The Method-specific Certification of the Cholesterol and Triglyceride Contents of a Pure and an Adulterated Butter Fat Reference Material.

Abstract not availableJRC.D-Institute for Reference Materials and Measurements (Geel

New European Metrology Network for advanced manufacturing

Advanced manufacturing has been identified as one of the key enabling technologies with applications in multiple industries. The growing importance of advanced manufacturing is reflected by an increased number of publications on this topic in recent years. Advanced manufacturing requires new and enhanced metrology methods to assure the quality of manufacturing processes and the resulting products. However, a high-level coordination of the metrology community is currently absent in this field and consequently this limits the impact of metrology developments on advanced manufacturing. In this article we introduce the new European Metrology Network (EMN) for Advanced Manufacturing within EURAMET, the European Association of National Metrology Institutes (NMIs). The EMN is intended to be operated sustainably by NMIs and Designated Institutes in close cooperation with stakeholders interested in advanced manufacturing. The objectives of the EMN are to set up a permanent stakeholder dialogue, to develop a Strategic Research Agenda for the metrology input required for advanced manufacturing technologies, to create and maintain a knowledge sharing programme and to implement a web-based service desk for stakeholders. The EMN development is supported by a Joint Network Project within the European Metrology Programme for Innovation and Research

Generating Dashboards Using Fine-Grained Components: A Case Study for a PhD Programme

Developing dashboards is a complex domain, especially when several stakeholders are involved; while some users could demand certain indicators, other users could demand specific visualizations or design features. Creating individual dashboards for each potential need would consume several resources and time, being an unfeasible approach. Also, user requirements must be thoroughly analyzed to understand their goals regarding the data to be explored, and other characteristics that could affect their user experience. All these necessities ask for a paradigm to foster reusability not only at development level but also at knowledge level. Some methodologies, like the Software Product Line paradigm, leverage domain knowledge and apply it to create a series of assets that can be composed, parameterized, or combined to obtain fully functional systems. This work presents an application of the SPL paradigm to the domain of information dashboards, with the goal of reducing their development time and increasing their effectiveness and user experience. Different dashboard configurations have been suggested to test the proposed approach in the context of the Education in the Knowledge Society PhD programme of the University of Salamanca

Pre-sleep feeding, sleep quality, and markers of recovery in division I NCAA female soccer players

Pre-sleep nutrition habits in elite female athletes have yet to be evaluated. A retrospective analysis was performed with 14 NCAA Division I female soccer players who wore a WHOOP, Inc. band – a wearable device that quantifies recovery by measuring sleep, activity, and heart rate metrics through actigraphy and photoplethysmography, respectively – 24 h a day for an entire competitive season to measure sleep and recovery. Pre-sleep food consumption data were collected via surveys every 3 days. Average pre-sleep nutritional intake (mean ± sd: kcals 330 ± 284; cho 46.2 ± 40.5 g; pro 7.6 ± 7.3 g; fat 12 ± 10.5 g) was recorded. Macronutrients and kcals were grouped into high and low categories based upon the 50th percentile of the mean to compare the impact of a high versus low pre-sleep intake on sleep and recovery variables. Sleep duration (p = 0.10, 0.69, 0.16, 0.17) and sleep disturbances (p = 0.42, 0.65, 0.81, 0.81) were not affected by high versus low kcal, PRO, fat, CHO intake, respectively. Recovery (p = 0.81, 0.06, 0.81, 0.92), RHR (p = 0.84, 0.64, 0.26, 0.66), or HRV (p = 0.84, 0.70, 0.76, 0.93) were also not affected by high versus low kcal, PRO, fat, or CHO consumption, respectively. Consuming a small meal before bed may have no impact on sleep or recovery

Selecting cash management models from a multiobjective perspective

[EN] This paper addresses the problem of selecting cash management models under different operating conditions from a multiobjective perspective considering not only cost but also risk. A number of models have been proposed to optimize corporate cash management policies. The impact on model performance of different operating conditions becomes an important issue. Here, we provide a range of visual and quantitative tools imported from Receiver Operating Characteristic (ROC) analysis. More precisely, we show the utility of ROC analysis from a triple perspective as a tool for: (1) showing model performance; (2) choosingmodels; and (3) assessing the impact of operating conditions on model performance. We illustrate the selection of cash management models by means of a numerical example.Work partially funded by projects Collectiveware TIN2015-66863-C2-1-R (MINECO/FEDER) and 2014 SGR 118.Salas-Molina, F.; Rodríguez-Aguilar, JA.; Díaz-García, P. (2018). Selecting cash management models from a multiobjective perspective. Annals of Operations Research. 261(1-2):275-288. https://doi.org/10.1007/s10479-017-2634-9S2752882611-2Ballestero, E. (2007). Compromise programming: A utility-based linear-quadratic composite metric from the trade-off between achievement and balanced (non-corner) solutions. European Journal of Operational Research, 182(3), 1369–1382.Ballestero, E., & Romero, C. (1998). Multiple criteria decision making and its applications to economic problems. Berlin: Springer.Bi, J., & Bennett, K. P. (2003). Regression error characteristic curves. In Proceedings of the 20th international conference on machine learning (ICML-03), pp. 43–50.Bradley, A. P. (1997). The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145–1159.da Costa Moraes, M. B., Nagano, M. S., & Sobreiro, V. A. (2015). Stochastic cash flow management models: A literature review since the 1980s. In Decision models in engineering and management (pp. 11–28). New York: Springer.Doumpos, M., & Zopounidis, C. (2007). Model combination for credit risk assessment: A stacked generalization approach. Annals of Operations Research, 151(1), 289–306.Drummond, C., & Holte, R. C. (2000). Explicitly representing expected cost: An alternative to roc representation. In Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 98–207). New York: ACM.Drummond, C., & Holte, R. C. (2006). Cost curves: An improved method for visualizing classifier performance. Machine Learning, 65(1), 95–130.Elkan, C. (2001). The foundations of cost-sensitive learning. In International joint conference on artificial intelligence (Vol. 17, pp. 973–978). Lawrence Erlbaum associates Ltd.Fawcett, T. (2006). An introduction to roc analysis. Pattern Recognition Letters, 27(8), 861–874.Flach, P. A. (2003). The geometry of roc space: understanding machine learning metrics through roc isometrics. In Proceedings of the 20th international conference on machine learning (ICML-03), pp. 194–201.Garcia-Bernabeu, A., Benito, A., Bravo, M., & Pla-Santamaria, D. (2016). Photovoltaic power plants: a multicriteria approach to investment decisions and a case study in western spain. Annals of Operations Research, 245(1–2), 163–175.Glasserman, P. (2003). Monte Carlo methods in financial engineering (Vol. 53). New York: Springer.Gregory, G. (1976). Cash flow models: a review. Omega, 4(6), 643–656.Hernández-Orallo, J. (2013). Roc curves for regression. Pattern Recognition, 46(12), 3395–3411.Hernández-Orallo, J., Flach, P., & Ferri, C. (2013). Roc curves in cost space. Machine Learning, 93(1), 71–91.Hernández-Orallo, J., Lachiche, N., & Martınez-Usó, A. (2014). Predictive models for multidimensional data when the resolution context changes. In Workshop on learning over multiple contexts at ECML, volume 2014.Metz, C. E. (1978). Basic principles of roc analysis. In Seminars in nuclear medicine (Vol. 8, pp. 283–298). Amsterdam: Elsevier.Miettinen, K. (2012). Nonlinear multiobjective optimization (Vol. 12). Berlin: Springer.Ringuest, J. L. (2012). Multiobjective optimization: Behavioral and computational considerations. Berlin: Springer.Ross, S. A., Westerfield, R., & Jordan, B. D. (2002). Fundamentals of corporate finance (sixth ed.). New York: McGraw-Hill.Salas-Molina, F., Pla-Santamaria, D., & Rodriguez-Aguilar, J. A. (2016). A multi-objective approach to the cash management problem. Annals of Operations Research, pp. 1–15.Srinivasan, V., & Kim, Y. H. (1986). Deterministic cash flow management: State of the art and research directions. Omega, 14(2), 145–166.Steuer, R. E., Qi, Y., & Hirschberger, M. (2007). Suitable-portfolio investors, nondominated frontier sensitivity, and the effect of multiple objectives on standard portfolio selection. Annals of Operations Research, 152(1), 297–317.Stone, B. K. (1972). The use of forecasts and smoothing in control limit models for cash management. Financial Management, 1(1), 72.Torgo, L. (2005). Regression error characteristic surfaces. In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining (pp. 697–702). ACM.Yu, P.-L. (1985). Multiple criteria decision making: concepts, techniques and extensions. New York: Plenum Press.Zeleny, M. (1982). Multiple criteria decision making. New York: McGraw-Hill

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Photovoltaic power plants: a multicriteria approach to investment decisions and a case study in western Spain

his paper proposes a compromise programming (CP) model to help investors decide whether to construct photovoltaic power plants with government financial support. For this purpose, we simulate an agreement between the government, who pursues political prices (guaranteed prices) as low as possible, and the project sponsor who wants returns (stochastic cash flows) as high as possible. The sponsor s decision depends on the positive or negative result of this simulation, the resulting simulated price being compared to the effective guaranteed price established by the country legislation for photovoltaic energy. To undertake the simulation, the CP model articulates variables such as ranges of guaranteed prices, tech- nical characteristics of the plant, expected energy to be generated over the investment life, investment cost, cash flow probabilities, and others. To determine the CP metric, risk aver- sion is assumed. As an actual application, a case study on photovoltaic power investment in Extremadura, western Spain, is developed in detail.Garcia-Bernabeu, A.; Benito Benito, A.; Bravo Selles, M.; Pla Santamaría, D. (2015). Photovoltaic power plants: a multicriteria approach to investment decisions and a case study in western Spain. Annals of Operations Research. 1-12. doi:10.1007/s10479-015-1836-2S112Andrews, R. W., Pollard, A., & Pearce, J. M. (2012). Improved parametric empirical determination of module short circuit current for modelling and optimization of solar photovoltaic systems. Solar Energy, 86(9), 2240–2254.Anwar, Y., & Mulyadi, M. S. (2011). Income tax incentives on renewable energy industry: Case of geothermal industry in USA and Indonesia. African Journal of Business Management, 5(31), 12264–12270.Aouni, B., & Kettani, O. (2001). Goal programming model: A glorious history and a promising future. European Journal of Operational Research, 133(2), 225–231.Ballestero, E. (1997). Selecting the CP metric: A risk aversion approach. European Journal of Operational Research, 97(3), 593–596.Ballestero, E. (2000). Project finance: A multicriteria approach to arbitration. Journal of Operational Research Society, 51, 183–197.Ballestero, E. (2007). Compromise programming: A utility-based linear-quadratic composite metric from the trade-off between achievement and balanced (non-corner) solutions. European Journal of Operational Research, 182(3), 1369–1382.Ballestero, E., Pérez-Gladish, B., Arenas-Parra, M., & BilbaoTerol, A. (2009). Selecting portfolios given multiple Eurostoxx-based uncertainty scenarios: A stochastic goal programming approach from fuzzy betas. INFOR: Information Systems and Operational Research, 47(1), 59–70.Ballestero, E., & Plà-Santamaría, D. (2003). Portfolio selection on the Madrid exchange: A compromise programming model. International Transactions in Operational Research, 10(1), 33–51.Ballestero, E., & Pla-Santamaria, D. (2004). Selecting portfolios for mutual funds. Omega, 32(5), 385–394.Ballestero, E., & Pla-Santamaria, D. (2005). Grading the performance of market indicators with utility benchmarks selected from Footsie: A 2000 case study. Applied Economics, 37(18), 2147–2160.Ballestero, E., & Romero, C. (1996). Portfolio selection: A compromise programming solution. Journal of the Operational Research Society, 47, 1377–1386.Bastian-Pinto, C., Brandão, L., & de Lemos Alves, M. (2010). Valuing the switching flexibility of the ethanol–gas flex fuel car. Annals of Operations Research, 176(1), 333–348.Branker, K., Pathak, M., & Pearce, J. M. (2011). A review of solar photovoltaic levelized cost of electricity. Renewable and Sustainable Energy Reviews, 15(9), 4470–4482.Casares, F., Lopez-Luque, R., Posadillo, R., & Varo-Martinez, M. (2014). Mathematical approach to the characterization of daily energy balance in autonomous photovoltaic solar systems. Energy, 72, 393–404.Chatterji, A. K., Levine, D. I., & Toffel, M. W. (2009). How well do social ratings actually measure corporate social responsibility? Journal of Economics & Management Strategy, 18(1), 125–169.Copeland, T. E., & Weston, J. (1988). Financial theory and corporate policy. Reading, Massachusetts: Addison-Wesley.Gallagher, K. S. (2013). Why & how governments support renewable energy. Daedalus, 142(1), 59–77.García-Cascales, M. S., Lamata, M. T., & Sánchez-Lozano, J. M. (2012). Evaluation of photovoltaic cells in a multi-criteria decision making process. Annals of Operations Research, 199(1), 373–391.Gupta, S. (2012). Financing renewable energy. In F. L. Toth (Ed.), Energy for development (pp. 171–186). Springer.Karaarslan, A. (2012). Obtaining renewable energy from piezoelectric ceramics using Sheppard–Taylor converter. International Review of Electrical Engineering, 7(2), 3949–3956.Koellner, T., Weber, O., Fenchel, M., & Scholz, R. (2005). Principles for sustainability rating of investment funds. Business Strategy and the Environment, 14(1), 54–70.Lorenzo, E., & Navarte, L. (2000). On the usefulness of stand-alone PV sizing methods. Progress in Photovoltaics: Research and Applications, 8(4), 391–409.Lüdeke-Freund, F., & Loock, M. (2011). Debt for brands: Tracking down a bias in financing photovoltaic projects in Germany. Journal of Cleaner Production, 19(12), 1356–1364.Mavrotas, G., Diakoulaki, D., & Capros, P. (2003). Combined MCDA-IP approach for project selection in the electricity market. Annals of Operations Research, 120(1–4), 159–170.Mendez-Rodriguez, P., Garcia Bernabeu, A., Hilario, A., & Perez-Gladish, B. (2013). Some effects on the efficient frontier of the investment strategy: A preliminary approach. Recta, 14, 131–144.Michelson, G., Wailes, N., Van Der Laan, S., & Frost, G. (2004). Ethical investment processes and outcomes. Journal of Business Ethics, 52(1), 1–10.Mills, S. J. (1994). Project finance for renewable energy. 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Feasibility analysis of solar photovoltaic commercial power generation in California. Engineering Management Journal, 24(4), 36–49.Yazdani-Chamzini, A., Fouladgar, M. M., Zavadskas, E. K., & Moini, S. H. H. (2013). Selecting the optimal renewable energy using multi criteria decision making. Journal of Business Economics and Management, 14(5), 957–978.Yu, P. (1985). Multiple criteria decision making: Concepts, techniques and extensions. New York: Springer.Zeleny, M. (1982). Multiple criteria decision making (Vol. 25). New York: McGraw-Hill.Zhao, R., Shi, G., Chen, H., Ren, A., & Finlow, D. (2011). Present status and prospects of photovoltaic market in China. Energy Policy, 39(4), 2204–2207

Placing the library at the heart of plagiarism prevention: The University of Bradford experience.

yesPlagiarism is a vexed issue for Higher Education, affecting student transition, retention and attainment. This paper reports on two initiatives from the University of Bradford library aimed at reducing student plagiarism. The first initiative is an intensive course for students who have contravened plagiarism regulations. The second course introduces new students to the concepts surrounding plagiarism with the aim to prevent plagiarism breaches. Since the Plagiarism Avoidance for New Students course was introduced there has been a significant drop in students referred to the disciplinary programme. This paper discusses the background to both courses and the challenges of implementation

Association of APOE polymorphism with chronic kidney disease in a nationally representative sample: a Third National Health and Nutrition Examination Survey (NHANES III) Genetic Study

<p>Abstract</p> <p>Background</p> <p>Apolipoprotein E polymorphisms (<it>APOE</it>) have been associated with lowered glomerular filtration rate (GFR) and chronic kidney disease (CKD) with e2 allele conferring risk and e4 providing protection. However, few data are available in non-European ethnic groups or in a population-based cohort.</p> <p>Methods</p> <p>The authors analyzed 5,583 individuals from the Third National Health and Nutrition Examination Survey (NHANES III) to determine association with estimated GFR by the Modification of Diet in Renal Disease (MDRD) equation and low-GFR cases. Low-GFR cases were defined as GFR <75 ml/min/1.73 m²; additionally, GFR was analyzed continuously.</p> <p>Results</p> <p>In univariate analysis, the e4 allele was negatively associated with low-GFR cases in non-Hispanic whites, odds ratio (OR): 0.76, 95% confidence interval (CI): 0.60, 0.97. In whites, there was a significant association between increasing <it>APOE </it>score (indicating greater number of e2 alleles) and higher prevalence of low-GFR cases (OR: 1.21, 95%CI: 1.01, 1.45). Analysis of continuous GFR in whites found the e4 allele was associated with higher levels of continuous GFR (β-coefficient: 2.57 ml/min/1.73 m², 95%CI: 0.005, 5.14); in non-Hispanic blacks the e2 allele was associated with lower levels of continuous GFR (β-coefficient: -3.73 ml/min/1.73 m², 95%CI: -6.61, -0.84). <it>APOE </it>e2 and e4 alleles were rare and not associated with low-GFR cases or continuous GFR in Mexican Americans.</p> <p>Conclusion</p> <p>In conclusion, the authors observed a weak association between the <it>APOE </it>e4 allele and low-GFR cases and continuous GFR in non-Hispanic whites, and the <it>APOE </it>e2 allele and continuous GFR in non-Hispanic blacks, but found no association with either measure of kidney function in Mexican Americans. Larger studies including multiethnic groups are needed to determine the significance of this association.</p