1、Energy storage component is essential in a solarassisted air conditioning system if the cooling demPattern information extraction from crystal structuresOriginal Research ArticleComputer Physics Communications, Volume 176, Issue 7, 1 April 2007, Pages 486-506Erhan Okuyan, Uur Gdkbay, Ouz GlserenClos
2、e preview| Related articles|Related reference work articles AbstractAbstract | Figures/TablesFigures/Tables | ReferencesReferences AbstractDetermining the crystal structure parameters of a material is an important issue in crystallography and material science. Knowing the crystal structure parameter
3、s helps in understanding the physical behavior of material. It can be difficult to obtain crystal parameters for complex structures, particularly those materials that show local symmetry as well as global symmetry. This work provides a tool that extracts crystal parameters such as primitive vectors,
4、 basis vectors and space groups from the atomic coordinates of crystal structures. A visualization tool for examining crystals is also provided. Accordingly, this work could help crystallographers, chemists and material scientists to analyze crystal structures efficiently. Program summaryTitle of pr
5、ogram: BilKristal Catalogue identifier: ADYU_v1_0 Program summary URL: http:/cpc.cs.qub.ac.uk/summaries/ADYU_v1_0 Program obtainable from: CPC Program Library, Queens University of Belfast, N. Ireland Licensing provisions: None Programming language used: C, C+, Microsoft .NET Framework 1.1 and OpenG
6、L Libraries Computer: Personal Computers with Windows operating system Operating system: Windows XP Professional RAM: 2060 MB No. of lines in distributed program, including test data, etc.:899779 No. of bytes in distributed program, including test date, etc.:9271521 Distribution format:tar.gz Extern
7、al routines/libraries: Microsoft .NET Framework 1.1. For visualization tool, graphics card driver should also support OpenGL Nature of problem: Determining crystal structure parameters of a material is a quite important issue in crystallography. Knowing the crystal structure parameters helps to unde
8、rstand physical behavior of material. For complex structures, particularly, for materials which also contain local symmetry as well as global symmetry, obtaining crystal parameters can be quite hard. Solution method: The tool extracts crystal parameters such as primitive vectors, basis vectors and i
9、dentify the space group from atomic coordinates of crystal structures. Restrictions: Assumptions are explained in the paper. However, none of them can be considered as a restriction onto the complexity of the problem. Running time: All the examples presented in the paper take less than 30 seconds on
10、 a 2.4 GHz Pentium 4 computer.Article Outline1. Introduction2. A framework for pattern information extraction 2.1. Data structures and indexing2.2. The stages of the proposed framework 2.2.1. Reading and indexing the data2.2.2. The algorithm for grouping identical atoms2.2.3. The algorithm for extra
11、cting primitive vectors 2.2.3.1. The vector set generation.2.2.3.2. Filtering out redundant vectors.2.2.3.3. Finding primitive vector alternatives.2.2.4. The clustering algorithm2.2.5. The algorithm for finding basis vectors2.2.6. The algorithm for identifying space group3. Implementation 3.1. Analy
12、zer3.2. VisualizationTool4. Experimental results and performance analysis 4.1. Extracting primitive vectors and basis vectors4.2. Space group identification4.3. Error handling4.4. Performance evaluation 4.4.1. Complexity analysis4.4.2. Dissected execution times for different stages4.5. The program f
13、iles4.6. Discussion5. ConclusionAcknowledgementsReferencesPurchase$ 31.50927Development and structure of synaptic contacts in DrosophilaReview ArticleSeminars in Cell & Developmental Biology, Volume 17, Issue 1, February 2006, Pages 20-30Andreas Prokop, Ian A. MeinertzhagenClose preview| Supplementa
14、ry content| Related articles|Related reference work articles AbstractAbstract | Figures/TablesFigures/Tables | ReferencesReferences AbstractStructural synapses are key regulators of information flow in neuronal networks. To understand the function and formation of neuronal circuits, the development
15、and function of synapses have therefore been intensely studied in both vertebrate and invertebrate species. Precise descriptions of synapses and their amenability to genetic analysis in the model organism Drosophila provide an efficient platform from which to explore mechanisms and principles of syn
16、apse formation, which find many counterparts in other animals. Here we summarise our knowledge of the structure of Drosophila synapses. Focussing on neuromuscular junctions and photoreceptor synapses, we provide an overview of mechanisms underlying the development of synaptic structure in Drosophila
17、.Article Outline1. Introduction2. The locations of synapses in the Drosophila nervous system3. The structure of Drosophila synapses 3.1. Physiological considerations3.2. General features of Drosophila synapses3.3. A common presynaptic component of fly synapses: the T-bar ribbon3.4. Other synaptic sp
18、ecialisations4. The development of synapses at NMJs and photoreceptor terminals 4.1. Connectivity in the neuromuscular system4.2. Formation of neuromuscular contacts4.3. Connectivity in the visual system4.4. Formation of photoreceptor contacts4.5. Developmental mechanisms establishing synaptic ultra
19、structure5. ConclusionsAcknowledgementsAppendix A. Supplementary dataReferencesPurchase$ 41.95928Fuzzy C-means based clustering for linearly and nonlinearly separable dataOriginal Research ArticlePattern Recognition, Volume 44, Issue 8, August 2011, Pages 1750-1760Du-Ming Tsai, Chung-Chan LinClose p
20、review| Related articles|Related reference work articles AbstractAbstract | Figures/TablesFigures/Tables | ReferencesReferences AbstractIn this paper we present a new distance metric that incorporates the distance variation in a cluster to regularize the distance between a data point and the cluster
21、 centroid. It is then applied to the conventional fuzzy C-means (FCM) clustering in data space and the kernel fuzzy C-means (KFCM) clustering in a high-dimensional feature space. Experiments on two-dimensional artificial data sets, real data sets from public data libraries and color image segmentati
22、on have shown that the proposed FCM and KFCM with the new distance metric generally have better performance on non-spherically distributed data with uneven density for linear and nonlinear separation.Article Outline1. Introduction2. Fuzzy C-means based clustering 2.1. Fuzzy C-means (FCM) clustering2
23、.2. New distance metric for FCM2.3. Kernel fuzzy C-means clustering2.4. New distance metric for KFCM2.5. Bandwidth setting3. Experimental results 3.1. Simulated data3.2. Real data sets3.3. Color image segmentation4. ConclusionsReferencesVitaePurchase$ 31.50Research highlights A new distance metric b
24、ased on distance variation is used to improve FCM clustering. The new distance metric can also be applied to kernel FCM for various data shapes. The proposed algorithms can be used for linearly and nonlinearly separable data.929Spacecraft destruction during re-entry latest results and development of
25、 the SCARAB software systemOriginal Research ArticleAdvances in Space Research, Volume 34, Issue 5, 2004, Pages 1055-1060T. Lips, B. Fritsche, G. Koppenwallner, H. KlinkradClose preview| Related articles|Related reference work articles AbstractAbstract AbstractThe calculation of destructive re-entri
26、es and the prediction of the related ground risk potential due to fragment objects reaching the ground have become of high interest in the past years. This was also evident during the re-entry of the MIR space station in 2001. In 1995, under ESA contract, HTG started an international cooperation wit
27、h other companies and institutes to develop the SCARAB software system (Spacecraft Atmospheric Re-Entry and Aerothermal Break-Up). SCARAB is a quasi-deterministic tool, modeling a re-entry object down to sub-system level. The resulting aerodynamic parameters and mass distribution allow calculating a
28、 realistic 6D re-entry trajectory. Geometry and mass are continuously updated during calculation. Multi-level fragmentations due to different destruction processes are considered. The SCARAB software has been applied to several projects, namely ATV (ESA), ROSAT (Germany), Ariane-5 (ESA) and BeppoSAX
29、 (Italy). The practical application of SCARAB to project work has been demonstrated. In addition SCARAB has been compared with NASAs ORSAT code. It has also been verified with experimental data gained from re-entry vehicles, break-up observations and wind-tunnel tests. SCARAB is now on the way to be
30、come the European standard software for re-entry destruction analysis.Purchase$ 31.50930Using the Attention Network Test to predict driving test scoresOriginal Research ArticleAccident Analysis & Prevention, Volume 41, Issue 1, January 2009, Pages 76-83Bruce Weaver, Michel Bdard, Jim McAuliffe, Mari
31、e ParkkariClose preview| Related articles|Related reference work articles AbstractAbstract | Figures/TablesFigures/Tables | ReferencesReferences AbstractDriving is a complex multi-factorial task that taps underlying mechanisms of cognition and attention. Not surprisingly, therefore, many tests of cognition and attention are significantly associated with driving outcomes. In this article, we introduce driving researchers and clinicians with an interest in driving to the Attention Network Test (ANT), which to our knowledge has not
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