Being Fred: Big stories, small stories and the accomplishment of a positive ageing identity

This is a postprint of an article published in Qualitative Research, Volume 9 (2), 83 - 99. © 2009 copyright SAGE Publications. Qualitative Research is available online at: http://www.uk.sagepub.com/journals.navThis article is informed by recent trends in narrative research that focus on the meaning-making actions of those involved in describing the life course. Drawing upon data generated during a series of interactive interviews with a 70-year-old physically active man named Fred, his story is presented to illustrate a strategic model of narrative activity. In particular, using the concepts of `big stories' and `small stories' as an analytical framework, we trace Fred's use of two specific identities; being fit and healthy , and being leisurely to analyse the ways that he accomplishes an ontological narrative where the plot line reads; `Life is what you make it'. The ways in which this narrative enables Fred to perform a narrative of positive self-ageing in his everyday life is illustrated. Finally, the analytical possibilities of being attentive to both big and small stories in narrative analysis are discussed

Big Data Transformation in Agriculture: From Precision Agriculture Towards Smart Farming

[EN] Big data is a concept that has changed the way to analyse data and information in different environments such as industry and recently, in agriculture. It is used to describe a large volume of data (structured or unstructured data), which are difficult to obtain, process or parse using conventional technologies and tools like relational databases or conventional statistics, in a reasonable time for their insight. However, Big Data is applied differently in each area to take advantage of its potential and capabilities. Specially in agriculture that presents more demanding conditions due to its inherent uncertainty, so Big Data methods and models from other environments cannot be used straight away in this area. In this paper, we present a review/update of term Big Data and analyse the evolution and the role of Big Data in agriculture outlined the element of collaboration.All authors acknowledge the partial support of Project 691249, RUC-APS: Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems, funded by the EU under its funding scheme H2020-MSCA-RISE-2015; and the project "Development of an integrated maturity model for agility, resilience and gender perspective in supply chains (MoMARGE). Application to the agricultural sector." Ref. GV/2017/025 funded by the Generalitat Valenciana. This first author was supported by the Aid Programme of Research and Development of Universitat Politecnica de Valencia [PAID-01-18].Rodríguez-Sánchez, MDLÁ.; Cuenca, L.; Ortiz Bas, Á. (2019). Big Data Transformation in Agriculture: From Precision Agriculture Towards Smart Farming. IFIP Advances in Information and Communication Technology. 568:467-474. https://doi.org/10.1007/978-3-030-28464-0_40S467474568Cox, M., Ellsworth, D.: Application-controlled demand paging for out-of-core visualization. In: Proceedings of the 8th Conference on Visualization 1997, p. 235. IEEE Computer Society Press (1997)Laney, D.: 3D data management: controlling data volume, velocity and variety. META Group Res. Note 6, 1 (2001)Beyer, M.A., Laney, D.: The Importance of “Big Data”: A Definition. Gartner, Stamford (2012)Kamilaris, A., et al.: A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture 143(C), 23–37 (2017)Marr, B.: How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read (2019). https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/#5671a61d60baNIST. The definition of Big Data. https://bigdatawg.nist.gov/home.phpIBM. The definition of Big Data. https://www.ibm.com/analytics/hadoop/big-data-analyticsOracle. The definition of Big Data. https://www.oracle.com/big-data/guide/what-is-big-data.htmlShahbaz, M., Gao, Ch., Zhai, L., Shahzad, F., Hu, Y.: Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change. J. Big Data 6 (2019). https://doi.org/10.1186/s40537-019-0170-yTrom, L., Cronje, J.: Analysis of data governance implications on big data. In: Arai, K., Bhatia, R. (eds.) FICC 2019. LNNS, vol. 69, pp. 645–654. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-12388-8_45Tao, F., et al.: A field programmable gate array implemented fibre channel switch for big data communication towards smart manufacturing. 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Navig. 49(1), 9–21 (1996)Robert, P.C.: Precision agriculture: research needs and status in the USA. In: Stafford, J.V. (ed.) Proceedings of the 2nd European Conference on Precision Agriculture, Part 1, pp. 19–33. Academic Press, SCI/Sheffield (1999)Long, D.S., Nielsen, G.A., Henry, M.P., Westcott, M.P.: Remote sensing for northern plains precision agriculture. In: Paper Presented at the Space 2000, pp. 208–214 (2000)Ge, Y., Thomasson, J.A., Sui, R.: Remote sensing of soil properties in precision agriculture: a review. Front. Earth Sci. 5(3), 229–238 (2011)Sundmaeker, H., Verdouw, C., Wolfert, S., Pérez L.: Internet of food and farm 2020. In: Paper presented at Digitising the Industry - Internet of Things Connecting Physical, Digital and Virtual Worlds, River Publishers, Gistrup/Delft, pp. 129–151 (2016)Barmpounakis, S., et al.: Management and control applications in agriculture domain via a FI Business-to-Business platform. Inf. Process. 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Factor Affecting Indonesian Charcoal Export To China

The purpose of this study was to determine the effect of Gross Domestic Product (GDP) and the Rupiah Exchange Rate on the Volume of Indonesian Shell Charcoal Exports to China. The data used in this study is secondary data that is time series. In this study, the method used as an analytical tool is Ordinary Least Square (OLS) to determine changes in the value of the dependent variable, namely the Export Volume of Indonesian Shell Charcoal which is influenced by the independent variable, namely China's Gross Domestic Product (GDP) and Exchange Rates using multiple linear regression techniques. The method used in this research is descriptive quantitative, namely explaining the results of computerization using the E-Views 9 program.The results of the analysis show that simultaneously China’s GDP and the Exchange Rate have a significant effect on the Export Volume of Indonesia Shell Charcoal to China in 2001 – 2020. The results of the analysis show partially China’s Gross Domestic Product (GDP) had no effect on the Export Volume of Indonesia Shell Charcoal to China in 2001 – 2020 and the Exchange Rate had a positive and significant effect on the Export Volume of Indonesia Shell Charcoal to China in 2001 – 2020.The result of the Coefficient of Determination (R2) is carried out to see how big the proportion of the influence of the independent variable on the dependent variable is. In this case, the value of the coefficient of determination used is Adjusted R-Squared which is the coefficient of determination that has been corrected by the number of variables and sample size. It is known that the Adjusted R-Squared value is 0,366601 or 36.66%. This indicates that a 36.66% change in the Volume of Indonesian Shell Charcoal Exports to China was caused by changes in China's Gross Domestic Product (GDP) and the Exchange Rate. While the remaining 63.34% changes in the Volume of Indonesian Shell Charcoal Exports to China were caused by changes in other variables that were not used in the study

Meeting report : Ocean ‘omics science, technology and cyberinfrastructure : current challenges and future requirements (August 20-23, 2013)

© The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Standards in Genomic Sciences 9 (2014): 1251-1258, doi:10.4056/sigs.5749944.The National Science Foundation’s EarthCube End User Workshop was held at USC’s Wrigley Marine Science Center on Catalina Island, California in August 2013. The workshop was designed to explore and characterise the needs and tools available to the community focusing on microbial and physical oceanography research with a particular focus on ‘omic research. The assembled researchers outlined the existing concerns regarding the vast data resources that are being generated, and how we will deal with these resources as their volume and diversity increases. Particular attention was focused on the tools for handling and analysing the existing data, and on the need for the construction and curation of diverse federated databases, as well as development of shared interoperable, “big-data capable” analytical tools. The key outputs from this workshop include (i) critical scientific challenges and cyberinfrastructure constraints, (ii) the current and future ocean ‘omics science grand challenges and questions, and (iii) data management, analytical and associated and cyber-infrastructure capabilities required to meet critical current and future scientific challenges. The main thrust of the meeting and the outcome of this report is a definition of the ‘omics tools, technologies and infrastructures that facilitate continued advance in ocean science biology, marine biogeochemistry, and biological oceanography.We gratefully acknowledge support for the Ocean ‘Omics EarthCube end-user workshop by the Geo-sciences Division of the U.S. National Science Foundation

STUDI APLIKATIF PENINGKATAN PENJUALAN MENU KATEGORI PUZZLE DAN PLOWHORSE MELALUI SUGGESTIVE SELLING DI 56 DEGREES BANDUNG

Penelitian ini dilatarbelakangi penurunan volume penjualan pada menu a la carte di 56 Degrees Bandung periode November 2016 – Oktober 2017. Restoran 56 Degrees belum menerapkan metode menu engineering sebagai salah satu alat untuk mengevaluasi menu yang dijual. Penelitian ini bertujuan untuk melihat pengaruh penerapan analisis menu engineering yang didukung dengan metode suggestive selling untuk menu kategori puzzle dan plowhorse sehingga dapat meningkatkan volume penjualannya. Jenis penelitian yang digunakan adalah jenis penelitian kuantitatif dan metode eksperimen. Sedangkan metode penelitian yang digunakan adalah metode verifikatif dengan analisis deskriptif. Data primer yang digunakan adalah data penjualan menu a’la carte di 56 Degrees periode November 2016 – Oktober 2017. Metode analisis data menggunakan pendekatan matriks menu engineering untuk mengetahui klasifikasi menu, dan analisis korelasi dan regresi untuk melihat tingkat pengaruh dari variabel-variabel yang diteliti. Berdasarkan hasil perhitungan menu engineering terdapat 39,45% atau 15 item menu dengan kategori puzzle dan plowhorse. Setelah dilakukan eksperimen penerapan suggestive selling pada menu puzzle dan plowhorse periode Januari 2018 - Maret 2018 didapatkan hasil 60% atau 9 item menu mengalami peningkatan penjualan dan 40% atau 6 item menu mengalami penurunan penjualan. Dengan hasil akhir kualitas menu maupun suggestive selling berpengaruh terhadap volume penjualan baik secara parsial maupun simultan. ;---This research are based on the decreasing selling volume of a la carte menu at 56 Degrees from November 2016 until October 2017. 56 Degrees café has not yet applying menu engineering methods as a tools to evaluate the menu that is sold. The purposes of this research is to see the effect of the application of the menu engineering analysis which is supported by suggestive selling method for puzzle and plowhorse menu category, so it can increase the selling volume. The types of this research is a quantitative research and the experiment method. Meanwhile the research methods that is used is verification method with descriptive analysis. The primer data that is used is the data sales of a la carte menu at 56 Degrees café from November 2016 until October 2017. To analyze the data itself is using matrix enclosing of menu engineering in order to classified the menu, and correlative and regression analysis to see the impact from the researched variabels. According to the menu engineering calculation there is 39,45% or 15 menu items within the puzzle and plowhorse category. After the experiment of suggestive selling was conducted the puzzle and plowhorse at January 2018 until march 2018 can be seen about 60% or 9 menu items are having an up selling and about 40% or 6 menu items are having a down selling. In conclusion, the quality of the menu and suggestive selling is having a big impact to the selling volume both partially and simultaneously