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Journal articles on the topic 'Large-scale study'

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Author: Grafiati
Published: 6 September 2023

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1

Carlson, Robert H. "Large-Scale Study." Oncology Times 34, no. 14 (July 2012): 8. http://dx.doi.org/10.1097/01.cot.0000418352.75540.03.

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2

Lee, Jin-Sung, Jung-Su Park, and Young-Shin Lee. "Study on the Computational Simulation of Large Scale Gap Test." Journal of the Korea Institute of Military Science and Technology 14, no. 5 (October 5, 2011): 932–40. http://dx.doi.org/10.9766/kimst.2011.14.5.932.

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3

Firat, Mehmet. "How Open and Distance Education Students use Technology? A Large Scale Study." New Trends and Issues Proceedings on Humanities and Social Sciences 3, no. 3 (March 22, 2017): 164–71. http://dx.doi.org/10.18844/gjhss.v3i3.1549.

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4

XU, Hong, and Zhiwei ZHOU. "ICONE19-43276 Preliminary study on In-Vessel Retention in large-scale advanced PWR." Proceedings of the International Conference on Nuclear Engineering (ICONE) 2011.19 (2011): _ICONE1943. http://dx.doi.org/10.1299/jsmeicone.2011.19._icone1943_117.

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5

Bonetta, Laura. "US plans large scale environmental study." Nature Medicine 7, no. 7 (July 2001): 756. http://dx.doi.org/10.1038/89842.

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6

Rouw, Romke, and Mercede Erfanian. "A Large-Scale Study of Misophonia." Journal of Clinical Psychology 74, no. 3 (May 31, 2017): 453–79. http://dx.doi.org/10.1002/jclp.22500.

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7

Maki, N., H. Oso, M. Asada, and R. Takahashi. "Design Study of Small-Scale to Large-Scale Superconducting Generators." IEEE Transactions on Appiled Superconductivity 14, no. 2 (June 2004): 887–91. http://dx.doi.org/10.1109/tasc.2004.830310.

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8

Fletcher, A. P. "Profile of a Large-Scale Cohort Study." Drugs 40, Supplement 5 (1990): 43–47. http://dx.doi.org/10.2165/00003495-199000405-00010.

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9

Sinno, Zeina, and Alan Conrad Bovik. "Large-Scale Study of Perceptual Video Quality." IEEE Transactions on Image Processing 28, no. 2 (February 2019): 612–27. http://dx.doi.org/10.1109/tip.2018.2869673.

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10

Thuillard, M., J.-L. Le Quellec, J. d’Huy, and Y. Berezkin. "A LARGE-SCALE STUDY OF WORLD MYTHS." Trames. Journal of the Humanities and Social Sciences 22, no. 4 (2018): 407. http://dx.doi.org/10.3176/tr.2018.4.05.

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11

Haug, Ursula. "Summary of a large-scale psychotherapy study." Psychoanalytic Psychotherapy 6, no. 2 (January 1992): 169–80. http://dx.doi.org/10.1080/02668739200700401.

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12

Shin, Seungwon, Guofei Gu, Narasimha Reddy, and Christopher P. Lee. "A Large-Scale Empirical Study of Conficker." IEEE Transactions on Information Forensics and Security 7, no. 2 (April 2012): 676–90. http://dx.doi.org/10.1109/tifs.2011.2173486.

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13

Plesums, C. A., and R. W. Bartels. "Large-scale image systems: USAA case study." IBM Systems Journal 29, no. 3 (1990): 343–55. http://dx.doi.org/10.1147/sj.293.0343.

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14

ITO, Suminobu. "2 ．Large Scale Cohort Study in Practice." Japanese Journal of Pharmacoepidemiology/Yakuzai ekigaku 27, no. 2 (October 20, 2022): 71–77. http://dx.doi.org/10.3820/jjpe.27.71.

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15

Patil, Dr Savita S., Dr Puranik S. C. Dr. Puranik S C, Dr Kulkarni K. K. Dr. Kulkarni K K, Dr Kudrimoti J. K. Dr. Kudrimoti J K, and Dr Kadgi N. V. Dr. Kadgi N V. "Large Scale Study of H1N1 Influenza Cases: A Novel Approach Using Post-Mortem Needle Biopsy." Paripex - Indian Journal Of Research 3, no. 2 (January 15, 2012): 301–4. http://dx.doi.org/10.15373/22501991/feb2014/112.

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16

Chang, B. H., M. V. Li, and Y. Zhou. "Comparative study of small scale and ‘large scale’ resistance spot welding." Science and Technology of Welding and Joining 6, no. 5 (October 2001): 273–80. http://dx.doi.org/10.1179/136217101101538875.

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17

Yeoh, G. H., S. C. P. Cheung, J. Y. Tu, and T. J. Barber. "Comparative Large Eddy Simulation study of a large-scale buoyant fire." Heat and Mass Transfer 47, no. 9 (March 4, 2011): 1197–208. http://dx.doi.org/10.1007/s00231-011-0771-2.

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18

YOSHINADA, Hiroshi, Shu TAKEDA, and Shinichi YOKOTA. "A Study on the Large-Scale Bilateral Manipulator." TRANSACTIONS OF THE JAPAN FLUID POWER SYSTEM SOCIETY 44, no. 3 (2013): 55–63. http://dx.doi.org/10.5739/jfps.44.55.

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19

YOSHINADA, Hiroshi, Shu TAKEDA, and Shinichi YOKOTA. "A Study on the Large-Scale Bilateral Manipulator." TRANSACTIONS OF THE JAPAN FLUID POWER SYSTEM SOCIETY 44, no. 4 (2013): 81–86. http://dx.doi.org/10.5739/jfps.44.81.

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20

Thuillard, M., J.-L. Le Quellec, J. d’Huy, and Y. Berezkin. "ANNEX. A LARGE-SCALE STUDY OF WORLD MYTHS." Trames. Journal of the Humanities and Social Sciences 22, no. 4 (2018): A1. http://dx.doi.org/10.3176/tr.2018.4.06.

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21

Douceur, John R., and William J. Bolosky. "A large-scale study of file-system contents." ACM SIGMETRICS Performance Evaluation Review 27, no. 1 (June 1999): 59–70. http://dx.doi.org/10.1145/301464.301480.

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22

Colantonio, M. C., and R. Pytlak. "Dynamic optimization of large scale systems: Case study." International Journal of Control 72, no. 9 (January 1999): 833–41. http://dx.doi.org/10.1080/002071799220759.

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23

Curnan, H., and P. Bokalo. "Large-Scale Hybrid Integrated Circuitry: A Case Study." IEEE Transactions on Components, Hybrids, and Manufacturing Technology 10, no. 4 (December 1987): 519–36. http://dx.doi.org/10.1109/tchmt.1987.1134792.

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24

Lam, Wing, Stefan Winter, Anjiang Wei, Tao Xie, Darko Marinov, and Jonathan Bell. "A large-scale longitudinal study of flaky tests." Proceedings of the ACM on Programming Languages 4, OOPSLA (November 13, 2020): 1–29. http://dx.doi.org/10.1145/3428270.

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25

Silva, Luciana L., Marco Tulio Valente, and Marcelo A. Maia. "Co-change patterns: A large scale empirical study." Journal of Systems and Software 152 (June 2019): 196–214. http://dx.doi.org/10.1016/j.jss.2019.03.014.

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26

Xiao, Xiangye, Qiong Luo, Zhisheng Li, Xing Xie, and Wei-Ying Ma. "A large-scale study on map search logs." ACM Transactions on the Web 4, no. 3 (July 2010): 1–33. http://dx.doi.org/10.1145/1806916.1806917.

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27

Kearney, A., and C. Forno. "A LARGE SCALE DEFORMATION STUDY USING MOIRÉ PHOTOGRAPHY." Photogrammetric Record 13, no. 74 (August 26, 2006): 217–24. http://dx.doi.org/10.1111/j.1477-9730.1989.tb00671.x.

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28

Ju, Shen-Haw, Yu-Cheng Huang, and Yin-Yu Huang. "Study of optimal large-scale offshore wind turbines." Renewable Energy 154 (July 2020): 161–74. http://dx.doi.org/10.1016/j.renene.2020.02.106.

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29

Avella, Pasquale, Antonio Sassano, and Igor Vasil'ev. "Computational study of large-scale p-Median problems." Mathematical Programming 109, no. 1 (June 2, 2006): 89–114. http://dx.doi.org/10.1007/s10107-005-0700-6.

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30

Gomes-Ruggiero, M. A., D. N. Kozakevich, and J. M. Martínez. "A numerical study on large-scale nonlinear solvers." Computers & Mathematics with Applications 32, no. 3 (August 1996): 1–13. http://dx.doi.org/10.1016/0898-1221(96)00109-5.

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31

LINDELOF, B. "PUVA and cancer: a large-scale epidemiological study." Lancet 338, no. 8759 (July 1991): 91–93. http://dx.doi.org/10.1016/0140-6736(91)90083-2.

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32

Park, Young Jae, Yoon Suk Chang, Jae Boong Choi, and Young Jin Kim. "Study on Large Scale Analysis for Infra-Structure." Solid State Phenomena 110 (March 2006): 169–76. http://dx.doi.org/10.4028/www.scientific.net/ssp.110.169.

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During the last two decades, the interests of large scale seamless analysis have been increased with respect to lifetime extension and management of infra-structure. And then, a seamless analysis of complex geometry is one of greatly interesting topic. However, there are still gaps between the industrial applications and fundamental academic studies owing to time consuming the modeling and analyzing process. To resolve these problems, the integrity evaluation procedure from knowledge-based information system (KIS) to ADVENTRUE has been proposed for the application of auto mesh generation and large scale analysis. The virtual plant which is used for the master of KIS, provides the 3D information of entities for mesh generation. And then, the ADVENTURE makes a domain decomposed mesh and analyzes it using hierarchical domain decomposition method. Considering the good applicability in both efficiency and exactness point of views, it is anticipated that the proposed study on large scale analysis can be used as a promising integrity evaluation method of infra-structures.
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33

Batchelor, M. T., B. I. Henry, and A. P. Roberts. "Comparative study of large-scale Laplacian growth patterns." Physical Review E 51, no. 1 (January 1, 1995): 807–10. http://dx.doi.org/10.1103/physreve.51.807.

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34

Hong, Lichan, Gregorio Convertino, and Ed Chi. "Language Matters In Twitter: A Large Scale Study." Proceedings of the International AAAI Conference on Web and Social Media 5, no. 1 (August 3, 2021): 518–21. http://dx.doi.org/10.1609/icwsm.v5i1.14184.

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Despite the widespread adoption of Twitter internationally, little research has investigated the differences among users of different languages. In prior research, the natural tendency has been to assume that the behaviors of English users generalize to other language users. We studied 62 million tweets collected over a four-week period and found that more than 100 languages were used. Only half of the tweets were in English (51%). Other popular languages including Japanese, Portuguese, Indonesian, and Spanish together accounted for 39% of the tweets. Examining users of the top 10 languages, we discovered cross-language differences in adoption of features such as URLs, hashtags, mentions, replies, and retweets. We discuss our work’s implications for research on large-scale social systems and design of cross-cultural communication tools.
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35

Nicolaisen, Jeppe, and Tove Faber Frandsen. "Zero impact: a large-scale study of uncitedness." Scientometrics 119, no. 2 (February 28, 2019): 1227–54. http://dx.doi.org/10.1007/s11192-019-03064-5.

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36

Grover, Mark L. "Large scale collection assessment." Collection Building 18, no. 2 (June 1, 1999): 58–66. http://dx.doi.org/10.1108/01604959910265797.

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An important responsibility of collection development libraries is to ensure that what is being collected is appropriate for the university’s curriculum and research needs. Unfortunately the large number of techniques that have been developed to measure collections work better with small college collections. As libraries grow and the range of library materials increase, many of these methods have proved to be inadequate. This study explores the use of statistics from the National Shelflist Count Project to gather enough valid statistical information to adequately assess large research collections.
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37

Lee, Ching-Pei, and Chih-Jen Lin. "Large-Scale Linear RankSVM." Neural Computation 26, no. 4 (April 2014): 781–817. http://dx.doi.org/10.1162/neco_a_00571.

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Linear rankSVM is one of the widely used methods for learning to rank. Although its performance may be inferior to nonlinear methods such as kernel rankSVM and gradient boosting decision trees, linear rankSVM is useful to quickly produce a baseline model. Furthermore, following its recent development for classification, linear rankSVM may give competitive performance for large and sparse data. A great deal of works have studied linear rankSVM. The focus is on the computational efficiency when the number of preference pairs is large. In this letter, we systematically study existing works, discuss their advantages and disadvantages, and propose an efficient algorithm. We discuss different implementation issues and extensions with detailed experiments. Finally, we develop a robust linear rankSVM tool for public use.
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38

Fialko, Laura, Philippa A. Garety, Elizabeth Kuipers, Graham Dunn, Paul E. Bebbington, David Fowler, and Daniel Freeman. "A large-scale validation study of the Medication Adherence Rating Scale (MARS)." Schizophrenia Research 100, no. 1-3 (March 2008): 53–59. http://dx.doi.org/10.1016/j.schres.2007.10.029.

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39

Surameery, Nigar M. Shafiq, and Dana Lattef Hussein. "Comparative Study of Classification Techniques For Large Scale Data - Case Study." Kurdistan Journal of Applied Research 2, no. 3 (August 27, 2017): 56–61. http://dx.doi.org/10.24017/science.2017.3.2.

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The existence of Massive datasets that are generated in many applications provides various opportunities and challenges. Especially, scalable mining of such large-scale datasets is a challenging issue that attracted some recent research. In the present study, the main focus is to analyse the classification techniques using WEKA machine learning workbench. Moreover, a large-scale dataset was used. This dataset comes from the protein structure prediction field. It has already been partitioned into training and test sets using the ten-fold cross-validation methodology. In this experiment, nine different methods have been tested. As a result, it became obvious that it is not applicable to test more than one classifier from the (tree) family in the same experiment. On the other hand, using (NaiveBayes) Classifier with the default properties of the attribute selection filter has a great time consuming. Finally, varying the parameters of the attribute selections should be prioritized for more accurate results.
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40

TAKANO, Naoki, Masaru ZAKO, and Yoshihiro OKUNO. "Study on 3D Large-scale Multi-scale Stress Analysis by Mesh Superposition Method." Proceedings of the JSME annual meeting 2003.7 (2003): 163–64. http://dx.doi.org/10.1299/jsmemecjo.2003.7.0_163.

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41

Kirkegaard, Emil O. W., and Helmuth Nyborg. "Pupil Size and Intelligence: A Large-Scale Replication Study." Mankind Quarterly 60, no. 4 (2020): 525–38. http://dx.doi.org/10.46469/mq.2020.60.4.5.

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42

DONG, Ji-guang, Wei-wei CHEN, Lang-jun TIAN, and Hai-jia WU. "Replica placement study in large-scale cloud storage system." Journal of Computer Applications 32, no. 3 (April 1, 2013): 620–24. http://dx.doi.org/10.3724/sp.j.1087.2012.00620.

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43

Till, Desmond, Graham McBride, Andrew Ball, Ken Taylor, and Eric Pyle. "Large-scale freshwater microbiological study: rationale, results and risks." Journal of Water and Health 6, no. 4 (March 1, 2008): 443–60. http://dx.doi.org/10.2166/wh.2008.071.

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A fifteen-month fortnightly survey of microbial health risk indicators and pathogens has been carried out at 25 freshwater recreational and water supply sites distributed throughout New Zealand, for: E. coli, Clostridium perfringens spores, F-RNA bacteriophage, somatic coliphage, human enteroviruses, human adenoviruses, Cryptosporidium oocysts, Giardia cysts, Salmonella and Campylobacter. Sites were selected to represent five geographical areas covering New Zealand and five categories of predominant environmental impact: birds, dairy farming, forestry/undeveloped, municipal, and sheep/pastoral farming. Six of the sites were also source waters for treated drinking-water supplies. Of the indicators, E. coli was detected in 99 % of all samples, with somatic coliphage being detected most of the time (89 %). Of the pathogens tested, Campylobacter and human adenoviruses were inferred to be the most likely to cause human waterborne illness to recreational freshwater users. Using data from all sites, an estimated 5 % of notified campylobacteriosis cases in New Zealand could be attributable to water contact recreation. The critical value for E.coli as an indicator of increased Campylobacter infection is in the range of 200–500 E. coli per 100 ml. This result has been used to derive new national water quality guidelines for recreational fresh water in New Zealand.
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44

Flores-Velazquez, J., and J. I. Montero. "COMPUTATIONAL FLUID DYNAMICS (CFD) STUDY OF LARGE SCALE SCREENHOUSES." Acta Horticulturae, no. 797 (September 2008): 117–22. http://dx.doi.org/10.17660/actahortic.2008.797.14.

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45

HASHIMOTO, T. "Recommendation for Large Scale Intervention Study on Industrial Population." Sangyo Igaku 34, no. 4 (1992): 309. http://dx.doi.org/10.1539/joh1959.34.309.

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46

Vilibic-Cavlek, Tatjana, Irena Tabain, Branko Kolaric, Klara Mihulja, Lana Blazevic, Maja Bogdanic, Dan Navolan, Natasa Beader, and Anna Mrzljak. "Parvovirus B19 in Croatia: A Large-Scale Seroprevalence Study." Medicina 57, no. 11 (November 21, 2021): 1279. http://dx.doi.org/10.3390/medicina57111279.

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Background and Objectives: Seroepidemiological studies indicate that parvovirus B19 circulates in all areas of the world, although with some differences. The aim of this study is to analyze the seroprevalence of parvovirus B19 in the Croatian population. Materials and Methods: From 2010 to 2021, 1538 serum samples from different populations were tested for the presence of parvovirus B19 IgM/IgG antibodies. Serological tests were performed using a commercial enzyme-linked immunosorbent assay. Results: IgG antibodies were detected in 986/64.1% of participants with differences (p < 0.001) among the following population groups: 42.4% of children and adolescents, 67.1% of the adult general population, 66.7% of hemodialysis patients, and 65.6% of liver transplant recipients. Seroprevalence increased with age, from 30.0% in the 6 months–9 years age group to 69.0% in the 40–49 years age group, and remained stable thereafter (68.8–73.3%). There was no difference in the seropositivity among males (66.1%) and females (63.1%), as well as the place of residence (suburban/rural 63.9%, urban 64.1%). IgM antibodies (current/recent infection) were found in 61/4.0% of participants with the highest seropositivity in the youngest age group (11.1%). In pregnant women, seroprevalence was higher in women with an unfavorable obstetric history compared with a normal pregnancy (IgG 71.0% vs. 62.6%; IgM 6.5% vs. 2.4%), but these differences were not significant. Logistic regression showed that the adult population had almost three times higher risk of IgG seropositivity compared to children/adolescents (general population OR = 2.777, 95% CI = 2.023–3.812; hemodialysis patients OR = 2.586, 95% CI = 1.531–4.367; and transplant patients OR = 2.717, 95% CI = 1.604–4.603). A one-year increase in age increased the risk of IgG seroprevalence (OR = 1.017; 95% CI = 1.011–1.022). Conclusions: Older age was the main risk factor for IgG seropositivity. Hemodialysis and organ transplantation seem unrelated to the increased parvovirus B19 seroprevalence. The role of parvovirus B19 in the etiology of TORCH infections needs to be studied further.
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47

Scharf, Andreas, Frank Mattern, Mohammed Al-Wardi, Gianluca Frijia, Daniel Moraetis, Bernhard Pracejus, Wilfried Bauer, and Ivan Callegari. "Chapter 4 Large-scale structure of the study area." Geological Society, London, Memoirs 54, no. 1 (2021): 61–66. http://dx.doi.org/10.1144/m54.4.

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AbstractThe Southeastern Oman Mountains are dominated by two major culminations: the Jabal Akhdar and Saih Hatat domes, surrounded by allochthonous and/or neo-autochthonous rocks. In the cores of both domes, folded autochthonous and par-autochthonous pre-Permian metasedimentary rocks are exposed, subjacent to the ‘Hercynian’ Unconformity. Above the unconformity are Permo--Mesozoic shelfal sedimentary rocks, characterized by carbonates. These sedimentary rocks were openly folded. The open folds are large-scale elongate structures that define the shapes of both domes. The main elongation direction is NW--SE. Doming is syn- to post-obductional. Most margins of the domes are marked by major post-obductional, extensional faults. Reactivated basement faults along the eastern margin of the Jabal Akhdar Dome may be responsible for the straight NNE-striking eastern margin which is perpendicular to the main elongation direction of the domes. The deep structure of both domes is poorly known. However, the Moho depth below the centre of the Jabal Akhdar Dome is at 50 km. We present a geological map of both domes, depicting the main faults and folds, and schematic cross-sections, parallel and perpendicular to the Oman Mountains.
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48

Polikarpova, Ilze, Roberts Kakis, Ieva Pakere, and Dagnija Blumberga. "Optimizing Large-Scale Solar Field Efficiency: Latvia Case Study." Energies 14, no. 14 (July 10, 2021): 4171. http://dx.doi.org/10.3390/en14144171.

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Solar energy transformation technologies are increasingly being used worldwide in the district heating sector. In the Baltic states, only one district heating company has implemented a large-scale solar collector field into its thermal energy production system, which is analyzed within this research. In this study, we analyzed the first year operation of the solar field, solar collector efficiency, and several influencing factors, i.e., ambient air temperature, heat carrier flow, and the temperature difference between the supply and return heat carrier temperatures. The study includes collecting and compilation of the data, analyzing influencing factors, and data analysis using the statistical analysis method. In addition, the research presents a simplified multi-regression model based on the actual performance of a large-scale solar field, which allows for forecasting the efficiency of solar collectors by taking into account the main operational parameters of the DH system. The results show that solar energy covers around 90% of the summer heat load of a particular district heating system. However, they also show room for improvements in producing all the necessary heat in the summer using solar energy. The regression analyses show that the most significant correlation between all parameters examined was obtained in May, reaching R2 = 0.9346 in solar field efficiency evaluation. This is due to several suitable conditions for solar energy production, i.e., placing solar collectors at an angle for them to be the most productive, having enough space in the storage tank, and the demand for thermal energy being still higher than in the summer months.
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49

Jung, Yong-Chul, Jia Xu, Jun Chen, Yeong C. Kim, David J. Winchester, and San Ming Wang. "Simplified DGS procedure for large-scale genome structural study." BioTechniques 47, no. 5 (November 2009): 969–71. http://dx.doi.org/10.2144/000113294.

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50

Lee, Eun-Kyung, Nayoung Hwang, and Yoondong Lee. "A study on high dimensional large-scale data visualization." Korean Journal of Applied Statistics 29, no. 6 (October 31, 2016): 1061–75. http://dx.doi.org/10.5351/kjas.2016.29.6.1061.

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