Interpretive structural modelling of risk sources in a virtual organisation

International audienceSpeedier network decision making together with shorter time to bring items to market together with lower network operating costs all result from enhanced knowledge sharing. In addition re-use of enterprise and network knowledge resulting from improved capture means that any risk of repeating earlier project work is limited, repetition of past mistakes is reduced. Decisions are made with greater awareness of any risks involved and therefore there is likely to be a reduction in costs arising from faulty decisions and failed collaborations. While there are many advantages attaching to the use of virtual organizations (VOs) there are also challenges, including risks that have become apparent through undertaking a review of the literature. In total 13 sources of risk were found stemming from the network related risks in a VO, where the emphasis of the study was placed,. This paper contains a thorough study that will identify these threats as well as gaining a sound understanding of them by examining them one by one as they have been identified by the literature and previous studies. Subsequently, their relative importance will be analysed through the use of Structural Modeling (ISM) using information gathered in a questionnaire

Integrating narratives into decision making for complex systems engineering design issues

Engineering decision making and design requires collaboration between groups from different disciplines, each with different tools, vocabulary, and concerns. Traditional engineering decision-making tools are generally based on understanding the decision makers’ values, modeling uncertainty with probability, and selecting the alternative that maximizes utility. This rational approach to decision making may not be well understood or used by many stakeholders involved in the engineering design process. Constructing narratives, a basic means of human communication, may aid in engineering communication and comprehension and help with decision making. Narratives represent events by means of a story and usually include characters or agents who cause events and to whom events happen. This paper recommends three methods for how the use of narrative can be applied to the area of engineering decision making. These methods include connecting the decision maker to the analysis, creating narrative simulations for training decision makers, and fostering consensus in problems with multiple stakeholders. An illustrative example of designing a better cookstove for the developing world demonstrates the role that understanding narratives of various stakeholders can play for accomplishing complex systems engineering

Driving Innovation through Big Open Linked Data (BOLD): Exploring Antecedents using Interpretive Structural Modelling

YesInnovation is vital to find new solutions to problems, increase quality, and improve profitability. Big open linked data (BOLD) is a fledgling and rapidly evolving field that creates new opportunities for innovation. However, none of the existing literature has yet considered the interrelationships between antecedents of innovation through BOLD. This research contributes to knowledge building through utilising interpretive structural modelling to organise nineteen factors linked to innovation using BOLD identified by experts in the field. The findings show that almost all the variables fall within the linkage cluster, thus having high driving and dependence powers, demonstrating the volatility of the process. It was also found that technical infrastructure, data quality, and external pressure form the fundamental foundations for innovation through BOLD. Deriving a framework to encourage and manage innovation through BOLD offers important theoretical and practical contributions

Ebola: translational science considerations

We are currently in the midst of the most aggressive and fulminating outbreak of Ebola-related disease, commonly referred to as “Ebola”, ever recorded. In less than a year, the Ebola virus (EBOV, Zaire ebolavirus species) has infected over 10,000 people, indiscriminately of gender or age, with a fatality rate of about 50%. Whereas at its onset this Ebola outbreak was limited to three countries in West Africa (Guinea, where it was first reported in late March 2014, Liberia, where it has been most rampant in its capital city, Monrovia and other metropolitan cities, and Sierra Leone), cases were later reported in Nigeria, Mali and Senegal, as well as in Western Europe (i.e., Madrid, Spain) and the US (i.e., Dallas, Texas; New York City) by late October 2014. World and US health agencies declared that the current Ebola virus disease (EVD) outbreak has a strong likelihood of growing exponentially across the world before an effective vaccine, treatment or cure can be developed, tested, validated and distributed widely. In the meantime, the spread of the disease may rapidly evolve from an epidemics to a full-blown pandemic. The scientific and healthcare communities actively research and define an emerging kaleidoscope of knowledge about critical translational research parameters, including the virology of EBOV, the molecular biomarkers of the pathological manifestations of EVD, putative central nervous system involvement in EVD, and the cellular immune surveillance to EBOV, patient-centered anthropological and societal parameters of EVD, as well as translational effectiveness about novel putative patient-targeted vaccine and pharmaceutical interventions, which hold strong promise, if not hope, to curb this and future Ebola outbreaks. This work reviews and discusses the principal known facts about EBOV and EVD, and certain among the most interesting ongoing or future avenues of research in the field, including vaccination programs for the wild animal vectors of the virus and the disease from global translational science perspective

The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar, and APOGEE-2 Data

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 survey that publicly releases infrared spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the subsurvey Time Domain Spectroscopic Survey data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey subsurvey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated value-added catalogs. This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper, Local Volume Mapper, and Black Hole Mapper surveys