A new Eliomys from the Upper Miocene of Spain and its implications for the phylogeny of genus

In this paper, we describe a previously unknown species of the glirid Eliomys from the Late Miocene and Early Pliocene Cabriel, Alcoy and Granada basins of southeastern Spain. Eliomys yevesi sp. nov. is characterized by its relative small size, narrow lingual wall and common presence of two centrolophs in the upper molars, and well-developed centrolophids in the lower molars. The new species is the probable ancestor of E. intermedius, which in turn represents the ancestor of the extant E. quercinus. According to its morphologic and biometric features, the origin of E. yevesi sp. nov. is likely to be found in some population of E. truci from the Late Miocene. Based on these affinities, we propose the lineage E. truci–E. yevesi sp. nov.–E. intermedius–E. quercinus, in which there is a trend towards the development of centrolophs, as well as the reduction of accessory crests

A Multi-Criteria Reference Point Based Approach for Assessing Regional Innovation Performance in Spain

[EN] The evaluation of regional innovation performance through composite innovation indices can serve as a valuable tool for policy-making. While discussion on the best methodology to construct composite innovation indices continues, we are interested in deepening the use of reference levels and the aggregation issue. So far, additive aggregation methods are, largely, the most widespread aggregation rule, thus allowing for full compensability among single indicators. In this paper, we present an integrated assessment methodology to evaluate regional innovation performance using the Multi-Reference Point based Weak and Strong Composite Indicator (MRP-WSCI) approach, which allows defining reference levels and different degrees of compensability. As an example of application to the Regional Innovation Scoreboard, the proposed technique is developed to measure the innovation performance of Spain¿s regions taking into account Spanish and European reference levels. The main features of the proposed approach are: (i) absolute or relative reference levels could be previously defined by the decision maker; (ii) by establishing the reference levels, the resulting composite innovation index is an easy-to-interpret measure; and (iii) the non-compensatory strong composite indicator provides an additional layer of information for policy-making (iv) a visualization tool called Light-Diagram is proposed to track the specific strengths and weaknesses of the regions' innovation performance.This research has been partially supported by the Spanish Ministry of Economy and Competitiveness (Project ECO2016-76567-C4-4-R), by the Regional Government of Andalucia (research group SEJ-417), and by the ERDF funds (Project UMA18-FEDERJA-065).Garcia-Bernabeu, A.; Cabello, JM.; Ruiz, F. (2020). A Multi-Criteria Reference Point Based Approach for Assessing Regional Innovation Performance in Spain. Mathematics. 8(5):1-21. https://doi.org/10.3390/math8050797S12185Hauser, C., Siller, M., Schatzer, T., Walde, J., & Tappeiner, G. (2018). Measuring regional innovation: A critical inspection of the ability of single indicators to shape technological change. Technological Forecasting and Social Change, 129, 43-55. doi:10.1016/j.techfore.2017.10.019Makkonen, 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/s11192-012-0753-2Asheim, B. T., Smith, H. L., & Oughton, C. (2011). Regional Innovation Systems: Theory, Empirics and Policy. Regional Studies, 45(7), 875-891. doi:10.1080/00343404.2011.596701Buesa, M., Heijs, J., & Baumert, T. (2010). The determinants of regional innovation in Europe: A combined factorial and regression knowledge production function approach. Research Policy, 39(6), 722-735. doi:10.1016/j.respol.2010.02.016Di Cagno, D., Fabrizi, A., Meliciani, V., & Wanzenböck, I. (2016). The impact of relational spillovers from joint research projects on knowledge creation across European regions. Technological Forecasting and Social Change, 108, 83-94. doi:10.1016/j.techfore.2016.04.021Capello, R., & Lenzi, C. (2012). Territorial patterns of innovation: a taxonomy of innovative regions in Europe. The Annals of Regional Science, 51(1), 119-154. doi:10.1007/s00168-012-0539-8Navarro, M., Gibaja, J. J., Bilbao-Osorio, B., & Aguado, R. (2009). Patterns of Innovation in EU-25 Regions: A Typology and Policy Recommendations. Environment and Planning C: Government and Policy, 27(5), 815-840. doi:10.1068/c0884rPinto, H. (2009). The Diversity of Innovation in the European Union: Mapping Latent Dimensions and Regional Profiles. European Planning Studies, 17(2), 303-326. doi:10.1080/09654310802553571Ruiz, 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.2019.04.003Hollenstein, H. (1996). A composite indicator of a firm’s innovativeness. An empirical analysis based on survey data for Swiss manufacturing. Research Policy, 25(4), 633-645. doi:10.1016/0048-7333(95)00874-8Gu *, W., & Tang, J. (2004). Link between innovation and productivity in Canadian manufacturing industries. Economics of Innovation and New Technology, 13(7), 671-686. doi:10.1080/1043890410001686806Tang, J., & Le, C. D. (2007). Multidimensional Innovation and Productivity. Economics of Innovation and New Technology, 16(7), 501-516. doi:10.1080/10438590600914585Kumar, S., Haleem, A., & Sushil. (2019). Assessing innovativeness of manufacturing firms using an intuitionistic fuzzy based MCDM framework. Benchmarking: An International Journal, 26(6), 1823-1844. doi:10.1108/bij-12-2017-0343Grupp, H., & Mogee, M. E. (2004). Indicators for national science and technology policy: how robust are composite indicators? Research Policy, 33(9), 1373-1384. doi:10.1016/j.respol.2004.09.007Schibany, A., & Streicher, G. (2008). The European Innovation Scoreboard: drowning by numbers? Science and Public Policy, 35(10), 717-732. doi:10.3152/030234208x398512Kozłowski, J. (2015). Innovation indices: the need for positioning them where they properly belong. Scientometrics, 104(3), 609-628. doi:10.1007/s11192-015-1632-4Carayannis, E. G., Goletsis, Y., & Grigoroudis, E. (2018). Composite innovation metrics: MCDA and the Quadruple Innovation Helix framework. Technological Forecasting and Social Change, 131, 4-17. doi:10.1016/j.techfore.2017.03.008Greco, 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/s11205-017-1832-9El 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-zRuiz, F., Cabello, J. M., & Luque, M. (2011). An application of reference point techniques to the calculation of synthetic sustainability indicators. Journal of the Operational Research Society, 62(1), 189-197. doi:10.1057/jors.2009.187Cabello, J. M., Ruiz, F., Pérez-Gladish, B., & Méndez-Rodríguez, P. (2014). Synthetic indicators of mutual funds’ environmental responsibility: An application of the Reference Point Method. European Journal of Operational Research, 236(1), 313-325. doi:10.1016/j.ejor.2013.11.031Ruiz, F., Cabello, J. M., & Pérez-Gladish, B. (2018). Building Ease-of-Doing-Business synthetic indicators using a double reference point approach. Technological Forecasting and Social Change, 131, 130-140. doi:10.1016/j.techfore.2017.06.005El Gibari, S., Gómez, T., & Ruiz, F. (2018). Evaluating university performance using reference point based composite indicators. Journal of Informetrics, 12(4), 1235-1250. doi:10.1016/j.joi.2018.10.003Mazziotta, M., & Pareto, A. (2017). Measuring Well-Being Over Time: The Adjusted Mazziotta–Pareto Index Versus Other Non-compensatory Indices. Social Indicators Research, 136(3), 967-976. doi:10.1007/s11205-017-1577-5Munda, G., & Nardo, M. (2009). Noncompensatory/nonlinear composite indicators for ranking countries: a defensible setting. Applied Economics, 41(12), 1513-1523. doi:10.1080/00036840601019364Cabello, J. M., Navarro, E., Prieto, F., Rodríguez, B., & Ruiz, F. (2014). Multicriteria development of synthetic indicators of the environmental profile of the Spanish regions. Ecological Indicators, 39, 10-23. doi:10.1016/j.ecolind.2013.11.01

A Reference Point-Based Proposal to Build Regional Quality of Life Composite Indicators

[EN] There is a growing interest in research on the role that space plays in defining and measuring well-being or quality of life. In this paper, we propose to evaluate the regional quality of life using the Multi-Reference Point based Weak Strong Composite Indicator approach, to further enhance the quality of the sub-national analysis. The major motivation is to facilitate assessing the regional quality of life performance at different geographical scales and compensability levels. As an example of application, we compute the composite indicators for 19 Spanish regions to paint a comprehensive picture of the regional quality of life using two different geographical scales: the Spanish and the European ones. Moreover, we provide warning signals to regional, national and European policy-makers on the quality of life dimensions in which each region needs further improvements.This research was partially funded by the Spanish Ministry of Economy and Competitiveness (Project PID2019-104263RB-C42), from the Regional Government of Andalucía (Project P18-RT-1566), and by the EU ERDF operative program (Project UMA18-FEDERJA-065)Garcia-Bernabeu, A.; Cabello, JM.; Ruiz, F. (2021). A Reference Point-Based Proposal to Build Regional Quality of Life Composite Indicators. Social Indicators Research (Online). 1-20. https://doi.org/10.1007/s11205-021-02818-0S120Blancas, F., Caballero, R., González, M., Lozano-Oyola, M., & Pérez, F. (2010). Goal programming synthetic indicators: An application for sustainable tourism in andalusian coastal counties. Ecological Economics, 69(11), 2158–2172.Boggia, A., Massei, G., Pace, E., Rocchi, L., Paolotti, L., & Attard, M. (2018). Spatial multicriteria analysis for sustainability assessment: A new model for decision making. Land Use Policy, 71, 281–292.Booysen, F. (2002). An overview and evaluation of composite indices of development. Social Indicators Research, 59(2), 115–151.Cabello, J. M., Ruiz, F., Pérez-Gladish, B., & Méndez-Rodríguez, P. (2014). Synthetic indicators of mutual fund’s environmental responsibility: An application of the Reference Point Method. European Journal of Operational Research, 236(1), 313–325.Costa, D. S. (2015). Reflective, causal, and composite indicators of quality of life: A conceptual or an empirical distinction? Quality of Life Research, 24(9), 2057–2065.Durand, M. (2015). The OCDE better life initiative: How’s life? and the measurement of well-being. Review of Income and Wealth, 61(1), 4–17.El Gibari, S., Cabello, J. M., Gómez, T., & Ruiz, F. (2021). Composite indicators as decision making tools: The joint use of compensatory and non-compensatory schemes. International Journal of Information Technology and Decision Making, 20(3), 847–879.El Gibari, S., Gómez, T., & Ruiz, F. (2018). Evaluating university performance using reference point based composite indicators. Journal of Informetrics, 12(4), 1235–1250.El Gibari, S., Gómez, T., & Ruiz, F. (2019). Building composite indicators using multicriteria methods: A review. Journal of Business Economics, 89(1), 1–24.European Commission: Eurostat quality of life database. (2020). url http://ec.europa.eu/eurostat/data/database.Freudenberg, M. (2003). Composite indicators of country performance.Garcia-Bernabeu, A., Cabello, J. M., & Ruiz, F. (2020). A multi-criteria reference point based approach for assessing regional innovation performance in Spain. Mathematics, 8(5), 797.Goerlich, F. J., & Reig, E. (2021). Quality of life ranking of spanish cities: A non-compensatory approach. Cities, 109, 102979.Greco, 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, 61–94.Greyling, T., & Tregenna, F. (2017). Construction and analysis of a composite quality of life index for a region of South Africa. Social Indicators Research, 131(3), 887–930.Hagerty, M. R., Cummins, R., Ferriss, A. L., Land, K., Michalos, A. C., Peterson, M., et al. (2001). Quality of life indexes for national policy: Review and agenda for research. Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique, 71(1), 58–78.INE: Indicadores de calidad de vida. (2020). url https://cutt.ly/Zj0L0qX.Ivaldi, E., Bonatti, G., Soliani, R., et al. (2014). Composite index for quality of life in italian cities: An application to urbes indicators. Review of Economics and Finance, 4(4)Karagiannis, R., & Karagiannis, G. (2020). Constructing composite indicators with shannon entropy: The case of human development index. Socio-Economic Planning Sciences, 70, 100701.Lagas, P., van Dongen, F., van Rijn, F., & Visser, H. (2015). Regional quality of living in Europe. Region, 2(2), 1–26.Malkina-Pykh, I. G., & Pykh, Y. A. (2008). Quality-of-life indicators at different scales: Theoretical background. Ecological Indicators, 8(6), 854–862.Marchante, A. J., & Ortega, B. (2006). Quality of life and economic convergence across Spanish regions, 1980–2001. Regional Studies, 40(5), 471–483.Mazziotta, M., & Pareto, A. (2016). On a generalized non-compensatory composite index for measuring socio-economic phenomena. Social Indicators Research, 127(3), 983–1003.Mazziotta, M., & Pareto, A. (2020). Composite indices construction: The performance interval approach. Social Indicators Research pp. 1–11.Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., Hoffman, A., & Giovannini, E. (2008). Handbook on constructing composite indicators.OECD: Handbook on constructing composite indicators: methodology and user guide. (2008). Paris: OECD publishing.Patil, G.R., & Sharma, G. (2020). Urban quality of life: An assessment and ranking for indian cities. Transport Policy.Royuela, V., Suriñach, J., & Reyes, M. (2003). Measuring quality of life in small areas over different periods of time. Social Indicators Research, 64(1), 51–74.Ruiz, F., Cabello, J. M., & Luque, M. (2011). An application of reference point techniques to the calculation of synthetic sustainability indicators. Journal of the Operational Research Society, 62(1), 189–197.Ruiz, F., Cabello, J. M., & Pérez-Gladish, B. (2018). Building ease-of-doing-business synthetic indicators using a double reference point approach. Technological Forecasting and Social Change, 131, 130–140.Ruiz, F., El Gibari, S., Cabello, J.M., & Gómez, T. (2019). MRP-WSCI: Multiple reference point based weak and strong composite indicators. Omega.Saisana, M., & Tarantola, S. (2002). State-of-the-art report on current methodologies and practices for composite indicator development. Ispra: Joint Research Centre.Stiglitz, J.E., Sen, A., Fitoussi, J.P., et al. (2009). Report by the commission on the measurement of economic performance and social progress

Estimation of the Interaction Forces in a Compliant pHRI Gripper

Physical human–robot interaction (pHRI) is an essential skill for robots expected to work with humans, such as assistive or rescue robots. However, due to hard safety and compliance constraints, pHRI is still underdeveloped in practice. Tactile sensing is vital for pHRI, as constant occlusions while grasping make it hard to rely on vision or range sensors alone. More specifically, measuring interaction forces in the gripper is crucial to avoid injuries, predict user intention and perform successful collaborative movements. This work exploits the inherent compliance of a gripper with four underactuated fingers which was previously designed by the authors and designed to manipulate human limbs. A new analytical model is proposed to calculate the external interaction forces by combining all finger forces, which are estimated by using the gripper proprioceptive sensor readings uniquely. An experimental evaluation of the method and an example application in a control system with active compliance have been included to evaluate performance. The results prove that the proposed finger arrangement offers good performance at measuring the lateral interaction forces and torque around the gripper’s Z-axis, providing a convenient and efficient way of implementing adaptive and compliant grasping for pHRI applications.This work was supported by the Universidad de Málaga, project UMA20-FEDERJA-052. Partial funding for open access charge: Universidad de Málag

Desarrollo Empresarial y Capital Humano en la región Sureste de Coahuila

El presente trabajo tiene como objetivo determinar si hay un efecto significativo entre capital humano y desarrollo empresarial en la región sureste del Estado de Coahuila, México. Se aplicó un instrumento a 243 personas, utilizando el software de AMOS PASS, en el cual se contemplaron los indicadores del capital humano y desarrollo empresarial, de acuerdo con la hipótesis las relaciones encontradas entre las varianzas de los constructos indican que existe un valor estadístico significativo que relaciona a estos constructos. En los resultados se encontró que algunas de las variables fueron: la consideración de pertenencia a la empresa, la percepción de cómo los ve la empresa en función de los conocimientos, y, el reconocimiento de que es el capital humano incrementado por parte de la empresa, demostrando que reconoce este valor como gestor del desarrollo empresarial. De esta manera el capital humano se constituye en uno de los principales factores intervinientes en el desarrollo empresarial. The present work determines if there is a significant effect between human capital and business development in the southeastern region of the State of Coahuila, Mexico. An instrument was applied to 243 people, using the AMOS PASS software, in which the indicators of human capital and business development were considered, according to the hypothesis, the relationships found between the variances of the constructs indicate that there is a significant statistical value that relates to these constructs. In the results it was found that some of the variables were: the consideration of belonging to the company, the perception of how the company sees them based on knowledge, and, the recognition that it is the human capital increased by the company, demonstrating that it recognizes this value as a manager of business development. In this way, human capital becomes one of the main factors involved in business development

New design parameters for biparabolic beach profiles (SW Cadiz, Spain)

165 profiles of seventy-one beaches along the Gulf of Cadiz (SW, Spain) were studied to improve the formulation of the beach profile in tidal seas. Maritime climate, degree of energy exposure and size of the sand grains were taken into account to study the two sections of the biparabolic profile. The objective of the study was the determination of more accurate formulations of the design parameters for the equilibrium profile that involves tidal seas. These formulations were modelled and validated based on existing profiles to quantify the error existing between the real profile and the modelling. This comparative analysis was extended by considering the formulations proposed by other authors. The best results were obtained with the proposal presented herein