315 research outputs found

    Benchmarking institutional and structural indicators in EU candidate and potential candidate countries

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    This paper reviews institutional and structural challenges in countries preparing for EU membership, i.e. Albania, Bosnia and Herzegovina, Kosovo*, the former Yugoslav Republic of Macedonia, Montenegro, Serbia and Turkey. Sound institutions and solid economic structures are not only the cornerstones of EU accession (as defined by the Copenhagen political and economic criteria), but are also crucial for achieving higher income levels and sustainable long-term growth. This paper finds that the EU candidate and potential candidate countries (EU CC/PCC) fare worse than the majority of EU Member States in a number of institutional and structural metrics, such as business environment, access to finance, judicial system, trade and competitiveness, labour market and education and institutional governance. When comparing EU CC/PCC among themselves, large intra-group disparities emerge. Countries such as the former Yugoslav Republic of Macedonia, Montenegro and, to a certain extent, Serbia and Turkey, tend to score on average higher than Albania, Bosnia and Herzegovina and Kosovo. While many EU CC/PCC have improved the quality of their institutions and economic governance over the past decade, it is crucial that they preserve the reform momentum to enable a sustainable convergence with the EU

    Panel on resources in Spain for the federated cloud

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    Trabajo presentado al Spanish JRU EGI-ENGAGE meeting celebrado en Madrid el 23 de febrero de 2015.EGI-InSPIRE RI-261323.N

    El acoso escolar dentro de las aulas: proyecto de innovación educativa a través del aprendizaje cooperativo en 3º de Educación Primaria

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    En los últimos años, se constata una preocupación por la conducta transgresora en las escuelas e instituciones. Estos comportamientos impiden el normal desarrollo del proceso de enseñanza-aprendizaje y afecta gravemente a las relaciones interpersonales entre profesores y alumnos (Aparicio ,2014). El proyecto a desarrollar y su posterior análisis se llevará a cabo durante el curso escolar de manera transversal ( 2º ciclo de Educación Primaria, 3º curso), ya que se observan ciertas conductas impropias, lo que repercute directamente en la convivencia entre los alumnos con importantes faltas de respeto. El objetivo del presente trabajo es prevenir el acoso escolar partiendo de la realidad, conociéndola y actuando en el momento oportuno. Para lograr ese objetivo, se trabajará con metodologías activas, basadas en el trabajo cooperativo y en actividades para fomentar la empatía. Una vez implementado el proyecto, se sintetizarán opiniones e impresiones de cada alumno sobre el proyecto, y de igual modo de sus padres y profesores, a través de diferentes herramientas evaluativas. Los resultados a obtener confirmarán una mejora o no en la actitud y relaciones entre el alumnado, lo cual nos permitirá comprobar si este proyecto ha valido para, no solamente erradicar esas conductas disruptivas, sino también para reducirlas y hacer reflexionar al alumnado antes de actuar.Máster en Formación de Educadores para la Intervención Sociocomunitari

    Responsabilidad social corporativa y su comunicación en Twitter: análisis del discurso y los sentimientos generados en la sociedad

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    [EN] The purpose of this work is to analyze on a global level, without focusing on any specific company or sector, the use that is being made of the social media Twitter to address the communication processes of CSR in companies. To do this, data was captured through programming in R and using an Application Program Interface (API) in Twitter. The data were analyzed using text mining techniques and graphical network analysis with the help of R and Gephi software, respectively. The results show that there are no notable groups or movements in the corporate sphere as generators of CSR content. The importance of the social perspective of CSR in communication processes should be emphasized. Finally, CSR generates positive feelings and emotions such as trust in society. It is concluded that there is no efficient use by companies of Twitter as a CSR communication tool, they are not taking into account the interests of their stakeholders, nor are they generating the dialogue and interaction necessary for communication to be effective. The main limitation is associated with the time period in which the sample was taken, which coincides with a period of strong social concern about the pandemic. It would be interesting for future research to analyze how the discourse changes over time, and to what extent an extraordinary social situation, such as the pandemic, is reflected in CSR communication.[ES] El objetivo de este trabajo es analizar a nivel global, sin centrarse en ninguna empresa o sector concreto, el uso que se está haciendo de la red social Twitter para abordar los procesos de comunicación de la RSC en las empresas. Para ello se hizo uso de captura de datos a través de programación en R y una Interfaz de Programación de Aplicaciones (API) en Twitter. Se trataron los datos mediante técnicas de minería de texto y análisis gráfico de redes con la ayuda respectivamente de los softwares R y Gephi. Los resultados muestran que no existen grupos o movimientos notables dentro del ámbito corporativo como generadores de contenido en materia de RSC. Además, se observa la importancia de la perspectiva social de la RSC en los procesos de comunicación de la misma. Por último, se pone de manifiesto que la RSC genera sentimientos positivos y emociones como la confianza en la sociedad. Se concluye que las empresas no están realizando un uso eficiente de Twitter como herramienta de comunicación de la RSC, no están teniendo en cuenta los intereses de sus partes interesadas, ni generando el diálogo y la interacción necesaria para que la comunicación sea eficaz. La principal limitación está asociada al periodo temporal en el que se toma la muestra que coincide con un momento de fuerte preocupación social por la pandemia. Sería interesante que futuras investigaciones analizaran cómo va cambiando el discurso a lo largo del tiempo, y en qué medida una situación social extraordinaria, como es el caso de la pandemia, se ve reflejada en la comunicación de la CSR.This activity has been partially funded by UNIR Research (http://research.unir.net), International University of La Rioja (UNIR, http://www.unir.net), Research Group TR3S-i “Liquid work and emerging risks in the information society”

    Turismo rural en España = Rural tourism in Spain

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    El turismo rural en España se concibe como una tipología turística relativamente nueva, enmarcado en el “turismo alternativo”. Los primeros viajes al medio rural surgieron a partir del éxodo rural de los años 60 del siglo XX; el desplazamiento de la población rural a las ciudades hizo que surgiera el interés por conocer sus antepasados. Su crecimiento y desarrollo se ha producido debido a las distintas acciones llevadas a cabo por parte de la Administración, como son el Programa Vacaciones en Casas de Labranza o los programas LEADER, y por las distintas motivaciones de los turistas a lo largo del tiempo, entre otros factores. A lo largo de este trabajo se profundiza en la descripción de las etapas de evolución y los factores de desarrollo y crecimiento del turismo rural en España, de las características de la oferta y la demanda actual, y de los impactos económicos, ambientales y sociales que produce en el entorno donde se practica. Además, se realiza un estudio estadístico mediante el análisis clásico de series temporales, con el fin de realizar una previsión en el número de turistas que se hospedarán en alojamientos de turismo rural en periodos posteriores

    Impact of adding prebiotics and probiotics on the characteristics of edible films and coatings- a review

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    This work was financially supported by the Spanish Ministry of Science and Innovation, through the project GRUPIN-SV-PA-21-AYUD/2021/51041 and by the grant Programa Severo Ochoa de 'Ayudas Predoctorales para la investigación y docencia' (grant number BP19-127 to Saez-Orviz, S.)

    Orchestrating Complex Application Architectures in Heterogeneous Clouds

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    [EN] Private cloud infrastructures are now widely deployed and adopted across technology industries and research institutions. Although cloud computing has emerged as a reality, it is now known that a single cloud provider cannot fully satisfy complex user requirements. This has resulted in a growing interest in developing hybrid cloud solutions that bind together distinct and heterogeneous cloud infrastructures. In this paper we describe the orchestration approach for heterogeneous clouds that has been implemented and used within the INDIGO-DataCloud project. This orchestration model uses existing open-source software like OpenStack and leverages the OASIS Topology and Specification for Cloud Applications (TOSCA) open standard as the modeling language. Our approach uses virtual machines and Docker containers in an homogeneous and transparent way providing consistent application deployment for the users. This approach is illustrated by means of two different use cases in different scientific communities, implemented using the INDIGO-DataCloud solutions.The authors want to acknowledge the support of the INDIGO-Datacloud (grant number 653549) project, funded by the European Commission's Horizon 2020 Framework Program.Caballer Fernández, M.; Zala, S.; López, Á.; Moltó, G.; Orviz, P.; Velten, M. (2018). Orchestrating Complex Application Architectures in Heterogeneous Clouds. Journal of Grid Computing. 16(1):3-18. https://doi.org/10.1007/s10723-017-9418-yS318161Aguilar Gómez, F., de Lucas, J.M., García, D., Monteoliva, A.: Hydrodynamics and water quality forecasting over a cloud computing environment: indigo-datacloud. In: EGU General Assembly Conference Abstracts, vol. 19, p 9684 (2017)de Alfonso, C., Caballer, M., Alvarruiz, F., Hernández, V.: An energy management system for cluster infrastructures. Comput. Electr. Eng. 39(8), 2579–2590 (2013). http://www.sciencedirect.com/science/article/pii/S0045790613001365Amazon Web Services (AWS): Amazon Web Services (AWS). https://aws.amazon.com/ (2017)Amazon Web Services (AWS): CloudFormation. https://aws.amazon.com/cloudformation/ (2017)Apache Software Foundation: Apache Mesos. http://mesos.apache.org/ (2017)ARIA, ARIA. http://ariatosca.incubator.apache.org/ (2017)Bumpus, W.: NIST Cloud Computing Standards Roadmap. Tech. rep., National Institute of Standards and Technology (NIST). https://doi.org/10.6028/NIST.SP.500-291r2 (2013)Caballer, M., Blanquer, I., Moltó, G., de Alfonso, C.: Dynamic management of virtual infrastructures. J Grid Comput. 13(1), 53–70 (2015). https://doi.org/10.1007/s10723-014-9296-5Campos Plasencia, I., Fernández-del Castillo, E., Heinemeyer, S., López García, Á., Pahlen, F., Borges, G.: Phenomenology tools on cloud infrastructures using OpenStack. Eur. Phys. J. C 73(4), 2375 (2013). https://doi.org/10.1140/epjc/s10052-013-2375-0Celar: Celar. http://www.cloudwatchhub.eu/celar (2017)Chen, Y., de Lucas, J.M., Aguilar, F., Fiore, S., Rossi, M., Ferrari, T.: Indigo: building a datacloud framework to support open science. In: EGU General Assembly Conference Abstracts, vol. 18, p 16610 (2016)Chronos: Chronos. https://mesos.github.io/chronos/ (2017)Cloudify: Cloudify. http://getcloudify.org (2017)Davidović, D., Cetinić, E., Skala, K.: European research area and digital humanitiesDistefano, S., Serazzi, G.: Performance driven WS orchestration and deployment in service oriented infrastructure. J Grid Comput. 12(2), 347–369 (2014). https://doi.org/10.1007/s10723-014-9293-8EGI FedCloud: EGI FedCloud. https://www.egi.eu/federation/egi-federated-cloud/ (2017)Eucalyptus: Eucalyptus. https://www.eucalyptus.com/ (2017)Fiore, S., D’Anca, A., Palazzo, C., Foster, I., Williams, D.N., Aloisio, G.: Ophidia: toward big data analytics for eScience. Procedia Comput. Sci. 18, 2376–2385 (2013). https://doi.org/10.1016/j.procs.2013.05.409Fiore, S., Palazzo, C., D’Anca, A., Elia, D., Londero, E., Knapic, C., Monna, S., Marcucci, N.M., Aguilar, F., Płóciennik, M., et al.: Big data analytics on large-scale scientific datasets in the indigo-datacloud project. In: Proceedings of the Computing Frontiers Conference, pp 343–348. ACM (2017)Fiore, S., Płóciennik, M., Doutriaux, C., Palazzo, C., Boutte, J., żok, T., Elia, D., Owsiak, M., D’Anca, A., Shaheen, Z., et al.: Distributed and cloud-based multi-model analytics experiments on large volumes of climate change data in the earth system grid federation eco-system. In: 2016 IEEE International Conference on Big Data (Big Data), pp 2911–2918. IEEE (2016)Galante, G., Erpen de Bona, L.C., Mury, A.R., Schulze, B., da Rosa Righi, R.: An analysis of public clouds elasticity in the execution of scientific applications: a survey. J Grid Comput.,1–24. https://doi.org/10.1007/s10723-016-9361-3 (2016)Google Cloud Platform (GCP): Google Cloud Platform (GCP). https://cloud.google.com/ (2017)Hochstein, L. (ed.): Ansible: Up and Running, Automating Configuration Management and Deployment the Easy Way. O’Reilly Media (2014)Idabc: European Interoperability Framework for pan-European eGovernment Services. European Commission version 1, 1–25. https://doi.org/10.1109/HICSS.2007.68 (2004)IM: IM. http://www.grycap.upv.es/im (2017)INDIGO-DataCloud: D1.8 - General Architecture. Tech. rep., INDIGO-DataCloud Consortium (2015)INDIGO-DataCloud: Ansible Galaxy repository for INDIGO-DataCloud. https://galaxy.ansible.com/indigo-dc/ (2017)INDIGO-DataCloud: Disvis/Powerfit Ansible Role in Ansible Galaxy. https://galaxy.ansible.com/indigo-dc/disvis-powerfit/ (2017)INDIGO-DataCloud: INDIGO-DataCloud. https://www.indigo-datacloud.eu/ (2017)INDIGO-DataCloud: INDIGO-DataCloud DockerHub application repository. https://hub.docker.com/u/indigodatacloudapps/ (2017)INDIGO-DataCloud: INDIGO-DataCloud PaaS Orchestrator. https://github.com/indigo-dc/orchestrator (2017)INDIGO-DataCloud: INDIGO-DataCloud RepoSync. https://github.com/indigo-dc/java-reposync (2017)INDIGO-DataCloud: INDIGO-DataCloud TOSCA templates. https://github.com/indigo-dc/tosca-templates (2017)INDIGO-DataCloud: TOSCA Across Clouds. https://github.com/indigo-dc/tosca-types/blob/master/examples/web_mysql_tosca_across_clouds.yaml (2017)INDIGO-DataCloud: TOSCA template for deploying an Elastic Mesos Cluster. http://github.com/indigo-dc/tosca-types/blob/master/examples/mesos_elastic_cluster.yaml (2017)INDIGO-DataCloud: TOSCA template for Powerfit application. https://github.com/indigo-dc/tosca-types/blob/master/examples/powerfit.yaml (2017)Kacsuk, P., Kecskemeti, G., Kertesz, A., Nemeth, Z., Kovȧcs, J., Farkas, Z.: Infrastructure aware scientific workflows and infrastructure aware workflow managers in science gateways. J Grid Comput., 641–654. https://doi.org/10.1007/s10723-016-9380-0 (2016)Korambath, P., Wang, J., Kumar, A., Hochstein, L., Schott, B., Graybill, R., Baldea, M., Davis, J.: Deploying kepler workflows as services on a cloud infrastructure for smart manufacturing. Procedia Comput. Sci. 29, 2254–2259 (2014)Koski, K., Hormia-Poutanen, K., Chatzopoulos, M., Legrė, Y., Day, B.: Position Paper: European Open Science Cloud for Research. Tech. Rep. october, EUDAT, LIBER, OpenAIRE, EGI, GĖANT Bari (2015)Krieger, M.T., Torreno, O., Trelles, O., Kranzlmüller, D.: Building an open source cloud environment with auto-scaling resources for executing bioinformatics and biomedical workflows. Futur. Gener. Comput. Syst. 67, 329–340 (2017). https://doi.org/10.1016/j.future.2016.02.008Kurkcuoglu Soner, Z., Bonvin, A.: Science in the clouds: virtualizing haddock powerfit and disvis using indigo-datacloud solutions (2016)Lipton, P.C.T., Moser, S.I., Palma, D.V., Spatzier, T.I.: Topology and Orchestration Specification for Cloud Applications. Tech. rep., OASIS Standard (2013)Liu, C., Mao, Y., Van der Merwe, J., Fernandez, M.: Cloud Resource Orchestration: a Data-Centric Approach. In: Proceedings of the Biennial Conference on Innovative Data Systems Research (CIDR), pp 1–8. Citeseer (2011)López García, Á., Fernández-del Castillo, E.: Analysis of scientific cloud computing requirements. In: Proceedings of the IBERGRID 2013 Conference, p 147 158 (2013)López García, Á., Fernández-del Castillo, E., Orviz Fernández, P.: Standards for enabling heterogeneous IaaS cloud federations. Comput. Standard Inter. 47, 19–23 (2016). https://doi.org/10.1016/j.csi.2016.02.002López García, Á., Zangrando, L., Sgaravatto, M., Llorens, V., Vallero, S., Zaccolo, V., Bagnasco, S., Taneja, S., Pra, S.D., Salomoni, D., Donvito G.: Improved cloud resource allocation: how INDIGO-datacloud is overcoming the current limitations in cloud schedulers. arXiv: 1707.06403 (2017)Lorido-Botran, T., Miguel-Alonso, J., Lozano, J.A.: A review of auto-scaling techniques for elastic applications in cloud environments. J Grid Comput. 12(4), 559–592 (2014). https://doi.org/10.1007/s10723-014-9314-7Marathon: Marathon. https://mesosphere.github.io/marathon/ (2017)Metsch, T., Edmonds, A.: Open Cloud Computing Interface-Infrastructure. Tech. rep., Open Grid Forum (2010)Metsch, T., Edmonds, A.: Open Cloud Computing Interface-RESTful HTTP Rendering. Tech. rep., Open Grid Forum (2011)Microsoft Azure: Microsoft Azure. https://azure.microsoft.com/ (2017)Moltó, G., Caballer, M., Pérez, A., Alfonso, D.C., Blanquer, I.: Coherent application delivery on hybrid distributed computing infrastructures of virtual machines and docker containers. In: 2017 25Th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). https://doi.org/10.1109/PDP.2017.29 , pp 486–490 (2017)Monna, S., Marcucci, N.M., Marinaro, G., Fiore, S., D’Anca, A., Antonacci, M., Beranzoli, L., Favali, P.: An Emso data case study within the indigo-Dc project. In: EGU General Assembly Conference Abstracts, vol. 19, p 12493 (2017)Nyrén, R., Metsch, T., Edmonds, A., Papaspyrou, A.: Open Cloud Computing Interface–Core. Tech. rep., Open Grid Forum (2010)OASIS: Organization for the Advancement of Structured Information Standards (OASIS). https://www.oasis-open.org (2015)Open Telekom Cloud (OTC): Open Telekom Cloud (OTC). https://cloud.telekom.de/en/ (2017)OpenNebula: OneFlow. http://docs.opennebula.org/5.2/advanced_components/application_flow_and_auto-scaling/index.html (2017)OpenNebula Project: OpenNebula. https://www.opennebula.org (2017)OpenStack Foundation: Heat Orchestration Template (HOT) Guide. https://docs.openstack.org/heat/latest/template_guide/hot_guide.html (2017)OpenStack Foundation: OpenStack. https://www.openstack.org (2017)OpenStack Foundation: Openstack Heat. http://wiki.openstack.org/wiki/Heat (2017)OpenStack Foundation: OpenStack Heat Translator. https://github.com/openstack/heat-translator (2017)OpenStack Foundation: OpenStack heat-translator project contribution statistics. http://stackalytics.com/?release=all&metric=commits&module=heat-translator (2017)OpenStack Foundation: OpenStack Tacker. https://wiki.openstack.org/wiki/Tacker (2017)OpenStack Foundation: OpenStack tosca-parser project contribution statistics. http://stackalytics.com/?release=all&metric=commits&module=tosca-parser (2017)OpenStack Foundation: TOSCA Parser. https://github.com/openstack/tosca-parser (2017)OpenTOSCA: OpenTOSCA. http://www.opentosca.org/ (2017)Owsiak, M., Plociennik, M., Palak, B., Zok, T., Reux, C., Di Gallo, L., Kalupin, D., Johnson, T., Schneider, M.: Running simultaneous kepler sessions for the parallelization of parametric scans and optimization studies applied to complex workflows. J Comput. Sci. 20, 103–111 (2017)Palma, D., Rutkowski, M., Spatzier T.: TOSCA Simple Profile in YAML Version 1.1. Tech. rep., OASIS Standard. http://docs.oasis-open.org/tosca/TOSCA-Simple-Profile-YAML/v1.1/TOSCA-Simple-Profile-YAML-v1.1.html (2016)Petcu, D.: Consuming resources and services from multiple clouds: from terminology to cloudware support. J Grid Comput. 12(2), 321–345 (2014). https://doi.org/10.1007/s10723-013-9290-3Plóciennik, M., Fiore, S., Donvito, G., Owsiak, M., Fargetta, M., Barbera, R., Bruno, R., Giorgio, E., Williams, D.N., Aloisio, G.: Two-level dynamic workflow orchestration in the INDIGO DataCloud for large-scale, climate change data analytics experiments. Procedia Comput. Sci. 80, 722–733 (2016). https://doi.org/10.1016/j.procs.2016.05.359Płóciennik, M., Fiore, S., Donvito, G., Owsiak, M., Fargetta, M., Barbera, R., Bruno, R., Giorgio, E., Williams, D.N., Aloisio, G.: Two-level dynamic workflow orchestration in the indigo datacloud for large-scale, climate change data analytics experiments. Procedia Comput. Sci. 80, 722–733 (2016)Python: Python Package Index (PyPI). https://pypi.python.org/pypi (2017)Ramakrishnan, L., Jackson, K.R., Canon, S., Cholia, S., Shalf, J.: Defining future platform requirements for e-Science clouds. In: Proceedings of the 1st ACM Symposium on Cloud Computing - SoCC ’10. https://doi.org/10.1145/1807128.1807145 , p 101 (2010)Ramakrishnan, L., Zbiegel, P.T.T.T.: Magellan: experiences from a science cloud. In: Proceedings of the 2Nd International Workshop on Scientific Cloud Computing. http://dl.acm.org/citation.cfm?id=1996119 , pp 49–58 (2011)Salomoni, D., Campos, I., Gaido, L., Donvito, G., Antonacci, M., Fuhrman, P., Marco, J., Lopez-Garcia, A., Orviz, P., Blanquer, I., et al.: Indigo-datacloud: foundations and architectural description of a platform as a service oriented to scientific computing. arXiv: http://arXiv.org/abs/1603.09536 (2016)Sánchez-Expósito, S., Martín, P., Ruiz, J.E., Verdes-Montenegro, L., Garrido, J., Sirvent, R., Falcó, A.R., Badia, R.M., Lezzi, D.: Web services as building blocks for science gateways in astrophysics. J Grid Comput. 14(4), 673–685 (2016). https://doi.org/10.1007/s10723-016-9382-ySlipStream: SlipStream. http://sixsq.com/products/slipstream/ (2017)Stockton, D.B., Santamaria, F.: Automating NEURON simulation deployment in cloud resources. Neuroinformatics 15(1), 51–70 (2017). https://doi.org/10.1007/s12021-016-9315-8Teckelmann, R., Reich, C., Sulistio, A.: Mapping of Cloud Standards to the Taxonomy of Interoperability in Iaas. In: 2011 IEEE Third International Conference on Cloud Computing Technology and Science (Cloudcom), pp 522–526. IEEE (2011)Toor, S., Osmani, L., Eerola, P., Kraemer, O., Lindén, T., Tarkoma, S., White, J.: A scalable infrastructure for CMS data analysis based on OpenStack Cloud and Gluster file system. J Phys.: Conf. Ser. 513(6), 062,047 (2014). https://doi.org/10.1088/1742-6596/513/6/062047 . http://stacks.iop.org/1742-6596/513/i=6/a=062047?key=crossref.84033a04265ce343371c7f38064e7143UK Government Cabinet Office: Open Standards Principles. https://www.gov.uk/government/publications/open-standards-principles/open-standards-principles (2015)Yangui, S., Marshall, I.J., Laisne, J.P., Tata, S.: Compatibleone: the open source cloud broker. J Grid Comput. 12(1), 93–109 (2014)Zhao, Y., Li, Y., Raicu, I., Lu, S., Tian, W., Liu, H.: Enabling scalable scientific workflow management in the cloud. Futur. Gener. Comput. Syst. 46, 3–16 (2015). https://doi.org/10.1016/j.future.2014.10.023van Zundert, G., Trellet, M., Schaarschmidt, J., Kurkcuoglu, Z., David, M., Verlato, M., Rosato, A., Bonvin, A.: The DisVis and PowerFit web servers: explorative and integrative modeling of biomolecular complexes. J. Mol. Biol. 429(3), 399–407 (2013). http://www.sciencedirect.com/science/article/pii/S002228361630527
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