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Статті в журналах з теми "Learning"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 26 жовтня 2024

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1

Kim, Hye-Jin. "M-Learning Application for Ubiquitous Learning Based on Android Web Platform." Journal of the Korea Academia-Industrial cooperation Society 12, no. 12 (December 31, 2011): 5564–69. http://dx.doi.org/10.5762/kais.2011.12.12.5564.

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2

Kim, Hye-Jin. "Educational Paradigm Shift from E-Learning to Mobile Learning Toward Ubiquitous Learning." Journal of the Korea Academia-Industrial cooperation Society 12, no. 11 (November 30, 2011): 4788–95. http://dx.doi.org/10.5762/kais.2011.12.11.4788.

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3

Chagas, Edgar Thiago De Oliveira. "Deep Learning e suas aplicações na atualidade." Revista Científica Multidisciplinar Núcleo do Conhecimento 04, no. 05 (May 8, 2019): 05–26. http://dx.doi.org/10.32749/nucleodoconhecimento.com.br/administracao/deep-learning.

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4

You, Yeong Mahn. "How Could We Marry Knowledge Management to e-Learning with Learning Objects?; The Possicilitiey and Limitations." Journal of Educational Technology 17, no. 2 (June 30, 2001): 53–89. http://dx.doi.org/10.17232/kset.17.2.53.

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5

Joan, D. R. Robert, and Dr S. P. Denisia Dr. S. P. Denisia. "Features of Electronic Learning, Mobile Learning and Virtual Learning." International Journal of Scientific Research 2, no. 4 (June 1, 2012): 73–75. http://dx.doi.org/10.15373/22778179/apr2013/29.

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6

Kulsum, Umi. "HYBRID LEARNING TIME MODIFICATION CAN IMPROVE LEARNING ACTIVITY AND LEARNING OUTCOMES." SCHOOL EDUCATION JOURNAL PGSD FIP UNIMED 11, no. 3 (December 23, 2021): 263–68. http://dx.doi.org/10.24114/sejpgsd.v11i3.27922.

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Анотація:

The purpose of this study was to determine the effectiveness of hybrid learning time modification in terms of learning outcomes; knowing the relationship between learning activities and learning outcomes and knowing the effect of hybrid and one other group is the conventional group (face-to-face only), this group is the control group.Collecting data using a learning activity questionnaire and a knowledge test to determine learning outcomes. Data analysis technique with Ancova. The results of the study: (1) hybrid learning time modification is effective in improving learning outcomes (2) significant relationship between learning activity and learning outcomes, significance 0.000; (3) there is a significant difference in the effect of variations in hybrid learning time modification on learning activity and learning outcomes, the significance of 0.037 Keywords: Time Modification, Hybrid Learning, Active Learning, Learning Outcomes

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7

Azis, Yunia Mulyani. "Sharing Time Learning (Face to Face and Online Learning) in Blended Learning." International Journal of Psychosocial Rehabilitation 24, no. 02 (February 20, 2020): 466–74. http://dx.doi.org/10.37200/ijpr/v24i2/pr200358.

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8

Bhoite, Dr Sudhakar D. "E-Learning: the Technology Enhanced Learning (TEL) Makes Learning Easier And Flexible." International Journal of Scientific Research 2, no. 9 (June 1, 2012): 84–85. http://dx.doi.org/10.15373/22778179/sep2013/29.

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9

Sirkemaa, Seppo J. "Analysing e-Learning and Modern Learning Environments." International Journal of Information and Education Technology 4, no. 2 (2014): 176–79. http://dx.doi.org/10.7763/ijiet.2014.v4.393.

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10

Karolczuk, Marzanna. "Blended learning (e-learning) in language studies." Prace Naukowe Akademii im. Jana Długosza w Częstochowie. Studia Neofilologiczne 13 (2017): 119–28. http://dx.doi.org/10.16926/sn.2017.13.09.

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11

Stonier, Keith A. "Learning for progression and employability or learning for learning's sake?" British Journal of Learning Disabilities 41, no. 4 (October 28, 2013): 249–55. http://dx.doi.org/10.1111/bld.12049.

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12

Chagas, Edgar Thiago De Oliveira. "Deep Learning and its applications today." Revista Científica Multidisciplinar Núcleo do Conhecimento 04, no. 05 (May 8, 2019): 05–26. http://dx.doi.org/10.32749/nucleodoconhecimento.com.br/business-administration/deep-learning-2.

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13

Fong, A. C. M., and G. Hong. "Boosted Supervised Intensional Learning Supported by Unsupervised Learning." International Journal of Machine Learning and Computing 11, no. 2 (March 2021): 98–102. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1020.

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Анотація:

Traditionally, supervised machine learning (ML) algorithms rely heavily on large sets of annotated data. This is especially true for deep learning (DL) neural networks, which need huge annotated data sets for good performance. However, large volumes of annotated data are not always readily available. In addition, some of the best performing ML and DL algorithms lack explainability – it is often difficult even for domain experts to interpret the results. This is an important consideration especially in safety-critical applications, such as AI-assisted medical endeavors, in which a DL’s failure mode is not well understood. This lack of explainability also increases the risk of malicious attacks by adversarial actors because these actions can become obscured in the decision-making process that lacks transparency. This paper describes an intensional learning approach which uses boosting to enhance prediction performance while minimizing reliance on availability of annotated data. The intensional information is derived from an unsupervised learning preprocessing step involving clustering. Preliminary evaluation on the MNIST data set has shown encouraging results. Specifically, using the proposed approach, it is now possible to achieve similar accuracy result as extensional learning alone while using only a small fraction of the original training data set.

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14

Arisanty, Deasy. "Improving Geography Learning through Project-based Learning Model." International Journal of Psychosocial Rehabilitation 24, no. 5 (March 31, 2020): 585–94. http://dx.doi.org/10.37200/ijpr/v24i5/pr201723.

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15

Khalikov, Azam, Sabina Smirnova, Gulnoza Baymatova, Gulshod Khamidova, and Mohidil Safarova. "The Basic Principles of Learning of Collaborative Learning." International Journal of Psychosocial Rehabilitation 24, Special Issue 1 (February 28, 2020): 402–8. http://dx.doi.org/10.37200/ijpr/v24sp1/pr201171.

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16

Kumar, Nikhil. "Effectiveness of E-Learning Through Learning Management System." Indian Journal of Applied Research 4, no. 5 (October 1, 2011): 340–41. http://dx.doi.org/10.15373/2249555x/may2014/101.

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17

V.L., Ramesh, and Narayanamurthy C. "Do Learning Styles Influence Learning Outcome in Pathology." Indian Journal of Pathology: Research and Practice 6, no. 3 (part-2) (2017): 696–700. http://dx.doi.org/10.21088/ijprp.2278.148x.6317.32.

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18

Tóthová, D., and K. Hennyeyová. "Preparation of e-learning methodology in distance learning." Agricultural Economics (Zemědělská ekonomika) 48, No. 8 (March 1, 2012): 364–66. http://dx.doi.org/10.17221/5336-agricecon.

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Анотація:

Education should be understood as a method of sharing and disseminating information. It deals not only with obtaining the right information in the right time but also with full understanding and processing of the information in the given context. Distance learning is a technology, where the distance between a teacher and a student, or among students, is characterized by the speed of the feedback, i.e. how quickly the student gets the feedback from his teacher – tutor. This type of education cannot exist without electronic education. It means providing the students with the content of a course via all electronic media including Internet, intranet and CD-ROMs. Before processing of the materials into this form, it is necessary to develop the methodology, which could be used effectively by distance learning project teams.

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19

R. D. Gomathi, R. D. Gomathi, and P. Kiruthika P. Kiruthika. "Activity Based Language Learning – an Effective Learning Method." Indian Journal of Applied Research 3, no. 11 (October 1, 2011): 254–55. http://dx.doi.org/10.15373/2249555x/nov2013/82.

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20

Kishore, Dr Kaushal, and Dr Amni Sahni. "Teaching Children with Learning Disabilities through Cooperative Learning." Indian Journal of Applied Research 1, no. 1 (October 1, 2011): 41–42. http://dx.doi.org/10.15373/2249555x/oct2011/13.

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21

Khidzir, Nik Zulkarnaen, Khairul Azhar Mat Daud, and Mohd Asrul Hery Ibrahim. "The Relationship among Student’s Domain of Learning Development Implementing Virtual Learning in Higher Learning Institutions." International Journal of Information and Education Technology 6, no. 6 (2016): 418–22. http://dx.doi.org/10.7763/ijiet.2016.v6.725.

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22

Marie Lockton, Marie Lockton, Anita Caduff Marie Lockton, Martin Rehm Anita Caduff, and Alan J. Daly Martin Rehm. "Extending the Learning Space: Cultivating an Online Learning Community in Support of District Professional Learning." 教育政策與管理 8, no. 8 (December 2022): 001–29. http://dx.doi.org/10.53106/251889252022120008001.

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23

Ventura, Michele Della. "Problem-Based Learning and e-Learning in Sound Recording." International Journal of Information and Education Technology 4, no. 5 (2014): 426–29. http://dx.doi.org/10.7763/ijiet.2014.v4.443.

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24

Son, Xiaodan, and Yang-soo Jung. "Exploring Influencing Factors of Smart Learning for English Learning." JOURNAL OF HUMANITIES STUDIES 110 (March 30, 2018): 295–324. http://dx.doi.org/10.46346/tjhs.110..11.

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25

Rego, Michelle. "The Global Learning Distinction: an Experiential Learning Research Project." JOURNAL OF INTERNATIONAL BUSINESS RESEARCH AND MARKETING 3, no. 3 (2018): 50–54. http://dx.doi.org/10.18775/jibrm.1849-8558.2015.33.3005.

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Анотація:

The purpose of this paper is to demonstrate the importance of integrating global learning and culturally-responsive teaching as part of a University curriculum to prepare graduates to enter a global workforce. According to the Association of American Colleges and Universities (AAC&U), global learning programs are an important aspect of culturally responsive teaching which help students to “understand and engage the diversities and commonalities among the world’s peoples, cultures, nations, and regions” (AAC&U, 2018). The objective of this experiential learning project, implemented at the College of Business at Johnson & Wales University in Providence, RI, USA, was to research ways to engage students in active global learning both inside and outside of the classroom. Specifically, the problem being explored included needs to assess student attitudes toward global learning (1), understand perceived benefits of a Global Learning Distinction program (2), and identify an opportunity for students to get involved in the promotion of this program prior to the full launch in 2018 (3). Focus group research results are discussed and recommendations for future research in Global Learning.

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26

Zitar, Raed Abu, Ammar EL-Hassan, and Oraib AL-Sahlee. "Deep Learning Recommendation System for Course Learning Outcomes Assessment." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (October 31, 2019): 1491–78. http://dx.doi.org/10.5373/jardcs/v11sp10/20192993.

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27

Akgül, İsmail, and Yıldız Aydın. "OBJECT RECOGNITION WITH DEEP LEARNING AND MACHINE LEARNING METHODS." NWSA Academic Journals 17, no. 4 (October 29, 2022): 54–61. http://dx.doi.org/10.12739/nwsa.2022.17.4.2a0189.

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28

Alla, Sri Sai Meghana, and Kavitha Athota. "Brain Tumor Detection Using Transfer Learning in Deep Learning." Indian Journal Of Science And Technology 15, no. 40 (October 27, 2022): 2093–102. http://dx.doi.org/10.17485/ijst/v15i40.1307.

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29

KLEMENT, Milan, and Miroslav CHRÁSKA. "TYPOLOGY OF STUDENTS ACCORDING TO THEIR LEVEL OF INTEREST IN THE EDUCATION THROUGH E-LEARNING: IS E-LEARNING SUITABLE FOR EVERYONE." Journal of Technology and Information 4, no. 2 (August 1, 2012): 5–11. http://dx.doi.org/10.5507/jtie.2012.025.

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30

Hancocks, Stephen. "Learning about learning." British Dental Journal 198, no. 4 (February 2005): 185. http://dx.doi.org/10.1038/sj.bdj.4812079.

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31

Eldridge, Amy. "Learning About Learning." Journal of Analytic Social Work 5, no. 2 (May 7, 1998): 77–84. http://dx.doi.org/10.1300/j408v05n02_05.

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32

Ozier, Marcia. "Learning From Learning." Contemporary Psychology: A Journal of Reviews 30, no. 11 (November 1985): 883–84. http://dx.doi.org/10.1037/023329.

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33

Koo, L. C. "Learning action learning." Journal of Workplace Learning 11, no. 3 (May 1999): 89–94. http://dx.doi.org/10.1108/13665629910264244.

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34

Sriram, Rishi. "Learning about Learning." About Campus: Enriching the Student Learning Experience 23, no. 1 (March 2018): 13–19. http://dx.doi.org/10.1177/1086482218765747.

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35

Siegler, Robert S. "Learning About Learning." Merrill-Palmer Quarterly 50, no. 3 (2004): 353–68. http://dx.doi.org/10.1353/mpq.2004.0025.

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36

Harker, Emily. "Learning about learning." Health Information & Libraries Journal 26, no. 2 (May 27, 2009): 156–60. http://dx.doi.org/10.1111/j.1471-1842.2009.00847.x.

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37

Halkier, Henrik. "Learning Transnational Learning." Regional Studies 48, no. 2 (January 13, 2014): 418–19. http://dx.doi.org/10.1080/00343404.2013.871146.

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38

Painter, Clare. "Learning about learning." Functions of Language 3, no. 1 (January 1, 1996): 95–125. http://dx.doi.org/10.1075/fol.3.1.05pai.

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Анотація:

Systemic-functional linguistics (SFL) is used as a framework within which a child's 'cognitive' development can be seen in linguistic terms as the building of a meaning potential which gains realisation in texts. Data are then presented from a diary study of one child's speech between the ages of two and a half and five years, focussing on the child's use of 'mental' and 'verbal' clauses (such as I think or she said) in order to reveal the child's understandings about information exchange, which constitutes the basis of learning. The naturalistic data display various developments in the child's construals of semiotic exchange, including exploration of 'false' information and the status of perceptual evidence. A general pattern emerges whereby the child moves out from representing and exploring the 'I-you-now' of the ongoing interaction, to a later construal of the world beyond this 'deictic centre', suggesting an intersubjective rather than an 'egocentric' starting point to development.

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39

Lewis, Ted G., and Peter J. Denning. "Learning machine learning." Communications of the ACM 61, no. 12 (November 20, 2018): 24–27. http://dx.doi.org/10.1145/3286868.

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40

Williams, Douglas A., and Jennifer L. Hurlburt. "Learning About Learning." Contemporary Psychology: A Journal of Reviews 43, no. 2 (February 1998): 135–36. http://dx.doi.org/10.1037/001521.

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41

Ende, Jack. "Learning about learning." Journal of General Internal Medicine 10, no. 3 (March 1995): 172–73. http://dx.doi.org/10.1007/bf02599678.

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42

Hariyanto, Didik. "The Design of Adaptive Learning System Based on the Collaboration of m-Learning and e-Learning Platform." Journal of Advances in Computer Networks 2, no. 4 (2014): 311–14. http://dx.doi.org/10.7763/jacn.2014.v2.131.

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43

Jovita Lantang Anetha, Gisela. "Development of a Discovery Learning Learning Model Combined with Literacy Activities and Educandy Games in Learning Mathematics." International Journal of Science and Research (IJSR) 12, no. 6 (June 5, 2023): 1850–57. http://dx.doi.org/10.21275/sr23612160341.

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44

Conradie, P. W. "Supporting Self-Directed Learning by Connectivism and Personal Learning Environments." International Journal of Information and Education Technology 4, no. 3 (2014): 254–59. http://dx.doi.org/10.7763/ijiet.2014.v4.408.

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45

Derus, Siti Rosminah M. D., and Ahmad Zamzuri Mohamad Ali. "Development of Hardware-Interfacing Learning Kit for Novice Learning Programming." International Journal of Information and Education Technology 6, no. 8 (2016): 647–51. http://dx.doi.org/10.7763/ijiet.2016.v6.767.

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46

Bridjeshpappula and Geetha narayanankannaiyan. "Assessment of students learning capability adapting activity based learning – STAD." International Journal of Psychosocial Rehabilitation 24, no. 04 (February 28, 2020): 2982–88. http://dx.doi.org/10.37200/ijpr/v24i4/pr201410.

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47

Singh, Gurjeet, and Raksha Singh. "DOMAINS OF LEARNING: ART OF LEARNING IN MEDICAL EDUCATION PROGRAM." Era's Journal of Medical Research 7, no. 1 (June 2020): 79–85. http://dx.doi.org/10.24041/ejmr2020.14.

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48

assahary, salman. "Integrative Learning Religious and Learning Enviroment at Adiwiyata Program School." mamangan 8, no. 2 (December 2019): 71–76. http://dx.doi.org/10.22202/mamangan.4145.

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49

Dimitrov, Kalin, and Eugenia Kovatcheva. "Best E-Learning Platforms for Blended Learning in Higher Education." Mathematics and Informatics LXV, no. 6 (December 29, 2022): 533–45. http://dx.doi.org/10.53656/math2022-6-2-bes.

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Анотація:

Over the past ten years or so both companies and academic institutions have made significant steps towards either adopting an e-learning infrastructure or upgrading the systems already being used. During the past few years, we witnessed an incredible proliferation of Learning Management Systems (LMS’s) and the number of features they offered and a strong push towards a blended-based learning approach to training and education. This paper deals with the comparison and presentation of the most popular LMS’s currently available on the market. In our research, we strove to not only summarize the most important information about the big players in the world of blended learning today, but to also direct the readers to the right questions they need to be asking when researching their options. Before a company or an academic institution can make an educated decision as to which system will work best in its case, it needs to carefully consider several important questions — it must decide on its budget, its expectations, its audience, the requirement for specific features, as well as why it needs an LMS in the first place. The effectiveness of gamified learning systems has been extensively researched and consistently praised for its immense positive effect on learners’ motivation and engagement. We firmly believe that in the years to come, gamification will transform both corporate and academic training and that is why, in our research, we paid special attention to any existing system gamification features or ones that may be in development. Introducing game dynamics into an LMS results in numerous benefits and is a sure bet for the future of blended learning.

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50

Wang, Yipu, and Stuart Perrin. "Deep Chinese Teaching and Learning Model Based on Deep Learning." International Journal of Languages, Literature and Linguistics 10, no. 1 (2024): 32–35. http://dx.doi.org/10.18178/ijlll.2024.10.1.479.

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Анотація:

Deep learning is a more situational and reflective way of learning that integrates complex knowledge and skills into intuitive thinking. As a language that closely combines sound, form and meaning, Chinese teaching and learning from the perspective of deep learning can help break through the limitations of the current teaching model that only focuses on certain language knowledge or cultural behaviors. This paper combines deep learning with international Chinese education, creates deep Chinese teaching and learning model including “four stages and ten steps”, and carries out practical application and teaching effect test. The results show that the deep Chinese teaching and learning model is conducive to improving students’ discourse presentation ability and comprehensive skills, cultivating the learners’ autonomous learning ability and intercultural communication competence, and strengthening the integration of language teaching and cultural teaching. At the same time, this model also has some limitations, need to be further adjusted and optimized.

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