Formative Assessment Framework Proposal for Transversal Competencies: Application to Analysis and Problem-Solving Competence

Purpose: In the last years, there is an increasing interest in the manner that transversal competences (TC) are introduced in the curricula. Transversal competences are generic and relevant skills that students have to develop through the several stages of the educational degrees. This paper analyses TCs in the context of the learning process of undergraduate and postgraduate courses. The main aim of this paper is to propose a framework to improve results. The framework facilities the student's training and one of the important pieces is undoubtedly that he has constant feedback from his assessments that allowing to improve the learning. An applying in the analysis and problem solving competence in the context of Master Degree in Advanced Engineering Production, Logistics and Supply Chain at the UPV is carried out. Design/methodology/approach: The work is the result of several years of professional experience in the application of the concept of transversal competence in the UPV with undergraduate and graduate students. As a result of this work and various educational innovation projects, a team of experts has been created, which has been discussing some aspects relevant to the improvement of the teaching-learning process. One of these areas of work has been in relation to the integration of various proposals on the application and deployment of transversal competences. With respect to this work, a conceptual proposal is proposed that has subsequently been empirically validated through the analysis of the results of several groups of students in a degree. Findings: The main result that is offered in the work is a framework that allows identifying the elements that are part of the learning process in the area of transversal competences. Likewise, the different items that are part of the framework are linked to the student's life cycle, and a temporal scope is established for their deployment. Practical implications: One of the most noteworthy practical implications is that the proposed framework includes a tool that allows a clear measurement of the student's evolution throughout his / her formative life cycle. In this way the student has a more consistent and robust vision of his / her training and the academic directors of the titles can have a vision of the impact of the decisions on the learning processes. Originality/value: The analysis of transversal competences is usually presented in the context of a subject. In this paper we propose an approach to cross-curricular competences but in the scope of the student's complete life cycle. The consideration of the entire formative process as well as the identification of the relevant elements that are part of this process are the most original aspects of the work.Peer Reviewe

Performance Management in Collaborative Networks: Difficulties and Barriers

Abstract. Global competitiveness obliges to enterprises to collaborate in many processes such as new product and services development in order to shorten the lifecycle, development and commercialization. Therefore, the competence has drifted from an individual focus to a supply chain management one and, from some years, to a collaborative enterprises network approach. It is common to find frameworks for measuring/managing the performance within extended enterprises, supply chains, virtual enterprises, etc. However, few authors deal with a higher level: the collaborative networks one. This concept of enterprises management set up bigger difficulties regarding not only from a conceptual and structural point of view but also considering both the design and posterior development of systems capable of managing the performance achieved in this type of organizations. This work describes both the main difficulties and barriers when trying to apply performance management concepts to collaborative networks. In this sense, it is highlighted the weaknesses of the existing intra-organizational frameworks that cannot be projected, as they are conceived, to manage performance within collaborative networks

Process improvement approaches for increasing the response of emergency departments against the Covid-19 pandemic: a systematic review

The COVID-19 pandemic has strongly affected the dynamics of Emergency Departments (EDs) worldwide and has accentuated the need for tackling different operational inefficiencies that decrease the quality of care provided to infected patients. The EDs continue to struggle against this outbreak by implementing strategies maximizing their performance within an uncertain healthcare environment. The efforts, however, have remained insufficient in view of the growing number of admissions and increased severity of the coronavirus disease. Therefore, the primary aim of this paper is to review the literature on process improvement interventions focused on increasing the ED response to the current COVID-19 outbreak to delineate future research lines based on the gaps detected in the practical scenario. Therefore, we applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to perform a review containing the research papers published between December 2019 and April 2021 using ISI Web of Science, Scopus, PubMed, IEEE, Google Scholar, and Science Direct databases. The articles were further classified taking into account the research domain, primary aim, journal, and publication year. A total of 65 papers disseminated in 51 journals were concluded to satisfy the inclusion criteria. Our review found that most applications have been directed towards predicting the health outcomes in COVID-19 patients through machine learning and data analytics techniques. In the overarching pandemic, healthcare decision makers are strongly recommended to integrate artificial intelligence techniques with approaches from the operations research (OR) and quality management domains to upgrade the ED performance under social-economic restrictions

Performance measurement in Judo: main KPIs, cluster categorization and causal relationships

[EN] Performance measurement in Judo usually focuses on some KPIs whose values indicate the final performance of the athlete. This paper deals with firstly identifying which these main Key Performance Indicators (KPIs) in Judo are. Once this is done, the KPIs are classified into four different clusters: Physical training, Specific training, Psychology and Lifestyle. Then, it moves into analyzing possible quantitative techniques to identify cause-effect relationships between KPIs in order to link not only the impact of the Judo KPIs with the achievement of the judoka’s strategic objectives but also to identify both the relative and the global importance of each of these KPIs. Finally, it points out the Analytic Network Technique as the one that could be ideally applied in this context and offers future research actions.Uriarte Marcos, S.; Rodriguez-Rodriguez, R.; Uriarte Marcos, M.; Alfaro-Saiz, J. (2019). Performance measurement in Judo: main KPIs, cluster categorization and causal relationships. International Journal of Production Management and Engineering. 7(2):145-150. https://doi.org/10.4995/ijpme.2019.12035SWORD1451507

Seeking organisational excellence by using the information coming from the EFQM excellence model as starting point: application to a real case

This paper describes how to use the information coming from applying the EFQM excellence model to analyse the perception that the members of an organisation have of it regarding their business vision. Such an analysis is made on the basis of the EFQM excellence model criteria and by applying statistical data analysis techniques. With this study, besides detecting both the strong and weak areas of actuation on which an organisation should focus and act, it is also possible to detect the relationships between the personal characteristics of members of the organisation and their business vision. The main goal is that organisations are able to reach excellence by jointly using an assessment method (the EFQM excellence model) and posterior statistical data analysis techniques (uni-variant and multi-variant). These techniques enable one to complement and enlarge the potential of the EFQM excellence model. Finally, the procedure is illustrated by presenting the main results of applying it to a real case of the Permanent Training Centre of the Polytechnic University of Valencia in Spain. © 2011 Taylor & Francis.Alfaro Saiz, JJ.; Carot Sierra, JM.; Rodríguez Rodríguez, R.; Jabaloyes Vivas, JM. (2011). Seeking organisational excellence by using the information coming from the EFQM excellence model as starting point: application to a real case. Total Quality Management and Business Excellence. 22(8):853-868. doi:10.1080/14783363.2011.597595S853868228Carlos Bou‐Llusar, J., Escrig‐Tena, A. B., Roca‐Puig, V., & Beltrán‐Martín, I. (2005). To what extent do enablers explain results in the EFQM excellence model? International Journal of Quality & Reliability Management, 22(4), 337-353. doi:10.1108/02656710510591192Calvo‐Mora, A., Leal, A., & Roldán, J. L. (2006). Using enablers of the EFQM model to manage institutions of higher education. Quality Assurance in Education, 14(2), 99-122. doi:10.1108/09684880610662006Dale, B. G., Zairi, M., Van der Wiele, A., & Williams, A. R. T. (2000). Quality is dead in Europe – long live excellence ‐ true or false? Measuring Business Excellence, 4(3), 4-10. doi:10.1108/13683040010377737Eskildsen, J. K., Kristensen, K., & Jørn Juhl, H. (2001). The criterion weights of the EFQM excellence model. International Journal of Quality & Reliability Management, 18(8), 783-795. doi:10.1108/eum0000000006033Farrar, M. (2000). Structuring success: A case study in the use of the EFQM Excellence Model in school improvement. Total Quality Management, 11(4-6), 691-696. doi:10.1080/09544120050008084Hides, M. T., Davies, J., & Jackson, S. (2004). Implementation of EFQM excellence model self‐assessment in the UK higher education sector – lessons learned from other sectors. The TQM Magazine, 16(3), 194-201. doi:10.1108/09544780410532936Li, M., & Yang, J. B. (2003). A decision model for self‐assessment of business process based on the EFQM excellence model. International Journal of Quality & Reliability Management, 20(2), 164-188. doi:10.1108/02656710310456608Martín‐Castilla, J. I., & Rodríguez‐Ruiz, Ó. (2008). EFQM model: knowledge governance and competitive advantage. Journal of Intellectual Capital, 9(1), 133-156. doi:10.1108/14691930810845858McAdam, R., & Welsh, W. (2000). A critical review of the business excellence quality model applied to further education colleges. Quality Assurance in Education, 8(3), 120-130. doi:10.1108/09684880010372716Ruiz-Carrillo, J. I. C., & Fernández-Ortiz, R. (2005). Theoretical foundation of the EFQM model: the resource-based view. Total Quality Management & Business Excellence, 16(1), 31-55. doi:10.1080/1478336042000309857Rusjan, B. (2005). Usefulness of the EFQM excellence model: Theoretical explanation of some conceptual and methodological issues. Total Quality Management & Business Excellence, 16(3), 363-380. doi:10.1080/14783360500053972José Tarí, J. (2006). An EFQM model self‐assessment exercise at a Spanish university. Journal of Educational Administration, 44(2), 170-188. doi:10.1108/09578230610652051Wongrassamee, S., Simmons, J. E. L., & Gardiner, P. D. (2003). Performance measurement tools: the Balanced Scorecard and the EFQM Excellence Model. Measuring Business Excellence, 7(1), 14-29. doi:10.1108/13683040310466690Yang, J. B., Dale, B. G., & Siow, C. H. R. (2001). Self-assessment of excellence: An application of the evidential reasoning approach. International Journal of Production Research, 39(16), 3789-3812. doi:10.1080/0020754011006907

A Hybrid Fuzzy Multi-criteria Decision Making Model to Evaluate the Overall Performance of Public Emergency Departments: A Case Study

[EN] Performance evaluation is relevant for supporting managerial decisions related to the improvement of public emergency departments (EDs). As different criteria from ED context and several alternatives need to be considered, selecting a suitable Multicriteria Decision-Making (MCDM) approach has become a crucial step for ED performance evaluation. Although some methodologies have been proposed to address this challenge, a more complete approach is still lacking. This paper bridges this gap by integrating three potent MCDM methods. First, the Fuzzy Analytic Hierarchy Process (FAHP) is used to determine the criteria and sub-criteria weights under uncertainty, followed by the interdependence evaluation via fuzzy Decision-Making Trial and Evaluation Laboratory(FDEMATEL). The fuzzy logic is merged with AHP and DEMATEL to illustrate vague judgments. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used for ranking EDs. This approach is validated in a real 3-ED cluster. The results revealed the critical role of Infrastructure (21.5%) in ED performance and the interactive nature of Patient safety (C+R =12.771). Furthermore, this paper evidences the weaknesses to be tackled for upgrading the performance of each ED.Ortiz-Barrios, M.; Alfaro Saiz, JJ. (2020). A Hybrid Fuzzy Multi-criteria Decision Making Model to Evaluate the Overall Performance of Public Emergency Departments: A Case Study. International Journal of Information Technology & Decision Making. 19(6):1485-1548. https://doi.org/10.1142/S0219622020500364S14851548196Lord, K., Parwani, V., Ulrich, A., Finn, E. B., Rothenberg, C., Emerson, B., … Venkatesh, A. K. (2018). Emergency department boarding and adverse hospitalization outcomes among patients admitted to a general medical service. The American Journal of Emergency Medicine, 36(7), 1246-1248. doi:10.1016/j.ajem.2018.03.043Sørup, C. M., Jacobsen, P., & Forberg, J. L. (2013). Evaluation of emergency department performance – a systematic review on recommended performance and quality-in-care measures. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 21(1). doi:10.1186/1757-7241-21-62Farokhi, S., & Roghanian, E. (2018). Determining quantitative targets for performance measures in the balanced scorecard method using response surface methodology. Management Decision, 56(9), 2006-2037. doi:10.1108/md-08-2017-0772Ortiz Barrios, M. A., & Felizzola Jiménez, H. (2016). Use of Six Sigma Methodology to Reduce Appointment Lead-Time in Obstetrics Outpatient Department. Journal of Medical Systems, 40(10). doi:10.1007/s10916-016-0577-3Sunder M., V., Ganesh, L. S., & Marathe, R. R. (2018). A morphological analysis of research literature on Lean Six Sigma for services. International Journal of Operations & Production Management, 38(1), 149-182. doi:10.1108/ijopm-05-2016-0273Bergeron, B. P. (2017). Performance Management in Healthcare. doi:10.4324/9781315102214Santos, S. P., Belton, V., Howick, S., & Pilkington, M. (2018). Measuring organisational performance using a mix of OR methods. Technological Forecasting and Social Change, 131, 18-30. doi:10.1016/j.techfore.2017.07.028Ho, W., & Ma, X. (2018). The state-of-the-art integrations and applications of the analytic hierarchy process. European Journal of Operational Research, 267(2), 399-414. doi:10.1016/j.ejor.2017.09.007Dargi, A., Anjomshoae, A., Galankashi, M. R., Memari, A., & Tap, M. B. M. (2014). Supplier Selection: A Fuzzy-ANP Approach. Procedia Computer Science, 31, 691-700. doi:10.1016/j.procs.2014.05.317Jing, M., Jie, Y., Shou-yi, L., & Lu, W. (2015). Application of fuzzy analytic hierarchy process in the risk assessment of dangerous small-sized reservoirs. International Journal of Machine Learning and Cybernetics, 9(1), 113-123. doi:10.1007/s13042-015-0363-4Samanlioglu, F., Taskaya, Y. E., Gulen, U. C., & Cokcan, O. (2018). A Fuzzy AHP–TOPSIS-Based Group Decision-Making Approach to IT Personnel Selection. International Journal of Fuzzy Systems, 20(5), 1576-1591. doi:10.1007/s40815-018-0474-7CHEN, M.-F., TZENG, G.-H., & TANG, T.-I. (2005). FUZZY MCDM APPROACH FOR EVALUATION OF EXPATRIATE ASSIGNMENTS. International Journal of Information Technology & Decision Making, 04(02), 277-296. doi:10.1142/s0219622005001520Gul, M., Celik, E., Gumus, A. T., & Guneri, A. F. (2016). Emergency department performance evaluation by an integrated simulation and interval type-2 fuzzy MCDM-based scenario analysis. European J. of Industrial Engineering, 10(2), 196. doi:10.1504/ejie.2016.075846Jovčić, Průša, Dobrodolac, & Švadlenka. (2019). A Proposal for a Decision-Making Tool in Third-Party Logistics (3PL) Provider Selection Based on Multi-Criteria Analysis and the Fuzzy Approach. Sustainability, 11(15), 4236. doi:10.3390/su11154236Saaty, T. L., & Vargas, L. G. (2012). Models, Methods, Concepts & Applications of the Analytic Hierarchy Process. International Series in Operations Research & Management Science. doi:10.1007/978-1-4614-3597-6Vargas, L. G. (2016). Voting with Intensity of Preferences. International Journal of Information Technology & Decision Making, 15(04), 839-859. doi:10.1142/s0219622016400058Lee, K.-C., Tsai, W.-H., Yang, C.-H., & Lin, Y.-Z. (2018). An MCDM approach for selecting green aviation fleet program management strategies under multi-resource limitations. Journal of Air Transport Management, 68, 76-85. doi:10.1016/j.jairtraman.2017.06.011Labib, A., & Read, M. (2015). A hybrid model for learning from failures: The Hurricane Katrina disaster. Expert Systems with Applications, 42(21), 7869-7881. doi:10.1016/j.eswa.2015.06.020Hosseini, S., & Khaled, A. A. (2016). A hybrid ensemble and AHP approach for resilient supplier selection. Journal of Intelligent Manufacturing, 30(1), 207-228. doi:10.1007/s10845-016-1241-yZavadskas, E. K., Govindan, K., Antucheviciene, J., & Turskis, Z. (2016). Hybrid multiple criteria decision-making methods: a review of applications for sustainability issues. Economic Research-Ekonomska Istraživanja, 29(1), 857-887. doi:10.1080/1331677x.2016.1237302Lolli, F., Balugani, E., Ishizaka, A., Gamberini, R., Butturi, M. A., Marinello, S., & Rimini, B. (2019). On the elicitation of criteria weights in PROMETHEE-based ranking methods for a mobile application. Expert Systems with Applications, 120, 217-227. doi:10.1016/j.eswa.2018.11.030De Almeida Filho, A. T., Clemente, T. R. N., Morais, D. C., & de Almeida, A. T. (2018). Preference modeling experiments with surrogate weighting procedures for the PROMETHEE method. European Journal of Operational Research, 264(2), 453-461. doi:10.1016/j.ejor.2017.08.006Sun, G., Guan, X., Yi, X., & Zhou, Z. (2018). An innovative TOPSIS approach based on hesitant fuzzy correlation coefficient and its applications. Applied Soft Computing, 68, 249-267. doi:10.1016/j.asoc.2018.04.004Frazão, T. D. C., Camilo, D. G. G., Cabral, E. L. S., & Souza, R. P. (2018). Multicriteria decision analysis (MCDA) in health care: a systematic review of the main characteristics and methodological steps. BMC Medical Informatics and Decision Making, 18(1). doi:10.1186/s12911-018-0663-1Ortiz-Barrios, M. A., Herrera-Fontalvo, Z., Rúa-Muñoz, J., Ojeda-Gutiérrez, S., De Felice, F., & Petrillo, A. (2018). An integrated approach to evaluate the risk of adverse events in hospital sector. Management Decision, 56(10), 2187-2224. doi:10.1108/md-09-2017-0917Al Salem, A. A., & Awasthi, A. (2018). Investigating rank reversal in reciprocal fuzzy preference relation based on additive consistency: Causes and solutions. Computers & Industrial Engineering, 115, 573-581. doi:10.1016/j.cie.2017.11.027Aires, R. F. de F., & Ferreira, L. (2019). A new approach to avoid rank reversal cases in the TOPSIS method. Computers & Industrial Engineering, 132, 84-97. doi:10.1016/j.cie.2019.04.023Emrouznejad, A., & Yang, G. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences, 61, 4-8. doi:10.1016/j.seps.2017.01.008Arya, A., & Yadav, S. P. (2017). Development of FDEA Models to Measure the Performance Efficiencies of DMUs. International Journal of Fuzzy Systems, 20(1), 163-173. doi:10.1007/s40815-017-0325-yMufazzal, S., & Muzakkir, S. M. (2018). A new multi-criterion decision making (MCDM) method based on proximity indexed value for minimizing rank reversals. Computers & Industrial Engineering, 119, 427-438. doi:10.1016/j.cie.2018.03.045Kaliszewski, I., & Podkopaev, D. (2016). Simple additive weighting—A metamodel for multiple criteria decision analysis methods. Expert Systems with Applications, 54, 155-161. doi:10.1016/j.eswa.2016.01.042Mousavi-Nasab, S. H., & Sotoudeh-Anvari, A. (2018). A new multi-criteria decision making approach for sustainable material selection problem: A critical study on rank reversal problem. Journal of Cleaner Production, 182, 466-484. doi:10.1016/j.jclepro.2018.02.062Chen, Z., Ming, X., Zhang, X., Yin, D., & Sun, Z. (2019). A rough-fuzzy DEMATEL-ANP method for evaluating sustainable value requirement of product service system. Journal of Cleaner Production, 228, 485-508. doi:10.1016/j.jclepro.2019.04.145Jumaah, F. M., Zadain, A. A., Zaidan, B. B., Hamzah, A. K., & Bahbibi, R. (2018). Decision-making solution based multi-measurement design parameter for optimization of GPS receiver tracking channels in static and dynamic real-time positioning multipath environment. Measurement, 118, 83-95. doi:10.1016/j.measurement.2018.01.011Singh, A., & Prasher, A. (2017). Measuring healthcare service quality from patients’ perspective: using Fuzzy AHP application. Total Quality Management & Business Excellence, 30(3-4), 284-300. doi:10.1080/14783363.2017.1302794Otay, İ., Oztaysi, B., Cevik Onar, S., & Kahraman, C. (2017). Multi-expert performance evaluation of healthcare institutions using an integrated intuitionistic fuzzy AHP&DEA methodology. Knowledge-Based Systems, 133, 90-106. doi:10.1016/j.knosys.2017.06.028Awasthi, A., Govindan, K., & Gold, S. (2018). Multi-tier sustainable global supplier selection using a fuzzy AHP-VIKOR based approach. International Journal of Production Economics, 195, 106-117. doi:10.1016/j.ijpe.2017.10.013Gul, M., Guneri, A. F., & Nasirli, S. M. (2018). A fuzzy-based model for risk assessment of routes in oil transportation. International Journal of Environmental Science and Technology, 16(8), 4671-4686. doi:10.1007/s13762-018-2078-zKazancoglu, Y., Kazancoglu, I., & Sagnak, M. (2018). Fuzzy DEMATEL-based green supply chain management performance. Industrial Management & Data Systems, 118(2), 412-431. doi:10.1108/imds-03-2017-0121Abdullah, L., & Zulkifli, N. (2015). Integration of fuzzy AHP and interval type-2 fuzzy DEMATEL: An application to human resource management. Expert Systems with Applications, 42(9), 4397-4409. doi:10.1016/j.eswa.2015.01.021Ashtiani, M., & Azgomi, M. A. (2016). A hesitant fuzzy model of computational trust considering hesitancy, vagueness and uncertainty. Applied Soft Computing, 42, 18-37. doi:10.1016/j.asoc.2016.01.023Zyoud, 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.2017.02.016Scholz, S., Ngoli, B., & Flessa, S. (2015). Rapid assessment of infrastructure of primary health care facilities – a relevant instrument for health care systems management. BMC Health Services Research, 15(1). doi:10.1186/s12913-015-0838-8Ivlev, I., Vacek, J., & Kneppo, P. (2015). Multi-criteria decision analysis for supporting the selection of medical devices under uncertainty. European Journal of Operational Research, 247(1), 216-228. doi:10.1016/j.ejor.2015.05.075Kovacs, E., Strobl, R., Phillips, A., Stephan, A.-J., Müller, M., Gensichen, J., & Grill, E. (2018). Systematic Review and Meta-analysis of the Effectiveness of Implementation Strategies for Non-communicable Disease Guidelines in Primary Health Care. Journal of General Internal Medicine, 33(7), 1142-1154. doi:10.1007/s11606-018-4435-5Morley, C., Unwin, M., Peterson, G. M., Stankovich, J., & Kinsman, L. (2018). Emergency department crowding: A systematic review of causes, consequences and solutions. PLOS ONE, 13(8), e0203316. doi:10.1371/journal.pone.0203316Hermann, R. M., Long, E., & Trotta, R. L. (2019). Improving Patients’ Experiences Communicating With Nurses and Providers in the Emergency Department. Journal of Emergency Nursing, 45(5), 523-530. doi:10.1016/j.jen.2018.12.001Hawley, K. L., Mazer-Amirshahi, M., Zocchi, M. S., Fox, E. R., & Pines, J. M. (2015). Longitudinal Trends in U.S. Drug Shortages for Medications Used in Emergency Departments (2001-2014). Academic Emergency Medicine, 23(1), 63-69. doi:10.1111/acem.12838Stang, A. S., Crotts, J., Johnson, D. W., Hartling, L., & Guttmann, A. (2015). Crowding Measures Associated With the Quality of Emergency Department Care: A Systematic Review. Academic Emergency Medicine, 22(6), 643-656. doi:10.1111/acem.12682Chanamool, N., & Naenna, T. (2016). Fuzzy FMEA application to improve decision-making process in an emergency department. Applied Soft Computing, 43, 441-453. doi:10.1016/j.asoc.2016.01.007Farup, P. G. (2015). Are measurements of patient safety culture and adverse events valid and reliable? Results from a cross sectional study. BMC Health Services Research, 15(1). doi:10.1186/s12913-015-0852-xCarter, E. J., Pouch, S. M., & Larson, E. L. (2013). The Relationship Between Emergency Department Crowding and Patient Outcomes: A Systematic Review. Journal of Nursing Scholarship, 46(2), 106-115. doi:10.1111/jnu.12055Ebben, R. H. A., Siqeca, F., Madsen, U. R., Vloet, L. C. M., & van Achterberg, T. (2018). Effectiveness of implementation strategies for the improvement of guideline and protocol adherence in emergency care: a systematic review. BMJ Open, 8(11), e017572. doi:10.1136/bmjopen-2017-017572Innes, G. D., Sivilotti, M. L. A., Ovens, H., McLelland, K., Dukelow, A., Kwok, E., … Chochinov, A. (2018). Emergency overcrowding and access block: A smaller problem than we think. CJEM, 21(2), 177-185. doi:10.1017/cem.2018.446Di Somma, S., Paladino, L., Vaughan, L., Lalle, I., Magrini, L., & Magnanti, M. (2014). Overcrowding in emergency department: an international issue. Internal and Emergency Medicine, 10(2), 171-175. doi:10.1007/s11739-014-1154-8Uthman, O. A., Walker, C., Lahiri, S., Jenkinson, D., Adekanmbi, V., Robertson, W., & Clarke, A. (2018). General practitioners providing non-urgent care in emergency department: a natural experiment. BMJ Open, 8(5), e019736. doi:10.1136/bmjopen-2017-019736Razzak, J. A., Baqir, S. M., Khan, U. R., Heller, D., Bhatti, J., & Hyder, A. A. (2013). Emergency and trauma care in Pakistan: a cross-sectional study of healthcare levels. Emergency Medicine Journal, 32(3), 207-213. doi:10.1136/emermed-2013-202590Dart, R. C., Goldfrank, L. R., Erstad, B. L., Huang, D. T., Todd, K. H., Weitz, J., … Anderson, V. E. (2018). Expert Consensus Guidelines for Stocking of Antidotes in Hospitals That Provide Emergency Care. Annals of Emergency Medicine, 71(3), 314-325.e1. doi:10.1016/j.annemergmed.2017.05.021Mkoka, D. A., Goicolea, I., Kiwara, A., Mwangu, M., & Hurtig, A.-K. (2014). 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Integrating Intangible Assets within Collaborative Networks Performance Management

Part 21: Sustainable Collaborative Networks IIInternational audienceIt is out of question the importance that intangible assets have acquired during the last decade. The management of such intangible assets is getting more and more the attention of both academics and professionals. It is aimed, through the application of different models and tools, to measure and manage them in order to achieve competitive advantages that will lead to better business sustainability. In this sense, organizations, taking into account both the intra and the inter-organizational level, must be able to seek and find the most efficient ways of integrating their intangible assets within their performance management systems. Up to now, most of the existing scientific works are focused on managing the intangible assets at the intra-organizational context. The key role that collaborative networks has got at the business ecosystem nowadays, and will have in the future, demands to define frameworks that deal with intangible assets management at the inter-enterprise context. This paper describes an approach of how to integrate intangible assets within collaborative networks. It follows a collaborative business processes approach, taking into account both the global ambit of the collaborative network as well as the individual enterprises that comprise the collaborative network ambit. The model includes a simplified and agile intangible assets management model, which might be used as a reference framework for collaborative networks in this field

Centrality dependence of the charged-particle multiplicity density at mid-rapidity in Pb-Pb collisions at sNN\sqrt{s_{NN}} = 2.76 TeV

The centrality dependence of the charged-particle multiplicity density at mid-rapidity in Pb-Pb collisions at $\sqrt{s_{NN}}$ = 2.76 TeV is presented. The charged-particle density normalized per participating nucleon pair increases by about a factor 2 from peripheral (70-80%) to central (0-5%) collisions. The centrality dependence is found to be similar to that observed at lower collision energies. The data are compared with models based on different mechanisms for particle production in nuclear collisions.The centrality dependence of the charged-particle multiplicity density at mid-rapidity in Pb-Pb collisions at $\sqrt{s_{\rm NN}}$ = 2.76 TeV is presented. The charged-particle density normalized per participating nucleon pair increases by about a factor 2 from peripheral (70-80%) to central (0-5%) collisions. The centrality dependence is found to be similar to that observed at lower collision energies. The data are compared with models based on different mechanisms for particle production in nuclear collisions

Suppression of charged particle production at large transverse momentum in central Pb–Pb collisions at √sNN=2.76 TeV

Inclusive transverse momentum spectra of primary charged particles in Pb–Pb collisions at √sNN=2.76 TeV have been measured by the ALICE Collaboration at the LHC. The data are presented for central and peripheral collisions, corresponding to 0–5% and 70–80% of the hadronic Pb–Pb cross section. The measured charged particle spectra in |η|<0.8 and 0.3<pT<20 GeV/c are compared to the expectation in pp collisions at the same sNN, scaled by the number of underlying nucleon–nucleon collisions. The comparison is expressed in terms of the nuclear modification factor RAA. The result indicates only weak medium effects (RAA≈0.7) in peripheral collisions. In central collisions, RAA reaches a minimum of about 0.14 at pT=6–7 GeV/c and increases significantly at larger pT. The measured suppression of high-pT particles is stronger than that observed at lower collision energies, indicating that a very dense medium is formed in central Pb–Pb collisions at the LHC