Las Nuevas Tecnologías y las Ciencias Humanas: El Caso de Internet y la Historia Actual

En esta comunicación se analizarán los puntos de convergencia y divergencia de la práctica de la Historia Actual con los nuevos paradigmas intelectuales de la "tercera cultura ", y la posibilidad de que desde la filosofía y organización de Internet se imponga un nuevo modelo de realización de los estudios humanísticos. Los rasgos principales de la sociedad informacional serán puestos en común con las nuevas fUentes, métodos y teorias de la Historia Actual. De esta forma, a partir del estudio de la cibersociedad y en general de la Revolución Científica-Tecnológica, se presentará el proceso de consilience (según la expresión de E. o. Wilson) y cuanto de éxito y de fracaso éste ha tenido a la hora de buscar una unidad del conocimiento, un acercamiento entre las ciencias naturales y las ciencias humanas. Los puntos a tratar en la comunicación por tanto, seran: sociedad informacional y cibersociedad; "la tercera cultura" y la consilience; origen, teoría y práctica de la Historia Actual y finalmente, síntesis de la posible simbiosis entre las nuevas tecnologías y la Historia Actual

Cibersociedad y ciencias humanas: el caso de la Historia Actual

La intención del presente artículo es presentar cómo los avances en el nuevo patrón de producción y difusión de conocimientos de la sociedad presente, ejemplificado en Internet, pueden influir en la reconceptualización de las ciencias humanas y en especial de la Historia Actual. Se analizarán los puntos de convergencia y divergencia de la práctica de la Historia Actual con los nuevos paradigmas intelectuales de la “tercera cultura”, y la posibilidad de que desde la filosofía y organización de Internet se imponga un nuevo modelo de realización de los estudios humanísticos. Los rasgos principales de la sociedad informacional serán puestos en común con las nuevas fuentes, métodos y teorías de la Historia Actual

Monitoring multidimensional phenomena with a multicriteria composite performance interval approach

[EN] In the last two decades, the construction of composite indicators to measure and compare multidimensional phenomena in a broad spectrum of domains has increased considerably. Different methodological approaches are used to summarise huge datasets of information in a single figure. This paper proposes a new approach that consists in computing a multicriteria composite performance interval based on different aggregation rules. The suggested approach provides an additional layer of information as the performance interval displays a lower bound from a non-compensability perspective, and an upper bound allowing for full-compensability. The outstanding features of this proposal are: 1) a distance-based multicriteria technique is taken as the baseline to construct the multicriteria performance interval; 2) the aggregation of distances/separation measures is made using particular cases of Minkowski Lp metric; 3) the span of the multicriteria performance interval can be considered as a sign of the dimensions or indicators balance.Garcia-Bernabeu, A.; Hilario Caballero, A.; Pla Santamaría, D.; Salas-Molina, F. (2021). Monitoring multidimensional phenomena with a multicriteria composite performance interval approach. International Journal of Multicriteria Decision Making (Online). 8(4):368-385. https://doi.org/10.1504/IJMCDM.2021.120760S3683858

Multiple-criteria cash-management policies with particular liquidity terms

[EN] Eliciting policies for cash management systems with multiple assets is by no means straightforward. Both the particular relationship between alternative assets and time delays from control decisions to availability of cash introduce additional difficulties. Here we propose a cash management model to derive short-term finance policies when considering multiple assets with different expected returns and particular liquidity terms for each alternative asset. In order to deal with the inherent uncertainty about the near future introduced by cash flows, we use forecasts as a key input to the model. We express uncertainty as lack of predictive accuracy and we derive a deterministic equivalent problem that depends on forecasting errors and preferences of cash managers. Since the assessment of the quality of forecasts is recommended, we describe a method to evaluate the impact of predictive accuracy in cash management policies. We illustrate this method through several numerical examples.Salas-Molina, F.; Pla Santamaría, D.; Garcia-Bernabeu, A.; Mayor-Vitoria, F. (2020). Multiple-criteria cash-management policies with particular liquidity terms. IMA Journal of Management Mathematics. 31(2):217-231. https://doi.org/10.1093/imaman/dpz010S217231312Abdelaziz, F. B., Aouni, B., & Fayedh, R. E. (2007). Multi-objective stochastic programming for portfolio selection. European Journal of Operational Research, 177(3), 1811-1823. doi:10.1016/j.ejor.2005.10.021Aouni, B., Ben Abdelaziz, F., & La Torre, D. (2012). The Stochastic Goal Programming Model: Theory and Applications. Journal of Multi-Criteria Decision Analysis, 19(5-6), 185-200. doi:10.1002/mcda.1466Aouni, B., Colapinto, C., & La Torre, D. (2014). Financial portfolio management through the goal programming model: Current state-of-the-art. European Journal of Operational Research, 234(2), 536-545. doi:10.1016/j.ejor.2013.09.040Baccarin, S. (2009). Optimal impulse control for a multidimensional cash management system with generalized cost functions. European Journal of Operational Research, 196(1), 198-206. doi:10.1016/j.ejor.2008.02.040Ballestero, E. (2001). Stochastic goal programming: A mean–variance approach. European Journal of Operational Research, 131(3), 476-481. doi:10.1016/s0377-2217(00)00084-9Ballestero, E., & Romero, C. (1998). Multiple Criteria Decision Making and its Applications to Economic Problems. doi:10.1007/978-1-4757-2827-9Bemporad, A., & Morari, M. (1999). Control of systems integrating logic, dynamics, and constraints. Automatica, 35(3), 407-427. doi:10.1016/s0005-1098(98)00178-2Cabello, J. G. (2013). Cash efficiency for bank branches. SpringerPlus, 2(1). doi:10.1186/2193-1801-2-334García Cabello, J., & Lobillo, F. J. (2017). Sound branch cash management for less: A low-cost forecasting algorithm under uncertain demand. Omega, 70, 118-134. doi:10.1016/j.omega.2016.09.005Charnes, A., & Cooper, W. W. (1959). Chance-Constrained Programming. Management Science, 6(1), 73-79. doi:10.1287/mnsc.6.1.73Charnes, A., & Cooper, W. W. (1977). Goal programming and multiple objective optimizations. European Journal of Operational Research, 1(1), 39-54. doi:10.1016/s0377-2217(77)81007-2Constantinides, G. M., & Richard, S. F. (1978). Existence of Optimal Simple Policies for Discounted-Cost Inventory and Cash Management in Continuous Time. Operations Research, 26(4), 620-636. doi:10.1287/opre.26.4.620Moraes, M. B. da C., & Nagano, M. S. (2014). Evolutionary models in cash management policies with multiple assets. Economic Modelling, 39, 1-7. doi:10.1016/j.econmod.2014.02.010Da Costa Moraes, M. B., Nagano, M. S., & Sobreiro, V. A. (2015). Stochastic Cash Flow Management Models: A Literature Review Since the 1980s. Decision Engineering, 11-28. doi:10.1007/978-3-319-11949-6_2Eppen, G. D., & Fama, E. F. (1969). Cash Balance and Simple Dynamic Portfolio Problems with Proportional Costs. International Economic Review, 10(2), 119. doi:10.2307/2525547Gormley, F. M., & Meade, N. (2007). The utility of cash flow forecasts in the management of corporate cash balances. European Journal of Operational Research, 182(2), 923-935. doi:10.1016/j.ejor.2006.07.041Gregory, G. (1976). Cash flow models: A review. Omega, 4(6), 643-656. doi:10.1016/0305-0483(76)90092-xHerrera-Cáceres, C. A., & Ibeas, A. (2016). Model predictive control of cash balance in a cash concentration and disbursements system. Journal of the Franklin Institute, 353(18), 4885-4923. doi:10.1016/j.jfranklin.2016.09.007Higson, A., Yoshikatsu, S., & Tippett, M. (2009). Organization size and the optimal investment in cash. IMA Journal of Management Mathematics, 21(1), 27-38. doi:10.1093/imaman/dpp015Miller, M. H., & Orr, D. (1966). A Model of the Demand for Money by Firms. The Quarterly Journal of Economics, 80(3), 413. doi:10.2307/1880728Miller, T. W., & Stone, B. K. (1985). Daily Cash Forecasting and Seasonal Resolution: Alternative Models and Techniques for Using the Distribution Approach. The Journal of Financial and Quantitative Analysis, 20(3), 335. doi:10.2307/2331034Penttinen, M. J. (1991). Myopic and stationary solutions for stochastic cash balance problems. European Journal of Operational Research, 52(2), 155-166. doi:10.1016/0377-2217(91)90077-9Prékopa, A. (1995). Stochastic Programming. doi:10.1007/978-94-017-3087-7Salas-Molina, F. (2017). Risk-sensitive control of cash management systems. Operational Research, 20(2), 1159-1176. doi:10.1007/s12351-017-0371-0Salas-Molina, F., Martin, F. J., Rodríguez-Aguilar, J. A., Serrà, J., & Arcos, J. L. (2017). Empowering cash managers to achieve cost savings by improving predictive accuracy. International Journal of Forecasting, 33(2), 403-415. doi:10.1016/j.ijforecast.2016.11.002Salas-Molina, F., Pla-Santamaria, D., & Rodriguez-Aguilar, J. A. (2016). A multi-objective approach to the cash management problem. Annals of Operations Research, 267(1-2), 515-529. doi:10.1007/s10479-016-2359-1Salas-Molina, F., Pla-Santamaria, D., & Rodríguez-Aguilar, J. A. (2017). Empowering Cash Managers Through Compromise Programming. Financial Decision Aid Using Multiple Criteria, 149-173. doi:10.1007/978-3-319-68876-3_7Salas-Molina, F., Rodríguez-Aguilar, J. A., & Pla-Santamaria, D. (2018). Boundless multiobjective models for cash management. The Engineering Economist, 63(4), 363-381. doi:10.1080/0013791x.2018.1456596Srinivasan, V., & Kim, Y. H. (1986). Deterministic cash flow management: State of the art and research directions. Omega, 14(2), 145-166. doi:10.1016/0305-0483(86)90017-4Stone, B. K. (1972). The Use of Forecasts and Smoothing in Control-Limit Models for Cash Management. Financial Management, 1(1), 72. doi:10.2307/3664955Stone, B. K., & Miller, T. W. (1987). Daily Cash Forecasting with Multiplicative Models of Cash Flow Patterns. Financial Management, 16(4), 45. doi:10.2307/366610

A Process Oriented MCDM Approach to Construct a Circular Economy Composite Index

[EN] The purpose of this contribution is to develop a Circular Economy Composite indicator to benchmark EU countries performance. Europe is at the forefront of the global transition towards a sustainable and circular economy. To this end, the European Commission has launched in 2015 a Circular Economy Action Plan including a monitoring framework to measure progress and to assess the effectiveness of initiatives towards the circular economy in the European Union (EU) and Member States. Still, this monitoring framework lacks a composite indicator at the national level to aggregate the circular economy dimensions into a single summary indicator. Although there is a wide range of sustainability composite indicators, no aggregate circular economy index exits to this date. We use a multi-criteria approach to construct a circular economy composite index based on TOPSIS (Technique for Order Preferences by Similarity to Ideal Solutions) methodology. In addition, we introduce a novel aggregation methodology for building a composite indicator where different levels of compensability for the distances to the ideal and anti-ideal (or negative-ideal) values of each indicator are considered. In order to illustrate the advantages of this proposal, we have applied it to evaluate the Circular Economy performance of EU Member States for the year 2016. This proposal can be a valuable tool for identifying areas in which the countries need to concentrate their efforts to boost their circular economy performance.Garcia-Bernabeu, A.; Hilario Caballero, A.; Pla Santamaría, D.; Salas-Molina, F. (2020). A Process Oriented MCDM Approach to Construct a Circular Economy Composite Index. Sustainability. 12(2):1-14. https://doi.org/10.3390/su12020618S114122Genovese, A., Acquaye, A. A., Figueroa, A., & Koh, S. C. L. (2017). Sustainable supply chain management and the transition towards a circular economy: Evidence and some applications. Omega, 66, 344-357. doi:10.1016/j.omega.2015.05.015Di Maio, F., & Rem, P. C. (2015). A Robust Indicator for Promoting Circular Economy through Recycling. Journal of Environmental Protection, 06(10), 1095-1104. doi:10.4236/jep.2015.610096Geng, Y., Sarkis, J., Ulgiati, S., & Zhang, P. (2013). Measuring China’s Circular Economy. Science, 339(6127), 1526-1527. doi:10.1126/science.1227059Geng, Y., Fu, J., Sarkis, J., & Xue, B. (2012). Towards a national circular economy indicator system in China: an evaluation and critical analysis. Journal of Cleaner Production, 23(1), 216-224. doi:10.1016/j.jclepro.2011.07.005Elia, V., Gnoni, M. G., & Tornese, F. (2017). Measuring circular economy strategies through index methods: A critical analysis. Journal of Cleaner Production, 142, 2741-2751. doi:10.1016/j.jclepro.2016.10.196Huijbregts, M. A. J., Rombouts, L. J. A., Hellweg, S., Frischknecht, R., Hendriks, A. J., van de Meent, D., … Struijs, J. (2006). Is Cumulative Fossil Energy Demand a Useful Indicator for the Environmental Performance of Products? Environmental Science & Technology, 40(3), 641-648. doi:10.1021/es051689gBrown, M. T., & Ulgiati, S. (2004). Energy quality, emergy, and transformity: H.T. Odum’s contributions to quantifying and understanding systems. Ecological Modelling, 178(1-2), 201-213. doi:10.1016/j.ecolmodel.2004.03.002Rees, W. E. (1992). Ecological footprints and appropriated carrying capacity: what urban economics leaves out. Environment and Urbanization, 4(2), 121-130. doi:10.1177/095624789200400212Wiedmann, T., & Barrett, J. (2010). A Review of the Ecological Footprint Indicator—Perceptions and Methods. Sustainability, 2(6), 1645-1693. doi:10.3390/su2061645Narodoslawsky, M., & Krotscheck, C. (1995). The sustainable process index (SPI): evaluating processes according to environmental compatibility. Journal of Hazardous Materials, 41(2-3), 383-397. doi:10.1016/0304-3894(94)00114-vMunda, G. (2005). «Measuring Sustainability»: A Multi-Criterion Framework. Environment, Development and Sustainability, 7(1), 117-134. doi:10.1007/s10668-003-4713-0Janeiro, L., & Patel, M. K. (2015). Choosing sustainable technologies. Implications of the underlying sustainability paradigm in the decision-making process. Journal of Cleaner Production, 105, 438-446. doi:10.1016/j.jclepro.2014.01.029Diaz-Balteiro, L., González-Pachón, J., & Romero, C. (2017). Measuring systems sustainability with multi-criteria methods: A critical review. European Journal of Operational Research, 258(2), 607-616. doi:10.1016/j.ejor.2016.08.075Wilson, M. C., & Wu, J. (2016). The problems of weak sustainability and associated indicators. International Journal of Sustainable Development & World Ecology, 24(1), 44-51. doi:10.1080/13504509.2015.1136360Arrow, K. J., Chenery, H. B., Minhas, B. S., & Solow, R. M. (1961). Capital-Labor Substitution and Economic Efficiency. The Review of Economics and Statistics, 43(3), 225. doi:10.2307/1927286Blackorby, C., Donaldson, D., & Weymark, J. A. (1982). A normative approach to industrial-performance evaluation and concentration indices. European Economic Review, 19(1), 89-121. doi:10.1016/0014-2921(82)90007-1Rennings, K., Ludwig Brockmann, K., & Bergmann, H. (1997). Voluntary agreements in environmental protection: experiences in Germany and future perspectives. Business Strategy and the Environment, 6(5), 245-263. doi:10.1002/(sici)1099-0836(199711)6:53.0.co;2-fMathews, J. A., & Tan, H. (2016). Circular economy: Lessons from China. Nature, 531(7595), 440-442. doi:10.1038/531440aCherchye, L., Moesen, W., Rogge, N., Van Puyenbroeck, T., Saisana, M., Saltelli, A., … Tarantola, S. (2008). Creating composite indicators with DEA and robustness analysis: the case of the Technology Achievement Index. Journal of the Operational Research Society, 59(2), 239-251. doi:10.1057/palgrave.jors.2602445Giannetti, B. F., Bonilla, S. H., Silva, C. C., & Almeida, C. M. V. B. (2009). The reliability of experts’ opinions in constructing a composite environmental index: The case of ESI 2005. Journal of Environmental Management, 90(8), 2448-2459. doi:10.1016/j.jenvman.2008.12.018Makkonen, T., & van der Have, R. P. (2012). Benchmarking regional innovative performance: composite measures and direct innovation counts. Scientometrics, 94(1), 247-262. doi:10.1007/s11192-012-0753-2Mazziotta, M., & Pareto, A. (2015). On a Generalized Non-compensatory Composite Index for Measuring Socio-economic Phenomena. Social Indicators Research, 127(3), 983-1003. doi:10.1007/s11205-015-0998-2Greco, M., Mazziotta, M., & Pareto, A. (2016). A Composite Index to Measure the Italian «Enological Vocation». Agriculture and Agricultural Science Procedia, 8, 691-697. doi:10.1016/j.aaspro.2016.02.045Greco, S., Ishizaka, A., Tasiou, M., & Torrisi, G. (2018). On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness. Social Indicators Research, 141(1), 61-94. doi:10.1007/s11205-017-1832-9Attardi, R., Cerreta, M., Sannicandro, V., & Torre, C. M. (2018). Non-compensatory composite indicators for the evaluation of urban planning policy: The Land-Use Policy Efficiency Index (LUPEI). European Journal of Operational Research, 264(2), 491-507. doi:10.1016/j.ejor.2017.07.064Angilella, S., Catalfo, P., Corrente, S., Giarlotta, A., Greco, S., & Rizzo, M. (2018). Robust sustainable development assessment with composite indices aggregating interacting dimensions: The hierarchical-SMAA-Choquet integral approach. Knowledge-Based Systems, 158, 136-153. doi:10.1016/j.knosys.2018.05.041Greco, S., Ishizaka, A., Tasiou, M., & Torrisi, G. (2019). Sigma-Mu efficiency analysis: A methodology for evaluating units through composite indicators. European Journal of Operational Research, 278(3), 942-960. doi:10.1016/j.ejor.2019.04.012Ruiz, F., El Gibari, S., Cabello, J. M., & Gómez, T. (2020). MRP-WSCI: Multiple reference point based weak and strong composite indicators. Omega, 95, 102060. doi:10.1016/j.omega.2019.04.003Sands, G. R., & Podmore, T. H. (2000). A generalized environmental sustainability index for agricultural systems. Agriculture, Ecosystems & Environment, 79(1), 29-41. doi:10.1016/s0167-8809(99)00147-4Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234-281. doi:10.1016/0022-2496(77)90033-5Singh, R. K., Murty, H. R., Gupta, S. K., & Dikshit, A. K. (2007). Development of composite sustainability performance index for steel industry. Ecological Indicators, 7(3), 565-588. doi:10.1016/j.ecolind.2006.06.004Ülengin, B., Ülengin, F., & Güvenç, Ü. (2001). A multidimensional approach to urban quality of life: The case of Istanbul. European Journal of Operational Research, 130(2), 361-374. doi:10.1016/s0377-2217(00)00047-3Buckland, S. T., Studeny, A. C., Magurran, A. E., Illian, J. B., & Newson, S. E. (2011). The geometric mean of relative abundance indices: a biodiversity measure with a difference. Ecosphere, 2(9), art100. doi:10.1890/es11-00186.1El Gibari, S., Gómez, T., & Ruiz, F. (2018). Building composite indicators using multicriteria methods: a review. Journal of Business Economics, 89(1), 1-24. doi:10.1007/s11573-018-0902-zGan, X., Fernandez, I. C., Guo, J., Wilson, M., Zhao, Y., Zhou, B., & Wu, J. (2017). When to use what: Methods for weighting and aggregating sustainability indicators. Ecological Indicators, 81, 491-502. doi:10.1016/j.ecolind.2017.05.068Li, H., Bao, W., Xiu, C., Zhang, Y., & Xu, H. (2010). Energy conservation and circular economy in China’s process industries. Energy, 35(11), 4273-4281. doi:10.1016/j.energy.2009.04.02

A Compact Representation of Preferences in Multiple Criteria Optimization Problems

[EN] A critical step in multiple criteria optimization is setting the preferences for all the criteria under consideration. Several methodologies have been proposed to compute the relative priority of criteria when preference relations can be expressed either by ordinal or by cardinal information. The analytic hierarchy process introduces relative priority levels and cardinal preferences. Lexicographical orders combine both ordinal and cardinal preferences and present the additional difficulty of establishing strict priority levels. To enhance the process of setting preferences, we propose a compact representation that subsumes the most common preference schemes in a single algebraic object. We use this representation to discuss the main properties of preferences within the context of multiple criteria optimization.Salas-Molina, F.; Pla Santamaría, D.; Garcia-Bernabeu, A.; Reig-Mullor, J. (2019). A Compact Representation of Preferences in Multiple Criteria Optimization Problems. Mathematics. 7(11):1-16. https://doi.org/10.3390/math7111092S116711Ahmadi, A., Ahmadi, M. R., & Nezhad, A. E. (2014). A Lexicographic Optimization and Augmented ϵ-constraint Technique for Short-term Environmental/Economic Combined Heat and Power Scheduling. Electric Power Components and Systems, 42(9), 945-958. doi:10.1080/15325008.2014.903542González-Arteaga, T., Alcantud, J. C. R., & de Andrés Calle, R. (2016). A new consensus ranking approach for correlated ordinal information based on Mahalanobis distance. Information Sciences, 372, 546-564. doi:10.1016/j.ins.2016.08.071Miettinen, K., & M�kel�, M. M. (2002). On scalarizing functions in multiobjective optimization. OR Spectrum, 24(2), 193-213. doi:10.1007/s00291-001-0092-9Ignizio, J. P. (1983). Generalized goal programming An overview. Computers & Operations Research, 10(4), 277-289. doi:10.1016/0305-0548(83)90003-5Sitorus, F., Cilliers, J. J., & Brito-Parada, P. R. (2019). Multi-criteria decision making for the choice problem in mining and mineral processing: Applications and trends. Expert Systems with Applications, 121, 393-417. doi:10.1016/j.eswa.2018.12.001Zyoud, S. H., & Fuchs-Hanusch, D. (2017). A bibliometric-based survey on AHP and TOPSIS techniques. Expert Systems with Applications, 78, 158-181. doi:10.1016/j.eswa.2017.02.016Erdoğan, M., & Kaya, İ. (2016). A combined fuzzy approach to determine the best region for a nuclear power plant in Turkey. Applied Soft Computing, 39, 84-93. doi:10.1016/j.asoc.2015.11.013Chen, Y., Liu, R., Barrett, D., Gao, L., Zhou, M., Renzullo, L., & Emelyanova, I. (2015). A spatial assessment framework for evaluating flood risk under extreme climates. Science of The Total Environment, 538, 512-523. doi:10.1016/j.scitotenv.2015.08.094Zammori, F. (2010). The analytic hierarchy and network processes: Applications to the US presidential election and to the market share of ski equipment in Italy. Applied Soft Computing, 10(4), 1001-1012. doi:10.1016/j.asoc.2009.07.013Carter, C. R., & Rogers, D. S. (2008). A framework of sustainable supply chain management: moving toward new theory. International Journal of Physical Distribution & Logistics Management, 38(5), 360-387. doi:10.1108/09600030810882816Ignizio, J. P. (1976). An Approach to the Capital Budgeting Problem with Multiple Objectives. The Engineering Economist, 21(4), 259-272. doi:10.1080/00137917608902798Lonergan, S. C., & Cocklin, C. (1988). The use of lexicographic goal programming in economic/ecolocical conflict analysis. Socio-Economic Planning Sciences, 22(2), 83-92. doi:10.1016/0038-0121(88)90020-1González-Pachón, J., & Romero, C. (2014). Properties underlying a preference aggregator based on satisficing logic. International Transactions in Operational Research, 22(2), 205-215. doi:10.1111/itor.1211

Teaching operations management to lawyers

[EN] This paper deals with a problem present in most Master in Business Administration (MBA) classrooms. This problem is the heterogeneity of students and their backgrounds. Different backgrounds pose a challenge to teachers that require the use of quantitative techniques such as optimization within an Operations Management subject. Engineers and other students with mathematical training probably get bored if the level is too low. On the contrary, Lawyers and other students without technical background usually find contents cumbersome. This paper aims to find a compromise between the heterogeneity of backgrounds and the fulfilment of learning objectives for students of Operations Management in an MBA. To this end, we propose a methodology to support the selection of teaching methods from a multiobjective perspective. The results derived from this methodology enable professors to consider their particular preferences and to integrate important decision-making principles by selecting the appropriate distance function to an ideal point that acts as a reference.Salas-Molina, F.; Vercher-Ferrandiz, M.; Pla Santamaría, D.; Garcia-Bernabeu, A. (2022). Teaching operations management to lawyers. IATED. 9293-9299. https://doi.org/10.21125/edulearn.2022.22399293929

Changes in serum metabolomics in idiopathic pulmonary fibrosis and effect of approved antifibrotic medication

Idiopathic pulmonary fibrosis (IPF) is a progressive disease with significant mortality and morbidity. Approval of antifibrotic therapy has ameliorated disease progression, but therapy response is heterogeneous and to date, adequate biomarkers predicting therapy response are lacking. In recent years metabolomic technology has improved and is broadly applied in cancer research thus enabling its use in other fields. Recently both aberrant metabolic and lipidomic pathways have been described to influence profibrotic responses. We thus aimed to characterize the metabolomic and lipidomic changes between IPF and healthy volunteers (HV) and analyze metabolomic changes following treatment with nintedanib and pirfenidone. We collected serial serum samples from two IPF cohorts from Germany (n = 122) and Spain (n = 21) and additionally age-matched healthy volunteers (n = 16). Metabolomic analysis of 630 metabolites covering 14 small molecule and 12 different lipid classes was carried out using flow injection analysis tandem mass spectrometry for lipids and liquid chromatography tandem mass spectrometry for small molecules. Levels were correlated with survival and disease severity. We identified 109 deregulated analytes in IPF compared to HV in cohort 1 and 112 deregulated analytes in cohort 2. Metabolites which were up-regulated in both cohorts were mainly triglycerides while the main class of down-regulated metabolites were phosphatidylcholines. Only a minority of de-regulated analytes were small molecules. Triglyceride subclasses were inversely correlated with baseline disease severity (GAP-score) and a clinical compound endpoint of lung function decline or death. No changes in the metabolic profiles were observed following treatment with pirfenidone. Nintedanib treatment induced up-regulation of triglycerides and phosphatidylcholines. Patients in whom an increase in these metabolites was observed showed a trend towards better survival using the 2-years composite endpoint (HR 2.46, p = 0.06). In conclusion, we report major changes in metabolites in two independent cohorts testing a large number of patients. Specific lipidic metabolite signatures may serve as biomarkers for disease progression or favorable treatment response to nintedanib

Metal content determination in biodiesel samples by microwave mineralization and ICP-AES

El trabajo comprende la puesta a punto de un método de digestión, mediante calentamiento de microondas, de muestras de biodiesel obtenidas mediante catálisis homogénea de aceites vegetales, para la determinación de 20 elementos mediante ICP-AES

Teaching case 3-2019: Are nuclear clefts or invaginations the niche of intranuclear inclusions in FTLD-TDP?

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