Clustering Process Analysis in a Large-Size Dropshaft and in a Hydraulic Jump

Recent efforts in the characterization of air-water flows properties have included some clustering process analysis. A cluster of bubbles is defined as a group of two or more bubbles, with a distinct separation from other bubbles before and after the cluster. The present paper compares the results of clustering processes two hydraulic structures. That is, a large-size dropshaft and a hydraulic jump in a rectangular horizontal channel. The comparison highlighted some significant differences in clustering production and structures. Both dropshaft and hydraulic jump flows are complex turbulent shear flows, and some clustering index may provide some measure of the bubble-turbulence interactions and associated energy dissipation.

Bubbly Flow Structure in Hydraulic Jump

In an open channel, a hydraulic jump is the rapid transition from super- to sub-critical flow associated with strong turbulence and air bubble entrainment in the mixing layer. New experiments were performed at relatively large Reynolds numbers using phase-detection probes. Some new signal analysis provided characteristic air-water time and length scales of the vortical structures advecting the air bubbles in the developing shear flow. An analysis of the longitudinal air-water flow structure suggested little bubble clustering in the mixing layer, although an interparticle arrival time analysis showed some preferential bubble clustering for small bubbles with chord times below 3 ms. Correlation analyses yielded longitudinal air-water time scales Txx*V1/d1 of about 0.8 in average. The transverse integral length scale Z/d1 of the eddies advecting entrained bubbles was typically between 0.25 and 0.4, irrespective of the inflow conditions within the range of the investigations. Overall the findings highlighted the complicated nature of the air-water flow

Innovation in industrial clusters: a survey of footwear companies in Brazil

The aim of this study is to characterize the relationships in innovation and business clustering processes in the productive chain of small and medium enterprises (SME) of Brazil. The object of study are SMEs the local procuctive cluster of the shoes in Franca, State of São Paulo. The conceptual model developed is based on the following constructs: vertical integration, innovation and characteristics of the cluster, and it is focused on identifying the agents that act predominantly in product innovation processes in the cluster. A survey was conducted. It was found that there is cooperation between the companies in the productive arrangement studied, and that shoe manufacturers are those who, predominantly, stimulate innovation within the cluster.

PRIM: Proximity imaging of green fluorescent protein-tagged polypeptides

We report a serendipitous discovery that extends the impressive catalog of reporter functions performed by green fluorescent protein (GFP) or its derivatives. When two GFP molecules are brought into proximity, changes in the relative intensities of green fluorescence emitted upon excitation at 395 vs. 475 nm result. These spectral changes provide a sensitive ratiometric index of the extent of self-association that can be exploited to quantitatively image homo-oligomerization or clustering processes of GFP-tagged proteins in vivo. The method, which we term proximity imaging (PRIM), complements fluorescence resonance energy transfer between a blue fluorescent protein donor and a GFP acceptor, a powerful method for imaging proximity relationships between different proteins. However, unlike fluorescence resonance energy transfer (which is a spectral interaction), PRIM depends on direct contact between two GFP modules, which can lead to structural perturbations and concomitant spectral changes within a module. Moreover, the precise spatial arrangement of the GFP molecules within a given dimer determines the magnitude and direction of the spectral change. We have used PRIM to detect FK1012-induced dimerization of GFP fused to FK506-binding protein and clustering of glycosylphosphatidylinositol-anchored GFP at cell surfaces.

Modelos baseados em agentes aplicados à dinâmica de preços do mercado imobiliário

Um dos aspectos regulatórios fundamentais para o mercado imobiliário no Brasil são os limites para obtenção de financiamento no Sistema Financeiro de Habitação. Esses limites podem ser definidos de forma a aumentar ou reduzir a oferta de crédito neste mercado, alterando o comportamento dos seus agentes e, com isso, o preço de mercado dos imóveis. Neste trabalho, propomos um modelo de formação de preços no mercado imobiliário brasileiro com base no comportamento dos agentes que o compõem. Os agentes vendedores têm comportamento heterogêneo e são influenciados pela demanda histórica, enquanto que os agentes compradores têm o seu comportamento determinado pela disponibilidade de crédito. Esta disponibilidade de crédito, por sua vez, é definida pelos limites para concessão de financiamento no Sistema Financeiro de Habitação. Verificamos que o processo markoviano que descreve preço de mercado converge para um sistema dinâmico determinístico quando o número de agentes aumenta, e analisamos o comportamento deste sistema dinâmico. Mostramos qual é a família de variáveis aleatórias que representa o comportamento dos agentes vendedores de forma que o sistema apresente um preço de equilíbrio não trivial, condizente com a realidade. Verificamos ainda que o preço de equilíbrio depende não só das regras de concessão de financiamento no Sistema Financeiro de Habitação, como também do preço de reserva dos compradores e da memória e da sensibilidade dos vendedores a alterações na demanda. A memória e a sensibilidade dos vendedores podem levar a oscilações de preços acima ou abaixo do preço de equilíbrio (típicas de processos de formação de bolhas); ou até mesmo a uma bifurcação de Neimark-Sacker, quando o sistema apresenta dinâmica oscilatória estável.

Air Entrainment Processes in a Full-Scale Rectangular Dropshaft at Large Flows

Rectangular dropshafts, commonly used in sewers and storm water systems, are characterised by significant flow aeration. New detailed air-water flow measurements were conducted in a near-full-scale dropshaft at large discharges. In the shaft pool and outflow channel, the results demonstrated the complexity of different competitive air entrainment mechanisms. Bubble size measurements showed a broad range of entrained bubble sizes. Analysis of streamwise distributions of bubbles suggested further some clustering process in the bubbly flow although, in the outflow channel, bubble chords were in average smaller than in the shaft pool. A robust hydrophone was tested to measure bubble acoustic spectra and to assess its field application potential. The acoustic results characterised accurately the order of magnitude of entrained bubble sizes, but the transformation from acoustic frequencies to bubble radii did not predict correctly the probability distribution functions of bubble sizes.

Astrocytic αVβ3 integrin inhibits neurite outgrowth and promotes retraction of neuronal processes by clustering Thy-1.

Thy-1 is a membrane glycoprotein suggested to stabilize or inhibit growth of neuronal processes. However, its precise function has remained obscure, because its endogenous ligand is unknown. We previously showed that Thy-1 binds directly to α(V)β(3) integrin in trans eliciting responses in astrocytes. Nonetheless, whether α(V)β(3) integrin might also serve as a Thy-1-ligand triggering a neuronal response has not been explored. Thus, utilizing primary neurons and a neuron-derived cell line CAD, Thy-1-mediated effects of α(V)β(3) integrin on growth and retraction of neuronal processes were tested. In astrocyte-neuron co-cultures, endogenous α(V)β(3) integrin restricted neurite outgrowth. Likewise, α(V)β(3)-Fc was sufficient to suppress neurite extension in Thy-1(+), but not in Thy-1(-) CAD cells. In differentiating primary neurons exposed to α(V)β(3)-Fc, fewer and shorter dendrites were detected. This effect was abolished by cleavage of Thy-1 from the neuronal surface using phosphoinositide-specific phospholipase C (PI-PLC). Moreover, α(V)β(3)-Fc also induced retraction of already extended Thy-1(+)-axon-like neurites in differentiated CAD cells as well as of axonal terminals in differentiated primary neurons. Axonal retraction occurred when redistribution and clustering of Thy-1 molecules in the plasma membrane was induced by α(V)β(3) integrin. Binding of α(V)β(3)-Fc was detected in Thy-1 clusters during axon retraction of primary neurons. Moreover, α(V)β(3)-Fc-induced Thy-1 clustering correlated in time and space with redistribution and inactivation of Src kinase. Thus, our data indicates that α(V)β(3) integrin is a ligand for Thy-1 that upon binding not only restricts the growth of neurites, but also induces retraction of already existing processes by inducing Thy-1 clustering. We propose that these events participate in bi-directional astrocyte-neuron communication relevant to axonal repair after neuronal damage.

Evolutionary Clustering of Complex Systems and Processes

In a paper the method of complex systems and processes clustering based use of genetic algorithm is offered. The aspects of its realization and shaping of fitness-function are considered. The solution of clustering task of Ukraine areas on socio-economic indexes is represented and comparative analysis with outcomes of classical methods is realized.

Measuring the interaction forces between protein inclusion bodies and an air bubble using an atomic force microscope

Interaction forces between protein inclusion bodies and an air bubble have been quantified using an atomic force microscope (AFM). The inclusion bodies were attached to the AFM tip by covalent bonds. Interaction forces measured in various buffer concentrations varied from 9.7 nN to 25.3 nN (+/- 4-11%) depending on pH. Hydrophobic forces provide a stronger contribution to overall interaction force than electrostatic double layer forces. It also appears that the ionic strength affects the interaction force in a complex way that cannot be directly predicted by DLVO theory. The effects of pH are significantly stronger for the inclusion body compared to the air bubble. This study provides fundamental information that will subsequently facilitate the rational design of flotation recovery system for inclusion bodies. It has also demonstrated the potential of AFM to facilitate the design of such processes from a practical viewpoint.

Task clustering on ETL systems – A pattern-oriented approach

Usually, data warehousing populating processes are data-oriented workflows composed by dozens of granular tasks that are responsible for the integration of data coming from different data sources. Specific subset of these tasks can be grouped on a collection together with their relationships in order to form higher- level constructs. Increasing task granularity allows for the generalization of processes, simplifying their views and providing methods to carry out expertise to new applications. Well-proven practices can be used to describe general solutions that use basic skeletons configured and instantiated according to a set of specific integration requirements. Patterns can be applied to ETL processes aiming to simplify not only a possible conceptual representation but also to reduce the gap that often exists between two design perspectives. In this paper, we demonstrate the feasibility and effectiveness of an ETL pattern-based approach using task clustering, analyzing a real world ETL scenario through the definitions of two commonly used clusters of tasks: a data lookup cluster and a data conciliation and integration cluster.

A nonlinear threshold model for the dependence of extremes of stationary sequences

One of the main implications of the efficient market hypothesis (EMH) is that expected future returns on financial assets are not predictable if investors are risk neutral. In this paper we argue that financial time series offer more information than that this hypothesis seems to supply. In particular we postulate that runs of very large returns can be predictable for small time periods. In order to prove this we propose a TAR(3,1)-GARCH(1,1) model that is able to describe two different types of extreme events: a first type generated by large uncertainty regimes where runs of extremes are not predictable and a second type where extremes come from isolated dread/joy events. This model is new in the literature in nonlinear processes. Its novelty resides on two features of the model that make it different from previous TAR methodologies. The regimes are motivated by the occurrence of extreme values and the threshold variable is defined by the shock affecting the process in the preceding period. In this way this model is able to uncover dependence and clustering of extremes in high as well as in low volatility periods. This model is tested with data from General Motors stocks prices corresponding to two crises that had a substantial impact in financial markets worldwide; the Black Monday of October 1987 and September 11th, 2001. By analyzing the periods around these crises we find evidence of statistical significance of our model and thereby of predictability of extremes for September 11th but not for Black Monday. These findings support the hypotheses of a big negative event producing runs of negative returns in the first case, and of the burst of a worldwide stock market bubble in the second example. JEL classification: C12; C15; C22; C51 Keywords and Phrases: asymmetries, crises, extreme values, hypothesis testing, leverage effect, nonlinearities, threshold models

BIO-INSPIRED COMPUTATIONAL TECHNIQUES APPLIED TO THE CLUSTERING AND VISUALIZATION OF SPATIO-TEMPORAL GEOSPATIAL DATA

The coverage and volume of geo-referenced datasets are extensive and incessantly¦growing. The systematic capture of geo-referenced information generates large volumes¦of spatio-temporal data to be analyzed. Clustering and visualization play a key¦role in the exploratory data analysis and the extraction of knowledge embedded in¦these data. However, new challenges in visualization and clustering are posed when¦dealing with the special characteristics of this data. For instance, its complex structures,¦large quantity of samples, variables involved in a temporal context, high dimensionality¦and large variability in cluster shapes.¦The central aim of my thesis is to propose new algorithms and methodologies for¦clustering and visualization, in order to assist the knowledge extraction from spatiotemporal¦geo-referenced data, thus improving making decision processes.¦I present two original algorithms, one for clustering: the Fuzzy Growing Hierarchical¦Self-Organizing Networks (FGHSON), and the second for exploratory visual data analysis:¦the Tree-structured Self-organizing Maps Component Planes. In addition, I present¦methodologies that combined with FGHSON and the Tree-structured SOM Component¦Planes allow the integration of space and time seamlessly and simultaneously in¦order to extract knowledge embedded in a temporal context.¦The originality of the FGHSON lies in its capability to reflect the underlying structure¦of a dataset in a hierarchical fuzzy way. A hierarchical fuzzy representation of¦clusters is crucial when data include complex structures with large variability of cluster¦shapes, variances, densities and number of clusters. The most important characteristics¦of the FGHSON include: (1) It does not require an a-priori setup of the number¦of clusters. (2) The algorithm executes several self-organizing processes in parallel.¦Hence, when dealing with large datasets the processes can be distributed reducing the¦computational cost. (3) Only three parameters are necessary to set up the algorithm.¦In the case of the Tree-structured SOM Component Planes, the novelty of this algorithm¦lies in its ability to create a structure that allows the visual exploratory data analysis¦of large high-dimensional datasets. This algorithm creates a hierarchical structure¦of Self-Organizing Map Component Planes, arranging similar variables' projections in¦the same branches of the tree. Hence, similarities on variables' behavior can be easily¦detected (e.g. local correlations, maximal and minimal values and outliers).¦Both FGHSON and the Tree-structured SOM Component Planes were applied in¦several agroecological problems proving to be very efficient in the exploratory analysis¦and clustering of spatio-temporal datasets.¦In this thesis I also tested three soft competitive learning algorithms. Two of them¦well-known non supervised soft competitive algorithms, namely the Self-Organizing¦Maps (SOMs) and the Growing Hierarchical Self-Organizing Maps (GHSOMs); and the¦third was our original contribution, the FGHSON. Although the algorithms presented¦here have been used in several areas, to my knowledge there is not any work applying¦and comparing the performance of those techniques when dealing with spatiotemporal¦geospatial data, as it is presented in this thesis.¦I propose original methodologies to explore spatio-temporal geo-referenced datasets¦through time. Our approach uses time windows to capture temporal similarities and¦variations by using the FGHSON clustering algorithm. The developed methodologies¦are used in two case studies. In the first, the objective was to find similar agroecozones¦through time and in the second one it was to find similar environmental patterns¦shifted in time.¦Several results presented in this thesis have led to new contributions to agroecological¦knowledge, for instance, in sugar cane, and blackberry production.¦Finally, in the framework of this thesis we developed several software tools: (1)¦a Matlab toolbox that implements the FGHSON algorithm, and (2) a program called¦BIS (Bio-inspired Identification of Similar agroecozones) an interactive graphical user¦interface tool which integrates the FGHSON algorithm with Google Earth in order to¦show zones with similar agroecological characteristics.

Scale decisions can reverse conclusions on community assembly processes.

AIM: Phylogenetic diversity patterns are increasingly being used to better understand the role of ecological and evolutionary processes in community assembly. Here, we quantify how these patterns are influenced by scale choices in terms of spatial and environmental extent and organismic scales. LOCATION: European Alps. METHODS: We applied 42 sampling strategies differing in their combination of focal scales. For each resulting sub-dataset, we estimated the phylogenetic diversity of the species pools, phylogenetic α-diversities of local communities, and statistics commonly used together with null models in order to infer non-random diversity patterns (i.e. phylogenetic clustering versus over-dispersion). Finally, we studied the effects of scale choices on these measures using regression analyses. RESULTS: Scale choices were decisive for revealing signals in diversity patterns. Notably, changes in focal scales sometimes reversed a pattern of over-dispersion into clustering. Organismic scale had a stronger effect than spatial and environmental extent. However, we did not find general rules for the direction of change from over-dispersion to clustering with changing scales. Importantly, these scale issues had only a weak influence when focusing on regional diversity patterns that change along abiotic gradients. MAIN CONCLUSIONS: Our results call for caution when combining phylogenetic data with distributional data to study how and why communities differ from random expectations of phylogenetic relatedness. These analyses seem to be robust when the focus is on relating community diversity patterns to variation in habitat conditions, such as abiotic gradients. However, if the focus is on identifying relevant assembly rules for local communities, the uncertainty arising from a certain scale choice can be immense. In the latter case, it becomes necessary to test whether emerging patterns are robust to alternative scale choices.

Spatial Scale of Clustering of Motor Vehicle Crash Types and Appropriate Countermeasures, 2009

This project analyzes the characteristics and spatial distributions of motor vehicle crash types in order to evaluate the degree and scale of their spatial clustering. Crashes occur as the result of a variety of vehicle, roadway, and human factors and thus vary in their clustering behavior. Clustering can occur at a variety of scales, from the intersection level, to the corridor level, to the area level. Conversely, other crash types are less linked to geographic factors and are more spatially “random.” The degree and scale of clustering have implications for the use of strategies to promote transportation safety. In this project, Iowa's crash database, geographic information systems, and recent advances in spatial statistics methodologies and software tools were used to analyze the degree and spatial scale of clustering for several crash types within the counties of the Iowa Northland Regional Council of Governments. A statistical measure called the K function was used to analyze the clustering behavior of crashes. Several methodological issues, related to the application of this spatial statistical technique in the context of motor vehicle crashes on a road network, were identified and addressed. These methods facilitated the identification of crash clusters at appropriate scales of analysis for each crash type. This clustering information is useful for improving transportation safety through focused countermeasures directly linked to crash causes and the spatial extent of identified problem locations, as well as through the identification of less location-based crash types better suited to non-spatial countermeasures. The results of the K function analysis point to the usefulness of the procedure in identifying the degree and scale at which crashes cluster, or do not cluster, relative to each other. Moreover, for many individual crash types, different patterns and processes and potentially different countermeasures appeared at different scales of analysis. This finding highlights the importance of scale considerations in problem identification and countermeasure formulation.

Deciphering the global organization of clustering in real complex networks

We uncover the global organization of clustering in real complex networks. To this end, we ask whether triangles in real networks organize as in maximally random graphs with given degree and clustering distributions, or as in maximally ordered graph models where triangles are forced into modules. The answer comes by way of exploring m-core landscapes, where the m-core is defined, akin to the k-core, as the maximal subgraph with edges participating in at least m triangles. This property defines a set of nested subgraphs that, contrarily to k-cores, is able to distinguish between hierarchical and modular architectures. We find that the clustering organization in real networks is neither completely random nor ordered although, surprisingly, it is more random than modular. This supports the idea that the structure of real networks may in fact be the outcome of self-organized processes based on local optimization rules, in contrast to global optimization principles.