Secondary attribute retrieval using tree data structures

Several techniques are known for searching an ordered collection of data. The techniques and analyses of retrieval methods based on primary attributes are straightforward. Retrieval using secondary attributes depends on several factors. For secondary attribute retrieval, the linear structures—inverted lists, multilists, doubly linked lists—and the recently proposed nonlinear tree structures—multiple attribute tree (MAT), K-d tree (kdT)—have their individual merits. It is shown in this paper that, of the two tree structures, MAT possesses several features of a systematic data structure for external file organisation which make it superior to kdT. Analytic estimates for the complexity of node searchers, in MAT and kdT for several types of queries, are developed and compared.

Incremental attribute evaluation through recursive procedures

Incremental semantic analysis in a programming environment based on Attribute Grammars is performed by an Incremental Attribute Evaluator (IAE). Current IAEs are either table-driven or make extensive use of graph structures to schedule reevaluation of attributes. A method of compiling an Ordered Attribute Grammar into mutually recursive procedures is proposed. These procedures form an optimal time Incremental Attribute Evaluator for the attribute grammar, which does not require any graphs or tables.

Tuning the Sensitivity of a Metal-Based Piezoresistive Sensor Using Electromigration

We report a simple method to enhance the piezoresistive sensitivity of a gold film by more than 30 times and demonstrate it using a microcantilever resonator. Our method depends on controlled electromigration that we use to tune the resistance and sensitivity of the piezoresistive sensor. We attribute the enhancement in strain sensitivity to the creation of an inhomogeneous conduction medium at a predefined location by directed and controlled electromigration. We understand this phenomenon with tunneling-percolation model, which was originally hypothesized to explain nonuniversal percolation behavior of composite materials. 2012-0174]

Regional flood frequency analysis using kernel- based fuzzy clustering approach

Regionalization approaches are widely used in water resources engineering to identify hydrologically homogeneous groups of watersheds that are referred to as regions. Pooled information from sites (depicting watersheds) in a region forms the basis to estimate quantiles associated with hydrological extreme events at ungauged/sparsely gauged sites in the region. Conventional regionalization approaches can be effective when watersheds (data points) corresponding to different regions can be separated using straight lines or linear planes in the space of watershed related attributes. In this paper, a kernel-based Fuzzy c-means (KFCM) clustering approach is presented for use in situations where such linear separation of regions cannot be accomplished. The approach uses kernel-based functions to map the data points from the attribute space to a higher-dimensional space where they can be separated into regions by linear planes. A procedure to determine optimal number of regions with the KFCM approach is suggested. Further, formulations to estimate flood quantiles at ungauged sites with the approach are developed. Effectiveness of the approach is demonstrated through Monte-Carlo simulation experiments and a case study on watersheds in United States. Comparison of results with those based on conventional Fuzzy c-means clustering, Region-of-influence approach and a prior study indicate that KFCM approach outperforms the other approaches in forming regions that are closer to being statistically homogeneous and in estimating flood quantiles at ungauged sites. Key Points Kernel-based regionalization approach is presented for flood frequency analysis Kernel procedure to estimate flood quantiles at ungauged sites is developed A set of fuzzy regions is delineated in Ohio, USA

Al based ultra-fine eutectic with high room temperature plasticity and elevated temperature strength

Developments of aluminum alloys that can retain strength at and above 250 degrees C present a significant challenge. In this paper we report an ultrafine scale Al-Fe-Ni eutectic alloy with less than 3.5 aa transition metals that exhibits room temperature ultimate tensile strength of similar to 400 MPa with a tensile ductility of 6-8%. The yield stress under compression at 300 degrees C was found to be 150 MPa. We attribute it to the refinement of the microstructure that is achieved by suction casting in copper mold. The characterization using scanning and transmission electron microscopy (SEM and TEM) reveals an unique composite structure that contains the Al-Al3Ni rod eutectic with spacing of similar to 90 nm enveloped by a lamellar eutectic of Al-Al9FeNi (similar to 140 nm). Observation of subsurface deformation under Vickers indentation using bonded interface technique reveals the presence of extensive shear banding during deformation that is responsible for the origin of ductility. The dislocation configuration in Al-Al3Ni eutectic colony indicates accommodation of plasticity in alpha-Al with dislocation accumulation at the alpha-Al/Al3Ni interface boundaries. In contrast the dislocation activities in the intermetallic lamellae are limited and contain set of planner dislocations across the plates. We present a detailed analysis of the fracture surface to rationalize the origin of the high strength and ductility in this class of potentially promising cast alloy. (C) 2015 Elsevier B.V. All rights reserved.