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Journal articles on the topic 'Point pattern analysis'

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Published: 4 June 2021
Last updated: 10 March 2023

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1

Gatrell, A. C. "Point pattern analysis." Applied Geography 9, no. 2 (April 1989): 140. http://dx.doi.org/10.1016/0143-6228(89)90059-3.

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2

Modi, Bharat P., and Dr Jayesh M. Patel. "User Behavior Pattern Analysis Mobile User’s Destination Point and their Service Utilization." International Journal of Scientific Research 3, no. 1 (June 1, 2012): 83–85. http://dx.doi.org/10.15373/22778179/jan2014/28.

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3

Robeson, Scott M., Ao Li, and Chunfeng Huang. "Point-pattern analysis on the sphere." Spatial Statistics 10 (November 2014): 76–86. http://dx.doi.org/10.1016/j.spasta.2014.10.001.

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4

Zimeras, Stelios. "Exploratory Point Pattern Analysis for Modeling Biological Data." International Journal of Systems Biology and Biomedical Technologies 2, no. 1 (January 2013): 1–13. http://dx.doi.org/10.4018/ijsbbt.2013010101.

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Data in the form of sets of points, irregular distributed in a region of space could be identified in varies biological applications for examples the cell nuclei in a microscope section of tissue. These kinds of data sets are defined as spatial point patterns and the presentation of the positions in the space are defined as points. The spatial pattern generated by a biological process, can be affected by the physical scale on which the process is observed. With these spatial maps, the biologists will usually want a detailed description of the observed patterns. One way to achieve this is by forming a parametric stochastic model and fitting it to the data. The estimated values of the parameters could be used to compare similar data sets providing statistical measures for fitting models. Also a fitted model can provide an explanation of the biological processes. Model fitting especially for large data sets is difficult. For that reason, statistical methods can apply with main purpose to formulate a hypothesis for the implementation of biological process. Spatial statistics could be implemented using advance statistical techniques that explicitly analyses and simulates point structures data sets. Typically spatial point patterns are data that explain the location of point events. The author’s interest is the investigation of the significance of these patterns. In this work, an investigation of biological spatial data is analyzed, using advance statistical modeling techniques like kriging.
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ARAÚJO, Edmary Silveira Barreto, João Domingos SCALON, and Lurimar Smera BATISTA. "EXPLORATORY SPECTRAL ANALYSIS IN THREE-DIMENSIONAL SPATIAL POINT PATTERNS." REVISTA BRASILEIRA DE BIOMETRIA 39, no. 1 (March 31, 2021): 177–93. http://dx.doi.org/10.28951/rbb.v39i1.524.

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A spatial point pattern is a collection of points irregularly located within a bounded area (2D) or space (3D) that have been generated by some form of stochastic mechanism. Examples of point patterns include locations of trees in a forest, of cases of a disease in a region, or of particles in a microscopic section of a composite material. Spatial Point pattern analysis is used mostly to determine the absence (completely spatial randomness) or presence (regularity and clustering) of spatial dependence structure of the locations. Methods based on the space domain are widely used for this purpose, while methods conducted in the frequency domain (spectral analysis) are still unknown to most researchers. Spectral analysis is a powerful tool to investigate spatial point patterns, since it does not assume any structural characteristics of the data (ex. isotropy), and uses only the autocovariance function, and its Fourier transform. There are some methods based on the spectral frameworks for analyzing 2D spatial point patterns. There is no such methods available for the 3D situation and, therefore, the aim of this work is to develop new methods based on spectral framework for the analysis of three-dimensional point patterns. The emphasis is on relating periodogram structure to the type of stochastic process which could have generated a 3D observed pattern. The results show that the methods based on spectral analysis developed in this work are able to identify patterns of three typical three-dimensional point processes, and can be used, concurrently, with analyzes in the space domain for a better characterization of spatial point patterns.
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6

Pourtaheri, Reza, and Mohammad Q. Vahidi-Asl. "Point pattern analysis of regional city distributions." Quality & Quantity 45, no. 6 (July 29, 2011): 1473–81. http://dx.doi.org/10.1007/s11135-011-9546-6.

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7

Norton-Wayne, L. "Pattern analysis for point-of-sale automation." Pattern Recognition Letters 6, no. 2 (July 1987): 139–43. http://dx.doi.org/10.1016/0167-8655(87)90094-8.

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8

Kosfeld, Reinhold, Hans-Friedrich Eckey, and Jørgen Lauridsen. "Spatial point pattern analysis and industry concentration." Annals of Regional Science 47, no. 2 (April 30, 2010): 311–28. http://dx.doi.org/10.1007/s00168-010-0385-5.

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9

Tanaka, Hajime, Takafumi Hayashi, and Toshio Nishi. "Digital image analysis of droplet patterns in polymer systems: Point pattern." Journal of Applied Physics 65, no. 12 (June 15, 1989): 4480–95. http://dx.doi.org/10.1063/1.343266.

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10

Carlsson, Gunnar. "Topological pattern recognition for point cloud data." Acta Numerica 23 (May 2014): 289–368. http://dx.doi.org/10.1017/s0962492914000051.

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In this paper we discuss the adaptation of the methods of homology from algebraic topology to the problem of pattern recognition in point cloud data sets. The method is referred to aspersistent homology, and has numerous applications to scientific problems. We discuss the definition and computation of homology in the standard setting of simplicial complexes and topological spaces, then show how one can obtain useful signatures, called barcodes, from finite metric spaces, thought of as sampled from a continuous object. We present several different cases where persistent homology is used, to illustrate the different ways in which the method can be applied.
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11

Rozas, V., and J. J. Camarero. "Spatial analysis techniques applied in forest ecology: point pattern analyses." Investigación Agraria: Sistemas y Recursos Forestales 14, no. 1 (April 1, 2005): 79. http://dx.doi.org/10.5424/srf/2005141-00875.

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12

Xinting, Wang, Hou Yali, Liang Cunzhu, Wang Wei, and Liu Fang. "Point pattern analysis based on different null models for detecting spatial patterns." Biodiversity Science 20, no. 2 (January 8, 2013): 151–58. http://dx.doi.org/10.3724/sp.j.1003.2012.08163.

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13

Xin-Ting, WANG, ZHANG Wei-Hua, JIANG Chao, and LIANG Cun-Zhu. "Point pattern analysis under conditions of replicated sampling." Chinese Journal of Plant Ecology 41, no. 5 (2017): 577–84. http://dx.doi.org/10.17521/cjpe.2016.0383.

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14

Rosenberg, Michael S. "Handbook of spatial point-pattern analysis in ecology." International Journal of Geographical Information Science 29, no. 9 (June 22, 2015): 1718–19. http://dx.doi.org/10.1080/13658816.2015.1059433.

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15

Ramsay, Paul M. "Handbook of spatial point-pattern analysis in ecology." Journal of Applied Statistics 42, no. 4 (December 9, 2014): 916–17. http://dx.doi.org/10.1080/02664763.2014.989467.

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16

DOUKISSAS, Leonidas, Yannis PSYCHARIS, and Anastasios KARAGANIS. "Location analysis of manufacturing activity in Greece: A point pattern analysis." European Journal of Geography 11, no. 3 (December 13, 2020): 108–25. http://dx.doi.org/10.48088/ejg.l.dou.11.3.108.125.

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This paper implements a point pattern analysis using a novel dataset with exact coordinates of statistical data for Greek manufacturing industry. Specifically, the dataset comprises the precise location of 2.452 observations of enterprises including 146.923 employees. For the year 2018 these industries are divided into twenty-four two-digit NACE 2 sub-industries of manufacturing activity. The method of point pattern analysis permits the estimation of the pattern in manufacturing activity across space. The highest agglomeration appears to be taking place in sectors of High (H) technological intensity as well as in the Middle High (M-H) sectors. In addition, sectors belonging to the middle category and the large category according to the number of employees tend to be more agglomerated in space. Findings reveal that the level of concentration or dispersion differs substantially among different sectors underlying the specialization and dispersion of economic activity in the country.
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17

Baddeley, A. J., R. A. Moyeed, C. V. Howard, and A. Boyde. "Analysis of a Three-Dimensional Point Pattern with Replication." Applied Statistics 42, no. 4 (1993): 641. http://dx.doi.org/10.2307/2986181.

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18

Shin, Hyun Su, Sang-Kyeong Lee, and Byoungkil Lee. "Selection of Spatial Regression Model Using Point Pattern Analysis." Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography 32, no. 3 (June 30, 2014): 225–31. http://dx.doi.org/10.7848/ksgpc.2014.32.3.225.

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19

Møller, Jesper, and Håkon Toftaker. "Geometric Anisotropic Spatial Point Pattern Analysis and Cox Processes." Scandinavian Journal of Statistics 41, no. 2 (January 2, 2014): 414–35. http://dx.doi.org/10.1111/sjos.12041.

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20

Lancaster, Jill, and Barbara J. Downes. "Spatial point pattern analysis of available and exploited resources." Ecography 27, no. 1 (February 2004): 94–102. http://dx.doi.org/10.1111/j.0906-7590.2004.03694.x.

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21

Lawrence, Thomas, Adrian Baddeley, Robin K. Milne, and Gopalan Nair. "Point pattern analysis on a region of a sphere." Stat 5, no. 1 (2016): 144–57. http://dx.doi.org/10.1002/sta4.108.

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22

He, Chen, Lingqiang Ran, Lei Wang, and Xueqing Li. "Point set surface compression based on shape pattern analysis." Multimedia Tools and Applications 76, no. 20 (October 1, 2016): 20545–65. http://dx.doi.org/10.1007/s11042-016-3991-0.

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23

Shi, Peijian, Jie Liu, and Zhen Yang. "Spatio-temporal point pattern analysis on Wenchuan strong earthquake." Earthquake Science 22, no. 3 (June 2009): 231–37. http://dx.doi.org/10.1007/s11589-009-0231-y.

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24

Rowlingson, B. S., and P. J. Diggle. "Splancs: Spatial point pattern analysis code in S-plus." Computers & Geosciences 19, no. 5 (May 1993): 627–55. http://dx.doi.org/10.1016/0098-3004(93)90099-q.

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25

Wallet, France, and Christophe Dussert. "Multifactorial Comparative Study of Spatial Point Pattern Analysis Methods." Journal of Theoretical Biology 187, no. 3 (August 1997): 437–47. http://dx.doi.org/10.1006/jtbi.1997.0445.

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26

Ben-Said, Mariem, Abdelilah Ghallab, Hajar Lamrhari, José Antonio Carreira, Juan Carlos Linares, and Lahcen Taïqui. "Characterizing spatial structure of Abies marocana forest through point pattern analysis." Forest Systems 29, no. 2 (August 25, 2020): e014. http://dx.doi.org/10.5424/fs/2020292-16754.

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Aim of study: Understanding small-scale patterns caused by stochastic factors or community interactions driving forest structure and diversity of Moroccan fir Abies marocana Trab.Area of study: Talassemtane fir forest, Talassemtane National Park, Rif Mountains, northern Morocco.Material and methods: Eight plots representative of the structural variability of A. marocana forests were selected, and all tree individuals with diameter at breast height (dbh) ≥2 cm were mapped and measured. We performed four types of spatial point pattern analyses: (1) Univariate analyses to explore the overall trees spatial pattern, (2) bivariate analyses to assess the spatial relationship between juveniles and A. marocana adults, (3) correlation between tree sizes (dbh) and distance between points pairs using the univariate mark correlation function, and (4) random labeling analyses between dominant and suppressed Moroccan fir individuals to assess competition patterns.Main results: We found a strong spatial aggregation of fir individuals and a positive intraspecific association between juveniles and adult trees. However, there were weak but significant distance-dependent effect on tree size and density-dependent effect on suppression pattern.Research highlights: Shade-tolerance, seed dispersal and/or microsite heterogeneity might play important roles in the observed fir patterns. Our results provide a basic knowledge on within-stand Moroccan fir spatial distribution, with implications for adaptive management of these relic forests, and prompting to further research to test advanced hypotheses.Additional key words: Moroccan fir; Talassemtane National Park; Rif forest; Spatial analysis; summary statistics; facilitation; aggregation.Abbreviations used: MF (Moroccan fir); Talassemtane National Park (TNP); dbh (diameter at breast height); Univariate pair-correlation function (g(r)); Heterogeneous Poisson (HP); Bivariate pair-correlation function (g12(r)); Univariate mark correlation function (kmm(r)), Mark connection function (p12(r)).
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27

Forcella, Frank, and Stephen J. Harvey. "Patterns of Weed Migration in Northwestern U.S.A." Weed Science 36, no. 2 (March 1988): 194–201. http://dx.doi.org/10.1017/s0043174500074701.

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Patterns of spread of 85 weed species alien to the northwestern U.S. were simulated using principal coordinate analysis on primarily herbaria-derived data. The primary pattern detected was a west-to-east migration of weeds. The point of introduction for most weeds following this pattern appeared to be near Portland, OR, an important shipping port in the past. An east-to-west migration represented a secondary pattern. The major cattle and wheat-cropping areas of east-central Montana were the apparent introduction points for weeds following this pattern. The grain and pulse region of eastern Washington represented the point of introduction of species following a third pattern of migration. These weeds subsequently spread southward to Utah and then laterally to the east and west. Historical weed migration patterns provide insight to probable points of origin and routes of currently spreading weeds as well as those that will spread in the future. Such insight may aid in the containment of future spreading weeds.
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28

Pan, Liang, Pengfei Wang, and Chee-Meng Chew. "PointAtrousNet: Point Atrous Convolution for Point Cloud Analysis." IEEE Robotics and Automation Letters 4, no. 4 (October 2019): 4035–41. http://dx.doi.org/10.1109/lra.2019.2927948.

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29

Yen, Thomas Y., and Robert G. Radwin. "Automated Job Analysis Using Biomechanical Data and Template Matching." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 41, no. 1 (October 1997): 712–16. http://dx.doi.org/10.1177/1071181397041001156.

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Spectral analysis of upper limb kinematic measurements has been previously demonstrated useful for quantifying physical stress properties of repetitive motion. The method requires manually separating the data into segments corresponding to individual tasks or work elements and computing power spectra. This study investigated using signal pattern recognition to help automate the analysis by separating the data through identification of stereotypical patterns in cyclical tasks. Joint angular data was collected for five industrial jobs using electrogoniometers attached to the wrist, elbow and shoulder of the dominant limb. A multimedia computer system along with the analyst interactively indicate element break points. The break points were also automatically identified using a template matching (TM) algorithm. The algorithm identified the cycle break points on average to within 0.997 s (S.D.=2.762 s) of the human analyst's reference break point. Nevertheless, automated break point identification should be useful for indicating approximate break point and then interactively fine-tuning the computer as a means for reducing the time required to perform an analysis.
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Jackson, A. G., and M. Rowe. "PC based diffraction pattern analysis programs." Proceedings, annual meeting, Electron Microscopy Society of America 46 (1988): 974–75. http://dx.doi.org/10.1017/s0424820100106934.

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The analysis of electron diffraction patterns can be done automatically using image techniques for collecting the diffraction pattern. This is useful if the material is well known. The alternative when the material is not well known is to do hand measurements on the plate and then enter that data into a program, or do the entire analysis by hand. We have developed a small software package called CRYSTAL, for a PC/AT which is an intermediate point between the fully automated approach and the approach by hand. These programs were written to be used in conjunction with the EMS programs in order to compare theoretical patterns with those obtained experimentally.The program menu is displayed by typing “CRYSTAL”. There are five choices available from the menu: 1) enter diffraction pattern data using the digitizer, 2) analysis of the zone and planes associated with the measured planes, 3) plot the theoretical diffraction pattern and/or the measured planes, either on the CRT or the plotter, 4) calculate x-ray planes and 20 angles, 5) return to DOS.
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31

Camacho, Ken, Raechel Portelli, Ashton Shortridge, and Bruno Takahashi. "Sentiment mapping: point pattern analysis of sentiment classified Twitter data." Cartography and Geographic Information Science 48, no. 3 (February 15, 2021): 241–57. http://dx.doi.org/10.1080/15230406.2020.1869999.

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32

Gatrell, Anthony C., Trevor C. Bailey, Peter J. Diggle, and Barry S. Rowlingson. "Spatial Point Pattern Analysis and Its Application in Geographical Epidemiology." Transactions of the Institute of British Geographers 21, no. 1 (1996): 256. http://dx.doi.org/10.2307/622936.

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33

Haffner, Matthew, and Jonathan C. Comer. "An interactive point pattern analysis web application and teaching exercise." Journal of Geography in Higher Education 43, no. 4 (September 6, 2019): 568–81. http://dx.doi.org/10.1080/03098265.2019.1660866.

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34

de Bruin, Karla G., Reinoud D. Stoel, and Josita C. M. Limborgh. "Improving the Point of Origin Determination in Bloodstain Pattern Analysis." Journal of Forensic Sciences 56, no. 6 (November 2011): 1476–82. http://dx.doi.org/10.1111/j.1556-4029.2011.01841.x.

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35

Li, Linling, Wei Qin, Lijun Bai, and Jie Tian. "Exploring vision-related acupuncture point specificity with multivoxel pattern analysis." Magnetic Resonance Imaging 28, no. 3 (April 2010): 380–87. http://dx.doi.org/10.1016/j.mri.2009.11.009.

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36

Parker, Joshua M., Eilon Sherman, Matthias van Der Raa, Detlef Lohse, Devaraj va da Meer, Larry Samelson, and Wolfgang Losert. "Cluster Analysis of Protein Point Pattern Sets using Minkowski Functionals." Biophysical Journal 104, no. 2 (January 2013): 511a. http://dx.doi.org/10.1016/j.bpj.2012.11.2822.

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37

Thomas, R. W., and D. E. Reeve. "The Role of Bose-Einstein Statistics in Point Pattern Analysis." Geographical Analysis 8, no. 2 (September 3, 2010): 113–36. http://dx.doi.org/10.1111/j.1538-4632.1976.tb01063.x.

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38

Lews, Ralph. "Redundancy as an Index of Change in Point Pattern Analysis." Geographical Analysis 11, no. 4 (September 3, 2010): 374–88. http://dx.doi.org/10.1111/j.1538-4632.1979.tb00703.x.

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39

Nieuwenhuizen, Roland, Wouter Zijl, and Marinus Van Veldhuizen. "Flow pattern analysis for a well defined by point sinks." Transport in Porous Media 21, no. 3 (December 1995): 209–23. http://dx.doi.org/10.1007/bf00617406.

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40

Nolze, G., C. Grosse, and A. Winkelmann. "Kikuchi pattern analysis of noncentrosymmetric crystals." Journal of Applied Crystallography 48, no. 5 (August 18, 2015): 1405–19. http://dx.doi.org/10.1107/s1600576715014016.

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Different models of Kikuchi pattern formation are compared with respect to their applicability to noncentrosymmetric crystals, and the breakdown of Friedel's rule in experimental electron backscatter diffraction (EBSD) patterns is discussed. DifferentAIIIBVsemiconductor materials are used to evaluate the resulting asymmetry of Kikuchi band profiles for polar lattice planes. By comparison with the characteristic etch pit morphology on a single-crystal surface, the polar character of the measured lattice planes can be assigned absolutely. The presented approach enables point-group-resolved orientation mapping, which goes beyond the commonly applied Laue group analysis in EBSD.
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HAKEN, HERMANN. "SYNERGETICS: FROM PATTERN FORMATION TO PATTERN ANALYSIS AND PATTERN RECOGNITION." International Journal of Bifurcation and Chaos 04, no. 05 (October 1994): 1069–83. http://dx.doi.org/10.1142/s0218127494000782.

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It is by now well known that numerous open systems in physics (fluids, plasmas, lasers, nonlinear optical devices, semiconductors), chemistry and biology (morphogenesis) may spontaneously develop spatial, temporal or spatiotemporal structures by self-organization. Quite often, striking analogies between the corresponding patterns can be observed in spite of the fact that the underlying systems are of quite a different nature. In this paper I shall first give an outline of general concepts that allow us to deal with the spontaneous formation of structures from a unifying point of view that is based on concepts of instability, order parameters and enslavement. We shall discuss a number of generalized Ginzburg-Landau equations. In most cases treated so far, theory started from microscopic or mesoscopic equations of motion from which the evolving structures were derived. In my paper I shall address two further problems that are in a way the reverse, namely (1) Can we derive order parameters and the basic modes from observed experimental data? (2) Can we construct systems by means of an underlying dynamics that are capable of producing patterns or structures that we prescribe? In order to address (1), a new variational principle that may be derived from path intergrals is introduced and illustrated by examples. An approach to the problem (2) is illustrated by the device of a computer that recognizes patterns and that may be realized by various kinds of spontaneous pattern formations in semiconductors and lasers.
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Tanaka, M. "Procedures for Point and Space Group Analysis." Proceedings, annual meeting, Electron Microscopy Society of America 47 (August 6, 1989): 488–89. http://dx.doi.org/10.1017/s042482010015441x.

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Description of general procedures for point- and space-group determination by convergent-beam electron diffraction(CBED) soon appears in a special issue of J. Electron Microsc. Tech. The essence of them was already given in the flow charts in ref.2. We here demonstrate the procedures using an example of La2 CuO4 - 6, which is the end member of a 40K class superconductor La2-x Mx CuO4 - 6 (M = Ba, Sr and Ca). The CBED method has utilized to date only dynamical extinction(GM lines) appearing in zeroth-order Laue-zone(ZOLZ) reflections for the space-group determination. We emphasize that the use of GM lines appearing in higher-order Laue-zone(HOLZ) reflections makes it easy to determine crystal space-groups.The substance was already known to belong to the orthorhombic system with the lattice parameters of a=5.3548Å, b=5.4006Å and c=13.1592Å. Fig. 1(a) shows a CBED pattern taken at the [001] incidence, Fig.l(b) being the central part of Fig.l(a). The whole pattern has a symmetry 2mm. This indicates that the crystal has two mirror symmetries perpendicular to each other and that the point group is mmm or mm2.
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43

Hills, C. R., and G. A. Poulter. "On-line diffraction pattern analysis and phase identification using a JEM 2000FX AEM with a macintosh-based program." Proceedings, annual meeting, Electron Microscopy Society of America 52 (1994): 1006–7. http://dx.doi.org/10.1017/s0424820100172760.

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A number of computer programs have been written to aid in the indexing of transmission electron diffraction pattcrns. These programs are useful for determining crystallographic orientation and for phase identification and often simplify the analysis of complex patterns. Over the last few years there has been a trend toward automated electron microscopy. It is natural to extend this automation to real time diffraction pattern analysis and phase identification using A/D data acquisition boards and computer software to interface the modern AEM with an electron diffraction database (EDD). This paper describes a real-time Macintosh-based system (hardware and software) for automated electron diffraction pattern analysis and phase identification developed for the JEM 2000FX AEM. Crystallographic analysis with this system is attractive because of the rapid analysis time, ease of implementation, and it is inexpensive compared to buying a digitizing board and video system.Computer-aided diffraction pattern-indexing programs typically require the user to input reciprocal lattice point spacings (r-spacings) and the interplanar angle measurements for at least three non-colinear lattice points in the pattern. It is also necessary to know the crystal structure and lattice constants of the sample.
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44

V A Siallagan, I Sembiring, S Sepriadi, and D Purba. "Financial Analysis of Broiler Chicken Farmers on Partnership Pattern and Independent Pattern." Jurnal Peternakan Integratif 8, no. 2 (November 30, 2020): 137–48. http://dx.doi.org/10.32734/jpi.v8i2.4801.

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Broiler farm business continues to grow. This research aims to find out the income and feasibility reviewed from financial aspect of broiler chicken farmers partnership patterns and independent patterns in Tanjung Morawa Sub-district. The type of data used in this study is primary data obtained through observation and interviews and secondary data. Determination of samples with purposive sampling with broiler chicken breeders partnership pattern as many as 9 people and broiler chicken farmer independent pattern as many as 3 people. Data analysis was observed by calculating revenue income and financial viability of Revenue Cost Ratio, Break Event Point, Return on Investman and Internal Rate of Return. The results showed that the average income per head breeder of partnership patterns with company A, company B and company C amounted to Rp 2,216, Rp2,407, Rp 2,310 and the average income of independent pattern farmers amounted to Rp 3,077. Broiler business farmers partnership obtained ROI of 63%-80%, IRR is greater than the interest rate, which is 22%-24% and R/C more than one. Based on the feasibility criteria of financial analysis of partner and independent pattern farms is feasible to run and develop.
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V A Siallagan, I Sembiring, S Sepriadi, and D Purba. "Financial Analysis of Broiler Chicken Farmers on Partnership Pattern and Independent Pattern." Jurnal Peternakan Integratif 8, no. 2 (November 30, 2020): 137–48. http://dx.doi.org/10.32734/jpi.v8i2.4801.

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Broiler farm business continues to grow. This research aims to find out the income and feasibility reviewed from financial aspect of broiler chicken farmers partnership patterns and independent patterns in Tanjung Morawa Sub-district. The type of data used in this study is primary data obtained through observation and interviews and secondary data. Determination of samples with purposive sampling with broiler chicken breeders partnership pattern as many as 9 people and broiler chicken farmer independent pattern as many as 3 people. Data analysis was observed by calculating revenue income and financial viability of Revenue Cost Ratio, Break Event Point, Return on Investman and Internal Rate of Return. The results showed that the average income per head breeder of partnership patterns with company A, company B and company C amounted to Rp 2,216, Rp2,407, Rp 2,310 and the average income of independent pattern farmers amounted to Rp 3,077. Broiler business farmers partnership obtained ROI of 63%-80%, IRR is greater than the interest rate, which is 22%-24% and R/C more than one. Based on the feasibility criteria of financial analysis of partner and independent pattern farms is feasible to run and develop.
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Parreño, Geneveve, Kyu Kon Kim, Changwan Kang, and Seungbae Choi. "A Study on Point Pattern Analysis of Earthquakes in the Philippines." Korean Data Analysis Society 19, no. 3 (June 30, 2017): 1143–54. http://dx.doi.org/10.37727/jkdas.2017.19.3.1143.

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Jin, Lan, Jonathan Reuning-Scherer, Jie Qiu, Qing Liu, Yawei Zhang, and Michelle Bell. "Spatial Point Pattern Analysis of Congenital Heart Defects in Lanzhou, China." ISEE Conference Abstracts 2017, no. 1 (February 2018): 561. http://dx.doi.org/10.1289/isee.2017.2017-561.

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Vacchiano, Giorgio, Daniele Castagneri, Fabio Meloni, Emanuele Lingua, and Renzo Motta. "Point pattern analysis of crown-to-crown interactions in mountain forests." Procedia Environmental Sciences 7 (2011): 269–74. http://dx.doi.org/10.1016/j.proenv.2011.07.047.

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Mongrain, Joanna, and Jessica F. Larsen. "Spatial point pattern analysis applied to bubble nucleation in silicate melts." Computers & Geosciences 35, no. 9 (September 2009): 1917–24. http://dx.doi.org/10.1016/j.cageo.2009.01.008.

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Madrid, A. E., J. M. Angulo, and J. Mateu. "Point Pattern Analysis of Spatial Deformation and Blurring Effects on Exceedances." Journal of Agricultural, Biological, and Environmental Statistics 21, no. 3 (August 8, 2016): 512–30. http://dx.doi.org/10.1007/s13253-016-0262-5.

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