Academic literature on the topic 'Mining statistics'

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Journal articles on the topic "Mining statistics"

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Hand, David J. "Statistics and data mining." ACM SIGKDD Explorations Newsletter 1, no. 1 (1999): 16–19. http://dx.doi.org/10.1145/846170.846171.

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Attwal, Kanwal Preet Singh, and Amardeep Singh Dhiman. "Exploring SPSS statistics for data mining and statistical modeling." International Journal of Statistics and Applied Mathematics 9, no. 3 (2024): 09–16. http://dx.doi.org/10.22271/maths.2024.v9.i3a.1721.

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Jin, Taolan, Bo Zhang, and Zhi Yang. "Cloud Statistics of Accounting Informatization Based on Statistics Mining." Computational Intelligence and Neuroscience 2022 (August 27, 2022): 1–10. http://dx.doi.org/10.1155/2022/3493678.

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With the rapid development of information technology, the amount of all kinds of data information is increasing rapidly. As an important means to collect, store, and manage massive data, and then analyze and predict the habits and characteristics of certain groups of people and even the development trend of a certain industry, big data technology provides a comprehensive strategic basis for management decision makers that the traditional processing mode cannot match. Contemporary management accounting serves the whole process of enterprise internal control, so it will produce a large number of
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Hand, David J. "Data Mining: Statistics and More?" American Statistician 52, no. 2 (1998): 112. http://dx.doi.org/10.2307/2685468.

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Hand, David J. "Data Mining: Statistics and More?" American Statistician 52, no. 2 (1998): 112–18. http://dx.doi.org/10.1080/00031305.1998.10480549.

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Hofmann, Heike, Antony Unwin, and Adalbert Wilhem. "Data Mining and Statistics — Introduction." Computational Statistics 16, no. 3 (2001): 317–21. http://dx.doi.org/10.1007/s001800100069.

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MacLaren, Graeme, James D. Fortenberry, and Heidi J. Dalton. "Lies, Statistics, and ECMO Data Mining." Pediatric Critical Care Medicine 17, no. 8 (2016): 799–802. http://dx.doi.org/10.1097/pcc.0000000000000870.

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Zhao, Chun-Mei, and Jing Luan. "Data mining: Going beyond traditional statistics." New Directions for Institutional Research 2006, no. 131 (2006): 7–16. http://dx.doi.org/10.1002/ir.184.

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Banks, David L. "Statistical data mining." WIREs Computational Statistics 2, no. 1 (2009): 9–25. http://dx.doi.org/10.1002/wics.53.

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Intelligence and Neuroscience, Computational. "Retracted: Cloud Statistics of Accounting Informatization Based on Statistics Mining." Computational Intelligence and Neuroscience 2023 (August 2, 2023): 1. http://dx.doi.org/10.1155/2023/9836184.

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Dissertations / Theses on the topic "Mining statistics"

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Luo, Man. "Data mining and classical statistics." Virtual Press, 2004. http://liblink.bsu.edu/uhtbin/catkey/1304657.

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This study introduces an overview of data mining. It suggests that methods derived from classical statistics are an integrated part of data mining. However, there are substantial differences between these two areas. Classical statistical models and non-statistical models used in data mining, such as regression trees and artificial neural networks, are presented to emphasize their unique approaches to extract information from data. In summation, this research provides some background to data mining and the role of classical statistics played in it.<br>Department of Mathematical Sciences
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Kamimura, Roy T. (Roy Tomoo). "Application of multivariate statistics to fermentation database mining." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/17437.

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Espy, John. "Data mining techniques for constructing jury selection models." Thesis, California State University, Long Beach, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1527548.

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<p> Jury selection can determine a case before it even begins. The goal is to predict whether a juror rules for the plaintiff or the defense in the medical malpractice trials that are conducted, and which variables are significant in predicting this. The data for the analysis were obtained from mock trials that simulated actual trials, with possible arguments from the defense and the plaintiff with ample discussion time. These mock trials were supplemented by surveys that attempted to capture the characteristics and attitudes of the mock juror and the case at hand. The data were modeled using
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Sharma, Gaurav Kumar. "Bayesian statistics and production reliability assessments for mining operations." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2741.

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This thesis presents a novel application of structural reliability concepts to assess the reliability of mining operations. “Limit-states” are defined to obtain the probability that the total productivity — measured in production time or economic gain — exceeds user-selected thresholds. Focus is on the impact of equipment downtime and other non-operating instances on the productivity and the economic costs of the operation. A comprehensive set of data gathered at a real-world mining facility is utilized to calibrate the probabilistic models. In particular, the utilization of Bayesian inference
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Li, Bin. "Statistical learning and predictive modeling in data mining." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1155058111.

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XU, Chen. "Customer lifetime value : an integrated data mining approach." Digital Commons @ Lingnan University, 2006. https://commons.ln.edu.hk/cds_etd/3.

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Customer Lifetime Value (CLV) ---which is a measure of the profit generating potential, or value, of a customer---is increasingly being considered a touchstone for customer relationship management. As the guide and benchmark for Customer Relationship Management (CRM) applications, CLV analysis has received increasing attention from both the marketing practitioners and researchers from different domains. Furthermore, the central challenge in predicting CLV is the precise calculation of customer’s length of service (LOS). There are several statistical approaches for this problem and several rese
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Annabathula, Ramesh. "A Web-based tool for analysis of crime laboratory data." Morgantown, W. Va. : [West Virginia University Libraries], 2007. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5048.

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Thesis (M.S.)--West Virginia University, 2007.<br>Title from document title page. Document formatted into pages; contains ix, 110 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 100-102).
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Padhye, Manoday D. "Use of data mining for investigation of crime patterns." Morgantown, W. Va. : [West Virginia University Libraries], 2006. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4836.

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Thesis (M.S.)--West Virginia University, 2006.<br>Title from document title page. Document formatted into pages; contains viii, 108 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 80-81).
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Wang, Dan Tong. "Outlier detection with data stream mining approach in high-dimenional time series data." Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691091.

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Haider, Syed Imran, and Raja M. Khurram Shahzad. "Detection of Spyware by Mining Executable Files." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3095.

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Malicious programs have been a serious threat for the confidentiality, integrity and availability of a system. Different researches have been done to detect them. Two approaches have been derived for it i.e. Signature Based Detection and Heuristic Based Detection. These approaches performed well against known malicious programs but cannot catch the new malicious programs. Different researchers tried to find new ways of detecting malicious programs. The application of data mining and machine learning is one of them and has shown good results compared to other approaches. A new category of malic
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Books on the topic "Mining statistics"

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1920-, Rao C. Radhakrishna, Wegman Edward J. 1943-, and Solka Jeffrey L. 1955-, eds. Data mining and data visualization. Elsevier North Holland, 2005.

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Giusti, Antonio. Classification and Data Mining. Springer Berlin Heidelberg, 2013.

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Giudici, Paolo. Applied Data Mining. John Wiley & Sons, Ltd., 2005.

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Tufféry, Stéphane. Data Mining and Statistics for Decision Making. John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9780470979174.

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Matwin, Stan, and Jan Mielniczuk, eds. Challenges in Computational Statistics and Data Mining. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-18781-5.

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Ellis, M. Fred. Illinois coal mining costs. The Department, 1992.

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A, Sinner George, and Job Service North Dakota. Research and Statistics Section., eds. Mining, second quarter, 1987. Job Service North Dakota, 1989.

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Job Service North Dakota. Research and Statistics Section., ed. Mining, second quarter, 1984. Job Service North Dakota, 1986.

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Giudici, Paolo. Applied data mining for business and industry. 2nd ed. John Wiley, 2009.

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Hendricks, P. A. Analysis of metal/nonmetal underground mining accidents involving mobile mining equipment. United States Dept. of the Interior, Bureau of Mines, 1994.

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Book chapters on the topic "Mining statistics"

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Wu, Di. "Data Statistics." In Data Mining with Python. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003462781-3.

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"Basic statistics." In Data Mining Algorithms. John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118950951.ch2.

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Banks, David L., and Stephen E. Fienberg. "Data Mining, Statistics." In Encyclopedia of Physical Science and Technology. Elsevier, 2003. http://dx.doi.org/10.1016/b0-12-227410-5/00164-2.

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Kaufman, Kenneth A., and Ryszard S. Michalski. "From Data Mining to Knowledge Mining." In Handbook of Statistics. Elsevier, 2005. http://dx.doi.org/10.1016/s0169-7161(04)24002-0.

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"Mining products." In International Trade Statistics. WTO, 2004. http://dx.doi.org/10.30875/c62e5871-en.

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Zhang, Yunquan, and Chuanhua Yu. "DATA MINING." In Handbook of Medical Statistics. WORLD SCIENTIFIC, 2017. http://dx.doi.org/10.1142/9789813148963_0015.

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"Mining." In Monthly Bulletin of Statistics, July 2017. UN, 2017. http://dx.doi.org/10.18356/930774c7-en-fr.

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"Mining." In Monthly Bulletin of Statistics, June 2017. UN, 2017. http://dx.doi.org/10.18356/93bad0ad-en-fr.

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"Mining." In Monthly Bulletin of Statistics (Ser. Q). UN, 2018. http://dx.doi.org/10.18356/4cfac460-en-fr.

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"Mining." In Monthly Bulletin of Statistics (Ser. Q). UN, 2015. http://dx.doi.org/10.18356/4e8a694d-en-fr.

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Conference papers on the topic "Mining statistics"

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Xie, Qinhua, Weicong Liu, Fan Yuan, Jifan Shi, Ziyu Liu, and Yanbing Zhang. "VidBot: Intelligent Video Learning Tool for Content Mining and Playback Traffic Statistics." In 2024 IEEE International Conference on Multimedia and Expo Workshops (ICMEW). IEEE, 2024. http://dx.doi.org/10.1109/icmew63481.2024.10645449.

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Musu, Wilem, Indra Samsie, Aldi Bastiatul Fawait, et al. "Trend and Correlation Analysis of Instagram Activity Using Data Mining and Statistics." In 2024 6th International Conference on Cybernetics and Intelligent System (ICORIS). IEEE, 2024. https://doi.org/10.1109/icoris63540.2024.10903903.

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Williams, Graham J. "Mining Multiple Models." In Proceedings of the International Statistics Workshop. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812772466_0022.

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"Data mining, statistics and biometrics." In 28th International Conference on Information Technology Interfaces, 2006. IEEE, 2006. http://dx.doi.org/10.1109/iti.2006.1708471.

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"Data mining, statistics and biometrics." In Proceedings of the ITI 2009 31st International Conference on Information Technology Interfaces (ITI). IEEE, 2009. http://dx.doi.org/10.1109/iti.2009.5196081.

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Lewis, Chris, and Noah Wardrip-Fruin. "Mining game statistics from web services." In the Fifth International Conference. ACM Press, 2010. http://dx.doi.org/10.1145/1822348.1822362.

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Duluta, Andreistefan, Stefan Mocanu, and Daniela Saru. "ON IMPROVING STUDENTS' PERFORMANCE THROUGH COMBINED STATISTICS AND DATA MINING ANALYSIS." In eLSE 2018. Carol I National Defence University Publishing House, 2018. http://dx.doi.org/10.12753/2066-026x-18-113.

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The learning process is (or should be) periodically monitored and evaluated in order to improve its results and efficiency. Unfortunately, most of the times, only the students' results are taken into consideration when making various reports, statistics or recommendations. However, it is obvious that, besides the instant or a period based student's performance, there are other external factors that affect in an objective manner the overall performance. Traditional investigation methods are based almost exclusively on statistical instruments and are not capable to reveal all aspects that lead t
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Moriya, Koichi, Shin Matsushima, and Kenji Yamanishi. "Traffic risk mining from heterogeneous road statistics." In 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2015. http://dx.doi.org/10.1109/dsaa.2015.7344889.

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Park, Inho, Kwang H. Lee, and Doheon Lee. "Mining cancer genes with running-sum statistics." In Proceeding of the third international workshop. ACM Press, 2009. http://dx.doi.org/10.1145/1651318.1651326.

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Martin, R. Douglas. "Data mining for outliers with robust statistics." In Tutorial notes of the seventh ACM SIGKDD international conference. ACM Press, 2001. http://dx.doi.org/10.1145/502786.502789.

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Reports on the topic "Mining statistics"

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Tumer, Kagan, and Joydeep Ghosh. Robust Order Statistics Based Ensembles for Distributed Data Mining. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada396346.

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Volkova, Nataliia P., Nina O. Rizun, and Maryna V. Nehrey. Data science: opportunities to transform education. [б. в.], 2019. http://dx.doi.org/10.31812/123456789/3241.

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The article concerns the issue of data science tools implementation, including the text mining and natural language processing algorithms for increasing the value of high education for development modern and technologically flexible society. Data science is the field of study that involves tools, algorithms, and knowledge of math and statistics to discover knowledge from the raw data. Data science is developing fast and penetrating all spheres of life. More people understand the importance of the science of data and the need for implementation in everyday life. Data science is used in business
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Vizmanos, Jana Flor, Jose Ramon Albert, Mika Muñoz, et al. Addressing Data Gaps with Innovative Data Sources. Philippine Institute for Development Studies, 2022. https://doi.org/10.62986/dp2022.55.

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With the advent of digital transformation, information and communications technology innovations have also led to a "data revolution" wherein more data is being captured, produced, stored, accessed, analyzed, archived, and reanalyzed at an exponential pace. An examination of new data sources, including big data and crowd-sourced data, can complement traditional sources of statistics and unlock insights that can ultimately lead to interventions for better outcomes by informing policies and actions toward attaining robust, sustainable, and inclusive development. This study will examine PIDS webs
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Бабець, Євген Костянтинович, Ірина Петрівна Антонік, Ірина Євгенівна Мельникова, and Антон Всеволодович Петрухін. nfluence of Mining and Concentration Works Activity on Land Resources. Petroșani, 2019. http://dx.doi.org/10.31812/123456789/3120.

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The research provides assessment of current and longer-term consequences of iron ore open pit mining for land resources of adjacent areas. There are applied methods of analysis of fund materials; comparison of topographic sheets and special maps, visual observation, soil testing, laboratory analyses and statistic processing of data obtained. It is revealed that facilities of iron ore mining and concentration waste accumulation (dumps and tailing ponds) are destructive factors for the local lithosphere, dust chemical contamination being the basic one. The steps aimed at reducing negative impact
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Skow, Jason, and Smitha Koduru. PR-244-163728-R01 In-Line Inspection Crack Tool Performance Evaluation Phase II. Pipeline Research Council International, Inc. (PRCI), 2018. http://dx.doi.org/10.55274/r0011531.

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After recent high profile pipeline incidents in the United States, the National Transportation Safety Board (NTSB) issued recommendations to review ILI technologies with respect to tool tolerance, probability of detection and probability of identi?cation, and to provide a model with detailed procedures for evaluating the e?ect of interacting corrosion and crack threats. Phase I of the PRCI project NDE-4E was initiated in 2013 to address the ?rst of these recommendations to the extent possible by mining existing historical operator data. The main goal of the project was to create a database of
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Lagria, Raymond Freth, and Brenda Quismorio. Analyzing Trends in APEC Using Data Analytics. Philippine Institute for Development Studies, 2022. https://doi.org/10.62986/dp2022.04.

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This paper shows that advanced analytics and traditional statistical techniques on available unstructured and structured data can be utilized to understand the themes put forward in APEC's yearly meetings and how member economies have supported these topics through the conduct of APEC projects. The application of text mining algorithms, such as topic modeling on the proceedings of APEC-level annual meetings, namely, APEC Economic Leaders' Meeting (AELM), APEC Annual Ministerial Meeting (AMM), and Senior Officials' Meeting (SOM), generated themes from the text which insight have been discussed.
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Weller, Joel I., Derek M. Bickhart, Micha Ron, Eyal Seroussi, George Liu, and George R. Wiggans. Determination of actual polymorphisms responsible for economic trait variation in dairy cattle. United States Department of Agriculture, 2015. http://dx.doi.org/10.32747/2015.7600017.bard.

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The project’s general objectives were to determine specific polymorphisms at the DNA level responsible for observed quantitative trait loci (QTLs) and to estimate their effects, frequencies, and selection potential in the Holstein dairy cattle breed. The specific objectives were to (1) localize the causative polymorphisms to small chromosomal segments based on analysis of 52 U.S. Holstein bulls each with at least 100 sons with high-reliability genetic evaluations using the a posteriori granddaughter design; (2) sequence the complete genomes of at least 40 of those bulls to 20 coverage; (3) de
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Hutchinson, M. L., J. E. L. Corry, and R. H. Madden. A review of the impact of food processing on antimicrobial-resistant bacteria in secondary processed meats and meat products. Food Standards Agency, 2020. http://dx.doi.org/10.46756/sci.fsa.bxn990.

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For meat and meat products, secondary processes are those that relate to the downstream of the primary chilling of carcasses. Secondary processes include maturation chilling, deboning, portioning, mincing and other operations such as thermal processing (cooking) that create fresh meat, meat preparations and ready-to-eat meat products. This review systematically identified and summarised information relating to antimicrobial resistance (AMR) during the manufacture of secondary processed meatand meat products (SPMMP). Systematic searching of eight literature databases was undertaken and the resu
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Statistical analysis of surface-water-quality data in and near the coal-mining region of southwestern Indiana, 1957-80. US Geological Survey, 1987. http://dx.doi.org/10.3133/wsp2291.

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Financial Stability Report - September 2015. Banco de la República, 2021. http://dx.doi.org/10.32468/rept-estab-fin.sem2.eng-2015.

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From this edition, the Financial Stability Report will have fewer pages with some changes in its structure. The purpose of this change is to present the most relevant facts of the financial system and their implications on the financial stability. This allows displaying the analysis more concisely and clearly, as it will focus on describing the evolution of the variables that have the greatest impact on the performance of the financial system, for estimating then the effect of a possible materialization of these risks on the financial health of the institutions. The changing dynamics of the ri
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