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Journal articles on the topic 'Sage'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 June 2025

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1

Lee, Sang-Gu, Kyung-Won Kim, and Jae Hwa Lee. "SAGE MATRIX CALCULATOR AND FULL SAGE CONTENTS FOR LINEAR ALGEBRA." Korean Journal of Mathematics 21, no. 4 (2013): 503–21. http://dx.doi.org/10.11568/kjm.2013.21.4.503.

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2

Pillai, Riya, Sakshi Pachlaniya, Rashi Malviya, and Niharika Jat. "Stock Sage." International Journal of Research Publication and Reviews 6, no. 4 (2025): 5815–19. https://doi.org/10.55248/gengpi.6.0425.14122.

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3

Feder, Toni. "A Besieged SAGE Is Safe, for Now." Physics Today 50, no. 6 (1997): 73. http://dx.doi.org/10.1063/1.881796.

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4

Martini, Nataly. "Sage." Journal of Primary Health Care 14, no. 1 (2022): 89–90. http://dx.doi.org/10.1071/hc19570.

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5

Parrell, Marnie. "Sage." Public 29, no. 57 (2018): 4. http://dx.doi.org/10.1386/public.29.57.4_7.

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6

Dhulipala, Laxman, Charles McGuffey, Hongbo Kang, et al. "Sage." Proceedings of the VLDB Endowment 13, no. 9 (2020): 1598–613. http://dx.doi.org/10.14778/3397230.3397251.

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7

Stein, William, and David Joyner. "SAGE." ACM SIGSAM Bulletin 39, no. 2 (2005): 61–64. http://dx.doi.org/10.1145/1101884.1101889.

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8

Godefroid, Patrice, Michael Y. Levin, and David Molnar. "SAGE." Communications of the ACM 55, no. 3 (2012): 40–44. http://dx.doi.org/10.1145/2093548.2093564.

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9

Cerina, L., and M. D. Santambrogio. "SAGe." ACM SIGBED Review 17, no. 1 (2020): 12–17. http://dx.doi.org/10.1145/3412821.3412823.

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10

Lee, Eunjae, Sam H. Noh, and Jiwon Seo. "Sage." Proceedings of the VLDB Endowment 15, no. 13 (2022): 3897–910. http://dx.doi.org/10.14778/3565838.3565844.

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We propose Sage, a system for uncertain network analysis. Algorithms for uncertain network analysis require large amounts of memory and computing resources as they sample a large number of network instances and run analysis on them. Sage makes uncertain network analysis simple and efficient. By extending the edge-centric programming model, Sage makes writing sampling-based analysis algorithms as simple as writing conventional graph algorithms in Pregel-like systems. Moreover, Sage proposes four optimization techniques, namely, deterministic sampling, hybrid gathering, schedule-aware caching, and copy-on-write attributes, that exploit common properties of uncertain network analysis. Extensive evaluation of Sage with eight algorithms on six real-world networks shows that the four optimizations in Sage jointly improve performance by up to 13.9X and on average 2.7X.

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11

Maret, Susan. "SAGE Navigator." Charleston Advisor 16, no. 3 (2015): 36–40. http://dx.doi.org/10.5260/chara.16.3.36.

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12

Boyd-Byrnes, Mary Kate. "SAGE Knowledge." Charleston Advisor 17, no. 2 (2015): 38–41. http://dx.doi.org/10.5260/chara.17.2.38.

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13

Saludes, Jordi, and Sebastian Xambó. "Multilingual Sage." Tbilisi Mathematical Journal 5, no. 2 (2012): 121–32. http://dx.doi.org/10.32513/tbilisi/1528768906.

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14

Kraus, Joseph R. "SAGE Campus." Charleston Advisor 23, no. 1 (2021): 49–52. http://dx.doi.org/10.5260/chara.23.1.49.

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The SAGE Campus platform provides 18 different courses with roughly 220 hours of online learning modules. The author reviewed the service from the perspective of a college student to see if it was an appropriate learning environment. The primary audience for the courses are graduate students in the social sciences, but undergraduate and graduate students of all disciplines may find courses that are worthwhile to investigate. At the time of the review, the course topics covered content such as information literacy, data management and other data science skills, research design, and how to get published. Many librarians and teaching faculty may recommend students take these courses to supplement their education. Students can learn through these courses in a self-paced manner, and there are no scores or grades associated with completion of a course. Overall, the SAGE Campus platform provides a low-stress way for students to enhance their understanding of many topics relevant to research in the social sciences.

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15

Wang, Weiqing, Hongzhi Yin, Ling Chen, Yizhou Sun, Shazia Sadiq, and Xiaofang Zhou. "ST-SAGE." ACM Transactions on Intelligent Systems and Technology 8, no. 3 (2017): 1–25. http://dx.doi.org/10.1145/3011019.

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16

Butter, Karen A. "Red Sage." Medical Reference Services Quarterly 13, no. 3 (1994): 75–81. http://dx.doi.org/10.1300/j115v13n03_07.

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17

Havird, David. "Becoming Sage." Literary Imagination 20, no. 1 (2018): 56–57. http://dx.doi.org/10.1093/litimag/imy010.

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18

Llewellin, Mary. "Parsley, sage…" Nursery World 2016, no. 7 (2016): 16–17. http://dx.doi.org/10.12968/nuwa.2016.7.16.

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19

Cotterman, Billie. "SAGE Video." Charleston Advisor 20, no. 4 (2019): 40–45. http://dx.doi.org/10.5260/chara.20.4.40.

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20

Glaser, Vicki. "SAGE Crossroads." Rejuvenation Research 7, no. 4 (2004): 267–70. http://dx.doi.org/10.1089/rej.2004.7.267.

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21

Malhi, Gin S. "SAGE decisions." Australian & New Zealand Journal of Psychiatry 46, no. 1 (2012): 5–6. http://dx.doi.org/10.1177/0004867411433165.

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22

Jost, Clémence. "Sage décision." Archimag N°364, no. 4 (2023): 1. http://dx.doi.org/10.3917/arma.364.0001.

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23

Martin, George M., and Don Kennedy. "SAGE Lessons." Science 313, no. 5783 (2006): 17. http://dx.doi.org/10.1126/science.1131700.

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24

Reynolds, Martin John. "Sage advice." Physics World 30, no. 6 (2017): 20. http://dx.doi.org/10.1088/2058-7058/30/6/40.

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25

Dodes, John E. "Sage advice." Journal of the American Dental Association 146, no. 4 (2015): 220. http://dx.doi.org/10.1016/j.adaj.2015.02.003.

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26

Lindstrom, Gary. "Sage commentary." International Journal of Parallel Programming 17, no. 1 (1988): 93. http://dx.doi.org/10.1007/bf01379324.

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27

Barber, Phyllis. "Wild Sage." Dialogue: A Journal of Mormon Thought 20, no. 2 (1987): 148–53. http://dx.doi.org/10.2307/45216019.

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28

Neher, Jon O. "Scottish Sage." Evidence-Based Practice 26, no. 3 (2023): 1. http://dx.doi.org/10.1097/ebp.0000000000001875.

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29

Olivetti, Katherine. "Museum Sage." Jung Journal 17, no. 4 (2023): 111–21. http://dx.doi.org/10.1080/19342039.2023.2258758.

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30

Deshpande, Renuka, Abhishek Mahadik, Mahesh Wagh, and Avin Walunj. "Stock Sage." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 1354–59. https://doi.org/10.22214/ijraset.2025.68523.

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Abstract: Stock market has been constant fascinating topic but since last few years’ stockholders desire to hardback return on day-to-day basis got glorified, then with the support of machine learning stockholders initiate stress-free approaches to get squint of forthcoming market trends. This Project benevolences Reinforcement Learning grounded methodology to forecast the characteristics of a specific stock, our method put greater concentration on sets of hundred days moving averages. Hundred days moving average is a technical method followed by the stock market professionals to forecast the forthcoming trends of market, which represents the current as well as the characteristics which is stock going to show in upcoming days. As we are scraping data with the API and creating our own dataset hence our method is comfortable with ambiguous data besides it provides output with high accuracy. The results indicate that our method attained superior upshot than other approaches.

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31

Havird, David. "Becoming Sage." Literary Imagination 20, no. 1 (2018): 56–57. https://doi.org/10.1353/lim.2018.a942481.

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32

Damadeo, R. P., J. M. Zawodny, L. W. Thomason, and N. Iyer. "SAGE version 7.0 algorithm: application to SAGE II." Atmospheric Measurement Techniques 6, no. 12 (2013): 3539–61. http://dx.doi.org/10.5194/amt-6-3539-2013.

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Abstract. This paper details the SAGE (Stratospheric Aerosol and Gas Experiment) version 7.0 algorithm and how it is applied to SAGE II. Changes made between the previous (v6.2) and current (v7.0) versions are described and their impacts on the data products explained for both coincident event comparisons and time-series analysis. Users of the data will notice a general improvement in all of the SAGE II data products, which are now in better agreement with more modern data sets (e.g., SAGE III) and more robust for use with trend studies.

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33

Damadeo, R. P., J. M. Zawodny, L. W. Thomason, and N. Iyer. "SAGE version 7.0 algorithm: application to SAGE II." Atmospheric Measurement Techniques Discussions 6, no. 3 (2013): 5101–71. http://dx.doi.org/10.5194/amtd-6-5101-2013.

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Abstract. This paper details the SAGE version 7.0 algorithm and how it is applied to SAGE II. Changes made between the previous (v6.2) and current (v7.0) versions are described and their impacts on the data products explained for both coincident event comparisons and time-series analysis. Users of the data will notice a general improvement in all of the SAGE II data products, which are now in better agreement with more modern data sets (e.g. SAGE III) and more robust for use with trend studies.

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34

Ghizzoni, C., E. Colombo, A. Bottini, V. Picci, and D. Atzei. "CHARACTERIZATION OF DESOLEANA SAGE, A NEW SAGE SPECIES." Acta Horticulturae, no. 333 (November 1993): 249–54. http://dx.doi.org/10.17660/actahortic.1993.333.30.

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35

Yi, Guo. "On the “Sage Meteorology” of the Jinsi Lu." Journal of Social Science and Humanities 6, no. 9 (2024): 51–53. http://dx.doi.org/10.53469/jssh.2024.6(09).10.

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The “Meteorology of Sages” is the final chapter of Jinsi Lu. The “weather of sages” is the reproduction of the ancient sages' temperament of Song Confucianism, which shows the absorption and transcendence of the previous Confucianism's internal saintly work, and is also the way of Song Confucianism's “Hsi Hsien Hsi Sheng” and learning to be a saint. In “Jinsi Lu - Sage Meteorology”, the “Sage Meteorology” is explained in terms of both external temperament and internal qualities, and a number of historical figures are classified as whether or not they are sages and whether or not they have the “Sage Meteorology”.

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36

Gavrin, V. N. "The Russian-American gallium experiment SAGE." Uspekhi Fizicheskih Nauk 181, no. 9 (2011): 975. http://dx.doi.org/10.3367/ufnr.0181.201109g.0975.

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37

Beck, Thomas J. "SAGE Business Researcher." Charleston Advisor 18, no. 1 (2016): 28–30. http://dx.doi.org/10.5260/chara.18.1.28.

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38

Quick, Julie. "SAGE Study Skills." Journal of Perioperative Practice 31, no. 4 (2021): 121. http://dx.doi.org/10.1177/17504589211002714.

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39

Kaufmann, E. N. "More SAGE Advice." MRS Bulletin 19, no. 9 (1994): 72. http://dx.doi.org/10.1557/s0883769400048089.

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40

Siboni, Marie-Élizabeth. "Être sage-femme." Empan 53, no. 1 (2004): 35. http://dx.doi.org/10.3917/empa.053.0035.

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41

Silver, Breezy D. "SAGE Skills: Business." Charleston Advisor 24, no. 2 (2022): 46–54. http://dx.doi.org/10.5260/chara.24.2.46.

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SAGE Skills: Business is a new product that covers essential skills that students will need to know in their careers, including data analysis, entrepreneurship, leadership, organizational communication, and professional reputation. It pulls information into a central location and includes text, videos, journal articles, case studies, comments from experts, tables, and more to help students learn in a self-paced environment. It also offers quizzes that help students test their knowledge. It requires students to take the initiative to use the resource, since there is no way for faculty to see students’ progress. However, that also allows it to be IP access controlled without the need for an account, unless desired by the user. Navigation of the resource could use some improvement. Links need to be updated and inaudible content replaced. It would also help if SAGE limited content to the current product and perhaps to some limited content owned by the institution instead of providing links to all of their content, which is confusing to users. The resource contains great content for students trying to learn necessary skills, but the interface and content could be improved.

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42

Murphy, Timothy. "The Drunken Sage." Hudson Review 48, no. 4 (1996): 626. http://dx.doi.org/10.2307/3852005.

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43

Thompson, Gary M. "Welcoming Sage Publications." Cornell Hotel and Restaurant Administration Quarterly 45, no. 1 (2004): 4–5. http://dx.doi.org/10.1177/0010880403260114.

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44

Patton, M. Q. "The Sage Difference." American Journal of Evaluation 10, no. 2 (1989): 87–89. http://dx.doi.org/10.1177/109821408901000215.

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45

Baldridge, W. Scott, Lawrence W. Braile, Shawn Biehler, et al. "SAGE at 30." Leading Edge 31, no. 6 (2012): 702–8. http://dx.doi.org/10.1190/tle31060702.1.

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46

Duevel, Casey. "SAGE Research Methods." Charleston Advisor 19, no. 4 (2018): 38–41. http://dx.doi.org/10.5260/chara.19.4.38.

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47

Weitzman, Jonathan B. "Annotation by SAGE." Genome Biology 3 (2002): spotlight—20020508–02. http://dx.doi.org/10.1186/gb-spotlight-20020508-02.

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48

Mason, Diana J. "The Sage Within." AJN, American Journal of Nursing 105, no. 5 (2005): 11. http://dx.doi.org/10.1097/00000446-200505000-00001.

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49

Lucier, Richard E., and Robert C. Badger. "Red Sage Project." Serials Librarian 24, no. 3-4 (1994): 129–34. http://dx.doi.org/10.1300/j123v24n03_16.

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50

Patton, Michael Quinn. "The sage difference." Evaluation Practice 10, no. 2 (1989): 87–89. http://dx.doi.org/10.1016/s0886-1633(89)80065-0.

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