Colour segmentation of multi variants tuberculosis sputum images using self organizing map

Science.gov (United States)

Rulaningtyas, Riries; Suksmono, Andriyan B.; Mengko, Tati L. R.; Saptawati, Putri

2017-05-01

Lung tuberculosis detection is still identified from Ziehl-Neelsen sputum smear images in low and middle countries. The clinicians decide the grade of this disease by counting manually the amount of tuberculosis bacilli. It is very tedious for clinicians with a lot number of patient and without standardization for sputum staining. The tuberculosis sputum images have multi variant characterizations in colour, because of no standardization in staining. The sputum has more variants colour and they are difficult to be identified. For helping the clinicians, this research examined the Self Organizing Map method for colouring image segmentation in sputum images based on colour clustering. This method has better performance than k-means clustering which also tried in this research. The Self Organizing Map could segment the sputum images with y good result and cluster the colours adaptively.

A Contribution to the Study of Ensemble of Self-Organizing Maps

Directory of Open Access Journals (Sweden)

Leandro Antonio Pasa

2015-01-01

Full Text Available This study presents a factorial experiment to investigate the ensemble of Kohonen Self-Organizing Maps. Clusters Validity Indexes and the Mean Square Quantization Error were used as a criterion for fusing Kohonen Maps, through three different equations and four approaches. Computational simulations were performed with traditional dataset, including those with high dimensionality, not linearly separable classes, Gaussian mixtures, almost touching clusters, and unbalanced classes, from the UCI Machine Learning Repository and from Fundamental Clustering Problems Suite, with variations in map size, number of ensemble components, and the percentage of dataset bagging. The proposed method achieves a better classification than a single Kohonen Map and we applied the Wilcoxon Signed Rank Test to evidence its effectiveness.

Theoretical and applied aspects of the self-organizing maps

OpenAIRE

Cottrell , Marie; Olteanu , Madalina; Rossi , Fabrice; Villa-Vialaneix , Nathalie

2016-01-01

International audience; The Self-Organizing Map (SOM) is widely used, easy to implement , has nice properties for data mining by providing both clustering and visual representation. It acts as an extension of the k-means algorithm that preserves as much as possible the topological structure of the data. However, since its conception, the mathematical study of the SOM remains difficult and has be done only in very special cases. In WSOM 2005, Jean-Claude Fort presented the state of the art, th...

Surface engineering with ion beams: from self-organized nanostructures to ultra-smooth surfaces

International Nuclear Information System (INIS)

Frost, F.; Ziberi, B.; Schindler, A.; Rauschenbach, B.

2008-01-01

Low-energy ion-beam sputtering, i.e. the removal of atoms from a surface due to the impact of energetic ions or atoms, is an inherent part of numerous surface processing techniques. Besides the actual removal of material, this surface erosion process often results in a pronounced alteration of the surface topography. Under certain conditions, sputtering results in the formation of well-ordered patterns. This self-organized pattern formation is related to a surface instability between curvature-dependent sputtering that roughens the surface and smoothing by different surface relaxation mechanisms. If the evolution of surface topography is dominated by relaxation mechanisms, surface smoothing can occur. In this presentation the current status of self-organized pattern formation and surface smoothing by low-energy ion-beam erosion of Si and Ge is summarized. In detail it will be shown that a multitude of patterns as well as ultra-smooth surfaces can develop, particularly on Si surfaces. Additionally, the most important experimental parameters that control these processes are discussed. Finally, examples are given for the application of low-energy ion beams as a novel approach for passive optical device engineering for many advanced optical applications. (orig.)

Asymmetric neighborhood functions accelerate ordering process of self-organizing maps

International Nuclear Information System (INIS)

Ota, Kaiichiro; Aoki, Takaaki; Kurata, Koji; Aoyagi, Toshio

2011-01-01

A self-organizing map (SOM) algorithm can generate a topographic map from a high-dimensional stimulus space to a low-dimensional array of units. Because a topographic map preserves neighborhood relationships between the stimuli, the SOM can be applied to certain types of information processing such as data visualization. During the learning process, however, topological defects frequently emerge in the map. The presence of defects tends to drastically slow down the formation of a globally ordered topographic map. To remove such topological defects, it has been reported that an asymmetric neighborhood function is effective, but only in the simple case of mapping one-dimensional stimuli to a chain of units. In this paper, we demonstrate that even when high-dimensional stimuli are used, the asymmetric neighborhood function is effective for both artificial and real-world data. Our results suggest that applying the asymmetric neighborhood function to the SOM algorithm improves the reliability of the algorithm. In addition, it enables processing of complicated, high-dimensional data by using this algorithm.

Self-Organization during Friction in Complex Surface Engineered Tribosystems

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Ben D. Beake

2010-02-01

Full Text Available Self-organization during friction in complex surface engineered tribosystems is investigated. The probability of self-organization in these complex tribosystems is studied on the basis of the theoretical concepts of irreversible thermodynamics. It is shown that a higher number of interrelated processes within the system result in an increased probability of self-organization. The results of this thermodynamic model are confirmed by the investigation of the wear performance of a novel Ti0.2Al0.55Cr0.2Si0.03Y0.02N/Ti0.25Al0.65Cr0.1N (PVD coating with complex nano-multilayered structure under extreme tribological conditions of dry high-speed end milling of hardened H13 tool steel.

Self-Organizing Maps Neural Networks Applied to the Classification of Ethanol Samples According to the Region of Commercialization

Directory of Open Access Journals (Sweden)

Aline Regina Walkoff

2017-10-01

Full Text Available Physical-chemical analysis data were collected, from 998 ethanol samples of automotive ethanol commercialized in the northern, midwestern and eastern regions of the state of Paraná. The data presented self-organizing maps (SOM neural networks, which classified them according to those regions. The self-organizing maps best configuration had a 45 x 45 topology and 5000 training epochs, with a final learning rate of 6.7x10-4, a final neighborhood relationship of 3x10-2 and a mean quantization error of 2x10-2. This neural network provided a topological map depicting three separated groups, each one corresponding to samples of a same region of commercialization. Four maps of weights, one for each parameter, were presented. The network established the pH was the most important variable for classification and electrical conductivity the least one. The self-organizing maps application allowed the segmentation of alcohol samples, therefore identifying them according to the region of commercialization. DOI: http://dx.doi.org/10.17807/orbital.v9i4.982

Authoring Tool for Identifying Learning Styles, Using Self-Organizing Maps on Mobile Devices

Directory of Open Access Journals (Sweden)

Ramón Zatarain Cabada

2011-05-01

Full Text Available This work explores a methodological proposal whose main objective is the identification of learning styles using a method of self-organizing maps designed to work, for the most part, on mobile devices. These maps can work in real time and without direct student interaction, which implies the absence of prior information. The results generated are an authoring tool for adaptive courses in Web 2.0 environments.

The morphological classification of normal and abnormal red blood cell using Self Organizing Map

Science.gov (United States)

Rahmat, R. F.; Wulandari, F. S.; Faza, S.; Muchtar, M. A.; Siregar, I.

2018-02-01

Blood is an essential component of living creatures in the vascular space. For possible disease identification, it can be tested through a blood test, one of which can be seen from the form of red blood cells. The normal and abnormal morphology of the red blood cells of a patient is very helpful to doctors in detecting a disease. With the advancement of digital image processing technology can be used to identify normal and abnormal blood cells of a patient. This research used self-organizing map method to classify the normal and abnormal form of red blood cells in the digital image. The use of self-organizing map neural network method can be implemented to classify the normal and abnormal form of red blood cells in the input image with 93,78% accuracy testing.

QUALITATIVE ANALYSIS OF OFFICIAL MILK CONTROL IN VALENCIA COMMUNITY (SPAIN BY SELF ORGANIZING MAPS

Directory of Open Access Journals (Sweden)

Carlos Javier Fernandez

2009-06-01

Full Text Available Breeding programs in dairy goats are mainly based on milk production and composition. Murciano-Granadina goats are located principally in the central and southern regions of Spain. This study is focused in Valencia Community (Spain and the objective is to study the Murciano-Granadina livestock based on the database from Murciano-Granadina Goat Breeders Association of Valencia (AMURVAL.  The aim of this study is to analyze the relationship among different variables related with milk production; milk yield, fat, protein, lactose, SCC, the number of births, lactation number and season. This analysis is carried out by using the Self Organizing Map. This tool allows mapping high-dimensional input spaces into much lower-dimensional spaces, thus making much more straightforward to understand any representation of data. These representations enable to visually extract qualitative relationships among variables (Visual Data Mining. A total of 3221 Murciano-Granadina dairy goats from AMURVAL were chosen. Self Organizing Maps (SOM were used to analyze data with the system identification toolbox of MATLAB v7. Data were obtained from Official Milk Control during 2006 campaign. SOM considered in this study is formed by 21´14 neurons (294 neurons; the chosen architecture is given by the range of the input variables used. The map shown that more than 70% of the goats has milk yield greater than 300 kg per lactation and goat, indicating good performance of farms. Besides, the SOM obtained indicate a group of neurons that included goats with high SSC (2%. The use of Self Organizing Maps in the descriptive analysis of this kind of data sets has proven to be highly valuable in extracting qualitative conclusions and guiding in improving the performance of farms.

Self-organizing maps based on limit cycle attractors.

Science.gov (United States)

Huang, Di-Wei; Gentili, Rodolphe J; Reggia, James A

2015-03-01

Recent efforts to develop large-scale brain and neurocognitive architectures have paid relatively little attention to the use of self-organizing maps (SOMs). Part of the reason for this is that most conventional SOMs use a static encoding representation: each input pattern or sequence is effectively represented as a fixed point activation pattern in the map layer, something that is inconsistent with the rhythmic oscillatory activity observed in the brain. Here we develop and study an alternative encoding scheme that instead uses sparsely-coded limit cycles to represent external input patterns/sequences. We establish conditions under which learned limit cycle representations arise reliably and dominate the dynamics in a SOM. These limit cycles tend to be relatively unique for different inputs, robust to perturbations, and fairly insensitive to timing. In spite of the continually changing activity in the map layer when a limit cycle representation is used, map formation continues to occur reliably. In a two-SOM architecture where each SOM represents a different sensory modality, we also show that after learning, limit cycles in one SOM can correctly evoke corresponding limit cycles in the other, and thus there is the potential for multi-SOM systems using limit cycles to work effectively as hetero-associative memories. While the results presented here are only first steps, they establish the viability of SOM models based on limit cycle activity patterns, and suggest that such models merit further study. Copyright © 2014 Elsevier Ltd. All rights reserved.

Image Fusion Based on the Self-Organizing Feature Map Neural Networks

Institute of Scientific and Technical Information of China (English)

ZHANG Zhaoli; SUN Shenghe

2001-01-01

This paper presents a new image datafusion scheme based on the self-organizing featuremap (SOFM) neural networks.The scheme consists ofthree steps:(1) pre-processing of the images,whereweighted median filtering removes part of the noisecomponents corrupting the image,(2) pixel clusteringfor each image using two-dimensional self-organizingfeature map neural networks,and (3) fusion of the im-ages obtained in Step (2) utilizing fuzzy logic,whichsuppresses the residual noise components and thusfurther improves the image quality.It proves thatsuch a three-step combination offers an impressive ef-fectiveness and performance improvement,which isconfirmed by simulations involving three image sen-sors (each of which has a different noise structure).

Visualizing the topical structure of the medical sciences: a self-organizing map approach.

Directory of Open Access Journals (Sweden)

André Skupin

Full Text Available We implement a high-resolution visualization of the medical knowledge domain using the self-organizing map (SOM method, based on a corpus of over two million publications. While self-organizing maps have been used for document visualization for some time, (1 little is known about how to deal with truly large document collections in conjunction with a large number of SOM neurons, (2 post-training geometric and semiotic transformations of the SOM tend to be limited, and (3 no user studies have been conducted with domain experts to validate the utility and readability of the resulting visualizations. Our study makes key contributions to all of these issues.Documents extracted from Medline and Scopus are analyzed on the basis of indexer-assigned MeSH terms. Initial dimensionality is reduced to include only the top 10% most frequent terms and the resulting document vectors are then used to train a large SOM consisting of over 75,000 neurons. The resulting two-dimensional model of the high-dimensional input space is then transformed into a large-format map by using geographic information system (GIS techniques and cartographic design principles. This map is then annotated and evaluated by ten experts stemming from the biomedical and other domains.Study results demonstrate that it is possible to transform a very large document corpus into a map that is visually engaging and conceptually stimulating to subject experts from both inside and outside of the particular knowledge domain. The challenges of dealing with a truly large corpus come to the fore and require embracing parallelization and use of supercomputing resources to solve otherwise intractable computational tasks. Among the envisaged future efforts are the creation of a highly interactive interface and the elaboration of the notion of this map of medicine acting as a base map, onto which other knowledge artifacts could be overlaid.

New Angle on the Parton Distribution Functions: Self-Organizing Maps

International Nuclear Information System (INIS)

Honkanen, H.; Liuti, S.

2009-01-01

Neural network (NN) algorithms have been recently applied to construct Parton Distribution Function (PDF) parametrizations, providing an alternative to standard global fitting procedures. Here we explore a novel technique using Self-Organizing Maps (SOMs). SOMs are a class of clustering algorithms based on competitive learning among spatially-ordered neurons. We train our SOMs with stochastically generated PDF samples. On every optimization iteration the PDFs are clustered on the SOM according to a user-defined feature and the most promising candidates are used as a seed for the subsequent iteration using the topology of the map to guide the PDF generating process. Our goal is a fitting procedure that, at variance with the standard neural network approaches, will allow for an increased control of the systematic bias by enabling user interaction in the various stages of the process.

Self-Organizing Maps for Fingerprint Image Quality Assessment

DEFF Research Database (Denmark)

Olsen, Martin Aastrup; Tabassi, Elham; Makarov, Anton

2013-01-01

Fingerprint quality assessment is a crucial task which needs to be conducted accurately in various phases in the biometric enrolment and recognition processes. Neglecting quality measurement will adversely impact accuracy and efficiency of biometric recognition systems (e.g. verification and iden......Fingerprint quality assessment is a crucial task which needs to be conducted accurately in various phases in the biometric enrolment and recognition processes. Neglecting quality measurement will adversely impact accuracy and efficiency of biometric recognition systems (e.g. verification...... machine learning techniques. We train a self-organizing map (SOM) to cluster blocks of fingerprint images based on their spatial information content. The output of the SOM is a high-level representation of the finger image, which forms the input to a Random Forest trained to learn the relationship between...

Self-organization of a self-assembled supramolecular rectangle, square, and three-dimensional cage on Au111 surfaces.

Science.gov (United States)

Yuan, Qun-Hui; Wan, Li-Jun; Jude, Hershel; Stang, Peter J

2005-11-23

The structure and conformation of three self-assembled supramolecular species, a rectangle, a square, and a three-dimensional cage, on Au111 surfaces were investigated by scanning tunneling microscopy. These supramolecular assemblies adsorb on Au111 surfaces and self-organize to form highly ordered adlayers with distinct conformations that are consistent with their chemical structures. The faces of the supramolecular rectangle and square lie flat on the surface, preserving their rectangle and square conformations, respectively. The three-dimensional cage also forms well-ordered adlayers on the gold surface, forming regular molecular rows of assemblies. When the rectangle and cage were mixed together, the assemblies separated into individual domains, and no mixed adlayers were observed. These results provide direct evidence of the noncrystalline solid-state structures of these assemblies and information about how they self-organize on Au111 surfaces, which is of importance in the potential manufacturing of functional nanostructures and devices.

Estimating temporal and spatial variation of ocean surface pCO2 in the North Pacific using a self-organizing map neural network technique

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S. Nakaoka

2013-09-01

Full Text Available This study uses a neural network technique to produce maps of the partial pressure of oceanic carbon dioxide (pCO2sea in the North Pacific on a 0.25° latitude × 0.25° longitude grid from 2002 to 2008. The pCO2sea distribution was computed using a self-organizing map (SOM originally utilized to map the pCO2sea in the North Atlantic. Four proxy parameters – sea surface temperature (SST, mixed layer depth, chlorophyll a concentration, and sea surface salinity (SSS – are used during the training phase to enable the network to resolve the nonlinear relationships between the pCO2sea distribution and biogeochemistry of the basin. The observed pCO2sea data were obtained from an extensive dataset generated by the volunteer observation ship program operated by the National Institute for Environmental Studies (NIES. The reconstructed pCO2sea values agreed well with the pCO2sea measurements, with the root-mean-square error ranging from 17.6 μatm (for the NIES dataset used in the SOM to 20.2 μatm (for independent dataset. We confirmed that the pCO2sea estimates could be improved by including SSS as one of the training parameters and by taking into account secular increases of pCO2sea that have tracked increases in atmospheric CO2. Estimated pCO2sea values accurately reproduced pCO2sea data at several time series locations in the North Pacific. The distributions of pCO2sea revealed by 7 yr averaged monthly pCO2sea maps were similar to Lamont-Doherty Earth Observatory pCO2sea climatology, allowing, however, for a more detailed analysis of biogeochemical conditions. The distributions of pCO2sea anomalies over the North Pacific during the winter clearly showed regional contrasts between El Niño and La Niña years related to changes of SST and vertical mixing.

Surface self-organization in multilayer film coatings

Science.gov (United States)

Shuvalov, Gleb M.; Kostyrko, Sergey A.

2017-12-01

It is a recognized fact that during film deposition and subsequent thermal processing the film surface evolves into an undulating profile. Surface roughness affects many important aspects in the engineering application of thin film materials such as wetting, heat transfer, mechanical, electromagnetic and optical properties. To accurately control the morphological surface modifications at the micro- and nanoscale and improve manufacturing techniques, we design a mathematical model of the surface self-organization process in multilayer film materials. In this paper, we consider a solid film coating with an arbitrary number of layers under plane strain conditions. The film surface has a small initial perturbation described by a periodic function. It is assumed that the evolution of the surface relief is governed by surface and volume diffusion. Based on Gibbs thermodynamics and linear theory of elasticity, we present a procedure for constructing a governing equation that gives the amplitude change of the surface perturbation with time. A parametric study of the evolution equation leads to the definition of a critical undulation wavelength that stabilizes the surface. As a numerical result, the influence of geometrical and physical parameters on the morphological stability of an isotropic two-layered film coating is analyzed.

Self-organizing maps for measuring similarity of audiovisual speech percepts

DEFF Research Database (Denmark)

Bothe, Hans-Heinrich

The goal of this work is to find a way to measure similarity of audiovisual speech percepts. Phoneme-related self-organizing maps (SOM) with a rectangular basis are trained with data material from a (labeled) video film. For the training, a combination of auditory speech features and corresponding....... Dependent on the training data, these other units may also be contextually immediate neighboring units. The poster demonstrates the idea with text material spoken by one individual subject using a set of simple audio-visual features. The data material for the training process consists of 44 labeled...... sentences in German with a balanced phoneme repertoire. As a result it can be stated that (i) the SOM can be trained to map auditory and visual features in a topology-preserving way and (ii) they show strain due to the influence of other audio-visual units. The SOM can be used to measure similarity amongst...

Intelligent Machine Vision for Automated Fence Intruder Detection Using Self-organizing Map

OpenAIRE

Veldin A. Talorete Jr.; Sherwin A Guirnaldo

2017-01-01

This paper presents an intelligent machine vision for automated fence intruder detection. A series of still captured images that contain fence events using Internet Protocol cameras was used as input data to the system. Two classifiers were used; the first is to classify human posture and the second one will classify intruder location. The system classifiers were implemented using Self-Organizing Map after the implementation of several image segmentation processes. The human posture classifie...

Pattern classification and recognition of invertebrate functional groups using self-organizing neural networks.

Science.gov (United States)

Zhang, WenJun

2007-07-01

Self-organizing neural networks can be used to mimic non-linear systems. The main objective of this study is to make pattern classification and recognition on sampling information using two self-organizing neural network models. Invertebrate functional groups sampled in the irrigated rice field were classified and recognized using one-dimensional self-organizing map and self-organizing competitive learning neural networks. Comparisons between neural network models, distance (similarity) measures, and number of neurons were conducted. The results showed that self-organizing map and self-organizing competitive learning neural network models were effective in pattern classification and recognition of sampling information. Overall the performance of one-dimensional self-organizing map neural network was better than self-organizing competitive learning neural network. The number of neurons could determine the number of classes in the classification. Different neural network models with various distance (similarity) measures yielded similar classifications. Some differences, dependent upon the specific network structure, would be found. The pattern of an unrecognized functional group was recognized with the self-organizing neural network. A relative consistent classification indicated that the following invertebrate functional groups, terrestrial blood sucker; terrestrial flyer; tourist (nonpredatory species with no known functional role other than as prey in ecosystem); gall former; collector (gather, deposit feeder); predator and parasitoid; leaf miner; idiobiont (acarine ectoparasitoid), were classified into the same group, and the following invertebrate functional groups, external plant feeder; terrestrial crawler, walker, jumper or hunter; neustonic (water surface) swimmer (semi-aquatic), were classified into another group. It was concluded that reliable conclusions could be drawn from comparisons of different neural network models that use different distance

Growing hierarchical probabilistic self-organizing graphs.

Science.gov (United States)

López-Rubio, Ezequiel; Palomo, Esteban José

2011-07-01

Since the introduction of the growing hierarchical self-organizing map, much work has been done on self-organizing neural models with a dynamic structure. These models allow adjusting the layers of the model to the features of the input dataset. Here we propose a new self-organizing model which is based on a probabilistic mixture of multivariate Gaussian components. The learning rule is derived from the stochastic approximation framework, and a probabilistic criterion is used to control the growth of the model. Moreover, the model is able to adapt to the topology of each layer, so that a hierarchy of dynamic graphs is built. This overcomes the limitations of the self-organizing maps with a fixed topology, and gives rise to a faithful visualization method for high-dimensional data.

Large-Scale Mapping of Carbon Stocks in Riparian Forests with Self-Organizing Maps and the k-Nearest-Neighbor Algorithm

Directory of Open Access Journals (Sweden)

Leonhard Suchenwirth

2014-07-01

Full Text Available Among the machine learning tools being used in recent years for environmental applications such as forestry, self-organizing maps (SOM and the k-nearest neighbor (kNN algorithm have been used successfully. We applied both methods for the mapping of organic carbon (Corg in riparian forests due to their considerably high carbon storage capacity. Despite the importance of floodplains for carbon sequestration, a sufficient scientific foundation for creating large-scale maps showing the spatial Corg distribution is still missing. We estimated organic carbon in a test site in the Danube Floodplain based on RapidEye remote sensing data and additional geodata. Accordingly, carbon distribution maps of vegetation, soil, and total Corg stocks were derived. Results were compared and statistically evaluated with terrestrial survey data for outcomes with pure remote sensing data and for the combination with additional geodata using bias and the Root Mean Square Error (RMSE. Results show that SOM and kNN approaches enable us to reproduce spatial patterns of riparian forest Corg stocks. While vegetation Corg has very high RMSEs, outcomes for soil and total Corg stocks are less biased with a lower RMSE, especially when remote sensing and additional geodata are conjointly applied. SOMs show similar percentages of RMSE to kNN estimations.

Autonomous Data Collection Using a Self-Organizing Map.

Science.gov (United States)

Faigl, Jan; Hollinger, Geoffrey A

2018-05-01

The self-organizing map (SOM) is an unsupervised learning technique providing a transformation of a high-dimensional input space into a lower dimensional output space. In this paper, we utilize the SOM for the traveling salesman problem (TSP) to develop a solution to autonomous data collection. Autonomous data collection requires gathering data from predeployed sensors by moving within a limited communication radius. We propose a new growing SOM that adapts the number of neurons during learning, which also allows our approach to apply in cases where some sensors can be ignored due to a lower priority. Based on a comparison with available combinatorial heuristic algorithms for relevant variants of the TSP, the proposed approach demonstrates improved results, while also being less computationally demanding. Moreover, the proposed learning procedure can be extended to cases where particular sensors have varying communication radii, and it can also be extended to multivehicle planning.

Comparison of brass alloys composition by laser-induced breakdown spectroscopy and self-organizing maps

Energy Technology Data Exchange (ETDEWEB)

Pagnotta, Stefano; Grifoni, Emanuela; Legnaioli, Stefano [Applied and Laser Spectroscopy Laboratory, ICCOM-CNR, Research Area of Pisa, Via G. Moruzzi 1, 56124 Pisa (Italy); Lezzerini, Marco [Department of Earth Sciences, University of Pisa, Via S. Maria 53, 56126 Pisa (Italy); Lorenzetti, Giulia [Applied and Laser Spectroscopy Laboratory, ICCOM-CNR, Research Area of Pisa, Via G. Moruzzi 1, 56124 Pisa (Italy); Palleschi, Vincenzo, E-mail: vincenzo.palleschi@cnr.it [Applied and Laser Spectroscopy Laboratory, ICCOM-CNR, Research Area of Pisa, Via G. Moruzzi 1, 56124 Pisa (Italy); Department of Civilizations and Forms of Knowledge, University of Pisa, Via L. Galvani 1, 56126 Pisa (Italy)

2015-01-01

In this paper we face the problem of assessing similarities in the composition of different metallic alloys, using the laser-induced breakdown spectroscopy technique. The possibility of determining the degree of similarity through the use of artificial neural networks and self-organizing maps is discussed. As an example, we present a case study involving the comparison of two historical brass samples, very similar in their composition. The results of the paper can be extended to many other situations, not necessarily associated with cultural heritage and archeological studies, where objects with similar composition have to be compared. - Highlights: • A method for assessing the similarity of materials analyzed by LIBS is proposed. • Two very similar fragments of historical brass were analyzed. • Using a simple artificial neural network the composition of the two alloys was determined. • The composition of the two brass alloys was the same within the experimental error. • Using self-organizing maps, the probability of the alloys to have the same composition was assessed.

Comparison of brass alloys composition by laser-induced breakdown spectroscopy and self-organizing maps

International Nuclear Information System (INIS)

Pagnotta, Stefano; Grifoni, Emanuela; Legnaioli, Stefano; Lezzerini, Marco; Lorenzetti, Giulia; Palleschi, Vincenzo

2015-01-01

In this paper we face the problem of assessing similarities in the composition of different metallic alloys, using the laser-induced breakdown spectroscopy technique. The possibility of determining the degree of similarity through the use of artificial neural networks and self-organizing maps is discussed. As an example, we present a case study involving the comparison of two historical brass samples, very similar in their composition. The results of the paper can be extended to many other situations, not necessarily associated with cultural heritage and archeological studies, where objects with similar composition have to be compared. - Highlights: • A method for assessing the similarity of materials analyzed by LIBS is proposed. • Two very similar fragments of historical brass were analyzed. • Using a simple artificial neural network the composition of the two alloys was determined. • The composition of the two brass alloys was the same within the experimental error. • Using self-organizing maps, the probability of the alloys to have the same composition was assessed

Manifold Learning with Self-Organizing Mapping for Feature Extraction of Nonlinear Faults in Rotating Machinery

Directory of Open Access Journals (Sweden)

Lin Liang

2015-01-01

Full Text Available A new method for extracting the low-dimensional feature automatically with self-organization mapping manifold is proposed for the detection of rotating mechanical nonlinear faults (such as rubbing, pedestal looseness. Under the phase space reconstructed by single vibration signal, the self-organization mapping (SOM with expectation maximization iteration algorithm is used to divide the local neighborhoods adaptively without manual intervention. After that, the local tangent space alignment algorithm is adopted to compress the high-dimensional phase space into low-dimensional feature space. The proposed method takes advantages of the manifold learning in low-dimensional feature extraction and adaptive neighborhood construction of SOM and can extract intrinsic fault features of interest in two dimensional projection space. To evaluate the performance of the proposed method, the Lorenz system was simulated and rotation machinery with nonlinear faults was obtained for test purposes. Compared with the holospectrum approaches, the results reveal that the proposed method is superior in identifying faults and effective for rotating machinery condition monitoring.

An Algorithm Based on the Self-Organized Maps for the Classification of Facial Features

Directory of Open Access Journals (Sweden)

Gheorghe Gîlcă

2015-12-01

Full Text Available This paper deals with an algorithm based on Self Organized Maps networks which classifies facial features. The proposed algorithm can categorize the facial features defined by the input variables: eyebrow, mouth, eyelids into a map of their grouping. The groups map is based on calculating the distance between each input vector and each output neuron layer , the neuron with the minimum distance being declared winner neuron. The network structure consists of two levels: the first level contains three input vectors, each having forty-one values, while the second level contains the SOM competitive network which consists of 100 neurons. The proposed system can classify facial features quickly and easily using the proposed algorithm based on SOMs.

Clustering analysis of malware behavior using Self Organizing Map

DEFF Research Database (Denmark)

Pirscoveanu, Radu-Stefan; Stevanovic, Matija; Pedersen, Jens Myrup

2016-01-01

For the time being, malware behavioral classification is performed by means of Anti-Virus (AV) generated labels. The paper investigates the inconsistencies associated with current practices by evaluating the identified differences between current vendors. In this paper we rely on Self Organizing...... Map, an unsupervised machine learning algorithm, for generating clusters that capture the similarities between malware behavior. A data set of approximately 270,000 samples was used to generate the behavioral profile of malicious types in order to compare the outcome of the proposed clustering...... approach with the labels collected from 57 Antivirus vendors using VirusTotal. Upon evaluating the results, the paper concludes on shortcomings of relying on AV vendors for labeling malware samples. In order to solve the problem, a cluster-based classification is proposed, which should provide more...

Characterization of Suicidal Behaviour with Self-Organizing Maps

Directory of Open Access Journals (Sweden)

José M. Leiva-Murillo

2013-01-01

Full Text Available The study of the variables involved in suicidal behavior is important from a social, medical, and economical point of view. Given the high number of potential variables of interest, a large population of subjects must be analysed in order to get conclusive results. In this paper, we describe a method based on self-organizing maps (SOMs for finding the most relevant variables even when their relation to suicidal behavior is strongly nonlinear. We have applied the method to a cohort with more than 8,000 subjects and 600 variables and discovered four groups of variables involved in suicidal behavior. According to the results, there are four main groups of risk factors that characterize the population of suicide attempters: mental disorders, alcoholism, impulsivity, and childhood abuse. The identification of specific subpopulations of suicide attempters is consistent with current medical knowledge and may provide a new avenue of research to improve the management of suicidal cases.

Curbing domestic violence: Instantiating C-K theory with formal concept analysis and emergent self-organizing maps

NARCIS (Netherlands)

Poelmans, J.; Elzinga, P.; Viaene, S.; Dedene, G.

2010-01-01

We propose a human-centred process for knowledge discovery from unstructured text that makes use of formal concept analysis and emergent self-organizing maps. The knowledge discovery process is conceptualized and interpreted as successive iterations through the concept-knowledge (C-K) theory design

Identification of lithofacies using Kohonen self-organizing maps

Science.gov (United States)

Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.

2002-01-01

Lithofacies identification is a primary task in reservoir characterization. Traditional techniques of lithofacies identification from core data are costly, and it is difficult to extrapolate to non-cored wells. We present a low-cost automated technique using Kohonen self-organizing maps (SOMs) to identify systematically and objectively lithofacies from well log data. SOMs are unsupervised artificial neural networks that map the input space into clusters in a topological form whose organization is related to trends in the input data. A case study used five wells located in Appleton Field, Escambia County, Alabama (Smackover Formation, limestone and dolomite, Oxfordian, Jurassic). A five-input, one-dimensional output approach is employed, assuming the lithofacies are in ascending/descending order with respect to paleoenvironmental energy levels. To consider the possible appearance of new logfacies not seen in training mode, which may potentially appear in test wells, the maximum number of outputs is set to 20 instead of four, the designated number of lithosfacies in the study area. This study found eleven major clusters. The clusters were compared to depositional lithofacies identified by manual core examination. The clusters were ordered by the SOM in a pattern consistent with environmental gradients inferred from core examination: bind/boundstone, grainstone, packstone, and wackestone. This new approach predicted lithofacies identity from well log data with 78.8% accuracy which is more accurate than using a backpropagation neural network (57.3%). The clusters produced by the SOM are ordered with respect to paleoenvironmental energy levels. This energy-related clustering provides geologists and petroleum engineers with valuable geologic information about the logfacies and their interrelationships. This advantage is not obtained in backpropagation neural networks and adaptive resonance theory neural networks. ?? 2002 Elsevier Science Ltd. All rights reserved.

Mapping the surface of Escherichia coli peptide deformylase by NMR with organic solvents.

Science.gov (United States)

Byerly, Douglas W; McElroy, Craig A; Foster, Mark P

2002-07-01

Identifying potential ligand binding sites on a protein surface is an important first step for targeted structure-based drug discovery. While performing control experiments with Escherichia coli peptide deformylase (PDF), we noted that the organic solvents used to solubilize some ligands perturbed many of the same resonances in PDF as the small molecule inhibitors. To further explore this observation, we recorded (15)N HSQC spectra of E. coli peptide deformylase (PDF) in the presence of trace quantities of several simple organic solvents (acetone, DMSO, ethanol, isopropanol) and identified their sites of interaction from local perturbation of amide chemical shifts. Analysis of the protein surface structure revealed that the ligand-induced shift perturbations map to the active site and one additional surface pocket. The correlation between sites of solvent and inhibitor binding highlights the utility of organic solvents to rapidly and effectively validate and characterize binding sites on proteins prior to designing a drug discovery screen. Further, the solvent-induced perturbations have implications for the use of organic solvents to dissolve candidate ligands in NMR-based screens.

Mesoscopic self-organization of a self-assembled supramolecular rectangle on highly oriented pyrolytic graphite and Au(111) surfaces.

Science.gov (United States)

Gong, Jian-Ru; Wan, Li-Jun; Yuan, Qun-Hui; Bai, Chun-Li; Jude, Hershel; Stang, Peter J

2005-01-25

A self-assembled supramolecular metallacyclic rectangle was investigated with scanning tunneling microscopy on highly oriented pyrolytic graphite and Au(111) surfaces. The rectangles spontaneously adsorb on both surfaces and self-organize into well ordered adlayers. On highly oriented pyrolytic graphite, the long edge of the rectangle stands on the surface, forming a 2D molecular network. In contrast, the face of the rectangle lays flat on the Au(111) surface, forming linear chains. The structures and intramolecular features obtained through high-resolution scanning tunneling microscopy imaging are discussed.

Self-Organizing Map Models of Language Acquisition

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Ping eLi

2013-11-01

Full Text Available Connectionist models have had a profound impact on theories of language. While most early models were inspired by the classic PDP architecture, recent models of language have explored various other types of models, including self-organizing models for language acquisition. In this paper we aim at providing a review of the latter type of models, and highlight a number of simulation experiments that we have conducted based on these models. We show that self-organizing connectionist models can provide significant insights into long-standing debates in both monolingual and bilingual language development.

Electrochemical Control of Peptide Self-Organization on Atomically Flat Solid Surfaces: A Case Study with Graphite.

Science.gov (United States)

Seki, Takakazu; So, Christopher R; Page, Tamon R; Starkebaum, David; Hayamizu, Yuhei; Sarikaya, Mehmet

2018-02-06

The nanoscale self-organization of biomolecules, such as proteins and peptides, on solid surfaces under controlled conditions is an important issue in establishing functional bio/solid soft interfaces for bioassays, biosensors, and biofuel cells. Electrostatic interaction between proteins and surfaces is one of the most essential parameters in the adsorption and self-assembly of proteins on solid surfaces. Although the adsorption of proteins has been studied with respect to the electrochemical surface potential, the self-assembly of proteins or peptides forming well-organized nanostructures templated by lattice structure of the solid surfaces has not been studied in the relation to the surface potential. In this work, we utilize graphite-binding peptides (GrBPs) selected by the phage display method to investigate the relationship between the electrochemical potential of the highly ordered pyrolytic graphite (HOPG) and peptide self-organization forming long-range-ordered structures. Under modulated electrical bias, graphite-binding peptides form various ordered structures, such as well-ordered nanowires, dendritic structures, wavy wires, amorphous (disordered) structures, and islands. A systematic investigation of the correlation between peptide sequence and self-organizational characteristics reveals that the presence of the bias-sensitive amino acid modules in the peptide sequence has a significant effect on not only surface coverage but also on the morphological features of self-assembled structures. Our results show a new method to control peptide self-assembly by means of applied electrochemical bias as well as peptide design-rules for the construction of functional soft bio/solid interfaces that could be integrated in a wide range of practical implementations.

Self-enhancement learning: target-creating learning and its application to self-organizing maps.

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Kamimura, Ryotaro

2011-05-01

In this article, we propose a new learning method called "self-enhancement learning." In this method, targets for learning are not given from the outside, but they can be spontaneously created within a neural network. To realize the method, we consider a neural network with two different states, namely, an enhanced and a relaxed state. The enhanced state is one in which the network responds very selectively to input patterns, while in the relaxed state, the network responds almost equally to input patterns. The gap between the two states can be reduced by minimizing the Kullback-Leibler divergence between the two states with free energy. To demonstrate the effectiveness of this method, we applied self-enhancement learning to the self-organizing maps, or SOM, in which lateral interactions were added to an enhanced state. We applied the method to the well-known Iris, wine, housing and cancer machine learning database problems. In addition, we applied the method to real-life data, a student survey. Experimental results showed that the U-matrices obtained were similar to those produced by the conventional SOM. Class boundaries were made clearer in the housing and cancer data. For all the data, except for the cancer data, better performance could be obtained in terms of quantitative and topological errors. In addition, we could see that the trustworthiness and continuity, referring to the quality of neighborhood preservation, could be improved by the self-enhancement learning. Finally, we used modern dimensionality reduction methods and compared their results with those obtained by the self-enhancement learning. The results obtained by the self-enhancement were not superior to but comparable with those obtained by the modern dimensionality reduction methods.

Identifying changes in dissolved organic matter content and characteristics by fluorescence spectroscopy coupled with self-organizing map and classification and regression tree analysis during wastewater treatment.

Science.gov (United States)

Yu, Huibin; Song, Yonghui; Liu, Ruixia; Pan, Hongwei; Xiang, Liancheng; Qian, Feng

2014-10-01

The stabilization of latent tracers of dissolved organic matter (DOM) of wastewater was analyzed by three-dimensional excitation-emission matrix (EEM) fluorescence spectroscopy coupled with self-organizing map and classification and regression tree analysis (CART) in wastewater treatment performance. DOM of water samples collected from primary sedimentation, anaerobic, anoxic, oxic and secondary sedimentation tanks in a large-scale wastewater treatment plant contained four fluorescence components: tryptophan-like (C1), tyrosine-like (C2), microbial humic-like (C3) and fulvic-like (C4) materials extracted by self-organizing map. These components showed good positive linear correlations with dissolved organic carbon of DOM. C1 and C2 were representative components in the wastewater, and they were removed to a higher extent than those of C3 and C4 in the treatment process. C2 was a latent parameter determined by CART to differentiate water samples of oxic and secondary sedimentation tanks from the successive treatment units, indirectly proving that most of tyrosine-like material was degraded by anaerobic microorganisms. C1 was an accurate parameter to comprehensively separate the samples of the five treatment units from each other, indirectly indicating that tryptophan-like material was decomposed by anaerobic and aerobic bacteria. EEM fluorescence spectroscopy in combination with self-organizing map and CART analysis can be a nondestructive effective method for characterizing structural component of DOM fractions and monitoring organic matter removal in wastewater treatment process. Copyright © 2014 Elsevier Ltd. All rights reserved.

Organic surfaces exposed by self-assembled organothiol monolayers: Preparation, characterization, and application

Science.gov (United States)

Kind, Martin; Wöll, Christof

2009-07-01

Organic surfaces play a major role in materials science. Most surfaces that we touch in our daily lives are made from organic materials, e.g., vegetables, fruit, skin, wood, and textiles made from natural fibers. In the context of biology, organic surfaces play a prominent role too, proteins docking onto cell surfaces are a good example. To better understand the characteristics of organic surfaces, including physico-chemical properties like wettability or chemical reactivities and physical properties like friction and lubrication, a structurally well-defined model system that can be investigated with numerous analytical techniques is desirable. In the last two decades, one particular system, self-assembled monolayers or SAMs, have demonstrated their suitability for this purpose. In particular, organothiols consisting of an organic molecule with an attached SH-group are well suited to fabricating structurally well-defined adlayers of monolayer thickness on gold substrates using a simple preparation procedure. These ultrathin monolayers expose an organic surface with properties that can be tailored by varying the type of organothiol employed. After a short introduction into the preparation of SAMs, this article provides an overview of the possibilities and limitations of organic surfaces exposed by Au-thiolate SAMs. Applications are as diverse as the metallization of organic surfaces, a fundamental problem in materials science, and the fabrication of surfaces that resist the adsorption of proteins. In addition to a number of different case studies, we will also discuss the most powerful analytical techniques needed to characterize these important model systems.

Self Organizing Maps to efficiently cluster and functionally interpret protein conformational ensembles

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Fabio Stella

2013-09-01

Full Text Available An approach that combines Self-Organizing maps, hierarchical clustering and network components is presented, aimed at comparing protein conformational ensembles obtained from multiple Molecular Dynamic simulations. As a first result the original ensembles can be summarized by using only the representative conformations of the clusters obtained. In addition the network components analysis allows to discover and interpret the dynamic behavior of the conformations won by each neuron. The results showed the ability of this approach to efficiently derive a functional interpretation of the protein dynamics described by the original conformational ensemble, highlighting its potential as a support for protein engineering.

Automatic lithofacies segmentation from well-logs data. A comparative study between the Self-Organizing Map (SOM) and Walsh transform

Science.gov (United States)

Aliouane, Leila; Ouadfeul, Sid-Ali; Rabhi, Abdessalem; Rouina, Fouzi; Benaissa, Zahia; Boudella, Amar

2013-04-01

The main goal of this work is to realize a comparison between two lithofacies segmentation techniques of reservoir interval. The first one is based on the Kohonen's Self-Organizing Map neural network machine. The second technique is based on the Walsh transform decomposition. Application to real well-logs data of two boreholes located in the Algerian Sahara shows that the Self-organizing map is able to provide more lithological details that the obtained lithofacies model given by the Walsh decomposition. Keywords: Comparison, Lithofacies, SOM, Walsh References: 1)Aliouane, L., Ouadfeul, S., Boudella, A., 2011, Fractal analysis based on the continuous wavelet transform and lithofacies classification from well-logs data using the self-organizing map neural network, Arabian Journal of geosciences, doi: 10.1007/s12517-011-0459-4 2) Aliouane, L., Ouadfeul, S., Djarfour, N., Boudella, A., 2012, Petrophysical Parameters Estimation from Well-Logs Data Using Multilayer Perceptron and Radial Basis Function Neural Networks, Lecture Notes in Computer Science Volume 7667, 2012, pp 730-736, doi : 10.1007/978-3-642-34500-5_86 3)Ouadfeul, S. and Aliouane., L., 2011, Multifractal analysis revisited by the continuous wavelet transform applied in lithofacies segmentation from well-logs data, International journal of applied physics and mathematics, Vol01 N01. 4) Ouadfeul, S., Aliouane, L., 2012, Lithofacies Classification Using the Multilayer Perceptron and the Self-organizing Neural Networks, Lecture Notes in Computer Science Volume 7667, 2012, pp 737-744, doi : 10.1007/978-3-642-34500-5_87 5) Weisstein, Eric W. "Fast Walsh Transform." From MathWorld--A Wolfram Web Resource. http://mathworld.wolfram.com/FastWalshTransform.html

Detecting domestic violence: Showcasing a knowledge browser based on formal concept analysis and emergent self organizing maps

NARCIS (Netherlands)

Elzinga, P.; Poelmans, J.; Viaene, S.; Dedene, G.; Cordeiro, J.; Filipe, J.

2009-01-01

Over 90% of the case data from police inquiries is stored as unstructured text in police databases. We use the combination of Formal Concept Analysis and Emergent Self Organizing Maps for exploring a dataset of unstructured police reports out of the Amsterdam-Amstelland police region in the

Mobile Anomaly Detection Based on Improved Self-Organizing Maps

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Chunyong Yin

2017-01-01

Full Text Available Anomaly detection has always been the focus of researchers and especially, the developments of mobile devices raise new challenges of anomaly detection. For example, mobile devices can keep connection with Internet and they are rarely turned off even at night. This means mobile devices can attack nodes or be attacked at night without being perceived by users and they have different characteristics from Internet behaviors. The introduction of data mining has made leaps forward in this field. Self-organizing maps, one of famous clustering algorithms, are affected by initial weight vectors and the clustering result is unstable. The optimal method of selecting initial clustering centers is transplanted from K-means to SOM. To evaluate the performance of improved SOM, we utilize diverse datasets and KDD Cup99 dataset to compare it with traditional one. The experimental results show that improved SOM can get higher accuracy rate for universal datasets. As for KDD Cup99 dataset, it achieves higher recall rate and precision rate.

Interpretation of fingerprint image quality features extracted by self-organizing maps

Science.gov (United States)

Danov, Ivan; Olsen, Martin A.; Busch, Christoph

2014-05-01

Accurate prediction of fingerprint quality is of significant importance to any fingerprint-based biometric system. Ensuring high quality samples for both probe and reference can substantially improve the system's performance by lowering false non-matches, thus allowing finer adjustment of the decision threshold of the biometric system. Furthermore, the increasing usage of biometrics in mobile contexts demands development of lightweight methods for operational environment. A novel two-tier computationally efficient approach was recently proposed based on modelling block-wise fingerprint image data using Self-Organizing Map (SOM) to extract specific ridge pattern features, which are then used as an input to a Random Forests (RF) classifier trained to predict the quality score of a propagated sample. This paper conducts an investigative comparative analysis on a publicly available dataset for the improvement of the two-tier approach by proposing additionally three feature interpretation methods, based respectively on SOM, Generative Topographic Mapping and RF. The analysis shows that two of the proposed methods produce promising results on the given dataset.

Self-organizing map models of language acquisition

Science.gov (United States)

Li, Ping; Zhao, Xiaowei

2013-01-01

Connectionist models have had a profound impact on theories of language. While most early models were inspired by the classic parallel distributed processing architecture, recent models of language have explored various other types of models, including self-organizing models for language acquisition. In this paper, we aim at providing a review of the latter type of models, and highlight a number of simulation experiments that we have conducted based on these models. We show that self-organizing connectionist models can provide significant insights into long-standing debates in both monolingual and bilingual language development. We suggest future directions in which these models can be extended, to better connect with behavioral and neural data, and to make clear predictions in testing relevant psycholinguistic theories. PMID:24312061

Hierarchical Self Organizing Map for Novelty Detection using Mobile Robot with Robust Sensor

International Nuclear Information System (INIS)

Sha'abani, M N A H; Miskon, M F; Sakidin, H

2013-01-01

This paper presents a novelty detection method based on Self Organizing Map neural network using a mobile robot. Based on hierarchical neural network, the network is divided into three networks; position, orientation and sensor measurement network. A simulation was done to demonstrate and validate the proposed method using MobileSim. Three cases of abnormal events; new, missing and shifted objects are employed for performance evaluation. The result of detection was then filtered for false positive detection. The result shows that the inspection produced less than 2% false positive detection at high sensitivity settings

Improving Security for SCADA Sensor Networks with Reputation Systems and Self-Organizing Maps

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Javier Blesa

2009-11-01

Full Text Available The reliable operation of modern infrastructures depends on computerized systems and Supervisory Control and Data Acquisition (SCADA systems, which are also based on the data obtained from sensor networks. The inherent limitations of the sensor devices make them extremely vulnerable to cyberwarfare/cyberterrorism attacks. In this paper, we propose a reputation system enhanced with distributed agents, based on unsupervised learning algorithms (self-organizing maps, in order to achieve fault tolerance and enhanced resistance to previously unknown attacks. This approach has been extensively simulated and compared with previous proposals.

Femtosecond pulse laser-induced self-organized nanostructures on the surface of ZnO crystal

International Nuclear Information System (INIS)

Zhong Minjian; Guo Guanglei; Yang Junyi; Ma Ninghua; Ye Guo; Ma Hongliang; Guo Xiaodong; Li Ruxin

2008-01-01

This paper reports self-organized nanostructures observed on the surface of ZnO crystal after irradiation by a focused beam of a femtosecond Ti:sapphire laser with a repetition rate of 250 kHz. For a linearly polarized femtosecond laser, the periodic nanograting structure on the ablation crater surface was promoted. The period of self-organization structures is about 180 nm. The grating orientation is adjusted by the laser polarization direction. A long range Bragg-like grating is formed by moving the sample at a speed of 10 μm/s. For a circularly polarized laser beam, uniform spherical nanoparticles were formed as a result of Coulomb explosion during the interaction of near-infrared laser with ZnO crystal

Clustering Multiple Sclerosis Subgroups with Multifractal Methods and Self-Organizing Map Algorithm

Science.gov (United States)

Karaca, Yeliz; Cattani, Carlo

Magnetic resonance imaging (MRI) is the most sensitive method to detect chronic nervous system diseases such as multiple sclerosis (MS). In this paper, Brownian motion Hölder regularity functions (polynomial, periodic (sine), exponential) for 2D image, such as multifractal methods were applied to MR brain images, aiming to easily identify distressed regions, in MS patients. With these regions, we have proposed an MS classification based on the multifractal method by using the Self-Organizing Map (SOM) algorithm. Thus, we obtained a cluster analysis by identifying pixels from distressed regions in MR images through multifractal methods and by diagnosing subgroups of MS patients through artificial neural networks.

Biomimetic approaches to create anti-reflection glass surfaces for solar cells using self-organizing techniques

International Nuclear Information System (INIS)

Achtelik, J.; Sievers, W.; Lindner, J.K.N.

2013-01-01

Highlights: ► Nanostructured glass surfaces with theoretically near-to-zero reflectivity in the UVNIR region. ► Simple fabrication process using self-organization during reactive ion etching proposed. ► Prediction of optical reflectivity from AFM measured surface morphology. -- Abstract: Aiming to diminish the reflection losses of glass covered light harvesting devices, the optical reflectivity of nanostructured glass surfaces is studied theoretically and experimentally. The work is inspired by the nanoscale roughness of insect eyes, which is tried to be replicated on a technical glass surface. To this end, the reflectivity of glass surfaces with topographies represented by linear, parabolic and Fermi-shaped glass/air fill factor profiles is calculated for normal incidence. It is shown that using the latter ones, an almost complete suppression of reflections can be achieved. A simple, self-organization technique to create such Fermi-shaped filling factor profiles in glass experimentally is also presented

Biomimetic approaches to create anti-reflection glass surfaces for solar cells using self-organizing techniques

Energy Technology Data Exchange (ETDEWEB)

Achtelik, J.; Sievers, W. [University of Paderborn, Department of Physics, 33098 Paderborn (Germany); Center of Optoelectronics and Photonics Paderborn CeOPP, 33098 Paderborn (Germany); Lindner, J.K.N., E-mail: lindner@physik.uni-paderborn.de [University of Paderborn, Department of Physics, 33098 Paderborn (Germany); Center of Optoelectronics and Photonics Paderborn CeOPP, 33098 Paderborn (Germany)

2013-05-15

Highlights: ► Nanostructured glass surfaces with theoretically near-to-zero reflectivity in the UVNIR region. ► Simple fabrication process using self-organization during reactive ion etching proposed. ► Prediction of optical reflectivity from AFM measured surface morphology. -- Abstract: Aiming to diminish the reflection losses of glass covered light harvesting devices, the optical reflectivity of nanostructured glass surfaces is studied theoretically and experimentally. The work is inspired by the nanoscale roughness of insect eyes, which is tried to be replicated on a technical glass surface. To this end, the reflectivity of glass surfaces with topographies represented by linear, parabolic and Fermi-shaped glass/air fill factor profiles is calculated for normal incidence. It is shown that using the latter ones, an almost complete suppression of reflections can be achieved. A simple, self-organization technique to create such Fermi-shaped filling factor profiles in glass experimentally is also presented.

The microscopic origin of self-organized nanostripe pattern formation on an electropolished aluminium surface

International Nuclear Information System (INIS)

Sarkar, Jaya; Basumallick, A; Khan, Gobinda Gopal

2009-01-01

By correlating the experimental evidence obtained from atomic force microscopy, conventional x-ray diffraction, and a surface sensitive modified x-ray diffraction technique with the results of density functional theory based computations, we demonstrate that self-organized nanostripe patterns formed on the electropolished surface of aluminium originate as a consequence of relaxation and reconstruction of the new surfaces exposed and textural changes at the surface caused by the dissolution during polishing.

Semi-automatic mapping of linear-trending bedforms using 'Self-Organizing Maps' algorithm

Science.gov (United States)

Foroutan, M.; Zimbelman, J. R.

2017-09-01

Increased application of high resolution spatial data such as high resolution satellite or Unmanned Aerial Vehicle (UAV) images from Earth, as well as High Resolution Imaging Science Experiment (HiRISE) images from Mars, makes it necessary to increase automation techniques capable of extracting detailed geomorphologic elements from such large data sets. Model validation by repeated images in environmental management studies such as climate-related changes as well as increasing access to high-resolution satellite images underline the demand for detailed automatic image-processing techniques in remote sensing. This study presents a methodology based on an unsupervised Artificial Neural Network (ANN) algorithm, known as Self Organizing Maps (SOM), to achieve the semi-automatic extraction of linear features with small footprints on satellite images. SOM is based on competitive learning and is efficient for handling huge data sets. We applied the SOM algorithm to high resolution satellite images of Earth and Mars (Quickbird, Worldview and HiRISE) in order to facilitate and speed up image analysis along with the improvement of the accuracy of results. About 98% overall accuracy and 0.001 quantization error in the recognition of small linear-trending bedforms demonstrate a promising framework.

Business Client Segmentation in Banking Using Self-Organizing Maps

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Bach Mirjana Pejić

2014-11-01

Full Text Available Segmentation in banking for the business client market is traditionally based on size measured in terms of income and the number of employees, and on statistical clustering methods (e.g. hierarchical clustering, k-means. The goal of the paper is to demonstrate that self-organizing maps (SOM effectively extend the pool of possible criteria for segmentation of the business client market with more relevant criteria, including behavioral, demographic, personal, operational, situational, and cross-selling products. In order to attain the goal of the paper, the dataset on business clients of several banks in Croatia, which, besides size, incorporates a number of different criteria, is analyzed using the SOM-Ward clustering algorithm of Viscovery SOMine software. The SOM-Ward algorithm extracted three segments that differ with respect to the attributes of foreign trade operations (import/export, annual income, origin of capital, important bank selection criteria, views on the loan selection and the industry. The analyzed segments can be used by banks for deciding on the direction of further marketing activities.

Segmentation of color images by chromaticity features using self-organizing maps

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Farid García-Lamont

2016-05-01

Full Text Available Usually, the segmentation of color images is performed using cluster-based methods and the RGB space to represent the colors. The drawback with these methods is the a priori knowledge of the number of groups, or colors, in the image; besides, the RGB space issensitive to the intensity of the colors. Humans can identify different sections within a scene by the chromaticity of its colors of, as this is the feature humans employ to tell them apart. In this paper, we propose to emulate the human perception of color by training a self-organizing map (SOM with samples of chromaticity of different colors. The image to process is mapped to the HSV space because in this space the chromaticity is decoupled from the intensity, while in the RGB space this is not possible. Our proposal does not require knowing a priori the number of colors within a scene, and non-uniform illumination does not significantly affect the image segmentation. We present experimental results using some images from the Berkeley segmentation database by employing SOMs with different sizes, which are segmented successfully using only chromaticity features.

Distributed Fast Self-Organized Maps for Massive Spectrophotometric Data Analysis †.

Science.gov (United States)

Dafonte, Carlos; Garabato, Daniel; Álvarez, Marco A; Manteiga, Minia

2018-05-03

Analyzing huge amounts of data becomes essential in the era of Big Data, where databases are populated with hundreds of Gigabytes that must be processed to extract knowledge. Hence, classical algorithms must be adapted towards distributed computing methodologies that leverage the underlying computational power of these platforms. Here, a parallel, scalable, and optimized design for self-organized maps (SOM) is proposed in order to analyze massive data gathered by the spectrophotometric sensor of the European Space Agency (ESA) Gaia spacecraft, although it could be extrapolated to other domains. The performance comparison between the sequential implementation and the distributed ones based on Apache Hadoop and Apache Spark is an important part of the work, as well as the detailed analysis of the proposed optimizations. Finally, a domain-specific visualization tool to explore astronomical SOMs is presented.

Better Organic Ternary Memory Performance through Self-Assembled Alkyltrichlorosilane Monolayers on Indium Tin Oxide (ITO) Surfaces.

Science.gov (United States)

Hou, Xiang; Cheng, Xue-Feng; Zhou, Jin; He, Jing-Hui; Xu, Qing-Feng; Li, Hua; Li, Na-Jun; Chen, Dong-Yun; Lu, Jian-Mei

2017-11-16

Recently, surface engineering of the indium tin oxide (ITO) electrode of sandwich-like organic electric memory devices was found to effectively improve their memory performances. However, there are few methods to modify the ITO substrates. In this paper, we have successfully prepared alkyltrichlorosilane self-assembled monolayers (SAMs) on ITO substrates, and resistive random access memory devices are fabricated on these surfaces. Compared to the unmodified ITO substrates, organic molecules (i.e., 2-((4-butylphenyl)amino)-4-((4-butylphenyl)iminio)-3-oxocyclobut-1-en-1-olate, SA-Bu) grown on these SAM-modified ITO substrates have rougher surface morphologies but a smaller mosaicity. The organic layer on the SAM-modified ITO further aged to eliminate the crystalline phase diversity. In consequence, the ternary memory yields are effectively improved to approximately 40-47 %. Our results suggest that the insertion of alkyltrichlorosilane self-assembled monolayers could be an efficient method to improve the performance of organic memory devices. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Self-organizing representations

Energy Technology Data Exchange (ETDEWEB)

Kohonen, T.

1983-01-01

A property which is commonplace in the brain but which has always been ignored in learning machines is the spatial order of the processing units. This order is clearly highly significant and in nature it develops gradually during the lifetime of the organism. It then serves as the basis for perceptual and cognitive processes, and memory, too. The spatial order in biological organisms is often believed to be genetically determined. It is therefore intriguing to learn that a meaningful and optimal spatial order is formed in an extremely simple self-organizing process whereby certain feature maps are formed automatically. 8 references.

Invertebrate diversity classification using self-organizing map neural network: with some special topological functions

Directory of Open Access Journals (Sweden)

WenJun Zhang

2014-06-01

Full Text Available In present study we used self-organizing map (SOM neural network to conduct the non-supervisory clustering of invertebrate orders in rice field. Four topological functions, i.e., cossintopf, sincostopf, acossintopf, and expsintopf, established on the template in toolbox of Matlab, were used in SOM neural network learning. Results showed that clusters were different when using different topological functions because different topological functions will generate different spatial structure of neurons in neural network. We may chose these functions and results based on comparison with the practical situation.

Functional grouping and establishment of distribution patterns of invasive plants in China using self-organizing maps and indicator species analysis

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Wang Zi-Bo

2009-01-01

Full Text Available In the present study, we introduce two techniques - self-organizing maps (SOM and indicator species analysis (INDVAL - for understanding the richness patterns of invasive species. We first employed SOM to identify functional groups and then used INDVAL to identify the representative areas characterizing these functional groups. Quantitative traits and distributional information on 127 invasive plants in 28 provinces of China were collected to form the matrices for our study. The results indicate Jiangsu to be the top province with the highest number of invasive species, while Ningxia was the lowest. Six functional groups were identified by the SOM method, and five of them were found to have significantly representative provinces by the INDVAL method. Our study represents the first attempt to combine self-organizing maps and indicator species analysis to assess the macro-scale distribution of exotic species.

Artificial neural network with self-organizing mapping for reactor stability monitoring

International Nuclear Information System (INIS)

Okumura, Motofumi; Tsuji, Masashi; Shimazu, Yoichiro; Narabayashi, Tadashi

2008-01-01

In BWR stability monitoring damping ratio has been used as a stability index. A method for estimating the damping ratio by applying Principal Component Analysis (PCA) to neutron detector signals measured with local power range monitors (LPRMs) had been developed; In this method, measured fluctuating signal is decomposed into some independent components and the signal component directly related to stability is extracted among them to determine the damping ratio. For online monitoring, it is necessary to select stability related signal component efficiently. The self-organizing map (SOM) is one of the artificial neural networks and has the characteristics such that online learning is possible without supervised learning within a relatively short time. In the present study, the SOM was applied to extract the relevant signal component more quickly and more accurately, and the availability was confirmed through the feasibility study. (author)

FPGA implementation of self organizing map with digital phase locked loops.

Science.gov (United States)

Hikawa, Hiroomi

2005-01-01

The self-organizing map (SOM) has found applicability in a wide range of application areas. Recently new SOM hardware with phase modulated pulse signal and digital phase-locked loops (DPLLs) has been proposed (Hikawa, 2005). The system uses the DPLL as a computing element since the operation of the DPLL is very similar to that of SOM's computation. The system also uses square waveform phase to hold the value of the each input vector element. This paper discuss the hardware implementation of the DPLL SOM architecture. For effective hardware implementation, some components are redesigned to reduce the circuit size. The proposed SOM architecture is described in VHDL and implemented on field programmable gate array (FPGA). Its feasibility is verified by experiments. Results show that the proposed SOM implemented on the FPGA has a good quantization capability, and its circuit size very small.

Distributed Fast Self-Organized Maps for Massive Spectrophotometric Data Analysis †

Directory of Open Access Journals (Sweden)

Carlos Dafonte

2018-05-01

Full Text Available Analyzing huge amounts of data becomes essential in the era of Big Data, where databases are populated with hundreds of Gigabytes that must be processed to extract knowledge. Hence, classical algorithms must be adapted towards distributed computing methodologies that leverage the underlying computational power of these platforms. Here, a parallel, scalable, and optimized design for self-organized maps (SOM is proposed in order to analyze massive data gathered by the spectrophotometric sensor of the European Space Agency (ESA Gaia spacecraft, although it could be extrapolated to other domains. The performance comparison between the sequential implementation and the distributed ones based on Apache Hadoop and Apache Spark is an important part of the work, as well as the detailed analysis of the proposed optimizations. Finally, a domain-specific visualization tool to explore astronomical SOMs is presented.

Expression cartography of human tissues using self organizing maps

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Löffler Markus

2011-07-01

Full Text Available Abstract Background Parallel high-throughput microarray and sequencing experiments produce vast quantities of multidimensional data which must be arranged and analyzed in a concerted way. One approach to addressing this challenge is the machine learning technique known as self organizing maps (SOMs. SOMs enable a parallel sample- and gene-centered view of genomic data combined with strong visualization and second-level analysis capabilities. The paper aims at bridging the gap between the potency of SOM-machine learning to reduce dimension of high-dimensional data on one hand and practical applications with special emphasis on gene expression analysis on the other hand. Results The method was applied to generate a SOM characterizing the whole genome expression profiles of 67 healthy human tissues selected from ten tissue categories (adipose, endocrine, homeostasis, digestion, exocrine, epithelium, sexual reproduction, muscle, immune system and nervous tissues. SOM mapping reduces the dimension of expression data from ten of thousands of genes to a few thousand metagenes, each representing a minicluster of co-regulated single genes. Tissue-specific and common properties shared between groups of tissues emerge as a handful of localized spots in the tissue maps collecting groups of co-regulated and co-expressed metagenes. The functional context of the spots was discovered using overrepresentation analysis with respect to pre-defined gene sets of known functional impact. We found that tissue related spots typically contain enriched populations of genes related to specific molecular processes in the respective tissue. Analysis techniques normally used at the gene-level such as two-way hierarchical clustering are better represented and provide better signal-to-noise ratios if applied to the metagenes. Metagene-based clustering analyses aggregate the tissues broadly into three clusters containing nervous, immune system and the remaining tissues

Mapping the Indonesian territory, based on pollution, social demography and geographical data, using self organizing feature map

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Hernawati, Kuswari; Insani, Nur; Bambang S. H., M.; Nur Hadi, W.; Sahid

2017-08-01

This research aims to mapping the 33 (thirty-three) provinces in Indonesia, based on the data on air, water and soil pollution, as well as social demography and geography data, into a clustered model. The method used in this study was unsupervised method that combines the basic concept of Kohonen or Self-Organizing Feature Maps (SOFM). The method is done by providing the design parameters for the model based on data related directly/ indirectly to pollution, which are the demographic and social data, pollution levels of air, water and soil, as well as the geographical situation of each province. The parameters used consists of 19 features/characteristics, including the human development index, the number of vehicles, the availability of the plant's water absorption and flood prevention, as well as geographic and demographic situation. The data used were secondary data from the Central Statistics Agency (BPS), Indonesia. The data are mapped into SOFM from a high-dimensional vector space into two-dimensional vector space according to the closeness of location in term of Euclidean distance. The resulting outputs are represented in clustered grouping. Thirty-three provinces are grouped into five clusters, where each cluster has different features/characteristics and level of pollution. The result can used to help the efforts on prevention and resolution of pollution problems on each cluster in an effective and efficient way.

The Relationship between Self-Assembly and Conformal Mappings

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Duque, Carlos; Santangelo, Christian

The isotropic growth of a thin sheet has been used as a way to generate programmed shapes through controlled buckling. We discuss how conformal mappings, which are transformations that locally preserve angles, provide a way to quantify the area growth needed to produce a particular shape. A discrete version of the conformal map can be constructed from circle packings, which are maps between packings of circles whose contact network is preserved. This provides a link to the self-assembly of particles on curved surfaces. We performed simulations of attractive particles on a curved surface using molecular dynamics. The resulting particle configurations were used to generate the corresponding discrete conformal map, allowing us to quantify the degree of area distortion required to produce a particular shape by finding particle configurations that minimize the area distortion.

Personal sleep pattern visualization using sequence-based kernel self-organizing map on sound data.

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Wu, Hongle; Kato, Takafumi; Yamada, Tomomi; Numao, Masayuki; Fukui, Ken-Ichi

2017-07-01

We propose a method to discover sleep patterns via clustering of sound events recorded during sleep. The proposed method extends the conventional self-organizing map algorithm by kernelization and sequence-based technologies to obtain a fine-grained map that visualizes the distribution and changes of sleep-related events. We introduced features widely applied in sound processing and popular kernel functions to the proposed method to evaluate and compare performance. The proposed method provides a new aspect of sleep monitoring because the results demonstrate that sound events can be directly correlated to an individual's sleep patterns. In addition, by visualizing the transition of cluster dynamics, sleep-related sound events were found to relate to the various stages of sleep. Therefore, these results empirically warrant future study into the assessment of personal sleep quality using sound data. Copyright © 2017 Elsevier B.V. All rights reserved.

Use of the self-organizing feature map to diagnose abnormal engineering change

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Lu, Ruei-Shan; Wu, Zhi-Ting; Peng, Kuo-Wei; Yu, Tai-Yi

2015-07-01

This study established identification manners with self-organizing feature map (SOM) to achieve the goal of monitoring Engineering Change (EC) based on historical data of a company that specializes in computers and peripherals. The product life cycle of this company is 3-6 months. The historical data were divided into three parts, each covering four months. The first part, comprising 2,343 records from January to April (the training period), comprise the Control Group. The second and third parts comprise Experimental Groups (EG) 1 and 2, respectively. For EG 1 and 2, the successful rate of recognizing information on abnormal ECs was approximately 96% and 95%, respectively. This paper shows the importance and screening procedures of abnormal engineering change for a particular company specializing in computers and peripherals.

An new self-organizing maps strategy for solving the traveling salesman problem

International Nuclear Information System (INIS)

Bai Yanping; Zhang Wendong; Jin Zhen

2006-01-01

This paper presents an approach to the well-known traveling salesman problem (TSP) using self-organizing maps (SOM). There are many types of SOM algorithms to solve the TSP found in the literature, whereas the purpose of this paper is to look for the incorporation of an efficient initialization methods and the definition of a parameters adaptation law to achieve better results and a faster convergence. Aspects of parameters adaptation, selecting the number of nodes of neurons, index of winner neurons and effect of the initial ordering of the cities, as well as the initial synaptic weights of the modified SOM algorithm are discussed. The complexity of the modified SOM algorithm is analyzed. The simulated results show an average deviation of 2.32% from the optimal tour length for a set of 12 TSP instances

An new self-organizing maps strategy for solving the traveling salesman problem

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Bai Yanping [Key Lab of Instrument Science and Dynamic Measurement of Ministry of Education, North University of China, No. 3, Xueyuan Road, TaiYuan, ShanXi 030051 (China)]. E-mail: baiyp@nuc.edu.cn; Zhang Wendong [Key Lab of Instrument Science and Dynamic Measurement of Ministry of Education, North University of China, No. 3, Xueyuan Road, TaiYuan, ShanXi 030051 (China)]. E-mail: wdzhang@nuc.edu.cn; Jin Zhen [Department of Applied Mathematics, North University of China, No. 3 Xueyuan Road, TaiYuan, ShanXi 030051 (China)

2006-05-15

This paper presents an approach to the well-known traveling salesman problem (TSP) using self-organizing maps (SOM). There are many types of SOM algorithms to solve the TSP found in the literature, whereas the purpose of this paper is to look for the incorporation of an efficient initialization methods and the definition of a parameters adaptation law to achieve better results and a faster convergence. Aspects of parameters adaptation, selecting the number of nodes of neurons, index of winner neurons and effect of the initial ordering of the cities, as well as the initial synaptic weights of the modified SOM algorithm are discussed. The complexity of the modified SOM algorithm is analyzed. The simulated results show an average deviation of 2.32% from the optimal tour length for a set of 12 TSP instances.

Analysis of Vehicle-Following Heterogeneity Using Self-Organizing Feature Maps

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Jie Yang

2014-01-01

Full Text Available A self-organizing feature map (SOM was used to represent vehicle-following and to analyze the heterogeneities in vehicle-following behavior. The SOM was constructed in such a way that the prototype vectors represented vehicle-following stimuli (the follower’s velocity, relative velocity, and gap while the output signals represented the response (the follower’s acceleration. Vehicle trajectories collected at a northbound segment of Interstate 80 Freeway at Emeryville, CA, were used to train the SOM. The trajectory information of two selected pairs of passenger cars was then fed into the trained SOM to identify similar stimuli experienced by the followers. The observed responses, when the stimuli were classified by the SOM into the same category, were compared to discover the interdriver heterogeneity. The acceleration profile of another passenger car was analyzed in the same fashion to observe the interdriver heterogeneity. The distribution of responses derived from data sets of car-following-car and car-following-truck, respectively, was compared to ascertain inter-vehicle-type heterogeneity.

A learning heuristic for space mapping and searching self-organizing systems using adaptive mesh refinement

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Phillips, Carolyn L.

2014-09-01

In a complex self-organizing system, small changes in the interactions between the system's components can result in different emergent macrostructures or macrobehavior. In chemical engineering and material science, such spontaneously self-assembling systems, using polymers, nanoscale or colloidal-scale particles, DNA, or other precursors, are an attractive way to create materials that are precisely engineered at a fine scale. Changes to the interactions can often be described by a set of parameters. Different contiguous regions in this parameter space correspond to different ordered states. Since these ordered states are emergent, often experiment, not analysis, is necessary to create a diagram of ordered states over the parameter space. By issuing queries to points in the parameter space (e.g., performing a computational or physical experiment), ordered states can be discovered and mapped. Queries can be costly in terms of resources or time, however. In general, one would like to learn the most information using the fewest queries. Here we introduce a learning heuristic for issuing queries to map and search a two-dimensional parameter space. Using a method inspired by adaptive mesh refinement, the heuristic iteratively issues batches of queries to be executed in parallel based on past information. By adjusting the search criteria, different types of searches (for example, a uniform search, exploring boundaries, sampling all regions equally) can be flexibly implemented. We show that this method will densely search the space, while preferentially targeting certain features. Using numerical examples, including a study simulating the self-assembly of complex crystals, we show how this heuristic can discover new regions and map boundaries more accurately than a uniformly distributed set of queries.

Descriptive Characteristics of Surface Water Quality in Hong Kong by a Self-Organising Map.

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An, Yan; Zou, Zhihong; Li, Ranran

2016-01-08

In this study, principal component analysis (PCA) and a self-organising map (SOM) were used to analyse a complex dataset obtained from the river water monitoring stations in the Tolo Harbor and Channel Water Control Zone (Hong Kong), covering the period of 2009-2011. PCA was initially applied to identify the principal components (PCs) among the nonlinear and complex surface water quality parameters. SOM followed PCA, and was implemented to analyze the complex relationships and behaviors of the parameters. The results reveal that PCA reduced the multidimensional parameters to four significant PCs which are combinations of the original ones. The positive and inverse relationships of the parameters were shown explicitly by pattern analysis in the component planes. It was found that PCA and SOM are efficient tools to capture and analyze the behavior of multivariable, complex, and nonlinear related surface water quality data.

Interconnected growing self-organizing maps for auditory and semantic acquisition modeling

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Mengxue eCao

2014-03-01

Full Text Available Based on the incremental nature of knowledge acquisition, in this study we propose a growing self-organizing neural network approach for modeling the acquisition of auditory and semantic categories. We introduce an Interconnected Growing Self-Organizing Maps (I-GSOM algorithm, which takes associations between auditory information and semantic information into consideration, in this paper. Direct phonetic--semantic association is simulated in order to model the language acquisition in early phases, such as the babbling and imitation stages, in which no phonological representations exist. Based on the I-GSOM algorithm, we conducted experiments using paired acoustic and semantic training data. We use a cyclical reinforcing and reviewing training procedure to model the teaching and learning process between children and their communication partners; a reinforcing-by-link training procedure and a link-forgetting procedure are introduced to model the acquisition of associative relations between auditory and semantic information. Experimental results indicate that (1 I-GSOM has good ability to learn auditory and semantic categories presented within the training data; (2 clear auditory and semantic boundaries can be found in the network representation; (3 cyclical reinforcing and reviewing training leads to a detailed categorization as well as to a detailed clustering, while keeping the clusters that have already been learned and the network structure that has already been developed stable; and (4 reinforcing-by-link training leads to well-perceived auditory--semantic associations. Our I-GSOM model suggests that it is important to associate auditory information with semantic information during language acquisition. Despite its high level of abstraction, our I-GSOM approach can be interpreted as a biologically-inspired neurocomputational model.

Seismic facies analysis from pre-stack data using self-organizing maps

International Nuclear Information System (INIS)

Kourki, Meysam; Ali Riahi, Mohammad

2014-01-01

In facies analysis, seismic data are clustered in different groups. Each group represents subsurface points with similar physical properties. Different groups can be related to differences in lithology, physical properties of rocks and fluid changes in the rocks. The supervised and unsupervised data clustering are known as two types of clustering architecture. In supervised clustering, the number of clusters is predefined, while in unsupervised clustering, a collection of patterns partitions into groups without predefined clusters. In this study, the pre-stack data clustering is used for seismic facies analysis. In this way, a horizon was selected from pre-stack data, followed by sorting of data using offset. A trace associated with each CDP is constructed, for which the first and second samples are related to the first and second offsets, respectively. The created trace is called consolidated trace which is characteristic of subsurface points. These consolidated traces are clustered by using self-organizing maps (SOM). In proposed pre-stack seismic data clustering, points with similar physical properties are placed in one cluster. Seismic data associated with hydrocarbon reservoirs have very different characteristics that are easily recognized. The efficiency of the proposed method was tested on both synthetic and real seismic data. The results showed that the algorithm improves the data classification and the points of different properties are noticeable in final maps. (paper)

APPLYING PRINCIPAL COMPONENT ANALYSIS, MULTILAYER PERCEPTRON AND SELF-ORGANIZING MAPS FOR OPTICAL CHARACTER RECOGNITION

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Khuat Thanh Tung

2016-11-01

Full Text Available Optical Character Recognition plays an important role in data storage and data mining when the number of documents stored as images is increasing. It is expected to find the ways to convert images of typewritten or printed text into machine-encoded text effectively in order to support for the process of information handling effectively. In this paper, therefore, the techniques which are being used to convert image into editable text in the computer such as principal component analysis, multilayer perceptron network, self-organizing maps, and improved multilayer neural network using principal component analysis are experimented. The obtained results indicated the effectiveness and feasibility of the proposed methods.

Identifying regions of interest in medical images using self-organizing maps.

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Teng, Wei-Guang; Chang, Ping-Lin

2012-10-01

Advances in data acquisition, processing and visualization techniques have had a tremendous impact on medical imaging in recent years. However, the interpretation of medical images is still almost always performed by radiologists. Developments in artificial intelligence and image processing have shown the increasingly great potential of computer-aided diagnosis (CAD). Nevertheless, it has remained challenging to develop a general approach to process various commonly used types of medical images (e.g., X-ray, MRI, and ultrasound images). To facilitate diagnosis, we recommend the use of image segmentation to discover regions of interest (ROI) using self-organizing maps (SOM). We devise a two-stage SOM approach that can be used to precisely identify the dominant colors of a medical image and then segment it into several small regions. In addition, by appropriately conducting the recursive merging steps to merge smaller regions into larger ones, radiologists can usually identify one or more ROIs within a medical image.

High-resolution Self-Organizing Maps for advanced visualization and dimension reduction.

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Saraswati, Ayu; Nguyen, Van Tuc; Hagenbuchner, Markus; Tsoi, Ah Chung

2018-05-04

Kohonen's Self Organizing feature Map (SOM) provides an effective way to project high dimensional input features onto a low dimensional display space while preserving the topological relationships among the input features. Recent advances in algorithms that take advantages of modern computing hardware introduced the concept of high resolution SOMs (HRSOMs). This paper investigates the capabilities and applicability of the HRSOM as a visualization tool for cluster analysis and its suitabilities to serve as a pre-processor in ensemble learning models. The evaluation is conducted on a number of established benchmarks and real-world learning problems, namely, the policeman benchmark, two web spam detection problems, a network intrusion detection problem, and a malware detection problem. It is found that the visualization resulted from an HRSOM provides new insights concerning these learning problems. It is furthermore shown empirically that broad benefits from the use of HRSOMs in both clustering and classification problems can be expected. Copyright © 2018 Elsevier Ltd. All rights reserved.

CUSTOMER SEGMENTATION DENGAN METODE SELF ORGANIZING MAP (STUDI KASUS: UD. FENNY

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A. A. Gde Bagus Ariana

2012-11-01

Full Text Available Saat ini persaingan bisnis pada perusahaan retail tidak hanya dengan menggunakan perangkat sistem informasi namun sudah dilengkapi dengan sistem pendukung keputusan. Salah satu metode sistem pendukung keputusan yang digunakan adalah data mining. Data mining digunakan untuk menemukan pola-pola yang tersembunyi pada database. UD. Fenny sebagai perusahaan retail ingin menemukan pola segmentasi pelanggan dengan menggunakan model RFM (Recency, Frequency, Monetary. Metode data mining untuk melakukan proses segmentasi adalah metode clustering. Clustering merupakan proses penggugusan data menjadi kelompok-kelompok yang memiliki kemiripan secara tidak terawasi (unsupervised. Sebelum melakukan proses clustering, dilakukan proses persiapan data dengan membuat datawarehouse menggunakan skema bintang (star scema. Selanjutnya dilakukan proses clustering dengan menggunakan metode Self Organizing Map (SOM/Kohonen. Metode ini merupakan salah satu model jaringan saraf tiruan yang menggunakan metode unsupervised. Dari hasil percobaan metode SOM melakukan proses clustering dan menggambarkan hasil clustering pada SOM plot. Dengan melakukan proses clustering, pihak pengambil keputusan dapat memahami segmentasi customer dan melakukan upaya peningkatan pelayanan customer.

Mapping visual cortex in monkeys and humans using surface-based atlases

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Van Essen, D. C.; Lewis, J. W.; Drury, H. A.; Hadjikhani, N.; Tootell, R. B.; Bakircioglu, M.; Miller, M. I.

2001-01-01

We have used surface-based atlases of the cerebral cortex to analyze the functional organization of visual cortex in humans and macaque monkeys. The macaque atlas contains multiple partitioning schemes for visual cortex, including a probabilistic atlas of visual areas derived from a recent architectonic study, plus summary schemes that reflect a combination of physiological and anatomical evidence. The human atlas includes a probabilistic map of eight topographically organized visual areas recently mapped using functional MRI. To facilitate comparisons between species, we used surface-based warping to bring functional and geographic landmarks on the macaque map into register with corresponding landmarks on the human map. The results suggest that extrastriate visual cortex outside the known topographically organized areas is dramatically expanded in human compared to macaque cortex, particularly in the parietal lobe.

Self-Organizing Maps on the Cell Broadband Engine Architecture

International Nuclear Information System (INIS)

McConnell, Sabine M

2010-01-01

We present and evaluate novel parallel implementations of Self-Organizing Maps for the Cell Broadband Engine Architecture. Motivated by the interactive nature of the data-mining process, we evaluate the scalability of the implementations on two clusters using different network characteristics and incarnations (PS3 TM console and PowerXCell 8i) of the architecture. Our implementations use varying combinations of the Power Processing Elements (PPEs) and Synergistic Processing Elements (SPEs) found in the Cell architecture. For a single processor, our implementation scaled well with the number of SPEs regardless of the incarnation. When combining multiple PS3 TM consoles, the synchronization over the slower network resulted in poor speedups and demonstrated that the use of such a low-cost cluster may be severely restricted, even without the use of SPEs. When using multiple SPEs for the PowerXCell 8i cluster, the speedup grew linearly with increasing number of SPEs for a given number of processors, and linear up to a maximum with the number of processors for a given number of SPEs. Our implementation achieved a worst-case efficiency of 67% for the maximum number of processing elements involved in the computation, but consistently higher values for smaller numbers of processing elements with speedups of up to 70.

Use of Self-Organizing Maps for Balanced Scorecard analysis to monitor the performance of dialysis clinic chains.

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Cattinelli, Isabella; Bolzoni, Elena; Barbieri, Carlo; Mari, Flavio; Martin-Guerrero, José David; Soria-Olivas, Emilio; Martinez-Martinez, José Maria; Gomez-Sanchis, Juan; Amato, Claudia; Stopper, Andrea; Gatti, Emanuele

2012-03-01

The Balanced Scorecard (BSC) is a validated tool to monitor enterprise performances against specific objectives. Through the choice and the evaluation of strategic Key Performance Indicators (KPIs), it provides a measure of the past company's outcome and allows planning future managerial strategies. The Fresenius Medical Care (FME) BSC makes use of 30 KPIs for a continuous quality improvement strategy within its dialysis clinics. Each KPI is monthly associated to a score that summarizes the clinic efficiency for that month. Standard statistical methods are currently used to analyze the BSC data and to give a comprehensive view of the corporate improvements to the top management. We herein propose the Self-Organizing Maps (SOMs) as an innovative approach to extrapolate information from the FME BSC data and to present it in an easy-readable informative form. A SOM is a computational technique that allows projecting high-dimensional datasets to a two-dimensional space (map), thus providing a compressed representation. The SOM unsupervised (self-organizing) training procedure results in a map that preserves similarity relations existing in the original dataset; in this way, the information contained in the high-dimensional space can be more easily visualized and understood. The present work demonstrates the effectiveness of the SOM approach in extracting useful information from the 30-dimensional BSC dataset: indeed, SOMs enabled both to highlight expected relationships between the KPIs and to uncover results not predictable with traditional analyses. Hence we suggest SOMs as a reliable complementary approach to the standard methods for BSC interpretation.

Comparison between genetic algorithm and self organizing map to detect botnet network traffic

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Yugandhara Prabhakar, Shinde; Parganiha, Pratishtha; Madhu Viswanatham, V.; Nirmala, M.

2017-11-01

In Cyber Security world the botnet attacks are increasing. To detect botnet is a challenging task. Botnet is a group of computers connected in a coordinated fashion to do malicious activities. Many techniques have been developed and used to detect and prevent botnet traffic and the attacks. In this paper, a comparative study is done on Genetic Algorithm (GA) and Self Organizing Map (SOM) to detect the botnet network traffic. Both are soft computing techniques and used in this paper as data analytics system. GA is based on natural evolution process and SOM is an Artificial Neural Network type, uses unsupervised learning techniques. SOM uses neurons and classifies the data according to the neurons. Sample of KDD99 dataset is used as input to GA and SOM.

Descriptive Characteristics of Surface Water Quality in Hong Kong by a Self-Organising Map

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Yan An

2016-01-01

Full Text Available In this study, principal component analysis (PCA and a self-organising map (SOM were used to analyse a complex dataset obtained from the river water monitoring stations in the Tolo Harbor and Channel Water Control Zone (Hong Kong, covering the period of 2009–2011. PCA was initially applied to identify the principal components (PCs among the nonlinear and complex surface water quality parameters. SOM followed PCA, and was implemented to analyze the complex relationships and behaviors of the parameters. The results reveal that PCA reduced the multidimensional parameters to four significant PCs which are combinations of the original ones. The positive and inverse relationships of the parameters were shown explicitly by pattern analysis in the component planes. It was found that PCA and SOM are efficient tools to capture and analyze the behavior of multivariable, complex, and nonlinear related surface water quality data.

An application of the Self Organizing Map Algorithm to computer aided classification of ASTER multispectral data

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Ferdinando Giacco

2008-01-01

Full Text Available In this paper we employ the Kohonen’s Self Organizing Map (SOM as a strategy for an unsupervised analysis of ASTER multispectral (MS images. In order to obtain an accurate clusterization we introduce as input for the network, in addition to spectral data, some texture measures extracted from IKONOS images, which gives a contribution to the classification of manmade structures. After clustering of SOM outcomes, we associated each cluster with a major land cover and compared them with prior knowledge of the scene analyzed.

Fe and Co nanostructures embedded into the Cu(100) surface: Self-Organization and magnetic properties

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Kolesnikov, S. V., E-mail: kolesnikov@physics.msu.ru; Klavsyuk, A. L.; Saletsky, A. M. [Moscow State University, Faculty of Physics (Russian Federation)

2015-10-15

The self-organization and magnetic properties of small iron and cobalt nanostructures embedded into the first layer of a Cu(100) surface are investigated using the self-learning kinetic Monte Carlo method and density functional theory. The similarities and differences between the Fe/Cu(100) and the Co/Cu(100) are underlined. The time evolution of magnetic properties of a copper monolayer with embedded magnetic atoms at 380 K is discussed.

Resting state cortico-cerebellar functional connectivity networks: A comparison of anatomical and self-organizing map approaches

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Jessica A Bernard

2012-08-01

Full Text Available The cerebellum plays a role in a wide variety of complex behaviors. In order to better understand the role of the cerebellum in human behavior, it is important to know how this structure interacts with cortical and other subcortical regions of the brain. To date, several studies have investigated the cerebellum using resting-state functional connectivity magnetic resonance imaging (fcMRI; Buckner et al., 2011; Krienen & Buckner, 2009; O’Reilly et al., 2009. However, none of this work has taken an anatomically-driven approach. Furthermore, though detailed maps of cerebral cortex and cerebellum networks have been proposed using different network solutions based on the cerebral cortex (Buckner et al., 2011, it remains unknown whether or not an anatomical lobular breakdown best encompasses the networks of the cerebellum. Here, we used fcMRI to create an anatomically-driven cerebellar connectivity atlas. Timecourses were extracted from the lobules of the right hemisphere and vermis. We found distinct networks for the individual lobules with a clear division into motor and non-motor regions. We also used a self-organizing map algorithm to parcellate the cerebellum. This allowed us to investigate redundancy and independence of the anatomically identified cerebellar networks. We found that while anatomical boundaries in the anterior cerebellum provide functional subdivisions of a larger motor grouping defined using our self-organizing map algorithm, in the posterior cerebellum, the lobules were made up of sub-regions associated with distinct functional networks. Together, our results indicate that the lobular boundaries of the human cerebellum are not indicative of functional boundaries, though anatomical divisions can be useful, as is the case of the anterior cerebellum. Additionally, driving the analyses from the cerebellum is key to determining the complete picture of functional connectivity within the structure.

Enhanced light trapping by focused ion beam (FIB) induced self-organized nanoripples on germanium (100) surface

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Kamaliya, Bhaveshkumar; Mote, Rakesh G.; Aslam, Mohammed; Fu, Jing

2018-03-01

In this paper, we demonstrate enhanced light trapping by self-organized nanoripples on the germanium surface. The enhanced light trapping leading to high absorption of light is confirmed by the experimental studies as well as the numerical simulations using the finite-difference time-domain method. We used gallium ion (Ga+) focused ion beam to enable the formation of the self-organized nanoripples on the germanium (100) surface. During the fabrication, the overlap of the scanning beam is varied from zero to negative value and found to influence the orientation of the nanoripples. Evolution of nanostructures with the variation of beam overlap is investigated. Parallel, perpendicular, and randomly aligned nanoripples with respect to the scanning direction are obtained via manipulation of the scanning beam overlap. 95% broadband absorptance is measured in the visible electromagnetic region for the nanorippled germanium surface. The reported light absorption enhancement can significantly improve the efficiency of germanium-silicon based photovoltaic systems.

An Anomaly Detection Algorithm of Cloud Platform Based on Self-Organizing Maps

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Jun Liu

2016-01-01

Full Text Available Virtual machines (VM on a Cloud platform can be influenced by a variety of factors which can lead to decreased performance and downtime, affecting the reliability of the Cloud platform. Traditional anomaly detection algorithms and strategies for Cloud platforms have some flaws in their accuracy of detection, detection speed, and adaptability. In this paper, a dynamic and adaptive anomaly detection algorithm based on Self-Organizing Maps (SOM for virtual machines is proposed. A unified modeling method based on SOM to detect the machine performance within the detection region is presented, which avoids the cost of modeling a single virtual machine and enhances the detection speed and reliability of large-scale virtual machines in Cloud platform. The important parameters that affect the modeling speed are optimized in the SOM process to significantly improve the accuracy of the SOM modeling and therefore the anomaly detection accuracy of the virtual machine.

PREFACE: Self-organized nanostructures

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Rousset, Sylvie; Ortega, Enrique

2006-04-01

In order to fabricate ordered arrays of nanostructures, two different strategies might be considered. The `top-down' approach consists of pushing the limit of lithography techniques down to the nanometre scale. However, beyond 10 nm lithography techniques will inevitably face major intrinsic limitations. An alternative method for elaborating ultimate-size nanostructures is based on the reverse `bottom-up' approach, i.e. building up nanostructures (and eventually assemble them to form functional circuits) from individual atoms or molecules. Scanning probe microscopies, including scanning tunnelling microscopy (STM) invented in 1982, have made it possible to create (and visualize) individual structures atom by atom. However, such individual atomic manipulation is not suitable for industrial applications. Self-assembly or self-organization of nanostructures on solid surfaces is a bottom-up approach that allows one to fabricate and assemble nanostructure arrays in a one-step process. For applications, such as high density magnetic storage, self-assembly appears to be the simplest alternative to lithography for massive, parallel fabrication of nanostructure arrays with regular sizes and spacings. These are also necessary for investigating the physical properties of individual nanostructures by means of averaging techniques, i.e. all those using light or particle beams. The state-of-the-art and the current developments in the field of self-organization and physical properties of assembled nanostructures are reviewed in this issue of Journal of Physics: Condensed Matter. The papers have been selected from among the invited and oral presentations of the recent summer workshop held in Cargese (Corsica, France, 17-23 July 2005). All authors are world-renowned in the field. The workshop has been funded by the Marie Curie Actions: Marie Curie Conferences and Training Courses series named `NanosciencesTech' supported by the VI Framework Programme of the European Community, by

The linkage between the lifestyle of knowledge-workers and their intra-metropolitan residential choice: A clustering approach based on self-organizing maps

DEFF Research Database (Denmark)

Frenkel, Amnon; Bendit, Edward; Kaplan, Sigal

2013-01-01

-Aviv metropolitan area and are analyzed with self-organizing maps for pattern recognition and classification. Five clusters are identified: nest-builders, bon-vivants, careerists, entrepreneurs and laid-back. Bon-vivants and entrepreneurs differ in their dwelling size and home-ownership, although both prefer...

Characterizing synoptic and cloud variability in the northern atlantic using self-organizing maps

Science.gov (United States)

Fish, Carly

Low-level clouds have a significant influence on the Earth's radiation budget and it is thus imperative to understand their behavior within the marine boundary layer (MBL). The cloud properties in the Northeast Atlantic region are highly variable in space and time and are a research focus for many atmospheric scientists. Characterizing the synoptic patterns in the region through the implementation of self-organizing maps (SOMs) enables a climatological grasp of cloud and atmospheric fields. ERA -- Interim and MODIS provide the platform to explore the variability in the Northeast Atlantic for over 30 years of data. Station data comes from CAP -- MBL on Graciosa Island in the Azores, which lies in a strong gradient of cloud and other atmospheric fields, offer an opportunity to incorporate an observational aspect for the years of 2009 and 2010.

Feature-based alert correlation in security systems using self organizing maps

Science.gov (United States)

Kumar, Munesh; Siddique, Shoaib; Noor, Humera

2009-04-01

The security of the networks has been an important concern for any organization. This is especially important for the defense sector as to get unauthorized access to the sensitive information of an organization has been the prime desire for cyber criminals. Many network security techniques like Firewall, VPN Concentrator etc. are deployed at the perimeter of network to deal with attack(s) that occur(s) from exterior of network. But any vulnerability that causes to penetrate the network's perimeter of defense, can exploit the entire network. To deal with such vulnerabilities a system has been evolved with the purpose of generating an alert for any malicious activity triggered against the network and its resources, termed as Intrusion Detection System (IDS). The traditional IDS have still some deficiencies like generating large number of alerts, containing both true and false one etc. By automatically classifying (correlating) various alerts, the high-level analysis of the security status of network can be identified and the job of network security administrator becomes much easier. In this paper we propose to utilize Self Organizing Maps (SOM); an Artificial Neural Network for correlating large amount of logged intrusion alerts based on generic features such as Source/Destination IP Addresses, Port No, Signature ID etc. The different ways in which alerts can be correlated by Artificial Intelligence techniques are also discussed. . We've shown that the strategy described in the paper improves the efficiency of IDS by better correlating the alerts, leading to reduced false positives and increased competence of network administrator.

Fault detection of sensors in nuclear reactors using self-organizing maps

Energy Technology Data Exchange (ETDEWEB)

Barbosa, Paulo Roberto; Tiago, Graziela Marchi [Instituto Federal de Educacao, Ciencia e Tecnologia de Sao Paulo (IFSP), Sao Paulo, SP (Brazil); Bueno, Elaine Inacio [Instituto Federal de Educacao, Ciencia e Tecnologia de Sao Paulo (IFSP), Guarulhos, SP (Brazil); Pereira, Iraci Martinez, E-mail: martinez@ipen.b [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

2011-07-01

In this work a Fault Detection System was developed based on the self-organizing maps methodology. This method was applied to the IEA-R1 research reactor at IPEN using a database generated by a theoretical model of the reactor. The IEA-R1 research reactor is a pool type reactor of 5 MW, cooled and moderated by light water, and uses graphite and beryllium as reflector. The theoretical model was developed using the Matlab Guide toolbox. The equations are based in the IEA-R1 mass and energy inventory balance and physical as well as operational aspects are taken into consideration. In order to test the model ability for fault detection, faults were artificially produced. As the value of the maximum calibration error for special thermocouples is +- 0.5 deg C, it had been inserted faults in the sensor signals with the purpose to produce the database considered in this work. The results show a high percentage of correct classification, encouraging the use of the technique for this type of industrial application. (author)

Artificial neural network with self-organizing mapping for reactor stability monitoring

International Nuclear Information System (INIS)

Okumura, Motofumi; Tsuji, Masashi; Shimazu, Yoichiro

2009-01-01

In boiling water reactor (BWR) stability monitoring, damping ratio has been used as a stability index. A method for estimating the damping ratio by applying Principal Component Analysis (PCA) to neutron detector signals measured with local power range monitors (LPRMs) had been developed; in this method, measured fluctuating signal is decomposed into some independent components and the signal components directly related to stability are extracted among them to determine the damping ratio. For online monitoring, it is necessary to select stability related signal components efficiently. The self-organizing map (SOM) is one of the artificial neural networks (ANNs) and has the characteristics such that online learning is possible without supervised learning within a relatively short time. In the present study, the SOM was applied to extract the relevant signal components more quickly and more accurately, and the availability was confirmed through the feasibility study. For realizing online stability monitoring only with ANNs, another type of ANN that performs online processing of PCA was combined with SOM. And stability monitoring performance was investigated. (author)

Fault detection of sensors in nuclear reactors using self-organizing maps

International Nuclear Information System (INIS)

Barbosa, Paulo Roberto; Tiago, Graziela Marchi; Bueno, Elaine Inacio; Pereira, Iraci Martinez

2011-01-01

In this work a Fault Detection System was developed based on the self-organizing maps methodology. This method was applied to the IEA-R1 research reactor at IPEN using a database generated by a theoretical model of the reactor. The IEA-R1 research reactor is a pool type reactor of 5 MW, cooled and moderated by light water, and uses graphite and beryllium as reflector. The theoretical model was developed using the Matlab Guide toolbox. The equations are based in the IEA-R1 mass and energy inventory balance and physical as well as operational aspects are taken into consideration. In order to test the model ability for fault detection, faults were artificially produced. As the value of the maximum calibration error for special thermocouples is +- 0.5 deg C, it had been inserted faults in the sensor signals with the purpose to produce the database considered in this work. The results show a high percentage of correct classification, encouraging the use of the technique for this type of industrial application. (author)

Predicting supramolecular self-assembly on reconstructed metal surfaces

Science.gov (United States)

Roussel, Thomas J.; Barrena, Esther; Ocal, Carmen; Faraudo, Jordi

2014-06-01

The prediction of supramolecular self-assembly onto solid surfaces is still challenging in many situations of interest for nanoscience. In particular, no previous simulation approach has been capable to simulate large self-assembly patterns of organic molecules over reconstructed surfaces (which have periodicities over large distances) due to the large number of surface atoms and adsorbing molecules involved. Using a novel simulation technique, we report here large scale simulations of the self-assembly patterns of an organic molecule (DIP) over different reconstructions of the Au(111) surface. We show that on particular reconstructions, the molecule-molecule interactions are enhanced in a way that long-range order is promoted. Also, the presence of a distortion in a reconstructed surface pattern not only induces the presence of long-range order but also is able to drive the organization of DIP into two coexisting homochiral domains, in quantitative agreement with STM experiments. On the other hand, only short range order is obtained in other reconstructions of the Au(111) surface. The simulation strategy opens interesting perspectives to tune the supramolecular structure by simulation design and surface engineering if choosing the right molecular building blocks and stabilising the chosen reconstruction pattern.The prediction of supramolecular self-assembly onto solid surfaces is still challenging in many situations of interest for nanoscience. In particular, no previous simulation approach has been capable to simulate large self-assembly patterns of organic molecules over reconstructed surfaces (which have periodicities over large distances) due to the large number of surface atoms and adsorbing molecules involved. Using a novel simulation technique, we report here large scale simulations of the self-assembly patterns of an organic molecule (DIP) over different reconstructions of the Au(111) surface. We show that on particular reconstructions, the molecule

Self-organized pattern on the surface of a metal anode in low-pressure DC discharge

Science.gov (United States)

Yaqi, YANG; Weiguo, LI

2018-03-01

Self-organization phenomena on the surface of a metal electrode in low-pressure DC discharge is studied. In this paper, we carry out laboratory investigations of self-organization in a low-pressure test platform for 100-200 mm rod-plane gaps with a needle tip, conical tip and hemispherical tip within 1-10 kPa. The factors influencing the pattern profile are the pressure value, gap length and shape of the electrode, and a variety of pattern structures are observed by changing these factors. With increasing pressure, first the pattern diameter increases and then decreases. With the needle tip, layer structure, single-ring structure and double-ring structure are displayed successively with increasing pressure. With the conical tip, the ring-like structure gradually forms separate spots with increasing pressure. With the hemispherical tip, there are anode spots inside the ring structure. With the increase of gap length, the diameter of the self-organized pattern increases and the profile of the pattern changes. The development process of the pattern contains three key stages: pattern enlargement, pattern stabilization and pattern shrink.

Segmentation of head magnetic resonance image using self-mapping characteristic

International Nuclear Information System (INIS)

Madokoro, Hirokazu; Sato, Kazuhito; Ishii, Masaki; Kadowaki, Sakura

2004-01-01

In this paper, we proposed a segmentation method, for head magnetic resonance (MR) images. Our method used self mapping characteristic of a self-organization map (SOM), and it does not need the setting of the representative point by the operator. We considered the continuity and boundary in the brain tissues by the definition of the local block. In the evaluation experiment, we obtained the segmentation result of matching anatomical structure information. In addition, our method applied the clinical MR images, it was possible to obtain the effective and objective result for supporting the diagnosis of the brain atrophy by the doctor. (author)

Nanosecond pulsed laser induced self-organized nano-dots patterns on GaSb surface

Energy Technology Data Exchange (ETDEWEB)

Yoshida, Yutaka, E-mail: yyoshida@cris.hokudai.ac.jp [Center for Advanced Research of Energy and Materials, Faculty of Engineering, Hokkaido University, N8, W13, Kita-ku, Sapporo 060-8628, Hokkaido (Japan); Creative Research Institution Sousei, Hokkaido University, N21, W10, Kita-ku, Sapporo 001-0021, Hokkaido (Japan); Oosawa, Kazuya; Wajima, Jyunya; Watanabe, Seiichi [Center for Advanced Research of Energy and Materials, Faculty of Engineering, Hokkaido University, N8, W13, Kita-ku, Sapporo 060-8628, Hokkaido (Japan); Matsuo, Yasutaka [Research Institute for Electronic Science, Hokkaido University, Sapporo 001-0020, Hokkaido (Japan); Kato, Takahiko [Hitachi Research Laboratory, Hitachi, Ltd., 7-1-1 Omika, Hitachi-shi 319-1292, Ibaraki-ken (Japan); Center for Advanced Research of Energy and Materials, Faculty of Engineering, Hokkaido University, N8, W13, Kita-ku, Sapporo 060-8628, Hokkaido (Japan)

2014-07-01

We report a technique for formation of two-dimensional (2D) nanodot (ND) patterns on gaillium antimoide (GaSb) using a nanosecond pulsed laser irradiation with 532 nm wavelength. The patterns have formed because of the interference and the self-organization under energy deposition of the laser irradiation, which induced the growth of NDs on the local area. The NDs are grown and shrunken in the pattern by energy depositions. In the laser irradiation with average laser energy density of 35 mJ cm⁻², large and small NDs are formed on GaSb surface. The large NDs have grown average diameter from 160 to 200 nm with increase of laser pulses, and the small NDs have shrunken average diameter from 75 to 30 nm. The critical dot size is required about 107 nm for growth of the NDs in the patterns. Nanosecond pulsed laser irradiation can control the self-organized ND size on GaSb in air as a function of the laser pulses.

Atmospheric Convective Organization: Self-Organized Criticality or Homeostasis?

Science.gov (United States)

Yano, Jun-Ichi

2015-04-01

Atmospheric convection has a tendency organized on a hierarchy of scales ranging from the mesoscale to the planetary scales, with the latter especially manifested by the Madden-Julian oscillation. The present talk examines two major possible mechanisms of self-organization identified in wider literature from a phenomenological thermodynamic point of view by analysing a planetary-scale cloud-resolving model simulation. The first mechanism is self-organized criticality. A saturation tendency of precipitation rate with the increasing column-integrated water, reminiscence of critical phenomena, indicates self-organized criticality. The second is a self-regulation mechanism that is known as homeostasis in biology. A thermodynamic argument suggests that such self-regulation maintains the column-integrated water below a threshold by increasing the precipitation rate. Previous analyses of both observational data as well as cloud-resolving model (CRM) experiments give mixed results. A satellite data analysis suggests self-organized criticality. Some observational data as well as CRM experiments support homeostasis. Other analyses point to a combination of these two interpretations. In this study, a CRM experiment over a planetary-scale domain with a constant sea-surface temperature is analyzed. This analysis shows that the relation between the column-integrated total water and precipitation suggests self-organized criticality, whereas the one between the column-integrated water vapor and precipitation suggests homeostasis. The concurrent presence of these two mechanisms are further elaborated by detailed statistical and budget analyses. These statistics are scale invariant, reflecting a spatial scaling of precipitation processes. These self-organization mechanisms are most likely be best theoretically understood by the energy cycle of the convective systems consisting of the kinetic energy and the cloud-work function. The author has already investigated the behavior of this

Cooperation-Controlled Learning for Explicit Class Structure in Self-Organizing Maps

Science.gov (United States)

Kamimura, Ryotaro

2014-01-01

We attempt to demonstrate the effectiveness of multiple points of view toward neural networks. By restricting ourselves to two points of view of a neuron, we propose a new type of information-theoretic method called “cooperation-controlled learning.” In this method, individual and collective neurons are distinguished from one another, and we suppose that the characteristics of individual and collective neurons are different. To implement individual and collective neurons, we prepare two networks, namely, cooperative and uncooperative networks. The roles of these networks and the roles of individual and collective neurons are controlled by the cooperation parameter. As the parameter is increased, the role of cooperative networks becomes more important in learning, and the characteristics of collective neurons become more dominant. On the other hand, when the parameter is small, individual neurons play a more important role. We applied the method to the automobile and housing data from the machine learning database and examined whether explicit class boundaries could be obtained. Experimental results showed that cooperation-controlled learning, in particular taking into account information on input units, could be used to produce clearer class structure than conventional self-organizing maps. PMID:25309950

Cooperation-Controlled Learning for Explicit Class Structure in Self-Organizing Maps

Directory of Open Access Journals (Sweden)

Ryotaro Kamimura

2014-01-01

Full Text Available We attempt to demonstrate the effectiveness of multiple points of view toward neural networks. By restricting ourselves to two points of view of a neuron, we propose a new type of information-theoretic method called “cooperation-controlled learning.” In this method, individual and collective neurons are distinguished from one another, and we suppose that the characteristics of individual and collective neurons are different. To implement individual and collective neurons, we prepare two networks, namely, cooperative and uncooperative networks. The roles of these networks and the roles of individual and collective neurons are controlled by the cooperation parameter. As the parameter is increased, the role of cooperative networks becomes more important in learning, and the characteristics of collective neurons become more dominant. On the other hand, when the parameter is small, individual neurons play a more important role. We applied the method to the automobile and housing data from the machine learning database and examined whether explicit class boundaries could be obtained. Experimental results showed that cooperation-controlled learning, in particular taking into account information on input units, could be used to produce clearer class structure than conventional self-organizing maps.

CLUSTER ANALYSIS UNTUK MEMPREDIKSI TALENTA PEMAIN BASKET MENGGUNAKAN JARINGAN SARAF TIRUAN SELF ORGANIZING MAPS (SOM

Directory of Open Access Journals (Sweden)

Gregorius Satia Budhi

2008-01-01

Full Text Available Basketball World has grown rapidly as the time goes on. This is signed by many competition and game all over the world. With the result there are many basketball players with their different playing characteristics. Demand for a coach or scout to look for or search great players to make a solid team as a coach requirement. With this application, a coach or scout will be helped in analyzing in decision making. This application uses Self Organizing Maps algorithm (SOM for Cluster Analysis. The real NBA player data is used for competitive learning or training process and real player data from Indonesian or Petra Christian University Basketball Players is used for testing process. The NBA Player data is prepared through cleaning process and then is transformed into a form that can be processed by SOM Algorithm. After that, the data is clustered with the SOM algorithm. The result of that clusters is displayed into a form that is easy to view and analyze. This result can be saved into a text file. By using the output / result of this application, that are the clusters of NBA player, the user can see the statistics of each cluster. With these cluster statistics coach or scout can predict the statistic and the position of a testing player who is in the same cluster. This information can give a support for the coach or scout to make a decision. Abstract in Bahasa Indonesia : Dunia bola basket telah berkembang dengan pesat seiring dengan berjalannya waktu. Hal ini ditandai dengan munculnya berbagai macam dan jenis kompetisi dan pertandingan baik dunia maupun dalam negeri. Sehingga makin banyak dilahirkannya pemain berbakat dengan berbagai karakteristik permainan yang berbeda. Tuntutan bagi seorang pelatih/pemandu bakat, untuk dapat melihat secara jeli dalam memenuhi kebutuhan tim untuk membentuk tim yang solid. Dengan dibuatnya aplikasi ini, maka akan membantu proses analisis dan pengambilan keputusan bagi pelatih maupun pemandu bakat Aplikasi ini

Modeling self-organization of novel organic materials

Science.gov (United States)

Sayar, Mehmet

In this thesis, the structural organization of oligomeric multi-block molecules is analyzed by computational analysis of coarse-grained models. These molecules form nanostructures with different dimensionalities, and the nanostructured nature of these materials leads to novel structural properties at different length scales. Previously, a number of oligomeric triblock rodcoil molecules have been shown to self-organize into mushroom shaped noncentrosymmetric nanostructures. Interestingly, thin films of these molecules contain polar domains and a finite macroscopic polarization. However, the fully polarized state is not the equilibrium state. In the first chapter, by solving a model with dipolar and Ising-like short range interactions, we show that polar domains are stable in films composed of aggregates as opposed to isolated molecules. Unlike classical molecular systems, these nanoaggregates have large intralayer spacings (a ≈ 6 nm), leading to a reduction in the repulsive dipolar interactions that oppose polar order within layers. This enables the formation of a striped pattern with polar domains of alternating directions. The energies of the possible structures at zero temperature are computed exactly and results of Monte Carlo simulations are provided at non-zero temperatures. In the second chapter, the macroscopic polarization of such nanostructured films is analyzed in the presence of a short range surface interaction. The surface interaction leads to a periodic domain structure where the balance between the up and down domains is broken, and therefore films of finite thickness have a net macroscopic polarization. The polarization per unit volume is a function of film thickness and strength of the surface interaction. Finally, in chapter three, self-organization of organic molecules into a network of one dimensional objects is analyzed. Multi-block organic dendron rodcoil molecules were found to self-organize into supramolecular nanoribbons (threads) and

Intelligent Machine Vision for Automated Fence Intruder Detection Using Self-organizing Map

Directory of Open Access Journals (Sweden)

Veldin A. Talorete Jr.

2017-03-01

Full Text Available This paper presents an intelligent machine vision for automated fence intruder detection. A series of still captured images that contain fence events using Internet Protocol cameras was used as input data to the system. Two classifiers were used; the first is to classify human posture and the second one will classify intruder location. The system classifiers were implemented using Self-Organizing Map after the implementation of several image segmentation processes. The human posture classifier is in charge of classifying the detected subject’s posture patterns from subject’s silhouette. Moreover, the Intruder Localization Classifier is in charge of classifying the detected pattern’s location classifier will estimate the location of the intruder with respect to the fence using geometric feature from images as inputs. The system is capable of activating the alarm, display the actual image and depict the location of the intruder when an intruder is detected. In detecting intruder posture, the system’s success rate of 88%. Overall system accuracy for day-time intruder localization is 83% and an accuracy of 88% for night-time intruder localization

Postprocessing of Accidental Scenarios by Semi-Supervised Self-Organizing Maps

Directory of Open Access Journals (Sweden)

Francesco Di Maio

2017-01-01

Full Text Available Integrated Deterministic and Probabilistic Safety Analysis (IDPSA of dynamic systems calls for the development of efficient methods for accidental scenarios generation. The necessary consideration of failure events timing and sequencing along the scenarios requires the number of scenarios to be generated to increase with respect to conventional PSA. Consequently, their postprocessing for retrieving safety relevant information regarding the system behavior is challenged because of the large amount of generated scenarios that makes the computational cost for scenario postprocessing enormous and the retrieved information difficult to interpret. In the context of IDPSA, the interpretation consists in the classification of the generated scenarios as safe, failed, Near Misses (NMs, and Prime Implicants (PIs. To address this issue, in this paper we propose the use of an ensemble of Semi-Supervised Self-Organizing Maps (SSSOMs whose outcomes are combined by a locally weighted aggregation according to two strategies: a locally weighted aggregation and a decision tree based aggregation. In the former, we resort to the Local Fusion (LF principle for accounting the classification reliability of the different SSSOM classifiers, whereas in the latter we build a classification scheme to select the appropriate classifier (or ensemble of classifiers, for the type of scenario to be classified. The two strategies are applied for the postprocessing of the accidental scenarios of a dynamic U-Tube Steam Generator (UTSG.

On the stability of the CO adsorption-induced and self-organized CuPt surface alloy

DEFF Research Database (Denmark)

Andersson, Klas Jerker; Chorkendorff, Ib

2010-01-01

The stability of the recently discovered CO-induced and self-organized CuPt surface alloy was explored at near ambient pressures of O-2 (200 mbar) at room temperature, in a CO + H-2 mix (P-tot = 220 mbar, 4% CO) from room temperature to 573 K, as well as in a CO + H2O mix (P-tot = 17 mbar, 50% CO...

An Information-Theoretic-Cluster Visualization for Self-Organizing Maps.

Science.gov (United States)

Brito da Silva, Leonardo Enzo; Wunsch, Donald C

2018-06-01

Improved data visualization will be a significant tool to enhance cluster analysis. In this paper, an information-theoretic-based method for cluster visualization using self-organizing maps (SOMs) is presented. The information-theoretic visualization (IT-vis) has the same structure as the unified distance matrix, but instead of depicting Euclidean distances between adjacent neurons, it displays the similarity between the distributions associated with adjacent neurons. Each SOM neuron has an associated subset of the data set whose cardinality controls the granularity of the IT-vis and with which the first- and second-order statistics are computed and used to estimate their probability density functions. These are used to calculate the similarity measure, based on Renyi's quadratic cross entropy and cross information potential (CIP). The introduced visualizations combine the low computational cost and kernel estimation properties of the representative CIP and the data structure representation of a single-linkage-based grouping algorithm to generate an enhanced SOM-based visualization. The visual quality of the IT-vis is assessed by comparing it with other visualization methods for several real-world and synthetic benchmark data sets. Thus, this paper also contains a significant literature survey. The experiments demonstrate the IT-vis cluster revealing capabilities, in which cluster boundaries are sharply captured. Additionally, the information-theoretic visualizations are used to perform clustering of the SOM. Compared with other methods, IT-vis of large SOMs yielded the best results in this paper, for which the quality of the final partitions was evaluated using external validity indices.

Gaia eclipsing binary and multiple systems. Supervised classification and self-organizing maps

Science.gov (United States)

Süveges, M.; Barblan, F.; Lecoeur-Taïbi, I.; Prša, A.; Holl, B.; Eyer, L.; Kochoska, A.; Mowlavi, N.; Rimoldini, L.

2017-07-01

Context. Large surveys producing tera- and petabyte-scale databases require machine-learning and knowledge discovery methods to deal with the overwhelming quantity of data and the difficulties of extracting concise, meaningful information with reliable assessment of its uncertainty. This study investigates the potential of a few machine-learning methods for the automated analysis of eclipsing binaries in the data of such surveys. Aims: We aim to aid the extraction of samples of eclipsing binaries from such databases and to provide basic information about the objects. We intend to estimate class labels according to two different, well-known classification systems, one based on the light curve morphology (EA/EB/EW classes) and the other based on the physical characteristics of the binary system (system morphology classes; detached through overcontact systems). Furthermore, we explore low-dimensional surfaces along which the light curves of eclipsing binaries are concentrated, and consider their use in the characterization of the binary systems and in the exploration of biases of the full unknown Gaia data with respect to the training sets. Methods: We have explored the performance of principal component analysis (PCA), linear discriminant analysis (LDA), Random Forest classification and self-organizing maps (SOM) for the above aims. We pre-processed the photometric time series by combining a double Gaussian profile fit and a constrained smoothing spline, in order to de-noise and interpolate the observed light curves. We achieved further denoising, and selected the most important variability elements from the light curves using PCA. Supervised classification was performed using Random Forest and LDA based on the PC decomposition, while SOM gives a continuous 2-dimensional manifold of the light curves arranged by a few important features. We estimated the uncertainty of the supervised methods due to the specific finite training set using ensembles of models constructed

Self-organizing maps applied to two-phase flow on natural circulation loop studies

Energy Technology Data Exchange (ETDEWEB)

Castro, Leonardo F.; Cunha, Kelly de P.; Andrade, Delvonei A.; Sabundjian, Gaiane; Torres, Walmir M.; Macedo, Luiz A.; Rocha, Marcelo da S.; Masotti, Paulo H.F.; Mesquita, Roberto N. de, E-mail: rnavarro@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

2015-07-01

Two-phase flow of liquid and gas is found in many closed circuits using natural circulation for cooling purposes. Natural circulation phenomenon is important on recent nuclear power plant projects for heat removal on 'loss of pump power' or 'plant shutdown' accidents. The accuracy of heat transfer estimation has been improved based on models that require precise prediction of pattern transitions of flow. Self-Organizing Maps are trained to digital images acquired on natural circulation flow instabilities. This technique will allow the selection of the more important characteristics associated with each flow pattern, enabling a better comprehension of each observed instability. This periodic flow oscillation behavior can be observed thoroughly in this facility due its glass-made tubes transparency. The Natural Circulation Facility (Circuito de Circulacao Natural - CCN) installed at Instituto de Pesquisas Energeticas e Nucleares, IPEN/CNEN, is an experimental circuit designed to provide thermal hydraulic data related to one and two phase flow under natural circulation conditions. (author)

Gene prediction using the Self-Organizing Map: automatic generation of multiple gene models.

Science.gov (United States)

Mahony, Shaun; McInerney, James O; Smith, Terry J; Golden, Aaron

2004-03-05

Many current gene prediction methods use only one model to represent protein-coding regions in a genome, and so are less likely to predict the location of genes that have an atypical sequence composition. It is likely that future improvements in gene finding will involve the development of methods that can adequately deal with intra-genomic compositional variation. This work explores a new approach to gene-prediction, based on the Self-Organizing Map, which has the ability to automatically identify multiple gene models within a genome. The current implementation, named RescueNet, uses relative synonymous codon usage as the indicator of protein-coding potential. While its raw accuracy rate can be less than other methods, RescueNet consistently identifies some genes that other methods do not, and should therefore be of interest to gene-prediction software developers and genome annotation teams alike. RescueNet is recommended for use in conjunction with, or as a complement to, other gene prediction methods.

Segmentation of Natural Gas Customers in Industrial Sector Using Self-Organizing Map (SOM) Method

Science.gov (United States)

Masbar Rus, A. M.; Pramudita, R.; Surjandari, I.

2018-03-01

The usage of the natural gas which is non-renewable energy, needs to be more efficient. Therefore, customer segmentation becomes necessary to set up a marketing strategy to be right on target or to determine an appropriate fee. This research was conducted at PT PGN using one of data mining method, i.e. Self-Organizing Map (SOM). The clustering process is based on the characteristic of its customers as a reference to create the customer segmentation of natural gas customers. The input variables of this research are variable of area, type of customer, the industrial sector, the average usage, standard deviation of the usage, and the total deviation. As a result, 37 cluster and 9 segment from 838 customer data are formed. These 9 segments then employed to illustrate the general characteristic of the natural gas customer of PT PGN.

Self-organizing neural networks for automatic detection and classification of contrast-enhancing lesions in dynamic MR-mammography

International Nuclear Information System (INIS)

Vomweg, T.W.; Teifke, A.; Kauczor, H.U.; Achenbach, T.; Rieker, O.; Schreiber, W.G.; Heitmann, K.R.; Beier, T.; Thelen, M.

2005-01-01

Purpose: Investigation and statistical evaluation of 'Self-Organizing Maps', a special type of neural networks in the field of artificial intelligence, classifying contrast enhancing lesions in dynamic MR-mammography. Material and Methods: 176 investigations with proven histology after core biopsy or operation were randomly divided into two groups. Several Self-Organizing Maps were trained by investigations of the first group to detect and classify contrast enhancing lesions in dynamic MR-mammography. Each single pixel's signal/time curve of all patients within the second group was analyzed by the Self-Organizing Maps. The likelihood of malignancy was visualized by color overlays on the MR-images. At last assessment of contrast-enhancing lesions by each different network was rated visually and evaluated statistically. Results: A well balanced neural network achieved a sensitivity of 90.5% and a specificity of 72.2% in predicting malignancy of 88 enhancing lesions. Detailed analysis of false-positive results revealed that every second fibroadenoma showed a 'typical malignant' signal/time curve without any chance to differentiate between fibroadenomas and malignant tissue regarding contrast enhancement alone; but this special group of lesions was represented by a well-defined area of the Self-Organizing Map. Discussion: Self-Organizing Maps are capable of classifying a dynamic signal/time curve as 'typical benign' or 'typical malignant'. Therefore, they can be used as second opinion. In view of the now known localization of fibroadenomas enhancing like malignant tumors at the Self-Organizing Map, these lesions could be passed to further analysis by additional post-processing elements (e.g., based on T2-weighted series or morphology analysis) in the future. (orig.)

A Self-Organizing Map-Based Approach to Generating Reduced-Size, Statistically Similar Climate Datasets

Science.gov (United States)

Cabell, R.; Delle Monache, L.; Alessandrini, S.; Rodriguez, L.

2015-12-01

Climate-based studies require large amounts of data in order to produce accurate and reliable results. Many of these studies have used 30-plus year data sets in order to produce stable and high-quality results, and as a result, many such data sets are available, generally in the form of global reanalyses. While the analysis of these data lead to high-fidelity results, its processing can be very computationally expensive. This computational burden prevents the utilization of these data sets for certain applications, e.g., when rapid response is needed in crisis management and disaster planning scenarios resulting from release of toxic material in the atmosphere. We have developed a methodology to reduce large climate datasets to more manageable sizes while retaining statistically similar results when used to produce ensembles of possible outcomes. We do this by employing a Self-Organizing Map (SOM) algorithm to analyze general patterns of meteorological fields over a regional domain of interest to produce a small set of "typical days" with which to generate the model ensemble. The SOM algorithm takes as input a set of vectors and generates a 2D map of representative vectors deemed most similar to the input set and to each other. Input predictors are selected that are correlated with the model output, which in our case is an Atmospheric Transport and Dispersion (T&D) model that is highly dependent on surface winds and boundary layer depth. To choose a subset of "typical days," each input day is assigned to its closest SOM map node vector and then ranked by distance. Each node vector is treated as a distribution and days are sampled from them by percentile. Using a 30-node SOM, with sampling every 20th percentile, we have been able to reduce 30 years of the Climate Forecast System Reanalysis (CFSR) data for the month of October to 150 "typical days." To estimate the skill of this approach, the "Measure of Effectiveness" (MOE) metric is used to compare area and overlap

Modeling hydrologic and geomorphic hazards across post-fire landscapes using a self-organizing map approach

Science.gov (United States)

Friedel, Michael J.

2011-01-01

Few studies attempt to model the range of possible post-fire hydrologic and geomorphic hazards because of the sparseness of data and the coupled, nonlinear, spatial, and temporal relationships among landscape variables. In this study, a type of unsupervised artificial neural network, called a self-organized map (SOM), is trained using data from 540 burned basins in the western United States. The sparsely populated data set includes variables from independent numerical landscape categories (climate, land surface form, geologic texture, and post-fire condition), independent landscape classes (bedrock geology and state), and dependent initiation processes (runoff, landslide, and runoff and landslide combination) and responses (debris flows, floods, and no events). Pattern analysis of the SOM-based component planes is used to identify and interpret relations among the variables. Application of the Davies-Bouldin criteria following k-means clustering of the SOM neurons identified eight conceptual regional models for focusing future research and empirical model development. A split-sample validation on 60 independent basins (not included in the training) indicates that simultaneous predictions of initiation process and response types are at least 78% accurate. As climate shifts from wet to dry conditions, forecasts across the burned landscape reveal a decreasing trend in the total number of debris flow, flood, and runoff events with considerable variability among individual basins. These findings suggest the SOM may be useful in forecasting real-time post-fire hazards, and long-term post-recovery processes and effects of climate change scenarios.

A NEW RECOGNITION TECHNIQUE NAMED SOMP BASED ON PALMPRINT USING NEURAL NETWORK BASED SELF ORGANIZING MAPS

Directory of Open Access Journals (Sweden)

A. S. Raja

2012-08-01

Full Text Available The word biometrics refers to the use of physiological or biological characteristics of human to recognize and verify the identity of an individual. Palmprint has become a new class of human biometrics for passive identification with uniqueness and stability. This is considered to be reliable due to the lack of expressions and the lesser effect of aging. In this manuscript a new Palmprint based biometric system based on neural networks self organizing maps (SOM is presented. The method is named as SOMP. The paper shows that the proposed SOMP method improves the performance and robustness of recognition. The proposed method is applied to a variety of datasets and the results are shown.

Rational design of organic semiconductors for texture control and self-patterning on halogenated surfaces

KAUST Repository

Ward, Jeremy W.

2014-05-15

Understanding the interactions at interfaces between the materials constituting consecutive layers within organic thin-film transistors (OTFTs) is vital for optimizing charge injection and transport, tuning thin-film microstructure, and designing new materials. Here, the influence of the interactions at the interface between a halogenated organic semiconductor (OSC) thin film and a halogenated self-assembled monolayer on the formation of the crystalline texture directly affecting the performance of OTFTs is explored. By correlating the results from microbeam grazing incidence wide angle X-ray scattering (μGIWAXS) measurements of structure and texture with OTFT characteristics, two or more interaction paths between the terminating atoms of the semiconductor and the halogenated surface are found to be vital to templating a highly ordered morphology in the first layer. These interactions are effective when the separating distance is lower than 2.5 dw, where dw represents the van der Waals distance. The ability to modulate charge carrier transport by several orders of magnitude by promoting "edge-on" versus "face-on" molecular orientation and crystallographic textures in OSCs is demonstrated. It is found that the "edge-on" self-assembly of molecules forms uniform, (001) lamellar-textured crystallites which promote high charge carrier mobility, and that charge transport suffers as the fraction of the "face-on" oriented crystallites increases. The role of interfacial halogenation in mediating texture formation and the self-patterning of organic semiconductor films, as well as the resulting effects on charge transport in organic thin-film transistors, are explored. The presence of two or more anchoring sites between a halogenated semiconductor and a halogenated self-assembled monolayer, closer than about twice the corresponding van der Waals distance, alter the microstructure and improve electrical properties. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Tailoring of quantum dot emission efficiency by localized surface plasmon polaritons in self-organized mesoscopic rings.

Science.gov (United States)

Margapoti, Emanuela; Gentili, Denis; Amelia, Matteo; Credi, Alberto; Morandi, Vittorio; Cavallini, Massimiliano

2014-01-21

We report on the tailoring of quantum dot (QD) emission efficiency by localized surface plasmon polaritons in self-organized mesoscopic rings. Ag nanoparticles (NPs) with CdSe QDs embedded in a polymeric matrix are spatially organised in mesoscopic rings and coupled in a tuneable fashion by breath figure formation. The mean distance between NPs and QDs and consequently the intensity of QD photoluminescence, which is enhanced by the coupling of surface plasmons and excitons, are tuned by acting on the NP concentration.

The concept of self-organizing systems. Why bother?

Science.gov (United States)

Elverfeldt, Kirsten v.; Embleton-Hamann, Christine; Slaymaker, Olav

2016-04-01

Complexity theory and the concept of self-organizing systems provide a rather challenging conceptual framework for explaining earth systems change. Self-organization - understood as the aggregate processes internal to an environmental system that lead to a distinctive spatial or temporal organization - reduces the possibility of implicating a specific process as being causal, and it poses some restrictions on the idea that external drivers cause a system to change. The concept of self-organizing systems suggests that many phenomena result from an orchestration of different mechanisms, so that no causal role can be assigned to an individual factor or process. The idea that system change can be due to system-internal processes of self-organization thus proves a huge challenge to earth system research, especially in the context of global environmental change. In order to understand the concept's implications for the Earth Sciences, we need to know the characteristics of self-organizing systems and how to discern self-organizing systems. Within the talk, we aim firstly at characterizing self-organizing systems, and secondly at highlighting the advantages and difficulties of the concept within earth system sciences. The presentation concludes that: - The concept of self-organizing systems proves especially fruitful for small-scale earth surface systems. Beach cusps and patterned ground are only two of several other prime examples of self-organizing earth surface systems. They display characteristics of self-organization like (i) system-wide order from local interactions, (ii) symmetry breaking, (iii) distributed control, (iv) robustness and resilience, (v) nonlinearity and feedbacks, (vi) organizational closure, (vii) adaptation, and (viii) variation and selection. - It is comparatively easy to discern self-organization in small-scale systems, but to adapt the concept to larger scale systems relevant to global environmental change research is more difficult: Self-organizing

The self-organizing map, a new approach to apprehend the Madden–Julian Oscillation influence on the intraseasonal variability of rainfall in the southern African region

CSIR Research Space (South Africa)

Oettli, P

2013-11-01

Full Text Available -linear classification method, the self-organizing map (SOM), a type of artificial neural network used to produce a low-dimensional representation of high-dimensional datasets, to capture more accurately the life cycle of the MJO and its global impacts...

Reaction-Map of Organic Chemistry

Science.gov (United States)

Murov, Steven

2007-01-01

The Reaction-Map of Organic Chemistry lists all the most commonly studied reactions in organic chemistry on one page. The discussed Reaction-Map will act as another learning aide for the students, making the study of organic chemistry much easier.

Surface mapping, organic matter and water stocks in peatlands of the Serra do Espinhaço meridional - Brazil

Directory of Open Access Journals (Sweden)

Márcio Luiz da Silva

2013-10-01

Full Text Available Peatlands are soil environments that store carbon and large amounts of water, due to their composition (90 % water, low hydraulic conductivity and a sponge-like behavior. It is estimated that peat bogs cover approximately 4.2 % of the Earth's surface and stock 28.4 % of the soil carbon of the planet. Approximately 612 000 ha of peatlands have been mapped in Brazil, but the peat bogs in the Serra do Espinhaço Meridional (SdEM were not included. The objective of this study was to map the peat bogs of the northern part of the SdEM and estimate the organic matter pools and water volume they stock. The peat bogs were pre-identified and mapped by GIS and remote sensing techniques, using ArcGIS 9.3, ENVI 4.5 and GPS Track Maker Pro software and the maps validated in the field. Six peat bogs were mapped in detail (1:20,000 and 1:5,000 by transects spaced 100 m and each transect were determined every 20 m, the UTM (Universal Transverse Mercator coordinates, depth and samples collected for characterization and determination of organic matter, according to the Brazilian System of Soil Classification. In the northern part of SdEM, 14,287.55 ha of peatlands were mapped, distributed over 1,180,109 ha, representing 1.2 % of the total area. These peatlands have an average volume of 170,021,845.00 m³ and stock 6,120,167 t (428.36 t ha-1 of organic matter and 142,138,262 m³ (9,948 m³ ha-1 of water. In the peat bogs of the Serra do Espinhaço Meridional, advanced stages of decomposing (sapric organic matter predominate, followed by the intermediate stage (hemic. The vertical growth rate of the peatlands ranged between 0.04 and 0.43 mm year-1, while the carbon accumulation rate varied between 6.59 and 37.66 g m-2 year-1. The peat bogs of the SdEM contain the headwaters of important water bodies in the basins of the Jequitinhonha and San Francisco Rivers and store large amounts of organic carbon and water, which is the reason why the protection and preservation

Transient classification for the IRIS reactor using self-organized maps built in free platform

International Nuclear Information System (INIS)

Doraskevicius Junior, Waldemar

2005-01-01

Kohonen's Self Organized Maps (SOM) were tested with data from several operational conditions of the nuclear reactor IRIS (International Reactor Innovative and Secure) to develop an effective tool in the classification and transient identification in nuclear reactors. The data were derived from 56 simulations of the operation of IRIS, from steady-state conditions to accidents. The digital system built for the tests was based on the JAVA platform for the portability and scalability, and for being one of the free development platforms. Satisfactory results of operation classification were obtained with reasonable processing time in personal computers; about two to five minutes were spent for ordination and convergence of the learning on the data base. The methodology of this work was extended to the supervision of logistics of natural gas for Brazilian pipelines, showing satisfactory results for the classification of deliveries for simultaneous measurement in several points. (author)

Self-Organizing Robots

CERN Document Server

Murata, Satoshi

2012-01-01

It is man’s ongoing hope that a machine could somehow adapt to its environment by reorganizing itself. This is what the notion of self-organizing robots is based on. The theme of this book is to examine the feasibility of creating such robots within the limitations of current mechanical engineering. The topics comprise the following aspects of such a pursuit: the philosophy of design of self-organizing mechanical systems; self-organization in biological systems; the history of self-organizing mechanical systems; a case study of a self-assembling/self-repairing system as an autonomous distributed system; a self-organizing robot that can create its own shape and robotic motion; implementation and instrumentation of self-organizing robots; and the future of self-organizing robots. All topics are illustrated with many up-to-date examples, including those from the authors’ own work. The book does not require advanced knowledge of mathematics to be understood, and will be of great benefit to students in the rob...

Using self-organizing maps to determine observation threshold limit predictions in highly variant data

Science.gov (United States)

Paganoni, C.A.; Chang, K.C.; Robblee, M.B.

2006-01-01

A significant data quality challenge for highly variant systems surrounds the limited ability to quantify operationally reasonable limits on the data elements being collected and provide reasonable threshold predictions. In many instances, the number of influences that drive a resulting value or operational range is too large to enable physical sampling for each influencer, or is too complicated to accurately model in an explicit simulation. An alternative method to determine reasonable observation thresholds is to employ an automation algorithm that would emulate a human analyst visually inspecting data for limits. Using the visualization technique of self-organizing maps (SOM) on data having poorly understood relationships, a methodology for determining threshold limits was developed. To illustrate this approach, analysis of environmental influences that drive the abundance of a target indicator species (the pink shrimp, Farfantepenaeus duorarum) provided a real example of applicability. The relationship between salinity and temperature and abundance of F. duorarum is well documented, but the effect of changes in water quality upstream on pink shrimp abundance is not well understood. The highly variant nature surrounding catch of a specific number of organisms in the wild, and the data available from up-stream hydrology measures for salinity and temperature, made this an ideal candidate for the approach to provide a determination about the influence of changes in hydrology on populations of organisms.

IDENTIFYING LOCAL SCALE CLIMATE ZONES OF URBAN HEAT ISLAND FROM HJ-1B SATELLITE DATA USING SELF-ORGANIZING MAPS

Directory of Open Access Journals (Sweden)

C. Z. Wei

2016-10-01

Full Text Available With the increasing acceleration of urbanization, the degeneration of the environment and the Urban Heat Island (UHI has attracted more and more attention. Quantitative delineation of UHI has become crucial for a better understanding of the interregional interaction between urbanization processes and the urban environment system. First of all, our study used medium resolution Chinese satellite data-HJ-1B as the Earth Observation data source to derive parameters, including the percentage of Impervious Surface Areas, Land Surface Temperature, Land Surface Albedo, Normalized Differential Vegetation Index, and object edge detector indicators (Mean of Inner Border, Mean of Outer border in the city of Guangzhou, China. Secondly, in order to establish a model to delineate the local climate zones of UHI, we used the Principal Component Analysis to explore the correlations between all these parameters, and estimate their contributions to the principal components of UHI zones. Finally, depending on the results of the PCA, we chose the most suitable parameters to classify the urban climate zones based on a Self-Organization Map (SOM. The results show that all six parameters are closely correlated with each other and have a high percentage of cumulative (95% in the first two principal components. Therefore, the SOM algorithm automatically categorized the city of Guangzhou into five classes of UHI zones using these six spectral, structural and climate parameters as inputs. UHI zones have distinguishable physical characteristics, and could potentially help to provide the basis and decision support for further sustainable urban planning.

Hierarchical organization versus self-organization

OpenAIRE

Busseniers, Evo

2014-01-01

In this paper we try to define the difference between hierarchical organization and self-organization. Organization is defined as a structure with a function. So we can define the difference between hierarchical organization and self-organization both on the structure as on the function. In the next two chapters these two definitions are given. For the structure we will use some existing definitions in graph theory, for the function we will use existing theory on (self-)organization. In the t...

Ionic self-assembly of surface functionalized metal-organic polyhedra nanocages and their ordered honeycomb architecture at the air/water interface.

Science.gov (United States)

Li, Yantao; Zhang, Daojun; Gai, Fangyuan; Zhu, Xingqi; Guo, Ya-nan; Ma, Tianliang; Liu, Yunling; Huo, Qisheng

2012-08-18

Metal-organic polyhedra (MOP) nanocages were successfully surface functionalized via ionic self-assembly and the ordered honeycomb architecture of the encapsulated MOP nanocages was also fabricated at the air/water surface. The results provide a novel synthetic method and membrane processing technique of amphiphilic MOP nanocages for various applications.

Assessment of self-organizing maps to analyze sole-carbon source utilization profiles.

Science.gov (United States)

Leflaive, Joséphine; Céréghino, Régis; Danger, Michaël; Lacroix, Gérard; Ten-Hage, Loïc

2005-07-01

The use of community-level physiological profiles obtained with Biolog microplates is widely employed to consider the functional diversity of bacterial communities. Biolog produces a great amount of data which analysis has been the subject of many studies. In most cases, after some transformations, these data were investigated with classical multivariate analyses. Here we provided an alternative to this method, that is the use of an artificial intelligence technique, the Self-Organizing Maps (SOM, unsupervised neural network). We used data from a microcosm study of algae-associated bacterial communities placed in various nutritive conditions. Analyses were carried out on the net absorbances at two incubation times for each substrates and on the chemical guild categorization of the total bacterial activity. Compared to Principal Components Analysis and cluster analysis, SOM appeared as a valuable tool for community classification, and to establish clear relationships between clusters of bacterial communities and sole-carbon sources utilization. Specifically, SOM offered a clear bidimensional projection of a relatively large volume of data and were easier to interpret than plots commonly obtained with multivariate analyses. They would be recommended to pattern the temporal evolution of communities' functional diversity.

Interfacial self-organization of bolaamphiphiles bearing mesogenic groups: relationships between the molecular structures and their self-organized morphologies.

Science.gov (United States)

Song, Bo; Liu, Guanqing; Xu, Rui; Yin, Shouchun; Wang, Zhiqiang; Zhang, Xi

2008-04-15

This article discusses the relationship between the molecular structure of bolaamphiphiles bearing mesogenic groups and their interfacial self-organized morphology. On the basis of the molecular structures of bolaamphiphiles, we designed and synthesized a series of molecules with different hydrophobic alkyl chain lengths, hydrophilic headgroups, mesogenic groups, and connectors between the alkyl chains and the mesogenic group. Through investigating their interfacial self-organization behavior, some experiential rules are summarized: (1) An appropriate alkyl chain length is necessary to form stable surface micelles; (2) different categories of headgroups have a great effect on the interfacial self-organized morphology; (3) different types of mesogenic groups have little effect on the structure of the interfacial assembly when it is changed from biphenyl to azobenzene or stilbene; (4) the orientation of the ester linker between the mesogenic group and alkyl chain can greatly influence the interfacial self-organization behavior. It is anticipated that this line of research may be helpful for the molecular engineering of bolaamphiphiles to form tailor-made morphologies.

Functional and structural mapping of human cerebral cortex: solutions are in the surfaces

Science.gov (United States)

Van Essen, D. C.; Drury, H. A.; Joshi, S.; Miller, M. I.

1998-01-01

The human cerebral cortex is notorious for the depth and irregularity of its convolutions and for its variability from one individual to the next. These complexities of cortical geography have been a chronic impediment to studies of functional specialization in the cortex. In this report, we discuss ways to compensate for the convolutions by using a combination of strategies whose common denominator involves explicit reconstructions of the cortical surface. Surface-based visualization involves reconstructing cortical surfaces and displaying them, along with associated experimental data, in various complementary formats (including three-dimensional native configurations, two-dimensional slices, extensively smoothed surfaces, ellipsoidal representations, and cortical flat maps). Generating these representations for the cortex of the Visible Man leads to a surface-based atlas that has important advantages over conventional stereotaxic atlases as a substrate for displaying and analyzing large amounts of experimental data. We illustrate this by showing the relationship between functionally specialized regions and topographically organized areas in human visual cortex. Surface-based warping allows data to be mapped from individual hemispheres to a surface-based atlas while respecting surface topology, improving registration of identifiable landmarks, and minimizing unwanted distortions. Surface-based warping also can aid in comparisons between species, which we illustrate by warping a macaque flat map to match the shape of a human flat map. Collectively, these approaches will allow more refined analyses of commonalities as well as individual differences in the functional organization of primate cerebral cortex.

Analysis of Flue Gas Emission Data from Fluidized Bed Combustion Using Self-Organizing Maps

Directory of Open Access Journals (Sweden)

Mika Liukkonen

2010-01-01

Full Text Available Efficient combustion of fuels with lower emissions levels has become a demanding task in modern power plants, and new tools are needed to diagnose their energy production. The goals of the study were to find dependencies between process variables and the concentrations of gaseous emission components and to create multivariate nonlinear models describing their formation in the process. First, a generic process model was created by using a self-organizing map, which was clustered with the k-means algorithm to create subsets representing the different states of the process. Characteristically, these process states may include high- and low- load situations and transition states where the load is increased or decreased. Then emission models were constructed for both the entire process and for the process state of high boiler load. The main conclusion is that the methodology used is able to reveal such phenomena that occur within the process states and that could otherwise be difficult to observe.

Estimation of austral summer net community production in the Amundsen Sea: Self-organizing map analysis approach

Science.gov (United States)

Park, K.; Hahm, D.; Lee, D. G.; Rhee, T. S.; Kim, H. C.

2014-12-01

The Amundsen Sea, Antarctica, has been known for one of the most susceptible region to the current climate change such as sea ice melting and sea surface temperature change. In the Southern Ocean, a predominant amount of primary production is occurring in the continental shelf region. Phytoplankton blooms take place during the austral summer due to the limited sunlit and sea ice cover. Thus, quantifying the variation of summer season net community production (NCP) in the Amundsen Sea is essential to analyze the influence of climate change to the variation of biogeochemical cycle in the Southern Ocean. During the past three years of 2011, 2012 and 2014 in austral summer, we have conducted underway observations of ΔO2/Ar and derived NCP of the Amundsen Sea. Despite the importance of NCP for understanding biological carbon cycle of the ocean, the observations are rather limited to see the spatio-temporal variation in the Amundsen Sea. Therefore, we applied self-organizing map (SOM) analysis to expand our observed data sets and estimate the NCP during the summer season. SOM analysis, a type of artificial neural network, has been proved to be a useful method for extracting and classifying features in geoscience. In oceanography, SOM has applied for the analysis of various properties of the seawater such as sea surface temperature, chlorophyll concentration, pCO2, and NCP. Especially it is useful to expand a spatial coverage of direct measurements or to estimate properties whose satellite observations are technically or spatially limited. In this study, we estimate summer season NCP and find a variables set which optimally delineates the NCP variation in the Amundsen Sea as well. Moreover, we attempt to analyze the interannual variation of the Amundsen Sea NCP by taking climatological factors into account for the SOM analysis.

Multiscale visual quality assessment for cluster analysis with self-organizing maps

Science.gov (United States)

Bernard, Jürgen; von Landesberger, Tatiana; Bremm, Sebastian; Schreck, Tobias

2011-01-01

Cluster analysis is an important data mining technique for analyzing large amounts of data, reducing many objects to a limited number of clusters. Cluster visualization techniques aim at supporting the user in better understanding the characteristics and relationships among the found clusters. While promising approaches to visual cluster analysis already exist, these usually fall short of incorporating the quality of the obtained clustering results. However, due to the nature of the clustering process, quality plays an important aspect, as for most practical data sets, typically many different clusterings are possible. Being aware of clustering quality is important to judge the expressiveness of a given cluster visualization, or to adjust the clustering process with refined parameters, among others. In this work, we present an encompassing suite of visual tools for quality assessment of an important visual cluster algorithm, namely, the Self-Organizing Map (SOM) technique. We define, measure, and visualize the notion of SOM cluster quality along a hierarchy of cluster abstractions. The quality abstractions range from simple scalar-valued quality scores up to the structural comparison of a given SOM clustering with output of additional supportive clustering methods. The suite of methods allows the user to assess the SOM quality on the appropriate abstraction level, and arrive at improved clustering results. We implement our tools in an integrated system, apply it on experimental data sets, and show its applicability.

Use of self-organizing maps for classification of defects in the tubes from the steam generator of nuclear power plants

International Nuclear Information System (INIS)

Mesquita, Roberto Navarro de

2002-01-01

This thesis obtains a new classification method for different steam generator tube defects in nuclear power plants using Eddy Current Test signals. The method uses self-organizing maps to compare different signal characteristics efficiency to identify and classify these defects. A multiple inference system is proposed which composes the different extracted characteristic trained maps classification to infer the final defect type. The feature extraction methods used are the Wavelet zero-crossings representation, the linear predictive coding (LPC), and other basic signal representations on time like module and phase. Many characteristic vectors are obtained with combinations of these extracted characteristics. These vectors are tested to classify the defects and the best ones are applied to the multiple inference system. A systematic study of pre-processing, calibration and analysis methods for the steam generator tube defect signals in nuclear power plants is done. The method efficiency is demonstrated and characteristic maps with the main prototypes are obtained for each steam generator tube defect type. (author)

Computerized mappings of the cerebral cortex: a multiresolution flattening method and a surface-based coordinate system

Science.gov (United States)

Drury, H. A.; Van Essen, D. C.; Anderson, C. H.; Lee, C. W.; Coogan, T. A.; Lewis, J. W.

1996-01-01

We present a new method for generating two-dimensional maps of the cerebral cortex. Our computerized, two-stage flattening method takes as its input any well-defined representation of a surface within the three-dimensional cortex. The first stage rapidly converts this surface to a topologically correct two-dimensional map, without regard for the amount of distortion introduced. The second stage reduces distortions using a multiresolution strategy that makes gross shape changes on a coarsely sampled map and further shape refinements on progressively finer resolution maps. We demonstrate the utility of this approach by creating flat maps of the entire cerebral cortex in the macaque monkey and by displaying various types of experimental data on such maps. We also introduce a surface-based coordinate system that has advantages over conventional stereotaxic coordinates and is relevant to studies of cortical organization in humans as well as non-human primates. Together, these methods provide an improved basis for quantitative studies of individual variability in cortical organization.

Re-discovering surface mass spectrometry: chemical mapping from micro to macro

Energy Technology Data Exchange (ETDEWEB)

Lloyd, K.G.; O' Keefe, D.P

2004-06-15

New developments in electronics, devices, micro-encapsulation, and other areas demand the ability to acquire molecularly-specific information from smaller and smaller features. ToF-SIMS provides molecularly-specific mass spectral data, but sufficient high-mass signal has historically been difficult to obtain from organic/polymeric surfaces in the point-mapping mode of operation. Use of chemometric data reduction methods and the development of heavier primary ion sources enhance and extend the chemical information in the mapping data. Large-area chemical mapping via sample stage rastering has also opened up new applications. This capability allows single-experiment mapping of large or multiple features, provides information on surface uniformity over end-use-relevant areas, and offers potential for combinatorial and other screening applications. Examples of these applications are presented.

Contrasting self-aggregation over land and ocean surfaces

Science.gov (United States)

Inda Diaz, H. A.; O'Brien, T. A.

2017-12-01

The spontaneous organization of convection into clusters, or self-aggregation, demonstrably changes the nature and statistics of precipitation. While there has been much recent progress in this area, the processes that control self-aggregation are still poorly understood. Most of the work to date has focused on self-aggregation over ocean-like surfaces, but it is particularly pressing to understand what controls convective aggregation over land, since the associated change in precipitation statistics—between non-aggregated and aggregated convection—could have huge impacts on society and infrastructure. Radiative-convective equilibrium (RCE), has been extensively used as an idealized framework to study the tropical atmosphere. Self-aggregation manifests in numerous numerical models of RCE, nevertheless, there is still a lack of understanding in how it relates to convective organization in the observed world. Numerous studies have examined self-aggregation using idealized Cloud Resolving Models (CRMs) and General Circulation Models over the ocean, however very little work has been done on RCE and self-aggregation over land. Idealized models of RCE over ocean have shown that aggregation is sensitive to sea surface temperature (SST), more intense precipitation occurs in aggregated systems, and a variety of feedbacks—such as surface flux, cloud radiative, and upgradient moisture transport— contribute to the maintenance of aggregation, however it is not clear if these results apply over land. Progress in this area could help relate understanding of self-aggregation in idealized simulations to observations. In order to explore the behavior of self-aggregation over land, we use a CRM to simulate idealized RCE over land. In particular, we examine the aggregation of convection and how it compares with aggregation over ocean. Based on previous studies, where a variety of different CRMs exhibit a SST threshold below which self-aggregation does not occur, we hypothesize

A self-assembled monolayer-assisted surface microfabrication and release technique

NARCIS (Netherlands)

Kim, B.J.; Liebau, M.; Huskens, Jurriaan; Reinhoudt, David; Brugger, J.P.

2001-01-01

This paper describes a method of thin film and MEMS processing which uses self-assembled monolayers as ultra-thin organic surface coating to enable a simple removal of microfabricated devices off the surface without wet chemical etching. A 1.5-nm thick self-assembled monolayer of

Chemotaxonomy of three genera of the Annonaceae family using self-organizing maps and 13C NMR data of diterpenes

International Nuclear Information System (INIS)

Scotti, Luciana; Tavares, Josean Fechine; Silva, Marcelo Sobral da; Falcao, Emanuela Viana; Silva, Luana de Morais e; Soares, Gabriela Cristina da Silva; Scotti, Marcus Tullius

2012-01-01

The Annonaceae family is distributed throughout Neotropical regions of the world. In Brazil, it covers nearly all natural formations particularly Annona, Xylopia and Polyalthia and is characterized chemically by the production of sources of terpenoids (mainly diterpenes), alkaloids, steroids, polyphenols and, flavonoids. Studies from 13 C NMR data of diterpenes related with their botanical occurrence were used to generate self-organizing maps. Results corroborate those in the literature obtained from morphological and molecular data for three genera and the model can be used to project other diterpenes. Therefore, the model produced can predict which genera are likely to contain a compound. (author)

SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance.

Science.gov (United States)

Sacha, Dominik; Kraus, Matthias; Bernard, Jurgen; Behrisch, Michael; Schreck, Tobias; Asano, Yuki; Keim, Daniel A

2018-01-01

Clustering is a core building block for data analysis, aiming to extract otherwise hidden structures and relations from raw datasets, such as particular groups that can be effectively related, compared, and interpreted. A plethora of visual-interactive cluster analysis techniques has been proposed to date, however, arriving at useful clusterings often requires several rounds of user interactions to fine-tune the data preprocessing and algorithms. We present a multi-stage Visual Analytics (VA) approach for iterative cluster refinement together with an implementation (SOMFlow) that uses Self-Organizing Maps (SOM) to analyze time series data. It supports exploration by offering the analyst a visual platform to analyze intermediate results, adapt the underlying computations, iteratively partition the data, and to reflect previous analytical activities. The history of previous decisions is explicitly visualized within a flow graph, allowing to compare earlier cluster refinements and to explore relations. We further leverage quality and interestingness measures to guide the analyst in the discovery of useful patterns, relations, and data partitions. We conducted two pair analytics experiments together with a subject matter expert in speech intonation research to demonstrate that the approach is effective for interactive data analysis, supporting enhanced understanding of clustering results as well as the interactive process itself.

Classification of passive auditory event-related potentials using discriminant analysis and self-organizing feature maps.

Science.gov (United States)

Schönweiler, R; Wübbelt, P; Tolloczko, R; Rose, C; Ptok, M

2000-01-01

Discriminant analysis (DA) and self-organizing feature maps (SOFM) were used to classify passively evoked auditory event-related potentials (ERP) P(1), N(1), P(2) and N(2). Responses from 16 children with severe behavioral auditory perception deficits, 16 children with marked behavioral auditory perception deficits, and 14 controls were examined. Eighteen ERP amplitude parameters were selected for examination of statistical differences between the groups. Different DA methods and SOFM configurations were trained to the values. SOFM had better classification results than DA methods. Subsequently, measures on another 37 subjects that were unknown for the trained SOFM were used to test the reliability of the system. With 10-dimensional vectors, reliable classifications were obtained that matched behavioral auditory perception deficits in 96%, implying central auditory processing disorder (CAPD). The results also support the assumption that CAPD includes a 'non-peripheral' auditory processing deficit. Copyright 2000 S. Karger AG, Basel.

Reduction of 4-dim self dual super Yang-Mills onto super Riemann surfaces

International Nuclear Information System (INIS)

Mendoza, A.; Restuccia, A.; Martin, I.

1990-05-01

Recently self dual super Yang-Mills over a super Riemann surface was obtained as the zero set of a moment map on the space of superconnections to the dual of the super Lie algebra of gauge transformations. We present a new formulation of 4-dim Euclidean self dual super Yang-Mills in terms of constraints on the supercurvature. By dimensional reduction we obtain the same set of superconformal field equations which define self dual connections on a super Riemann surface. (author). 10 refs

Cooperation in carbon source degradation shapes spatial self-organization of microbial consortia on hydrated surfaces.

Science.gov (United States)

Tecon, Robin; Or, Dani

2017-03-06

Mounting evidence suggests that natural microbial communities exhibit a high level of spatial organization at the micrometric scale that facilitate ecological interactions and support biogeochemical cycles. Microbial patterns are difficult to study definitively in natural environments due to complex biodiversity, observability and variable physicochemical factors. Here, we examine how trophic dependencies give rise to self-organized spatial patterns of a well-defined bacterial consortium grown on hydrated surfaces. The model consortium consisted of two Pseudomonas putida mutant strains that can fully degrade the aromatic hydrocarbon toluene. We demonstrated that obligate cooperation in toluene degradation (cooperative mutualism) favored convergence of 1:1 partner ratio and strong intermixing at the microscale (10-100 μm). In contrast, competition for benzoate, a compound degraded independently by both strains, led to distinct segregation patterns. Emergence of a persistent spatial pattern has been predicted for surface attached microbial activity in liquid films that mediate diffusive exchanges while permitting limited cell movement (colony expansion). This study of a simple microbial consortium offers mechanistic glimpses into the rules governing the assembly and functioning of complex sessile communities, and points to general principles of spatial organization with potential applications for natural and engineered microbial systems.

Content-based image retrieval using a signature graph and a self-organizing map

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Van Thanh The

2016-06-01

Full Text Available In order to effectively retrieve a large database of images, a method of creating an image retrieval system CBIR (contentbased image retrieval is applied based on a binary index which aims to describe features of an image object of interest. This index is called the binary signature and builds input data for the problem of matching similar images. To extract the object of interest, we propose an image segmentation method on the basis of low-level visual features including the color and texture of the image. These features are extracted at each block of the image by the discrete wavelet frame transform and the appropriate color space. On the basis of a segmented image, we create a binary signature to describe the location, color and shape of the objects of interest. In order to match similar images, we provide a similarity measure between the images based on binary signatures. Then, we present a CBIR model which combines a signature graph and a self-organizing map to cluster and store similar images. To illustrate the proposed method, experiments on image databases are reported, including COREL,Wang and MSRDI.

Social-Ecological Patterns of Soil Heavy Metals Based on a Self-Organizing Map (SOM: A Case Study in Beijing, China

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Binwu Wang

2014-03-01

Full Text Available The regional management of trace elements in soils requires understanding the interaction between the natural system and human socio-economic activities. In this study, a social-ecological patterns of heavy metals (SEPHM approach was proposed to identify the heavy metal concentration patterns and processes in different ecoregions of Beijing (China based on a self-organizing map (SOM. Potential ecological risk index (RI values of Cr, Ni, Zn, Hg, Cu, As, Cd and Pb were calculated for 1,018 surface soil samples. These data were averaged in accordance with 253 communities and/or towns, and compared with demographic, agriculture structure, geomorphology, climate, land use/cover, and soil-forming parent material to discover the SEPHM. Multivariate statistical techniques were further applied to interpret the control factors of each SEPHM. SOM application clustered the 253 towns into nine groups on the map size of 12 × 7 plane (quantization error 1.809; topographic error, 0.0079. The distribution characteristics and Spearman rank correlation coefficients of RIs were strongly associated with the population density, vegetation index, industrial and mining land percent and road density. The RIs were relatively high in which towns in a highly urbanized area with large human population density exist, while low RIs occurred in mountainous and high vegetation cover areas. The resulting dataset identifies the SEPHM of Beijing and links the apparent results of RIs to driving factors, thus serving as an excellent data source to inform policy makers for legislative and land management actions.

Using Self-Organizing Map (SOM) Clusters of Ozonesonde Profiles to Evaluate Climatologies and Create Linkages between Meteorology and Pollution

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Stauffer, R. M.; Thompson, A. M.; Young, G. S.; Oltmans, S. J.; Johnson, B.

2016-12-01

Ozone (O3) climatologies are typically created by averaging ozonesonde profiles on a monthly or seasonal basis, either for specific regions or zonally. We demonstrate the advantages of using a statistical clustering technique, self-organizing maps (SOM), over this simple averaging, through analysis of more than 4500 sonde profiles taken from the long-term US sites at Boulder, CO; Huntsville, AL; Trinidad Head, CA; and Wallops Island, VA. First, we apply SOM to O3 mixing ratios from surface to 12 km amsl. At all four sites, profiles in SOM clusters exhibit similar tropopause height, 500 hPa height and temperature, and total and tropospheric column O3. Second, when profiles from each SOM cluster are compared to monthly O3 means, near-tropopause O3 in three of the clusters is double (over +100 ppbv) the climatological O3 mixing ratio. The three clusters include 13-16% of all profiles, mostly from winter and spring. Large mid-tropospheric deviations from monthly means are found in two highly-populated clusters that represent either distinctly polluted (summer) or clean O3 (fall-winter, high tropopause) profiles. Thus, SOM indeed appear to represent US O3 profile statistics better than conventional climatologies. In the case of Trinidad Head, SOM clusters of O3 profile data from the lower troposphere (surface-6 km amsl) can discriminate background vs polluted O3 and the meteorology associated with each. Two of nine O3 clusters exhibit thin layers ( 100s of m thick) of high O3, typically between 1 and 4 km. Comparisons between clusters and downwind, high-altitude surface O3 measurements display a marked impact of the elevated tropospheric O­­3. Days corresponding to the high O3 clusters exhibit hourly surface O3 anomalies at surface sites of +5 -10 ppbv compared to a climatology; the anomalies can last up to four days. We also explore applications of SOM to tropical ozonesonde profiles, where tropospheric O3 variability is generally smaller.

Cell shape characterization and classification with discrete Fourier transforms and self-organizing maps.

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Kriegel, Fabian L; Köhler, Ralf; Bayat-Sarmadi, Jannike; Bayerl, Simon; Hauser, Anja E; Niesner, Raluca; Luch, Andreas; Cseresnyes, Zoltan

2018-03-01

Cells in their natural environment often exhibit complex kinetic behavior and radical adjustments of their shapes. This enables them to accommodate to short- and long-term changes in their surroundings under physiological and pathological conditions. Intravital multi-photon microscopy is a powerful tool to record this complex behavior. Traditionally, cell behavior is characterized by tracking the cells' movements, which yields numerous parameters describing the spatiotemporal characteristics of cells. Cells can be classified according to their tracking behavior using all or a subset of these kinetic parameters. This categorization can be supported by the a priori knowledge of experts. While such an approach provides an excellent starting point for analyzing complex intravital imaging data, faster methods are required for automated and unbiased characterization. In addition to their kinetic behavior, the 3D shape of these cells also provide essential clues about the cells' status and functionality. New approaches that include the study of cell shapes as well may also allow the discovery of correlations amongst the track- and shape-describing parameters. In the current study, we examine the applicability of a set of Fourier components produced by Discrete Fourier Transform (DFT) as a tool for more efficient and less biased classification of complex cell shapes. By carrying out a number of 3D-to-2D projections of surface-rendered cells, the applied method reduces the more complex 3D shape characterization to a series of 2D DFTs. The resulting shape factors are used to train a Self-Organizing Map (SOM), which provides an unbiased estimate for the best clustering of the data, thereby characterizing groups of cells according to their shape. We propose and demonstrate that such shape characterization is a powerful addition to, or a replacement for kinetic analysis. This would make it especially useful in situations where live kinetic imaging is less practical or not

Implementation of Self Organizing Map (SOM) as decision support: Indonesian telematics services MSMEs empowerment

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Tosida, E. T.; Maryana, S.; Thaheer, H.; Hardiani

2017-01-01

Information technology and communication (telematics) is one of the most rapidly developing business sectors in Indonesia. It has strategic position in its contribution towards planning and implementation of developmental, economics, social, politics and defence strategies in business, communication and education. Aid absorption for the national telecommunication SMEs is relatively low; therefore, improvement is needed using analysis on business support cluster of which basis is types of business. In the study, the business support cluster analysis is specifically implemented for Indonesian telecommunication service. The data for the business are obtained from the National Census of Economic (Susenas 2006). The method used to develop cluster model is an Artificial Neural Network (ANN) system called Self-Organizing Maps (SOM) algorithm. Based on Index of Davies Bouldin (IDB), the accuracy level of the cluster model is 0.37 or can be categorized as good. The cluster model is developed to find out telecommunication business clusters that has influence towards the national economy so that it is easier for the government to supervise telecommunication business.

An effective self-assessment based on concept map extraction from test-sheet for personalized learning

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Liew, Keng-Hou; Lin, Yu-Shih; Chang, Yi-Chun; Chu, Chih-Ping

2013-12-01

Examination is a traditional way to assess learners' learning status, progress and performance after a learning activity. Except the test grade, a test sheet hides some implicit information such as test concepts, their relationships, importance, and prerequisite. The implicit information can be extracted and constructed a concept map for considering (1) the test concepts covered in the same question means these test concepts have strong relationships, and (2) questions in the same test sheet means the test concepts are relative. Concept map has been successfully employed in many researches to help instructors and learners organize relationships among concepts. However, concept map construction depends on experts who need to take effort and time for the organization of the domain knowledge. In addition, the previous researches regarding to automatic concept map construction are limited to consider all learners of a class, which have not considered personalized learning. To cope with this problem, this paper proposes a new approach to automatically extract and construct concept map based on implicit information in a test sheet. Furthermore, the proposed approach also can help learner for self-assessment and self-diagnosis. Finally, an example is given to depict the effectiveness of proposed approach.

Integrable mappings via rational elliptic surfaces

International Nuclear Information System (INIS)

Tsuda, Teruhisa

2004-01-01

We present a geometric description of the QRT map (which is an integrable mapping introduced by Quispel, Roberts and Thompson) in terms of the addition formula of a rational elliptic surface. By this formulation, we classify all the cases when the QRT map is periodic; and show that its period is 2, 3, 4, 5 or 6. A generalization of the QRT map which acts birationally on a pencil of K3 surfaces, or Calabi-Yau manifolds, is also presented

Chemotaxonomy of three genera of the Annonaceae family using self-organizing maps and {sup 13}C NMR data of diterpenes

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Scotti, Luciana; Tavares, Josean Fechine; Silva, Marcelo Sobral da [Universidade Federal da Paraiba (UFPB), Joao Pessoa, PB (Brazil). Dept. de Ciencias Farmaceuticas; Falcao, Emanuela Viana; Silva, Luana de Morais e; Soares, Gabriela Cristina da Silva; Scotti, Marcus Tullius, E-mail: mtscotti@ccae.ufpb.br [Universidade Federal da Paraiba (UFPB), Rio Tinto, PB (Brazil). Dept. de Engenharia e Meio Ambiente

2012-07-01

The Annonaceae family is distributed throughout Neotropical regions of the world. In Brazil, it covers nearly all natural formations particularly Annona, Xylopia and Polyalthia and is characterized chemically by the production of sources of terpenoids (mainly diterpenes), alkaloids, steroids, polyphenols and, flavonoids. Studies from {sup 13}C NMR data of diterpenes related with their botanical occurrence were used to generate self-organizing maps. Results corroborate those in the literature obtained from morphological and molecular data for three genera and the model can be used to project other diterpenes. Therefore, the model produced can predict which genera are likely to contain a compound. (author)

Self-organizing plasmas

International Nuclear Information System (INIS)

Hayashi, T.; Sato, T.

1999-01-01

The primary purpose of this paper is to extract a grand view of self-organization through an extensive computer simulation of plasmas. The assertion is made that self-organization is governed by three key processes, i.e. the existence of an open complex system, the existence of information (energy) sources and the existence of entropy generation and expulsion processes. We find that self-organization takes place in an intermittent fashion when energy is supplied continuously from outside. In contrast, when the system state is suddenly changed into a non-equilibrium state externally, the system evolves stepwise and reaches a minimum energy state. We also find that the entropy production rate is maximized whenever a new ordered structure is created and that if the entropy generated during the self-organizing process is expelled from the system, then the self-organized structure becomes more prominent and clear. (author)

Patterns identification in supervisory systems of nuclear reactors installations and gas pipelines systems using self-organizing maps

International Nuclear Information System (INIS)

Doraskevicius Junior, Waldemar

2005-01-01

Self-Organizing Maps, SOM, of Kohonen were studied, implemented and tested with the aim of developing, for the energy branch, an effective tool especially for transient identification in nuclear reactors and for gas pipelines networks logistic supervision, by classifying operations and identifying transients or abnormalities. The digital system for the test was developed in Java platform, for the portability and scalability, and for belonging to free development platforms. The system, executed in personal computers, showed satisfactory results to aid in decision taking, by classifying IRIS (International Reactor Innovative and Secure) reactor operation conditions (data from simulator) and by classifying Southeast (owner: TRANSPETRO - Brazil) gas pipeline network. Various adaptations were needed for such business, as new topologies for the output layer of artificial neural network and particular preparation for the input data. (author)

A Graphical, Self-Organizing Approach to Classifying Electronic Meeting Output.

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Orwig, Richard E.; Chen, Hsinchun; Nunamaker, Jay F., Jr.

1997-01-01

Describes research using an artificial intelligence approach in the application of a Kohonen Self-Organizing Map (SOM) to the problem of classification of electronic brainstorming output and an evaluation of the results. The graphical representation of textual data produced by the Kohonen SOM suggests many opportunities for improving information…

Redox potential distribution of an organic-rich contaminated site obtained by the inversion of self-potential data

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Abbas, M.; Jardani, A.; Soueid Ahmed, A.; Revil, A.; Brigaud, L.; Bégassat, Ph.; Dupont, J. P.

2017-11-01

Mapping the redox potential of shallow aquifers impacted by hydrocarbon contaminant plumes is important for the characterization and remediation of such contaminated sites. The redox potential of groundwater is indicative of the biodegradation of hydrocarbons and is important in delineating the shapes of contaminant plumes. The self-potential method was used to reconstruct the redox potential of groundwater associated with an organic-rich contaminant plume in northern France. The self-potential technique is a passive technique consisting in recording the electrical potential distribution at the surface of the Earth. A self-potential map is essentially the sum of two contributions, one associated with groundwater flow referred to as the electrokinetic component, and one associated with redox potential anomalies referred to as the electroredox component (thermoelectric and diffusion potentials are generally negligible). A groundwater flow model was first used to remove the electrokinetic component from the observed self-potential data. Then, a residual self-potential map was obtained. The source current density generating the residual self-potential signals is assumed to be associated with the position of the water table, an interface characterized by a change in both the electrical conductivity and the redox potential. The source current density was obtained through an inverse problem by minimizing a cost function including a data misfit contribution and a regularizer. This inversion algorithm allows the determination of the vertical and horizontal components of the source current density taking into account the electrical conductivity distribution of the saturated and non-saturated zones obtained independently by electrical resistivity tomography. The redox potential distribution was finally determined from the inverted residual source current density. A redox map was successfully built and the estimated redox potential values correlated well with in

Self-organizing maps applied to two-phase flow on natural circulation loop study

International Nuclear Information System (INIS)

Castro, Leonardo Ferreira

2016-01-01

Two-phase flow of liquid and gas is found in many closed circuits using natural circulation for cooling purposes. Natural circulation phenomenon is important on recent nuclear power plant projects for decay heat removal. The Natural Circulation Facility (Circuito de Circulacao Natural CCN) installed at Instituto de Pesquisas Energeticas e Nucleares, IPEN/CNEN, is an experimental circuit designed to provide thermal hydraulic data related to single and two-phase flow under natural circulation conditions. This periodic flow oscillation behavior can be observed thoroughly in this facility due its glass-made tubes transparency. The heat transfer estimation has been improved based on models that require precise prediction of pattern transitions of flow. This work presents experiments realized at CCN to visualize natural circulation cycles in order to classify two-phase flow patterns associated with phase transients and static instabilities of flow. Images are compared and clustered using Kohonen Self-organizing Maps (SOM's) applied on different digital image features. The Full Frame Discret Cosine Transform (FFDCT) coefficients were used as input for the classification task, enabling good results. FFDCT prototypes obtained can be associated to each flow pattern, enabling a better comprehension of each observed instability. A systematic test methodology was used to verify classifier robustness.

Self-assembly of fatty acids on hydroxylated Al surface and effects of their stability on wettability and nanoscale organization.

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Liascukiene, Irma; Steffenhagen, Marie; Asadauskas, Svajus J; Lambert, Jean-François; Landoulsi, Jessem

2014-05-27

The self-assembly of fatty acids (FA) on the surfaces of inorganic materials is a relevant way to control their wetting properties. While the mechanism of adsorption on model flat substrate is well described in the literature, interfacial processes remain poorly documented on nanostructured surfaces. In this study, we report the self-assembly of a variety of FA on a hydroxylated Al surface which exhibits a random nanoscale organization. Our results revealed a peculiar fingerprint due to the FA self-assembly which consists in the formation of aligned nanopatterns in a state of hierarchical nanostructuration, regardless of the molecular structure of the FA (chain length, level of unsaturation). After a significant removal of adsorbed FA using UV/O3 treatment, a complete wetting was reached, and a noticeable disturbance of the surface morphology was observed, evidencing the pivotal role of FA molecules to maintain these nanostructures. The origin of wetting properties was investigated prior to and after conditioning of FA-modified samples taking into account key parameters, namely the surface roughness and its composition. For this purpose, the Wenzel roughness, defined as the third moment of power spectral density, was used, as it is sensitive to high spatial frequency and thus to the obtained hierarchical level of nanostructuration. Our results revealed that no correlation can be made between water contact angles (θ(w)) and the Wenzel roughness. By contrast, θ(w) strongly increased with the amount of -CHx- groups exhibited by adsorbed FA. These findings suggest that the main origin of hydrophobization is the presence of self-assembled molecules and that the surface roughness has only a small contribution to the wettability.

Performance Comparison of Reputation Assessment Techniques Based on Self-Organizing Maps in Wireless Sensor Networks

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Sabrina Sicari

2017-01-01

Full Text Available Many solutions based on machine learning techniques have been proposed in literature aimed at detecting and promptly counteracting various kinds of malicious attack (data violation, clone, sybil, neglect, greed, and DoS attacks, which frequently affect Wireless Sensor Networks (WSNs. Besides recognizing the corrupted or violated information, also the attackers should be identified, in order to activate the proper countermeasures for preserving network’s resources and to mitigate their malicious effects. To this end, techniques adopting Self-Organizing Maps (SOM for intrusion detection in WSN were revealed to represent a valuable and effective solution to the problem. In this paper, the mechanism, namely, Good Network (GoNe, which is based on SOM and is able to assess the reliability of the sensor nodes, is compared with another relevant and similar work existing in literature. Extensive performance simulations, in terms of nodes’ classification, attacks’ identification, data accuracy, energy consumption, and signalling overhead, have been carried out in order to demonstrate the better feasibility and efficiency of the proposed solution in WSN field.

Self-organizing map analysis using multivariate data from theophylline powders predicted by a thin-plate spline interpolation.

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Yasuda, Akihito; Onuki, Yoshinori; Kikuchi, Shingo; Takayama, Kozo

2010-11-01

The quality by design concept in pharmaceutical formulation development requires establishment of a science-based rationale and a design space. We integrated thin-plate spline (TPS) interpolation and Kohonen's self-organizing map (SOM) to visualize the latent structure underlying causal factors and pharmaceutical responses. As a model pharmaceutical product, theophylline powders were prepared based on the standard formulation. The angle of repose, compressibility, cohesion, and dispersibility were measured as the response variables. These responses were predicted quantitatively on the basis of a nonlinear TPS. A large amount of data on these powders was generated and classified into several clusters using an SOM. The experimental values of the responses were predicted with high accuracy, and the data generated for the powders could be classified into several distinctive clusters. The SOM feature map allowed us to analyze the global and local correlations between causal factors and powder characteristics. For instance, the quantities of microcrystalline cellulose (MCC) and magnesium stearate (Mg-St) were classified distinctly into each cluster, indicating that the quantities of MCC and Mg-St were crucial for determining the powder characteristics. This technique provides a better understanding of the relationships between causal factors and pharmaceutical responses in theophylline powder formulations. © 2010 Wiley-Liss, Inc. and the American Pharmacists Association

Self-Mapping in Treating Suicide Ideation: A Case Study

Science.gov (United States)

Robertson, Lloyd Hawkeye

2011-01-01

This case study traces the development and use of a self-mapping exercise in the treatment of a youth who had been at risk for re-attempting suicide. A life skills exercise was modified to identify units of culture called "memes" from which a map of the youth's self was prepared. A successful treatment plan followed the mapping exercise. The…

Topology assisted self-organization of colloidal nanoparticles: application to 2D large-scale nanomastering

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Hind Kadiri

2014-08-01

Full Text Available Our aim was to elaborate a novel method for fully controllable large-scale nanopatterning. We investigated the influence of the surface topology, i.e., a pre-pattern of hydrogen silsesquioxane (HSQ posts, on the self-organization of polystyrene beads (PS dispersed over a large surface. Depending on the post size and spacing, long-range ordering of self-organized polystyrene beads is observed wherein guide posts were used leading to single crystal structure. Topology assisted self-organization has proved to be one of the solutions to obtain large-scale ordering. Besides post size and spacing, the colloidal concentration and the nature of solvent were found to have a significant effect on the self-organization of the PS beads. Scanning electron microscope and associated Fourier transform analysis were used to characterize the morphology of the ordered surfaces. Finally, the production of silicon molds is demonstrated by using the beads as a template for dry etching.

75 FR 63878 - Self-Regulatory Organizations; Self-Regulatory Organizations; Notice of Filing and Immediate...

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2010-10-18

...-Regulatory Organizations; Self-Regulatory Organizations; Notice of Filing and Immediate Effectiveness of...(b)(1). \\2\\ 17 CFR 240.19b-4. I. Self-Regulatory Organization's Statement of the Terms of Substance... Public Reference Room. II. Self-Regulatory Organization's Statement of the Purpose of, and Statutory...

Assembly, Structure, and Functionality of Metal-Organic Networks and Organic Semiconductor Layers at Surfaces

Science.gov (United States)

Tempas, Christopher D.

Self-assembled nanostructures at surfaces show promise for the development of next generation technologies including organic electronic devices and heterogeneous catalysis. In many cases, the functionality of these nanostructures is not well understood. This thesis presents strategies for the structural design of new on-surface metal-organic networks and probes their chemical reactivity. It is shown that creating uniform metal sites greatly increases selectivity when compared to ligand-free metal islands. When O2 reacts with single-site vanadium centers, in redox-active self-assembled coordination networks on the Au(100) surface, it forms one product. When O2 reacts with vanadium metal islands on the same surface, multiple products are formed. Other metal-organic networks described in this thesis include a mixed valence network containing Pt0 and PtII and a network where two Fe centers reside in close proximity. This structure is stable to temperatures >450 °C. These new on-surface assemblies may offer the ability to perform reactions of increasing complexity as future heterogeneous catalysts. The functionalization of organic semiconductor molecules is also shown. When a few molecular layers are grown on the surface, it is seen that the addition of functional groups changes both the film's structure and charge transport properties. This is due to changes in both first layer packing structure and the pi-electron distribution in the functionalized molecules compared to the original molecule. The systems described in this thesis were studied using high-resolution scanning tunneling microscopy, non-contact atomic force microscopy, and X-ray photoelectron spectroscopy. Overall, this work provides strategies for the creation of new, well-defined on-surface nanostructures and adds additional chemical insight into their properties.

A simple method to prepare self-assembled organic-organic heterobilayers on metal substrates

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L. D. Sun

2011-06-01

Full Text Available We demonstrate a self-assembly based simple method to prepare organic-organic heterobilayers on a metal substrate. By either sequential- or co-deposition of para-sexiphenyl (p-6P and pentacene molecules onto the Cu(110 surface in ultrahigh vacuum, p-6P/pentacene/Cu(110 heterobilayer is synthesized at room temperature. The layer sequence of the heterostructure is independent of the growth scenario indicating the p-6P/pentacene/Cu(110 is a self-assembled structure with lowest energy. Besides, the bilayer shows a very high orientational ordering and is thermally stable up to 430K.

Application Self-organizing Map Type in a Study of the Profile of Gasoline C Commercialized in the Eastern and Northern Parana Regions

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Lívia Ramazzoti Silva

2015-06-01

Full Text Available Artificial neural networks self-organizing map type (SOM was used to classify samples of automotive gasoline C marketed in the eastern and northern regions of the state of Paraná, Brazil. The input order of parameters in the network were the values of temperature of the first drop, the 10, 50 and 90% distilled bulk, the final boiling point, density, residue content and alcohol content. A network with a topology of 25x25 and 5000 training epochs was used. The weight maps of input parameters for the trained network identified that the most important parameters for classifying samples were the temperature of the first drop and the temperature of the 10% and 50% of the distilled fuel. DOI: http://dx.doi.org/10.17807/orbital.v7i2.732 

Applications of Self-Organizing Maps for Ecomorphological Investigations through Early Ontogeny of Fish

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Russo, Tommaso; Scardi, Michele; Cataudella, Stefano

2014-01-01

We propose a new graphical approach to the analysis of multi-temporal morphological and ecological data concerning the life history of fish, which can typically serves models in ecomorphological investigations because they often undergo significant ontogenetic changes. These changes can be very complex and difficult to describe, so that visualization, abstraction and interpretation of the underlying relationships are often impeded. Therefore, classic ecomorphological analyses of covariation between morphology and ecology, performed by means of multivariate techniques, may result in non-exhaustive models. The Self Organizing map (SOM) is a new, effective approach for pursuing this aim. In this paper, lateral outlines of larval stages of gilthead sea bream (Sparus aurata) and dusky grouper (Epinephelus marginatus) were recorded and broken down using by means of Elliptic Fourier Analysis (EFA). Gut contents of the same specimens were also collected and analyzed. Then, shape and trophic habits data were examined by SOM, which allows both a powerful visualization of shape changes and an easy comparison with trophic habit data, via their superimposition onto the trained SOM. Thus, the SOM provides a direct visual approach for matching morphological and ecological changes during fish ontogenesis. This method could be used as a tool to extract and investigate relationships between shape and other sinecological or environmental variables, which cannot be taken into account simultaneously using conventional statistical methods. PMID:24466185

Adaptive monitoring of emissions in energy boilers using self-organizing maps: An application to a biomass-fired CFB (circulating fluidized bed)

International Nuclear Information System (INIS)

Liukkonen, M.; Hiltunen, T.

2014-01-01

Improvement of energy efficiency, reduction of operating costs, and reduction of harmful emissions released into the atmosphere are issues of major concern in modern energy plants. While air emissions have to be restricted due to tightening environmental legislation, at the same time it is ever more important to be able to respond quickly to any changes in the load demand or fuel quality. As unpredictability increases with changing fuel quality and more complex operational strategies, undesired phenomena such as increased emission release rates may become more likely. Therefore, it is crucial that emission monitoring systems are able to adapt to varying conditions, and advanced methodologies are needed for monitoring and decision-support. In this paper a novel approach for advanced monitoring of emissions in CFB (circulating fluidized bed) boilers is described. In this approach a model based on SOM (self-organizing maps) is updated regularly to respond to the prevailing condition of the boiler. After creating each model a new set of measurements is input to the system, and the current state of the process is determined using vector distance calculation. Finally, the system evaluates the current condition and may alert if a preset limit defined for each emission component is exceeded. - Highlights: • An adaptive monitoring approach based on self-organizing maps is presented. • The system can monitor the current state of a combustion process and its emissions. • The system is designed to alert when the preset limits defined for emissions are exceeded. • Due to regular updating routine the system is able to adapt to changing conditions. • The application is demonstrated using data from a biomass-fired energy boiler

The Performance of the Smart Cities in China—A Comparative Study by Means of Self-Organizing Maps and Social Networks Analysis

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Dong Lu

2015-06-01

Full Text Available Smart cities link the city services, citizens, resource and infrastructures together and form the heart of the modern society. As a “smart” ecosystem, smart cities focus on sustainable growth, efficiency, productivity and environmentally friendly development. By comparing with the European Union, North America and other countries, smart cities in China are still in the preliminary stage. This study offers a comparative analysis of ten smart cities in China on the basis of an extensive database covering two time periods: 2005–2007 and 2008–2010. The unsupervised computational neural network self-organizing map (SOM analysis is adopted to map out the various cities based on their performance. The demonstration effect and mutual influences between these ten smart cities are also discussed by using social network analysis. Based on the smart city performance and cluster network, current problems for smart city development in China were pointed out. Future research directions for smart city research are discussed at the end this paper.

Evolution of self-organization in nano-structured PVD coatings under extreme tribological conditions

Energy Technology Data Exchange (ETDEWEB)

Fox-Rabinovich, G., E-mail: gfox@mcmaster.ca [Department of Mechanical Engineering, McMaster University, 1280 Main St. W., Hamilton, ON, Canada L8S 4L7 (Canada); Kovalev, A. [Surface Phenomena Researches Group, CNIICHERMET, 9/23, 2-nd Baumanskaya Street, Moscow 105005 (Russian Federation); Aguirre, M.H. [Laboratory of Advanced Microscopy, Institute of Nanoscience of Aragón, University of Zaragoza, 50018 Zaragoza (Spain); Yamamoto, K. [Materials Research Laboratory, Kobe Steel Ltd, 1-5-5 Takatsuda-dai, Nishi-ku, Kobe 651-2271, Hyogo (Japan); Veldhuis, S. [Department of Mechanical Engineering, McMaster University, 1280 Main St. W., Hamilton, ON, Canada L8S 4L7 (Canada); Gershman, I. [All-Russian Railway Research Institute, 10 Third Mytishchinskaya Street, Moscow 29851 (Russian Federation); Rashkovskiy, A. [Surface Phenomena Researches Group, CNIICHERMET, 9/23, 2-nd Baumanskaya Street, Moscow 105005 (Russian Federation); Endrino, J.L. [Albengoa Research, Energia Solar 1, Palmas Altas, Seville 41014 (Spain); Beake, B. [Micro Materials Limited, Willow House, Yale Business Village, Ellice Way, Wrexham LL13 7YL (United Kingdom); Dosbaeva, G. [Department of Mechanical Engineering, McMaster University, 1280 Main St. W., Hamilton, ON, Canada L8S 4L7 (Canada); Wainstein, D. [Surface Phenomena Researches Group, CNIICHERMET, 9/23, 2-nd Baumanskaya Street, Moscow 105005 (Russian Federation); Yuan, Junifeng; Bunting, J.W. [Department of Mechanical Engineering, McMaster University, 1280 Main St. W., Hamilton, ON, Canada L8S 4L7 (Canada)

2014-04-01

Highlights: • The evolution of self-organization under extreme frictional conditions has been studied. • Comprehensive characterization of the tribo-films was made using various surface analytical techniques. • During the running-in stage, mullite tribo-ceramics predominate on the surface of the nano-multilayer coating, establishing a functional hierarchy within the layer of tribo-films. • It is possible to control tribo-film evolution during self-organization by means of an increase in structural complexity and the non-equilibrium state of the surface engineered layer. - Abstract: The evolution of the self-organization process where dissipative structures are formed under the extreme frictional conditions associated with high performance dry machining of hardened steels has been studied in detail. The emphasis was on the progressive studies of surface transformations within multilayer and monolayer TiAlCrSiYN-based PVD coatings during the running-in stage of wear when self-organization process occurs. The coating layer was characterized by high resolution electron energy-loss spectroscopy (HREELS). It is shown that the nano-multilayer coating possesses higher non-equilibrium structure in comparison to the monolayer. Comprehensive studies of the tribo-films (dissipative structures) formed on the friction surface were made using a number of advanced surface characterization techniques such as X-ray photoelectron spectroscopy (XPS) and X-ray absorption near edge structure (XANES). The data obtained for the tribo-films was combined with the detailed TEM studies of the structural and phase transformations within the underlying coating layer. This data was related to the micro-mechanical characteristics of the coating layer and its wear resistance. It was demonstrated that the evolution of the self-organization process is strongly controlled by the characteristics of the tribo-films formed at different stages of the wear process. Within running-in stage (after

Complete Surface Mapping of ICF Shells

International Nuclear Information System (INIS)

Stephens, R.B.; Olson, D.; Huang, H.; Gibson, J.B.

2004-01-01

Inertial confinement fusion shells have previously been evaluated on the basis of microscopic examination for local defects and limited surface profiling to represent their average fluctuation power. Since defects are local, and don't always have visible edges, this approach both misses some important fluctuations and doesn't properly represent the spatially dependent surface fluctuation power. We have taken the first step toward correcting this problem by demonstrating the capability to completely map the surface of a NIF shell with the resolution to account for all modes. This allows complete accounting of all the surface fluctuations. In the future this capability could be used for valuable shells to generate a complete r(θ, φ) surface map for accurate 3-D modeling of a shot

COMPLETE SURFACE MAPPING OF ICF SHELLS

International Nuclear Information System (INIS)

STEPHENS, R.B.; OLSON, D.; HUANG, H.; GIBSON, J.B.

2003-09-01

OAK-B135 Inertial confinement fusion shells have previously been evaluated on the basis of microscopic examination for local defects and limited surface profiling to represent their average fluctuation power. Since defects are local, and don't always have visible edges, this approach both misses some important fluctuations and doesn't properly represent the spatially dependent surface fluctuation power. they have taken the first step toward correcting this problem by demonstrating the capability to completely map the surface of a NIF shell with the resolution to account for all modes. This allows complete accounting of all the surface fluctuations. In the future this capability could be used for valuable shells to generate a complete r(θ,ψ) surface map for accurate 3-D modeling of a shot

Self-assembled magnetic nitride dots on Cu(100) surfaces

NARCIS (Netherlands)

Gallego, JM; Grachev, SY; Passeggi, MCG; Sacharowitz, F; Ecija, D; Miranda, R; Boerma, DO

We describe here a procedure for the direct fabrication of a self-organized, ordered pattern of Fe4N magnetic dots on an otherwise clean Cu(100) surface. It is based on the evaporation of Fe in a flux of atomic N produced by a plasma source onto a Cu(100) surface kept at 700 K. The large-scale

Self-organizing map analysis using multivariate data from theophylline tablets predicted by a thin-plate spline interpolation.

Science.gov (United States)

Yasuda, Akihito; Onuki, Yoshinori; Obata, Yasuko; Yamamoto, Rie; Takayama, Kozo

2013-01-01

The "quality by design" concept in pharmaceutical formulation development requires the establishment of a science-based rationale and a design space. We integrated thin-plate spline (TPS) interpolation and Kohonen's self-organizing map (SOM) to visualize the latent structure underlying causal factors and pharmaceutical responses. As a model pharmaceutical product, theophylline tablets were prepared based on a standard formulation. The tensile strength, disintegration time, and stability of these variables were measured as response variables. These responses were predicted quantitatively based on nonlinear TPS. A large amount of data on these tablets was generated and classified into several clusters using an SOM. The experimental values of the responses were predicted with high accuracy, and the data generated for the tablets were classified into several distinct clusters. The SOM feature map allowed us to analyze the global and local correlations between causal factors and tablet characteristics. The results of this study suggest that increasing the proportion of microcrystalline cellulose (MCC) improved the tensile strength and the stability of tensile strength of these theophylline tablets. In addition, the proportion of MCC has an optimum value for disintegration time and stability of disintegration. Increasing the proportion of magnesium stearate extended disintegration time. Increasing the compression force improved tensile strength, but degraded the stability of disintegration. This technique provides a better understanding of the relationships between causal factors and pharmaceutical responses in theophylline tablet formulations.

Ordination of self-organizing feature map neural networks and its application to the study of plant communities

Institute of Scientific and Technical Information of China (English)

Jintun ZHANG; Dongping MENG; Yuexiang XI

2009-01-01

A self-organizing feature map (SOFM) neural network is a powerful tool in analyzing and solving complex, non-linear problems. According to its features, a SOFM is entirely compatible with ordination studies of plant communities. In our present work, mathematical principles, and ordination techniques and procedures are introduced. A SOFM ordination was applied to the study of plant communities in the middle of the Taihang mountains. The ordination was carried out by using the NNTool box in MATLAB. The results of 68 quadrats of plant communities were distributed in SOFM space. The ordination axes showed the ecological gradients clearly and provided the relationships between communities with ecological meaning. The results are consistent with the reality of vegetation in the study area. This suggests that SOFM ordination is an effective technique in plant ecology. During ordination procedures, it is easy to carry out clustering of communities and so it is beneficial for combining classification and ordination in vegetation studies.

Temporal Comparison Between NIRS and EEG Signals During a Mental Arithmetic Task Evaluated with Self-Organizing Maps.

Science.gov (United States)

Oyama, Katsunori; Sakatani, Kaoru

2016-01-01

Simultaneous monitoring of brain activity with near-infrared spectroscopy and electroencephalography allows spatiotemporal reconstruction of the hemodynamic response regarding the concentration changes in oxyhemoglobin and deoxyhemoglobin that are associated with recorded brain activity such as cognitive functions. However, the accuracy of state estimation during mental arithmetic tasks is often different depending on the length of the segment for sampling of NIRS and EEG signals. This study compared the results of a self-organizing map and ANOVA, which were both used to assess the accuracy of state estimation. We conducted an experiment with a mental arithmetic task performed by 10 participants. The lengths of the segment in each time frame for observation of NIRS and EEG signals were compared with the 30-s, 1-min, and 2-min segment lengths. The optimal segment lengths were different for NIRS and EEG signals in the case of classification of feature vectors into the states of performing a mental arithmetic task and being at rest.

Modeling Poroelastic Wave Propagation in a Real 2-D Complex Geological Structure Obtained via Self-Organizing Maps

Science.gov (United States)

Itzá Balam, Reymundo; Iturrarán-Viveros, Ursula; Parra, Jorge O.

2018-03-01

Two main stages of seismic modeling are geological model building and numerical computation of seismic response for the model. The quality of the computed seismic response is partly related to the type of model that is built. Therefore, the model building approaches become as important as seismic forward numerical methods. For this purpose, three petrophysical facies (sands, shales and limestones) are extracted from reflection seismic data and some seismic attributes via the clustering method called Self-Organizing Maps (SOM), which, in this context, serves as a geological model building tool. This model with all its properties is the input to the Optimal Implicit Staggered Finite Difference (OISFD) algorithm to create synthetic seismograms for poroelastic, poroacoustic and elastic media. The results show a good agreement between observed and 2-D synthetic seismograms. This demonstrates that the SOM classification method enables us to extract facies from seismic data and allows us to integrate the lithology at the borehole scale with the 2-D seismic data.

Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015 Using Self-Organizing Maps

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Adeoluwa Akande

2017-01-01

Full Text Available The explosion of data in the information age has provided an opportunity to explore the possibility of characterizing the climate patterns using data mining techniques. Nigeria has a unique tropical climate with two precipitation regimes: low precipitation in the north leading to aridity and desertification and high precipitation in parts of the southwest and southeast leading to large scale flooding. In this research, four indices have been used to characterize the intensity, frequency, and amount of rainfall over Nigeria. A type of Artificial Neural Network called the self-organizing map has been used to reduce the multiplicity of dimensions and produce four unique zones characterizing extreme precipitation conditions in Nigeria. This approach allowed for the assessment of spatial and temporal patterns in extreme precipitation in the last three decades. Precipitation properties in each cluster are discussed. The cluster closest to the Atlantic has high values of precipitation intensity, frequency, and duration, whereas the cluster closest to the Sahara Desert has low values. A significant increasing trend has been observed in the frequency of rainy days at the center of the northern region of Nigeria.

Visualization of amino acid composition differences between processed protein from different animal species by self-organizing feature maps

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Xingfan ZHOU,Zengling YANG,Longjian CHEN,Lujia HAN

2016-06-01

Full Text Available Amino acids are the dominant organic components of processed animal proteins, however there has been limited investigation of differences in their composition between various protein sources. Information on these differences will not only be helpful for their further utilization but also provide fundamental information for developing species-specific identification methods. In this study, self-organizing feature maps (SOFM were used to visualize amino acid composition of fish meal, and meat and bone meal (MBM produced from poultry, ruminants and swine. SOFM display the similarities and differences in amino acid composition between protein sources and effectively improve data transparency. Amino acid composition was shown to be useful for distinguishing fish meal from MBM due to their large concentration differences between glycine, lysine and proline. However, the amino acid composition of the three MBMs was quite similar. The SOFM results were consistent with those obtained by analysis of variance and principal component analysis but more straightforward. SOFM was shown to have a robust sample linkage capacity and to be able to act as a powerful means to link different sample for further data mining.

Plate Tectonics as a Far-From-Equilibrium Self-Organized Dissipative System

Science.gov (United States)

Anderson, D. L.

2001-12-01

A fluid above the critical Rayleigh number is far from equilibrium and spontaneously organizes itself into patterns involving the collective motion of large numbers of molecules which are resisted by the viscosity of the fluid. No external template is involved in forming the pattern. In 1928 Pearson showed that Bénard's experiments were driven by variations in surface tension at the top of the fluid and the surface motions drove convection in the fluid. In this case, the surface organized itself AND the underlying fluid. Both internal buoyancy driven flow and flow driven by surface forces can be far-from-equilibrium self-organized open systems that receive energy and matter from the environment. In the Earth, the cold thermal boundary layer at the surface drives plate tectonics and introduces temperature, shear and pressure gradients into the mantle that drive mantle convection. The mantle provides energy and material but may not provide the template. Plate tectonics is therefore a candidate for a far-from-equilibrium dissipative self-organizing system. Alternatively, one could view mantle convection as the self-organized system and the plates as simply the surface manifestation. Lithospheric architecture also imposes lateral temperature gradients onto the mantle which can drive and organize flow. Far-from-equilibrium self-organization requires; an open system, interacting parts, nonlinearities or feedbacks, an outside steady source of energy or matter, multiple possible states and a source of dissipation. In uniform fluids viscosity is the source of dissipation. Sources of dissipation in the plate system include bending, breaking, folding, shearing, tearing, collision and basal drag. These can change rapidly, in contrast to plate driving forces, and introduce the sort of fluctuations that can reorganize far-from-equilibrium systems. Global plate reorganizations can alternatively be thought of as convective overturns of the mantle, or thermal weakening of plates

Fermi surface mapping: Techniques and visualization

International Nuclear Information System (INIS)

Rotenberg, E.; Denlinger, J.D.; Kevan, S.D.

1997-01-01

Angle-resolved photoemission (ARP) of valence bands is a mature technique that has achieved spectacular success in band-mapping metals, semiconductors, and insulators. The purpose of the present study was the development of experimental and analytical techniques in ARP which take advantage of third generation light sources. Here the authors studied the relatively simple Cu surface in preparation for other metals. Copper and related metals themselves are of current interest, especially due to its role as an interlayer in spin valves and other magnetic heterostructures. A major goal of this study was the development of a systematic technique to quickly (i.e. in a few hours of synchrotron beamtime) measure the FS and separate it into bulk and surface FS's. Often, one needs to avoid bulk features altogether, which one can achieve by carefully mapping their locations in k-space. The authors will also show how they systematically map Fermi surfaces throughout large volumes of k-space, and, by processing the resulting volume data sets, provide intuitive pictures of FS's, both bulk and surface

Application of a self-organizing map and positive matrix factorization to investigate the spatial distributions and sources of polycyclic aromatic hydrocarbons in soils from Xiangfen County, northern China.

Science.gov (United States)

Tao, Shi-Yang; Zhong, Bu-Qing; Lin, Yan; Ma, Jin; Zhou, Yongzhang; Hou, Hong; Zhao, Long; Sun, Zaijin; Qin, Xiaopeng; Shi, Huading

2017-07-01

The concentrations of 16 priority polycyclic aromatic hydrocarbons (PAHs) were measured in 128 surface soil samples from Xiangfen County, northern China. The total mass concentration of these PAHs ranged from 52 to 10,524ng/g, with a mean of 723ng/g. Four-ring PAHs contributed almost 50% of the total PAH burden. A self-organizing map and positive matrix factorization were applied to investigate the spatial distribution and source apportionment of PAHs. Three emission sources of PAHs were identified, namely, coking ovens (21.9%), coal/biomass combustion (60.1%), and anthracene oil (18.0%). High concentrations of low-molecular-weight PAHs were particularly apparent in the coking plant zone in the region around Gucheng Town. High-molecular-weight PAHs mainly originated from coal/biomass combustion around Gucheng Town, Xincheng Town, and Taosi Town. PAHs in the soil of Xiangfen County are unlikely to pose a significant cancer risk for the population. Copyright © 2017 Elsevier Inc. All rights reserved.

Discovery of possible gene relationships through the application of self-organizing maps to DNA microarray databases.

Science.gov (United States)

Chavez-Alvarez, Rocio; Chavoya, Arturo; Mendez-Vazquez, Andres

2014-01-01

DNA microarrays and cell cycle synchronization experiments have made possible the study of the mechanisms of cell cycle regulation of Saccharomyces cerevisiae by simultaneously monitoring the expression levels of thousands of genes at specific time points. On the other hand, pattern recognition techniques can contribute to the analysis of such massive measurements, providing a model of gene expression level evolution through the cell cycle process. In this paper, we propose the use of one of such techniques--an unsupervised artificial neural network called a Self-Organizing Map (SOM)-which has been successfully applied to processes involving very noisy signals, classifying and organizing them, and assisting in the discovery of behavior patterns without requiring prior knowledge about the process under analysis. As a test bed for the use of SOMs in finding possible relationships among genes and their possible contribution in some biological processes, we selected 282 S. cerevisiae genes that have been shown through biological experiments to have an activity during the cell cycle. The expression level of these genes was analyzed in five of the most cited time series DNA microarray databases used in the study of the cell cycle of this organism. With the use of SOM, it was possible to find clusters of genes with similar behavior in the five databases along two cell cycles. This result suggested that some of these genes might be biologically related or might have a regulatory relationship, as was corroborated by comparing some of the clusters obtained with SOMs against a previously reported regulatory network that was generated using biological knowledge, such as protein-protein interactions, gene expression levels, metabolism dynamics, promoter binding, and modification, regulation and transport of proteins. The methodology described in this paper could be applied to the study of gene relationships of other biological processes in different organisms.

Molecular surface mesh generation by filtering electron density map.

Science.gov (United States)

Giard, Joachim; Macq, Benoît

2010-01-01

Bioinformatics applied to macromolecules are now widely spread and in continuous expansion. In this context, representing external molecular surface such as the Van der Waals Surface or the Solvent Excluded Surface can be useful for several applications. We propose a fast and parameterizable algorithm giving good visual quality meshes representing molecular surfaces. It is obtained by isosurfacing a filtered electron density map. The density map is the result of the maximum of Gaussian functions placed around atom centers. This map is filtered by an ideal low-pass filter applied on the Fourier Transform of the density map. Applying the marching cubes algorithm on the inverse transform provides a mesh representation of the molecular surface.

Molecular Surface Mesh Generation by Filtering Electron Density Map

Directory of Open Access Journals (Sweden)

Joachim Giard

2010-01-01

Full Text Available Bioinformatics applied to macromolecules are now widely spread and in continuous expansion. In this context, representing external molecular surface such as the Van der Waals Surface or the Solvent Excluded Surface can be useful for several applications. We propose a fast and parameterizable algorithm giving good visual quality meshes representing molecular surfaces. It is obtained by isosurfacing a filtered electron density map. The density map is the result of the maximum of Gaussian functions placed around atom centers. This map is filtered by an ideal low-pass filter applied on the Fourier Transform of the density map. Applying the marching cubes algorithm on the inverse transform provides a mesh representation of the molecular surface.

Surface-enhanced absorption by self-organized silver films with aciniform-like nanoaggregates at elevated temperatures

Energy Technology Data Exchange (ETDEWEB)

Kim, Sang Woo, E-mail: swkim@kist.re.kr [Korea Institute of Science and Technology, Clean Energy Research Center (Korea, Republic of)

2012-01-15

Morphological evolution of silver nanocomposite films prepared by the wet colloidal route and surface-enhanced phenomena on aggregate nanostructures evolved during annealing were investigated. Dramatic changes in morphologies of particles and pores incurred by rearragement, coarsening, premelting, and dewetting of the silver clusters at different concentrations (i.e., mass thicknesses). At a higher mass thickness, the morphological transitions from self-organized nanoaggregates with aciniform pattern at 300 Degree-Sign C to elongated and coarsened particles with circular holes at 400 Degree-Sign C to island clusters at 500 Degree-Sign C occurred in the films. The peculiar absorption with a much redder and broader surface plasmon feature, which gone far beyond the theoretical prediction, induced by the formation of aciniform nanoaggregates embedded in the porous polymer matrix at a critical mass thickness of 9.6 nm during partial degradation of the PVP polymer and rearrangement of silver clusters at 300 Degree-Sign C. The surface-enhanced absorption was dramatically reduced by the elemination of the aggregate nanostructures and the spontaneous formation of the silver nanoisland film at the dewetting temperature of 500 Degree-Sign C.

Self-organized critical model for protein folding

Science.gov (United States)

Moret, M. A.

2011-09-01

The major factor that drives a protein toward collapse and folding is the hydrophobic effect. At the folding process a hydrophobic core is shielded by the solvent-accessible surface area of the protein. We study the fractal behavior of 5526 protein structures present in the Brookhaven Protein Data Bank. Power laws of protein mass, volume and solvent-accessible surface area are measured independently. The present findings indicate that self-organized criticality is an alternative explanation for the protein folding. Also we note that the protein packing is an independent and constant value because the self-similar behavior of the volumes and protein masses have the same fractal dimension. This power law guarantees that a protein is a complex system. From the analyzed data, q-Gaussian distributions seem to fit well this class of systems.

Brain Basis of Self: Self-Organization and Lessons from Dreaming

Directory of Open Access Journals (Sweden)

David eKahn

2013-07-01

Full Text Available Through dreaming a different facet of the self is created as a result of a self-organizing process in the brain. Self-organization in biological systems often happens as an answer to an environmental change for which the existing system cannot cope; self-organization creates a system that can cope in the newly changed environment. In dreaming, self-organization serves the function of organizing disparate memories into a dream since the dreamer herself is not able to control how individual memories become weaved into a dream. The self-organized dream provides, thereby, a wide repertoire of experiences; this expanded repertoire of experience results in an expansion of the self beyond that obtainable when awake. Since expression of the self is associated with activity in specific areas of the brain, the article also discusses the brain basis of the self by reviewing studies of brain injured patients, discussing brain imaging studies in normal brain functioning when focused, when daydreaming and when asleep and dreaming.

Clustering self-organizing maps (SOM) method for human papillomavirus (HPV) DNA as the main cause of cervical cancer disease

Science.gov (United States)

Bustamam, A.; Aldila, D.; Fatimah, Arimbi, M. D.

2017-07-01

One of the most widely used clustering method, since it has advantage on its robustness, is Self-Organizing Maps (SOM) method. This paper discusses the application of SOM method on Human Papillomavirus (HPV) DNA which is the main cause of cervical cancer disease, the most dangerous cancer in developing countries. We use 18 types of HPV DNA-based on the newest complete genome. By using open-source-based program R, clustering process can separate 18 types of HPV into two different clusters. There are two types of HPV in the first cluster while 16 others in the second cluster. The analyzing result of 18 types HPV based on the malignancy of the virus (the difficultness to cure). Two of HPV types the first cluster can be classified as tame HPV, while 16 others in the second cluster are classified as vicious HPV.

Adaptive self-assembly and induced-fit transformations of anion-binding metal-organic macrocycles

Science.gov (United States)

Zhang, Ting; Zhou, Li-Peng; Guo, Xiao-Qing; Cai, Li-Xuan; Sun, Qing-Fu

2017-06-01

Container-molecules are attractive to chemists due to their unique structural characteristics comparable to enzymes and receptors in nature. We report here a family of artificial self-assembled macrocyclic containers that feature induced-fit transformations in response to different anionic guests. Five metal-organic macrocycles with empirical formula of MnL2n (M=Metal L=Ligand n=3, 4, 5, 6, 7) are selectively obtained starting from one simple benzimidazole-based ligand and square-planar palladium(II) ions, either by direct anion-adaptive self-assembly or induced-fit transformations. Hydrogen-bonding interactions between the inner surface of the macrocycles and the anionic guests dictate the shape and size of the product. A comprehensive induced-fit transformation map across all the MnL2n species is drawn, with a representative reconstitution process from Pd7L14 to Pd3L6 traced in detail, revealing a gradual ring-shrinking mechanism. We envisage that these macrocyclic molecules with adjustable well-defined hydrogen-bonding pockets will find wide applications in molecular sensing or catalysis.

Spiking neurons in a hierarchical self-organizing map model can learn to develop spatial and temporal properties of entorhinal grid cells and hippocampal place cells.

Directory of Open Access Journals (Sweden)

Praveen K Pilly

Full Text Available Medial entorhinal grid cells and hippocampal place cells provide neural correlates of spatial representation in the brain. A place cell typically fires whenever an animal is present in one or more spatial regions, or places, of an environment. A grid cell typically fires in multiple spatial regions that form a regular hexagonal grid structure extending throughout the environment. Different grid and place cells prefer spatially offset regions, with their firing fields increasing in size along the dorsoventral axes of the medial entorhinal cortex and hippocampus. The spacing between neighboring fields for a grid cell also increases along the dorsoventral axis. This article presents a neural model whose spiking neurons operate in a hierarchy of self-organizing maps, each obeying the same laws. This spiking GridPlaceMap model simulates how grid cells and place cells may develop. It responds to realistic rat navigational trajectories by learning grid cells with hexagonal grid firing fields of multiple spatial scales and place cells with one or more firing fields that match neurophysiological data about these cells and their development in juvenile rats. The place cells represent much larger spaces than the grid cells, which enable them to support navigational behaviors. Both self-organizing maps amplify and learn to categorize the most frequent and energetic co-occurrences of their inputs. The current results build upon a previous rate-based model of grid and place cell learning, and thus illustrate a general method for converting rate-based adaptive neural models, without the loss of any of their analog properties, into models whose cells obey spiking dynamics. New properties of the spiking GridPlaceMap model include the appearance of theta band modulation. The spiking model also opens a path for implementation in brain-emulating nanochips comprised of networks of noisy spiking neurons with multiple-level adaptive weights for controlling autonomous

Controlling Schottky energy barriers in organic electronic devices using self-assembled monolayers

Science.gov (United States)

Campbell, I. H.; Rubin, S.; Zawodzinski, T. A.; Kress, J. D.; Martin, R. L.; Smith, D. L.; Barashkov, N. N.; Ferraris, J. P.

1996-11-01

We demonstrate tuning of Schottky energy barriers in organic electronic devices by utilizing chemically tailored electrodes. The Schottky energy barrier of Ag on poly[2-methoxy, 5-(2'-ethyl-hexyloxy)- 1,4-phenylene was tuned over a range of more than 1 eV by using self-assembled monolayers (SAM's) to attach oriented dipole layers to the Ag prior to device fabrication. Kelvin probe measurements were used to determine the effect of the SAM's on the Ag surface potential. Ab initio Hartree-Fock calculations of the molecular dipole moments successfully describe the surface potential changes. The chemically tailored electrodes were then incorporated in organic diode structures and changes in the metal/organic Schottky energy barriers were measured using an electroabsorption technique. These results demonstrate the use of self-assembled monolayers to control metal/organic interfacial electronic properties. They establish a physical principle for manipulating the relative energy levels between two materials and demonstrate an approach to improve metal/organic contacts in organic electronic devices.

Contractive type non-self mappings on metric spaces of hyperbolic type

Science.gov (United States)

Ciric, Ljubomir B.

2006-05-01

Let (X,d) be a metric space of hyperbolic type and K a nonempty closed subset of X. In this paper we study a class of mappings from K into X (not necessarily self-mappings on K), which are defined by the contractive condition (2.1) below, and a class of pairs of mappings from K into X which satisfy the condition (2.28) below. We present fixed point and common fixed point theorems which are generalizations of the corresponding fixed point theorems of Ciric [L.B. Ciric, Quasi-contraction non-self mappings on Banach spaces, Bull. Acad. Serbe Sci. Arts 23 (1998) 25-31; L.B. Ciric, J.S. Ume, M.S. Khan, H.K.T. Pathak, On some non-self mappings, Math. Nachr. 251 (2003) 28-33], Rhoades [B.E. Rhoades, A fixed point theorem for some non-self mappings, Math. Japon. 23 (1978) 457-459] and many other authors. Some examples are presented to show that our results are genuine generalizations of known results from this area.

Modeling of Instabilities and Self-organization at the Frictional Interface

Science.gov (United States)

Mortazavi, Vahid

frictional surface to exhibit "self-protection" and "self-healing" properties. Hence, this research is dealing with the fundamental concepts that allow the possibility of the development of a new generation of tribosystem and materials that reinforce such properties. In chapter 2, we investigate instabilities due to the temperature-dependency of the coefficient of friction. The temperature-dependency of the coefficient of friction can have a significant effect on the frictional sliding stability, by leading to the formation of "hot" and "cold" spots on the contacting surfaces. We formulate a stability criterion and perform a case study of a brake disk. In chapter 3, we study frictional running-in. Running-in is a transient period on the onset of the frictional sliding, in which friction and wear decrease to their stationary values. In this research, running-in is interpreted as friction-induced self-organization process. We introduce a theoretical model of running-in and investigate rough profile evolution assuming that its kinetics is driven by two opposite processes or events, i.e., smoothening which is typical for the deformation-driven friction and wear, and roughening which is typical for the adhesion-driven friction and wear. In chapter 4, we investigate the possibility of the so-called Turing-type pattern formation during friction. Turing or reaction-diffusion systems describe variations of spatial concentrations of chemical components with time due to local chemical reactions coupled with diffusion. During friction, the patterns can form at the sliding interface due to the mass transfer (diffusion), heat transfer, various tribochemical reactions, and wear. In chapter 5, we investigate how interfacial patterns including propagating trains of stick and slip zones form due to dynamic sliding instabilities. These can be categorized as self-organized patterns. We treat stick and slip as two phases at the interface, and study the effects related to phase transitions. Our

Assessment of habitat conditions using Self-Organizing Feature Maps for reintroduction/introduction of Aldrovanda vesiculosa L. in Poland

Directory of Open Access Journals (Sweden)

Piotr Kosiba

2011-07-01

Full Text Available The study objects were Aldrovanda vesiculosa L., an endangered species and fifty five water sites in Poland. The aim of the present work was to test the Self-Organizing Feature Map in order to examine and predict water properties and type of trophicity for restoration of the rare plant. Descriptive statistical parameters have been calculated, analysis of variance and cluster analysis were carried out and SOFM model has been constructed for analysed sites. The results of SOFM model and cluster analysis were compared. The study revealed that the ordination of individuals and groups of neurons in topological map of sites are similar in relation to dendrogram of cluster analysis, but not identical. The constructed SOFM model is related with significantly different contents of chemical water properties and type of trophicity. It appeared that sites with A. vesiculosa are predominantly distrophic and eutrophic waters shifted to distrophicity. The elevated model showed the sites with chemical properties favourable for restoration the species. Determined was the range of ecological tolerance of the species in relation to habitat conditions as stenotopic or relatively stenotopic in respect of the earlier accepted eutrophic status. The SOFM appeared to be a useful technique for ordination of ecological data and provides a novel framework for the discovery and forecasting of ecosystem properties constituting a validation of the SOFM method in this type of studies.

Classification of sediments by means of Self-Organizing Maps and sediment quality guidelines in sites of the southern Spanish coastline

Directory of Open Access Journals (Sweden)

O. VESES

2013-08-01

Full Text Available This study was carried out to classify 112 marine and estuarine sites of the southern Spanish coastline (about 918 km long according to similar sediment characteristics by means of artificial neural networks (ANNs such as Self-Organizing Maps (SOM and sediment quality guidelines from a dataset consisted of 16 physical and chemical parameters including sediment granulometry, trace and major elements, total N and P and organic carbon content. The use of ANNs such as SOM made possible the classification of the sampling sites according to their similar chemical characteristics. Visual correlations between geochemical parameters were extracted due to the powerful visual characteristics (component planes of the SOM revealing that ANNs are an excellent tool to be incorporated in sediment quality assessments. Besides, almost 20% of the sites were classified as medium-high or high priority sites in order to take future remediation actions due to their high mean Effects Range-Median Quotient (m-ERMQ value. Priority sites included the estuaries of the major rivers (Tinto, Odiel, Palmones, etc. and several locations along the eastern coastline.

From self-organization to self-assembly: a new materialism?

Science.gov (United States)

Vincent, Bernadette Bensaude

2016-09-01

While self-organization has been an integral part of academic discussions about the distinctive features of living organisms, at least since Immanuel Kant's Critique of Judgement, the term 'self-assembly' has only been used for a few decades as it became a hot research topic with the emergence of nanotechnology. Could it be considered as an attempt at reducing vital organization to a sort of assembly line of molecules? Considering the context of research on self-assembly I argue that the shift of attention from self-organization to self-assembly does not really challenge the boundary between chemistry and biology. Self-assembly was first and foremost investigated in an engineering context as a strategy for manufacturing without human intervention and did not raise new perspectives on the emergence of vital organization itself. However self-assembly implies metaphysical assumptions that this paper tries to disentangle. It first describes the emergence of self-assembly as a research field in the context of materials science and nanotechnology. The second section outlines the metaphysical implications and will emphasize a sharp contrast between the ontology underlying two practices of self-assembly developed under the umbrella of synthetic biology. And unexpectedly, we shall see that chemists are less on the reductionist side than most synthetic biologists. Finally, the third section ventures some reflections on the kind of design involved in self-assembly practices.

Pseudo-periodic maps and degeneration of Riemann surfaces

CERN Document Server

Matsumoto, Yukio

2011-01-01

The first part of the book studies pseudo-periodic maps of a closed surface of genus greater than or equal to two. This class of homeomorphisms was originally introduced by J. Nielsen in 1944 as an extension of periodic maps. In this book, the conjugacy classes of the (chiral) pseudo-periodic mapping classes are completely classified, and Nielsen’s incomplete classification is corrected. The second part applies the results of the first part to the topology of degeneration of Riemann surfaces. It is shown that the set of topological types of all the singular fibers appearing in one-parameter holomorphic families of Riemann surfaces is in a bijective correspondence with the set of conjugacy classes of the pseudo-periodic maps of negative twists. The correspondence is given by the topological monodromy.

Self-organized classification of boundary layer meteorology and associated characteristics of air quality in Beijing

Science.gov (United States)

Liao, Zhiheng; Sun, Jiaren; Yao, Jialin; Liu, Li; Li, Haowen; Liu, Jian; Xie, Jielan; Wu, Dui; Fan, Shaojia

2018-05-01

Self-organizing maps (SOMs; a feature-extracting technique based on an unsupervised machine learning algorithm) are used to classify atmospheric boundary layer (ABL) meteorology over Beijing through detecting topological relationships among the 5-year (2013-2017) radiosonde-based virtual potential temperature profiles. The classified ABL types are then examined in relation to near-surface pollutant concentrations to understand the modulation effects of the changing ABL meteorology on Beijing's air quality. Nine ABL types (i.e., SOM nodes) are obtained through the SOM classification technique, and each is characterized by distinct dynamic and thermodynamic conditions. In general, the self-organized ABL types are able to distinguish between high and low loadings of near-surface pollutants. The average concentrations of PM2.5, NO2 and CO dramatically increased from the near neutral (i.e., Node 1) to strong stable conditions (i.e., Node 9) during all seasons except for summer. Since extremely strong stability can isolate the near-surface observations from the influence of elevated SO2 pollution layers, the highest average SO2 concentrations are typically observed in Node 3 (a layer with strong stability in the upper ABL) rather than Node 9. In contrast, near-surface O3 shows an opposite dependence on atmospheric stability, with the lowest average concentration in Node 9. Analysis of three typical pollution months (i.e., January 2013, December 2015 and December 2016) suggests that the ABL types are the primary drivers of day-to-day variations in Beijing's air quality. Assuming a fixed relationship between ABL type and PM2.5 loading for different years, the relative (absolute) contributions of the ABL anomaly to elevated PM2.5 levels are estimated to be 58.3 % (44.4 µg m-3) in January 2013, 46.4 % (22.2 µg m-3) in December 2015 and 73.3 % (34.6 µg m-3) in December 2016.

Innovative Mechanism of Rural Organization Based on Self-Organization

OpenAIRE

Wang, Xing jin; Gao, Bing

2011-01-01

The paper analyzes the basic situation for the formation of innovative rural organizations with the form of self-organization; revels the features of self-organization, including the four aspects of openness of rural organization, innovation of rural organization is far away from equilibrium, the non-linear response mechanism of rural organization innovation and the random rise and fall of rural organization innovation. The evolution mechanism of rural organization innovation is reveled accor...

Walkable self-overlapping virtual reality maze and map visualization demo

DEFF Research Database (Denmark)

Serubugo, Sule; Skantarova, Denisa; Evers, Nicolaj

2017-01-01

This paper describes our demonstration of a walkable self-overlapping maze and its corresponding map to facilitate asymmetric collaboration for room-scale virtual reality setups in public places.......This paper describes our demonstration of a walkable self-overlapping maze and its corresponding map to facilitate asymmetric collaboration for room-scale virtual reality setups in public places....

Innovative Mechanism of Rural Organization Based on Self-Organization

Institute of Scientific and Technical Information of China (English)

2011-01-01

The paper analyzes the basic situation of the formation of innovative rural organizations with the form of self-organization;reveals the features of self-organization,including the four aspects of openness of rural organization,innovation of rural organization far away from equilibrium,the non-linear response mechanism of rural organization innovation and the random rise and fall of rural organization innovation.The evolution mechanism of rural organization innovation is revealed according to the growth stage,the ideal stage,the decline and the fall stage.The paper probes into the basic restriction mechanism of the self-organization evaluation of rural organization from three aspects,including target recognition,path dependence and knowledge sharing.The basic measures on cultivating the innovative mechanism of rural organization are put forward.Firstly,constructing the dissipative structure of rural organization innovation;secondly,cultivating the dynamic study capability of rural organization innovation system;thirdly,selecting the step-by-step evolution strategy of rural organization innovation system.

Self-Organization and the Self-Assembling Process in Tissue Engineering

Science.gov (United States)

Eswaramoorthy, Rajalakshmanan; Hadidi, Pasha; Hu, Jerry C.

2015-01-01

In recent years, the tissue engineering paradigm has shifted to include a new and growing subfield of scaffoldless techniques which generate self-organizing and self-assembling tissues. This review aims to provide a cogent description of this relatively new research area, with special emphasis on applications toward clinical use and research models. Particular emphasis is placed on providing clear definitions of self-organization and the self-assembling process, as delineated from other scaffoldless techniques in tissue engineering and regenerative medicine. Significantly, during formation, self-organizing and self-assembling tissues display biological processes similar to those that occur in vivo. These help lead to the recapitulation of native tissue morphological structure and organization. Notably, functional properties of these tissues also approach native tissue values; some of these engineered tissues are already in clinical trials. This review aims to provide a cohesive summary of work in this field, and to highlight the potential of self-organization and the self-assembling process to provide cogent solutions to current intractable problems in tissue engineering. PMID:23701238

Your space or mine? Mapping self in time.

Directory of Open Access Journals (Sweden)

Brittany M Christian

Full Text Available While humans are capable of mentally transcending the here and now, this faculty for mental time travel (MTT is dependent upon an underlying cognitive representation of time. To this end, linguistic, cognitive and behavioral evidence has revealed that people understand abstract temporal constructs by mapping them to concrete spatial domains (e.g. past=backward, future=forward. However, very little research has investigated factors that may determine the topographical characteristics of these spatiotemporal maps. Guided by the imperative role of episodic content for retrospective and prospective thought (i.e., MTT, here we explored the possibility that the spatialization of time is influenced by the amount of episodic detail a temporal unit contains. In two experiments, participants mapped temporal events along mediolateral (Experiment 1 and anterioposterior (Experiment 2 spatial planes. Importantly, the temporal units varied in self-relevance as they pertained to temporally proximal or distal events in the participant's own life, the life of a best friend or the life of an unfamiliar other. Converging evidence from both experiments revealed that the amount of space used to represent time varied as a function of target (self, best friend or unfamiliar other and temporal distance. Specifically, self-time was represented as occupying more space than time pertaining to other targets, but only for temporally proximal events. These results demonstrate the malleability of space-time mapping and suggest that there is a self-specific conceptualization of time that may influence MTT as well as other temporally relevant cognitive phenomena.

Self-organized Learning Environments

DEFF Research Database (Denmark)

Dalsgaard, Christian; Mathiasen, Helle

2007-01-01

system actively. The two groups used the system in their own way to support their specific activities and ways of working. The paper concludes that self-organized learning environments can strengthen the development of students’ academic as well as social qualifications. Further, the paper identifies......The purpose of the paper is to discuss the potentials of using a conference system in support of a project based university course. We use the concept of a self-organized learning environment to describe the shape of the course. In the paper we argue that educational technology, such as conference...... systems, has a potential to support students’ development of self-organized learning environments and facilitate self-governed activities in higher education. The paper is based on an empirical study of two project groups’ use of a conference system. The study showed that the students used the conference...

Controlling Schottky energy barriers in organic electronic devices using self-assembled monolayers

Energy Technology Data Exchange (ETDEWEB)

Campbell, I.H.; Rubin, S.; Zawodzinski, T.A.; Kress, J.D.; Martin, R.L.; Smith, D.L. [Los Alamos National Laboratory, Los Alamos, New Mexico 87545 (United States); Barashkov, N.N.; Ferraris, J.P. [The University of Texas at Dallas, Richardson, Texas 75083 (United States)

1996-11-01

We demonstrate tuning of Schottky energy barriers in organic electronic devices by utilizing chemically tailored electrodes. The Schottky energy barrier of Ag on poly[2-methoxy], 5-(2{prime}-ethyl-hexyloxy)- 1,4-phenylene was tuned over a range of more than 1 eV by using self-assembled monolayers (SAM{close_quote}s) to attach oriented dipole layers to the Ag prior to device fabrication. Kelvin probe measurements were used to determine the effect of the SAM{close_quote}s on the Ag surface potential. {ital Ab} {ital initio} Hartree-Fock calculations of the molecular dipole moments successfully describe the surface potential changes. The chemically tailored electrodes were then incorporated in organic diode structures and changes in the metal/organic Schottky energy barriers were measured using an electroabsorption technique. These results demonstrate the use of self-assembled monolayers to control metal/organic interfacial electronic properties. They establish a physical principle for manipulating the relative energy levels between two materials and demonstrate an approach to improve metal/organic contacts in organic electronic devices. {copyright} {ital 1996 The American Physical Society.}

Controlling Schottky energy barriers in organic electronic devices using self-assembled monolayers

International Nuclear Information System (INIS)

Campbell, I.H.; Rubin, S.; Zawodzinski, T.A.; Kress, J.D.; Martin, R.L.; Smith, D.L.; Barashkov, N.N.; Ferraris, J.P.

1996-01-01

We demonstrate tuning of Schottky energy barriers in organic electronic devices by utilizing chemically tailored electrodes. The Schottky energy barrier of Ag on poly[2-methoxy], 5-(2'-ethyl-hexyloxy)- 1,4-phenylene was tuned over a range of more than 1 eV by using self-assembled monolayers (SAM close-quote s) to attach oriented dipole layers to the Ag prior to device fabrication. Kelvin probe measurements were used to determine the effect of the SAM close-quote s on the Ag surface potential. Ab initio Hartree-Fock calculations of the molecular dipole moments successfully describe the surface potential changes. The chemically tailored electrodes were then incorporated in organic diode structures and changes in the metal/organic Schottky energy barriers were measured using an electroabsorption technique. These results demonstrate the use of self-assembled monolayers to control metal/organic interfacial electronic properties. They establish a physical principle for manipulating the relative energy levels between two materials and demonstrate an approach to improve metal/organic contacts in organic electronic devices. copyright 1996 The American Physical Society

Surface structure and stereochemical properties of self-assembled monolayer materials. Final Report

International Nuclear Information System (INIS)

Scoles, Giacinto

2006-01-01

This document reports the progress the authors have made in support of their proposal to generate well-characterized, well-ordered organic surfaces and to impinge upon the array of oriented organic molecules a well-collimated beam of radical atoms at a well-defined angle of incidence. Using the intensity of helium atom diffraction from the organic surface as a measure of the number of unreacted molecules at the surface, the authors will measure the rate of the reaction. They will then vary the angle of incidence of the reactive atom beam and repeat the measurement. In this manner they plan to map out the reactivity of the molecules on the surface as a function of the angle of incidence of the reactive moiety. To carry out this experiment requires that two fields of research be brought together: (1) molecular beam technology and (2) the science/art of growing well-ordered organic surfaces. The first half of this report describes recent helium diffraction results from molecular beam deposited organic monolayers (structural layer characterization work). The second half reports progress in constructing and characterizing the reactive atom (oxygen) beam source.

Localized aliphatic organic material on the surface of Ceres

Science.gov (United States)

De Sanctis, M. C.; Ammannito, E.; McSween, H. Y.; Raponi, A.; Marchi, S.; Capaccioni, F.; Capria, M. T.; Carrozzo, F. G.; Ciarniello, M.; Fonte, S.; Formisano, M.; Frigeri, A.; Giardino, M.; Longobardo, A.; Magni, G.; McFadden, L. A.; Palomba, E.; Pieters, C. M.; Tosi, F.; Zambon, F.; Raymond, C. A.; Russell, C. T.

2017-02-01

Organic compounds occur in some chondritic meteorites, and their signatures on solar system bodies have been sought for decades. Spectral signatures of organics have not been unambiguously identified on the surfaces of asteroids, whereas they have been detected on cometary nuclei. Data returned by the Visible and InfraRed Mapping Spectrometer on board the Dawn spacecraft show a clear detection of an organic absorption feature at 3.4 micrometers on dwarf planet Ceres. This signature is characteristic of aliphatic organic matter and is mainly localized on a broad region of ~1000 square kilometers close to the ~50-kilometer Ernutet crater. The combined presence on Ceres of ammonia-bearing hydrated minerals, water ice, carbonates, salts, and organic material indicates a very complex chemical environment, suggesting favorable environments to prebiotic chemistry.

Influences of surface charge, size, and concentration of colloidal nanoparticles on fabrication of self-organized porous silica in film and particle forms.

Science.gov (United States)

Nandiyanto, Asep Bayu Dani; Suhendi, Asep; Arutanti, Osi; Ogi, Takashi; Okuyama, Kikuo

2013-05-28

Studies on preparation of porous material have attracted tremendous attention because existence of pores can provide material with excellent performances. However, current preparation reports described successful production of porous material with only partial information on charges, interactions, sizes, and compositions of the template and host materials. In this report, influences of self-assembly parameters (i.e., surface charge, size, and concentration of colloidal nanoparticles) on self-organized porous material fabrication were investigated. Silica nanoparticles (as a host material) and polystyrene (PS) spheres (as a template) were combined to produce self-assembly porous materials in film and particle forms. The experimental results showed that the porous structure and pore size were controllable and strongly depended on the self-assembly parameters. Materials containing highly ordered pores were effectively created only when process parameters fall within appropriate conditions (i.e., PS surface charge ≤ -30 mV; silica-to-PS size ratio ≤0.078; and silica-to-PS mass ratio of about 0.50). The investigation of the self-assembly parameter landscape was also completed using geometric considerations. Because optimization of these parameters provides significant information in regard to practical uses, results of this report could be relevant to other functional properties.

Self-Organization in Embedded Real-Time Systems

CERN Document Server

Brinkschulte, Uwe; Rettberg, Achim

2013-01-01

This book describes the emerging field of self-organizing, multicore, distributed and real-time embedded systems.  Self-organization of both hardware and software can be a key technique to handle the growing complexity of modern computing systems. Distributed systems running hundreds of tasks on dozens of processors, each equipped with multiple cores, requires self-organization principles to ensure efficient and reliable operation. This book addresses various, so-called Self-X features such as self-configuration, self-optimization, self-adaptation, self-healing and self-protection. Presents open components for embedded real-time adaptive and self-organizing applications; Describes innovative techniques in: scheduling, memory management, quality of service, communications supporting organic real-time applications; Covers multi-/many-core embedded systems supporting real-time adaptive systems and power-aware, adaptive hardware and software systems; Includes case studies of open embedded real-time self-organizi...

NOAA Office of Exploration and Research > About OER > Organization > Map of

Science.gov (United States)

About OER Overview Organization Guiding Documents Organizational Structure Map of Staff and Affiliate of Staff and Affiliate Locations About OER Organization Map of Staff and Affiliate Locations Home About OER Overview Organization Guiding Documents Organizational Structure Map of Staff and Affiliate

Constructing Ozone Profile Climatologies with Self-Organizing Maps: Illustrations with CONUS Ozonesonde Data

Science.gov (United States)

Thompson, A. M.; Stauffer, R. M.; Young, G. S.

2015-12-01

Ozone (O3) trends analysis is typically performed with monthly or seasonal averages. Although this approach works well for stratospheric or total O3, uncertainties in tropospheric O3 amounts may be large due to rapid meteorological changes near the tropopause and in the lower free troposphere (LFT) where pollution has a days-weeks lifetime. We use self-organizing maps (SOM), a clustering technique, as an alternative for creating tropospheric climatologies from O3 soundings. In a previous study of 900 tropical ozonesondes, clusters representing >40% of profiles deviated > 1-sigma from mean O­3. Here SOM are based on 15 years of data from four sites in the contiguous US (CONUS; Boulder, CO; Huntsville, AL; Trinidad Head, CA; Wallops Island, VA). Ozone profiles from 2 - 12 km are used to evaluate the impact of tropopause variability on climatology; 2 - 6 km O3 profile segments are used for the LFT. Near-tropopause O­3 is twice the mean O­3 mixing ratio in three clusters of 2 - 12 km O3, representing > 15% of profiles at each site. Large mid and lower-tropospheric O3 deviations from monthly means are found in clusters of both 2 - 12 and 2 - 6 km O3. Positive offsets result from pollution and stratosphere-to-troposphere exchange. In the LFT the lowest tropospheric O3 is associated with subtropical air. Some clusters include profiles with common seasonality but other factors, e.g., tropopause height or LFT column amount, characterize other SOM nodes. Thus, as for tropical profiles, CONUS O­3 averages can be a poor choice for a climatology.

Estimation Algorithm of Machine Operational Intention by Bayes Filtering with Self-Organizing Map

Directory of Open Access Journals (Sweden)

Satoshi Suzuki

2012-01-01

Full Text Available We present an intention estimator algorithm that can deal with dynamic change of the environment in a man-machine system and will be able to be utilized for an autarkical human-assisting system. In the algorithm, state transition relation of intentions is formed using a self-organizing map (SOM from the measured data of the operation and environmental variables with the reference intention sequence. The operational intention modes are identified by stochastic computation using a Bayesian particle filter with the trained SOM. This method enables to omit the troublesome process to specify types of information which should be used to build the estimator. Applying the proposed method to the remote operation task, the estimator's behavior was analyzed, the pros and cons of the method were investigated, and ways for the improvement were discussed. As a result, it was confirmed that the estimator can identify the intention modes at 44–94 percent concordance ratios against normal intention modes whose periods can be found by about 70 percent of members of human analysts. On the other hand, it was found that human analysts' discrimination which was used as canonical data for validation differed depending on difference of intention modes. Specifically, an investigation of intentions pattern discriminated by eight analysts showed that the estimator could not identify the same modes that human analysts could not discriminate. And, in the analysis of the multiple different intentions, it was found that the estimator could identify the same type of intention modes to human-discriminated ones as well as 62–73 percent when the first and second dominant intention modes were considered.

Sustained activity in hierarchical modular neural networks: self-organized criticality and oscillations

Directory of Open Access Journals (Sweden)

Sheng-Jun Wang

2011-06-01

Full Text Available Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. They are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality. We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. It was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We find that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and self-organized criticality, which are not present in the respective random networks. The underlying mechanism is that each dense module cannot sustain activity on its own, but displays self-organized criticality in the presence of weak perturbations. The hierarchical modular networks provide the coupling among subsystems with self-organized criticality. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivityof critical state and predictability and timing of oscillations for efficient

Screen media usage, sleep time and academic performance in adolescents: clustering a self-organizing maps analysis.

Science.gov (United States)

Peiró-Velert, Carmen; Valencia-Peris, Alexandra; González, Luis M; García-Massó, Xavier; Serra-Añó, Pilar; Devís-Devís, José

2014-01-01

Screen media usage, sleep time and socio-demographic features are related to adolescents' academic performance, but interrelations are little explored. This paper describes these interrelations and behavioral profiles clustered in low and high academic performance. A nationally representative sample of 3,095 Spanish adolescents, aged 12 to 18, was surveyed on 15 variables linked to the purpose of the study. A Self-Organizing Maps analysis established non-linear interrelationships among these variables and identified behavior patterns in subsequent cluster analyses. Topological interrelationships established from the 15 emerging maps indicated that boys used more passive videogames and computers for playing than girls, who tended to use mobile phones to communicate with others. Adolescents with the highest academic performance were the youngest. They slept more and spent less time using sedentary screen media when compared to those with the lowest performance, and they also showed topological relationships with higher socioeconomic status adolescents. Cluster 1 grouped boys who spent more than 5.5 hours daily using sedentary screen media. Their academic performance was low and they slept an average of 8 hours daily. Cluster 2 gathered girls with an excellent academic performance, who slept nearly 9 hours per day, and devoted less time daily to sedentary screen media. Academic performance was directly related to sleep time and socioeconomic status, but inversely related to overall sedentary screen media usage. Profiles from the two clusters were strongly differentiated by gender, age, sedentary screen media usage, sleep time and academic achievement. Girls with the highest academic results had a medium socioeconomic status in Cluster 2. Findings may contribute to establishing recommendations about the timing and duration of screen media usage in adolescents and appropriate sleep time needed to successfully meet the demands of school academics and to improve

Screen media usage, sleep time and academic performance in adolescents: clustering a self-organizing maps analysis.

Directory of Open Access Journals (Sweden)

Carmen Peiró-Velert

Full Text Available Screen media usage, sleep time and socio-demographic features are related to adolescents' academic performance, but interrelations are little explored. This paper describes these interrelations and behavioral profiles clustered in low and high academic performance. A nationally representative sample of 3,095 Spanish adolescents, aged 12 to 18, was surveyed on 15 variables linked to the purpose of the study. A Self-Organizing Maps analysis established non-linear interrelationships among these variables and identified behavior patterns in subsequent cluster analyses. Topological interrelationships established from the 15 emerging maps indicated that boys used more passive videogames and computers for playing than girls, who tended to use mobile phones to communicate with others. Adolescents with the highest academic performance were the youngest. They slept more and spent less time using sedentary screen media when compared to those with the lowest performance, and they also showed topological relationships with higher socioeconomic status adolescents. Cluster 1 grouped boys who spent more than 5.5 hours daily using sedentary screen media. Their academic performance was low and they slept an average of 8 hours daily. Cluster 2 gathered girls with an excellent academic performance, who slept nearly 9 hours per day, and devoted less time daily to sedentary screen media. Academic performance was directly related to sleep time and socioeconomic status, but inversely related to overall sedentary screen media usage. Profiles from the two clusters were strongly differentiated by gender, age, sedentary screen media usage, sleep time and academic achievement. Girls with the highest academic results had a medium socioeconomic status in Cluster 2. Findings may contribute to establishing recommendations about the timing and duration of screen media usage in adolescents and appropriate sleep time needed to successfully meet the demands of school academics and

Structural characterization of self-assembled semiconductor islands by three-dimensional X-ray diffraction mapping in reciprocal space

International Nuclear Information System (INIS)

Holy, V.; Mundboth, K.; Mokuta, C.; Metzger, T.H.; Stangl, J.; Bauer, G.; Boeck, T.; Schmidbauer, M.

2008-01-01

For the first time self-organized epitaxially grown semiconductor islands were investigated by a full three-dimensional mapping of the scattered X-ray intensity in reciprocal space. Intensity distributions were measured in a coplanar diffraction geometry around symmetric and asymmetric Bragg reflections. The 3D intensity maps were compared with theoretical simulations based on continuum-elasticity simulations of internal strains in the islands and on kinematical scattering theory whereby local chemical composition and strain profiles of the islands were retrieved

Self-organizing sensing and actuation for automatic control

Science.gov (United States)

Cheng, George Shu-Xing

2017-07-04

A Self-Organizing Process Control Architecture is introduced with a Sensing Layer, Control Layer, Actuation Layer, Process Layer, as well as Self-Organizing Sensors (SOS) and Self-Organizing Actuators (SOA). A Self-Organizing Sensor for a process variable with one or multiple input variables is disclosed. An artificial neural network (ANN) based dynamic modeling mechanism as part of the Self-Organizing Sensor is described. As a case example, a Self-Organizing Soft-Sensor for CFB Boiler Bed Height is presented. Also provided is a method to develop a Self-Organizing Sensor.

Using parallel factor analysis modeling (PARAFAC) and self-organizing maps to track senescence-induced patterns in leaf litter leachate

Science.gov (United States)

Wheeler, K. I.; Levia, D. F., Jr.; Hudson, J. E.

2017-12-01

As trees undergo autumnal processes such as resorption, senescence, and leaf abscission, the dissolved organic matter (DOM) contribution of leaf litter leachate to streams changes. However, little research has investigated how the fluorescent DOM (FDOM) changes throughout the autumn and how this differs inter- and intraspecifically. Two of the major impacts of global climate change on forested ecosystems include altering phenology and causing forest community species and subspecies composition restructuring. We examined changes in FDOM in leachate from American beech (Fagus grandifolia Ehrh.) leaves in Maryland, Rhode Island, Vermont, and North Carolina and yellow poplar (Liriodendron tulipifera L.) leaves from Maryland throughout three different phenophases: green, senescing, and freshly abscissed. Beech leaves from Maryland and Rhode Island have previously been identified as belonging to the same distinct genetic cluster and beech trees from Vermont and the study site in North Carolina from the other. FDOM in samples was characterized using excitation-emission matrices (EEMs) and a six-component parallel factor analysis (PARAFAC) model was created to identify components. Self-organizing maps (SOMs) were used to visualize variation and patterns in the PARAFAC component proportions of the leachate samples. Phenophase and species had the greatest influence on determining where a sample mapped on the SOM when compared to genetic clusters and geographic origin. Throughout senescence, FDOM from all the trees transitioned from more protein-like components to more humic-like ones. Percent greenness of the sampled leaves and the proportion of the tyrosine-like component 1 were found to significantly differ between the two genetic beech clusters. This suggests possible differences in photosynthesis and resorption between the two genetic clusters of beech. The use of SOMs to visualize differences in patterns of senescence between the different species and genetic

Application of self-organizing feature maps to analyze the relationships between ignitable liquids and selected mass spectral ions.

Science.gov (United States)

Frisch-Daiello, Jessica L; Williams, Mary R; Waddell, Erin E; Sigman, Michael E

2014-03-01

The unsupervised artificial neural networks method of self-organizing feature maps (SOFMs) is applied to spectral data of ignitable liquids to visualize the grouping of similar ignitable liquids with respect to their American Society for Testing and Materials (ASTM) class designations and to determine the ions associated with each group. The spectral data consists of extracted ion spectra (EIS), defined as the time-averaged mass spectrum across the chromatographic profile for select ions, where the selected ions are a subset of ions from Table 2 of the ASTM standard E1618-11. Utilization of the EIS allows for inter-laboratory comparisons without the concern of retention time shifts. The trained SOFM demonstrates clustering of the ignitable liquid samples according to designated ASTM classes. The EIS of select samples designated as miscellaneous or oxygenated as well as ignitable liquid residues from fire debris samples are projected onto the SOFM. The results indicate the similarities and differences between the variables of the newly projected data compared to those of the data used to train the SOFM. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

System and method employing a self-organizing map load feature database to identify electric load types of different electric loads

Science.gov (United States)

Lu, Bin; Harley, Ronald G.; Du, Liang; Yang, Yi; Sharma, Santosh K.; Zambare, Prachi; Madane, Mayura A.

2014-06-17

A method identifies electric load types of a plurality of different electric loads. The method includes providing a self-organizing map load feature database of a plurality of different electric load types and a plurality of neurons, each of the load types corresponding to a number of the neurons; employing a weight vector for each of the neurons; sensing a voltage signal and a current signal for each of the loads; determining a load feature vector including at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the loads; and identifying by a processor one of the load types by relating the load feature vector to the neurons of the database by identifying the weight vector of one of the neurons corresponding to the one of the load types that is a minimal distance to the load feature vector.

Molecular self-assembly approaches for supramolecular electronic and organic electronic devices

Science.gov (United States)

Yip, Hin-Lap

Molecular self-assembly represents an efficient bottom-up strategy to generate structurally well-defined aggregates of semiconducting pi-conjugated materials. The capability of tuning the chemical structures, intermolecular interactions and nanostructures through molecular engineering and novel materials processing renders it possible to tailor a large number of unprecedented properties such as charge transport, energy transfer and light harvesting. This approach does not only benefit traditional electronic devices based on bulk materials, but also generate a new research area so called "supramolecular electronics" in which electronic devices are built up with individual supramolecular nanostructures with size in the sub-hundred nanometers range. My work combined molecular self-assembly together with several novel materials processing techniques to control the nucleation and growth of organic semiconducting nanostructures from different type of pi-conjugated materials. By tailoring the interactions between the molecules using hydrogen bonds and pi-pi stacking, semiconducting nanoplatelets and nanowires with tunable sizes can be fabricated in solution. These supramolecular nanostructures were further patterned and aligned on solid substrates through printing and chemical templating methods. The capability to control the different hierarchies of organization on surface provides an important platform to study their structural-induced electronic properties. In addition to using molecular self-assembly to create different organic nanostructures, functional self-assembled monolayer (SAM) formed by spontaneous chemisorption on surfaces was used to tune the interfacial property in organic solar cells. Devices showed dramatically improved performance when appropriate SAMs were applied to optimize the contact property for efficiency charge collection.

Self-Organizing Maps: A Data Mining Tool for the Analysis of Airborne Geophysical Data Collected over the Brazilian Amazon

Science.gov (United States)

Carneiro, C.; Fraser, S. J.; Crosta, A. P.; Silva, A.; Barros, C.

2011-12-01

Regional airborne geophysical data sets are being collected worldwide to promote mineral exploration and resource development. These data sets often are collected over highly prospective terranes, where access is limited or there are environmental concerns. Such regional surveys typically consist of two or more sensor packages being flown in an aircraft over the survey area and vast amounts of near-continuous data can be acquired in a relatively short time. Increasingly, there is also a need to process such data in a timely fashion to demonstrate the data's value and indicate the potential return or value of the survey to the funding agency. To assist in the timely analysis of such regional data sets, we have used an exploratory data mining approach: the Self Organizing Map (SOM). Because SOM is based on vector quantization and measures of vector similarity, it is an ideal tool to analyze a data set consisting of disparate geophysical input parameters to look for relationships and trends. We report on our use of SOM to analyze part of a regional airborne geophysical survey collected over the prospective Anapu-Tuere region of the Brazilian Amazon. Magnetic and spectrometric gamma ray data were used as input to our SOM analysis, and the results used to discriminate and identify various rock types and produce a "pseudo" geological map over the study area. The ability of SOM to define discrete domains of rock-types with similar properties allowed us to expand upon existing geological knowledge of the area for mapping purposes; and, often it was the combination of the magnetic and radiometric responses that identified a lithology's unique response. One particular unit was identified that had an association with known gold mineralization, which consequently highlighted the prospectivity of that unit elsewhere in the survey area. Our results indicate that SOM can be used for the semi-automatic analysis of regional airborne geophysical data to assist in geological mapping

Spot profile analysis and lifetime mapping in ultrafast electron diffraction: Lattice excitation of self-organized Ge nanostructures on Si(001

Directory of Open Access Journals (Sweden)

T. Frigge

2015-05-01

Full Text Available Ultrafast high energy electron diffraction in reflection geometry is employed to study the structural dynamics of self-organized Germanium hut-, dome-, and relaxed clusters on Si(001 upon femtosecond laser excitation. Utilizing the difference in size and strain state the response of hut- and dome clusters can be distinguished by a transient spot profile analysis. Surface diffraction from {105}-type facets provide exclusive information on hut clusters. A pixel-by-pixel analysis of the dynamics of the entire diffraction pattern gives time constants of 40, 160, and 390 ps, which are assigned to the cooling time constants for hut-, dome-, and relaxed clusters.

Crystal step edges can trap electrons on the surfaces of n-type organic semiconductors.

Science.gov (United States)

He, Tao; Wu, Yanfei; D'Avino, Gabriele; Schmidt, Elliot; Stolte, Matthias; Cornil, Jérôme; Beljonne, David; Ruden, P Paul; Würthner, Frank; Frisbie, C Daniel

2018-05-30

Understanding relationships between microstructure and electrical transport is an important goal for the materials science of organic semiconductors. Combining high-resolution surface potential mapping by scanning Kelvin probe microscopy (SKPM) with systematic field effect transport measurements, we show that step edges can trap electrons on the surfaces of single crystal organic semiconductors. n-type organic semiconductor crystals exhibiting positive step edge surface potentials display threshold voltages that increase and carrier mobilities that decrease with increasing step density, characteristic of trapping, whereas crystals that do not have positive step edge surface potentials do not have strongly step density dependent transport. A device model and microelectrostatics calculations suggest that trapping can be intrinsic to step edges for crystals of molecules with polar substituents. The results provide a unique example of a specific microstructure-charge trapping relationship and highlight the utility of surface potential imaging in combination with transport measurements as a productive strategy for uncovering microscopic structure-property relationships in organic semiconductors.

Real-space Mapping of Surface Trap States in CIGSe Nanocrystals using 4D Electron Microscopy

KAUST Repository

Bose, Riya

2016-05-26

Surface trap states in semiconductor copper indium gallium selenide nanocrystals (NCs) which serve as undesirable channels for non-radiative carrier recombination, remain a great challenge impeding the development of solar and optoelectronics devices based on these NCs. In order to design efficient passivation techniques to minimize these trap states, a precise knowledge about the charge carrier dynamics on the NCs surface is essential. However, selective mapping of surface traps requires capabilities beyond the reach of conventional laser spectroscopy and static electron microscopy; it can only be accessed by using a one-of-a-kind, second-generation four-dimensional scanning ultrafast electron microscope (4D S-UEM) with sub-picosecond temporal and nanometer spatial resolutions. Here, we precisely map the surface charge carrier dynamics of copper indium gallium selenide NCs before and after surface passivation in real space and time using S-UEM. The time-resolved snapshots clearly demonstrate that the density of the trap states is significantly reduced after zinc sulfide (ZnS) shelling. Furthermore, removal of trap states and elongation of carrier lifetime are confirmed by the increased photocurrent of the self-biased photodetector fabricated using the shelled NCs.

Real-space Mapping of Surface Trap States in CIGSe Nanocrystals using 4D Electron Microscopy

KAUST Repository

Bose, Riya; Bera, Ashok; Parida, Manas R.; Adhikari, Aniruddha; Shaheen, Basamat; Alarousu, Erkki; Sun, Jingya; Wu, Tao; Bakr, Osman; Mohammed, Omar F.

2016-01-01

Surface trap states in semiconductor copper indium gallium selenide nanocrystals (NCs) which serve as undesirable channels for non-radiative carrier recombination, remain a great challenge impeding the development of solar and optoelectronics devices based on these NCs. In order to design efficient passivation techniques to minimize these trap states, a precise knowledge about the charge carrier dynamics on the NCs surface is essential. However, selective mapping of surface traps requires capabilities beyond the reach of conventional laser spectroscopy and static electron microscopy; it can only be accessed by using a one-of-a-kind, second-generation four-dimensional scanning ultrafast electron microscope (4D S-UEM) with sub-picosecond temporal and nanometer spatial resolutions. Here, we precisely map the surface charge carrier dynamics of copper indium gallium selenide NCs before and after surface passivation in real space and time using S-UEM. The time-resolved snapshots clearly demonstrate that the density of the trap states is significantly reduced after zinc sulfide (ZnS) shelling. Furthermore, removal of trap states and elongation of carrier lifetime are confirmed by the increased photocurrent of the self-biased photodetector fabricated using the shelled NCs.

3D silicon breast surface mapping via structured light profilometry

Science.gov (United States)

Vairavan, R.; Ong, N. R.; Sauli, Z.; Kirtsaeng, S.; Sakuntasathien, S.; Shahimin, M. M.; Alcain, J. B.; Lai, S. L.; Paitong, P.; Retnasamy, V.

2017-09-01

Digital fringe projection technique is one of the promising optical methods for 3D surface imaging as it demonstrates non contact and non invasive characteristics. The potential of this technique matches the requirement for human body evaluation, as it is vital for disease diagnosis and for treatment option selection. Thus, the digital fringe projection has addressed this requirement with its wide clinical related application and studies. However, the application of this technique for 3D surface mapping of the breast is very minimal. Hence, in this work, the application of digital fringe projection for 3D breast surface mapping is reported. Phase shift fringe projection technique was utilized to perform the 3D breast surface mapping. Maiden results have confirmed the feasibility of using the digital fringe projection method for 3D surface mapping of the breast and it can be extended for breast cancer detection.

Spatial Mapping of Organic Carbon in Returned Samples from Mars

Science.gov (United States)

Siljeström, S.; Fornaro, T.; Greenwalt, D.; Steele, A.

2018-04-01

To map organic material spatially to minerals present in the sample will be essential for the understanding of the origin of any organics in returned samples from Mars. It will be shown how ToF-SIMS may be used to map organics in samples from Mars.

Synthesis Road Map Problems in Organic Chemistry

Science.gov (United States)

Schaller, Chris P.; Graham, Kate J.; Jones, T. Nicholas

2014-01-01

Road map problems ask students to integrate their knowledge of organic reactions with pattern recognition skills to "fill in the blanks" in the synthesis of an organic compound. Students are asked to identify familiar organic reactions in unfamiliar contexts. A practical context, such as a medicinally useful target compound, helps…

Surface characterization of sulfur and alkanethiol self-assembled monolayers on Au(111)

International Nuclear Information System (INIS)

Vericat, C; Vela, M E; Benitez, G A; Gago, J A Martin; Torrelles, X; Salvarezza, R C

2006-01-01

In the last two decades surface science techniques have decisively contributed to our present knowledge of alkanethiol self-assembled monolayers (SAMs) on solid surfaces. These organic layers have been a challenge for surface scientists, in particular because of the soft nature of the organic material (which can be easily damaged by irradiation), the large number of atoms present in the molecules, and the complex physical chemistry involved in the self-assembly process. This challenge has been motivated by the appealing technological applications of SAMs that cover many fields of the emerging area of nanotechnology. Sulfur (S) is closely related to alkanethiols and can be used to understand basic aspects of the surface structure of SAMs. In this review we focus on the atomic/molecular structures of S-containing SAMs on Au(111). Particular emphasis is given to the substrate, adsorption sites, chemical state of the S-metal bond and also to the experimental and theoretical tools used to study these structures at the atomic or molecular levels. (topical review)

Surface characterization of sulfur and alkanethiol self-assembled monolayers on Au(111)

Energy Technology Data Exchange (ETDEWEB)

Vericat, C [Instituto de Investigaciones FisicoquImicas Teoricas y Aplicadas (INIFTA), Universidad Nacional de La Plata-CONICET, Sucursal 4 Casilla de Correo 16 (1900) La Plata (Argentina); Vela, M E [Instituto de Investigaciones FisicoquImicas Teoricas y Aplicadas (INIFTA), Universidad Nacional de La Plata-CONICET, Sucursal 4 Casilla de Correo 16 (1900) La Plata (Argentina); Benitez, G A [Instituto de Investigaciones FisicoquImicas Teoricas y Aplicadas (INIFTA), Universidad Nacional de La Plata-CONICET, Sucursal 4 Casilla de Correo 16 (1900) La Plata (Argentina); Gago, J A Martin [Centro de AstrobiologIa (CSIC-INTA), 28850 Torrejon de Ardoz Madrid (Spain); Torrelles, X [Instituto de Ciencia de Materiales de Barcelona (ICMAB), Barcelona (Spain); Salvarezza, R C [Instituto de Investigaciones FisicoquImicas Teoricas y Aplicadas (INIFTA), Universidad Nacional de La Plata-CONICET, Sucursal 4 Casilla de Correo 16 (1900) La Plata (Argentina)

2006-12-06

In the last two decades surface science techniques have decisively contributed to our present knowledge of alkanethiol self-assembled monolayers (SAMs) on solid surfaces. These organic layers have been a challenge for surface scientists, in particular because of the soft nature of the organic material (which can be easily damaged by irradiation), the large number of atoms present in the molecules, and the complex physical chemistry involved in the self-assembly process. This challenge has been motivated by the appealing technological applications of SAMs that cover many fields of the emerging area of nanotechnology. Sulfur (S) is closely related to alkanethiols and can be used to understand basic aspects of the surface structure of SAMs. In this review we focus on the atomic/molecular structures of S-containing SAMs on Au(111). Particular emphasis is given to the substrate, adsorption sites, chemical state of the S-metal bond and also to the experimental and theoretical tools used to study these structures at the atomic or molecular levels. (topical review)

Chaoticity of interval self-maps with positive entropy

International Nuclear Information System (INIS)

Xiong Jincheng.

1988-12-01

Li and Yorke originally introduced the notion of chaos for continuous self-map of the interval I = (0,1). In the present paper we show that an interval self-map with positive topological entropy has a chaoticity more complicated than the chaoticity in the sense of Li and Yorke. The main result is that if f:I → I is continuous and has a periodic point with odd period > 1 then there exists a closed subset K of I invariant with respect to f such that the periodic points are dense in K, the periods of periodic points in K form an infinite set and f|K is topologically mixing. (author). 9 refs

The Self-Organized Archive: SPASE, PDS and Archive Cooperatives

Science.gov (United States)

King, T. A.; Hughes, J. S.; Roberts, D. A.; Walker, R. J.; Joy, S. P.

2005-05-01

Information systems with high quality metadata enable uses and services which often go beyond the original purpose. There are two types of metadata: annotations which are items that comment on or describe the content of a resource and identification attributes which describe the external properties of the resource itself. For example, annotations may indicate which columns are present in a table of data, whereas an identification attribute would indicate source of the table, such as the observatory, instrument, organization, and data type. When the identification attributes are collected and used as the basis of a search engine, a user can constrain on an attribute, the archive can then self-organize around the constraint, presenting the user with a particular view of the archive. In an archive cooperative where each participating data system or archive may have its own metadata standards, providing a multi-system search engine requires that individual archive metadata be mapped to a broad based standard. To explore how cooperative archives can form a larger self-organized archive we will show how the Space Physics Archive Search and Extract (SPASE) data model will allow different systems to create a cooperative and will use Planetary Data System (PDS) plus existing space physics activities as a demonstration.

Mapping the global land surface using 1 km AVHRR data

Science.gov (United States)

Lauer, D.T.; Eidenshink, J.C.

1998-01-01

The scientific requirements for mapping the global land surface using 1 km advanced very high resolution radiometer (AVHRR) data have been set forth by the U.S. Global Change Research Program; the International Geosphere Biosphere Programme (IGBP); The United Nations; the National Oceanic and Atmospheric Administration (NOAA); the Committee on Earth Observations Satellites; and the National Aeronautics and Space Administration (NASA) mission to planet Earth (MTPE) program. Mapping the global land surface using 1 km AVHRR data is an international effort to acquire, archive, process, and distribute 1 km AVHRR data to meet the needs of the international science community. A network of AVHRR receiving stations, along with data recorded by NOAA, has been acquiring daily global land coverage since April 1, 1992. A data set of over 70,000 AVHRR images is archived and distributed by the United States Geological Survey (USGS) EROS Data Center, and the European Space Agency. Under the guidance of the IGBP, processing standards have been developed for calibration, atmospheric correction, geometric registration, and the production of global 10-day maximum normalized difference vegetation index (NDVI) composites. The major uses of the composites are for the study of surface vegetation condition, mapping land cover, and deriving biophysical characteristics of terrestrial ecosystems. A time-series of 54 10-day global vegetation index composites for the period of April 1, 1992 through September 1993 has been produced. The production of a time-series of 33 10-day global vegetation index composites using NOAA-14 data for the period of February 1, 1995 through December 31, 1995 is underway. The data products are available from the USGS, in cooperation with NASA's MTPE program and other international organizations.

Method for Pre-Conditioning a Measured Surface Height Map for Model Validation

Science.gov (United States)

Sidick, Erkin

2012-01-01

This software allows one to up-sample or down-sample a measured surface map for model validation, not only without introducing any re-sampling errors, but also eliminating the existing measurement noise and measurement errors. Because the re-sampling of a surface map is accomplished based on the analytical expressions of Zernike-polynomials and a power spectral density model, such re-sampling does not introduce any aliasing and interpolation errors as is done by the conventional interpolation and FFT-based (fast-Fourier-transform-based) spatial-filtering method. Also, this new method automatically eliminates the measurement noise and other measurement errors such as artificial discontinuity. The developmental cycle of an optical system, such as a space telescope, includes, but is not limited to, the following two steps: (1) deriving requirements or specs on the optical quality of individual optics before they are fabricated through optical modeling and simulations, and (2) validating the optical model using the measured surface height maps after all optics are fabricated. There are a number of computational issues related to model validation, one of which is the "pre-conditioning" or pre-processing of the measured surface maps before using them in a model validation software tool. This software addresses the following issues: (1) up- or down-sampling a measured surface map to match it with the gridded data format of a model validation tool, and (2) eliminating the surface measurement noise or measurement errors such that the resulted surface height map is continuous or smoothly-varying. So far, the preferred method used for re-sampling a surface map is two-dimensional interpolation. The main problem of this method is that the same pixel can take different values when the method of interpolation is changed among the different methods such as the "nearest," "linear," "cubic," and "spline" fitting in Matlab. The conventional, FFT-based spatial filtering method used to

Self-organization phenomena in plasma physics

International Nuclear Information System (INIS)

Sanduloviciu, M.; Popescu, S.

2001-01-01

The self-assembling in nature and laboratory of structures in systems away from thermodynamic equilibrium is one of the problems that mostly fascinates the scientists working in all branches of science. In this context a substantial progress has been obtained by investigating the appearance of spatial and spatiotemporal patterns in plasma. These experiments revealed the presence of a scenario of self-organization able to suggest an answer to the central problem of the 'Science of Complexity', why matter transits spontaneously from a disordered into an ordered state? Based on this scenario of self-organization we present arguments proving the possibility to explain the challenging problems of nonequilibrium physics in general. These problems refer to: (i) genuine origin of phase transitions observed in gaseous conductors and semiconductors; (ii) the elucidation of the role played by self-organization in the simulation of oscillations; (iii) the physical basis of anomalous transport of matter and energy with special reference to the possibilities of improving the economical performance of fusion devices; (iv) the possibility to use self-confined gaseous space charged configurations as an alternative to the magnetically confined plasma used at present in fusion devices. In other branches of sciences, as for instance in Biology, the self-organization scenario reveals a new insight into a mechanism able to explain the appearance of the simplest possible space charge configuration able to evolve, under suitable conditions, into prebiotic structures. Referring to phenomena observed in nature, the same self-organization scenario suggests plausible answers to the appearance of ball lightening but also to the origin of the flickering phenomena observed in the light emission of the Sun and stars. For theory the described self-organization scenario offers a new physical basis for many problems of nonlinear science not solved yet and also a new model for the so-called 'self

Self-organizing map network-based precipitation regionalization for the Tibetan Plateau and regional precipitation variability

Science.gov (United States)

Wang, Nini; Yin, Jianchuan

2017-12-01

A precipitation-based regionalization for the Tibetan Plateau (TP) was investigated for regional precipitation trend analysis and frequency analysis using data from 1113 grid points covering the period 1900-2014. The results utilizing self-organizing map (SOM) network suggest that four clusters of precipitation coherent zones can be identified, including the southwestern edge, the southern edge, the southeastern region, and the north central region. Regionalization results of the SOM network satisfactorily represent the influences of the atmospheric circulation systems such as the East Asian summer monsoon, the south Asian summer monsoon, and the mid-latitude westerlies. Regionalization results also well display the direct impacts of physical geographical features of the TP such as orography, topography, and land-sea distribution. Regional-scale annual precipitation trend as well as regional differences of annual and seasonal total precipitation were investigated by precipitation index such as precipitation concentration index (PCI) and Standardized Anomaly Index (SAI). Results demonstrate significant negative long-term linear trends in southeastern TP and the north central part of the TP, indicating arid and semi-arid regions in the TP are getting drier. The empirical mode decomposition (EMD) method shows an evolution of the main cycle with 4 and 12 months for all the representative grids of four sub-regions. The cross-wavelet analysis suggests that predominant and effective period of Indian Ocean Dipole (IOD) on monthly precipitation is around ˜12 months, except for the representative grid of the northwestern region.

Self-Organization of Motor-Propelled Cytoskeletal Filaments at Topographically Defined Borders

Directory of Open Access Journals (Sweden)

Alf Månsson

2012-01-01

Full Text Available Self-organization phenomena are of critical importance in living organisms and of great interest to exploit in nanotechnology. Here we describe in vitro self-organization of molecular motor-propelled actin filaments, manifested as a tendency of the filaments to accumulate in high density close to topographically defined edges on nano- and microstructured surfaces. We hypothesized that this “edge-tracing” effect either (1 results from increased motor density along the guiding edges or (2 is a direct consequence of the asymmetric constraints on stochastic changes in filament sliding direction imposed by the edges. The latter hypothesis is well captured by a model explicitly defining the constraints of motility on structured surfaces in combination with Monte-Carlo simulations [cf. Nitta et al. (2006] of filament sliding. In support of hypothesis 2 we found that the model reproduced the edge tracing effect without the need to assume increased motor density at the edges. We then used model simulations to elucidate mechanistic details. The results are discussed in relation to nanotechnological applications and future experiments to test model predictions.

Surface morphology and dewettability of self-organized thermosets involving epoxy and POSS-capped poly(ethylene oxide) telechelics

International Nuclear Information System (INIS)

Wang, Lei; Zheng, Sixun

2012-01-01

A heptaphenyl polyhedral oligomeric silsesquioxane-capped poly(ethylene oxide) (POSS-capped PEO) telechelics was synthesized via the Huisgen 1,3-dipolar cycloaddition between 3-azidopropylheptaphenyl POSS and α,ω-dialkynyl-terminated poly(ethylene oxide). The organic–inorganic amphiphile was incorporated into epoxy to obtain the organic–inorganic nanocomposites. The morphology of the nanocomposites was investigated by means of atomic force microscopy (AFM) and dynamic mechanical thermal analysis (DMTA). It was found that the epoxy thermosets containing POSS-capped PEO telechelics were microphase-separated. The formation of the nanophases in the thermosets followed a self-assembly mechanism. The static contact angle measurements show that the nanocomposites displayed a significant enhancement in surface hydrophobicity as well as reduction in surface free energy. The improvement in surface dewettability was ascribed to the enrichment of POSS cages at the surface of the nanocomposites and the formation of the specific surface morphology as evidenced by X-ray photoelectron spectroscopy (XPS) and surface atomic force microscopy (AFM). -- Highlights: ► POSS-capped PEO telechelics was synthesized via click chemistry approach. ► The organic–inorganic amphiphile can be self-assembled into the nanophases in epoxy. ► The hybrid nanocomposites were successfully prepared via a self-assembly approach. ► The nanocomposites displayed a significant enhancement in surface hydrophobicity.

Self-organizing feature map (neural networks) as a tool to select the best indicator of road traffic pollution (soil, leaves or bark of Robinia pseudoacacia L.).

Science.gov (United States)

Samecka-Cymerman, A; Stankiewicz, A; Kolon, K; Kempers, A J

2009-07-01

Concentrations of the elements Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn were measured in the leaves and bark of Robinia pseudoacacia and the soil in which it grew, in the town of Oleśnica (SW Poland) and at a control site. We selected this town because emission from motor vehicles is practically the only source of air pollution, and it seemed interesting to evaluate its influence on soil and plants. The self-organizing feature map (SOFM) yielded distinct groups of soils and R. pseudoacacia leaves and bark, depending on traffic intensity. Only the map classifying bark samples identified an additional group of highly polluted sites along the main highway from Wrocław to Warszawa. The bark of R. pseudoacacia seems to be a better bioindicator of long-term cumulative traffic pollution in the investigated area, while leaves are good indicators of short-term seasonal accumulation trends.

Unprecedented Self-Organized Monolayer of a Ru(II) Complex by Diazonium Electroreduction.

Science.gov (United States)

Nguyen, Van Quynh; Sun, Xiaonan; Lafolet, Frédéric; Audibert, Jean-Frédéric; Miomandre, Fabien; Lemercier, Gilles; Loiseau, Frédérique; Lacroix, Jean-Christophe

2016-08-03

A new heteroleptic polypyridyle Ru(II) complex was synthesized and deposited on surface by the diazonium electroreduction process. It yields to the covalent grafting of a monolayer. The functionalized surface was characterized by XPS, electrochemistry, AFM, and STM. A precise organization of the molecules within the monolayer is observed with parallel linear stripes separated by a distance of 3.8 nm corresponding to the lateral size of the molecule. Such organization suggests a strong cooperative process in the deposition process. This strategy is an original way to obtain well-controlled and stable functionalized surfaces for potential applications related to the photophysical properties of the grafted chromophore. As an exciting result, it is the first example of a self-organized monolayer (SOM) obtained using diazonium electroreduction.

Usage of self-organizing neural networks in evaluation of consumer behaviour

Directory of Open Access Journals (Sweden)

Jana Weinlichová

2010-01-01

Full Text Available This article deals with evaluation of consumer data by Artificial Intelligence methods. In methodical part there are described learning algorithms for Kohonen maps on the principle of supervised learning, unsupervised learning and semi-supervised learning. The principles of supervised learning and unsupervised learning are compared. On base of binding conditions of these principles there is pointed out an advantage of semi-supervised learning. Three algorithms are described for the semi-supervised learning: label propagation, self-training and co-training. Especially usage of co-training in Kohonen map learning seems to be promising point of other research. In concrete application of Kohonen neural network on consumer’s expense the unsupervised learning method has been chosen – the self-organization. So the features of data are evaluated by clustering method called Kohonen maps. These input data represents consumer expenses of households in countries of European union and are characterised by 12-dimension vector according to commodity classification. The data are evaluated in several years, so we can see their distribution, similarity or dissimilarity and also their evolution. In the article we discus other usage of this method for this type of data and also comparison of our results with results reached by hierarchical cluster analysis.

Rational design of organic semiconductors for texture control and self-patterning on halogenated surfaces

KAUST Repository

Ward, Jeremy W.; Li, Ruipeng; Obaid, Abdulmalik; Payne, Marcia M.; Smilgies, Detlef Matthias; Anthony, John Edward; Amassian, Aram; Jurchescu, Oana D.

2014-01-01

new materials. Here, the influence of the interactions at the interface between a halogenated organic semiconductor (OSC) thin film and a halogenated self-assembled monolayer on the formation of the crystalline texture directly affecting

Self-organized pattern formation of biomolecules at silicon surfaces: Intended application of a dislocation network

International Nuclear Information System (INIS)

Kittler, M.; Yu, X.; Vyvenko, O.F.; Birkholz, M.; Seifert, W.; Reiche, M.; Wilhelm, T.; Arguirov, T.; Wolff, A.; Fritzsche, W.; Seibt, M.

2006-01-01

Defined placement of biomolecules at Si surfaces is a precondition for a successful combination of Si electronics with biological applications. We aim to realize this by Coulomb interaction of biomolecules with dislocations in Si. The dislocations form charged lines and they will be surrounded with a space charge region being connected with an electric field. The electric stray field in a solution of biomolecules, caused by dislocations located close to the Si surface, was estimated to yield values up to few kVcm -1 . A regular dislocation network can be formed by wafer direct bonding at the interface between the bonded wafers in case of misorientation. The adjustment of misorientation allows the variation of the distance between dislocations in a range from 10 nm to a few μm. This is appropriate for nanobiotechnology dealing with protein or DNA molecules with sizes in the nm and lower μm range. Actually, we achieved a distance between the dislocations of 10-20 nm. Also the existence of a distinct electric field formed by the dislocation network was demonstrated by the technique of the electron-beam-induced current (EBIC). Because of the relatively short range of the field, the dislocations have to be placed close to the surface. We positioned the dislocation network in an interface being 200 nm parallel to the Si surface by layer transfer techniques using hydrogen implantation and bonding. Based on EBIC and luminescence data we postulate a barrier of the dislocations at the as bonded interface < 100 meV. We plan to dope the dislocations with metal atoms to increase the electric field. We demonstrated that regular periodic dislocation networks close to the Si surface formed by bonding are realistic candidates for self-organized placing of biomolecules. Experiments are underway to test whether biomolecules decorate the pattern of the dislocation lines

Filtering Color Mapped Textures and Surfaces

OpenAIRE

Heitz , Eric; Nowrouzezahrai , Derek; Poulin , Pierre; Neyret , Fabrice

2013-01-01

International audience; Color map textures applied directly to surfaces, to geometric microsurface details, or to procedural functions (such as noise), are commonly used to enhance visual detail. Their simplicity and ability to mimic a wide range of realistic appearances have led to their adoption in many rendering problems. As with any textured or geometric detail, proper filtering is needed to reduce aliasing when viewed across a range of distances, but accurate and efficient color map filt...

Non-Taylor magnetohydrodynamic self-organization

International Nuclear Information System (INIS)

Zhu, Shao-ping; Horiuchi, Ritoku; Sato, Tetsuya.

1994-10-01

A self-organization process in a plasma with a finite pressure is investigated by means of a three-dimensional magnetohydrodynamic simulation. It is demonstrated that a non-Taylor finite β self-organized state is realized in which a perpendicular component of the electric current is generated and the force-free(parallel) current decreases until they reach to almost the same level. The self-organized state is described by an MHD force-balance relation, namely, j perpendicular = B x ∇p/B·B and j parallel = μB where μ is not a constant, and the pressure structure resembles the structure of the toroidal magnetic field intensity. Unless an anomalous perpendicular thermal conduction arises, the plasma cannot relax to a Taylor state but to a non-Taylor (non-force-free) self-organized state. This state becomes more prominent for a weaker resistivity condition. The non-Taylor state has a rather universal property, for example, independence of the initial β value. Another remarkable finding is that the Taylor's conjecture of helicity conservation is, in a strict sense, not valid. The helicity dissipation occurs and its rate slows down critically in accordance with the stepwise relaxation of the magnetic energy. It is confirmed that the driven magnetic reconnection caused by the nonlinearly excited plasma kink flows plays the leading role in all of these key features of the non-Taylor self-organization. (author)

Investigation of Supramolecular Coordination Self-Assembly and Polymerization Confined on Metal Surfaces Using Scanning Tunneling Microscopy

Science.gov (United States)

Lin, Tao

Organic molecules are envisioned as the building blocks for design and fabrication of functional devices in future, owing to their versatility, low cost and flexibility. Although some devices such as organic light-emitting diode (OLED) have been already applied in our daily lives, the field is still in its infancy and numerous challenges still remain. In particular, fundamental understanding of the process of organic material fabrication at a molecular level is highly desirable. This thesis focuses on the design and fabrication of supramolecular and macromolecular nanostructures on a Au(111) surface through self-assembly, polymerization and a combination of two. We used scanning tunneling microscopy (STM) as an experimental tool and Monte Carlo (MC) and kinetic Monte Carlo (KMC) simulations as theoretical tools to characterize the structures of these systems and to investigate the mechanisms of the self-assembly and polymerization processes at a single-molecular level. The results of this thesis consist of four parts as below: Part I addresses the mechanisms of two-dimensional multicomponent supramolecular self-assembly via pyridyl-Fe-terpyridyl coordination. Firstly, we studied four types of self-assembled metal-organic systems exhibiting different dimensionalities using specifically-designed molecular building blocks. We found that the two-dimensional system is under thermodynamic controls while the systems of lower dimension are under kinetic controls. Secondly, we studied the self-assembly of a series of cyclic supramolecular polygons. Our results indicate that the yield of on-surface cyclic polygon structures is very low independent of temperature and concentration and this phenomenon can be attributed to a subtle competition between kinetic and thermodynamic controls. These results shed light on thermodynamic and kinetic controls in on-surface coordination self-assembly. Part II addresses the two-dimensional supramolecular self-assembly of porphyrin

Self-organized neural network for the quality control of 12-lead ECG signals

International Nuclear Information System (INIS)

Chen, Yun; Yang, Hui

2012-01-01

Telemedicine is very important for the timely delivery of health care to cardiovascular patients, especially those who live in the rural areas of developing countries. However, there are a number of uncertainty factors inherent to the mobile-phone-based recording of electrocardiogram (ECG) signals such as personnel with minimal training and other extraneous noises. PhysioNet organized a challenge in 2011 to develop efficient algorithms that can assess the ECG signal quality in telemedicine settings. This paper presents our efforts in this challenge to integrate multiscale recurrence analysis with a self-organizing map for controlling the ECG signal quality. As opposed to directly evaluating the 12-lead ECG, we utilize an information-preserving transform, i.e. Dower transform, to derive the 3-lead vectorcardiogram (VCG) from the 12-lead ECG in the first place. Secondly, we delineate the nonlinear and nonstationary characteristics underlying the 3-lead VCG signals into multiple time-frequency scales. Furthermore, a self-organizing map is trained, in both supervised and unsupervised ways, to identify the correlations between signal quality and multiscale recurrence features. The efficacy and robustness of this approach are validated using real-world ECG recordings available from PhysioNet. The average performance was demonstrated to be 95.25% for the training dataset and 90.0% for the independent test dataset with unknown labels. (paper)

Modelling and mapping the topsoil organic carbon content for Tanzania

Science.gov (United States)

Kempen, Bas; Kaaya, Abel; Ngonyani Mhaiki, Consolatha; Kiluvia, Shani; Ruiperez-Gonzalez, Maria; Batjes, Niels; Dalsgaard, Soren

2014-05-01

Soil organic carbon (SOC), held in soil organic matter, is a key indicator of soil health and plays an important role in the global carbon cycle. The soil can act as a net source or sink of carbon depending on land use and management. Deforestation and forest degradation lead to the release of vast amounts of carbon from the soil in the form of greenhouse gasses, especially in tropical countries. Tanzania has a high deforestation rate: it is estimated that the country loses 1.1% of its total forested area annually. During 2010-2013 Tanzania has been a pilot country under the UN-REDD programme. This programme has supported Tanzania in its initial efforts towards reducing greenhouse gas emission from forest degradation and deforestation and towards preserving soil carbon stocks. Formulation and implementation of the national REDD strategy requires detailed information on the five carbon pools among these the SOC pool. The spatial distribution of SOC contents and stocks was not available for Tanzania. The initial aim of this research, was therefore to develop high-resolution maps of the SOC content for the country. The mapping exercise was carried out in a collaborative effort with four Tanzanian institutes and data from the Africa Soil Information Service initiative (AfSIS). The mapping exercise was provided with over 3200 field observations on SOC from four sources; this is the most comprehensive soil dataset collected in Tanzania so far. The main source of soil samples was the National Forest Monitoring and Assessment (NAFORMA). The carbon maps were generated by means of digital soil mapping using regression-kriging. Maps at 250 m spatial resolution were developed for four depth layers: 0-10 cm, 10-20 cm, 20-30 cm, and 0-30 cm. A total of 37 environmental GIS data layers were prepared for use as covariates in the regression model. These included vegetation indices, terrain parameters, surface temperature, spectral reflectances, a land cover map and a small

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

Science.gov (United States)

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

2014-06-01

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

Unraveling atomic-level self-organization at the plasma-material interface

Science.gov (United States)

Allain, J. P.; Shetty, A.

2017-07-01

The intrinsic dynamic interactions at the plasma-material interface and critical role of irradiation-driven mechanisms at the atomic scale during exposure to energetic particles require a priori the use of in situ surface characterization techniques. Characterization of ‘active’ surfaces during modification at atomic-scale levels is becoming more important as advances in processing modalities are limited by an understanding of the behavior of these surfaces under realistic environmental conditions. Self-organization from exposure to non-equilibrium and thermalized plasmas enable dramatic control of surface morphology, topography, composition, chemistry and structure yielding the ability to tune material properties with an unprecedented level of control. Deciphering self-organization mechanisms of nanoscale morphology (e.g. nanodots, ripples) and composition on a variety of materials including: compound semiconductors, semiconductors, ceramics, polymers and polycrystalline metals via low-energy ion-beam assisted plasma irradiation are critical to manipulate functionality in nanostructured systems. By operating at ultra-low energies near the damage threshold, irradiation-driven defect engineering can be optimized and surface-driven mechanisms controlled. Tunability of optical, electronic, magnetic and bioactive properties is realized by reaching metastable phases controlled by atomic-scale irradiation-driven mechanisms elucidated by novel in situ diagnosis coupled to atomistic-level computational tools. Emphasis will be made on tailored surface modification from plasma-enhanced environments on particle-surface interactions and their subsequent modification of hard and soft matter interfaces. In this review, we examine current trends towards in situ and in operando surface and sub-surface characterization to unravel atomic-scale mechanisms at the plasma-material interface. This work will emphasize on recent advances in the field of plasma and ion

Novel alternating polymer adsorption/surface activation self-assembled film based on hydrogen bond

Energy Technology Data Exchange (ETDEWEB)

Zhang Yongjun; Yang Shuguang; Guan Ying; Miao Xiaopeng; Cao Weixiao; Xu Jian

2003-08-01

By combining hydrogen bonding layer-by-layer self-assembly and the stepwise chemisorption method, a new alternating polymer adsorption/surface activation self-assembly method was developed. First a layer of diphenylamine-4-diazonium-formaldehyde resin (diazo resin or DR) is deposited on a substrate. In the following surface activation step, the diazonium groups on the surface couple with resorcin in the outside solution. The deposition of another layer of DR is feasible due to the formation of hydrogen bond between the diazonium group of DR and the hydroxy group of the resorcin moieties. The resulting film is photosensitive. After UV irradiation, the film becomes very stable towards polar organic solvents.

Self-organizing neural networks for automatic detection and classification of contrast-enhancing lesions in dynamic MR-mammography; Selbstorganisierende neuronale Netze zur automatischen Detektion und Klassifikation von Kontrast(mittel)-verstaerkten Laesionen in der dynamischen MR-Mammographie

Energy Technology Data Exchange (ETDEWEB)

Vomweg, T.W.; Teifke, A.; Kauczor, H.U.; Achenbach, T.; Rieker, O.; Schreiber, W.G.; Heitmann, K.R.; Beier, T.; Thelen, M. [Klinik und Poliklinik fuer Radiologie, Klinikum der Univ. Mainz (Germany)

2005-05-01

Purpose: Investigation and statistical evaluation of 'Self-Organizing Maps', a special type of neural networks in the field of artificial intelligence, classifying contrast enhancing lesions in dynamic MR-mammography. Material and Methods: 176 investigations with proven histology after core biopsy or operation were randomly divided into two groups. Several Self-Organizing Maps were trained by investigations of the first group to detect and classify contrast enhancing lesions in dynamic MR-mammography. Each single pixel's signal/time curve of all patients within the second group was analyzed by the Self-Organizing Maps. The likelihood of malignancy was visualized by color overlays on the MR-images. At last assessment of contrast-enhancing lesions by each different network was rated visually and evaluated statistically. Results: A well balanced neural network achieved a sensitivity of 90.5% and a specificity of 72.2% in predicting malignancy of 88 enhancing lesions. Detailed analysis of false-positive results revealed that every second fibroadenoma showed a 'typical malignant' signal/time curve without any chance to differentiate between fibroadenomas and malignant tissue regarding contrast enhancement alone; but this special group of lesions was represented by a well-defined area of the Self-Organizing Map. Discussion: Self-Organizing Maps are capable of classifying a dynamic signal/time curve as 'typical benign' or 'typical malignant'. Therefore, they can be used as second opinion. In view of the now known localization of fibroadenomas enhancing like malignant tumors at the Self-Organizing Map, these lesions could be passed to further analysis by additional post-processing elements (e.g., based on T2-weighted series or morphology analysis) in the future. (orig.)

Self-organized internal architectures of chiral micro-particles

International Nuclear Information System (INIS)

Provenzano, Clementina; Mazzulla, Alfredo; Desiderio, Giovanni; Pagliusi, Pasquale; De Santo, Maria P.; Cipparrone, Gabriella; Perrotta, Ida

2014-01-01

The internal architecture of polymeric self-assembled chiral micro-particles is studied by exploring the effect of the chirality, of the particle sizes, and of the interface/surface properties in the ordering of the helicoidal planes. The experimental investigations, performed by means of different microscopy techniques, show that the polymeric beads, resulting from light induced polymerization of cholesteric liquid crystal droplets, preserve both the spherical shape and the internal self-organized structures. The method used to create the micro-particles with controlled internal chiral architectures presents great flexibility providing several advantages connected to the acquired optical and photonics capabilities and allowing to envisage novel strategies for the development of chiral colloidal systems and materials

Self-organizing feature map (neural networks) as a tool to select the best indicator of road traffic pollution (soil, leaves or bark of Robinia pseudoacacia L.)

Energy Technology Data Exchange (ETDEWEB)

Samecka-Cymerman, A., E-mail: sameckaa@biol.uni.wroc.p [Department of Ecology, Biogeochemistry and Environmental Protection, Wroclaw University, ul. Kanonia 6/8, 50-328 Wroclaw (Poland); Stankiewicz, A.; Kolon, K. [Department of Ecology, Biogeochemistry and Environmental Protection, Wroclaw University, ul. Kanonia 6/8, 50-328 Wroclaw (Poland); Kempers, A.J. [Department of Environmental Sciences, Radboud University of Nijmegen, Toernooiveld, 6525 ED Nijmegen (Netherlands)

2009-07-15

Concentrations of the elements Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn were measured in the leaves and bark of Robinia pseudoacacia and the soil in which it grew, in the town of Olesnica (SW Poland) and at a control site. We selected this town because emission from motor vehicles is practically the only source of air pollution, and it seemed interesting to evaluate its influence on soil and plants. The self-organizing feature map (SOFM) yielded distinct groups of soils and R. pseudoacacia leaves and bark, depending on traffic intensity. Only the map classifying bark samples identified an additional group of highly polluted sites along the main highway from Wroclaw to Warszawa. The bark of R. pseudoacacia seems to be a better bioindicator of long-term cumulative traffic pollution in the investigated area, while leaves are good indicators of short-term seasonal accumulation trends. - Once trained, SOFM could be used in the future to recognize types of pollution.

Mapping Soil Organic Matter with Hyperspectral Imaging

Science.gov (United States)

Moni, Christophe; Burud, Ingunn; Flø, Andreas; Rasse, Daniel

2014-05-01

Soil organic matter (SOM) plays a central role for both food security and the global environment. Soil organic matter is the 'glue' that binds soil particles together, leading to positive effects on soil water and nutrient availability for plant growth and helping to counteract the effects of erosion, runoff, compaction and crusting. Hyperspectral measurements of samples of soil profiles have been conducted with the aim of mapping soil organic matter on a macroscopic scale (millimeters and centimeters). Two soil profiles have been selected from the same experimental site, one from a plot amended with biochar and another one from a control plot, with the specific objective to quantify and map the distribution of biochar in the amended profile. The soil profiles were of size (30 x 10 x 10) cm3 and were scanned with two pushbroomtype hyperspectral cameras, one which is sensitive in the visible wavelength region (400 - 1000 nm) and one in the near infrared region (1000 - 2500 nm). The images from the two detectors were merged together into one full dataset covering the whole wavelength region. Layers of 15 mm were removed from the 10 cm high sample such that a total of 7 hyperspectral images were obtained from the samples. Each layer was analyzed with multivariate statistical techniques in order to map the different components in the soil profile. Moreover, a 3-dimensional visalization of the components through the depth of the sample was also obtained by combining the hyperspectral images from all the layers. Mid-infrared spectroscopy of selected samples of the measured soil profiles was conducted in order to correlate the chemical constituents with the hyperspectral results. The results show that hyperspectral imaging is a fast, non-destructive technique, well suited to characterize soil profiles on a macroscopic scale and hence to map elements and different organic matter quality present in a complete pedon. As such, we were able to map and quantify biochar in our

Structural and electronic properties of single molecules and organic layers on surfaces

NARCIS (Netherlands)

Sotthewes, Kai

2016-01-01

Single molecules and organic layers on well-defined solid surfaces have attracted tremendous attention owing to their interesting physical and chemical properties. The ultimate utility of single molecules or self-assembled monolayers (SAMs) for potential applications is critically dependent on the

Visual data mining with self-organising maps for ventricular fibrillation analysis.

Science.gov (United States)

Rosado-Muñoz, Alfredo; Martínez-Martínez, José M; Escandell-Montero, Pablo; Soria-Olivas, Emilio

2013-08-01

Detection of ventricular fibrillation (VF) at an early stage is being deeply studied in order to lower the risk of sudden death and allows the specialist to have greater reaction time to give the patient a good recovering therapy. Some works are focusing on detecting VF based on numerical analysis of time-frequency distributions, but in general the methods used do not provide insight into the problem. However, this study proposes a new methodology in order to obtain information about this problem. This work uses a supervised self-organising map (SOM) to obtain visually information among four important groups of patients: VF (ventricular fibrillation), VT (ventricular tachycardia), HP (healthy patients) and AHR (other anomalous heart rates and noise). A total number of 27 variables were obtained from continuous surface ECG recordings in standard databases (MIT and AHA), providing information in the time, frequency, and time-frequency domains. self-organising maps (SOMs), trained with 11 of the 27 variables, were used to extract knowledge about the variable values for each group of patients. Results show that the SOM technique allows to determine the profile of each group of patients, assisting in gaining a deeper understanding of this clinical problem. Additionally, information about the most relevant variables is given by the SOM analysis. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Self-organization in irregular landscapes: Detecting autogenic interactions from field data using descriptive statistics and dynamical systems theory

Science.gov (United States)

Larsen, L.; Watts, D.; Khurana, A.; Anderson, J. L.; Xu, C.; Merritts, D. J.

2015-12-01

The classic signal of self-organization in nature is pattern formation. However, the interactions and feedbacks that organize depositional landscapes do not always result in regular or fractal patterns. How might we detect their existence and effects in these "irregular" landscapes? Emergent landscapes such as newly forming deltaic marshes or some restoration sites provide opportunities to study the autogenic processes that organize landscapes and their physical signatures. Here we describe a quest to understand autogenic vs. allogenic controls on landscape evolution in Big Spring Run, PA, a landscape undergoing restoration from bare-soil conditions to a target wet meadow landscape. The contemporary motivation for asking questions about autogenic vs. allogenic controls is to evaluate how important initial conditions or environmental controls may be for the attainment of management objectives. However, these questions can also inform interpretation of the sedimentary record by enabling researchers to separate signals that may have arisen through self-organization processes from those resulting from environmental perturbations. Over three years at Big Spring Run, we mapped the dynamic evolution of floodplain vegetation communities and distributions of abiotic variables and topography. We used principal component analysis and transition probability analysis to detect associative interactions between vegetation and geomorphic variables and convergent cross-mapping on lidar data to detect causal interactions between biomass and topography. Exploratory statistics revealed that plant communities with distinct morphologies exerted control on landscape evolution through stress divergence (i.e., channel initiation) and promoting the accumulation of fine sediment in channels. Together, these communities participated in a negative feedback that maintains low energy and multiple channels. Because of the spatially explicit nature of this feedback, causal interactions could not

Substrate morphology induced self-organization into carbon nanotube arrays, ropes, and agglomerates.

Science.gov (United States)

Huang, Jia-Qi; Zhang, Qiang; Xu, Guang-Hui; Qian, Wei-Zhong; Wei, Fei

2008-10-29

In this paper, hydrophobic carbon nanotube (CNT) arrays, ropes, and agglomerates were synthesized through self-organization on quartz substrates with different micro-structures under the same growth condition. On a flat substrate, a uniform woven structure was formed which resulted in a synchronous growth into an array. When the substrate with 10 µm round concaves distributed on the surface was adopted, the woven structure was sporadic and a CNT cluster was grown in the concave. With further growth, CNT ropes were self-organized. Subsequently, when the substrate consisting of irregular ∼100 nm gaps was used, the initial woven structure was high density, thus resulting in the formation of CNT agglomerates. Study results showed that CNT arrays grown on the flat substrate were of the highest purity and had a contact angle of 153.8 ± 0.9°. Thus, the self-organization behavior among CNTs was in situ modulated by different substrate morphology without further treatments. This provides us with an additional understanding of the self-organization of CNTs during growth, as well as strategies for the controllable synthesis of CNTs with fixed properties.

Substrate morphology induced self-organization into carbon nanotube arrays, ropes, and agglomerates

International Nuclear Information System (INIS)

Huang Jiaqi; Zhang Qiang; Xu Guanghui; Qian Weizhong; Wei Fei

2008-01-01

In this paper, hydrophobic carbon nanotube (CNT) arrays, ropes, and agglomerates were synthesized through self-organization on quartz substrates with different micro-structures under the same growth condition. On a flat substrate, a uniform woven structure was formed which resulted in a synchronous growth into an array. When the substrate with 10 μm round concaves distributed on the surface was adopted, the woven structure was sporadic and a CNT cluster was grown in the concave. With further growth, CNT ropes were self-organized. Subsequently, when the substrate consisting of irregular ∼100 nm gaps was used, the initial woven structure was high density, thus resulting in the formation of CNT agglomerates. Study results showed that CNT arrays grown on the flat substrate were of the highest purity and had a contact angle of 153.8 ± 0.9 0 . Thus, the self-organization behavior among CNTs was in situ modulated by different substrate morphology without further treatments. This provides us with an additional understanding of the self-organization of CNTs during growth, as well as strategies for the controllable synthesis of CNTs with fixed properties.

The dynamics of ant mosaics in tropical rainforests characterized using the Self-Organizing Map algorithm.

Science.gov (United States)

Dejean, Alain; Azémar, Frédéric; Céréghino, Régis; Leponce, Maurice; Corbara, Bruno; Orivel, Jérôme; Compin, Arthur

2016-08-01

Ants, the most abundant taxa among canopy-dwelling animals in tropical rainforests, are mostly represented by territorially dominant arboreal ants (TDAs) whose territories are distributed in a mosaic pattern (arboreal ant mosaics). Large TDA colonies regulate insect herbivores, with implications for forestry and agronomy. What generates these mosaics in vegetal formations, which are dynamic, still needs to be better understood. So, from empirical research based on 3 Cameroonian tree species (Lophira alata, Ochnaceae; Anthocleista vogelii, Gentianaceae; and Barteria fistulosa, Passifloraceae), we used the Self-Organizing Map (SOM, neural network) to illustrate the succession of TDAs as their host trees grow and age. The SOM separated the trees by species and by size for L. alata, which can reach 60 m in height and live several centuries. An ontogenic succession of TDAs from sapling to mature trees is shown, and some ecological traits are highlighted for certain TDAs. Also, because the SOM permits the analysis of data with many zeroes with no effect of outliers on the overall scatterplot distributions, we obtained ecological information on rare species. Finally, the SOM permitted us to show that functional groups cannot be selected at the genus level as congeneric species can have very different ecological niches, something particularly true for Crematogaster spp., which include a species specifically associated with B. fistulosa, nondominant species and TDAs. Therefore, the SOM permitted the complex relationships between TDAs and their growing host trees to be analyzed, while also providing new information on the ecological traits of the ant species involved. © 2015 Institute of Zoology, Chinese Academy of Sciences.

Mimosa Origami: A nanostructure-enabled directional self-organization regime of materials

Science.gov (United States)

Wong, William S. Y.; Li, Minfei; Nisbet, David R.; Craig, Vincent S. J.; Wang, Zuankai; Tricoli, Antonio

2016-01-01

One of the innate fundamentals of living systems is their ability to respond toward distinct stimuli by various self-organization behaviors. Despite extensive progress, the engineering of spontaneous motion in man-made inorganic materials still lacks the directionality and scale observed in nature. We report the directional self-organization of soft materials into three-dimensional geometries by the rapid propagation of a folding stimulus along a predetermined path. We engineer a unique Janus bilayer architecture with superior chemical and mechanical properties that enables the efficient transformation of surface energy into directional kinetic and elastic energies. This Janus bilayer can respond to pinpoint water stimuli by a rapid, several-centimeters-long self-assembly that is reminiscent of the Mimosa pudica’s leaflet folding. The Janus bilayers also shuttle water at flow rates up to two orders of magnitude higher than traditional wicking-based devices, reaching velocities of 8 cm/s and flow rates of 4.7 μl/s. This self-organization regime enables the ease of fabricating curved, bent, and split flexible channels with lengths greater than 10 cm, demonstrating immense potential for microfluidics, biosensors, and water purification applications. PMID:28861471

Single atom self-diffusion on nickel surfaces

International Nuclear Information System (INIS)

Tung, R.T.; Graham, W.R.

1980-01-01

Results of a field ion microscope study of single atom self-diffusion on Ni(311), (331), (110), (111) and (100) planes are presented, including detailed information on the self-diffusion parameters on (311), (331), and (110) surfaces, and activation energies for diffusion on the (111), and (100) surfaces. Evidence is presented for the existence of two types of adsorption site and surface site geometry for single nickel atoms on the (111) surface. The presence of adsorbed hydrogen on the (110), (311), and (331) surfaces is shown to lower the onset temperature for self-diffusion on these planes. (orig.)

Highly accurate surface maps from profilometer measurements

Science.gov (United States)

Medicus, Kate M.; Nelson, Jessica D.; Mandina, Mike P.

2013-04-01

Many aspheres and free-form optical surfaces are measured using a single line trace profilometer which is limiting because accurate 3D corrections are not possible with the single trace. We show a method to produce an accurate fully 2.5D surface height map when measuring a surface with a profilometer using only 6 traces and without expensive hardware. The 6 traces are taken at varying angular positions of the lens, rotating the part between each trace. The output height map contains low form error only, the first 36 Zernikes. The accuracy of the height map is ±10% of the actual Zernike values and within ±3% of the actual peak to valley number. The calculated Zernike values are affected by errors in the angular positioning, by the centering of the lens, and to a small effect, choices made in the processing algorithm. We have found that the angular positioning of the part should be better than 1?, which is achievable with typical hardware. The centering of the lens is essential to achieving accurate measurements. The part must be centered to within 0.5% of the diameter to achieve accurate results. This value is achievable with care, with an indicator, but the part must be edged to a clean diameter.

Reproducibility of crop surface maps extracted from Unmanned Aerial Vehicle (UAV) derived digital surface maps

KAUST Repository

Parkes, Stephen

2016-10-25

Crop height measured from UAVs fitted with commercially available RGB cameras provide an affordable alternative to retrieve field scale high resolution estimates. The study presents an assessment of between flight reproducibility of Crop Surface Maps (CSM) extracted from Digital Surface Maps (DSM) generated by Structure from Motion (SfM) algorithms. Flights were conducted over a centre pivot irrigation system covered with an alfalfa crop. An important step in calculating the absolute crop height from the UAV derived DSM is determining the height of the underlying terrain. Here we use automatic thresholding techniques applied to RGB vegetation index maps to classify vegetated and soil pixels. From interpolation of classified soil pixels, a terrain map is calculated and subtracted from the DSM. The influence of three different thresholding techniques on CSMs are investigated. Median Alfalfa crop heights determined with the different thresholding methods varied from 18cm for K means thresholding to 13cm for Otsu thresholding methods. Otsu thresholding also gave the smallest range of crop heights and K means thresholding the largest. Reproducibility of median crop heights between flight surveys was 4-6cm for all thresholding techniques. For the flight conducted later in the afternoon shadowing caused soil pixels to be classified as vegetation in key locations around the domain, leading to lower crop height estimates. The range of crop heights was similar for both flights using K means thresholding (35-36cm), local minimum thresholding depended on whether raw or normalised RGB intensities were used to calculate vegetation indices (30-35cm), while Otsu thresholding had a smaller range of heights and varied most between flights (26-30cm). This study showed that crop heights from multiple survey flights are comparable, however, they were dependent on the thresholding method applied to classify soil pixels and the time of day the flight was conducted.

Reproducibility of crop surface maps extracted from Unmanned Aerial Vehicle (UAV) derived digital surface maps

KAUST Repository

Parkes, Stephen; McCabe, Matthew; Al-Mashhawari, Samir K.; Rosas, Jorge

2016-01-01

Crop height measured from UAVs fitted with commercially available RGB cameras provide an affordable alternative to retrieve field scale high resolution estimates. The study presents an assessment of between flight reproducibility of Crop Surface Maps (CSM) extracted from Digital Surface Maps (DSM) generated by Structure from Motion (SfM) algorithms. Flights were conducted over a centre pivot irrigation system covered with an alfalfa crop. An important step in calculating the absolute crop height from the UAV derived DSM is determining the height of the underlying terrain. Here we use automatic thresholding techniques applied to RGB vegetation index maps to classify vegetated and soil pixels. From interpolation of classified soil pixels, a terrain map is calculated and subtracted from the DSM. The influence of three different thresholding techniques on CSMs are investigated. Median Alfalfa crop heights determined with the different thresholding methods varied from 18cm for K means thresholding to 13cm for Otsu thresholding methods. Otsu thresholding also gave the smallest range of crop heights and K means thresholding the largest. Reproducibility of median crop heights between flight surveys was 4-6cm for all thresholding techniques. For the flight conducted later in the afternoon shadowing caused soil pixels to be classified as vegetation in key locations around the domain, leading to lower crop height estimates. The range of crop heights was similar for both flights using K means thresholding (35-36cm), local minimum thresholding depended on whether raw or normalised RGB intensities were used to calculate vegetation indices (30-35cm), while Otsu thresholding had a smaller range of heights and varied most between flights (26-30cm). This study showed that crop heights from multiple survey flights are comparable, however, they were dependent on the thresholding method applied to classify soil pixels and the time of day the flight was conducted.

Multi-channel Analysis of Passive Surface Waves (MAPS)

Science.gov (United States)

Xia, J.; Cheng, F. Mr; Xu, Z.; Wang, L.; Shen, C.; Liu, R.; Pan, Y.; Mi, B.; Hu, Y.

2017-12-01

Urbanization is an inevitable trend in modernization of human society. In the end of 2013 the Chinese Central Government launched a national urbanization plan—"Three 100 Million People", which aggressively and steadily pushes forward urbanization. Based on the plan, by 2020, approximately 100 million people from rural areas will permanently settle in towns, dwelling conditions of about 100 million people in towns and villages will be improved, and about 100 million people in the central and western China will permanently settle in towns. China's urbanization process will run at the highest speed in the urbanization history of China. Environmentally friendly, non-destructive and non-invasive geophysical assessment method has played an important role in the urbanization process in China. Because human noise and electromagnetic field due to industrial life, geophysical methods already used in urban environments (gravity, magnetics, electricity, seismic) face great challenges. But humanity activity provides an effective source of passive seismic methods. Claerbout pointed out that wavefileds that are received at one point with excitation at the other point can be reconstructed by calculating the cross-correlation of noise records at two surface points. Based on this idea (cross-correlation of two noise records) and the virtual source method, we proposed Multi-channel Analysis of Passive Surface Waves (MAPS). MAPS mainly uses traffic noise recorded with a linear receiver array. Because Multi-channel Analysis of Surface Waves can produces a shear (S) wave velocity model with high resolution in shallow part of the model, MPAS combines acquisition and processing of active source and passive source data in a same flow, which does not require to distinguish them. MAPS is also of ability of real-time quality control of noise recording that is important for near-surface applications in urban environment. The numerical and real-world examples demonstrated that MAPS can be

Self-assembly of nanosize coordination cages on si(100) surfaces.

Science.gov (United States)

Busi, Marco; Laurenti, Marco; Condorelli, Guglielmo G; Motta, Alessandro; Favazza, Maria; Fragalà, Ignazio L; Montalti, Marco; Prodi, Luca; Dalcanale, Enrico

2007-01-01

Bottom-up fabrication of 3D organic nanostructures on Si(100) surfaces has been achieved by a two-step procedure. Tetradentate cavitand 1 was grafted on the Si surface together with 1-octene (Oct) as a spatial spectator by photochemical hydrosilylation. Ligand exchange between grafted cavitand 1 and self-assembled homocage 2, derived from cavitand 5 bearing a fluorescence marker, led to the formation of coordination cages on Si(100). Formation, quantification, and distribution of the nanoscale molecular containers on a silicon surface was assessed by using three complementary analytical techniques (AFM, XPS, and fluorescence) and validated by control experiments on cavitand-free silicon surfaces. Interestingly, the fluorescence of pyrene at approximately 4 nm above the Si(100) surface can be clearly observed.

Laser Control of Self-Organization Process in Microscopic Region and Fabrication of Fine Microporous Structure

OpenAIRE

Matsumura, Yukimasa; Inami, Wataru; Kawata, Yoshimasa

2012-01-01

We present a controlling technique of microporous structure by laser irradiation during self-organization process. Self-organization process is fabrication method of microstructure. Polymer solution was dropped on the substrate at high humid condition. Water in air appears dropping air temperature below the dew point. The honeycomb structure with regularly aligned pores on the film was fabricated by attaching water droplets onto the solution surface. We demonstrate that it was possible to pre...

Tracking senescence-induced patterns in leaf litter leachate using parallel factor analysis (PARAFAC) modeling and self-organizing maps

Science.gov (United States)

Wheeler, K. I.; Levia, D. F.; Hudson, J. E.

2017-09-01

In autumn, the dissolved organic matter (DOM) contribution of leaf litter leachate to streams in forested watersheds changes as trees undergo resorption, senescence, and leaf abscission. Despite its biogeochemical importance, little work has investigated how leaf litter leachate DOM changes throughout autumn and how any changes might differ interspecifically and intraspecifically. Since climate change is expected to cause vegetation migration, it is necessary to learn how changes in forest composition could affect DOM inputs via leaf litter leachate. We examined changes in leaf litter leachate fluorescent DOM (FDOM) from American beech (Fagus grandifolia Ehrh.) leaves in Maryland, Rhode Island, Vermont, and North Carolina and from yellow poplar (Liriodendron tulipifera L.) leaves from Maryland. FDOM in leachate samples was characterized by excitation-emission matrices (EEMs). A six-component parallel factor analysis (PARAFAC) model was created to identify components that accounted for the majority of the variation in the data set. Self-organizing maps (SOM) compared the PARAFAC component proportions of leachate samples. Phenophase and species exerted much stronger influence on the determination of a sample's SOM placement than geographic origin. As expected, FDOM from all trees transitioned from more protein-like components to more humic-like components with senescence. Percent greenness of sampled leaves and the proportion of tyrosine-like component 1 were found to be significantly different between the two genetic beech clusters, suggesting differences in photosynthesis and resorption. Our results highlight the need to account for interspecific and intraspecific variations in leaf litter leachate FDOM throughout autumn when examining the influence of allochthonous inputs to streams.

Functionalization of silicon nanowire surfaces with metal-organic frameworks

KAUST Repository

Liu, Nian

2011-12-28

Metal-organic frameworks (MOFs) and silicon nanowires (SiNWs) have been extensively studied due to their unique properties; MOFs have high porosity and specific surface area with well-defined nanoporous structure, while SiNWs have valuable one-dimensional electronic properties. Integration of the two materials into one composite could synergistically combine the advantages of both materials and lead to new applications. We report the first example of a MOF synthesized on surface-modified SiNWs. The synthesis of polycrystalline MOF-199 (also known as HKUST-1) on SiNWs was performed at room temperature using a step-by-step (SBS) approach, and X-ray photoelectron spectroscopy, X-ray diffraction, scanning electron microscopy, transmission electron microscopy, and energy dispersive spectroscopy elemental mapping were used to characterize the material. Matching of the SiNW surface functional groups with the MOF organic linker coordinating groups was found to be critical for the growth. Additionally, the MOF morphology can by tuned by changing the soaking time, synthesis temperature and precursor solution concentration. This SiNW/MOF hybrid structure opens new avenues for rational design of materials with novel functionalities. © 2011 Tsinghua University Press and Springer-Verlag Berlin Heidelberg.

Surface mineral maps of Afghanistan derived from HyMap imaging spectrometer data, version 2

Science.gov (United States)

Kokaly, Raymond F.; King, Trude V.V.; Hoefen, Todd M.

2013-01-01

This report presents a new version of surface mineral maps derived from HyMap imaging spectrometer data collected over Afghanistan in the fall of 2007. This report also describes the processing steps applied to the imaging spectrometer data. The 218 individual flight lines composing the Afghanistan dataset, covering more than 438,000 square kilometers, were georeferenced to a mosaic of orthorectified Landsat images. The HyMap data were converted from radiance to reflectance using a radiative transfer program in combination with ground-calibration sites and a network of cross-cutting calibration flight lines. The U.S. Geological Survey Material Identification and Characterization Algorithm (MICA) was used to generate two thematic maps of surface minerals: a map of iron-bearing minerals and other materials, which have their primary absorption features at the shorter wavelengths of the reflected solar wavelength range, and a map of carbonates, phyllosilicates, sulfates, altered minerals, and other materials, which have their primary absorption features at the longer wavelengths of the reflected solar wavelength range. In contrast to the original version, version 2 of these maps is provided at full resolution of 23-meter pixel size. The thematic maps, MICA summary images, and the material fit and depth images are distributed in digital files linked to this report, in a format readable by remote sensing software and Geographic Information Systems (GIS). The digital files can be downloaded from http://pubs.usgs.gov/ds/787/downloads/.

Regional quantitative analysis of cortical surface maps of FDG PET images

CERN Document Server

Protas, H D; Hayashi, K M; Chin Lung, Yu; Bergsneider, M; Sung Cheng, Huang

2006-01-01

Cortical surface maps are advantageous for visualizing the 3D profile of cortical gray matter development and atrophy, and for integrating structural and functional images. In addition, cortical surface maps for PET data, when analyzed in conjunction with structural MRI data allow us to investigate, and correct for, partial volume effects. Here we compared quantitative regional PET values based on a 3D cortical surface modeling approach with values obtained directly from the 3D FDG PET images in various atlas-defined regions of interest (ROIs; temporal, parietal, frontal, and occipital lobes). FDG PET and 3D MR (SPGR) images were obtained and aligned to ICBM space for 15 normal subjects. Each image was further elastically warped in 2D parameter space of the cortical surface, to align major cortical sulci. For each point within a 15 mm distance of the cortex, the value of the PET intensity was averaged to give a cortical surface map of FDG uptake. The average PET values on the cortical surface map were calcula...

Complexity of a kind of interval continuous self-map of finite type

International Nuclear Information System (INIS)

Wang Lidong; Chu Zhenyan; Liao Gongfu

2011-01-01

Highlights: → We find the Hausdorff dimension for an interval continuous self-map f of finite type is s element of (0,1) on a non-wandering set. → f| Ω(f) has positive topological entropy. → f| Ω(f) is chaotic such as Devaney chaos, Kato chaos, two point distributional chaos and so on. - Abstract: An interval map is called finitely typal, if the restriction of the map to non-wandering set is topologically conjugate with a subshift of finite type. In this paper, we prove that there exists an interval continuous self-map of finite type such that the Hausdorff dimension is an arbitrary number in the interval (0, 1), discuss various chaotic properties of the map and the relations between chaotic set and the set of recurrent points.

Complexity of a kind of interval continuous self-map of finite type

Energy Technology Data Exchange (ETDEWEB)

Wang Lidong, E-mail: wld@dlnu.edu.cn [Institute of Mathematics, Dalian Nationalities University, Dalian 116600 (China); Institute of Mathematics, Jilin Normal University, Siping 136000 (China); Chu Zhenyan, E-mail: chuzhenyan8@163.com [Institute of Mathematics, Dalian Nationalities University, Dalian 116600 (China) and Institute of Mathematics, Jilin University, Changchun 130023 (China); Liao Gongfu, E-mail: liaogf@email.jlu.edu.cn [Institute of Mathematics, Jilin University, Changchun 130023 (China)

2011-10-15

Highlights: > We find the Hausdorff dimension for an interval continuous self-map f of finite type is s element of (0,1) on a non-wandering set. > f|{sub {Omega}(f)} has positive topological entropy. > f|{sub {Omega}(f)} is chaotic such as Devaney chaos, Kato chaos, two point distributional chaos and so on. - Abstract: An interval map is called finitely typal, if the restriction of the map to non-wandering set is topologically conjugate with a subshift of finite type. In this paper, we prove that there exists an interval continuous self-map of finite type such that the Hausdorff dimension is an arbitrary number in the interval (0, 1), discuss various chaotic properties of the map and the relations between chaotic set and the set of recurrent points.

Cooperation in carbon source degradation shapes spatial self-organization of microbial consortia on hydrated surfaces

OpenAIRE

Tecon, Robin; Or, Dani

2017-01-01

Mounting evidence suggests that natural microbial communities exhibit a high level of spatial organization at the micrometric scale that facilitate ecological interactions and support biogeochemical cycles. Microbial patterns are difficult to study definitively in natural environments due to complex biodiversity, observability and variable physicochemical factors. Here, we examine how trophic dependencies give rise to self-organized spatial patterns of a well-defined bacterial consortium grow...

A buffer material optimal design in the radioactive wastes geological disposal using the satisficing trade-off method and the self-organizing map

International Nuclear Information System (INIS)

Okamoto, Takashi; Hanaoka, Yuya; Aiyoshi, Eitaro; Kobayashi, Yoko

2012-01-01

In this paper, we consider a multi-objective optimization method in order to obtain a preferred solution for the buffer material optimal design problem in the high-level radioactive wastes geological disposal. The buffer material optimal design problem is formulated as a constrained multi-objective optimization problem. Its Pareto optimal solutions are distributed evenly on whole bounds of the feasible region. Hence, we develop a search method to find a preferred solution easily for a decision maker from the Pareto optimal solutions which are distributed evenly and vastly. In the preferred solution search method, the visualization technique of a Pareto optimal solution set using the self-organizing map is introduced into the satisficing trade-off method which is the interactive method to obtain a Pareto optimal solution that satisfies a decision maker. We confirm the effectiveness of the preferred solution search method in the buffer material optimal design problem. (author)

Ellipsometry of functional organic surfaces and films

CERN Document Server

Eichhorn, Klaus-Jochen

2018-01-01

This new edition provides a state-of-the-art survey of ellipsometric methods used to study organic films and surfaces, from laboratory to synchrotron applications, with a special focus on in-situ use in processing environments and at solid-liquid interfaces. Thanks to the development of functional organic, meta- and hybrid materials for new optical, electronic, sensing and biotechnological devices, the ellipsometric analysis of optical and material properties has made tremendous strides over the past few years. The second edition has been updated to reflect the latest advances in ellipsometric methods. The new content focuses on the study of anisotropic materials, conjugated polymers, polarons, self-assembled monolayers, industrial membranes, adsorption of proteins, enzymes and RGD-peptides, as well as the correlation of ellipsometric spectra to structure and molecular interactions.

Ray-map migration of transmitted surface waves

KAUST Repository

Li, Jing

2016-08-25

Near-surface normal faults can sometimes separate two distinct zones of velocity heterogeneity, where the medium on one side of the fault has a faster velocity than on the other side. Therefore, the slope of surface-wave arrivals in a common-shot gather should abruptly change near the surface projection of the fault. We present ray-map imaging method that migrates transmitted surface waves to the fault plane, and therefore it roughly estimates the orientation, depth, and location of the near-surface fault. The main benefits of this method are that it is computationally inexpensive and robust in the presence of noise.

Differentiating the Spatiotemporal Distribution of Natural and Anthropogenic Processes on River Water-Quality Variation Using a Self-Organizing Map With Factor Analysis.

Science.gov (United States)

Wang, Yeuh-Bin; Liu, Chen-Wuing; Lee, Jin-Jing

2015-08-01

To elucidate the historical improvement and advanced measure of river water quality in the Taipei metropolitan area, this study applied the self-organizing map (SOM) technique with factor analysis (FA) to differentiate the spatiotemporal distribution of natural and anthropogenic processes on river water-quality variation spanning two decades. The SOM clustered river water quality into five groups: very low pollution, low pollution, moderate pollution, high pollution, and very high pollution. FA was then used to extract four latent factors that dominated water quality from 1991 to 2011 including three anthropogenic process factors (organic, industrial, and copper pollution) and one natural process factor [suspended solids (SS) pollution]. The SOM revealed that the water quality improved substantially over time. However, the downstream river water quality was still classified as high pollution because of an increase in anthropogenic activity. FA showed the spatiotemporal pattern of each factor score decreasing over time, but the organic pollution factor downstream of the Tamsui River, as well as the SS factor scores in the upstream major tributary (the Dahan Stream), remained within the high pollution level. Therefore, we suggest that public sewage-treatment plants should be upgraded from their current secondary biological processing to advanced treatment processing. The conservation of water and soil must also be reinforced to decrease the SS loading of the Dahan Stream from natural erosion processes in the future.

Self-cleaning efficiency of artificial superhydrophobic surfaces.

Science.gov (United States)

Bhushan, Bharat; Jung, Yong Chae; Koch, Kerstin

2009-03-03

The hierarchical structured surface of the lotus (Nelumbo nucifera, Gaertn.) leaf provides a model for the development of biomimetic self-cleaning surfaces. On these water-repellent surfaces, water droplets move easily at a low inclination of the leaf and collect dirt particles adhering to the leaf surface. Flat hydrophilic and hydrophobic, nanostructured, microstructured, and hierarchical structured superhydrophobic surfaces were fabricated, and a systematic study of wettability and adhesion properties was carried out. The influence of contact angle hysteresis on self-cleaning by water droplets was studied at different tilt angles (TA) of the specimen surfaces (3 degrees for Lotus wax, 10 degrees for n-hexatriacontane, as well as 45 degrees for both types of surfaces). At 3 degrees and 10 degrees TA, no surfaces were cleaned by moving water applied onto the surfaces with nearly zero kinetic energy, but most particles were removed from hierarchical structured surfaces, and a certain amount of particles were captured between the asperities of the micro- and hierarchical structured surfaces. After an increase of the TA to 45 degrees (larger than the tilt angles of all structured surfaces), as usually used for industrial self-cleaning tests, all nanostructured surfaces were cleaned by water droplets moving over the surfaces followed by hierarchical and microstructures. Droplets applied onto the surfaces with some pressure removed particles residues and led to self-cleaning by a combination of sliding and rolling droplets. Geometrical scale effects were responsible for superior performance of nanostructured surfaces.

The genotype-phenotype map of an evolving digital organism

OpenAIRE

Fortuna, Miguel A.; Zaman, Luis; Ofria, Charles; Wagner, Andreas

2017-01-01

To understand how evolving systems bring forth novel and useful phenotypes, it is essential to understand the relationship between genotypic and phenotypic change. Artificial evolving systems can help us understand whether the genotype-phenotype maps of natural evolving systems are highly unusual, and it may help create evolvable artificial systems. Here we characterize the genotype-phenotype map of digital organisms in Avida, a platform for digital evolution. We consider digital organisms fr...

Quantification of Hepatorenal Index for Computer-Aided Fatty Liver Classification with Self-Organizing Map and Fuzzy Stretching from Ultrasonography

Directory of Open Access Journals (Sweden)

Kwang Baek Kim

2015-01-01

Full Text Available Accurate measures of liver fat content are essential for investigating hepatic steatosis. For a noninvasive inexpensive ultrasonographic analysis, it is necessary to validate the quantitative assessment of liver fat content so that fully automated reliable computer-aided software can assist medical practitioners without any operator subjectivity. In this study, we attempt to quantify the hepatorenal index difference between the liver and the kidney with respect to the multiple severity status of hepatic steatosis. In order to do this, a series of carefully designed image processing techniques, including fuzzy stretching and edge tracking, are applied to extract regions of interest. Then, an unsupervised neural learning algorithm, the self-organizing map, is designed to establish characteristic clusters from the image, and the distribution of the hepatorenal index values with respect to the different levels of the fatty liver status is experimentally verified to estimate the differences in the distribution of the hepatorenal index. Such findings will be useful in building reliable computer-aided diagnostic software if combined with a good set of other characteristic feature sets and powerful machine learning classifiers in the future.

Simple lecture demonstrations of instability and self-organization

International Nuclear Information System (INIS)

Mayer, V V; Varaksina, E I; Saranin, V A

2014-01-01

A dielectric liquid layer with an electric field created inside it is proposed as a means for demonstrating the phenomenon of self-organization. The field is produced by the distributed charge transferred by a corona discharge from the tip to the liquid surface. The theory of the phenomenon is presented. An analogy with the Rayleigh – Taylor instability is drawn and a comparison with the Benard instability is given. The practicality of the method for both natural sciences and the humanities is discussed. (methodological notes)

Achieving Optimal Self-Adaptivity for Dynamic Tuning of Organic Semiconductors through Resonance Engineering.

Science.gov (United States)

Tao, Ye; Xu, Lijia; Zhang, Zhen; Chen, Runfeng; Li, Huanhuan; Xu, Hui; Zheng, Chao; Huang, Wei

2016-08-03

Current static-state explorations of organic semiconductors for optimal material properties and device performance are hindered by limited insights into the dynamically changed molecular states and charge transport and energy transfer processes upon device operation. Here, we propose a simple yet successful strategy, resonance variation-based dynamic adaptation (RVDA), to realize optimized self-adaptive properties in donor-resonance-acceptor molecules by engineering the resonance variation for dynamic tuning of organic semiconductors. Organic light-emitting diodes hosted by these RVDA materials exhibit remarkably high performance, with external quantum efficiencies up to 21.7% and favorable device stability. Our approach, which supports simultaneous realization of dynamically adapted and selectively enhanced properties via resonance engineering, illustrates a feasible design map for the preparation of smart organic semiconductors capable of dynamic structure and property modulations, promoting the studies of organic electronics from static to dynamic.

Air transport pilots' information priorities for surface moving maps

Science.gov (United States)

2003-10-13

The use of a surface map display for operations on or near the airport surface (taxi out, takeoff, final approach and landing, taxi in) is expected to enhance safety. There is a lack of research, however, detailing how the airport surface should be d...

Self-assembling triblock proteins for biofunctional surface modification

Science.gov (United States)

Fischer, Stephen E.

of the triblock protein hydrogels, and the ease of introducing multiple functionalities to a substrate surface, a surface coating is tailored for neural stem cell culture in order to improve proliferation on the scaffold, while maintaining the stem cell phenotype. These studies demonstrate the unique advantages of genetic engineering over traditional techniques for surface modification. In addition to their unmatched sequence fidelity, recombinant proteins can easily be modified with bioactive ligands and their organization into coherent, supramolecular structures mimics natural self-assembly processes.

‘’Mapping Self- and Co-regulation Approaches in the EU Context’’ : Explorative Study for the European Commission, DG Connect

NARCIS (Netherlands)

Senden, L.A.J.; Kica, E.; Klinger, Kilian; Hiemstra, M.I.

2015-01-01

This report presents a first mapping or inventory of the different approaches to self- and co- regulation (SCR) that can be found within the EU context, in a number of Member States (MS) and international organizations. The report consists of four sections. Whereas the first section provides an

Topography evolution of 500 keV Ar(4+) ion beam irradiated InP(100) surfaces - formation of self-organized In-rich nano-dots and scaling laws.

Science.gov (United States)

Sulania, Indra; Agarwal, Dinesh C; Kumar, Manish; Kumar, Sunil; Kumar, Pravin

2016-07-27

We report the formation of self-organized nano-dots on the surface of InP(100) upon irradiating it with a 500 keV Ar(4+) ion beam. The irradiation was carried out at an angle of 25° with respect to the normal at the surface with 5 different fluences ranging from 1.0 × 10(15) to 1.0 × 10(17) ions per cm(2). The morphology of the ion-irradiated surfaces was examined by atomic force microscopy (AFM) and the formation of the nano-dots on the irradiated surfaces was confirmed. The average size of the nano-dots varied from 44 ± 14 nm to 94 ± 26 nm with increasing ion fluence. As a function of the ion fluence, the variation in the average size of the nano-dots has a great correlation with the surface roughness, which changes drastically up to the ion fluence of 1.0 × 10(16) ions per cm(2) and attains almost a saturation level for further irradiation. The roughness and the growth exponent values deduced from the scaling laws suggest that the kinetic sputtering and the large surface diffusion steps of the atoms are the primary reasons for the formation of the self-organized nanodots on the surface. X-ray photo-electron spectroscopy (XPS) studies show that the surface stoichiometry changes with the ion fluence. With irradiation, the surface becomes more indium (In)-rich owing to the preferential sputtering of the phosphorus atoms (P) and the pure metallic In nano-dots evolve at the highest ion fluence. The cross-sectional scanning electron microscopy (SEM) analysis of the sample irradiated with the highest fluence showed the absence of the nanostructuring beneath the surface. The surface morphological changes at this medium energy ion irradiation are discussed in correlation with the low and high energy experiments to shed more light on the mechanism of the well separated nano-dot formation.

Corrected body surface potential mapping.

Science.gov (United States)

Krenzke, Gerhard; Kindt, Carsten; Hetzer, Roland

2007-02-01

In the method for body surface potential mapping described here, the influence of thorax shape on measured ECG values is corrected. The distances of the ECG electrodes from the electrical heart midpoint are determined using a special device for ECG recording. These distances are used to correct the ECG values as if they had been measured on the surface of a sphere with a radius of 10 cm with its midpoint localized at the electrical heart midpoint. The equipotential lines of the electrical heart field are represented on the virtual surface of such a sphere. It is demonstrated that the character of a dipole field is better represented if the influence of the thorax shape is reduced. The site of the virtual reference electrode is also important for the dipole character of the representation of the electrical heart field.

Self-organized pattern formation upon femtosecond laser ablation by circularly polarized light

International Nuclear Information System (INIS)

Varlamova, Olga; Costache, Florenta; Reif, Juergen; Bestehorn, Michael

2006-01-01

Surface ripples generation upon femtosecond laser ablation is attributed to self-organized structure formation from instability. We report that linear arrangements are observed not only for linearly polarized light but also for ablation with circularly polarized light. Long ordered chains of spherical nanoparticles, reminding of bead-strings are almost parallel but exhibit typical non-linear dynamics features such as bifurcations. In a first attempt to understand the self-assembly, we rely on models recently developed for the description of similar structures upon ion beam erosion and for the simulation of instabilities in thin liquid films. Our picture describes an unstable surface layer, non-uniformly eroded through Coulomb repulsion between individual positive charges

Development, application and evaluation of a computational tool for management high voltage break disconnector based on self-organizing maps and image processing

International Nuclear Information System (INIS)

Freitas Colaco, Daniel; Alexandria, Auzuir R. de; Cortez, Paulo Cesar; Frota, Joao Batista B.; Nunes de Lima, Jose Nunes de; Albuquerque, Victor Hugo C. de

2010-01-01

This work has the objective of developing, analysing and applying a new tool for management the status of break disconnectors in high voltage substations from digital images. This tool uses a non-supervised kind of artificial neural network using the Kohonen learning algorithm, known as a self-organizing maps. In order to develop the proposed tool, C/C++ programming language, provided with easily used interfaces, is used. In order to obtain the results, three environments are considered: one for laboratory simulation and two pilot projects installed in the Fortaleza II/CHESF substation. These pilots are used for 230 kV EV-2000 type and 500 kV semi-pantographic type break disconnector management tests. The results prove the developed system's efficiency, because it is able to detect 100% of open and closed identification situations. However, the neural network utilised for management break disconnectors has become suitable for installation in high voltage substations in order to support the maintenance team in safely handling these disconnectors.

Development, application and evaluation of a computational tool for management high voltage break disconnector based on self-organizing maps and image processing

Energy Technology Data Exchange (ETDEWEB)

Freitas Colaco, Daniel, E-mail: colaco@deti.ufc.b [Universidade Federal do Ceara (UFC), Centro de Tecnologia (CT), Departamento de Engenharia de Teleinformatica - DETI, Campus do PICI S/N, Bloco 723, 60455-970 Fortaleza, Ceara (Brazil); Alexandria, Auzuir R. de, E-mail: auzuir@ifce.edu.b [Instituto Federal de Educacao, Ciencia e Tecnologia do Ceara (IFCE), Area da industria, Nucleo de Simulacao Computacional-N5IMCO, Campus Fortaleza, Av. Treze de Maio, 2081, 60040-531 Fortaleza, Ceara (Brazil); Cortez, Paulo Cesar, E-mail: cortez@deti.ufc.b [Universidade Federal do Ceara (UFC), Centro de Tecnologia (CT), Departamento de Engenharia de Teleinformatica - DETI, Campus do PICI S/N, Bloco 723, 60455-970 Fortaleza, Ceara (Brazil); Frota, Joao Batista B., E-mail: jb@ifce.edu.b [Instituto Federal de Educacao, Ciencia e Tecnologia do Ceara (IFCE), Area da industria, Nucleo de Simulacao Computacional-N5IMCO, Campus Fortaleza, Av. Treze de Maio, 2081, 60040-531 Fortaleza, Ceara (Brazil); Nunes de Lima, Jose Nunes de, E-mail: josenl@chesf.gov.b [Companhia Hidro Eletrica do Sao Francisco (CHESF), Rua Delmiro Gouveia, 333, 50761-901 Recife, Pernambuco (Brazil); Albuquerque, Victor Hugo C. de, E-mail: victor.albuquerque@fe.up.p [Universidade de Fortaleza (UNIFOR), Centro de Ciencias Tecnologicas (CCT), Nucleo de Pesquisas Tecnologicas - NPT, Av. Washington Soares, 1321, Sala NPT/CCT, CEP 60.811-905, Edson Queiroz (Brazil); Universidade Federal da Paraiba (UFPB), Departamento de Engenharia Mecanica (DEM), Cidade Universitaria, S/N, 58059-900 Joao Pessoa, Paraiba (Brazil)

2010-11-15

This work has the objective of developing, analysing and applying a new tool for management the status of break disconnectors in high voltage substations from digital images. This tool uses a non-supervised kind of artificial neural network using the Kohonen learning algorithm, known as a self-organizing maps. In order to develop the proposed tool, C/C++ programming language, provided with easily used interfaces, is used. In order to obtain the results, three environments are considered: one for laboratory simulation and two pilot projects installed in the Fortaleza II/CHESF substation. These pilots are used for 230 kV EV-2000 type and 500 kV semi-pantographic type break disconnector management tests. The results prove the developed system's efficiency, because it is able to detect 100% of open and closed identification situations. However, the neural network utilised for management break disconnectors has become suitable for installation in high voltage substations in order to support the maintenance team in safely handling these disconnectors.

Portraits of self-organization in fish schools interacting with robots

Science.gov (United States)

Aureli, M.; Fiorilli, F.; Porfiri, M.

2012-05-01

In this paper, we propose an enabling computational and theoretical framework for the analysis of experimental instances of collective behavior in response to external stimuli. In particular, this work addresses the characterization of aggregation and interaction phenomena in robot-animal groups through the exemplary analysis of fish schooling in the vicinity of a biomimetic robot. We adapt global observables from statistical mechanics to capture the main features of the shoal collective motion and its response to the robot from experimental observations. We investigate the shoal behavior by using a diffusion mapping analysis performed on these global observables that also informs the definition of relevant portraits of self-organization.

Analyzing the effectiveness of flare dispensing programs against pulse width modulation seekers using self-organizing maps

Science.gov (United States)

Şahingil, Mehmet C.; Aslan, Murat Š.

2013-10-01

Infrared guided missile seekers utilizing pulse width modulation in target tracking is one of the threats against air platforms. To be able to achieve a "soft-kill" protection of own platform against these type of threats, one needs to examine carefully the seeker operating principle with its special electronic counter-counter measure (ECCM) capability. One of the cost-effective ways of soft kill protection is to use flare decoys in accordance with an optimized dispensing program. Such an optimization requires a good understanding of the threat seeker, capabilities of the air platform and engagement scenario information between them. Modeling and simulation is very powerful tool to achieve a valuable insight and understand the underlying phenomenology. A careful interpretation of simulation results is crucial to infer valuable conclusions from the data. In such an interpretation there are lots of factors (features) which affect the results. Therefore, powerful statistical tools and pattern recognition algorithms are of special interest in the analysis. In this paper, we show how self-organizing maps (SOMs), which is one of those powerful tools, can be used in analyzing the effectiveness of various flare dispensing programs against a PWM seeker. We perform several Monte Carlo runs for a typical engagement scenario in a MATLAB-based simulation environment. In each run, we randomly change the flare dispending program and obtain corresponding class: "successful" or "unsuccessful", depending on whether the corresponding flare dispensing program deceives the seeker or not, respectively. Then, in the analysis phase, we use SOMs to interpret and visualize the results.

Quantifying Postural Control during Exergaming Using Multivariate Whole-Body Movement Data: A Self-Organizing Maps Approach.

Directory of Open Access Journals (Sweden)

Mike van Diest

Full Text Available Exergames are becoming an increasingly popular tool for training balance ability, thereby preventing falls in older adults. Automatic, real time, assessment of the user's balance control offers opportunities in terms of providing targeted feedback and dynamically adjusting the gameplay to the individual user, yet algorithms for quantification of balance control remain to be developed. The aim of the present study was to identify movement patterns, and variability therein, of young and older adults playing a custom-made weight-shifting (ice-skating exergame.Twenty older adults and twenty young adults played a weight-shifting exergame under five conditions of varying complexity, while multi-segmental whole-body movement data were captured using Kinect. Movement coordination patterns expressed during gameplay were identified using Self Organizing Maps (SOM, an artificial neural network, and variability in these patterns was quantified by computing Total Trajectory Variability (TTvar. Additionally a k Nearest Neighbor (kNN classifier was trained to discriminate between young and older adults based on the SOM features.Results showed that TTvar was significantly higher in older adults than in young adults, when playing the exergame under complex task conditions. The kNN classifier showed a classification accuracy of 65.8%.Older adults display more variable sway behavior than young adults, when playing the exergame under complex task conditions. The SOM features characterizing movement patterns expressed during exergaming allow for discriminating between young and older adults with limited accuracy. Our findings contribute to the development of algorithms for quantification of balance ability during home-based exergaming for balance training.

Actomyosin-based Self-organization of cell internalization during C. elegans gastrulation

Directory of Open Access Journals (Sweden)

Pohl Christian

2012-11-01

Full Text Available Abstract Background Gastrulation is a key transition in embryogenesis; it requires self-organized cellular coordination, which has to be both robust to allow efficient development and plastic to provide adaptability. Despite the conservation of gastrulation as a key event in Metazoan embryogenesis, the morphogenetic mechanisms of self-organization (how global order or coordination can arise from local interactions are poorly understood. Results We report a modular structure of cell internalization in Caenorhabditis elegans gastrulation that reveals mechanisms of self-organization. Cells that internalize during gastrulation show apical contractile flows, which are correlated with centripetal extensions from surrounding cells. These extensions converge to seal over the internalizing cells in the form of rosettes. This process represents a distinct mode of monolayer remodeling, with gradual extrusion of the internalizing cells and simultaneous tissue closure without an actin purse-string. We further report that this self-organizing module can adapt to severe topological alterations, providing evidence of scalability and plasticity of actomyosin-based patterning. Finally, we show that globally, the surface cell layer undergoes coplanar division to thin out and spread over the internalizing mass, which resembles epiboly. Conclusions The combination of coplanar division-based spreading and recurrent local modules for piecemeal internalization constitutes a system-level solution of gradual volume rearrangement under spatial constraint. Our results suggest that the mode of C. elegans gastrulation can be unified with the general notions of monolayer remodeling and with distinct cellular mechanisms of actomyosin-based morphogenesis.

Self-organized carbon-rich stripe formation from competitive carbon and aluminium segregation at Fe0.85Al0.15(1 1 0) surfaces

Science.gov (United States)

Dai, Zongbei; Borghetti, Patrizia; Mouchaal, Younes; Chenot, Stéphane; David, Pascal; Jupille, Jacques; Cabailh, Gregory; Lazzari, Rémi

2018-06-01

By combining Scanning Tunnelling Microscopy, Low Energy Electron Diffraction and X-ray Photoelectron Spectroscopy, it was found that the surface of A2 random alloy Fe0.85Al0.15(1 1 0) is significantly influenced by the segregation of aluminium but also of carbon bulk impurities. Below ∼ 900 K, carbon segregates in the form of self-organized protruding stripes separated by ∼ 5 nm that run along the [ 0 0 1 ] B bulk direction and cover up to 34% of the surface. Their C 1s spectroscopic signature that is dominated by graphitic carbon peaks around 900 K. Above this temperature, the surface carbon concentration decays by redissolution in the bulk, whereas an intense aluminium segregation is observed giving rise to a hexagonal superstructure. The present findings is interpreted by a competitive segregation between the two elements.

Preliminary Correlation Map of Geomorphic Surfaces in North-Central Frenchman Flat, Nevada Test Site

International Nuclear Information System (INIS)

Bechtel Nevada

2005-01-01

This correlation map (scale = 1:12,000) presents the results of a mapping initiative that was part of the comprehensive site characterization required to operate the Area 5 Radioactive Waste Management Site, a low-level radioactive waste disposal facility located in northern Frenchman Flat at the Nevada Test Site. Eight primary map units are recognized for Quaternary surfaces: remnants of six alluvial fan or terrace surfaces, one unit that includes colluvial aprons associated with hill slopes, and one unit for anthropogenically disturbed surfaces. This surficial geology map provides fundamental data on natural processes for reconstruction of the Quaternary history of northern Frenchman Flat, which in turn will aid in the understanding of the natural processes that act to develop the landscape, and the time-frames involved in landscape development. The mapping was conducted using color and color-infrared aerial photographs and field verification of map unit composition and boundaries. Criteria for defining the map unit composition of geomorphic surface units are based on relative geomorphic position, landform morphology, and degree of preservation of surface morphology. The bedrock units identified on this map were derived from previous published mapping efforts and are included for completeness

MAP SERVICES FOR MANAGEMENT OF HUNTING ORGANIZATIONS (THE CASE OF HUNTING ORGANIZATION “MEDVEDICA”

Directory of Open Access Journals (Sweden)

S. A. Zaichenko

2013-01-01

Full Text Available The current state map support of the system of hunting management requires updating an information database and the creation of new schemes of hunting organization. In this case the beneficial is using of satellite imagery data for the mapping and also for important environmental research. Presentation of the results in the form of Internet web services provides broad benefits to the paper version of the maps.

Hierarchical surfaces for enhanced self-cleaning applications

Science.gov (United States)

Fernández, Ariadna; Francone, Achille; Thamdrup, Lasse H.; Johansson, Alicia; Bilenberg, Brian; Nielsen, Theodor; Guttmann, Markus; Sotomayor Torres, Clivia M.; Kehagias, Nikolaos

2017-04-01

In this study we present a flexible and adaptable fabrication method to create complex hierarchical structures over inherently hydrophobic resist materials. We have tested these surfaces for their superhydrophobic behaviour and successfully verified their self-cleaning properties. The followed approach allow us to design and produce superhydrophobic surfaces in a reproducible manner. We have analysed different combination of hierarchical micro-nanostructures for their application to self-cleaning surfaces. A static contact angle value of 170° with a hysteresis of 4° was achieved without the need of any additional chemical treatment on the fabricated hierarchical structures. Dynamic effects were analysed on these surfaces, obtaining a remarkable self-cleaning effect as well as a good robustness over impacting droplets.

Segmentation and profiling consumers in a multi-channel environment using a combination of self-organizing maps (SOM method, and logistic regression

Directory of Open Access Journals (Sweden)

Seyed Ali Akbar Afjeh

2014-05-01

Full Text Available Market segmentation plays essential role on understanding the behavior of people’s interests in purchasing various products and services through various channels. This paper presents an empirical investigation to shed light on consumer’s purchasing attitude as well as gathering information in multi-channel environment. The proposed study of this paper designed a questionnaire and distributed it among 800 people who were at least 18 years of age and had some experiences on purchasing goods and services on internet, catalog or regular shopping centers. Self-organizing map, SOM, clustering technique was performed based on consumer’s interest in gathering information as well as purchasing products through internet, catalog and shopping centers and determined four segments. There were two types of questions for the proposed study of this paper. The first group considered participants’ personal characteristics such as age, gender, income, etc. The second group of questions was associated with participants’ psychographic characteristics including price consciousness, quality consciousness, time pressure, etc. Using multinominal logistic regression technique, the study determines consumers’ behaviors in each four segments.

Self organized criticality

International Nuclear Information System (INIS)

Creutz, M.

1993-03-01

Self organized criticality refers to the tendency of highly dissipative systems to drive themselves to a critical state. This has been proposed to explain why observed physics often displays a wide disparity of length and time scales. The phenomenon can be studied in simple cellular automaton models

Nanopatched Graphene with Molecular Self-Assembly Toward Graphene-Organic Hybrid Soft Electronics.

Science.gov (United States)

Kang, Boseok; Lee, Seong Kyu; Jung, Jaehyuck; Joe, Minwoong; Lee, Seon Baek; Kim, Jinsung; Lee, Changgu; Cho, Kilwon

2018-04-30

Increasing the mechanical durability of large-area polycrystalline single-atom-thick materials is a necessary step toward the development of practical and reliable soft electronics based on these materials. Here, it is shown that the surface assembly of organosilane by weak epitaxy forms nanometer-thick organic patches on a monolayer graphene surface and dramatically increases the material's resistance to harsh postprocessing environments, thereby increasing the number of ways in which graphene can be processed. The nanopatched graphene with the improved mechanical durability enables stable operation when used as transparent electrodes of wearable strain sensors. Also, the nanopatched graphene applied as an electrode modulates the molecular orientation of deposited organic semiconductor layers, and yields favorable nominal charge injection for organic transistors. These results demonstrate the potential for use of self-assembled organic nanopatches in graphene-based soft electronics. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Explaining the “how” of self-esteem development : The self-organizing self-esteem model

NARCIS (Netherlands)

de Ruiter, Naomi M.P.; van Geert, Paul L.C.; Kunnen, E. Saskia

2017-01-01

The current article proposes a theoretical model of self-esteem called the Self-Organizing Self-Esteem (SOSE) model. The model provides an integrative framework for conceptualizing and understanding the intrinsic dynamics of self-esteem and the role of the context across 3 levels of development: The

Eastern Denali Fault surface trace map, eastern Alaska and Yukon, Canada

Science.gov (United States)

Bender, Adrian M.; Haeussler, Peter J.

2017-05-04

We map the 385-kilometer (km) long surface trace of the right-lateral, strike-slip Denali Fault between the Totschunda-Denali Fault intersection in Alaska, United States and the village of Haines Junction, Yukon, Canada. In Alaska, digital elevation models based on light detection and ranging and interferometric synthetic aperture radar data enabled our fault mapping at scales of 1:2,000 and 1:10,000, respectively. Lacking such resources in Yukon, we developed new structure-from-motion digital photogrammetry products from legacy aerial photos to map the fault surface trace at a scale of 1:10,000 east of the international border. The section of the fault that we map, referred to as the Eastern Denali Fault, did not rupture during the 2002 Denali Fault earthquake (moment magnitude 7.9). Seismologic, geodetic, and geomorphic evidence, along with a paleoseismic record of past ground-rupturing earthquakes, demonstrate Holocene and contemporary activity on the fault, however. This map of the Eastern Denali Fault surface trace complements other data sets by providing an openly accessible digital interpretation of the location, length, and continuity of the fault’s surface trace based on the accompanying digital topography dataset. Additionally, the digitized fault trace may provide geometric constraints useful for modeling earthquake scenarios and related seismic hazard.

Self-organization in metal complexes

International Nuclear Information System (INIS)

Radecka-Paryzek, W.

1999-01-01

Inorganic self-organization involves the spontaneous generation of well-defined supramolecular architectures from metal ions and organic ligands. The basic concept of supramolecular chemistry is a molecular recognition. When the substrate are metal ions, recognition is expressed in the stability and selectivity of metal ion complexation by organic ligands and depends on the geometry of the ligand and on their binding sites that it contains. The combination of the geometric features of the ligand units and the coordination geometries of the metal ions provides very efficient tool for the synthesis of novel, intriguing and highly sophisticated species such as catenanes, box structures, double and triple helicates with a variety of interesting properties. The article will focus on the examples of inorganic self-organization involving the templating as a first step for the assembly of supramolecular structures of high complexity. (author)

A strategy for tissue self-organization that is robust to cellular heterogeneity and plasticity.

Science.gov (United States)

Cerchiari, Alec E; Garbe, James C; Jee, Noel Y; Todhunter, Michael E; Broaders, Kyle E; Peehl, Donna M; Desai, Tejal A; LaBarge, Mark A; Thomson, Matthew; Gartner, Zev J

2015-02-17

Developing tissues contain motile populations of cells that can self-organize into spatially ordered tissues based on differences in their interfacial surface energies. However, it is unclear how self-organization by this mechanism remains robust when interfacial energies become heterogeneous in either time or space. The ducts and acini of the human mammary gland are prototypical heterogeneous and dynamic tissues comprising two concentrically arranged cell types. To investigate the consequences of cellular heterogeneity and plasticity on cell positioning in the mammary gland, we reconstituted its self-organization from aggregates of primary cells in vitro. We find that self-organization is dominated by the interfacial energy of the tissue-ECM boundary, rather than by differential homo- and heterotypic energies of cell-cell interaction. Surprisingly, interactions with the tissue-ECM boundary are binary, in that only one cell type interacts appreciably with the boundary. Using mathematical modeling and cell-type-specific knockdown of key regulators of cell-cell cohesion, we show that this strategy of self-organization is robust to severe perturbations affecting cell-cell contact formation. We also find that this mechanism of self-organization is conserved in the human prostate. Therefore, a binary interfacial interaction with the tissue boundary provides a flexible and generalizable strategy for forming and maintaining the structure of two-component tissues that exhibit abundant heterogeneity and plasticity. Our model also predicts that mutations affecting binary cell-ECM interactions are catastrophic and could contribute to loss of tissue architecture in diseases such as breast cancer.

Self-organizing networks

DEFF Research Database (Denmark)

Marchetti, Nicola; Prasad, Neeli R.; Johansson, Johan

2010-01-01

In this paper, a general overview of Self-Organizing Networks (SON), and the rationale and state-of-the-art of wireless SON are first presented. The technical and business requirements are then briefly treated, and the research challenges within the field of SON are highlighted. Thereafter, the r...

Surface-assisted DNA self-assembly: An enzyme-free strategy towards formation of branched DNA lattice

International Nuclear Information System (INIS)

Bhanjadeo, Madhabi M.; Nayak, Ashok K.; Subudhi, Umakanta

2017-01-01

DNA based self-assembled nanostructures and DNA origami has proven useful for organizing nanomaterials with firm precision. However, for advanced applications like nanoelectronics and photonics, large-scale organization of self-assembled branched DNA (bDNA) into periodic lattices is desired. In this communication for the first time we report a facile method of self-assembly of Y-shaped bDNA nanostructures on the cationic surface of Aluminum (Al) foil to prepare periodic two dimensional (2D) bDNA lattice. Particularly those Y-shaped bDNA structures having smaller overhangs and unable to self-assemble in solution, they are easily assembled on the surface of Al foil in the absence of ligase. Field emission scanning electron microscopy (FESEM) analysis shows homogenous distribution of two-dimensional bDNA lattices across the Al foil. When the assembled bDNA structures were recovered from the Al foil and electrophoresed in nPAGE only higher order polymeric bDNA structures were observed without a trace of monomeric structures which confirms the stability and high yield of the bDNA lattices. Therefore, this enzyme-free economic and efficient strategy for developing bDNA lattices can be utilized in assembling various nanomaterials for functional molecular components towards development of DNA based self-assembled nanodevices. - Highlights: • Al foil surface-assisted self-assembly of monomeric structures into larger branched DNA lattice. • FESEM study confirms the uniform distribution of two-dimensional bDNA lattice structures across the surface of Al foil. • Enzyme-free and economic strategy to prepare higher order structures from simpler DNA nanostructures have been confirmed by recovery assay. • Use of well proven sequences for the preparation of pure Y-shaped monomeric DNA nanostructure with high yield.

Hierarchical surfaces for enhanced self-cleaning applications

International Nuclear Information System (INIS)

Fernández, Ariadna; Francone, Achille; Sotomayor Torres, Clivia M; Kehagias, Nikolaos; Thamdrup, Lasse H; Johansson, Alicia; Bilenberg, Brian; Nielsen, Theodor; Guttmann, Markus

2017-01-01

In this study we present a flexible and adaptable fabrication method to create complex hierarchical structures over inherently hydrophobic resist materials. We have tested these surfaces for their superhydrophobic behaviour and successfully verified their self-cleaning properties. The followed approach allow us to design and produce superhydrophobic surfaces in a reproducible manner. We have analysed different combination of hierarchical micro-nanostructures for their application to self-cleaning surfaces. A static contact angle value of 170° with a hysteresis of 4° was achieved without the need of any additional chemical treatment on the fabricated hierarchical structures. Dynamic effects were analysed on these surfaces, obtaining a remarkable self-cleaning effect as well as a good robustness over impacting droplets. (paper)

Self-organized nanostructures in silicon and glass for MEMS, MOEMS and BioMEMS

International Nuclear Information System (INIS)

Lilienthal, K.; Fischer, M.; Stubenrauch, M.; Schober, A.

2010-01-01

The utilization of self-organization in the process workflows for Micro-Electro-Mechanical-Systems (MEMS) and their derivatives is a smart way to get large areas of nanostructured surfaces for various applications. The generation of nano-masking spots by self-organizing residues in the plasma can lead to needle- or tube-like structures on the surface after (deep-) reactive ion etching. With lengths of 3 up to 25 μm and 150 up to 500 nm in diameter for silicon broad applications in the fields of micro fluidics with catalysts, micro-optical or mechanical mountings or carrier wafer bonding in microelectronics are possible. Now, we also developed dry etching processes for fused silica which shows analogue properties to 'Black Silicon' and investigated these glass nanostructures by a first parameter study to identify new usable structures and hybrids. This innovative starting point allows the transfer of 'Black Silicon' technologies and its applications to another important material class in micro- and nanotechnologies, fused silica.

Self-organized nanostructures in silicon and glass for MEMS, MOEMS and BioMEMS

Energy Technology Data Exchange (ETDEWEB)

Lilienthal, K., E-mail: katharina.lilienthal@tu-ilmenau.de [Research Group ' Micro fluidics and Biosensors' , Ilmenau University of Technology, Institute of Micro- and Nanotechnologies, D-98693 Ilmenau (Germany); Fischer, M. [Research Group ' Micro fluidics and Biosensors' , Ilmenau University of Technology, Institute of Micro- and Nanotechnologies, D-98693 Ilmenau (Germany); Stubenrauch, M. [Department of Micromechanical Systems, Ilmenau University of Technology, Institute of Micro- and Nanotechnologies, D-98693 Ilmenau (Germany); Schober, A. [Research Group ' Micro fluidics and Biosensors' , Ilmenau University of Technology, Institute of Micro- and Nanotechnologies, D-98693 Ilmenau (Germany)

2010-05-25

The utilization of self-organization in the process workflows for Micro-Electro-Mechanical-Systems (MEMS) and their derivatives is a smart way to get large areas of nanostructured surfaces for various applications. The generation of nano-masking spots by self-organizing residues in the plasma can lead to needle- or tube-like structures on the surface after (deep-) reactive ion etching. With lengths of 3 up to 25 {mu}m and 150 up to 500 nm in diameter for silicon broad applications in the fields of micro fluidics with catalysts, micro-optical or mechanical mountings or carrier wafer bonding in microelectronics are possible. Now, we also developed dry etching processes for fused silica which shows analogue properties to 'Black Silicon' and investigated these glass nanostructures by a first parameter study to identify new usable structures and hybrids. This innovative starting point allows the transfer of 'Black Silicon' technologies and its applications to another important material class in micro- and nanotechnologies, fused silica.

Assessment of the Eutrophication-Related Environmental Parameters in Two Mediterranean Lakes by Integrating Statistical Techniques and Self-Organizing Maps.

Science.gov (United States)

Hadjisolomou, Ekaterini; Stefanidis, Konstantinos; Papatheodorou, George; Papastergiadou, Evanthia

2018-03-19

During the last decades, Mediterranean freshwater ecosystems, especially lakes, have been under severe pressure due to increasing eutrophication and water quality deterioration. In this article, we compared the effectiveness of different data analysis methods by assessing the contribution of environmental parameters to eutrophication processes. For this purpose, principal components analysis (PCA), cluster analysis, and a self-organizing map (SOM) were applied, using water quality data from two transboundary lakes of North Greece. SOM is considered as an advanced and powerful data analysis tool because of its ability to represent complex and nonlinear relationships among multivariate data sets. The results of PCA and cluster analysis agreed with the SOM results, although the latter provided more information because of the visualization abilities regarding the parameters' relationships. Besides nutrients that were found to be a key factor for controlling chlorophyll-a (Chl - a), water temperature was related positively with algal production, while the Secchi disk depth parameter was found to be highly important and negatively related toeutrophic conditions. In general, the SOM results were more specific and allowed direct associations between the water quality variables. Our work showed that SOMs can be used effectively in limnological studies to produce robust and interpretable results, aiding scientists and managers to cope with environmental problems such as eutrophication.

Assessment of the Eutrophication-Related Environmental Parameters in Two Mediterranean Lakes by Integrating Statistical Techniques and Self-Organizing Maps

Directory of Open Access Journals (Sweden)

Ekaterini Hadjisolomou

2018-03-01

Full Text Available During the last decades, Mediterranean freshwater ecosystems, especially lakes, have been under severe pressure due to increasing eutrophication and water quality deterioration. In this article, we compared the effectiveness of different data analysis methods by assessing the contribution of environmental parameters to eutrophication processes. For this purpose, principal components analysis (PCA, cluster analysis, and a self-organizing map (SOM were applied, using water quality data from two transboundary lakes of North Greece. SOM is considered as an advanced and powerful data analysis tool because of its ability to represent complex and nonlinear relationships among multivariate data sets. The results of PCA and cluster analysis agreed with the SOM results, although the latter provided more information because of the visualization abilities regarding the parameters’ relationships. Besides nutrients that were found to be a key factor for controlling chlorophyll-a (Chl-a, water temperature was related positively with algal production, while the Secchi disk depth parameter was found to be highly important and negatively related toeutrophic conditions. In general, the SOM results were more specific and allowed direct associations between the water quality variables. Our work showed that SOMs can be used effectively in limnological studies to produce robust and interpretable results, aiding scientists and managers to cope with environmental problems such as eutrophication.

Self-organizing periodicity in development: organ positioning in plants.

Science.gov (United States)

Bhatia, Neha; Heisler, Marcus G

2018-02-08

Periodic patterns during development often occur spontaneously through a process of self-organization. While reaction-diffusion mechanisms are often invoked, other types of mechanisms that involve cell-cell interactions and mechanical buckling have also been identified. Phyllotaxis, or the positioning of plant organs, has emerged as an excellent model system to study the self-organization of periodic patterns. At the macro scale, the regular spacing of organs on the growing plant shoot gives rise to the typical spiral and whorled arrangements of plant organs found in nature. In turn, this spacing relies on complex patterns of cell polarity that involve feedback between a signaling molecule - the plant hormone auxin - and its polar, cell-to-cell transport. Here, we review recent progress in understanding phyllotaxis and plant cell polarity and highlight the development of new tools that can help address the remaining gaps in our understanding. © 2018. Published by The Company of Biologists Ltd.

USING STROKE-BASED OR CHARACTER-BASED SELF-ORGANIZING MAPS IN THE RECOGNITION OF ONLINE, CONNECTED CURSIVE SCRIPT

NARCIS (Netherlands)

SCHOMAKER, L

Comparisons are made between a number of stroke-based and character-based recognizers of connected cursive script. In both approaches a Kohonen self-organizing neural network is used as a feature-vector quantizer. It is found that a ''best match only'' character-based recognizer performs better than

Studies on Manfred Eigen's model for the self-organization of information processing.

Science.gov (United States)

Ebeling, W; Feistel, R

2018-05-01

In 1971, Manfred Eigen extended the principles of Darwinian evolution to chemical processes, from catalytic networks to the emergence of information processing at the molecular level, leading to the emergence of life. In this paper, we investigate some very general characteristics of this scenario, such as the valuation process of phenotypic traits in a high-dimensional fitness landscape, the effect of spatial compartmentation on the valuation, and the self-organized transition from structural to symbolic genetic information of replicating chain molecules. In the first part, we perform an analysis of typical dynamical properties of continuous dynamical models of evolutionary processes. In particular, we study the mapping of genotype to continuous phenotype spaces following the ideas of Wright and Conrad. We investigate typical features of a Schrödinger-like dynamics, the consequences of the high dimensionality, the leading role of saddle points, and Conrad's extra-dimensional bypass. In the last part, we discuss in brief the valuation of compartment models and the self-organized emergence of molecular symbols at the beginning of life.

Modeling Directional Selectivity Using Self-Organizing Delay-Aadaptation Maps

OpenAIRE

Tversky, Mr. Tal; Miikkulainen, Dr. Risto

2002-01-01

Using a delay adaptation learning rule, we model the activity-dependent development of directionally selective cells in the primary visual cortex. Based on input stimuli, a learning rule shifts delays to create synchronous arrival of spikes at cortical cells. As a result, delays become tuned creating a smooth cortical map of direction selectivity. This result demonstrates how delay adaption can serve as a powerful abstraction for modeling temporal learning in the brain.

Domination, self-determination and circular organizing

NARCIS (Netherlands)

Romme, A.G.L.

2002-01-01

The emergence of self-organizing forms of control, based on the idea of self-determination, have challenged traditional forms of control based on the concept of domination. As such, self-determination has been put forward as an alternative rather than as a complement to domination. This paper

Geomorphic Surface Maps of Northern Frenchman Flat, Nevada Test Site, Southern Nevada

International Nuclear Information System (INIS)

Bechtel Nevada

2005-01-01

Large-scale (1:6000) surficial geology maps of northern Frenchman Flat were developed in 1995 as part of comprehensive site characterization required to operate a low-level radioactive waste disposal facility in that area. Seven surficial geology maps provide fundamental data on natural processes and are the platform needed to reconstruct the Quaternary history of northern Frenchman Flat. Reconstruction of the Quaternary history provides an understanding of the natural processes that act to develop the landscape, and the time-frames involved in landscape development. The mapping was conducted using color and color-infrared aerial photographs and field verification of map unit composition and boundaries. Criteria for defining the map unit composition of geomorphic surface units are based on relative geomorphic position, landform morphology, and degree of preservation of surface morphology. Seven geomorphic surfaces (Units 1 through 7) are recognized, spanning from the early Quaternary to present time

Profile of the biodiesel B100 commercialized in the region of Londrina: application of artificial neural networks of the type self organizing maps

Directory of Open Access Journals (Sweden)

Vilson Machado de Campos Filho

2015-10-01

Full Text Available The 97 samples were grouped according to the year of analysis. For each year, letters from A to D were attributed, between 2010 and 2013; A (33 B (25 C (24 and D (15. The parameters of compliance previously analyzed are those established by the National Agency of Petroleum, Natural Gas and Biofuels (ANP, through resolution ANP 07/2008. The parameters analyzed were density, flash point, peroxide and acid value. The observed values were presented to Artificial Neural Network (ANN Self Organizing MAP (SOM in order to classify, by physical-chemical properties, each sample from year of production. The ANN was trained on different days and randomly divided samples into two groups, training and test set. It was found that SOM network differentiated samples by the year and the compliance parameters, allowing to identify that the density and the flash point were the most significant compliance parameters, so good for the distinction and classification of these samples.

Self-organizing maps as a chemometric tool for aromatic pattern recognition of soluble coffee - doi: 10.4025/actascitechnol.v34i1.10892

Directory of Open Access Journals (Sweden)

Evandro Bona

2011-11-01

Full Text Available The electronic nose (EN is an instrument very used for food flavor analysis. However, it is also necessary to integrate the equipment with a multivariable pattern recognition system, and to this end the principal component analysis (PCA is the first choice. Alternatively, self-organizing maps (SOM had been also suggested, since they are a nonlinear and reliable technique. In this study SOM were used to distinguish soluble coffee according to EN data. The proposed methodology had identified all of the seven coffees evaluated; in addition, the groups and relationships detected were similar to those obtained through PCA. Also, the analysis of network weights allowed gathering the e-nose sensors into 4 groups according to the behavior regarding the samples. Results confirm SOM as an efficient tool to EN data pos-processing, and have showed the methodology as a promising choice for the development of new products and quality control of soluble coffee.

Self-Organization Activities of College Students: Challenges and Opportunities

Science.gov (United States)

Shmurygina, Natalia; Bazhenova, Natalia; Bazhenov, Ruslan; Nikolaeva, Natalia; Tcytcarev, Andrey

2016-01-01

The article provides the analysis of self-organization activities of college students related to their participation in youth associations activities. The purpose of research is to disclose a degree of students' activities demonstration based on self-organization processes, assessment of existing self-organization practices of the youth,…

Surface tension driven aggregation of organic nanowires via lab in a droplet.

Science.gov (United States)

Gu, Jianmin; Yin, Baipeng; Fu, Shaoyan; Feng, Man; Zhang, Ziming; Dong, Haiyun; Gao, Faming; Zhao, Yong Sheng

2018-06-05

Directing the architecture of complex organic nanostructures is desirable and still remains a challenge in areas of materials science due to their structure-dependent collective optoelectronic properties. Herein, we demonstrate a simple and versatile solution strategy that allows surface tension to drive low-dimensional nanostructures to aggregate into complex structures via a lab in a droplet technique. By selecting a suitable combination of a solvent and an anti-solvent with controllable surface tension difference, the droplets can be automatically cracked into micro-droplets, which provides an aggregation force directed toward the centre of the droplet to drive the low-dimensional building blocks to form the special aggregations during the self-assembly process. This synthetic strategy has been shown to be universal for organic materials, which is beneficial for further optimizing the optoelectronic properties. These results contribute to gaining an insightful understanding on the detailed growth mechanism of complex organic nanostructures and greatly promoting the development of organic nanophotonics.

Identification of Outlier Loci Responding to Anthropogenic and Natural Selection Pressure in Stream Insects Based on a Self-Organizing Map

Directory of Open Access Journals (Sweden)

Bin Li

2016-05-01

Full Text Available Water quality maintenance should be considered from an ecological perspective since water is a substrate ingredient in the biogeochemical cycle and is closely linked with ecosystem functioning and services. Addressing the status of live organisms in aquatic ecosystems is a critical issue for appropriate prediction and water quality management. Recently, genetic changes in biological organisms have garnered more attention due to their in-depth expression of environmental stress on aquatic ecosystems in an integrative manner. We demonstrate that genetic diversity would adaptively respond to environmental constraints in this study. We applied a self-organizing map (SOM to characterize complex Amplified Fragment Length Polymorphisms (AFLP of aquatic insects in six streams in Japan with natural and anthropogenic variability. After SOM training, the loci compositions of aquatic insects effectively responded to environmental selection pressure. To measure how important the role of loci compositions was in the population division, we altered the AFLP data by flipping the existence of given loci individual by individual. Subsequently we recognized the cluster change of the individuals with altered data using the trained SOM. Based on SOM recognition of these altered data, we determined the outlier loci (over 90th percentile that showed drastic changes in their belonging clusters (D. Subsequently environmental responsiveness (Ek’ was also calculated to address relationships with outliers in different species. Outlier loci were sensitive to slightly polluted conditions including Chl-a, NH4-N, NOX-N, PO4-P, and SS, and the food material, epilithon. Natural environmental factors such as altitude and sediment additionally showed relationships with outliers in somewhat lower levels. Poly-loci like responsiveness was detected in adapting to environmental constraints. SOM training followed by recognition shed light on developing algorithms de novo to

First-principles study on the strain effect of the Cu(0-bar 0-bar 1)-c(2x2)N self-organized structure

International Nuclear Information System (INIS)

Yoshimoto, Yoshihide; Tsuneyuki, Shinji

2004-01-01

Nitrogen atoms adsorbed on Cu(0-bar 0-bar 1) surface are known to form a self-organized structure, in which islands of nitrogen-adsorbed region are arranged into a square lattice. To clarify the mechanism of the self-organization, the strain effect in this surface is investigated by first-principles theoretical calculations. The difference between the calculated surface stress of Cu(0-bar 0-bar 1)-c(2x2)N surface and that of clean Cu(0-bar 0-bar 1) surface is in good agreement with the value estimated from experiments. In the stripe model of the self-organized surface, the top-most Cu atoms are largely displaced in lateral directions, while the nitrogen atoms at the edge of its nitrogen-adsorbed region slightly protrude in the surface normal. These results are consistent with observations. Spontaneous formation of the clean Cu region is also confirmed by calculating the formation energy within the stripe model. The formation energy is fitted well by a function deduced from the theory of elasticity. Nevertheless, the parameter of the fitting cannot be explained only by the difference of the surface stresses

Self-organizing of critical state in granulated superconductors

International Nuclear Information System (INIS)

Ginzburg, S.L.; Savitskaya, N.E.

2000-01-01

Critical state in granulated superconductors was studied on the basis of two mathematical models - the system of differential equations for calibration and invariant difference of phases and a simplified model describing the system of associated images and equivalent to the standard models to study self-organizing criticality. The critical state of granulated superconductors in all studied cases was shown to be self-organized. Besides, it is shown that the applied models are practically equivalent ones, that is they both show similar critical behavior and lead to coincidence of noncritical phenomena. For the first time one showed that the occurrence of self-organized critically within the system of nonlinear differential equations and its equivalence to self-organized critically in the standard models [ru

Complex Systems and Self-organization Modelling

CERN Document Server

Bertelle, Cyrille; Kadri-Dahmani, Hakima

2009-01-01

The concern of this book is the use of emergent computing and self-organization modelling within various applications of complex systems. The authors focus their attention both on the innovative concepts and implementations in order to model self-organizations, but also on the relevant applicative domains in which they can be used efficiently. This book is the outcome of a workshop meeting within ESM 2006 (Eurosis), held in Toulouse, France in October 2006.

Self-Organization of Friction Surface of Fe-Mn-C-B Coating With Increased Resistance to Abrasion / Samoorganizacja Powierzchni Tarcia Powłoki Fe-Mn-C-B O Zwiększonej Odporności Na Zużycie Ścierne

Directory of Open Access Journals (Sweden)

Barszcz M.

2015-12-01

Full Text Available The paper concerns the research on self-organization of the surface of coating of hypoeutectic alloy Fe-Mn-C-B modified Si, Ni, Cr, Cu with friction with C45 steel. The coatings were obtained by arc welding using a flux-cored wire. Tests of resistance to wear were carried out for hypoeutectic coatings with use of the friction pair pin-on-disc in the conditions of sliding friction, in model lubricating environments. The surface-active (glycerol oil and inactive (Vaseline grease lubricant was used. Tribological tests carried out showed that cooperation of hypoeutectic alloy coating with counterbody of C45 steel with lubrication with surface-active lubricant results in a significant improvement in tribological properties than in case of the lubrication with surface-inactive lubricant. The resulting effect is related to the self-organization of friction surface. After deposition and wear resistance tests, the friction surface microstructure was analysed, as well as the surface and depth distribution of the elements.

Self-Organized Construction with Continuous Building Material

DEFF Research Database (Denmark)

Heinrich, Mary Katherine; Wahby, Mostafa; Divband Soorati, Mohammad

2016-01-01

Self-organized construction with continuous, structured building material, as opposed to modular units, offers new challenges to the robot-based construction process and lends the opportunity for increased flexibility in constructed artifact properties, such as shape and deformation. As an example...... investigation, we look at continuous filaments organized into braided structures, within the context of bio-hybrids constructing architectural artifacts. We report the result of an early swarm robot experiment. The robots successfully constructed a braid in a self-organized process. The construction process can...... be extended by using different materials and by embedding sensors during the self-organized construction directly into the braided structure. In future work, we plan to apply dedicated braiding robot hardware and to construct sophisticated 3-d structures with local variability in patterns of filament...

Self organising maps for visualising and modelling.

Science.gov (United States)

Brereton, Richard G

2012-05-02

The paper describes the motivation of SOMs (Self Organising Maps) and how they are generally more accessible due to the wider available modern, more powerful, cost-effective computers. Their advantages compared to Principal Components Analysis and Partial Least Squares are discussed. These allow application to non-linear data, are not so dependent on least squares solutions, normality of errors and less influenced by outliers. In addition there are a wide variety of intuitive methods for visualisation that allow full use of the map space. Modern problems in analytical chemistry include applications to cultural heritage studies, environmental, metabolomic and biological problems result in complex datasets. Methods for visualising maps are described including best matching units, hit histograms, unified distance matrices and component planes. Supervised SOMs for classification including multifactor data and variable selection are discussed as is their use in Quality Control. The paper is illustrated using four case studies, namely the Near Infrared of food, the thermal analysis of polymers, metabolomic analysis of saliva using NMR, and on-line HPLC for pharmaceutical process monitoring.

Self organising maps for visualising and modelling

Science.gov (United States)

2012-01-01

The paper describes the motivation of SOMs (Self Organising Maps) and how they are generally more accessible due to the wider available modern, more powerful, cost-effective computers. Their advantages compared to Principal Components Analysis and Partial Least Squares are discussed. These allow application to non-linear data, are not so dependent on least squares solutions, normality of errors and less influenced by outliers. In addition there are a wide variety of intuitive methods for visualisation that allow full use of the map space. Modern problems in analytical chemistry include applications to cultural heritage studies, environmental, metabolomic and biological problems result in complex datasets. Methods for visualising maps are described including best matching units, hit histograms, unified distance matrices and component planes. Supervised SOMs for classification including multifactor data and variable selection are discussed as is their use in Quality Control. The paper is illustrated using four case studies, namely the Near Infrared of food, the thermal analysis of polymers, metabolomic analysis of saliva using NMR, and on-line HPLC for pharmaceutical process monitoring. PMID:22594434

Regulating DNA Self-assembly by DNA-Surface Interactions.

Science.gov (United States)

Liu, Longfei; Li, Yulin; Wang, Yong; Zheng, Jianwei; Mao, Chengde

2017-12-14

DNA self-assembly provides a powerful approach for preparation of nanostructures. It is often studied in bulk solution and involves only DNA-DNA interactions. When confined to surfaces, DNA-surface interactions become an additional, important factor to DNA self-assembly. However, the way in which DNA-surface interactions influence DNA self-assembly is not well studied. In this study, we showed that weak DNA-DNA interactions could be stabilized by DNA-surface interactions to allow large DNA nanostructures to form. In addition, the assembly can be conducted isothermally at room temperature in as little as 5 seconds. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Topological mappings of video and audio data.

Science.gov (United States)

Fyfe, Colin; Barbakh, Wesam; Ooi, Wei Chuan; Ko, Hanseok

2008-12-01

We review a new form of self-organizing map which is based on a nonlinear projection of latent points into data space, identical to that performed in the Generative Topographic Mapping (GTM).(1) But whereas the GTM is an extension of a mixture of experts, this model is an extension of a product of experts.(2) We show visualisation and clustering results on a data set composed of video data of lips uttering 5 Korean vowels. Finally we note that we may dispense with the probabilistic underpinnings of the product of experts and derive the same algorithm as a minimisation of mean squared error between the prototypes and the data. This leads us to suggest a new algorithm which incorporates local and global information in the clustering. Both ot the new algorithms achieve better results than the standard Self-Organizing Map.

Self-induced free surface oscillations caused by water jet

International Nuclear Information System (INIS)

Fukaya, M.; Madarame, H.; Okamoto, K.; Iida, M.; Someya, S.

1995-01-01

The interaction between the high speed flow and the free surfaces could induced surface oscillations. Recently, some kinds of self-induced free surface oscillations caused by water jet were discovered, e.g., a self-induced sloshing, 'Jet-Flutter' and a self-induced manometer oscillation. These oscillations have many different characteristics with each other. In this study, the similarities and differences of these oscillations are examined, and the geometrical effects on the phenomena are experimentally investigated. The self-induced sloshing and the Jet-Flutter have different dimensionless traveling times, which suggests a difference in the energy supply mechanism. When the distance between the inlet and the outlet is small in a vessel, the self-induced manometer oscillation could occur in the multi-free-surface system. (author)

A Forecasting Approach Combining Self-Organizing Map with Support Vector Regression for Reservoir Inflow during Typhoon Periods

Directory of Open Access Journals (Sweden)

Gwo-Fong Lin

2016-01-01

Full Text Available This study describes the development of a reservoir inflow forecasting model for typhoon events to improve short lead-time flood forecasting performance. To strengthen the forecasting ability of the original support vector machines (SVMs model, the self-organizing map (SOM is adopted to group inputs into different clusters in advance of the proposed SOM-SVM model. Two different input methods are proposed for the SVM-based forecasting method, namely, SOM-SVM1 and SOM-SVM2. The methods are applied to an actual reservoir watershed to determine the 1 to 3 h ahead inflow forecasts. For 1, 2, and 3 h ahead forecasts, improvements in mean coefficient of efficiency (MCE due to the clusters obtained from SOM-SVM1 are 21.5%, 18.5%, and 23.0%, respectively. Furthermore, improvement in MCE for SOM-SVM2 is 20.9%, 21.2%, and 35.4%, respectively. Another SOM-SVM2 model increases the SOM-SVM1 model for 1, 2, and 3 h ahead forecasts obtained improvement increases of 0.33%, 2.25%, and 10.08%, respectively. These results show that the performance of the proposed model can provide improved forecasts of hourly inflow, especially in the proposed SOM-SVM2 model. In conclusion, the proposed model, which considers limit and higher related inputs instead of all inputs, can generate better forecasts in different clusters than are generated from the SOM process. The SOM-SVM2 model is recommended as an alternative to the original SVR (Support Vector Regression model because of its accuracy and robustness.

Evaluating Spatial Variability in Sediment and Phosphorus Concentration-Discharge Relationships Using Bayesian Inference and Self-Organizing Maps

Science.gov (United States)

Underwood, Kristen L.; Rizzo, Donna M.; Schroth, Andrew W.; Dewoolkar, Mandar M.

2017-12-01

Given the variable biogeochemical, physical, and hydrological processes driving fluvial sediment and nutrient export, the water science and management communities need data-driven methods to identify regions prone to production and transport under variable hydrometeorological conditions. We use Bayesian analysis to segment concentration-discharge linear regression models for total suspended solids (TSS) and particulate and dissolved phosphorus (PP, DP) using 22 years of monitoring data from 18 Lake Champlain watersheds. Bayesian inference was leveraged to estimate segmented regression model parameters and identify threshold position. The identified threshold positions demonstrated a considerable range below and above the median discharge—which has been used previously as the default breakpoint in segmented regression models to discern differences between pre and post-threshold export regimes. We then applied a Self-Organizing Map (SOM), which partitioned the watersheds into clusters of TSS, PP, and DP export regimes using watershed characteristics, as well as Bayesian regression intercepts and slopes. A SOM defined two clusters of high-flux basins, one where PP flux was predominantly episodic and hydrologically driven; and another in which the sediment and nutrient sourcing and mobilization were more bimodal, resulting from both hydrologic processes at post-threshold discharges and reactive processes (e.g., nutrient cycling or lateral/vertical exchanges of fine sediment) at prethreshold discharges. A separate DP SOM defined two high-flux clusters exhibiting a bimodal concentration-discharge response, but driven by differing land use. Our novel framework shows promise as a tool with broad management application that provides insights into landscape drivers of riverine solute and sediment export.

Quantum self-organization and nuclear collectivities

Science.gov (United States)

Otsuka, T.; Tsunoda, Y.; Togashi, T.; Shimizu, N.; Abe, T.

2018-02-01

The quantum self-organization is introduced as one of the major underlying mechanisms of the quantum many-body systems. In the case of atomic nuclei as an example, two types of the motion of nucleons, single-particle states and collective modes, dominate the structure of the nucleus. The outcome of the collective mode is determined basically by the balance between the effect of the mode-driving force (e.g., quadrupole force for the ellipsoidal deformation) and the resistance power against it. The single-particle energies are one of the sources to produce such resistance power: a coherent collective motion is more hindered by larger gaps between relevant single particle states. Thus, the single-particle state and the collective mode are “enemies” each other. However, the nuclear forces are demonstrated to be rich enough so as to enhance relevant collective mode by reducing the resistance power by changing singleparticle energies for each eigenstate through monopole interactions. This will be verified with the concrete example taken from Zr isotopes. Thus, when the quantum self-organization occurs, single-particle energies can be self-organized, being enhanced by (i) two quantum liquids, e.g., protons and neutrons, (ii) two major force components, e.g., quadrupole interaction (to drive collective mode) and monopole interaction (to control resistance). In other words, atomic nuclei are not necessarily like simple rigid vases containing almost free nucleons, in contrast to the naïve Fermi liquid picture. Type II shell evolution is considered to be a simple visible case involving excitations across a (sub)magic gap. The quantum self-organization becomes more important in heavier nuclei where the number of active orbits and the number of active nucleons are larger. The quantum self-organization is a general phenomenon, and is expected to be found in other quantum systems.

High-resolution charge carrier mobility mapping of heterogeneous organic semiconductors

Science.gov (United States)

Button, Steven W.; Mativetsky, Jeffrey M.

2017-08-01

Organic electronic device performance is contingent on charge transport across a heterogeneous landscape of structural features. Methods are therefore needed to unravel the effects of local structure on overall electrical performance. Using conductive atomic force microscopy, we construct high-resolution out-of-plane hole mobility maps from arrays of 5000 to 16 000 current-voltage curves. To demonstrate the efficacy of this non-invasive approach for quantifying and mapping local differences in electrical performance due to structural heterogeneities, we investigate two thin film test systems, one bearing a heterogeneous crystal structure [solvent vapor annealed 5,11-Bis(triethylsilylethynyl)anthradithiophene (TES-ADT)—a small molecule organic semiconductor] and one bearing a heterogeneous chemical composition [p-DTS(FBTTh2)2:PC71BM—a high-performance organic photovoltaic active layer]. TES-ADT shows nearly an order of magnitude difference in hole mobility between semicrystalline and crystalline areas, along with a distinct boundary between the two regions, while p-DTS(FBTTh2)2:PC71BM exhibits subtle local variations in hole mobility and a nanoscale domain structure with features below 10 nm in size. We also demonstrate mapping of the built-in potential, which plays a significant role in organic light emitting diode and organic solar cell operation.

Relativistic fluid theories - Self organization

International Nuclear Information System (INIS)

Mahajan, S.M.; Hazeltine, R.D.; Yoshida, Z.

2003-01-01

Developments in two distinct but related subjects are reviewed: 1) Formulation and investigation of closed fluid theories which transcend the limitations of standard magnetohydrodynamics (MHD), in particular, theories which are valid in the long mean free path limit and in which pressure anisotropy, heat flow, and arbitrarily strong sheared flows are treated consistently, and 2) Exploitation of the two-fluid theories to derive new plasma configurations in which the flow-field is a co-determinant of the overall dynamics; some of these states belong to the category of self-organized relaxed states. Physical processes which may provide a route to self-organization and complexity are also explored. (author)

Self-organized molecular films with long-range quasiperiodic order.

Science.gov (United States)

Fournée, Vincent; Gaudry, Émilie; Ledieu, Julian; de Weerd, Marie-Cécile; Wu, Dongmei; Lograsso, Thomas

2014-04-22

Self-organized molecular films with long-range quasiperiodic order have been grown by using the complex potential energy landscape of quasicrystalline surfaces as templates. The long-range order arises from a specific subset of quasilattice sites acting as preferred adsorption sites for the molecules, thus enforcing a quasiperiodic structure in the film. These adsorption sites exhibit a local 5-fold symmetry resulting from the cut by the surface plane through the cluster units identified in the bulk solid. Symmetry matching between the C60 fullerene and the substrate leads to a preferred adsorption configuration of the molecules with a pentagonal face down, a feature unique to quasicrystalline surfaces, enabling efficient chemical bonding at the molecule-substrate interface. This finding offers opportunities to investigate the physical properties of model 2D quasiperiodic systems, as the molecules can be functionalized to yield architectures with tailor-made properties.

Self-Organized Transport System

Science.gov (United States)

2009-09-28

This report presents the findings of the simulation model for a self-organized transport system where traffic lights communicate with neighboring traffic lights and make decisions locally to adapt to traffic conditions in real time. The model is insp...

Optical electronics self-organized integration and applications

CERN Document Server

Yoshimura, Tetsuzo

2012-01-01

IntroductionFrom Electronics to Optical ElectronicsAnalysis Tools for Optical CircuitsSelf-Organized Optical Waveguides: Theoretical AnalysisSelf-Organized Optical Waveguides: Experimental DemonstrationsOptical Waveguide Films with Vertical Mirrors 3-D Optical Circuits with Stacked Waveguide Films Heterogeneous Thin-Film Device IntegrationOptical Switches OE Hardware Built by Optical ElectronicsIntegrated Solar Energy Conversion SystemsFuture Challenges.

Self-organizing networks for extracting jet features

International Nuclear Information System (INIS)

Loennblad, L.; Peterson, C.; Pi, H.; Roegnvaldsson, T.

1991-01-01

Self-organizing neural networks are briefly reviewed and compared with supervised learning algorithms like back-propagation. The power of self-organization networks is in their capability of displaying typical features in a transparent manner. This is successfully demonstrated with two applications from hadronic jet physics; hadronization model discrimination and separation of b.c. and light quarks. (orig.)

Driving Solar Giant Cells through the Self-organization of Near-surface Plumes

Science.gov (United States)

Nelson, Nicholas J.; Featherstone, Nicholas A.; Miesch, Mark S.; Toomre, Juri

2018-06-01

Global 3D simulations of solar giant-cell convection have provided significant insight into the processes which yield the Sun’s observed differential rotation and cyclic dynamo action. However, as we move to higher-resolution simulations a variety of codes have encountered what has been termed the convection conundrum. As these simulations increase in resolution and hence the level of turbulence achieved, they tend to produce weak or even anti-solar differential rotation patterns associated with a weak rotational influence (high Rossby number) due to large convective velocities. One potential culprit for this convection conundrum is the upper boundary condition applied in most simulations, which is generally impenetrable. Here we present an alternative stochastic plume boundary condition which imposes small-scale convective plumes designed to mimic near-surface convective downflows, thus allowing convection to carry the majority of the outward solar energy flux up to and through our simulated upper boundary. The use of a plume boundary condition leads to significant changes in the convective driving realized in the simulated domain and thus to the convective energy transport, the dominant scale of the convective enthalpy flux, and the relative strength of the strongest downflows, the downflow network, and the convective upflows. These changes are present even far from the upper boundary layer. Additionally, we demonstrate that, in spite of significant changes, giant cell morphology in the convective patterns is still achieved with self-organization of the imposed boundary plumes into downflow lanes, cellular patterns, and even rotationally aligned banana cells in equatorial regions. This plume boundary presents an alternative pathway for 3D global convection simulations where driving is non-local and may provide a new approach toward addressing the convection conundrum.

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

Energy Technology Data Exchange (ETDEWEB)

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

2014-06-01

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

An atlas of the smaller maps in orientable and nonorientable surfaces

CERN Document Server

Jackson, David

2000-01-01

Maps are beguilingly simple structures with deep and ubiquitous properties. They arise in an essential way in many areas of mathematics and mathematical physics, but require considerable time and computational effort to generate. Few collected drawings are available for reference, and little has been written, in book form, about their enumerative aspects. An Atlas of the Smaller Maps in Orientable and Nonorientable Surfaces is the first book to provide complete collections of maps along with their vertex and face partitions, number of rootings, and an index number for cross referencing. It provides an explanation of axiomatization and encoding, and serves as an introduction to maps as a combinatorial structure. The Atlas lists the maps first by genus and number of edges, and gives the embeddings of all graphs with at most five edges in orientable surfaces, thus presenting the genus distribution for each graph. Exemplifying the use of the Atlas, the authors explore two substantial conjectures with origins in ...

Complexity in plasma: From self-organization to geodynamo

International Nuclear Information System (INIS)

Sato, T.

1996-01-01

A central theme of open-quote open-quote Complexity close-quote close-quote is the question of the creation of ordered structure in nature (self-organization). The assertion is made that self-organization is governed by three key processes, i.e., energy pumping, entropy expulsion and nonlinearity. Extensive efforts have been done to confirm this assertion through computer simulations of plasmas. A system exhibits markedly different features in self-organization, depending on whether the energy pumping is instantaneous or continuous, or whether the produced entropy is expulsed or reserved. The nonlinearity acts to bring a nonequilibrium state into a bifurcation, thus resulting in a new structure along with an anomalous entropy production. As a practical application of our grand view of self-organization a preferential generation of a dipole magnetic field is successfully demonstrated. copyright 1996 American Institute of Physics

Evaluation of Changes in Effluent Quality from Industrial Complexes on the Korean Nationwide Scale Using a Self-Organizing Map

Directory of Open Access Journals (Sweden)

Mi-Jung Bae

2012-04-01

Full Text Available One of the major issues related to the environment in the 21st century is sustainable development. The innovative economic growth policy has supported relatively successful economic development, but poor environmental conservation efforts, have consequently resulted in serious water quality pollution issues. Hence, assessments of water quality and health are fundamental processes towards conserving and restoring aquatic ecosystems. In this study, we characterized spatial and temporal changes in water quality (specifically physico-chemical variables plus priority and non-priority pollutants of discharges from industrial complexes on a national scale in Korea. The data were provided by the Water Quality Monitoring Program operated by the Ministry of Environment, Korea and were measured from 1989 to 2008 on a monthly basis at 61 effluent monitoring sites located at industrial complexes. Analysis of monthly and annual changes in water quality, using the seasonal Mann-Kendall test, indicated an improvement in water quality, which was inferred from a continuous increase in dissolved oxygen and decrease in other water quality factors. A Self-Organizing Map, which is an unsupervised artificial neural network, also indicated an improvement of effluent water quality, by showing spatial and temporal differences in the effluent water quality as well as in the occurrence of priority pollutants. Finally, our results suggested that continued long-term monitoring is necessary to establish plans and policies for wastewater management and health assessment.

Evaluation of Changes in Effluent Quality from Industrial Complexes on the Korean Nationwide Scale Using a Self-Organizing Map

Science.gov (United States)

Bae, Mi-Jung; Kim, Jun-Su; Park, Young-Seuk

2012-01-01

One of the major issues related to the environment in the 21st century is sustainable development. The innovative economic growth policy has supported relatively successful economic development, but poor environmental conservation efforts, have consequently resulted in serious water quality pollution issues. Hence, assessments of water quality and health are fundamental processes towards conserving and restoring aquatic ecosystems. In this study, we characterized spatial and temporal changes in water quality (specifically physico-chemical variables plus priority and non-priority pollutants) of discharges from industrial complexes on a national scale in Korea. The data were provided by the Water Quality Monitoring Program operated by the Ministry of Environment, Korea and were measured from 1989 to 2008 on a monthly basis at 61 effluent monitoring sites located at industrial complexes. Analysis of monthly and annual changes in water quality, using the seasonal Mann-Kendall test, indicated an improvement in water quality, which was inferred from a continuous increase in dissolved oxygen and decrease in other water quality factors. A Self-Organizing Map, which is an unsupervised artificial neural network, also indicated an improvement of effluent water quality, by showing spatial and temporal differences in the effluent water quality as well as in the occurrence of priority pollutants. Finally, our results suggested that continued long-term monitoring is necessary to establish plans and policies for wastewater management and health assessment. PMID:22690190

Influence of oxygen gas on characteristics of self-organized luminous pattern formation observed in an atmospheric dc glow discharge using a liquid electrode

International Nuclear Information System (INIS)

Shirai, Naoki; Uchida, Satoshi; Tochikubo, Fumiyoshi

2014-01-01

Self-organized luminous pattern formation is observed in the liquid surface of an atmospheric dc glow discharge using a liquid electrode with a miniature helium flow. The factors affecting pattern formation are the gap length, discharge current, helium mass flow rate and polarity. The pattern shape depends on the conductivity and temperature of the liquid electrode. A variety of patterns were observed by changing the conductivity and temperature of the liquid. We clarified that the self-organized pattern formation depends on the amount of electronegative gas, such as oxygen, in the gas in the electrode gap. When an oxygen gas flow was fed to the liquid surface from the outside in an obliquely downward direction, namely, the amount of oxygen gas on the liquid surface was increased locally, self-organized pattern formation was observed in the region with the increased amount of oxygen gas. When the amount of oxygen in the gas in the gap was changed by using a sheath flow system, the appearance of the pattern changed. The presence of oxygen gas strongly affected the self-organized pattern formation of the atmospheric dc discharge using a liquid anode. (paper)

The role of hierarchy in self-organizing systems

NARCIS (Netherlands)

Ollfen, van W.; Romme, A.G.L.

1995-01-01

This paper discusses the role of hierarchy in human systems. Two kinds of self-organizing processes are distinguished: conservative and dissipative self-organization. The former leads to rather stable, specialistic systems, whereas the latter leads to continuously changing generalistic systems. When

The role of crystal orientation and surface proximity in the self-similar behavior of deformed Cu single crystals

Energy Technology Data Exchange (ETDEWEB)

Pang, Judy W.L., E-mail: pangj@ornl.gov [Materials Science and Technology Division, Oak Ridge National Laboratory, 1 Behtel Valley Road, Oak Ridge, TN 37831 (United States); Ice, Gene E. [Materials Science and Technology Division, Oak Ridge National Laboratory, 1 Behtel Valley Road, Oak Ridge, TN 37831 (United States); Liu Wenjun [Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439 (United States)

2010-11-25

We report on novel 3D spatially resolved X-ray diffraction microscopy studies of self-affine behavior in deformed single crystals. This study extends surface profile measurements of self-affined morphology changes in single crystals during deformation to include local lattice rotations and sub-surface behavior. Investigations were made on the spatial correlation of the local lattice rotations in 8% tensile deformed Cu single crystals oriented with [1 2 3], [1 1 1] and [0 0 1] axes parallel to the tensile axis. The nondestructive depth-resolved measurements were made over a length scale of one to hundreds of micrometers. Self-affined correlation was found both at the surface and below the surface of the samples. A universal exponent for the power-law similar to that observed with surface profile methods is found at the surface of all samples but crystallographically sensitive changes are observed as a function of depth. Correlation lengths of the self-affine behavior vary with the [1 2 3] crystal exhibiting the longest self-affine length scale of 70 {mu}m with only 18 {mu}m for the [1 1 1] and [0 0 1] crystals. These measurements illuminate the transition from surface-like to bulk-like deformation behavior and provide new quantitative information to guide emerging models of self-organized structures in plasticity.

Integrative mapping analysis of chicken microchromosome 16 organization

Directory of Open Access Journals (Sweden)

Bed'hom Bertrand

2010-11-01

Full Text Available Abstract Background The chicken karyotype is composed of 39 chromosome pairs, of which 9 still remain totally absent from the current genome sequence assembly, despite international efforts towards complete coverage. Some others are only very partially sequenced, amongst which microchromosome 16 (GGA16, particularly under-represented, with only 433 kb assembled for a full estimated size of 9 to 11 Mb. Besides the obvious need of full genome coverage with genetic markers for QTL (Quantitative Trait Loci mapping and major genes identification studies, there is a major interest in the detailed study of this chromosome because it carries the two genetically independent MHC complexes B and Y. In addition, GGA16 carries the ribosomal RNA (rRNA genes cluster, also known as the NOR (nucleolus organizer region. The purpose of the present study is to construct and present high resolution integrated maps of GGA16 to refine its organization and improve its coverage with genetic markers. Results We developed 79 STS (Sequence Tagged Site markers to build a physical RH (radiation hybrid map and 34 genetic markers to extend the genetic map of GGA16. We screened a BAC (Bacterial Artificial Chromosome library with markers for the MHC-B, MHC-Y and rRNA complexes. Selected clones were used to perform high resolution FISH (Fluorescent In Situ Hybridization mapping on giant meiotic lampbrush chromosomes, allowing meiotic mapping in addition to the confirmation of the order of the three clusters along the chromosome. A region with high recombination rates and containing PO41 repeated elements separates the two MHC complexes. Conclusions The three complementary mapping strategies used refine greatly our knowledge of chicken microchromosome 16 organisation. The characterisation of the recombination hotspots separating the two MHC complexes demonstrates the presence of PO41 repetitive sequences both in tandem and inverted orientation. However, this region still needs to

Updated global soil map for the Weather Research and Forecasting model and soil moisture initialization for the Noah land surface model

Science.gov (United States)

DY, C. Y.; Fung, J. C. H.

2016-08-01

A meteorological model requires accurate initial conditions and boundary conditions to obtain realistic numerical weather predictions. The land surface controls the surface heat and moisture exchanges, which can be determined by the physical properties of the soil and soil state variables, subsequently exerting an effect on the boundary layer meteorology. The initial and boundary conditions of soil moisture are currently obtained via National Centers for Environmental Prediction FNL (Final) Operational Global Analysis data, which are collected operationally in 1° by 1° resolutions every 6 h. Another input to the model is the soil map generated by the Food and Agriculture Organization of the United Nations - United Nations Educational, Scientific and Cultural Organization (FAO-UNESCO) soil database, which combines several soil surveys from around the world. Both soil moisture from the FNL analysis data and the default soil map lack accuracy and feature coarse resolutions, particularly for certain areas of China. In this study, we update the global soil map with data from Beijing Normal University in 1 km by 1 km grids and propose an alternative method of soil moisture initialization. Simulations of the Weather Research and Forecasting model show that spinning-up the soil moisture improves near-surface temperature and relative humidity prediction using different types of soil moisture initialization. Explanations of that improvement and improvement of the planetary boundary layer height in performing process analysis are provided.

Self-organized computation with unreliable, memristive nanodevices

International Nuclear Information System (INIS)

Snider, G S

2007-01-01

Nanodevices have terrible properties for building Boolean logic systems: high defect rates, high variability, high death rates, drift, and (for the most part) only two terminals. Economical assembly requires that they be dynamical. We argue that strategies aimed at mitigating these limitations, such as defect avoidance/reconfiguration, or applying coding theory to circuit design, present severe scalability and reliability challenges. We instead propose to mitigate device shortcomings and exploit their dynamical character by building self-organizing, self-healing networks that implement massively parallel computations. The key idea is to exploit memristive nanodevice behavior to cheaply implement adaptive, recurrent networks, useful for complex pattern recognition problems. Pulse-based communication allows the designer to make trade-offs between power consumption and processing speed. Self-organization sidesteps the scalability issues of characterization, compilation and configuration. Network dynamics supplies a graceful response to device death. We present simulation results of such a network-a self-organized spatial filter array-that demonstrate its performance as a function of defects and device variation

Magnetic-field-dependent morphology of self-organized Fe on stepped Si(111) surfaces

International Nuclear Information System (INIS)

Cougo dos Santos, M.; Geshev, J.; Pereira, L. G.; Schmidt, J. E.

2009-01-01

The present work reports on Fe thin films grown on vicinal Si(111) substrates via rf magnetron sputtering. The dependencies of the growth mode and magnetic properties of the obtained iron nanostructures on both crystallographic surface orientation and on the direction of the very weak stray magnetic field from the magnetron gun were studied. Scanning tunneling microscopy images showed strong dependence of the Fe grains' orientation on the stray field direction in relation to the substrate's steps demonstrating that, under appropriately directed magnetic field, Si surfaces can be used as templates for well-defined self-assembled iron nanostructures. Magneto-optical Kerr effect hysteresis loops showed an easy-axis coercivity almost one order of magnitude smaller for the film deposited with stray field applied along the steps, accompanied with a change in the magnetization reversal mode. Phenomenological models involving coherent rotation and/or domain-wall unpinning were used for the interpretation of these results.

Use of roughness maps in visualisation of surfaces

DEFF Research Database (Denmark)

Seitavuopio, Paulus; Rantanen, Jukka; Yliruusi, Jouko

2005-01-01

monohydrate, theophylline anhydrate, sodium chloride and potassium chloride. The roughness determinations were made by a laser profilometer. The new matrix method gives detailed roughness maps, which are able to show local variations in surface roughness values and provide an illustrative picture...

Deliberative Self-Organizing Traffic Lights with Elementary Cellular Automata

Directory of Open Access Journals (Sweden)

Jorge L. Zapotecatl

2017-01-01

Full Text Available Self-organizing traffic lights have shown considerable improvements compared to traditional methods in computer simulations. Self-organizing methods, however, use sophisticated sensors, increasing their cost and limiting their deployment. We propose a novel approach using simple sensors to achieve self-organizing traffic light coordination. The proposed approach involves placing a computer and a presence sensor at the beginning of each block; each such sensor detects a single vehicle. Each computer builds a virtual environment simulating vehicle movement to predict arrivals and departures at the downstream intersection. At each intersection, a computer receives information across a data network from the computers of the neighboring blocks and runs a self-organizing method to control traffic lights. Our simulations showed a superior performance for our approach compared with a traditional method (a green wave and a similar performance (close to optimal compared with a self-organizing method using sophisticated sensors but at a lower cost. Moreover, the developed sensing approach exhibited greater robustness against sensor failures.

Electrocardiographic markers of ischemia during mental stress testing in postinfarction patients. Role of body surface mapping

International Nuclear Information System (INIS)

Bosimini, E.; Galli, M.; Guagliumi, G.; Giubbini, R.; Tavazzi, L.

1991-01-01

In patients with coronary artery disease, radionuclide investigations have documented a high incidence of mental stress-induced myocardial ischemia in the absence of significant electrocardiographic changes and/or angina. To investigate the causes of the low electrocardiographic sensitivity, we recorded body surface maps during mental arithmetic in 22 normal volunteers and 37 postinfarction patients with residual exercise ischemia. Myocardial perfusion was studied with thallium-201 or technetium-99 (SESTAMIBI) planar scans. In 14 patients, body surface maps were also recorded during atrial pacing at the heart rate values achieved during mental stress. While taking the body surface maps, the area from J point to 80 msec after this point (ST-80) was analyzed by integral maps, difference maps, and departure maps. The body surface mapping criteria for ischemia were a new negative area on the integral maps, a negative potential of more than 2 SD from mean normal values on the difference maps, and a negative departure index of more than 2. Scintigraphy showed asymptomatic myocardial hypoperfusion in 33 patients. Eight patients had significant ST segment depression. The ST-80 integral and difference maps identified 17 ischemic patients. Twenty-four patients presented abnormal departure maps. One patient presented ST depression and abnormal body surface maps without reversible tracer defect. In 14 of 14 patients, atrial pacing did not reproduce the body surface map abnormalities. The analyses of the other electrocardiographic variables showed that in patients with mental stress-induced perfusion defects, only changes of T apex-T offset (aT-eT) interval in Frank leads and changes of maximum negative potential value of aT-eT integral maps significantly differed from those of normal subjects

Cancer Risk Map for the Surface of Mars

Science.gov (United States)

Kim, Myung-Hee Y.; Cucinotta, Francis A.

2011-01-01

We discuss calculations of the median and 95th percentile cancer risks on the surface of Mars for different solar conditions. The NASA Space Radiation Cancer Risk 2010 model is used to estimate gender and age specific cancer incidence and mortality risks for astronauts exploring Mars. Organ specific fluence spectra and doses for large solar particle events (SPE) and galactic cosmic rays (GCR) at various levels of solar activity are simulated using the HZETRN/QMSFRG computer code, and the 2010 version of the Badhwar and O Neill GCR model. The NASA JSC propensity model of SPE fluence and occurrence is used to consider upper bounds on SPE fluence for increasing mission lengths. In the transport of particles through the Mars atmosphere, a vertical distribution of Mars atmospheric thickness is calculated from the temperature and pressure data of Mars Global Surveyor, and the directional cosine distribution is implemented to describe the spherically distributed atmospheric distance along the slant path at each elevation on Mars. The resultant directional shielding by Mars atmosphere at each elevation is coupled with vehicle and body shielding for organ dose estimates. Astronaut cancer risks are mapped on the global topography of Mars, which was measured by the Mars Orbiter Laser Altimeter. Variation of cancer risk on the surface of Mars is due to a 16-km elevation range, and the large difference is obtained between the Tharsis Montes (Ascraeus, Pavonis, and Arsia) and the Hellas impact basin. Cancer incidence risks are found to be about 2-fold higher than mortality risks with a disproportionate increase in skin and thyroid cancers for all astronauts and breast cancer risk for female astronauts. The number of safe days on Mars to be below radiation limits at the 95th percent confidence level is reported for several Mission design scenarios.

Association mapping of main tomato fruit sugars and organic acids

Directory of Open Access Journals (Sweden)

Jiantao Zhao

2016-08-01

Full Text Available Association mapping has been widely used to map the significant associated loci responsible for natural variation in complex traits and are valuable for crop improvement. Sugars and organic acids are the most important metabolites in tomato fruits. We used a collection of 174 tomato accessions composed of S. lycopersicum (123 accessions and S. lycopersicum var cerasiforme (51 accessions to detect significantly associated loci controlling the variation of main sugars and organic acids. The accessions were genotyped with 182 SSRs spreading over the tomato genome. Association mapping was conducted on the main sugars and organic acids detected by gas chromatography-mass spectrometer (GC-MS over two years using the mixed linear model (MLM. We detected a total of 58 significantly associated loci (P<0.001 for the 17 sugars and organic acids, including fructose, glucose, sucrose, citric acid, malic acid. These results not only co-localized with several reported QTLs, including fru9.1/PV, suc9.1/PV, ca2.1/HS, ca3.1/PV, ca4.1/PV and ca8.1/PV, but also provided a list of candidate significantly associated loci to be functionally validated. These significantly associated loci could be used for deciphering the genetic architecture of tomato fruit sugars and organic acids and for tomato quality breeding.

Metal complexation and monolayer self-assembly of the bio-organic semiconductor Alizarin

Energy Technology Data Exchange (ETDEWEB)

Uppal, Neeti [Dept. Earth and Environmental Sciences, Ludwig-Maximilians-Universitaet Muenchen (LMU) and Center for NanoSciences (CeNS), Muenchen (Germany); Institut fuer Physik, Universitaet Augsburg (Germany); Gast, Norbert [Dept. Earth and Environmental Sciences, Ludwig-Maximilians-Universitaet Muenchen (LMU) and Center for NanoSciences (CeNS), Muenchen (Germany); Zentrum Neue Technologien, Deutsches Museum, Muenchen (Germany); Bueno, Martin [Fakultaet Feinwerk- und Mikrotechnik, Physikalische Technik, Hochschule Muenchen (Germany); Heckl, Wolfgang M. [Dept. of Physics, Technische Universitaet Muenchen (TUM), Garching (Germany); Zentrum Neue Technologien, Deutsches Museum, Muenchen (Germany); Trixler, Frank [Dept. Earth and Environmental Sciences, Ludwig-Maximilians-Universitaet Muenchen (LMU) and Center for NanoSciences (CeNS), Muenchen (Germany); Dept. of Physics, Technische Universitaet Muenchen (TUM), Garching (Germany); Zentrum Neue Technologien, Deutsches Museum, Muenchen (Germany)

2010-07-01

Organic Solid/Solid Wetting Deposition (OSWD) (Trixler et al.: Chem.Eur.J. 13 (2007), 7785) enables to deposit insoluble molecules such as organic pigments and semiconductors on substrate surfaces under ambient conditions. We explore the potential of OSWD to grow and manipulate monolayers of biomolecules and their chelates on graphite and use Alizarin as a model system - a natural organic compound which occurs mainly as an anthraquinone glycoside in plants. Our investigations via Scanning Tunneling Microscopy (STM), Tunneling Spectroscopy (TS) and Molecular Modelling reveal that OSWD works also with bio-organic molecules and chelate complexes and show that the advantages of OSWD (self-assembly under ambient conditions in a non-solvent environment, nanomanipulation via molecular extraction) can all be tapped.

Self-organized fluorescent nanosensors for ratiometric Pb2+ detection.

Science.gov (United States)

Arduini, Maria; Mancin, Fabrizio; Tecilla, Paolo; Tonellato, Umberto

2007-07-31

Silica nanoparticles (60 nm diameter) doped with fluorescent dyes and functionalized on the surface with thiol groups have been proved to be efficient fluorescent chemosensors for Pb2+ ions. The particles can detect a 1 microM metal ion concentration with a good selectivity, suffering only interference from Cu2+ ions. Analyte binding sites are provided by the simple grafting of the thiol groups on the nanoparticles. Once bound to the particles surface, the Pb2+ ions quench the emission of the reporting dyes embedded. Sensor performances can be improved by taking advantage of the ease of production of multishell silica particles. On one hand, signaling units can be concentrated in the external shells, allowing a closer interaction with the surface-bound analyte. On the other, a second dye can be buried in the particle core, far enough from the surface to be unaffected by the Pb2+ ions, thus producing a reference signal. In this way, a ratiometric system is easily prepared by simple self-organization of the particle components.

Singularity spectrum of self-organized criticality

International Nuclear Information System (INIS)

Canessa, E.

1992-10-01

I introduce a simple continuous probability theory based on the Ginzburg-Landau equation that provides for the first time a common analytical basis to relate and describe the main features of two seemingly different phenomena of condensed-matter physics, namely self-organized criticality and multifractality. Numerical support is given by a comparison with reported simulation data. Within the theory the origin of self-organized critical phenomena is analysed in terms of a nonlinear singularity spectrum different form the typical convex shape due to multifractal measures. (author). 29 refs, 5 figs

A data-mining framework for exploring the multi-relation between fish species and water quality through self-organizing map.

Science.gov (United States)

Tsai, Wen-Ping; Huang, Shih-Pin; Cheng, Su-Ting; Shao, Kwang-Tsao; Chang, Fi-John

2017-02-01

The steep slopes of rivers can easily lead to large variations in river water quality during typhoon seasons in Taiwan, which may poses significant impacts on riverine eco-hydrological environments. This study aims to investigate the relationship between fish communities and water quality by using artificial neural networks (ANNs) for comprehending the upstream eco-hydrological system in northern Taiwan. We collected a total of 276 heterogeneous datasets with 8 water quality parameters and 25 fish species from 10 sampling sites. The self-organizing feature map (SOM) was used to cluster, analyze and visualize the heterogeneous datasets. Furthermore, the structuring index (SI) was adopted to determine the relative importance of each input variable of the SOM and identify the indicator factors. The clustering results showed that the SOM could suitably reflect the spatial characteristics of fishery sampling sites. Besides, the patterns of water quality parameters and fish species could be distinguishably (visually) classified into three eco-water quality groups: 1) typical upstream freshwater fishes that depended the most on dissolved oxygen (DO); 2) typical middle-lower reach riverine freshwater fishes that depended the most on total phosphorus (TP) and ammonia nitrogen; and 3) low lands or pond (reservoirs) freshwater fishes that depended the most on water temperature, suspended solids and chemical oxygen demand. According to the results of the SI, the representative indicators of water quality parameters and fish species consisted of DO, TP and Onychostoma barbatulum. This grouping result suggested that the methodology can be used as a guiding reference to comprehensively relate ecology to water quality. Our methods offer a cost-effective alternative to more traditional methods for identifying key water quality factors relating to fish species. In addition, visualizing the constructed topological maps of the SOM could produce detailed inter-relation between water

Information Driven Ecohydrologic Self-Organization

Directory of Open Access Journals (Sweden)

Benjamin L. Ruddell

2010-09-01

Full Text Available Variability plays an important role in the self-organized interaction between vegetation and its environment, yet the principles that characterize the role of the variability in these interactions remain elusive. To address this problem, we study the dependence between a number of variables measured at flux towers by quantifying the information flow between the different variables along with the associated time lag. By examining this network of feedback loops for seven ecosystems in different climate regions, we find that: (1 the feedback tends to maximize information production in the entire system, and the latter increases with increasing variability within the whole system; and (2 variables that participate in feedback exhibit moderated variability. Self-organization arises as a tradeoff where the ability of the total system to maximize information production through feedback is limited by moderate variability of the participating variables. This relationship between variability and information production leads to the emergence of ordered organization.

Tensor SOM and tensor GTM: Nonlinear tensor analysis by topographic mappings.

Science.gov (United States)

Iwasaki, Tohru; Furukawa, Tetsuo

2016-05-01

In this paper, we propose nonlinear tensor analysis methods: the tensor self-organizing map (TSOM) and the tensor generative topographic mapping (TGTM). TSOM is a straightforward extension of the self-organizing map from high-dimensional data to tensorial data, and TGTM is an extension of the generative topographic map, which provides a theoretical background for TSOM using a probabilistic generative model. These methods are useful tools for analyzing and visualizing tensorial data, especially multimodal relational data. For given n-mode relational data, TSOM and TGTM can simultaneously organize a set of n-topographic maps. Furthermore, they can be used to explore the tensorial data space by interactively visualizing the relationships between modes. We present the TSOM algorithm and a theoretical description from the viewpoint of TGTM. Various TSOM variations and visualization techniques are also described, along with some applications to real relational datasets. Additionally, we attempt to build a comprehensive description of the TSOM family by adapting various data structures. Copyright © 2016 Elsevier Ltd. All rights reserved.

Self-organization of single filaments and diffusive plasmas during a single pulse in dielectric-barrier discharges

International Nuclear Information System (INIS)

Babaeva, Natalia Yu; Kushner, Mark J

2014-01-01

Self-organization of filaments in dielectric-barrier discharges (DBDs) probably has many origins. However, the dominant cause is proposed to be the accumulation of charge on the surfaces of the bounding dielectrics that reinforces successive discharge pulses to occur at the same locations. A secondary cause is the electrostatic repulsion of individual plasma filaments. Self-organization typically develops over many discharge pulses. In this paper, we discuss the results of a computational investigation of plasma filaments in overvoltage DBDs that, under select conditions, display self-organized patterns (SOPs) of plasma density during a single discharge pulse. (Overvoltage refers to the rapid application of a voltage in excess of the quasi-dc breakdown voltage.) The origin of the SOPs is a synergistic relationship between the speed of the surface-ionization waves that propagate along each dielectric and the rate at which avalanche occurs across the gap. For our test conditions, SOPs were not observed at lower voltages and gradually formed at higher voltages. The same conditions that result in SOPs, i.e. the application of an overvoltage, also produce more diffuse discharges. A transition from a single narrow filament to a more diffuse structure was observed as overvoltage was approached. The sensitivity of SOPs to the orientation and permittivity of the bounding dielectrics is discussed. (paper)

Developing Coastal Surface Roughness Maps Using ASTER and QuickBird Data Sources

Science.gov (United States)

Spruce, Joe; Berglund, Judith; Davis, Bruce

2006-01-01

This viewgraph presentation regards one element of a larger project on the integration of NASA science models and data into the Hazards U.S. Multi-Hazard (HAZUS-MH) Hurricane module for hurricane damage and loss risk assessment. HAZUS-MH is a decision support tool being developed by the National Institute of Building Sciences for the Federal Emergency Management Agency (FEMA). It includes the Hurricane Module, which employs surface roughness maps made from National Land Cover Data (NLCD) maps to estimate coastal hurricane wind damage and loss. NLCD maps are produced and distributed by the U.S. Geological Survey. This presentation discusses an effort to improve upon current HAZUS surface roughness maps by employing ASTER multispectral classifications with QuickBird "ground reference" imagery.

Self-Organization during Friction of Slide Bearing Antifriction Materials

Directory of Open Access Journals (Sweden)

Iosif S. Gershman

2015-12-01

Full Text Available This article discusses the peculiarities of self-organization behavior and formation of dissipative structures during friction of antifriction alloys for slide bearings against a steel counterbody. It shows that during self-organization, the moment of friction in a tribosystem may be decreasing with the load growth and in the bifurcations of the coefficient of friction with respect to load. Self-organization and the formation of dissipative structures lead to an increase in the seizure load.

Concept and Feasibility Study of Self-Organized Electrochemical Devices

National Research Council Canada - National Science Library

Moorehead, William

2002-01-01

.... In this work, using attractive and repulsive London-van der Waals forces, a self-organized, interpenetrating, separator-free rechargeable lithium ion battery called a self-organized battery system (SBS) is proposed...

Self-organization of spatial patterning in human embryonic stem cells

Science.gov (United States)

Deglincerti, Alessia; Etoc, Fred; Ozair, M. Zeeshan; Brivanlou, Ali H.

2017-01-01

The developing embryo is a remarkable example of self-organization, where functional units are created in a complex spatio-temporal choreography. Recently, human embryonic stem cells (ESCs) have been used to recapitulate in vitro the self-organization programs that are executed in the embryo in vivo. This represents a unique opportunity to address self-organization in humans that is otherwise not addressable with current technologies. In this essay, we review the recent literature on self-organization of human ESCs, with a particular focus on two examples: formation of embryonic germ layers and neural rosettes. Intriguingly, both activation and elimination of TGFβ signaling can initiate self-organization, albeit with different molecular underpinnings. We discuss the mechanisms underlying the formation of these structures in vitro and explore future challenges in the field. PMID:26970615

Multicellular Self-Organization of P. aeruginosa due to Interactions with Secreted Trails.

Science.gov (United States)

Gelimson, Anatolij; Zhao, Kun; Lee, Calvin K; Kranz, W Till; Wong, Gerard C L; Golestanian, Ramin

2016-10-21

Guided movement in response to slowly diffusing polymeric trails provides a unique mechanism for self-organization of some microorganisms. To elucidate how this signaling route leads to microcolony formation, we experimentally probe the trajectory and orientation of Pseudomonas aeruginosa that propel themselves on a surface using type IV pili motility appendages, which preferentially attach to deposited exopolysaccharides. We construct a stochastic model by analyzing single-bacterium trajectories and show that the resulting theoretical prediction for the many-body behavior of the bacteria is in quantitative agreement with our experimental characterization of how cells explore the surface via a power-law strategy.

Emergence or self-organization?: Look to the soil population.

Science.gov (United States)

Addiscott, Tom

2011-07-01

EMERGENCE IS NOT WELL DEFINED, BUT ALL EMERGENT SYSTEMS HAVE THE FOLLOWING CHARACTERISTICS: the whole is more than the sum of the parts, they show bottom-up rather top-down organization and, if biological, they involve chemical signaling. Self-organization can be understood in terms of the second and third stages of thermodynamics enabling these stages used as analogs of ecosystem functioning. The second stage system was suggested earlier to provide a useful analog of the behavior of natural and agricultural ecosystems subjected to perturbations, but for this it needs the capacity for self-organization. Considering the hierarchy of the ecosystem suggests that this self-organization is provided by the third stage, whose entropy maximization acts as an analog of that of the soil population when it releases small molecules from much larger molecules in dead plant matter. This it does as vigorously as conditions allow. Through this activity, the soil population confers self-organization at both the ecosystem and the global level. The soil population has been seen as both emergent and self-organizing, supporting the suggestion that the two concepts are are so closely linked as to be virtually interchangeable. If this idea is correct one of the characteristics of a biological emergent system seems to be the ability to confer self-organization on an ecosystem or other entity which may be larger than itself. The beehive and the termite colony are emergent systems which share this ability.

Deterministic self-organization: Ordered positioning of InAs quantum dots by self-organized anisotropic strain engineering on patterned GaAs(311)B

International Nuclear Information System (INIS)

Selcuk, E.; Hamhuis, G.J.; Noetzel, R.

2009-01-01

Laterally ordered InGaAs quantum dot (QD) arrays, InAs QD molecules, and single InAs QDs in a spot-like periodic arrangement are created by self-organized anisotropic strain engineering of InGaAs/GaAs superlattice (SL) templates on planar GaAs (311)B substrates in molecular beam epitaxy. On shallow- and deep-patterned substrates the respectively generated steps and facets guide the self-organization process during SL template formation to create more complex ordering such as periodic stripes, depending on pattern design. Here we demonstrate for patterns such as shallow- and deepetched round holes and deep-etched zigzag mesas that the self-organized periodic arrangement of QD molecules and single QDs is spatially locked to the pattern sidewalls and corners. This extends the concept of guided self-organization to deterministic self-organization. Absolute position control of the QDs is achieved without one-to-one pattern definition. This guarantees the excellent arrangement control of the ordered QD molecules and single QDs with strong photoluminescence emission up to room temperature, which is required for future quantum functional devices. (copyright 2009 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

Chemical reactivity of self-organized alumina nanopores in aqueous medium

International Nuclear Information System (INIS)

Rocca, E.; Vantelon, D.; Gehin, A.; Augros, M.; Viola, A.

2011-01-01

This work is devoted to the characterization of the structure and chemistry of small self-organized nanopores of aluminum oxide in aqueous medium (diameter 4 /AlO 6 clusters is proposed to describe the amorphous oxide constituting the walls of the nanostructure. X-ray absorption near edge spectroscopy measurements, electrokinetic measurements and O 18 tracer experiments bring to light the structural changes and the specific diffusion mechanism in the nanometer network. Immersion in boiling water induces both the transformation of AlO 4 to AlO 6 clusters and the release of sulfate species by hydrolysis. Water molecules rapidly diffuse in the nanostructure, but ion diffusion is selective because of surface positive charges and overlap of the surface electric field in very small pores.

Selective Binding, Self-Assembly and Nanopatterning of the Creutz-Taube Ion on Surfaces

Directory of Open Access Journals (Sweden)

Qingling Hang

2009-02-01

Full Text Available The surface attachment properties of the Creutz-Taube ion, i.e., [(NH35Ru(pyrazineRu(NH35]5+, on both hydrophilic and hydrophobic types of surfaces were investigated using X-ray photoelectron spectroscopy (XPS. The results indicated that the Creutz-Taube ions only bound to hydrophilic surfaces, such as SiO2 and –OH terminated organic SAMs on gold substrates. No attachment of the ions on hydrophobic surfaces such as –CH3 terminated organic SAMs and poly(methylmethacrylate (PMMA thin films covered gold or SiO2 substrates was observed. Further ellipsometric, atomic force microscopy (AFM and time-dependent XPS studies suggested that the attached cations could form an inorganic analog of the self-assembled monolayer on SiO2 substrate with a “lying-down” orientation. The strong electrostatic interaction between the highly charged cations and the anionic SiO2 surface was believed to account for these observations. Based on its selective binding property, patterning of wide (~200 nm and narrow (~35 nm lines of the Creutz-Taube ions on SiO2 surface were demonstrated through PMMA electron resist masks written by electron beam lithography (EBL.

Templated dewetting: designing entirely self-organized platforms for photocatalysis.

Science.gov (United States)

Altomare, Marco; Nguyen, Nhat Truong; Schmuki, Patrik

2016-12-01

Formation and dispersion of metal nanoparticles on oxide surfaces in site-specific or even arrayed configuration are key in various technological processes such as catalysis, photonics, electrochemistry and for fabricating electrodes, sensors, memory devices, and magnetic, optical, and plasmonic platforms. A crucial aspect towards an efficient performance of many of these metal/metal oxide arrangements is a reliable fabrication approach. Since the early works on graphoepitaxy in the 70s, solid state dewetting of metal films on patterned surfaces has been much explored and regarded as a most effective tool to form defined arrays of ordered metal particles on a desired substrate. While templated dewetting has been studied in detail, particularly from a mechanistic perspective on lithographically patterned Si surfaces, the resulting outstanding potential of its applications on metal oxide semiconductors, such as titania, has received only limited attention. In this perspective we illustrate how dewetting and particularly templated dewetting can be used to fabricate highly efficient metal/TiO 2 photocatalyst assemblies e.g. for green hydrogen evolution. A remarkable advantage is that the synthesis of such photocatalysts is completely based on self-ordering principles: anodic self-organized TiO 2 nanotube arrays that self-align to a highest degree of hexagonal ordering are an ideal topographical substrate for a second self-ordering process, that is, templated-dewetting of sputter-deposited metal thin films. The controllable metal/semiconductor coupling delivers intriguing features and functionalities. We review concepts inherent to dewetting and particularly templated dewetting, and outline a series of effective tools that can be synergistically interlaced to reach fine control with nanoscopic precision over the resulting metal/TiO 2 structures (in terms of e.g. high ordering, size distribution, site specific placement, alloy formation) to maximize their photocatalytic

Self-Organization of Spatial Patterning in Human Embryonic Stem Cells.

Science.gov (United States)

Deglincerti, Alessia; Etoc, Fred; Ozair, M Zeeshan; Brivanlou, Ali H

2016-01-01

The developing embryo is a remarkable example of self-organization, where functional units are created in a complex spatiotemporal choreography. Recently, human embryonic stem cells (ESCs) have been used to recapitulate in vitro the self-organization programs that are executed in the embryo in vivo. This represents an unique opportunity to address self-organization in humans that is otherwise not addressable with current technologies. In this chapter, we review the recent literature on self-organization of human ESCs, with a particular focus on two examples: formation of embryonic germ layers and neural rosettes. Intriguingly, both activation and elimination of TGFβ signaling can initiate self-organization, albeit with different molecular underpinnings. We discuss the mechanisms underlying the formation of these structures in vitro and explore future challenges in the field. © 2016 Elsevier Inc. All rights reserved.

Revisit to the helicity and the generalized self-organization theory

International Nuclear Information System (INIS)

Kondoh, Y.; Takahashi, T.; Momota, H.

2000-09-01

It is clarified that the so-caned 'helicity conservation law' is never the conservation equation of the helicity K itself', but is merely 'the time change rate equation of K', which is passively and resultantly determined by the mutually independent volume and surface integral terms. It is shown that since the total helicity K can never be conserved in the real experimental systems, the conjecture of the total helicity invariance is not physically available to real magnetized plasmas in an exact sense. The well-known relaxation theory by Dr. J. B. Taylor is clarified to be neither the variational principle nor the energy principle, but be merely a mathematical calculation, using the variational calculus in order to find the minimum magnetic energy solution from the set of solutions having the same value of K. With the use of auto-correlations for physical quantities, it is presented that a novel basic formulation of an extended generalized self-organization theory, which is not based on neither the variational principle nor the energy principle. It is clarified that conservation equations concerning with all physical quantities for the dynamic system of interest are naturally embedded in the formulation of the generalized self-organization theory. The self-organized states of every physical quantities of interest may be realized during their own phases and the dynamical system may evolve repeatedly those out of phase organizations, depending on boundary conditions and input powers. It is shown that the conservation laws can be used to extend conventional methods of plasma current drives by energy injections with use of various types of energies, such as magnetic energies, electromagnetic wave energies, internal energies of plasmoids by plasma guns, which induce the thermal plasma flow velocity, various particle beam energies, and so on. (author)

Revisit to the helicity and the generalized self-organization theory

Energy Technology Data Exchange (ETDEWEB)

Kondoh, Y.; Takahashi, T. [Dept. of Electronic Engineering, Gunma Univ., Kiryu, Gunma (Japan); Momota, H. [Illinois Univ., Illinois (United States)

2000-09-01

It is clarified that the so-caned 'helicity conservation law' is never the conservation equation of the helicity K itself', but is merely 'the time change rate equation of K', which is passively and resultantly determined by the mutually independent volume and surface integral terms. It is shown that since the total helicity K can never be conserved in the real experimental systems, the conjecture of the total helicity invariance is not physically available to real magnetized plasmas in an exact sense. The well-known relaxation theory by Dr. J. B. Taylor is clarified to be neither the variational principle nor the energy principle, but be merely a mathematical calculation, using the variational calculus in order to find the minimum magnetic energy solution from the set of solutions having the same value of K. With the use of auto-correlations for physical quantities, it is presented that a novel basic formulation of an extended generalized self-organization theory, which is not based on neither the variational principle nor the energy principle. It is clarified that conservation equations concerning with all physical quantities for the dynamic system of interest are naturally embedded in the formulation of the generalized self-organization theory. The self-organized states of every physical quantities of interest may be realized during their own phases and the dynamical system may evolve repeatedly those out of phase organizations, depending on boundary conditions and input powers. It is shown that the conservation laws can be used to extend conventional methods of plasma current drives by energy injections with use of various types of energies, such as magnetic energies, electromagnetic wave energies, internal energies of plasmoids by plasma guns, which induce the thermal plasma flow velocity, various particle beam energies, and so on. (author)

Self-organizing maps of Kohonen (SOM) applied to multidimensional monitoring data of the IEA-R1 nuclear research reactor

International Nuclear Information System (INIS)

Affonso, Gustavo S.; Pereira, Iraci M.; Mesquita, Roberto N. de; Bueno, Elaine I.

2011-01-01

Multivariate statistics comprise a set of statistical methods used in situations where many variables are database space subsets. Initially applied to human, social and biological sciences, these methods are being applied to many other areas such as education, geology, chemistry, physics, engineering, and many others. This spectra expansion was possible due to recent technological development of computation hardware and software that allows high and complex databases to be treated iteratively enabling further analysis. Following this trend, the neural networks called Self-Organizing Maps are turning into a powerful tool on visualization of implicit and unknown correlations in big sized database sets. Originally created by Kohonen in 1981, it was applied to speech recognition tasks. The SOM is being used as a comparative parameter to evaluate the performance of new multidimensional analysis methodologies. Most of methods require good variable input selection criteria and SOM has contributed to clustering, classification and prediction of multidimensional engineering process variables. This work proposes a method of applying SOM to a set of 58 IEA-R1 operational variables at IPEN research reactor which are monitored by a Data Acquisition System (DAS). This data set includes variables as temperature, flow mass rate, coolant level, nuclear radiation, nuclear power and control bars position. DAS enables the creation and storage of historical data which are used to contribute to Failure Detection and Monitoring System development. Results show good agreement with previous studies using other methods as GMDH and other predictive methods. (author)

Applying self-organizing map and modified radial based neural network for clustering and routing optimal path in wireless network

Science.gov (United States)

Hoomod, Haider K.; Kareem Jebur, Tuka

2018-05-01

Mobile ad hoc networks (MANETs) play a critical role in today’s wireless ad hoc network research and consist of active nodes that can be in motion freely. Because it consider very important problem in this network, we suggested proposed method based on modified radial basis function networks RBFN and Self-Organizing Map SOM. These networks can be improved by the use of clusters because of huge congestion in the whole network. In such a system, the performance of MANET is improved by splitting the whole network into various clusters using SOM. The performance of clustering is improved by the cluster head selection and number of clusters. Modified Radial Based Neural Network is very simple, adaptable and efficient method to increase the life time of nodes, packet delivery ratio and the throughput of the network will increase and connection become more useful because the optimal path has the best parameters from other paths including the best bitrate and best life link with minimum delays. Proposed routing algorithm depends on the group of factors and parameters to select the path between two points in the wireless network. The SOM clustering average time (1-10 msec for stall nodes) and (8-75 msec for mobile nodes). While the routing time range (92-510 msec).The proposed system is faster than the Dijkstra by 150-300%, and faster from the RBFNN (without modify) by 145-180%.

Self-organizing maps of Kohonen (SOM) applied to multidimensional monitoring data of the IEA-R1 nuclear research reactor

Energy Technology Data Exchange (ETDEWEB)

Affonso, Gustavo S.; Pereira, Iraci M.; Mesquita, Roberto N. de, E-mail: rnavarro@ipen.b [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil); Bueno, Elaine I., E-mail: ebueno@ifsp.gov.b [Instituto Federal de Educacao, Ciencia e Tecnologia de Sao Paulo (IFSP), SP (Brazil)

2011-07-01

Multivariate statistics comprise a set of statistical methods used in situations where many variables are database space subsets. Initially applied to human, social and biological sciences, these methods are being applied to many other areas such as education, geology, chemistry, physics, engineering, and many others. This spectra expansion was possible due to recent technological development of computation hardware and software that allows high and complex databases to be treated iteratively enabling further analysis. Following this trend, the neural networks called Self-Organizing Maps are turning into a powerful tool on visualization of implicit and unknown correlations in big sized database sets. Originally created by Kohonen in 1981, it was applied to speech recognition tasks. The SOM is being used as a comparative parameter to evaluate the performance of new multidimensional analysis methodologies. Most of methods require good variable input selection criteria and SOM has contributed to clustering, classification and prediction of multidimensional engineering process variables. This work proposes a method of applying SOM to a set of 58 IEA-R1 operational variables at IPEN research reactor which are monitored by a Data Acquisition System (DAS). This data set includes variables as temperature, flow mass rate, coolant level, nuclear radiation, nuclear power and control bars position. DAS enables the creation and storage of historical data which are used to contribute to Failure Detection and Monitoring System development. Results show good agreement with previous studies using other methods as GMDH and other predictive methods. (author)

Methodical Aspects of Applying Strategy Map in an Organization

Directory of Open Access Journals (Sweden)

Piotr Markiewicz

2013-06-01

Full Text Available One of important aspects of strategic management is the instrumental aspect included in a rich set of methods and techniques used at particular stages of strategic management process. The object of interest in this study is the development of views and the implementation of strategy as an element of strategic management and instruments in the form of methods and techniques. The commonly used method in strategy implementation and measuring progress is Balanced Scorecard (BSC. The method was created as a result of implementing the project “Measuring performance in the Organization of the future” of 1990, completed by a team under the supervision of David Norton (Kaplan, Norton 2002. The developed method was used first of all to evaluate performance by decomposition of a strategy into four perspectives and identification of measures of achievement. In the middle of 1990s the method was improved by enriching it, first of all, with a strategy map, in which the process of transition of intangible assets into tangible financial effects is reflected (Kaplan, Norton 2001. Strategy map enables illustration of cause and effect relationship between processes in all four perspectives and performance indicators at the level of organization. The purpose of the study being prepared is to present methodical conditions of using strategy maps in the strategy implementation process in organizations of different nature.

Use of self-organizing maps for classification of defects in the tubes from the steam generator of nuclear power plants; Classificacao de defeitos em tubos de gerador de vapor de plantas nucleares utilizando mapas auto-organizaveis

Energy Technology Data Exchange (ETDEWEB)

Mesquita, Roberto Navarro de

2002-07-01

This thesis obtains a new classification method for different steam generator tube defects in nuclear power plants using Eddy Current Test signals. The method uses self-organizing maps to compare different signal characteristics efficiency to identify and classify these defects. A multiple inference system is proposed which composes the different extracted characteristic trained maps classification to infer the final defect type. The feature extraction methods used are the Wavelet zero-crossings representation, the linear predictive coding (LPC), and other basic signal representations on time like module and phase. Many characteristic vectors are obtained with combinations of these extracted characteristics. These vectors are tested to classify the defects and the best ones are applied to the multiple inference system. A systematic study of pre-processing, calibration and analysis methods for the steam generator tube defect signals in nuclear power plants is done. The method efficiency is demonstrated and characteristic maps with the main prototypes are obtained for each steam generator tube defect type. (author)

Intelligent self-organization methods for wireless ad hoc sensor networks based on limited resources

Science.gov (United States)

Hortos, William S.

2006-05-01

A wireless ad hoc sensor network (WSN) is a configuration for area surveillance that affords rapid, flexible deployment in arbitrary threat environments. There is no infrastructure support and sensor nodes communicate with each other only when they are in transmission range. To a greater degree than the terminals found in mobile ad hoc networks (MANETs) for communications, sensor nodes are resource-constrained, with limited computational processing, bandwidth, memory, and power, and are typically unattended once in operation. Consequently, the level of information exchange among nodes, to support any complex adaptive algorithms to establish network connectivity and optimize throughput, not only deplete those limited resources and creates high overhead in narrowband communications, but also increase network vulnerability to eavesdropping by malicious nodes. Cooperation among nodes, critical to the mission of sensor networks, can thus be disrupted by the inappropriate choice of the method for self-organization. Recent published contributions to the self-configuration of ad hoc sensor networks, e.g., self-organizing mapping and swarm intelligence techniques, have been based on the adaptive control of the cross-layer interactions found in MANET protocols to achieve one or more performance objectives: connectivity, intrusion resistance, power control, throughput, and delay. However, few studies have examined the performance of these algorithms when implemented with the limited resources of WSNs. In this paper, self-organization algorithms for the initiation, operation and maintenance of a network topology from a collection of wireless sensor nodes are proposed that improve the performance metrics significant to WSNs. The intelligent algorithm approach emphasizes low computational complexity, energy efficiency and robust adaptation to change, allowing distributed implementation with the actual limited resources of the cooperative nodes of the network. Extensions of the

Riemannian metric optimization on surfaces (RMOS) for intrinsic brain mapping in the Laplace-Beltrami embedding space.

Science.gov (United States)

Gahm, Jin Kyu; Shi, Yonggang

2018-05-01

Surface mapping methods play an important role in various brain imaging studies from tracking the maturation of adolescent brains to mapping gray matter atrophy patterns in Alzheimer's disease. Popular surface mapping approaches based on spherical registration, however, have inherent numerical limitations when severe metric distortions are present during the spherical parameterization step. In this paper, we propose a novel computational framework for intrinsic surface mapping in the Laplace-Beltrami (LB) embedding space based on Riemannian metric optimization on surfaces (RMOS). Given a diffeomorphism between two surfaces, an isometry can be defined using the pullback metric, which in turn results in identical LB embeddings from the two surfaces. The proposed RMOS approach builds upon this mathematical foundation and achieves general feature-driven surface mapping in the LB embedding space by iteratively optimizing the Riemannian metric defined on the edges of triangular meshes. At the core of our framework is an optimization engine that converts an energy function for surface mapping into a distance measure in the LB embedding space, which can be effectively optimized using gradients of the LB eigen-system with respect to the Riemannian metrics. In the experimental results, we compare the RMOS algorithm with spherical registration using large-scale brain imaging data, and show that RMOS achieves superior performance in the prediction of hippocampal subfields and cortical gyral labels, and the holistic mapping of striatal surfaces for the construction of a striatal connectivity atlas from substantia nigra. Copyright © 2018 Elsevier B.V. All rights reserved.

Site-selective growth of surface-anchored metal-organic frameworks on self-assembled monolayer patterns prepared by AFM nanografting

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Tatjana Ladnorg

2013-10-01

Full Text Available Surface anchored metal-organic frameworks, SURMOFs, are highly porous materials, which can be grown on modified substrates as highly oriented, crystalline coatings by a quasi-epitaxial layer-by-layer method (liquid-phase epitaxy, or LPE. The chemical termination of the supporting substrate is crucial, because the most convenient method for substrate modification is the formation of a suitable self-assembled monolayer. The choice of a particular SAM also allows for control over the orientation of the SURMOF. Here, we demonstrate for the first time the site-selective growth of the SURMOF HKUST-1 on thiol-based self-assembled monolayers patterned by the nanografting technique, with an atomic force microscope as a structuring tool. Two different approaches were applied: The first one is based on 3-mercaptopropionic acid molecules which are grafted in a 1-decanethiolate SAM, which serves as a matrix for this nanolithography. The second approach uses 16-mercaptohexadecanoic acid, which is grafted in a matrix of an 1-octadecanethiolate SAM. In both cases a site-selective growth of the SURMOF is observed. In the latter case the roughness of the HKUST-1 is found to be significantly higher than for the 1-mercaptopropionic acid. The successful grafting process was verified by time-of-flight secondary ion mass spectrometry and atomic force microscopy. The SURMOF structures grown via LPE were investigated and characterized by atomic force microscopy and Fourier-transform infrared microscopy.

Site-selective growth of surface-anchored metal-organic frameworks on self-assembled monolayer patterns prepared by AFM nanografting

Science.gov (United States)

Ladnorg, Tatjana; Welle, Alexander; Heißler, Stefan; Wöll, Christof

2013-01-01

Summary Surface anchored metal-organic frameworks, SURMOFs, are highly porous materials, which can be grown on modified substrates as highly oriented, crystalline coatings by a quasi-epitaxial layer-by-layer method (liquid-phase epitaxy, or LPE). The chemical termination of the supporting substrate is crucial, because the most convenient method for substrate modification is the formation of a suitable self-assembled monolayer. The choice of a particular SAM also allows for control over the orientation of the SURMOF. Here, we demonstrate for the first time the site-selective growth of the SURMOF HKUST-1 on thiol-based self-assembled monolayers patterned by the nanografting technique, with an atomic force microscope as a structuring tool. Two different approaches were applied: The first one is based on 3-mercaptopropionic acid molecules which are grafted in a 1-decanethiolate SAM, which serves as a matrix for this nanolithography. The second approach uses 16-mercaptohexadecanoic acid, which is grafted in a matrix of an 1-octadecanethiolate SAM. In both cases a site-selective growth of the SURMOF is observed. In the latter case the roughness of the HKUST-1 is found to be significantly higher than for the 1-mercaptopropionic acid. The successful grafting process was verified by time-of-flight secondary ion mass spectrometry and atomic force microscopy. The SURMOF structures grown via LPE were investigated and characterized by atomic force microscopy and Fourier-transform infrared microscopy. PMID:24205458

Detecting tactical patterns in basketball: comparison of merge self-organising maps and dynamic controlled neural networks.

Science.gov (United States)

Kempe, Matthias; Grunz, Andreas; Memmert, Daniel

2015-01-01

The soaring amount of data, especially spatial-temporal data, recorded in recent years demands for advanced analysis methods. Neural networks derived from self-organizing maps established themselves as a useful tool to analyse static and temporal data. In this study, we applied the merge self-organising map (MSOM) to spatio-temporal data. To do so, we investigated the ability of MSOM's to analyse spatio-temporal data and compared its performance to the common dynamical controlled network (DyCoN) approach to analyse team sport position data. The position data of 10 players were recorded via the Ubisense tracking system during a basketball game. Furthermore, three different pre-selected plays were recorded for classification. Following data preparation, the different nets were trained with the data of the first half. The training success of both networks was evaluated by achieved entropy. The second half of the basketball game was presented to both nets for automatic classification. Both approaches were able to present the trained data extremely well and to detect the pre-selected plays correctly. In conclusion, MSOMs are a useful tool to analyse spatial-temporal data, especially in team sports. By their direct inclusion of different time length of tactical patterns, they open up new opportunities within team sports.

Self-organization at the frictional interface for green tribology.

Science.gov (United States)

Nosonovsky, Michael

2010-10-28

Despite the fact that self-organization during friction has received relatively little attention from tribologists so far, it has the potential for the creation of self-healing and self-lubricating materials, which are important for green or environment-friendly tribology. The principles of the thermodynamics of irreversible processes and of the nonlinear theory of dynamical systems are used to investigate the formation of spatial and temporal structures during friction. The transition to the self-organized state with low friction and wear occurs through destabilization of steady-state (stationary) sliding. The criterion for destabilization is formulated and several examples are discussed: the formation of a protective film, microtopography evolution and slip waves. The pattern formation may involve self-organized criticality and reaction-diffusion systems. A special self-healing mechanism may be embedded into the material by coupling the corresponding required forces. The analysis provides the structure-property relationship, which can be applied for the design optimization of composite self-lubricating and self-healing materials for various ecologically friendly applications and green tribology.

A concept mapping study on organic food consumers in Shanghai, China.

Science.gov (United States)

Hasimu, Huliyeti; Marchesini, Sergio; Canavari, Maurizio

2017-01-01

Despite some similarities with developed countries, the growth of organic market in China seems to follow a different path. Thus, important questions are how Chinese urban consumers perceive organic food, and what are the main concepts associated to the organic attribute. We aimed at representing in graphic form the network of mental associations with the organic concept. We used an adapted version of the "Brand concept mapping" method to acquire, process, and draw individual concept networks perceived by 50 organic food consumers in Shanghai. We then analyzed the data using network and cluster analysis to create aggregated maps for two distinct groups of consumers. Similarly to their peers in developed countries, Chinese consumers perceive organic food as healthy, safe and expensive. However, organic is not necessarily synonymous with natural produce in China, also due to a translation of the term that conveys the idea of a "technology advanced" product. Organic overlaps with the green food label in terms of image and positioning in the market, since they are easily associated and often confused. The two groups we identified show clear differences in the way the organic concept is associated to other concepts and features. The study provides useful information for practitioners: marketers of organic products in China should invest in communication to emphasize the differences with Green Food products and they should consider the possibility of segmenting organic consumers; Chinese policy makers should consider implementing information campaigns aimed at achieving a better understanding of the features of these quality labels among consumers. For researchers, the study confirms that the BCM method is effective and its integration with network and cluster analysis improves the interpretation of individual and aggregated maps. Copyright © 2016 Elsevier Ltd. All rights reserved.

Self-organization of physical fields and spin

International Nuclear Information System (INIS)

Pestov, I.B.

2008-01-01

The subject of the present investigation is the laws of intrinsic self-organization of fundamental physical fields. In the framework of the Theory of Self-Organization the geometrical and physical nature of spin phenomena is uncovered. The key points are spin symmetry (the fundamental realization of the concept of geometrical internal symmetry) and the spinning field (space of defining representation of spin symmetry). It is shown that the essence of spin is the bipolar structure of spin symmetry induced by the gravitational potentials. The bipolar structure provides natural violation of spin symmetry and leads to spinstatics (theory of spinning field outside the time) and spindynamics. The equations of spinstatics and spindynamics are derived. It is shown that Sommerfeld's formula can be derived from the equations of spindynamics and hence the correspondence principle is valid. This means that the Theory of Self-Organization provides the new understanding of spin phenomena

Sensitivity of biogenic volatile organic compounds to land surface parameterizations and vegetation distributions in California

Energy Technology Data Exchange (ETDEWEB)

Zhao, Chun; Huang, Maoyi; Fast, Jerome D.; Berg, Larry K.; Qian, Yun; Guenther, Alex; Gu, Dasa; Shrivastava, Manish; Liu, Ying; Walters, Stacy; Pfister, Gabriele; Jin, Jiming; Shilling, John E.; Warneke, Carsten

2016-01-01

Current climate models still have large uncertainties in estimating biogenic trace gases, which can significantly affect atmospheric chemistry and secondary aerosol formation that ultimately influences air quality and aerosol radiative forcing. These uncertainties result from many factors, including uncertainties in land surface processes and specification of vegetation types, both of which can affect the simulated near-surface fluxes of biogenic volatile organic compounds (BVOCs). In this study, the latest version of Model of Emissions of Gases and Aerosols from Nature (MEGAN v2.1) is coupled within the land surface scheme CLM4 (Community Land Model version 4.0) in the Weather Research and Forecasting model with chemistry (WRF-Chem). In this implementation, MEGAN v2.1 shares a consistent vegetation map with CLM4 for estimating BVOC emissions. This is unlike MEGAN v2.0 in the public version of WRF-Chem that uses a stand-alone vegetation map that differs from what is used by land surface schemes. This improved modeling framework is used to investigate the impact of two land surface schemes, CLM4 and Noah, on BVOCs and examine the sensitivity of BVOCs to vegetation distributions in California. The measurements collected during the Carbonaceous Aerosol and Radiative Effects Study (CARES) and the California Nexus of Air Quality and Climate Experiment (CalNex) conducted in June of 2010 provided an opportunity to evaluate the simulated BVOCs. Sensitivity experiments show that land surface schemes do influence the simulated BVOCs, but the impact is much smaller than that of vegetation distributions. This study indicates that more effort is needed to obtain the most appropriate and accurate land cover data sets for climate and air quality models in terms of simulating BVOCs, oxidant chemistry and, consequently, secondary organic aerosol formation.

Self-organised wires and antiwires on semiconductor surfaces

International Nuclear Information System (INIS)

Srivastava, G.P.; Miwa, R.H.

2004-01-01

We present an overview of self-organised formation of lines of (semi)metal elements on semiconductor surfaces, together with an ab initio theoretical modelling of such quasi-one-dimensional systems. Results of calculations are presented for the atomic geometry, electronic states, and dispersion of the most tightly bound image state (IS) for a self-organised thin indium chain on the silicon surface forming the Si(1 1 1)-In(4 x 1) nanowire system. It is pointed out that strong anisotropic behaviour of the image state observed in inverse photoemission measurements originates from the anisotropy in the surface corrugation potential. Results are also presented for theoretically simulated STM images of self-organised Bi-lines on the Si(0 0 1) surface, which suggest a low density of states close to the valence band maximum localized on the Bi-lines, supporting a proposed model of a quantum antiwire system

Remote sensing the sea surface CO2 of the Baltic Sea using the SOMLO methodology

Science.gov (United States)

Parard, G.; Charantonis, A. A.; Rutgerson, A.

2015-06-01

Studies of coastal seas in Europe have noted the high variability of the CO2 system. This high variability, generated by the complex mechanisms driving the CO2 fluxes, complicates the accurate estimation of these mechanisms. This is particularly pronounced in the Baltic Sea, where the mechanisms driving the fluxes have not been characterized in as much detail as in the open oceans. In addition, the joint availability of in situ measurements of CO2 and of sea-surface satellite data is limited in the area. In this paper, we used the SOMLO (self-organizing multiple linear output; Sasse et al., 2013) methodology, which combines two existing methods (i.e. self-organizing maps and multiple linear regression) to estimate the ocean surface partial pressure of CO2 (pCO2) in the Baltic Sea from the remotely sensed sea surface temperature, chlorophyll, coloured dissolved organic matter, net primary production, and mixed-layer depth. The outputs of this research have a horizontal resolution of 4 km and cover the 1998-2011 period. These outputs give a monthly map of the Baltic Sea at a very fine spatial resolution. The reconstructed pCO2 values over the validation data set have a correlation of 0.93 with the in situ measurements and a root mean square error of 36 μatm. Removing any of the satellite parameters degraded this reconstructed CO2 flux, so we chose to supply any missing data using statistical imputation. The pCO2 maps produced using this method also provide a confidence level of the reconstruction at each grid point. The results obtained are encouraging given the sparsity of available data, and we expect to be able to produce even more accurate reconstructions in coming years, given the predicted acquisition of new data.

A self-organized internal models architecture for coding sensory-motor schemes

Directory of Open Access Journals (Sweden)

Esaú eEscobar Juárez

2016-04-01

Full Text Available Cognitive robotics research draws inspiration from theories and models on cognition, as conceived by neuroscience or cognitive psychology, to investigate biologically plausible computational models in artificial agents. In this field, the theoretical framework of Grounded Cognition provides epistemological and methodological grounds for the computational modeling of cognition. It has been stressed in the literature that textit{simulation}, textit{prediction}, and textit{multi-modal integration} are key aspects of cognition and that computational architectures capable of putting them into play in a biologically plausible way are a necessity.Research in this direction has brought extensive empirical evidencesuggesting that textit{Internal Models} are suitable mechanisms forsensory-motor integration. However, current Internal Models architectures show several drawbacks, mainly due to the lack of a unified substrate allowing for a true sensory-motor integration space, enabling flexible and scalable ways to model cognition under the embodiment hypothesis constraints.We propose the Self-Organized Internal ModelsArchitecture (SOIMA, a computational cognitive architecture coded by means of a network of self-organized maps, implementing coupled internal models that allow modeling multi-modal sensory-motor schemes. Our approach addresses integrally the issues of current implementations of Internal Models.We discuss the design and features of the architecture, and provide empirical results on a humanoid robot that demonstrate the benefits and potentialities of the SOIMA concept for studying cognition in artificial agents.

17 CFR 201.420 - Appeal of determinations by self-regulatory organizations.

Science.gov (United States)

2010-04-01

... self-regulatory organizations. 201.420 Section 201.420 Commodity and Securities Exchanges SECURITIES... Review § 201.420 Appeal of determinations by self-regulatory organizations. (a) Application for review... by a self-regulatory organization determination as to which a notice is required to be filed with the...

Organic Chemistry Self Instructional Package 2: Methane.

Science.gov (United States)

Zdravkovich, V.

This booklet, one of a series of 17 developed at Prince George's Community College, Largo, Maryland, provides an individualized, self-paced undergraduate organic chemistry instruction module designed to augment any course in organic chemistry but particularly those taught using the text "Organic Chemistry" by Morrison and Boyd. The…

Organic Chemistry Self Instructional Package 12: Alkynes.

Science.gov (United States)

Zdravkovich, V.

This booklet, one of a series of 17 developed at Prince George's Community College, Largo, Maryland, provides an individualized, self-paced undergraduate organic chemistry instruction module designed to augment any course in organic chemistry but particularly those taught using the text "Organic Chemistry" by Morrison and Boyd. The…

Self-organized titanium oxide nano-channels for resistive memory application

Energy Technology Data Exchange (ETDEWEB)

Barman, A.; Saini, C. P.; Dhar, S.; Kanjilal, A., E-mail: aloke.kanjilal@snu.edu.in [Department of Physics, School of Natural Sciences, Shiv Nadar University, NH-91, Tehsil Dadri, Gautam Buddha Nagar, Uttar Pradesh 201 314 (India); Sarkar, P. [Department of Physics, National Institute of Technology, Silchar, Assam 788 010 (India); Satpati, B.; Bhattacharyya, S. R. [Surface Physics and Material Science Division, Saha Institute of Nuclear Physics, 1/AF Bidhannagar, Kolkata 700 064 (India); Kabiraj, D.; Kanjilal, D. [Inter-University Accelerator Centre, Aruna Asaf Ali Marg, New Delhi 110 067 (India)

2015-12-14

Towards developing next generation scalable TiO{sub 2}-based resistive switching (RS) memory devices, the efficacy of 50 keV Ar{sup +}-ion irradiation to achieve self-organized nano-channel based structures at a threshold fluence of 5 × 10{sup 16} ions/cm{sup 2} at ambient temperature is presented. Although x-ray diffraction results suggest the amorphization of as-grown TiO{sub 2} layers, detailed transmission electron microscopy study reveals fluence-dependent evolution of voids and eventual formation of self-organized nano-channels between them. Moreover, gradual increase of TiO/Ti{sub 2}O{sub 3} in the near surface region, as monitored by x-ray photoelectron spectroscopy, establishes the upsurge in oxygen deficient centers. The impact of structural and chemical modification on local RS behavior has also been investigated by current-voltage measurements in conductive atomic force microscopy, while memory application is manifested by fabricating Pt/TiO{sub 2}/Pt/Ti/SiO{sub 2}/Si devices. Finally, the underlying mechanism of our experimental results has been analyzed and discussed in the light of oxygen vacancy migration through nano-channels.

Application of Topic Map on Knowledge Organization

Directory of Open Access Journals (Sweden)

Sou-shan Wu

2003-06-01

Full Text Available Knowledge management (KM has received much attention from both academics and practitioners in the past few years. Following the KM trend, many organizations have built their own knowledge repositories or data warehouses. However, information or knowledge is still scattered everywhere without being properly managed. The rapid growth of the Internet accelerates the creation of unstructured and unclassified information and causes the explosion of information overload. The effort of browsing information through general-purpose search engines turns out to be tedious and painstaking. Hence, an effective technology to solve this information retrieval problem is much needed. The purpose of this research is to explore the application of text mining technique in organizing knowledge stored in unstructured natural language text documents. Major components of text mining techniques required for topic map in particular will be presented in detail.Two sets of unstructured documents are utilized to demonstrate the usage of SOM for topic categorization. The first set of documents is a collection of speeches given by Y.C. Wang, Chairman of the Taiwan Plastics Group, and the other is the collection of all laws and regulations related to securities and future markets in Taiwan. We also try to apply text mining to these two sets of documents to generate their respective topic maps, thus revealing the differences between organizing explicit and tacit knowledge as well as the difficulties associated with tacit knowledge.[Article content in Chinese

Measuring the Complexity of Self-Organizing Traffic Lights

Directory of Open Access Journals (Sweden)

Darío Zubillaga

2014-04-01

Full Text Available We apply measures of complexity, emergence, and self-organization to an urban traffic model for comparing a traditional traffic-light coordination method with a self-organizing method in two scenarios: cyclic boundaries and non-orientable boundaries. We show that the measures are useful to identify and characterize different dynamical phases. It becomes clear that different operation regimes are required for different traffic demands. Thus, not only is traffic a non-stationary problem, requiring controllers to adapt constantly; controllers must also change drastically the complexity of their behavior depending on the demand. Based on our measures and extending Ashby’s law of requisite variety, we can say that the self-organizing method achieves an adaptability level comparable to that of a living system.

Optical properties of Au colloids self-organized into rings via copolymer templates

Energy Technology Data Exchange (ETDEWEB)

Lamarre, S. S. [Laval University, Department of Chemistry (Canada); Sarrazin, A.; Proust, J.; Yockell-Lelievre, H.; Plain, J. [University of Technology of Troyes, Laboratory of Nanotechnology, Instrumentation and Optics, Charles Delaunay Institute (France); Ritcey, A. M. [Laval University, Department of Chemistry (Canada); Maurer, T., E-mail: thomas.maurer@utt.fr [University of Technology of Troyes, Laboratory of Nanotechnology, Instrumentation and Optics, Charles Delaunay Institute (France)

2013-05-15

The investigation of the localized surface plasmon resonance for plasmonic nanoparticles has opened new perspectives for optical nanosensors. Nowadays, an issue in plasmonics is the development of large scale and low cost devices. We focus here on the Langmuir-Blodgett technique to self-organize gold nanoparticles ({approx}7 nm) into rings ({approx}60 nm) via polystyrene-b-polymethylmethacrylate templates. In particular, we investigated the optical properties of organized gold nanoparticle rings over large areas and report experimental evidence for plasmon resonances of both individual nanoparticles and collective modes. This paves the way for designing devices with multiple resonances in the visible-infra-red spectrum and developing optical sensors.

Required Equipment for Photo-Switchable Donor-Acceptor (D-A) Dyad Interfacial Self-Assembled Monolayers for Organic Photovoltaic Cells

Science.gov (United States)

2014-01-24

Interfacial Tuning via Electron-Blocking/Hole-Transport Layers and Indium Tin Oxide Surface Treatment in Bulk- Heterojunction Organic Photovoltaic Cells...devices Figure 3 shows the compounds we prepared to assemble on gold (Au) surfaces. Results of TPA-C60 dyads (1 and 2) self-assembled on Au electrodes...surface hydroxyl groups, respectively, we decided to prepare compounds 5-7 to attach as SAMs, see Figure 5. Difficulties and unexpected problems

Pure Surface Texture Mapping Technology and it's Application for Mirror Image

Directory of Open Access Journals (Sweden)

Wei Feng Wang

2013-02-01

Full Text Available Based on the study of pure surface texture mapping technology, pure texture surface rendering method is proposed. The method is combined pure surface texture rendering and view mirror, real-time rendering has an index of refraction, reflection, and the flow of water ripple effect. Through the experimental verification of the validity of the algorithm.

Sea surface temperature mapping using a thermal infrared scanner

Digital Repository Service at National Institute of Oceanography (India)

RameshKumar, M.R; Pandya, R; Mathur, K.M.; Charyulu, R; Rao, L.V.G.

1 metre water column below the sea surface. A thermal infrared scanner developed by the Space Applications Centre (ISRO), Ahmedabad was operated on board R.V. Gaveshani in April/May 1984 for mapping SST over the eastern Arabian Sea. SST values...

Low temperature self-cleaning properties of superhydrophobic surfaces

Science.gov (United States)

Wang, Fajun; Shen, Taohua; Li, Changquan; Li, Wen; Yan, Guilong

2014-10-01

Outdoor surfaces are usually dirty surfaces. Ice accretion on outdoor surfaces could lead to serious accidents. In the present work, the superhydrophobic surface based on 1H, 1H, 2H, 2H-Perfluorodecanethiol (PFDT) modified Ag/PDMS composite was prepared to investigate the anti-icing property and self-cleaning property at temperatures below freezing point. The superhydrophobic surface was deliberately polluted with activated carbon before testing. It was observed that water droplet picked up dusts on the cold superhydrophobic surface and took it away without freezing at a measuring temperature of -10 °C. While on a smooth PFDT surface and a rough surface base on Ag/PDMS composite without PFDT modification, water droplets accumulated and then froze quickly at the same temperature. However, at even lower temperature of -12 °C, the superhydrophobic surface could not prevent the surface water from icing. In addition, it was observed that the frost layer condensed from the moisture pay an important role in determining the low temperature self-cleaning properties of a superhydrophobic surface.

Self-cleaning Foliar Surfaces Characterization using RIMAPS Technique and Variogram Method

International Nuclear Information System (INIS)

Rosi, Pablo E.

2002-01-01

Along the last ten years many important studies about characterization of self-cleaning foliar surfaces have been done and focused new interest on this kind of surfaces.These studies were possible due to the development of a novel preparation technique for this biological material that let us observe the delicate structures of a foliar surface under scanning electron microscope (S.E.M.).This technique consists of replacing the natural water of the specimen by glycerol. Digital S.E.M. images from both self-cleaning and non-self-cleaning foliar surfaces were obtained and analyzed using RIMAPS technique and Variograms method. Our results revealed the existence of a common and exclusive geometrical pattern that is found in species which present self-cleaning foliar surfaces.This pattern combines at least nine different directions.The results from the Variograms method showed that the stomata play a key role in the determination of foliar surface roughness. In addition, spectra from RIMAPS technique constitute a fingerprint of a foliar surface so they can be used to find evolutionary relationships among species.Further studies will provide more detailed information to fully elucidate the self-cleaning pattern, so it might be possible to reproduce it on an artificial surface and make it self-cleaning

Non-contact adhesion to self-affine surfaces: A theoretical model

Energy Technology Data Exchange (ETDEWEB)

Makeev, Maxim A., E-mail: makeev@umich.edu

2013-11-22

Strength of adhesion between materials is known to be strongly influenced by interface irregularities. In this work, I devise a perturbative approach to describe the effect of self-affine roughness on non-contact adhesive interactions. The hierarchy of the obtained analytical solutions is the following. First, analytical formulae are deduced to describe roughness corrections to the van der Waals interaction energies between a hemi-space adherend, bounded by a self-affine surface, and a point-like adherent. Second, the problem of two hemi-spaces, one of which has a planar surface, and the other is bounded by a self-affine surface, is solved analytically. In the latter case, a numerical analysis is performed to delineate the behavior of the roughness corrections as a function of the parameters, characterizing self-affine fractal surface roughness. The problem of two hemi-spaces, both bounded by self-affine fractal surfaces, is also addressed in this work. The model's predictions are compared with previously reported theoretical results and available experimental data.

Order out of Randomness: Self-Organization Processes in Astrophysics

Science.gov (United States)

Aschwanden, Markus J.; Scholkmann, Felix; Béthune, William; Schmutz, Werner; Abramenko, Valentina; Cheung, Mark C. M.; Müller, Daniel; Benz, Arnold; Chernov, Guennadi; Kritsuk, Alexei G.; Scargle, Jeffrey D.; Melatos, Andrew; Wagoner, Robert V.; Trimble, Virginia; Green, William H.

2018-03-01

Self-organization is a property of dissipative nonlinear processes that are governed by a global driving force and a local positive feedback mechanism, which creates regular geometric and/or temporal patterns, and decreases the entropy locally, in contrast to random processes. Here we investigate for the first time a comprehensive number of (17) self-organization processes that operate in planetary physics, solar physics, stellar physics, galactic physics, and cosmology. Self-organizing systems create spontaneous " order out of randomness", during the evolution from an initially disordered system to an ordered quasi-stationary system, mostly by quasi-periodic limit-cycle dynamics, but also by harmonic (mechanical or gyromagnetic) resonances. The global driving force can be due to gravity, electromagnetic forces, mechanical forces (e.g., rotation or differential rotation), thermal pressure, or acceleration of nonthermal particles, while the positive feedback mechanism is often an instability, such as the magneto-rotational (Balbus-Hawley) instability, the convective (Rayleigh-Bénard) instability, turbulence, vortex attraction, magnetic reconnection, plasma condensation, or a loss-cone instability. Physical models of astrophysical self-organization processes require hydrodynamic, magneto-hydrodynamic (MHD), plasma, or N-body simulations. Analytical formulations of self-organizing systems generally involve coupled differential equations with limit-cycle solutions of the Lotka-Volterra or Hopf-bifurcation type.

Organic matter composition of soil macropore surfaces under different agricultural management practices

Science.gov (United States)

Glæsner, Nadia; Leue, Marin; Magid, Jacob; Gerke, Horst H.

2016-04-01

Understanding the heterogeneous nature of soil, i.e. properties and processes occurring specifically at local scales is essential for best managing our soil resources for agricultural production. Examination of intact soil structures in order to obtain an increased understanding of how soil systems operate from small to large scale represents a large gap within soil science research. Dissolved chemicals, nutrients and particles are transported through the disturbed plow layer of agricultural soil, where after flow through the lower soil layers occur by preferential flow via macropores. Rapid movement of water through macropores limit the contact between the preferentially moving water and the surrounding soil matrix, therefore contact and exchange of solutes in the water is largely restricted to the surface area of the macropores. Organomineral complex coated surfaces control sorption and exchange properties of solutes, as well as availability of essential nutrients to plant roots and to the preferentially flowing water. DRIFT (Diffuse Reflectance infrared Fourier Transform) Mapping has been developed to examine composition of organic matter coated macropores. In this study macropore surfaces structures will be determined for organic matter composition using DRIFT from a long-term field experiment on waste application to agricultural soil (CRUCIAL, close to Copenhagen, Denmark). Parcels with 5 treatments; accelerated household waste, accelerated sewage sludge, accelerated cattle manure, NPK and unfertilized, will be examined in order to study whether agricultural management have an impact on the organic matter composition of intact structures.

Excited-state potential-energy surfaces of metal-adsorbed organic molecules from linear expansion Δ-self-consistent field density-functional theory (ΔSCF-DFT).

Science.gov (United States)

Maurer, Reinhard J; Reuter, Karsten

2013-07-07

Accurate and efficient simulation of excited state properties is an important and much aspired cornerstone in the study of adsorbate dynamics on metal surfaces. To this end, the recently proposed linear expansion Δ-self-consistent field method by Gavnholt et al. [Phys. Rev. B 78, 075441 (2008)] presents an efficient alternative to time consuming quasi-particle calculations. In this method, the standard Kohn-Sham equations of density-functional theory are solved with the constraint of a non-equilibrium occupation in a region of Hilbert-space resembling gas-phase orbitals of the adsorbate. In this work, we discuss the applicability of this method for the excited-state dynamics of metal-surface mounted organic adsorbates, specifically in the context of molecular switching. We present necessary advancements to allow for a consistent quality description of excited-state potential-energy surfaces (PESs), and illustrate the concept with the application to Azobenzene adsorbed on Ag(111) and Au(111) surfaces. We find that the explicit inclusion of substrate electronic states modifies the topologies of intra-molecular excited-state PESs of the molecule due to image charge and hybridization effects. While the molecule in gas phase shows a clear energetic separation of resonances that induce isomerization and backreaction, the surface-adsorbed molecule does not. The concomitant possibly simultaneous induction of both processes would lead to a significantly reduced switching efficiency of such a mechanism.

Tropospheric Ozonesonde Profiles at Long-term U.S. Monitoring Sites: 1. A Climatology Based on Self-Organizing Maps