X-Ray Scanner for Atlas Barrel TRT Modules

X-ray scanners for gain mapping of ATLAS Barrel Transition Radiation Tracker (TRT) modules were developed at Hampton University for quality assurance purposes. Gas gain variations for each straw of the TRT modules were used to decide whether wires should be removed or restrung, and to evaluate overall module quality.Comment: Conference proceeding in the XXIV Physics in Collisions Conference (PIC04), Boston, USA, June 2004, 3 pages, LaTeX, 6 eps figures. MONP0

A Technique of Direct Tension Measurement of a Strung Fine Wire

We present a new technique of direct measurement of wire tensions in wire chambers. A specially designed circuit plucks the wire using the Lorentz force and measures the frequency of damped transverse oscillations of the wire. The technique avoids the usual time-consuming necessity of tuning circuit parameter to a resonance. It allows a fast and convenient determination of tensions and is straightforward to implement.Comment: 15 pages with 9 figure

An Efficient Algorithm for Bulk-Loading xBR+ -trees

A major part of the interface to a database is made up of the queries that can be addressed to this database and answered (processed) in an efficient way, contributing to the quality of the developed software. Efficiently processed spatial queries constitute a fundamental part of the interface to spatial databases due to the wide area of applications that may address such queries, like geographical information systems (GIS), location-based services, computer visualization, automated mapping, facilities management, etc. Another important capability of the interface to a spatial database is to offer the creation of efficient index structures to speed up spatial query processing. The xBR + -tree is a balanced disk-resident quadtree-based index structure for point data, which is very efficient for processing such queries. Bulk-loading refers to the process of creating an index from scratch, when the dataset to be indexed is available beforehand, instead of creating the index gradually (and more slowly), when the dataset elements are inserted one-by-one. In this paper, we present an algorithm for bulk-loading xBR + -trees for big datasets residing on disk, using a limited amount of main memory. The resulting tree is not only built fast, but exhibits high performance in processing a broad range of spatial queries, where one or two datasets are involved. To justify these characteristics, using real and artificial datasets of various cardinalities, first, we present an experimental comparison of this algorithm vs. a previous version of the same algorithm and STR, a popular algorithm of bulk-loading R-trees, regarding tree creation time and the characteristics of the trees created, and second, we experimentally compare the query efficiency of bulk-loaded xBR + -trees vs. bulk-loaded R-trees, regarding I/O and execution time. Thus, this paper contributes to the implementation of spatial database interfaces and the efficient storage organization for big spatial data management

New Plane-Sweep Algorithms for Distance-Based Join Queries in Spatial Databases

Efficient and effective processing of the distance-based join query (DJQ) is of great importance in spatial databases due to the wide area of applications that may address such queries (mapping, urban planning, transportation planning, resource management, etc.). The most representative and studied DJQs are the K Closest Pairs Query (KCPQ) and εDistance Join Query (εDJQ). These spatial queries involve two spatial data sets and a distance function to measure the degree of closeness, along with a given number of pairs in the final result (K) or a distance threshold (ε). In this paper, we propose four new plane-sweep-based algorithms for KCPQs and their extensions for εDJQs in the context of spatial databases, without the use of an index for any of the two disk-resident data sets (since, building and using indexes is not always in favor of processing performance). They employ a combination of plane-sweep algorithms and space partitioning techniques to join the data sets. Finally, we present results of an extensive experimental study, that compares the efficiency and effectiveness of the proposed algorithms for KCPQs and εDJQs. This performance study, conducted on medium and big spatial data sets (real and synthetic) validates that the proposed plane-sweep-based algorithms are very promising in terms of both efficient and effective measures, when neither inputs are indexed. Moreover, the best of the new algorithms is experimentally compared to the best algorithm that is based on the R-tree (a widely accepted access method), for KCPQs and εDJQs, using the same data sets. This comparison shows that the new algorithms outperform R-tree based algorithms, in most cases

Efficient query processing on large spatial databases A performance study

Processing of spatial queries has been studied extensively in the literature. In most cases, it is accomplished by indexing spatial data using spatial access methods. Spatial indexes, such as those based on the Quadtree, are important in spatial databases for efficient execution of queries involving spatial constraints and objects. In this paper, we study a recent balanced disk-based index structure for point data, called xBR + -tree, that belongs to the Quadtree family and hierarchically decomposes space in a regular manner. For the most common spatial queries, like Point Location, Window, Distance Range, Nearest Neighbor and Distance-based Join, the R-tree family is a very popular choice of spatial index, due to its excellent query performance. For this reason, we compare the performance of the xBR + -tree with respect to the R ∗ -tree and the R + -tree for tree building and processing the most studied spatial queries. To perform this comparison, we utilize existing algorithms and present new ones. We demonstrate through extensive experimental performance results (I/O efficiency and execution time), based on medium and large real and synthetic datasets, that the xBR + -tree is a big winner in execution time in all cases and a winner in I/O in most cases

Guanylyl cyclase activation reverses resistive breathing–induced lung injury and inflammation

Inspiratory resistive breathing (RB), encountered in obstructive lung diseases, induces lung injury. The soluble guanylyl cyclase (sGC)/cyclic guanosine monophosphate (cGMP) pathway is down-regulated in chronic and acute animal models of RB, such as asthma, chronic obstructive pulmonary disease, and in endotoxin-induced acute lung injury. Our objectives were to: (1) characterize the effects of increased concurrent inspiratory and expiratory resistance in mice via tracheal banding; and (2) investigate the contribution of the sGC/cGMP pathway in RB-induced lung injury. Anesthetized C57BL/6 mice underwent RB achieved by restricting tracheal surface area to 50% (tracheal banding). RB for 24 hours resulted in increased bronchoalveolar lavage fluid cellularity and protein content, marked leukocyte infiltration in the lungs, and perturbed respiratory mechanics (increased tissue resistance and elasticity, shifted static pressure–volume curve right and downwards, decreased static compliance), consistent with the presence of acute lung injury. RB down-regulated sGC expression in the lung. All manifestations of lung injury caused by RB were exacerbated by the administration of the sGC inhibitor, 1H-[1,2,4]oxodiazolo[4,3-]quinoxalin-l-one, or when RB was performed using sGCα1 knockout mice. Conversely, restoration of sGC signaling by prior administration of the sGC activator BAY 58-2667 (Bayer, Leverkusen, Germany) prevented RB-induced lung injury. Strikingly, direct pharmacological activation of sGC with BAY 58-2667 24 hours after RB reversed, within 6 hours, the established lung injury. These findings raise the possibility that pharmacological targeting of the sGC–cGMP axis could be used to ameliorate lung dysfunction in obstructive lung diseases

Distance Range Queries in SpatialHadoop

Efficient processing of Distance Range Queries (DRQs) is of great importance in spatial databases due to the wide area of applications. This type of spatial query is characterized by a distance range over one or two datasets. The most representative and known DRQs are the ε Distance Range Query (εDRQ) and the ε Distance Range Join Query (εDRJQ). Given the increasing volume of spatial data, it is difficult to perform a DRQ on a centralized machine efficiently. Moreover, the εDRJQ is an expensive spatial operation, since it can be considered a combination of the εDR and the spatial join queries. For this reason, this paper addresses the problem of computing DRQs on big spatial datasets in SpatialHadoop, an extension of Hadoop that supports spatial operations efficiently, and proposes new algorithms in SpatialHadoop to perform efficient parallel DRQs on large-scale spatial datasets. We have evaluated the performance of the proposed algorithms in several situations with big synthetic and real-world datasets. The experiments have demonstrated the efficiency and scalability of our proposal

Enhancing SpatialHadoop with Closest Pair Queries

Given two datasets P and Q, the K Closest Pair Query (KCPQ) finds the K closest pairs of objects from P ×Q. It is an operation widely adopted by many spatial and GIS applications. As a combination of the K Nearest Neighbor (KNN) and the spatial join queries, KCPQ is an expensive operation. Given the increasing volume of spatial data, it is difficult to perform a KCPQ on a centralized machine efficiently. For this reason, this paper addresses the problem of computing the KCPQ on big spatial datasets in SpatialHadoop, an extension of Hadoop that supports spatial operations efficiently, and proposes a novel algorithm in SpatialHadoop to perform efficient parallel KCPQ on large-scale spatial datasets. We have evaluated the performance of the algorithm in several situations with big synthetic and real-world datasets. The experiments have demonstrated the efficiency and scalability of our proposal