European countries’ vulnerability to COVID-19: multicriteria decision-making techniques

COVID-19 has triggered an unprecedented health crisis, crippling economic activity around the world. The aim of this paper is to analyse European countries’ vulnerability to the associated consequences. The analysis will focus on three areas that a priori are expected to be most severely affected by the pandemic – health, society and work – examining the possible relationship with countries’ wealth. The multicriteria decision-making Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) will be used to generate a ranking of countries based on criteria that define each of these three areas. The findings will provide authorities with quantitative information to guide their aid policies. The results show that Eastern European countries should direct their resources towards addressing health-related and social issues. Conversely, those that have higher GDP per capita and that have been hardest hit by coronavirus will have to make changes to their labour systems in order to minimize the fallout

Emerging Countries as the Main Destinations for European Value-Added Exports

Nowadays, production chains may cross the borders of several continents in search of greater profitability. In order to more accurately calculate countries’ foreign demand, value-added exports should be used rather than gross exports. This study takes the value-added exports calculated for European Union countries and uses extended gravity models to analyze the determinants of this trade, differentiating between countries according to the main destinations for their value-added, USA, Russia and China. The results reveal certain changes according to the economic period analyzed and the destination of the goods, with respect to key variables such as the wealth of the exporting country, the level of logistics performance and distance. In 2014, China registered an improvement in its position compared to Russia

Efficiency of airlines: Hub and Spoke versus Point-to-Point

Purpose – The purpose of this paper is to study the efficiency and financial situation of Spanish airlines by conducting a comparative analysis of those operating in hubs and those that employ the point-to-point system. Design/methodology/approach – Data envelopment analysis and accounting rates are implemented to do so. Findings – The results show that hubs do not result in the companies that use them being efficient. Instead, it is the charter, low-cost and private flight operators that best manage their resources. Originality/value – The study makes a novel contribution to the literature, as there has been no research on Spanish airlines that compares the two types of operators (hubs and point to point).Martí Selva, ML.; Puertas Medina, RM.; Calafat Marzal, MC. (2015). Efficiency of airlines: Hub and Spoke versus Point-to-Point. Journal of Economic Studies. 42(1):157-166. doi:10.1108/JES-07-2013-0095S157166421Salem Al‐Eraqi, A., Mustafa, A., & Tajudin Khader, A. (2010). An extended DEA windows analysis: Middle East and East African seaports. Journal of Economic Studies, 37(2), 208-218. doi:10.1108/01443581011043591Alarcon, S. (2008), “Endeudamientos y eficiencia en las empresas agrarias”,Revista Española de Financiación y Contabilidad, Vol. 37 No. 138, pp. 211-230.Alderighi, M., Cento, A., Nijkamp, P., & Rietveld, P. (2005). Network competition—the coexistence of hub-and-spoke and point-to-point systems. Journal of Air Transport Management, 11(5), 328-334. doi:10.1016/j.jairtraman.2005.07.006Assaf, A. G., & Josiassen, A. (2011). The operational performance of UK airlines: 2002‐2007. Journal of Economic Studies, 38(1), 5-16. doi:10.1108/01443581111096114Assaf, A. G., & Josiassen, A. (2012). European vs. U.S. airlines: Performance comparison in a dynamic market. Tourism Management, 33(2), 317-326. doi:10.1016/j.tourman.2011.03.012Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30(9), 1078-1092. doi:10.1287/mnsc.30.9.1078Campa, F. and Campa, R. (2009), “Estructuras de oferta en transporte aéreo: modelos punto a punto y de red”,Harvard Deusto Business Review, No. 179, pp. 42-50.Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. doi:10.1016/0377-2217(78)90138-8Coto-Millan, P. , Inglada, V. and Rodriguez-Alvarez, A. (1999), “Economic and technical efficiency in the world air industry”,International Journal of Transport Economics, Vol. 26 No. 2, pp. 119-235.Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253. doi:10.2307/2343100Garcia, J. (2005), “El nuevo modelo aeroportuario europeo: más competencia, mejor regulación”,Universia Business Review, Vol. 4 No. 8, pp. 86-102.Goncharuk, A. G. (2007). Impact of political changes on industrial efficiency: a case of Ukraine. Journal of Economic Studies, 34(4), 324-340. doi:10.1108/01443580710817443Good, D. H., Röller, L.-H., & Sickles, R. C. (1995). Airline efficiency differences between Europe and the US: Implications for the pace of EC integration and domestic regulation. European Journal of Operational Research, 80(3), 508-518. doi:10.1016/0377-2217(94)00134-xHendricks, K., Piccione, M., & Tan, G. (1997). Entry and Exit in Hub-Spoke Networks. The RAND Journal of Economics, 28(2), 291. doi:10.2307/2555806Mulwa, M. R., Emrouznejad, A., & Murithi, F. M. (2009). Impact of liberalization on efficiency and productivity of sugar industry in Kenya. Journal of Economic Studies, 36(3), 250-264. doi:10.1108/01443580910983843Sellers, R. and Mas, F.J. (2009), “Determinantes de la eficiencia en el canal de distribución: análisis en agencias de viajes”,Revista Española de Investigación de Marketing ESIC, Vol. 13 No. 1, pp. 97-115.Zhang, B. , Wang, J. , Liu, C. and Zhao, Y. (2012), “Evaluating the technical efficiency of Chinese airport airside activities”,Journal of Air Transport Management, Vol. 20, May, pp. 23-27.Zhang, T., & Garvey, E. (2008). A comparative analysis of multi-output frontier models. Journal of Zhejiang University-SCIENCE A, 9(10), 1426-1436. doi:10.1631/jzus.a082012

Acute DOB and PMA Administration Impairs Motor and Sensorimotor Responses in Mice and Causes Hallucinogenic Effects in Adult Zebrafish

The drastic increase in hallucinogenic compounds in illicit drug markets of new psychoactive substances (NPS) is a worldwide threat. Among these, 2, 5-dimetoxy-4-bromo-amphetamine (DOB) and paramethoxyamphetamine (PMA; marketed as "ecstasy") are frequently purchased on the dark web and consumed for recreational purposes during rave/dance parties. In fact, these two substances seem to induce the same effects as MDMA, which could be due to their structural similarities. According to users, DOB and PMA share the same euphoric effects: increasing of the mental state, increasing sociability and empathy. Users also experienced loss of memory, temporal distortion, and paranoia following the repetition of the same thought. The aim of this study was to investigate the effect of the acute systemic administration of DOB and PMA (0.01-30 mg/kg; i.p.) on motor, sensorimotor (visual, acoustic, and tactile), and startle/PPI responses in CD-1 male mice. Moreover, the pro-psychedelic effect of DOB (0.075-2 mg/kg) and PMA (0.0005-0.5 mg/kg) was investigated by using zebrafish as a model. DOB and PMA administration affected spontaneous locomotion and impaired behaviors and startle/PPI responses in mice. In addition, the two compounds promoted hallucinatory states in zebrafish by reducing the hallucinatory score and swimming activity in hallucinogen-like states

Medidas sanitarias y fitosanitarias en las importaciones agroalimentarias de la Unión Europea: los efectos reputación a lo largo del tiempo

[EN] The EU’s Rapid Alert System for Food and Feed provides information on sanitary and phytosanitary (SPS) notifications. With a set of data from the 1998-2013 period, we test the hypothesis that past notifications can determine current notifications. This is the “reputation effect”, meaning that inspectors may tend to target products or countries with previous SPS problems. We analyze the scope of the reputation effect over time. We used two count data models to estimate the distribution of current notifications. In line with previous literature, our findings indicate that reputation does affect current EU notifications. Furthermore, we identify some relevant exporter countries for which reputation is long-lasting.[ES] El sistema de alerta rápida para alimentos de la UE informa sobre notificaciones sanitarias y fitosanitarias. Con datos del periodo 1998-2013, se comprueba la hipótesis de si notificaciones pasadas afectan a las notificaciones presentes. Se trata del efecto reputación, que implica que los inspectores pue-den dirigir sus inspecciones a productos o países que hayan tenido previamente problemas sanitarios y fi-tosanitarios; también se analiza el alcance temporal de la reputación. Se utilizan dos modelos de recuento para estimar la distribución de las notificaciones actuales. Los resultados muestran que la reputación in-fluye en las notificaciones actuales de la UE. Además, se identifican varios exportadores relevantes para los que la reputación tiene un efecto duraderoThe authors are grateful for the comments of two anonymous referees on a previous draft of this paper. V. Martinez-Gomez and L. Marti acknowledge the financial support of the Generalitat Valenciana, project GV/2015/073.Taghouti, I.; Martinez Gómez, VD.; Marti, L. (2017). Sanitary and Phytosanitary measures in agri-food imports from the European Union: Reputation effects over time. Economía Agraria y Recursos Naturales - Agricultural and Resource Economics. 16(2):69-88. https://doi.org/10.7201/earn.2016.02.03SWORD698816

Vehiculation of active principles as a way to create smart and biofunctional textiles

In some specific fields of application (e.g., cosmetics, pharmacy), textile substrates need to incorporate sensible molecules (active principles) that can be affected if they are sprayed freely on the surface of fabrics. The effect is not controlled and sometimes this application is consequently neglected. Microencapsulation and functionalization using biocompatible vehicles and polymers has recently been demonstrated as an interesting way to avoid these problems. The use of defined structures (polymers) that protect the active principle allows controlled drug delivery and regulation of the dosing in every specific case. Many authors have studied the use of three different methodologies to incorporate active principles into textile substrates, and assessed their quantitative behavior. Citronella oil, as a natural insect repellent, has been vehicularized with two different protective substances; cyclodextrine (CD), which forms complexes with it, and microcapsules of gelatin-arabic gum. The retention capability of the complexes and microcapsules has been assessed using an in vitro experiment. Structural characteristics have been evaluated using thermogravimetric methods and microscopy. The results show very interesting long-term capability of dosing and promising applications for home use and on clothes in environmental conditions with the need to fight against insects. Ethyl hexyl methoxycinnamate (EHMC) and gallic acid (GA) have both been vehicularized using two liposomic-based structures: Internal wool lipids (IWL) and phosphatidylcholine (PC). They were applied on polyamide and cotton substrates and the delivery assessed. The amount of active principle in the different layers of skin was determined in vitro using a Franz-cell diffusion chamber. The results show many new possibilities for application in skin therapeutics. Biofunctional devices with controlled functionality can be built using textile substrates and vehicles. As has been demonstrated, their behavior can be assessed using in vitro methods that make extrapolation to their final applications possiblePostprint (published version

Sustainability in universities: DEA-Greenmetric

[EN] Many universities are currently doing important work not only on environmental issues, but also on social and economic matters, thereby covering the three dimensions of sustainability. This paper uses Data Envelopment Analysis to construct a synthetic indicator based on the variables that make up the UI GreenMetric. The aim is to quantify universities' contribution to sustainability, rank all the campuses accordingly, and evaluate specific aspects of their related institutional policies. First, cluster analysis is applied, yielding four homogeneous groups of universities. DEA is then applied to these clusters in order to construct the synthetic indicator. The proposed indicator, DEA-GreenMetric, reveals that the US and the UK are the countries that are home to the greatest number of universities actively involved in all aspects of sustainability. In addition, this new index provides a complete ranking of universities, circumventing the issue of the duplicate scores assigned by UI GreenMetric. Lastly, it can be seen that greater efforts are required for universities to improve their performance relating to environmental variables (energy, water use and waste treatment) than to make improvements in infrastructure, transport or education.Puertas Medina, RM.; Martí Selva, ML. (2019). Sustainability in universities: DEA-Greenmetric. Sustainability. 11(14):1-17. https://doi.org/10.3390/su11143766S1171114Kuhlman, T., & Farrington, J. (2010). What is Sustainability? Sustainability, 2(11), 3436-3448. doi:10.3390/su2113436Castellani, V., & Sala, S. (2010). Sustainable performance index for tourism policy development. Tourism Management, 31(6), 871-880. doi:10.1016/j.tourman.2009.10.001Togtokh, C. (2011). Time to stop celebrating the polluters. Nature, 479(7373), 269-269. doi:10.1038/479269aSharp, L. (2002). Green campuses: the road from little victories to systemic transformation. International Journal of Sustainability in Higher Education, 3(2), 128-145. doi:10.1108/14676370210422357Shriberg, M. (2002). Institutional assessment tools for sustainability in higher education. International Journal of Sustainability in Higher Education, 3(3), 254-270. doi:10.1108/14676370210434714Balsas, C. J. . (2003). Sustainable transportation planning on college campuses. Transport Policy, 10(1), 35-49. doi:10.1016/s0967-070x(02)00028-8Lukman, R., Krajnc, D., & Glavič, P. (2010). University ranking using research, educational and environmental indicators. Journal of Cleaner Production, 18(7), 619-628. doi:10.1016/j.jclepro.2009.09.015Baboulet, O., & Lenzen, M. (2010). Evaluating the environmental performance of a university. Journal of Cleaner Production, 18(12), 1134-1141. doi:10.1016/j.jclepro.2010.04.006Alshuwaikhat, H. M., & Abubakar, I. (2008). An integrated approach to achieving campus sustainability: assessment of the current campus environmental management practices. Journal of Cleaner Production, 16(16), 1777-1785. doi:10.1016/j.jclepro.2007.12.002Lukman, R., & Glavič, P. (2006). What are the key elements of a sustainable university? Clean Technologies and Environmental Policy, 9(2), 103-114. doi:10.1007/s10098-006-0070-7León-Fernández, Y., & Domínguez-Vilches, E. (2015). Environmental management and sustainability in higher education. International Journal of Sustainability in Higher Education, 16(4), 440-455. doi:10.1108/ijshe-07-2013-0084Velazquez, L., Munguia, N., Platt, A., & Taddei, J. (2006). Sustainable university: what can be the matter? Journal of Cleaner Production, 14(9-11), 810-819. doi:10.1016/j.jclepro.2005.12.008Grindsted, T. S. (2011). Sustainable Universities From Declarations on Sustainability in Higher Education to National Law. SSRN Electronic Journal. doi:10.2139/ssrn.2697465Suwartha, N., & Sari, R. F. (2013). Evaluating UI GreenMetric as a tool to support green universities development: assessment of the year 2011 ranking. Journal of Cleaner Production, 61, 46-53. doi:10.1016/j.jclepro.2013.02.034Sonetti, G., Lombardi, P., & Chelleri, L. (2016). True Green and Sustainable University Campuses? Toward a Clusters Approach. Sustainability, 8(1), 83. doi:10.3390/su8010083Drahein, A. D., De Lima, E. P., & Da Costa, S. E. G. (2019). Sustainability assessment of the service operations at seven higher education institutions in Brazil. Journal of Cleaner Production, 212, 527-536. doi:10.1016/j.jclepro.2018.11.293Parvez, N., & Agrawal, A. (2019). Assessment of sustainable development in technical higher education institutes of India. Journal of Cleaner Production, 214, 975-994. doi:10.1016/j.jclepro.2018.12.305Ragazzi, M., & Ghidini, F. (2017). Environmental sustainability of universities: critical analysis of a green ranking. Energy Procedia, 119, 111-120. doi:10.1016/j.egypro.2017.07.054Abbott, M., & Doucouliagos, C. (2003). The efficiency of Australian universities: a data envelopment analysis. Economics of Education Review, 22(1), 89-97. doi:10.1016/s0272-7757(01)00068-1Kuiper, F. K., & Fisher, L. (1975). 391: A Monte Carlo Comparison of Six Clustering Procedures. Biometrics, 31(3), 777. doi:10.2307/2529565Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253. doi:10.2307/2343100Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. doi:10.1016/0377-2217(78)90138-8Adler, N., Friedman, L., & Sinuany-Stern, Z. (2002). Review of ranking methods in the data envelopment analysis context. European Journal of Operational Research, 140(2), 249-265. doi:10.1016/s0377-2217(02)00068-1Doyle, J., & Green, R. (1994). Efficiency and Cross-efficiency in DEA: Derivations, Meanings and Uses. Journal of the Operational Research Society, 45(5), 567-578. doi:10.1057/jors.1994.84Angulo-Meza, L., & Lins, M. P. E. (2002). Annals of Operations Research, 116(1/4), 225-242. doi:10.1023/a:1021340616758Hashimoto, A., & Ishikawa, H. (1993). Using DEA to evaluate the state of society as measured by multiple social indicators. Socio-Economic Planning Sciences, 27(4), 257-268. doi:10.1016/0038-0121(93)90019-fHashimoto, A., & Kodama, M. (1997). Social Indicators Research, 40(3), 359-373. doi:10.1023/a:1006804520184Zhu, J. (2001). Multidimensional quality-of-life measure with an application to Fortune’s best cities. Socio-Economic Planning Sciences, 35(4), 263-284. doi:10.1016/s0038-0121(01)00009-xMurias, P., Martinez, F., & De Miguel, C. (2006). An Economic Wellbeing Index for the Spanish Provinces: A Data Envelopment Analysis Approach. 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