives. The results were favorably compared with those obtained by a reference spectrophotometric method. The methods are validated according to the ICH guidelines and accuracy, precision and repeatability are found to be within the acceptable limit. PMID:23374392

  6. The Crucial Role of Amateur-Professional Networks in the Golden Age of Large Surveys (Abstract)

    Science.gov (United States)

    Rodriguez, J. E.

    2017-06-01

    (Abstract only) With ongoing projects such as HATNet, SuperWASP, KELT, MEarth, and the CoRoT and Kepler/K2 mission, we are in a golden era of large photometric surveys. In addition, LSST and TESS will be coming online in the next three to five years. The combination of all these projects will increased the number of photometrically monitored stars by orders of magnitude. It is expected that these surveys will enhance our knowledge of circumstellar architecture and the early stages of stellar and planetary formation, while providing a better understanding of exoplanet demographics. However, the success of these surveys will be dependent on simultaneous and continued follow up by large networks. With federal scientific funding reduced over the past few years, the availability of astronomical observations has been directly affected. Fortunately, ground based amateur-professional networks like the AAVSO and the KELT Follow-up Network (KELT-FUN) are already providing access to an international, independent resource for professional grade astronomical observations. These networks have both multi-band photometric and spectroscopic capabilities. I provide an overview of the ongoing and future surveys, highlight past and current contributions by amateur-professional networks to scientific discovery, and discuss the role of these networks in upcoming projects.

  7. Local Social Networks

    OpenAIRE

    Sapuppo, Antonio; Sørensen, Lene Tolstrup

    2011-01-01

    Online social networks have become essential for many users in their daily communication. Through a combination of the online social networks with opportunistic networks, a new concept arises: Local Social Networks. The target of local social networks is to promote social networking benefits in physical environment in order to leverage personal affinities in the users' surroundings. The purpose of this paper is to present and discuss the concept of local social networks as a new social commun...

  8. Gene identification for risk of relapse in stage I lung adenocarcinoma patients: a combined methodology of gene expression profiling and computational gene network analysis.

    Science.gov (United States)

    Ludovini, Vienna; Bianconi, Fortunato; Siggillino, Annamaria; Piobbico, Danilo; Vannucci, Jacopo; Metro, Giulio; Chiari, Rita; Bellezza, Guido; Puma, Francesco; Della Fazia, Maria Agnese; Servillo, Giuseppe; Crinò, Lucio

    2016-05-24

    Risk assessment and treatment choice remains a challenge in early non-small-cell lung cancer (NSCLC). The aim of this study was to identify novel genes involved in the risk of early relapse (ER) compared to no relapse (NR) in resected lung adenocarcinoma (AD) patients using a combination of high throughput technology and computational analysis. We identified 18 patients (n.13 NR and n.5 ER) with stage I AD. Frozen samples of patients in ER, NR and corresponding normal lung (NL) were subjected to Microarray technology and quantitative-PCR (Q-PCR). A gene network computational analysis was performed to select predictive genes. An independent set of 79 ADs stage I samples was used to validate selected genes by Q-PCR.From microarray analysis we selected 50 genes, using the fold change ratio of ER versus NR. They were validated both in pool and individually in patient samples (ER and NR) by Q-PCR. Fourteen increased and 25 decreased genes showed a concordance between two methods. They were used to perform a computational gene network analysis that identified 4 increased (HOXA10, CLCA2, AKR1B10, FABP3) and 6 decreased (SCGB1A1, PGC, TFF1, PSCA, SPRR1B and PRSS1) genes. Moreover, in an independent dataset of ADs samples, we showed that both high FABP3 expression and low SCGB1A1 expression was associated with a worse disease-free survival (DFS).Our results indicate that it is possible to define, through gene expression and computational analysis, a characteristic gene profiling of patients with an increased risk of relapse that may become a tool for patient selection for adjuvant therapy.

  9. An integrated anti-arrhythmic target network of a Chinese medicine compound, Wenxin Keli, revealed by combined machine learning and molecular pathway analysis.

    Science.gov (United States)

    Wang, Taiyi; Lu, Ming; Du, Qunqun; Yao, Xi; Zhang, Peng; Chen, Xiaonan; Xie, Weiwei; Li, Zheng; Ma, Yuling; Zhu, Yan

    2017-05-02

    Wenxin Keli (WK), a Chinese patent medicine, is known to be effective against cardiac arrhythmias and heart failure. Although a number of electrophysiological findings regarding its therapeutic effect have been reported, the active components and system-level characterizations of the component-target interactions of WK have yet to be elucidated. In the current study, we present the first report of a new protective effect of WK on suppressing anti-arrhythmic-agent-induced arrhythmias. In a model of isolated guinea pig hearts, rapid perfusion of quinidine altered the heart rate and prolonged the Q-T interval. Pretreatment with WK significantly prevented quinidine-induced arrhythmias. To explain the therapeutic and protective effects of WK, we constructed an integrated multi-target pharmacological mechanism prediction workflow in combination with machine learning and molecular pathway analysis. This workflow had the ability to predict and rank the probability of each compound interacting with 1715 target proteins simultaneously. The ROC value statistics showed that 97.786% of the values for target prediction were larger than 0.8. We applied this model to carry out target prediction and network analysis for the identified components of 5 herbs in WK. Using the 124 potential anti-arrhythmic components and the 30 corresponding protein targets obtained, an integrative anti-arrhythmic molecular mechanism of WK was proposed. Emerging drug/target networks suggested ion channel and intracellular calcium and autonomic nervous and hormonal regulation had critical roles in WK-mediated anti-arrhythmic activity. A validation of the proposed mechanisms was achieved by demonstrating that calaxin, one of the WK components from Gansong, dose-dependently blocked its predicted target Ca V 1.2 channel in an electrophysiological assay.

  10. Three-level prediction of protein function by combining profile-sequence search, profile-profile search, and domain co-occurrence networks.

    Science.gov (United States)

    Wang, Zheng; Cao, Renzhi; Cheng, Jianlin

    2013-01-01

    Predicting protein function from sequence is useful for biochemical experiment design, mutagenesis analysis, protein engineering, protein design, biological pathway analysis, drug design, disease diagnosis, and genome annotation as a vast number of protein sequences with unknown function are routinely being generated by DNA, RNA and protein sequencing in the genomic era. However, despite significant progresses in the last several years, the accuracy of protein function prediction still needs to be improved in order to be used effectively in practice, particularly when little or no homology exists between a target protein and proteins with annotated function. Here, we developed a method that integrated profile-sequence alignment, profile-profile alignment, and Domain Co-Occurrence Networks (DCN) to predict protein function at different levels of complexity, ranging from obvious homology, to remote homology, to no homology. We tested the method blindingly in the 2011 Critical Assessment of Function Annotation (CAFA). Our experiments demonstrated that our three-level prediction method effectively increased the recall of function prediction while maintaining a reasonable precision. Particularly, our method can predict function terms defined by the Gene Ontology more accurately than three standard baseline methods in most situations, handle multi-domain proteins naturally, and make ab initio function prediction when no homology exists. These results show that our approach can combine complementary strengths of most widely used BLAST-based function prediction methods, rarely used in function prediction but more sensitive profile-profile comparison-based homology detection methods, and non-homology-based domain co-occurrence networks, to effectively extend the power of function prediction from high homology, to low homology, to no homology (ab initio cases).

  11. Falls Risk Prediction for Older Inpatients in Acute Care Medical Wards: Is There an Interest to Combine an Early Nurse Assessment and the Artificial Neural Network Analysis?

    Science.gov (United States)

    Beauchet, O; Noublanche, F; Simon, R; Sekhon, H; Chabot, J; Levinoff, E J; Kabeshova, A; Launay, C P

    2018-01-01

    Identification of the risk of falls is important among older inpatients. This study aims to examine performance criteria (i.e.; sensitivity, specificity, positive predictive value, negative predictive value and accuracy) for fall prediction resulting from a nurse assessment and an artificial neural networks (ANNs) analysis in older inpatients hospitalized in acute care medical wards. A total of 848 older inpatients (mean age, 83.0±7.2 years; 41.8% female) admitted to acute care medical wards in Angers University hospital (France) were included in this study using an observational prospective cohort design. Within 24 hours after admission of older inpatients, nurses performed a bedside clinical assessment. Participants were separated into non-fallers and fallers (i.e.; ≥1 fall during hospitalization stay). The analysis was conducted using three feed forward ANNs (multilayer perceptron [MLP], averaged neural network, and neuroevolution of augmenting topologies [NEAT]). Seventy-three (8.6%) participants fell at least once during their hospital stay. ANNs showed a high specificity, regardless of which ANN was used, and the highest value reported was with MLP (99.8%). In contrast, sensitivity was lower, with values ranging between 98.4 to 14.8%. MLP had the highest accuracy (99.7). Performance criteria for fall prediction resulting from a bedside nursing assessment and an ANNs analysis was associated with a high specificity but a low sensitivity, suggesting that this combined approach should be used more as a diagnostic test than a screening test when considering older inpatients in acute care medical ward.

  12. A systematic review and network meta-analysis of neoadjuvant therapy combined with surgery for patients with resectable esophageal squamous cell carcinoma.

    Science.gov (United States)

    Huang, Yuanwei; Wang, Haidong; Luo, Ganfeng; Zhang, Yanting; Wang, Li; Li, Ke

    2017-02-01

    The role of neoadjuvant therapy combined with surgery for treating esophageal squamous cell carcinoma (ESCC) remains controversial. We performed a network meta-analysis to synthesize direct and indirect evidence to identify the optimal therapeutic method for ESCC. We identified 15 randomized controlled trials that compared any of the following 4 therapeutic measures: surgery alone (S), preoperative chemotherapy followed by surgery (CTS), preoperative radiotherapy followed by surgery (RTS), and preoperative chemoradiotherapy followed by surgery (CRTS). The main outcomes were 5-year survival, rate of radical resection, operative mortality and postoperative complications. Network meta-analysis showed that CRTS was associated with improved survival as compared with S (OR = 1.50 [95% CI 1.21 to 1.97]) and decreased occurrence of complications as compared with RTS (OR = 0.50 [95% CI 0.22 to 0.99]). Direct evidence revealed CRTS associated with improved survival (OR = 1.61 [95% CI 1.01 to 2.57]) and radical resection (OR = 4.01 [95% CI 1.66 to 9.69]) as compared with S. In terms of radical resection, CTS was more effective than S (OR = 1.73 [95% CI 1.09 to 2.76]). Findings for CTS and RTS did not differ for 5-year survival, operative mortality and postoperative complications. Overall, CRTS might be the best choice for resectable ESCC because it could increase the radical resection rate and lower the occurrence of complications, thereby prolonging survival time. Copyright © 2016 IJS Publishing Group Ltd. Published by Elsevier Ltd. All rights reserved.

  13. Comparative efficacy and acceptability of antidepressants, psychological interventions, and their combination for depressive disorder in children and adolescents: protocol for a network meta-analysis.

    Science.gov (United States)

    Zhou, Xinyu; Cipriani, Andrea; Zhang, Yuqing; Cuijpers, Pim; Hetrick, Sarah E; Weisz, John R; Pu, Juncai; Giovane, Cinzia Del; Furukawa, Toshiaki A; Barth, Jürgen; Coghill, David; Leucht, Stefan; Yang, Lining; Ravindran, Arun V; Xie, Peng

    2017-08-11

    Depressive disorder is common in children and adolescents, with important consequences and serious impairments in terms of personal and social functioning. While both pharmacological and psychological interventions have been shown to be effective, there is still uncertainty about the balance between these and what treatment strategy should be preferred in clinical practice. Therefore, we aim to compare and rank in a network meta-analysis (NMA) the commonly used psychological, pharmacological and combined interventions for depressive disorder in children and adolescents. We will update the literature search of two previous NMAs for the identification of trials of antidepressant and psychotherapy alone for depressive disorder in children and adolescents. For identification of trials of combination interventions, seven databases (PubMed, EMBASE, CENTRAL (Cochrane Central Register of Controlled Trials), Web of Science, PsycINFO, CINAHL, LiLACS) will be searched from date of inception. We will also search ClinicalTrials.gov, the WHO International Clinical Trials Registry Platform and check relevant reports on the US Food and Drug Administration website for unpublished data. Building on our previous findings in the field, we will include any commonly prescribed oral antidepressants and any manualised or structured psychotherapies, as well as their combinations. Randomised controlled trials assessing any active intervention against active comparator or pill placebo/psychological controls in acute treatment for depressive disorder in children and adolescents will be included. The primary outcomes will be efficacy (mean change in depressive symptoms), and acceptability of treatment (dropout rate due to any cause). The secondary outcomes will be remission rate, tolerability of treatment (dropouts for adverse events), as well as suicide-related outcomes (suicidal behaviour or ideation). We will perform Bayesian NMAs for all relative outcome measures. Subgroup analyses and

  14. 'You're in FunDzaland': Pre-service teachers (reimagine audience on a creative writing course

    Directory of Open Access Journals (Sweden)

    Belinda Mendelowitz

    2016-07-01

    Full Text Available This study explores how collaborative writing for a digital platform can enable students to (re imagine audience. Although in the context of process writing peer feedback is foreground, in practice, its effectiveness is uneven. The digital revolution offers new opportunities for alternative peer feedback through collaborative writing and re-imagining self and other in the process. This study examines data from a creative writing course in which pre-service teachers wrote collaborative short stories for the FunDza digital site and individual reflective essays about the process. The study’s research questions are the following: (1 what were the affordances of this multilayered audience for engaging the students’ imaginations? (2 How did this process of (reimagining audience impact on students’ conceptions of themselves as writers? The data set comprised 16 collaboratively authored stories (published on the site and 34 individual reflective essays. Six of the latter were selected for detailed analysis. Hence, the data for this study encompass detailed analysis of two groups’ reflective essays on the process of writing their stories. These groups were selected because they exemplified contrasting collaborative, imaginative writing processes. Group 1 was familiar with the FunDza audience and context, while Group 2 struggled to imagine it. Thematic content analysis was used for analysis. Each essay was read first in relation to the entire data set, then in relation to the other reflections in the author’s group. The combination of gearing stories towards the FunDza audience and writing stories collaboratively created two sets of audiences that writers needed to hold in mind simultaneously. Analysis indicates that both audiences challenged students to make imaginative leaps into the minds of an unfamiliar audience, deepening their understanding of the writing process. It also highlights students’ mastery of writing discourses and increasing

  15. ECHN honors cancer survivors with fun, food and inspirational stories.

    Science.gov (United States)

    Botvin, Judith D

    2005-01-01

    A nostalgia theme was fully explored by Eastern Connecticut Health Network (ECHN), Manchester, Conn., in its celebration of Cancer Survivors Day, June 6. The observance is sponsored by the national Cancer Survivors Day organization. This year more than 700 facilities across the country observed the occasion.

  16. Space Age Multi-CPU Computer Network Is Just for Fun and Education, Too.

    Science.gov (United States)

    Technological Horizons in Education, 1980

    1980-01-01

    Describes the Sesame Place's Computer Gallery, 56 Apple II computers linked by three Nestar Cluster/One Model A hard disc systems, the first commercial permanent educational play park. Programs for this hands-on indoor/outdoor park as well as a description of the facility are given. (JN)

  17. Theories of the deep: combining salience and network analyses to produce mental model visualizations of a coastal British Columbia food web

    Directory of Open Access Journals (Sweden)

    Jordan Levine

    2015-12-01

    Full Text Available Arriving at shared mental models among multiple stakeholder groups can be crucial for successful management of contested social-ecological systems (SES. Academia can help by first eliciting stakeholders' initial, often tacit, beliefs about a SES, and representing them in useful ways. We demonstrate a new recombination of techniques for this purpose, focusing specifically on tacit beliefs about food webs. Our approach combines freelisting and sorting techniques, salience analysis, and ultimately network analysis, to produce accessible visualizations of aggregate mental models that can then be used to facilitate discussion or generate further hypotheses about cognitive drivers of conflict. The case study we draw upon to demonstrate this technique is Clayoquot Sound UNESCO Biosphere Reserve, on the west coast of British Columbia, Canada. There, an immanent upsurge in the sea otter (Enhydra lutris population, which competes with humans for shellfish, has produced tension among government managers, and both First Nations and non-First Nations residents. Our approach helps explain this tension by visually highlighting which trophic relationships appear most cognitively salient among the lay public. We also include speculative representations of models held by managers, and pairs of contrasting demographic subgroups, to further demonstrate potential uses of the method.

  18. A comparison of Spectral Angle Mapper and Artificial Neural Network classifiers combined with Landsat TM imagery analysis for obtaining burnt area mapping.

    Science.gov (United States)

    Petropoulos, George P; Vadrevu, Krishna Prasad; Xanthopoulos, Gavriil; Karantounias, George; Scholze, Marko

    2010-01-01

    Satellite remote sensing, with its unique synoptic coverage capabilities, can provide accurate and immediately valuable information on fire analysis and post-fire assessment, including estimation of burnt areas. In this study the potential for burnt area mapping of the combined use of Artificial Neural Network (ANN) and Spectral Angle Mapper (SAM) classifiers with Landsat TM satellite imagery was evaluated in a Mediterranean setting. As a case study one of the most catastrophic forest fires, which occurred near the capital of Greece during the summer of 2007, was used. The accuracy of the two algorithms in delineating the burnt area from the Landsat TM imagery, acquired shortly after the fire suppression, was determined by the classification accuracy results of the produced thematic maps. In addition, the derived burnt area estimates from the two classifiers were compared with independent estimates available for the study region, obtained from the analysis of higher spatial resolution satellite data. In terms of the overall classification accuracy, ANN outperformed (overall accuracy 90.29%, Kappa coefficient 0.878) the SAM classifier (overall accuracy 83.82%, Kappa coefficient 0.795). Total burnt area estimates from the two classifiers were found also to be in close agreement with the other available estimates for the study region, with a mean absolute percentage difference of ≈ 1% for ANN and ≈ 6.5% for SAM. The study demonstrates the potential of the examined here algorithms in detecting burnt areas in a typical Mediterranean setting.

  19. Inequalities and Duality in Gene Coexpression Networks of HIV-1 Infection Revealed by the Combination of the Double-Connectivity Approach and the Gini's Method

    Directory of Open Access Journals (Sweden)

    Chuang Ma

    2011-01-01

    Full Text Available The symbiosis (Sym and pathogenesis (Pat is a duality problem of microbial infection, including HIV/AIDS. Statistical analysis of inequalities and duality in gene coexpression networks (GCNs of HIV-1 infection may gain novel insights into AIDS. In this study, we focused on analysis of GCNs of uninfected subjects and HIV-1-infected patients at three different stages of viral infection based on data deposited in the GEO database of NCBI. The inequalities and duality in these GCNs were analyzed by the combination of the double-connectivity (DC approach and the Gini's method. DC analysis reveals that there are significant differences between positive and negative connectivity in HIV-1 stage-specific GCNs. The inequality measures of negative connectivity and edge weight are changed more significantly than those of positive connectivity and edge weight in GCNs from the HIV-1 uninfected to the AIDS stages. With the permutation test method, we identified a set of genes with significant changes in the inequality and duality measure of edge weight. Functional analysis shows that these genes are highly enriched for the immune system, which plays an essential role in the Sym-Pat duality (SPD of microbial infections. Understanding of the SPD problems of HIV-1 infection may provide novel intervention strategies for AIDS.

  20. A Comparison of Spectral Angle Mapper and Artificial Neural Network Classifiers Combined with Landsat TM Imagery Analysis for Obtaining Burnt Area Mapping

    Directory of Open Access Journals (Sweden)

    Marko Scholze

    2010-03-01

    Full Text Available Satellite remote sensing, with its unique synoptic coverage capabilities, can provide accurate and immediately valuable information on fire analysis and post-fire assessment, including estimation of burnt areas. In this study the potential for burnt area mapping of the combined use of Artificial Neural Network (ANN and Spectral Angle Mapper (SAM classifiers with Landsat TM satellite imagery was evaluated in a Mediterranean setting. As a case study one of the most catastrophic forest fires, which occurred near the capital of Greece during the summer of 2007, was used. The accuracy of the two algorithms in delineating the burnt area from the Landsat TM imagery, acquired shortly after the fire suppression, was determined by the classification accuracy results of the produced thematic maps. In addition, the derived burnt area estimates from the two classifiers were compared with independent estimates available for the study region, obtained from the analysis of higher spatial resolution satellite data. In terms of the overall classification accuracy, ANN outperformed (overall accuracy 90.29%, Kappa coefficient 0.878 the SAM classifier (overall accuracy 83.82%, Kappa coefficient 0.795. Total burnt area estimates from the two classifiers were found also to be in close agreement with the other available estimates for the study region, with a mean absolute percentage difference of ~1% for ANN and ~6.5% for SAM. The study demonstrates the potential of the examined here algorithms in detecting burnt areas in a typical Mediterranean setting.

  1. Combining Personality Traits with Traditional Risk Factors for Coronary Stenosis: An Artificial Neural Networks Solution in Patients with Computed Tomography Detected Coronary Artery Disease

    Directory of Open Access Journals (Sweden)

    Angelo Compare

    2013-01-01

    Full Text Available Background. Coronary artery disease (CAD is a complex, multifactorial disease in which personality seems to play a role but with no definition in combination with other risk factors. Objective. To explore the nonlinear and simultaneous pathways between traditional and personality traits risk factors and coronary stenosis by Artificial Neural Networks (ANN data mining analysis. Method. Seventy-five subjects were examined for traditional cardiac risk factors and personality traits. Analyses were based on a new data mining method using a particular artificial adaptive system, the autocontractive map (AutoCM. Results. Several traditional Cardiovascular Risk Factors (CRF present significant relations with coronary artery plaque (CAP presence or severity. Moreover, anger turns out to be the main factor of personality for CAP in connection with numbers of traditional risk factors. Hidden connection map showed that anger, hostility, and the Type D personality subscale social inhibition are the core factors related to the traditional cardiovascular risk factors (CRF specifically by hypertension. Discussion. This study shows a nonlinear and simultaneous pathway between traditional risk factors and personality traits associated with coronary stenosis in CAD patients without history of cardiovascular disease. In particular, anger seems to be the main personality factor for CAP in addition to traditional risk factors.

  2. A stock market forecasting model combining two-directional two-dimensional principal component analysis and radial basis function neural network.

    Science.gov (United States)

    Guo, Zhiqiang; Wang, Huaiqing; Yang, Jie; Miller, David J

    2015-01-01

    In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron.

  3. A stock market forecasting model combining two-directional two-dimensional principal component analysis and radial basis function neural network.

    Directory of Open Access Journals (Sweden)

    Zhiqiang Guo

    Full Text Available In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D2PCA and a Radial Basis Function Neural Network (RBFNN to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA and independent component analysis (ICA. The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron.

  4. Fun with Foodella: A Pilot Study for Determining the Efficacy of a 2nd Grade Nutrition and Physical Activity Curriculum

    Science.gov (United States)

    Winter, Elizabeth M.; Stluka, Suzanne; Wells, Karlys; Wey, Howard; Kemmer, Teresa M.

    2012-01-01

    Fun with Foodella is a nutrition and physical activity workbook designed for elementary-aged youth. The objective was to determine if the Fun with Foodella program increased participant preference for fruit, vegetables, low-fat dairy products, and physical activity. Four intervention (53 students) and four control (68 students) schools…

  5. Improved diagnostic accuracy of Alzheimer's disease by combining regional cortical thickness and default mode network functional connectivity: Validated in the Alzheimer's disease neuroimaging initiative set

    Energy Technology Data Exchange (ETDEWEB)

    Park, Ji Eun; Park, Bum Woo; Kim, Sang Joon; Kim, Ho Sung; Choi, Choong Gon; Jung, Seung Jung; Oh, Joo Young; Shim, Woo Hyun [Dept. of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul (Korea, Republic of); Lee, Jae Hong; Roh, Jee Hoon [University of Ulsan College of Medicine, Asan Medical Center, Seoul (Korea, Republic of)

    2017-11-15

    To identify potential imaging biomarkers of Alzheimer's disease by combining brain cortical thickness (CThk) and functional connectivity and to validate this model's diagnostic accuracy in a validation set. Data from 98 subjects was retrospectively reviewed, including a study set (n = 63) and a validation set from the Alzheimer's Disease Neuroimaging Initiative (n = 35). From each subject, data for CThk and functional connectivity of the default mode network was extracted from structural T1-weighted and resting-state functional magnetic resonance imaging. Cortical regions with significant differences between patients and healthy controls in the correlation of CThk and functional connectivity were identified in the study set. The diagnostic accuracy of functional connectivity measures combined with CThk in the identified regions was evaluated against that in the medial temporal lobes using the validation set and application of a support vector machine. Group-wise differences in the correlation of CThk and default mode network functional connectivity were identified in the superior temporal (p < 0.001) and supramarginal gyrus (p = 0.007) of the left cerebral hemisphere. Default mode network functional connectivity combined with the CThk of those two regions were more accurate than that combined with the CThk of both medial temporal lobes (91.7% vs. 75%). Combining functional information with CThk of the superior temporal and supramarginal gyri in the left cerebral hemisphere improves diagnostic accuracy, making it a potential imaging biomarker for Alzheimer's disease.

  6. Observing System Simulation Experiments for Fun and Profit

    Science.gov (United States)

    Prive, Nikki C.

    2015-01-01

    Observing System Simulation Experiments can be powerful tools for evaluating and exploring both the behavior of data assimilation systems and the potential impacts of future observing systems. With great power comes great responsibility - given a pure modeling framework, how can we be sure our results are meaningful? The challenges and pitfalls of OSSE calibration and validation will be addressed, as well as issues of incestuousness, selection of appropriate metrics, and experiment design. The use of idealized observational networks to investigate theoretical ideas in a fully complex modeling framework will also be discussed

  7. Cyber Network Mission Dependencies

    Science.gov (United States)

    2015-09-18

    or she must make. Network Mapping System ( NeMS ) is a software-based tool created by the Lawrence Livermore National Laboratory to discover and map...network assets in support of cyber situational awareness [10]. NeMS combines both active probes and passive monitoring of network data to map the network...security settings in order to maximize efficiency without disrupting network activities. Tests of NeMS in control networks yielded great results, as

  8. Calcium-aluminum-rich inclusions with fractionation and unknown nuclear effects (FUN CAIs)

    DEFF Research Database (Denmark)

    Krot, Alexander N.; Nagashima, Kazuhide; Wasserburg, Gerald J.

    2014-01-01

    and gas-melt oxygen-isotope exchange in a 16O-poor gaseous reservoir that resulted in crystallization of 16O-depleted fassaite, melilite and plagioclase. The final oxygen isotopic compositions of melilite and plagioclase in the CV FUN CAIs may have been established on the CV parent asteroid as a result...

  9. Emerald Dragon Bites vs Veggie Beans: Fun Food Names Increase Children's Consumption of Novel Healthy Foods

    Science.gov (United States)

    Musher-Eizenman, Dara R.; Oehlhof, Marissa Wagner; Young, Kathleen M.; Hauser, Jessica C.; Galliger, Courtney; Sommer, Alyssa

    2011-01-01

    Caregivers often struggle with food neophobia on the part of young children. This study examined whether labeling novel healthy foods with fun names would increase children's willingness to try those foods and encourage them to eat more of those foods in a child care setting. Thirty-nine toddler and preschool age children (mean age = 3.9 years)…

  10. Keeping It Fun in Youth Sport: What Coaches Should Know and Do

    Science.gov (United States)

    Martin, Nicole J.

    2014-01-01

    Children and adolescents participate in sport for a variety of reasons including to learn new skills, to have fun, for peer affiliation, to experience excitement, to exercise and increase fitness, and for competition; and coincidentally, withdraw when these motives are left unmet. The literature is well established on the influence coaching…

  11. The FunFOLD2 server for the prediction of protein–ligand interactions

    Science.gov (United States)

    Roche, Daniel B.; Buenavista, Maria T.; McGuffin, Liam J.

    2013-01-01

    The FunFOLD2 server is a new independent server that integrates our novel protein–ligand binding site and quality assessment protocols for the prediction of protein function (FN) from sequence via structure. Our guiding principles were, first, to provide a simple unified resource to make our function prediction software easily accessible to all via a simple web interface and, second, to produce integrated output for predictions that can be easily interpreted. The server provides a clean web interface so that results can be viewed on a single page and interpreted by non-experts at a glance. The output for the prediction is an image of the top predicted tertiary structure annotated to indicate putative ligand-binding site residues. The results page also includes a list of the most likely binding site residues and the types of predicted ligands and their frequencies in similar structures. The protein–ligand interactions can also be interactively visualized in 3D using the Jmol plug-in. The raw machine readable data are provided for developers, which comply with the Critical Assessment of Techniques for Protein Structure Prediction data standards for FN predictions. The FunFOLD2 webserver is freely available to all at the following web site: http://www.reading.ac.uk/bioinf/FunFOLD/FunFOLD_form_2_0.html. PMID:23761453

  12. Food & Fun op de boerderij : Consumenten komen graag op de boerderij

    NARCIS (Netherlands)

    Stil, L.; PPO Akkerbouw, Groene Ruimte en Vollegrondsgroente

    2009-01-01

    Een Food- & Fun-boerderij geeft consumenten een goed gevoel en daarom komen ze er graag. Ze komen om te genieten van het buiten zijn en omdat ze er wat kunnen leren. De ervaring van het boerenleven zorgt voor een stapje uit de dagelijkse werkelijkheid met rust, ruimte, ritme en natuur van de

  13. Mobility should be fun. A consumer (law) perspective on border check technology

    National Research Council Canada - National Science Library

    De Hert, Paul; Bellanova, Rocco

    2011-01-01

    ...: "We cannot make it fun, but we can make it efficient." When traveling, we are asked to pass on data, give body samples, and pass through body scanners in the name of the general interest and in the name of our safety...

  14. Função Poética e Televisão

    Directory of Open Access Journals (Sweden)

    Anna Maria Balogh

    1990-10-01

    Full Text Available Ao empreendermos uma rápida usca nos guardados da memória, verificamos a existência de conceitos arraigados no tocante ao que se considera artístico". Alguns dos conceitos de "artisticidade" revelam vinculações estreitas com as funções "estética" e "poética".

  15. Disentangling fun and enjoyment in exergames using an expanded design, play, experience framework: A narrative review

    Science.gov (United States)

    With exergames (as with physical activity in general), more intense and longer-duration game play should accrue more health benefits. Exergames, however, appear to be played for relatively short durations, often at medium or lower intensities. Ostensibly games are played for fun or enjoyment. Enhanc...

  16. Food & Fun op de boerderij : consumentenpercepties - hoe beleven consumenten multifunctionele 'biologische' landbouw

    NARCIS (Netherlands)

    Jong, de D.; Kamstra, J.H.; Roest, A.E.; Winter, de M.A.

    2009-01-01

    Consumers of Food and Fun farms are questioned via group interviews on their perception when doing activities of buying products. Consumers like to visit these farms because they get a good feeling. However, consumers do have own perceptions of what the farm is

  17. Udspring og fald i Alison Bechdels grafiske erindringsværk: "Fun Home. A Family Tragicomic"

    DEFF Research Database (Denmark)

    Gammelgaard, Lasse

    2012-01-01

    Alison Bechdel’s Fun Home. A Family Tragicomic is a graphic memoir about the author’s own coming-out story and her father’s homosexuality and (probable) suicide. It endeavors to give an accurate account of the past, but the telling is simultaneously replete with fictionalising elements. This paper...

  18. Sun Protection is Fun! A Skin Cancer Prevention Program for Preschools.

    Science.gov (United States)

    Tripp, Mary K.; Herrmann, Nancy B.; Parcel, Guy S.; Chamberlin, Robert M.; Gritz, Ellen R.

    2000-01-01

    Describes the Sun Protection is Fun! skin cancer prevention program for preschool children that features intervention methods grounded in social cognitive theory and emphasizes symbolic modeling, vicarious learning, enactive mastery experiences, and persuasion. Program components include a curriculum and teacher's guide, videos, newsletters,…

  19. Generational Attitudes toward Workplace Fun and Their Relationship to Job Satisfaction

    Science.gov (United States)

    Attebery, Esther

    2017-01-01

    Purpose: The purpose of this quantitative study was to examine attitudes toward workplace fun and overall job satisfaction of baby boomer, Generation X, and millennial staff employees at a Christian university in California, and determine if there is a predictive relationship between them. Conceptual Framework: The framework was developed from…

  20. A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network

    Directory of Open Access Journals (Sweden)

    Han Kyungsook

    2010-06-01

    Full Text Available Abstract Background Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited for annotating gene functions and dissecting specific pathway structures. However, our understanding is rather limited to the relationship between double concurrent perturbation and various higher level phenotypic changes, e.g. those in cells, tissues or organs. Modifier screens, such as synthetic genetic arrays (SGA can help us to understand the phenotype caused by combined gene mutations. Unfortunately, exhaustive tests on all possible combined mutations in any genome are vulnerable to combinatorial explosion and are infeasible either technically or financially. Therefore, an accurate computational approach to predict genetic interaction is highly desirable, and such methods have the potential of alleviating the bottleneck on experiment design. Results In this work, we introduce a computational systems biology approach for the accurate prediction of pairwise synthetic genetic interactions (SGI. First, a high-coverage and high-precision functional gene network (FGN is constructed by integrating protein-protein interaction (PPI, protein complex and gene expression data; then, a graph-based semi-supervised learning (SSL classifier is utilized to identify SGI, where the topological properties of protein pairs in weighted FGN is used as input features of the classifier. We compare the proposed SSL method with the state-of-the-art supervised classifier, the support vector machines (SVM, on a benchmark dataset in S. cerevisiae to validate our method's ability to distinguish synthetic genetic interactions from non-interaction gene pairs. Experimental results show that the proposed method can accurately predict genetic interactions in S. cerevisiae (with a sensitivity of 92% and specificity of 91%. Noticeably, the SSL method is more efficient than SVM, especially for

  1. Roles of the combined irrigation, drainage, and storage of the canal network in improving water reuse in the irrigation districts along the lower Yellow River, China

    Science.gov (United States)

    Liu, Lei; Luo, Yi; He, Chansheng; Lai, Jianbin; Li, Xiubin

    2010-09-01

    SummaryThe commonly used irrigation system in the irrigation districts (with a combined irrigation area of 3.334 × 10 6 ha) along the lower Yellow River of China is canal network. It delivers water from the Yellow River to the fields, collects surface runoff and drainage from cropland, and stores both of them for subsequent irrigation uses. This paper developed a new combined irrigation, drainage, and storage (CIDS) module for the SWAT2000 model, simulated the multiple roles of the CIDS canal system, and estimated its performance in improving water reuse in the irrigation districts under different irrigation and water diversion scenarios. The simulation results show that the annual evapotranspiration (ET) of the double-cropping winter wheat and summer maize was the highest under the full irrigation scenario (automatic irrigation), and the lowest under the no irrigation scenario. It varied between these two values when different irrigation schedules were adopted. Precipitation could only meet the water requirement of the double-cropping system by 62-96% on an annual basis; that of the winter wheat by 32-36%, summer maize by 92-123%, and cotton by 87-98% on a seasonal basis. Hence, effective irrigation management for winter wheat is critical to ensure high wheat yield in the study area. Runoff generation was closely related to precipitation and influenced by irrigation. The highest and lowest annual runoff accounted for 19% and 11% of the annual precipitation under the full irrigation and no irrigation scenarios, respectively. Nearly 70% of the annual runoff occurred during months of July and August due to the concentrated precipitation in these 2 months. The CIDS canals play an important role in delivering the diversion water from the Yellow River, intercepting the surface runoff and drainage from cropland (inflow of the CIDS canal) and recharging the shallow aquifer for later use. Roughly 14-26% of the simulated total flow in the CIDS canal system recharged

  2. Mapping of rock types using a joint approach by combining the multivariate statistics, self-organizing map and Bayesian neural networks: an example from IODP 323 site

    Science.gov (United States)

    Karmakar, Mampi; Maiti, Saumen; Singh, Amrita; Ojha, Maheswar; Maity, Bhabani Sankar

    2017-07-01

    Modeling and classification of the subsurface lithology is very important to understand the evolution of the earth system. However, precise classification and mapping of lithology using a single framework are difficult due to the complexity and the nonlinearity of the problem driven by limited core sample information. Here, we implement a joint approach by combining the unsupervised and the supervised methods in a single framework for better classification and mapping of rock types. In the unsupervised method, we use the principal component analysis (PCA), K-means cluster analysis (K-means), dendrogram analysis, Fuzzy C-means (FCM) cluster analysis and self-organizing map (SOM). In the supervised method, we use the Bayesian neural networks (BNN) optimized by the Hybrid Monte Carlo (HMC) (BNN-HMC) and the scaled conjugate gradient (SCG) (BNN-SCG) techniques. We use P-wave velocity, density, neutron porosity, resistivity and gamma ray logs of the well U1343E of the Integrated Ocean Drilling Program (IODP) Expedition 323 in the Bering Sea slope region. While the SOM algorithm allows us to visualize the clustering results in spatial domain, the combined classification schemes (supervised and unsupervised) uncover the different patterns of lithology such of as clayey-silt, diatom-silt and silty-clay from an un-cored section of the drilled hole. In addition, the BNN approach is capable of estimating uncertainty in the predictive modeling of three types of rocks over the entire lithology section at site U1343. Alternate succession of clayey-silt, diatom-silt and silty-clay may be representative of crustal inhomogeneity in general and thus could be a basis for detail study related to the productivity of methane gas in the oceans worldwide. Moreover, at the 530 m depth down below seafloor (DSF), the transition from Pliocene to Pleistocene could be linked to lithological alternation between the clayey-silt and the diatom-silt. The present results could provide the basis for

  3. Application of a Combined Model with Autoregressive Integrated Moving Average (ARIMA) and Generalized Regression Neural Network (GRNN) in Forecasting Hepatitis Incidence in Heng County, China

    Science.gov (United States)

    Liang, Hao; Gao, Lian; Liang, Bingyu; Huang, Jiegang; Zang, Ning; Liao, Yanyan; Yu, Jun; Lai, Jingzhen; Qin, Fengxiang; Su, Jinming; Ye, Li; Chen, Hui

    2016-01-01

    Background Hepatitis is a serious public health problem with increasing cases and property damage in Heng County. It is necessary to develop a model to predict the hepatitis epidemic that could be useful for preventing this disease. Methods The autoregressive integrated moving average (ARIMA) model and the generalized regression neural network (GRNN) model were used to fit the incidence data from the Heng County CDC (Center for Disease Control and Prevention) from January 2005 to December 2012. Then, the ARIMA-GRNN hybrid model was developed. The incidence data from January 2013 to December 2013 were used to validate the models. Several parameters, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and mean square error (MSE), were used to compare the performance among the three models. Results The morbidity of hepatitis from Jan 2005 to Dec 2012 has seasonal variation and slightly rising trend. The ARIMA(0,1,2)(1,1,1)12 model was the most appropriate one with the residual test showing a white noise sequence. The smoothing factor of the basic GRNN model and the combined model was 1.8 and 0.07, respectively. The four parameters of the hybrid model were lower than those of the two single models in the validation. The parameters values of the GRNN model were the lowest in the fitting of the three models. Conclusions The hybrid ARIMA-GRNN model showed better hepatitis incidence forecasting in Heng County than the single ARIMA model and the basic GRNN model. It is a potential decision-supportive tool for controlling hepatitis in Heng County. PMID:27258555

  4. Electronic Books: Presentation Software Makes Writing More Fun.

    Science.gov (United States)

    Hodges, Bob

    1999-01-01

    Describes the use of presentation software such as PowerPoint with elementary school students to create electronic books that use a combination of text, audio, and graphics. Discusses introducing the concept, planning the story on paper with the help of a worksheet, creating the story on the computer, and sharing stories. (LRW)

  5. Local Social Networks

    DEFF Research Database (Denmark)

    Sapuppo, Antonio; Sørensen, Lene Tolstrup

    2011-01-01

    in physical environment in order to leverage personal affinities in the users' surroundings. The purpose of this paper is to present and discuss the concept of local social networks as a new social communication system. Particularly, the preliminary architecture and the prototype of local social networks......Online social networks have become essential for many users in their daily communication. Through a combination of the online social networks with opportunistic networks, a new concept arises: Local Social Networks. The target of local social networks is to promote social networking benefits...

  6. Isolated heart transplant and combined heart-liver transplant in adult congenital heart disease patients: Insights from the united network of organ sharing.

    Science.gov (United States)

    Bradley, Elisa A; Pinyoluksana, Krong-On; Moore-Clingenpeel, Melissa; Miao, Yongjie; Daniels, Curt

    2017-02-01

    The aging patient with severe congenital heart disease (CHD) faces many challenges: heart failure, arrhythmia, and in the Fontan patient, liver disease. Our goal was to define combined heart liver transplant (CHLT) and isolated orthotopic heart transplant (OHT) outcomes in U.S. adult CHD patients. The U.S. United Network for Organ Sharing (UNOS) thoracic and liver databases were queried for cardiac and CHD diagnoses, from inception-2014. In CHLT, CHD made up 22% of waitlist patients (non-CHD n=262 vs. CHD n=58), and 20% of transplanted patients (non-CHD n=137 vs. CHD n=27). Liver function tests in the non-CHD and CHD groups were similar and there was no difference in CHD and non-CHD survival (HR 0.93, CI: 0.36-2.38, p 0.48). In isolated OHT, CHD patients comprised 2% of those listed (non-CHD n=74,080 vs. CHD n=1599) and transplanted (non-CHD n=48,985 vs. CHD n=967) and had higher early (<1year) mortality (HR 1.36, CI: 1.18-1.57, p<0.0001), but better long-term survival (HR 0.66, CI; 0.57-0.76, p<0.001) than non-CHD. Both groups benefitted from mechanical support when used (non-CHD HR 0.34, CI: 0.31-0.37 and CHD HR 0.14, CI: 0.03-0.58) and prior sternotomy had no effect on mortality in CHD (HR 0.63, CI: 0.15-2.58). Survival of CHD patients undergoing CHLT is no different than in non-CHD, encouraging consideration of CHLT when clinically appropriate. Short-term mortality is higher in CHD (vs. non-CHD) patients undergoing OHT, regardless of prior cardiac surgery status. Modifications to CHD classification within UNOS would help better understand CHD CHLT and OHT outcomes. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  7. Fuel for Fun: a cluster-randomized controlled study of cooking skills, eating behaviors, and physical activity of 4th graders and their families

    Directory of Open Access Journals (Sweden)

    Leslie Cunningham-Sabo

    2016-05-01

    parents over the length of the project. Discussion The Fuel for Fun study design allows for impact assessment of school-, family- and online parent-based intervention components separately and in combination. Study strengths include use of theory- and evidence-based programs, valid child and parent self-report instruments, and objective measures of food, cooking, and physical activity behaviors at the individual, family and school levels. Parent involvement and engagement is examined through multiple strategies. Trial registration Clinicaltrials.gov registration number NCT02491294 . Registered 7 July, 2015.

  8. Functional and white matter abnormalities in the language network in patients with schizophrenia: a combined study with diffusion tensor imaging and functional magnetic resonance imaging.

    Science.gov (United States)

    Leroux, Elise; Delcroix, Nicolas; Alary, Mathieu; Razafimandimby, Annick; Brazo, Perrine; Delamillieure, Pascal; Dollfus, Sonia

    2013-10-01

    Schizophrenia is a mental disorder characterized by functional abnormalities in the language network. Anatomical white matter (WM) abnormalities (volume and integrity) have also been reported for this pathology. Nevertheless, few studies have investigated anatomo-functional relationships in schizophrenia, and none has focused on the language comprehension network in relation to various diffusion parameters. We hypothesized that the WM abnormalities that are reflected by several diffusion parameters underlie functional deficits in the language network. Eighteen DSM-IV patients with schizophrenia and 18 healthy controls without any significant differences in sex, age, or level of education were included. First, functional brain activation within the language network was estimated. Then, using diffusion tensor imaging, fractional anisotropy (FA), radial diffusivity (RD), and mean diffusivity (MD) values were extracted within WM regions adjacent to this network and their anatomo-functional relationships were investigated. Compared with healthy participants, both functional and diffusion deficits were observed in patients with schizophrenia. Primarily, an altered diffusion-functional relationship was observed in patients in the left middle temporal region: functional activations were positively correlated with FA, but were negatively correlated with RD. Our findings indicate a close relationship between diffusion and functional deficits in patients with schizophrenia, suggesting that WM integrity disturbance might be one cause of functional alterations in the language network in patients with schizophrenia. Thus, the present multimodal study improves our understanding of the pathophysiology of schizophrenia. © 2013 Elsevier B.V. All rights reserved.

  9. For fun and profit a history of the free and open source software revolution

    CERN Document Server

    Tozzi, Christopher

    2017-01-01

    In the 1980s, there was a revolution with far-reaching consequences -- a revolution to restore software freedom. In the early 1980s, after decades of making source code available with programs, most programmers ceased sharing code freely. A band of revolutionaries, self-described "hackers," challenged this new norm by building operating systems with source code that could be freely shared. In For Fun and Profit, Christopher Tozzi offers an account of the free and open source software (FOSS) revolution, from its origins as an obscure, marginal effort by a small group of programmers to the widespread commercial use of open source software today. Tozzi explains FOSS's historical trajectory, shaped by eccentric personalities -- including Richard Stallman and Linus Torvalds -- and driven both by ideology and pragmatism, by fun and profit. Tozzi examines hacker culture and its influence on the Unix operating system, the reaction to Unix's commercialization, and the history of early Linux development. He describes ...

  10. New(er) Kids on the Block - Voices of Junior FUN Faculty.

    Science.gov (United States)

    Dickinson, Shelly D

    2009-01-01

    How do good teachers get that way? While practice is certainly important, good ideas are essential. The first goal of the "New(er) Kids on the Block" plenary session at the 2008 PKAL/FUN Workshop was to highlight the notable things junior FUN faculty are doing in the classroom and the lab. Happily, both younger and more seasoned faculty colleagues shared a multitude of pedagogical ideas, many of which are briefly described here. The second goal of the session was to provide a place for junior faculty to ask questions of senior faculty. This broader goal was less directly met, possibly because of time constraints, possibly because of the nature of the group setting. In future workshops, arranging a large session for the exchange of ideas and a smaller session for mentoring type activities might be advisable.

  11. Application of FUN3D Solver for Aeroacoustics Simulation of a Nose Landing Gear Configuration

    Science.gov (United States)

    Vatsa, Veer N.; Lockard, David P.; Khorrami, Mehdi R.

    2011-01-01

    Numerical simulations have been performed for a nose landing gear configuration corresponding to the experimental tests conducted in the Basic Aerodynamic Research Tunnel at NASA Langley Research Center. A widely used unstructured grid code, FUN3D, is examined for solving the unsteady flow field associated with this configuration. A series of successively finer unstructured grids has been generated to assess the effect of grid refinement. Solutions have been obtained on purely tetrahedral grids as well as mixed element grids using hybrid RANS/LES turbulence models. The agreement of FUN3D solutions with experimental data on the same size mesh is better on mixed element grids compared to pure tetrahedral grids, and in general improves with grid refinement.

  12. Fundamentals of Stochastic Networks

    CERN Document Server

    Ibe, Oliver C

    2011-01-01

    An interdisciplinary approach to understanding queueing and graphical networks In today's era of interdisciplinary studies and research activities, network models are becoming increasingly important in various areas where they have not regularly been used. Combining techniques from stochastic processes and graph theory to analyze the behavior of networks, Fundamentals of Stochastic Networks provides an interdisciplinary approach by including practical applications of these stochastic networks in various fields of study, from engineering and operations management to communications and the physi

  13. Designing Slow Fun! Physical Therapy Games to Remedy the Negative Consequences of Spasticity

    OpenAIRE

    Vanden Abeele, Vero; Geurts, Luc; Husson, Jelle; Windey, Frederik; Annema, Jan Henk; Verstraete, Mathijs; Desmet, Stef

    2010-01-01

    Spasticity is a motor disorder defined by involuntary muscle contractions, resulting in uncoordinated gait, stiff body posture and shortening of range of limb movement. The first line treatment of spasticity is physical and occupational therapy, involving physical exercises that focus on stretching and strengthening of muscles. In this paper, we report on the difficulty of designing fun games that build upon these physical exercises and remedy the negative consequences of...

  14. Kid-Friendly Veggies and Fruits: 10 Tips for Making Healthy Food Choices More Fun for Children

    Science.gov (United States)

    ... canned, and even overripe fruits. Try bananas, the freezer (rinse first). Make “popsicles” by inserting sticks berries, ... veggies or fruits into a fun shape or design. 5 fruity peanut butterfly Start with carrot sticks ...

  15. Smiling is fun: a Coping with Stress and Emotion Regulation Program.

    Science.gov (United States)

    Botella, Cristina; Mira, Adriana; Garcia-Palacios, Azucena; Quero, Soledad; Navarro, Ma Vicenta; Riera López Del Amo, Antonio; Molinari, Guadalupe; Castilla, Diana; Moragrega, Inés; Soler, Carla; Alcañiz, Mariano; Baños, Rosa Maria

    2012-01-01

    Emotional disorders (Anxiety disorders and Mood disorders) are one of the most common health problems worldwide, and their economic costs are very high. People suffering from emotional disorders often use maladaptive emotion regulation strategies and have low coping behaviour that contributes to the presence of clinical symptoms. For this reason, it is important to develop strategies to monitor coping and promote emotion regulation in people exposed to high levels of stress. Information and Communication Technologies (ICT) can help us in this task. Recent systematic reviews of literature on evidence-based CBT treatments delivered via the Internet show that these approaches are effective. We have developed an intervention program ICT based: Coping with Stress and Emotion Regulation Program (Smiling is Fun), a self-applied program via the Internet. Smiling is Fun follows a transdiagnostic perspective, and it is based on CBT techniques. However, it also includes other psychological strategies to improve positive mood. The aim of the present work is to describe Smiling is Fun and the study designed to test its efficacy.

  16. PENGEMBANGAN MODEL PEMBELAJARAN SENAM HEALTY FUN UNTUK ANAK SLB DI KOTA MAGELANG

    Directory of Open Access Journals (Sweden)

    Bernadeta Yovina Anggitasari

    2017-02-01

    Full Text Available The purpose of research is to produce healty Gymnastics Instructional Model Development Fun, Grade V and VI in SLB-C Magelang City. This research is the development of the procedures of product development; (1 analysis, (2 develop initial product form, (3 expert validation test, (4 the first product revision, the revision of the product based on the evaluation of experts and trial I (20 students. Revised used for improvements to the initial product, (5 the trial II (30 students, (6 the revision of the final product is done based on the results of the field trials, (7 the results of a late model gymnastics healty fun for students of classes V and VI which is generated through the revision II trial. Based on the results of research trials I obtained a percentage of 87% (good, the expert evaluation is obtained percentage of 89% (good. II trial research results obtained by the percentage of 96% (excellent. From the trial I and II, there was an increase by a margin of 7%. It can be concluded that the development of gymnastics healty fun learning model can be used as an alternative to teachers provide learning materials rhythmic gymnastics penjasorkes particularly good for classes V and VI SLB-C in Magelang and gymnastic activities that teachers do on a regular basis

  17. Language Micro-gaming: Fun and Informal Microblogging Activities for Language Learning

    Science.gov (United States)

    Perifanou, Maria A.

    'Learning is an active process of constructing rather than acquiring knowledge and instruction is a process of supporting that construction rather than communicating knowledge' [1]. Can this process of learning be fun for the learner? Successful learning involves a mixture of work and fun. One of the recent web 2.0 services that can offer great possibilities for learning is Microblogging [2]. This kind of motivation can raise students' natural curiosity and interest which promotes learning. Play can also promote excitement, enjoyment, and a relaxing atmosphere. As Vygotsky (1933) [3] advocates, play creates a zone of proximal development (ZDP) in children. According to Vygotsky, the ZDP is the distance between one's actual developmental level and one's potential developmental level when interacting with someone and/or something in the social environment [4]. Play can be highly influential in learning. What happens when play becomes informal learning supported by web 2.0 technologies? Practical ideas applied in an Italian foreign language classroom using microblogging to promote fun and informal learning showed that microblogging can enhance motivation, participation, collaboration and practice in basic language skills.

  18. Cisco Networking All-in-One For Dummies

    CERN Document Server

    Tetz, Edward

    2011-01-01

    A helpful guide on all things Cisco Do you wish that the complex topics of routers, switches, and networking could be presented in a simple, understandable presentation? With Cisco Networking All-in-One For Dummies, they are! This expansive reference is packed with all the information you need to learn to use Cisco routers and switches to develop and manage secure Cisco networks. This straightforward-by-fun guide offers expansive coverage of Cisco and breaks down intricate subjects such as networking, virtualization, and database technologies into easily digestible pieces. Drills down complex

  19. A combined finite element-Langevin dynamics (FEM-LD) approach for analyzing the mechanical response of bio-polymer networks

    Science.gov (United States)

    Lin, Yuan; Wei, X.; Qian, J.; Sze, K. Y.; Shenoy, V. B.

    2014-01-01

    A Langevin dynamics based formulation is proposed to describe the shape fluctuations of biopolymer filaments. We derive a set of stochastic partial differential equations (SPDEs) to describe the temporal evolution of the shape of semiflexible filaments and show that the solutions of these equations reduce to predictions from classical modal analysis. A finite element formulation to solve these SPDEs is also developed where, besides entropy, the finite deformation of the filaments has been taken into account. The validity of the proposed finite element-Langevin dynamics (FEM-LD) approach is verified by comparing the simulation results with a variety of theoretical predictions. The method is then applied to study the mechanical behavior of randomly cross-linked F-actin networks. We find that as deformation progresses, the response of such networks undergoes transitions from being entropy dominated to being governed by filament bending and then, eventually, to being dictated by filament stretching. The levels of macroscopic stress at which these transitions take place were found to be around 1% and 10%, respectively, of the initial bulk modulus of the network, in agreement with recent experimental observations.

  20. Cyber-Physical Test Platform for Microgrids: Combining Hardware, Hardware-in-the-Loop, and Network-Simulator-in-the-Loop

    Energy Technology Data Exchange (ETDEWEB)

    Nelson, Austin; Chakraborty, Sudipta; Wang, Dexin; Singh, Pawan; Cui, Qiang; Yang, Liuqing; Suryanarayanan, Siddharth

    2016-11-14

    This paper presents a cyber-physical testbed, developed to investigate the complex interactions between emerging microgrid technologies such as grid-interactive power sources, control systems, and a wide variety of communication platforms and bandwidths. The cyber-physical testbed consists of three major components for testing and validation: real time models of a distribution feeder model with microgrid assets that are integrated into the National Renewable Energy Laboratory's (NREL) power hardware-in-the-loop (PHIL) platform; real-time capable network-simulator-in-the-loop (NSIL) models; and physical hardware including inverters and a simple system controller. Several load profiles and microgrid configurations were tested to examine the effect on system performance with increasing channel delays and router processing delays in the network simulator. Testing demonstrated that the controller's ability to maintain a target grid import power band was severely diminished with increasing network delays and laid the foundation for future testing of more complex cyber-physical systems.

  1. Combining control electronics with SOA to equalize packet-to-packet power variations for optical 3R regeneration in optical networks at 10 Gbit/s

    DEFF Research Database (Denmark)

    Wessing, Henrik; Lavigne, B.; Sørensen, Brian Michael

    2004-01-01

    We report on the combined effects of control electronics and a SOA as to suppress packet to packet power fluctuations. Associated to a SOA-MZI based 3R regenerator, we demonstrate a power dynamic range of 9 dB.......We report on the combined effects of control electronics and a SOA as to suppress packet to packet power fluctuations. Associated to a SOA-MZI based 3R regenerator, we demonstrate a power dynamic range of 9 dB....

  2. Direct Quantification of Cd2+ in the Presence of Cu2+ by a Combination of Anodic Stripping Voltammetry Using a Bi-Film-Modified Glassy Carbon Electrode and an Artificial Neural Network

    OpenAIRE

    Zhao, Guo; Wang, Hui; Liu, Gang

    2017-01-01

    In this study, a novel method based on a Bi/glassy carbon electrode (Bi/GCE) for quantitatively and directly detecting Cd2+ in the presence of Cu2+ without further electrode modifications by combining square-wave anodic stripping voltammetry (SWASV) and a back-propagation artificial neural network (BP-ANN) has been proposed. The influence of the Cu2+ concentration on the stripping response to Cd2+ was studied. In addition, the effect of the ferrocyanide concentration on the SWASV detection of...

  3. FUN CITY

    DEFF Research Database (Denmark)

    down the consquences of these developments, to elocidate the interplay between funscapes and fear culture, and to account for the meaning of new concepts and new phenomena such as "event culture", "urban scenography", "experience economy","city branding" and "cultural planning".......Once the blues guitarist B.B. King sang that when he "didn't wanna live no more", he would go shopping instead. Now, however, shopping has become a lifestyle... The city of today has become "Disneyfied" and "Tivolized". It has become a scene for events. The aim of the book is to encircle and pin...

  4. FUN CITY

    DEFF Research Database (Denmark)

    down the consquences of these developments, to elocidate the interplay between funscapes and fear culture, and to account for the meaning of new concepts and new phenomena such as "event culture", "urban scenography", "experience economy","city branding" and "cultural planning"....

  5. Snow Fun.

    Science.gov (United States)

    Finlay, Joy

    1988-01-01

    Describes several learning activities that can be done with children in the snow. Includes shake paintings, snow sculpture, snow "snakes," snow-ball contests, an igloo experience, and how to make snowshoes. (TW)

  6. B-jet and c-jet identification with Neural Networks as well as combination of multivariate analyses for the search for of multivariate analyses for the search for single top-quark production

    Energy Technology Data Exchange (ETDEWEB)

    Renz, Manuel; /Karlsruhe U., EKP

    2008-06-01

    into half. In the second part of this diploma thesis, a method for the combination of three multivariate single-top analyses using an integrated luminosity of 2.2 fb{sup -1} is presented. For this purpose the discriminants of the Likelihood Function analysis, the Matrix Element method and the Neural Network analysis are used as input variables to a neural network. Overall four different networks are trained, one for events with two or three jets and one or two SecVtx tags, respectively. Using a binned likelihood function, the outputs of these networks are fitted to the output distribution of observed events. A single top-quark production cross section of {sigma}{sub single-top} = 2.2{sub -0.7}{sup +0.8} pb is measured. Ensemble tests are performed for the calculation of the sensitivity and observed significance, which are found to be 4.8{sigma} and 3.9{sigma}, respectively. Hence the improvement of this combination is roughly 8% in comparison with sensitivities found by the individual analyses. Due to the proportionality of {sigma}{sub single-top} and |V{sub tb}|{sup 2} and under the assumption V{sub tb} >> V{sub ts}, V{sub td}, a value for |V{sub tb}| is quoted: |V{sub tb}| = 0.88{sub -0.12}{sup +0.14}(exp.) {+-} 0.07(theo.). It can be seen, that the given uncertainties are too large for a verification or falsification of the unitarity assumption of the CKM-matrix. Parallel to this combination a further combination method (NEAT-combination) has been developed. This combination uses a neural network trained with a neuroevolution technique, which optimizes the neural network architecture and weights through the use of genetic algorithms. In this analysis an improvement of roughly 12% could be reached. In figure 7.1 the current situation for the measurement of the single top-quark production cross section is summarized. After collecting more data, CDF will be able to observe single top-quark production with a significance larger than 5.0{sigma}. Nevertheless, the cross

  7. Efeitos do diabetes mellitus sobre a função testicular de ratos Wistar

    OpenAIRE

    Marcia Cury Cioffi

    2006-01-01

    Utilizaram-se 27 ratos Wistar, machos com 98 dias de idade, originados do Biotério da FMVZ-USP, com o objetivo de avaliar os possíveis efeitos do diabetes mellitus, sobre a função reprodutiva relacionada ao macho.Os animais foram divididos em três grupos, grupo A (GA) constituído de 10 animais sadios, grupo B (GB) constituído de oito ratos Wistar, com diabetes mellitus induzida quimicamente através da administração intraperitonial de estreptozotocina (65mg/Kg) e grupo C (GC) constituído por n...

  8. Uncertainty Quantification of the FUN3D-Predicted NASA CRM Flutter Boundary

    Science.gov (United States)

    Stanford, Bret K.; Massey, Steven J.

    2017-01-01

    A nonintrusive point collocation method is used to propagate parametric uncertainties of the flexible Common Research Model, a generic transport configuration, through the unsteady aeroelastic CFD solver FUN3D. A range of random input variables are considered, including atmospheric flow variables, structural variables, and inertial (lumped mass) variables. UQ results are explored for a range of output metrics (with a focus on dynamic flutter stability), for both subsonic and transonic Mach numbers, for two different CFD mesh refinements. A particular focus is placed on computing failure probabilities: the probability that the wing will flutter within the flight envelope.

  9. Córtex pré-frontal, funções executivas e comportamento criminal

    OpenAIRE

    Seruca, Tânia Catarina Mira

    2013-01-01

    Tese de Doutoramento em Psicologia - Área de Especialidade Psicobiologia. Os comportamentos anti-sociais têm sido associados ao funcionamento pré-frontal, cuja disfunção pode levar a perturbações emocionais e/ou alteração das Funções Executivas relacionadas com a organização temporal do comportamento, planeamento, conceptualização e flexibilidade cognitiva. As disfunções executivas caracterizam-se, fundamentalmente, por impulsividade elevada, diminuição do controlo inibitório, ...

  10. On the way to fun an emotion-based approach to successful game design

    CERN Document Server

    Dillon, Roberto

    2010-01-01

    On the Way to Fun outlines a fine framework linking human emotions and instincts to successful game design, blending a theoretical framework with keys to analyzing game play. The framework is then applied to both successful and unsuccessful games to make for a fine survey for any who want to properly design and develop ideas to maximum benefit.-Midwest Book Review, January 2011I love the '6-11 Framework'. It's a brilliant analysis. Wish I'd thought of it. Emotion is essential to establishing a deep connection with games. So many games lack it, and this book shows the way. The analyses of retro

  11. Mapping real-time air pollution health risk for environmental management: Combining mobile and stationary air pollution monitoring with neural network models.

    Science.gov (United States)

    Adams, Matthew D; Kanaroglou, Pavlos S

    2016-03-01

    Air pollution poses health concerns at the global scale. The challenge of managing air pollution is significant because of the many air pollutants, insufficient funds for monitoring and abatement programs, and political and social challenges in defining policy to limit emissions. Some governments provide citizens with air pollution health risk information to allow them to limit their exposure. However, many regions still have insufficient air pollution monitoring networks to provide real-time mapping. Where available, these risk mapping systems either provide absolute concentration data or the concentrations are used to derive an Air Quality Index, which provides the air pollution risk for a mix of air pollutants with a single value. When risk information is presented as a single value for an entire region it does not inform on the spatial variation within the region. Without an understanding of the local variation residents can only make a partially informed decision when choosing daily activities. The single value is typically provided because of a limited number of active monitoring units in the area. In our work, we overcome this issue by leveraging mobile air pollution monitoring techniques, meteorological information and land use information to map real-time air pollution health risks. We propose an approach that can provide improved health risk information to the public by applying neural network models within a framework that is inspired by land use regression. Mobile air pollution monitoring campaigns were conducted across Hamilton from 2005 to 2013. These mobile air pollution data were modelled with a number of predictor variables that included information on the surrounding land use characteristics, the meteorological conditions, air pollution concentrations from fixed location monitors, and traffic information during the time of collection. Fine particulate matter and nitrogen dioxide were both modelled. During the model fitting process we reserved

  12. Estimation of probability for the presence of claw and digital skin diseases by combining cow- and herd-level information using a Bayesian network

    DEFF Research Database (Denmark)

    Ettema, Jehan Frans; Østergaard, Søren; Kristensen, Anders Ringgaard

    2009-01-01

    Cross sectional data on the prevalence of claw and (inter) digital skin diseases on 4854 Holstein Friesian cows in 50 Danish dairy herds was used in a Bayesian network to create herd specific probability distributions for the presence of lameness causing diseases. Parity and lactation stage...... probabilities and random herd effects are used to formulate cow-level probability distributions of disease presence in a specific Danish dairy herd. By step-wise inclusion of information on cow- and herd-level risk factors, lameness prevalence and clinical diagnosis of diseases on cows in the herd, the Bayesian...

  13. Empathy and well-being correlate with centrality in different social networks.

    Science.gov (United States)

    Morelli, Sylvia A; Ong, Desmond C; Makati, Rucha; Jackson, Matthew O; Zaki, Jamil

    2017-09-12

    Individuals benefit from occupying central roles in social networks, but little is known about the psychological traits that predict centrality. Across four college freshman dorms (n = 193), we characterized individuals with a battery of personality questionnaires and also asked them to nominate dorm members with whom they had different types of relationships. This revealed several social networks within dorm communities with differing characteristics. In particular, additional data showed that networks varied in the degree to which nominations depend on (i) trust and (ii) shared fun and excitement. Networks more dependent upon trust were further defined by fewer connections than those more dependent on fun. Crucially, network and personality features interacted to predict individuals' centrality: people high in well-being (i.e., life satisfaction and positive emotion) were central to networks characterized by fun, whereas people high in empathy were central to networks characterized by trust. Together, these findings provide network-based corroboration of psychological evidence that well-being is socially attractive, whereas empathy supports close relationships. More broadly, these data highlight how an individual's personality relates to the roles that they play in sustaining their community.

  14. Medidas psicofísicas e eletrofisiológicas da função visual do recém nascido: uma revisão

    Directory of Open Access Journals (Sweden)

    Marcelo Fernandes da Costa

    2006-01-01

    Full Text Available O sistema visual apresenta muitas funções ao nascimento. O processo de amadurecimento destas funções demanda um tempo variado. Neste trabalho, inicialmente descreveremos como a psicofísica e a eletrofisiologia visual tem colaborado para a medida e o estudo do desenvolvimento de três funções visuais: acuidade visual, sensibilidade ao contraste e visão de cores. Num segundo momento, discutimos sobre como a medida e o desenvolvimento destas funções podem estar prejudicados em patologias que afetam o sistema visual, como a prematuridade e a paralisia cerebral.

  15. Systematic review and network meta-analysis of combination and monotherapy treatments in disease-modifying antirheumatic drug-experienced patients with rheumatoid arthritis: analysis of American College of Rheumatology criteria scores 20, 50, and 70

    Directory of Open Access Journals (Sweden)

    Orme ME

    2012-12-01

    Full Text Available Michelle E Orme,1 Katherine S MacGilchrist,2 Stephen Mitchell,2 Dean Spurden,3 Alex Bird31Icera Consulting, Swindon, Wiltshire, UK; 2Systematic Review Department, Abacus International, Bicester, Oxfordshire, UK; 3Pfizer UK Limited, Tadworth, Surrey, UKBackground: Biologic disease-modifying antirheumatic drugs (bDMARDs extend the treatment choices for rheumatoid arthritis patients with suboptimal response or intolerance to conventional DMARDs. The objective of this systematic review and meta-analysis was to compare the relative efficacy of EU-licensed bDMARD combination therapy or monotherapy for patients intolerant of or contraindicated to continued methotrexate.Methods: Comprehensive, structured literature searches were conducted in Medline, Embase, and the Cochrane Library, as well as hand-searching of conference proceedings and reference lists. Phase II or III randomized controlled trials reporting American College of Rheumatology (ACR criteria scores of 20, 50, and 70 between 12 and 30 weeks' follow-up and enrolling adult patients meeting ACR classification criteria for rheumatoid arthritis previously treated with and with an inadequate response to conventional DMARDs were eligible. To estimate the relative efficacy of treatments whilst preserving the randomized comparisons within each trial, a Bayesian network meta-analysis was conducted in WinBUGS using fixed and random-effects, logit-link models fitted to the binomial ACR 20/50/70 trial data.Results: The systematic review identified 10,625 citations, and after a review of 2450 full-text papers, there were 29 and 14 eligible studies for the combination and monotherapy meta-analyses, respectively. In the combination analysis, all licensed bDMARD combinations had significantly higher odds of ACR 20/50/70 compared to DMARDs alone, except for the rituximab comparison, which did not reach significance for the ACR 70 outcome (based on the 95% credible interval. The etanercept combination was

  16. An integrated approach (CLuster Analysis Integration Method) to combine expression data and protein-protein interaction networks in agrigenomics: application on Arabidopsis thaliana.

    Science.gov (United States)

    Santoni, Daniele; Swiercz, Aleksandra; Zmieńko, Agnieszka; Kasprzak, Marta; Blazewicz, Marek; Bertolazzi, Paola; Felici, Giovanni

    2014-02-01

    Experimental co-expression data and protein-protein interaction networks are frequently used to analyze the interactions among genes or proteins. Recent studies have investigated methods to integrate these two sources of information. We propose a new method to integrate co-expression data obtained through DNA microarray analysis (MA) and protein-protein interaction (PPI) network data, and apply it to Arabidopsis thaliana. The proposed method identifies small subsets of highly interacting proteins. Based on the analysis of the basis of co-localization and mRNA developmental expression, we show that these groups provide important biological insights; additionally, these subsets are significantly enriched with respect to KEGG Pathways and can be used to predict successfully whether proteins belong to known pathways. Thus, the method is able to provide relevant biological information and support the functional identification of complex genetic traits of economic value in plant agrigenomics research. The method has been implemented in a prototype software tool named CLAIM (CLuster Analysis Integration Method) and can be downloaded from http://bio.cs.put.poznan.pl/research_fields . CLAIM is based on the separate clustering of MA and PPI data; the clusters are merged in a special graph; cliques of this graph are subsets of strongly connected proteins. The proposed method was successfully compared with existing methods. CLAIM appears to be a useful semi-automated tool for protein functional analysis and warrants further evaluation in agrigenomics research.

  17. Combined genome-wide expression profiling and targeted RNA interference in primary mouse macrophages reveals perturbation of transcriptional networks associated with interferon signalling

    Directory of Open Access Journals (Sweden)

    Craigon Marie

    2009-08-01

    Full Text Available Abstract Background Interferons (IFNs are potent antiviral cytokines capable of reprogramming the macrophage phenotype through the induction of interferon-stimulated genes (ISGs. Here we have used targeted RNA interference to suppress the expression of a number of key genes associated with IFN signalling in murine macrophages prior to stimulation with interferon-gamma. Genome-wide changes in transcript abundance caused by siRNA activity were measured using exon-level microarrays in the presence or absence of IFNγ. Results Transfection of murine bone-marrow derived macrophages (BMDMs with a non-targeting (control siRNA and 11 sequence-specific siRNAs was performed using a cationic lipid transfection reagent (Lipofectamine2000 prior to stimulation with IFNγ. Total RNA was harvested from cells and gene expression measured on Affymetrix GeneChip Mouse Exon 1.0 ST Arrays. Network-based analysis of these data revealed six siRNAs to cause a marked shift in the macrophage transcriptome in the presence or absence IFNγ. These six siRNAs targeted the Ifnb1, Irf3, Irf5, Stat1, Stat2 and Nfkb2 transcripts. The perturbation of the transcriptome by the six siRNAs was highly similar in each case and affected the expression of over 600 downstream transcripts. Regulated transcripts were clustered based on co-expression into five major groups corresponding to transcriptional networks associated with the type I and II IFN response, cell cycle regulation, and NF-KB signalling. In addition we have observed a significant non-specific immune stimulation of cells transfected with siRNA using Lipofectamine2000, suggesting use of this reagent in BMDMs, even at low concentrations, is enough to induce a type I IFN response. Conclusion Our results provide evidence that the type I IFN response in murine BMDMs is dependent on Ifnb1, Irf3, Irf5, Stat1, Stat2 and Nfkb2, and that siRNAs targeted to these genes results in perturbation of key transcriptional networks associated

  18. The role of induction and adjuvant chemotherapy in combination with concurrent chemoradiotherapy for nasopharyngeal cancer: a Bayesian network meta-analysis of published randomized controlled trials

    Directory of Open Access Journals (Sweden)

    Yu HL

    2016-01-01

    Full Text Available Hongliang Yu,1,* Dayong Gu,1,* Xia He,1 Xianshu Gao,2 Xiuhua Bian1 1Department of Radiation Oncology, Jiangsu Cancer Hospital affiliated with Nanjing Medical University, Nanjing, 2Department of Radiation Oncology, Peking University First Hospital, Peking University, Beijing, People’s Republic of China *These authors contributed equally to this work Abstract: Whether the addition of induction chemotherapy (IC or adjuvant chemotherapy (AC to concurrent chemoradiotherapy (CCRT is superior to CCRT alone for locally advanced nasopharyngeal cancer is unknown. A Bayesian network meta-analysis was performed to investigate the efficacy of CCRT, IC + CCRT, and CCRT + AC on locally advanced nasopharyngeal cancer. The overall survival (OS with hazard ratios (HRs and locoregional recurrence rates (LRRs and distant metastasis rates (DMRs with risk ratios (RRs were investigated. After a comprehensive database search, eleven studies involving 2,626 assigned patients were included in this network meta-analysis. Compared with CCRT alone, IC + CCRT resulted in no significant improvement in OS or LRR and a marginal improvement in DMR (OS: HR =0.67, 95% credible interval (CrI 0.32–1.18; LRR: RR =1.79, 95% CrI 0.80–3.51; DMR: RR =1.79, 95% CrI 0.24–1.04 and CCRT + AC exhibited no beneficial effects on any of the endpoints of OS, LRR, or DMR (OS: HR =0.99, 95% CrI 0.64–1.43; LRR: RR =0.78, 95% CrI 0.43–1.32; DMR: RR =0.85, 95% CrI 0.57–1.24. As a conclusion, for locally advanced nasopharyngeal cancer, no significant differences in the treatment efficacies of CCRT, IC + CCRT, and CCRT + AC were found, with the exception of a marginally significant improvement in distant control observed following IC + CCRT compared with CCRT alone. Keywords: concurrent chemotherapy, induction chemotherapy, adjuvant chemotherapy, radiotherapy, nasopharyngeal cancer, network meta-analysis

  19. Nuclear networking.

    Science.gov (United States)

    Xie, Wei; Burke, Brian

    2017-07-04

    Nuclear lamins are intermediate filament proteins that represent important structural components of metazoan nuclear envelopes (NEs). By combining proteomics and superresolution microscopy, we recently reported that both A- and B-type nuclear lamins form spatially distinct filament networks at the nuclear periphery of mouse fibroblasts. In particular, A-type lamins exhibit differential association with nuclear pore complexes (NPCs). Our studies reveal that the nuclear lamina network in mammalian somatic cells is less ordered and more complex than that of amphibian oocytes, the only other system in which the lamina has been visualized at high resolution. In addition, the NPC component Tpr likely links NPCs to the A-type lamin network, an association that appears to be regulated by C-terminal modification of various A-type lamin isoforms. Many questions remain, however, concerning the structure and assembly of lamin filaments, as well as with their mode of association with other nuclear components such as peripheral chromatin.

  20. Radiation Coupling with the FUN3D Unstructured-Grid CFD Code

    Science.gov (United States)

    Wood, William A.

    2012-01-01

    The HARA radiation code is fully-coupled to the FUN3D unstructured-grid CFD code for the purpose of simulating high-energy hypersonic flows. The radiation energy source terms and surface heat transfer, under the tangent slab approximation, are included within the fluid dynamic ow solver. The Fire II flight test, at the Mach-31 1643-second trajectory point, is used as a demonstration case. Comparisons are made with an existing structured-grid capability, the LAURA/HARA coupling. The radiative surface heat transfer rates from the present approach match the benchmark values within 6%. Although radiation coupling is the focus of the present work, convective surface heat transfer rates are also reported, and are seen to vary depending upon the choice of mesh connectivity and FUN3D ux reconstruction algorithm. On a tetrahedral-element mesh the convective heating matches the benchmark at the stagnation point, but under-predicts by 15% on the Fire II shoulder. Conversely, on a mixed-element mesh the convective heating over-predicts at the stagnation point by 20%, but matches the benchmark away from the stagnation region.

  1. Aeroacoustic Simulations of a Nose Landing Gear with FUN3D: A Grid Refinement Study

    Science.gov (United States)

    Vatsa, Veer N.; Khorrami, Mehdi R.; Lockard, David P.

    2017-01-01

    A systematic grid refinement study is presented for numerical simulations of a partially-dressed, cavity-closed (PDCC) nose landing gear configuration that was tested in the University of Florida's open-jet acoustic facility known as the UFAFF. The unstructured-grid flow solver FUN3D is used to compute the unsteady flow field for this configuration. Mixed-element grids generated using the Pointwise (Registered Trademark) grid generation software are used for numerical simulations. Particular care is taken to ensure quality cells and proper resolution in critical areas of interest in an effort to minimize errors introduced by numerical artifacts. A set of grids was generated in this manner to create a family of uniformly refined grids. The finest grid was then modified to coarsen the wall-normal spacing to create a grid suitable for the wall-function implementation in FUN3D code. A hybrid Reynolds-averaged Navier-Stokes/large eddy simulation (RANS/LES) turbulence modeling approach is used for these simulations. Time-averaged and instantaneous solutions obtained on these grids are compared with the measured data. These CFD solutions are used as input to a FfowcsWilliams-Hawkings (FW-H) noise propagation code to compute the farfield noise levels. The agreement of the computed results with the experimental data improves as the grid is refined.

  2. Provas da função pulmonar. Controlo de qualidade. Aspectos gerais (1a Parte

    Directory of Open Access Journals (Sweden)

    J.M. Reis Ferreira

    1999-07-01

    Full Text Available RESUMO: Os autores procuraram neste artigo sensibilizar todos os que lidam de perto com a realização dos testes da função respiratória, para a importância da qualidade e dos meios disponiveis para conseguir o seu controlo. Foram deste modo encarados os diferentes factores intervenientes na qualidade, analisandoos de acordo com os dados fornecidos fundamentalmente pela experiência. Foram assim focados, neste primeiro artigo, apenas aspectos gerais trazidos pela prática do dia a dia, a que se seguirá a publicação de novo texto em que serão, dentro do mesmo âmbito, encaradas as diferentes técnicas actualmente usadas para a avaliação da Função Respiratória.REV PORT PNEUMOL 1999; V (4: 393-404 ABSTRACT: The authors claim for the importance of the quality control in order to assure clinically useful results and a well-functioning lung function laboratory, and analyse briefly the different general factors that could be involved in the lack of quality.REV PORTPNEUMOL 1999; V (4: 393-404 Key-words: Quality control, Equipment, Calibration, Accuracity, Colaboration, Palavras-chave: Controlo de qualidade, Equipamento, Calibração, Precisão, Exactidão;Colaboração

  3. Função de drenagem purificadora do sistema nervoso central pelo liquido cefalorraquiano

    Directory of Open Access Journals (Sweden)

    João Baptista dos Reis-filho

    1983-03-01

    Full Text Available Por motivo da ausência de um sistema linfático no encéfalo, o produto derivado de seu metabolismo somente pode ser removido por duas vias, pelo fluxo sanguíneo capilar ou pela sua transferência ao LCR. Davson havia sugerido que esta segunda forma seria possível, o LCR exercendo uma função de eliminação depuradora para o tecido nervoso e, posteriormente, juntamente com Oldendorf, demonstrou experimentalmente ser esta hipótese admissível. De acordo com esta concepção, o produto do metabolismo encefálico não aproveitável se difundiria em condições normais para o LCR e daqui seria removido pelo mecanismo habitual de sua reabsorção. Da mesma forma, qualquer soluto que atravessasse a barreira hematencefálica em proporção prejudicial seria desviado do tecido nervoso, evitando-se que sua concentração mais elevada perturbasse a função cerebral. Por meio da sua reabsorção nas vilosidades da aracnóide, o LCR eliminaria lentamente este soluto que passou do sangue para o SNC. De modo semelhante, a água em excesso poderia ser removida do tecido nervoso em pacientes com edema encefálico.

  4. FunGene-DB: a web-based tool for Polyporales strains authentication.

    Science.gov (United States)

    Navarro, David; Favel, Anne; Chabrol, Olivier; Pontarotti, Pierre; Haon, Mireille; Lesage-Meessen, Laurence

    2012-10-31

    Polyporales are extensively studied wood-decaying fungi with applications in white and green biotechnologies and in medicinal chemistry. We developed an open-access, user-friendly, bioinformatics tool named FunGene-DB (http://www.fungene-db.org). The goal was to facilitate the molecular authentication of Polyporales strains and fruit-bodies, otherwise subjected to morphological studies. This tool includes a curated database that contains ITS1-5.8S-ITS2 rDNA genes screened through a semi-automated pipeline from the International Nucleotide Sequence Database (INSD), and the similarity search BLASTn program. Today, the web-accessible database compiles 2379 accepted sequences, among which 386 were selected as reference sequences (most often fully identified ITS sequences for which a voucher, strain or specimen, has been deposited in a public-access collection). The restriction of the database to one reference sequence per species (or per clade for species complex) allowed most often unequivocal analysis. We conclude that FunGene-DB is a promising tool for molecular authentication of Polyporales. It should be especially useful for scientists who are not expert mycologists but who need to check the identity of strains (e.g. for culture collections, for applied microbiology). Copyright © 2012 Elsevier B.V. All rights reserved.

  5. Aeroacoustic Simulation of Nose Landing Gear on Adaptive Unstructured Grids With FUN3D

    Science.gov (United States)

    Vatsa, Veer N.; Khorrami, Mehdi R.; Park, Michael A.; Lockard, David P.

    2013-01-01

    Numerical simulations have been performed for a partially-dressed, cavity-closed nose landing gear configuration that was tested in NASA Langley s closed-wall Basic Aerodynamic Research Tunnel (BART) and in the University of Florida's open-jet acoustic facility known as the UFAFF. The unstructured-grid flow solver FUN3D, developed at NASA Langley Research center, is used to compute the unsteady flow field for this configuration. Starting with a coarse grid, a series of successively finer grids were generated using the adaptive gridding methodology available in the FUN3D code. A hybrid Reynolds-averaged Navier-Stokes/large eddy simulation (RANS/LES) turbulence model is used for these computations. Time-averaged and instantaneous solutions obtained on these grids are compared with the measured data. In general, the correlation with the experimental data improves with grid refinement. A similar trend is observed for sound pressure levels obtained by using these CFD solutions as input to a FfowcsWilliams-Hawkings noise propagation code to compute the farfield noise levels. In general, the numerical solutions obtained on adapted grids compare well with the hand-tuned enriched fine grid solutions and experimental data. In addition, the grid adaption strategy discussed here simplifies the grid generation process, and results in improved computational efficiency of CFD simulations.

  6. O sujeito psicótico e a função do delírio

    OpenAIRE

    Briggs, Raquel; Rinaldi, Doris

    2014-01-01

    Este trabalho aborda o conceito de delírio e sua função na estrutura psicótica. A psicanálise considera o delírio, por um lado, fenômeno elementar e, por outro, tentativa de cura, portador de uma verdade. O presente trabalho objetiva abordar a estruturação delirante, assim como a função da mesma para o sujeito paranoico, no sentido de situar a direção de tratamento na clínica da paranoia. A partir de um caso clínico e embasando-se nos conceitos da psicanálise, discute-se a função do mesmo par...

  7. Combined Molecular Dynamics, Atoms in Molecules, and IR Studies of the Bulk Monofluoroethanol and Bulk Ethanol To Understand the Role of Organic Fluorine in the Hydrogen Bond Network.

    Science.gov (United States)

    Biswas, Biswajit; Mondal, Saptarsi; Singh, Prashant Chandra

    2017-02-16

    The presence of the fluorocarbon group in fluorinated alcohols makes them an important class of molecules that have diverse applications in the field of separation techniques, synthetic chemistry, polymer industry, and biology. In this paper, we have performed the density function theory calculation along with atom in molecule analysis, molecular dynamics simulation, and IR measurements of bulk monofluoroethanol (MFE) and compared them with the data for bulk ethanol (ETH) to understand the effect of the fluorocarbon group in the structure and the hydrogen bond network of bulk MFE. It has been found that the intramolecular O-H···F hydrogen bond is almost absent in bulk MFE. Molecular dynamics simulation and density function theory calculation along with atom in molecule analysis clearly depict that in the case of bulk MFE, a significant amount of intermolecular O-H···F and C-H···F hydrogen bonds are present along with the intermolecular O-H···O hydrogen bond. The presence of intermolecular O-H···F and C-H···F hydrogen bonds causes the difference in the IR spectrum of bulk MFE as compared to bulk ETH. This study clearly depicts that the organic fluorine (fluorocarbon) of MFE acts as a hydrogen bond acceptor and plays a significant role in the structure and hydrogen bond network of bulk MFE through the formation of weak O-H···F as well C-H···F hydrogen bonds, which may be one of the important reasons behind the unique behavior of the fluoroethanols.

  8. Object detection approach using generative sparse, hierarchical networks with top-down and lateral connections for combining texture/color detection and shape/contour detection

    Science.gov (United States)

    Paiton, Dylan M.; Kenyon, Garrett T.; Brumby, Steven P.; Schultz, Peter F.; George, John S.

    2015-07-28

    An approach to detecting objects in an image dataset may combine texture/color detection, shape/contour detection, and/or motion detection using sparse, generative, hierarchical models with lateral and top-down connections. A first independent representation of objects in an image dataset may be produced using a color/texture detection algorithm. A second independent representation of objects in the image dataset may be produced using a shape/contour detection algorithm. A third independent representation of objects in the image dataset may be produced using a motion detection algorithm. The first, second, and third independent representations may then be combined into a single coherent output using a combinatorial algorithm.

  9. Object detection approach using generative sparse, hierarchical networks with top-down and lateral connections for combining texture/color detection and shape/contour detection

    Science.gov (United States)

    Paiton, Dylan M.; Kenyon, Garrett T.; Brumby, Steven P.; Schultz, Peter F.; George, John S.

    2016-10-25

    An approach to detecting objects in an image dataset may combine texture/color detection, shape/contour detection, and/or motion detection using sparse, generative, hierarchical models with lateral and top-down connections. A first independent representation of objects in an image dataset may be produced using a color/texture detection algorithm. A second independent representation of objects in the image dataset may be produced using a shape/contour detection algorithm. A third independent representation of objects in the image dataset may be produced using a motion detection algorithm. The first, second, and third independent representations may then be combined into a single coherent output using a combinatorial algorithm.

  10. Comparison of palbociclib in combination with letrozole or fulvestrant with endocrine therapies for advanced/metastatic breast cancer: network meta-analysis.

    Science.gov (United States)

    Chirila, Costel; Mitra, Debanjali; Colosia, Ann; Ling, Caroline; Odom, Dawn; Iyer, Shrividya; Kaye, James A

    2017-08-01

    Palbociclib is the first cyclin-dependent kinase 4/6 inhibitor approved in the United States for HR+/HER2- advanced/metastatic breast cancer, in combination with letrozole as initial endocrine-based therapy in postmenopausal women or with fulvestrant in women with disease progression following endocrine therapy. We compared progression-free survival (PFS) and discontinuations due to adverse events for palbociclib combinations against other endocrine therapies using a mixed-treatment comparison meta-analysis of randomized, controlled trials. A systematic literature review identified relevant trials. Separate analyses were conducted for each palbociclib combination using a Bayesian approach. Treatment rankings were established using the surface under the cumulative ranking curve (SUCRA). Sixty-five unique studies met inclusion criteria. Palbociclib plus letrozole had the highest SUCRA value (99.9%) and was associated with significantly longer PFS than all comparators in treatment-naïve patients (hazard ratios [HRs] ranged from 0.41 to 0.58). Palbociclib plus fulvestrant had the second highest SUCRA value (93.9%) and, in previously treated patients, yielded significantly longer PFS than most comparators (HRs ranged from 0.26 to 0.46); the exception was everolimus plus exemestane, with similar PFS (HR, 1.04; 95% credible interval [CrI], 0.58-1.76). Palbociclib plus fulvestrant was associated with significantly lower odds of discontinuation due to adverse events than everolimus plus exemestane (odds ratio, 0.14; 95% CrI, 0.05-0.39). The results suggest that the two palbociclib combinations yielded significantly greater PFS than endocrine therapy in treatment-naïve and previously treated patients with advanced/metastatic breast cancer. Palbociclib plus fulvestrant was associated with significantly less toxicity than everolimus plus exemestane.

  11. Tissue segmentation-assisted analysis of fMRI for human motor response: an approach combining artificial neural network and fuzzy C means

    OpenAIRE

    Chiu, MJ; Lin, CC; Chuang, KH; Chen, JH; Huang, KM

    2001-01-01

    The authors have developed an automated algorithm for segmentation of magnetic resonance images (MRI) of the human brain. They investigated the quantitative analysis of tissue-specific human motor response through an approach combining gradient echo functional MRI and automated segmentation analysis. Fifteen healthy volunteers, placed in a 1.5 T clinical MR imager, performed a self-paced finger opposition throughout the activation periods. T1-weighted images (WI), T2WI, and proton density WI ...

  12. NETWORK CODING BY BEAM FORMING

    DEFF Research Database (Denmark)

    2013-01-01

    Network coding by beam forming in networks, for example, in single frequency networks, can provide aid in increasing spectral efficiency. When network coding by beam forming and user cooperation are combined, spectral efficiency gains may be achieved. According to certain embodiments, a method...

  13. Partilha de sentidos-e-significados atribuídos à função do tutor presencial

    OpenAIRE

    Silva Filho, Leonardo Nogueira da

    2015-01-01

    Este trabalho tem como objetivo investigar as funções do tutor presencial no processo de ensino-aprendizagem do aluno de educação a distância. Para nortear nosso estudo, propomos a seguinte pergunta de pesquisa: Que sentidos-e-significados são partilhados entre tutores presenciais, sobre suas funções, em conversas reflexivas? Esta pesquisa está ancorada em duas bases teóricas: (1) a teoria sócio-histórica e cultural, discutida a partir dos escritos de Vygotsky (1934/2002; 1932/1984), e de alg...

  14. FeUdal Networks for Hierarchical Reinforcement Learning

    OpenAIRE

    Vezhnevets, Alexander Sasha; Osindero, Simon; Schaul, Tom; Heess, Nicolas; Jaderberg, Max; Silver, David; Kavukcuoglu, Koray

    2017-01-01

    We introduce FeUdal Networks (FuNs): a novel architecture for hierarchical reinforcement learning. Our approach is inspired by the feudal reinforcement learning proposal of Dayan and Hinton, and gains power and efficacy by decoupling end-to-end learning across multiple levels -- allowing it to utilise different resolutions of time. Our framework employs a Manager module and a Worker module. The Manager operates at a lower temporal resolution and sets abstract goals which are conveyed to and e...

  15. Cognitive-Behavioural Analysis System of Psychotherapy (CBASP), a drug, or their combination: differential therapeutics for persistent depressive disorder: a study protocol of an individual participant data network meta-analysis

    Science.gov (United States)

    Schramm, Elisabeth; Weitz, Erica S; Salanti, Georgia; Efthimiou, Orestis; Michalak, Johannes; Watanabe, Norio; Keller, Martin B; Kocsis, James H; Klein, Daniel N; Cuijpers, Pim

    2016-01-01

    Introduction Despite important advances in psychological and pharmacological treatments of persistent depressive disorders in the past decades, their responses remain typically slow and poor, and differential responses among different modalities of treatments or their combinations are not well understood. Cognitive-Behavioural Analysis System of Psychotherapy (CBASP) is the only psychotherapy that has been specifically designed for chronic depression and has been examined in an increasing number of trials against medications, alone or in combination. When several treatment alternatives are available for a certain condition, network meta-analysis (NMA) provides a powerful tool to examine their relative efficacy by combining all direct and indirect comparisons. Individual participant data (IPD) meta-analysis enables exploration of impacts of individual characteristics that lead to a differentiated approach matching treatments to specific subgroups of patients. Methods and analysis We will search for all randomised controlled trials that compared CBASP, pharmacotherapy or their combination, in the treatment of patients with persistent depressive disorder, in Cochrane CENTRAL, PUBMED, SCOPUS and PsycINFO, supplemented by personal contacts. Individual participant data will be sought from the principal investigators of all the identified trials. Our primary outcomes are depression severity as measured on a continuous observer-rated scale for depression, and dropouts for any reason as a proxy measure of overall treatment acceptability. We will conduct a one-step IPD-NMA to compare CBASP, medications and their combinations, and also carry out a meta-regression to identify their prognostic factors and effect moderators. The model will be fitted in OpenBUGS, using vague priors for all location parameters. For the heterogeneity we will use a half-normal prior on the SD. Ethics and dissemination This study requires no ethical approval. We will publish the findings in a peer

  16. Neural networks combined with region growing techniques for tumor detection in [18F]-fluorothymidine dynamic positron emission tomography breast cancer studies

    Science.gov (United States)

    Cseh, Zoltan; Kenny, Laura; Swingland, James; Bose, Subrata; Turheimer, Federico E.

    2013-03-01

    Early detection and precise localization of malignant tumors has been a primary challenge in medical imaging in recent years. Functional modalities play a continuously increasing role in these efforts. Image segmentation algorithms which enable automatic, accurate tumor visualization and quantification on noisy positron emission tomography (PET) images would significantly improve the quality of treatment planning processes and in turn, the success of treatments. In this work a novel multistep method has been applied in order to identify tumor regions in 4D dynamic [18F] fluorothymidine (FLT) PET studies of patients with locally advanced breast cancer. In order to eliminate the effect of inherently detectable high inhomogeneity inside tumors, specific voxel-kinetic classes were initially introduced by finding characteristic FLT-uptake curves with K-means algorithm on a set of voxels collected from each tumor. Image voxel sets were then split based on voxel time-activity curve (TAC) similarities, and models were generated separately on each voxel set. At first, artificial neural networks, in comparison with linear classification algorithms were applied to distinguish tumor and healthy regions relying on the characteristics of TACs of the individual voxels. The outputs of the best model with very high specificity were then used as input seeds for region shrinking and growing techniques, the application of which considerably enhanced the sensitivity and specificity (78.65% +/- 0.65% and 98.98% +/- 0.03%, respectively) of the final image segmentation model.

  17. An Efficient Approach for Lipase-Catalyzed Synthesis of Retinyl Laurate Nutraceutical by Combining Ultrasound Assistance and Artificial Neural Network Optimization.

    Science.gov (United States)

    Huang, Shang-Ming; Li, Hsin-Ju; Liu, Yung-Chuan; Kuo, Chia-Hung; Shieh, Chwen-Jen

    2017-11-15

    Although retinol is an important nutrient, retinol is highly sensitive to oxidation. At present, some ester forms of retinol are generally used in nutritional supplements because of its stability and bioavailability. However, such esters are commonly synthesized by chemical procedures which are harmful to the environment. Thus, this study utilized a green method using lipase as a catalyst with sonication assistance to produce a retinol derivative named retinyl laurate. Moreover, the process was optimized by an artificial neural network (ANN). First, a three-level-four-factor central composite design (CCD) was employed to design 27 experiments, which the highest relative conversion was 82.64%. Further, the optimal architecture of the CCD-employing ANN was developed, including the learning Levenberg-Marquardt algorithm, the transfer function (hyperbolic tangent), iterations (10,000), and the nodes of the hidden layer (6). The best performance of the ANN was evaluated by the root mean squared error (RMSE) and the coefficient of determination (R²) from predicting and observed data, which displayed a good data-fitting property. Finally, the process performed with optimal parameters actually obtained a relative conversion of 88.31% without long-term reactions, and the lipase showed great reusability for biosynthesis. Thus, this study utilizes green technology to efficiently produce retinyl laurate, and the bioprocess is well established by ANN-mediated modeling and optimization.

  18. Combination of artificial neural network and genetic algorithm method for modeling of methylene blue adsorption onto wood sawdust from water samples.

    Science.gov (United States)

    Khajeh, Mostafa; Sarafraz-Yazdi, Ali; Natavan, Zahra Bameri

    2016-03-01

    The aim of this research was to develop a low price and environmentally friendly adsorbent with abundant of source to remove methylene blue (MB) from water samples. Sawdust solid-phase extraction coupled with high-performance liquid chromatography was used for the extraction and determination of MB. In this study, an experimental data-based artificial neural network model is constructed to describe the performance of sawdust solid-phase extraction method for various operating conditions. The pH, time, amount of sawdust, and temperature were the input variables, while the percentage of extraction of MB was the output. The optimum operating condition was then determined by genetic algorithm method. The optimized conditions were obtained as follows: 11.5, 22.0 min, 0.3 g, and 26.0°C for pH of the solution, extraction time, amount of adsorbent, and temperature, respectively. Under these optimum conditions, the detection limit and relative standard deviation were 0.067 μg L(-1) and <2.4%, respectively. The Langmuir and Freundlich adsorption models were applied to describe the isotherm constant and for the removal and determination of MB from water samples. © The Author(s) 2013.

  19. An Efficient Approach for Lipase-Catalyzed Synthesis of Retinyl Laurate Nutraceutical by Combining Ultrasound Assistance and Artificial Neural Network Optimization

    Directory of Open Access Journals (Sweden)

    Shang-Ming Huang

    2017-11-01

    Full Text Available Although retinol is an important nutrient, retinol is highly sensitive to oxidation. At present, some ester forms of retinol are generally used in nutritional supplements because of its stability and bioavailability. However, such esters are commonly synthesized by chemical procedures which are harmful to the environment. Thus, this study utilized a green method using lipase as a catalyst with sonication assistance to produce a retinol derivative named retinyl laurate. Moreover, the process was optimized by an artificial neural network (ANN. First, a three-level-four-factor central composite design (CCD was employed to design 27 experiments, which the highest relative conversion was 82.64%. Further, the optimal architecture of the CCD-employing ANN was developed, including the learning Levenberg-Marquardt algorithm, the transfer function (hyperbolic tangent, iterations (10,000, and the nodes of the hidden layer (6. The best performance of the ANN was evaluated by the root mean squared error (RMSE and the coefficient of determination (R2 from predicting and observed data, which displayed a good data-fitting property. Finally, the process performed with optimal parameters actually obtained a relative conversion of 88.31% without long-term reactions, and the lipase showed great reusability for biosynthesis. Thus, this study utilizes green technology to efficiently produce retinyl laurate, and the bioprocess is well established by ANN-mediated modeling and optimization.

  20. Effective professional networking.

    Science.gov (United States)

    Goolsby, Mary Jo; Knestrick, Joyce M

    2017-08-01

    The reasons for nurse practitioners to develop a professional network are boundless and are likely to change over time. Networking opens doors and creates relationships that support new opportunities, personal development, collaborative research, policy activism, evidence-based practice, and more. Successful professional networking involves shared, mutually beneficial interactions between individuals and/or individuals and groups, regardless of whether it occurs face to face or electronically. This article combines nuggets from the literature with guidance based on the authors' combined experience in networking activities at the local, national, and international levels. ©2017 American Association of Nurse Practitioners.

  1. Improving health-related fitness in children: the fit-4-Fun randomized controlled trial study protocol

    Directory of Open Access Journals (Sweden)

    Eather Narelle

    2011-12-01

    Full Text Available Abstract Background Declining levels of physical fitness in children are linked to an increased risk of developing poor physical and mental health. Physical activity programs for children that involve regular high intensity physical activity, along with muscle and bone strengthening activities, have been identified by the World Health Organisation as a key strategy to reduce the escalating burden of ill health caused by non-communicable diseases. This paper reports the rationale and methods for a school-based intervention designed to improve physical fitness and physical activity levels of Grades 5 and 6 primary school children. Methods/Design Fit-4-Fun is an 8-week multi-component school-based health-related fitness education intervention and will be evaluated using a group randomized controlled trial. Primary schools from the Hunter Region in NSW, Australia, will be invited to participate in the program in 2011 with a target sample size of 128 primary schools children (age 10-13. The Fit-4-Fun program is theoretically grounded and will be implemented applying the Health Promoting Schools framework. Students will participate in weekly curriculum-based health and physical education lessons, daily break-time physical activities during recess and lunch, and will complete an 8-week (3 × per week home activity program with their parents and/or family members. A battery of six health-related fitness assessments, four days of pedometery-assessed physical activity and a questionnaire, will be administered at baseline, immediate post-intervention (2-months and at 6-months (from baseline to determine intervention effects. Details of the methodological aspects of recruitment, inclusion criteria, randomization, intervention program, assessments, process evaluation and statistical analyses are described. Discussion The Fit-4-Fun program is an innovative school-based intervention targeting fitness improvements in primary school children. The program will

  2. Os nomes em função adjetiva não predicativa: contrastes

    Directory of Open Access Journals (Sweden)

    Juliana Caires Saad

    2001-02-01

    Full Text Available

    As duas primeiras seções deste artigo tratam de nomes que ocupam a segunda posição em grupos N1 N2 do português do Brasil. Investigamos um corpus de 224 ocorrências extraídas de amostras de literatura romanesca, jornalística, dramática, técnica e oratória. O objetivo era uma proposta de tratamento lexicográfico para tais nomes num dicionário de usos do português. Tentamos responder a questões sobre a classificação e as funções de N2, com base nas seguintes características: função qualificadora; possibilidade de gradação; coordenação com adjetivos; ausência de função temática; concordância. Utilizando essas propriedades, estabelecemos uma hierarquia para a classificação de N2: os que exibem a maior parte das propriedades acima seriam classificados como adjetivos; os demais mantêm o estatuto de substantivo. Na terceira seção discutimos a análise de 372 nomes do inglês (extraídos de jornais e revistas, de acordo com os mesmos critérios, e concluímos que, embora a maioria dos substantivos adnominais do inglês não possa ser classificada como adjetivo, alguns substantivos qualificadores sofrem a conversão. Os adjetivos não predicativos, porém, nunca mudam de categoria. A comparação entre o inglês e o português, na seção 4, mostra que o comportamento dos nomes não predicativos difere quantitativamente, mas não qualitativamente, nas duas línguas.

  3. Marcadores práticos de função renal em pacientes com cistinose

    Directory of Open Access Journals (Sweden)

    Luciana Pache de Faria Guimaraes

    2012-09-01

    Full Text Available INTRODUÇÃO: Cistinose é uma doença sistêmica, autossômica recessiva, que leva à insuficiência renal crônica na infância, a não ser que o tratamento com cisteamina seja iniciado precocemente. Mesmo nestas condições, os pacientes evoluem para doença renal crônica terminal por volta da segunda década da vida. Portanto, a avaliação da função renal é essencial neste grupo de pacientes. OBJETIVO: Avaliar e correlacionar a cistatina C, creatinina sérica e o clearance de creatinina pela Fórmula de Schwartz em pacientes com cistinose, com diferentes graus de função renal. MÉTODOS: Foram incluídos pacientes com menos de 18 anos de idade, com diferentes níveis de função renal, de acordo com o KDOQI em estágios 1 a 4. Nenhum dos pacientes estava em terapia de substituição renal. Foram medidos os seguintes parâmetros: cistatina C, creatinina sérica e o clearance de creatinina pela fórmula de Schwartz. RESULTADOS: Foram analisadas 103 amostras de sangue de 26 pacientes. Foi detectada correlação significativa entre creatinina sérica e cistatina C (r = 0,81, p < 0,0001, cistatina C e o clearance de creatinina pela fórmula de Schwartz (r = -0,84, p < 0,0001 e creatinina sérica e clearance de creatinina (r = -0,97, p < 0,0001. CONCLUSÕES: A medida da cistatina não mostrou nenhuma vantagem sobre a creatinina sérica e o clearance de creatinina pela fórmula de Schwartz em pacientes com cistinose para avaliar o ritmo de filtração glomerular. Este é o primeiro relato sobre o valor da creatinina sérica, do clearance de creatinina pela fórmula de Schwartz e da cistatina C em pacientes com cistinose.

  4. COMBINING MULTI-LEVEL AND NETWORK GOVERNANCE WITH A SPILLOVER EFFECT: THE CASE OF THE EUROPEAN “INNOVATION UNION” FLAGSHIP INITIATIVE

    Directory of Open Access Journals (Sweden)

    OANA-ANDREEA ION

    2011-04-01

    Full Text Available The purpose of this paper is to demonstrate the possibility of a theoretical up-grade to the framework offered by the theory of governance: from a middle-range theory to a full theory through adding a neofunctionalist component that would enhance its explanatory capabilities by projecting them at the systemic level. The authors have chosen, as a case study, the flagship initiative "Innovation Union" within the Europe 2020 Strategy; this initiative provides a set of actions that can be undertaken at different levels of political authority (supranational, national, etc. and involving several types of actors (state, supranational, non-state, etc., context which validates the theoretical components of governance, represented by multi-level governance and network governance. The authors consider that the integration of the research policy of the Member States will produce a spillover effect (in neofunctionalist terms on other policy areas; the argument is based on the fact that the Europe 2020 Strategy, in general, and the flagship initiative "Innovation Union", in particular, require concerted actions within different policy directions (research, education, industrial policy, fiscal policy, employment, communications, environment, etc., context that determines an "integration" trend of these policies on the basis of a spillover process. The authors believe that the integration of all policy areas involved in the flagship initiative "Innovation Union" would lead, through a spillover effect, to a better European economic integration. The normative foundation of the analysis is the Treaty of Lisbon, as the flagship initiative is part of the research and development policy of the European Union, in which the EU currently holds not only the competence to support, coordinate and complement the actions undertaken by the Member States, but also to define and implement programs.

  5. Source locations of continuous tremor by combined analyses of array and network seismometers : A case study for the 2011 eruption of Shinmoe-dake, Japan

    Science.gov (United States)

    Ichihara, Mie; Matsumoto, Satoshi

    2017-04-01

    The 2011 eruption of Shinmoe-dake, Japan, was one of the common cases in that geophysical monitoring system was improved after eruption became very active. We used tremor recorded after the main eruption phases by a dense seismic array and many network stations to calibrate the site effects and regional attenuation factor. The calibration was used in estimating the source locations of volcanic tremor before and during the eruption from the amplitude distribution at the limited available seismic stations. The stability of the algorithm was improved by the careful selection of time windows in which signal from a single source dominated. The result was compared with multi-parametric data including infrasound, tilt, and video records. The tremor source depth beneath the crater varied for one week before the onset of the eruption. Upward motion of the source from a depth to the shallow water table was found on three separate occasions, each of which occurred following shallow inflation sometimes with a minor eruption. This change in depth is interpreted as a result of fluid movement, which transported sufficient heat to trigger a larger eruption. In contrast to the upward motion of the source after the precursory events, the source tends to move downward after explosive eruptions. Such upward/downward movements could be used as indicators of how an eruption proceeds. Although seismic array processing methods are powerful tools for locating tremor, a dense array with a sufficient performance requires considerable effort to maintain and is rarely available, especially before a noticeable eruption occurs. This study demonstrates that even a seismic array deployed after an eruption is useful in assessing processes preceding the eruption.

  6. Using Self-Organizing Neural Network Map Combined with Ward's Clustering Algorithm for Visualization of Students' Cognitive Structural Models about Aliveness Concept.

    Science.gov (United States)

    Yorek, Nurettin; Ugulu, Ilker; Aydin, Halil

    2016-01-01

    We propose an approach to clustering and visualization of students' cognitive structural models. We use the self-organizing map (SOM) combined with Ward's clustering to conduct cluster analysis. In the study carried out on 100 subjects, a conceptual understanding test consisting of open-ended questions was used as a data collection tool. The results of analyses indicated that students constructed the aliveness concept by associating it predominantly with human. Motion appeared as the most frequently associated term with the aliveness concept. The results suggest that the aliveness concept has been constructed using anthropocentric and animistic cognitive structures. In the next step, we used the data obtained from the conceptual understanding test for training the SOM. Consequently, we propose a visualization method about cognitive structure of the aliveness concept.

  7. Tip Deflection Determination of a Barrel for the Effect of an Accelerating Projectile Before Firing Using Finite Element and Artificial Neural Network Combined Algorithm

    Directory of Open Access Journals (Sweden)

    Mehmet Akif Koç

    Full Text Available Abstract For realistic applications, design and control engineers have limited modelling options in dealing with some vibration problems that hold many nonlinearity such as non-uniform geometry, variable velocity loadings, indefinite damping cases, etc. For these reasons numerous time consuming experimental studies at high costs must be done for determining the actual behaviour such nonlinear systems. However, using advantages of multiple computational methods like Finite Element Method (FEM together with an Artificial Intelligence (ANN, many complicated engineering problems can be handled and solved to some extent. This study, proposes a new collective method to deal with the nonlinear vibrations of the barrels in order to fulfil accurate shooting expectancy. Using known analytical methods, in practical, to determine dynamic behaviour of the barrel beam is not possible for all conditions of firing that include numerous varieties of ammunition for different purposes, and each projectile of different ammunition has different mass and exit velocity. In order to cover all cases this study proposes a new method that combines a precise FEM with ANN, and can be used for determining the exact dynamic behaviour of a barrel for some cases and then for precisely predicting the behaviour for all other possible cases of firing. In this study, the whole nonlinear behaviour of an antiaircraft barrel were obtained with 3.5% accuracy errors by ANN trained by FEM using calculated analysis results of ammunitions for a particular range. The proposed FEM-ANN combined method can be very useful for design and control engineers in design and control of barrels in order to compensate the effect of nonlinear vibrations of a barrel for achieving a higher shooting accuracy; and can reduce high-cost experimental works.

  8. Tissue segmentation-assisted analysis of fMRI for human motor response: an approach combining artificial neural network and fuzzy C means.

    Science.gov (United States)

    Chiu, M J; Lin, C C; Chuang, K H; Chen, J H; Huang, K M

    2001-03-01

    The authors have developed an automated algorithm for segmentation of magnetic resonance images (MRI) of the human brain. They investigated the quantitative analysis of tissue-specific human motor response through an approach combining gradient echo functional MRI and automated segmentation analysis. Fifteen healthy volunteers, placed in a 1.5 T clinical MR imager, performed a self-paced finger opposition throughout the activation periods. T1-weighted images (WI), T2WI, and proton density WI were acquired for segmentation analysis. Single-slice axial T2* fast low-angle shot (FLASH) images were obtained during the functional study. Pixelwise cross-correlation analysis was performed to obtain an activation map. A cascaded algorithm, combining Kohonen feature maps and fuzzy C means, was applied for segmentation. After processing, masks for gray matter, white matter, small vessels, and large vessels were generated. Tissue-specific analysis showed a signal change rate of 4.53% in gray matter, 2.98% in white matter, 5.79% in small vessels, and 7.24% in large vessels. Different temporal patterns as well as different levels of activation were identified in the functional response from various types of tissue. High correlation exists between cortical gray matter and subcortical white matter (r = 0.957), while the vessel behaves somewhat different temporally. The cortical gray matter fits best to the assumed input function (r = 0.957) followed by subcortical white matter (r = 0.829) and vessels (r = 0.726). The automated algorithm of tissue-specific analysis thus can assist functional MRI studies with different modalities of response in different brain regions.

  9. Learning Networks, Networked Learning

    NARCIS (Netherlands)

    Sloep, Peter; Berlanga, Adriana

    2010-01-01

    Sloep, P. B., & Berlanga, A. J. (2011). Learning Networks, Networked Learning [Redes de Aprendizaje, Aprendizaje en Red]. Comunicar, XIX(37), 55-63. Retrieved from http://dx.doi.org/10.3916/C37-2011-02-05

  10. NASA's unique networking environment

    Science.gov (United States)

    Johnson, Marjory J.

    1988-01-01

    Networking is an infrastructure technology; it is a tool for NASA to support its space and aeronautics missions. Some of NASA's networking problems are shared by the commercial and/or military communities, and can be solved by working with these communities. However, some of NASA's networking problems are unique and will not be addressed by these other communities. Individual characteristics of NASA's space-mission networking enviroment are examined, the combination of all these characteristics that distinguish NASA's networking systems from either commercial or military systems is explained, and some research areas that are important for NASA to pursue are outlined.

  11. Manufacturing network evolution

    DEFF Research Database (Denmark)

    Yang, Cheng; Farooq, Sami; Johansen, John

    2011-01-01

    Purpose – This paper examines the effect of changes at the manufacturing plant level on other plants in the manufacturing network and also investigates the role of manufacturing plants on the evolution of a manufacturing network. Design/methodology/approach –The research questions are developed......, the complex phenomenon of a manufacturing network evolution is observed by combining the analysis of a manufacturing plant and network level. The historical trajectories of manufacturing networks that are presented in the case studies are examined in order to understand and determine the future shape...

  12. Efficacy of propidium iodide and FUN-1 stains for assessing viability in basidiospores of Rhizopogon roseolus.

    Science.gov (United States)

    Fernández-Miranda, Elena; Majada, Juan; Casares, Abelardo

    2017-01-01

    The use of spores in applications of ectomycorrhizal fungi requires information regarding spore viability and germination, especially in genera such as Rhizopogon with high rates of spore dormancy. The authors developed a protocol to assess spore viability of Rhizopogon roseolus using four vital stains to quantify spore viability and germination and to optimize storage procedures. They showed that propidium iodide is an excellent stain for quantifying nonviable spores. Observing red fluorescent intravacuolar structures following staining with 2-chloro-4-(2,3-dihydro-3-methyl-(benzo-1,3-thiazol-2-yl)-methylidene)-1-phenylquinolinium iodide (FUN-1) can help identify viable spores that are activated. At 6 mo and 1 y, the spores kept in a water suspension survived better than those left within intact, dry gasterocarps. Our work highlights the importance of temperature, nutrients, and vitamins for maturation and germination of spores of R. roseolus during 1 y of storage.

  13. Creating Evaluation Profiles for Games Designed to be Fun: An Interpretive Framework for Serious Game Mechanics

    DEFF Research Database (Denmark)

    Ulrich, Frank; Helms, Niels Henrik

    2017-01-01

    Background. Games can be great pedagogical tools for educators and students. COTS games (commercialoff-the-shelf) are designed for the pure purpose of leisure but can also contain educational value. Aim. In this paper, we address the potential of COTS games as serious games. We develop...... an interpretive evaluation framework that can identify the educational value in COTS games. Application. The presented framework can create evaluative profiles of the learning, social, game, and immersive mechanics of COTS games as educational tools. Moreover, the framework can position COTS games between four...... enables critical reflection on the game mechanics; thereby capturing the complexity of the game mechanics that makes COTS game both educational and fun to play....

  14. Fun Science: The Use of Variable Manipulation to Avoid Content Instruction

    Science.gov (United States)

    Peters-Burton, Erin E.; Hiller, Suzanne E.

    2013-02-01

    This study examined the beliefs and rationale pre-service elementary teachers used to choose activities for upper-elementary students in a 1-week intensive science camp. Six undergraduate elementary pre-service teachers were observed as they took a semester-long science methods class that culminated in a 1-week science camp. This qualitative, phenomenological study found that counselors chose activities with the possibility of fun being a priority rather than teaching content, even after they were confronted with campers who demanded more content. Additionally, all six of the counselors agreed that activities involving variable manipulation were the most successful, even though content knowledge was not required to complete the activities. The counselors felt the variable manipulation activities were successful because students were constructing products and therefore getting to the end of the activity. Implications include building an awareness of the complexity of self-efficacy of science teaching and outcome expectancy to improve teacher education programs.

  15. Efeito do transplante renal na morfologia e função cardíaca

    OpenAIRE

    Francival Leite de Souza; Francisco das Chagas Monteiro Junior; Natalino Salgado Filho

    2012-01-01

    O envolvimento cardíaco é muito frequente nos portadores de doença renal crônica em diálise. O transplante renal resulta em redução da morbidade e mortalidade em relação aos pacientes em diálise. O objetivo desta revisão foi abordar o efeito do transplante renal na estrutura e função cardíaca avaliada pela ecodopplercardiografia. Desde a década de 1980, os estudos já demonstravam tendência à melhora nos parâmetros cardíacos após o transplante renal. Com a melhora dos métodos de imagens ao eco...

  16. FUN3D Analyses in Support of the Second Aeroelastic Prediction Workshop

    Science.gov (United States)

    Chwalowski, Pawel; Heeg, Jennifer

    2016-01-01

    This paper presents the computational aeroelastic results generated in support of the second Aeroelastic Prediction Workshop for the Benchmark Supercritical Wing (BSCW) configurations and compares them to the experimental data. The computational results are obtained using FUN3D, an unstructured grid Reynolds- Averaged Navier-Stokes solver developed at NASA Langley Research Center. The analysis results include aerodynamic coefficients and surface pressures obtained for steady-state, static aeroelastic equilibrium, and unsteady flow due to a pitching wing or flutter prediction. Frequency response functions of the pressure coefficients with respect to the angular displacement are computed and compared with the experimental data. The effects of spatial and temporal convergence on the computational results are examined.

  17. The ELEPHANT criteria in medical education: can medical education be fun?

    Science.gov (United States)

    Gifford, Hugh; Varatharaj, Aravinthan

    2010-01-01

    'Hilarity and a good nature [and] a breezy cheerfulness help enormously in the study and in the practice of medicine,' said Sir William Osler, Regius Professor of Medicine at Oxford, pioneering medical educationalist, and arguably one of the greatest physicians of all time (Osler W. 1905 ). We present evidence that (1) Encouraging Learning, (2) Entertaining People, and (3) Having a Nice Time are dangerously powerful adjuncts to medical education. These are, by acronym, the ELEPHANT criteria. Encouraging is the motivating heart of the matter. Entertainment engages the mind and has been shown to enhance working memory and recall. Enjoyment is associated with deep learning, which comes with a whole host of benefits. However, learning in fear and misery can be an effective tool--but for other reasons--and the pessimistic personality type may respond badly to 'fun learning.' Even so, medical education that fulfills the ELEPHANT criteria can be an effective tool in training young doctors.

  18. Paratireóides: estrutura, funções e patologia

    OpenAIRE

    Prospero,José Donato de; Baptista,Pedro Pericles Ribeiro; Amary,Maria Fernanda Carriel; Santos,Priscila Pizzo Crêm dos

    2009-01-01

    Os autores tecem considerações sobre a estrutura e funções normais das glândulas paratireóides como introdução à patologia e as repercussões clinico - patológicas tanto do excesso como da redução do paratormônio. Maior ênfase é dedicada ao hiperparatireoidismo primário quanto às causas, a fisiopatologia das alterações, os aspectos anatomopatológicos macro e microscópicos das lesões e sua patogenia, na "Osteite fibrocistica" ou "doença de von Recklinghausen dos ossos" com a correlação aos aspe...

  19. Computational Analysis of the Transonic Dynamics Tunnel Using FUN3D

    Energy Technology Data Exchange (ETDEWEB)

    Chwalowski, Pawel; Quon, Eliot; Brynildsen, Scott E.

    2016-01-04

    This paper presents results from an explanatory two-year effort of applying Computational Fluid Dynamics (CFD) to analyze the empty-tunnel flow in the NASA Langley Research Center Transonic Dynamics Tunnel (TDT). The TDT is a continuous-flow, closed circuit, 16- x 16-foot slotted-test-section wind tunnel, with capabilities to use air or heavy gas as a working fluid. In this study, experimental data acquired in the empty tunnel using the R-134a test medium was used to calibrate the computational data. The experimental calibration data includes wall pressures, boundary-layer profiles, and the tunnel centerline Mach number profiles. Subsonic and supersonic flow regimes were considered, focusing on Mach 0.5, 0.7 and Mach 1.1 in the TDT test section. This study discusses the computational domain, boundary conditions, and initial conditions selected in the resulting steady-state analyses using NASA's FUN3D CFD software.

  20. Does the Animal Fun program improve social-emotional and behavioural outcomes in children aged 4-6 years?

    NARCIS (Netherlands)

    Piek, J.P.; Kane, R.; Rigoli, D.; McLaren, S.; Roberts, C.M.; Rooney, R.; Jensen, L.; Dender, A.; Packer, T.L.; Straker, L.

    2015-01-01

    Animal Fun was designed to enhance motor and social development in young children. Its efficacy in improving motor skills was presented previously using a randomised controlled trial and a multivariate nested cohort design. Based on the Environmental Stress Hypothesis, it was argued that the program

  1. Looking Forward to Monday Morning: Ideas for Recognition and Appreciation Activities and Fun Things to Do at Work for Educators

    Science.gov (United States)

    Hodges, Diane

    2004-01-01

    In this book, a former human resources director and school administrator, shares numerous staff appreciation and recognition activities that can be implemented to promote a positive environment and inspire staff members to look forward to the beginning of each new week. This insightful text presents low-cost, fun ideas that will help staff…

  2. Predictive power of task orientation, general self-efficacy and self-determined motivation on fun and boredom

    Directory of Open Access Journals (Sweden)

    Lorena Ruiz-González

    2015-12-01

    Full Text Available Abstract The aim of this study was to test the predictive power of dispositional orientations, general self-efficacy and self-determined motivation on fun and boredom in physical education classes, with a sample of 459 adolescents between 13 and 18 with a mean age of 15 years (SD = 0.88. The adolescents responded to four Likert scales: Perceptions of Success Questionnaire, General Self-Efficacy Scale, Sport Motivation Scale and Intrinsic Satisfaction Questionnaire in Sport. The results showed the structural regression model showed that task orientation and general self-efficacy positively predicted self-determined motivation and this in turn positively predicted more fun and less boredom in physical education classes. Consequently, the promotion of an educational task-oriented environment where learners perceive their progress and make them feel more competent, will allow them to overcome the intrinsically motivated tasks, and therefore they will have more fun. Pedagogical implications for less boredom and more fun in physical education classes are discussed.

  3. MODEL PENGEMBANGAN PERMAINAN FUN HOCKEY PADA SISWA KELAS XI SMA NEGERI 1 BAWANG KECAMATAN BAWANG KABUPATEN BATANG TAHUN 2014.

    Directory of Open Access Journals (Sweden)

    Wahyu Putri Vembriana Dewi

    2015-07-01

    Full Text Available The purpose of this study was to produce a model of the development of the game Fun Hockey in the XI student class of SMAN 1 Bawang, Bawang Subdistrict, Batang. The method used is the development of Borg & Gall, namely: (1 to analyze the products that will be developed that are obtained from the information collection, including field observations and study of literature, (2 develop a form of initial product model game Fun Hockey, (3 expert validation test which uses a physical education expert (hockey skills and learning experts physical education in high school, as well as small scale test, using questionnaires and consultations later in the analysis, (4 the first product revision, revision of the product based on the results of expert evaluation and testing of small-scale (12 students, (5 field trials (28 students, (6 the revision of the final product is done based on the results of field trials, (7 the outcome Fun Hockey game for students of XI class generated through the revision of field trials. From the data on the differences can concluded that the Fun Hockey Game model development can be implemented as an alternative model for students learning physical education XI class SMAN 1 Bawang, Bawang Subdistrict, Batang.

  4. Teaching Case: IS Security Requirements Identification from Conceptual Models in Systems Analysis and Design: The Fun & Fitness, Inc. Case

    Science.gov (United States)

    Spears, Janine L.; Parrish, James L., Jr.

    2013-01-01

    This teaching case introduces students to a relatively simple approach to identifying and documenting security requirements within conceptual models that are commonly taught in systems analysis and design courses. An introduction to information security is provided, followed by a classroom example of a fictitious company, "Fun &…

  5. Soil science is way more fun than a proper job (Philippe Duchaufour Medal Lecture)

    Science.gov (United States)

    Smith, Pete

    2017-04-01

    Having now worked in soil science and climate change for over 20 years, I find myself giving one of the "old man / old woman" lectures at the EGU2017. You probably get picked to do this when your peers think that you are about to die soon, so I had better make the most of my time left! We are very fortunate to have a career in science, and to belong to the soils, and the wider, biogeosciences communities. If ever you get fed up with your teaching load, with your experiment that won't work, your model that you can't get running, or your paper that reviewers do not realise for the gem that you know it is, remember that we could be doing a 9 to 5 job, stuck in an office, with no opportunities to meet, talk and have fun with others from around the world with whom we share the same passion. I hope you enjoy your research careers and the time you spend with your work friends as much as I have. In this presentation I will reflect on how much I have learned about soils, climate, and the politics of how things get done over the past 20 years, and I will pick out some changes in our understanding of soils, and their role in the world as I go. I will draw on examples not only from my own work, but those of others, and will reflect on the some of the fun I have had while doing this "job".

  6. FUn: A Framework for Interactive Visualizations of Large, High Dimensional Datasets on the Web.

    Science.gov (United States)

    Probst, Daniel; Reymond, Jean-Louis

    2017-11-24

    During the past decade, big data has become a major tool in scientific endeavors. While statistical methods and algorithms are well-suited for analyzing and summarizing enormous amounts of data, the results do not allow for a visual inspection of the entire data. Current scientific software, including R packages and Python libraries such as ggplot2, matplotlib, and plot.ly, do not support interactive visualizations of datasets exceeding 100,000 data points on the web. Other solutions enable the web-based visualization of big data only through data reduction or statistical representations. However, recent hardware developments, especially advancements in graphical processing units (GPUs), allow for the rendering of millions of data points on a wide range of consumer hardware like laptops, tablets and mobile phones. Similar to the challenges and opportunities brought to virtually every scientific field by big data, both the visualization of and interaction with copious amounts of data is both demanding and holds great promise. Here we present FUn, a framework consisting of a client (Faerun) and server (Underdark) module, facilitating the creation of web-based, interactive 3D visualizations of large data sets, enabling record level visual inspection. We also introduce a reference implementation providing access to SureChEMBL, a database containing patent information on more than 17 million chemical compounds. The source code and the most recent builds of Faerun and Underdark, Lore.js and the data preprocessing toolchain used in the reference implementation, are available on the project website (http://doc.gdb.tools/fun/). daniel.probst@dcb.unibe.ch, jean-louis.reymond@dcb.unibe.ch. Supplementary data are available at Bioinformatics online.

  7. Systematic review and network meta-analysis of the efficacy and safety of tumour necrosis factor inhibitor–methotrexate combination therapy versus triple therapy in rheumatoid arthritis

    Science.gov (United States)

    Fleischmann, Roy; Tongbram, Vanita; van Vollenhoven, Ronald; Tang, Derek H; Chung, James; Collier, David; Urs, Shilpa; Ndirangu, Kerigo; Wells, George; Pope, Janet

    2017-01-01

    Objective Clinical trials have not consistently demonstrated differences between tumour necrosis factor inhibitor (TNFi) plus methotrexate and triple therapy (methotrexate plus hydroxychloroquine plus sulfasalazine) in rheumatoid arthritis (RA). The study objective was to estimate the efficacy, radiographic benefits, safety and patient-reported outcomes of TNFi–methotrexate versus triple therapy in patients with RA. Methods A systematic review and network meta-analysis (NMA) of randomised controlled trials of TNFi–methotrexate or triple therapy as one of the treatment arms in patients with an inadequate response to or who were naive to methotrexate was conducted. American College of Rheumatology 70% response criteria (ACR70) at 6 months was the prespecified primary endpoint to evaluate depth of response. Data from direct and indirect comparisons between TNFi–methotrexate and triple therapy were pooled and quantitatively analysed using fixed-effects and random-effects Bayesian models. Results We analysed 33 studies in patients with inadequate response to methotrexate and 19 in patients naive to methotrexate. In inadequate responders, triple therapy was associated with lower odds of achieving ACR70 at 6 months compared with TNFi–methotrexate (OR 0.35, 95% credible interval (CrI) 0.19 to 0.64). Most secondary endpoints tended to favour TNFi–methotrexate in terms of OR direction; however, no clear increased likelihood of achieving these endpoints was observed for either therapy. The odds of infection were lower with triple therapy than with TNFi−methotrexate (OR 0.08, 95% CrI 0.00 to 0.57). There were no differences observed between the two regimens in patients naive to methotrexate. Conclusions In this NMA, triple therapy was associated with 65% lower odds of achieving ACR70 at 6 months compared with TNFi–methotrexate in patients with inadequate response to methotrexate. Although secondary endpoints numerically favoured TNFi–methotrexate, no

  8. Um estudo de funções polinomiais de 1º e 2º graus em ambiente informatizado

    Directory of Open Access Journals (Sweden)

    João Batista Silva Caires

    2012-12-01

    Full Text Available Neste artigo pretende-se relatar as investigações realizadas sobre o ensino-aprendizagem de funções polinomiais do primeiro e do segundo graus com o uso do computador. O conteúdo de funções tem importância nos estudos em Matemática, pois aparece em todos os níveis de ensino da educação básica, tanto no segundo segmento do ensino fundamental, quanto e principalmente no ensino médio. Para alcançar os objetivos que tinham como foco principal analisar como se dava a aprendizagem de funções em ambientes informatizados e, como foco secundário, verificar como estudantes construíam o conceito de funções, utilizando interpretação de gráficos em ambientes informatizados e, também, verificar se esses estudantes conseguem aplicar os conceitos aprendidos na resolução de atividades no computador. A pesquisa teve abordagem qualitativa, com metodologia da pesquisa-ação, realizada como estudo de caso em uma turma com dezenove alunos, desenvolvidas em cinco aulas de cinqüenta minutos, no Laboratório de Informática de uma Escola Pública Estadual Baiana, na cidade de Jequié. Os dados foram complementados com entrevistas realizadas com dois alunos da turma pesquisada e dois professores de matemática da escola. Com os resultados percebeu-se que as atividades de ensino associadas ao uso do computador propiciaram a construção de conceitos sobre função, pelos discentes, e que houve melhoria perceptível na aprendizagem das funções trabalhadas, tanto que desapertou o interesse de professores de matemática da escola em utilizar o laboratório de informática em suas aulas.Palavras-chave: matemática; educação matemática; função; informática.

  9. Stochastic Pooling Networks

    OpenAIRE

    McDonnell, Mark D; Amblard, Pierre-Olivier; Stocks, Nigel G.

    2009-01-01

    We introduce and define the concept of a stochastic pooling network (SPN), as a model for sensor systems where redundancy and two forms of 'noise' -- lossy compression and randomness -- interact in surprising ways. Our approach to analyzing SPNs is information theoretic. We define an SPN as a network with multiple nodes that each produce noisy and compressed measurements of the same information. An SPN must combine all these measurements into a single further compressed network output, in a w...

  10. Designing of an artificial neural network model to evaluate the association of three combined Y-specific microsatellite loci on the actual and predicted postthaw motility in crossbred bull semen.

    Science.gov (United States)

    Deb, Rajib; Singh, Umesh; Raja, Thirvvothur Venkatesan; Kumar, Sushil; Tyagi, Shrikant; Alyethodi, Rafeeque R; Alex, Rani; Sengar, Gyanendra; Sharma, Sheetal

    2015-06-01

    The freezing of bull semen significantly hamper the motility of sperm which reduces the conception rate in dairy cattle. The prediction of postthaw motility (PTM) before freezing will be useful to take the decision on discarding or freezing of the germplasm. The artificial neural network (ANN) methodology found to be useful in prediction and classification problems related to animal science, and hence, the present study was undertaken to compare the efficiency of ANN in prediction of PTM on the basis of the number of ejaculates, volume, and concentration of sperms. The combined effect of Y-specific microsatellite alleles on the actual and predicted PTM was also studied. The results revealed that the prediction accuracy of PTM based on the semen quality parameters was comparatively lower because of higher variability in the data set. The ANN gave better prediction accuracy (34.88%) than the multiple regression analysis models (32.04%). The root mean square error was lower for ANN (8.4353) than that in the multiple regression analysis (8.6168). The haplotype or combined effect of microsatellite alleles on actual and predicted PTM was found to be highly significant (P bulls. Copyright © 2015 Elsevier Inc. All rights reserved.

  11. Optimization of solid-phase extraction using artificial neural networks and response surface methodology in combination with experimental design for determination of gold by atomic absorption spectrometry in industrial wastewater samples.

    Science.gov (United States)

    Ebrahimzadeh, H; Tavassoli, N; Sadeghi, O; Amini, M M

    2012-08-15

    Solid-phase extraction (SPE) is often used for preconcentration and determination of metal ions from industrial and natural samples. A traditional single variable approach (SVA) is still often carried out for optimization in analytical chemistry. Since there is always a risk of not finding the real optimum by single variation method, more advanced optimization approaches such as multivariable approach (MVA) should be applied. Applying MVA optimization can save both time and chemical materials, and consequently decrease analytical costs. Nowadays, using artificial neural network (ANN) and response surface methodology (RSM) in combination with experimental design (MVA) are rapidly developing. After prediction of model equation in RSM and training of artificial neurons in ANNs, the products were used for estimation of the response of the 27 experimental runs. In the present work, the optimization of SPE using single variation method and optimization by ANN and RSM in combination with central composite design (CCD) are compared and the latter approach is practically illustrated. Copyright © 2012 Elsevier B.V. All rights reserved.

  12. Extracorporeal shock wave therapy, ultrasound-guided percutaneous lavage, corticosteroid injection and combined treatment for the treatment of rotator cuff calcific tendinopathy: a network meta-analysis of RCTs.

    Science.gov (United States)

    Arirachakaran, Alisara; Boonard, Manusuk; Yamaphai, Sarunpong; Prommahachai, Akom; Kesprayura, Suraphol; Kongtharvonskul, Jatupon

    2017-04-01

    Treatment of calcific tendinitis using extracorporeal shock wave therapy (ESWT), ultrasound-guided percutaneous lavage (UGPL or barbotage), subacromial corticosteroid injection (SAI) and combined treatment is still controversial. This systematic review and meta-regression aimed to compare clinical outcomes between treatments. Relevant RCTs were identified using PubMed and Scopus search engines to date of September 23, 2015. Seven of 920 studies identified were eligible. Compared to the other treatments, the results of this study indicate that ESWT significantly improved CMS and VAS when compared to placebo. Barbotage plus ESWT significantly improved CMS, VAS and decreased size of calcium deposit when compared to ESWT, while barbotage plus SAI significantly improved CMS and decreased size of calcium deposit when compared to SAI. There have no different adverse effects of all treatment groups. Multiple active treatment comparisons indicated that barbotage plus SAI significantly improved VAS and size of calcium deposit when compared to other groups, while barbotage plus SAI improved CMS when compared to other groups. But there was no significant difference. The network meta-analysis suggested that combined US-guided needling and subacromial corticosteroid injection significantly decreased shoulder pain VAS, improved CMS score and decreased the size of calcium deposits, while also lowering risks of adverse event when compared to barbotage plus ESWT, ESWT and subacromial corticosteroid injection; therefore, the evidence points to UGPL as being the treatment of choice for nonsurgical options of treatment in calcific tendinitis of the shoulder. Level of evidence I.

  13. Lymphatic Education & Research Network

    Science.gov (United States)

    Lymphatic Education & Research Network Donate Now Become a Supporting Member X Living with LYMPHEDEMA AND Lymphatic Disease FAQs About ... December 8, 2017 11.08.2017 The Lymphatic Education & Research Network… Read More > ASRM LE&RN Combined ...

  14. Quantifying randomness in real networks

    Science.gov (United States)

    Orsini, Chiara; Dankulov, Marija M.; Colomer-de-Simón, Pol; Jamakovic, Almerima; Mahadevan, Priya; Vahdat, Amin; Bassler, Kevin E.; Toroczkai, Zoltán; Boguñá, Marián; Caldarelli, Guido; Fortunato, Santo; Krioukov, Dmitri

    2015-10-01

    Represented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the dk-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks--the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain--and find that many important local and global structural properties of these networks are closely reproduced by dk-random graphs whose degree distributions, degree correlations and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation of network randomness, and release software to generate dk-random graphs.

  15. Cognitive-Behavioural Analysis System of Psychotherapy (CBASP), a drug, or their combination: differential therapeutics for persistent depressive disorder: a study protocol of an individual participant data network meta-analysis.

    Science.gov (United States)

    Furukawa, Toshi A; Schramm, Elisabeth; Weitz, Erica S; Salanti, Georgia; Efthimiou, Orestis; Michalak, Johannes; Watanabe, Norio; Cipriani, Andrea; Keller, Martin B; Kocsis, James H; Klein, Daniel N; Cuijpers, Pim

    2016-05-04

    Despite important advances in psychological and pharmacological treatments of persistent depressive disorders in the past decades, their responses remain typically slow and poor, and differential responses among different modalities of treatments or their combinations are not well understood. Cognitive-Behavioural Analysis System of Psychotherapy (CBASP) is the only psychotherapy that has been specifically designed for chronic depression and has been examined in an increasing number of trials against medications, alone or in combination. When several treatment alternatives are available for a certain condition, network meta-analysis (NMA) provides a powerful tool to examine their relative efficacy by combining all direct and indirect comparisons. Individual participant data (IPD) meta-analysis enables exploration of impacts of individual characteristics that lead to a differentiated approach matching treatments to specific subgroups of patients. We will search for all randomised controlled trials that compared CBASP, pharmacotherapy or their combination, in the treatment of patients with persistent depressive disorder, in Cochrane CENTRAL, PUBMED, SCOPUS and PsycINFO, supplemented by personal contacts. Individual participant data will be sought from the principal investigators of all the identified trials. Our primary outcomes are depression severity as measured on a continuous observer-rated scale for depression, and dropouts for any reason as a proxy measure of overall treatment acceptability. We will conduct a one-step IPD-NMA to compare CBASP, medications and their combinations, and also carry out a meta-regression to identify their prognostic factors and effect moderators. The model will be fitted in OpenBUGS, using vague priors for all location parameters. For the heterogeneity we will use a half-normal prior on the SD. This study requires no ethical approval. We will publish the findings in a peer-reviewed journal. The study results will contribute to more

  16. Fulcrum Network Codes

    DEFF Research Database (Denmark)

    2015-01-01

    Fulcrum network codes, which are a network coding framework, achieve three objectives: (i) to reduce the overhead per coded packet to almost 1 bit per source packet; (ii) to operate the network using only low field size operations at intermediate nodes, dramatically reducing complexity...... in the network; and (iii) to deliver an end-to-end performance that is close to that of a high field size network coding system for high-end receivers while simultaneously catering to low-end ones that can only decode in a lower field size. Sources may encode using a high field size expansion to increase...... the number of dimensions seen by the network using a linear mapping. Receivers can tradeoff computational effort with network delay, decoding in the high field size, the low field size, or a combination thereof....

  17. Os chamados castella do Sudoeste: arquitectura, cronología e funções

    Directory of Open Access Journals (Sweden)

    Fabião, Carlos

    2002-12-01

    Full Text Available The well known tower-type buildings seem to be a peculiar issue of the Southwest Roman settlement of the Iberian Peninsula, not related at all with pre-Roman architectural traditions. Despite the excavation undertook in some of them, the lack of publication leaves to many unanswered questions. We may say that they were not military buildings, despite the semblance with Castelo da Lousa, Mourão, a true Roman fort, near Guadiana's river; we also know that there's a widespread of this particular settlement type and not the specific concentration on the Castro Verde / Almodovar area. It seems that this peculiar settlement pattern was developed between the second half of the first Century BC and the first half of the first A. D., maybe before the setting of the classical rural farms and uillae.De há longa data são conhecidos vários edifícios de feição turriforme e organização complexa no Sudoeste Peninsular. Embora alguns tenham sido objecto de investigação, a ausência de publicação extensa dos dados que forneceram, inibe uma correcta interpretação das suas funções e tem alimentado um longo debate inconclusivo. No estado actual dos conhecimentos, parece claro que estes edifícios são romanos, sem nenhuma ligação com o mundo indígena; não terão tido uma função militar, em sentido estrito, contrariamente ao que sucede com o Castelo da Lousa, Mourão, uma verdadeira fortaleza, bem distinta dos restantes conhecidos; resulta claro, hoje, que a suposta concentração deste tipo de arquitectura numa região concreta será mais aparente que real. Constituem um modelo de povoamento ensaiado pelos romanos no Sudoeste num lapso de tempo compreendido entre a Segunda metade do século I a. C. e os inícios do I d.C, provavelmente, antecedendo a estabilização do modelo rural clássico.

  18. Clarifying off-target effects for torcetrapib using network pharmacology and reverse docking approach

    Directory of Open Access Journals (Sweden)

    Fan Shengjun

    2012-12-01

    Full Text Available Abstract Background Torcetrapib, a cholesteryl ester transfer protein (CETP inhibitor which raises high-density lipoprotein (HDL cholesterol and reduces low-density lipoprotein (LDL cholesterol level, has been documented to increase mortality and cardiac events associated with adverse effects. However, it is still unclear the underlying mechanisms of the off-target effects of torcetrapib. Results In the present study, we developed a systems biology approach by combining a human reassembled signaling network with the publicly available microarray gene expression data to provide unique insights into the off-target adverse effects for torcetrapib. Cytoscape with three plugins including BisoGenet, NetworkAnalyzer and ClusterONE was utilized to establish a context-specific drug-gene interaction network. The DAVID functional annotation tool was applied for gene ontology (GO analysis, while pathway enrichment analysis was clustered by ToppFun. Furthermore, potential off-targets of torcetrapib were predicted by a reverse docking approach. In general, 10503 nodes were retrieved from the integrative signaling network and 47660 inter-connected relations were obtained from the BisoGenet plugin. In addition, 388 significantly up-regulated genes were detected by Significance Analysis of Microarray (SAM in adrenal carcinoma cells treated with torcetrapib. After constructing the human signaling network, the over-expressed microarray genes were mapped to illustrate the context-specific network. Subsequently, three conspicuous gene regulatory networks (GRNs modules were unearthed, which contributed to the off-target effects of torcetrapib. GO analysis reflected dramatically over-represented biological processes associated with torcetrapib including activation of cell death, apoptosis and regulation of RNA metabolic process. Enriched signaling pathways uncovered that IL-2 Receptor Beta Chain in T cell Activation, Platelet-Derived Growth Factor Receptor (PDGFR beta

  19. Evaluation of Pore Networks in Caprocks at Geologic Storage Sites: A Combined Study using High Temperature and Pressure Reaction Experiments, Small Angle Neutron Scattering, and Focused Ion Beam-Scanning Electron Microscopy

    Science.gov (United States)

    Mouzakis, K. M.; Sitchler, A.; Wang, X.; McCray, J. E.; Kaszuba, J. P.; Rother, G.; Dewers, T. A.; Heath, J. E.

    2011-12-01

    Low permeability rock units, often shales or mudstones, that overlie geologic formations under consideration for CO2 sequestration will help contain injected CO2. CO2 that does flow through these rocks will dissolve into the porewaters, creating carbonic acid lowering the pH. This perturbation of the system may result in mineral dissolution or precipitation, which can change the pore structure and impact the flow properties of the caprocks. In order to investigate the impacts that reaction can have on caprock pore structure, we performed a combination of high pressure high temperature reaction experiments, small angle neutron scattering (SANS) experiments and high resolution focused ion beam-scanning electron microscope (FIB-SEM) imaging on samples from the Gothic shale and Marine Tuscaloosa Group. Small angle neutron scattering was performed on unreacted and reacted caprocks at the High Flux Isotope Reactor at Oak Ridge National Laboratory. New precipitates and pores are observed in high-resolution images of the reacted samples. The precipitates have been preliminarily identified as gypsum or anhydrite, and sulfide minerals. Results from small angle neutron scattering, a technique that provides information about pores and pore/mineral interfaces at scales ~ 5 to 300 nm, show an increased porosity and specific surface area after reaction with brine and CO2. However, there appear to be differences in how the pore networks change between the two samples that are related to sample mineralogy and original pore network structure. Changes to pores and formation of new pores may lead to different capillary sealing behavior and permeability. This combination of controlled laboratory experiments, neutron scattering and high-resolution imaging provides detailed information about the geochemical processes that occur at the pore scale as CO2 reacts with rocks underground. Such information is integral to the evaluation of large-scale CO2 sequestration as a feasible technology

  20. Global Operations Networks

    DEFF Research Database (Denmark)

    In the current context of global economic liberalisation and technological advancements, industrial companies are less likely to generate value in the traditional vertically integrated chain. Instead, they are doing so by means of elaborate cross-border and cross-organisational networks. As a rule...... and processes in global operations networks, and • Trajectories and reconfiguration of global operations networks. The themes are intended to incorporate elements which in combination provide a comprehensive multidisciplinary view on operations networks. Behind these themes lay clusters of questions and topics...

  1. Estudo de descontinuidades crustais na província borborema usando a função do receptor

    OpenAIRE

    Pavão, Cesar Garcia

    2011-01-01

    As estimativas das espessuras da crosta, da crosta superior e da razão V p=V s são essenciais para o detalhamento de estruturas e feições geológicas, além de corroborarem para o entendimento da evolução tectônica regional. O estudo da crosta usando Função do Receptor é realizado com a onda P de um telessismo que atinge uma interface, sob a estação, com um ângulo próximo a vertical. Através da deconvolução da componente horizontal pela vertical, obtém-se a Função do Receptor. O sismograma sint...

  2. Conceito e evolução da função logística

    OpenAIRE

    Servera-Francés, David

    2011-01-01

    A função logística adquiriu, nos últimos anos, uma importância máxima na competitividade das empresas. Em especial por sua capacidade para gerar valor para o consumidor final. Esta importância, aliada à confusão de termos existentes (logística, transporte, distribuição física, Supply Chain Management...), nos levou a realizar o presente trabalho, com o qual pretendemos, através da revisão da literatura, oferecer maior claridade sobre o conceito de função logística e sua evolução histórica....

  3. Estresse e função reprodutiva feminina Stress and female reproductive function

    Directory of Open Access Journals (Sweden)

    Simone da Nóbrega Tomaz Moreira

    2005-03-01

    Full Text Available Este artigo discute a relação do estresse sobre a função reprodutiva, considerando que a infertilidade pode ter causas psicológicas (hipótese da psicogênese ou pode ser a origem do estresse psicológico. A presença do estresse tem o potencial de ativar o eixo hipotálamo-hipófise-adrenal, o qual, inibe o eixo hipotálamo-hipófise-ovariano, levando à paralisação temporária das menstruações. Esse processo pode resultar em infertilidade transitória para as mulheres. Os autores enfatizam a necessidade de uma abordagem psicológica nos serviços de reprodução, objetivando trabalhar as tensões e frustrações advindas da infertilidade e do seu tratamento.This article discusses the relationship between stress and reproduction considering that infertility could bear psychological causes (psychogenesis hypothesis or could be caused by psychological stress. Stress has the potential of activating the hypothalamus-hypophisis-adrenal axis inhibiting the hypothalamus-hypophisis-ovarian axis leading to temporary menstruation paralysis. This process could result in a transitory infertility of women. The authors emphasize the need of a psychological approach in reproduction services with the objective of treating tensions and frustrations derived from infertility and its treatment.

  4. Aeroacoustic Simulations of a Nose Landing Gear Using FUN3D on Pointwise Unstructured Grids

    Science.gov (United States)

    Vatsa, Veer N.; Khorrami, Mehdi R.; Rhoads, John; Lockard, David P.

    2015-01-01

    Numerical simulations have been performed for a partially-dressed, cavity-closed (PDCC) nose landing gear configuration that was tested in the University of Florida's open-jet acoustic facility known as the UFAFF. The unstructured-grid flow solver FUN3D is used to compute the unsteady flow field for this configuration. Mixed-element grids generated using the Pointwise(TradeMark) grid generation software are used for these simulations. Particular care is taken to ensure quality cells and proper resolution in critical areas of interest in an effort to minimize errors introduced by numerical artifacts. A hybrid Reynolds-averaged Navier-Stokes/large eddy simulation (RANS/LES) turbulence model is used for these simulations. Solutions are also presented for a wall function model coupled to the standard turbulence model. Time-averaged and instantaneous solutions obtained on these Pointwise grids are compared with the measured data and previous numerical solutions. The resulting CFD solutions are used as input to a Ffowcs Williams-Hawkings noise propagation code to compute the farfield noise levels in the flyover and sideline directions. The computed noise levels compare well with previous CFD solutions and experimental data.

  5. When the going gets tough...: Self-motivation is associated with invigoration and fun.

    Science.gov (United States)

    Kazén, Miguel; Kuhl, Julius; Leicht, Eva-Maria

    2015-11-01

    Personality systems interaction (PSI) theory postulates two executive control modes in volitional action: Self-control and self-regulation (self-motivation). Self-control should deplete energy whereas self-motivation should maintain energy and result in invigoration. There were three groups of participants: Self-control, self-motivation, and pretend, who performed a resource-demanding Stroop-Shift and an anagram task. Performance and energy expenditure were examined in each task. Compared to the other groups, the self-motivation group showed increments in blood glucose throughout the experiment, indicating invigoration, and had better performance on the difficult Stroop-Shift task than the self-control group. Additionally, for the self-motivation group anagram performance correlated with less effort and ease of concentration and was moderated by fun in the task. These results are consonant with the predictions of PSI and self-determination theories. It is concluded that self-control depletes resources whereas self-motivation is associated with invigoration in carrying resource-demanding tasks.

  6. Emergent complex network geometry.

    Science.gov (United States)

    Wu, Zhihao; Menichetti, Giulia; Rahmede, Christoph; Bianconi, Ginestra

    2015-05-18

    Networks are mathematical structures that are universally used to describe a large variety of complex systems such as the brain or the Internet. Characterizing the geometrical properties of these networks has become increasingly relevant for routing problems, inference and data mining. In real growing networks, topological, structural and geometrical properties emerge spontaneously from their dynamical rules. Nevertheless we still miss a model in which networks develop an emergent complex geometry. Here we show that a single two parameter network model, the growing geometrical network, can generate complex network geometries with non-trivial distribution of curvatures, combining exponential growth and small-world properties with finite spectral dimensionality. In one limit, the non-equilibrium dynamical rules of these networks can generate scale-free networks with clustering and communities, in another limit planar random geometries with non-trivial modularity. Finally we find that these properties of the geometrical growing networks are present in a large set of real networks describing biological, social and technological systems.

  7. Função motora e qualidade de vida de indivíduos com paralisia cerebral

    Directory of Open Access Journals (Sweden)

    Maria Tereza Artero Prado

    2013-08-01

    Full Text Available Objetivo: Avaliar e correlacionar a função motora grossa e a qualidade de vida de crianças com PC. Métodos: Foi verificado em 20 crianças o tipo clínico e anatômico, a função motora grossa pela GMFM (Gross Motor Function Measure e a qualidade de vida pelo Questionário de Qualidade de Vida Pediátrico (PedsQL. Foi utilizado o teste de Shapiro-Wilk e o de Kruskal-Wallis. Para correlacionar utilizou-se da Análise de Correlação Canônica, o teste da Razão de Verossimilhança e a Estatística Multivariada de Wilks Lambda, com nível de significância de 5%. Resultados: Apresentaram idade média de 8,4±4,03 anos. Os indivíduos com hemiparesia espástica apresentaram função motora significativamente maior que os com tetraparesia atetóide e espástica. Verificou-se a existência de alta correlação canônica significativa entre as variáveis da GMFM e PedsQL. Conclusão: Os achados demonstram que quanto maior for o comprometimento da função motora grossa menor será a sua qualidade de vida.

  8. Using FUN3D for Aeroelastic, Sonic Boom, and AeroPropulsoServoElastic (APSE) Analyses of a Supersonic Configuration

    Science.gov (United States)

    Silva, Walter A.; Sanetrik, Mark D.; Chwalowski, Pawel; Connolly, Joseph; Kopasakis, George

    2016-01-01

    An overview of recent applications of the FUN3D CFD code to computational aeroelastic, sonic boom, and aeropropulsoservoelasticity (APSE) analyses of a low-boom supersonic configuration is presented. The overview includes details of the computational models developed including multiple unstructured CFD grids suitable for aeroelastic and sonic boom analyses. In addition, aeroelastic Reduced-Order Models (ROMs) are generated and used to rapidly compute the aeroelastic response and utter boundaries at multiple flight conditions.

  9. Combination vaccines

    Directory of Open Access Journals (Sweden)

    David AG Skibinski

    2011-01-01

    Full Text Available The combination of diphtheria, tetanus, and pertussis vaccines into a single product has been central to the protection of the pediatric population over the past 50 years. The addition of inactivated polio, Haemophilus influenzae, and hepatitis B vaccines into the combination has facilitated the introduction of these vaccines into recommended immunization schedules by reducing the number of injections required and has therefore increased immunization compliance. However, the development of these combinations encountered numerous challenges, including the reduced response to Haemophilus influenzae vaccine when given in combination; the need to consolidate the differences in the immunization schedule (hepatitis B; and the need to improve the safety profile of the diphtheria, tetanus, and pertussis combination. Here, we review these challenges and also discuss future prospects for combination vaccines.

  10. Estimativa da área foliar de nabo forrageiro em função de dimensões foliares

    Directory of Open Access Journals (Sweden)

    Alberto Cargnelutti Filho

    2012-01-01

    Full Text Available O objetivo deste trabalho foi desenvolver um modelo para estimar a área foliar de nabo forrageiro (Raphanus sativus L. var. oleiferus Metzg determinada por fotos digitais, em função do comprimento, ou da largura e/ou do produto comprimento vezes largura da folha. Aos 76 dias após a semeadura, foram coletadas 557 folhas da haste principal de 92 plantas, sendo mensurados o comprimento (C e a largura (L de cada folha, e calculado o produto comprimento × largura (C×L. Após, determinou-se a área foliar (Y, por meio do método de fotos digitais. Do total de folhas, separaram-se, aleatoriamente, 450 folhas para a construção de modelos do tipo quadrático, potência e linear de Y em função de C, da L, e/ou de C×L. 107 folhas foram usadas para a validação dos modelos. O modelo do tipo potência da área foliar obtida por meio do método de fotos digitais (Ŷ=0,6843x0,9221, R²=0,9862 em função do produto comprimento × largura é adequado para estimar a área foliar de nabo forrageiro.

  11. O sujeito psicótico e a função do delírio

    Directory of Open Access Journals (Sweden)

    Raquel Briggs

    2014-09-01

    Full Text Available Este trabalho aborda o conceito de delírio e sua função na estrutura psicótica. A psicanálise considera o delírio, por um lado, fenômeno elementar e, por outro, tentativa de cura, portador de uma verdade. O presente trabalho objetiva abordar a estruturação delirante, assim como a função da mesma para o sujeito paranoico, no sentido de situar a direção de tratamento na clínica da paranoia. A partir de um caso clínico e embasando-se nos conceitos da psicanálise, discute-se a função do mesmo para o sujeito. Leva-se em conta a invenção do sujeito para além do delírio, a partir de uma estabilização que, entretanto, não acontece sem ele.

  12. Direct Quantification of Cd2+ in the Presence of Cu2+ by a Combination of Anodic Stripping Voltammetry Using a Bi-Film-Modified Glassy Carbon Electrode and an Artificial Neural Network

    Science.gov (United States)

    Zhao, Guo; Wang, Hui; Liu, Gang

    2017-01-01

    In this study, a novel method based on a Bi/glassy carbon electrode (Bi/GCE) for quantitatively and directly detecting Cd2+ in the presence of Cu2+ without further electrode modifications by combining square-wave anodic stripping voltammetry (SWASV) and a back-propagation artificial neural network (BP-ANN) has been proposed. The influence of the Cu2+ concentration on the stripping response to Cd2+ was studied. In addition, the effect of the ferrocyanide concentration on the SWASV detection of Cd2+ in the presence of Cu2+ was investigated. A BP-ANN with two inputs and one output was used to establish the nonlinear relationship between the concentration of Cd2+ and the stripping peak currents of Cu2+ and Cd2+. The factors affecting the SWASV detection of Cd2+ and the key parameters of the BP-ANN were optimized. Moreover, the direct calibration model (i.e., adding 0.1 mM ferrocyanide before detection), the BP-ANN model and other prediction models were compared to verify the prediction performance of these models in terms of their mean absolute errors (MAEs), root mean square errors (RMSEs) and correlation coefficients. The BP-ANN model exhibited higher prediction accuracy than the direct calibration model and the other prediction models. Finally, the proposed method was used to detect Cd2+ in soil samples with satisfactory results. PMID:28671628

  13. Direct Quantification of Cd2+ in the Presence of Cu2+ by a Combination of Anodic Stripping Voltammetry Using a Bi-Film-Modified Glassy Carbon Electrode and an Artificial Neural Network.

    Science.gov (United States)

    Zhao, Guo; Wang, Hui; Liu, Gang

    2017-07-03

    Abstract: In this study, a novel method based on a Bi/glassy carbon electrode (Bi/GCE) for quantitatively and directly detecting Cd2+ in the presence of Cu2+ without further electrode modifications by combining square-wave anodic stripping voltammetry (SWASV) and a back-propagation artificial neural network (BP-ANN) has been proposed. The influence of the Cu2+ concentration on the stripping response to Cd2+ was studied. In addition, the effect of the ferrocyanide concentration on the SWASV detection of Cd2+ in the presence of Cu2+ was investigated. A BP-ANN with two inputs and one output was used to establish the nonlinear relationship between the concentration of Cd2+ and the stripping peak currents of Cu2+ and Cd2+. The factors affecting the SWASV detection of Cd2+ and the key parameters of the BP-ANN were optimized. Moreover, the direct calibration model (i.e., adding 0.1 mM ferrocyanide before detection), the BP-ANN model and other prediction models were compared to verify the prediction performance of these models in terms of their mean absolute errors (MAEs), root mean square errors (RMSEs) and correlation coefficients. The BP-ANN model exhibited higher prediction accuracy than the direct calibration model and the other prediction models. Finally, the proposed method was used to detect Cd2+ in soil samples with satisfactory results.

  14. Declarative Networking

    CERN Document Server

    Loo, Boon Thau

    2012-01-01

    Declarative Networking is a programming methodology that enables developers to concisely specify network protocols and services, which are directly compiled to a dataflow framework that executes the specifications. Declarative networking proposes the use of a declarative query language for specifying and implementing network protocols, and employs a dataflow framework at runtime for communication and maintenance of network state. The primary goal of declarative networking is to greatly simplify the process of specifying, implementing, deploying and evolving a network design. In addition, decla

  15. Avaliação da função renal em idosos: um estudo de base populacional

    Directory of Open Access Journals (Sweden)

    Marina Constante Dutra

    2014-09-01

    Full Text Available Introdução: A doença renal crônica (DRC atinge todas as faixas etárias e sua prevalência tem aumentando nos últimos anos. A DRC é dividida em seis estágios de acordo com o grau de função renal do paciente: 1. Função renal normal sem lesão renal; 2. Fase de lesão com função renal normal; 3. Fase de insuficiência renal funcional ou leve; 4. Fase de insuficiência renal laboratorial ou moderada; 5. Fase de insuficiência renal clínica ou grave; 6. Fase terminal de insuficiência renal crônica. Objetivo: O objetivo do estudo foi avaliar a função renal dos pacientes idosos e presença de fatores associados a estas alterações. Métodos: Estudo transversal de base populacional. Foram estudados idosos entre setembro de 2010 e maio de 2011. A função renal foi avaliada pela creatinina sérica, sendo estimada a taxa de filtração glomerular pela fórmula de CKD-EPI. Resultados: Foram estudados 822 idosos, sendo 61,6% mulheres, 92,2% brancos e a maioria tinha entre 60-69 anos (61,0%. Com relação à taxa de filtração renal, 26,2% tinham a taxa normal, 60,2% discreta diminuição, 13,0% moderada diminuição, 0,5% dano renal grave e 0,1% insuficiência renal. A idade mais avançada foi associada a dano renal pela diminuição da taxa de filtração glomerular (p < 0,001. Além disso, foram fatores associados de forma independente a diminuição da taxa de filtração renal a obesidade, hipertensão arterial sistêmica e tabagismo. Conclusão: A grande maioria dos idosos estudados tinha algum dano renal, mesmo que considerado discreto, e 13,6% tinha disfunção de grau moderado ou superior.

  16. THE ROLES OF TRANSLATED YOUNG ADULT FICTION IN ACHIEVEMENT OF YOUNG ADULT DEVELOPMENT IN PITIMOSS FUN LIBRARY

    Directory of Open Access Journals (Sweden)

    Fuji Muliasari

    2017-02-01

    Full Text Available Abstrak. Fiksi merupakan salah satu jenis koleksi yang perlu dimiliki oleh perpustakaan. Fiksi terdiri atas fiksi dalam negeri dan fiksi terjemahan. Pemilihannya tidak hanya berdasarkan pada unsur hiburan, melainkan juga kriteria lain seperti pencapaian perkembangan pembaca. Penelitian ini bertujuan untuk mendeskripsikan peran fiksi remaja terjemahan dalam pencapaian perkembangan remaja pada aspek kognitif, sosial, dan afektif. Secara teoritis, penelitian ini bermanfaat bagi pengembangan ilmu perpustakaan dan informasi, terutama berkaitan dengan teori seleksi fiksi dan biblioterapi. Sementara secara praktis, penelitian ini memberikan manfaat bagi pihak penyelenggara Pitimoss Fun Library dan peneliti selanjutnya. Penelitian ini dilakukan dengan menggunakan pendekatan kualitatif. Sementara metode penelitian yang dipilih ialah metode deskriptif dengan model studi kasus interpretatif. Jawaban atas pertanyaan penelitian diperoleh dari keterangan enam informan dan satu informan kunci. Enam informan dipilih dengan metode purposive sampling. Sementara informan kunci merupakan seorang biblioterapis. Teknik pengumpulan data yang digunakan ialah observasi, wawancara, telaah dokumen, dan pencarian data online. Keseluruhan data penelitian yang diperoleh kemudian dianalisis dengan menggunakan teknik analisis naratif. Hasil penelitian menunjukan bahwa fiksi remaja terjemahan yang tersedia di Pitimoss Fun Library memiliki peran dalam pencapaian ketiga aspek perkembangan. Kendati begitu, peranan ini tidak hanya berlaku satu arah. Sebab pencapaian aspek perkembangan remaja juga menentukan seberapa besar peran dari pengalaman membaca serta tanggapan atas muatan kontroversial dalam fiksi remaja terjemahan. Upaya yang dapat dilakukan oleh Pitimoss Fun Library untuk memaksimalkan peran fiksi remaja terjemahan ialah dengan menjadikan perkembangan remaja sebagai kriteria pengadaan koleksi serta pemberian bimbingan dan rekomendasi bacaan yang sesuai dengan usia

  17. Funções Executivas na Dislexia do Desenvolvimento: Revendo Evidências de Pesquisas

    Directory of Open Access Journals (Sweden)

    Giovanna Beatriz Kalva MEDINA

    Full Text Available RESUMO: o objetivo deste artigo é fazer uma revisão sistemática de literatura a fim de analisar produções científicas que abordam as funções executivas (FE e a dislexia. O método consiste num levantamento de artigos publicados na Biblioteca Virtual em Saúde, no Portal de Periódicos da CAPES e na PUBMED. Foram selecionados 28 trabalhos, sendo 9 do PUBMED, 14 dos Periódicos da CAPES, 4 da BVS e um manualmente. Resultados indicaram que os estudos selecionados não avaliam as FE da mesma forma. Alguns avaliam as FE como um componente global, o qual demonstra pouca diferença comparando os disléxicos a leitores típicos, outros avaliam os componentes principais das FE, que são o Controle Inibitório, a Flexibilidade Cognitiva e a Memória de Trabalho (MT, e um terceiro grupo de estudos avalia a MT também em seus subcomponentes: MT visuoespacial, executivo central e MT verbal ou fonológica. É possível concluir que o estudo das FE em disléxicos está em pleno desenvolvimento. Nos últimos 5 anos, o interesse nesta temática aumentou, inclusive no Brasil, expresso pelo aumento no número de estudos publicados. Porém, ainda há muito que se conhecer, principalmente sobre o efeito de intervenções envolvendo as FE.

  18. Social networking spaces from Facebook to Twitter and everything in between

    CERN Document Server

    Kelsey, Todd

    2010-01-01

    What the heck is Facebook? Twitter? Blogging? This book answers these questions and explains how to use a variety of social networking sites to keep in touch, stay in business, and have fun. This book covers the main social networking ""spaces,"" and introduces some of the ways people are enjoying them within a family or business context. It includes information on posting pictures, using add-ons, and working with Facebook and LinkedIn groups. It also covers the phenomenon of Twitter, including how it has grown and the road ahead. This book also covers how you can use the various networks toge

  19. combination Dictionary

    African Journals Online (AJOL)

    rbr

    advanced-level Spanish-speaking EFL learners. 2. Word combinations ... a certain process of segregation as a separate branch of linguistics. While lexi- ... As substantiated by Ilson's investigation of lexicographic practices, most dictionaries ...

  20. Possibilities of personalized advertising campaigns application on social networks

    Directory of Open Access Journals (Sweden)

    Vasković Jelena V.

    2015-01-01

    Full Text Available Development of new technologies and the emergence of sites and applications that are primarily intended for fun, considerably changed the way of communication among people. Social networks can be stated as a preferred product of modern society that have become an essential form of communication especially among young people, but also in older generations. The most popular social network in our country is Facebook which has over 3.5 million users. This kind of popularity led this social network into a position to become a place where many companies want to promote their products and services. Facebook has a mechanism that allows page administrators to easily target a group of potential consumers and to present them a desired message. This paper will analyze the advertising possibility through social networks. Also, the example of the campaign implementation for the Facebook page that is primarily engaged in the sale will be shown.

  1. Improving the Resilience of Major Ports and Critical Supply Chains to Extreme Coastal Flooding: a Combined Artificial Neural Network and Hydrodynamic Simulation Approach to Predicting Tidal Surge Inundation of Port Infrastructure and Impact on Operations.

    Science.gov (United States)

    French, J.

    2015-12-01

    Ports are vital to the global economy, but assessments of global exposure to flood risk have generally focused on major concentrations of population or asset values. Few studies have examined the impact of extreme inundation events on port operation and critical supply chains. Extreme water levels and recurrence intervals have conventionally been estimated via analysis of historic water level maxima, and these vary widely depending on the statistical assumptions made. This information is supplemented by near-term forecasts from operational surge-tide models, which give continuous water levels but at considerable computational cost. As part of a NERC Infrastructure and Risk project, we have investigated the impact of North Sea tidal surges on the Port of Immingham, eastern, UK. This handles the largest volume of bulk cargo in the UK and flows of coal and biomass that are critically important for national energy security. The port was partly flooded during a major tidal surge in 2013. This event highlighted the need for improved local forecasts of surge timing in relation to high water, with a better indication of flood depth and duration. We address this problem using a combination of data-driven and numerical hydrodynamic models. An Artificial Neural Network (ANN) is first used to predict the surge component of water level from meteorological data. The input vector comprises time-series of local wind (easterly and northerly wind stress) and pressure, as well as regional pressure and pressure gradients from stations between the Shetland Islands and the Humber estuary. The ANN achieves rms errors of around 0.1 m and can generate short-range (~ 3 to 12 hour) forecasts given real-time input data feeds. It can also synthesize water level events for a wider range of tidal and meteorological forcing combinations than contained in the observational records. These are used to force Telemac2D numerical floodplain simulations using a LiDAR digital elevation model of the port

  2. The network researchers' network

    DEFF Research Database (Denmark)

    Henneberg, Stephan C.; Jiang, Zhizhong; Naudé, Peter

    2009-01-01

    The Industrial Marketing and Purchasing (IMP) Group is a network of academic researchers working in the area of business-to-business marketing. The group meets every year to discuss and exchange ideas, with a conference having been held every year since 1984 (there was no meeting in 1987). In thi......The Industrial Marketing and Purchasing (IMP) Group is a network of academic researchers working in the area of business-to-business marketing. The group meets every year to discuss and exchange ideas, with a conference having been held every year since 1984 (there was no meeting in 1987......). In this paper, based upon the papers presented at the 22 conferences held to date, we undertake a Social Network Analysis in order to examine the degree of co-publishing that has taken place between this group of researchers. We identify the different components in this database, and examine the large main...

  3. Neural network design with combined backpropagation and creeping random search learning algorithms applied to the determination of retained austenite in TRIP steels; Diseno de redes neuronales con aprendizaje combinado de retropropagacion y busqueda aleatoria progresiva aplicado a la determinacion de austenita retenida en aceros TRIP

    Energy Technology Data Exchange (ETDEWEB)

    Toda-Caraballo, I.; Garcia-Mateo, C.; Capdevila, C.

    2010-07-01

    At the beginning of the decade of the nineties, the industrial interest for TRIP steels leads to a significant increase of the investigation and application in this field. In this work, the flexibility of neural networks for the modelling of complex properties is used to tackle the problem of determining the retained austenite content in TRIP-steel. Applying a combination of two learning algorithms (backpropagation and creeping-random-search) for the neural network, a model has been created that enables the prediction of retained austenite in low-Si / low-Al multiphase steels as a function of processing parameters. (Author). 34 refs.

  4. NETWORKS OF QUALITY IMPROVEMENT

    OpenAIRE

    Cevallos A., Juan; Universidad Nacional Mayor de San Marcos

    2014-01-01

    This article deals about the need of better highly-powered tools for quality improvement. A creative combination of Continuous Improvement Philosophy, Systems General Theory and Network General Theory is used, with the purpose of developing a Quality Improvement Network allowing an optimization of systems and processes within organizations. El artículo trata sobre la necesidad de herramientas con mayor potencia para la mejora de la calidad. Se utiliza la combinación, de manera creativa, de...

  5. Neural network applications

    Science.gov (United States)

    Padgett, Mary L.; Desai, Utpal; Roppel, T.A.; White, Charles R.

    1993-01-01

    A design procedure is suggested for neural networks which accommodates the inclusion of such knowledge-based systems techniques as fuzzy logic and pairwise comparisons. The use of these procedures in the design of applications combines qualitative and quantitative factors with empirical data to yield a model with justifiable design and parameter selection procedures. The procedure is especially relevant to areas of back-propagation neural network design which are highly responsive to the use of precisely recorded expert knowledge.

  6. Security Shift in Future Network Architectures

    NARCIS (Netherlands)

    Hartog, T.; Schotanus, H.A.; Verkoelen, C.A.A.

    2010-01-01

    In current practice military communication infrastructures are deployed as stand-alone networked information systems. Network-Enabled Capabilities (NEC) and combined military operations lead to new requirements which current communication architectures cannot deliver. This paper informs IT

  7. Network cosmology.

    Science.gov (United States)

    Krioukov, Dmitri; Kitsak, Maksim; Sinkovits, Robert S; Rideout, David; Meyer, David; Boguñá, Marián

    2012-01-01

    Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology.

  8. Network Cosmology

    Science.gov (United States)

    Krioukov, Dmitri; Kitsak, Maksim; Sinkovits, Robert S.; Rideout, David; Meyer, David; Boguñá, Marián

    2012-01-01

    Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology. PMID:23162688

  9. Nudging children towards whole wheat bread: a field experiment on the influence of fun bread roll shape on breakfast consumption.

    Science.gov (United States)

    van Kleef, Ellen; Vrijhof, Milou; Polet, Ilse A; Vingerhoeds, Monique H; de Wijk, René A

    2014-09-02

    Many children do not eat enough whole grains, which may have negative health consequences. Intervention research is increasingly focusing on nudging as a way to influence food choices by affecting unconscious behavioural processes. The aim of this field study was to examine whether the shape of bread rolls is able to shift children's bread choices from white to whole wheat during breakfast to increase whole grain intake. In a between-subjects experiment conducted at twelve primary schools in the Netherlands, with school as the unit of condition assignment, children were exposed to an assortment of white and whole wheat bread rolls, both varying in shape (regular versus fun). Children were free to choose the type and number of bread rolls and toppings to eat during breakfast. Consumption of bread rolls was measured at class level via the number of bread rolls before and after breakfast. In addition, children (N = 1113) responded to a survey including questions about the breakfast. Results of the field experiment showed that about 76% of bread consumption consisted of white bread rolls. Consumption of white bread rolls did not differ according to shape (all P-values > 0.18). However, presenting fun-shaped whole wheat bread rolls almost doubled consumption of whole wheat bread (P = 0.001), particularly when the simultaneously presented white bread rolls had a regular shape (interaction P = 0.02). Survey results suggest that slight increases in perceived pleasure and taste are associated with these effects. Overall, presenting whole wheat bread in fun shapes may be helpful in increasing consumption of whole wheat bread in children. Future research could examine how improving the visual appeal of healthy foods may lead to sustained behaviour changes.

  10. Social support from teachers mediates physical activity behavior change in children participating in the Fit-4-Fun intervention.

    Science.gov (United States)

    Eather, Narelle; Morgan, Philip J; Lubans, David R

    2013-05-28

    Few studies have examined the mediators of behavior change in successful school-based physical activity interventions. The aim of this study was to explore potential mediators of physical activity in the Fit-4-Fun program for primary school children. Group randomized controlled trial. Four primary schools were recruited in April, 2011 and randomized by school into intervention or control conditions. Participants included 213 children (mean age = 10.7 years ± 0.6; 52.2% female) with the treatment group (n = 118) completing the 8-week multi-component Fit-4-Fun program. Participants were assessed at baseline, 3- and 6-months. Physical activity was measured using Yamax SW700 pedometers (mean steps/day) and questionnaires were used to assess constructs from Social Cognitive Theory and Competence Motivation Theory. Hypothesized mediators measured included social support from peers, parents and teachers; physical activity self-efficacy (barrier and task); enjoyment; and perceived school physical environment. Mediation was assessed using Preacher and Hayes' multiple mediation regression SPSS macro. Action theory (A), conceptual theory (B) and the significance of the product of coefficients (AB) are reported. The intervention had a significant effect on physical activity (pperceived school environment (A=0.28, pperceived school environment (A=0.058, pperceived school environment approached significance (AB = 434, CI= -415 to 1507 steps, proportion= 13%). The Fit-4-Fun program successfully targeted social support for physical activity provided by classroom teachers which contributed to improved physical activity in children. These results demonstrate that classroom teachers play a key role in influencing physical activity behavior outcomes in children.Trial Registration No: ACTRN12611000976987.

  11. Fun with Mission Control: Learning Science and Technology by Sitting in the Driver's Seat

    Science.gov (United States)

    Fitzpatrick, A. J.; Fisher, D. K.; Leon, N.; Novati, A.; Chmielewski, A. B.; Karlson, D. K.

    2012-12-01

    We will demonstrate and discuss iOS games we have developed that simulate real space mission scenarios in simplified form. These games are designed to appeal to multiple generations, while educating and informing the player about the mission science and technology. Such interactive games for mobile devices can reach an audience that might otherwise be inaccessible. However, developing in this medium comes with its own set of challenges. Touch screen input demands a different type of interface and defines new rules for user interaction. Communicating informative messages to an audience on the go also poses unique challenges. The organization and delivery of the content needs to consider that the users are often distracted by their environments or have only short blocks of time in which to become involved with the activity. The first game, "Comet Quest," simulates the Rosetta mission. Rosetta, sponsored by the European Space Agency, with important contributions from NASA, is on its way to Comet 67P/Churyumov-Gerasimenko. It will orbit the comet and drop a lander on the nucleus. It will continue to orbit for two years as the comet approaches the Sun. Both orbiter and lander will make measurements and observations and transmit the data to Earth, in the first close study of a comet's evolution as it journeys to the inner solar system. In "Comet Quest," the player controls the release of the lander and records and transmits all the science data. The game is fun and challenging, no matter the player's skill level. Comet Quest includes a "Learn more" feature, with questions and simple, concise answers about comets and the Rosetta mission. "Rescue 406!" is another simulation game, this one enacting the process of rescuing individuals in distress using the Search And Rescue Satellite-Aided Tracking system, SARSAT. Development of this game was sponsored by NOAA's Geostationary Operational Environmental Satellite, R-series, program (GOES-R). This game incorporates the major

  12. Wireless rechargeable sensor networks

    CERN Document Server

    Yang, Yuanyuan

    2015-01-01

    This SpringerBrief provides a concise guide to applying wireless energy transfer techniques in traditional battery-powered sensor networks. It examines the benefits and challenges of wireless power including efficiency and reliability. The authors build a wireless rechargeable sensor networks from scratch and aim to provide perpetual network operation. Chapters cover a wide range of topics from the collection of energy information and recharge scheduling to joint design with typical sensing applications such as data gathering. Problems are approached using a natural combination of probability

  13. From fun and excitement to joy and trouble. An explorative study of three Danish father's experiences around birth

    DEFF Research Database (Denmark)

    Hall, Elisabeth

    1995-01-01

    at the end of the pregnancy, love at first sight at the birth, at which they all attended and took an active part, awakening when the new family was united at home and when they came to realize how much effort is needed in caring for an infant, and joy and trouble three months later. It is suggested...... first-time fathers were interviewed at three different times: in the last month of pregnancy, two weeks after the birth of their child, and again three months later. Data were analyzed in several steps using a hermeneutical approach. The fathers' experiences were identified as fun and excitement...

  14. Preditores de função ventricular esquerda global na síndrome metabólica

    Directory of Open Access Journals (Sweden)

    Branislava Aleksa Ivanovic

    2011-05-01

    Full Text Available FUNDAMENTO: A síndrome metabólica (SM representa um conjunto de fatores de risco cardiovascular que agem de forma sinérgica. OBJETIVO: O objetivo desse estudo foi determinar quais parâmetros estavam associados de forma independente à função global do ventrículo esquerdo (VE em indivíduos com SM, estimada através do índice Tei. MÉTODOS: O estudo incluiu 234 indivíduos com SM e 96 controles ajustados por idade. A SM foi definida pela presença de três ou mais critérios da ATP-NCEP III. Todos os indivíduos foram submetidos a testes laboratoriais e ecocardiograma bidimensional e com Doppler pulsátil e tecidual. Intervalos de tempo apropriados no Doppler tecidual para a estimativa do índice Tei também foram avaliados. RESULTADOS: O índice Tei estava aumentado em todos os indivíduos com SM (0,35 ± 0,05 vs 0,49 ± 0,10, p < 0,001. Análise de regressão múltipla dos parâmetros clínicos mostrou que a pressão arterial sistólica (β= 0,289, p < 0,001, glicemia de jejum (β= 0,205, p = 0,009, índice de massa do VE (β= 0,301, p < 0,001, E/e'septal (β= 0,267, p < 0,001 e e'septal (β= -0,176, p = 0,011 estavam independentemente associados com a função ventricular esquerda global estimada pelo índice Tei. CONCLUSÃO: A SM teve um impacto significante na função global do VE. A pressão arterial sistólica, glicemia de jejum, índice de massa do VE E/e'septal, e e'septal estavam independentemente associados com a função global do VE.

  15. Efeitos da Effleurage de diferentes pressões na função cardíaca

    OpenAIRE

    Moreira, Nídia Maria Gomes

    2014-01-01

    Projeto de Graduação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Licenciada em Fisioterapia Objectivo: Determinar os efeitos da effleurage de diferentes pressões na função cardíaca. Metodologia: Foram seleccionadas 30 pessoas com as idades compreendidas entre os 20 a 25 anos todos do género masculino e sem patologias. Estes jovens foram divididos aleatoriamente em três grupos: grupo controlo (GC) (N=10), grupo effleurage superficial (GES) (...

  16. Efeitos de diferentes graus de sensibilidade a insulina na função endotelial de pacientes obesos

    Directory of Open Access Journals (Sweden)

    Roberto Galvão

    2012-01-01

    Full Text Available FUNDAMENTO: A obesidade derivada da deposição de gordura intra-abdominal tende a aumentar a produção de hormônios e citoquinas, piorando a sensibilidade a insulina e levando a disfunção endotelial. A hiperinsulinemia é considerada um fator de risco independente para doença isquêmica cardíaca e é uma causa de disfunção endotelial em indivíduos saudáveis. OBJETIVO: Avaliar o impacto de diferentes graus de resistência a insulina, medida pelo HOMA-IR (Homeostasis Model Assessment of Insulin Resistance, sobre a função endotelial de obesos, pacientes não diabéticos, sem história prévia de eventos cardiovasculares e diversos componentes da síndrome metabólica. MÉTODOS: Um total de 40 indivíduos obesos foi submetido a medidas antropométricas, pressão arterial de consultório, MAPA e exames laboratoriais, além de avaliação ultrassonográfica não invasiva da função endotelial. Os pacientes foram divididos em três grupos de acordo com o grau de resistência a insulina: pacientes com valores de HOMA-IR entre 0,590 e 1,082 foram incluídos no Grupo 1 (n = 13; entre 1,083 e 1,410 no Grupo 2 (n = 14; e entre 1,610 e 2,510 no Grupo 3 (n = 13. RESULTADOS: Encontramos uma diferença significativa na vasodilatação mediada por fluxo no Grupo 3 em relação ao Grupo 1 (9,2 ± 7,0 vs 18,0 ± 7,5 %, p = 0,006. Houve uma correlação negativa entre a função endotelial e insulina, HOMA-IR e triglicérides. CONCLUSÃO: Nosso estudo sugere que leves alterações nos níveis de resistência a insulina avaliada pelo HOMA-IR podem causar algum impacto sobre a função vasodilatadora do endotélio em indivíduos obesos não complicados com diferentes fatores de risco cardiovascular.

  17. O uso da modelagem para o ensino da função seno no ensino médio

    OpenAIRE

    Santos, Ricardo Ferreira dos

    2014-01-01

    Esta pesquisa se insere nos estudos de utilização da Modelagem Matemática como estratégia de ensino. Nela é apresentada uma atividade de modelagem para o ensino da função seno. A pesquisa teve dois objetivos principais: analisar os efeitos de uma modelagem matemática no Ensino Médio com vistas à alcançar uma aprendizagem significativa; e avaliar uma proposta de abordagem para a modelagem, por meio de etapas e fases. Nesse segundo caso pretendeu-se verificar se o protoganismo do professor na a...

  18. Winning Combinations

    DEFF Research Database (Denmark)

    Criscuolo, Paola; Laursen, Keld; Reichstein, Toke

    2017-01-01

    Searching for the most rewarding sources of innovative ideas remains a key challenge in management of technological innovation. Yet, little is known about which combinations of internal and external knowledge sources are triggers for innovation. Extending theories about searching for innovation, ...

  19. Adaptive parallel logic networks

    Science.gov (United States)

    Martinez, Tony R.; Vidal, Jacques J.

    1988-01-01

    Adaptive, self-organizing concurrent systems (ASOCS) that combine self-organization with massive parallelism for such applications as adaptive logic devices, robotics, process control, and system malfunction management, are presently discussed. In ASOCS, an adaptive network composed of many simple computing elements operating in combinational and asynchronous fashion is used and problems are specified by presenting if-then rules to the system in the form of Boolean conjunctions. During data processing, which is a different operational phase from adaptation, the network acts as a parallel hardware circuit.

  20. Comparative efficacy of indacaterol 150 µg and 300 µg versus fixed-dose combinations of formoterol + budesonide or salmeterol + fluticasone for the treatment of chronic obstructive pulmonary disease – a network meta-analysis

    Directory of Open Access Journals (Sweden)

    Cope S

    2011-06-01

    Full Text Available Shannon Cope1, Gorana Capkun-Niggli2, Rupert Gale3, José R Jardim4, Jeroen P Jansen11Mapi Values, Boston, MA, USA; 2Health Economics and Outcomes Research, Novartis Pharma AG, Basel, Switzerland; 3Novartis Horsham Research Centre, Horsham, UK; 4Respiratory Division, Federal University of São Paulo, BrazilObjective: To compare efficacy of indacaterol to that of fixed-dose combination (FDC formoterol and budesonide (FOR/BUD and FDC salmeterol and fluticasone (SAL/FP for the treatment of chronic obstructive pulmonary disease (COPD based on the available randomized clinical trials (RCTs.Methods: Fifteen placebo-controlled RCTs were included that evaluated: indacaterol 150 µg (n = 5 studies, indacaterol 300 µg (n = 4, FOR/BUD 9/160 µg (n = 2, FOR/BUD 9/320 µg (n = 3, SAL/FP 50/500 µg (n = 5, and SAL/FP 50/250 µg (n = 1. Outcomes of interest were trough forced expiratory volume in 1 second (FEV1, total scores for St. George's Respiratory Questionnaire (SGRQ, and transition dyspnea index (TDI. All trials were analyzed simultaneously using a Bayesian network meta-analysis and relative treatment effects between all regimens were obtained. Treatment-by-covariate interactions were included where possible to improve the similarity of the trials.Results: Indacaterol 150 µg resulted in a higher change from baseline (CFB in FEV1 at 12 weeks compared to FOR/BUD 9/160 µg (difference in CFB 0.11 L [95% credible intervals: 0.08, 0.13] and FOR/BUD 9/320 µg (0.09 L [0.06, 0.11] and was comparable to SAL/FP 50/250 µg (0.02 L [–0.04, 0.08] and SAL/FP 50/500 µg (0.03 L [0.00, 0.06]. Similar results were observed for indacaterol 300 µg at 12 weeks and indacaterol 150/300 µg at 6 months. Indacaterol 150 µg demonstrated comparable improvement in SGRQ total score at 6 months versus FOR/BUD (both doses, and SAL/FP 50/500 µg (–2.16 point improvement [–4.96, 0.95]. Indacaterol 150 and 300 µg demonstrated comparable TDI scores versus SAL/FP 50/250 µg