Use of the neighboring orbital model for analysis of electronic coupling in Class III intervalence compounds

International Nuclear Information System (INIS)

Nelsen, Stephen F.; Weaver, Michael N.; Luo Yun; Lockard, Jenny V.; Zink, Jeffrey I.

2006-01-01

Symmetrical charge-delocalized intervalence radical ions should not be described by the traditional two-state model that has been so successful for their localized counterparts. If they lack direct overlap between their charge-bearing units (M), their diabatic orbitals have an equal energy pair of symmetrized M-centered combination orbitals that are symmetric (S) or antisymmetric (A) with respect to a symmetry element at the center of the molecule. The M combination orbitals will mix separately with bridge orbitals of the same symmetry. We call the simplest useful model for this situation the neighboring orbital model, which uses the S and A bridge orbitals of high overlap that lie closest in energy to the M orbital pair, resulting in two two-state models that have a common energy for one pair. This model is developed quantitatively, and examples having 1, 3, 5, and 7 electrons in the neighboring orbitals are illustrated

A two-step nearest neighbors algorithm using satellite imagery for predicting forest structure within species composition classes

Science.gov (United States)

Ronald E. McRoberts

2009-01-01

Nearest neighbors techniques have been shown to be useful for predicting multiple forest attributes from forest inventory and Landsat satellite image data. However, in regions lacking good digital land cover information, nearest neighbors selected to predict continuous variables such as tree volume must be selected without regard to relevant categorical variables such...

ENTROPY CHARACTERISTICS IN MODELS FOR COORDINATION OF NEIGHBORING ROAD SECTIONS

Directory of Open Access Journals (Sweden)

N. I. Kulbashnaya

2016-01-01

Full Text Available The paper considers an application of entropy characteristics as criteria to coordinate traffic conditions at neighboring road sections. It has been proved that the entropy characteristics are widely used in the methods that take into account information influence of the environment on drivers and in the mechanisms that create such traffic conditions which ensure preservation of the optimal level of driver’s emotional tension during the drive. Solution of such problem is considered in the aspect of coordination of traffic conditions at neighboring road sections that, in its turn, is directed on exclusion of any driver’s transitional processes. Methodology for coordination of traffic conditions at neighboring road sections is based on the E. V. Gavrilov’s concept on coordination of some parameters of road sections which can be expressed in the entropy characteristics. The paper proposes to execute selection of coordination criteria according to accident rates because while moving along neighboring road sections traffic conditions change drastically that can result in creation of an accident situation. Relative organization of a driver’s perception field and driver’s interaction with the traffic environment has been selected as entropy characteristics. Therefore, the given characteristics are made conditional to the road accidents rate. The investigation results have revealed a strong correlation between the relative organization of the driver’s perception field and the relative organization of the driver’s interaction with the traffic environment and the accident rate. Results of the executed experiment have proved an influence of the accident rate on the investigated entropy characteristics.

Anderson localization in one-dimensional quasiperiodic lattice models with nearest- and next-nearest-neighbor hopping

International Nuclear Information System (INIS)

Gong, Longyan; Feng, Yan; Ding, Yougen

2017-01-01

Highlights: • Quasiperiodic lattice models with next-nearest-neighbor hopping are studied. • Shannon information entropies are used to reflect state localization properties. • Phase diagrams are obtained for the inverse bronze and golden means, respectively. • Our studies present a more complete picture than existing works. - Abstract: We explore the reduced relative Shannon information entropies SR for a quasiperiodic lattice model with nearest- and next-nearest-neighbor hopping, where an irrational number is in the mathematical expression of incommensurate on-site potentials. Based on SR, we respectively unveil the phase diagrams for two irrationalities, i.e., the inverse bronze mean and the inverse golden mean. The corresponding phase diagrams include regions of purely localized phase, purely delocalized phase, pure critical phase, and regions with mobility edges. The boundaries of different regions depend on the values of irrational number. These studies present a more complete picture than existing works.

Anderson localization in one-dimensional quasiperiodic lattice models with nearest- and next-nearest-neighbor hopping

Energy Technology Data Exchange (ETDEWEB)

Gong, Longyan, E-mail: lygong@njupt.edu.cn [Information Physics Research Center and Department of Applied Physics, Nanjing University of Posts and Telecommunications, Nanjing, 210003 (China); Institute of Signal Processing and Transmission, Nanjing University of Posts and Telecommunications, Nanjing, 210003 (China); National Laboratory of Solid State Microstructures, Nanjing University, Nanjing 210093 (China); Feng, Yan; Ding, Yougen [Information Physics Research Center and Department of Applied Physics, Nanjing University of Posts and Telecommunications, Nanjing, 210003 (China); Institute of Signal Processing and Transmission, Nanjing University of Posts and Telecommunications, Nanjing, 210003 (China)

2017-02-12

Highlights: • Quasiperiodic lattice models with next-nearest-neighbor hopping are studied. • Shannon information entropies are used to reflect state localization properties. • Phase diagrams are obtained for the inverse bronze and golden means, respectively. • Our studies present a more complete picture than existing works. - Abstract: We explore the reduced relative Shannon information entropies SR for a quasiperiodic lattice model with nearest- and next-nearest-neighbor hopping, where an irrational number is in the mathematical expression of incommensurate on-site potentials. Based on SR, we respectively unveil the phase diagrams for two irrationalities, i.e., the inverse bronze mean and the inverse golden mean. The corresponding phase diagrams include regions of purely localized phase, purely delocalized phase, pure critical phase, and regions with mobility edges. The boundaries of different regions depend on the values of irrational number. These studies present a more complete picture than existing works.

The square Ising model with second-neighbor interactions and the Ising chain in a transverse field

International Nuclear Information System (INIS)

Grynberg, M.D.; Tanatar, B.

1991-06-01

We consider the thermal and critical behaviour of the square Ising lattice with frustrated first - and second-neighbor interactions. A low-temperature domain wall analysis including kinks and dislocations shows that there is a close relation between this classical model and the Hamiltonian of an Ising chain in a transverse field provided that the ratio of the next-nearest to nearest-neighbor coupling, is close to 1/2. Due to the field inversion symmetry of the Ising chain Hamiltonian, the thermal properties of the classical system are symmetrical with respect to this coupling ratio. In the neighborhood of this regime critical exponents of the model turn out to belong to the Ising universality class. Our results are compared with previous Monte Carlo simulations. (author). 23 refs, 6 figs

Frog sound identification using extended k-nearest neighbor classifier

Science.gov (United States)

Mukahar, Nordiana; Affendi Rosdi, Bakhtiar; Athiar Ramli, Dzati; Jaafar, Haryati

2017-09-01

Frog sound identification based on the vocalization becomes important for biological research and environmental monitoring. As a result, different types of feature extractions and classifiers have been employed to evaluate the accuracy of frog sound identification. This paper presents a frog sound identification with Extended k-Nearest Neighbor (EKNN) classifier. The EKNN classifier integrates the nearest neighbors and mutual sharing of neighborhood concepts, with the aims of improving the classification performance. It makes a prediction based on who are the nearest neighbors of the testing sample and who consider the testing sample as their nearest neighbors. In order to evaluate the classification performance in frog sound identification, the EKNN classifier is compared with competing classifier, k -Nearest Neighbor (KNN), Fuzzy k -Nearest Neighbor (FKNN) k - General Nearest Neighbor (KGNN)and Mutual k -Nearest Neighbor (MKNN) on the recorded sounds of 15 frog species obtained in Malaysia forest. The recorded sounds have been segmented using Short Time Energy and Short Time Average Zero Crossing Rate (STE+STAZCR), sinusoidal modeling (SM), manual and the combination of Energy (E) and Zero Crossing Rate (ZCR) (E+ZCR) while the features are extracted by Mel Frequency Cepstrum Coefficient (MFCC). The experimental results have shown that the EKNCN classifier exhibits the best performance in terms of accuracy compared to the competing classifiers, KNN, FKNN, GKNN and MKNN for all cases.

NeighborHood

OpenAIRE

Corominola Ocaña, Víctor

2015-01-01

NeighborHood és una aplicació basada en el núvol, adaptable a qualsevol dispositiu (mòbil, tablet, desktop). L'objectiu d'aquesta aplicació és poder permetre als usuaris introduir a les persones del seu entorn més immediat i que aquestes persones siguin visibles per a la resta d'usuaris. NeighborHood es una aplicación basada en la nube, adaptable a cualquier dispositivo (móvil, tablet, desktop). El objetivo de esta aplicación es poder permitir a los usuarios introducir a las personas de su...

Does a pear growl? Interference from semantic properties of orthographic neighbors.

Science.gov (United States)

Pecher, Diane; de Rooij, Jimmy; Zeelenberg, René

2009-07-01

In this study, we investigated whether semantic properties of a word's orthographic neighbors are activated during visual word recognition. In two experiments, words were presented with a property that was not true for the word itself. We manipulated whether the property was true for an orthographic neighbor of the word. Our results showed that rejection of the property was slower and less accurate when the property was true for a neighbor than when the property was not true for a neighbor. These findings indicate that semantic information is activated before orthographic processing is finished. The present results are problematic for the links model (Forster, 2006; Forster & Hector, 2002) that was recently proposed in order to bring form-first models of visual word recognition into line with previously reported findings (Forster & Hector, 2002; Pecher, Zeelenberg, & Wagenmakers, 2005; Rodd, 2004).

Predicting Audience Location on the Basis of the k-Nearest Neighbor Multilabel Classification

Directory of Open Access Journals (Sweden)

Haitao Wu

2014-01-01

Full Text Available Understanding audience location information in online social networks is important in designing recommendation systems, improving information dissemination, and so on. In this paper, we focus on predicting the location distribution of audiences on YouTube. And we transform this problem to a multilabel classification problem, while we find there exist three problems when the classical k-nearest neighbor based algorithm for multilabel classification (ML-kNN is used to predict location distribution. Firstly, the feature weights are not considered in measuring the similarity degree. Secondly, it consumes considerable computing time in finding similar items by traversing all the training set. Thirdly, the goal of ML-kNN is to find relevant labels for every sample which is different from audience location prediction. To solve these problems, we propose the methods of measuring similarity based on weight, quickly finding similar items, and ranking a specific number of labels. On the basis of these methods and the ML-kNN, the k-nearest neighbor based model for audience location prediction (AL-kNN is proposed for predicting audience location. The experiments based on massive YouTube data show that the proposed model can more accurately predict the location of YouTube video audience than the ML-kNN, MLNB, and Rank-SVM methods.

Hole motion in the t-J and Hubbard models: Effect of a next-nearest-neighbor hopping

International Nuclear Information System (INIS)

Gagliano, E.; Bacci, S.; Dagotto, E.

1990-01-01

Using exact diagonalization techniques, we study one dynamical hole in the two-dimensional t-J and Hubbard models on a square lattice including a next-nearest-neighbor hopping t'. We present the phase diagram in the parameter space (J/t,t'/t), discussing the ground-state properties of the hole. At J=0, a crossing of levels exists at some value of t' separating a ferromagnetic from an antiferromagnetic ground state. For nonzero J, at least four different regions appear where the system behaves like an antiferromagnet or a (not fully saturated) ferromagnet. We study the quasiparticle behavior of the hole, showing that for small values of |t'| the previously presented string picture is still valid. We also find that, for a realistic set of parameters derived from the Cu-O Hamiltonian, the hole has momentum (π/2,π/2), suggesting an enhancement of the p-wave superconducting mode due to the second-neighbor interactions in the spin-bag picture. Results for the t-t'-U model are also discussed with conclusions similar to those of the t-t'-J model. In general we found that t'=0 is not a singular point of these models

Identifying influential neighbors in animal flocking.

Directory of Open Access Journals (Sweden)

Li Jiang

2017-11-01

Full Text Available Schools of fish and flocks of birds can move together in synchrony and decide on new directions of movement in a seamless way. This is possible because group members constantly share directional information with their neighbors. Although detecting the directionality of other group members is known to be important to maintain cohesion, it is not clear how many neighbors each individual can simultaneously track and pay attention to, and what the spatial distribution of these influential neighbors is. Here, we address these questions on shoals of Hemigrammus rhodostomus, a species of fish exhibiting strong schooling behavior. We adopt a data-driven analysis technique based on the study of short-term directional correlations to identify which neighbors have the strongest influence over the participation of an individual in a collective U-turn event. We find that fish mainly react to one or two neighbors at a time. Moreover, we find no correlation between the distance rank of a neighbor and its likelihood to be influential. We interpret our results in terms of fish allocating sequential and selective attention to their neighbors.

Identifying influential neighbors in animal flocking.

Science.gov (United States)

Jiang, Li; Giuggioli, Luca; Perna, Andrea; Escobedo, Ramón; Lecheval, Valentin; Sire, Clément; Han, Zhangang; Theraulaz, Guy

2017-11-01

Schools of fish and flocks of birds can move together in synchrony and decide on new directions of movement in a seamless way. This is possible because group members constantly share directional information with their neighbors. Although detecting the directionality of other group members is known to be important to maintain cohesion, it is not clear how many neighbors each individual can simultaneously track and pay attention to, and what the spatial distribution of these influential neighbors is. Here, we address these questions on shoals of Hemigrammus rhodostomus, a species of fish exhibiting strong schooling behavior. We adopt a data-driven analysis technique based on the study of short-term directional correlations to identify which neighbors have the strongest influence over the participation of an individual in a collective U-turn event. We find that fish mainly react to one or two neighbors at a time. Moreover, we find no correlation between the distance rank of a neighbor and its likelihood to be influential. We interpret our results in terms of fish allocating sequential and selective attention to their neighbors.

Neighbor Rupture Degree of Some Middle Graphs

Directory of Open Access Journals (Sweden)

Gökşen BACAK-TURAN

2017-12-01

Full Text Available Networks have an important place in our daily lives. Internet networks, electricity networks, water networks, transportation networks, social networks and biological networks are some of the networks we run into every aspects of our lives. A network consists of centers connected by links. A network is represented when centers and connections modelled by vertices and edges, respectively. In consequence of the failure of some centers or connection lines, measurement of the resistance of the network until the communication interrupted is called vulnerability of the network. In this study, neighbor rupture degree which is a parameter that explores the vulnerability values of the resulting graphs due to the failure of some centers of a communication network and its neighboring centers becoming nonfunctional were applied to some middle graphs and neighbor rupture degree of the $M(C_{n},$ $M(P_{n},$ $M(K_{1,n},$ $M(W_{n},$ $M(P_{n}\\times K_{2}$ and $M(C_{n}\\times K_{2}$ have been found.

A Novel Hybrid Model Based on Extreme Learning Machine, k-Nearest Neighbor Regression and Wavelet Denoising Applied to Short-Term Electric Load Forecasting

Directory of Open Access Journals (Sweden)

Weide Li

2017-05-01

Full Text Available Electric load forecasting plays an important role in electricity markets and power systems. Because electric load time series are complicated and nonlinear, it is very difficult to achieve a satisfactory forecasting accuracy. In this paper, a hybrid model, Wavelet Denoising-Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EWKM, which combines k-Nearest Neighbor (KNN and Extreme Learning Machine (ELM based on a wavelet denoising technique is proposed for short-term load forecasting. The proposed hybrid model decomposes the time series into a low frequency-associated main signal and some detailed signals associated with high frequencies at first, then uses KNN to determine the independent and dependent variables from the low-frequency signal. Finally, the ELM is used to get the non-linear relationship between these variables to get the final prediction result for the electric load. Compared with three other models, Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EKM, Wavelet Denoising-Extreme Learning Machine (WKM and Wavelet Denoising-Back Propagation Neural Network optimized by k-Nearest Neighbor Regression (WNNM, the model proposed in this paper can improve the accuracy efficiently. New South Wales is the economic powerhouse of Australia, so we use the proposed model to predict electric demand for that region. The accurate prediction has a significant meaning.

Learning tag relevance by neighbor voting for social image retrieval

NARCIS (Netherlands)

Li, X.; Snoek, C.G.M.; Worring, M.

2008-01-01

Social image retrieval is important for exploiting the increasing amounts of amateur-tagged multimedia such as Flickr images. Since amateur tagging is known to be uncontrolled, ambiguous, and personalized, a fundamental problem is how to reliably interpret the relevance of a tag with respect to the

A Comparison of the Spatial Linear Model to Nearest Neighbor (k-NN) Methods for Forestry Applications

Science.gov (United States)

Jay M. Ver Hoef; Hailemariam Temesgen; Sergio Gómez

2013-01-01

Forest surveys provide critical information for many diverse interests. Data are often collected from samples, and from these samples, maps of resources and estimates of aerial totals or averages are required. In this paper, two approaches for mapping and estimating totals; the spatial linear model (SLM) and k-NN (k-Nearest Neighbor) are compared, theoretically,...

Estimating cavity tree and snag abundance using negative binomial regression models and nearest neighbor imputation methods

Science.gov (United States)

Bianca N.I. Eskelson; Hailemariam Temesgen; Tara M. Barrett

2009-01-01

Cavity tree and snag abundance data are highly variable and contain many zero observations. We predict cavity tree and snag abundance from variables that are readily available from forest cover maps or remotely sensed data using negative binomial (NB), zero-inflated NB, and zero-altered NB (ZANB) regression models as well as nearest neighbor (NN) imputation methods....

ACTION RECOGNITION USING SALIENT NEIGHBORING HISTOGRAMS

DEFF Research Database (Denmark)

Ren, Huamin; Moeslund, Thomas B.

2013-01-01

Combining spatio-temporal interest points with Bag-of-Words models achieves state-of-the-art performance in action recognition. However, existing methods based on “bag-ofwords” models either are too local to capture the variance in space/time or fail to solve the ambiguity problem in spatial...... and temporal dimensions. Instead, we propose a salient vocabulary construction algorithm to select visual words from a global point of view, and form compact descriptors to represent discriminative histograms in the neighborhoods. Those salient neighboring histograms are then trained to model different actions...

Grain price spikes and beggar-thy-neighbor policy responses

DEFF Research Database (Denmark)

Boysen, Ole; Jensen, Hans Grinsted

on the agenda of various international policy fora, including the annual meetings of G20 countries in recent years. For that reason, recent studies have attempted to quantify the extent to which such policy actions contributed to the rise in food prices. A study by Jensen & Anderson (2014) uses the global AGE...... model GTAP and the corresponding database to quantify the global policy actions contributions to the raise in food prices by modeling the changes in distortions to agricultural incentives in the period 2006 to 2008. We link the results from this global model into a national AGE model, highlighting how...... global "Beggar-thy-Neighbor Policy Responses" impacted on poor households in Uganda. More specifically we examine the following research questions: What were the Ugandan economy-wide and poverty impacts of the price spikes? What was the impact of other countries "Beggar-thy-Neighbor Policy Responses...

Dimensionality reduction with unsupervised nearest neighbors

CERN Document Server

Kramer, Oliver

2013-01-01

This book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustr...

Inferring feature relevances from metric learning

DEFF Research Database (Denmark)

Schulz, Alexander; Mokbel, Bassam; Biehl, Michael

2015-01-01

Powerful metric learning algorithms have been proposed in the last years which do not only greatly enhance the accuracy of distance-based classifiers and nearest neighbor database retrieval, but which also enable the interpretability of these operations by assigning explicit relevance weights...

Recrafting the neighbor-joining method

Directory of Open Access Journals (Sweden)

Pedersen Christian NS

2006-01-01

Full Text Available Abstract Background The neighbor-joining method by Saitou and Nei is a widely used method for constructing phylogenetic trees. The formulation of the method gives rise to a canonical Θ(n3 algorithm upon which all existing implementations are based. Results In this paper we present techniques for speeding up the canonical neighbor-joining method. Our algorithms construct the same phylogenetic trees as the canonical neighbor-joining method. The best-case running time of our algorithms are O(n2 but the worst-case remains O(n3. We empirically evaluate the performance of our algoritms on distance matrices obtained from the Pfam collection of alignments. The experiments indicate that the running time of our algorithms evolve as Θ(n2 on the examined instance collection. We also compare the running time with that of the QuickTree tool, a widely used efficient implementation of the canonical neighbor-joining method. Conclusion The experiments show that our algorithms also yield a significant speed-up, already for medium sized instances.

Passage relevance models for genomics search

Directory of Open Access Journals (Sweden)

Frieder Ophir

2009-03-01

Full Text Available Abstract We present a passage relevance model for integrating syntactic and semantic evidence of biomedical concepts and topics using a probabilistic graphical model. Component models of topics, concepts, terms, and document are represented as potential functions within a Markov Random Field. The probability of a passage being relevant to a biologist's information need is represented as the joint distribution across all potential functions. Relevance model feedback of top ranked passages is used to improve distributional estimates of query concepts and topics in context, and a dimensional indexing strategy is used for efficient aggregation of concept and term statistics. By integrating multiple sources of evidence including dependencies between topics, concepts, and terms, we seek to improve genomics literature passage retrieval precision. Using this model, we are able to demonstrate statistically significant improvements in retrieval precision using a large genomics literature corpus.

The influence of further-neighbor spin-spin interaction on a ground state of 2D coupled spin-electron model in a magnetic field

Science.gov (United States)

Čenčariková, Hana; Strečka, Jozef; Gendiar, Andrej; Tomašovičová, Natália

2018-05-01

An exhaustive ground-state analysis of extended two-dimensional (2D) correlated spin-electron model consisting of the Ising spins localized on nodal lattice sites and mobile electrons delocalized over pairs of decorating sites is performed within the framework of rigorous analytical calculations. The investigated model, defined on an arbitrary 2D doubly decorated lattice, takes into account the kinetic energy of mobile electrons, the nearest-neighbor Ising coupling between the localized spins and mobile electrons, the further-neighbor Ising coupling between the localized spins and the Zeeman energy. The ground-state phase diagrams are examined for a wide range of model parameters for both ferromagnetic as well as antiferromagnetic interaction between the nodal Ising spins and non-zero value of external magnetic field. It is found that non-zero values of further-neighbor interaction leads to a formation of new quantum states as a consequence of competition between all considered interaction terms. Moreover, the new quantum states are accompanied with different magnetic features and thus, several kinds of field-driven phase transitions are observed.

Neighbor discovery in multi-hop wireless networks: evaluation and dimensioning with interferences considerations

Directory of Open Access Journals (Sweden)

Elyes Ben Hamida

2008-04-01

Full Text Available In this paper, we study the impact of collisions and interferences on a neighbor discovery process in the context of multi-hop wireless networks. We consider three models in which interferences and collisions are handled in very different ways. From an ideal channel where simultaneous transmissions do not interfere, we derive an alternate channel where simultaneous transmissions are considered two-by-two under the form of collisions, to finally reach a more realistic channel where simultaneous transmissions are handled under the form of shot-noise interferences. In these models, we analytically compute the link probability success between two neighbors as well as the expected number of nodes that correctly receive a Hello packet. Using this analysis, we show that if the neighbor discovery process is asymptotically equivalent in the three models, it offers very different behaviors locally in time. In particular, the scalability of the process is not the same depending on the way interferences are handled. Finally, we apply our results to the dimensioning of a Hello protocol parameters. We propose a method to adapt the protocol parameters to meet application constraints on the neighbor discovery process and to minimize the protocol energy consumption.

Haldane to Dimer Phase Transition in the Spin-1 Haldane System with Bond-Alternating Nearest-Neighbor and Uniform Next-Nearest-Neighbor Exchange Interactions

OpenAIRE

Takashi, Tonegawa; Makoto, Kaburagi; Takeshi, Nakao; Department of Physics, Faculty of Science, Kobe University; Faculty of Cross-Cultural Studies, Kobe University; Department of Physics, Faculty of Science, Kobe University

1995-01-01

The Haldane to dimer phase transition is studied in the spin-1 Haldane system with bond-alternating nearest-neighbor and uniform next-nearest-neighbor exchange interactions, where both interactions are antiferromagnetic and thus compete with each other. By using a method of exact diagonalization, the ground-state phase diagram on the ratio of the next-nearest-neighbor interaction constant to the nearest-neighbor one versus the bond-alternation parameter of the nearest-neighbor interactions is...

Novel qsar combination forecast model for insect repellent coupling support vector regression and k-nearest-neighbor

International Nuclear Information System (INIS)

Wang, L.F.; Bai, L.Y.

2013-01-01

To improve the precision of quantitative structure-activity relationship (QSAR) modeling for aromatic carboxylic acid derivatives insect repellent, a novel nonlinear combination forecast model was proposed integrating support vector regression (SVR) and K-nearest neighbor (KNN): Firstly, search optimal kernel function and nonlinearly select molecular descriptors by the rule of minimum MSE value using SVR. Secondly, illuminate the effects of all descriptors on biological activity by multi-round enforcement resistance-selection. Thirdly, construct the sub-models with predicted values of different KNN. Then, get the optimal kernel and corresponding retained sub-models through subtle selection. Finally, make prediction with leave-one-out (LOO) method in the basis of reserved sub-models. Compared with previous widely used models, our work shows significant improvement in modeling performance, which demonstrates the superiority of the present combination forecast model. (author)

Recrafting the Neighbor-Joining Method

DEFF Research Database (Denmark)

Mailund; Brodal, Gerth Stølting; Fagerberg, Rolf

2006-01-01

Background: The neighbor-joining method by Saitou and Nei is a widely used method for constructing phylogenetic trees. The formulation of the method gives rise to a canonical Θ(n3) algorithm upon which all existing implementations are based. Methods: In this paper we present techniques for speeding...... up the canonical neighbor-joining method. Our algorithms construct the same phylogenetic trees as the canonical neighbor-joining method. The best-case running time of our algorithms are O(n2) but the worst-case remains O(n3). We empirically evaluate the performance of our algoritms on distance...... matrices obtained from the Pfam collection of alignments. Results: The experiments indicate that the running time of our algorithms evolve as Θ(n2) on the examined instance collection. We also compare the running time with that of the QuickTree tool, a widely used efficient implementation of the canonical...

Quantum Algorithm for K-Nearest Neighbors Classification Based on the Metric of Hamming Distance

Science.gov (United States)

Ruan, Yue; Xue, Xiling; Liu, Heng; Tan, Jianing; Li, Xi

2017-11-01

K-nearest neighbors (KNN) algorithm is a common algorithm used for classification, and also a sub-routine in various complicated machine learning tasks. In this paper, we presented a quantum algorithm (QKNN) for implementing this algorithm based on the metric of Hamming distance. We put forward a quantum circuit for computing Hamming distance between testing sample and each feature vector in the training set. Taking advantage of this method, we realized a good analog for classical KNN algorithm by setting a distance threshold value t to select k - n e a r e s t neighbors. As a result, QKNN achieves O( n 3) performance which is only relevant to the dimension of feature vectors and high classification accuracy, outperforms Llyod's algorithm (Lloyd et al. 2013) and Wiebe's algorithm (Wiebe et al. 2014).

Heterogeneous autoregressive model with structural break using nearest neighbor truncation volatility estimators for DAX.

Science.gov (United States)

Chin, Wen Cheong; Lee, Min Cherng; Yap, Grace Lee Ching

2016-01-01

High frequency financial data modelling has become one of the important research areas in the field of financial econometrics. However, the possible structural break in volatile financial time series often trigger inconsistency issue in volatility estimation. In this study, we propose a structural break heavy-tailed heterogeneous autoregressive (HAR) volatility econometric model with the enhancement of jump-robust estimators. The breakpoints in the volatility are captured by dummy variables after the detection by Bai-Perron sequential multi breakpoints procedure. In order to further deal with possible abrupt jump in the volatility, the jump-robust volatility estimators are composed by using the nearest neighbor truncation approach, namely the minimum and median realized volatility. Under the structural break improvements in both the models and volatility estimators, the empirical findings show that the modified HAR model provides the best performing in-sample and out-of-sample forecast evaluations as compared with the standard HAR models. Accurate volatility forecasts have direct influential to the application of risk management and investment portfolio analysis.

A Markov chain Monte Carlo Expectation Maximization Algorithm for Statistical Analysis of DNA Sequence Evolution with Neighbor-Dependent Substitution Rates

DEFF Research Database (Denmark)

Hobolth, Asger

2008-01-01

-dimensional integrals required in the EM algorithm are estimated using MCMC sampling. The MCMC sampler requires simulation of sample paths from a continuous time Markov process, conditional on the beginning and ending states and the paths of the neighboring sites. An exact path sampling algorithm is developed......The evolution of DNA sequences can be described by discrete state continuous time Markov processes on a phylogenetic tree. We consider neighbor-dependent evolutionary models where the instantaneous rate of substitution at a site depends on the states of the neighboring sites. Neighbor......-dependent substitution models are analytically intractable and must be analyzed using either approximate or simulation-based methods. We describe statistical inference of neighbor-dependent models using a Markov chain Monte Carlo expectation maximization (MCMC-EM) algorithm. In the MCMC-EM algorithm, the high...

Attribute Weighting Based K-Nearest Neighbor Using Gain Ratio

Science.gov (United States)

Nababan, A. A.; Sitompul, O. S.; Tulus

2018-04-01

K- Nearest Neighbor (KNN) is a good classifier, but from several studies, the result performance accuracy of KNN still lower than other methods. One of the causes of the low accuracy produced, because each attribute has the same effect on the classification process, while some less relevant characteristics lead to miss-classification of the class assignment for new data. In this research, we proposed Attribute Weighting Based K-Nearest Neighbor Using Gain Ratio as a parameter to see the correlation between each attribute in the data and the Gain Ratio also will be used as the basis for weighting each attribute of the dataset. The accuracy of results is compared to the accuracy acquired from the original KNN method using 10-fold Cross-Validation with several datasets from the UCI Machine Learning repository and KEEL-Dataset Repository, such as abalone, glass identification, haberman, hayes-roth and water quality status. Based on the result of the test, the proposed method was able to increase the classification accuracy of KNN, where the highest difference of accuracy obtained hayes-roth dataset is worth 12.73%, and the lowest difference of accuracy obtained in the abalone dataset of 0.07%. The average result of the accuracy of all dataset increases the accuracy by 5.33%.

Reduction in predator defense in the presence of neighbors in a colonial fish.

Directory of Open Access Journals (Sweden)

Franziska C Schädelin

Full Text Available Predation pressure has long been considered a leading explanation of colonies, where close neighbors may reduce predation via dilution, alarming or group predator attacks. Attacking predators may be costly in terms of energy and survival, leading to the question of how neighbors contribute to predator deterrence in relationship to each other. Two hypotheses explaining the relative efforts made by neighbors are byproduct-mutualism, which occurs when breeders inadvertently attack predators by defending their nests, and reciprocity, which occurs when breeders deliberately exchange predator defense efforts with neighbors. Most studies investigating group nest defense have been performed with birds. However, colonial fish may constitute a more practical model system for an experimental approach because of the greater ability of researchers to manipulate their environment. We investigated in the colonial fish, Neolamprologus caudopunctatus, whether prospecting pairs preferred to breed near conspecifics or solitarily, and how breeders invested in anti-predator defense in relation to neighbors. In a simple choice test, prospecting pairs selected breeding sites close to neighbors versus a solitary site. Predators were then sequentially presented to the newly established test pairs, the previously established stimulus pairs or in between the two pairs. Test pairs attacked the predator eight times more frequently when they were presented on their non-neighbor side compared to between the two breeding sites, where stimulus pairs maintained high attack rates. Thus, by joining an established pair, test pairs were able to reduce their anti-predator efforts near neighbors, at no apparent cost to the stimulus pairs. These findings are unlikely to be explained by reciprocity or byproduct-mutualism. Our results instead suggest a commensal relationship in which new pairs exploit the high anti-predator efforts of established pairs, which invest similarly with or

The distribution of the number of node neighbors in random hypergraphs

International Nuclear Information System (INIS)

López, Eduardo

2013-01-01

Hypergraphs, the generalization of graphs in which edges become conglomerates of r nodes called hyperedges of rank r ⩾ 2, are excellent models to study systems with interactions that are beyond the pairwise level. For hypergraphs, the node degree ℓ (number of hyperedges connected to a node) and the number of neighbors k of a node differ from each other in contrast to the case of graphs, where counting the number of edges is equivalent to counting the number of neighbors. In this paper, I calculate the distribution of the number of node neighbors in random hypergraphs in which hyperedges of uniform rank r have a homogeneous (equal for all hyperedges) probability p to appear. This distribution is equivalent to the degree distribution of ensembles of graphs created as projections of hypergraph or bipartite network ensembles, where the projection connects any two nodes in the projected graph when they are also connected in the hypergraph or bipartite network. The calculation is non-trivial due to the possibility that neighbor nodes belong simultaneously to multiple hyperedges (node overlaps). From the exact results, the traditional asymptotic approximation to the distribution in the sparse regime (small p) where overlaps are ignored is rederived and improved; the approximation exhibits Poisson-like behavior accompanied by strong fluctuations modulated by power-law decays in the system size N with decay exponents equal to the minimum number of overlapping nodes possible for a given number of neighbors. It is shown that the dense limit cannot be explained if overlaps are ignored, and the correct asymptotic distribution is provided. The neighbor distribution requires the calculation of a new combinatorial coefficient Q r−1 (k, ℓ), which counts the number of distinct labeled hypergraphs of k nodes, ℓ hyperedges of rank r − 1, and where every node is connected to at least one hyperedge. Some identities of Q r−1 (k, ℓ) are derived and applied to the

A Markov chain Monte Carlo Expectation Maximization Algorithm for Statistical Analysis of DNA Sequence Evolution with Neighbor-Dependent Substitution Rates

DEFF Research Database (Denmark)

Hobolth, Asger

2008-01-01

The evolution of DNA sequences can be described by discrete state continuous time Markov processes on a phylogenetic tree. We consider neighbor-dependent evolutionary models where the instantaneous rate of substitution at a site depends on the states of the neighboring sites. Neighbor...

The 'neighbor effect'. Simulating dynamics in consumer preferences for new vehicle technologies

International Nuclear Information System (INIS)

Mau, Paulus; Eyzaguirre, Jimena; Jaccard, Mark; Tiedemann, Kenneth; Collins-Dodd, Colleen

2008-01-01

Understanding consumer behaviour is essential in designing policies that efficiently increase the uptake of clean technologies over the long-run. Expert opinion or qualitative market analyses have tended to be the sources of this information. However, greater scrutiny on governments increasingly demands the use of reliable and credible evidence to support policy decisions. While discrete choice research and modeling techniques have been applied to estimate consumer preferences for technologies, these methods often assume static preferences. This study builds on the application of discrete choice research and modeling to capture dynamics in consumer preferences. We estimate Canadians' preferences for new vehicle technologies under different market assumptions, using responses from two national surveys focused on hybrid gas-electric vehicles and hydrogen fuel cell vehicles. The results support the relevance of a range of vehicle attributes beyond the purchase price in shaping consumer preferences towards clean vehicle technologies. They also corroborate our hypothesis that the degree of market penetration of clean vehicle technologies is an influence on people's preferences ('the neighbor effect'). Finally, our results provide behavioural parameters for the energy-economy model CIMS, which we use here to show the importance of including consumer preference dynamics when setting policies to encourage the uptake of clean technologies. (author)

Mixed random walks with a trap in scale-free networks including nearest-neighbor and next-nearest-neighbor jumps

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Zhang, Zhongzhi; Dong, Yuze; Sheng, Yibin

2015-10-01

Random walks including non-nearest-neighbor jumps appear in many real situations such as the diffusion of adatoms and have found numerous applications including PageRank search algorithm; however, related theoretical results are much less for this dynamical process. In this paper, we present a study of mixed random walks in a family of fractal scale-free networks, where both nearest-neighbor and next-nearest-neighbor jumps are included. We focus on trapping problem in the network family, which is a particular case of random walks with a perfect trap fixed at the central high-degree node. We derive analytical expressions for the average trapping time (ATT), a quantitative indicator measuring the efficiency of the trapping process, by using two different methods, the results of which are consistent with each other. Furthermore, we analytically determine all the eigenvalues and their multiplicities for the fundamental matrix characterizing the dynamical process. Our results show that although next-nearest-neighbor jumps have no effect on the leading scaling of the trapping efficiency, they can strongly affect the prefactor of ATT, providing insight into better understanding of random-walk process in complex systems.

Scalable Nearest Neighbor Algorithms for High Dimensional Data.

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Muja, Marius; Lowe, David G

2014-11-01

For many computer vision and machine learning problems, large training sets are key for good performance. However, the most computationally expensive part of many computer vision and machine learning algorithms consists of finding nearest neighbor matches to high dimensional vectors that represent the training data. We propose new algorithms for approximate nearest neighbor matching and evaluate and compare them with previous algorithms. For matching high dimensional features, we find two algorithms to be the most efficient: the randomized k-d forest and a new algorithm proposed in this paper, the priority search k-means tree. We also propose a new algorithm for matching binary features by searching multiple hierarchical clustering trees and show it outperforms methods typically used in the literature. We show that the optimal nearest neighbor algorithm and its parameters depend on the data set characteristics and describe an automated configuration procedure for finding the best algorithm to search a particular data set. In order to scale to very large data sets that would otherwise not fit in the memory of a single machine, we propose a distributed nearest neighbor matching framework that can be used with any of the algorithms described in the paper. All this research has been released as an open source library called fast library for approximate nearest neighbors (FLANN), which has been incorporated into OpenCV and is now one of the most popular libraries for nearest neighbor matching.

Lectures on the nearest neighbor method

CERN Document Server

Biau, Gérard

2015-01-01

This text presents a wide-ranging and rigorous overview of nearest neighbor methods, one of the most important paradigms in machine learning. Now in one self-contained volume, this book systematically covers key statistical, probabilistic, combinatorial and geometric ideas for understanding, analyzing and developing nearest neighbor methods. Gérard Biau is a professor at Université Pierre et Marie Curie (Paris). Luc Devroye is a professor at the School of Computer Science at McGill University (Montreal).   .

Dimensional testing for reverse k-nearest neighbor search

DEFF Research Database (Denmark)

Casanova, Guillaume; Englmeier, Elias; Houle, Michael E.

2017-01-01

Given a query object q, reverse k-nearest neighbor (RkNN) search aims to locate those objects of the database that have q among their k-nearest neighbors. In this paper, we propose an approximation method for solving RkNN queries, where the pruning operations and termination tests are guided...... by a characterization of the intrinsic dimensionality of the data. The method can accommodate any index structure supporting incremental (forward) nearest-neighbor search for the generation and verification of candidates, while avoiding impractically-high preprocessing costs. We also provide experimental evidence...

Introduction to machine learning: k-nearest neighbors.

Science.gov (United States)

Zhang, Zhongheng

2016-06-01

Machine learning techniques have been widely used in many scientific fields, but its use in medical literature is limited partly because of technical difficulties. k-nearest neighbors (kNN) is a simple method of machine learning. The article introduces some basic ideas underlying the kNN algorithm, and then focuses on how to perform kNN modeling with R. The dataset should be prepared before running the knn() function in R. After prediction of outcome with kNN algorithm, the diagnostic performance of the model should be checked. Average accuracy is the mostly widely used statistic to reflect the kNN algorithm. Factors such as k value, distance calculation and choice of appropriate predictors all have significant impact on the model performance.

Numerical Simulation of the Diffusion Processes in Nanoelectrode Arrays Using an Axial Neighbor Symmetry Approximation.

Science.gov (United States)

Peinetti, Ana Sol; Gilardoni, Rodrigo S; Mizrahi, Martín; Requejo, Felix G; González, Graciela A; Battaglini, Fernando

2016-06-07

Nanoelectrode arrays have introduced a complete new battery of devices with fascinating electrocatalytic, sensitivity, and selectivity properties. To understand and predict the electrochemical response of these arrays, a theoretical framework is needed. Cyclic voltammetry is a well-fitted experimental technique to understand the undergoing diffusion and kinetics processes. Previous works describing microelectrode arrays have exploited the interelectrode distance to simulate its behavior as the summation of individual electrodes. This approach becomes limited when the size of the electrodes decreases to the nanometer scale due to their strong radial effect with the consequent overlapping of the diffusional fields. In this work, we present a computational model able to simulate the electrochemical behavior of arrays working either as the summation of individual electrodes or being affected by the overlapping of the diffusional fields without previous considerations. Our computational model relays in dividing a regular electrode array in cells. In each of them, there is a central electrode surrounded by neighbor electrodes; these neighbor electrodes are transformed in a ring maintaining the same active electrode area than the summation of the closest neighbor electrodes. Using this axial neighbor symmetry approximation, the problem acquires a cylindrical symmetry, being applicable to any diffusion pattern. The model is validated against micro- and nanoelectrode arrays showing its ability to predict their behavior and therefore to be used as a designing tool.

A Compositional Relevance Model for Adaptive Information Retrieval

Science.gov (United States)

Mathe, Nathalie; Chen, James; Lu, Henry, Jr. (Technical Monitor)

1994-01-01

There is a growing need for rapid and effective access to information in large electronic documentation systems. Access can be facilitated if information relevant in the current problem solving context can be automatically supplied to the user. This includes information relevant to particular user profiles, tasks being performed, and problems being solved. However most of this knowledge on contextual relevance is not found within the contents of documents, and current hypermedia tools do not provide any easy mechanism to let users add this knowledge to their documents. We propose a compositional relevance network to automatically acquire the context in which previous information was found relevant. The model records information on the relevance of references based on user feedback for specific queries and contexts. It also generalizes such information to derive relevant references for similar queries and contexts. This model lets users filter information by context of relevance, build personalized views of documents over time, and share their views with other users. It also applies to any type of multimedia information. Compared to other approaches, it is less costly and doesn't require any a priori statistical computation, nor an extended training period. It is currently being implemented into the Computer Integrated Documentation system which enables integration of various technical documents in a hypertext framework.

Do alcohol compliance checks decrease underage sales at neighboring establishments?

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Erickson, Darin J; Smolenski, Derek J; Toomey, Traci L; Carlin, Bradley P; Wagenaar, Alexander C

2013-11-01

Underage alcohol compliance checks conducted by law enforcement agencies can reduce the likelihood of illegal alcohol sales at checked alcohol establishments, and theory suggests that an alcohol establishment that is checked may warn nearby establishments that compliance checks are being conducted in the area. In this study, we examined whether the effects of compliance checks diffuse to neighboring establishments. We used data from the Complying with the Minimum Drinking Age trial, which included more than 2,000 compliance checks conducted at more than 900 alcohol establishments. The primary outcome was the sale of alcohol to a pseudo-underage buyer without the need for age identification. A multilevel logistic regression was used to model the effect of a compliance check at each establishment as well as the effect of compliance checks at neighboring establishments within 500 m (stratified into four equal-radius concentric rings), after buyer, license, establishment, and community-level variables were controlled for. We observed a decrease in the likelihood of establishments selling alcohol to underage youth after they had been checked by law enforcement, but these effects quickly decayed over time. Establishments that had a close neighbor (within 125 m) checked in the past 90 days were also less likely to sell alcohol to young-appearing buyers. The spatial effect of compliance checks on other establishments decayed rapidly with increasing distance. Results confirm the hypothesis that the effects of police compliance checks do spill over to neighboring establishments. These findings have implications for the development of an optimal schedule of police compliance checks.

Cultural macroevolution on neighbor graphs : vertical and horizontal transmission among Western North American Indian societies.

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Towner, Mary C; Grote, Mark N; Venti, Jay; Borgerhoff Mulder, Monique

2012-09-01

What are the driving forces of cultural macroevolution, the evolution of cultural traits that characterize societies or populations? This question has engaged anthropologists for more than a century, with little consensus regarding the answer. We develop and fit autologistic models, built upon both spatial and linguistic neighbor graphs, for 44 cultural traits of 172 societies in the Western North American Indian (WNAI) database. For each trait, we compare models including or excluding one or both neighbor graphs, and for the majority of traits we find strong evidence in favor of a model which uses both spatial and linguistic neighbors to predict a trait's distribution. Our results run counter to the assertion that cultural trait distributions can be explained largely by the transmission of traits from parent to daughter populations and are thus best analyzed with phylogenies. In contrast, we show that vertical and horizontal transmission pathways can be incorporated in a single model, that both transmission modes may indeed operate on the same trait, and that for most traits in the WNAI database, accounting for only one mode of transmission would result in a loss of information.

Effect of Floquet engineering on the p-wave superconductor with second-neighbor couplings

Science.gov (United States)

Li, X. P.; Li, C. F.; Wang, L. C.; Zhou, L.

2018-06-01

The influence of the Floquet engineering on a particular one-dimensional p-wave superconductor, Kitaev model, with second-neighbor couplings is investigated in this paper. The effective Hamiltonians in the rotated reference frames have been obtained, and the convergent regions of the approximated Hamiltonian as well as the topological phase diagrams have been analyzed and discussed. We show that by modulating the external driving field amplitude, frequency as well as the second-neighbor hopping amplitude, the rich phase diagrams and transitions between different topological phases can be obtained.

The temporal-relevance temporal-uncertainty model of prospective duration judgment.

Science.gov (United States)

Zakay, Dan

2015-12-15

A model aimed at explaining prospective duration judgments in real life settings (as well as in the laboratory) is presented. The model is based on the assumption that situational meaning is continuously being extracted by humans' perceptual and cognitive information processing systems. Time is one of the important dimensions of situational meaning. Based on the situational meaning, a value for Temporal Relevance is set. Temporal Relevance reflects the importance of temporal aspects for enabling adaptive behavior in a specific moment in time. When Temporal Relevance is above a certain threshold a prospective duration judgment process is evoked automatically. In addition, a search for relevant temporal information is taking place and its outcomes determine the level of Temporal Uncertainty which reflects the degree of knowledge one has regarding temporal aspects of the task to be performed. The levels of Temporal Relevance and Temporal Uncertainty determine the amount of attentional resources allocated for timing by the executive system. The merit of the model is in connecting timing processes with the ongoing general information processing stream. The model rests on findings in various domains which indicate that cognitive-relevance and self-relevance are powerful determinants of resource allocation policy. The feasibility of the model is demonstrated by analyzing various temporal phenomena. Suggestions for further empirical validation of the model are presented. Copyright © 2015 Elsevier Inc. All rights reserved.

Error Analysis for RADAR Neighbor Matching Localization in Linear Logarithmic Strength Varying Wi-Fi Environment

Directory of Open Access Journals (Sweden)

Mu Zhou

2014-01-01

Full Text Available This paper studies the statistical errors for the fingerprint-based RADAR neighbor matching localization with the linearly calibrated reference points (RPs in logarithmic received signal strength (RSS varying Wi-Fi environment. To the best of our knowledge, little comprehensive analysis work has appeared on the error performance of neighbor matching localization with respect to the deployment of RPs. However, in order to achieve the efficient and reliable location-based services (LBSs as well as the ubiquitous context-awareness in Wi-Fi environment, much attention has to be paid to the highly accurate and cost-efficient localization systems. To this end, the statistical errors by the widely used neighbor matching localization are significantly discussed in this paper to examine the inherent mathematical relations between the localization errors and the locations of RPs by using a basic linear logarithmic strength varying model. Furthermore, based on the mathematical demonstrations and some testing results, the closed-form solutions to the statistical errors by RADAR neighbor matching localization can be an effective tool to explore alternative deployment of fingerprint-based neighbor matching localization systems in the future.

Error Analysis for RADAR Neighbor Matching Localization in Linear Logarithmic Strength Varying Wi-Fi Environment

Science.gov (United States)

Tian, Zengshan; Xu, Kunjie; Yu, Xiang

2014-01-01

This paper studies the statistical errors for the fingerprint-based RADAR neighbor matching localization with the linearly calibrated reference points (RPs) in logarithmic received signal strength (RSS) varying Wi-Fi environment. To the best of our knowledge, little comprehensive analysis work has appeared on the error performance of neighbor matching localization with respect to the deployment of RPs. However, in order to achieve the efficient and reliable location-based services (LBSs) as well as the ubiquitous context-awareness in Wi-Fi environment, much attention has to be paid to the highly accurate and cost-efficient localization systems. To this end, the statistical errors by the widely used neighbor matching localization are significantly discussed in this paper to examine the inherent mathematical relations between the localization errors and the locations of RPs by using a basic linear logarithmic strength varying model. Furthermore, based on the mathematical demonstrations and some testing results, the closed-form solutions to the statistical errors by RADAR neighbor matching localization can be an effective tool to explore alternative deployment of fingerprint-based neighbor matching localization systems in the future. PMID:24683349

A dumbed-down approach to unite Fermilab, its neighbors

CERN Multimedia

Constable, B

2004-01-01

"...Fermilab is reaching out to its suburban neighbors...With the nation on orange alert, Fermilab scientists no longer can sit on the front porch and invite neighbors in for coffee and quasars" (1 page).

Local Order in the Unfolded State: Conformational Biases and Nearest Neighbor Interactions

Directory of Open Access Journals (Sweden)

Siobhan Toal

2014-07-01

Full Text Available The discovery of Intrinsically Disordered Proteins, which contain significant levels of disorder yet perform complex biologically functions, as well as unwanted aggregation, has motivated numerous experimental and theoretical studies aimed at describing residue-level conformational ensembles. Multiple lines of evidence gathered over the last 15 years strongly suggest that amino acids residues display unique and restricted conformational preferences in the unfolded state of peptides and proteins, contrary to one of the basic assumptions of the canonical random coil model. To fully understand residue level order/disorder, however, one has to gain a quantitative, experimentally based picture of conformational distributions and to determine the physical basis underlying residue-level conformational biases. Here, we review the experimental, computational and bioinformatic evidence for conformational preferences of amino acid residues in (mostly short peptides that can be utilized as suitable model systems for unfolded states of peptides and proteins. In this context particular attention is paid to the alleged high polyproline II preference of alanine. We discuss how these conformational propensities may be modulated by peptide solvent interactions and so called nearest-neighbor interactions. The relevance of conformational propensities for the protein folding problem and the understanding of IDPs is briefly discussed.

Pollinator-mediated interactions in experimental arrays vary with neighbor identity.

Science.gov (United States)

Ha, Melissa K; Ivey, Christopher T

2017-02-01

Local ecological conditions influence the impact of species interactions on evolution and community structure. We investigated whether pollinator-mediated interactions between coflowering plants vary with plant density, coflowering neighbor identity, and flowering season. We conducted a field experiment in which flowering time and floral neighborhood were manipulated in a factorial design. Early- and late-flowering Clarkia unguiculata plants were placed into arrays with C. biloba neighbors, noncongeneric neighbors, additional conspecific plants, or no additional plants as a density control. We compared whole-plant pollen limitation of seed set, pollinator behavior, and pollen deposition among treatments. Interactions mediated by shared pollinators depended on the identity of the neighbor and possibly changed through time, although flowering-season comparisons were compromised by low early-season plant survival. Interactions with conspecific neighbors were likely competitive late in the season. Interactions with C. biloba appeared to involve facilitation or neutral interactions. Interactions with noncongeners were more consistently competitive. The community composition of pollinators varied among treatment combinations. Pollinator-mediated interactions involved competition and likely facilitation, depending on coflowering neighbor. Experimental manipulation helped to reveal context-dependent variation in indirect biotic interactions. © 2017 Botanical Society of America.

The clinic as a good corporate neighbor.

Science.gov (United States)

Sass, Hans-Martin

2013-02-01

Clinics today specialize in health repair services similar to car repair shops; procedures and prices are standardized, regulated, and inflexibly uniform. Clinics of the future have to become Health Care Centers in order to be more respected and more effective corporate neighbors in offering outreach services in health education and preventive health care. The traditional concept of care for health is much broader than repair management and includes the promotion of lay health competence and responsibility in healthy social and natural environments. The corporate profile and ethics of the clinic as a good and competitive local neighbor will have to focus on [a] better personalized care, [b] education and services in preventive care, [c] direct or web-based information and advice for general, seasonal, or age related health risks, and on developing and improving trustworthy character traits of the clinic as a corporate person and a good neighbor.

Diagnostic tools for nearest neighbors techniques when used with satellite imagery

Science.gov (United States)

Ronald E. McRoberts

2009-01-01

Nearest neighbors techniques are non-parametric approaches to multivariate prediction that are useful for predicting both continuous and categorical forest attribute variables. Although some assumptions underlying nearest neighbor techniques are common to other prediction techniques such as regression, other assumptions are unique to nearest neighbor techniques....

Unwanted Behaviors and Nuisance Behaviors Among Neighbors in a Belgian Community Sample.

Science.gov (United States)

Michaux, Emilie; Groenen, Anne; Uzieblo, Katarzyna

2015-06-30

Unwanted behaviors between (ex-)intimates have been extensively studied, while those behaviors within other contexts such as neighbors have received much less scientific consideration. Research indicates that residents are likely to encounter problem behaviors from their neighbors. Besides the lack of clarity in the conceptualization of problem behaviors among neighbors, little is known on which types of behaviors characterize neighbor problems. In this study, the occurrence of two types of problem behaviors encountered by neighbors was explored within a Belgian community sample: unwanted behaviors such as threats and neighbor nuisance issues such as noise nuisance. By clearly distinguishing those two types of behaviors, this study aimed at contributing to the conceptualization of neighbor problems. Next, the coping strategies used to deal with the neighbor problems were investigated. Our results indicated that unwanted behaviors were more frequently encountered by residents compared with nuisance problems. Four out of 10 respondents reported both unwanted pursuit behavior and nuisance problems. It was especially unlikely to encounter nuisance problems in isolation of unwanted pursuit behaviors. While different coping styles (avoiding the neighbor, confronting the neighbor, and enlisting help from others) were equally used by the stalked participants, none of them was perceived as being more effective in reducing the stalking behaviors. Strikingly, despite being aware of specialized help services such as community mediation services, only a very small subgroup enlisted this kind of professional help. © The Author(s) 2015.

Secure Nearest Neighbor Query on Crowd-Sensing Data

Directory of Open Access Journals (Sweden)

Ke Cheng

2016-09-01

Full Text Available Nearest neighbor queries are fundamental in location-based services, and secure nearest neighbor queries mainly focus on how to securely and quickly retrieve the nearest neighbor in the outsourced cloud server. However, the previous big data system structure has changed because of the crowd-sensing data. On the one hand, sensing data terminals as the data owner are numerous and mistrustful, while, on the other hand, in most cases, the terminals find it difficult to finish many safety operation due to computation and storage capability constraints. In light of they Multi Owners and Multi Users (MOMU situation in the crowd-sensing data cloud environment, this paper presents a secure nearest neighbor query scheme based on the proxy server architecture, which is constructed by protocols of secure two-party computation and secure Voronoi diagram algorithm. It not only preserves the data confidentiality and query privacy but also effectively resists the collusion between the cloud server and the data owners or users. Finally, extensive theoretical and experimental evaluations are presented to show that our proposed scheme achieves a superior balance between the security and query performance compared to other schemes.

Co-Expression of Neighboring Genes in the Zebrafish (Danio rerio Genome

Directory of Open Access Journals (Sweden)

Daryi Wang

2009-08-01

Full Text Available Neighboring genes in the eukaryotic genome have a tendency to express concurrently, and the proximity of two adjacent genes is often considered a possible explanation for their co-expression behavior. However, the actual contribution of the physical distance between two genes to their co-expression behavior has yet to be defined. To further investigate this issue, we studied the co-expression of neighboring genes in zebrafish, which has a compact genome and has experienced a whole genome duplication event. Our analysis shows that the proportion of highly co-expressed neighboring pairs (Pearson’s correlation coefficient R>0.7 is low (0.24% ~ 0.67%; however, it is still significantly higher than that of random pairs. In particular, the statistical result implies that the co-expression tendency of neighboring pairs is negatively correlated with their physical distance. Our findings therefore suggest that physical distance may play an important role in the co-expression of neighboring genes. Possible mechanisms related to the neighboring genes’ co-expression are also discussed.

On relevant boundary perturbations of unitary minimal models

International Nuclear Information System (INIS)

Recknagel, A.; Roggenkamp, D.; Schomerus, V.

2000-01-01

We consider unitary Virasoro minimal models on the disk with Cardy boundary conditions and discuss deformations by certain relevant boundary operators, analogous to tachyon condensation in string theory. Concentrating on the least relevant boundary field, we can perform a perturbative analysis of renormalization group fixed points. We find that the systems always flow towards stable fixed points which admit no further (non-trivial) relevant perturbations. The new conformal boundary conditions are in general given by superpositions of 'pure' Cardy boundary conditions

New Sliding Puzzle with Neighbors Swap Motion

OpenAIRE

Prihardono, Ariyanto; Kawagoe, Kenichi

2015-01-01

The sliding puzzles (15-puzzle, 8-puzzle, 5-puzzle) are known to have 2 kind of puz-zle: solvable puzzle and unsolvable puzzle. In this thesis, we make a new puzzle with only 1 kind of it, solvable puzzle. This new puzzle is made by adopting sliding puzzle with several additional rules from M13 puzzle; the puzzle that is formed form The Mathieu group M13. This puzzle has a movement that called a neighbors swap motion, a rule of movement that enables every neighboring points to swap. This extr...

The Influence of Neighbor Effect and Urbanization Toward Organ Donation in Thailand.

Science.gov (United States)

Wongboonsin, Kua; Jindahra, Pavitra; Teerakapibal, Surat

2018-03-01

Toward population wellness, an extreme scarcity of organ supply is proven to be an enormous hindrance. Preferences toward organ donation are vital to raise the organ donation rate. Notably, the area people live in can address the social influence on individual preference toward organ donation. This article studies the impact of the neighbor effect on organ donation decisions, addressing the social influence of urbanization on preferences. How neighborhood-specific variables, population density, and socioeconomic status drive the neighbor effect is investigated. The pursuit of organ donor traits is to be answered. The study uses organ donation interview survey data and neighborhood-specific data from Thailand to estimate a series of logistic regression models. Individuals residing in urban areas exhibit a greater likelihood to sign the donor card than those in rural areas. The neighborhood socioeconomic status is the key driver. An individual is more willing to be an organ donor when having neighbors with higher socioeconomic statuses. Results also reveal positive influences of males and education on the organ donation rate. This article documents the "neighbor effect" on the organ donation decision via living area type, offering an alternative exposition in raising the organ donation rate. In shifting the society norm toward organ donation consent, policy-makers should acknowledge the benefit of urbanization on organ donation decision derived from resourceful urban areas. Moreover, raising education levels does improve not only citizens' well-being but also their tendency to exhibit an altruistic act toward others.

The role of orthography in the semantic activation of neighbors.

Science.gov (United States)

Hino, Yasushi; Lupker, Stephen J; Taylor, Tamsen E

2012-09-01

There is now considerable evidence that a letter string can activate semantic information appropriate to its orthographic neighbors (e.g., Forster & Hector's, 2002, TURPLE effect). This phenomenon is the focus of the present research. Using Japanese words, we examined whether semantic activation of neighbors is driven directly by orthographic similarity alone or whether there is also a role for phonological similarity. In Experiment 1, using a relatedness judgment task in which a Kanji word-Katakana word pair was presented on each trial, an inhibitory effect was observed when the initial Kanji word was related to an orthographic and phonological neighbor of the Katakana word target but not when the initial Kanji word was related to a phonological but not orthographic neighbor of the Katakana word target. This result suggests that phonology plays little, if any, role in the activation of neighbors' semantics when reading familiar words. In Experiment 2, the targets were transcribed into Hiragana, a script they are typically not written in, requiring readers to engage in phonological coding. In that experiment, inhibitory effects were observed in both conditions. This result indicates that phonologically mediated semantic activation of neighbors will emerge when phonological processing is necessary in order to understand a written word (e.g., when that word is transcribed into an unfamiliar script). PsycINFO Database Record (c) 2012 APA, all rights reserved.

A new approach to very short term wind speed prediction using k-nearest neighbor classification

International Nuclear Information System (INIS)

Yesilbudak, Mehmet; Sagiroglu, Seref; Colak, Ilhami

2013-01-01

Highlights: ► Wind speed parameter was predicted in an n-tupled inputs using k-NN classification. ► The effects of input parameters, nearest neighbors and distance metrics were analyzed. ► Many useful and reasonable inferences were uncovered using the developed model. - Abstract: Wind energy is an inexhaustible energy source and wind power production has been growing rapidly in recent years. However, wind power has a non-schedulable nature due to wind speed variations. Hence, wind speed prediction is an indispensable requirement for power system operators. This paper predicts wind speed parameter in an n-tupled inputs using k-nearest neighbor (k-NN) classification and analyzes the effects of input parameters, nearest neighbors and distance metrics on wind speed prediction. The k-NN classification model was developed using the object oriented programming techniques and includes Manhattan and Minkowski distance metrics except from Euclidean distance metric on the contrary of literature. The k-NN classification model which uses wind direction, air temperature, atmospheric pressure and relative humidity parameters in a 4-tupled space achieved the best wind speed prediction for k = 5 in the Manhattan distance metric. Differently, the k-NN classification model which uses wind direction, air temperature and atmospheric pressure parameters in a 3-tupled inputs gave the worst wind speed prediction for k = 1 in the Minkowski distance metric

Incorporating the information from direct and indirect neighbors into fitness evaluation enhances the cooperation in the social dilemmas

International Nuclear Information System (INIS)

Hu, Menglong; Wang, Juan; Kong, Lingcong; An, Kang; Bi, Tao; Guo, Baohong; Dong, Enzeng

2015-01-01

Highlights: •A novel fitness evaluation method integrating environmental information is presented. •The introduction of neighbors’ payoff favors the promotion of cooperation in the PDG. •The role of direct neighbors becomes much more prominent. •In the SDG, the cooperative behavior is also improved by this new mechanism. -- Abstract: We propose an improved fitness evaluation method to investigate the evolution of cooperation in the spatial social dilemmas. In our model, a focal player’s fitness is calculated as the linear combination of his own payoff, the average payoffs of direct and indirect neighbors in which two independent selection parameters (α and β) are used to control the proportion of various payoff contribution to the current fitness. Then, the fitness-based strategy update rule is still Fermi-like, and asynchronous update is adopted here. A large plethora of numerical simulations are performed to validate the behaviors of the current model, and the results unambiguously demonstrate that the cooperation level is greatly enhanced by introducing the payoffs from the surrounding players. In particular, the influence of direct neighbors become more evident when compared with indirect neighbors since the correlation between focal players and their direct neighbors is much closer. Current outcomes are significant for us to further illustrate the origin and emergence of cooperation within a wide variety of natural and man-made systems

Improved Fuzzy K-Nearest Neighbor Using Modified Particle Swarm Optimization

Science.gov (United States)

Jamaluddin; Siringoringo, Rimbun

2017-12-01

Fuzzy k-Nearest Neighbor (FkNN) is one of the most powerful classification methods. The presence of fuzzy concepts in this method successfully improves its performance on almost all classification issues. The main drawbackof FKNN is that it is difficult to determine the parameters. These parameters are the number of neighbors (k) and fuzzy strength (m). Both parameters are very sensitive. This makes it difficult to determine the values of ‘m’ and ‘k’, thus making FKNN difficult to control because no theories or guides can deduce how proper ‘m’ and ‘k’ should be. This study uses Modified Particle Swarm Optimization (MPSO) to determine the best value of ‘k’ and ‘m’. MPSO is focused on the Constriction Factor Method. Constriction Factor Method is an improvement of PSO in order to avoid local circumstances optima. The model proposed in this study was tested on the German Credit Dataset. The test of the data/The data test has been standardized by UCI Machine Learning Repository which is widely applied to classification problems. The application of MPSO to the determination of FKNN parameters is expected to increase the value of classification performance. Based on the experiments that have been done indicating that the model offered in this research results in a better classification performance compared to the Fk-NN model only. The model offered in this study has an accuracy rate of 81%, while. With using Fk-NN model, it has the accuracy of 70%. At the end is done comparison of research model superiority with 2 other classification models;such as Naive Bayes and Decision Tree. This research model has a better performance level, where Naive Bayes has accuracy 75%, and the decision tree model has 70%

Color and neighbor edge directional difference feature for image retrieval

Institute of Scientific and Technical Information of China (English)

Chaobing Huang; Shengsheng Yu; Jingli Zhou; Hongwei Lu

2005-01-01

@@ A novel image feature termed neighbor edge directional difference unit histogram is proposed, in which the neighbor edge directional difference unit is defined and computed for every pixel in the image, and is used to generate the neighbor edge directional difference unit histogram. This histogram and color histogram are used as feature indexes to retrieve color image. The feature is invariant to image scaling and translation and has more powerful descriptive for the natural color images. Experimental results show that the feature can achieve better retrieval performance than other color-spatial features.

Nearest neighbors by neighborhood counting.

Science.gov (United States)

Wang, Hui

2006-06-01

Finding nearest neighbors is a general idea that underlies many artificial intelligence tasks, including machine learning, data mining, natural language understanding, and information retrieval. This idea is explicitly used in the k-nearest neighbors algorithm (kNN), a popular classification method. In this paper, this idea is adopted in the development of a general methodology, neighborhood counting, for devising similarity functions. We turn our focus from neighbors to neighborhoods, a region in the data space covering the data point in question. To measure the similarity between two data points, we consider all neighborhoods that cover both data points. We propose to use the number of such neighborhoods as a measure of similarity. Neighborhood can be defined for different types of data in different ways. Here, we consider one definition of neighborhood for multivariate data and derive a formula for such similarity, called neighborhood counting measure or NCM. NCM was tested experimentally in the framework of kNN. Experiments show that NCM is generally comparable to VDM and its variants, the state-of-the-art distance functions for multivariate data, and, at the same time, is consistently better for relatively large k values. Additionally, NCM consistently outperforms HEOM (a mixture of Euclidean and Hamming distances), the "standard" and most widely used distance function for multivariate data. NCM has a computational complexity in the same order as the standard Euclidean distance function and NCM is task independent and works for numerical and categorical data in a conceptually uniform way. The neighborhood counting methodology is proven sound for multivariate data experimentally. We hope it will work for other types of data.

Neighboring Genes Show Correlated Evolution in Gene Expression

Science.gov (United States)

Ghanbarian, Avazeh T.; Hurst, Laurence D.

2015-01-01

When considering the evolution of a gene’s expression profile, we commonly assume that this is unaffected by its genomic neighborhood. This is, however, in contrast to what we know about the lack of autonomy between neighboring genes in gene expression profiles in extant taxa. Indeed, in all eukaryotic genomes genes of similar expression-profile tend to cluster, reflecting chromatin level dynamics. Does it follow that if a gene increases expression in a particular lineage then the genomic neighbors will also increase in their expression or is gene expression evolution autonomous? To address this here we consider evolution of human gene expression since the human-chimp common ancestor, allowing for both variation in estimation of current expression level and error in Bayesian estimation of the ancestral state. We find that in all tissues and both sexes, the change in gene expression of a focal gene on average predicts the change in gene expression of neighbors. The effect is highly pronounced in the immediate vicinity (genes increasing their expression in humans tend to avoid nuclear lamina domains and be enriched for the gene activator 5-hydroxymethylcytosine, we conclude that, most probably owing to chromatin level control of gene expression, a change in gene expression of one gene likely affects the expression evolution of neighbors, what we term expression piggybacking, an analog of hitchhiking. PMID:25743543

Stress and adaptation : Toward ecologically relevant animal models

NARCIS (Netherlands)

Koolhaas, Jaap M.; Boer, Sietse F. de; Buwalda, Bauke

Animal models have contributed considerably to the current understanding of mechanisms underlying the role of stress in health and disease. Despite the progress made already, much more can be made by more carefully exploiting animals' and humans' shared biology, using ecologically relevant models.

Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm

Directory of Open Access Journals (Sweden)

E. Parvinnia

2014-01-01

Full Text Available Electroencephalogram (EEG signals are often used to diagnose diseases such as seizure, alzheimer, and schizophrenia. One main problem with the recorded EEG samples is that they are not equally reliable due to the artifacts at the time of recording. EEG signal classification algorithms should have a mechanism to handle this issue. It seems that using adaptive classifiers can be useful for the biological signals such as EEG. In this paper, a general adaptive method named weighted distance nearest neighbor (WDNN is applied for EEG signal classification to tackle this problem. This classification algorithm assigns a weight to each training sample to control its influence in classifying test samples. The weights of training samples are used to find the nearest neighbor of an input query pattern. To assess the performance of this scheme, EEG signals of thirteen schizophrenic patients and eighteen normal subjects are analyzed for the classification of these two groups. Several features including, fractal dimension, band power and autoregressive (AR model are extracted from EEG signals. The classification results are evaluated using Leave one (subject out cross validation for reliable estimation. The results indicate that combination of WDNN and selected features can significantly outperform the basic nearest-neighbor and the other methods proposed in the past for the classification of these two groups. Therefore, this method can be a complementary tool for specialists to distinguish schizophrenia disorder.

Local randomization in neighbor selection improves PRM roadmap quality

KAUST Repository

McMahon, Troy; Jacobs, Sam; Boyd, Bryan; Tapia, Lydia; Amato, Nancy M.

2012-01-01

Probabilistic Roadmap Methods (PRMs) are one of the most used classes of motion planning methods. These sampling-based methods generate robot configurations (nodes) and then connect them to form a graph (roadmap) containing representative feasible pathways. A key step in PRM roadmap construction involves identifying a set of candidate neighbors for each node. Traditionally, these candidates are chosen to be the k-closest nodes based on a given distance metric. In this paper, we propose a new neighbor selection policy called LocalRand(k,K'), that first computes the K' closest nodes to a specified node and then selects k of those nodes at random. Intuitively, LocalRand attempts to benefit from random sampling while maintaining the higher levels of local planner success inherent to selecting more local neighbors. We provide a methodology for selecting the parameters k and K'. We perform an experimental comparison which shows that for both rigid and articulated robots, LocalRand results in roadmaps that are better connected than the traditional k-closest policy or a purely random neighbor selection policy. The cost required to achieve these results is shown to be comparable to k-closest. © 2012 IEEE.

Local randomization in neighbor selection improves PRM roadmap quality

KAUST Repository

McMahon, Troy

2012-10-01

Probabilistic Roadmap Methods (PRMs) are one of the most used classes of motion planning methods. These sampling-based methods generate robot configurations (nodes) and then connect them to form a graph (roadmap) containing representative feasible pathways. A key step in PRM roadmap construction involves identifying a set of candidate neighbors for each node. Traditionally, these candidates are chosen to be the k-closest nodes based on a given distance metric. In this paper, we propose a new neighbor selection policy called LocalRand(k,K\\'), that first computes the K\\' closest nodes to a specified node and then selects k of those nodes at random. Intuitively, LocalRand attempts to benefit from random sampling while maintaining the higher levels of local planner success inherent to selecting more local neighbors. We provide a methodology for selecting the parameters k and K\\'. We perform an experimental comparison which shows that for both rigid and articulated robots, LocalRand results in roadmaps that are better connected than the traditional k-closest policy or a purely random neighbor selection policy. The cost required to achieve these results is shown to be comparable to k-closest. © 2012 IEEE.

The Islands Approach to Nearest Neighbor Querying in Spatial Networks

DEFF Research Database (Denmark)

Huang, Xuegang; Jensen, Christian Søndergaard; Saltenis, Simonas

2005-01-01

, and versatile approach to k nearest neighbor computation that obviates the need for using several k nearest neighbor approaches for supporting a single service scenario. The experimental comparison with the existing techniques uses real-world road network data and considers both I/O and CPU performance...

Handling Neighbor Discovery and Rendezvous Consistency with Weighted Quorum-Based Approach.

Science.gov (United States)

Own, Chung-Ming; Meng, Zhaopeng; Liu, Kehan

2015-09-03

Neighbor discovery and the power of sensors play an important role in the formation of Wireless Sensor Networks (WSNs) and mobile networks. Many asynchronous protocols based on wake-up time scheduling have been proposed to enable neighbor discovery among neighboring nodes for the energy saving, especially in the difficulty of clock synchronization. However, existing researches are divided two parts with the neighbor-discovery methods, one is the quorum-based protocols and the other is co-primality based protocols. Their distinction is on the arrangements of time slots, the former uses the quorums in the matrix, the latter adopts the numerical analysis. In our study, we propose the weighted heuristic quorum system (WQS), which is based on the quorum algorithm to eliminate redundant paths of active slots. We demonstrate the specification of our system: fewer active slots are required, the referring rate is balanced, and remaining power is considered particularly when a device maintains rendezvous with discovered neighbors. The evaluation results showed that our proposed method can effectively reschedule the active slots and save the computing time of the network system.

Handling Neighbor Discovery and Rendezvous Consistency with Weighted Quorum-Based Approach

Directory of Open Access Journals (Sweden)

Chung-Ming Own

2015-09-01

Full Text Available Neighbor discovery and the power of sensors play an important role in the formation of Wireless Sensor Networks (WSNs and mobile networks. Many asynchronous protocols based on wake-up time scheduling have been proposed to enable neighbor discovery among neighboring nodes for the energy saving, especially in the difficulty of clock synchronization. However, existing researches are divided two parts with the neighbor-discovery methods, one is the quorum-based protocols and the other is co-primality based protocols. Their distinction is on the arrangements of time slots, the former uses the quorums in the matrix, the latter adopts the numerical analysis. In our study, we propose the weighted heuristic quorum system (WQS, which is based on the quorum algorithm to eliminate redundant paths of active slots. We demonstrate the specification of our system: fewer active slots are required, the referring rate is balanced, and remaining power is considered particularly when a device maintains rendezvous with discovered neighbors. The evaluation results showed that our proposed method can effectively reschedule the active slots and save the computing time of the network system.

Accelerating distributed average consensus by exploring the information of second-order neighbors

Energy Technology Data Exchange (ETDEWEB)

Yuan Deming [School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu (China); Xu Shengyuan, E-mail: syxu02@yahoo.com.c [School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu (China); Zhao Huanyu [School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu (China); Chu Yuming [Department of Mathematics, Huzhou Teacher' s College, Huzhou 313000, Zhejiang (China)

2010-05-17

The problem of accelerating distributed average consensus by using the information of second-order neighbors in both the discrete- and continuous-time cases is addressed in this Letter. In both two cases, when the information of second-order neighbors is used in each iteration, the network will converge with a speed faster than the algorithm only using the information of first-order neighbors. Moreover, the problem of using partial information of second-order neighbors is considered, and the edges are not chosen randomly from second-order neighbors. In the continuous-time case, the edges are chosen by solving a convex optimization problem which is formed by using the convex relaxation method. In the discrete-time case, for small network the edges are chosen optimally via the brute force method. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed algorithm.

Finger vein identification using fuzzy-based k-nearest centroid neighbor classifier

Science.gov (United States)

Rosdi, Bakhtiar Affendi; Jaafar, Haryati; Ramli, Dzati Athiar

2015-02-01

In this paper, a new approach for personal identification using finger vein image is presented. Finger vein is an emerging type of biometrics that attracts attention of researchers in biometrics area. As compared to other biometric traits such as face, fingerprint and iris, finger vein is more secured and hard to counterfeit since the features are inside the human body. So far, most of the researchers focus on how to extract robust features from the captured vein images. Not much research was conducted on the classification of the extracted features. In this paper, a new classifier called fuzzy-based k-nearest centroid neighbor (FkNCN) is applied to classify the finger vein image. The proposed FkNCN employs a surrounding rule to obtain the k-nearest centroid neighbors based on the spatial distributions of the training images and their distance to the test image. Then, the fuzzy membership function is utilized to assign the test image to the class which is frequently represented by the k-nearest centroid neighbors. Experimental evaluation using our own database which was collected from 492 fingers shows that the proposed FkNCN has better performance than the k-nearest neighbor, k-nearest-centroid neighbor and fuzzy-based-k-nearest neighbor classifiers. This shows that the proposed classifier is able to identify the finger vein image effectively.

On Competitiveness of Nearest-Neighbor-Based Music Classification: A Methodological Critique

DEFF Research Database (Denmark)

Pálmason, Haukur; Jónsson, Björn Thór; Amsaleg, Laurent

2017-01-01

The traditional role of nearest-neighbor classification in music classification research is that of a straw man opponent for the learning approach of the hour. Recent work in high-dimensional indexing has shown that approximate nearest-neighbor algorithms are extremely scalable, yielding results...... of reasonable quality from billions of high-dimensional features. With such efficient large-scale classifiers, the traditional music classification methodology of aggregating and compressing the audio features is incorrect; instead the approximate nearest-neighbor classifier should be given an extensive data...... collection to work with. We present a case study, using a well-known MIR classification benchmark with well-known music features, which shows that a simple nearest-neighbor classifier performs very competitively when given ample data. In this position paper, we therefore argue that nearest...

Beyond formal groups: neighboring acts and watershed protection in Appalachia

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Heather Lukacs

2016-09-01

Full Text Available This paper explores how watershed organizations in Appalachia have persisted in addressing water quality issues in areas with a history of coal mining. We identified two watershed groups that have taken responsibility for restoring local creeks that were previously highly degraded and sporadically managed. These watershed groups represent cases of self-organized commons governance in resource-rich, economically poor Appalachian communities. We describe the extent and characteristics of links between watershed group volunteers and watershed residents who are not group members. Through surveys, participant observation, and key-informant consultation, we found that neighbors – group members as well as non-group-members – supported the group's function through informal neighboring acts. Past research has shown that local commons governance institutions benefit from being nested in supportive external structures. We found that the persistence and success of community watershed organizations depends on the informal participation of local residents, affirming the necessity of looking beyond formal, organized groups to understand the resources, expertise, and information needed to address complex water pollution at the watershed level. Our findings augment the concept of nestedness in commons governance to include that of a formal organization acting as a neighbor that exchanges informal neighboring acts with local residents. In this way, we extend the concept of neighboring to include interactions between individuals and a group operating in the same geographic area.

Recursive nearest neighbor search in a sparse and multiscale domain for comparing audio signals

DEFF Research Database (Denmark)

Sturm, Bob L.; Daudet, Laurent

2011-01-01

We investigate recursive nearest neighbor search in a sparse domain at the scale of audio signals. Essentially, to approximate the cosine distance between the signals we make pairwise comparisons between the elements of localized sparse models built from large and redundant multiscale dictionaries...

Learn good from bad: Effects of good and bad neighbors in spatial prisoners' dilemma games

Science.gov (United States)

Lu, Peng

2015-10-01

Cooperation is vital for the human society and this study focuses on how to promote cooperation. In our stratification model, there exist three classes: two minorities are elites who are prone to cooperate and scoundrels who are born to defect; one majority is the class of common people. Agents of these three classes interact with each other on a square lattice. Commons' cooperation and its factors are investigated. Contradicting our common sense, it indicates that elites play a negative role while scoundrels play a positive one in promoting commons' cooperation. Besides, effects of good and bad neighbors vary with temptation. When the temptation is smaller the positive effect is able to overcome the negative effect, but the later prevails when the temptation is larger. It concludes that common people are more prone to cooperate in harsh environment with bad neighbors, and a better environment with good neighbors merely leads to laziness and free riding of commons.

Plant neighbor identity influences plant biochemistry and physiology related to defense.

Science.gov (United States)

Broz, Amanda K; Broeckling, Corey D; De-la-Peña, Clelia; Lewis, Matthew R; Greene, Erick; Callaway, Ragan M; Sumner, Lloyd W; Vivanco, Jorge M

2010-06-17

Chemical and biological processes dictate an individual organism's ability to recognize and respond to other organisms. A small but growing body of evidence suggests that plants may be capable of recognizing and responding to neighboring plants in a species specific fashion. Here we tested whether or not individuals of the invasive exotic weed, Centaurea maculosa, would modulate their defensive strategy in response to different plant neighbors. In the greenhouse, C. maculosa individuals were paired with either conspecific (C. maculosa) or heterospecific (Festuca idahoensis) plant neighbors and elicited with the plant defense signaling molecule methyl jasmonate to mimic insect herbivory. We found that elicited C. maculosa plants grown with conspecific neighbors exhibited increased levels of total phenolics, whereas those grown with heterospecific neighbors allocated more resources towards growth. To further investigate these results in the field, we conducted a metabolomics analysis to explore chemical differences between individuals of C. maculosa growing in naturally occurring conspecific and heterospecific field stands. Similar to the greenhouse results, C. maculosa individuals accumulated higher levels of defense-related secondary metabolites and lower levels of primary metabolites when growing in conspecific versus heterospecific field stands. Leaf herbivory was similar in both stand types; however, a separate field study positively correlated specialist herbivore load with higher densities of C. maculosa conspecifics. Our results suggest that an individual C. maculosa plant can change its defensive strategy based on the identity of its plant neighbors. This is likely to have important consequences for individual and community success.

Mountain tourism development in Serbia and neighboring countries

Directory of Open Access Journals (Sweden)

Krunić Nikola

2010-01-01

Full Text Available Mountain areas with their surroundings are important parts of tourism regions with potentials for all-season tourism development and complementary activities. Development possibilities are based on size of high mountain territory, nature protection regimes, infrastructural equipment, provided conditions for leisure and recreation as well as involvement of local population in processes of development and protection. This paper analyses the key aspects of tourism development, winter tourism in high-mountain areas of Serbia and some neighboring countries (Slovakia, Romania, Bulgaria, and Greece. Common determinants of cohesion between nature protection and mountain tourism development, national development policies, applied models and concepts and importance of trans-border cooperation are indicated.

Geometric k-nearest neighbor estimation of entropy and mutual information

Science.gov (United States)

Lord, Warren M.; Sun, Jie; Bollt, Erik M.

2018-03-01

Nonparametric estimation of mutual information is used in a wide range of scientific problems to quantify dependence between variables. The k-nearest neighbor (knn) methods are consistent, and therefore expected to work well for a large sample size. These methods use geometrically regular local volume elements. This practice allows maximum localization of the volume elements, but can also induce a bias due to a poor description of the local geometry of the underlying probability measure. We introduce a new class of knn estimators that we call geometric knn estimators (g-knn), which use more complex local volume elements to better model the local geometry of the probability measures. As an example of this class of estimators, we develop a g-knn estimator of entropy and mutual information based on elliptical volume elements, capturing the local stretching and compression common to a wide range of dynamical system attractors. A series of numerical examples in which the thickness of the underlying distribution and the sample sizes are varied suggest that local geometry is a source of problems for knn methods such as the Kraskov-Stögbauer-Grassberger estimator when local geometric effects cannot be removed by global preprocessing of the data. The g-knn method performs well despite the manipulation of the local geometry. In addition, the examples suggest that the g-knn estimators can be of particular relevance to applications in which the system is large, but the data size is limited.

Some Observations about the Nearest-Neighbor Model of the Error Threshold

International Nuclear Information System (INIS)

Gerrish, Philip J.

2009-01-01

I explore some aspects of the 'error threshold' - a critical mutation rate above which a population is nonviable. The phase transition that occurs as mutation rate crosses this threshold has been shown to be mathematically equivalent to the loss of ferromagnetism that occurs as temperature exceeds the Curie point. I will describe some refinements and new results based on the simplest of these mutation models, will discuss the commonly unperceived robustness of this simple model, and I will show some preliminary results comparing qualitative predictions with simulations of finite populations adapting at high mutation rates. I will talk about how these qualitative predictions are relevant to biomedical science and will discuss how my colleagues and I are looking for phase-transition signatures in real populations of Escherichia coli that go extinct as a result of excessive mutation.

Detect thy neighbor: Identity recognition at the root level in plants

NARCIS (Netherlands)

Chen, B.J.W.; During, H.J.; Anten, N.P.R.

2012-01-01

Some plant species increase root allocation at the expense of reproduction in the presence of non-self and non-kin neighbors, indicating the capacity of neighbor-identityrecognition at the rootlevel. Yet in spite of the potential consequences of rootidentityrecognition for the relationship between

Neighboring trees affect ectomycorrhizal fungal community composition in a woodland-forest ecotone.

Science.gov (United States)

Hubert, Nathaniel A; Gehring, Catherine A

2008-09-01

Ectomycorrhizal fungi (EMF) are frequently species rich and functionally diverse; yet, our knowledge of the environmental factors that influence local EMF diversity and species composition remains poor. In particular, little is known about the influence of neighboring plants on EMF community structure. We tested the hypothesis that the EMF of plants with heterospecific neighbors would differ in species richness and community composition from the EMF of plants with conspecific neighbors. We conducted our study at the ecotone between pinyon (Pinus edulis)-juniper (Juniperus monosperma) woodland and ponderosa pine (Pinus ponderosa) forest in northern Arizona, USA where the dominant trees formed associations with either EMF (P. edulis and P. ponderosa) or arbuscular mycorrhizal fungi (AMF; J. monosperma). We also compared the EMF communities of pinyon and ponderosa pines where their rhizospheres overlapped. The EMF community composition, but not species richness of pinyon pines was significantly influenced by neighboring AM juniper, but not by neighboring EM ponderosa pine. Ponderosa pine EMF communities were different in species composition when growing in association with pinyon pine than when growing in association with a conspecific. The EMF communities of pinyon and ponderosa pines were similar where their rhizospheres overlapped consisting of primarily the same species in similar relative abundance. Our findings suggest that neighboring tree species identity shaped EMF community structure, but that these effects were specific to host-neighbor combinations. The overlap in community composition between pinyon pine and ponderosa pine suggests that these tree species may serve as reservoirs of EMF inoculum for one another.

Plant neighbor identity influences plant biochemistry and physiology related to defense

Directory of Open Access Journals (Sweden)

Callaway Ragan M

2010-06-01

Full Text Available Abstract Background Chemical and biological processes dictate an individual organism's ability to recognize and respond to other organisms. A small but growing body of evidence suggests that plants may be capable of recognizing and responding to neighboring plants in a species specific fashion. Here we tested whether or not individuals of the invasive exotic weed, Centaurea maculosa, would modulate their defensive strategy in response to different plant neighbors. Results In the greenhouse, C. maculosa individuals were paired with either conspecific (C. maculosa or heterospecific (Festuca idahoensis plant neighbors and elicited with the plant defense signaling molecule methyl jasmonate to mimic insect herbivory. We found that elicited C. maculosa plants grown with conspecific neighbors exhibited increased levels of total phenolics, whereas those grown with heterospecific neighbors allocated more resources towards growth. To further investigate these results in the field, we conducted a metabolomics analysis to explore chemical differences between individuals of C. maculosa growing in naturally occurring conspecific and heterospecific field stands. Similar to the greenhouse results, C. maculosa individuals accumulated higher levels of defense-related secondary metabolites and lower levels of primary metabolites when growing in conspecific versus heterospecific field stands. Leaf herbivory was similar in both stand types; however, a separate field study positively correlated specialist herbivore load with higher densities of C. maculosa conspecifics. Conclusions Our results suggest that an individual C. maculosa plant can change its defensive strategy based on the identity of its plant neighbors. This is likely to have important consequences for individual and community success.

The spectrum and the quantum Hall effect on the square lattice with next-nearest-neighbor hopping: Statistics of holons and spinons in the t-J model

International Nuclear Information System (INIS)

Hatsugai, Y.; Kohmoto, M.

1992-01-01

We investigate the energy spectrum and the Hall effect of electrons on the square lattice with next-nearest-neighbor (NNN) hopping as well as nearest-neighbor hopping. General rational values of magnetic flux per unit cell φ=p/q are considered. In the absence of NNN hopping, the two bands at the center touch for q even, thus the Hall conductance is not well defined at half filling. An energy gap opens there by introducing NNN hoping. When φ=1/2, the NNN model coincides with the mean field Hamiltonian for the chiral spin state proposed by Wen, Wilczek and Zee (WWZ). The Hall conductance is calculated from the Diophantine equation and the E-φ diagram. We find that gaps close for other fillings at certain values of NNN hopping strength. The quantized value of the Hall conductance changes once this phenomenon occurs. In a mean field treatment of the t-J model, the effective Hamiltonian is the same as our NNN model. From this point of view, the statistics of the quasi-particles is not always semion and depends on the filling and the strength of the mean field. (orig.)

A multilevel-skin neighbor list algorithm for molecular dynamics simulation

Science.gov (United States)

Zhang, Chenglong; Zhao, Mingcan; Hou, Chaofeng; Ge, Wei

2018-01-01

Searching of the interaction pairs and organization of the interaction processes are important steps in molecular dynamics (MD) algorithms and are critical to the overall efficiency of the simulation. Neighbor lists are widely used for these steps, where thicker skin can reduce the frequency of list updating but is discounted by more computation in distance check for the particle pairs. In this paper, we propose a new neighbor-list-based algorithm with a precisely designed multilevel skin which can reduce unnecessary computation on inter-particle distances. The performance advantages over traditional methods are then analyzed against the main simulation parameters on Intel CPUs and MICs (many integrated cores), and are clearly demonstrated. The algorithm can be generalized for various discrete simulations using neighbor lists.

Effects of second neighbor interactions on skyrmion lattices in chiral magnets

International Nuclear Information System (INIS)

Oliveira, E A S; Silva, R L; Silva, R C; Pereira, A R

2017-01-01

In this paper we investigate the influences of the second neighbor interactions on a skyrmion lattice in two-dimensional chiral magnets. Such a system contains the exchange and the Dzyaloshinskii–Moriya for the spin interactions and therefore, we analyse three situations: firstly, the second neighbor interaction is present only in the exchange coupling; secondly, it is present only in the Dzyaloshinskii–Moriya coupling. Finally, the second neighbor interactions are present in both exchange and Dzyaloshinskii–Moriya couplings. We show that such effects cause important modifications to the helical and skyrmion phases when an external magnetic field is applied. (paper)

Improving Fraudster Detection in Online Auctions by Using Neighbor-Driven Attributes

Directory of Open Access Journals (Sweden)

Jun-Lin Lin

2015-12-01

Full Text Available Online auction websites use a simple reputation system to help their users to evaluate the trustworthiness of sellers and buyers. However, to improve their reputation in the reputation system, fraudulent users can easily deceive the reputation system by creating fake transactions. This inflated-reputation fraud poses a major problem for online auction websites because it can lead legitimate users into scams. Numerous approaches have been proposed in the literature to address this problem, most of which involve using social network analysis (SNA to derive critical features (e.g., k-core, center weight, and neighbor diversity for distinguishing fraudsters from legitimate users. This paper discusses the limitations of these SNA features and proposes a class of SNA features referred to as neighbor-driven attributes (NDAs. The NDAs of users are calculated from the features of their neighbors. Because fraudsters require collusive neighbors to provide them with positive ratings in the reputation system, using NDAs can be helpful for detecting fraudsters. Although the idea of NDAs is not entirely new, experimental results on a real-world dataset showed that using NDAs improves classification accuracy compared with state-of-the-art methods that use the k-core, center weight, and neighbor diversity.

Model of inter-cell interference phenomenon in 10 nm magnetic tunnel junction with perpendicular anisotropy array due to oscillatory stray field from neighboring cells

Science.gov (United States)

Ohuchida, Satoshi; Endoh, Tetsuo

2018-06-01

In this paper, we propose a new model of inter-cell interference phenomenon in a 10 nm magnetic tunnel junction with perpendicular anisotropy (p-MTJ) array and investigated the interference effect between a program cell and unselected cells due to the oscillatory stray field from neighboring cells by Landau–Lifshitz–Gilbert micromagnetic simulation. We found that interference brings about a switching delay in a program cell and excitation of magnetization precession in unselected cells even when no programing current passes through. The origin of interference is ferromagnetic resonance between neighboring cells. During the interference period, the precession frequency of the program cell is 20.8 GHz, which synchronizes with that of the theoretical precession frequency f = γH eff in unselected cells. The disturbance strength of unselected cells decreased to be inversely proportional to the cube of the distance from the program cell, which is in good agreement with the dependence of stray field on the distance from the program cell calculated by the dipole approximation method.

River Flow Prediction Using the Nearest Neighbor Probabilistic Ensemble Method

Directory of Open Access Journals (Sweden)

H. Sanikhani

2016-02-01

Full Text Available Introduction: In the recent years, researchers interested on probabilistic forecasting of hydrologic variables such river flow.A probabilistic approach aims at quantifying the prediction reliability through a probability distribution function or a prediction interval for the unknown future value. The evaluation of the uncertainty associated to the forecast is seen as a fundamental information, not only to correctly assess the prediction, but also to compare forecasts from different methods and to evaluate actions and decisions conditionally on the expected values. Several probabilistic approaches have been proposed in the literature, including (1 methods that use resampling techniques to assess parameter and model uncertainty, such as the Metropolis algorithm or the Generalized Likelihood Uncertainty Estimation (GLUE methodology for an application to runoff prediction, (2 methods based on processing the forecast errors of past data to produce the probability distributions of future values and (3 methods that evaluate how the uncertainty propagates from the rainfall forecast to the river discharge prediction, as the Bayesian forecasting system. Materials and Methods: In this study, two different probabilistic methods are used for river flow prediction.Then the uncertainty related to the forecast is quantified. One approach is based on linear predictors and in the other, nearest neighbor was used. The nonlinear probabilistic ensemble can be used for nonlinear time series analysis using locally linear predictors, while NNPE utilize a method adapted for one step ahead nearest neighbor methods. In this regard, daily river discharge (twelve years of Dizaj and Mashin Stations on Baranduz-Chay basin in west Azerbijan and Zard-River basin in Khouzestan provinces were used, respectively. The first six years of data was applied for fitting the model. The next three years was used to calibration and the remained three yeas utilized for testing the models

Exactly solvable spin-1 Ising–Heisenberg diamond chain with the second-neighbor interaction between nodal spins

International Nuclear Information System (INIS)

Hovhannisyan, V V; Ananikian, N S; Strečka, J

2016-01-01

The spin-1 Ising–Heisenberg diamond chain with the second-neighbor interaction between nodal spins is rigorously solved using the transfer-matrix method. In particular, exact results for the ground state, magnetization process and specific heat are presented and discussed. It is shown that further-neighbor interaction between nodal spins gives rise to three novel ground states with a translationally broken symmetry, but at the same time, does not increases the total number of intermediate plateaus in a zero-temperature magnetization curve compared with the simplified model without this interaction term. The zero-field specific heat displays interesting thermal dependencies with a single- or double-peak structure. (paper)

Towards increased policy relevance in energy modeling

Energy Technology Data Exchange (ETDEWEB)

Worrell, Ernst; Ramesohl, Stephan; Boyd, Gale

2003-07-29

Historically, most energy models were reasonably equipped to assess the impact of a subsidy or change in taxation, but are often insufficient to assess the impact of more innovative policy instruments. We evaluate the models used to assess future energy use, focusing on industrial energy use. We explore approaches to engineering-economic analysis that could help improve the realism and policy relevance of engineering-economic modeling frameworks. We also explore solutions to strengthen the policy usefulness of engineering-economic analysis that can be built from a framework of multi-disciplinary cooperation. We focus on the so-called ''engineering-economic'' (or ''bottom-up'') models, as they include the amount of detail that is commonly needed to model policy scenarios. We identify research priorities for the modeling framework, technology representation in models, policy evaluation and modeling of decision-making behavior.

Incidence and Prevalence of Tuberculosis in Iran and Neighboring Countries

Directory of Open Access Journals (Sweden)

Arezoo Tavakoli

2017-07-01

Full Text Available Background Tuberculosis is one of the major public health concerns in many countries, however the available and effective treatment is known. Tuberculosis typically determined with socio-economic problems such as war, malnutrition and HIV prevalence. In Iran, many progresses are carried to control tuberculosis but, different factors such as immigration from neighboring countries are affective to tuberculosis infection. Objectives In this paper, the incidence and prevalence of tuberculosis is evaluated in different regions of Iran and neighboring countries. Methods The data are collected from different and valid sources such as Scopus, Pubmed and also many reports from world health organization (WHO and center of disease control and prevention (CDC for a period of 25 years (1990 - 2015 evaluated for Iran and neighboring countries. Results This study as a descriptive- analytical research is conducted cross- sectional among Iran and neighboring countries since 1990. The information is obtained from exact and valid informative data from web of sciences. The east and west border countries of Iran which are faced with war and immigration in Afghanistan, Pakistan and Iraq are source of tuberculosis infection that effect on tuberculosis prevalence in Iran. The data were analyzed by SPSS 22 and Excel 2013. Conclusions The incidence of tuberculosis in Iran has been decreased because of many controlling actions such as BCG vaccination, electronic reporting system for tuberculosis and free access to tuberculosis medication. Some of Iran neighboring countries such as Tajikistan and Pakistan have the highest incidence of tuberculosis which known as a challenge for tuberculosis control in Iran while Saudi Arabia and Turkey have the lowest incidence.

ReliefSeq: a gene-wise adaptive-K nearest-neighbor feature selection tool for finding gene-gene interactions and main effects in mRNA-Seq gene expression data.

Directory of Open Access Journals (Sweden)

Brett A McKinney

Full Text Available Relief-F is a nonparametric, nearest-neighbor machine learning method that has been successfully used to identify relevant variables that may interact in complex multivariate models to explain phenotypic variation. While several tools have been developed for assessing differential expression in sequence-based transcriptomics, the detection of statistical interactions between transcripts has received less attention in the area of RNA-seq analysis. We describe a new extension and assessment of Relief-F for feature selection in RNA-seq data. The ReliefSeq implementation adapts the number of nearest neighbors (k for each gene to optimize the Relief-F test statistics (importance scores for finding both main effects and interactions. We compare this gene-wise adaptive-k (gwak Relief-F method with standard RNA-seq feature selection tools, such as DESeq and edgeR, and with the popular machine learning method Random Forests. We demonstrate performance on a panel of simulated data that have a range of distributional properties reflected in real mRNA-seq data including multiple transcripts with varying sizes of main effects and interaction effects. For simulated main effects, gwak-Relief-F feature selection performs comparably to standard tools DESeq and edgeR for ranking relevant transcripts. For gene-gene interactions, gwak-Relief-F outperforms all comparison methods at ranking relevant genes in all but the highest fold change/highest signal situations where it performs similarly. The gwak-Relief-F algorithm outperforms Random Forests for detecting relevant genes in all simulation experiments. In addition, Relief-F is comparable to the other methods based on computational time. We also apply ReliefSeq to an RNA-Seq study of smallpox vaccine to identify gene expression changes between vaccinia virus-stimulated and unstimulated samples. ReliefSeq is an attractive tool for inclusion in the suite of tools used for analysis of mRNA-Seq data; it has power to

Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN classification method

Directory of Open Access Journals (Sweden)

D.A. Adeniyi

2016-01-01

Full Text Available The major problem of many on-line web sites is the presentation of many choices to the client at a time; this usually results to strenuous and time consuming task in finding the right product or information on the site. In this work, we present a study of automatic web usage data mining and recommendation system based on current user behavior through his/her click stream data on the newly developed Really Simple Syndication (RSS reader website, in order to provide relevant information to the individual without explicitly asking for it. The K-Nearest-Neighbor (KNN classification method has been trained to be used on-line and in Real-Time to identify clients/visitors click stream data, matching it to a particular user group and recommend a tailored browsing option that meet the need of the specific user at a particular time. To achieve this, web users RSS address file was extracted, cleansed, formatted and grouped into meaningful session and data mart was developed. Our result shows that the K-Nearest Neighbor classifier is transparent, consistent, straightforward, simple to understand, high tendency to possess desirable qualities and easy to implement than most other machine learning techniques specifically when there is little or no prior knowledge about data distribution.

Evidence for cultural differences between neighboring chimpanzee communities.

Science.gov (United States)

Luncz, Lydia V; Mundry, Roger; Boesch, Christophe

2012-05-22

The majority of evidence for cultural behavior in animals has come from comparisons between populations separated by large geographical distances that often inhabit different environments. The difficulty of excluding ecological and genetic variation as potential explanations for observed behaviors has led some researchers to challenge the idea of animal culture. Chimpanzees (Pan troglodytes verus) in the Taï National Park, Côte d'Ivoire, crack Coula edulis nuts using stone and wooden hammers and tree root anvils. In this study, we compare for the first time hammer selection for nut cracking across three neighboring chimpanzee communities that live in the same forest habitat, which reduces the likelihood of ecological variation. Furthermore, the study communities experience frequent dispersal of females at maturity, which eliminates significant genetic variation. We compared key ecological factors, such as hammer availability and nut hardness, between the three neighboring communities and found striking differences in group-specific hammer selection among communities despite similar ecological conditions. Differences were found in the selection of hammer material and hammer size in response to changes in nut resistance over time. Our findings highlight the subtleties of cultural differences in wild chimpanzees and illustrate how cultural knowledge is able to shape behavior, creating differences among neighboring social groups. Copyright © 2012 Elsevier Ltd. All rights reserved.

A User-Centered Approach to Adaptive Hypertext Based on an Information Relevance Model

Science.gov (United States)

Mathe, Nathalie; Chen, James

1994-01-01

Rapid and effective to information in large electronic documentation systems can be facilitated if information relevant in an individual user's content can be automatically supplied to this user. However most of this knowledge on contextual relevance is not found within the contents of documents, it is rather established incrementally by users during information access. We propose a new model for interactively learning contextual relevance during information retrieval, and incrementally adapting retrieved information to individual user profiles. The model, called a relevance network, records the relevance of references based on user feedback for specific queries and user profiles. It also generalizes such knowledge to later derive relevant references for similar queries and profiles. The relevance network lets users filter information by context of relevance. Compared to other approaches, it does not require any prior knowledge nor training. More importantly, our approach to adaptivity is user-centered. It facilitates acceptance and understanding by users by giving them shared control over the adaptation without disturbing their primary task. Users easily control when to adapt and when to use the adapted system. Lastly, the model is independent of the particular application used to access information, and supports sharing of adaptations among users.

Multiple k Nearest Neighbor Query Processing in Spatial Network Databases

DEFF Research Database (Denmark)

Xuegang, Huang; Jensen, Christian Søndergaard; Saltenis, Simonas

2006-01-01

This paper concerns the efficient processing of multiple k nearest neighbor queries in a road-network setting. The assumed setting covers a range of scenarios such as the one where a large population of mobile service users that are constrained to a road network issue nearest-neighbor queries...... for points of interest that are accessible via the road network. Given multiple k nearest neighbor queries, the paper proposes progressive techniques that selectively cache query results in main memory and subsequently reuse these for query processing. The paper initially proposes techniques for the case...... where an upper bound on k is known a priori and then extends the techniques to the case where this is not so. Based on empirical studies with real-world data, the paper offers insight into the circumstances under which the different proposed techniques can be used with advantage for multiple k nearest...

Improving Recommendations in Tag-based Systems with Spectral Clustering of Tag Neighbors

DEFF Research Database (Denmark)

Pan, Rong; Xu, Guandong; Dolog, Peter

2012-01-01

Tag as a useful metadata reflects the collaborative and conceptual features of documents in social collaborative annotation systems. In this paper, we propose a collaborative approach for expanding tag neighbors and investigate the spectral clustering algorithm to filter out noisy tag neighbors...... in order to get appropriate recommendation for users. The preliminary experiments have been conducted on MovieLens dataset to compare our proposed approach with the traditional collaborative filtering recommendation approach and naive tag neighbors expansion approach in terms of precision, and the result...... demonstrates that our approach could considerably improve the performance of recommendations....

Local biotic adaptation of trees and shrubs to plant neighbors

Science.gov (United States)

Grady, Kevin C.; Wood, Troy E.; Kolb, Thomas E.; Hersch-Green, Erika; Shuster, Stephen M.; Gehring, Catherine A.; Hart, Stephen C.; Allan, Gerard J.; Whitham, Thomas G.

2017-01-01

Natural selection as a result of plant–plant interactions can lead to local biotic adaptation. This may occur where species frequently interact and compete intensely for resources limiting growth, survival, and reproduction. Selection is demonstrated by comparing a genotype interacting with con- or hetero-specific sympatric neighbor genotypes with a shared site-level history (derived from the same source location), to the same genotype interacting with foreign neighbor genotypes (from different sources). Better genotype performance in sympatric than allopatric neighborhoods provides evidence of local biotic adaptation. This pattern might be explained by selection to avoid competition by shifting resource niches (differentiation) or by interactions benefitting one or more members (facilitation). We tested for local biotic adaptation among two riparian trees, Populus fremontii and Salix gooddingii, and the shrub Salix exigua by transplanting replicated genotypes from multiple source locations to a 17 000 tree common garden with sympatric and allopatric treatments along the Colorado River in California. Three major patterns were observed: 1) across species, 62 of 88 genotypes grew faster with sympatric neighbors than allopatric neighbors; 2) these growth rates, on an individual tree basis, were 44, 15 and 33% higher in sympatric than allopatric treatments for P. fremontii, S. exigua and S. gooddingii, respectively, and; 3) survivorship was higher in sympatric treatments for P. fremontiiand S. exigua. These results support the view that fitness of foundation species supporting diverse communities and dominating ecosystem processes is determined by adaptive interactions among multiple plant species with the outcome that performance depends on the genetic identity of plant neighbors. The occurrence of evolution in a plant-community context for trees and shrubs builds on ecological evolutionary research that has demonstrated co-evolution among herbaceous taxa, and

Nearest Neighbor Search in the Metric Space of a Complex Network for Community Detection

Directory of Open Access Journals (Sweden)

Suman Saha

2016-03-01

Full Text Available The objective of this article is to bridge the gap between two important research directions: (1 nearest neighbor search, which is a fundamental computational tool for large data analysis; and (2 complex network analysis, which deals with large real graphs but is generally studied via graph theoretic analysis or spectral analysis. In this article, we have studied the nearest neighbor search problem in a complex network by the development of a suitable notion of nearness. The computation of efficient nearest neighbor search among the nodes of a complex network using the metric tree and locality sensitive hashing (LSH are also studied and experimented. For evaluation of the proposed nearest neighbor search in a complex network, we applied it to a network community detection problem. Experiments are performed to verify the usefulness of nearness measures for the complex networks, the role of metric tree and LSH to compute fast and approximate node nearness and the the efficiency of community detection using nearest neighbor search. We observed that nearest neighbor between network nodes is a very efficient tool to explore better the community structure of the real networks. Several efficient approximation schemes are very useful for large networks, which hardly made any degradation of results, whereas they save lot of computational times, and nearest neighbor based community detection approach is very competitive in terms of efficiency and time.

Disentangling neighbors and extended range density oscillations in monatomic amorphous semiconductors.

Science.gov (United States)

Roorda, S; Martin, C; Droui, M; Chicoine, M; Kazimirov, A; Kycia, S

2012-06-22

High energy x-ray diffraction measurements of pure amorphous Ge were made and its radial distribution function (RDF) was determined at high resolution, revealing new information on the atomic structure of amorphous semiconductors. Fine structure in the second peak in the RDF provides evidence that a fraction of third neighbors are closer than some second neighbors; taking this into account leads to a narrow distribution of tetrahedral bond angles, (8.5 ± 0.1)°. A small peak which appears near 5 Å upon thermal annealing shows that some ordering in the dihedral bond-angle distribution takes place during structural relaxation. Extended range order is detected (in both a-Ge and a-Si) which persists to beyond 20 Å, and both the periodicity and its decay length increase upon thermal annealing. Previously, the effect of structural relaxation was only detected at intermediate range, involving reduced tetrahedral bond-angle distortions. These results enhance our understanding of the atomic order in continuous random networks and place significantly more stringent requirements on computer models intending to describe these networks, or their alternatives which attempt to describe the structure in terms of an arrangement of paracrystals.

Relevant criteria for testing the quality of turbulence models

DEFF Research Database (Denmark)

Frandsen, Sten Tronæs; Ejsing Jørgensen, Hans; Sørensen, J.D.

2007-01-01

Seeking relevant criteria for testing the quality of turbulence models, the scale of turbulence and the gust factor have been estimated from data and compared with predictions from first-order models of these two quantities. It is found that the mean of the measured length scales is approx. 10......% smaller than the IEC model, for wind turbine hub height levels. The mean is only marginally dependent on trends in time series. It is also found that the coefficient of variation of the measured length scales is about 50%. 3sec and 10sec pre-averaging of wind speed data are relevant for MW-size wind...... turbines when seeking wind characteristics that correspond to one blade and the entire rotor, respectively. For heights exceeding 50-60m the gust factor increases with wind speed. For heights larger the 60-80m, present assumptions on the value of the gust factor are significantly conservative, both for 3...

Working with Family, Friend, and Neighbor Caregivers: Lessons from Four Diverse Communities

Science.gov (United States)

Powell, Douglas R.

2011-01-01

This article is excerpted from "Who's Watching the Babies? Improving the Quality of Family, Friend, and Neighbor Care" by Douglas R. Powell ("ZERO TO THREE," 2008). The article explores questions about program development and implementation strategies for supporting Family, Friend, and Neighbor (FFN) caregivers: How do programs and their host…

Aftershock identification problem via the nearest-neighbor analysis for marked point processes

Science.gov (United States)

Gabrielov, A.; Zaliapin, I.; Wong, H.; Keilis-Borok, V.

2007-12-01

The centennial observations on the world seismicity have revealed a wide variety of clustering phenomena that unfold in the space-time-energy domain and provide most reliable information about the earthquake dynamics. However, there is neither a unifying theory nor a convenient statistical apparatus that would naturally account for the different types of seismic clustering. In this talk we present a theoretical framework for nearest-neighbor analysis of marked processes and obtain new results on hierarchical approach to studying seismic clustering introduced by Baiesi and Paczuski (2004). Recall that under this approach one defines an asymmetric distance D in space-time-energy domain such that the nearest-neighbor spanning graph with respect to D becomes a time- oriented tree. We demonstrate how this approach can be used to detect earthquake clustering. We apply our analysis to the observed seismicity of California and synthetic catalogs from ETAS model and show that the earthquake clustering part is statistically different from the homogeneous part. This finding may serve as a basis for an objective aftershock identification procedure.

The Application of Determining Students’ Graduation Status of STMIK Palangkaraya Using K-Nearest Neighbors Method

Science.gov (United States)

Rusdiana, Lili; Marfuah

2017-12-01

K-Nearest Neighbors method is one of methods used for classification which calculate a value to find out the closest in distance. It is used to group a set of data such as students’ graduation status that are got from the amount of course credits taken by them, the grade point average (AVG), and the mini-thesis grade. The study is conducted to know the results of using K-Nearest Neighbors method on the application of determining students’ graduation status, so it can be analyzed from the method used, the data, and the application constructed. The aim of this study is to find out the application results by using K-Nearest Neighbors concept to determine students’ graduation status using the data of STMIK Palangkaraya students. The development of the software used Extreme Programming, since it was appropriate and precise for this study which was to quickly finish the project. The application was created using Microsoft Office Excel 2007 for the training data and Matlab 7 to implement the application. The result of K-Nearest Neighbors method on the application of determining students’ graduation status was 92.5%. It could determine the predicate graduation of 94 data used from the initial data before the processing as many as 136 data which the maximal training data was 50data. The K-Nearest Neighbors method is one of methods used to group a set of data based on the closest value, so that using K-Nearest Neighbors method agreed with this study. The results of K-Nearest Neighbors method on the application of determining students’ graduation status was 92.5% could determine the predicate graduation which is the maximal training data. The K-Nearest Neighbors method is one of methods used to group a set of data based on the closest value, so that using K-Nearest Neighbors method agreed with this study.

Genetic mouse models relevant to schizophrenia: taking stock and looking forward.

Science.gov (United States)

Harrison, Paul J; Pritchett, David; Stumpenhorst, Katharina; Betts, Jill F; Nissen, Wiebke; Schweimer, Judith; Lane, Tracy; Burnet, Philip W J; Lamsa, Karri P; Sharp, Trevor; Bannerman, David M; Tunbridge, Elizabeth M

2012-03-01

Genetic mouse models relevant to schizophrenia complement, and have to a large extent supplanted, pharmacological and lesion-based rat models. The main attraction is that they potentially have greater construct validity; however, they share the fundamental limitations of all animal models of psychiatric disorder, and must also be viewed in the context of the uncertain and complex genetic architecture of psychosis. Some of the key issues, including the choice of gene to target, the manner of its manipulation, gene-gene and gene-environment interactions, and phenotypic characterization, are briefly considered in this commentary, illustrated by the relevant papers reported in this special issue. Copyright © 2011 Elsevier Ltd. All rights reserved.

Mathematical Properties Relevant to Geomagnetic Field Modeling

DEFF Research Database (Denmark)

Sabaka, Terence J.; Hulot, Gauthier; Olsen, Nils

2010-01-01

be directly measured. In this chapter, the mathematical foundation of global (as opposed to regional) geomagnetic field modeling is reviewed, and the spatial modeling of the field in spherical coordinates is focussed. Time can be dealt with as an independent variable and is not explicitly considered......Geomagnetic field modeling consists in converting large numbers of magnetic observations into a linear combination of elementary mathematical functions that best describes those observations.The set of numerical coefficients defining this linear combination is then what one refers.......The relevant elementary mathematical functions are introduced, their properties are reviewed, and how they can be used to describe the magnetic field in a source-free (such as the Earth’s neutral atmosphere) or source-dense (such as the ionosphere) environment is explained. Completeness and uniqueness...

Mathematical Properties Relevant to Geomagnetic Field Modeling

DEFF Research Database (Denmark)

Sabaka, Terence J.; Hulot, Gauthier; Olsen, Nils

2014-01-01

be directly measured. In this chapter, the mathematical foundation of global (as opposed to regional) geomagnetic field modeling is reviewed, and the spatial modeling of the field in spherical coordinates is focused. Time can be dealt with as an independent variable and is not explicitly considered......Geomagnetic field modeling consists in converting large numbers of magnetic observations into a linear combination of elementary mathematical functions that best describes those observations. The set of numerical coefficients defining this linear combination is then what one refers....... The relevant elementary mathematical functions are introduced, their properties are reviewed, and how they can be used to describe the magnetic field in a source-free (such as the Earth’s neutral atmosphere) or source-dense (such as the ionosphere) environment is explained. Completeness and uniqueness...

Macroscale hydrologic modeling of ecologically relevant flow metrics

Science.gov (United States)

Wenger, Seth J.; Luce, Charles H.; Hamlet, Alan F.; Isaak, Daniel J.; Neville, Helen M.

2010-09-01

Stream hydrology strongly affects the structure of aquatic communities. Changes to air temperature and precipitation driven by increased greenhouse gas concentrations are shifting timing and volume of streamflows potentially affecting these communities. The variable infiltration capacity (VIC) macroscale hydrologic model has been employed at regional scales to describe and forecast hydrologic changes but has been calibrated and applied mainly to large rivers. An important question is how well VIC runoff simulations serve to answer questions about hydrologic changes in smaller streams, which are important habitat for many fish species. To answer this question, we aggregated gridded VIC outputs within the drainage basins of 55 streamflow gages in the Pacific Northwest United States and compared modeled hydrographs and summary metrics to observations. For most streams, several ecologically relevant aspects of the hydrologic regime were accurately modeled, including center of flow timing, mean annual and summer flows and frequency of winter floods. Frequencies of high and low flows in the summer were not well predicted, however. Predictions were worse for sites with strong groundwater influence, and some sites showed errors that may result from limitations in the forcing climate data. Higher resolution (1/16th degree) modeling provided small improvements over lower resolution (1/8th degree). Despite some limitations, the VIC model appears capable of representing several ecologically relevant hydrologic characteristics in streams, making it a useful tool for understanding the effects of hydrology in delimiting species distributions and predicting the potential effects of climate shifts on aquatic organisms.

Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification

National Research Council Canada - National Science Library

Han, Euihong; Karypis, George; Kumar, Vipin

1999-01-01

.... The authors present a nearest neighbor classification scheme for text categorization in which the importance of discriminating words is learned using mutual information and weight adjustment techniques...

Other relevant numerical modelling papers

International Nuclear Information System (INIS)

Chartier, M.

1989-01-01

The ocean modelling is a rapidly evolving science and a large number of results have been published. Several categories of papers are of particular interest for this review: the papers published by the international atomic institutions, such as the NEA (for the CRESP or Subseabed Programs), the IAEA (for example the Safety Series, the Technical Report Series or the TECDOC), and the ICRP, and the papers concerned by more fundamental research, which are published in specific scientific literature. This paper aims to list some of the most relevant publications for the CRESP purposes. It means by no way to be exhaustive, but informative on the incontestable progress recently achieved in that field. One should note that some of these papers are so recent that their final version has not yet been published

Intertwining personal and reward relevance: evidence from the drift-diffusion model.

Science.gov (United States)

Yankouskaya, A; Bührle, R; Lugt, E; Stolte, M; Sui, J

2018-01-24

In their seminal paper 'Is our self nothing but reward', Northoff and Hayes (Biol Psychiatry 69(11):1019-1025, Northoff, Hayes, Biological Psychiatry 69(11):1019-1025, 2011) proposed three models of the relationship between self and reward and opened a continuing debate about how these different fields can be linked. To date, none of the proposed models received strong empirical support. The present study tested common and distinct effects of personal relevance and reward values by de-componenting different stages of perceptual decision making using a drift-diffusion approach. We employed a recently developed associative matching paradigm where participants (N = 40) formed mental associations between five geometric shapes and five labels referring personal relevance in the personal task, or five shape-label pairings with different reward values in the reward task and then performed a matching task by indicating whether a displayed shape-label pairing was correct or incorrect. We found that common effects of personal relevance and monetary reward were manifested in the facilitation of behavioural performance for high personal relevance and high reward value as socially important signals. The differential effects between personal and monetary relevance reflected non-decisional time in a perceptual decision process, and task-specific prioritization of stimuli. Our findings support the parallel processing model (Northoff & Hayes, Biol Psychiatry 69(11):1019-1025, Northoff, Hayes, Biological Psychiatry 69(11):1019-1025, 2011) and suggest that self-specific processing occurs in parallel with high reward processing. Limitations and further directions are discussed.

SibRank: Signed bipartite network analysis for neighbor-based collaborative ranking

Science.gov (United States)

Shams, Bita; Haratizadeh, Saman

2016-09-01

Collaborative ranking is an emerging field of recommender systems that utilizes users' preference data rather than rating values. Unfortunately, neighbor-based collaborative ranking has gained little attention despite its more flexibility and justifiability. This paper proposes a novel framework, called SibRank that seeks to improve the state of the art neighbor-based collaborative ranking methods. SibRank represents users' preferences as a signed bipartite network, and finds similar users, through a novel personalized ranking algorithm in signed networks.

Experimental Models of Vaginal Candidiasis and Their Relevance to Human Candidiasis

Science.gov (United States)

Sobel, Jack D.

2016-01-01

Vulvovaginal candidiasis (VVC) is a high-incidence disease seriously affecting the quality of life of women worldwide, particularly in its chronic, recurrent forms (RVVC), and with no definitive cure or preventive measure. Experimental studies in currently used rat and mouse models of vaginal candidiasis have generated a large mass of data on pathogenicity determinants and inflammation and immune responses of potential importance for the control of human pathology. However, reflection is necessary about the relevance of these rodent models to RVVC. Here we examine the chemical, biochemical, and biological factors that determine or contrast the forms of the disease in rodent models and in women and highlight the differences between them. We also appeal for approaches to improve or replace the current models in order to enhance their relevance to human infection. PMID:26883592

Surface Energy Balance in Jakarta and Neighboring Regions As Simulated Using Fifth Mesoscale Model (MM5

Directory of Open Access Journals (Sweden)

Yopi Ilhamsyah

2014-04-01

Full Text Available The objective of the present research was to assess the surface energy balance particularly in terms of the computed surface energy and radiation balance and the development of boundary layer over Jakarta and Neighboring Regions (JNR by means of numerical model of fifth generation of Mesoscale Model (MM5. The MM5 with four domains of 9 kilometers in spatial resolution presenting the outermost and the innermost of JNR is utilized. The research focuses on the third and fourth domains covering the entire JNR. The description between radiation and energy balance at the surface is obtained from the model. The result showed that energy balance is higher in the city area during daytime. Meanwhile, energy components, e.g., surface sensible and latent heat flux showed that at the sea and in the city areas were higher than other areas. Moreover, ground flux showed eastern region was higher than others. In general, radiation and energy balance was higher in the daytime and lower in the nighttime for all regions. The calculation of Bowen Ratio, the ratio of surface sensible and latent heat fluxes, was also higher in the city area, reflecting the dominations of urban and built-up land in the region. Meanwhile, Bowen Ratio in the rural area dominated by irrigated cropland was lower. It is consistent with changes of land cover properties, e.g. albedo, soil moisture, and thermal characteristics. In addition, the boundary layer is also higher in the city. Meanwhile western region dominated by suburban showed higher boundary layer instead of eastern region.

Nearest unlike neighbor (NUN): an aid to decision confidence estimation

Science.gov (United States)

Dasarathy, Belur V.

1995-09-01

The concept of nearest unlike neighbor (NUN), proposed and explored previously in the design of nearest neighbor (NN) based decision systems, is further exploited in this study to develop a measure of confidence in the decisions made by NN-based decision systems. This measure of confidence, on the basis of comparison with a user-defined threshold, may be used to determine the acceptability of the decision provided by the NN-based decision system. The concepts, associated methodology, and some illustrative numerical examples using the now classical Iris data to bring out the ease of implementation and effectiveness of the proposed innovations are presented.

Low-spin identical bands in neighboring odd-A and even-even nuclei

International Nuclear Information System (INIS)

Baktash, C.; Winchell, D.F.; Garrett, J.D.; Smith, A.

1992-01-01

A comprehensive study of odd-A rotational bands in normally deformed rare-earth nuclei indicates that a large number of seniority-one configurations (21% for odd-Z nuclei) at low spin have moments of inertia nearly identical to that of the seniority-zero configuration of the neighboring even-even nucleus with one less nucleon. It is difficult to reconcile these results with conventional models of nuclear pair correlation, which predict variations of about 15% in the moments of inertia of configurations differing by one unit in seniority

Bacterial genomes lacking long-range correlations may not be modeled by low-order Markov chains: the role of mixing statistics and frame shift of neighboring genes.

Science.gov (United States)

Cocho, Germinal; Miramontes, Pedro; Mansilla, Ricardo; Li, Wentian

2014-12-01

We examine the relationship between exponential correlation functions and Markov models in a bacterial genome in detail. Despite the well known fact that Markov models generate sequences with correlation function that decays exponentially, simply constructed Markov models based on nearest-neighbor dimer (first-order), trimer (second-order), up to hexamer (fifth-order), and treating the DNA sequence as being homogeneous all fail to predict the value of exponential decay rate. Even reading-frame-specific Markov models (both first- and fifth-order) could not explain the fact that the exponential decay is very slow. Starting with the in-phase coding-DNA-sequence (CDS), we investigated correlation within a fixed-codon-position subsequence, and in artificially constructed sequences by packing CDSs with out-of-phase spacers, as well as altering CDS length distribution by imposing an upper limit. From these targeted analyses, we conclude that the correlation in the bacterial genomic sequence is mainly due to a mixing of heterogeneous statistics at different codon positions, and the decay of correlation is due to the possible out-of-phase between neighboring CDSs. There are also small contributions to the correlation from bases at the same codon position, as well as by non-coding sequences. These show that the seemingly simple exponential correlation functions in bacterial genome hide a complexity in correlation structure which is not suitable for a modeling by Markov chain in a homogeneous sequence. Other results include: use of the (absolute value) second largest eigenvalue to represent the 16 correlation functions and the prediction of a 10-11 base periodicity from the hexamer frequencies. Copyright © 2014 Elsevier Ltd. All rights reserved.

Towards Increased Relevance: Context-Adapted Models of the Learning Organization

Science.gov (United States)

Örtenblad, Anders

2015-01-01

Purpose: The purposes of this paper are to take a closer look at the relevance of the idea of the learning organization for organizations in different generalized organizational contexts; to open up for the existence of multiple, context-adapted models of the learning organization; and to suggest a number of such models.…

Mapping DNA methylation by transverse current sequencing: Reduction of noise from neighboring nucleotides

Science.gov (United States)

Alvarez, Jose; Massey, Steven; Kalitsov, Alan; Velev, Julian

Nanopore sequencing via transverse current has emerged as a competitive candidate for mapping DNA methylation without needed bisulfite-treatment, fluorescent tag, or PCR amplification. By eliminating the error producing amplification step, long read lengths become feasible, which greatly simplifies the assembly process and reduces the time and the cost inherent in current technologies. However, due to the large error rates of nanopore sequencing, single base resolution has not been reached. A very important source of noise is the intrinsic structural noise in the electric signature of the nucleotide arising from the influence of neighboring nucleotides. In this work we perform calculations of the tunneling current through DNA molecules in nanopores using the non-equilibrium electron transport method within an effective multi-orbital tight-binding model derived from first-principles calculations. We develop a base-calling algorithm accounting for the correlations of the current through neighboring bases, which in principle can reduce the error rate below any desired precision. Using this method we show that we can clearly distinguish DNA methylation and other base modifications based on the reading of the tunneling current.

[Galaxy/quasar classification based on nearest neighbor method].

Science.gov (United States)

Li, Xiang-Ru; Lu, Yu; Zhou, Jian-Ming; Wang, Yong-Jun

2011-09-01

With the wide application of high-quality CCD in celestial spectrum imagery and the implementation of many large sky survey programs (e. g., Sloan Digital Sky Survey (SDSS), Two-degree-Field Galaxy Redshift Survey (2dF), Spectroscopic Survey Telescope (SST), Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) program and Large Synoptic Survey Telescope (LSST) program, etc.), celestial observational data are coming into the world like torrential rain. Therefore, to utilize them effectively and fully, research on automated processing methods for celestial data is imperative. In the present work, we investigated how to recognizing galaxies and quasars from spectra based on nearest neighbor method. Galaxies and quasars are extragalactic objects, they are far away from earth, and their spectra are usually contaminated by various noise. Therefore, it is a typical problem to recognize these two types of spectra in automatic spectra classification. Furthermore, the utilized method, nearest neighbor, is one of the most typical, classic, mature algorithms in pattern recognition and data mining, and often is used as a benchmark in developing novel algorithm. For applicability in practice, it is shown that the recognition ratio of nearest neighbor method (NN) is comparable to the best results reported in the literature based on more complicated methods, and the superiority of NN is that this method does not need to be trained, which is useful in incremental learning and parallel computation in mass spectral data processing. In conclusion, the results in this work are helpful for studying galaxies and quasars spectra classification.

Plant Clonal Integration Mediates the Horizontal Redistribution of Soil Resources, Benefiting Neighboring Plants.

Science.gov (United States)

Ye, Xue-Hua; Zhang, Ya-Lin; Liu, Zhi-Lan; Gao, Shu-Qin; Song, Yao-Bin; Liu, Feng-Hong; Dong, Ming

2016-01-01

Resources such as water taken up by plants can be released into soils through hydraulic redistribution and can also be translocated by clonal integration within a plant clonal network. We hypothesized that the resources from one (donor) microsite could be translocated within a clonal network, released into different (recipient) microsites and subsequently used by neighbor plants in the recipient microsite. To test these hypotheses, we conducted two experiments in which connected and disconnected ramet pairs of Potentilla anserina were grown under both homogeneous and heterogeneous water regimes, with seedlings of Artemisia ordosica as neighbors. The isotopes [(15)N] and deuterium were used to trace the translocation of nitrogen and water, respectively, within the clonal network. The water and nitrogen taken up by P. anserina ramets in the donor microsite were translocated into the connected ramets in the recipient microsites. Most notably, portions of the translocated water and nitrogen were released into the recipient microsite and were used by the neighboring A. ordosica, which increased growth of the neighboring A. ordosica significantly. Therefore, our hypotheses were supported, and plant clonal integration mediated the horizontal hydraulic redistribution of resources, thus benefiting neighboring plants. Such a plant clonal integration-mediated resource redistribution in horizontal space may have substantial effects on the interspecific relations and composition of the community and consequently on ecosystem processes.

The N400 as a snapshot of interactive processing: evidence from regression analyses of orthographic neighbor and lexical associate effects

Science.gov (United States)

Laszlo, Sarah; Federmeier, Kara D.

2010-01-01

Linking print with meaning tends to be divided into subprocesses, such as recognition of an input's lexical entry and subsequent access of semantics. However, recent results suggest that the set of semantic features activated by an input is broader than implied by a view wherein access serially follows recognition. EEG was collected from participants who viewed items varying in number and frequency of both orthographic neighbors and lexical associates. Regression analysis of single item ERPs replicated past findings, showing that N400 amplitudes are greater for items with more neighbors, and further revealed that N400 amplitudes increase for items with more lexical associates and with higher frequency neighbors or associates. Together, the data suggest that in the N400 time window semantic features of items broadly related to inputs are active, consistent with models in which semantic access takes place in parallel with stimulus recognition. PMID:20624252

Low-spin identical bands in neighboring odd-A and even-even nuclei

International Nuclear Information System (INIS)

Baktash, C.; Winchell, D.F.; Garrett, J.D.; Smith, A.

1993-01-01

A comprehensive study of odd-A rotational bands in normally deformed rare-earth nuclei indicates that a large number of seniority-one configurations (21 % for odd-Z nuclei) at low spin have moments of inertia nearly identical to that of the seniority-zero configuration of the neighboring even-even nucleus with one less nucleon. It is difficult to reconcile these results with conventional models of nuclear pair correlation, which predict variations of about 15% in the moments of inertia of configurations differing by one unit in seniority. (orig.)

Neighbor Discovery Algorithm in Wireless Local Area Networks Using Multi-beam Directional Antennas

Science.gov (United States)

Wang, Jin; Peng, Wei; Liu, Song

2017-10-01

Neighbor discovery is an important step for Wireless Local Area Networks (WLAN) and the use of multi-beam directional antennas can greatly improve the network performance. However, most neighbor discovery algorithms in WLAN, based on multi-beam directional antennas, can only work effectively in synchronous system but not in asynchro-nous system. And collisions at AP remain a bottleneck for neighbor discovery. In this paper, we propose two asynchrono-us neighbor discovery algorithms: asynchronous hierarchical scanning (AHS) and asynchronous directional scanning (ADS) algorithm. Both of them are based on three-way handshaking mechanism. AHS and ADS reduce collisions at AP to have a good performance in a hierarchical way and directional way respectively. In the end, the performance of the AHS and ADS are tested on OMNeT++. Moreover, it is analyzed that different application scenarios and the factors how to affect the performance of these algorithms. The simulation results show that AHS is suitable for the densely populated scenes around AP while ADS is suitable for that most of the neighborhood nodes are far from AP.

Using K-Nearest Neighbor in Optical Character Recognition

Directory of Open Access Journals (Sweden)

Veronica Ong

2016-03-01

Full Text Available The growth in computer vision technology has aided society with various kinds of tasks. One of these tasks is the ability of recognizing text contained in an image, or usually referred to as Optical Character Recognition (OCR. There are many kinds of algorithms that can be implemented into an OCR. The K-Nearest Neighbor is one such algorithm. This research aims to find out the process behind the OCR mechanism by using K-Nearest Neighbor algorithm; one of the most influential machine learning algorithms. It also aims to find out how precise the algorithm is in an OCR program. To do that, a simple OCR program to classify alphabets of capital letters is made to produce and compare real results. The result of this research yielded a maximum of 76.9% accuracy with 200 training samples per alphabet. A set of reasons are also given as to why the program is able to reach said level of accuracy.

An Improvement To The k-Nearest Neighbor Classifier For ECG Database

Science.gov (United States)

Jaafar, Haryati; Hidayah Ramli, Nur; Nasir, Aimi Salihah Abdul

2018-03-01

The k nearest neighbor (kNN) is a non-parametric classifier and has been widely used for pattern classification. However, in practice, the performance of kNN often tends to fail due to the lack of information on how the samples are distributed among them. Moreover, kNN is no longer optimal when the training samples are limited. Another problem observed in kNN is regarding the weighting issues in assigning the class label before classification. Thus, to solve these limitations, a new classifier called Mahalanobis fuzzy k-nearest centroid neighbor (MFkNCN) is proposed in this study. Here, a Mahalanobis distance is applied to avoid the imbalance of samples distribition. Then, a surrounding rule is employed to obtain the nearest centroid neighbor based on the distributions of training samples and its distance to the query point. Consequently, the fuzzy membership function is employed to assign the query point to the class label which is frequently represented by the nearest centroid neighbor Experimental studies from electrocardiogram (ECG) signal is applied in this study. The classification performances are evaluated in two experimental steps i.e. different values of k and different sizes of feature dimensions. Subsequently, a comparative study of kNN, kNCN, FkNN and MFkCNN classifier is conducted to evaluate the performances of the proposed classifier. The results show that the performance of MFkNCN consistently exceeds the kNN, kNCN and FkNN with the best classification rates of 96.5%.

Remaining Useful Life Estimation of Insulated Gate Biploar Transistors (IGBTs Based on a Novel Volterra k-Nearest Neighbor Optimally Pruned Extreme Learning Machine (VKOPP Model Using Degradation Data

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Zhen Liu

2017-11-01

Full Text Available The insulated gate bipolar transistor (IGBT is a kind of excellent performance switching device used widely in power electronic systems. How to estimate the remaining useful life (RUL of an IGBT to ensure the safety and reliability of the power electronics system is currently a challenging issue in the field of IGBT reliability. The aim of this paper is to develop a prognostic technique for estimating IGBTs’ RUL. There is a need for an efficient prognostic algorithm that is able to support in-situ decision-making. In this paper, a novel prediction model with a complete structure based on optimally pruned extreme learning machine (OPELM and Volterra series is proposed to track the IGBT’s degradation trace and estimate its RUL; we refer to this model as Volterra k-nearest neighbor OPELM prediction (VKOPP model. This model uses the minimum entropy rate method and Volterra series to reconstruct phase space for IGBTs’ ageing samples, and a new weight update algorithm, which can effectively reduce the influence of the outliers and noises, is utilized to establish the VKOPP network; then a combination of the k-nearest neighbor method (KNN and least squares estimation (LSE method is used to calculate the output weights of OPELM and predict the RUL of the IGBT. The prognostic results show that the proposed approach can predict the RUL of IGBT modules with small error and achieve higher prediction precision and lower time cost than some classic prediction approaches.

The Neighboring Column Approximation (NCA) – A fast approach for the calculation of 3D thermal heating rates in cloud resolving models

International Nuclear Information System (INIS)

Klinger, Carolin; Mayer, Bernhard

2016-01-01

Due to computational costs, radiation is usually neglected or solved in plane parallel 1D approximation in today's numerical weather forecast and cloud resolving models. We present a fast and accurate method to calculate 3D heating and cooling rates in the thermal spectral range that can be used in cloud resolving models. The parameterization considers net fluxes across horizontal box boundaries in addition to the top and bottom boundaries. Since the largest heating and cooling rates occur inside the cloud, close to the cloud edge, the method needs in first approximation only the information if a grid box is at the edge of a cloud or not. Therefore, in order to calculate the heating or cooling rates of a specific grid box, only the directly neighboring columns are used. Our so-called Neighboring Column Approximation (NCA) is an analytical consideration of cloud side effects which can be considered a convolution of a 1D radiative transfer result with a kernel or radius of 1 grid-box (5 pt stencil) and which does usually not break the parallelization of a cloud resolving model. The NCA can be easily applied to any cloud resolving model that includes a 1D radiation scheme. Due to the neglect of horizontal transport of radiation further away than one model column, the NCA works best for model resolutions of about 100 m or lager. In this paper we describe the method and show a set of applications of LES cloud field snap shots. Correction terms, gains and restrictions of the NCA are described. Comprehensive comparisons to the 3D Monte Carlo Model MYSTIC and a 1D solution are shown. In realistic cloud fields, the full 3D simulation with MYSTIC shows cooling rates up to −150 K/d (100 m resolution) while the 1D solution shows maximum coolings of only −100 K/d. The NCA is capable of reproducing the larger 3D cooling rates. The spatial distribution of the heating and cooling is improved considerably. Computational costs are only a factor of 1.5–2 higher compared to a 1D

Bioprinting towards Physiologically Relevant Tissue Models for Pharmaceutics.

Science.gov (United States)

Peng, Weijie; Unutmaz, Derya; Ozbolat, Ibrahim T

2016-09-01

Improving the ability to predict the efficacy and toxicity of drug candidates earlier in the drug discovery process will speed up the introduction of new drugs into clinics. 3D in vitro systems have significantly advanced the drug screening process as 3D tissue models can closely mimic native tissues and, in some cases, the physiological response to drugs. Among various in vitro systems, bioprinting is a highly promising technology possessing several advantages such as tailored microarchitecture, high-throughput capability, coculture ability, and low risk of cross-contamination. In this opinion article, we discuss the currently available tissue models in pharmaceutics along with their limitations and highlight the possibilities of bioprinting physiologically relevant tissue models, which hold great potential in drug testing, high-throughput screening, and disease modeling. Copyright © 2016 Elsevier Ltd. All rights reserved.

Nearest Neighbor Networks: clustering expression data based on gene neighborhoods

Directory of Open Access Journals (Sweden)

Olszewski Kellen L

2007-07-01

Full Text Available Abstract Background The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks; these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes (e.g. ribosomes. Results We developed Nearest Neighbor Networks (NNN, a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods. Conclusion The Nearest Neighbor Networks algorithm is a valuable clustering method that effectively groups genes that are likely to be functionally related. It is particularly attractive due to its simplicity, its success in the

Correction of dental artifacts within the anatomical surface in PET/MRI using active shape models and k-nearest-neighbors

DEFF Research Database (Denmark)

Ladefoged, Claes N.; Andersen, Flemming L.; Keller, Sune H.

2014-01-01

n combined PET/MR, attenuation correction (AC) is performed indirectly based on the available MR image information. Metal implant-induced susceptibility artifacts and subsequent signal voids challenge MR-based AC. Several papers acknowledge the problem in PET attenuation correction when dental...... artifacts are ignored, but none of them attempts to solve the problem. We propose a clinically feasible correction method which combines Active Shape Models (ASM) and k- Nearest-Neighbors (kNN) into a simple approach which finds and corrects the dental artifacts within the surface boundaries of the patient...... anatomy. ASM is used to locate a number of landmarks in the T1-weighted MR-image of a new patient. We calculate a vector of offsets from each voxel within a signal void to each of the landmarks. We then use kNN to classify each voxel as belonging to an artifact or an actual signal void using this offset...

Near Neighbor Distribution in Sets of Fractal Nature

Czech Academy of Sciences Publication Activity Database

Jiřina, Marcel

2013-01-01

Roč. 5, č. 1 (2013), s. 159-166 ISSN 2150-7988 R&D Projects: GA MŠk(CZ) LG12020 Institutional support: RVO:67985807 Keywords : nearest neighbor * fractal set * multifractal * Erlang distribution Subject RIV: BB - Applied Statistics, Operational Research http://www.mirlabs.org/ijcisim/regular_papers_2013/Paper91.pdf

A Novel Preferential Diffusion Recommendation Algorithm Based on User’s Nearest Neighbors

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Fuguo Zhang

2017-01-01

Full Text Available Recommender system is a very efficient way to deal with the problem of information overload for online users. In recent years, network based recommendation algorithms have demonstrated much better performance than the standard collaborative filtering methods. However, most of network based algorithms do not give a high enough weight to the influence of the target user’s nearest neighbors in the resource diffusion process, while a user or an object with high degree will obtain larger influence in the standard mass diffusion algorithm. In this paper, we propose a novel preferential diffusion recommendation algorithm considering the significance of the target user’s nearest neighbors and evaluate it in the three real-world data sets: MovieLens 100k, MovieLens 1M, and Epinions. Experiments results demonstrate that the novel preferential diffusion recommendation algorithm based on user’s nearest neighbors can significantly improve the recommendation accuracy and diversity.

Models of the Economic Growth and their Relevance

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Nicolae MOROIANU

2012-06-01

Full Text Available Until few years ago, the economic growth was something perfect normal, part of an era marked by the transformation speed. Normality itself has been transformed and we currently are influenced by other rules, unknown yet, which should answer the question: “How do we return to the economic growth?” The economic growth and the models aiming to solve this problem concern the economic history even since its beginnings. In this paper we would like to find out what is the relevance that the well-known macroeconomic models still have and which might be their applicability level in a framework created by a black swan event type.

k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks

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Z. Martinasek

2016-06-01

Full Text Available Power analysis presents the typical example of successful attacks against trusted cryptographic devices such as RFID (Radio-Frequency IDentifications and contact smart cards. In recent years, the cryptographic community has explored new approaches in power analysis based on machine learning models such as Support Vector Machine (SVM, RF (Random Forest and Multi-Layer Perceptron (MLP. In this paper, we made an extensive comparison of machine learning algorithms in the power analysis. For this purpose, we implemented a verification program that always chooses the optimal settings of individual machine learning models in order to obtain the best classification accuracy. In our research, we used three datasets, the first containing the power traces of an unprotected AES (Advanced Encryption Standard implementation. The second and third datasets are created independently from public available power traces corresponding to a masked AES implementation (DPA Contest v4. The obtained results revealed some interesting facts, namely, an elementary k-NN (k-Nearest Neighbors algorithm, which has not been commonly used in power analysis yet, shows great application potential in practice.

Control-relevant modeling and simulation of a SOFC-GT hybrid system

OpenAIRE

Rambabu Kandepu; Lars Imsland; Christoph Stiller; Bjarne A. Foss; Vinay Kariwala

2006-01-01

In this paper, control-relevant models of the most important components in a SOFC-GT hybrid system are described. Dynamic simulations are performed on the overall hybrid system. The model is used to develop a simple control structure, but the simulations show that more elaborate control is needed.

Structure of the first- and second-neighbor shells of simulated water: Quantitative relation to translational and orientational order

Science.gov (United States)

Yan, Zhenyu; Buldyrev, Sergey V.; Kumar, Pradeep; Giovambattista, Nicolas; Debenedetti, Pablo G.; Stanley, H. Eugene

2007-11-01

We perform molecular dynamics simulations of water using the five-site transferable interaction potential (TIP5P) model to quantify structural order in both the first shell (defined by four nearest neighbors) and second shell (defined by twelve next-nearest neighbors) of a central water molecule. We find that the anomalous decrease of orientational order upon compression occurs in both shells, but the anomalous decrease of translational order upon compression occurs mainly in the second shell. The decreases of translational order and orientational order upon compression (called the “structural anomaly”) are thus correlated only in the second shell. Our findings quantitatively confirm the qualitative idea that the thermodynamic, structural, and hence dynamic anomalies of water are related to changes upon compression in the second shell.

Velocity correlations and spatial dependencies between neighbors in a unidirectional flow of pedestrians

Science.gov (United States)

Porzycki, Jakub; WÄ s, Jarosław; Hedayatifar, Leila; Hassanibesheli, Forough; Kułakowski, Krzysztof

2017-08-01

The aim of the paper is an analysis of self-organization patterns observed in the unidirectional flow of pedestrians. On the basis of experimental data from Zhang et al. [J. Zhang et al., J. Stat. Mech. (2011) P06004, 10.1088/1742-5468/2011/06/P06004], we analyze the mutual positions and velocity correlations between pedestrians when walking along a corridor. The angular and spatial dependencies of the mutual positions reveal a spatial structure that remains stable during the crowd motion. This structure differs depending on the value of n , for the consecutive n th -nearest-neighbor position set. The preferred position for the first-nearest neighbor is on the side of the pedestrian, while for further neighbors, this preference shifts to the axis of movement. The velocity correlations vary with the angle formed by the pair of neighboring pedestrians and the direction of motion and with the time delay between pedestrians' movements. The delay dependence of the correlations shows characteristic oscillations, produced by the velocity oscillations when striding; however, a filtering of the main frequency of individual striding out reduces the oscillations only partially. We conclude that pedestrians select their path directions so as to evade the necessity of continuously adjusting their speed to their neighbors'. They try to keep a given distance, but follow the person in front of them, as well as accepting and observing pedestrians on their sides. Additionally, we show an empirical example that illustrates the shape of a pedestrian's personal space during movement.

Hyperplane distance neighbor clustering based on local discriminant analysis for complex chemical processes monitoring

Energy Technology Data Exchange (ETDEWEB)

Lu, Chunhong; Xiao, Shaoqing; Gu, Xiaofeng [Jiangnan University, Wuxi (China)

2014-11-15

The collected training data often include both normal and faulty samples for complex chemical processes. However, some monitoring methods, such as partial least squares (PLS), principal component analysis (PCA), independent component analysis (ICA) and Fisher discriminant analysis (FDA), require fault-free data to build the normal operation model. These techniques are applicable after the preliminary step of data clustering is applied. We here propose a novel hyperplane distance neighbor clustering (HDNC) based on the local discriminant analysis (LDA) for chemical process monitoring. First, faulty samples are separated from normal ones using the HDNC method. Then, the optimal subspace for fault detection and classification can be obtained using the LDA approach. The proposed method takes the multimodality within the faulty data into account, and thus improves the capability of process monitoring significantly. The HDNC-LDA monitoring approach is applied to two simulation processes and then compared with the conventional FDA based on the K-nearest neighbor (KNN-FDA) method. The results obtained in two different scenarios demonstrate the superiority of the HDNC-LDA approach in terms of fault detection and classification accuracy.

Hyperplane distance neighbor clustering based on local discriminant analysis for complex chemical processes monitoring

International Nuclear Information System (INIS)

Lu, Chunhong; Xiao, Shaoqing; Gu, Xiaofeng

2014-01-01

The collected training data often include both normal and faulty samples for complex chemical processes. However, some monitoring methods, such as partial least squares (PLS), principal component analysis (PCA), independent component analysis (ICA) and Fisher discriminant analysis (FDA), require fault-free data to build the normal operation model. These techniques are applicable after the preliminary step of data clustering is applied. We here propose a novel hyperplane distance neighbor clustering (HDNC) based on the local discriminant analysis (LDA) for chemical process monitoring. First, faulty samples are separated from normal ones using the HDNC method. Then, the optimal subspace for fault detection and classification can be obtained using the LDA approach. The proposed method takes the multimodality within the faulty data into account, and thus improves the capability of process monitoring significantly. The HDNC-LDA monitoring approach is applied to two simulation processes and then compared with the conventional FDA based on the K-nearest neighbor (KNN-FDA) method. The results obtained in two different scenarios demonstrate the superiority of the HDNC-LDA approach in terms of fault detection and classification accuracy

A Peep into the Uncertainty-Complexity-Relevance Modeling Trilemma through Global Sensitivity and Uncertainty Analysis

Science.gov (United States)

Munoz-Carpena, R.; Muller, S. J.; Chu, M.; Kiker, G. A.; Perz, S. G.

2014-12-01

Model Model complexity resulting from the need to integrate environmental system components cannot be understated. In particular, additional emphasis is urgently needed on rational approaches to guide decision making through uncertainties surrounding the integrated system across decision-relevant scales. However, in spite of the difficulties that the consideration of modeling uncertainty represent for the decision process, it should not be avoided or the value and science behind the models will be undermined. These two issues; i.e., the need for coupled models that can answer the pertinent questions and the need for models that do so with sufficient certainty, are the key indicators of a model's relevance. Model relevance is inextricably linked with model complexity. Although model complexity has advanced greatly in recent years there has been little work to rigorously characterize the threshold of relevance in integrated and complex models. Formally assessing the relevance of the model in the face of increasing complexity would be valuable because there is growing unease among developers and users of complex models about the cumulative effects of various sources of uncertainty on model outputs. In particular, this issue has prompted doubt over whether the considerable effort going into further elaborating complex models will in fact yield the expected payback. New approaches have been proposed recently to evaluate the uncertainty-complexity-relevance modeling trilemma (Muller, Muñoz-Carpena and Kiker, 2011) by incorporating state-of-the-art global sensitivity and uncertainty analysis (GSA/UA) in every step of the model development so as to quantify not only the uncertainty introduced by the addition of new environmental components, but the effect that these new components have over existing components (interactions, non-linear responses). Outputs from the analysis can also be used to quantify system resilience (stability, alternative states, thresholds or tipping

Control-relevant modeling and simulation of a SOFC-GT hybrid system

Directory of Open Access Journals (Sweden)

Rambabu Kandepu

2006-07-01

Full Text Available In this paper, control-relevant models of the most important components in a SOFC-GT hybrid system are described. Dynamic simulations are performed on the overall hybrid system. The model is used to develop a simple control structure, but the simulations show that more elaborate control is needed.

A Novel AMR-WB Speech Steganography Based on Diameter-Neighbor Codebook Partition

Directory of Open Access Journals (Sweden)

Junhui He

2018-01-01

Full Text Available Steganography is a means of covert communication without revealing the occurrence and the real purpose of communication. The adaptive multirate wideband (AMR-WB is a widely adapted format in mobile handsets and is also the recommended speech codec for VoLTE. In this paper, a novel AMR-WB speech steganography is proposed based on diameter-neighbor codebook partition algorithm. Different embedding capacity may be achieved by adjusting the iterative parameters during codebook division. The experimental results prove that the presented AMR-WB steganography may provide higher and flexible embedding capacity without inducing perceptible distortion compared with the state-of-the-art methods. With 48 iterations of cluster merging, twice the embedding capacity of complementary-neighbor-vertices-based embedding method may be obtained with a decrease of only around 2% in speech quality and much the same undetectability. Moreover, both the quality of stego speech and the security regarding statistical steganalysis are better than the recent speech steganography based on neighbor-index-division codebook partition.

Linear perturbation renormalization group for the two-dimensional Ising model with nearest- and next-nearest-neighbor interactions in a field

Science.gov (United States)

Sznajd, J.

2016-12-01

The linear perturbation renormalization group (LPRG) is used to study the phase transition of the weakly coupled Ising chains with intrachain (J ) and interchain nearest-neighbor (J1) and next-nearest-neighbor (J2) interactions forming the triangular and rectangular lattices in a field. The phase diagrams with the frustration point at J2=-J1/2 for a rectangular lattice and J2=-J1 for a triangular lattice have been found. The LPRG calculations support the idea that the phase transition is always continuous except for the frustration point and is accompanied by a divergence of the specific heat. For the antiferromagnetic chains, the external field does not change substantially the shape of the phase diagram. The critical temperature is suppressed to zero according to the power law when approaching the frustration point with an exponent dependent on the value of the field.

Belowground neighbor perception in Arabidopsis thaliana studied by transcriptome analysis: roots of Hieracium pilosella cause biotic stress

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Christoph eSchmid

2013-08-01

Full Text Available Root-root interactions are much more sophisticated than previously thought, yet the mechanisms of belowground neighbor perception remain largely obscure. Genome-wide transcriptome analyses allow detailed insight into plant reactions to environmental cues.A root interaction trial was set up to explore both morphological and whole genome transcriptional responses in roots of Arabidopsis thaliana in the presence or absence of an inferior competitor, Hieracium pilosella.Neighbor perception was indicated by Arabidopsis roots predominantly growing away from the neighbor (segregation, while solitary plants placed more roots towards the middle of the pot. Total biomass remained unaffected. Database comparisons in transcriptome analysis revealed considerable similarity between Arabidopsis root reactions to neighbors and reactions to pathogens. Detailed analyses of the functional category ‘biotic stress’ using MapMan tools found the sub-category ‘pathogenesis-related proteins’ highly significantly induced. A comparison to a study on intraspecific competition brought forward a core of genes consistently involved in reactions to neighbor roots.We conclude that beyond resource depletion roots perceive neighboring roots or their associated microorganisms by a relatively uniform mechanism that involves the strong induction of pathogenesis-related proteins. In an ecological context the findings reveal that belowground neighbor detection may occur independently of resource depletion, allowing for a time advantage for the root to prepare for potential interactions.

Collective Behaviors of Mobile Robots Beyond the Nearest Neighbor Rules With Switching Topology.

Science.gov (United States)

Ning, Boda; Han, Qing-Long; Zuo, Zongyu; Jin, Jiong; Zheng, Jinchuan

2018-05-01

This paper is concerned with the collective behaviors of robots beyond the nearest neighbor rules, i.e., dispersion and flocking, when robots interact with others by applying an acute angle test (AAT)-based interaction rule. Different from a conventional nearest neighbor rule or its variations, the AAT-based interaction rule allows interactions with some far-neighbors and excludes unnecessary nearest neighbors. The resulting dispersion and flocking hold the advantages of scalability, connectivity, robustness, and effective area coverage. For the dispersion, a spring-like controller is proposed to achieve collision-free coordination. With switching topology, a new fixed-time consensus-based energy function is developed to guarantee the system stability. An upper bound of settling time for energy consensus is obtained, and a uniform time interval is accordingly set so that energy distribution is conducted in a fair manner. For the flocking, based on a class of generalized potential functions taking nonsmooth switching into account, a new controller is proposed to ensure that the same velocity for all robots is eventually reached. A co-optimizing problem is further investigated to accomplish additional tasks, such as enhancing communication performance, while maintaining the collective behaviors of mobile robots. Simulation results are presented to show the effectiveness of the theoretical results.

Mapping wildland fuels and forest structure for land management: a comparison of nearest neighbor imputation and other methods

Science.gov (United States)

Kenneth B. Pierce; Janet L. Ohmann; Michael C. Wimberly; Matthew J. Gregory; Jeremy S. Fried

2009-01-01

Land managers need consistent information about the geographic distribution of wildland fuels and forest structure over large areas to evaluate fire risk and plan fuel treatments. We compared spatial predictions for 12 fuel and forest structure variables across three regions in the western United States using gradient nearest neighbor (GNN) imputation, linear models (...

Tensile twin nucleation events coupled to neighboring slip observed in three dimensions

International Nuclear Information System (INIS)

Lind, J.; Li, S.F.; Pokharel, R.; Lienert, U.; Rollett, A.D.; Suter, R.M.

2014-01-01

Low-symmetry crystals and polycrystals have anisotropic mechanical properties which, given better understanding of their deformation modes, could lead to development of next generation materials. Understanding how grains in a bulk polycrystal interact will guide and improve material modeling. Here, we show that tensile twins, in hexagonal close-packed metals, form where the macroscopic stress does not generate appropriate shear stress and vice versa. We use non-destructive high-energy X-ray diffraction microscopy to map local crystal orientations in three dimensions in a series of tensile strain states in a zirconium polycrystal. Twins and intragranular orientation variations are observed and it is found that deformation-induced rotations in neighboring grains are spatially correlated with many twins. We conclude that deformation twinning involves complex multigrain interactions which must be included in polycrystal plasticity models

The influence of neighbors' family size preference on progression to high parity births in rural Nepal.

Science.gov (United States)

Jennings, Elyse A; Barber, Jennifer S

2013-03-01

Large families can have a negative impact on the health and well-being of women, children, and their communities. Seventy-three percent of the individuals in our rural Nepalese sample report that two children is their ideal number, yet about half of the married women continue childbearing after their second child. Using longitudinal data from the Chitwan Valley Family Study, we explore the influence of women's and neighbors' family size preferences on women's progression to high parity births, comparing this influence across two cohorts. We find that neighbors' family size preferences influence women's fertility, that older cohorts of women are more influenced by their neighbors' preferences than are younger cohorts of women, and that the influence of neighbors' preferences is independent of women's own preferences. © 2013 The Population Council, Inc.

Clustered K nearest neighbor algorithm for daily inflow forecasting

NARCIS (Netherlands)

Akbari, M.; Van Overloop, P.J.A.T.M.; Afshar, A.

2010-01-01

Instance based learning (IBL) algorithms are a common choice among data driven algorithms for inflow forecasting. They are based on the similarity principle and prediction is made by the finite number of similar neighbors. In this sense, the similarity of a query instance is estimated according to

Contrasting demographic histories of the neighboring bonobo and chimpanzee

DEFF Research Database (Denmark)

Hvilsom, Christina; Carlsen, Frands; Heller, Rasmus

2014-01-01

of the neighboring bonobo remained constant. The changes in population size are likely linked to changes in habitat area due to climate oscillations during the late Pleistocene. Furthermore, the timing of population expansion for the rainforest-adapted chimpanzee is concurrent with the expansion of the savanna...

Predicting the severity of nuclear power plant transients using nearest neighbors modeling optimized by genetic algorithms on a parallel computer

International Nuclear Information System (INIS)

Lin, J.; Bartal, Y.; Uhrig, R.E.

1995-01-01

The importance of automatic diagnostic systems for nuclear power plants (NPPs) has been discussed in numerous studies, and various such systems have been proposed. None of those systems were designed to predict the severity of the diagnosed scenario. A classification and severity prediction system for NPP transients is developed. The system is based on nearest neighbors modeling, which is optimized using genetic algorithms. The optimization process is used to determine the most important variables for each of the transient types analyzed. An enhanced version of the genetic algorithms is used in which a local downhill search is performed to further increase the accuracy achieved. The genetic algorithms search was implemented on a massively parallel supercomputer, the KSR1-64, to perform the analysis in a reasonable time. The data for this study were supplied by the high-fidelity simulator of the San Onofre unit 1 pressurized water reactor

Phase Transition and Critical Values of a Nearest-Neighbor System with Uncountable Local State Space on Cayley Trees

International Nuclear Information System (INIS)

Jahnel, Benedikt; Külske, Christof; Botirov, Golibjon I.

2014-01-01

We consider a ferromagnetic nearest-neighbor model on a Cayley tree of degree k ⩾ 2 with uncountable local state space [0,1] where the energy function depends on a parameter θ ∊[0, 1). We show that for 0 ⩽ θ ⩽ 5 3 k the model has a unique translation-invariant Gibbs measure. If 5 3 k < θ < 1 , there is a phase transition, in particular there are three translation-invariant Gibbs measures

Economic evaluation of health benefits of mercury emission controls for China and the neighboring countries in East Asia

International Nuclear Information System (INIS)

Zhang, Wei; Zhen, Gengchong; Chen, Long; Wang, Huanhuan; Li, Ying; Ye, Xuejie; Tong, Yindong; Zhu, Yan; Wang, Xuejun

2017-01-01

Globally, coal-fired power plant (CFPP) is a major source of mercury. China is developing its first National Implementation Plan on Mercury Control, which priorities the control of emissions from CFPPs. While social benefits play an important role in designing environmental policies in China, the benefits associated with mercury control are not yet understood, mainly due to the scientific challenges to trace mercury's emissions-to-impacts path. This study evaluates the benefits of mercury reductions in China's CFPPs for China and its three neighboring countries in East Asia. Four policy scenarios are analyzed following the policies-to-impacts path, which links a global atmospheric model to health benefit analysis models to estimate the economic gains from avoided mercury-related adverse health outcomes under each scenario, and take into account key uncertainties in the path. Under the most stringent scenario, the benefits of mercury reduction by 2030 are projected to be $432 billion (95% CI: $166–941 billion), with the benefits for China and the neighboring countries accounting for 96% and 4% of the total benefits, respectively. Policy scenario analysis indicates that coal washing generates the greatest benefits in the near term, whereas upgrading air pollution control devices maximizes health benefits in the long term. - Highlights: • Benefits of mercury controls for China and neighboring countries are analyzed. • Policy analysis shows that coal washing generates the largest benefits in near term. • Upgrading air pollution control devices maximizes health benefits in long term. • For mercury controls, local policies contribute most to local benefits.

The relevance of non-human primate and rodent malaria models for humans

Directory of Open Access Journals (Sweden)

Riley Eleanor

2011-02-01

Full Text Available Abstract At the 2010 Keystone Symposium on "Malaria: new approaches to understanding Host-Parasite interactions", an extra scientific session to discuss animal models in malaria research was convened at the request of participants. This was prompted by the concern of investigators that skepticism in the malaria community about the use and relevance of animal models, particularly rodent models of severe malaria, has impacted on funding decisions and publication of research using animal models. Several speakers took the opportunity to demonstrate the similarities between findings in rodent models and human severe disease, as well as points of difference. The variety of malaria presentations in the different experimental models parallels the wide diversity of human malaria disease and, therefore, might be viewed as a strength. Many of the key features of human malaria can be replicated in a variety of nonhuman primate models, which are very under-utilized. The importance of animal models in the discovery of new anti-malarial drugs was emphasized. The major conclusions of the session were that experimental and human studies should be more closely linked so that they inform each other, and that there should be wider access to relevant clinical material.

Towards policy relevant environmental modeling: contextual validity and pragmatic models

Science.gov (United States)

Miles, Scott B.

2000-01-01

"What makes for a good model?" In various forms, this question is a question that, undoubtedly, many people, businesses, and institutions ponder with regards to their particular domain of modeling. One particular domain that is wrestling with this question is the multidisciplinary field of environmental modeling. Examples of environmental models range from models of contaminated ground water flow to the economic impact of natural disasters, such as earthquakes. One of the distinguishing claims of the field is the relevancy of environmental modeling to policy and environment-related decision-making in general. A pervasive view by both scientists and decision-makers is that a "good" model is one that is an accurate predictor. Thus, determining whether a model is "accurate" or "correct" is done by comparing model output to empirical observations. The expected outcome of this process, usually referred to as "validation" or "ground truthing," is a stamp on the model in question of "valid" or "not valid" that serves to indicate whether or not the model will be reliable before it is put into service in a decision-making context. In this paper, I begin by elaborating on the prevailing view of model validation and why this view must change. Drawing from concepts coming out of the studies of science and technology, I go on to propose a contextual view of validity that can overcome the problems associated with "ground truthing" models as an indicator of model goodness. The problem of how we talk about and determine model validity has much to do about how we perceive the utility of environmental models. In the remainder of the paper, I argue that we should adopt ideas of pragmatism in judging what makes for a good model and, in turn, developing good models. From such a perspective of model goodness, good environmental models should facilitate communication, convey—not bury or "eliminate"—uncertainties, and, thus, afford the active building of consensus decisions, instead

Quality and efficiency in high dimensional Nearest neighbor search

KAUST Repository

Tao, Yufei; Yi, Ke; Sheng, Cheng; Kalnis, Panos

2009-01-01

Nearest neighbor (NN) search in high dimensional space is an important problem in many applications. Ideally, a practical solution (i) should be implementable in a relational database, and (ii) its query cost should grow sub-linearly with the dataset size, regardless of the data and query distributions. Despite the bulk of NN literature, no solution fulfills both requirements, except locality sensitive hashing (LSH). The existing LSH implementations are either rigorous or adhoc. Rigorous-LSH ensures good quality of query results, but requires expensive space and query cost. Although adhoc-LSH is more efficient, it abandons quality control, i.e., the neighbor it outputs can be arbitrarily bad. As a result, currently no method is able to ensure both quality and efficiency simultaneously in practice. Motivated by this, we propose a new access method called the locality sensitive B-tree (LSB-tree) that enables fast highdimensional NN search with excellent quality. The combination of several LSB-trees leads to a structure called the LSB-forest that ensures the same result quality as rigorous-LSH, but reduces its space and query cost dramatically. The LSB-forest also outperforms adhoc-LSH, even though the latter has no quality guarantee. Besides its appealing theoretical properties, the LSB-tree itself also serves as an effective index that consumes linear space, and supports efficient updates. Our extensive experiments confirm that the LSB-tree is faster than (i) the state of the art of exact NN search by two orders of magnitude, and (ii) the best (linear-space) method of approximate retrieval by an order of magnitude, and at the same time, returns neighbors with much better quality. © 2009 ACM.

Symmetric Link Key Management for Secure Neighbor Discovery in a Decentralized Wireless Sensor Network

Science.gov (United States)

2017-09-01

KEY MANAGEMENT FOR SECURE NEIGHBOR DISCOVERY IN A DECENTRALIZED WIRELESS SENSOR NETWORK by Kelvin T. Chew September 2017 Thesis Advisor...and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington, DC 20503. 1. AGENCY USE ONLY (Leave blank) 2. REPORT...DATE September 2017 3. REPORT TYPE AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE SYMMETRIC LINK KEY MANAGEMENT FOR SECURE NEIGHBOR

Protein function prediction using neighbor relativity in protein-protein interaction network.

Science.gov (United States)

Moosavi, Sobhan; Rahgozar, Masoud; Rahimi, Amir

2013-04-01

There is a large gap between the number of discovered proteins and the number of functionally annotated ones. Due to the high cost of determining protein function by wet-lab research, function prediction has become a major task for computational biology and bioinformatics. Some researches utilize the proteins interaction information to predict function for un-annotated proteins. In this paper, we propose a novel approach called "Neighbor Relativity Coefficient" (NRC) based on interaction network topology which estimates the functional similarity between two proteins. NRC is calculated for each pair of proteins based on their graph-based features including distance, common neighbors and the number of paths between them. In order to ascribe function to an un-annotated protein, NRC estimates a weight for each neighbor to transfer its annotation to the unknown protein. Finally, the unknown protein will be annotated by the top score transferred functions. We also investigate the effect of using different coefficients for various types of functions. The proposed method has been evaluated on Saccharomyces cerevisiae and Homo sapiens interaction networks. The performance analysis demonstrates that NRC yields better results in comparison with previous protein function prediction approaches that utilize interaction network. Copyright © 2012 Elsevier Ltd. All rights reserved.

Model Predictive Engine Air-Ratio Control Using Online Sequential Relevance Vector Machine

Directory of Open Access Journals (Sweden)

Hang-cheong Wong

2012-01-01

Full Text Available Engine power, brake-specific fuel consumption, and emissions relate closely to air ratio (i.e., lambda among all the engine variables. An accurate and adaptive model for lambda prediction is essential to effective lambda control for long term. This paper utilizes an emerging technique, relevance vector machine (RVM, to build a reliable time-dependent lambda model which can be continually updated whenever a sample is added to, or removed from, the estimated lambda model. The paper also presents a new model predictive control (MPC algorithm for air-ratio regulation based on RVM. This study shows that the accuracy, training, and updating time of the RVM model are superior to the latest modelling methods, such as diagonal recurrent neural network (DRNN and decremental least-squares support vector machine (DLSSVM. Moreover, the control algorithm has been implemented on a real car to test. Experimental results reveal that the control performance of the proposed relevance vector machine model predictive controller (RVMMPC is also superior to DRNNMPC, support vector machine-based MPC, and conventional proportional-integral (PI controller in production cars. Therefore, the proposed RVMMPC is a promising scheme to replace conventional PI controller for engine air-ratio control.

ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms

DEFF Research Database (Denmark)

Aumüller, Martin; Bernhardsson, Erik; Faithfull, Alexander

2017-01-01

This paper describes ANN-Benchmarks, a tool for evaluating the performance of in-memory approximate nearest neighbor algorithms. It provides a standard interface for measuring the performance and quality achieved by nearest neighbor algorithms on different standard data sets. It supports several...... visualise these as images, Open image in new window plots, and websites with interactive plots. ANN-Benchmarks aims to provide a constantly updated overview of the current state of the art of k-NN algorithms. In the short term, this overview allows users to choose the correct k-NN algorithm and parameters...... for their similarity search task; in the longer term, algorithm designers will be able to use this overview to test and refine automatic parameter tuning. The paper gives an overview of the system, evaluates the results of the benchmark, and points out directions for future work. Interestingly, very different...

Compensation phenomena of a mixed spin-2 and spin-12 Heisenberg ferrimagnetic model: Green function study

International Nuclear Information System (INIS)

Li Jun; Wei Guozhu; Du An

2005-01-01

The compensation and critical behaviors of a mixed spin-2 and spin-12 Heisenberg ferrimagnetic system on a square lattice are investigated theoretically by the two-time Green's function technique, which takes into account the quantum nature of Heisenberg spins. The model can be relevant for understanding the magnetic behavior of the new class of organometallic ferromagnetic materials that exhibit spontaneous magnetic properties at room temperature. We carry out the calculation of the sublattice magnetizations and the spin-wave spectra of the ground state. In particular, we have studied the effects of the nearest, next-nearest-neighbor interactions, the crystal field and the external magnetic field on the compensation temperature and the critical temperature. When only the nearest-neighbor interactions and the crystal field are included, no compensation temperature exists; when the next-nearest-neighbor interaction between spin-12 is taken into account and exceeds a minimum value, a compensation point appears and it is basically unchanged for other parameters in Hamiltonian fixed. The next-nearest-neighbor interactions between spin-2 and the external magnetic field have the effects of changing the compensation temperature and there is a narrow range of parameters of the Hamiltonian for which the model has the compensation temperatures and compensation temperature exists only for a small value of them

A multimedia retrieval framework based on semi-supervised ranking and relevance feedback.

Science.gov (United States)

Yang, Yi; Nie, Feiping; Xu, Dong; Luo, Jiebo; Zhuang, Yueting; Pan, Yunhe

2012-04-01

We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.

Nearest neighbor 3D segmentation with context features

Science.gov (United States)

Hristova, Evelin; Schulz, Heinrich; Brosch, Tom; Heinrich, Mattias P.; Nickisch, Hannes

2018-03-01

Automated and fast multi-label segmentation of medical images is challenging and clinically important. This paper builds upon a supervised machine learning framework that uses training data sets with dense organ annotations and vantage point trees to classify voxels in unseen images based on similarity of binary feature vectors extracted from the data. Without explicit model knowledge, the algorithm is applicable to different modalities and organs, and achieves high accuracy. The method is successfully tested on 70 abdominal CT and 42 pelvic MR images. With respect to ground truth, an average Dice overlap score of 0.76 for the CT segmentation of liver, spleen and kidneys is achieved. The mean score for the MR delineation of bladder, bones, prostate and rectum is 0.65. Additionally, we benchmark several variations of the main components of the method and reduce the computation time by up to 47% without significant loss of accuracy. The segmentation results are - for a nearest neighbor method - surprisingly accurate, robust as well as data and time efficient.

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

International Nuclear Information System (INIS)

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

2007-01-01

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

A multiple relevance feedback strategy with positive and negative models.

Directory of Open Access Journals (Sweden)

Yunlong Ma

Full Text Available A commonly used strategy to improve search accuracy is through feedback techniques. Most existing work on feedback relies on positive information, and has been extensively studied in information retrieval. However, when a query topic is difficult and the results from the first-pass retrieval are very poor, it is impossible to extract enough useful terms from a few positive documents. Therefore, the positive feedback strategy is incapable to improve retrieval in this situation. Contrarily, there is a relatively large number of negative documents in the top of the result list, and it has been confirmed that negative feedback strategy is an important and useful way for adapting this scenario by several recent studies. In this paper, we consider a scenario when the search results are so poor that there are at most three relevant documents in the top twenty documents. Then, we conduct a novel study of multiple strategies for relevance feedback using both positive and negative examples from the first-pass retrieval to improve retrieval accuracy for such difficult queries. Experimental results on these TREC collections show that the proposed language model based multiple model feedback method which is generally more effective than both the baseline method and the methods using only positive or negative model.

Chirality dependence of dipole matrix element of carbon nanotubes in axial magnetic field: A third neighbor tight binding approach

Science.gov (United States)

Chegel, Raad; Behzad, Somayeh

2014-02-01

We have studied the electronic structure and dipole matrix element, D, of carbon nanotubes (CNTs) under magnetic field, using the third nearest neighbor tight binding model. It is shown that the 1NN and 3NN-TB band structures show differences such as the spacing and mixing of neighbor subbands. Applying the magnetic field leads to breaking the degeneracy behavior in the D transitions and creates new allowed transitions corresponding to the band modifications. It is found that |D| is proportional to the inverse tube radius and chiral angle. Our numerical results show that amount of filed induced splitting for the first optical peak is proportional to the magnetic field by the splitting rate ν11. It is shown that ν11 changes linearly and parabolicly with the chiral angle and radius, respectively.

Optimizing Neighbor Discovery for Ad hoc Networks based on the Bluetooth PAN Profile

DEFF Research Database (Denmark)

Kuijpers, Gerben; Nielsen, Thomas Toftegaard; Prasad, Ramjee

2002-01-01

IP layer neighbor discovery mechanisms rely highly on broadcast/multicast capabilities of the underlying link layer. The Bluetooth personal area network (PAN) profile has no native link layer broadcast/multicast capabilities and can only emulate this by repeatedly unicast link layer frames....... This paper introduces a neighbor discovery mechanism that utilizes the resources in the Bluetooth PAN profile more efficient. The performance of the new mechanism is investigated using a IPv6 network simulator and compared with emulated broadcasting. It is shown that the signaling overhead can...

Structural Model Error and Decision Relevancy

Science.gov (United States)

Goldsby, M.; Lusk, G.

2017-12-01

The extent to which climate models can underwrite specific climate policies has long been a contentious issue. Skeptics frequently deny that climate models are trustworthy in an attempt to undermine climate action, whereas policy makers often desire information that exceeds the capabilities of extant models. While not skeptics, a group of mathematicians and philosophers [Frigg et al. (2014)] recently argued that even tiny differences between the structure of a complex dynamical model and its target system can lead to dramatic predictive errors, possibly resulting in disastrous consequences when policy decisions are based upon those predictions. They call this result the Hawkmoth effect (HME), and seemingly use it to rebuke rightwing proposals to forgo mitigation in favor of adaptation. However, a vigorous debate has emerged between Frigg et al. on one side and another philosopher-mathematician pair [Winsberg and Goodwin (2016)] on the other. On one hand, Frigg et al. argue that their result shifts the burden to climate scientists to demonstrate that their models do not fall prey to the HME. On the other hand, Winsberg and Goodwin suggest that arguments like those asserted by Frigg et al. can be, if taken seriously, "dangerous": they fail to consider the variety of purposes for which models can be used, and thus too hastily undermine large swaths of climate science. They put the burden back on Frigg et al. to show their result has any effect on climate science. This paper seeks to attenuate this debate by establishing an irenic middle position; we find that there is more agreement between sides than it first seems. We distinguish a `decision standard' from a `burden of proof', which helps clarify the contributions to the debate from both sides. In making this distinction, we argue that scientists bear the burden of assessing the consequences of HME, but that the standard Frigg et al. adopt for decision relevancy is too strict.

Integration and analysis of neighbor discovery and link quality estimation in wireless sensor networks.

Science.gov (United States)

Radi, Marjan; Dezfouli, Behnam; Abu Bakar, Kamalrulnizam; Abd Razak, Shukor

2014-01-01

Network connectivity and link quality information are the fundamental requirements of wireless sensor network protocols to perform their desired functionality. Most of the existing discovery protocols have only focused on the neighbor discovery problem, while a few number of them provide an integrated neighbor search and link estimation. As these protocols require a careful parameter adjustment before network deployment, they cannot provide scalable and accurate network initialization in large-scale dense wireless sensor networks with random topology. Furthermore, performance of these protocols has not entirely been evaluated yet. In this paper, we perform a comprehensive simulation study on the efficiency of employing adaptive protocols compared to the existing nonadaptive protocols for initializing sensor networks with random topology. In this regard, we propose adaptive network initialization protocols which integrate the initial neighbor discovery with link quality estimation process to initialize large-scale dense wireless sensor networks without requiring any parameter adjustment before network deployment. To the best of our knowledge, this work is the first attempt to provide a detailed simulation study on the performance of integrated neighbor discovery and link quality estimation protocols for initializing sensor networks. This study can help system designers to determine the most appropriate approach for different applications.

Integration and Analysis of Neighbor Discovery and Link Quality Estimation in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Marjan Radi

2014-01-01

Full Text Available Network connectivity and link quality information are the fundamental requirements of wireless sensor network protocols to perform their desired functionality. Most of the existing discovery protocols have only focused on the neighbor discovery problem, while a few number of them provide an integrated neighbor search and link estimation. As these protocols require a careful parameter adjustment before network deployment, they cannot provide scalable and accurate network initialization in large-scale dense wireless sensor networks with random topology. Furthermore, performance of these protocols has not entirely been evaluated yet. In this paper, we perform a comprehensive simulation study on the efficiency of employing adaptive protocols compared to the existing nonadaptive protocols for initializing sensor networks with random topology. In this regard, we propose adaptive network initialization protocols which integrate the initial neighbor discovery with link quality estimation process to initialize large-scale dense wireless sensor networks without requiring any parameter adjustment before network deployment. To the best of our knowledge, this work is the first attempt to provide a detailed simulation study on the performance of integrated neighbor discovery and link quality estimation protocols for initializing sensor networks. This study can help system designers to determine the most appropriate approach for different applications.

Transfer-Efficient Face Routing Using the Planar Graphs of Neighbors in High Density WSNs

Directory of Open Access Journals (Sweden)

Eun-Seok Cho

2017-10-01

Full Text Available Face routing has been adopted in wireless sensor networks (WSNs where topological changes occur frequently or maintaining full network information is difficult. For message forwarding in networks, a planar graph is used to prevent looping, and because long edges are removed by planarization and the resulting planar graph is composed of short edges, and messages are forwarded along multiple nodes connected by them even though they can be forwarded directly. To solve this, face routing using information on all nodes within 2-hop range was adopted to forward messages directly to the farthest node within radio range. However, as the density of the nodes increases, network performance plunges because message transfer nodes receive and process increased node information. To deal with this problem, we propose a new face routing using the planar graphs of neighboring nodes to improve transfer efficiency. It forwards a message directly to the farthest neighbor and reduces loads and processing time by distributing network graph construction and planarization to the neighbors. It also decreases the amount of location information to be transmitted by sending information on the planar graph nodes rather than on all neighboring nodes. Simulation results show that it significantly improves transfer efficiency.

The relevance of non-human primate and rodent malaria models for humans

OpenAIRE

Langhorne, Jean; Buffet, Pierre; Galinski, Mary; Good, Michael; Harty, John; Leroy, Didier; Mota, Maria M; Pasini, Erica; Renia, Laurent; Riley, Eleanor; Stins, Monique; Duffy, Patrick

2011-01-01

Abstract At the 2010 Keystone Symposium on "Malaria: new approaches to understanding Host-Parasite interactions", an extra scientific session to discuss animal models in malaria research was convened at the request of participants. This was prompted by the concern of investigators that skepticism in the malaria community about the use and relevance of animal models, particularly rodent models of severe malaria, has impacted on funding decisions and publication of research using animal models....

Control Relevant Modeling and Design of Scramjet-Powered Hypersonic Vehicles

Science.gov (United States)

Dickeson, Jeffrey James

This report provides an overview of scramjet-powered hypersonic vehicle modeling and control challenges. Such vehicles are characterized by unstable non-minimum phase dynamics with significant coupling and low thrust margins. Recent trends in hypersonic vehicle research are summarized. To illustrate control relevant design issues and tradeoffs, a generic nonlinear 3DOF longitudinal dynamics model capturing aero-elastic-propulsive interactions for wedge-shaped vehicle is used. Limitations of the model are discussed and numerous modifications have been made to address control relevant needs. Two different baseline configurations are examined over a two-stage to orbit ascent trajectory. The report highlights how vehicle level-flight static (trim) and dynamic properties change over the trajectory. Thermal choking constraints are imposed on control system design as a direct consequence of having a finite FER margin. The implication of this state-dependent nonlinear FER margin constraint, the right half plane (RHP) zero, and lightly damped flexible modes, on control system bandwidth (BW) and FPA tracking has been discussed. A control methodology has been proposed that addresses the above dynamics while providing some robustness to modeling uncertainty. Vehicle closure (the ability to fly a trajectory segment subject to constraints) is provided through a proposed vehicle design methodology. The design method attempts to use open loop metrics whenever possible to design the vehicle. The design method is applied to a vehicle/control law closed loop nonlinear simulation for validation. The 3DOF longitudinal modeling results are validated against a newly released NASA 6DOF code.

75 FR 62412 - Notice of Proposed Information Collection: Comment Request; HUD-Owned Real Estate-Good Neighbor...

Science.gov (United States)

2010-10-08

... DEPARTMENT OF HOUSING AND URBAN DEVELOPMENT [Docket No. FR-5380-N-36] Notice of Proposed Information Collection: Comment Request; HUD- Owned Real Estate-Good Neighbor Next Door Program AGENCY: Office... information: Title of Proposal: HUD-Owned Real Estate-Good Neighbor Next Door Program. OMB Control Number, if...

Green function study of a mixed spin-((3)/(2)) and spin-((1)/(2)) Heisenberg ferrimagnetic model

International Nuclear Information System (INIS)

Li Jun; Wei Guozhu; Du An

2004-01-01

The magnetic properties of a mixed spin-((3)/(2)) and spin-((1)/(2)) Heisenberg ferrimagnetic system on a square lattice are investigated theoretically by a multisublattice Green-function technique which takes into account the quantum nature of Heisenberg spins. This model can be relevant for understanding the magnetic behavior of the new class of organometallic materials that exhibit spontaneous magnetic moments at room temperature. We discuss the spontaneous magnetic moments and the finite-temperature phase diagram. We find that there is no compensation point at finite temperature when only the nearest-neighbor interaction and the single-ion anisotropy are included. When the next-nearest-neighbor interaction between spin-((1)/(2)) is taken into account and exceeds a minimum value, a compensation point appears and it is basically unchanged for other values in Hamiltonian fixed. The next-nearest-neighbor interaction between spin-((3)/(2)) has the effect of changing the compensation temperature

On Feature Relevance in Image-Based Prediction Models: An Empirical Study

DEFF Research Database (Denmark)

Konukoglu, E.; Ganz, Melanie; Van Leemput, Koen

2013-01-01

Determining disease-related variations of the anatomy and function is an important step in better understanding diseases and developing early diagnostic systems. In particular, image-based multivariate prediction models and the “relevant features” they produce are attracting attention from the co...

Local and neighboring patch conditions alter sex-specific movement in banana weevils.

Science.gov (United States)

Carval, Dominique; Perrin, Benjamin; Duyck, Pierre-François; Tixier, Philippe

2015-12-01

Understanding the mechanisms underlying the movements and spread of a species over time and space is a major concern of ecology. Here, we assessed the effects of an individual's sex and the density and sex ratio of conspecifics in the local and neighboring environment on the movement probability of the banana weevil, Cosmopolites sordidus. In a "two patches" experiment, we used radiofrequency identification tags to study the C. sordidus movement response to patch conditions. We showed that local and neighboring densities of conspecifics affect the movement rates of individuals but that the density-dependent effect can be either positive or negative depending on the relative densities of conspecifics in local and neighboring patches. We demonstrated that sex ratio also influences the movement of C. sordidus, that is, the weevil exhibits nonfixed sex-biased movement strategies. Sex-biased movement may be the consequence of intrasexual competition for resources (i.e., oviposition sites) in females and for mates in males. We also detected a high individual variability in the propensity to move. Finally, we discuss the role of demographic stochasticity, sex-biased movement, and individual heterogeneity in movement on the colonization process.

Impact of Training Bolivian Farmers on Integrated Pest Management and Diffusion of Knowledge to Neighboring Farmers.

Science.gov (United States)

Jørs, Erik; Konradsen, Flemming; Huici, Omar; Morant, Rafael C; Volk, Julie; Lander, Flemming

2016-01-01

Teaching farmers integrated pest management (IPM) in farmer field schools (FFS) has led to reduced pesticide use and safer handling. This article evaluates the long-term impact of training farmers on IPM and the diffusion of knowledge from trained farmers to neighboring farmers, a subject of importance to justify training costs and to promote a healthy and sustainable agriculture. Training on IPM of farmers took place from 2002 to 2004 in their villages in La Paz County, Bolivia, whereas dissemination of knowledge from trained farmer to neighboring farmer took place until 2009. To evaluate the impact of the intervention, self-reported knowledge and practice on pesticide handling and IPM among trained farmers (n = 23) and their neighboring farmers (n = 47) were analyzed in a follow-up study and compared in a cross-sectional analysis with a control group of farmers (n = 138) introduced in 2009. Variables were analyzed using χ2 test and analysis of variance (ANOVA). Trained farmers improved and performed significantly better in all tested variables than their neighboring farmers, although the latter also improved their performance from 2002 to 2009. Including a control group showed an increasing trend in all variables, with the control farmers having the poorest performance and trained farmers the best. The same was seen in an aggregated variable where trained farmers had a mean score of 16.55 (95% confidence interval [CI]: 15.45-17.65), neighboring farmers a mean score of 11.97 (95% CI: 10.56-13.38), and control farmers a mean score of 9.18 (95% CI: 8.55-9.80). Controlling for age and living altitude did not change these results. Trained farmers and their neighboring farmers improved and maintained knowledge and practice on IPM and pesticide handling. Diffusion of knowledge from trained farmers might explain the better performance of the neighboring farmers compared with the control farmers. Dissemination of knowledge can contribute to justify the cost and convince

Cryptosporidiosis in Saudi Arabia and neighboring countries

International Nuclear Information System (INIS)

Areeshi, Mohammed Y.; Hart, C.A.; Beeching, N.J.

2007-01-01

Cryptosporidium is a coccidian protozoan parasite of the intestinal tract that causes severe and sometimes fatal watery diarrhea in immunocompromised patients and self-limiting but prolonged diarrheal disease in immunocompetent individuals. It exists naturally in animals and can be zoonotic. Although cryptosporidiosis is a significant cause of diarrheal disease in both developing and developed countries, it is more prevalent in developing countries and in tropical environments. We examined the epidemiology and disease burden of Cryptosporidium in Saudi Arabia and neighboring countries by reviewing 23 published studies of Cryptosporidium and etiology of diarrhea in between 1986 and 2006. The prevalence of Cryptosporidium infection in human's ranged from 1% to 37% with a median of 4%, while in animals it was for different species of animals and geographic locations of the studies. Most cases of cryptosporidiosis occurred among children less than 7 years of age and particularly in the first two years of life. The seasonality of Cryptosporidium varied depending on the geographic locations of the studies but it generally most prevalent in the rainy season. The most commonly identified species was Cryptosporidium parvum while C.hominis was detected only in one study from Kuwait. The cumulative experience from Saudi Arabia and four neighboring countries (Kuwait, Oman, Jordan and Iraq) suggest that Cryptosporidium is an important cause of diarrhea in human and cattle. However, the findings of this review also demonstrate the limitations of the available data regarding Cryptosporidium species and strains in circulation in these countries. (author)

Heart murmur detection based on wavelet transformation and a synergy between artificial neural network and modified neighbor annealing methods.

Science.gov (United States)

Eslamizadeh, Gholamhossein; Barati, Ramin

2017-05-01

Early recognition of heart disease plays a vital role in saving lives. Heart murmurs are one of the common heart problems. In this study, Artificial Neural Network (ANN) is trained with Modified Neighbor Annealing (MNA) to classify heart cycles into normal and murmur classes. Heart cycles are separated from heart sounds using wavelet transformer. The network inputs are features extracted from individual heart cycles, and two classification outputs. Classification accuracy of the proposed model is compared with five multilayer perceptron trained with Levenberg-Marquardt, Extreme-learning-machine, back-propagation, simulated-annealing, and neighbor-annealing algorithms. It is also compared with a Self-Organizing Map (SOM) ANN. The proposed model is trained and tested using real heart sounds available in the Pascal database to show the applicability of the proposed scheme. Also, a device to record real heart sounds has been developed and used for comparison purposes too. Based on the results of this study, MNA can be used to produce considerable results as a heart cycle classifier. Copyright © 2017 Elsevier B.V. All rights reserved.

Developing predictive systems models to address complexity and relevance for ecological risk assessment.

Science.gov (United States)

Forbes, Valery E; Calow, Peter

2013-07-01

Ecological risk assessments (ERAs) are not used as well as they could be in risk management. Part of the problem is that they often lack ecological relevance; that is, they fail to grasp necessary ecological complexities. Adding realism and complexity can be difficult and costly. We argue that predictive systems models (PSMs) can provide a way of capturing complexity and ecological relevance cost-effectively. However, addressing complexity and ecological relevance is only part of the problem. Ecological risk assessments often fail to meet the needs of risk managers by not providing assessments that relate to protection goals and by expressing risk in ratios that cannot be weighed against the costs of interventions. Once more, PSMs can be designed to provide outputs in terms of value-relevant effects that are modulated against exposure and that can provide a better basis for decision making than arbitrary ratios or threshold values. Recent developments in the modeling and its potential for implementation by risk assessors and risk managers are beginning to demonstrate how PSMs can be practically applied in risk assessment and the advantages that doing so could have. Copyright © 2013 SETAC.

Common Nearest Neighbor Clustering—A Benchmark

Directory of Open Access Journals (Sweden)

Oliver Lemke

2018-02-01

Full Text Available Cluster analyses are often conducted with the goal to characterize an underlying probability density, for which the data-point density serves as an estimate for this probability density. We here test and benchmark the common nearest neighbor (CNN cluster algorithm. This algorithm assigns a spherical neighborhood R to each data point and estimates the data-point density between two data points as the number of data points N in the overlapping region of their neighborhoods (step 1. The main principle in the CNN cluster algorithm is cluster growing. This grows the clusters by sequentially adding data points and thereby effectively positions the border of the clusters along an iso-surface of the underlying probability density. This yields a strict partitioning with outliers, for which the cluster represents peaks in the underlying probability density—termed core sets (step 2. The removal of the outliers on the basis of a threshold criterion is optional (step 3. The benchmark datasets address a series of typical challenges, including datasets with a very high dimensional state space and datasets in which the cluster centroids are aligned along an underlying structure (Birch sets. The performance of the CNN algorithm is evaluated with respect to these challenges. The results indicate that the CNN cluster algorithm can be useful in a wide range of settings. Cluster algorithms are particularly important for the analysis of molecular dynamics (MD simulations. We demonstrate how the CNN cluster results can be used as a discretization of the molecular state space for the construction of a core-set model of the MD improving the accuracy compared to conventional full-partitioning models. The software for the CNN clustering is available on GitHub.

Highly Relevant Mentoring (HRM) as a Faculty Development Model for Web-Based Instruction

Science.gov (United States)

Carter, Lorraine; Salyers, Vincent; Page, Aroha; Williams, Lynda; Albl, Liz; Hofsink, Clarence

2012-01-01

This paper describes a faculty development model called the highly relevant mentoring (HRM) model; the model includes a framework as well as some practical strategies for meeting the professional development needs of faculty who teach web-based courses. The paper further emphasizes the need for faculty and administrative buy-in for HRM and…

Raman scattering mediated by neighboring molecules

Science.gov (United States)

Williams, Mathew D.; Bradshaw, David S.; Andrews, David L.

2016-05-01

Raman scattering is most commonly associated with a change in vibrational state within individual molecules, the corresponding frequency shift in the scattered light affording a key way of identifying material structures. In theories where both matter and light are treated quantum mechanically, the fundamental scattering process is represented as the concurrent annihilation of a photon from one radiation mode and creation of another in a different mode. Developing this quantum electrodynamical formulation, the focus of the present work is on the spectroscopic consequences of electrodynamic coupling between neighboring molecules or other kinds of optical center. To encompass these nanoscale interactions, through which the molecular states evolve under the dual influence of the input light and local fields, this work identifies and determines two major mechanisms for each of which different selection rules apply. The constituent optical centers are considered to be chemically different and held in a fixed orientation with respect to each other, either as two components of a larger molecule or a molecular assembly that can undergo free rotation in a fluid medium or as parts of a larger, solid material. The two centers are considered to be separated beyond wavefunction overlap but close enough together to fall within an optical near-field limit, which leads to high inverse power dependences on their local separation. In this investigation, individual centers undergo a Stokes transition, whilst each neighbor of a different species remains in its original electronic and vibrational state. Analogous principles are applicable for the anti-Stokes case. The analysis concludes by considering the experimental consequences of applying this spectroscopic interpretation to fluid media; explicitly, the selection rules and the impact of pressure on the radiant intensity of this process.

Raman scattering mediated by neighboring molecules

Energy Technology Data Exchange (ETDEWEB)

Williams, Mathew D.; Bradshaw, David S.; Andrews, David L., E-mail: david.andrews@physics.org [School of Chemistry, University of East Anglia, Norwich NR4 7TJ (United Kingdom)

2016-05-07

Raman scattering is most commonly associated with a change in vibrational state within individual molecules, the corresponding frequency shift in the scattered light affording a key way of identifying material structures. In theories where both matter and light are treated quantum mechanically, the fundamental scattering process is represented as the concurrent annihilation of a photon from one radiation mode and creation of another in a different mode. Developing this quantum electrodynamical formulation, the focus of the present work is on the spectroscopic consequences of electrodynamic coupling between neighboring molecules or other kinds of optical center. To encompass these nanoscale interactions, through which the molecular states evolve under the dual influence of the input light and local fields, this work identifies and determines two major mechanisms for each of which different selection rules apply. The constituent optical centers are considered to be chemically different and held in a fixed orientation with respect to each other, either as two components of a larger molecule or a molecular assembly that can undergo free rotation in a fluid medium or as parts of a larger, solid material. The two centers are considered to be separated beyond wavefunction overlap but close enough together to fall within an optical near-field limit, which leads to high inverse power dependences on their local separation. In this investigation, individual centers undergo a Stokes transition, whilst each neighbor of a different species remains in its original electronic and vibrational state. Analogous principles are applicable for the anti-Stokes case. The analysis concludes by considering the experimental consequences of applying this spectroscopic interpretation to fluid media; explicitly, the selection rules and the impact of pressure on the radiant intensity of this process.

Mouse models of ageing and their relevance to disease.

Science.gov (United States)

Kõks, Sulev; Dogan, Soner; Tuna, Bilge Guvenc; González-Navarro, Herminia; Potter, Paul; Vandenbroucke, Roosmarijn E

2016-12-01

Ageing is a process that gradually increases the organism's vulnerability to death. It affects different biological pathways, and the underlying cellular mechanisms are complex. In view of the growing disease burden of ageing populations, increasing efforts are being invested in understanding the pathways and mechanisms of ageing. We review some mouse models commonly used in studies on ageing, highlight the advantages and disadvantages of the different strategies, and discuss their relevance to disease susceptibility. In addition to addressing the genetics and phenotypic analysis of mice, we discuss examples of models of delayed or accelerated ageing and their modulation by caloric restriction. Copyright © 2016 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Detecting PM2.5's Correlations between Neighboring Cities Using a Time-Lagged Cross-Correlation Coefficient.

Science.gov (United States)

Wang, Fang; Wang, Lin; Chen, Yuming

2017-08-31

In order to investigate the time-dependent cross-correlations of fine particulate (PM2.5) series among neighboring cities in Northern China, in this paper, we propose a new cross-correlation coefficient, the time-lagged q-L dependent height crosscorrelation coefficient (denoted by p q (τ, L)), which incorporates the time-lag factor and the fluctuation amplitude information into the analogous height cross-correlation analysis coefficient. Numerical tests are performed to illustrate that the newly proposed coefficient ρ q (τ, L) can be used to detect cross-correlations between two series with time lags and to identify different range of fluctuations at which two series possess cross-correlations. Applying the new coefficient to analyze the time-dependent cross-correlations of PM2.5 series between Beijing and the three neighboring cities of Tianjin, Zhangjiakou, and Baoding, we find that time lags between the PM2.5 series with larger fluctuations are longer than those between PM2.5 series withsmaller fluctuations. Our analysis also shows that cross-correlations between the PM2.5 series of two neighboring cities are significant and the time lags between two PM2.5 series of neighboring cities are significantly non-zero. These findings providenew scientific support on the view that air pollution in neighboring cities can affect one another not simultaneously but with a time lag.

A Scalable Approach to Modeling Cascading Risk in the MDAP Network

Science.gov (United States)

2014-05-01

Populate Decision Process Model. • Identify challenges to data acquisition. Legend: ATIE_MOD Automated Text & Image  Extraction Module  IID_MOD...8217:~ TI ~.O.Y <D1Y o:yle-~Jti<NI:Aboolate:tos>:J14 : lert•tl ::J!i <DtV o; vlc "~’"""’al>oolote:tos~: 3l4: 1•tt:t’l...DAES, PE docs, SARS – Topic models built from MDAP hub data seem to be relevant to neighbors. – Challenges : Formatting and Content inconsistencies

Political economy models and agricultural policy formation : empirical applicability and relevance for the CAP

NARCIS (Netherlands)

Zee, van der F.A.

1997-01-01

This study explores the relevance and applicability of political economy models for the explanation of agricultural policies. Part I (chapters 4-7) takes a general perspective and evaluates the empirical applicability of voting models and interest group models to agricultural policy

Electronic transport of molecular nanowires by considering of electron hopping energy between the second neighbors

Directory of Open Access Journals (Sweden)

H Rabani

2015-07-01

Full Text Available In this paper, we study the electronic conductance of molecular nanowires by considering the electron hopping between the first and second neighbors with the help Green’s function method at the tight-binding approach. We investigate three types of structures including linear uniform and periodic chains as well as poly(p-phenylene molecule which are embedded between two semi-infinite metallic leads. The results show that in the second neighbor approximation, the resonance, anti-resonance and Fano phenomena occur in the conductance spectra of these structures. Moreover, a new gap is observed at edge of the lead energy band wich its width depends on the value of the electron hopping energy between the second neighbors. In the systems including intrinsic gap, this hopping energy shifts the gap in the energy spectra.

Implementation of Nearest Neighbor using HSV to Identify Skin Disease

Science.gov (United States)

Gerhana, Y. A.; Zulfikar, W. B.; Ramdani, A. H.; Ramdhani, M. A.

2018-01-01

Today, Android is one of the most widely used operating system in the world. Most of android device has a camera that could capture an image, this feature could be optimized to identify skin disease. The disease is one of health problem caused by bacterium, fungi, and virus. The symptoms of skin disease usually visible. In this work, the symptoms that captured as image contains HSV in every pixel of the image. HSV can extracted and then calculate to earn euclidean value. The value compared using nearest neighbor algorithm to discover closer value between image testing and image training to get highest value that decide class label or type of skin disease. The testing result show that 166 of 200 or about 80% is accurate. There are some reasons that influence the result of classification model like number of image training and quality of android device’s camera.

Multi-strategy based quantum cost reduction of linear nearest-neighbor quantum circuit

Science.gov (United States)

Tan, Ying-ying; Cheng, Xue-yun; Guan, Zhi-jin; Liu, Yang; Ma, Haiying

2018-03-01

With the development of reversible and quantum computing, study of reversible and quantum circuits has also developed rapidly. Due to physical constraints, most quantum circuits require quantum gates to interact on adjacent quantum bits. However, many existing quantum circuits nearest-neighbor have large quantum cost. Therefore, how to effectively reduce quantum cost is becoming a popular research topic. In this paper, we proposed multiple optimization strategies to reduce the quantum cost of the circuit, that is, we reduce quantum cost from MCT gates decomposition, nearest neighbor and circuit simplification, respectively. The experimental results show that the proposed strategies can effectively reduce the quantum cost, and the maximum optimization rate is 30.61% compared to the corresponding results.

Thermodynamic systematics of oxides of americium, curium, and neighboring elements

International Nuclear Information System (INIS)

Morss, L.R.

1984-01-01

Recently-obtained calorimetric data on the sesquioxides and dioxides of americium and curium are summarized. These data are combined with other properties of the actinide elements to elucidate the stability relationships among these oxides and to predict the behavior of neighboring actinide oxides. 45 references, 4 figures, 5 tables

A Hybrid Instance Selection Using Nearest-Neighbor for Cross-Project Defect Prediction

Institute of Scientific and Technical Information of China (English)

Duksan Ryu; Jong-In Jang; Jongmoon Baik; Member; ACM; IEEE

2015-01-01

Software defect prediction (SDP) is an active research field in software engineering to identify defect-prone modules. Thanks to SDP, limited testing resources can be effectively allocated to defect-prone modules. Although SDP requires suffcient local data within a company, there are cases where local data are not available, e.g., pilot projects. Companies without local data can employ cross-project defect prediction (CPDP) using external data to build classifiers. The major challenge of CPDP is different distributions between training and test data. To tackle this, instances of source data similar to target data are selected to build classifiers. Software datasets have a class imbalance problem meaning the ratio of defective class to clean class is far low. It usually lowers the performance of classifiers. We propose a Hybrid Instance Selection Using Nearest-Neighbor (HISNN) method that performs a hybrid classification selectively learning local knowledge (via k-nearest neighbor) and global knowledge (via na¨ıve Bayes). Instances having strong local knowledge are identified via nearest-neighbors with the same class label. Previous studies showed low PD (probability of detection) or high PF (probability of false alarm) which is impractical to use. The experimental results show that HISNN produces high overall performance as well as high PD and low PF.

Sequence correction of random coil chemical shifts: correlation between neighbor correction factors and changes in the Ramachandran distribution

DEFF Research Database (Denmark)

Kjærgaard, Magnus; Poulsen, Flemming Martin

2011-01-01

Random coil chemical shifts are necessary for secondary chemical shift analysis, which is the main NMR method for identification of secondary structure in proteins. One of the largest challenges in the determination of random coil chemical shifts is accounting for the effect of neighboring residues....... The contributions from the neighboring residues are typically removed by using neighbor correction factors determined based on each residue's effect on glycine chemical shifts. Due to its unusual conformational freedom, glycine may be particularly unrepresentative for the remaining residue types. In this study, we...... in the conformational ensemble are an important source of neighbor effects in disordered proteins. Glutamine derived random coil chemical shifts and correction factors modestly improve our ability to predict (13)C chemical shifts of intrinsically disordered proteins compared to existing datasets, and may thus improve...

Sistem Rekomendasi Pada E-Commerce Menggunakan K-Nearest Neighbor

Directory of Open Access Journals (Sweden)

Chandra Saha Dewa Prasetya

2017-09-01

The growing number of product information available on the internet brings challenges to both customer and online businesses in the e-commerce environment. Customer often have difficulty when looking for products on the internet because of the number of products sold on the internet. In addition, online businessman often experience difficulties because they has much data about products, customers and transactions, thus causing online businessman have difficulty to promote the right product to a particular customer target. A recommendation system was developed to address those problem with various methods such as Collaborative Filtering, ContentBased, and Hybrid. Collaborative filtering method uses customer’s rating data, content based using product content such as title or description, and hybrid using both as the basis of the recommendation. In this research, the k-nearest neighbor algorithm is used to determine the top-n product recommendations for each buyer. The result of this research method Content Based outperforms other methods because the sparse data, that is the condition where the number of rating given by the customers is relatively little compared the number of products available in e-commerce. Keywords: recomendation system, k-nearest neighbor, collaborative filtering, content based.

Forest structure of Mediterranean yew (Taxus baccata L. populations and neighbor effects on juvenile yew performance in the NE Iberian Peninsula

Directory of Open Access Journals (Sweden)

Pere Casals

2015-12-01

Full Text Available Aim of study: In the Mediterranean region, yew (Taxus baccata L. usually grows with other tree species in mixed forests. Yew recruitment and juvenile growth may depend on the structure of the forest and the net balance between competition for soil water and nutrients with neighbors and facilitation that these neighbors exert by protecting the plants from direct sun exposure. This study aims, at a regional scale, to analyze the structure of forests containing yew, and, on an individual level, to analyze the effect of the surrounding vegetation structure on the performance of yew juveniles.Area of study: The structural typologies of yew populations were defined based on field inventories conducted in 55 plots distributed in 14 localities in the North-Eastern (NE Iberian Peninsula, covering a wide range of yew distribution in the area. In a second step, an analysis of neighboring species' effects on juveniles was conducted based on the data from 103 plots centered in yew juveniles in five localities.Main Results: A cluster analysis classified the inventoried stands into four forest structural types: two multi-stratified forests with scattered yew and two yew groves. Multiple regression modeling showed that the δ13C measured in last year's leaves positively relates to the basal area of conifer neighbors, but negatively with the cover of the yew crown by other trees.Research highlights: At a stand-level, the density of recruits and juveniles (625 ± 104 recruits ha-1, 259 ± 55 juveniles ha-1 in mixed forests was found to be higher than that on yew dominant stands (181 ± 88 recruits ha-1 and 57 ± 88 juveniles ha-1. At an individual-level, the water stress (estimated from leaf δ13C of yew juveniles seems alleviated by the crown cover by neighbors while it increases with the basal area of conifers. Yew conservation should focus on selective felling for the reduction of basal area of neighbors surrounding the target tree, but avoid affecting the

Fracton topological order from nearest-neighbor two-spin interactions and dualities

Science.gov (United States)

Slagle, Kevin; Kim, Yong Baek

2017-10-01

Fracton topological order describes a remarkable phase of matter, which can be characterized by fracton excitations with constrained dynamics and a ground-state degeneracy that increases exponentially with the length of the system on a three-dimensional torus. However, previous models exhibiting this order require many-spin interactions, which may be very difficult to realize in a real material or cold atom system. In this work, we present a more physically realistic model which has the so-called X-cube fracton topological order [Vijay, Haah, and Fu, Phys. Rev. B 94, 235157 (2016), 10.1103/PhysRevB.94.235157] but only requires nearest-neighbor two-spin interactions. The model lives on a three-dimensional honeycomb-based lattice with one to two spin-1/2 degrees of freedom on each site and a unit cell of six sites. The model is constructed from two orthogonal stacks of Z2 topologically ordered Kitaev honeycomb layers [Kitaev, Ann. Phys. 321, 2 (2006), 10.1016/j.aop.2005.10.005], which are coupled together by a two-spin interaction. It is also shown that a four-spin interaction can be included to instead stabilize 3+1D Z2 topological order. We also find dual descriptions of four quantum phase transitions in our model, all of which appear to be discontinuous first-order transitions.

Crimean-Congo hemorrhagic fever in Iran and neighboring countries

DEFF Research Database (Denmark)

Chinikar, S; Ghiasi, Seyed Mojtaba; Hewson, R

2010-01-01

Crimean-Congo hemorrhagic fever (CCHF) is a zoonotic viral disease that is asymptomatic in infected livestock, but a serious threat to humans. Human infections begin with nonspecific febrile symptoms, but progress to a serious hemorrhagic syndrome with a case fatality rate of 2-50%. Although the ...... in Iran and neighboring countries and provide evidence of over 5000 confirmed cases of CCHF in a single period/season....

The surprising power of neighborly advice.

Science.gov (United States)

Gilbert, Daniel T; Killingsworth, Matthew A; Eyre, Rebecca N; Wilson, Timothy D

2009-03-20

Two experiments revealed that (i) people can more accurately predict their affective reactions to a future event when they know how a neighbor in their social network reacted to the event than when they know about the event itself and (ii) people do not believe this. Undergraduates made more accurate predictions about their affective reactions to a 5-minute speed date (n = 25) and to a peer evaluation (n = 88) when they knew only how another undergraduate had reacted to these events than when they had information about the events themselves. Both participants and independent judges mistakenly believed that predictions based on information about the event would be more accurate than predictions based on information about how another person had reacted to it.

The impact of vacant, tax-delinquent, and foreclosed property on sales prices of neighboring homes

OpenAIRE

Stephan Whitaker; Thomas J. Fitzpatrick

2012-01-01

In this empirical analysis, we estimate the impact of vacancy, neglect associated with property-tax delinquency, and foreclosures on the value of neighboring homes using parcel-level observations. Numerous studies have estimated the impact of foreclosures on neighboring properties, and these papers theorize that the foreclosure impact works partially through creating vacant and neglected homes. To our knowledge, this is only the second attempt to estimate the impact of vacancy itself and the ...

Many Neighbors are not Silent. fMRI Evidence for Global Lexical Activity in Visual Word Recognition.

Directory of Open Access Journals (Sweden)

Mario eBraun

2015-07-01

Full Text Available Many neurocognitive studies investigated the neural correlates of visual word recognition, some of which manipulated the orthographic neighborhood density of words and nonwords believed to influence the activation of orthographically similar representations in a hypothetical mental lexicon. Previous neuroimaging research failed to find evidence for such global lexical activity associated with neighborhood density. Rather, effects were interpreted to reflect semantic or domain general processing. The present fMRI study revealed effects of lexicality, orthographic neighborhood density and a lexicality by orthographic neighborhood density interaction in a silent reading task. For the first time we found greater activity for words and nonwords with a high number of neighbors. We propose that this activity in the dorsomedial prefrontal cortex reflects activation of orthographically similar codes in verbal working memory thus providing evidence for global lexical activity as the basis of the neighborhood density effect. The interaction of lexicality by neighborhood density in the ventromedial prefrontal cortex showed lower activity in response to words with a high number compared to nonwords with a high number of neighbors. In the light of these results the facilitatory effect for words and inhibitory effect for nonwords with many neighbors observed in previous studies can be understood as being due to the operation of a fast-guess mechanism for words and a temporal deadline mechanism for nonwords as predicted by models of visual word recognition. Furthermore, we propose that the lexicality effect with higher activity for words compared to nonwords in inferior parietal and middle temporal cortex reflects the operation of an identification mechanism and based on local lexico-semantic activity.

Resampling nucleotide sequences with closest-neighbor trimming and its comparison to other methods.

Directory of Open Access Journals (Sweden)

Kouki Yonezawa

Full Text Available A large number of nucleotide sequences of various pathogens are available in public databases. The growth of the datasets has resulted in an enormous increase in computational costs. Moreover, due to differences in surveillance activities, the number of sequences found in databases varies from one country to another and from year to year. Therefore, it is important to study resampling methods to reduce the sampling bias. A novel algorithm-called the closest-neighbor trimming method-that resamples a given number of sequences from a large nucleotide sequence dataset was proposed. The performance of the proposed algorithm was compared with other algorithms by using the nucleotide sequences of human H3N2 influenza viruses. We compared the closest-neighbor trimming method with the naive hierarchical clustering algorithm and [Formula: see text]-medoids clustering algorithm. Genetic information accumulated in public databases contains sampling bias. The closest-neighbor trimming method can thin out densely sampled sequences from a given dataset. Since nucleotide sequences are among the most widely used materials for life sciences, we anticipate that our algorithm to various datasets will result in reducing sampling bias.

Proteomic characterization of host response to Yersinia pestis and near neighbors

International Nuclear Information System (INIS)

Chromy, Brett A.; Perkins, Julie; Heidbrink, Jenny L.; Gonzales, Arlene D.; Murphy, Gloria A.; Fitch, J. Patrick; McCutchen-Maloney, Sandra L.

2004-01-01

Host-pathogen interactions result in protein expression changes within both the host and the pathogen. Here, results from proteomic characterization of host response following exposure to Yersinia pestis, the causative agent of plague, and to two near neighbors, Yersinia pseudotuberculosis and Yersinia enterocolitica, are reported. Human monocyte-like cells were chosen as a model for macrophage immune response to pathogen exposure. Two-dimensional electrophoresis followed by mass spectrometry was used to identify host proteins with differential expression following exposure to these three closely related Yersinia species. This comparative proteomic characterization of host response clearly shows that host protein expression patterns are distinct for the different pathogen exposures, and contributes to further understanding of Y. pestis virulence and host defense mechanisms. This work also lays the foundation for future studies aimed at defining biomarkers for presymptomatic detection of plague

Seismic clusters analysis in Northeastern Italy by the nearest-neighbor approach

Science.gov (United States)

Peresan, Antonella; Gentili, Stefania

2018-01-01

The main features of earthquake clusters in Northeastern Italy are explored, with the aim to get new insights on local scale patterns of seismicity in the area. The study is based on a systematic analysis of robustly and uniformly detected seismic clusters, which are identified by a statistical method, based on nearest-neighbor distances of events in the space-time-energy domain. The method permits us to highlight and investigate the internal structure of earthquake sequences, and to differentiate the spatial properties of seismicity according to the different topological features of the clusters structure. To analyze seismicity of Northeastern Italy, we use information from local OGS bulletins, compiled at the National Institute of Oceanography and Experimental Geophysics since 1977. A preliminary reappraisal of the earthquake bulletins is carried out and the area of sufficient completeness is outlined. Various techniques are considered to estimate the scaling parameters that characterize earthquakes occurrence in the region, namely the b-value and the fractal dimension of epicenters distribution, required for the application of the nearest-neighbor technique. Specifically, average robust estimates of the parameters of the Unified Scaling Law for Earthquakes, USLE, are assessed for the whole outlined region and are used to compute the nearest-neighbor distances. Clusters identification by the nearest-neighbor method turn out quite reliable and robust with respect to the minimum magnitude cutoff of the input catalog; the identified clusters are well consistent with those obtained from manual aftershocks identification of selected sequences. We demonstrate that the earthquake clusters have distinct preferred geographic locations, and we identify two areas that differ substantially in the examined clustering properties. Specifically, burst-like sequences are associated with the north-western part and swarm-like sequences with the south-eastern part of the study

The neighbor enclosed area tracking algorithm and its application to cyclone merger in the midlatitudes

Science.gov (United States)

Inatsu, Masaru; Amada, Shotarou; Satake, Yuya

2010-05-01

The neighbor enclosed area tracking (NEAT) algorithm is proposed as an alternative method to conventional point-to-point cyclone tracking approaches. Most automated Lagrangian tracking algorithms contain three procedures: cyclone identification, cyclone tracking, and quantification of cyclone intensity and activity. The cyclone identification was simply based on a comparison of neighboring grid points; cyclone tracking mainly employed a near-neighbor point search to neighbor-time cyclone-center datasets; and cyclone intensity and activity are mainly quantified as cyclone track density, and other accompanying products such as genesis and lysis densities, mean lifetime, average moving vector, and mean growth rate can also be obtained in the final procedure. But a crucial problem in the above technique is its requirement of some complicated connecting conditions for near-neighbor tracking. To overcome the problem, NEAT completes cyclone identification and cyclone tracking in a single process of equivalent labeling for spatiotemporally connected domains, i.e., if two spatially enclosed areas in a neighboring time frame overlap, they should be connected. NEAT enables us to count the genesis and tracks of individual cyclones as the conventional tracking. Moreover, NEAT has the ability to produce fruitful information on cyclone mergers and separations, cyclone shape, and material transport by individual eddies (the latter two features will be reported elsewhere). There are many possible applications of NEAT to meteorology and oceanography, but now we focus on the situation, well-known by Japanese synopticians, that two cyclones pass respectively over the north and south of Japan and then they frequently merge and are rapidly deepened in the western Pacific. For the case, the southern cyclones tend to be stimulated just above the sea surface temperature front to the north of oceanic western boundary currents, while the northern cyclones, moving eastward along the polar

Stuttering Attitudes among Turkish Family Generations and Neighbors from Representative Samples

Science.gov (United States)

Ozdemir, R. Sertan; St. Louis, Kenneth O.; Topbas, Seyhun

2011-01-01

Purpose: Attitudes toward stuttering, measured by the "Public Opinion Survey of Human Attributes-Stuttering" ("POSHA-S"), are compared among (a) two different representative samples; (b) family generations (children, parents, and either grandparents or uncles and aunts) and neighbors; (c) children, parents, grandparents/adult…

The Patient-Centered Medical Home Neighbor: A Critical Concept for a Redesigned Healthcare Delivery System

Science.gov (United States)

2011-01-25

Sharing Knowledge: Achieving Breakthrough Performance 2010 Military Health System Conference The Patient -Centered Medical Home Neighbor: A Critical...DATE 25 JAN 2011 2. REPORT TYPE 3. DATES COVERED 00-00-2011 to 00-00-2011 4. TITLE AND SUBTITLE The Patient -Centered Medical Home Neighbor: A...Conference What is the Patient -Centered Medical Home?  …a vision of health care as it should be  …a framework for organizing systems of care at both the

Single cell transcriptomics of neighboring hyphae of Aspergillus niger

Science.gov (United States)

2011-01-01

Single cell profiling was performed to assess differences in RNA accumulation in neighboring hyphae of the fungus Aspergillus niger. A protocol was developed to isolate and amplify RNA from single hyphae or parts thereof. Microarray analysis resulted in a present call for 4 to 7% of the A. niger genes, of which 12% showed heterogeneous RNA levels. These genes belonged to a wide range of gene categories. PMID:21816052

Microscopic theory of the nearest-neighbor valence bond sector of the spin-1/2 kagome antiferromagnet

Science.gov (United States)

Ralko, Arnaud; Mila, Frédéric; Rousochatzakis, Ioannis

2018-03-01

The spin-1/2 Heisenberg model on the kagome lattice, which is closely realized in layered Mott insulators such as ZnCu3(OH) 6Cl2 , is one of the oldest and most enigmatic spin-1/2 lattice models. While the numerical evidence has accumulated in favor of a quantum spin liquid, the debate is still open as to whether it is a Z2 spin liquid with very short-range correlations (some kind of resonating valence bond spin liquid), or an algebraic spin liquid with power-law correlations. To address this issue, we have pushed the program started by Rokhsar and Kivelson in their derivation of the effective quantum dimer model description of Heisenberg models to unprecedented accuracy for the spin-1/2 kagome, by including all the most important virtual singlet contributions on top of the orthogonalization of the nearest-neighbor valence bond singlet basis. Quite remarkably, the resulting picture is a competition between a Z2 spin liquid and a diamond valence bond crystal with a 12-site unit cell, as in the density-matrix renormalization group simulations of Yan et al. Furthermore, we found that, on cylinders of finite diameter d , there is a transition between the Z2 spin liquid at small d and the diamond valence bond crystal at large d , the prediction of the present microscopic description for the two-dimensional lattice. These results show that, if the ground state of the spin-1/2 kagome antiferromagnet can be described by nearest-neighbor singlet dimers, it is a diamond valence bond crystal, and, a contrario, that, if the system is a quantum spin liquid, it has to involve long-range singlets, consistent with the algebraic spin liquid scenario.

Equation of state experiments and theory relevant to planetary modelling

International Nuclear Information System (INIS)

Ross, M.; Graboske, H.C. Jr.; Nellis, W.J.

1981-01-01

In recent years there have been a number of static and shockwave experiments on the properties of planetary materials. The highest pressure measurements, and the ones most relevant to planetary modelling, have been obtained by shock compression. Of particular interest to the Jovian group are results for H 2 , H 2 O, CH 4 and NH 3 . Although the properties of metallic hydrogen have not been measured, they have been the subject of extensive calculations. In addition recent shock wave experiments on iron report to have detected melting under Earth core conditions. From this data theoretical models have been developed for computing the equations of state of materials used in planetary studies. A compelling feature that has followed from the use of improved material properties is a simplification in the planetary models. (author)

Competing growth processes induced by next-nearest-neighbor interactions: Effects on meandering wavelength and stiffness

Science.gov (United States)

Blel, Sonia; Hamouda, Ajmi BH.; Mahjoub, B.; Einstein, T. L.

2017-02-01

In this paper we explore the meandering instability of vicinal steps with a kinetic Monte Carlo simulations (kMC) model including the attractive next-nearest-neighbor (NNN) interactions. kMC simulations show that increase of the NNN interaction strength leads to considerable reduction of the meandering wavelength and to weaker dependence of the wavelength on the deposition rate F. The dependences of the meandering wavelength on the temperature and the deposition rate obtained with simulations are in good quantitative agreement with the experimental result on the meandering instability of Cu(0 2 24) [T. Maroutian et al., Phys. Rev. B 64, 165401 (2001), 10.1103/PhysRevB.64.165401]. The effective step stiffness is found to depend not only on the strength of NNN interactions and the Ehrlich-Schwoebel barrier, but also on F. We argue that attractive NNN interactions intensify the incorporation of adatoms at step edges and enhance step roughening. Competition between NNN and nearest-neighbor interactions results in an alternative form of meandering instability which we call "roughening-limited" growth, rather than attachment-detachment-limited growth that governs the Bales-Zangwill instability. The computed effective wavelength and the effective stiffness behave as λeff˜F-q and β˜eff˜F-p , respectively, with q ≈p /2 .

Effect of electrode design on crosstalk between neighboring organic field-effect transistors based on one single crystal

Science.gov (United States)

Li, Mengjie; Tang, Qingxin; Tong, Yanhong; Zhao, Xiaoli; Zhou, Shujun; Liu, Yichun

2018-03-01

The design of high-integration organic circuits must be such that the interference between neighboring devices is eliminated. Here, rubrene crystals were used to study the effect of the electrode design on crosstalk between neighboring organic field-effect transistors (OFETs). Results show that a decreased source/drain interval and gate electrode width can decrease the diffraction distance of the current, and therefore can weaken the crosstalk. In addition, the inherent low carrier concentration in organic semiconductors can create a high-resistance barrier at the space between gate electrodes of neighboring devices, limiting or even eliminating the crosstalk as a result of the gate electrode width being smaller than the source/drain electrode width.

Relevant Criteria for Testing the Quality of Models for Turbulent Wind Speed Fluctuations

DEFF Research Database (Denmark)

Frandsen, Sten Tronæs; Ejsing Jørgensen, Hans; Sørensen, John Dalsgaard

2008-01-01

Seeking relevant criteria for testing the quality of turbulence models, the scale of turbulence and the gust factor have been estimated from data and compared with predictions from first-order models of these two quantities. It is found that the mean of the measured length scales is approximately...... 10% smaller than the IEC model for wind turbine hub height levels. The mean is only marginally dependent on trends in time series. It is also found that the coefficient of variation of the measured length scales is about 50%. 3  s and 10  s preaveraging of wind speed data are relevant for megawatt......-size wind turbines when seeking wind characteristics that correspond to one blade and the entire rotor, respectively. For heights exceeding 50-60  m, the gust factor increases with wind speed. For heights larger than 60-80  m, present assumptions on the value of the gust factor are significantly...

Prediction of microbe-disease association from the integration of neighbor and graph with collaborative recommendation model.

Science.gov (United States)

Huang, Yu-An; You, Zhu-Hong; Chen, Xing; Huang, Zhi-An; Zhang, Shanwen; Yan, Gui-Ying

2017-10-16

Accumulating clinical researches have shown that specific microbes with abnormal levels are closely associated with the development of various human diseases. Knowledge of microbe-disease associations can provide valuable insights for complex disease mechanism understanding as well as the prevention, diagnosis and treatment of various diseases. However, little effort has been made to predict microbial candidates for human complex diseases on a large scale. In this work, we developed a new computational model for predicting microbe-disease associations by combining two single recommendation methods. Based on the assumption that functionally similar microbes tend to get involved in the mechanism of similar disease, we adopted neighbor-based collaborative filtering and a graph-based scoring method to compute association possibility of microbe-disease pairs. The promising prediction performance could be attributed to the use of hybrid approach based on two single recommendation methods as well as the introduction of Gaussian kernel-based similarity and symptom-based disease similarity. To evaluate the performance of the proposed model, we implemented leave-one-out and fivefold cross validations on the HMDAD database, which is recently built as the first database collecting experimentally-confirmed microbe-disease associations. As a result, NGRHMDA achieved reliable results with AUCs of 0.9023 ± 0.0031 and 0.9111 in the validation frameworks of fivefold CV and LOOCV. In addition, 78.2% microbe samples and 66.7% disease samples are found to be consistent with the basic assumption of our work that microbes tend to get involved in the similar disease clusters, and vice versa. Compared with other methods, the prediction results yielded by NGRHMDA demonstrate its effective prediction performance for microbe-disease associations. It is anticipated that NGRHMDA can be used as a useful tool to search the most potential microbial candidates for various diseases, and therefore

The nearest neighbor and the bayes error rates.

Science.gov (United States)

Loizou, G; Maybank, S J

1987-02-01

The (k, l) nearest neighbor method of pattern classification is compared to the Bayes method. If the two acceptance rates are equal then the asymptotic error rates satisfy the inequalities Ek,l + 1 ¿ E*(¿) ¿ Ek,l dE*(¿), where d is a function of k, l, and the number of pattern classes, and ¿ is the reject threshold for the Bayes method. An explicit expression for d is given which is optimal in the sense that for some probability distributions Ek,l and dE* (¿) are equal.

Cell membrane disruption stimulates cAMP and Ca2+ signaling to potentiate cell membrane resealing in neighboring cells

Directory of Open Access Journals (Sweden)

Tatsuru Togo

2017-12-01

Full Text Available Disruption of cellular plasma membranes is a common event in many animal tissues, and the membranes are usually rapidly resealed. Moreover, repeated membrane disruptions within a single cell reseal faster than the initial wound in a protein kinase A (PKA- and protein kinase C (PKC-dependent manner. In addition to wounded cells, recent studies have demonstrated that wounding of Madin-Darby canine kidney (MDCK cells potentiates membrane resealing in neighboring cells in the short-term by purinergic signaling, and in the long-term by nitric oxide/protein kinase G signaling. In the present study, real-time imaging showed that cell membrane disruption stimulated cAMP synthesis and Ca2+ mobilization from intracellular stores by purinergic signaling in neighboring MDCK cells. Furthermore, inhibition of PKA and PKC suppressed the ATP-mediated short-term potentiation of membrane resealing in neighboring cells. These results suggest that cell membrane disruption stimulates PKA and PKC via purinergic signaling to potentiate cell membrane resealing in neighboring MDCK cells.

Fast Most Similar Neighbor (MSN) classifiers for Mixed Data

OpenAIRE

Hernández Rodríguez, Selene

2010-01-01

The k nearest neighbor (k-NN) classifier has been extensively used in Pattern Recognition because of its simplicity and its good performance. However, in large datasets applications, the exhaustive k-NN classifier becomes impractical. Therefore, many fast k-NN classifiers have been developed; most of them rely on metric properties (usually the triangle inequality) to reduce the number of prototype comparisons. Hence, the existing fast k-NN classifiers are applicable only when the comparison f...

Quantum decoration transformation for spin models

Energy Technology Data Exchange (ETDEWEB)

Braz, F.F.; Rodrigues, F.C.; Souza, S.M. de; Rojas, Onofre, E-mail: ors@dfi.ufla.br

2016-09-15

It is quite relevant the extension of decoration transformation for quantum spin models since most of the real materials could be well described by Heisenberg type models. Here we propose an exact quantum decoration transformation and also showing interesting properties such as the persistence of symmetry and the symmetry breaking during this transformation. Although the proposed transformation, in principle, cannot be used to map exactly a quantum spin lattice model into another quantum spin lattice model, since the operators are non-commutative. However, it is possible the mapping in the “classical” limit, establishing an equivalence between both quantum spin lattice models. To study the validity of this approach for quantum spin lattice model, we use the Zassenhaus formula, and we verify how the correction could influence the decoration transformation. But this correction could be useless to improve the quantum decoration transformation because it involves the second-nearest-neighbor and further nearest neighbor couplings, which leads into a cumbersome task to establish the equivalence between both lattice models. This correction also gives us valuable information about its contribution, for most of the Heisenberg type models, this correction could be irrelevant at least up to the third order term of Zassenhaus formula. This transformation is applied to a finite size Heisenberg chain, comparing with the exact numerical results, our result is consistent for weak xy-anisotropy coupling. We also apply to bond-alternating Ising–Heisenberg chain model, obtaining an accurate result in the limit of the quasi-Ising chain.

Quantum decoration transformation for spin models

International Nuclear Information System (INIS)

Braz, F.F.; Rodrigues, F.C.; Souza, S.M. de; Rojas, Onofre

2016-01-01

It is quite relevant the extension of decoration transformation for quantum spin models since most of the real materials could be well described by Heisenberg type models. Here we propose an exact quantum decoration transformation and also showing interesting properties such as the persistence of symmetry and the symmetry breaking during this transformation. Although the proposed transformation, in principle, cannot be used to map exactly a quantum spin lattice model into another quantum spin lattice model, since the operators are non-commutative. However, it is possible the mapping in the “classical” limit, establishing an equivalence between both quantum spin lattice models. To study the validity of this approach for quantum spin lattice model, we use the Zassenhaus formula, and we verify how the correction could influence the decoration transformation. But this correction could be useless to improve the quantum decoration transformation because it involves the second-nearest-neighbor and further nearest neighbor couplings, which leads into a cumbersome task to establish the equivalence between both lattice models. This correction also gives us valuable information about its contribution, for most of the Heisenberg type models, this correction could be irrelevant at least up to the third order term of Zassenhaus formula. This transformation is applied to a finite size Heisenberg chain, comparing with the exact numerical results, our result is consistent for weak xy-anisotropy coupling. We also apply to bond-alternating Ising–Heisenberg chain model, obtaining an accurate result in the limit of the quasi-Ising chain.

Association of food access and neighbor relationships with diet and underweight among community-dwelling older Japanese.

Science.gov (United States)

Nakamura, Hideko; Nakamura, Mieko; Okada, Eisaku; Ojima, Toshiyuki; Kondo, Katsunori

2017-11-01

Food access is important for maintaining dietary variety, which predicts underweight. The aim of this study was to examine the association of food access and neighbor relationships with eating and underweight. We analyzed cross-sectional data from 102,869 Japanese individuals aged 65 years or older. The perceived availability of food was assessed using the presence or absence of food stores within 1 km of the home. Level of relationships with neighbors was also assessed. The odds ratios (ORs) and 95% confidence intervals (CIs) for infrequent food intake and underweight were determined using logistic regression analysis. The proportion of men and women having low access to food was 25-30%. Having low food access (OR 1.18; 95% CI, 1.12-1.25 for men and OR 1.26; 95% CI, 1.19-1.33 for women) and a low level of relationship with neighbors (OR 1.38; 95% CI, 1.31-1.45 for men and OR 1.57; 95% CI, 1.48-1.67 for women) was associated with infrequent intake of fruits and vegetables in both sexes. Association between low food access and infrequent intake of fruits and vegetables was higher among men with low levels of neighbor relationship (OR 1.34; 95% CI, 1.23-1.46) than among men with high levels of relationship (OR 1.10; 95% CI, 1.03-1.18). Low perceived availability of food is a risk factor for low dietary variety among older people. Furthermore, high levels of relationship with neighbors may relieve the harmful effect of low food access. Copyright © 2017 The Authors. Production and hosting by Elsevier B.V. All rights reserved.

Hierarchical Bayesian nonparametric mixture models for clustering with variable relevance determination.

Science.gov (United States)

Yau, Christopher; Holmes, Chris

2011-07-01

We propose a hierarchical Bayesian nonparametric mixture model for clustering when some of the covariates are assumed to be of varying relevance to the clustering problem. This can be thought of as an issue in variable selection for unsupervised learning. We demonstrate that by defining a hierarchical population based nonparametric prior on the cluster locations scaled by the inverse covariance matrices of the likelihood we arrive at a 'sparsity prior' representation which admits a conditionally conjugate prior. This allows us to perform full Gibbs sampling to obtain posterior distributions over parameters of interest including an explicit measure of each covariate's relevance and a distribution over the number of potential clusters present in the data. This also allows for individual cluster specific variable selection. We demonstrate improved inference on a number of canonical problems.

Political economy models and agricultural policy formation : empirical applicability and relevance for the CAP

OpenAIRE

Zee, van der, F.A.

1997-01-01

This study explores the relevance and applicability of political economy models for the explanation of agricultural policies. Part I (chapters 4-7) takes a general perspective and evaluates the empirical applicability of voting models and interest group models to agricultural policy formation in industrialised market economics. Part II (chapters 8-11) focuses on the empirical applicability of political economy models to agricultural policy formation and agricultural policy developmen...

Elliptic Painlevé equations from next-nearest-neighbor translations on the E_8^{(1)} lattice

Science.gov (United States)

Joshi, Nalini; Nakazono, Nobutaka

2017-07-01

The well known elliptic discrete Painlevé equation of Sakai is constructed by a standard translation on the E_8(1) lattice, given by nearest neighbor vectors. In this paper, we give a new elliptic discrete Painlevé equation obtained by translations along next-nearest-neighbor vectors. This equation is a generic (8-parameter) version of a 2-parameter elliptic difference equation found by reduction from Adler’s partial difference equation, the so-called Q4 equation. We also provide a projective reduction of the well known equation of Sakai.

Culturally relevant model program to prevent and reduce agricultural injuries.

Science.gov (United States)

Helitzer, D L; Hathorn, G; Benally, J; Ortega, C

2014-07-01

Limited research has explored pesticide injury prevention among American Indian farmers. In a five-year agricultural intervention, a university-community partnership, including the University of New Mexico School of Medicine, New Mexico State University, Shiprock Area Cooperative Extension Service, and Navajo Nation communities, used a culturally relevant model to introduce and maintain safe use of integrated pest management techniques. We applied the Diffusion of Innovations theory and community-based approaches to tailor health promotion strategies for our intervention. In a longitudinal study with repeated measures, we trained six "model farmers" to be crop management experts in pesticide safety, application, and control. Subsequently, these model farmers worked with 120 farm families randomized into two groups: intervention (Group 1) and delayed intervention (Group 2). Measurements included a walk-through analysis, test of knowledge and attitudes, and yield analysis. Both groups demonstrated improvements in pesticide storage behaviors after training. Test scores regarding safety practices improved significantly: from 57.3 to 72.4 for Group 1 and from 52.6 to 76.3 for Group 2. Group 1 maintained their knowledge and safety practices after the intervention. Attitudes about pesticides and communication of viewpoints changed across the study years. With pesticides and fertilizer, the number of corn ears increased by 56.3% and yield (kg m(-2)) of alfalfa increased by 41.2%. The study combined traditional farming practices with culturally relevant approaches and behavior change theory to affect knowledge, safety practices, attitudes, communication channels, and crop yield. Storage behaviors, use of pesticides and safety and application equipment, and safety practice knowledge changed significantly, as did attitudes about social networking, social support, and the compatibility and relative advantage of pesticides for farms.

Response properties of neighboring neurons in the auditory midbrain for pure-tone stimulation: a tetrode study.

Science.gov (United States)

Seshagiri, Chandran V; Delgutte, Bertrand

2007-10-01

The complex anatomical structure of the central nucleus of the inferior colliculus (ICC), the principal auditory nucleus in the midbrain, may provide the basis for functional organization of auditory information. To investigate this organization, we used tetrodes to record from neighboring neurons in the ICC of anesthetized cats and studied the similarity and difference among the responses of these neurons to pure-tone stimuli using widely used physiological characterizations. Consistent with the tonotopic arrangement of neurons in the ICC and reports of a threshold map, we found a high degree of correlation in the best frequencies (BFs) of neighboring neurons, which were mostly binaural beats. However, the characteristic phases (CPs) of neighboring neurons revealed a significant correlation. Because the CP is related to the neural mechanisms generating the ITD sensitivity, this result is consistent with segregation of inputs to the ICC from the lateral and medial superior olives.

Implementation of the k -Neighbors Technique in a recommender algorithm for a purchasing system using NFC and Android

Directory of Open Access Journals (Sweden)

Oscar Arley Riveros

2017-01-01

Full Text Available Introduction: This paper aims to present the design of a mobile application involving NFC technology and a collaborative recommendation algorithm under the K-neighbors technique, allowing to observe personalized suggestions for each client. Objective: Design and develop a mobile application, using NFC technologies and K-Neighbors Technique in a recommendation algorithm, for a Procurement System. Methodology: The process followed for the design and development of the application focuses on: • Review of the state of the art in mobile shopping systems. • State-of-the-art construction in the use of NFC technology and AI techniques for recommending systems focused on K-Neighbors Algorithms • Proposed system design • Parameterization and implementation of the K-Neighbors Technique and integration of NFC Technology • Proposed System Implementation and Testing. Results: Among the results obtained are detailed: • Mobile application that integrates Android, NFC Technologies and a Technique of Algorithm Recommendation • Parameterization of the K-Neighbors Technique, to be used within the recommended algorithm. • Implementation of functional requirements that allow the generation of personalized recommendations for purchase to the user, user ratings Conclusions: The k-neighbors technique in a recommendation algorithm allows the client to provide a series of recommendations with a level of security, since this algorithm performs calculations taking into account multiple parameters and contrasts the results obtained for other users, finding the articles with a Greater degree of similarity with the customer profile. This algorithm starts from a sample of similar, complementary and other unrelated products, applying its respective formulation, we obtain that the recommendation is made only with the complementary products that obtained higher qualification; Making a big difference with most recommending systems on the market, which are limited to

Prediction and characterization of heat-affected zone formation due to neighboring nickel-aluminum multilayer foil reaction

Energy Technology Data Exchange (ETDEWEB)

Adams, David P. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Hirschfeld, Deidre A. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Hooper, Ryan J. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Manuel, Michelle V. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

2015-09-01

Reactive multilayer foils have the potential to be used as local high intensity heat sources for a variety of applications. Much of the past research effort concerning these materials have focused on understanding the structure-property relationships of the foils that govern the energy released during a reaction. To enhance the ability of researchers to more rapidly develop technologies based on reactive multilayer foils, a deeper and more predictive understanding of the relationship between the heat released from the foil and microstructural evolution in the neighboring materials is needed. This work describes the development of a numerical model for the purpose of evaluating new foil-substrate combinations for screening and optimization. The model is experimentally validated using a commercially available Ni-Al multilayer foils and different alloys.

Neighboring Structure Visualization on a Grid-based Layout.

Science.gov (United States)

Marcou, G; Horvath, D; Varnek, A

2017-10-01

Here, we describe an algorithm to visualize chemical structures on a grid-based layout in such a way that similar structures are neighboring. It is based on structure reordering with the help of the Hilbert Schmidt Independence Criterion, representing an empirical estimate of the Hilbert-Schmidt norm of the cross-covariance operator. The method can be applied to any layout of bi- or three-dimensional shape. The approach is demonstrated on a set of dopamine D5 ligands visualized on squared, disk and spherical layouts. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

The Relevance of Grain Dissection for Grain Size Reduction in Polar Ice: Insights from Numerical Models and Ice Core Microstructure Analysis

Directory of Open Access Journals (Sweden)

Florian Steinbach

2017-09-01

Full Text Available The flow of ice depends on the properties of the aggregate of individual ice crystals, such as grain size or lattice orientation distributions. Therefore, an understanding of the processes controlling ice micro-dynamics is needed to ultimately develop a physically based macroscopic ice flow law. We investigated the relevance of the process of grain dissection as a grain-size-modifying process in natural ice. For that purpose, we performed numerical multi-process microstructure modeling and analyzed microstructure and crystallographic orientation maps from natural deep ice-core samples from the North Greenland Eemian Ice Drilling (NEEM project. Full crystallographic orientations measured by electron backscatter diffraction (EBSD have been used together with c-axis orientations using an optical technique (Fabric Analyser. Grain dissection is a feature of strain-induced grain boundary migration. During grain dissection, grain boundaries bulge into a neighboring grain in an area of high dislocation energy and merge with the opposite grain boundary. This splits the high dislocation-energy grain into two parts, effectively decreasing the local grain size. Currently, grain size reduction in ice is thought to be achieved by either the progressive transformation from dislocation walls into new high-angle grain boundaries, called subgrain rotation or polygonisation, or bulging nucleation that is assisted by subgrain rotation. Both our time-resolved numerical modeling and NEEM ice core samples show that grain dissection is a common mechanism during ice deformation and can provide an efficient process to reduce grain sizes and counter-act dynamic grain-growth in addition to polygonisation or bulging nucleation. Thus, our results show that solely strain-induced boundary migration, in absence of subgrain rotation, can reduce grain sizes in polar ice, in particular if strain energy gradients are high. We describe the microstructural characteristics that can be

The patient-centered medical home neighbor: A primary care physician's view.

Science.gov (United States)

Sinsky, Christine A

2011-01-04

The American College of Physicians' position paper on the patient-centered medical home neighbor (PCMH-N) extends the work of the patient-centered medical home (PCMH) as a means of improving the delivery of health care. Recognizing that the PCMH does not exist in isolation, the PCMH-N concept outlines expectations for comanagement, communication, and care coordination and broadens responsibility for safe, effective, and efficient care beyond primary care to include physicians of all specialties. As such, it is a fitting follow-up to the PCMH and moves further down the road toward improved care for complex patients. Yet, there is more work to be done. Truly transforming the U.S. health care system around personalized medical homes embedded in highly functional medical neighborhoods will require better staffing models; more robust electronic information tools; aligned incentives for quality and efficiency within payment and regulatory policies; and a culture of greater engagement of patients, their families, and communities.

The relevance of existing health communication models in the email age: An

Science.gov (United States)

Fage-Butler, Antoinette Mary; Jensen, Matilde Nisbeth

2015-01-01

Email communication is being integrated relatively slowly into doctor–patient communication. Patients have expressed enthusiasm for the medium, while doctors are generally more reluctant. As existing health communication models have characteristically assumed the co-presence of doctor and patient and primarily reflect medical practitioners’ perspectives, their suitability in relation to email communication and patients’ perspectives warrants further investigation. Following a two-step process and using the methodology of the integrative literature review, 29 articles from 2004–2014 are analysed with the aim of investigating the advantages and disadvantages of the medium of email from the patient’s perspective. The findings are compared to the health communication models of biomedicine, patient-centeredness, patient education and patient empowerment to investigate these models’ relevance for doctor–patient email communication. Results show that patients identify numerous advantages with email communication, including improved convenience and access, more detailed informational exchanges, greater reflection opportunities, freedom from the medical gaze and the potential to level out power imbalances, as well as a number of primarily medium-related disadvantages. The findings indicate that email can counteract some of the communicative problems associated with biomedicine and suggest the ongoing relevance of aspects of the models of patient empowerment, patient-centeredness and patient education for email communication.

Monte Carlo study of a ferrimagnetic mixed-spin (2, 5/2) system with the nearest and next-nearest neighbors exchange couplings

Science.gov (United States)

Bi, Jiang-lin; Wang, Wei; Li, Qi

2017-07-01

In this paper, the effects of the next-nearest neighbors exchange couplings on the magnetic and thermal properties of the ferrimagnetic mixed-spin (2, 5/2) Ising model on a 3D honeycomb lattice have been investigated by the use of Monte Carlo simulation. In particular, the influences of exchange couplings (Ja, Jb, Jan) and the single-ion anisotropy(Da) on the phase diagrams, the total magnetization, the sublattice magnetization, the total susceptibility, the internal energy and the specific heat have been discussed in detail. The results clearly show that the system can express the critical and compensation behavior within the next-nearest neighbors exchange coupling. Great deals of the M curves such as N-, Q-, P- and L-types have been discovered, owing to the competition between the exchange coupling and the temperature. Compared with other theoretical and experimental works, our results have an excellent consistency with theirs.

Observing Literacy Practices in Neighbor Institutions

DEFF Research Database (Denmark)

Reusch, Charlotte

’procedures on language and literacy. Based on this material, we developed an observation scheme and a guide for preschool teachers to follow, inspired by an action learning concept.During fall 2015, a pilot project is carried out. Preschool teachers from one institution visit a neighbor institution one by one during...... work hours, in order to observe and register how language and literacy events look like there. Afterwards, they share their registrations at a team meeting, and discuss and decide which procedures to test in their own institution. Thus, they form a professional learning network. In the pilot project......The Danish National Centre for Reading and a municipality in southern Denmark cooperate to develop a program to improve preschool children’s early literacy skills. The project aims to support preschool teachers’ ability to create a rich literacy environment for children age 3‒6. Recent research...

Family influences on mania-relevant cognitions and beliefs: a cognitive model of mania and reward.

Science.gov (United States)

Chen, Stephen H; Johnson, Sheri L

2012-07-01

The present study proposed and tested a cognitive model of mania and reward. Undergraduates (N = 284; 68.4% female; mean age = 20.99 years, standard deviation ± 3.37) completed measures of family goal setting and achievement values, personal reward-related beliefs, cognitive symptoms of mania, and risk for mania. Correlational analyses and structural equation modeling supported two distinct, but related facets of mania-relevant cognition: stably present reward-related beliefs and state-dependent cognitive symptoms in response to success and positive emotion. Results also indicated that family emphasis on achievement and highly ambitious extrinsic goals were associated with these mania-relevant cognitions. Finally, controlling for other factors, cognitive symptoms in response to success and positive emotion were uniquely associated with lifetime propensity towards mania symptoms. Results support the merit of distinguishing between facets of mania-relevant cognition and the importance of the family in shaping both aspects of cognition. © 2012 Wiley Periodicals, Inc.

Long-term effect of September 11 on the political behavior of victims’ families and neighbors

Science.gov (United States)

Hersh, Eitan D.

2013-01-01

This article investigates the long-term effect of September 11, 2001 on the political behaviors of victims’ families and neighbors. Relative to comparable individuals, family members and residential neighbors of victims have become—and have stayed—significantly more active in politics in the last 12 years, and they have become more Republican on account of the terrorist attacks. The method used to demonstrate these findings leverages the random nature of the terrorist attack to estimate a causal effect and exploits new techniques to link multiple, individual-level, governmental databases to measure behavioral change without relying on surveys or aggregate analysis. PMID:24324145

Long-term effect of September 11 on the political behavior of victims' families and neighbors.

Science.gov (United States)

Hersh, Eitan D

2013-12-24

This article investigates the long-term effect of September 11, 2001 on the political behaviors of victims' families and neighbors. Relative to comparable individuals, family members and residential neighbors of victims have become--and have stayed--significantly more active in politics in the last 12 years, and they have become more Republican on account of the terrorist attacks. The method used to demonstrate these findings leverages the random nature of the terrorist attack to estimate a causal effect and exploits new techniques to link multiple, individual-level, governmental databases to measure behavioral change without relying on surveys or aggregate analysis.

Estimating forest attribute parameters for small areas using nearest neighbors techniques

Science.gov (United States)

Ronald E. McRoberts

2012-01-01

Nearest neighbors techniques have become extremely popular, particularly for use with forest inventory data. With these techniques, a population unit prediction is calculated as a linear combination of observations for a selected number of population units in a sample that are most similar, or nearest, in a space of ancillary variables to the population unit requiring...

Applying an efficient K-nearest neighbor search to forest attribute imputation

Science.gov (United States)

Andrew O. Finley; Ronald E. McRoberts; Alan R. Ek

2006-01-01

This paper explores the utility of an efficient nearest neighbor (NN) search algorithm for applications in multi-source kNN forest attribute imputation. The search algorithm reduces the number of distance calculations between a given target vector and each reference vector, thereby, decreasing the time needed to discover the NN subset. Results of five trials show gains...

Location Criteria Relevant for Sustainability of Social Housing Model

Directory of Open Access Journals (Sweden)

Petković-Grozdanović Nataša

2016-01-01

Full Text Available Social housing models, which had began to develop during the last century, for their only objective had a need to overcome the housing problems of socially vulnerable categories. However, numerous studies have shown that these social categories, because of their low social status, are highly susceptible to various psychological and sociological problems. On the other hand a low level of quality, which was common for social housing dwellings, has further aggravated these problems by initiating trouble behaviours among tenants, affecting social exclusion and segregation. Contemporary social housing models are therefore conceptualized in a way to provide a positive psycho-sociological impact on their tenants. Therefore the planning approach in social housing should be such to: support important functions in daily life routines; promote tolerance and cooperation; influence on a sense of social order and belonging; affect the socialization of the tenant and their integration into the wider community; and improve social cohesion. Analysis of the influential location parameters of immediate and wider social housing environment strive to define the ones relevant to the life quality of social housing tenants and therefore influence on the sustainability of social housing model.

Influence of geometry on light harvesting in dendrimeric systems. II. nth-nearest neighbor effects and the onset of percolation

International Nuclear Information System (INIS)

Bentz, Jonathan L.; Kozak, John J.

2006-01-01

We explore the effect of imposing different constraints (biases, boundary conditions) on the mean time to trapping (or mean walklength) for a particle (excitation) migrating on a finite dendrimer lattice with a centrally positioned trap. By mobilizing the theory of finite Markov processes, we are able to obtain exact analytic expressions for site-specific walklengths as well as the overall walklength for both nearest-neighbor and second-nearest-neighbor displacements. This allows the comparison with and generalization of earlier results [A. Bar-Haim, J. Klafter, J. Phys. Chem. B 102 (1998) 1662; A. Bar-Haim, J. Klafter, J. Lumin. 76, 77 (1998) 197; O. Flomenbom, R.J. Amir, D. Shabat, J. Klafter, J. Lumin. 111 (2005) 315; J.L. Bentz, F.N. Hosseini, J.J. Kozak, Chem. Phys. Lett. 370 (2003) 319]. A novel feature of this work is the establishment of a connection between the random walk models studied here and percolation theory. The full dynamical behavior was also determined via solution of the stochastic master equation, and the results obtained compared with recent spectroscopic experiments

Influence of geometry on light harvesting in dendrimeric systems. II. nth-nearest neighbor effects and the onset of percolation

Energy Technology Data Exchange (ETDEWEB)

Bentz, Jonathan L. [Department of Chemistry, Iowa State University, Ames, IA, 50011 (United States)]. E-mail: jnbntz@iastate.edu; Kozak, John J. [Beckman Institute, California Institute of Technology, 1200 E. California Boulevard, Pasadena, CA 91125-7400 (United States)

2006-11-15

We explore the effect of imposing different constraints (biases, boundary conditions) on the mean time to trapping (or mean walklength) for a particle (excitation) migrating on a finite dendrimer lattice with a centrally positioned trap. By mobilizing the theory of finite Markov processes, we are able to obtain exact analytic expressions for site-specific walklengths as well as the overall walklength for both nearest-neighbor and second-nearest-neighbor displacements. This allows the comparison with and generalization of earlier results [A. Bar-Haim, J. Klafter, J. Phys. Chem. B 102 (1998) 1662; A. Bar-Haim, J. Klafter, J. Lumin. 76, 77 (1998) 197; O. Flomenbom, R.J. Amir, D. Shabat, J. Klafter, J. Lumin. 111 (2005) 315; J.L. Bentz, F.N. Hosseini, J.J. Kozak, Chem. Phys. Lett. 370 (2003) 319]. A novel feature of this work is the establishment of a connection between the random walk models studied here and percolation theory. The full dynamical behavior was also determined via solution of the stochastic master equation, and the results obtained compared with recent spectroscopic experiments.

Present status on atomic and molecular data relevant to fusion plasma diagnostics and modeling

International Nuclear Information System (INIS)

Tawara, H.

1997-01-01

This issue is the collection of the paper presented status on atomic and molecular data relevant to fusion plasma diagnostics and modeling. The 10 of the presented papers are indexed individually. (J.P.N.)

Power-Controlled MAC Protocols with Dynamic Neighbor Prediction for Ad hoc Networks

Institute of Scientific and Technical Information of China (English)

LI Meng; ZHANG Lin; XIAO Yong-kang; SHAN Xiu-ming

2004-01-01

Energy and bandwidth are the scarce resources in ad hoc networks because most of the mobile nodes are battery-supplied and share the exclusive wireless medium. Integrating the power control into MAC protocol is a promising technique to fully exploit these precious resources of ad hoc wireless networks. In this paper, a new intelligent power-controlled Medium Access Control (MAC) (iMAC) protocol with dynamic neighbor prediction is proposed. Through the elaborate design of the distributed transmit-receive strategy of mobile nodes, iMAC greatly outperforms the prevailing IEEE 802.11 MAC protocols in not only energy conservation but also network throughput. Using the Dynamic Neighbor Prediction (DNP), iMAC performs well in mobile scenes. To the best of our knowledge, iMAC is the first protocol that considers the performance deterioration of power-controlled MAC protocols in mobile scenes and then proposes a solution. Simulation results indicate that DNP is important and necessary for power-controlled MAC protocols in mobile ad hoc networks.

Efficient and accurate nearest neighbor and closest pair search in high-dimensional space

KAUST Repository

Tao, Yufei; Yi, Ke; Sheng, Cheng; Kalnis, Panos

2010-01-01

Nearest Neighbor (NN) search in high-dimensional space is an important problem in many applications. From the database perspective, a good solution needs to have two properties: (i) it can be easily incorporated in a relational database, and (ii

Reduction of Conflicts in Mining Development Using "Good Neighbor Agreements"

Science.gov (United States)

Masaitis, A.

2013-05-01

New environmental and social challenges for the mining industry in both developed and developing countries show the obvious need to implement "responsible" mining practices that include improved community involvement. Good Neighbor Agreements (GNA's) are a relatively new mechanism for improving communication and trust between a mining company and the community. The focus of a GNA will be to provide a written and enforceable agreement, negotiated between the concerned public and the respective mining company to respond to concerns from the public, and also provide a mechanism for conflict resolution, when there is mutual benefit to maintain a working relationship. Development of GNA's, a recently evolving process that promotes environmentally sound relationships between mines and the surrounding communities. Modify and apply the resulting GNA formulas to the developing countries and countries with transitional economies. This is particularly important for countries that have poorly functioning regulatory systems that cannot guarantee a healthy and safe environment for the communities. The fundamental questions addressed by this research. 1. This is a three-year research project started in August 2012 at the University of Nevada, Reno (UNR) to develop a Good Neighbor Agreements standards as well as to investigate the details of mine development. 2. Identify spheres of possible cooperation between mining companies, government organizations, and the Non-Governmental Organizations (NGO's). Use this cooperation to develop international standards for the GNA, to promote exchange of environmental information, and exchange of successful environmental, health, and safety practices between mining operations from different countries. Discussion: The Good Neighbor Agreement currently evolving will address the following: 1. Provide an economically viable mechanism for developing a partnership between mining operations and the local communities that will increase mining industry

Spatiotemporal distribution of Oklahoma earthquakes: Exploring relationships using a nearest-neighbor approach

Science.gov (United States)

Vasylkivska, Veronika S.; Huerta, Nicolas J.

2017-07-01

Determining the spatiotemporal characteristics of natural and induced seismic events holds the opportunity to gain new insights into why these events occur. Linking the seismicity characteristics with other geologic, geographic, natural, or anthropogenic factors could help to identify the causes and suggest mitigation strategies that reduce the risk associated with such events. The nearest-neighbor approach utilized in this work represents a practical first step toward identifying statistically correlated clusters of recorded earthquake events. Detailed study of the Oklahoma earthquake catalog's inherent errors, empirical model parameters, and model assumptions is presented. We found that the cluster analysis results are stable with respect to empirical parameters (e.g., fractal dimension) but were sensitive to epicenter location errors and seismicity rates. Most critically, we show that the patterns in the distribution of earthquake clusters in Oklahoma are primarily defined by spatial relationships between events. This observation is a stark contrast to California (also known for induced seismicity) where a comparable cluster distribution is defined by both spatial and temporal interactions between events. These results highlight the difficulty in understanding the mechanisms and behavior of induced seismicity but provide insights for future work.

Chaotic and stable perturbed maps: 2-cycles and spatial models

Science.gov (United States)

Braverman, E.; Haroutunian, J.

2010-06-01

As the growth rate parameter increases in the Ricker, logistic and some other maps, the models exhibit an irreversible period doubling route to chaos. If a constant positive perturbation is introduced, then the Ricker model (but not the classical logistic map) experiences period doubling reversals; the break of chaos finally gives birth to a stable two-cycle. We outline the maps which demonstrate a similar behavior and also study relevant discrete spatial models where the value in each cell at the next step is defined only by the values at the cell and its nearest neighbors. The stable 2-cycle in a scalar map does not necessarily imply 2-cyclic-type behavior in each cell for the spatial generalization of the map.

BROA: An agent-based model to recommend relevant Learning Objects from Repository Federations adapted to learner profile

Directory of Open Access Journals (Sweden)

Paula A. Rodríguez

2013-03-01

Full Text Available Learning Objects (LOs are distinguished from traditional educational resources for their easy and quickly availability through Web-based repositories, from which they are accessed through their metadata. In addition, having a user profile allows an educational recommender system to help the learner to find the most relevant LOs based on their needs and preferences. The aim of this paper is to propose an agent-based model so-called BROA to recommend relevant LOs recovered from Repository Federations as well as LOs adapted to learner profile. The model proposed uses both role and service models of GAIA methodology, and the analysis models of the MAS-CommonKADS methodology. A prototype was built based on this model and validated to obtain some assessing results that are finally presented.

Gastronomy Tourism in Several Neighbor Countries of Indonesia: a Brief Review

Directory of Open Access Journals (Sweden)

Kurniasih Sukenti

2014-04-01

Full Text Available Gastronomy tourism, also called culinary tourism or food tourism, is a kind of tourism that provide attractions based on the culinary aspect owned by a country, region, or area. It is not only offers food and beverages as the main objects in its attractions, but also everything related to food activities ranging from food ingredients, preparation, processing, serving, as well as the cultural and local values. A well-managed culinary tourism will be a supportive program in developing and enhancing the tourism sector in a country. The objective of this paper is to describe the profile of gastronomy tourism in several neighbor countries of Indonesia, i.e. Hongkong, Singapore, Thailand, and Malaysia. This brief review is also discussed the potential of Indonesia gastronomy in supporting government’s tourism program. Basically, Indonesia has more enormous potential asset in managing its cultural heritages in term of culinary than its neighbor countries. A well-managed gastronomy tourism plays not only an important role in enhancing the economic sector, but also contribute in preserving the natural and cultural resources. Keywords: gastronomy tourism, culinary tourism, food tourism.

Social aggregation in pea aphids: experiment and random walk modeling.

Directory of Open Access Journals (Sweden)

Christa Nilsen

Full Text Available From bird flocks to fish schools and ungulate herds to insect swarms, social biological aggregations are found across the natural world. An ongoing challenge in the mathematical modeling of aggregations is to strengthen the connection between models and biological data by quantifying the rules that individuals follow. We model aggregation of the pea aphid, Acyrthosiphon pisum. Specifically, we conduct experiments to track the motion of aphids walking in a featureless circular arena in order to deduce individual-level rules. We observe that each aphid transitions stochastically between a moving and a stationary state. Moving aphids follow a correlated random walk. The probabilities of motion state transitions, as well as the random walk parameters, depend strongly on distance to an aphid's nearest neighbor. For large nearest neighbor distances, when an aphid is essentially isolated, its motion is ballistic with aphids moving faster, turning less, and being less likely to stop. In contrast, for short nearest neighbor distances, aphids move more slowly, turn more, and are more likely to become stationary; this behavior constitutes an aggregation mechanism. From the experimental data, we estimate the state transition probabilities and correlated random walk parameters as a function of nearest neighbor distance. With the individual-level model established, we assess whether it reproduces the macroscopic patterns of movement at the group level. To do so, we consider three distributions, namely distance to nearest neighbor, angle to nearest neighbor, and percentage of population moving at any given time. For each of these three distributions, we compare our experimental data to the output of numerical simulations of our nearest neighbor model, and of a control model in which aphids do not interact socially. Our stochastic, social nearest neighbor model reproduces salient features of the experimental data that are not captured by the control.

The LAILAPS search engine: a feature model for relevance ranking in life science databases.

Science.gov (United States)

Lange, Matthias; Spies, Karl; Colmsee, Christian; Flemming, Steffen; Klapperstück, Matthias; Scholz, Uwe

2010-03-25

Efficient and effective information retrieval in life sciences is one of the most pressing challenge in bioinformatics. The incredible growth of life science databases to a vast network of interconnected information systems is to the same extent a big challenge and a great chance for life science research. The knowledge found in the Web, in particular in life-science databases, are a valuable major resource. In order to bring it to the scientist desktop, it is essential to have well performing search engines. Thereby, not the response time nor the number of results is important. The most crucial factor for millions of query results is the relevance ranking. In this paper, we present a feature model for relevance ranking in life science databases and its implementation in the LAILAPS search engine. Motivated by the observation of user behavior during their inspection of search engine result, we condensed a set of 9 relevance discriminating features. These features are intuitively used by scientists, who briefly screen database entries for potential relevance. The features are both sufficient to estimate the potential relevance, and efficiently quantifiable. The derivation of a relevance prediction function that computes the relevance from this features constitutes a regression problem. To solve this problem, we used artificial neural networks that have been trained with a reference set of relevant database entries for 19 protein queries. Supporting a flexible text index and a simple data import format, this concepts are implemented in the LAILAPS search engine. It can easily be used both as search engine for comprehensive integrated life science databases and for small in-house project databases. LAILAPS is publicly available for SWISSPROT data at http://lailaps.ipk-gatersleben.de.

A Fast Exact k-Nearest Neighbors Algorithm for High Dimensional Search Using k-Means Clustering and Triangle Inequality.

Science.gov (United States)

Wang, Xueyi

2012-02-08

The k-nearest neighbors (k-NN) algorithm is a widely used machine learning method that finds nearest neighbors of a test object in a feature space. We present a new exact k-NN algorithm called kMkNN (k-Means for k-Nearest Neighbors) that uses the k-means clustering and the triangle inequality to accelerate the searching for nearest neighbors in a high dimensional space. The kMkNN algorithm has two stages. In the buildup stage, instead of using complex tree structures such as metric trees, kd-trees, or ball-tree, kMkNN uses a simple k-means clustering method to preprocess the training dataset. In the searching stage, given a query object, kMkNN finds nearest training objects starting from the nearest cluster to the query object and uses the triangle inequality to reduce the distance calculations. Experiments show that the performance of kMkNN is surprisingly good compared to the traditional k-NN algorithm and tree-based k-NN algorithms such as kd-trees and ball-trees. On a collection of 20 datasets with up to 10(6) records and 10(4) dimensions, kMkNN shows a 2-to 80-fold reduction of distance calculations and a 2- to 60-fold speedup over the traditional k-NN algorithm for 16 datasets. Furthermore, kMkNN performs significant better than a kd-tree based k-NN algorithm for all datasets and performs better than a ball-tree based k-NN algorithm for most datasets. The results show that kMkNN is effective for searching nearest neighbors in high dimensional spaces.

Keeping up With The Neighbors: Nonproliferation and Implementation of UNSCR 1540

Science.gov (United States)

2016-02-15

be respectful of the rule of law and a competitive participatory democracy , yet fail to implement UNSCR 1540, just like its neighbors. Discussion...risk-taking between 1816 and 1992. They found a strong association between conservative governmental decision-making and not only democracies , but...specifically those democracies with highly competitive political systems.46 In addition, Bruce Bueno de Mesquita, et.al. found a significant

Credit scoring analysis using weighted k nearest neighbor

Science.gov (United States)

Mukid, M. A.; Widiharih, T.; Rusgiyono, A.; Prahutama, A.

2018-05-01

Credit scoring is a quatitative method to evaluate the credit risk of loan applications. Both statistical methods and artificial intelligence are often used by credit analysts to help them decide whether the applicants are worthy of credit. These methods aim to predict future behavior in terms of credit risk based on past experience of customers with similar characteristics. This paper reviews the weighted k nearest neighbor (WKNN) method for credit assessment by considering the use of some kernels. We use credit data from a private bank in Indonesia. The result shows that the Gaussian kernel and rectangular kernel have a better performance based on the value of percentage corrected classified whose value is 82.4% respectively.

Reentrant behavior in the nearest-neighbor Ising antiferromagnet in a magnetic field

Science.gov (United States)

Neto, Minos A.; de Sousa, J. Ricardo

2004-12-01

Motived by the H-T phase diagram in the bcc Ising antiferromagnetic with nearest-neighbor interactions obtained by Monte Carlo simulation [Landau, Phys. Rev. B 16, 4164 (1977)] that shows a reentrant behavior at low temperature, with two critical temperatures in magnetic field about 2% greater than the critical value Hc=8J , we apply the effective field renormalization group (EFRG) approach in this model on three-dimensional lattices (simple cubic-sc and body centered cubic-bcc). We find that the critical curve TN(H) exhibits a maximum point around of H≃Hc only in the bcc lattice case. We also discuss the critical behavior by the effective field theory in clusters with one (EFT-1) and two (EFT-2) spins, and a reentrant behavior is observed for the sc and bcc lattices. We have compared our results of EFRG in the bcc lattice with Monte Carlo and series expansion, and we observe a good accordance between the methods.

Malaria in pregnancy: the relevance of animal models for vaccine development.

Science.gov (United States)

Doritchamou, Justin; Teo, Andrew; Fried, Michal; Duffy, Patrick E

2017-10-06

Malaria during pregnancy due to Plasmodium falciparum or P. vivax is a major public health problem in endemic areas, with P. falciparum causing the greatest burden of disease. Increasing resistance of parasites and mosquitoes to existing tools, such as preventive antimalarial treatments and insecticide-treated bed nets respectively, is eroding the partial protection that they offer to pregnant women. Thus, development of effective vaccines against malaria during pregnancy is an urgent priority. Relevant animal models that recapitulate key features of the pathophysiology and immunology of malaria in pregnant women could be used to accelerate vaccine development. This review summarizes available rodent and nonhuman primate models of malaria in pregnancy, and discusses their suitability for studies of biologics intended to prevent or treat malaria in this vulnerable population.

Relevance of Discrecionary Accruals in Ohlson Model: the Case of Mexico

Directory of Open Access Journals (Sweden)

Rocío Durán-Vázquez

2012-01-01

Full Text Available This study applied the modified Jones´ model (1991 for selected companies of Mexico. This model aims to assess the impact of Discretionary Accrual Information (DAI on financial reporting statements, in order to identify the value relevance of “earnings quality”. We applied methodological criteria of Chung et al (2005 and Mukit & Iskandar (2009. We analyzed financial information of the 35 stock included in the Index of Prices and Quotations (IPC of the Mexican Stock Exchange (BMV for the period 2000 to 2011. 19 companies met the specifications of the model, for 48 quarters of information. The analysis was done in three parts: first, an analysis of the modified Jones´ model under panel data considerations by using fixed effects and adjustments of performing autocorrelation of order 1; second, a correlation analysis between the residuals of the modified Jones´ model and the return of stock price in 3 annual closings years of study: 2007, 2008 and 2009; and third, we incorporated this variable (DAI in the Ohlson model (of the financial and corporate accounting literature and we tested it with panel data analysis, under fixed effects, throughout the study period.

Fusion yield rate recovery by escaping hot-spot fast ions in the neighboring fuel layer

Science.gov (United States)

Tang, Xian-Zhu; McDevitt, C. J.; Guo, Zehua; Berk, H. L.

2014-02-01

Free-streaming loss by fast ions can deplete the tail population in the hot spot of an inertial confinement fusion (ICF) target. Escaping fast ions in the neighboring fuel layer of a cryogenic target can produce a surplus of fast ions locally. In contrast to the Knudsen layer effect that reduces hot-spot fusion reactivity due to tail ion depletion, the inverse Knudsen layer effect increases fusion reactivity in the neighboring fuel layer. In the case of a burning ICF target in the presence of significant hydrodynamic mix which aggravates the Knudsen layer effect, the yield recovery largely compensates for the yield reduction. For mix-dominated sub-ignition targets, the yield reduction is the dominant process.

Linking electricity and water models to assess electricity choices at water-relevant scales

International Nuclear Information System (INIS)

Sattler, S; Rogers, J; Macknick, J; Lopez, A; Yates, D; Flores-Lopez, F

2012-01-01

Hydrology/water management and electricity generation projections have been modeled separately, but there has been little effort in intentionally and explicitly linking the two sides of the water–energy nexus. This paper describes a platform for assessing power plant cooling water withdrawals and consumption under different electricity pathways at geographic and time scales appropriate for both electricity and hydrology/water management. This platform uses estimates of regional electricity generation by the Regional Energy Deployment System (ReEDS) as input to a hydrologic and water management model—the Water Evaluation and Planning (WEAP) system. In WEAP, this electricity use represents thermoelectric cooling water withdrawals and consumption within the broader, regional water resource context. Here we describe linking the electricity and water models, including translating electricity generation results from ReEDS-relevant geographies to the water-relevant geographies of WEAP. The result of this analysis is water use by the electric sector at the regional watershed level, which is used to examine the water resource implications of these electricity pathways. (letter)

Carbon-hydrogen defects with a neighboring oxygen atom in n-type Si

Science.gov (United States)

Gwozdz, K.; Stübner, R.; Kolkovsky, Vl.; Weber, J.

2017-07-01

We report on the electrical activation of neutral carbon-oxygen complexes in Si by wet-chemical etching at room temperature. Two deep levels, E65 and E75, are observed by deep level transient spectroscopy in n-type Czochralski Si. The activation enthalpies of E65 and E75 are obtained as EC-0.11 eV (E65) and EC-0.13 eV (E75). The electric field dependence of their emission rates relates both levels to single acceptor states. From the analysis of the depth profiles, we conclude that the levels belong to two different defects, which contain only one hydrogen atom. A configuration is proposed, where the CH1BC defect, with hydrogen in the bond-centered position between neighboring C and Si atoms, is disturbed by interstitial oxygen in the second nearest neighbor position to substitutional carbon. The significant reduction of the CH1BC concentration in samples with high oxygen concentrations limits the use of this defect for the determination of low concentrations of substitutional carbon in Si samples.

Spatial correlations between browsing on balsam fir by white-tailed deer and the nutritional value of neighboring winter forage.

Science.gov (United States)

Champagne, Emilie; Moore, Ben D; Côté, Steeve D; Tremblay, Jean-Pierre

2018-03-01

Associational effects, that is, the influence of neighboring plants on herbivory suffered by a plant, are an outcome of forage selection. Although forage selection is a hierarchical process, few studies have investigated associational effects at multiple spatial scales. Because the nutritional quality of plants can be spatially structured, it might differently influence associational effects across multiple scales. Our objective was to determine the radius of influence of neighbor density and nutritional quality on balsam fir ( Abies balsamea ) herbivory by white-tailed deer ( Odocoileus virginianus ) in winter. We quantified browsing rates on fir and the density and quality of neighboring trees in a series of 10-year-old cutovers on Anticosti Island (Canada). We used cross-correlations to investigate relationships between browsing rates and the density and nutritional quality of neighboring trees at distances up to 1,000 m. Balsam fir and white spruce ( Picea glauca ) fiber content and dry matter in vitro true digestibility were correlated with fir browsing rate at the finest extra-patch scale (across distance of up to 50 m) and between cutover areas (300-400 m). These correlations suggest associational effects, that is, low nutritional quality of neighbors reduces the likelihood of fir herbivory (associational defense). Our results may indicate associational effects mediated by intraspecific variation in plant quality and suggest that these effects could occur at scales from tens to hundreds of meters. Understanding associational effects could inform strategies for restoration or conservation; for example, planting of fir among existing natural regeneration could be concentrated in areas of low nutritional quality.

How fear-relevant illusory correlations might develop and persist in anxiety disorders: A model of contributing factors.

Science.gov (United States)

Wiemer, Julian; Pauli, Paul

2016-12-01

Fear-relevant illusory correlations (ICs) are defined as the overestimation of the relationship between a fear-relevant stimulus and aversive consequences. ICs reflect biased cognitions affecting the learning and unlearning of fear in anxiety disorders, and a deeper understanding might help to improve treatment. A model for the maintenance of ICs is proposed that highlights the importance of amplified aversiveness and salience of fear-relevant outcomes, impaired executive contingency monitoring and an availability heuristic. The model explains why ICs are enhanced in high fearful individuals and allows for some implications that might be applied to augment the effectiveness of cognitive behavior therapy, such as emotion regulation and the direction of attention to non-aversive experiences. Finally, we suggest possible future research directions and an alternative measure of ICs. Copyright © 2016 Elsevier Ltd. All rights reserved.

Structural characterisation of medically relevant protein assemblies by integrating mass spectrometry with computational modelling.

Science.gov (United States)

Politis, Argyris; Schmidt, Carla

2018-03-20

Structural mass spectrometry with its various techniques is a powerful tool for the structural elucidation of medically relevant protein assemblies. It delivers information on the composition, stoichiometries, interactions and topologies of these assemblies. Most importantly it can deal with heterogeneous mixtures and assemblies which makes it universal among the conventional structural techniques. In this review we summarise recent advances and challenges in structural mass spectrometric techniques. We describe how the combination of the different mass spectrometry-based methods with computational strategies enable structural models at molecular levels of resolution. These models hold significant potential for helping us in characterizing the function of protein assemblies related to human health and disease. In this review we summarise the techniques of structural mass spectrometry often applied when studying protein-ligand complexes. We exemplify these techniques through recent examples from literature that helped in the understanding of medically relevant protein assemblies. We further provide a detailed introduction into various computational approaches that can be integrated with these mass spectrometric techniques. Last but not least we discuss case studies that integrated mass spectrometry and computational modelling approaches and yielded models of medically important protein assembly states such as fibrils and amyloids. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

The influence of As/III pressure ratio on nitrogen nearest-neighbor environments in as-grown GaInNAs quantum wells

International Nuclear Information System (INIS)

Kudrawiec, R.; Poloczek, P.; Misiewicz, J.; Korpijaervi, V.-M.; Laukkanen, P.; Pakarinen, J.; Dumitrescu, M.; Guina, M.; Pessa, M.

2009-01-01

The energy fine structure, corresponding to different nitrogen nearest-neighbor environments, was observed in contactless electroreflectance (CER) spectra of as-grown GaInNAs quantum wells (QWs) obtained at various As/III pressure ratios. In the spectral range of the fundamental transition, two CER resonances were detected for samples grown at low As pressures whereas only one CER resonance was observed for samples obtained at higher As pressures. This resonance corresponds to the most favorable nitrogen nearest-neighbor environment in terms of the total crystal energy. It means that the nitrogen nearest-neighbor environment in GaInNAs QWs can be controlled in molecular beam epitaxy process by As/III pressure ratio.

Diagnosis of Diabetes Diseases Using an Artificial Immune Recognition System2 (AIRS2) with Fuzzy K-nearest Neighbor

OpenAIRE

CHIKH, Mohamed Amine; SAIDI, Meryem; SETTOUTI, Nesma

2012-01-01

The use of expert systems and artificial intelligence techniques in disease diagnosis has been increasing gradually. Artificial Immune Recognition System (AIRS) is one of the methods used in medical classification problems. AIRS2 is a more efficient version of the AIRS algorithm. In this paper, we used a modified AIRS2 called MAIRS2 where we replace the K- nearest neighbors algorithm with the fuzzy K-nearest neighbors to improve the diagnostic accuracy of diabetes diseases. The diabetes disea...

On the difference in oscillator strengths of inner shell excitations in noble gases and their alkali neighbors

International Nuclear Information System (INIS)

Amusia, M.Y.; Baltenkov, A.S.; Zhuravleva, G.I.

1995-01-01

It is demonstrated that the oscillator strength of resonant inner-shell excitation in a noble gas atom is considerably smaller than that in its alkali neighbor because in the latter case the effective charge acting upon excited electron is much bigger. With increase of the excitation's principal quantum number the difference between line intensities in noble gases and their alkali neighbors rapidly disappears. The calculations are performed in the Hartree-Fock approximation and with inclusion of rearrangement effects due to inner vacancy creation and its Auger decay. A paper has been submitted for publication

Repository environmental parameters and models/methodologies relevant to assessing the performance of high-level waste packages in basalt, tuff, and salt

Energy Technology Data Exchange (ETDEWEB)

Claiborne, H.C.; Croff, A.G.; Griess, J.C.; Smith, F.J.

1987-09-01

This document provides specifications for models/methodologies that could be employed in determining postclosure repository environmental parameters relevant to the performance of high-level waste packages for the Basalt Waste Isolation Project (BWIP) at Richland, Washington, the tuff at Yucca Mountain by the Nevada Test Site, and the bedded salt in Deaf Smith County, Texas. Guidance is provided on the identify of the relevant repository environmental parameters; the models/methodologies employed to determine the parameters, and the input data base for the models/methodologies. Supporting studies included are an analysis of potential waste package failure modes leading to identification of the relevant repository environmental parameters, an evaluation of the credible range of the repository environmental parameters, and a summary of the review of existing models/methodologies currently employed in determining repository environmental parameters relevant to waste package performance. 327 refs., 26 figs., 19 tabs.

Repository environmental parameters and models/methodologies relevant to assessing the performance of high-level waste packages in basalt, tuff, and salt

International Nuclear Information System (INIS)

Claiborne, H.C.; Croff, A.G.; Griess, J.C.; Smith, F.J.

1987-09-01

This document provides specifications for models/methodologies that could be employed in determining postclosure repository environmental parameters relevant to the performance of high-level waste packages for the Basalt Waste Isolation Project (BWIP) at Richland, Washington, the tuff at Yucca Mountain by the Nevada Test Site, and the bedded salt in Deaf Smith County, Texas. Guidance is provided on the identify of the relevant repository environmental parameters; the models/methodologies employed to determine the parameters, and the input data base for the models/methodologies. Supporting studies included are an analysis of potential waste package failure modes leading to identification of the relevant repository environmental parameters, an evaluation of the credible range of the repository environmental parameters, and a summary of the review of existing models/methodologies currently employed in determining repository environmental parameters relevant to waste package performance. 327 refs., 26 figs., 19 tabs

Nearest neighbors EPR superhyperfine interaction in divalent iridium complexes in alkali halide host lattice

International Nuclear Information System (INIS)

Pinhal, N.M.; Vugman, N.V.

1983-01-01

Further splitting of chlorine superhyperfine lines on the EPR spectrum of the [Ir (CN) 4 Cl 2 ] 4 - molecular species in NaCl latice indicates a super-superhyperfine interaction with the nearest neighbors sodium atoms. (Author) [pt

Automated analysis of long-term grooming behavior in Drosophila using a k-nearest neighbors classifier

Science.gov (United States)

Allen, Victoria W; Shirasu-Hiza, Mimi

2018-01-01

Despite being pervasive, the control of programmed grooming is poorly understood. We addressed this gap by developing a high-throughput platform that allows long-term detection of grooming in Drosophila melanogaster. In our method, a k-nearest neighbors algorithm automatically classifies fly behavior and finds grooming events with over 90% accuracy in diverse genotypes. Our data show that flies spend ~13% of their waking time grooming, driven largely by two major internal programs. One of these programs regulates the timing of grooming and involves the core circadian clock components cycle, clock, and period. The second program regulates the duration of grooming and, while dependent on cycle and clock, appears to be independent of period. This emerging dual control model in which one program controls timing and another controls duration, resembles the two-process regulatory model of sleep. Together, our quantitative approach presents the opportunity for further dissection of mechanisms controlling long-term grooming in Drosophila. PMID:29485401

Modelling low energy electron and positron tracks in biologically relevant media

International Nuclear Information System (INIS)

Blanco, F.; Munoz, A.; Almeida, D.; Ferreira da Silva, F.; Limao-Vieira, P.; Fuss, M.C.; Sanz, A.G.; Garcia, G.

2013-01-01

This colloquium describes an approach to incorporate into radiation damage models the effect of low and intermediate energy (0-100 eV) electrons and positrons, slowing down in biologically relevant materials (water and representative biomolecules). The core of the modelling procedure is a C++ computing programme named 'Low Energy Particle Track Simulation (LEPTS)', which is compatible with available general purpose Monte Carlo packages. Input parameters are carefully selected from theoretical and experimental cross section data and energy loss distribution functions. Data sources used for this purpose are reviewed showing examples of electron and positron cross section and energy loss data for interactions with different media of increasing complexity: atoms, molecules, clusters and condense matter. Finally, we show how such a model can be used to develop an effective dosimetric tool at the molecular level (i.e. nanodosimetry). Recent experimental developments to study the fragmentation induced in biologically material by charge transfer from neutrals and negative ions are also included. (authors)

A dynamic evolutionary clustering perspective: Community detection in signed networks by reconstructing neighbor sets

Science.gov (United States)

Chen, Jianrui; Wang, Hua; Wang, Lina; Liu, Weiwei

2016-04-01

Community detection in social networks has been intensively studied in recent years. In this paper, a novel similarity measurement is defined according to social balance theory for signed networks. Inter-community positive links are found and deleted due to their low similarity. The positive neighbor sets are reconstructed by this method. Then, differential equations are proposed to imitate the constantly changing states of nodes. Each node will update its state based on the difference between its state and average state of its positive neighbors. Nodes in the same community will evolve together with time and nodes in the different communities will evolve far away. Communities are detected ultimately when states of nodes are stable. Experiments on real world and synthetic networks are implemented to verify detection performance. The thorough comparisons demonstrate the presented method is more efficient than two acknowledged better algorithms.

77 FR 50504 - Good Neighbor Environmental Board Notification of Public Advisory Committee Teleconference

Science.gov (United States)

2012-08-21

... recommendations to the President and Congress on environmental and infrastructure issues along the U.S. border with Mexico. Purpose of Meeting: The purpose of this teleconference is to discuss the Good Neighbor Environmental Board's Fifteenth Report. The report will focus on water infrastructure issues in the U.S.-Mexico...

77 FR 13599 - Good Neighbor Environmental Board; Notification of Public Advisory Committee Teleconference

Science.gov (United States)

2012-03-07

... recommendations to the President and Congress on environmental and infrastructure issues along the U.S. border with Mexico. Purpose of Meeting: The purpose of this teleconference is to discuss the Good Neighbor Environmental Board's Fifteenth Report. The report will focus on water infrastructure issues in the U.S.-Mexico...

Using small XML elements to support relevance

NARCIS (Netherlands)

G. Ramirez Camps (Georgina); T.H.W. Westerveld (Thijs); A.P. de Vries (Arjen)

2006-01-01

htmlabstractSmall XML elements are often estimated relevant by the retrieval model but they are not desirable retrieval units. This paper presents a generic model that exploits the information obtained from small elements. We identify relationships between small and relevant elements and use this

Giant Planets: Good Neighbors for Habitable Worlds?

Science.gov (United States)

Georgakarakos, Nikolaos; Eggl, Siegfried; Dobbs-Dixon, Ian

2018-04-01

The presence of giant planets influences potentially habitable worlds in numerous ways. Massive celestial neighbors can facilitate the formation of planetary cores and modify the influx of asteroids and comets toward Earth analogs later on. Furthermore, giant planets can indirectly change the climate of terrestrial worlds by gravitationally altering their orbits. Investigating 147 well-characterized exoplanetary systems known to date that host a main-sequence star and a giant planet, we show that the presence of “giant neighbors” can reduce a terrestrial planet’s chances to remain habitable, even if both planets have stable orbits. In a small fraction of systems, however, giant planets slightly increase the extent of habitable zones provided that the terrestrial world has a high climate inertia. In providing constraints on where giant planets cease to affect the habitable zone size in a detrimental fashion, we identify prime targets in the search for habitable worlds.

Finite temperature magnon spectra in yttrium iron garnet from a mean field approach in a tight-binding model

Science.gov (United States)

Shen, Ka

2018-04-01

We study magnon spectra at finite temperature in yttrium iron garnet using a tight-binding model with nearest-neighbor exchange interaction. The spin reduction due to thermal magnon excitation is taken into account via the mean field approximation to the local spin and is found to be different at two sets of iron atoms. The resulting temperature dependence of the spin wave gap shows good agreement with experiment. We find that only two magnon modes are relevant to the ferromagnetic resonance.

Chaotic Synchronization in Nearest-Neighbor Coupled Networks of 3D CNNs

OpenAIRE

Serrano-Guerrero, H.; Cruz-Hernández, C.; López-Gutiérrez, R.M.; Cardoza-Avendaño, L.; Chávez-Pérez, R.A.

2013-01-01

In this paper, a synchronization of Cellular Neural Networks (CNNs) in nearest-neighbor coupled arrays, is numerically studied. Synchronization of multiple chaotic CNNs is achieved by appealing to complex systems theory. In particular, we consider dynamical networks composed by 3D CNNs, as interconnected nodes, where the interactions in the networks are defined by coupling the first state of each node. Four cases of interest are considered: i) synchronization without chaotic master, ii) maste...

Determinants of dermal exposure relevant for exposure modelling in regulatory risk assessment.

Science.gov (United States)

Marquart, J; Brouwer, D H; Gijsbers, J H J; Links, I H M; Warren, N; van Hemmen, J J

2003-11-01

Risk assessment of chemicals requires assessment of the exposure levels of workers. In the absence of adequate specific measured data, models are often used to estimate exposure levels. For dermal exposure only a few models exist, which are not validated externally. In the scope of a large European research programme, an analysis of potential dermal exposure determinants was made based on the available studies and models and on the expert judgement of the authors of this publication. Only a few potential determinants appear to have been studied in depth. Several studies have included clusters of determinants into vaguely defined parameters, such as 'task' or 'cleaning and maintenance of clothing'. Other studies include several highly correlated parameters, such as 'amount of product handled', 'duration of task' and 'area treated', and separation of these parameters to study their individual influence is not possible. However, based on the available information, a number of determinants could clearly be defined as proven or highly plausible determinants of dermal exposure in one or more exposure situation. This information was combined with expert judgement on the scientific plausibility of the influence of parameters that have not been extensively studied and on the possibilities to gather relevant information during a risk assessment process. The result of this effort is a list of determinants relevant for dermal exposure models in the scope of regulatory risk assessment. The determinants have been divided into the major categories 'substance and product characteristics', 'task done by the worker', 'process technique and equipment', 'exposure control measures', 'worker characteristics and habits' and 'area and situation'. To account for the complex nature of the dermal exposure processes, a further subdivision was made into the three major processes 'direct contact', 'surface contact' and 'deposition'.

Loving All Your Neighbors: Why Community Colleges Need the Academic Study of Religion

Science.gov (United States)

Maley, Melissa

2013-01-01

This chapter explains how the study of world religions prepares the community college student to become a better citizen, worker, and neighbor. The effective middle between the pitfalls of religious relativism and religious dominance in a world religions classroom is central to this discussion of teaching critical thinking, empathy, and…

Dose distribution in the thyroid and neighboring regions in therapy with 131I

International Nuclear Information System (INIS)

Monteiro, Rommel Barbosa; Bonifacio, Daniel Alexandre Baptista; Sa, Lidia Vasconcellos de

2013-01-01

In this work, simulations were performed with two types of computer simulators: the MIRD phantom and voxel phantom MASH, both of type adult male and in the standing position, coupled to the computational tool GATE (Geant4 Application for Tomographic Emission), to obtain the dose deposited in thyroid and neighboring regions

Profiles of Dialogue for Relevance

Directory of Open Access Journals (Sweden)

Douglas Walton

2016-12-01

Full Text Available This paper uses argument diagrams, argumentation schemes, and some tools from formal argumentation systems developed in artificial intelligence to build a graph-theoretic model of relevance shown to be applicable (with some extensions as a practical method for helping a third party judge issues of relevance or irrelevance of an argument in real examples. Examples used to illustrate how the method works are drawn from disputes about relevance in natural language discourse, including a criminal trial and a parliamentary debate.

Review: To be or not to be an identifiable model. Is this a relevant question in animal science modelling?

Science.gov (United States)

Muñoz-Tamayo, R; Puillet, L; Daniel, J B; Sauvant, D; Martin, O; Taghipoor, M; Blavy, P

2018-04-01

What is a good (useful) mathematical model in animal science? For models constructed for prediction purposes, the question of model adequacy (usefulness) has been traditionally tackled by statistical analysis applied to observed experimental data relative to model-predicted variables. However, little attention has been paid to analytic tools that exploit the mathematical properties of the model equations. For example, in the context of model calibration, before attempting a numerical estimation of the model parameters, we might want to know if we have any chance of success in estimating a unique best value of the model parameters from available measurements. This question of uniqueness is referred to as structural identifiability; a mathematical property that is defined on the sole basis of the model structure within a hypothetical ideal experiment determined by a setting of model inputs (stimuli) and observable variables (measurements). Structural identifiability analysis applied to dynamic models described by ordinary differential equations (ODEs) is a common practice in control engineering and system identification. This analysis demands mathematical technicalities that are beyond the academic background of animal science, which might explain the lack of pervasiveness of identifiability analysis in animal science modelling. To fill this gap, in this paper we address the analysis of structural identifiability from a practitioner perspective by capitalizing on the use of dedicated software tools. Our objectives are (i) to provide a comprehensive explanation of the structural identifiability notion for the community of animal science modelling, (ii) to assess the relevance of identifiability analysis in animal science modelling and (iii) to motivate the community to use identifiability analysis in the modelling practice (when the identifiability question is relevant). We focus our study on ODE models. By using illustrative examples that include published

Neighbor Detection Induces Organ-Specific Transcriptomes, Revealing Patterns Underlying Hypocotyl-Specific Growth.

Science.gov (United States)

Kohnen, Markus V; Schmid-Siegert, Emanuel; Trevisan, Martine; Petrolati, Laure Allenbach; Sénéchal, Fabien; Müller-Moulé, Patricia; Maloof, Julin; Xenarios, Ioannis; Fankhauser, Christian

2016-12-01

In response to neighbor proximity, plants increase the growth of specific organs (e.g., hypocotyls) to enhance access to sunlight. Shade enhances the activity of Phytochrome Interacting Factors (PIFs) by releasing these bHLH transcription factors from phytochrome B-mediated inhibition. PIFs promote elongation by inducing auxin production in cotyledons. In order to elucidate spatiotemporal aspects of the neighbor proximity response, we separately analyzed gene expression patterns in the major light-sensing organ (cotyledons) and in rapidly elongating hypocotyls of Arabidopsis thaliana PIFs initiate transcriptional reprogramming in both organs within 15 min, comprising regulated expression of several early auxin response genes. This suggests that hypocotyl growth is elicited by both local and distal auxin signals. We show that cotyledon-derived auxin is both necessary and sufficient to initiate hypocotyl growth, but we also provide evidence for the functional importance of the local PIF-induced response. With time, the transcriptional response diverges increasingly between organs. We identify genes whose differential expression may underlie organ-specific elongation. Finally, we uncover a growth promotion gene expression signature shared between different developmentally regulated growth processes and responses to the environment in different organs. © 2016 American Society of Plant Biologists. All rights reserved.

δ-Generalized Labeled Multi-Bernoulli Filter Using Amplitude Information of Neighboring Cells

Directory of Open Access Journals (Sweden)

Chao Liu

2018-04-01

Full Text Available The amplitude information (AI of echoed signals plays an important role in radar target detection and tracking. A lot of research shows that the introduction of AI enables the tracking algorithm to distinguish targets from clutter better and then improves the performance of data association. The current AI-aided tracking algorithms only consider the signal amplitude in the range-azimuth cell where measurement exists. However, since radar echoes always contain backscattered signals from multiple cells, the useful information of neighboring cells would be lost if directly applying those existing methods. In order to solve this issue, a new δ-generalized labeled multi-Bernoulli (δ-GLMB filter is proposed. It exploits the AI of radar echoes from neighboring cells to construct a united amplitude likelihood ratio, and then plugs it into the update process and the measurement-track assignment cost matrix of the δ-GLMB filter. Simulation results show that the proposed approach has better performance in target’s state and number estimation than that of the δ-GLMB only using single-cell AI in low signal-to-clutter-ratio (SCR environment.

New Perspectives on Rodent Models of Advanced Paternal Age: Relevance to Autism

Directory of Open Access Journals (Sweden)

Claire J Foldi

2011-06-01

Full Text Available Offspring of older fathers have an increased risk of various adverse health outcomes, including autism and schizophrenia. With respect to biological mechanisms for this association, there are many more germline cell divisions in the life history of a sperm relative to that of an oocyte. This leads to more opportunities for copy error mutations in germ cells from older fathers. Evidence also suggests that epigenetic patterning in the sperm from older men is altered. Rodent models provide an experimental platform to examine the association between paternal age and brain development. Several rodent models of advanced paternal age (APA have been published with relevance to intermediate phenotypes related to autism. All four published APA models vary in key features creating a lack of consistency with respect to behavioural phenotypes. A consideration of common phenotypes that emerge from these APA-related mouse models may be informative in the exploration of the molecular and neurobiological correlates of APA.

Nostalgia's place among self-relevant emotions.

Science.gov (United States)

van Tilburg, Wijnand A P; Wildschut, Tim; Sedikides, Constantine

2017-07-24

How is nostalgia positioned among self-relevant emotions? We tested, in six studies, which self-relevant emotions are perceived as most similar versus least similar to nostalgia, and what underlies these similarities/differences. We used multidimensional scaling to chart the perceived similarities/differences among self-relevant emotions, resulting in two-dimensional models. The results were revealing. Nostalgia is positioned among self-relevant emotions characterised by positive valence, an approach orientation, and low arousal. Nostalgia most resembles pride and self-compassion, and least resembles embarrassment and shame. Our research pioneered the integration of nostalgia among self-relevant emotions.

Studying nearest neighbor correlations by atom probe tomography (APT) in metallic glasses as exemplified for Fe40Ni40B20 glassy ribbons

KAUST Repository

Shariq, Ahmed

2012-01-01

A next nearest neighbor evaluation procedure of atom probe tomography data provides distributions of the distances between atoms. The width of these distributions for metallic glasses studied so far is a few Angstrom reflecting the spatial resolution of the analytical technique. However, fitting Gaussian distributions to the distribution of atomic distances yields average distances with statistical uncertainties of 2 to 3 hundredth of an Angstrom. Fe 40Ni40B20 metallic glass ribbons are characterized this way in the as quenched state and for a state heat treated at 350 °C for 1 h revealing a change in the structure on the sub-nanometer scale. By applying the statistical tool of the χ2 test a slight deviation from a random distribution of B-atoms in the as quenched sample is perceived, whereas a pronounced elemental inhomogeneity of boron is detected for the annealed state. In addition, the distance distribution of the first fifteen atomic neighbors is determined by using this algorithm for both annealed and as quenched states. The next neighbor evaluation algorithm evinces a steric periodicity of the atoms when the next neighbor distances are normalized by the first next neighbor distance. A comparison of the nearest neighbor atomic distribution for as quenched and annealed state shows accumulation of Ni and B. Moreover, it also reveals the tendency of Fe and B to move slightly away from each other, an incipient step to Ni rich boride formation. © 2011 Elsevier B.V.

Impact relevance and usability of high resolution climate modeling and data

Energy Technology Data Exchange (ETDEWEB)

Arnott, James C. [Aspen Global Change Inst., Basalt, CO (United States)

2016-10-30

The Aspen Global Change Institute hosted a technical science workshop entitled, “Impact Relevance and Usability of High-Resolution Climate Modeling and Datasets,” on August 2-7, 2015 in Aspen, CO. Kate Calvin (Pacific Northwest National Laboratory), Andrew Jones (Lawrence Berkeley National Laboratory) and Jean-François Lamarque (NCAR) served as co-chairs for the workshop. The meeting included the participation of 29 scientists for a total of 145 participant days. Following the workshop, workshop co-chairs authored a meeting report published in Eos on April 27, 2016. Insights from the workshop directly contributed to the formation of a new DOE-supported project co-led by workshop co-chair Andy Jones. A subset of meeting participants continue to work on a publication on institutional innovations that can support the usability of high resolution modeling, among other sources of climate information.

Functionally relevant climate variables for arid lands: Aclimatic water deficit approach for modelling desert shrub distributions

Science.gov (United States)

Thomas E. Dilts; Peter J. Weisberg; Camie M. Dencker; Jeanne C. Chambers

2015-01-01

We have three goals. (1) To develop a suite of functionally relevant climate variables for modelling vegetation distribution on arid and semi-arid landscapes of the Great Basin, USA. (2) To compare the predictive power of vegetation distribution models based on mechanistically proximate factors (water deficit variables) and factors that are more mechanistically removed...

Development of a novel, physiologically relevant cytotoxicity model: Application to the study of chemotherapeutic damage to mesenchymal stromal cells

International Nuclear Information System (INIS)

May, Jennifer E.; Morse, H. Ruth; Xu, Jinsheng; Donaldson, Craig

2012-01-01

There is an increasing need for development of physiologically relevant in-vitro models for testing toxicity, however determining toxic effects of agents which undergo extensive hepatic metabolism can be particularly challenging. If a source of such metabolic enzymes is inadequate within a model system, toxicity from prodrugs may be grossly underestimated. Conversely, the vast majority of agents are detoxified by the liver, consequently toxicity from such agents may be overestimated. In this study we describe the development of a novel in-vitro model, which could be adapted for any toxicology setting. The model utilises HepG2 liver spheroids as a source of metabolic enzymes, which have been shown to more closely resemble human liver than traditional monolayer cultures. A co-culture model has been developed enabling the effect of any metabolised agent on another cell type to be assessed. This has been optimised to enable the study of damaging effects of chemotherapy on mesenchymal stem cells (MSC), the supportive stem cells of the bone marrow. Several optimisation steps were undertaken, including determining optimal culture conditions, confirmation of hepatic P450 enzyme activity and ensuring physiologically relevant doses of chemotherapeutic agents were appropriate for use within the model. The developed model was subsequently validated using several chemotherapeutic agents, both prodrugs and active drugs, with resulting MSC damage closely resembling effects seen in patients following chemotherapy. Minimal modifications would enable this novel co-culture model to be utilised as a general toxicity model, contributing to the drive to reduce animal safety testing and enabling physiologically relevant in-vitro study. -- Highlights: ► An in vitro model was developed for study of drugs requiring hepatic metabolism ► HepG2 spheroids were utilised as a physiologically relevant source of liver enzymes ► The model was optimised to enable study of chemotherapeutic

Development of a novel, physiologically relevant cytotoxicity model: Application to the study of chemotherapeutic damage to mesenchymal stromal cells

Energy Technology Data Exchange (ETDEWEB)

May, Jennifer E., E-mail: Jennifer2.May@uwe.ac.uk; Morse, H. Ruth, E-mail: Ruth.Morse@uwe.ac.uk; Xu, Jinsheng, E-mail: Jinsheng.Xu@uwe.ac.uk; Donaldson, Craig, E-mail: Craig.Donaldson@uwe.ac.uk

2012-09-15

There is an increasing need for development of physiologically relevant in-vitro models for testing toxicity, however determining toxic effects of agents which undergo extensive hepatic metabolism can be particularly challenging. If a source of such metabolic enzymes is inadequate within a model system, toxicity from prodrugs may be grossly underestimated. Conversely, the vast majority of agents are detoxified by the liver, consequently toxicity from such agents may be overestimated. In this study we describe the development of a novel in-vitro model, which could be adapted for any toxicology setting. The model utilises HepG2 liver spheroids as a source of metabolic enzymes, which have been shown to more closely resemble human liver than traditional monolayer cultures. A co-culture model has been developed enabling the effect of any metabolised agent on another cell type to be assessed. This has been optimised to enable the study of damaging effects of chemotherapy on mesenchymal stem cells (MSC), the supportive stem cells of the bone marrow. Several optimisation steps were undertaken, including determining optimal culture conditions, confirmation of hepatic P450 enzyme activity and ensuring physiologically relevant doses of chemotherapeutic agents were appropriate for use within the model. The developed model was subsequently validated using several chemotherapeutic agents, both prodrugs and active drugs, with resulting MSC damage closely resembling effects seen in patients following chemotherapy. Minimal modifications would enable this novel co-culture model to be utilised as a general toxicity model, contributing to the drive to reduce animal safety testing and enabling physiologically relevant in-vitro study. -- Highlights: ► An in vitro model was developed for study of drugs requiring hepatic metabolism ► HepG2 spheroids were utilised as a physiologically relevant source of liver enzymes ► The model was optimised to enable study of chemotherapeutic

Measurement of near neighbor separations of surface atoms

International Nuclear Information System (INIS)

Cohen, P.I.

Two techniques are being developed to measure the nearest neighbor distances of atoms at the surfaces of solids. Both measures extended fine structure in the excitation probability of core level electrons which are excited by an incident electron beam. This is an important problem because the structures of most surface systems are as yet unknown, even though the location of surface atoms is the basis for any quantitative understanding of the chemistry and physics of surfaces and interfaces. These methods would allow any laboratory to make in situ determinations of surface structure in conjunction with most other laboratory probes of surfaces. Each of these two techniques has different advantages; further, the combination of the two will increase confidence in the results by reducing systematic error in the data analysis

Radiative energy loss of neighboring subjets arXiv

CERN Document Server

Mehtar-Tani, Yacine

We compute the in-medium energy loss probability distribution of two neighboring subjets at leading order, in the large-$N_c$ approximation. Our result exhibits a gradual onset of color decoherence of the system and accounts for two expected limiting cases. When the angular separation is smaller than the characteristic angle for medium-induced radiation, the two-pronged substructure lose energy coherently as a single color charge, namely that of the parent parton. At large angular separation the two subjets lose energy independently. Our result is a first step towards quantifying effects of energy loss as a result of the fluctuation of the multi-parton jet substructure and therefore goes beyond the standard approach to jet quenching based on single parton energy loss. We briefly discuss applications to jet observables in heavy-ion collisions.

Stem cell therapy for joint problems using the horse as a clinically relevant animal model

DEFF Research Database (Denmark)

Koch, Thomas Gadegaard; Betts, Dean H.

2007-01-01

of experimentally induced lesions. The horse lends itself as a good animal model of spontaneous joint disorders that are clinically relevant to similar human disorders. Equine stem cell and tissue engineering studies may be financially feasible to principal investigators and small biotechnology companies...

Historical harvests reduce neighboring old-growth basal area across a forest landscape.

Science.gov (United States)

Bell, David M; Spies, Thomas A; Pabst, Robert

2017-07-01

While advances in remote sensing have made stand, landscape, and regional assessments of the direct impacts of disturbance on forests quite common, the edge influence of timber harvesting on the structure of neighboring unharvested forests has not been examined extensively. In this study, we examine the impact of historical timber harvests on basal area patterns of neighboring old-growth forests to assess the magnitude and scale of harvest edge influence in a forest landscape of western Oregon, USA. We used lidar data and forest plot measurements to construct 30-m resolution live tree basal area maps in lower and middle elevation mature and old-growth forests. We assessed how edge influence on total, upper canopy, and lower canopy basal area varied across this forest landscape as a function of harvest characteristics (i.e., harvest size and age) and topographic conditions in the unharvested area. Upper canopy, lower canopy, and total basal area increased with distance from harvest edge and elevation. Forests within 75 m of harvest edges (20% of unharvested forests) had 4% to 6% less live tree basal area compared with forest interiors. An interaction between distance from harvest edge and elevation indicated that elevation altered edge influence in this landscape. We observed a positive edge influence at low elevations (800 m). Surprisingly, we found no or weak effects of harvest age (13-60 yr) and harvest area (0.2-110 ha) on surrounding unharvested forest basal area, implying that edge influence was relatively insensitive to the scale of disturbance and multi-decadal recovery processes. Our study indicates that the edge influence of past clearcutting on the structure of neighboring uncut old-growth forests is widespread and persistent. These indirect and diffuse legacies of historical timber harvests complicate forest management decision-making in old-growth forest landscapes by broadening the traditional view of stand boundaries. Furthermore, the consequences

Relevant Subspace Clustering

DEFF Research Database (Denmark)

Müller, Emmanuel; Assent, Ira; Günnemann, Stephan

2009-01-01

Subspace clustering aims at detecting clusters in any subspace projection of a high dimensional space. As the number of possible subspace projections is exponential in the number of dimensions, the result is often tremendously large. Recent approaches fail to reduce results to relevant subspace...... clusters. Their results are typically highly redundant, i.e. many clusters are detected multiple times in several projections. In this work, we propose a novel model for relevant subspace clustering (RESCU). We present a global optimization which detects the most interesting non-redundant subspace clusters...... achieves top clustering quality while competing approaches show greatly varying performance....

Looking for a relevant potential evapotranspiration model at the watershed scale

Science.gov (United States)

Oudin, L.; Hervieu, F.; Michel, C.; Perrin, C.; Anctil, F.; Andréassian, V.

2003-04-01

In this paper, we try to identify the most relevant approach to calculate Potential Evapotranspiration (PET) for use in a daily watershed model, to try to bring an answer to the following question: "how can we use commonly available atmospheric parameters to represent the evaporative demand at the catchment scale?". Hydrologists generally see the Penman model as the ideal model regarding to its good adequacy with lysimeter measurements and its physically-based formulation. However, in real-world engineering situations, where meteorological stations are scarce, hydrologists are often constrained to use other PET formulae with less data requirements or/and long-term average of PET values (the rationale being that PET is an inherently conservative variable). We chose to test 28 commonly used PET models coupled with 4 different daily watershed models. For each test, we compare both PET input options: actual data and long-term average data. The comparison is made in terms of streamflow simulation efficiency, over a large sample of 308 watersheds. The watersheds are located in France, Australia and the United States of America and represent varied climates. Strikingly, we find no systematic improvements of the watershed model efficiencies when using actual PET series instead of long-term averages. This suggests either that watershed models may not conveniently use the climatic information contained in PET values or that formulae are only awkward indicators of the real PET which watershed models need.

Neighboring Hetero-Atom Assistance of Sacrificial Amines to Hydrogen Evolution Using Pt-Loaded TiO2-Photocatalyst

Directory of Open Access Journals (Sweden)

Masahide Yasuda

2014-05-01

Full Text Available Photocatalytic H2 evolution was examined using Pt-loaded TiO2-photocatalyst in the presence of amines as sacrificial agents. In the case of amines with all of the carbon attached to the hetero-atom such as 2-aminoethanol, 1,2-diamonoethane, 2-amino-1,3-propanediol, and 3-amino-1,2-propanediol, they were completely decomposed into CO2 and water to quantitatively evolve H2. On the other hand, the amines with both hetero-atoms and one methyl group at the β-positions (neighboring carbons of amino group such as 2-amino-1-propanol and 1,2-diaminopropane were partially decomposed. Also, the photocatalytic H2 evolution using amines without the hetero-atoms at the β-positions such as ethylamine, propylamine, 1-butylamine, 1,3-diaminopropane, 2-propylamine, and 2-butylamine was inefficient. Thus, it was found that the neighboring hetero-atom strongly assisted the degradation of sacrificial amines. Moreover, rate constants for H2 evolution were compared among amines. In conclusion, the neighboring hetero-atom did not affect the rate constants but enhanced the yield of hydrogen evolution.

Thyroid monitoring for residents of disaster-affected and neighboring areas

International Nuclear Information System (INIS)

Ito, Shigeki

2014-01-01

The devastating environmental contamination caused by the nuclear disaster at the Fukushima Daiichi Nuclear Power Station of The Tokyo Electric Power Company is exposing the residents of the disaster-affected areas to health risks attributable to radiation exposure, and fear of the development of 131 I-induced thyroid cancer, which is a stochastic effect of radiation and is particularly high. As part of the response to nuclear disasters by the government of the municipality where the nuclear power station is located and in operation and by the governments of neighboring municipalities, it is necessary to conduct thyroid monitoring for the purpose of alleviating the fears of residents of the disaster-affected areas as well as those living in the contaminated, even if only slightly, neighboring areas (local residents). This health monitoring needs to be implemented without delay in the case of a disaster along with dissemination of a portable type thyroid monitoring system available at evacuation centers, etc. for assessing thyroid exposure doses. The establishment of a system for developing personnel ready to perform monitoring is also essential. Assessing thyroid exposure doses is indispensable as a means of assuring local residents not only of safety but also of security from the risks of radiation. To date, contamination has not been detected in people, except for residents contaminated by a large amount of iodine, by employing the mobile type of thyroid monitoring system. However, when local residents seeking security desire thyroid monitoring, it is preferable that a portable type simplified thyroid monitoring system be used as a means of ensuring security against radiation. (author)

Distance-Constraint k-Nearest Neighbor Searching in Mobile Sensor Networks.

Science.gov (United States)

Han, Yongkoo; Park, Kisung; Hong, Jihye; Ulamin, Noor; Lee, Young-Koo

2015-07-27

The κ-Nearest Neighbors ( κNN) query is an important spatial query in mobile sensor networks. In this work we extend κNN to include a distance constraint, calling it a l-distant κ-nearest-neighbors (l-κNN) query, which finds the κ sensor nodes nearest to a query point that are also at or greater distance from each other. The query results indicate the objects nearest to the area of interest that are scattered from each other by at least distance l. The l-κNN query can be used in most κNN applications for the case of well distributed query results. To process an l-κNN query, we must discover all sets of κNN sensor nodes and then find all pairs of sensor nodes in each set that are separated by at least a distance l. Given the limited battery and computing power of sensor nodes, this l-κNN query processing is problematically expensive in terms of energy consumption. In this paper, we propose a greedy approach for l-κNN query processing in mobile sensor networks. The key idea of the proposed approach is to divide the search space into subspaces whose all sides are l. By selecting κ sensor nodes from the other subspaces near the query point, we guarantee accurate query results for l-κNN. In our experiments, we show that the proposed method exhibits superior performance compared with a post-processing based method using the κNN query in terms of energy efficiency, query latency, and accuracy.

Crack propagation rate modelling for 316SS exposed to PWR-relevant conditions

International Nuclear Information System (INIS)

Vankeerberghen, M.; Weyns, G.; Gavrilov, S.; Martens, B.; Deconinck, J.

2009-01-01

The crack propagation rate of Type 316 stainless steel in boric acid-lithium hydroxide solutions under PWR-relevant conditions was modelled. A film rupture/dissolution/repassivation mechanism is assumed and extended to cold worked materials by including a stress-dependent bare metal dissolution current density. The chemical and electrochemical conditions within the crack are calculated by finite element calculations, an analytical expression is used for the crack-tip strain rate and the crack-tip stress is assumed equal to 2.5 times the yield stress (plane-strain). First the model was calibrated against a literature published data set. Afterwards, the influence of various variables - dissolved hydrogen, boric acid and lithium hydroxide content, stress intensity, crack length, temperature, flow rate - was studied. Finally, other published crack growth rate tests were modelled and the calculated crack growth rates were found to be in reasonable agreement with the reported ones

Mapping change of older forest with nearest-neighbor imputation and Landsat time-series

Science.gov (United States)

Janet L. Ohmann; Matthew J. Gregory; Heather M. Roberts; Warren B. Cohen; Robert E. Kennedy; Zhiqiang. Yang

2012-01-01

The Northwest Forest Plan (NWFP), which aims to conserve late-successional and old-growth forests (older forests) and associated species, established new policies on federal lands in the Pacific Northwest USA. As part of monitoring for the NWFP, we tested nearest-neighbor imputation for mapping change in older forest, defined by threshold values for forest attributes...

Penerapan Metode K-nearest Neighbor pada Penentuan Grade Dealer Sepeda Motor

OpenAIRE

Leidiyana, Henny

2017-01-01

The mutually beneficial cooperation is a very important thing for a leasing and dealer. Incentives for marketing is given in order to get consumers as much as possible. But sometimes the surveyor objectivity is lost due to the conspiracy on the field of marketing and surveyors. To overcome this, leasing a variety of ways one of them is doing ranking against the dealer. In this study the application of the k-Nearest Neighbor method and Euclidean distance measurement to determine the grade deal...

Who's your neighbor? Acoustic cues to individual identity in red squirrel Tamiasciurus hudsonicus rattle calls

Directory of Open Access Journals (Sweden)

Shannon M. DIGWEED, Drew RENDALL, Teana IMBEAU

2012-10-01

Full Text Available North American red squirrels Tamiasciurus hudsonicus often produce a loud territorial rattle call when conspecifics enter or invade a territory. Previous playback experiments suggest that the territorial rattle call may indicate an invader's identity as squirrels responded more intensely to calls played from strangers than to calls played from neighbors. This dear-enemy effect is well known in a variety of bird and mammal species and functions to reduce aggressive interactions between known neighbors. However, although previous experiments on red squirrels suggest some form of individual differentiation and thus recognition, detailed acoustic analysis of potential acoustic cues in rattle calls have not been conducted. If calls function to aid in conspecific identification in order to mitigate aggressive territorial interactions, we would expect that individual recognition cues would be acoustically represented. Our work provides a detailed analysis of acoustic cues to identity within rattle calls. A total of 225 calls across 32 individual squirrels from Sheep River Provincial Park, Kananaskis, AB, Canada, were analyzed with discriminant function analysis for potential acoustic cues to individual identity. Initial analysis of all individuals revealed a reliable acoustic differentiation across individuals. A more detailed analysis of clusters of neighboring squirrels was performed and results again indicated a statistically significant likelihood that calls were assigned correctly to specific squirrels (55%-75% correctly assigned; in other words squirrels have distinct voices that should allow for individual identification and discrimination by conspecifics [Current Zoology 58 (5: 758–764, 2012].

Spin canting in a Dy-based single-chain magnet with dominant next-nearest-neighbor antiferromagnetic interactions

Science.gov (United States)

Bernot, K.; Luzon, J.; Caneschi, A.; Gatteschi, D.; Sessoli, R.; Bogani, L.; Vindigni, A.; Rettori, A.; Pini, M. G.

2009-04-01

We investigate theoretically and experimentally the static magnetic properties of single crystals of the molecular-based single-chain magnet of formula [Dy(hfac)3NIT(C6H4OPh)]∞ comprising alternating Dy3+ and organic radicals. The magnetic molar susceptibility χM displays a strong angular variation for sample rotations around two directions perpendicular to the chain axis. A peculiar inversion between maxima and minima in the angular dependence of χM occurs on increasing temperature. Using information regarding the monomeric building block as well as an ab initio estimation of the magnetic anisotropy of the Dy3+ ion, this “anisotropy-inversion” phenomenon can be assigned to weak one-dimensional ferromagnetism along the chain axis. This indicates that antiferromagnetic next-nearest-neighbor interactions between Dy3+ ions dominate, despite the large Dy-Dy separation, over the nearest-neighbor interactions between the radicals and the Dy3+ ions. Measurements of the field dependence of the magnetization, both along and perpendicularly to the chain, and of the angular dependence of χM in a strong magnetic field confirm such an interpretation. Transfer-matrix simulations of the experimental measurements are performed using a classical one-dimensional spin model with antiferromagnetic Heisenberg exchange interaction and noncollinear uniaxial single-ion anisotropies favoring a canted antiferromagnetic spin arrangement, with a net magnetic moment along the chain axis. The fine agreement obtained with experimental data provides estimates of the Hamiltonian parameters, essential for further study of the dynamics of rare-earth-based molecular chains.

No difference in the intention to engage others in academic transgression among medical students from neighboring countries: a cross-national study on medical students from Bosnia and Herzegovina, Croatia, and Macedonia.

Science.gov (United States)

Đogaš, Varja; Donev, Doncho M; Kukolja-Taradi, Sunčana; Đogaš, Zoran; Ilakovac, Vesna; Novak, Anita; Jerončić, Ana

2016-08-31

To asses if the level of intention to engage others in academic transgressions was comparable among medical students from five schools from neighboring Southern-European countries: Croatia, Bosnia and Herzegovina, and Macedonia; and medical students from western EU studying at Split, Croatia. Five medical schools were surveyed in 2011, with ≥87% of the targeted population sampled and a response rate of ≥76%. Students' intention to engage a family member, friend, colleague, or a stranger in academic transgression was measured using a previously validated the Intention to Engage Others in Academic Transgression (IEOAT) questionnaire and compared with their intention to ask others for a non-academic, material favor. Data on students' motivation measured by Work Preference Inventory scale, and general data were also collected. Multiple linear regression models of the intention to engage others in a particular behavior were developed. The most important determinants of the intention to engage others in academic transgression were psychological factors, such as intention to ask others for a material favor, or students' motivation (median determinant's β of 0.18, P≤0.045 for all), whereas social and cultural factors associated with the country of origin were either weak (median β of 0.07, P≤0.031) or not relevant. A significant proportion of students were aware of the ethical violations in academic transgressions (P≤0.004 for all transgressions), but a large proportion of students also perceived academic cheating as a collective effort and were likely to engage people randomly (P≤0.001 for all, but the most severe transgression). This collective effort was more pronounced for academic than non-academic behavior. Culture differences among neighboring Southern-European countries were not an important determinant of the intention to engage others in academic cheating.

China’s Stock Market Integration with a Leading Power and a Close Neighbor

Directory of Open Access Journals (Sweden)

Zheng Yi

2009-12-01

Full Text Available Current integration and co-movement among international stock markets has been boosted by increased globalization of the world economy, and profit-chasing capital surfing across borders. With a reputation as the fastest growing economy in the world, China’s stock market has continued gaining momentum during recent years and incurred growing attention from academicians, as well as practitioners. Taking into account economic and geographical considerations, the US and Hong Kong are considerably the most comparable stock markets to China. The usual vector error correction model (VECM could overlook the long memory feature of cointegration residual series, which can in turn exert bias on the resulting inferences. To overcome its limitations, we employ a fractionally integrated VECM (FIVECM in this paper to investigate the long-term cointegration relations binding China’s stock market to the aforementioned stock markets. In addition, by augmenting the FIVECM with multivariate GARCH model, the return transmission and volatility spillover between market return series were revealed simultaneously. Our empirical results show that China’s stock market is fractionally cointegrated with the two markets, and it appears that China’s stock market has stronger ties with its neighboring Hong Kong market than with the world superpower, the US market.

Decision-relevant evaluation of climate models: A case study of chill hours in California

Science.gov (United States)

Jagannathan, K. A.; Jones, A. D.; Kerr, A. C.

2017-12-01

The past decade has seen a proliferation of different climate datasets with over 60 climate models currently in use. Comparative evaluation and validation of models can assist practitioners chose the most appropriate models for adaptation planning. However, such assessments are usually conducted for `climate metrics' such as seasonal temperature, while sectoral decisions are often based on `decision-relevant outcome metrics' such as growing degree days or chill hours. Since climate models predict different metrics with varying skill, the goal of this research is to conduct a bottom-up evaluation of model skill for `outcome-based' metrics. Using chill hours (number of hours in winter months where temperature is lesser than 45 deg F) in Fresno, CA as a case, we assess how well different GCMs predict the historical mean and slope of chill hours, and whether and to what extent projections differ based on model selection. We then compare our results with other climate-based evaluations of the region, to identify similarities and differences. For the model skill evaluation, historically observed chill hours were compared with simulations from 27 GCMs (and multiple ensembles). Model skill scores were generated based on a statistical hypothesis test of the comparative assessment. Future projections from RCP 8.5 runs were evaluated, and a simple bias correction was also conducted. Our analysis indicates that model skill in predicting chill hour slope is dependent on its skill in predicting mean chill hours, which results from the non-linear nature of the chill metric. However, there was no clear relationship between the models that performed well for the chill hour metric and those that performed well in other temperature-based evaluations (such winter minimum temperature or diurnal temperature range). Further, contrary to conclusions from other studies, we also found that the multi-model mean or large ensemble mean results may not always be most appropriate for this

Moderate-resolution data and gradient nearest neighbor imputation for regional-national risk assessment

Science.gov (United States)

Kenneth B. Jr. Pierce; C. Kenneth Brewer; Janet L. Ohmann

2010-01-01

This study was designed to test the feasibility of combining a method designed to populate pixels with inventory plot data at the 30-m scale with a new national predictor data set. The new national predictor data set was developed by the USDA Forest Service Remote Sensing Applications Center (hereafter RSAC) at the 250-m scale. Gradient Nearest Neighbor (GNN)...

Obstacle Detection for Intelligent Transportation Systems Using Deep Stacked Autoencoder and k-Nearest Neighbor Scheme

KAUST Repository

Dairi, Abdelkader; Harrou, Fouzi; Sun, Ying; Senouci, Mohamed

2018-01-01

Obstacle detection is an essential element for the development of intelligent transportation systems so that accidents can be avoided. In this study, we propose a stereovisionbased method for detecting obstacles in urban environment. The proposed method uses a deep stacked auto-encoders (DSA) model that combines the greedy learning features with the dimensionality reduction capacity and employs an unsupervised k-nearest neighbors algorithm (KNN) to accurately and reliably detect the presence of obstacles. We consider obstacle detection as an anomaly detection problem. We evaluated the proposed method by using practical data from three publicly available datasets, the Malaga stereovision urban dataset (MSVUD), the Daimler urban segmentation dataset (DUSD), and Bahnhof dataset. Also, we compared the efficiency of DSA-KNN approach to the deep belief network (DBN)-based clustering schemes. Results show that the DSA-KNN is suitable to visually monitor urban scenes.

Obstacle Detection for Intelligent Transportation Systems Using Deep Stacked Autoencoder and k-Nearest Neighbor Scheme

KAUST Repository

Dairi, Abdelkader

2018-04-30

Obstacle detection is an essential element for the development of intelligent transportation systems so that accidents can be avoided. In this study, we propose a stereovisionbased method for detecting obstacles in urban environment. The proposed method uses a deep stacked auto-encoders (DSA) model that combines the greedy learning features with the dimensionality reduction capacity and employs an unsupervised k-nearest neighbors algorithm (KNN) to accurately and reliably detect the presence of obstacles. We consider obstacle detection as an anomaly detection problem. We evaluated the proposed method by using practical data from three publicly available datasets, the Malaga stereovision urban dataset (MSVUD), the Daimler urban segmentation dataset (DUSD), and Bahnhof dataset. Also, we compared the efficiency of DSA-KNN approach to the deep belief network (DBN)-based clustering schemes. Results show that the DSA-KNN is suitable to visually monitor urban scenes.

State-space prediction model for chaotic time series

Science.gov (United States)

Alparslan, A. K.; Sayar, M.; Atilgan, A. R.

1998-08-01

A simple method for predicting the continuation of scalar chaotic time series ahead in time is proposed. The false nearest neighbors technique in connection with the time-delayed embedding is employed so as to reconstruct the state space. A local forecasting model based upon the time evolution of the topological neighboring in the reconstructed phase space is suggested. A moving root-mean-square error is utilized in order to monitor the error along the prediction horizon. The model is tested for the convection amplitude of the Lorenz model. The results indicate that for approximately 100 cycles of the training data, the prediction follows the actual continuation very closely about six cycles. The proposed model, like other state-space forecasting models, captures the long-term behavior of the system due to the use of spatial neighbors in the state space.

Multiagent data warehousing and multiagent data mining for cerebrum/cerebellum modeling

Science.gov (United States)

Zhang, Wen-Ran

2002-03-01

An algorithm named Neighbor-Miner is outlined for multiagent data warehousing and multiagent data mining. The algorithm is defined in an evolving dynamic environment with autonomous or semiautonomous agents. Instead of mining frequent itemsets from customer transactions, the new algorithm discovers new agents and mining agent associations in first-order logic from agent attributes and actions. While the Apriori algorithm uses frequency as a priory threshold, the new algorithm uses agent similarity as priory knowledge. The concept of agent similarity leads to the notions of agent cuboid, orthogonal multiagent data warehousing (MADWH), and multiagent data mining (MADM). Based on agent similarities and action similarities, Neighbor-Miner is proposed and illustrated in a MADWH/MADM approach to cerebrum/cerebellum modeling. It is shown that (1) semiautonomous neurofuzzy agents can be identified for uniped locomotion and gymnastic training based on attribute relevance analysis; (2) new agents can be discovered and agent cuboids can be dynamically constructed in an orthogonal MADWH, which resembles an evolving cerebrum/cerebellum system; and (3) dynamic motion laws can be discovered as association rules in first order logic. Although examples in legged robot gymnastics are used to illustrate the basic ideas, the new approach is generally suitable for a broad category of data mining tasks where knowledge can be discovered collectively by a set of agents from a geographically or geometrically distributed but relevant environment, especially in scientific and engineering data environments.

Morphological type correlation between nearest neighbor pairs of galaxies

Science.gov (United States)

Yamagata, Tomohiko

1990-01-01

Although the morphological type of galaxies is one of the most fundamental properties of galaxies, its origin and evolutionary processes, if any, are not yet fully understood. It has been established that the galaxy morphology strongly depends on the environment in which the galaxy resides (e.g., Dressler 1980). Galaxy pairs correspond to the smallest scales of galaxy clustering and may provide important clues to how the environment influences the formation and evolution of galaxies. Several investigators pointed out that there is a tendency for pair galaxies to have similar morphological types (Karachentsev and Karachentseva 1974, Page 1975, Noerdlinger 1979). Here, researchers analyze morphological type correlation for 18,364 nearest neighbor pairs of galaxies identified in the magnetic tape version of the Center for Astrophysics Redshift Catalogue.

Relevance of the ICRP biokinetic model for dietary organically bound tritium

International Nuclear Information System (INIS)

Trivedi, A.

1999-10-01

Ingested dietary tritium can participate in metabolic processes, and become synthesized into organically bound tritium in the tissues and organs. The distribution and retention of the organically bound tritium throughout the body are much different than tritium in the body water. The International Commission on Radiological Protection (ICRP) Publication 56 (1989) has a biokinetic model to calculate dose from the ingestion of organically bound dietary tritium. The model predicts that the dose from the ingestion of organically bound dietary tritium is about 2.3 times higher than from the ingestion of the same activity of tritiated water. Under steady-state conditions, the calculated dose rate (using the first principle approach) from the ingestion of dietary organically bound tritium can be twice that from the ingestion of tritiated water. For an adult, the upper-bound dose estimate for the ingestion of dietary organically bound tritium is estimated to be close to 2.3 times higher than that of tritiated water. Therefore, given the uncertainty in the dose calculation with respect to the actual relevant dose, the ICRP biokinetic model for organically bound tritium is sufficient for dosimetry for adults. (author)

Relevance in the science classroom: A multidimensional analysis

Science.gov (United States)

Hartwell, Matthew F.

While perceived relevance is considered a fundamental component of adaptive learning, the experience of relevance and its conceptual definition have not been well described. The mixed-methods research presented in this dissertation aimed to clarify the conceptual meaning of relevance by focusing on its phenomenological experience from the students' perspective. Following a critical literature review, I propose an identity-based model of perceived relevance that includes three components: a contextual target, an identity target, and a connection type, or lens. An empirical investigation of this model that consisted of two general phases was implemented in four 9th grade-biology classrooms. Participants in Phase 1 (N = 118) completed a series of four open-ended writing activities focused on eliciting perceived personal connections to academic content. Exploratory qualitative content analysis of a 25% random sample of the student responses was used to identify the main meaning-units of the proposed model as well as different dimensions of student relevance perceptions. These meaning-units and dimensions provided the basis for the construction of a conceptual mapping sentence capturing students' perceived relevance, which was then applied in a confirmatory analysis to all other student responses. Participants in Phase 2 (N = 139) completed a closed survey designed based on the mapping sentence to assess their perceived relevance of a biology unit. The survey also included scales assessing other domain-level motivational processes. Exploratory factor analysis and non-metric multidimensional scaling indicated a coherent conceptual structure, which included a primary interpretive relevance dimension. Comparison of the conceptual structure across various groups (randomly-split sample, gender, academic level, domain-general motivational profiles) provided support for its ubiquity and insight into variation in the experience of perceived relevance among students of different

Why relevance theory is relevant for lexicography

DEFF Research Database (Denmark)

Bothma, Theo; Tarp, Sven

2014-01-01

This article starts by providing a brief summary of relevance theory in information science in relation to the function theory of lexicography, explaining the different types of relevance, viz. objective system relevance and the subjective types of relevance, i.e. topical, cognitive, situational...... that is very important for lexicography as well as for information science, viz. functional relevance. Since all lexicographic work is ultimately aimed at satisfying users’ information needs, the article then discusses why the lexicographer should take note of all these types of relevance when planning a new...... dictionary project, identifying new tasks and responsibilities of the modern lexicographer. The article furthermore discusses how relevance theory impacts on teaching dictionary culture and reference skills. By integrating insights from lexicography and information science, the article contributes to new...

Designing lattice structures with maximal nearest-neighbor entanglement

Energy Technology Data Exchange (ETDEWEB)

Navarro-Munoz, J C; Lopez-Sandoval, R [Instituto Potosino de Investigacion CientIfica y Tecnologica, Camino a la presa San Jose 2055, 78216 San Luis Potosi (Mexico); Garcia, M E [Theoretische Physik, FB 18, Universitaet Kassel and Center for Interdisciplinary Nanostructure Science and Technology (CINSaT), Heinrich-Plett-Str.40, 34132 Kassel (Germany)

2009-08-07

In this paper, we study the numerical optimization of nearest-neighbor concurrence of bipartite one- and two-dimensional lattices, as well as non-bipartite two-dimensional lattices. These systems are described in the framework of a tight-binding Hamiltonian while the optimization of concurrence was performed using genetic algorithms. Our results show that the concurrence of the optimized lattice structures is considerably higher than that of non-optimized systems. In the case of one-dimensional chains, the concurrence increases dramatically when the system begins to dimerize, i.e., it undergoes a structural phase transition (Peierls distortion). This result is consistent with the idea that entanglement is maximal or shows a singularity near quantum phase transitions. Moreover, the optimization of concurrence in two-dimensional bipartite and non-bipartite lattices is achieved when the structures break into smaller subsystems, which are arranged in geometrically distinguishable configurations.

Stripe order from the perspective of the Hubbard model

Energy Technology Data Exchange (ETDEWEB)

Devereaux, Thomas Peter

2018-03-01

A microscopic understanding of the strongly correlated physics of the cuprates must account for the translational and rotational symmetry breaking that is present across all cuprate families, commonly in the form of stripes. Here we investigate emergence of stripes in the Hubbard model, a minimal model believed to be relevant to the cuprate superconductors, using determinant quantum Monte Carlo (DQMC) simulations at finite temperatures and density matrix renormalization group (DMRG) ground state calculations. By varying temperature, doping, and model parameters, we characterize the extent of stripes throughout the phase diagram of the Hubbard model. Our results show that including the often neglected next-nearest-neighbor hopping leads to the absence of spin incommensurability upon electron-doping and nearly half-filled stripes upon hole-doping. The similarities of these findings to experimental results on both electron and hole-doped cuprate families support a unified description across a large portion of the cuprate phase diagram.

On the ""early-time"" evolution of variables relevant to turbulence models for Rayleigh-Taylor instability

Energy Technology Data Exchange (ETDEWEB)

Rollin, Bertrand [Los Alamos National Laboratory; Andrews, Malcolm J [Los Alamos National Laboratory

2010-01-01

We present our progress toward setting initial conditions in variable density turbulence models. In particular, we concentrate our efforts on the BHR turbulence model for turbulent Rayleigh-Taylor instability. Our approach is to predict profiles of relevant parameters before the fully turbulent regime and use them as initial conditions for the turbulence model. We use an idealized model of the mixing between two interpenetrating fluids to define the initial profiles for the turbulence model parameters. Velocities and volume fractions used in the idealized mixing model are obtained respectively from a set of ordinary differential equations modeling the growth of the Rayleigh-Taylor instability and from an idealization of the density profile in the mixing layer. A comparison between predicted initial profiles for the turbulence model parameters and initial profiles of the parameters obtained from low Atwood number three dimensional simulations show reasonable agreement.

Software Helps Retrieve Information Relevant to the User

Science.gov (United States)

Mathe, Natalie; Chen, James

2003-01-01

The Adaptive Indexing and Retrieval Agent (ARNIE) is a code library, designed to be used by an application program, that assists human users in retrieving desired information in a hypertext setting. Using ARNIE, the program implements a computational model for interactively learning what information each human user considers relevant in context. The model, called a "relevance network," incrementally adapts retrieved information to users individual profiles on the basis of feedback from the users regarding specific queries. The model also generalizes such knowledge for subsequent derivation of relevant references for similar queries and profiles, thereby, assisting users in filtering information by relevance. ARNIE thus enables users to categorize and share information of interest in various contexts. ARNIE encodes the relevance and structure of information in a neural network dynamically configured with a genetic algorithm. ARNIE maintains an internal database, wherein it saves associations, and from which it returns associated items in response to a query. A C++ compiler for a platform on which ARNIE will be utilized is necessary for creating the ARNIE library but is not necessary for the execution of the software.

The Tragedy of Your Upstairs Neighbors: Is the Airbnb Negative Externality Internalized?

OpenAIRE

Horton, John J.

2015-01-01

A commonly expressed concern about the rise of the peer-to-peer rental market Airbnb is that hosts---those renting out their properties---impose costs on their unwitting neighbors. I consider the question of whether apartment building owners will, in a competitive rental market, set a building-specific Airbnb hosting policy that is socially efficient. I find that if tenants can sort across apartments based on the owners policy then the equilibrium fraction of buildings allowing Airbnb listing...

Interacting Effects Induced by Two Neighboring Pits Considering Relative Position Parameters and Pit Depth

Directory of Open Access Journals (Sweden)

Yongfang Huang

2017-04-01

Full Text Available For pre-corroded aluminum alloy 7075-T6, the interacting effects of two neighboring pits on the stress concentration are comprehensively analyzed by considering various relative position parameters (inclination angle θ and dimensionless spacing parameter λ and pit depth (d with the finite element method. According to the severity of the stress concentration, the critical corrosion regions, bearing high susceptibility to fatigue damage, are determined for intersecting and adjacent pits, respectively. A straightforward approach is accordingly proposed to conservatively estimate the combined stress concentration factor induced by two neighboring pits, and a concrete application example is presented. It is found that for intersecting pits, the normalized stress concentration factor Ktnor increases with the increase of θ and λ and always reaches its maximum at θ = 90°, yet for adjacent pits, Ktnor decreases with the increase of λ and the maximum value appears at a slight asymmetric location. The simulations reveal that Ktnor follows a linear and an exponential relationship with the dimensionless depth parameter Rd for intersecting and adjacent cases, respectively.

Data delivery method based on neighbor nodes' information in a mobile ad hoc network.

Science.gov (United States)

Kashihara, Shigeru; Hayashi, Takuma; Taenaka, Yuzo; Okuda, Takeshi; Yamaguchi, Suguru

2014-01-01

This paper proposes a data delivery method based on neighbor nodes' information to achieve reliable communication in a mobile ad hoc network (MANET). In a MANET, it is difficult to deliver data reliably due to instabilities in network topology and wireless network condition which result from node movement. To overcome such unstable communication, opportunistic routing and network coding schemes have lately attracted considerable attention. Although an existing method that employs such schemes, MAC-independent opportunistic routing and encoding (MORE), Chachulski et al. (2007), improves the efficiency of data delivery in an unstable wireless mesh network, it does not address node movement. To efficiently deliver data in a MANET, the method proposed in this paper thus first employs the same opportunistic routing and network coding used in MORE and also uses the location information and transmission probabilities of neighbor nodes to adapt to changeable network topology and wireless network condition. The simulation experiments showed that the proposed method can achieve efficient data delivery with low network load when the movement speed is relatively slow.

Data Delivery Method Based on Neighbor Nodes’ Information in a Mobile Ad Hoc Network

Directory of Open Access Journals (Sweden)

Shigeru Kashihara

2014-01-01

Full Text Available This paper proposes a data delivery method based on neighbor nodes’ information to achieve reliable communication in a mobile ad hoc network (MANET. In a MANET, it is difficult to deliver data reliably due to instabilities in network topology and wireless network condition which result from node movement. To overcome such unstable communication, opportunistic routing and network coding schemes have lately attracted considerable attention. Although an existing method that employs such schemes, MAC-independent opportunistic routing and encoding (MORE, Chachulski et al. (2007, improves the efficiency of data delivery in an unstable wireless mesh network, it does not address node movement. To efficiently deliver data in a MANET, the method proposed in this paper thus first employs the same opportunistic routing and network coding used in MORE and also uses the location information and transmission probabilities of neighbor nodes to adapt to changeable network topology and wireless network condition. The simulation experiments showed that the proposed method can achieve efficient data delivery with low network load when the movement speed is relatively slow.

Application of all relevant feature selection for failure analysis of parameter-induced simulation crashes in climate models

Science.gov (United States)

Paja, W.; Wrzesień, M.; Niemiec, R.; Rudnicki, W. R.

2015-07-01

The climate models are extremely complex pieces of software. They reflect best knowledge on physical components of the climate, nevertheless, they contain several parameters, which are too weakly constrained by observations, and can potentially lead to a crash of simulation. Recently a study by Lucas et al. (2013) has shown that machine learning methods can be used for predicting which combinations of parameters can lead to crash of simulation, and hence which processes described by these parameters need refined analyses. In the current study we reanalyse the dataset used in this research using different methodology. We confirm the main conclusion of the original study concerning suitability of machine learning for prediction of crashes. We show, that only three of the eight parameters indicated in the original study as relevant for prediction of the crash are indeed strongly relevant, three other are relevant but redundant, and two are not relevant at all. We also show that the variance due to split of data between training and validation sets has large influence both on accuracy of predictions and relative importance of variables, hence only cross-validated approach can deliver robust prediction of performance and relevance of variables.

Air Pollution from Livestock Farms Is Associated with Airway Obstruction in Neighboring Residents.

Science.gov (United States)

Borlée, Floor; Yzermans, C Joris; Aalders, Bernadette; Rooijackers, Jos; Krop, Esmeralda; Maassen, Catharina B M; Schellevis, François; Brunekreef, Bert; Heederik, Dick; Smit, Lidwien A M

2017-11-01

Livestock farm emissions may not only affect respiratory health of farmers but also of neighboring residents. To explore associations between spatial and temporal variation in pollutant emissions from livestock farms and lung function in a general, nonfarming, rural population in the Netherlands. We conducted a cross-sectional study in 2,308 adults (age, 20-72 yr). A pulmonary function test was performed measuring prebronchodilator and post-bronchodilator FEV 1 , FVC, FEV 1 /FVC, and maximum mid-expiratory flow (MMEF). Spatial exposure was assessed as (1) number of farms within 500 m and 1,000 m of the home, (2) distance to the nearest farm, and (3) modeled annual average fine dust emissions from farms within 500 m and 1,000 m of the home address. Temporal exposure was assessed as week-average ambient particulate matter livestock farms within a 1,000-m buffer from the home address and MMEF, which was more pronounced in participants without atopy. No associations were found with other spatial exposure variables. Week-average particulate matter livestock air pollution emissions are associated with lung function deficits in nonfarming residents.

Perspectives on creating clinically relevant blast models for mild traumatic brain injury and post traumatic stress disorder symptoms

Directory of Open Access Journals (Sweden)

Lisa eBrenner

2012-03-01

Full Text Available Military personnel are returning from Iraq and Afghanistan and reporting non-specific physical (somatic, behavioral, psychological, and cognitive symptoms. Many of these symptoms are frequently associated with mild traumatic brain injury (mTBI and/or post traumatic stress disorder (PTSD. Despite significant attention and advances in assessment and intervention for these two conditions, challenges persist. To address this, clinically relevant blast models are essential in the full characterization of this type of injury, as well as in the testing and identification of potential treatment strategies. In this publication, existing diagnostic challenges and current treatment practices for mTBI and/or PTSD will be summarized, along with suggestions regarding how what has been learned from existing models of PTSD and traditional mechanism (e.g., non-blast TBI can be used to facilitate the development of clinically relevant blast models.

Fidelity in Animal Modeling: Prerequisite for a Mechanistic Research Front Relevant to the Inflammatory Incompetence of Acute Pediatric Malnutrition

Science.gov (United States)

Woodward, Bill

2016-01-01

Inflammatory incompetence is characteristic of acute pediatric protein-energy malnutrition, but its underlying mechanisms remain obscure. Perhaps substantially because the research front lacks the driving force of a scholarly unifying hypothesis, it is adrift and research activity is declining. A body of animal-based research points to a unifying paradigm, the Tolerance Model, with some potential to offer coherence and a mechanistic impetus to the field. However, reasonable skepticism prevails regarding the relevance of animal models of acute pediatric malnutrition; consequently, the fundamental contributions of the animal-based component of this research front are largely overlooked. Design-related modifications to improve the relevance of animal modeling in this research front include, most notably, prioritizing essential features of pediatric malnutrition pathology rather than dietary minutiae specific to infants and children, selecting windows of experimental animal development that correspond to targeted stages of pediatric immunological ontogeny, and controlling for ontogeny-related confounders. In addition, important opportunities are presented by newer tools including the immunologically humanized mouse and outbred stocks exhibiting a magnitude of genetic heterogeneity comparable to that of human populations. Sound animal modeling is within our grasp to stimulate and support a mechanistic research front relevant to the immunological problems that accompany acute pediatric malnutrition. PMID:27077845

Fidelity in Animal Modeling: Prerequisite for a Mechanistic Research Front Relevant to the Inflammatory Incompetence of Acute Pediatric Malnutrition.

Science.gov (United States)

Woodward, Bill

2016-04-11

Inflammatory incompetence is characteristic of acute pediatric protein-energy malnutrition, but its underlying mechanisms remain obscure. Perhaps substantially because the research front lacks the driving force of a scholarly unifying hypothesis, it is adrift and research activity is declining. A body of animal-based research points to a unifying paradigm, the Tolerance Model, with some potential to offer coherence and a mechanistic impetus to the field. However, reasonable skepticism prevails regarding the relevance of animal models of acute pediatric malnutrition; consequently, the fundamental contributions of the animal-based component of this research front are largely overlooked. Design-related modifications to improve the relevance of animal modeling in this research front include, most notably, prioritizing essential features of pediatric malnutrition pathology rather than dietary minutiae specific to infants and children, selecting windows of experimental animal development that correspond to targeted stages of pediatric immunological ontogeny, and controlling for ontogeny-related confounders. In addition, important opportunities are presented by newer tools including the immunologically humanized mouse and outbred stocks exhibiting a magnitude of genetic heterogeneity comparable to that of human populations. Sound animal modeling is within our grasp to stimulate and support a mechanistic research front relevant to the immunological problems that accompany acute pediatric malnutrition.

Nearest-neighbor Kitaev exchange blocked by charge order in electron-doped α -RuCl3

Science.gov (United States)

Koitzsch, A.; Habenicht, C.; Müller, E.; Knupfer, M.; Büchner, B.; Kretschmer, S.; Richter, M.; van den Brink, J.; Börrnert, F.; Nowak, D.; Isaeva, A.; Doert, Th.

2017-10-01

A quantum spin liquid might be realized in α -RuCl3 , a honeycomb-lattice magnetic material with substantial spin-orbit coupling. Moreover, α -RuCl3 is a Mott insulator, which implies the possibility that novel exotic phases occur upon doping. Here, we study the electronic structure of this material when intercalated with potassium by photoemission spectroscopy, electron energy loss spectroscopy, and density functional theory calculations. We obtain a stable stoichiometry at K0.5RuCl3 . This gives rise to a peculiar charge disproportionation into formally Ru2 + (4 d6 ) and Ru3 + (4 d5 ). Every Ru 4 d5 site with one hole in the t2 g shell is surrounded by nearest neighbors of 4 d6 character, where the t2 g level is full and magnetically inert. Thus, each type of Ru site forms a triangular lattice, and nearest-neighbor interactions of the original honeycomb are blocked.

Probability distributions for first neighbor distances between resonances that belong to two different families

International Nuclear Information System (INIS)

Difilippo, F.C.

1994-01-01

For a mixture of two families of resonances, we found the probability distribution for the distance, as first neighbors, between resonances that belong to different families. Integration of this distribution gives the probability of accidental overlapping of resonances of one isotope by resonances of the other, provided that the resonances of each isotope belong to a single family. (author)

Clinical and Neurobiological Relevance of Current Animal Models of Autism Spectrum Disorders

Science.gov (United States)

Kim, Ki Chan; Gonzales, Edson Luck; Lázaro, María T.; Choi, Chang Soon; Bahn, Geon Ho; Yoo, Hee Jeong; Shin, Chan Young

2016-01-01

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social and communication impairments, as well as repetitive and restrictive behaviors. The phenotypic heterogeneity of ASD has made it overwhelmingly difficult to determine the exact etiology and pathophysiology underlying the core symptoms, which are often accompanied by comorbidities such as hyperactivity, seizures, and sensorimotor abnormalities. To our benefit, the advent of animal models has allowed us to assess and test diverse risk factors of ASD, both genetic and environmental, and measure their contribution to the manifestation of autistic symptoms. At a broader scale, rodent models have helped consolidate molecular pathways and unify the neurophysiological mechanisms underlying each one of the various etiologies. This approach will potentially enable the stratification of ASD into clinical, molecular, and neurophenotypic subgroups, further proving their translational utility. It is henceforth paramount to establish a common ground of mechanistic theories from complementing results in preclinical research. In this review, we cluster the ASD animal models into lesion and genetic models and further classify them based on the corresponding environmental, epigenetic and genetic factors. Finally, we summarize the symptoms and neuropathological highlights for each model and make critical comparisons that elucidate their clinical and neurobiological relevance. PMID:27133257

Neighboring Optimal Aircraft Guidance in a General Wind Environment

Science.gov (United States)

Jardin, Matthew R. (Inventor)

2003-01-01

Method and system for determining an optimal route for an aircraft moving between first and second waypoints in a general wind environment. A selected first wind environment is analyzed for which a nominal solution can be determined. A second wind environment is then incorporated; and a neighboring optimal control (NOC) analysis is performed to estimate an optimal route for the second wind environment. In particular examples with flight distances of 2500 and 6000 nautical miles in the presence of constant or piecewise linearly varying winds, the difference in flight time between a nominal solution and an optimal solution is 3.4 to 5 percent. Constant or variable winds and aircraft speeds can be used. Updated second wind environment information can be provided and used to obtain an updated optimal route.

Enhanced Approximate Nearest Neighbor via Local Area Focused Search.

Energy Technology Data Exchange (ETDEWEB)

Gonzales, Antonio [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Blazier, Nicholas Paul [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-02-01

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

Use of an ecologically relevant modelling approach to improve remote sensing-based schistosomiasis risk profiling

Directory of Open Access Journals (Sweden)

Yvonne Walz

2015-11-01

Full Text Available Schistosomiasis is a widespread water-based disease that puts close to 800 million people at risk of infection with more than 250 million infected, mainly in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and the frequency, duration and extent of human bodies exposed to infested water sources during human water contact. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. Since schistosomiasis risk profiling based on remote sensing data inherits a conceptual drawback if school-based disease prevalence data are directly related to the remote sensing measurements extracted at the location of the school, because the disease transmission usually does not exactly occur at the school, we took the local environment around the schools into account by explicitly linking ecologically relevant environmental information of potential disease transmission sites to survey measurements of disease prevalence. Our models were validated at two sites with different landscapes in Côte d’Ivoire using high- and moderateresolution remote sensing data based on random forest and partial least squares regression. We found that the ecologically relevant modelling approach explained up to 70% of the variation in Schistosoma infection prevalence and performed better compared to a purely pixelbased modelling approach. Furthermore, our study showed that model performance increased as a function of enlarging the school catchment area, confirming the hypothesis that suitable environments for schistosomiasis transmission rarely occur at the location of survey measurements.

Evaluation of the suitability of free-energy minimization using nearest-neighbor energy parameters for RNA secondary structure prediction

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Cobaugh Christian W

2004-08-01

Full Text Available Abstract Background A detailed understanding of an RNA's correct secondary and tertiary structure is crucial to understanding its function and mechanism in the cell. Free energy minimization with energy parameters based on the nearest-neighbor model and comparative analysis are the primary methods for predicting an RNA's secondary structure from its sequence. Version 3.1 of Mfold has been available since 1999. This version contains an expanded sequence dependence of energy parameters and the ability to incorporate coaxial stacking into free energy calculations. We test Mfold 3.1 by performing the largest and most phylogenetically diverse comparison of rRNA and tRNA structures predicted by comparative analysis and Mfold, and we use the results of our tests on 16S and 23S rRNA sequences to assess the improvement between Mfold 2.3 and Mfold 3.1. Results The average prediction accuracy for a 16S or 23S rRNA sequence with Mfold 3.1 is 41%, while the prediction accuracies for the majority of 16S and 23S rRNA structures tested are between 20% and 60%, with some having less than 20% prediction accuracy. The average prediction accuracy was 71% for 5S rRNA and 69% for tRNA. The majority of the 5S rRNA and tRNA sequences have prediction accuracies greater than 60%. The prediction accuracy of 16S rRNA base-pairs decreases exponentially as the number of nucleotides intervening between the 5' and 3' halves of the base-pair increases. Conclusion Our analysis indicates that the current set of nearest-neighbor energy parameters in conjunction with the Mfold folding algorithm are unable to consistently and reliably predict an RNA's correct secondary structure. For 16S or 23S rRNA structure prediction, Mfold 3.1 offers little improvement over Mfold 2.3. However, the nearest-neighbor energy parameters do work well for shorter RNA sequences such as tRNA or 5S rRNA, or for larger rRNAs when the contact distance between the base-pairs is less than 100 nucleotides.

Automatic classification and detection of clinically relevant images for diabetic retinopathy

Science.gov (United States)

Xu, Xinyu; Li, Baoxin

2008-03-01

We proposed a novel approach to automatic classification of Diabetic Retinopathy (DR) images and retrieval of clinically-relevant DR images from a database. Given a query image, our approach first classifies the image into one of the three categories: microaneurysm (MA), neovascularization (NV) and normal, and then it retrieves DR images that are clinically-relevant to the query image from an archival image database. In the classification stage, the query DR images are classified by the Multi-class Multiple-Instance Learning (McMIL) approach, where images are viewed as bags, each of which contains a number of instances corresponding to non-overlapping blocks, and each block is characterized by low-level features including color, texture, histogram of edge directions, and shape. McMIL first learns a collection of instance prototypes for each class that maximizes the Diverse Density function using Expectation- Maximization algorithm. A nonlinear mapping is then defined using the instance prototypes and maps every bag to a point in a new multi-class bag feature space. Finally a multi-class Support Vector Machine is trained in the multi-class bag feature space. In the retrieval stage, we retrieve images from the archival database who bear the same label with the query image, and who are the top K nearest neighbors of the query image in terms of similarity in the multi-class bag feature space. The classification approach achieves high classification accuracy, and the retrieval of clinically-relevant images not only facilitates utilization of the vast amount of hidden diagnostic knowledge in the database, but also improves the efficiency and accuracy of DR lesion diagnosis and assessment.

Adaptive behavior of neighboring neurons during adaptation-induced plasticity of orientation tuning in V1

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Shumikhina Svetlana

2009-12-01

Full Text Available Abstract Background Sensory neurons display transient changes of their response properties following prolonged exposure to an appropriate stimulus (adaptation. In adult cat primary visual cortex, orientation-selective neurons shift their preferred orientation after being adapted to a non-preferred orientation. The direction of those shifts, towards (attractive or away (repulsive from the adapter depends mostly on adaptation duration. How the adaptive behavior of a neuron is related to that of its neighbors remains unclear. Results Here we show that in most cases (75%, cells shift their preferred orientation in the same direction as their neighbors. We also found that cells shifting preferred orientation differently from their neighbors (25% display three interesting properties: (i larger variance of absolute shift amplitude, (ii wider tuning bandwidth and (iii larger range of preferred orientations among the cluster of cells. Several response properties of V1 neurons depend on their location within the cortical orientation map. Our results suggest that recording sites with both attractive and repulsive shifts following adaptation may be located in close proximity to iso-orientation domain boundaries or pinwheel centers. Indeed, those regions have a more diverse orientation distribution of local inputs that could account for the three properties above. On the other hand, sites with all cells shifting their preferred orientation in the same direction could be located within iso-orientation domains. Conclusions Our results suggest that the direction and amplitude of orientation preference shifts in V1 depend on location within the orientation map. This anisotropy of adaptation-induced plasticity, comparable to that of the visual cortex itself, could have important implications for our understanding of visual adaptation at the psychophysical level.

Truncated Calogero-Sutherland models on a circle

Science.gov (United States)

Tummuru, Tarun R.; Jain, Sudhir R.; Khare, Avinash

2017-12-01

We investigate a quantum many-body system with particles moving in a circle and subject to two-body and three-body potentials. This class of models, in which the range of interaction r can be set to a certain number of neighbors, extrapolates from a system with interactions up to next-to-nearest neighbors and the celebrated Calogero-Sutherland model. The exact ground state energy and a part of the excitation spectrum have been obtained.

The effect of near laterally and vertically neighboring quantum dots on the composition of uncapped InxGa1−xAs/GaAs quantum dots

International Nuclear Information System (INIS)

Donglin, Wang; Zhongyuan, Yu; Yumin, Liu; Han, Ye; Pengfei, Lu; Xiaotao, Guo; Long, Zhao; Xia, Xin

2010-01-01

The composition of quantum dots has a direct effect on the optical and electronic properties of quantum-dot-based devices. In this paper, we combine the method of moving asymptotes and finite element tools to compute the composition distribution by minimizing the Gibbs free energy of quantum dots, and use this method to study the effect of near laterally and vertically neighboring quantum dots on the composition distribution. The simulation results indicate that the effect from the laterally neighboring quantum dot is very small, and the vertically neighboring quantum dot can significantly influence the composition by the coupled strain field

Filtered selection coupled with support vector machines generate a functionally relevant prediction model for colorectal cancer

Directory of Open Access Journals (Sweden)

Gabere MN

2016-06-01

Full Text Available Musa Nur Gabere,1 Mohamed Aly Hussein,1 Mohammad Azhar Aziz2 1Department of Bioinformatics, King Abdullah International Medical Research Center/King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia; 2Colorectal Cancer Research Program, Department of Medical Genomics, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia Purpose: There has been considerable interest in using whole-genome expression profiles for the classification of colorectal cancer (CRC. The selection of important features is a crucial step before training a classifier.Methods: In this study, we built a model that uses support vector machine (SVM to classify cancer and normal samples using Affymetrix exon microarray data obtained from 90 samples of 48 patients diagnosed with CRC. From the 22,011 genes, we selected the 20, 30, 50, 100, 200, 300, and 500 genes most relevant to CRC using the minimum-redundancy–maximum-relevance (mRMR technique. With these gene sets, an SVM model was designed using four different kernel types (linear, polynomial, radial basis function [RBF], and sigmoid.Results: The best model, which used 30 genes and RBF kernel, outperformed other combinations; it had an accuracy of 84% for both ten fold and leave-one-out cross validations in discriminating the cancer samples from the normal samples. With this 30 genes set from mRMR, six classifiers were trained using random forest (RF, Bayes net (BN, multilayer perceptron (MLP, naïve Bayes (NB, reduced error pruning tree (REPT, and SVM. Two hybrids, mRMR + SVM and mRMR + BN, were the best models when tested on other datasets, and they achieved a prediction accuracy of 95.27% and 91.99%, respectively, compared to other mRMR hybrid models (mRMR + RF, mRMR + NB, mRMR + REPT, and mRMR + MLP. Ingenuity pathway analysis was used to analyze the functions of the 30 genes selected for this model and their potential association with CRC: CDH3, CEACAM7, CLDN1, IL8, IL6R, MMP1

A content relevance model for social media health information.

Science.gov (United States)

Prybutok, Gayle Linda; Koh, Chang; Prybutok, Victor R

2014-04-01

Consumer health informatics includes the development and implementation of Internet-based systems to deliver health risk management information and health intervention applications to the public. The application of consumer health informatics to educational and interventional efforts such as smoking reduction and cessation has garnered attention from both consumers and health researchers in recent years. Scientists believe that smoking avoidance or cessation before the age of 30 years can prevent more than 90% of smoking-related cancers and that individuals who stop smoking fare as well in preventing cancer as those who never start. The goal of this study was to determine factors that were most highly correlated with content relevance for health information provided on the Internet for a study group of 18- to 30-year-old college students. Data analysis showed that the opportunity for convenient entertainment, social interaction, health information-seeking behavior, time spent surfing on the Internet, the importance of available activities on the Internet (particularly e-mail), and perceived site relevance for Internet-based sources of health information were significantly correlated with content relevance for 18- to 30-year-old college students, an educated subset of this population segment.

The Relevance Voxel Machine (RVoxM): A Self-Tuning Bayesian Model for Informative Image-Based Prediction

DEFF Research Database (Denmark)

Sabuncu, Mert R.; Van Leemput, Koen

2012-01-01

This paper presents the relevance voxel machine (RVoxM), a dedicated Bayesian model for making predictions based on medical imaging data. In contrast to the generic machine learning algorithms that have often been used for this purpose, the method is designed to utilize a small number of spatially...

Who's Watching the Babies? Improving the Quality of Family, Friend, and Neighbor Child Care

Science.gov (United States)

Powell, Douglas R.

2008-01-01

One of the important influences on a child's development is the quality of his or her early care and education experiences. It is estimated that more than 1 million children in the U.S. are cared for while their parents are at work by nonlicensed caregivers who are family, friends, or neighbors - and these caregivers can be difficult to reach…

Behavior change communication activities improve infant and young child nutrition knowledge and practice of neighboring non-participants in a cluster-randomized trial in rural Bangladesh.

Science.gov (United States)

Hoddinott, John; Ahmed, Ishita; Ahmed, Akhter; Roy, Shalini

2017-01-01

To examine the impact on infant and young child nutrition knowledge and practice of mothers who were neighbors of mothers participating in a nutrition Behavior Change Communication (BCC) intervention in rural Bangladesh. We analyzed data from 300 mothers whose neighbor participated in a nutrition BCC intervention and 600 mothers whose neighbor participated in an intervention that did not include BCC. We constructed measures capturing mothers' knowledge of infant and young child nutrition (IYCN) and measures of food consumption by children 6-24m. The effect on these outcomes of exposure to a neighbor receiving a nutrition BCC intervention was estimated using ordinary least squares and probit regressions. The study was registered with ClinicalTrials.gov (Study ID: NCT02237144). Having a neighboring mother participate in a nutrition BCC intervention increased non-participant mothers' IYCN knowledge by 0.17 SD (translating to 0.3 more correct answers). They were 14.1 percentage points more likely to feed their 6-24m children legumes and nuts; 11.6 percentage points more likely to feed these children vitamin A rich fruits and vegetables; and 10.0 percentage points more likely to feed these children eggs. Children of non-participant mothers who had a neighboring mother participate in a nutrition BCC intervention were 13.8 percentage points more likely to meet World Health Organization (WHO) guidelines for minimum diet diversity, 11.9 percentage points more likely to meet WHO guidelines for minimum acceptable diet, and 10.3 percentage points more likely to meet WHO guidelines for minimum meal frequency for children who continue to be breastfed after age 6m. Children aged 0-6m of non-participant mothers who are neighbors of mothers receiving BCC were 7.1 percentage points less likely to have ever consumed water-based liquids. Studies of nutrition BCC that do not account for information spillovers to non-participants may underestimate its benefits in terms of IYCN knowledge

Behavior change communication activities improve infant and young child nutrition knowledge and practice of neighboring non-participants in a cluster-randomized trial in rural Bangladesh.

Directory of Open Access Journals (Sweden)

John Hoddinott

Full Text Available To examine the impact on infant and young child nutrition knowledge and practice of mothers who were neighbors of mothers participating in a nutrition Behavior Change Communication (BCC intervention in rural Bangladesh.We analyzed data from 300 mothers whose neighbor participated in a nutrition BCC intervention and 600 mothers whose neighbor participated in an intervention that did not include BCC. We constructed measures capturing mothers' knowledge of infant and young child nutrition (IYCN and measures of food consumption by children 6-24m. The effect on these outcomes of exposure to a neighbor receiving a nutrition BCC intervention was estimated using ordinary least squares and probit regressions. The study was registered with ClinicalTrials.gov (Study ID: NCT02237144.Having a neighboring mother participate in a nutrition BCC intervention increased non-participant mothers' IYCN knowledge by 0.17 SD (translating to 0.3 more correct answers. They were 14.1 percentage points more likely to feed their 6-24m children legumes and nuts; 11.6 percentage points more likely to feed these children vitamin A rich fruits and vegetables; and 10.0 percentage points more likely to feed these children eggs. Children of non-participant mothers who had a neighboring mother participate in a nutrition BCC intervention were 13.8 percentage points more likely to meet World Health Organization (WHO guidelines for minimum diet diversity, 11.9 percentage points more likely to meet WHO guidelines for minimum acceptable diet, and 10.3 percentage points more likely to meet WHO guidelines for minimum meal frequency for children who continue to be breastfed after age 6m. Children aged 0-6m of non-participant mothers who are neighbors of mothers receiving BCC were 7.1 percentage points less likely to have ever consumed water-based liquids.Studies of nutrition BCC that do not account for information spillovers to non-participants may underestimate its benefits in terms of

Promoting culturally competent chronic pain management using the clinically relevant continuum model.

Science.gov (United States)

Monsivais, Diane B

2011-06-01

This article reviews the culture of biomedicine and current practices in pain management education, which often merge to create a hostile environment for effective chronic pain care. Areas of cultural tensions in chronic pain frequently involve the struggle to achieve credibility regarding one's complaints of pain (or being believed that the pain is real) and complying with pain medication protocols. The clinically relevant continuum model is presented as a framework allowing providers to approach care from an evidence-based, culturally appropriate (patient centered) perspective that takes into account the highest level of evidence available, provider expertise, and patient preferences and values. Copyright © 2011 Elsevier Inc. All rights reserved.

Ising percolation in a three-state majority vote model

Energy Technology Data Exchange (ETDEWEB)

Balankin, Alexander S., E-mail: abalankin@ipn.mx [Grupo Mecánica Fractal, ESIME, Instituto Politécnico Nacional, México D.F., 07738 (Mexico); Martínez-Cruz, M.A.; Gayosso Martínez, Felipe [Grupo Mecánica Fractal, ESIME, Instituto Politécnico Nacional, México D.F., 07738 (Mexico); Mena, Baltasar [Laboratorio de Ingeniería y Procesos Costeros, Instituto de Ingeniería, Universidad Nacional Autónoma de México, Sisal, Yucatán, 97355 (Mexico); Tobon, Atalo; Patiño-Ortiz, Julián; Patiño-Ortiz, Miguel; Samayoa, Didier [Grupo Mecánica Fractal, ESIME, Instituto Politécnico Nacional, México D.F., 07738 (Mexico)

2017-02-05

Highlights: • Three-state non-consensus majority voter model is introduced. • Phase transition in the absorbing state non-consensus is revealed. • The percolation transition belongs to the universality class of Ising percolation. • The effect of an updating rule for a tie between voter neighbors is highlighted. - Abstract: In this Letter, we introduce a three-state majority vote model in which each voter adopts a state of a majority of its active neighbors, if exist, but the voter becomes uncommitted if its active neighbors are in a tie, or all neighbors are the uncommitted. Numerical simulations were performed on square lattices of different linear size with periodic boundary conditions. Starting from a random distribution of active voters, the model leads to a stable non-consensus state in which three opinions coexist. We found that the “magnetization” of the non-consensus state and the concentration of uncommitted voters in it are governed by an initial composition of system and are independent of the lattice size. Furthermore, we found that a configuration of the stable non-consensus state undergoes a second order percolation transition at a critical concentration of voters holding the same opinion. Numerical simulations suggest that this transition belongs to the same universality class as the Ising percolation. These findings highlight the effect of an updating rule for a tie between voter neighbors on the critical behavior of models obeying the majority vote rule whenever a strict majority exists.

Ising percolation in a three-state majority vote model

International Nuclear Information System (INIS)

Balankin, Alexander S.; Martínez-Cruz, M.A.; Gayosso Martínez, Felipe; Mena, Baltasar; Tobon, Atalo; Patiño-Ortiz, Julián; Patiño-Ortiz, Miguel; Samayoa, Didier

2017-01-01

Highlights: • Three-state non-consensus majority voter model is introduced. • Phase transition in the absorbing state non-consensus is revealed. • The percolation transition belongs to the universality class of Ising percolation. • The effect of an updating rule for a tie between voter neighbors is highlighted. - Abstract: In this Letter, we introduce a three-state majority vote model in which each voter adopts a state of a majority of its active neighbors, if exist, but the voter becomes uncommitted if its active neighbors are in a tie, or all neighbors are the uncommitted. Numerical simulations were performed on square lattices of different linear size with periodic boundary conditions. Starting from a random distribution of active voters, the model leads to a stable non-consensus state in which three opinions coexist. We found that the “magnetization” of the non-consensus state and the concentration of uncommitted voters in it are governed by an initial composition of system and are independent of the lattice size. Furthermore, we found that a configuration of the stable non-consensus state undergoes a second order percolation transition at a critical concentration of voters holding the same opinion. Numerical simulations suggest that this transition belongs to the same universality class as the Ising percolation. These findings highlight the effect of an updating rule for a tie between voter neighbors on the critical behavior of models obeying the majority vote rule whenever a strict majority exists.

DETERMINATION OF RELEVANT FEATURES OF A SCALE MODEL FOR A 55 000 DWT BULK CARRIER NECESSARY TO STUDY THE SHIP MANEUVERABILITY

Directory of Open Access Journals (Sweden)

ALECU TOMA

2016-06-01

Full Text Available The study method of a ship behavior based on practical tests on scale models is widely used both leading scientists and engineers, architects and researchers in the naval field. In this paper we propose to determine the parameters of a ship handling characteristics relevant to study the 55,000 dwt bulk carrier using a scale model. Scientific background for practical experimentation of this techniques necessary to built a scale model ship consists in applying the principles of similarity or "similitude". The scale model achieved by applying the laws of similarity must allow, through approximations available in certain circumstances, finding relevant parameters needed to simplify and solve the Navier-Stokes equations. These parameters are necessary for modeling the interaction between hull of the real ship and the fluid motion.

A γ dose distribution evaluation technique using the k-d tree for nearest neighbor searching

International Nuclear Information System (INIS)

Yuan Jiankui; Chen Weimin

2010-01-01

Purpose: The authors propose an algorithm based on the k-d tree for nearest neighbor searching to improve the γ calculation time for 2D and 3D dose distributions. Methods: The γ calculation method has been widely used for comparisons of dose distributions in clinical treatment plans and quality assurances. By specifying the acceptable dose and distance-to-agreement criteria, the method provides quantitative measurement of the agreement between the reference and evaluation dose distributions. The γ value indicates the acceptability. In regions where γ≤1, the predefined criterion is satisfied and thus the agreement is acceptable; otherwise, the agreement fails. Although the concept of the method is not complicated and a quick naieve implementation is straightforward, an efficient and robust implementation is not trivial. Recent algorithms based on exhaustive searching within a maximum radius, the geometric Euclidean distance, and the table lookup method have been proposed to improve the computational time for multidimensional dose distributions. Motivated by the fact that the least searching time for finding a nearest neighbor can be an O(log N) operation with a k-d tree, where N is the total number of the dose points, the authors propose an algorithm based on the k-d tree for the γ evaluation in this work. Results: In the experiment, the authors found that the average k-d tree construction time per reference point is O(log N), while the nearest neighbor searching time per evaluation point is proportional to O(N 1/k ), where k is between 2 and 3 for two-dimensional and three-dimensional dose distributions, respectively. Conclusions: Comparing with other algorithms such as exhaustive search and sorted list O(N), the k-d tree algorithm for γ evaluation is much more efficient.

On the ""early-time"" evolution of variables relevant to turbulence models for the Rayleigh-Taylor instability

Energy Technology Data Exchange (ETDEWEB)

Rollin, Bertrand [Los Alamos National Laboratory; Andrews, Malcolm J [Los Alamos National Laboratory

2010-01-01

We present our progress toward setting initial conditions in variable density turbulence models. In particular, we concentrate our efforts on the BHR turbulence model for turbulent Rayleigh-Taylor instability. Our approach is to predict profiles of relevant variables before fully turbulent regime and use them as initial conditions for the turbulence model. We use an idealized model of mixing between two interpenetrating fluids to define the initial profiles for the turbulence model variables. Velocities and volume fractions used in the idealized mixing model are obtained respectively from a set of ordinary differential equations modeling the growth of the Rayleigh-Taylor instability and from an idealization of the density profile in the mixing layer. A comparison between predicted profiles for the turbulence model variables and profiles of the variables obtained from low Atwood number three dimensional simulations show reasonable agreement.

The value relevance of environmental emissions

Directory of Open Access Journals (Sweden)

Melinda Lydia Nelwan

2016-07-01

Full Text Available This study examines whether environmental performance has value relevance by investigating the relations between environmental emissions and stock prices for the U.S. public companies. The previous studies argued that the conjectured relations between accounting performance measures and environmental performance do not have a strong theoretical basis, and the modeling of relations between market per-formance measures and environmental performance do not adequately consider the relevance of accounting performance to market value. Therefore, this study examines whether publicly reported environmental emissions provide incremental information to accounting earnings in pricing companies stocks. It is done among the complete set of industries covered by Toxics Release Inventory (TRI reporting for the period 2007 to 2010. Using Ohlson model but modified to include different types of emis-sions, it is found that ground emissions (underground injection and land emissions are value relevant but other emission types (air and water and transferred-out emis-sions appear to not provide incremental information in the valuation model. The result in this study raise concerns that different types of emissions are assessed differently by the market, confirming that studies should not aggregate such measures.

Detection of Burkholderia pseudomallei O-antigen serotypes in near-neighbor species

Directory of Open Access Journals (Sweden)

Stone Joshua K

2012-11-01

Full Text Available Abstract Background Burkholderia pseudomallei is the etiological agent of melioidosis and a CDC category B select agent with no available effective vaccine. Previous immunizations in mice have utilized the lipopolysaccharide (LPS as a potential vaccine target because it is known as one of the most important antigenic epitopes in B. pseudomallei. Complicating this strategy are the four different B. pseudomallei LPS O-antigen types: A, B, B2, and rough. Sero-crossreactivity is common among O-antigens of Burkholderia species. Here, we identified the presence of multiple B. pseudomallei O-antigen types and sero-crossreactivity in its near-neighbor species. Results PCR screening of O-antigen biosynthesis genes, phenotypic characterization using SDS-PAGE, and immunoblot analysis showed that majority of B. mallei and B. thailandensis strains contained the typical O-antigen type A. In contrast, most of B. ubonensis and B. thailandensis-like strains expressed the atypical O-antigen types B and B2, respectively. Most B. oklahomensis strains expressed a distinct and non-seroreactive O-antigen type, except strain E0147 which expressed O-antigen type A. O-antigen type B2 was also detected in B. thailandensis 82172, B. ubonensis MSMB108, and Burkholderia sp. MSMB175. Interestingly, B. thailandensis-like MSMB43 contained a novel serotype B positive O-antigen. Conclusions This study expands the number of species which express B. pseudomallei O-antigen types. Further work is required to elucidate the full structures and how closely these are to the B. pseudomallei O-antigens, which will ultimately determine the efficacy of the near-neighbor B serotypes for vaccine development.

Polymers with nearest- and next nearest-neighbor interactions on the Husimi lattice

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Oliveira, Tiago J.

2016-04-01

The exact grand-canonical solution of a generalized interacting self-avoid walk (ISAW) model, placed on a Husimi lattice built with squares, is presented. In this model, beyond the traditional interaction {ω }1={{{e}}}{ɛ 1/{k}BT} between (nonconsecutive) monomers on nearest-neighbor (NN) sites, an additional energy {ɛ }2 is associated to next-NN (NNN) monomers. Three definitions of NNN sites/interactions are considered, where each monomer can have, effectively, at most two, four, or six NNN monomers on the Husimi lattice. The phase diagrams found in all cases have (qualitatively) the same thermodynamic properties: a non-polymerized (NP) and a polymerized (P) phase separated by a critical and a coexistence surface that meet at a tricritical (θ-) line. This θ-line is found even when one of the interactions is repulsive, existing for {ω }1 in the range [0,∞ ), i.e., for {ɛ }1/{k}BT in the range [-∞ ,∞ ). Thus, counterintuitively, a θ-point exists even for an infinite repulsion between NN monomers ({ω }1=0), being associated to a coil-‘soft globule’ transition. In the limit of an infinite repulsive force between NNN monomers, however, the coil-globule transition disappears, and only NP-P continuous transition is observed. This particular case, with {ω }2=0, is also solved exactly on the square lattice, using a transfer matrix calculation where a discontinuous NP-P transition is found. For attractive and repulsive forces between NN and NNN monomers, respectively, the model becomes quite similar to the semiflexible-ISAW one, whose crystalline phase is not observed here, as a consequence of the frustration due to competing NN and NNN forces. The mapping of the phase diagrams in canonical ones is discussed and compared with recent results from Monte Carlo simulations on the square lattice.

The big seven model of personality and its relevance to personality pathology.

Science.gov (United States)

Simms, Leonard J

2007-02-01

Proponents of the Big Seven model of personality have suggested that Positive Valence (PV) and Negative Valence (NV) are independent of the Big Five personality dimensions and may be particularly relevant to personality disorder. These hypotheses were tested with 403 undergraduates who completed a Big Seven measure and markers of the Big Five and personality pathology. Results revealed that PV and NV incrementally predicted personality pathology dimensions beyond those predicted by multiple markers of the Big Five. However, factor analyses suggested that PV and NV might be best understood as specific, maladaptive aspects of positive emotionality and low agreeableness, respectively, as opposed to independent factors of personality. Implications for the description of normal and abnormal personality are discussed.

Nuclear models relevant to evaluation

International Nuclear Information System (INIS)

Arthur, E.D.; Chadwick, M.B.; Hale, G.M.; Young, P.G.

1991-01-01

The widespread use of nuclear models continues in the creation of data evaluations. The reasons include extension of data evaluations to higher energies, creation of data libraries for isotopic components of natural materials, and production of evaluations for radiative target species. In these cases, experimental data are often sparse or nonexistent. As this trend continues, the nuclear models employed in evaluation work move towards more microscopically-based theoretical methods, prompted in part by the availability of increasingly powerful computational resources. Advances in nuclear models applicable to evaluation will be reviewed. These include advances in optical model theory, microscopic and phenomenological state and level density theory, unified models that consistently describe both equilibrium and nonequilibrium reaction mechanism, and improved methodologies for calculation of prompt radiation from fission. 84 refs., 8 figs

Impact of Training Bolivian Farmers on Integrated Pest Management and Diffusion of Knowledge to Neighboring Farmers

DEFF Research Database (Denmark)

Jørs, Erik; Konradsen, Flemming; Huici, Omar

2016-01-01

of importance to justify training costs and to promote a healthy and sustainable agriculture. Training on IPM of farmers took place from 2002 to 2004 in their villages in La Paz County, Bolivia, while dissemination of knowledge from trained farmer to neighboring farmer took place until 2009. To evaluate...

From research excellence to brand relevance: A model for higher education reputation building

Directory of Open Access Journals (Sweden)

Nina Overton-de Klerk

2016-05-01

Full Text Available In this article we propose a novel approach to reputation development at higher education institutions. Global reputation development at higher education institutions is largely driven by research excellence, is predominantly measured by research output, and is predominantly reflected in hierarchical university rankings. The ranking becomes equated with brand equity. We argue that the current approach to reputation development in higher education institutions is modernist and linear. This is strangely out-of-kilter with the complexities of a transforming society in flux, the demands of a diversity of stakeholders, and the drive towards transdisciplinarity, laterality, reflexivity and relevance in science. Good research clearly remains an important ingredient of a university's brand value. However, a case can be made for brand relevance, co-created in collaboration with stakeholders, as an alternative and non-linear way of differentiation. This approach is appropriate in light of challenges in strategic science globally as well as trends and shifts in the emerging paradigm of strategic communication. In applying strategic communication principles to current trends and issues in strategic science and the communication thereof, an alternative model for strategic reputation building at higher education institutions is developed.

Canine intrahepatic vasculature: is a functional anatomic model relevant to the dog?

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Hall, Jon L; Mannion, Paddy; Ladlow, Jane F

2015-01-01

To clarify canine intrahepatic portal and hepatic venous system anatomy using corrosion casting and advanced imaging and to devise a novel functional anatomic model of the canine liver to investigate whether this could help guide the planning and surgical procedure of partial hepatic lobectomy and interventional radiological procedures. Prospective experimental study. Adult Greyhound cadavers (n = 8). Portal and hepatic vein corrosion casts of healthy livers were assessed using computed tomography (CT). The hepatic lobes have a consistent hilar hepatic and portal vein supply with some variation in the number of intrahepatic branches. For all specimens, 3 surgically resectable areas were identified in the left lateral lobe and 2 surgically resectable areas were identified in the right medial lobe as defined by a functional anatomic model. CT of detailed acrylic casts allowed complex intrahepatic vascular relationships to be investigated and compared with previous studies. Improving understanding of the intrahepatic vascular supply facilitates interpretation of advanced images in clinical patients, the planning and performance of surgical procedures, and may facilitate interventional vascular procedures, such as intravenous embolization of portosystemic shunts. Functional division of the canine liver similar to human models is possible. The left lateral and right medial lobes can be consistently divided into surgically resectable functional areas and partial lobectomies can be performed following a functional model; further study in clinically affected animals would be required to investigate the relevance of this functional model in the dog. © Copyright 2014 by The American College of Veterinary Surgeons.

On the hyperporous non-linear elasticity model for fusion-relevant pebble beds

International Nuclear Information System (INIS)

Di Maio, P.A.; Giammusso, R.; Vella, G.

2010-01-01

Packed pebble beds are particular granular systems composed of a large amount of small particles, arranged in irregular lattices and surrounded by a gas filling interstitial spaces. Due to their heterogeneous structure, pebble beds have non-linear and strongly coupled thermal and mechanical behaviours whose constitutive models seem limited, being not suitable for fusion-relevant design-oriented applications. Within the framework of the modelling activities promoted for the lithiated ceramics and beryllium pebble beds foreseen in the Helium-Cooled Pebble Bed breeding blanket concept of DEMO, at the Department of Nuclear Engineering of the University of Palermo (DIN) a thermo-mechanical constitutive model has been set-up assuming that pebble beds can be considered as continuous, homogeneous and isotropic media. The present paper deals with the DIN non-linear elasticity constitutive model, based on the assumption that during the reversible straining of a pebble bed its effective logarithmic bulk modulus depends on the equivalent pressure according to a modified power law and its effective Poisson modulus remains constant. In these hypotheses the functional dependence of the effective tangential and secant bed deformation moduli on either the equivalent pressure or the volumetric strain have been derived in a closed analytical form. A procedure has been, then, defined to assess the model parameters for a given pebble bed from its oedometric test results and it has been applied to both polydisperse lithium orthosilicate and single size beryllium pebble beds.

Theories, models and frameworks used in capacity building interventions relevant to public health: a systematic review.

Science.gov (United States)

Bergeron, Kim; Abdi, Samiya; DeCorby, Kara; Mensah, Gloria; Rempel, Benjamin; Manson, Heather

2017-11-28

There is limited research on capacity building interventions that include theoretical foundations. The purpose of this systematic review is to identify underlying theories, models and frameworks used to support capacity building interventions relevant to public health practice. The aim is to inform and improve capacity building practices and services offered by public health organizations. Four search strategies were used: 1) electronic database searching; 2) reference lists of included papers; 3) key informant consultation; and 4) grey literature searching. Inclusion and exclusion criteria are outlined with included papers focusing on capacity building, learning plans, professional development plans in combination with tools, resources, processes, procedures, steps, model, framework, guideline, described in a public health or healthcare setting, or non-government, government, or community organizations as they relate to healthcare, and explicitly or implicitly mention a theory, model and/or framework that grounds the type of capacity building approach developed. Quality assessment were performed on all included articles. Data analysis included a process for synthesizing, analyzing and presenting descriptive summaries, categorizing theoretical foundations according to which theory, model and/or framework was used and whether or not the theory, model or framework was implied or explicitly identified. Nineteen articles were included in this review. A total of 28 theories, models and frameworks were identified. Of this number, two theories (Diffusion of Innovations and Transformational Learning), two models (Ecological and Interactive Systems Framework for Dissemination and Implementation) and one framework (Bloom's Taxonomy of Learning) were identified as the most frequently cited. This review identifies specific theories, models and frameworks to support capacity building interventions relevant to public health organizations. It provides public health practitioners

Diagnosis of diabetes diseases using an Artificial Immune Recognition System2 (AIRS2) with fuzzy K-nearest neighbor.

Science.gov (United States)

Chikh, Mohamed Amine; Saidi, Meryem; Settouti, Nesma

2012-10-01

The use of expert systems and artificial intelligence techniques in disease diagnosis has been increasing gradually. Artificial Immune Recognition System (AIRS) is one of the methods used in medical classification problems. AIRS2 is a more efficient version of the AIRS algorithm. In this paper, we used a modified AIRS2 called MAIRS2 where we replace the K- nearest neighbors algorithm with the fuzzy K-nearest neighbors to improve the diagnostic accuracy of diabetes diseases. The diabetes disease dataset used in our work is retrieved from UCI machine learning repository. The performances of the AIRS2 and MAIRS2 are evaluated regarding classification accuracy, sensitivity and specificity values. The highest classification accuracy obtained when applying the AIRS2 and MAIRS2 using 10-fold cross-validation was, respectively 82.69% and 89.10%.

Inverse analyses of effective diffusion parameters relevant for a two-phase moisture model of cementitious materials

DEFF Research Database (Denmark)

Addassi, Mouadh; Johannesson, Björn; Wadsö, Lars

2018-01-01

Here we present an inverse analyses approach to determining the two-phase moisture transport properties relevant to concrete durability modeling. The purposed moisture transport model was based on a continuum approach with two truly separate equations for the liquid and gas phase being connected...... test, and, (iv) capillary suction test. Mass change over time, as obtained from the drying test, the two different cup test intervals and the capillary suction test, was used to obtain the effective diffusion parameters using the proposed inverse analyses approach. The moisture properties obtained...

Application of all-relevant feature selection for the failure analysis of parameter-induced simulation crashes in climate models

Science.gov (United States)

Paja, Wiesław; Wrzesien, Mariusz; Niemiec, Rafał; Rudnicki, Witold R.

2016-03-01

Climate models are extremely complex pieces of software. They reflect the best knowledge on the physical components of the climate; nevertheless, they contain several parameters, which are too weakly constrained by observations, and can potentially lead to a simulation crashing. Recently a study by Lucas et al. (2013) has shown that machine learning methods can be used for predicting which combinations of parameters can lead to the simulation crashing and hence which processes described by these parameters need refined analyses. In the current study we reanalyse the data set used in this research using different methodology. We confirm the main conclusion of the original study concerning the suitability of machine learning for the prediction of crashes. We show that only three of the eight parameters indicated in the original study as relevant for prediction of the crash are indeed strongly relevant, three others are relevant but redundant and two are not relevant at all. We also show that the variance due to the split of data between training and validation sets has a large influence both on the accuracy of predictions and on the relative importance of variables; hence only a cross-validated approach can deliver a robust prediction of performance and relevance of variables.

Ising percolation in a three-state majority vote model

Science.gov (United States)

Balankin, Alexander S.; Martínez-Cruz, M. A.; Gayosso Martínez, Felipe; Mena, Baltasar; Tobon, Atalo; Patiño-Ortiz, Julián; Patiño-Ortiz, Miguel; Samayoa, Didier

2017-02-01

In this Letter, we introduce a three-state majority vote model in which each voter adopts a state of a majority of its active neighbors, if exist, but the voter becomes uncommitted if its active neighbors are in a tie, or all neighbors are the uncommitted. Numerical simulations were performed on square lattices of different linear size with periodic boundary conditions. Starting from a random distribution of active voters, the model leads to a stable non-consensus state in which three opinions coexist. We found that the "magnetization" of the non-consensus state and the concentration of uncommitted voters in it are governed by an initial composition of system and are independent of the lattice size. Furthermore, we found that a configuration of the stable non-consensus state undergoes a second order percolation transition at a critical concentration of voters holding the same opinion. Numerical simulations suggest that this transition belongs to the same universality class as the Ising percolation. These findings highlight the effect of an updating rule for a tie between voter neighbors on the critical behavior of models obeying the majority vote rule whenever a strict majority exists.

Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials

International Nuclear Information System (INIS)

Thompson, A.P.; Swiler, L.P.; Trott, C.R.; Foiles, S.M.; Tucker, G.J.

2015-01-01

We present a new interatomic potential for solids and liquids called Spectral Neighbor Analysis Potential (SNAP). The SNAP potential has a very general form and uses machine-learning techniques to reproduce the energies, forces, and stress tensors of a large set of small configurations of atoms, which are obtained using high-accuracy quantum electronic structure (QM) calculations. The local environment of each atom is characterized by a set of bispectrum components of the local neighbor density projected onto a basis of hyperspherical harmonics in four dimensions. The bispectrum components are the same bond-orientational order parameters employed by the GAP potential [1]. The SNAP potential, unlike GAP, assumes a linear relationship between atom energy and bispectrum components. The linear SNAP coefficients are determined using weighted least-squares linear regression against the full QM training set. This allows the SNAP potential to be fit in a robust, automated manner to large QM data sets using many bispectrum components. The calculation of the bispectrum components and the SNAP potential are implemented in the LAMMPS parallel molecular dynamics code. We demonstrate that a previously unnoticed symmetry property can be exploited to reduce the computational cost of the force calculations by more than one order of magnitude. We present results for a SNAP potential for tantalum, showing that it accurately reproduces a range of commonly calculated properties of both the crystalline solid and the liquid phases. In addition, unlike simpler existing potentials, SNAP correctly predicts the energy barrier for screw dislocation migration in BCC tantalum

Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials

Energy Technology Data Exchange (ETDEWEB)

Thompson, Aidan P. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States). Multiscale Science Dept.; Swiler, Laura P. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States). Optimization and Uncertainty Quantification Dept.; Trott, Christian R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Scalable Algorithms Dept.; Foiles, Stephen M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Computational Materials and Data Science Dept.; Tucker, Garritt J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Computational Materials and Data Science Dept.; Drexel Univ., Philadelphia, PA (United States). Dept. of Materials Science and Engineering

2015-03-15

Here, we present a new interatomic potential for solids and liquids called Spectral Neighbor Analysis Potential (SNAP). The SNAP potential has a very general form and uses machine-learning techniques to reproduce the energies, forces, and stress tensors of a large set of small configurations of atoms, which are obtained using high-accuracy quantum electronic structure (QM) calculations. The local environment of each atom is characterized by a set of bispectrum components of the local neighbor density projected onto a basis of hyperspherical harmonics in four dimensions. The bispectrum components are the same bond-orientational order parameters employed by the GAP potential [1]. The SNAP potential, unlike GAP, assumes a linear relationship between atom energy and bispectrum components. The linear SNAP coefficients are determined using weighted least-squares linear regression against the full QM training set. This allows the SNAP potential to be fit in a robust, automated manner to large QM data sets using many bispectrum components. The calculation of the bispectrum components and the SNAP potential are implemented in the LAMMPS parallel molecular dynamics code. We demonstrate that a previously unnoticed symmetry property can be exploited to reduce the computational cost of the force calculations by more than one order of magnitude. We present results for a SNAP potential for tantalum, showing that it accurately reproduces a range of commonly calculated properties of both the crystalline solid and the liquid phases. In addition, unlike simpler existing potentials, SNAP correctly predicts the energy barrier for screw dislocation migration in BCC tantalum.

Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials

Energy Technology Data Exchange (ETDEWEB)

Thompson, A.P., E-mail: athomps@sandia.gov [Multiscale Science Department, Sandia National Laboratories, PO Box 5800, MS 1322, Albuquerque, NM 87185 (United States); Swiler, L.P., E-mail: lpswile@sandia.gov [Optimization and Uncertainty Quantification Department, Sandia National Laboratories, PO Box 5800, MS 1318, Albuquerque, NM 87185 (United States); Trott, C.R., E-mail: crtrott@sandia.gov [Scalable Algorithms Department, Sandia National Laboratories, PO Box 5800, MS 1322, Albuquerque, NM 87185 (United States); Foiles, S.M., E-mail: foiles@sandia.gov [Computational Materials and Data Science Department, Sandia National Laboratories, PO Box 5800, MS 1411, Albuquerque, NM 87185 (United States); Tucker, G.J., E-mail: gtucker@coe.drexel.edu [Computational Materials and Data Science Department, Sandia National Laboratories, PO Box 5800, MS 1411, Albuquerque, NM 87185 (United States); Department of Materials Science and Engineering, Drexel University, Philadelphia, PA 19104 (United States)

2015-03-15

We present a new interatomic potential for solids and liquids called Spectral Neighbor Analysis Potential (SNAP). The SNAP potential has a very general form and uses machine-learning techniques to reproduce the energies, forces, and stress tensors of a large set of small configurations of atoms, which are obtained using high-accuracy quantum electronic structure (QM) calculations. The local environment of each atom is characterized by a set of bispectrum components of the local neighbor density projected onto a basis of hyperspherical harmonics in four dimensions. The bispectrum components are the same bond-orientational order parameters employed by the GAP potential [1]. The SNAP potential, unlike GAP, assumes a linear relationship between atom energy and bispectrum components. The linear SNAP coefficients are determined using weighted least-squares linear regression against the full QM training set. This allows the SNAP potential to be fit in a robust, automated manner to large QM data sets using many bispectrum components. The calculation of the bispectrum components and the SNAP potential are implemented in the LAMMPS parallel molecular dynamics code. We demonstrate that a previously unnoticed symmetry property can be exploited to reduce the computational cost of the force calculations by more than one order of magnitude. We present results for a SNAP potential for tantalum, showing that it accurately reproduces a range of commonly calculated properties of both the crystalline solid and the liquid phases. In addition, unlike simpler existing potentials, SNAP correctly predicts the energy barrier for screw dislocation migration in BCC tantalum.

Humanitarian Cleft Lip/Palate Surgeries in Buddhist Thailand and Neighboring Countries.

Science.gov (United States)

Uemura, Tetsuji; Preeyanont, Piyoros; Udnoon, Sopridee

2015-06-01

This study evaluates surgeries done on patients with cleft lip and/or palate in Thailand and its neighboring countries from 1988 to 2008. This 21-year-long volunteer surgical mission was sponsored by Duang-Kaew Foundation, a volunteer organization. Countries involved, besides Thailand, were Vietnam, Myanmar, Laos, Cambodia, China, Sri Lanka, Bhutan, and India. The same surgical method for primary and secondary repair of lip and/or palate was used throughout: Onizuka method by single surgeon, the second author mainly. We assessed, by way of the patients' medical records including their background, the results of surgeries. The healing rates and complication rates associated with patients for primary and secondary repair of lip and/or palate. The study consisted of a total of 6832 patients: 3120 with cleft lip (CL); 2190 with cleft palate (CP); and 1522 with cleft lip and palate (CLP). Their primary cases were 675 (CL), 799 (CP), and 301 (CLP). All CP operations were done under general anesthesia. Of the CL surgeries, 10% of adult cases were done under local anesthesia. Of all the patients, 78%, or 5329, had one surgery; and 22%, or 1503, had 2 or more surgeries. Good healing was seen in 73.3%, whereas wound infection was noted in 2.0% and healing by second intention was in 1.2% of all cases. It is important that the Onizuka method was the only method used in all the countries throughout the mission period. The method has an advantage over other methods in that its design is simple enough so that even a beginning plastic surgeon can easily master, and operative results are constantly good regardless of who did the operation. The Duang-Kaew Foundation's long-term surgical program helped reduce the number of untreated patients to manageable levels for local health care providers in Thailand and neighboring countries for as long as 21 years.

Tularemia, a re-emerging infectious disease in Iran and neighboring countrie

Science.gov (United States)

Zargar, Afsaneh; Maurin, Max; Mostafavi, Ehsan

2015-01-01

OBJECTIVES: Tularemia is a zoonotic disease transmitted by direct contact with infected animals and through arthropod bites, inhalation of contaminated aerosols, ingestion of contaminated meat or water, and skin contact with any infected material. It is widespread throughout the northern hemisphere, including Iran and its neighbors to the north, northeast, and northwest. METHODS: In this paper, the epidemiology of tularemia as a re-emerging infectious disease in the world with a focus on Iran and the neighboring countries is reviewed. RESULTS: In Iran, positive serological tests were first reported in 1973, in wildlife and domestic livestock in the northwestern and southeastern parts of the country. The first human case was reported in 1980 in the southwest of Iran, and recent studies conducted among at-risk populations in the western, southeastern, and southwestern parts of Iran revealed seroprevalences of 14.4, 6.52, and 6%, respectively. CONCLUSIONS: Several factors may explain the absence of reported tularemia cases in Iran since 1980. Tularemia may be underdiagnosed in Iran because Francisella tularensis subspecies holarctica is likely to be the major etiological agent and usually causes mild to moderately severe disease. Furthermore, tularemia is not a disease extensively studied in the medical educational system in Iran, and empirical therapy may be effective in many cases. Finally, it should be noted that laboratories capable of diagnosing tularemia have only been established in the last few years. Since both recent and older studies have consistently found tularemia antibodies in humans and animals, the surveillance of this disease should receive more attention. In particular, it would be worthwhile for clinical researchers to confirm tularemia cases more often by isolating F. tularensis from infected humans and animals. PMID:25773439

Characterization and spatial modeling of urban sprawl in the Wuhan Metropolitan Area, China

Science.gov (United States)

Zeng, Chen; Liu, Yaolin; Stein, Alfred; Jiao, Limin

2015-02-01

Urban sprawl has led to environmental problems and large losses of arable land in China. In this study, we monitor and model urban sprawl by means of a combination of remote sensing, geographical information system and spatial statistics. We use time-series data to explore the potential socio-economic driving forces behind urban sprawl, and spatial models in different scenarios to explore the spatio-temporal interactions. The methodology is applied to the city of Wuhan, China, for the period from 1990 to 2013. The results reveal that the built-up land has expanded and has dispersed in urban clusters. Population growth, and economic and transportation development are still the main causes of urban sprawl; however, when they have developed to certain levels, the area affected by construction in urban areas (Jian Cheng Qu (JCQ)) and the area of cultivated land (ACL) tend to be stable. Spatial regression models are shown to be superior to the traditional models. The interaction among districts with the same administrative status is stronger than if one of those neighbors is in the city center and the other in the suburban area. The expansion of urban built-up land is driven by the socio-economic development at the same period, and greatly influenced by its spatio-temporal neighbors. We conclude that the integration of remote sensing, a geographical information system, and spatial statistics offers an excellent opportunity to explore the spatio-temporal variation and interactions among the districts in the sprawling metropolitan areas. Relevant regulations to control the urban sprawl process are suggested accordingly.

Lab-on-a-brane: A novel physiologically relevant planar arterial model to study transendothelial transport

Science.gov (United States)

Budhwani, Karim Ismail

The tremendous quality of life impact notwithstanding, cardiovascular diseases and Cancer add up to over US$ 700bn each year in financial costs alone. Aging and population growth are expected to further expand the problem space while drug research and development remain expensive. However, preclinical costs can be substantially mitigated by substituting animal models with in vitro devices that accurately model human cardiovascular transport. Here we present a novel physiologically relevant lab-on-a-brane that simulates in vivo pressure, flow, strain, and shear waveforms associated with normal and pathological conditions in large and small blood vessels for studying molecular transport across the endothelial monolayer. The device builds upon previously demonstrated integrated microfluidic loop design by: (a) introducing nanoscale pores in the substrate membrane to enable transmembrane molecular transport, (b) transforming the substrate membrane into a nanofibrous matrix for 3D smooth muscle cell (SMC) tissue culture, (c) integrating electrospinning fabrication methods, (d) engineering an invertible sandwich cell culture device architecture, and (e) devising a healthy co-culture mechanism for human arterial endothelial cell (HAEC) monolayer and multiple layers of human smooth muscle cells (HSMC) to accurately mimic arterial anatomy. Structural and mechanical characterization was conducted using confocal microscopy, SEM, stress/strain analysis, and infrared spectroscopy. Transport was characterized using FITC-Dextran hydraulic permeability protocol. Structure and transport characterization successfully demonstrate device viability as a physiologically relevant arterial mimic for testing transendothelial transport. Thus, our lab-on-a-brane provides a highly effective and efficient, yet considerably inexpensive, physiologically relevant alternative for pharmacokinetic evaluation; possibly reducing animals used in pre-clinical testing, clinical trials cost from false

Systematic Assessment of Neutron and Gamma Backgrounds Relevant to Operational Modeling and Detection Technology Implementation

Energy Technology Data Exchange (ETDEWEB)

Archer, Daniel E. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Hornback, Donald Eric [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Johnson, Jeffrey O. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Nicholson, Andrew D. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Patton, Bruce W. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Peplow, Douglas E. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Miller, Thomas Martin [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Ayaz-Maierhafer, Birsen [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2015-01-01

This report summarizes the findings of a two year effort to systematically assess neutron and gamma backgrounds relevant to operational modeling and detection technology implementation. The first year effort focused on reviewing the origins of background sources and their impact on measured rates in operational scenarios of interest. The second year has focused on the assessment of detector and algorithm performance as they pertain to operational requirements against the various background sources and background levels.

Role of calibration, validation, and relevance in multi-level uncertainty integration

International Nuclear Information System (INIS)

Li, Chenzhao; Mahadevan, Sankaran

2016-01-01

Calibration of model parameters is an essential step in predicting the response of a complicated system, but the lack of data at the system level makes it impossible to conduct this quantification directly. In such a situation, system model parameters are estimated using tests at lower levels of complexity which share the same model parameters with the system. For such a multi-level problem, this paper proposes a methodology to quantify the uncertainty in the system level prediction by integrating calibration, validation and sensitivity analysis at different levels. The proposed approach considers the validity of the models used for parameter estimation at lower levels, as well as the relevance at the lower level to the prediction at the system level. The model validity is evaluated using a model reliability metric, and models with multivariate output are considered. The relevance is quantified by comparing Sobol indices at the lower level and system level, thus measuring the extent to which a lower level test represents the characteristics of the system so that the calibration results can be reliably used in the system level. Finally the results of calibration, validation and relevance analysis are integrated in a roll-up method to predict the system output. - Highlights: • Relevance analysis to quantify the closeness of two models. • Stochastic model reliability metric to integrate multiple validation experiments. • Extend the model reliability metric to deal with multivariate output. • Roll-up formula to integrate calibration, validation, and relevance.

The Limits to Relevance

Science.gov (United States)

Averill, M.; Briggle, A.

2006-12-01

Science policy and knowledge production lately have taken a pragmatic turn. Funding agencies increasingly are requiring scientists to explain the relevance of their work to society. This stems in part from mounting critiques of the "linear model" of knowledge production in which scientists operating according to their own interests or disciplinary standards are presumed to automatically produce knowledge that is of relevance outside of their narrow communities. Many contend that funded scientific research should be linked more directly to societal goals, which implies a shift in the kind of research that will be funded. While both authors support the concept of useful science, we question the exact meaning of "relevance" and the wisdom of allowing it to control research agendas. We hope to contribute to the conversation by thinking more critically about the meaning and limits of the term "relevance" and the trade-offs implicit in a narrow utilitarian approach. The paper will consider which interests tend to be privileged by an emphasis on relevance and address issues such as whose goals ought to be pursued and why, and who gets to decide. We will consider how relevance, narrowly construed, may actually limit the ultimate utility of scientific research. The paper also will reflect on the worthiness of research goals themselves and their relationship to a broader view of what it means to be human and to live in society. Just as there is more to being human than the pragmatic demands of daily life, there is more at issue with knowledge production than finding the most efficient ways to satisfy consumer preferences or fix near-term policy problems. We will conclude by calling for a balanced approach to funding research that addresses society's most pressing needs but also supports innovative research with less immediately apparent application.

Investment Incentives and Effective Tax Rates in the Philippines; A Comparison With Neighboring Countries

OpenAIRE

Alexander D Klemm; Dennis P Botman; Reza Baqir

2008-01-01

We compare the general tax provisions and investment incentives in the Philippines to six other east-Asian economies-Malaysia, Indonesia, Lao, Vietnam, Cambodia, and Thailand. We calculate effective tax rates and find that general effective tax rates are relatively high in the Philippines, while investment incentives are comparable to those in neighboring countries. Tax holidays are most attractive for very profitable firms, creating redundancy, and for investment in short-lived assets. We al...

Specific Protein Markers for Stem Cell Cross-Talk with Neighboring Cells in the Environment

OpenAIRE

Park, Kyung Soo; Shin, Seung Won; Choi, Jeong-Woo; Um, Soong Ho

2013-01-01

A stem cell interacts with the neighboring cells in its environment. To maintain a living organism’s metabolism, either cell-cell or cell-environment interactions may be significant. Usually, these cells communicate with each other through biological signaling by interactive behaviors of primary proteins or complementary chemicals. The signaling intermediates offer the stem cell’s functionality on its metabolism. With the rapid advent of omics technologies, various specific markers by which s...

Neighbor-directed histidine N(τ) alkylation. A route to imidazolium-containing phosphopeptide macrocycles

Energy Technology Data Exchange (ETDEWEB)

Qian, Wen-Jian [National Cancer Inst., Frederick, MD (United States); Park, Jung-Eun [National Cancer Inst., Bethesda, MD (United States); Grant, Robert [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Lai, Christopher C. [National Cancer Inst., Frederick, MD (United States); Kelley, James A. [National Cancer Inst., Frederick, MD (United States); Yaffe, Michael B. [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Lee, Kyung S. [National Cancer Inst., Bethesda, MD (United States); Burke, Terrence R. [National Cancer Inst., Frederick, MD (United States)

2015-07-07

Our recently discovered, selective, on-resin route to N(τ)-alkylated imidazolium-containing histidine residues affords new strategies for peptide mimetic design. In this, we demonstrate the use of this chemistry to prepare a series of macrocyclic phosphopeptides, in which imidazolium groups serve as ring-forming junctions. These cationic moieties subsequently serve to charge-mask the phosphoamino acid group that directed their formation. Furthermore, neighbor-directed histidine N(τ)-alkylation opens the door to new families of phosphopeptidomimetics for use in a range of chemical biology contexts.

A Tissue Relevance and Meshing Method for Computing Patient-Specific Anatomical Models in Endoscopic Sinus Surgery Simulation

Science.gov (United States)

Audette, M. A.; Hertel, I.; Burgert, O.; Strauss, G.

This paper presents on-going work on a method for determining which subvolumes of a patient-specific tissue map, extracted from CT data of the head, are relevant to simulating endoscopic sinus surgery of that individual, and for decomposing these relevant tissues into triangles and tetrahedra whose mesh size is well controlled. The overall goal is to limit the complexity of the real-time biomechanical interaction while ensuring the clinical relevance of the simulation. Relevant tissues are determined as the union of the pathology present in the patient, of critical tissues deemed to be near the intended surgical path or pathology, and of bone and soft tissue near the intended path, pathology or critical tissues. The processing of tissues, prior to meshing, is based on the Fast Marching method applied under various guises, in a conditional manner that is related to tissue classes. The meshing is based on an adaptation of a meshing method of ours, which combines the Marching Tetrahedra method and the discrete Simplex mesh surface model to produce a topologically faithful surface mesh with well controlled edge and face size as a first stage, and Almost-regular Tetrahedralization of the same prescribed mesh size as a last stage.

False-nearest-neighbors algorithm and noise-corrupted time series

International Nuclear Information System (INIS)

Rhodes, C.; Morari, M.

1997-01-01

The false-nearest-neighbors (FNN) algorithm was originally developed to determine the embedding dimension for autonomous time series. For noise-free computer-generated time series, the algorithm does a good job in predicting the embedding dimension. However, the problem of predicting the embedding dimension when the time-series data are corrupted by noise was not fully examined in the original studies of the FNN algorithm. Here it is shown that with large data sets, even small amounts of noise can lead to incorrect prediction of the embedding dimension. Surprisingly, as the length of the time series analyzed by FNN grows larger, the cause of incorrect prediction becomes more pronounced. An analysis of the effect of noise on the FNN algorithm and a solution for dealing with the effects of noise are given here. Some results on the theoretically correct choice of the FNN threshold are also presented. copyright 1997 The American Physical Society

Nearest Neighbor Estimates of Entropy for Multivariate Circular Distributions

Directory of Open Access Journals (Sweden)

Neeraj Misra

2010-05-01

Full Text Available In molecular sciences, the estimation of entropies of molecules is important for the understanding of many chemical and biological processes. Motivated by these applications, we consider the problem of estimating the entropies of circular random vectors and introduce non-parametric estimators based on circular distances between n sample points and their k th nearest neighbors (NN, where k (≤ n – 1 is a fixed positive integer. The proposed NN estimators are based on two different circular distances, and are proven to be asymptotically unbiased and consistent. The performance of one of the circular-distance estimators is investigated and compared with that of the already established Euclidean-distance NN estimator using Monte Carlo samples from an analytic distribution of six circular variables of an exactly known entropy and a large sample of seven internal-rotation angles in the molecule of tartaric acid, obtained by a realistic molecular-dynamics simulation.

A Distributed Approach to Continuous Monitoring of Constrained k-Nearest Neighbor Queries in Road Networks

Directory of Open Access Journals (Sweden)

Hyung-Ju Cho

2012-01-01

Full Text Available Given two positive parameters k and r, a constrained k-nearest neighbor (CkNN query returns the k closest objects within a network distance r of the query location in road networks. In terms of the scalability of monitoring these CkNN queries, existing solutions based on central processing at a server suffer from a sudden and sharp rise in server load as well as messaging cost as the number of queries increases. In this paper, we propose a distributed and scalable scheme called DAEMON for the continuous monitoring of CkNN queries in road networks. Our query processing is distributed among clients (query objects and server. Specifically, the server evaluates CkNN queries issued at intersections of road segments, retrieves the objects on the road segments between neighboring intersections, and sends responses to the query objects. Finally, each client makes its own query result using this server response. As a result, our distributed scheme achieves close-to-optimal communication costs and scales well to large numbers of monitoring queries. Exhaustive experimental results demonstrate that our scheme substantially outperforms its competitor in terms of query processing time and messaging cost.

Do family order and neighbor intervention against intimate partner violence protect children from abuse? Findings from Kathmandu.

Science.gov (United States)

Emery, Clifton R; Thapa, Sirjana; Do, Mi Hyang; Chan, Ko Ling

2015-03-01

Drawing on previous research on intimate partner violence, child maltreatment, and informal social control, we hypothesized relationships between child abuse severity and (1) protective informal social control of intimate partner violence (ISC_IPV) by neighbors, (2) intimate terrorism, (3) family order, and (4) the power of mothers in intimate relationships. In what we believe may be a first study of physical child abuse by parents in Nepal, we used a three stage cluster approach to draw a random sample of 300 families in Kathmandu. Random effects regression models were used to test the study hypotheses. The analyses found support for hypotheses one and two, but with an important caveat. Although observed (actual) protective ISC_IPV had the hypothesized negative association with child abuse severity, in one of our models perceived protective ISC_IPV was positively associated with child abuse severity. The models clarify that the overall direction of protective ISC_IPV appears to be negative (protective), but the positive finding is important to consider for both research and practice. A significant relationship between family order and child abuse severity was found, but the direction was negative rather than positive as in hypothesis three. Implications for neighborhood research and typological research on IPV and child maltreatment are discussed. Copyright © 2014 Elsevier Ltd. All rights reserved.

The indirect effects of cheatgrass invasion: Grasshopper herbivory on native grasses determined by neighboring cheatgrass abundance

Science.gov (United States)

Julie Beckstead; Susan E. Meyer; Carol K. Augsperger

2008-01-01

Invasion biology has focused on the direct effects of plant invasion and has generally overlooked indirect interactions. Here we link theories of invasion biology and herbivory to explore an indirect effect of one invading species on associational herbivory (the effect of neighboring plants on herbivory) of native species. We studied a Great Basin shadscale (...

Towards and Effective Financial Management: Relevance of Dividend Discount Model in Stock Price Valuation

Directory of Open Access Journals (Sweden)

Ana Mugoša

2015-06-01

Full Text Available The aim of this paper is to analyze the relevance of dividend discount model, i.e. its specific form in stock price estimation known as Gordon growth model. The expected dividends can be a measure of cash flows returned to the stockholder. In this context, the model is useful for assessment of how risk factors, such as interest rates and changing inflation rates, affect stock returns. This is especially important in case when investors are value oriented, i.e. when expected dividends are theirmain investing drivers. We compared the estimated with the actual stock price values and tested the statistical significance of price differences in 199 publicly traded European companies for the period2010-2013. Statistical difference between pairs of price series (actual and estimated was tested using Wilcoxon and Kruskal-Wallis tests of median and distribution equality. The hypothesis that Gordon growth model cannot be reliable measure of stock price valuation on European equity market over period of 2010-2013 due to influence of the global financial crisis was rejected with 95% confidence. Gordon growth model has proven to be reliable measure of stock price valuation even over period of strong global financial crisis influence.

Sequential nearest-neighbor effects on computed {sup 13}C{sup {alpha}} chemical shifts

Energy Technology Data Exchange (ETDEWEB)

Vila, Jorge A. [Cornell University, Baker Laboratory of Chemistry and Chemical Biology (United States); Serrano, Pedro; Wuethrich, Kurt [The Scripps Research Institute, Department of Molecular Biology (United States); Scheraga, Harold A., E-mail: has5@cornell.ed [Cornell University, Baker Laboratory of Chemistry and Chemical Biology (United States)

2010-09-15

To evaluate sequential nearest-neighbor effects on quantum-chemical calculations of {sup 13}C{sup {alpha}} chemical shifts, we selected the structure of the nucleic acid binding (NAB) protein from the SARS coronavirus determined by NMR in solution (PDB id 2K87). NAB is a 116-residue {alpha}/{beta} protein, which contains 9 prolines and has 50% of its residues located in loops and turns. Overall, the results presented here show that sizeable nearest-neighbor effects are seen only for residues preceding proline, where Pro introduces an overestimation, on average, of 1.73 ppm in the computed {sup 13}C{sup {alpha}} chemical shifts. A new ensemble of 20 conformers representing the NMR structure of the NAB, which was calculated with an input containing backbone torsion angle constraints derived from the theoretical {sup 13}C{sup {alpha}} chemical shifts as supplementary data to the NOE distance constraints, exhibits very similar topology and comparable agreement with the NOE constraints as the published NMR structure. However, the two structures differ in the patterns of differences between observed and computed {sup 13}C{sup {alpha}} chemical shifts, {Delta}{sub ca,i}, for the individual residues along the sequence. This indicates that the {Delta}{sub ca,i} -values for the NAB protein are primarily a consequence of the limited sampling by the bundles of 20 conformers used, as in common practice, to represent the two NMR structures, rather than of local flaws in the structures.

More Realistic Face Model Surface Improves Relevance of Pediatric In-Vitro Aerosol Studies.

Science.gov (United States)

Amirav, Israel; Halamish, Asaf; Gorenberg, Miguel; Omar, Hamza; Newhouse, Michael T

2015-01-01

Various hard face models are commonly used to evaluate the efficiency of aerosol face masks. Softer more realistic "face" surface materials, like skin, deform upon mask application and should provide more relevant in-vitro tests. Studies that simultaneously take into consideration many of the factors characteristic of the in vivo face are lacking. These include airways, various application forces, comparison of various devices, comparison with a hard-surface model and use of a more representative model face based on large numbers of actual faces. To compare mask to "face" seal and aerosol delivery of two pediatric masks using a soft vs. a hard, appropriately representative, pediatric face model under various applied forces. Two identical face models and upper airways replicas were constructed, the only difference being the suppleness and compressibility of the surface layer of the "face." Integrity of the seal and aerosol delivery of two different masks [AeroChamber (AC) and SootherMask (SM)] were compared using a breath simulator, filter collection and realistic applied forces. The soft "face" significantly increased the delivery efficiency and the sealing characteristics of both masks. Aerosol delivery with the soft "face" was significantly greater for the SM compared to the AC (pmasks was observed with the hard "face." The material and pliability of the model "face" surface has a significant influence on both the seal and delivery efficiency of face masks. This finding should be taken into account during in-vitro aerosol studies.

FCNN-MR: A Parallel Instance Selection Method Based on Fast Condensed Nearest Neighbor Rule

OpenAIRE

Lu Si; Jie Yu; Shasha Li; Jun Ma; Lei Luo; Qingbo Wu; Yongqi Ma; Zhengji Liu

2017-01-01

Instance selection (IS) technique is used to reduce the data size to improve the performance of data mining methods. Recently, to process very large data set, several proposed methods divide the training set into some disjoint subsets and apply IS algorithms independently to each subset. In this paper, we analyze the limitation of these methods and give our viewpoint about how to divide and conquer in IS procedure. Then, based on fast condensed nearest neighbor (FCNN) rul...

The Impactof the Kurdish Question on Turkey's Relations with its Middle Eastern neighbors

OpenAIRE

Asil, Muhammet Ali

2013-01-01

Tezin basılısı İstanbul Şehir Üniversitesi Kütüphanesi'ndedir. This dissertation analyzes the “Kurdish Question” from an International Relations perspective. Focusing on the impact of the Kurdish question on Turkey’s relations in the last decade with its Middle Eastern neighbors, i.e. Iran, Syria, and Iraq, and with the European Union; this study shows how Turkey-Middle East and Turkey-EU relations are shaped differently. In the search for reasons for this difference, Realist and Liberal I...

Radionuclide content of an exhumed canyon vessel and neighboring soil

International Nuclear Information System (INIS)

Holcomb, H.P.

1976-11-01

The long-term hazard potential associated with burial of process equipment from radiochemical separations plants is being evaluated. As part of this evaluation, a feed adjustment tank was exhumed eighteen years after burial. The tank had been in service in the fuel reprocessing plant for twenty-nine months before it was retired. Assay of the exhumed tank indicated that 7 mg (0.4 mCi) of 239 Pu and 1 mCi of 137 Cs remained on its surfaces; 1.1 mg (0.07 mCi) 239 Pu, 0.4 mCi 137 Cs, and 3.5 mCi 90 Sr were found in neighboring soil. The vessel and surrounding soil have met the present guidelines (less than or equal to 10 nCi/g) of the U. S. Energy Research and Development Administration (ERDA) for nonretrievable waste

A Local Weighted Nearest Neighbor Algorithm and a Weighted and Constrained Least-Squared Method for Mixed Odor Analysis by Electronic Nose Systems

Directory of Open Access Journals (Sweden)

Jyuo-Min Shyu

2010-11-01

Full Text Available A great deal of work has been done to develop techniques for odor analysis by electronic nose systems. These analyses mostly focus on identifying a particular odor by comparing with a known odor dataset. However, in many situations, it would be more practical if each individual odorant could be determined directly. This paper proposes two methods for such odor components analysis for electronic nose systems. First, a K-nearest neighbor (KNN-based local weighted nearest neighbor (LWNN algorithm is proposed to determine the components of an odor. According to the component analysis, the odor training data is firstly categorized into several groups, each of which is represented by its centroid. The examined odor is then classified as the class of the nearest centroid. The distance between the examined odor and the centroid is calculated based on a weighting scheme, which captures the local structure of each predefined group. To further determine the concentration of each component, odor models are built by regressions. Then, a weighted and constrained least-squares (WCLS method is proposed to estimate the component concentrations. Experiments were carried out to assess the effectiveness of the proposed methods. The LWNN algorithm is able to classify mixed odors with different mixing ratios, while the WCLS method can provide good estimates on component concentrations.

Neighboring phosphoSer-Pro motifs in the undefined domain of IRAK1 impart bivalent advantage for Pin1 binding.

Science.gov (United States)

Rogals, Monique J; Greenwood, Alexander I; Kwon, Jeahoo; Lu, Kun Ping; Nicholson, Linda K

2016-12-01

The peptidyl prolyl isomerase Pin1 has two domains that are considered to be its binding (WW) and catalytic (PPIase) domains, both of which interact with phosphorylated Ser/Thr-Pro motifs. This shared specificity might influence substrate selection, as many known Pin1 substrates have multiple sequentially close phosphoSer/Thr-Pro motifs, including the protein interleukin-1 receptor-associated kinase-1 (IRAK1). The IRAK1 undefined domain (UD) contains two sets of such neighboring motifs (Ser131/Ser144 and Ser163/Ser173), suggesting possible bivalent interactions with Pin1. Using a series of NMR titrations with 15N-labeled full-length Pin1 (Pin1-FL), PPIase, or WW domain and phosphopeptides representing the Ser131/Ser144 and Ser163/Ser173 regions of IRAK1-UD, bivalent interactions were investigated. Binding studies using singly phosphorylated peptides showed that individual motifs displayed weak affinities (> 100 μm) for Pin1-FL and each isolated domain. Analysis of dually phosphorylated peptides binding to Pin1-FL showed that inclusion of bivalent states was necessary to fit the data. The resulting complex model and fitted parameters were applied to predict the impact of bivalent states at low micromolar concentrations, demonstrating significant affinity enhancement for both dually phosphorylated peptides (3.5 and 24 μm for peptides based on the Ser131/Ser144 and Ser163/Ser173 regions, respectively). The complementary technique biolayer interferometry confirmed the predicted affinity enhancement for a representative set of singly and dually phosphorylated Ser131/Ser144 peptides at low micromolar concentrations, validating model predictions. These studies provide novel insights regarding the complexity of interactions between Pin1 and activated IRAK1, and more broadly suggest that phosphorylation of neighboring Ser/Thr-Pro motifs in proteins might provide competitive advantage at cellular concentrations for engaging with Pin1. © 2016 Federation of European

Health behavior change models and their socio-cultural relevance for breast cancer screening in African American women.

Science.gov (United States)

Ashing-Giwa, K

1999-01-01

Models of health behavior provide the conceptual bases for most of the breast cancer screening intervention studies. These models were not designed for and have not been adequately tested with African American women. The models discussed in this paper are: The Health Belief Model, the Theory of Reasoned Action/Theory of Planned Behavior, and the Transtheoretical Model. This paper will examine the socio-cultural relevance of these health behavior models, and discuss specific socio-cultural dimensions that are not accounted for by these paradigms. It is critical that researchers include socio-cultural dimensions, such as interconnectedness, health socialization, ecological factors and health care system factors into their intervention models with African American women. Comprehensive and socio-culturally based investigations are necessary to guide the scientific and policy challenge for reducing breast cancer mortality in African American women.

Wildflower Plantings Do Not Compete With Neighboring Almond Orchards for Pollinator Visits.

Science.gov (United States)

Lundin, Ola; Ward, Kimiora L; Artz, Derek R; Boyle, Natalie K; Pitts-Singer, Theresa L; Williams, Neal M

2017-06-01

The engineering of flowering agricultural field borders has emerged as a research and policy priority to mitigate threats to pollinators. Studies have, however, rarely addressed the potential that flowering field borders might compete with neighboring crops for pollinator visits if they both are in bloom at the same time, despite this being a concern expressed by growers. We evaluated how wildflower plantings added to orchard borders in a large (512 ha) commercial almond orchard affected honey bee and wild bee visitation to orchard borders and the crop. The study was conducted over two consecutive seasons using three large (0.48 ha) wildflower plantings paired with control orchard borders in a highly simplified agricultural landscape in California. Honey bee (Apis mellifera L.) and wild bee visitation to wildflower plots were at least an order of magnitude higher than to control plots, but increased honey bee visitation to wildflower plots did not lead to any detectable shifts in honey bee visitation to almond flowers in the neighboring orchard. Wild bees were rarely observed visiting almond flowers irrespective of border treatment, indicating a limited short-term potential for augmenting crop pollination using wild bees in highly simplified agricultural landscapes. Although further studies are warranted on bee visitation and crop yield from spatially independent orchards, this study indicates that growers can support bees with alternative forage in almond orchards without risking competition between the wildflower plantings and the crop. © The Authors 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

A model of negotiation scenarios based on time, relevance andcontrol used to define advantageous positions in a negotiation

Directory of Open Access Journals (Sweden)

Omar Guillermo Rojas Altamirano

2016-04-01

Full Text Available Models that apply to negotiation are based on different perspectives that range from the relationship between the actors, game theory or the steps in a procedure. This research proposes a model of negotiation scenarios that considers three factors (time, relevance and control, which are displayed as the most important in a negotiation. These factors interact with each other and create different scenarios for each of the actors involved in a negotiation. The proposed model not only facilitates the creation of a negotiation strategy but also an ideal choice of effective tactics.

Sex and gonadal hormones in mouse models of Alzheimer’s disease: what is relevant to the human condition?

Directory of Open Access Journals (Sweden)

Dubal Dena B

2012-11-01

Full Text Available Abstract Biologic sex and gonadal hormones matter in human aging and diseases of aging such as Alzheimer’s – and the importance of studying their influences relates directly to human health. The goal of this article is to review the literature to date on sex and hormones in mouse models of Alzheimer’s disease (AD with an exclusive focus on interpreting the relevance of findings to the human condition. To this end, we highlight advances in AD and in sex and hormone biology, discuss what these advances mean for merging the two fields, review the current mouse model literature, raise major unresolved questions, and offer a research framework that incorporates human reproductive aging for future studies aimed at translational discoveries in this important area. Unraveling human relevant pathways in sex and hormone-based biology may ultimately pave the way to novel and urgently needed treatments for AD and other neurodegenerative diseases.

Combining stated and revealed choice research to simulate the neighbor effect: The case of hybrid-electric vehicles

Energy Technology Data Exchange (ETDEWEB)

Axsen, Jonn [Institute of Transportation Studies, Univ. of California at Davis, 2028 Academic Surge, One Shields Avenue, Davis, CA 95616 (United States); Mountain, Dean C. [DeGroote School of Business, McMaster Univ., 1280 Main Street West, Hamilton, ON L8S 4M4 (Canada); Jaccard, Mark [School of Resource and Environmental Management, Simon Fraser Univ., 8888 Univ. Drive, Burnaby, BC V5A 1S6 (Canada)

2009-08-15

According to intuition and theories of diffusion, consumer preferences develop along with technological change. However, most economic models designed for policy simulation unrealistically assume static preferences. To improve the behavioral realism of an energy-economy policy model, this study investigates the ''neighbor effect'', where a new technology becomes more desirable as its adoption becomes more widespread in the market. We measure this effect as a change in aggregated willingness to pay under different levels of technology penetration. Focusing on hybrid-electric vehicles (HEVs), an online survey experiment collected stated preference (SP) data from 535 Canadian and 408 Californian vehicle owners under different hypothetical market conditions. Revealed preference (RP) data was collected from the same respondents by eliciting the year, make and model of recent vehicle purchases from regions with different degrees of HEV popularity: Canada with 0.17% new market share, and California with 3.0% new market share. We compare choice models estimated from RP data only with three joint SP-RP estimation techniques, each assigning a different weight to the influence of SP and RP data in coefficient estimates. Statistically, models allowing more RP influence outperform SP influenced models. However, results suggest that because the RP data in this study is afflicted by multicollinearity, techniques that allow more SP influence in the beta estimates while maintaining RP data for calibrating vehicle class constraints produce more realistic estimates of willingness to pay. Furthermore, SP influenced coefficient estimates also translate to more realistic behavioral parameters for CIMS, allowing more sensitivity to policy simulations. (author)

Combining stated and revealed choice research to simulate the neighbor effect: The case of hybrid-electric vehicles

International Nuclear Information System (INIS)

Axsen, Jonn; Mountain, Dean C.; Jaccard, Mark

2009-01-01

According to intuition and theories of diffusion, consumer preferences develop along with technological change. However, most economic models designed for policy simulation unrealistically assume static preferences. To improve the behavioral realism of an energy-economy policy model, this study investigates the ''neighbor effect'', where a new technology becomes more desirable as its adoption becomes more widespread in the market. We measure this effect as a change in aggregated willingness to pay under different levels of technology penetration. Focusing on hybrid-electric vehicles (HEVs), an online survey experiment collected stated preference (SP) data from 535 Canadian and 408 Californian vehicle owners under different hypothetical market conditions. Revealed preference (RP) data was collected from the same respondents by eliciting the year, make and model of recent vehicle purchases from regions with different degrees of HEV popularity: Canada with 0.17% new market share, and California with 3.0% new market share. We compare choice models estimated from RP data only with three joint SP-RP estimation techniques, each assigning a different weight to the influence of SP and RP data in coefficient estimates. Statistically, models allowing more RP influence outperform SP influenced models. However, results suggest that because the RP data in this study is afflicted by multicollinearity, techniques that allow more SP influence in the beta estimates while maintaining RP data for calibrating vehicle class constraints produce more realistic estimates of willingness to pay. Furthermore, SP influenced coefficient estimates also translate to more realistic behavioral parameters for CIMS, allowing more sensitivity to policy simulations. (author)

A conscious mouse model of gastric ileus using clinically relevant endpoints

Directory of Open Access Journals (Sweden)

Shao Yuanlin

2005-06-01

Full Text Available Abstract Background Gastric ileus is an unsolved clinical problem and current treatment is limited to supportive measures. Models of ileus using anesthetized animals, muscle strips or isolated smooth muscle cells do not adequately reproduce the clinical situation. Thus, previous studies using these techniques have not led to a clear understanding of the pathophysiology of ileus. The feasibility of using food intake and fecal output as simple, clinically relevant endpoints for monitoring ileus in a conscious mouse model was evaluated by assessing the severity and time course of various insults known to cause ileus. Methods Delayed food intake and fecal output associated with ileus was monitored after intraperitoneal injection of endotoxin, laparotomy with bowel manipulation, thermal injury or cerulein induced acute pancreatitis. The correlation of decreased food intake after endotoxin injection with gastric ileus was validated by measuring gastric emptying. The effect of endotoxin on general activity level and feeding behavior was also determined. Small bowel transit was measured using a phenol red marker. Results Each insult resulted in a transient and comparable decrease in food intake and fecal output consistent with the clinical picture of ileus. The endpoints were highly sensitive to small changes in low doses of endotoxin, the extent of bowel manipulation, and cerulein dose. The delay in food intake directly correlated with delayed gastric emptying. Changes in general activity and feeding behavior were insufficient to explain decreased food intake. Intestinal transit remained unchanged at the times measured. Conclusion Food intake and fecal output are sensitive markers of gastric dysfunction in four experimental models of ileus. In the mouse, delayed gastric emptying appears to be the major cause of the anorexic effect associated with ileus. Gastric dysfunction is more important than small bowel dysfunction in this model. Recovery of

A Novel Quantum Solution to Privacy-Preserving Nearest Neighbor Query in Location-Based Services

Science.gov (United States)

Luo, Zhen-yu; Shi, Run-hua; Xu, Min; Zhang, Shun

2018-04-01

We present a cheating-sensitive quantum protocol for Privacy-Preserving Nearest Neighbor Query based on Oblivious Quantum Key Distribution and Quantum Encryption. Compared with the classical related protocols, our proposed protocol has higher security, because the security of our protocol is based on basic physical principles of quantum mechanics, instead of difficulty assumptions. Especially, our protocol takes single photons as quantum resources and only needs to perform single-photon projective measurement. Therefore, it is feasible to implement this protocol with the present technologies.

Relevance of the c-statistic when evaluating risk-adjustment models in surgery.

Science.gov (United States)

Merkow, Ryan P; Hall, Bruce L; Cohen, Mark E; Dimick, Justin B; Wang, Edward; Chow, Warren B; Ko, Clifford Y; Bilimoria, Karl Y

2012-05-01

The measurement of hospital quality based on outcomes requires risk adjustment. The c-statistic is a popular tool used to judge model performance, but can be limited, particularly when evaluating specific operations in focused populations. Our objectives were to examine the interpretation and relevance of the c-statistic when used in models with increasingly similar case mix and to consider an alternative perspective on model calibration based on a graphical depiction of model fit. From the American College of Surgeons National Surgical Quality Improvement Program (2008-2009), patients were identified who underwent a general surgery procedure, and procedure groups were increasingly restricted: colorectal-all, colorectal-elective cases only, and colorectal-elective cancer cases only. Mortality and serious morbidity outcomes were evaluated using logistic regression-based risk adjustment, and model c-statistics and calibration curves were used to compare model performance. During the study period, 323,427 general, 47,605 colorectal-all, 39,860 colorectal-elective, and 21,680 colorectal cancer patients were studied. Mortality ranged from 1.0% in general surgery to 4.1% in the colorectal-all group, and serious morbidity ranged from 3.9% in general surgery to 12.4% in the colorectal-all procedural group. As case mix was restricted, c-statistics progressively declined from the general to the colorectal cancer surgery cohorts for both mortality and serious morbidity (mortality: 0.949 to 0.866; serious morbidity: 0.861 to 0.668). Calibration was evaluated graphically by examining predicted vs observed number of events over risk deciles. For both mortality and serious morbidity, there was no qualitative difference in calibration identified between the procedure groups. In the present study, we demonstrate how the c-statistic can become less informative and, in certain circumstances, can lead to incorrect model-based conclusions, as case mix is restricted and patients become

Deep learning relevance

DEFF Research Database (Denmark)

Lioma, Christina; Larsen, Birger; Petersen, Casper

2016-01-01

train a Recurrent Neural Network (RNN) on existing relevant information to that query. We then use the RNN to "deep learn" a single, synthetic, and we assume, relevant document for that query. We design a crowdsourcing experiment to assess how relevant the "deep learned" document is, compared...... to existing relevant documents. Users are shown a query and four wordclouds (of three existing relevant documents and our deep learned synthetic document). The synthetic document is ranked on average most relevant of all....

“Zebrafishing” for Novel Genes Relevant to the Glomerular Filtration Barrier

Directory of Open Access Journals (Sweden)

Nils Hanke

2013-01-01

Full Text Available Data for genes relevant to glomerular filtration barrier function or proteinuria is continually increasing in an era of microarrays, genome-wide association studies, and quantitative trait locus analysis. Researchers are limited by published literature searches to select the most relevant genes to investigate. High-throughput cell cultures and other in vitro systems ultimately need to demonstrate proof in an in vivo model. Generating mammalian models for the genes of interest is costly and time intensive, and yields only a small number of test subjects. These models also have many pitfalls such as possible embryonic mortality and failure to generate phenotypes or generate nonkidney specific phenotypes. Here we describe an in vivo zebrafish model as a simple vertebrate screening system to identify genes relevant to glomerular filtration barrier function. Using our technology, we are able to screen entirely novel genes in 4–6 weeks in hundreds of live test subjects at a fraction of the cost of a mammalian model. Our system produces consistent and reliable evidence for gene relevance in glomerular kidney disease; the results then provide merit for further analysis in mammalian models.

Building good relationships with neighbors of Japan's oldest plant, Tsuruga

International Nuclear Information System (INIS)

Hata, Emi

1992-01-01

Since its establishment in 1957 as a pioneer company of nuclear power development in Japan, the Japan Atomic Power Company (JAPC) has gained a great deal of experience with construction and operation of four nuclear power plants - one gas-cooled reactor, two boiling water reactors (BWRs), and one pressurized water reactor (PWR) - at two sites, Tsuruga and Tokai. To gain the understanding and cooperation of the local community, the Tsuruga station must keep running. Each employee is encouraged to make every possible effort not only to ensure the safe and reliable operation of the two units, but also to ensure conscientious coexistence and coprosperity within the local community. The Tsuruga office in the city and the Public Relations (PR) Pavilion (visitor's center) at the site work together as an open window of communication with the local community. Under these basic philosophies, various good neighbor activities are developed and carried out

The media effect in Axelrod's model explained

Science.gov (United States)

Peres, L. R.; Fontanari, J. F.

2011-11-01

We revisit the problem of introducing an external global field —the mass media— in Axelrod's model of social dynamics, where in addition to their nearest neighbors, the agents can interact with a virtual neighbor whose cultural features are fixed from the outset. The finding that this apparently homogenizing field actually increases the cultural diversity has been considered a puzzle since the phenomenon was first reported more than a decade ago. Here we offer a simple explanation for it, which is based on the pedestrian observation that Axelrod's model exhibits more cultural diversity, i.e., more distinct cultural domains, when the agents are allowed to interact solely with the media field than when they can interact with their neighbors as well. In this perspective, it is the local homogenizing interactions that work towards making the absorbing configurations less fragmented as compared with the extreme situation in which the agents interact with the media only.

Perceptual inequality between two neighboring time intervals defined by sound markers: correspondence between neurophysiological and psychological data

Directory of Open Access Journals (Sweden)

Takako eMitsudo

2014-09-01

Full Text Available Brain activity related to time estimation processes in humans was analyzed using a perceptual phenomenon called auditory temporal assimilation. In a typical stimulus condition, two neighboring time intervals (T1 and T2 in this order are perceived as equal even when the physical lengths of these time intervals are considerably different. Our previous event-related potential (ERP study demonstrated that a slow negative component (SNCt appears in the right-frontal brain area (around the F8 electrode after T2, which is associated with judgment of the equality/inequality of T1 and T2. In the present study, we conducted two ERP experiments to further confirm the robustness of the SNCt. The stimulus patterns consisted of two neighboring time intervals marked by three successive tone bursts. Thirteen participants only listened to the patterns in the first session, and judged the equality/inequality of T1 and T2 in the next session. Behavioral data showed typical temporal assimilation. The ERP data revealed that three components (N1; contingent negative variation, CNV; and SNCt emerged related to the temporal judgment. The N1 appeared in the central area, and its peak latencies corresponded to the physical timing of each marker onset. The CNV component appeared in the frontal area during T2 presentation, and its amplitude increased as a function of T1. The SNCt appeared in the right-frontal area after the presentation of T1 and T2, and its magnitude was larger for the temporal patterns causing perceptual inequality. The SNCt was also correlated with the perceptual equality/inequality of the same stimulus pattern, and continued up to about 400 ms after the end of T2. These results suggest that the SNCt can be a signature of equality/inequality judgment, which derives from the comparison of the two neighboring time intervals.

Classification of matrix-product ground states corresponding to one-dimensional chains of two-state sites of nearest neighbor interactions

International Nuclear Information System (INIS)

Fatollahi, Amir H.; Khorrami, Mohammad; Shariati, Ahmad; Aghamohammadi, Amir

2011-01-01

A complete classification is given for one-dimensional chains with nearest-neighbor interactions having two states in each site, for which a matrix product ground state exists. The Hamiltonians and their corresponding matrix product ground states are explicitly obtained.

Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor

OpenAIRE

Samir Brahim Belhaouari

2009-01-01

By taking advantage of both k-NN which is highly accurate and K-means cluster which is able to reduce the time of classification, we can introduce Cluster-k-Nearest Neighbor as "variable k"-NN dealing with the centroid or mean point of all subclasses generated by clustering algorithm. In general the algorithm of K-means cluster is not stable, in term of accuracy, for that reason we develop another algorithm for clustering our space which gives a higher accuracy than K-means cluster, less ...

Refuse dumps from leaf-cutting ant nests reduce the intensity of above-ground competition among neighboring plants in a Patagonian steppe

Science.gov (United States)

Farji-Brener, Alejandro G.; Lescano, María Natalia

2017-11-01

In arid environments, the high availability of sunlight due to the scarcity of trees suggests that plant competition take place mainly belowground for water and nutrients. However, the occurrence of soil disturbances that increase nutrient availability and thereby promote plant growth may enhance shoot competition between neighboring plants. We conducted a greenhouse experiment to evaluate the influence of the enriched soil patches generated by the leaf-cutting ant, Acromyrmex lobicornis, on the performance of the alien forb Carduus thoermeri (Asteraceae) under different intraspecific competition scenarios. Our results showed that substrate type and competition scenario affected mainly aboveground plant growth. As expected, plants growing without neighbors and in nutrient-rich ant refuse dumps showed more aboveground biomass than plants growing with neighbors and in nutrient-poor steppe soils. However, aboveground competition was more intense in nutrient-poor substrates: plants under shoot and full competition growing in the nutrient-rich ant refuse dumps showed higher biomass than those growing on steppe soils. Belowground biomass was similar among focal plants growing under different substrate type. Our results support the traditional view that increments in resource availability reduce competition intensity. Moreover, the fact that seedlings in this sunny habitat mainly compete aboveground illustrates how limiting factors may be scale-dependent and change in importance as plants grow.

Integrating retention soil filters into urban hydrologic models - Relevant processes and important parameters

Science.gov (United States)

Bachmann-Machnik, Anna; Meyer, Daniel; Waldhoff, Axel; Fuchs, Stephan; Dittmer, Ulrich

2018-04-01

Retention Soil Filters (RSFs), a form of vertical flow constructed wetlands specifically designed for combined sewer overflow (CSO) treatment, have proven to be an effective tool to mitigate negative impacts of CSOs on receiving water bodies. Long-term hydrologic simulations are used to predict the emissions from urban drainage systems during planning of stormwater management measures. So far no universally accepted model for RSF simulation exists. When simulating hydraulics and water quality in RSFs, an appropriate level of detail must be chosen for reasonable balancing between model complexity and model handling, considering the model input's level of uncertainty. The most crucial parameters determining the resultant uncertainties of the integrated sewer system and filter bed model were identified by evaluating a virtual drainage system with a Retention Soil Filter for CSO treatment. To determine reasonable parameter ranges for RSF simulations, data of 207 events from six full-scale RSF plants in Germany were analyzed. Data evaluation shows that even though different plants with varying loading and operation modes were examined, a simple model is sufficient to assess relevant suspended solids (SS), chemical oxygen demand (COD) and NH4 emissions from RSFs. Two conceptual RSF models with different degrees of complexity were assessed. These models were developed based on evaluation of data from full scale RSF plants and column experiments. Incorporated model processes are ammonium adsorption in the filter layer and degradation during subsequent dry weather period, filtration of SS and particulate COD (XCOD) to a constant background concentration and removal of solute COD (SCOD) by a constant removal rate during filter passage as well as sedimentation of SS and XCOD in the filter overflow. XCOD, SS and ammonium loads as well as ammonium concentration peaks are discharged primarily via RSF overflow not passing through the filter bed. Uncertainties of the integrated

Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines.

Science.gov (United States)

Majid, Abdul; Ali, Safdar; Iqbal, Mubashar; Kausar, Nabeela

2014-03-01

This study proposes a novel prediction approach for human breast and colon cancers using different feature spaces. The proposed scheme consists of two stages: the preprocessor and the predictor. In the preprocessor stage, the mega-trend diffusion (MTD) technique is employed to increase the samples of the minority class, thereby balancing the dataset. In the predictor stage, machine-learning approaches of K-nearest neighbor (KNN) and support vector machines (SVM) are used to develop hybrid MTD-SVM and MTD-KNN prediction models. MTD-SVM model has provided the best values of accuracy, G-mean and Matthew's correlation coefficient of 96.71%, 96.70% and 71.98% for cancer/non-cancer dataset, breast/non-breast cancer dataset and colon/non-colon cancer dataset, respectively. We found that hybrid MTD-SVM is the best with respect to prediction performance and computational cost. MTD-KNN model has achieved moderately better prediction as compared to hybrid MTD-NB (Naïve Bayes) but at the expense of higher computing cost. MTD-KNN model is faster than MTD-RF (random forest) but its prediction is not better than MTD-RF. To the best of our knowledge, the reported results are the best results, so far, for these datasets. The proposed scheme indicates that the developed models can be used as a tool for the prediction of cancer. This scheme may be useful for study of any sequential information such as protein sequence or any nucleic acid sequence. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Nearest neighbor spacing distributions of low-lying levels of vibrational nuclei

International Nuclear Information System (INIS)

Abul-Magd, A.Y.; Simbel, M.H.

1996-01-01

Energy-level statistics are considered for nuclei whose Hamiltonian is divided into intrinsic and collective-vibrational terms. The levels are described as a random superposition of independent sequences, each corresponding to a given number of phonons. The intrinsic motion is assumed chaotic. The level spacing distribution is found to be intermediate between the Wigner and Poisson distributions and similar in form to the spacing distribution of a system with classical phase space divided into separate regular and chaotic domains. We have obtained approximate expressions for the nearest neighbor spacing and cumulative spacing distribution valid when the level density is described by a constant-temperature formula and not involving additional free parameters. These expressions have been able to achieve good agreement with the experimental spacing distributions. copyright 1996 The American Physical Society

Relevant pH and lipase for in vitro models of gastric digestion.

Science.gov (United States)

Sams, Laura; Paume, Julie; Giallo, Jacqueline; Carrière, Frédéric

2016-01-01

The development of in vitro digestion models relies on the availability of in vivo data such as digestive enzyme levels and pH values recorded in the course of meal digestion. The variations of these parameters along the GI tract are important for designing dynamic digestion models but also static models for which the choice of representative conditions of the gastric and intestinal conditions is critical. Simulating gastric digestion with a static model and a single set of parameters is particularly challenging because the variations in pH and enzyme concentration occurring in the stomach are much broader than those occurring in the small intestine. A review of the literature on this topic reveals that most models of gastric digestion use very low pH values that are not representative of the fed conditions. This is illustrated here by showing the variations in gastric pH as a function of meal gastric emptying instead of time. This representation highlights those pH values that are the most relevant for testing meal digestion in the stomach. Gastric lipolysis is still largely ignored or is performed with microbial lipases. In vivo data on gastric lipase and lipolysis have however been collected in humans and dogs during test meals. The biochemical characterization of gastric lipase has shown that this enzyme is rather unique among lipases: (i) stability and activity in the pH range 2 to 7 with an optimum at pH 4-5.4; (ii) high tensioactivity that allows resistance to bile salts and penetration into phospholipid layers covering TAG droplets; (iii) sn-3 stereospecificity for TAG hydrolysis; and (iv) resistance to pepsin. Most of these properties have been known for more than two decades and should provide a rational basis for the replacement of gastric lipase by other lipases when gastric lipase is not available.

Unsynchronized influenza epidemics in two neighboring subtropical cities

Directory of Open Access Journals (Sweden)

Xiujuan Tang

2018-04-01

Full Text Available Objective: The aim of this study was to examine the synchrony of influenza epidemics between Hong Kong and Shenzhen, two neighboring subtropical cities in South China. Methods: Laboratory-confirmed influenza data for the period January 2006 to December 2016 were obtained from the Shenzhen Center for Disease Control and Prevention and the Department of Health in Hong Kong. The population data were retrieved from the 2011 population censuses. The weekly rates of laboratory-confirmed influenza cases were compared between Shenzhen and Hong Kong. Results: Unsynchronized influenza epidemics between Hong Kong and Shenzhen were frequently observed during the study period. Influenza A/H1N1 caused a more severe pandemic in Hong Kong in 2009, but the subsequent seasonal epidemics showed similar magnitudes in both cities. Two influenza A/H3N2 dominant epidemic waves were seen in Hong Kong in 2015, but these epidemics were very minor in Shenzhen. More influenza B epidemics occurred in Shenzhen than in Hong Kong. Conclusions: Influenza epidemics appeared to be unsynchronized between Hong Kong and Shenzhen most of the time. Given the close geographical locations of these two cities, this could be due to the strikingly different age structures of their populations. Keywords: Influenza epidemics, Synchrony, Shenzhen, Hong Kong

Allotment gardening and health: a comparative survey among allotment gardeners and their neighbors without an allotment.

Science.gov (United States)

van den Berg, Agnes E; van Winsum-Westra, Marijke; de Vries, Sjerp; van Dillen, Sonja M E

2010-11-23

The potential contribution of allotment gardens to a healthy and active life-style is increasingly recognized, especially for elderly populations. However, few studies have empirically examined beneficial effects of allotment gardening. In the present study the health, well-being and physical activity of older and younger allotment gardeners was compared to that of controls without an allotment. A survey was conducted among 121 members of 12 allotment sites in the Netherlands and a control group of 63 respondents without an allotment garden living next to the home addresses of allotment gardeners. The survey included five self-reported health measures (perceived general health, acute health complaints, physical constraints, chronic illnesses, and consultations with GP), four self-reported well-being measures (stress, life satisfaction, loneliness, and social contacts with friends) and one measure assessing self-reported levels of physical activity in summer. Respondents were divided into a younger and older group at the median of 62 years which equals the average retirement age in the Netherlands. After adjusting for income, education level, gender, stressful life events, physical activity in winter, and access to a garden at home as covariates, both younger and older allotment gardeners reported higher levels of physical activity during the summer than neighbors in corresponding age categories. The impacts of allotment gardening on health and well-being were moderated by age. Allotment gardeners of 62 years and older scored significantly or marginally better on all measures of health and well-being than neighbors in the same age category. Health and well-being of younger allotment gardeners did not differ from younger neighbors. The greater health and well-being benefits of allotment gardening for older gardeners may be related to the finding that older allotment gardeners were more oriented towards gardening and being active, and less towards passive relaxation

Value-Relevance of Biological Assets under IFRS

OpenAIRE

Rute Gonçalves; Patrícia Lopes

2015-01-01

Using 389 firm-year observations of listed firms worldwide in 27 countries that adopted International Financial Reporting Standards (IFRS) until 2010, for the period 2011-2013, the purpose of this paper is to examine the value-relevance of fair value accounting of biological assets. In order to operationalize it as the book value’s ability to explain market equity value, this study adjusts the Ohlson model. The results support that recognized biological assets are value-relevant. After includ...

Extracting the relevant delays in time series modelling

DEFF Research Database (Denmark)

Goutte, Cyril

1997-01-01

selection, and more precisely stepwise forward selection. The method is compared to other forward selection schemes, as well as to a nonparametric tests aimed at estimating the embedding dimension of time series. The final application extends these results to the efficient estimation of FIR filters on some......In this contribution, we suggest a convenient way to use generalisation error to extract the relevant delays from a time-varying process, i.e. the delays that lead to the best prediction performance. We design a generalisation-based algorithm that takes its inspiration from traditional variable...

Action detection by double hierarchical multi-structure space-time statistical matching model

Science.gov (United States)

Han, Jing; Zhu, Junwei; Cui, Yiyin; Bai, Lianfa; Yue, Jiang

2018-03-01

Aimed at the complex information in videos and low detection efficiency, an actions detection model based on neighboring Gaussian structure and 3D LARK features is put forward. We exploit a double hierarchical multi-structure space-time statistical matching model (DMSM) in temporal action localization. First, a neighboring Gaussian structure is presented to describe the multi-scale structural relationship. Then, a space-time statistical matching method is proposed to achieve two similarity matrices on both large and small scales, which combines double hierarchical structural constraints in model by both the neighboring Gaussian structure and the 3D LARK local structure. Finally, the double hierarchical similarity is fused and analyzed to detect actions. Besides, the multi-scale composite template extends the model application into multi-view. Experimental results of DMSM on the complex visual tracker benchmark data sets and THUMOS 2014 data sets show the promising performance. Compared with other state-of-the-art algorithm, DMSM achieves superior performances.

Interacting-fermion approximation in the two-dimensional ANNNI model

International Nuclear Information System (INIS)

Grynberg, M.D.; Ceva, H.

1990-12-01

We investigate the effect of including domain-walls interactions in the two-dimensional axial next-nearest-neighbor Ising or ANNNI model. At low temperatures this problem is reduced to a one-dimensional system of interacting fermions which can be treated exactly. It is found that the critical boundaries of the low-temperature phases are in good agreement with those obtained using a free-fermion approximation. In contrast with the monotonic behavior derived from the free-fermion approach, the wall density or wave number displays reentrant phenomena when the ratio of the next-nearest-neighbor and nearest-neighbor interactions is greater than one-half. (author). 17 refs, 2 figs

A lattice gas model on a tangled chain

International Nuclear Information System (INIS)

Mejdani, R.

1993-04-01

We have used a model of a lattice gas defined on a tangled chain to study the enzyme kinetics by a modified transfer matrix method. By using a simple iterative algorithm we have obtained different kinds of saturation curves for different configurations of the tangled chain and different types of the additional interactions. In some special cases of configurations and interactions we have found the same equations for the saturation curves, which we have obtained before studying the lattice gas model with nearest neighbor interactions or the lattice gas model with alternate nearest neighbor interactions, using different techniques as the correlated walks' theory, the partition point technique or the transfer matrix model. This more general model and the new results could be useful for the experimental investigations. (author). 20 refs, 6 figs

Predicting persistence in the sediment compartment with a new automatic software based on the k-Nearest Neighbor (k-NN) algorithm.

Science.gov (United States)

Manganaro, Alberto; Pizzo, Fabiola; Lombardo, Anna; Pogliaghi, Alberto; Benfenati, Emilio

2016-02-01

The ability of a substance to resist degradation and persist in the environment needs to be readily identified in order to protect the environment and human health. Many regulations require the assessment of persistence for substances commonly manufactured and marketed. Besides laboratory-based testing methods, in silico tools may be used to obtain a computational prediction of persistence. We present a new program to develop k-Nearest Neighbor (k-NN) models. The k-NN algorithm is a similarity-based approach that predicts the property of a substance in relation to the experimental data for its most similar compounds. We employed this software to identify persistence in the sediment compartment. Data on half-life (HL) in sediment were obtained from different sources and, after careful data pruning the final dataset, containing 297 organic compounds, was divided into four experimental classes. We developed several models giving satisfactory performances, considering that both the training and test set accuracy ranged between 0.90 and 0.96. We finally selected one model which will be made available in the near future in the freely available software platform VEGA. This model offers a valuable in silico tool that may be really useful for fast and inexpensive screening. Copyright © 2015 Elsevier Ltd. All rights reserved.

Lysenin Toxin Membrane Insertion Is pH-Dependent but Independent of Neighboring Lysenins.

Science.gov (United States)

Munguira, Ignacio L B; Takahashi, Hirohide; Casuso, Ignacio; Scheuring, Simon

2017-11-07

Pore-forming toxins form a family of proteins that act as virulence factors of pathogenic bacteria, but similar proteins are found in all kingdoms of life, including the vertebrate immune system. They are secreted as soluble monomers that oligomerize on target membranes in the so-called prepore state; after activation, they insert into the membrane and adopt the pore state. Lysenin is a pore-forming toxin from the earthworm Eisenida foetida, of which both the soluble and membrane-inserted structures are solved. However, the activation and membrane-insertion mechanisms have remained elusive. Here, we used high-speed atomic force microscopy to directly visualize the membrane-insertion mechanism. Changing the environmental pH from pH 7.5 to below pH 6.0 favored membrane insertion. We detected a short α-helix in the soluble structure that comprised three glutamic acids (Glu92, Glu94, and Glu97) that we hypothesized may represent a pH-sensor (as in similar toxins, e.g., Listeriolysin). Mutant lysenin still can form pores, but mutating these glutamic acids to glutamines rendered the toxin pH-insensitive. On the other hand, toxins in the pore state did not favor insertion of neighboring prepores; indeed, pore insertion breaks the hexagonal ordered domains of prepores and separates from neighboring molecules in the membrane. pH-dependent activation of toxins may represent a common feature of pore-forming toxins. High-speed atomic force microscopy with single-molecule resolution at high temporal resolution and the possibility of exchanging buffers during the experiments presents itself as a unique tool for the study of toxin-state conversion. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Synergistic effects in threshold models on networks

Science.gov (United States)

Juul, Jonas S.; Porter, Mason A.

2018-01-01

Network structure can have a significant impact on the propagation of diseases, memes, and information on social networks. Different types of spreading processes (and other dynamical processes) are affected by network architecture in different ways, and it is important to develop tractable models of spreading processes on networks to explore such issues. In this paper, we incorporate the idea of synergy into a two-state ("active" or "passive") threshold model of social influence on networks. Our model's update rule is deterministic, and the influence of each meme-carrying (i.e., active) neighbor can—depending on a parameter—either be enhanced or inhibited by an amount that depends on the number of active neighbors of a node. Such a synergistic system models social behavior in which the willingness to adopt either accelerates or saturates in a way that depends on the number of neighbors who have adopted that behavior. We illustrate that our model's synergy parameter has a crucial effect on system dynamics, as it determines whether degree-k nodes are possible or impossible to activate. We simulate synergistic meme spreading on both random-graph models and networks constructed from empirical data. Using a heterogeneous mean-field approximation, which we derive under the assumption that a network is locally tree-like, we are able to determine which synergy-parameter values allow degree-k nodes to be activated for many networks and for a broad family of synergistic models.

A meta-analysis of the abscopal effect in preclinical models: Is the biologically effective dose a relevant physical trigger?

Directory of Open Access Journals (Sweden)

Raffaella Marconi

Full Text Available Preclinical in vivo studies using small animals are considered crucial in translational cancer research and clinical implementation of novel treatments. This is of paramount relevance in radiobiology, especially for any technological developments permitted to deliver high doses in single or oligo-fractionated regimens, such as stereotactic ablative radiotherapy (SABR. In this context, clinical success in cancer treatment needs to be guaranteed, sparing normal tissue and preventing the potential spread of disease or local recurrence. In this work we introduce a new dose-response relationship based on relevant publications concerning preclinical models with regard to delivered dose, fractionation schedule and occurrence of biological effects on non-irradiated tissue, abscopal effects.We reviewed relevant publications on murine models and the abscopal effect in radiation cancer research following PRISMA methodology. In particular, through a log-likelihood method, we evaluated whether the occurrence of abscopal effects may be related to the biologically effective dose (BED. To this aim, studies accomplished with different tumor histotypes were considered in our analysis including breast, colon, lung, fibrosarcoma, pancreas, melanoma and head and neck cancer. For all the tumors, the α / β ratio was assumed to be 10 Gy, as generally adopted for neoplastic cells.Our results support the hypothesis that the occurrence rate of abscopal effects in preclinical models increases with BED. In particular, the probability of revealing abscopal effects is 50% when a BED of 60 Gy is generated.Our study provides evidence that SABR treatments associated with high BEDs could be considered an effective strategy in triggering the abscopal effect, thus shedding light on the promising outcomes revealed in clinical practice.

Translating hydrologically-relevant variables from the ice sheet model SICOPOLIS to the Greenland Analog Project hydrologic modeling domain

Science.gov (United States)

Vallot, Dorothée; Applegate, Patrick; Pettersson, Rickard

2013-04-01

Projecting future climate and ice sheet development requires sophisticated models and extensive field observations. Given the present state of our knowledge, it is very difficult to say what will happen with certainty. Despite the ongoing increase in atmospheric greenhouse gas concentrations, the possibility that a new ice sheet might form over Scandinavia in the far distant future cannot be excluded. The growth of a new Scandinavian Ice Sheet would have important consequences for buried nuclear waste repositories. The Greenland Analogue Project, initiated by the Swedish Nuclear Fuel and Waste Management Company (SKB), is working to assess the effects of a possible future ice sheet on groundwater flow by studying a constrained domain in Western Greenland by field measurements (including deep bedrock drilling in front of the ice sheet) combined with numerical modeling. To address the needs of the GAP project, we interpolated results from an ensemble of ice sheet model runs to the smaller and more finely resolved modeling domain used in the GAP project's hydrologic modeling. Three runs have been chosen with three fairly different positive degree-day factors among those that reproduced the modern ice margin at the borehole position. The interpolated results describe changes in hydrologically-relevant variables over two time periods, 115 ka to 80 ka, and 20 ka to 1 ka. In the first of these time periods, the ice margin advances over the model domain; in the second time period, the ice margin retreats over the model domain. The spatially-and temporally dependent variables that we treated include the ice thickness, basal melting rate, surface mass balance, basal temperature, basal thermal regime (frozen or thawed), surface temperature, and basal water pressure. The melt flux is also calculated.

K-Nearest Neighbor Intervals Based AP Clustering Algorithm for Large Incomplete Data

Directory of Open Access Journals (Sweden)

Cheng Lu

2015-01-01

Full Text Available The Affinity Propagation (AP algorithm is an effective algorithm for clustering analysis, but it can not be directly applicable to the case of incomplete data. In view of the prevalence of missing data and the uncertainty of missing attributes, we put forward a modified AP clustering algorithm based on K-nearest neighbor intervals (KNNI for incomplete data. Based on an Improved Partial Data Strategy, the proposed algorithm estimates the KNNI representation of missing attributes by using the attribute distribution information of the available data. The similarity function can be changed by dealing with the interval data. Then the improved AP algorithm can be applicable to the case of incomplete data. Experiments on several UCI datasets show that the proposed algorithm achieves impressive clustering results.

From localized to extended states in a time-dependent quantum model

International Nuclear Information System (INIS)

Jose, J.V.

1986-01-01

The problem of a particle inside a rigid box with one of the walls oscillating periodically in time is studied quantum mechanically. In the classical limit, this model was introduced by Fermi in the context of cosmic ray physics. The classical solutions can go from being quasiperiodic to chaotic, as a function of the amplitude of the wall oscillation. In the quantum case, the authors calculate the spectral properties of the corresponding evolution operator, i.e.: the quasi-energy eigenvalues and eigenvectors. The specific form of the wall oscillation, e.g. iota(t) = √ 1 + 2δabsolute value of t, with absolute value of t ≤ 1/2, and iota(t + 1) = iota(t), is essential to the solutions presented here. It is found that as h increases with δ fixed, the nearest neighbor separation between quasi-energy eigenvalues changes from showing no energy level repulsion to energy level repulsion. This transition, from Poisson-like statistics to Gaussian-Orthogonal-Ensemble-like statistics is tested by looking at the distribution of quasi-energy level nearest neighbor separations and the Δ/sub e/(L) statistics. these results are also correlated to a transition between localized to extended states in energy space. The possible relevance of the results presented here to experiments in quasi-one-dimensional atoms is also discussed

Mitochondrial metabolism in early neural fate and its relevance for neuronal disease modeling.

Science.gov (United States)

Lorenz, Carmen; Prigione, Alessandro

2017-12-01

Modulation of energy metabolism is emerging as a key aspect associated with cell fate transition. The establishment of a correct metabolic program is particularly relevant for neural cells given their high bioenergetic requirements. Accordingly, diseases of the nervous system commonly involve mitochondrial impairment. Recent studies in animals and in neural derivatives of human pluripotent stem cells (PSCs) highlighted the importance of mitochondrial metabolism for neural fate decisions in health and disease. The mitochondria-based metabolic program of early neurogenesis suggests that PSC-derived neural stem cells (NSCs) may be used for modeling neurological disorders. Understanding how metabolic programming is orchestrated during neural commitment may provide important information for the development of therapies against conditions affecting neural functions, including aging and mitochondrial disorders. Copyright © 2017. Published by Elsevier Ltd.

Inferring relevance in a changing world

Directory of Open Access Journals (Sweden)

Robert C Wilson

2012-01-01

Full Text Available Reinforcement learning models of human and animal learning usually concentrate on how we learn the relationship between different stimuli or actions and rewards. However, in real world situations stimuli are ill-defined. On the one hand, our immediate environment is extremely multi-dimensional. On the other hand, in every decision-making scenario only a few aspects of the environment are relevant for obtaining reward, while most are irrelevant. Thus a key question is how do we learn these relevant dimensions, that is, how do we learn what to learn about? We investigated this process of representation learning experimentally, using a task in which one stimulus dimension was relevant for determining reward at each point in time. As in real life situations, in our task the relevant dimension can change without warning, adding ever-present uncertainty engendered by a constantly changing environment. We show that human performance on this task is better described by a suboptimal strategy based on selective attention and serial hypothesis testing rather than a normative strategy based on probabilistic inference. From this, we conjecture that the problem of inferring relevance in general scenarios is too computationally demanding for the brain to solve optimally. As a result the brain utilizes approximations, employing these even in simplified scenarios in which optimal representation learning is tractable, such as the one in our experiment.

The Effects of Capital Outflows from Neighboring Countries on a Home Country’s Terms of Trade and Real Exchange Rate: The Case of East Asia

Directory of Open Access Journals (Sweden)

Sammo Kang

2005-06-01

Full Text Available While there is an extensive body of empirical analyses showing that currency crises tend to be regionally concentrated to specific areas and contagious to countries with high levels of trade, there has been insufficient research on the mechanisms underlying such tendencies. Using a two¡ⓒcountry model, we investigate the possibility of deterioration in the terms of trade and a rise in the real exchange rate of a home country in the case of capital outflows from its trade partner. In addition, an empirical analysis of East Asian countries conclusively shows that some countries conform to the model. Generally, neighboring countries trade extensively with one another for reasons like low logistics costs. This paper finds that such patterns of trade can be one reason for a currency crisis being regional.

Using language models to identify relevant new information in inpatient clinical notes.

Science.gov (United States)

Zhang, Rui; Pakhomov, Serguei V; Lee, Janet T; Melton, Genevieve B

2014-01-01

Redundant information in clinical notes within electronic health record (EHR) systems is ubiquitous and may negatively impact the use of these notes by clinicians, and, potentially, the efficiency of patient care delivery. Automated methods to identify redundant versus relevant new information may provide a valuable tool for clinicians to better synthesize patient information and navigate to clinically important details. In this study, we investigated the use of language models for identification of new information in inpatient notes, and evaluated our methods using expert-derived reference standards. The best method achieved precision of 0.743, recall of 0.832 and F1-measure of 0.784. The average proportion of redundant information was similar between inpatient and outpatient progress notes (76.6% (SD=17.3%) and 76.7% (SD=14.0%), respectively). Advanced practice providers tended to have higher rates of redundancy in their notes compared to physicians. Future investigation includes the addition of semantic components and visualization of new information.

Effective model with strong Kitaev interactions for α -RuCl3

Science.gov (United States)

Suzuki, Takafumi; Suga, Sei-ichiro

2018-04-01

We use an exact numerical diagonalization method to calculate the dynamical spin structure factors of three ab initio models and one ab initio guided model for a honeycomb-lattice magnet α -RuCl3 . We also use thermal pure quantum states to calculate the temperature dependence of the heat capacity, the nearest-neighbor spin-spin correlation function, and the static spin structure factor. From the results obtained from these four effective models, we find that, even when the magnetic order is stabilized at low temperature, the intensity at the Γ point in the dynamical spin structure factors increases with increasing nearest-neighbor spin correlation. In addition, we find that the four models fail to explain heat-capacity measurements whereas two of the four models succeed in explaining inelastic-neutron-scattering experiments. In the four models, when temperature decreases, the heat capacity shows a prominent peak at a high temperature where the nearest-neighbor spin-spin correlation function increases. However, the peak temperature in heat capacity is too low in comparison with that observed experimentally. To address these discrepancies, we propose an effective model that includes strong ferromagnetic Kitaev coupling, and we show that this model quantitatively reproduces both inelastic-neutron-scattering experiments and heat-capacity measurements. To further examine the adequacy of the proposed model, we calculate the field dependence of the polarized terahertz spectra, which reproduces the experimental results: the spin-gapped excitation survives up to an onset field where the magnetic order disappears and the response in the high-field region is almost linear. Based on these numerical results, we argue that the low-energy magnetic excitation in α -RuCl3 is mainly characterized by interactions such as off-diagonal interactions and weak Heisenberg interactions between nearest-neighbor pairs, rather than by the strong Kitaev interactions.

Spiral correlations in frustrated one-dimensional spin-1/2 Heisenberg J1-J2-J3 ferromagnets

International Nuclear Information System (INIS)

Zinke, R; Richter, J; Drechsler, S-L

2010-01-01

We use the coupled cluster method for infinite chains complemented by exact diagonalization of finite periodic chains to discuss the influence of a third-neighbor exchange J 3 on the ground state of the spin- 1/2 Heisenberg chain with ferromagnetic nearest-neighbor interaction J 1 and frustrating antiferromagnetic next-nearest-neighbor interaction J 2 . A third-neighbor exchange J 3 might be relevant to describe the magnetic properties of the quasi-one-dimensional edge-shared cuprates, such as LiVCuO 4 or LiCu 2 O 2 . In particular, we calculate the critical point J 2 c as a function of J 3 , where the ferromagnetic ground state gives way for a ground state with incommensurate spiral correlations. For antiferromagnetic J 3 the ferro-spiral transition is always continuous and the critical values J 2 c of the classical and the quantum model coincide. On the other hand, for ferromagnetic J 3 ∼ 1 | the critical value J 2 c of the quantum model is smaller than that of the classical model. Moreover, the transition becomes discontinuous, i.e. the model exhibits a quantum tricritical point. We also calculate the height of the jump of the spiral pitch angle at the discontinuous ferro-spiral transition.

Relevance Theory as model for analysing visual and multimodal communication

NARCIS (Netherlands)

Forceville, C.; Machin, D.

2014-01-01

Elaborating on my earlier work (Forceville 1996: chapter 5, 2005, 2009; see also Yus 2008), I will here sketch how discussions of visual and multimodal discourse can be embedded in a more general theory of communication and cognition: Sperber and Wilson’s Relevance Theory/RT (Sperber and Wilson

A method of neighbor classes based SVM classification for optical printed Chinese character recognition.

Science.gov (United States)

Zhang, Jie; Wu, Xiaohong; Yu, Yanmei; Luo, Daisheng

2013-01-01

In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR.

Gene Expression Analysis to Assess the Relevance of Rodent Models to Human Lung Injury.

Science.gov (United States)

Sweeney, Timothy E; Lofgren, Shane; Khatri, Purvesh; Rogers, Angela J

2017-08-01

The relevance of animal models to human diseases is an area of intense scientific debate. The degree to which mouse models of lung injury recapitulate human lung injury has never been assessed. Integrating data from both human and animal expression studies allows for increased statistical power and identification of conserved differential gene expression across organisms and conditions. We sought comprehensive integration of gene expression data in experimental acute lung injury (ALI) in rodents compared with humans. We performed two separate gene expression multicohort analyses to determine differential gene expression in experimental animal and human lung injury. We used correlational and pathway analyses combined with external in vitro gene expression data to identify both potential drivers of underlying inflammation and therapeutic drug candidates. We identified 21 animal lung tissue datasets and three human lung injury bronchoalveolar lavage datasets. We show that the metasignatures of animal and human experimental ALI are significantly correlated despite these widely varying experimental conditions. The gene expression changes among mice and rats across diverse injury models (ozone, ventilator-induced lung injury, LPS) are significantly correlated with human models of lung injury (Pearson r = 0.33-0.45, P human lung injury. Predicted therapeutic targets, peptide ligand signatures, and pathway analyses are also all highly overlapping. Gene expression changes are similar in animal and human experimental ALI, and provide several physiologic and therapeutic insights to the disease.

The LAILAPS Search Engine: Relevance Ranking in Life Science Databases

Directory of Open Access Journals (Sweden)

Lange Matthias

2010-06-01

Full Text Available Search engines and retrieval systems are popular tools at a life science desktop. The manual inspection of hundreds of database entries, that reflect a life science concept or fact, is a time intensive daily work. Hereby, not the number of query results matters, but the relevance does. In this paper, we present the LAILAPS search engine for life science databases. The concept is to combine a novel feature model for relevance ranking, a machine learning approach to model user relevance profiles, ranking improvement by user feedback tracking and an intuitive and slim web user interface, that estimates relevance rank by tracking user interactions. Queries are formulated as simple keyword lists and will be expanded by synonyms. Supporting a flexible text index and a simple data import format, LAILAPS can easily be used both as search engine for comprehensive integrated life science databases and for small in-house project databases.

Truncated Calogero-Sutherland models

Science.gov (United States)

Pittman, S. M.; Beau, M.; Olshanii, M.; del Campo, A.

2017-05-01

A one-dimensional quantum many-body system consisting of particles confined in a harmonic potential and subject to finite-range two-body and three-body inverse-square interactions is introduced. The range of the interactions is set by truncation beyond a number of neighbors and can be tuned to interpolate between the Calogero-Sutherland model and a system with nearest and next-nearest neighbors interactions discussed by Jain and Khare. The model also includes the Tonks-Girardeau gas describing impenetrable bosons as well as an extension with truncated interactions. While the ground state wave function takes a truncated Bijl-Jastrow form, collective modes of the system are found in terms of multivariable symmetric polynomials. We numerically compute the density profile, one-body reduced density matrix, and momentum distribution of the ground state as a function of the range r and the interaction strength.

Allotment gardening and health: a comparative survey among allotment gardeners and their neighbors without an allotment

Directory of Open Access Journals (Sweden)

van Winsum-Westra Marijke

2010-11-01

Full Text Available Abstract Background The potential contribution of allotment gardens to a healthy and active life-style is increasingly recognized, especially for elderly populations. However, few studies have empirically examined beneficial effects of allotment gardening. In the present study the health, well-being and physical activity of older and younger allotment gardeners was compared to that of controls without an allotment. Methods A survey was conducted among 121 members of 12 allotment sites in the Netherlands and a control group of 63 respondents without an allotment garden living next to the home addresses of allotment gardeners. The survey included five self-reported health measures (perceived general health, acute health complaints, physical constraints, chronic illnesses, and consultations with GP, four self-reported well-being measures (stress, life satisfaction, loneliness, and social contacts with friends and one measure assessing self-reported levels of physical activity in summer. Respondents were divided into a younger and older group at the median of 62 years which equals the average retirement age in the Netherlands. Results After adjusting for income, education level, gender, stressful life events, physical activity in winter, and access to a garden at home as covariates, both younger and older allotment gardeners reported higher levels of physical activity during the summer than neighbors in corresponding age categories. The impacts of allotment gardening on health and well-being were moderated by age. Allotment gardeners of 62 years and older scored significantly or marginally better on all measures of health and well-being than neighbors in the same age category. Health and well-being of younger allotment gardeners did not differ from younger neighbors. The greater health and well-being benefits of allotment gardening for older gardeners may be related to the finding that older allotment gardeners were more oriented towards gardening

Exposure measurement in the neighboring hospital beds during an x-ray procedure in hospitalization unit

Energy Technology Data Exchange (ETDEWEB)

Goto, Rafael E.; Capeleti, Felipe F.; Cabete, Henrique V., E-mail: rafael.goto@fcmsantacasasp.edu.br, E-mail: felipe.capeleti@fcmsantacasasp.edu.br, E-mail: henrique@gmpbrasil.com.br [Faculdade de Ciencias Medicas da Santa Casa Sao Paulo, SP (Brazil); GMP Consultoria em Radioprotecao e Fisica Medica e Assessoria LTDA, Sao Paulo, SP (Brazil)

2017-11-01

There are lots of discussion about the exposure in hospitalization units in Brazil, especially around labor legislation and economic advantages of unhealthiness. With the attention focused on hospitalized patients, there were measured the exposure in neighboring beds of the patient submitted to an X-ray procedure with a mobile X-ray system that could be used to illustrate the discussion with consistent values. The most common X-ray procedure made in hospitalization units are chests images with techniques between 70 to 120 kV and 5 to 20 mAs. The measurement was made during routine exposure and simulations using a scattering phantom with Radcal AccuPro electrometer and 1800cc ionization chamber in a private hospital and a philanthropic hospital, both in Sao Paulo, Brazil. The ionization chambers are placed at 2 meters distance of the patient exposed of both sides during the routine procedure. During the simulation, a nylon phantom of 20 centimeters thick and 30 x 30 cm² size was placed on the bed, a typical exposure technique was used and the exposure was measured surrounding the phantom at 0.6, 1.0 and 2.0 meters distance for scattered radiation characterization. Initial results showed that the neighboring exposure at about 2 meters distance from the exposed patient bed have low values, even when exposure is integrated during the length of hospital stay. Therefore, the exposure in hospitalization units are very low compared to the exams doses. (author). (author)

The Relevance Aura of Bibliographic Records.

Science.gov (United States)

Brooks, Terrence A.

1997-01-01

Analyzes relevance assessments of topical descriptors for bibliographic records for two dimensions: (1) a vertical conceptual hierarchy of broad to narrow descriptors, and (2) a horizontal linkage of related terms. The data were analyzed for a semantic distance and semantic direction effect as postulated by the Semantic Distance Model. (Author/LRW)

Regional and global modeling estimates of policy relevant background ozone over the United States

Science.gov (United States)

Emery, Christopher; Jung, Jaegun; Downey, Nicole; Johnson, Jeremiah; Jimenez, Michele; Yarwood, Greg; Morris, Ralph

2012-02-01

Policy Relevant Background (PRB) ozone, as defined by the US Environmental Protection Agency (EPA), refers to ozone concentrations that would occur in the absence of all North American anthropogenic emissions. PRB enters into the calculation of health risk benefits, and as the US ozone standard approaches background levels, PRB is increasingly important in determining the feasibility and cost of compliance. As PRB is a hypothetical construct, modeling is a necessary tool. Since 2006 EPA has relied on global modeling to establish PRB for their regulatory analyses. Recent assessments with higher resolution global models exhibit improved agreement with remote observations and modest upward shifts in PRB estimates. This paper shifts the paradigm to a regional model (CAMx) run at 12 km resolution, for which North American boundary conditions were provided by a low-resolution version of the GEOS-Chem global model. We conducted a comprehensive model inter-comparison, from which we elucidate differences in predictive performance against ozone observations and differences in temporal and spatial background variability over the US. In general, CAMx performed better in replicating observations at remote monitoring sites, and performance remained better at higher concentrations. While spring and summer mean PRB predicted by GEOS-Chem ranged 20-45 ppb, CAMx predicted PRB ranged 25-50 ppb and reached well over 60 ppb in the west due to event-oriented phenomena such as stratospheric intrusion and wildfires. CAMx showed a higher correlation between modeled PRB and total observed ozone, which is significant for health risk assessments. A case study during April 2006 suggests that stratospheric exchange of ozone is underestimated in both models on an event basis. We conclude that wildfires, lightning NO x and stratospheric intrusions contribute a significant level of uncertainty in estimating PRB, and that PRB will require careful consideration in the ozone standard setting process.

Relevance of the Lin's and Host hydropedological models to predict grape yield and wine quality

Directory of Open Access Journals (Sweden)

E. A. C. Costantini

2009-09-01

Full Text Available The adoption of precision agriculture in viticulture could be greatly enhanced by the diffusion of straightforward and easy to be applied hydropedological models, able to predict the spatial variability of available soil water. The Lin's and Host hydropedological models were applied to standard soil series descriptions and hillslope position, to predict the distribution of hydrological functional units in two vineyard and their relevance for grape yield and wine quality. A three-years trial was carried out in Chianti (Central Italy on Sangiovese. The soils of the vineyards differentiated in structure, porosity and related hydropedological characteristics, as well as in salinity. Soil spatial variability was deeply affected by earth movement carried out before vine plantation. Six plots were selected in the different hydrological functional units of the two vineyards, that is, at summit, backslope and footslope morphological positions, to monitor soil hydrology, grape production and wine quality. Plot selection was based upon a cluster analysis of local slope, topographic wetness index (TWI, and cumulative moisture up to the root limiting layer, appreciated by means of a detailed combined geophysical survey. Water content, redox processes and temperature were monitored, as well as yield, phenological phases, and chemical analysis of grapes. The isotopic ratio δ¹³C was measured in the wine ethanol upon harvesting to evaluate the degree of stress suffered by vines. The grapes in each plot were collected for wine making in small barrels. The wines obtained were analysed and submitted to a blind organoleptic testing.

The results demonstrated that the combined application of the two hydropedological models can be used for the prevision of the moisture status of soils cultivated with grape during summertime in Mediterranean climate. As correctly foreseen by the models, the amount of mean daily transpirable soil water (TSW during

Translational relevance of rodent models of hypothalamic-pituitary-adrenal function and stressors in adolescence

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Cheryl M. McCormick

2017-02-01

Full Text Available Elevations in glucocorticoids that result from environmental stressors can have programming effects on brain structure and function when the exposure occurs during sensitive periods that involve heightened neural development. In recent years, adolescence has gained increasing attention as another sensitive period of development, a period in which pubertal transitions may increase the vulnerability to stressors. There are similarities in physical and behavioural development between humans and rats, and rats have been used effectively as an animal model of adolescence and the unique plasticity of this period of ontogeny. This review focuses on benefits and challenges of rats as a model for translational research on hypothalamic-pituitary-adrenal (HPA function and stressors in adolescence, highlighting important parallels and contrasts between adolescent rats and humans, and we review the main stress procedures that are used in investigating HPA stress responses and their consequences in adolescence in rats. We conclude that a greater focus on timing of puberty as a factor in research in adolescent rats may increase the translational relevance of the findings.

Intake and transformation to a glycoside of (Z)-3-hexenol from infested neighbors reveals a mode of plant odor reception and defense

Science.gov (United States)

Sugimoto, Koichi; Matsui, Kenji; Iijima, Yoko; Akakabe, Yoshihiko; Muramoto, Shoko; Ozawa, Rika; Uefune, Masayoshi; Sasaki, Ryosuke; Alamgir, Kabir Md.; Akitake, Shota; Nobuke, Tatsunori; Galis, Ivan; Aoki, Koh; Shibata, Daisuke; Takabayashi, Junji

2014-01-01

Plants receive volatile compounds emitted by neighboring plants that are infested by herbivores, and consequently the receiver plants begin to defend against forthcoming herbivory. However, to date, how plants receive volatiles and, consequently, how they fortify their defenses, is largely unknown. In this study, we found that undamaged tomato plants exposed to volatiles emitted by conspecifics infested with common cutworms (exposed plants) became more defensive against the larvae than those exposed to volatiles from uninfested conspecifics (control plants) in a constant airflow system under laboratory conditions. Comprehensive metabolite analyses showed that only the amount of (Z)-3-hexenylvicianoside (HexVic) was higher in exposed than control plants. This compound negatively affected the performance of common cutworms when added to an artificial diet. The aglycon of HexVic, (Z)-3-hexenol, was obtained from neighboring infested plants via the air. The amount of jasmonates (JAs) was not higher in exposed plants, and HexVic biosynthesis was independent of JA signaling. The use of (Z)-3-hexenol from neighboring damaged conspecifics for HexVic biosynthesis in exposed plants was also observed in an experimental field, indicating that (Z)-3-hexenol intake occurred even under fluctuating environmental conditions. Specific use of airborne (Z)-3-hexenol to form HexVic in undamaged tomato plants reveals a previously unidentified mechanism of plant defense. PMID:24778218

Familiarity breeds contempt: combining proximity loggers and GPS reveals female white-tailed deer (Odocoileus virginianus) avoiding close contact with neighbors.

Science.gov (United States)

Tosa, Marie I; Schauber, Eric M; Nielsen, Clayton K

2015-01-01

Social interactions can influence infectious disease dynamics, particularly for directly transmitted pathogens. Therefore, reliable information on contact frequency within and among groups can better inform disease modeling and management. We compared three methods of assessing contact patterns: (1) space-use overlap (volume of interaction [VI]), (2) direct contact rates measured by simultaneous global positioning system (GPS) locations (<10 m apart), and (3) direct contact rates measured by proximity loggers (PLs; 1-m detection) among female white-tailed deer (Odocoileus virginianus). We calculated the PL∶GPS contact ratios to see whether both devices reveal similar contact patterns and thus predict similar pathogen transmission patterns. Contact rates measured by GPS and PLs were similarly high for two within-group dyads (pairs of deer in the same social groups). Dyads representing separate but neighboring groups (high VI) had PL∶GPS contact ratios near zero, whereas dyads further apart (intermediate VI) had higher PL∶GPS contact ratios. Social networks based on PL contacts showed the fewest connected individuals and lowest mean centrality measures; network metrics were intermediate when based on GPS contacts and greatest when based on VI. Thus, the VI network portrayed animals to be more uniformly and strongly connected than did the PL network. We conclude that simultaneous GPS locations, compared with PLs, substantially underestimate the impact of group membership on direct contact rates of female deer and make networks appear more connected. We also present evidence that deer coming within the general vicinity of each other are less likely to come in close contact if they are in neighboring social groups than deer whose home ranges overlap little if at all. Combined, these results provide evidence that direct transmission of disease agents among female and juvenile white-tailed deer is likely to be constrained both spatially and by social structure, more

Adenovirus-Mediated Delivery of Catalase to Retinal Pigment Epithelial Cells Protects Neighboring Photoreceptors from Photo-Oxidative Stress

OpenAIRE

Rex, T.S.; Tsui, I.; Hahn, P.; Maguire, A.M.; Duan, D.; Bennett, J.; Dunaief, J.L.

2004-01-01

Oxidative stress is involved in the pathogenesis of many diseases. Overexpression of antioxidant enzymes by gene therapy may protect tissues from oxidative damage. Because the reactive oxygen species hydrogen peroxide can diffuse across cell membranes, we hypothesized that overexpression of the antioxidant catalase within certain cells might protect neighboring cells. To test this hypothesis, we transduced retinal pigment epithelial (RPE) cells in vitro and in vivo with adenovirus carrying th...

On the particle-hole symmetry of the fermionic spinless Hubbard model in D=1

Directory of Open Access Journals (Sweden)

M.T. Thomaz

2014-06-01

Full Text Available We revisit the particle-hole symmetry of the one-dimensional (D=1 fermionic spinless Hubbard model, associating that symmetry to the invariance of the Helmholtz free energy of the one-dimensional spin-1/2 XXZ Heisenberg model, under reversal of the longitudinal magnetic field and at any finite temperature. Upon comparing two regimes of that chain model so that the number of particles in one regime equals the number of holes in the other, one finds that, in general, their thermodynamics is similar, but not identical: both models share the specific heat and entropy functions, but not the internal energy per site, the first-neighbor correlation functions, and the number of particles per site. Due to that symmetry, the difference between the first-neighbor correlation functions is proportional to the z-component of magnetization of the XXZ Heisenberg model. The results presented in this paper are valid for any value of the interaction strength parameter V, which describes the attractive/null/repulsive interaction of neighboring fermions.

Is QCD relevant to nuclear physics

International Nuclear Information System (INIS)

Thomas, A.W.

1985-01-01

A review is given of recent work on baryon structure in a number of QCD-motivated models. After establishing a prima facie case that the quark model should be relevant in a consistent description of the nucleus over a wide range of momentum transfer, the author looks for experimental confirmation. The discussion includes the search for exotic states, for a six quark component of the deuteron, and an up to date report on the interpretation of the EMC effect. (Auth.)

The Sznajd Model with Team Work