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Journal articles on the topic 'Term-set'

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Author: Grafiati

Published: 30 May 2022

Last updated: 31 May 2022

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1

Forster, T. E. "Term models for weak set theories with a universal set." Journal of Symbolic Logic 52, no. 2 (June 1987): 374–87. http://dx.doi.org/10.2307/2274387.

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We shall be concerned here with weak axiomatic systems of set theory with a universal set. The language in which they are expressed is that of set theory—two primitive predicates, = and ϵ, and no function symbols (though some function symbols will be introduced by definitional abbreviation). All the theories will have stratified axioms only, and they will all have Ext (extensionality: (∀x)(∀y)(x = y· ↔ ·(∀z)(z ϵ x ↔ z ϵ y))). In fact, in addition to extensionality, they have only axioms saying that the universe is closed under certain set-theoretic operations, viz. all of the formand these will always include singleton, i.e., ι′x exists if x does (the iota notation for singleton, due to Russell and Whitehead, is used here to avoid confusion with {x: Φ}, set abstraction), and also x ∪ y, x ∩ y and − x (the complement of x). The system with these axioms is called NF2 in the literature (see [F]). The other axioms we consider will be those giving ⋃x, ⋂x, {y: y ⊆x} and {y: x ⊆ y}. We will frequently have occasion to bear in mind that 〈 V, ⊆ 〉 is a Boolean algebra in any theory extending NF2. There is no use of the axiom of choice at any point in this paper. Since the systems with which we will be concerned exhibit this feature of having, in addition to extensionality, only axioms stating that V is closed under certain operations, we will be very interested in terms of the theories in question. A T-term, for T such a theory, is a thing (with no free variables) built up from V or ∧ by means of the T-operations, which are of course the operations that the axioms of T say the universe is closed under.

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2

Venzin, Megan. "Promote One‐Time and Set‐Term Volunteering." Volunteer Management Report 24, no. 7 (June 13, 2019): 1. http://dx.doi.org/10.1002/vmr.31205.

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3

Leber, Andrew B., Jun-Ichiro Kawahara, and Yuji Gabari. "Long-term abstract learning of attentional set." Journal of Experimental Psychology: Human Perception and Performance 35, no. 5 (2009): 1385–97. http://dx.doi.org/10.1037/a0016470.

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4

R hner, G. Bruggmoser, F. Heinemann, N. Hodapp. "Prediction of long-term cancer survival from a short-term data set?" Physics in Medicine and Biology 48, no. 12 (June 4, 2003): L35. http://dx.doi.org/10.1088/0031-9155/48/12/102.

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5

Liu Xinglin, Zheng Qilun, Ma Qianli, and Lin Guli. "Text Similarity Computing Based on Thematic Term Set." International Journal of Advancements in Computing Technology 4, no. 6 (April 15, 2012): 338–45. http://dx.doi.org/10.4156/ijact.vol4.issue6.39.

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6

Nirmala, K., and M. Pushpa. "Feature based Text Classification using Application Term Set." International Journal of Computer Applications 52, no. 10 (August 30, 2012): 1–3. http://dx.doi.org/10.5120/8235-1439.

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7

Mayr, Ulrich, and Reinhold Kliegl. "Task-set switching and long-term memory retrieval." Journal of Experimental Psychology: Learning, Memory, and Cognition 26, no. 5 (2000): 1124–40. http://dx.doi.org/10.1037/0278-7393.26.5.1124.

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8

Buckley, Ralf, and Paula Brough. "Mental health: set up long-term cohort studies." Nature 595, no. 7867 (July 13, 2021): 352. http://dx.doi.org/10.1038/d41586-021-01924-w.

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9

Lee, Tae-Woo. "Relation of PSM and Altruism in Set Term." Korean Journal of Local Government Studies 25, no. 4 (February 28, 2022): 91–109. http://dx.doi.org/10.20484/klog.25.4.4.

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10

Gao, Shuang, Lei Dong, Xiao Zhong Liao, and Yang Gao. "Long-Term Wind Power Prediction Based on Rough Set." Applied Mechanics and Materials 329 (June 2013): 411–15. http://dx.doi.org/10.4028/www.scientific.net/amm.329.411.

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In long-term wind power prediction, dealing with the relevant factors correctly is the key point to improve the prediction accuracy. This paper presents a prediction method with rough set analysis. The key factors that affect the wind power prediction are identified by rough set theory. The chaotic characteristics of wind speed time series are analyzed. The rough set neural network prediction model is built by adding the key factors as the additional inputs to the chaotic neural network model. Data of Fujin wind farm are used for this paper to verify the new method of long-term wind power prediction. The results show that rough set method is a useful tool in long-term prediction of wind power.

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11

Pi, Zhi Xian, Ru Zhi Xu, and Jian Guo. "The Short-Term Load Forecasting Based on Rough Set." Advanced Materials Research 383-390 (November 2011): 5023–27. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.5023.

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Short-term load forecasting in power system is an important daily work in Dispatch Operation Department of Power System. The level of forecasting accuracy directly affects the operating economy and supply quality of power system.This paper adopts the rough sets to forecast short-term load. It designs an overall structure of forecasting the short-term load based on the rough sets, applies the rough sets to analyze the importance of attribute of each condition on decision-making attribute and then gets a reduced forecasting system, lists examples to forecast short-term load on the basis of the real historical data, and compare the results with the traditional decision-making tree algorithm. The results of this study prove that the rough sets is much practical in short-term load forecasting.

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12

Pushpa, M. "Semi Automated Text Categorization using Demonstration Based Term Set." International Journal of Computer Science, Engineering and Applications 2, no. 4 (August 31, 2012): 71–77. http://dx.doi.org/10.5121/ijcsea.2012.2408.

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13

Halkier, Asger, Trygve Helgaker, Wim Klopper, and Jeppe Olsen. "Basis-set convergence of the two-electron Darwin term." Chemical Physics Letters 319, no. 3-4 (March 2000): 287–95. http://dx.doi.org/10.1016/s0009-2614(00)00161-5.

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14

LIU, Xinglin. "Improved algorithm of thematic term extraction based on increment term-set frequency from Chinese document." Journal of Computer Applications 33, no. 9 (November 6, 2013): 2546–49. http://dx.doi.org/10.3724/sp.j.1087.2013.02546.

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15

Chen, Liang-Yu, Ming-Hsien Lin, Li-Ning Peng, and Liang-Kung Chen. "Applications of Minimum Data Set in long-term care research." Aging Medicine and Healthcare (Journal of Clinical Gerontology & Geriatrics) 9, no. 4 (December 30, 2018): 118–25. http://dx.doi.org/10.33879/jcgg.2018.1811.

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16

Cowper, Andy. "Commission set up to solve funding of long-term care." Nursing Older People 22, no. 6 (June 24, 2010): 6–7. http://dx.doi.org/10.7748/nop.22.6.6.s10.

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17

Drevelle, Vincent, and Philippe Bonnifait. "Localization Confidence Domains via Set Inversion on Short-Term Trajectory." IEEE Transactions on Robotics 29, no. 5 (October 2013): 1244–56. http://dx.doi.org/10.1109/tro.2013.2262776.

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18

Marris, Emma. "Bush set to keep core science team for second term." Nature 432, no. 7014 (November 2004): 135. http://dx.doi.org/10.1038/432135b.

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19

Christal, A., D. H. K. Davies, J. Warren, and J. Macintosh. "The management of long term set-aside for nature conservation." Botanical Journal of Scotland 49, no. 2 (January 1997): 415–24. http://dx.doi.org/10.1080/03746609708684886.

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20

Yu, Wangwang, Hui Zhang, and Boquan Li. "Comparison and operators based on uncertain probabilistic linguistic term set." Journal of Intelligent & Fuzzy Systems 36, no. 6 (June 11, 2019): 6359–79. http://dx.doi.org/10.3233/jifs-182639.

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21

Favero, Carlo A., Linlin Niu, and Luca Sala. "Term Structure Forecasting: No-Arbitrage Restrictions versus Large Information Set." Journal of Forecasting 31, no. 2 (June 21, 2010): 124–56. http://dx.doi.org/10.1002/for.1181.

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22

Bailey, Aileen M. "Long-Term Retention of Olfactory Discrimination Learning Set in Rats." Psychological Record 56, no. 2 (April 2006): 219–31. http://dx.doi.org/10.1007/bf03395546.

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23

Corbet, Sarah A. "Insects, plants and succession: advantages of long-term set-aside." Agriculture, Ecosystems & Environment 53, no. 3 (May 1995): 201–17. http://dx.doi.org/10.1016/0167-8809(94)00581-x.

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24

Nakagawa, Shin-ichi, Masao Utiyama, Makoto Misumi, Akira Shimazu, and Yoshinori Sakai. "Establishment of Corpus-based Cancer Specific Term Set and its Characteristics." Journal of Natural Language Processing 16, no. 2 (2009): 3–44. http://dx.doi.org/10.5715/jnlp.16.2_3.

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25

Kulev, Nikolay. "Analysis of definition set formed by given definitions of term cybersecurity." Journal of Defence & Security Technologies 2 (2020): 3–10. http://dx.doi.org/10.46713/jdst.002.01.

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26

Jabalameli, Mehdi, Ala Arman, and Mohammadali Nematbakhsh. "Improving the Efficiency of Term Weighting in Set of Dynamic Documents." International Journal of Modern Education and Computer Science 7, no. 2 (February 8, 2015): 42–47. http://dx.doi.org/10.5815/ijmecs.2015.02.06.

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27

Lee, Joon Ho, Myoung Ho Kim, and Young Hwa Cho. "Using term dependencies of a thesaurus in the fuzzy set model." Microprocessing and Microprogramming 39, no. 2-5 (December 1993): 105–8. http://dx.doi.org/10.1016/0165-6074(93)90067-u.

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28

Celotto, Emilio, Andrea Ellero, and Paola Ferretti. "Short-medium Term Tourist Services Demand Forecasting with Rough Set Theory." Procedia Economics and Finance 3 (2012): 62–67. http://dx.doi.org/10.1016/s2212-5671(12)00121-9.

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29

Alawneh, Ahmed S., and Abdulla A. Sharo. "Estimation of long-term set-up of driven piles in sand." Geomechanics and Geoengineering 15, no. 4 (July 24, 2019): 281–96. http://dx.doi.org/10.1080/17486025.2019.1641630.

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30

Trewin, Blair, Karl Braganza, Robert Fawcett, Simon Grainger, Branislava Jovanovic, David Jones, David Martin, Robert Smalley, and Vanessa Webb. "An updated long‐term homogenized daily temperature data set for Australia." Geoscience Data Journal 7, no. 2 (June 8, 2020): 149–69. http://dx.doi.org/10.1002/gdj3.95.

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31

Hall, Tasha, Jordana van Geest, and Jonathan Love. "Long-term field data set supports the chloride-dependent nitrite guideline." Integrated Environmental Assessment and Management 12, no. 4 (September 18, 2016): 824–25. http://dx.doi.org/10.1002/ieam.1805.

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32

Ben Salah, M., A. Mitiche, and I. Ben Ayed. "Effective Level Set Image Segmentation With a Kernel Induced Data Term." IEEE Transactions on Image Processing 19, no. 1 (January 2010): 220–32. http://dx.doi.org/10.1109/tip.2009.2032940.

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33

Zhou, Hang, Elena Aizen, and Vladimir Aizen. "Constructing a long-term monthly climate data set in central Asia." International Journal of Climatology 38, no. 3 (September 10, 2017): 1463–75. http://dx.doi.org/10.1002/joc.5259.

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34

Menard, Scott, and Delbert S. Elliot. "Data set comparability and short-term trends in crime and delinquency." Journal of Criminal Justice 21, no. 5 (January 1993): 433–45. http://dx.doi.org/10.1016/0047-2352(93)90030-q.

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35

Xiao, Zhi, Shi-Jie Ye, Bo Zhong, and Cai-Xin Sun. "BP neural network with rough set for short term load forecasting." Expert Systems with Applications 36, no. 1 (January 2009): 273–79. http://dx.doi.org/10.1016/j.eswa.2007.09.031.

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36

杨, 福义. "Research on the Definition and Mathematical Model of Core Term Set." Interdisciplinary Science Letters 05, no. 04 (2021): 56–66. http://dx.doi.org/10.12677/isl.2021.54008.

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37

Bechrakis, D. A., J. P. Deane, and E. J. McKeogh. "Wind resource assessment of an area using short term data correlated to a long term data set." Solar Energy 76, no. 6 (2004): 725–32. http://dx.doi.org/10.1016/j.solener.2004.01.004.

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38

BURRIS, STANLEY, and JOHN LAWRENCE. "TERM REWRITE RULES FOR FINITE FIELDS." International Journal of Algebra and Computation 01, no. 03 (September 1991): 353–69. http://dx.doi.org/10.1142/s0218196791000249.

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Let F1, …, Fk be finite fields with distinct characteristics. We give a finite set of equations which axiomatize the equational theory of F1, …, Fk and then use these axioms to find a finite set of AC-term rewrite rules which is complete for this theory. This gives finite sets of complete AC-term rewrite rules for most instances of xm ≈ x rings by adding new rules to the usual AC-term rewrite rules for commutative rings. The first case for which we do not find a complete set of AC-term rewrite rules is x22 ≈ x, and we doubt that such rules can be found. If R is a set of AC-term rewrite rules from which one can derive x(y + z) → xy + xz, then we show R cannot be complete for x22 ≈ x rings.

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39

ZENG, Xiping, Wei-Kuo TAO, and Joanne SIMPSON. "A Set of Prognostic Variables for Long-Term Cloud-Resolving Model Simulations." Journal of the Meteorological Society of Japan 86, no. 6 (2008): 839–56. http://dx.doi.org/10.2151/jmsj.86.839.

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40

Sheremata, Summer L., and Sarah Shomstein. "Task set induces dynamic reallocation of resources in visual short-term memory." Psychonomic Bulletin & Review 24, no. 4 (January 3, 2017): 1113–20. http://dx.doi.org/10.3758/s13423-016-1181-8.

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41

Rat, A. C., J. Coste, A. Saraux, B. Mazières, E. Verrouil, B. Fautrel, P. Fardellone, et al. "369 KHOALA (KNEE AND HIP OSTEOARTHRITIS LONG TERM ASSESSMENT) COHORT: SET UP." Osteoarthritis and Cartilage 17 (September 2009): S195. http://dx.doi.org/10.1016/s1063-4584(09)60391-5.

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42

Yang, Gang, Yan Wang, and Lei Guo. "A sparser reduced set density estimator by introducing weighted l1 penalty term." Pattern Recognition Letters 58 (June 2015): 15–22. http://dx.doi.org/10.1016/j.patrec.2015.01.016.

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43

Ma, Ruyun, and Jiemei Li. "Global Bifurcation for Second-Order Neumann Problem with a Set-Valued Term." International Journal of Differential Equations 2009 (2009): 1–16. http://dx.doi.org/10.1155/2009/373851.

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We study the global bifurcation of the differential inclusion of the form−(ku′)′+g(⋅,u)∈μF(⋅,u), u′(0)=0=u′(1), whereFis a “set-valued representation” of a function with jump discontinuities along the line segment[0,1]×{0}. The proof relies on a Sturm-Liouville version of Rabinowitz's bifurcation theorem and an approximation procedure.

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44

Evans, Jeff. "Updated Polio Vaccine Recommendations Set For Optimum Early and Long-Term Protection." Pediatric News 43, no. 9 (September 2009): 4. http://dx.doi.org/10.1016/s0031-398x(09)70251-6.

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45

Greener, J., and J. R. Gillmor. "Long-term growth of core-set curl in poly(ethylene terephthalate) film." Journal of Polymer Science Part B: Polymer Physics 39, no. 17 (2001): 1957–67. http://dx.doi.org/10.1002/polb.1171.

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46

Emmert, J. T. "A long-term data set of globally averaged thermospheric total mass density." Journal of Geophysical Research: Space Physics 114, A6 (June 2009): n/a. http://dx.doi.org/10.1029/2009ja014102.

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47

Laude, Audrey. "Bioenergy with carbon capture and storage: are short-term issues set aside?" Mitigation and Adaptation Strategies for Global Change 25, no. 2 (April 2, 2019): 185–203. http://dx.doi.org/10.1007/s11027-019-09856-7.

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48

Chen, Hanhua, Jun Yan, Hai Jin, Yunhao Liu, and Lionel M. Ni. "TSS: Efficient Term Set Search in Large Peer-to-Peer Textual Collections." IEEE Transactions on Computers 59, no. 7 (July 2010): 969–80. http://dx.doi.org/10.1109/tc.2010.81.

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49

Reckerth, Anne, Willibald Stichler, Axel Schmidt, and Christine Stumpp. "Long-term data set analysis of stable isotopic composition in German rivers." Journal of Hydrology 552 (September 2017): 718–31. http://dx.doi.org/10.1016/j.jhydrol.2017.07.022.

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50

van Steenwijk, R. "Experimental set-up for long-term intracellular measurements on contracting isolated hearts." Medical and Biological Engineering and Computing 25, no. 1 (January 1987): 93–95. http://dx.doi.org/10.1007/bf02442828.

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