Support vector regression based modeling of pier scour using field data

This paper investigates the potential of support vector machines based regression approach to model the local scour around bridge piers using field data. A dataset of consisting of 232 pier scour measurements taken from BSDMS were used for this analysis. Results obtained by using radial basis function and polynomial kernel based Support vector regression were compared with four empirical relation as well as with a backpropagation neural network and generalized regression neural network. A total of 154 data were used for training different algorithms whereas remaining 78 data were used to test the created model. A coefficient of determination value of 0.897 (root mean square error=0.356) was achieved by radial basis kernel based support vector regression in comparison to 0.880 and 0.835 (root mean square error=0.388 and 0.438) by backpropagation neural and generalized regression neural network. Comparisons of results with four predictive equations suggest an improved performance by support vector regression. Results with dimensionless data using all three algorithms suggest a better performance by dimensional data with this dataset. Sensitivity analysis suggests the importance of depth of flow and pier width in predicting the scour depth when using support vector regression based modeling approach.     

Application of support vector machines in scour prediction on grade-control structures

Research into the problem of predicting the maximum depth of scour on grade-control structures like sluice gates, weirs and check dams, etc., has been mainly of an experimental nature and several investigators have proposed a number of empirical relations for a particular situation. These traditional scour prediction equations, although offer some guidance on the likely magnitude of maximum scour depth, yet applicable to a limited range of the situations. It appears from the literature review that a regression mathematical model for predicting maximum depth of scour under all circumstances is not currently available. This paper explores the potential of support vector machines in modeling the scour from the available laboratory and field data obtained form the earlier published studies. To compare the results, a recently proposed empirical relation and a feed forward back propagation neural network model are also used in the present study. The outcome from the support vector machines-based modeling approach suggests a better performance in comparison to both the empirical relation and back propagation neural network approach with the laboratory data. The results also suggest an encouraging performance by the support vector machines learning technique in comparison to both empirical relation as well as neural network approach in scaling up the results from laboratory to field conditions for the purpose of scour prediction.     

The interaction between skill, postures, forces and back pain in wool handling

Wool handling is an important rural occupation where workers process 200 or more fleeces daily, separating them into various quality components. Loads and postures they experience carry substantial risk of low back pain (LBP). Although a formal skill training structure exists, interaction with loads and LBP is unknown. We examined whether skill and LBP influenced trunk postures and loads of 60 wool handlers representing 3 skill levels. LBP prevalence ranged from 20% for junior (lowest skill) to 45% for open class (highest skill) wool handlers. Open class wool handlers demonstrated increased lateral bend and more axially twisted postures, generating greater medio-lateral shear forces and lateral bend and axial twist moments. LBP was associated with open class wool handlers spending more time in severe axially twisted postures. These findings suggest that skill-based training needs to be reviewed to reduce the quantity of axially twisted posture which may help reduce the prevalence of LBP in this workforce.     

Relational mountain (density) clustering method and web log analysis

The mountain clustering method and the subtractive clustering method are useful methods for finding cluster centers based on local density in object data. These methods have been extended to shell clustering. In this article, we propose a relational mountain clustering method (RMCM), which produces a set of (proto) typical objects as well as a crisp partition of the objects generating the relation, using a new concept that we call relational density. We exemplify RMCM by clustering several relational data sets that come from object data. Finally, RMCM is applied to web log analysis, where it produces useful user profiles from web log data. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 375–392, 2005.     

Semiconduction properties of some polyene-iodine charge-transfer complexes and their application in solid-state batteries

The conjugated polyenes -carotene, lutein, retinoic acid and -apo-8-carotenal are shown to form charge-transfer (CT) complexes with the electron acceptor iodine. The conductivity increases by several orders of magnitude and the activation energy decreases on CT complex formation. Using these complexes as cathodic material, batteries with the configuration Mg/(polyene-iodine CT complex) /graphite are developed. Different battery parameters are evaluated. The effects of ambient temperature and humidity on battery performance are also studied. Results show that a -apo-8-carotenal-l₂ based battery has the maximum power density and longest self-life and is suitable for use as a micro-electronic gadget energizer.     

Fuzzy medial axis transformation (fmat): Image representation,skeleton extraction and uncertainty management in computer vision

The problem of extraction of medial axis transformation of a gray image with reference to skeletonization, image representation and uncertainty management in a vision system has been addressed. The Fuzzy Medial Axis Transformation (fmat) of a fuzzy setf is a set of fuzzy disks whose sup isf. Unfortunately, specifying thefmat sometimes requires more storage space than specifyingf itself. The present paper describes some techniques to improve the compact representation offmat; thereby making it practically useful to an image for its skeleton extraction and compact representation, for shape analysis and template matching, for representation and retrieval, for uncertainty management in recognition and for creating new images of various poses. The algorithms involve reduction of redundancy infmat, its approximation, and reduction of the searching spaces for its computation. Computational aspects for the convenience of writing an efficient program have been described. Some applications of thefmat have also been mentioned. This work was done while SKP held annrc-nasa Senior Research Associateship at the Lyndon B Johnson Space Center, Houston, Texas.     

Wave propagation in a thinly two layered laminated medium with stress couples under initial stresses

Summary The present paper is an attempt to provide an approximate treatment based on Biots theory of incremental deformation to study the wave propagation in two thinly layered laminated medium under initial stresses. The cross-sectional distortion which plays an important role in the coupling of adherent layers is taken into account. The theory is derived in the context of plane strain deformation and the frequency equation for phase velocity of waves propagated has been obtained. It has been shown that under certain conditions when wave length becomes small compared to thickness of each layer, the wave approaches to Rayleigh waves at the two outer surfaces with the possibility of Stoneley waves at the interface.

Notation and Nomenclature

For Medium—I M _i, L_i elastic coefficients - C _i bending moment - h _i thickness - P _i initial stress - _i fraction of the total thickness occupied by theith layer - b _i couple-stress coefficient For the equivalent anisotropic continuum H total thickness - M, L elastic coefficients - P average initial stress - s ₁₁ ⁽¹⁾ ,s ₂₂,s ₁₂ ⁽¹⁾ incremental stress components - u ₁,v ₁ displacement components alongx, y directions respectively - ₁ density - e _xx,e _yy,e _xy ⁽¹⁾ strain components with respect to rotated axes - w ₁ rotational component aboutz-axis - f ₁ x, f ₁ y components of incremental boundary forces per unit initial area For Medium-II N _j,Q _j elastic coefficients - D _j bending moment - K _j thickness - R _j initial stress - _j fraction of the total thickness occupied by thejth layer - g _j couple-stress coefficients For the equivalent anisotropic continuum K total thickness - N, Q elastic coefficients - R average initial stress - s ₁₁ ⁽²⁾ ,s ₂₂,s ₁₂ ⁽²⁾ incremental stress components - u ₂,v ₂ displacement components alongx, y directions respectively - ₂ density - e _xx, e_yy,e _xy ⁽²⁾ strain components with respect to rotated axes - w ₂ rotational component aboutz-axis - f ₂ x, f ₂ y components of incremental boundary forces per unit initial area With 5 Figures     

An improved microfluidics approach for monitoring real-time interaction profiles of ultrafast molecular recognition

Our study illustrates the development of a microfluidics (MF) platform combining fluorescence microscopy and femtosecond/picosecond-resolved spectroscopy to investigate ultrafast chemical processes in liquid-phase diffusion-controlled reactions. By controlling the flow rates of two reactants in a specially designed MF chip, sub-100 ns time resolution for the exploration of chemical intermediates of the reaction in the MF channel has been achieved. Our system clearly rules out the possibility of formation of any intermediate reaction product in a so-called fast ionic reaction between sodium hydroxide and phenolphthalein, and reveals a microsecond time scale associated with the formation of the reaction product. We have also used the developed system for the investigation of intermediate states in the molecular recognition of various macromolecular self-assemblies (micelles) and genomic DNA by small organic ligands (Hoechst 33258 and ethidium bromide). We propose our MF-based system to be an alternative to the existing millisecond-resolved "stopped-flow" technique for a broad range of time-resolved (sub-100 ns to minutes) experiments on complex chemical∕biological systems.