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Journal articles on the topic 'Criticality'

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Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

Senthil, Todadri, Leon Balents, Subir Sachdev, Ashvin Vishwanath, and Matthew P. A. Fisher. "Deconfined Criticality Critically Defined." Journal of the Physical Society of Japan 74, Suppl (January 2005): 1–9. http://dx.doi.org/10.1143/jpsjs.74s.1.

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2

Banegas, Darío Luis, and Luis S. Villacañas de Castro. "Criticality." ELT Journal 70, no. 4 (June 13, 2016): 455–57. http://dx.doi.org/10.1093/elt/ccw048.

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3

Sornette, Didier, Anders Johansen, and Ivan Dornic. "Mapping Self-Organized Criticality onto Criticality." Journal de Physique I 5, no. 3 (March 1995): 325–35. http://dx.doi.org/10.1051/jp1:1995129.

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4

Willinger, W., R. Govindan, S. Jamin, V. Paxson, and S. Shenker. "Scaling phenomena in the Internet: Critically examining criticality." Proceedings of the National Academy of Sciences 99, Supplement 1 (February 19, 2002): 2573–80. http://dx.doi.org/10.1073/pnas.012583099.

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5

Huang, Y., H. Saleur, C. Sammis, and D. Sornette. "Precursors, aftershocks, criticality and self-organized criticality." Europhysics Letters (EPL) 41, no. 1 (January 1, 1998): 43–48. http://dx.doi.org/10.1209/epl/i1998-00113-x.

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6

Zimmer, Markus Philipp, Polyxeni Vassilakopoulou, Miria Grisot, and Marko Niemimaa. "Criticality and Values in Digital Transformation Research: Insights from a Workshop." Communications of the Association for Information Systems 53, no. 1 (2023): 964–83. http://dx.doi.org/10.17705/1cais.05341.

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Digital transformation can positively or negatively contribute to societies, organizations, and individuals depending on the values inscribed in the underlying digital technologies. This highlights the importance for researchers to critically examine digital technologies’ value inscriptions, how technology use enacts these values and the bearing of these values on research. This paper draws on the pre-ICIS 2022 IFIP 8.2 OASIS workshop on “Criticality and Values in Digital Transformation Research" to highlight four ways researchers can practice criticality, that is, how they can identify and reflect on the values that underlie digital phenomena. The types of criticality are phenomenon-based, method-based, theory-based, and self-reflexive criticality. Criticality alone does not constitute critical social research. However, criticality sensitizes researchers to consciously engage with values, which can feed into critical research’s elements of insight, critique, and transformation. Criticality can inform insight by surfacing values; providing the basis for critique by confronting readers with alternative values; and supporting transformation by proposing alternative value inscriptions. Hence, we take criticality as pivotal for understanding how digital transformation can contribute to building a better world and we invite the IS community to practice and discuss criticality, values, and reflexivity to drive positive change.

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7

Stajic, Jelena. "Pervasive criticality." Science 372, no. 6545 (May 27, 2021): 929.3–929. http://dx.doi.org/10.1126/science.372.6545.929-c.

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8

Drummond, P. D., S. Chaturvedi, K. Dechoum, and J. Comey. "Quantum Criticality." Zeitschrift für Naturforschung A 56, no. 1-2 (February 1, 2001): 133–39. http://dx.doi.org/10.1515/zna-2001-0120.

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Abstract We investigate the theory of quantum fluctuations in non-equilibrium systems having large critical fluctuations. This allows us to treat the limits imposed by nonlinearities to quantum squeezing and noise reduction, and also to envisage future tests of quantum theory in regions of macroscopic quantum fluctuations. A long-term objective of this research is to identify suitable physical systems in which macroscopic 'Schrödinger cat'-like behaviour may be observed. We investigate two systems in particular of much current experimental interest, namely the degenerate parametric oscillator near threshold, and the evaporatively cooled (BEC). We compare the results obtained in the positive-P representation, as a fully quantum mechanical calculation, with the truncated Wigner phase space equation, also known as semi-classical theory. We show when these results agree and differ in calculations taken beyond the linearized approximation. In the region where the largest quantum fluctuations and Schrödinger cat-like behaviour might be expected, we find that the quantum predictions correspond very closely to the semi-classical theory. Nature abhors observing a Schrödinger cat. -Pacs: 03.65.Bz

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9

Coleman, Piers, and Andrew J. Schofield. "Quantum criticality." Nature 433, no. 7023 (January 2005): 226–29. http://dx.doi.org/10.1038/nature03279.

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10

Cantarella, Jason, Joseph H. G. Fu, Robert B. Kusner, and John M. Sullivan. "Ropelength criticality." Geometry & Topology 18, no. 4 (October 2, 2014): 1973–2043. http://dx.doi.org/10.2140/gt.2014.18.1973.

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11

Sachdev, Subir, and Bernhard Keimer. "Quantum criticality." Physics Today 64, no. 2 (February 2011): 29–35. http://dx.doi.org/10.1063/1.3554314.

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12

Du Bois, Kristof, Stijn Eyerman, Jennifer B. Sartor, and Lieven Eeckhout. "Criticality stacks." ACM SIGARCH Computer Architecture News 41, no. 3 (June 26, 2013): 511–22. http://dx.doi.org/10.1145/2508148.2485966.

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13

Bagnoli, F., P. Palmerini, and R. Rechtman. "Algorithmic mapping from criticality to self-organized criticality." Physical Review E 55, no. 4 (April 1, 1997): 3970–76. http://dx.doi.org/10.1103/physreve.55.3970.

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14

Fadlelseed, Sajid, Raimund Kirner, and Catherine Menon. "ATMP-CA: Optimising Mixed-Criticality Systems Considering Criticality Arithmetic." Electronics 10, no. 11 (June 6, 2021): 1352. http://dx.doi.org/10.3390/electronics10111352.

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Many safety-critical systems use criticality arithmetic, an informal practice of implementing a higher-criticality function by combining several lower-criticality redundant components or tasks. This lowers the cost of development, but existing mixed-criticality schedulers may act incorrectly as they lack the knowledge that the lower-criticality tasks are operating together to implement a single higher-criticality function. In this paper, we propose a solution to this problem by presenting a mixed-criticality mid-term scheduler that considers where criticality arithmetic is used in the system. As this scheduler, which we term ATMP-CA, is a mid-term scheduler, it changes the configuration of the system when needed based on the recent history of deadline misses. We present the results from a series of experiments that show that ATMP-CA’s operation provides a smoother degradation of service compared with reference schedulers that do not consider the use of criticality arithmetic.

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15

Cai, Wan Tong, Wen Ying Liu, Wei Zheng, and Chen Liang. "The Analysis of Influences of Source-Network-Load's Operation Mode on Power Grids Self-Organized Criticality." Advanced Materials Research 732-733 (August 2013): 1375–81. http://dx.doi.org/10.4028/www.scientific.net/amr.732-733.1375.

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The distribution of power flow is a determinant of power grids self-organized criticality, and the uniformity of power flows distribution can be quantified by power flow entropy. The theory of power flow entropy is applied in this article to the research of influencing factors of power grids self-organized criticality. First the mathematics mechanism of self-organized criticalitys quantifying by power flow entropy is researched. And then the influence of start-up mode (source), running state (network), load distribution (load) on power flow entropy is analyzed respectively. Finally, the Hexi grid located in Gansu province is employed to verify the critical influences of source-network-loads operation mode on power grids self-organized criticality.

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16

Friman, B., G. A. Almási, and K. Redlich. "Modeling Chiral Criticality." Acta Physica Polonica B Proceedings Supplement 10, no. 3 (2017): 567. http://dx.doi.org/10.5506/aphyspolbsupp.10.567.

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17

Mejía D., Andrés. "Reconstruction in Criticality." Inquiry: Critical Thinking Across the Disciplines 21, no. 1 (2001): 17–31. http://dx.doi.org/10.5840/inquiryctnews200121122.

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18

Berdanier, William, Michael Kolodrubetz, S. A. Parameswaran, and Romain Vasseur. "Floquet quantum criticality." Proceedings of the National Academy of Sciences 115, no. 38 (August 29, 2018): 9491–96. http://dx.doi.org/10.1073/pnas.1805796115.

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We study transitions between distinct phases of one-dimensional periodically driven (Floquet) systems. We argue that these are generically controlled by infinite-randomness fixed points of a strong-disorder renormalization group procedure. Working in the fermionic representation of the prototypical Floquet Ising chain, we leverage infinite randomness physics to provide a simple description of Floquet (multi)criticality in terms of a distinct type of domain wall associated with time translational symmetry-breaking and the formation of “Floquet time crystals.” We validate our analysis via numerical simulations of free-fermion models sufficient to capture the critical physics.

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19

Tebbens, Sarah F., and Stephen M. Burroughs. "Self-Similar Criticality." Fractals 11, no. 03 (September 2003): 221–31. http://dx.doi.org/10.1142/s0218348x03002117.

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Cumulative frequency-size distributions associated with many natural phenomena follow a power law. Self-organized criticality (SOC) models have been used to model characteristics associated with these natural systems. As originally proposed, SOC models generate event frequency-size distributions that follow a power law with a single scaling exponent. Natural systems often exhibit power law frequency-size distributions with a range of scaling exponents. We modify the forest fire SOC model to produce a range of scaling exponents. In our model, uniform energy (material) input produces events initiated on a self-similar distribution of critical grid cells. An event occurs when material is added to a critical cell, causing that material and all material in occupied non-diagonal adjacent cells to leave the grid. The scaling exponent of the resulting cumulative frequency-size distribution depends on the fractal dimension of the critical cells. Since events occur on a self-similar distribution of critical cells, we call this model Self-Similar Criticality (SSC). The SSC model may provide a link between fractal geometry in nature and observed power law frequency-size distributions for many natural systems.

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20

Collins, Joshua C., and Dominique T. Chlup. "Criticality in Practice." Advances in Developing Human Resources 16, no. 4 (July 28, 2014): 481–98. http://dx.doi.org/10.1177/1523422314544295.

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21

Narayan, Awadhesh, Andrés Cano, Alexander V. Balatsky, and Nicola A. Spaldin. "Multiferroic quantum criticality." Nature Materials 18, no. 3 (December 31, 2018): 223–28. http://dx.doi.org/10.1038/s41563-018-0255-6.

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22

Rowley, S. E., L. J. Spalek, R. P. Smith, M. P. M. Dean, M. Itoh, J. F. Scott, G. G. Lonzarich, and S. S. Saxena. "Ferroelectric quantum criticality." Nature Physics 10, no. 5 (March 30, 2014): 367–72. http://dx.doi.org/10.1038/nphys2924.

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23

Srinivasan, Sudhir, and Paul F. Reynolds. "Super-criticality revisited." ACM SIGSIM Simulation Digest 25, no. 1 (July 1995): 130–36. http://dx.doi.org/10.1145/214283.214323.

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24

Turcotte, Donald L. "Self-organized criticality." Reports on Progress in Physics 62, no. 10 (September 28, 1999): 1377–429. http://dx.doi.org/10.1088/0034-4885/62/10/201.

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25

Bak, Per, Chao Tang, and Kurt Wiesenfeld. "Self-organized criticality." Physical Review A 38, no. 1 (July 1, 1988): 364–74. http://dx.doi.org/10.1103/physreva.38.364.

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26

Ju, Roy Dz-ching, Alvin R. Lebeck, and Chris Wilkerson. "Locality vs. criticality." ACM SIGARCH Computer Architecture News 29, no. 2 (May 2001): 132–43. http://dx.doi.org/10.1145/384285.379258.

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27

Bose, Indrani, and Sayantari Ghosh. "Bifurcation and criticality." Journal of Statistical Mechanics: Theory and Experiment 2019, no. 4 (April 26, 2019): 043403. http://dx.doi.org/10.1088/1742-5468/ab11d8.

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28

Bissett, Ngaire, and Sharon Saunders. "Criticality and Collegiality." Journal of Management Education 39, no. 5 (November 14, 2014): 597–625. http://dx.doi.org/10.1177/1052562914557281.

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29

Alstrøm, Preben, Tomas Bohr, Kim Christensen, Henrik Flyvbjerg, Mogens Høgh Jensen, Benny Lautrup, and Kim Sneppen. "Complexity and criticality." Physica A: Statistical Mechanics and its Applications 340, no. 4 (September 2004): iv—vi. http://dx.doi.org/10.1016/j.physa.2004.05.001.

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30

Krotov, Dmitry, Julien O. Dubuis, Thomas Gregor, and William Bialek. "Morphogenesis at criticality." Proceedings of the National Academy of Sciences 111, no. 10 (February 10, 2014): 3683–88. http://dx.doi.org/10.1073/pnas.1324186111.

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Spatial patterns in the early fruit fly embryo emerge from a network of interactions among transcription factors, the gap genes, driven by maternal inputs. Such networks can exhibit many qualitatively different behaviors, separated by critical surfaces. At criticality, we should observe strong correlations in the fluctuations of different genes around their mean expression levels, a slowing of the dynamics along some but not all directions in the space of possible expression levels, correlations of expression fluctuations over long distances in the embryo, and departures from a Gaussian distribution of these fluctuations. Analysis of recent experiments on the gap gene network shows that all these signatures are observed, and that the different signatures are related in ways predicted by theory. Although there might be other explanations for these individual phenomena, the confluence of evidence suggests that this genetic network is tuned to criticality.

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31

Malone, Kareen. "Criticality is perilous." Journal of Theoretical and Philosophical Psychology 31, no. 3 (2011): 200–204. http://dx.doi.org/10.1037/a0024724.

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32

Buchel, Alex, and Chris Pagnutti. "Transport at criticality." Nuclear Physics B 834, no. 1-2 (July 2010): 222–36. http://dx.doi.org/10.1016/j.nuclphysb.2010.03.016.

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33

McKelvain, Boyd J. "Determining military criticality." Society 23, no. 5 (July 1986): 19–21. http://dx.doi.org/10.1007/bf02695552.

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34

Vasilyev, O. A., A. Maciołek, and S. Dietrich. "Criticality senses topology." EPL (Europhysics Letters) 128, no. 2 (December 9, 2019): 20002. http://dx.doi.org/10.1209/0295-5075/128/20002.

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35

Bak, Per, and Kan Chen. "Self-Organized Criticality." Scientific American 264, no. 1 (January 1991): 46–53. http://dx.doi.org/10.1038/scientificamerican0191-46.

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36

Tang, Chao. "Self-Organized Criticality." IFAC Proceedings Volumes 27, no. 1 (March 1994): 29–30. http://dx.doi.org/10.1016/s1474-6670(17)46153-2.

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37

Bak, Per. "Self-organized criticality." Physica A: Statistical Mechanics and its Applications 163, no. 1 (February 1990): 403–9. http://dx.doi.org/10.1016/0378-4371(90)90348-v.

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38

Moss, T. R., and J. Woodhouse. "Criticality analysis revisited." Quality and Reliability Engineering International 15, no. 2 (March 1999): 117–21. http://dx.doi.org/10.1002/(sici)1099-1638(199903/04)15:2<117::aid-qre238>3.0.co;2-i.

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39

Naito, Yoshitaka, Toshihiro Yamamoto, Tsuyoshi Misawa, and Yuichi Yamane. "Review of studies on criticality safety evaluation and criticality experiment methods." Journal of Nuclear Science and Technology 50, no. 11 (November 2013): 1045–61. http://dx.doi.org/10.1080/00223131.2013.831238.

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40

McCarthy, Mark D. "Critically Teaching Criticality?: Modeling Social and Pedagogical Inquiry with Literary Texts." Studying Teacher Education 14, no. 2 (March 14, 2018): 174–93. http://dx.doi.org/10.1080/17425964.2018.1449103.

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41

Koch, Björn, Fernando Peñaherrera, and Alexandra Pehlken. "Criticality and LCA – Building comparison values to show the impact of criticality on LCA." European Journal of Sustainable Development 8, no. 4 (October 1, 2019): 304. http://dx.doi.org/10.14207/ejsd.2019.v8n4p304.

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Including criticality into Life Cycle Assessment (LCA) has always been challenging to achieve but desirable to accomplish. In this article, we present a new approach for the evaluation of resource consumption of products by building comparison values based on Life Cycle Impact Assessment (LCIA) combined with weighted criticality values to show the direct impacts of criticality on LCA results. For this purpose, we develop an impact indicator based on the Abiotic Depletion Potential (ADP) of natural resources and use the two main parameters defined by the EU to determine the criticality of a material - the economic importance and the supply risk – in our case studies to build the Criticality Weighted Abiotic Depletion Potentials (CWADPs), one for each parameter. These indicators allow identifying and measuring the impacts of criticality when comparing the results of resource depletion using the ADP methodology and the results that incorporate criticality. The comparison of the CWADPs to the corresponding EU criticality values and its thresholds it reflects the equivalent criticality of the assessed product. This information reflects the impacts of criticality on LCA and assesses the total resource consumption of critical materials in a system.Keywords: Life Cycle Assessment, criticality, resources, materials, sustainability indicator

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42

Liao, Ming Li, Yung Tsan Jou, and Cheng Shih Liaw. "Amalgamated Criticality Analysis Methodology." Advanced Materials Research 679 (April 2013): 101–6. http://dx.doi.org/10.4028/www.scientific.net/amr.679.101.

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Failure mode effects and criticality analysis (FMECA) is a widely useful design tool for enhancing product quality, safety and reliability. Most of the current FMECA procedure is in accordance with MIL-STD-1629A by which to conduct FMECA and criticality analysis, which is able to prioritize the failure modes and undertake limited corrective actions toward eliminating product risks. However, the criticality analysis calculation and its interpretation for a failure mode have some mathematical difficulties and erroneous omissions. To resolve these problems, this study proposes a new amalgamated criticality analysis methodology, which is knowledge-based to obtain the four different factor criteria and then using the maximal entropy ordered weighted geometric averaging (ME-OWGA) approach to compute the criticality numbers for a system. This study evaluates criticality analysis in the context of a communication system; the experimental results demonstrate that the proposed method is both accurate and provides discriminating analysis information that helps decision making in product design processes.

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43

Williams, T. M. "Criticality in Stochastic Networks." Journal of the Operational Research Society 43, no. 4 (April 1992): 353. http://dx.doi.org/10.2307/2583158.

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44

Benini, Leonardo. "Out-of-equilibrium criticality." Nature Physics 17, no. 11 (November 2021): 1191. http://dx.doi.org/10.1038/s41567-021-01413-x.

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45

Wu, Xiao-Chuan, Wenjie Ji, and Cenke Xu. "Categorical symmetries at criticality." Journal of Statistical Mechanics: Theory and Experiment 2021, no. 7 (July 1, 2021): 073101. http://dx.doi.org/10.1088/1742-5468/ac08fe.

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46

Harré, Michael S. "Entropy, Economics, and Criticality." Entropy 24, no. 2 (January 28, 2022): 210. http://dx.doi.org/10.3390/e24020210.

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Information theory is a well-established method for the study of many phenomena and more than 70 years after Claude Shannon first described it in A Mathematical Theory of Communication it has been extended well beyond Shannon’s initial vision. It is now an interdisciplinary tool that is used from ‘causal’ information flow to inferring complex computational processes and it is common to see it play an important role in fields as diverse as neuroscience, artificial intelligence, quantum mechanics, and astrophysics. In this article, I provide a selective review of a specific aspect of information theory that has received less attention than many of the others: as a tool for understanding, modelling, and detecting non-linear phenomena in finance and economics. Although some progress has been made in this area, it is still an under-developed area that I argue has considerable scope for further development.

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47

Colineau, E., R. Eloirdi, J. C. Griveau, P. Gaczynski, and A. B. Shick. "NpCoGe, near quantum criticality?" Journal of the Korean Physical Society 62, no. 10 (May 2013): 1539–41. http://dx.doi.org/10.3938/jkps.62.1539.

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48

Ovchinnikov, Igor V., Wenyuan Li, Yuquan Sun, Andrew E. Hudson, Karlheinz Meier, Robert N. Schwartz, and Kang L. Wang. "Criticality or Supersymmetry Breaking?" Symmetry 12, no. 5 (May 12, 2020): 805. http://dx.doi.org/10.3390/sym12050805.

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In many stochastic dynamical systems, ordinary chaotic behavior is preceded by a full-dimensional phase that exhibits 1/f-type power spectra and/or scale-free statistics of (anti)instantons such as neuroavalanches, earthquakes, etc. In contrast with the phenomenological concept of self-organized criticality, the recently found approximation-free supersymmetric theory of stochastics (STS) identifies this phase as the noise-induced chaos (N-phase), i.e., the phase where the topological supersymmetry pertaining to all stochastic dynamical systems is broken spontaneously by the condensation of the noise-induced (anti)instantons. Here, we support this picture in the context of neurodynamics. We study a 1D chain of neuron-like elements and find that the dynamics in the N-phase is indeed featured by positive stochastic Lyapunov exponents and dominated by (anti)instantonic processes of (creation) annihilation of kinks and antikinks, which can be viewed as predecessors of boundaries of neuroavalanches. We also construct the phase diagram of emulated stochastic neurodynamics on Spikey neuromorphic hardware and demonstrate that the width of the N-phase vanishes in the deterministic limit in accordance with STS. As a first result of the application of STS to neurodynamics comes the conclusion that a conscious brain can reside only in the N-phase.

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49

Klusoň, J. "Branes at quantum criticality." Journal of High Energy Physics 2009, no. 07 (July 22, 2009): 079. http://dx.doi.org/10.1088/1126-6708/2009/07/079.

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50

Xi, Li-Feng. "Criticality of plane arcs*." Nonlinearity 16, no. 2 (February 10, 2003): 647–60. http://dx.doi.org/10.1088/0951-7715/16/2/316.

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