We asked whether and how generations vary in their perceptions on moral matters ranging from their justifications of crime and questions concerning bodily autonomy. In our exploratory study using data from the World Values Survey, we found that Generations Y and Z are more likely than their older counterparts to justify crimes, such as cheating on taxes or stealing property, and to favor greater bodily autonomy in issues such as suicide and abortion. They also rank lower the importance of God and national pride. Implications are offered for employers who wish to motivate and incentivize a multi-generational workforce.

Leading in in extremis situations, when lives are in peril, remains one of the least addressed areas of leadership research. Little is known about how leaders make sense in these dangerous situations and communicate these contexts to others. Because most of the literature on in extremis is theoretical, we sought empirical evidence of how sensemaking proceeds in practice. A qualitative study was conducted based on interviews with 30 Army leaders who had recently led teams in combat. Our findings suggest that during these life-threatening situations, sensemaking and sensegiving are actually occurring simultaneously, the type of training leaders receive is critical, and a sense of duty can influence a person’s role as a leader. Our findings have implications for both theory and practice since crisis leadership is now a coveted executive quality for leadership competency.

In extremis leadership is a situation in which the leader's life and those of his/her team are, or are perceived to be, in danger. Because dangerous situations are difficult to study, and most of the literature is theoretical, little is known about how leaders communicate with their teams and make sense of these contexts. We proposed research to address this gap and understand how leaders sensemake in in extremis situations and sensegive to their teams and how this affects in extremist situation outcomes. Our study was a qualitative study of thirty US Army leaders from West Point who recently have returned from combat tours in the Middle East. Our data demonstrate that sensemaking and sensegiving in that context differ from those processes in more commonly studied benign situations. We found that leaders who have mental flexibility, sense of duty, and self-confidence were the best prepared for these dangerous situations. Our study has implications for both theory and practice.

The CARMEN Virtual Laboratory (VL) is a cloud-based platform which allows neuroscientists to store, share, develop, execute, reproduce and publicise their work. This paper describes new functionality in the CARMEN VL: an interactive publications repository. This new facility allows users to link data and software to publications. This enables other users to examine data and software associated with the publication and execute the associated software within the VL using the same data as the authors used in the publication. The cloud-based architecture and SaaS (Software as a Service) framework allows vast data sets to be uploaded and analysed using software services. Thus, this new interactive publications facility allows others to build on research results through reuse. This aligns with recent developments by funding agencies, institutions, and publishers with a move to open access research. Open access provides reproducibility and verification of research resources and results. Publications and their associated data and software will be assured of long-term preservation and curation in the repository. Further, analysing research data and the evaluations described in publications frequently requires a number of execution stages many of which are iterative. The VL provides a scientific workflow environment to combine software services into a processing tree. These workflows can also be associated with publications and executed by users. The VL also provides a secure environment where users can decide the access rights for each resource to ensure copyright and privacy restrictions are met.

In recent years, the range of sensing technologies has expanded rapidly, whereas sensor devices have become cheaper. This has led to a rapid expansion in condition monitoring of systems, structures, vehicles, and machinery using sensors. Key factors are the recent advances in networking technologies such as wireless communication and mobile ad hoc networking coupled with the technology to integrate devices. Wireless sensor networks (WSNs) can be used for monitoring the railway infrastructure such as bridges, rail tracks, track beds, and track equipment along with vehicle health monitoring such as chassis, bogies, wheels, and wagons. Condition monitoring reduces human inspection requirements through automated monitoring, reduces maintenance through detecting faults before they escalate, and improves safety and reliability. This is vital for the development, upgrading, and expansion of railway networks. This paper surveys these wireless sensors network technology for monitoring in the railway industry for analyzing systems, structures, vehicles, and machinery. This paper focuses on practical engineering solutions, principally, which sensor devices are used and what they are used for; and the identification of sensor configurations and network topologies. It identifies their respective motivations and distinguishes their advantages and disadvantages in a comparative review.

The disruptive innovation theory of Clayton Christensen was heavily criticized in an article in The New Yorker magazine by Jill Lepore. Her article evaluated Christensen’s research methodology and analysis and found both lacking. Lepore’s criticisms identified several symptoms of problems within the research, but stopped short of presenting root causes. This article looks at Lepore’s criticisms and diagnoses three root causes for the symptoms recognized by Lepore: a lack of an adequately constrained definition of the term disruptive innovation; a failure to identify and maintain a consistent unit of analysis in the research; and a failure to account adequately for managerial agency. This article then discusses solutions to these issues and how scholars might move research into disruptive innovation forward.

AWE is replacing its current Mevex X-ray machine (~800kV, 35kA, 60ns pulse) to improve reliability and maintainability. A design using a Linear Transformer Driver (LTD) insulated with dry air at atmospheric pressure has been developed by ITHPP. A testbed using 3 cavities, made from 2 bricks of two GA35460M 8nF, 100kV capacitors, a multigap multichannel switch and a magnetic core, has been developed and extensively tested [1]. This led to the development of the full generator that uses 17 cavities in series to produce an 800kV output voltage on a vacuum insulated line and a 23-25Ω diode. The ~4.3m long inner stalk is cantilevered by the back of the pulser and is positioned by a 3 point adjustable support. This setting is completed by 2 internal tensioning cables to compensate for flexing and provide fine adjustment of the diode end. The assembly of the generator has been done on the 1 st half of 2015. The machine can be operated with negative and positive polarity outputs to satisfy operational requirements given by AWE. The tests results of this generator in both polarities are presented in terms of output performances. The reproducibility and reliability of the full system is also analyzed.

Event Abstract Back to Event Development of a workflow system for the CARMEN Neuroscience Portal Colin Ingram1*, Mark Jessop2, Michael Weeks3, Jim Austin2 and Martyn Fletcher3 1 Newcastle university, UK 2 University of York, United Kingdom 3 University of York , United Kingdom We discuss the development of a workflow system for the CARMEN neuroscience portal. CARMEN is a virtual (browser- based) collaboration environment for the analysis and management of electrophysiology data. It has been designed to facilitate community based sharing of tools, services and data relating to neurophysiology research. To date, CARMEN has provided mechanisms to allow legacy software code and applications from the lab to be deployed, via a ligtweight wrapping process, as interactive services that run on the CARMEN cloud resource. This is a software as a service (SaaS) delivery model. This approach offers many benefits to the neuroscience community, including the reuse of software applications, the ability to compare processing algorithms and access to considerable computing CARMEN resource for high intensity computing tasks. However, many researchers would derive extra benefit from being able to string combine analysis services together in an orchestrated processing pipeline, or workflow. The CARMEN project has now addressed this requirement and developed a workflow generation and execution system within the platform. CARMEN has relatively specific workflow requirements due to its cloud execution model and the use of the it's NDF data format (www.carmen.org.uk/standards/CarmenDataSpecs.pdf). Hence, although we evaluated current workflow tools, such as Taverna (www.taverna.org.uk/) and E-Science Central (www.esciencecentral.co.uk/), it was found that neither met the functional requirements and a bespoke workflow service was developed. The CARMEN Workflow Tool is Java-based and designed to make use of CARMEN Services and NDF. The workflow tool supports both data and control flow, and allows parallel execution of services. Using a Service Invocation API to invoke CARMEN services simplifies the workflow infrastructure substantially. This API allows the workflow engine to make use of our dynamic service deployment and execution system, achieving scalable heterogeneous distributed processing. The complete workflow tool consists of a graphical design tool, a workflow engine, and access to a library of CARMEN services and common workflow tasks. The poster will provide an overview of the main design considerations for the workflow system and provide an overview of its use within CARMEN. We provide details of the workflow execution engine and describe the XML scripting approach that has been developed to control the workflow orchestration. The workflow description XML schema follows a similar form to myGrid’s SCUFL (Simple Conceptual Unified Flow Language - www.mygrid.org.uk/dev/wiki/display/developer/SCUFL2) script but modified to suit our service and data description formats. Example workflows for neurophysiology will be presented, demonstrating the flexibility of the workflow tool to support complex analysis pipelines. Keywords: Infrastructural and portal services, Electrophysiology, workflow development, CARMEN, Software Development Conference: 5th INCF Congress of Neuroinformatics, Munich, Germany, 10 Sep - 12 Sep, 2012. Presentation Type: Poster Topic: Neuroinformatics Citation: Ingram C, Jessop M, Weeks M, Austin J and Fletcher M (2014). Development of a workflow system for the CARMEN Neuroscience Portal. Front. Neuroinform. Conference Abstract: 5th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2014.08.00068 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 21 Mar 2013; Published Online: 27 Feb 2014. * Correspondence: Dr. Colin Ingram, Newcastle university, York, UK, C.d.ingram@Newcastle.ac.uk Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Colin Ingram Mark Jessop Michael Weeks Jim Austin Martyn Fletcher Google Colin Ingram Mark Jessop Michael Weeks Jim Austin Martyn Fletcher Google Scholar Colin Ingram Mark Jessop Michael Weeks Jim Austin Martyn Fletcher PubMed Colin Ingram Mark Jessop Michael Weeks Jim Austin Martyn Fletcher Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Event Abstract Back to Event The CARMEN data sharing portal project: what have we learned? Leslie S. Smith1*, Jim Austin2, Stephen Eglen3, Tom Jackson2, Mark Jessop2, Bojian Liang2, Michael Weeks2 and Evelyne Sernagor4 1 University of Stirling, Computing Science and Mathematics, United Kingdom 2 University of York, Computer Science, United Kingdom 3 University of Cambridge, Applied Mathematics and Theoretical Physics, United Kingdom 4 Newcastle University, Institute of Neuroscience, United Kingdom The UK CARMEN project represents one of the first major efforts at sharing electrophysiological datasets, and the techniques for processing them, using a portal. It started in 2006 (with the late Professor Colin Ingram as Principal Investigator), with funding from the UK EPSRC, and this was followed on with funding from the UK BBSRC. It has been providing a gradually improving service for about five years, starting from the capability or sharing data, and adding services, and workflows. It has its own internal data format, Neural Data Format: by converting proprietary dataset types to this format using a service, we enable services and workflows that process this format to be applicable to datasets originating from many different types of recording platforms. Given the experience that we have gained from running this service, what have we learned? What would we do differently if we were to start again? Is there still interest in this type of capability, or has the world moved onwards? We recently put out a questionnaire to all registered users of CARMEN, and we have now some feedback from registered users, and (perhaps equally importantly) from people who registered and did not end up using the system. In general, the use of the system for secure data sharing and exchange seems to have been the most popular. Certainly, in the design of the system, we were very aware that geographically distributed neuroinformaticians and neuroscientists wanted to share their datasets, and to be able to do so in a way that was secure. This seems to have been one of the successes of the system. Yet had we only wanted to do that, we could have put together a much simpler system altogether! Certainly, it is the case that some users have used the system in a much more powerful way, as evidenced by the recent paper [Eglen et al 2014]. But such types of users have been relatively few. What is it that has put users off from more sophisticated interaction with what is in essence a platform that could be used for extended analysis and sharing of data from many different laboratories? One issue has been speed of access. Although the network at the server end is fast, many users do not have such fast access from their laboratories. The result is that uploading large datasets (and indeed downloading them as well) can be slow. There is little that the CARMEN staff can really do to help here, because the problem lies at the users end, and is not under the control of CARMEN itself. Many users complained that the use of the services and workflows was difficult. They reported that it was difficult to work out exactly how to use them, and even to find out exactly what services were available precisely. This is a bit disappointing: a great deal of time was spent in trying to make services usable, and in enabling effective search techniques, and providing information on these services within the system. But perhaps the system is complex to use, and many users, more used to expensively developed sites that were easier to use, did not spend the time really finding out what could be done. That said, it is clear that running services was relatively complex, and further, that running multiple services (and workflows) was really quite difficult to organise. There does seem to be some agreement that using CARMEN services and workflows for cross-dataset analysis (i.e. on datasets from a number of sources) is of interest, however, very little data on CARMEN has been made public, so unless users have direct access to datasets that they can then upload, this type of activity has been difficult. Some users simply did not like the concept: they wanted something that was decentralised, and could use many local machines. Some wanted a more professionally designed “look and feel” as well. For the first of these, the issue of dataset size is problematic - indeed, that was the reason for the basic design, with the concept of bringing the processing to the data, rather than the other way around. For the second, we too would have liked to employ more professional designers, but the budget did not stretch that far. Another suggestion is direct integration on to the systems that the neurophysiologists are already using. This would be a great idea, but there are many such systems (although integrating it on to Matlab, which is often used for initial data analysis would be a possibility). In addition, the CARMEN project has been running at a time of rapid technological change within the Internet. Much of the user-facing processing was designed initially to use Java applets, because in that way we could provide systems that enabled uploading and downloading in a secure and effective fashion. But times have moved on, and one would now expect to use a mixture of HTML5 and JavaScript for these types of purposes. The datasets are very complex, particularly when one includes the multiplicity of data types in electrophysiological datasets (simple time series, excerpted sections, spikes, etc., plus the metadata that describes the representation, and the experiments that produced the dataset). Neural Data Format (NDF) caters for these. At the time that the NDF was designed, HDF5 was not really able to work with data in the way that we desired. This is no longer the case, and were we to redesign NDF, we would now use an HDF5 based format. This would be a major task, but we can get around the issues by creating services to translate between HDF5 and NDF. It is worth noting that HDF5 alone does not solve the problem. Indeed one of the INCF Task Forces has been developing an HDF5 format for this type of application, and this work is only now nearing completion. Another aspect of technological change lies in data display. When CARMEN started, there was no straightforward way of enabling complex data display in a browser (short of a very complex Java applet). As a result, we used a proprietary piece of software for data display. Now, however, thanks to the large expansion in the capabilities of JavaScript, this is no longer the case. Reading over the users comments, it appears that CARMEN, or a portal like it, remains a popular idea: however, it needs to be easy to use, both for upload/download and for running services and workflows. Documentation needs to be better, and easy to find (perhaps easy to find is critically important here). We are planning a new project proposal, and we will be taking these issues into account. Acknowledgements UK EPSRC grant EP/E002331/1 and BBSRC grant BB/IO01042/1. References [Eglen et al 2014] A data repository and analysis framework for spontaneous neural activity recordings in developing retina, SJ Eglen, M Weeks, M Jessop, J Simonotto, T Jackson and E Sernagor, GigaScience, 3:3, 2014, doi:10.1186/2047-217X-3-3 Keywords: neuroscience data portal, data processing services, data processing workflows, data sharing, electrophysiological time series data Conference: Neuroinformatics 2014, Leiden, Netherlands, 25 Aug - 27 Aug, 2014. Presentation Type: Poster, not to be considered for oral presentation Topic: Infrastructural and portal services Citation: Smith LS, Austin J, Eglen S, Jackson T, Jessop M, Liang B, Weeks M and Sernagor E (2014). The CARMEN data sharing portal project: what have we learned?. Front. Neuroinform. Conference Abstract: Neuroinformatics 2014. doi: 10.3389/conf.fninf.2014.18.00068 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 28 Apr 2014; Published Online: 04 Jun 2014. * Correspondence: Prof. Leslie S Smith, University of Stirling, Computing Science and Mathematics, Stirling, Scotland, FK9 4LA, United Kingdom, l.s.smith@cs.stir.ac.uk Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Leslie S Smith Jim Austin Stephen Eglen Tom Jackson Mark Jessop Bojian Liang Michael Weeks Evelyne Sernagor Google Leslie S Smith Jim Austin Stephen Eglen Tom Jackson Mark Jessop Bojian Liang Michael Weeks Evelyne Sernagor Google Scholar Leslie S Smith Jim Austin Stephen Eglen Tom Jackson Mark Jessop Bojian Liang Michael Weeks Evelyne Sernagor PubMed Leslie S Smith Jim Austin Stephen Eglen Tom Jackson Mark Jessop Bojian Liang Michael Weeks Evelyne Sernagor Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Under in extremis conditions, when lives are at stake, astutely reading a situation (situation awareness) and belief in one’s ability to manage it (self-efficacy) are crucial leader skills (Sweeney, Matthews, & Lester, 2011). We report on a survey of 514 military, firefighting, and law enforcement leaders and their experiences during in extremis conditions. Our earlier qualitative study identified several characteristics that helped leaders during these tense times. This study examines the moderating effect of four human qualities identified in the qualitative study (self-esteem, self-sacrifice, mental flexibility and altruism) on situation awareness and self-efficacy as they relate to survival criteria in life threatening situations. This study also examined the similarities and differences among the three groups of hazardous occupations, and discovered when taken as a whole, each of the factors were significant, but the specific details for each group diverged. We theorize that differing priorities among the organizational missions give rise to these disparities. Even though all three are often grouped as one in most in extremis research (Sweeney, Matthews, Lester, 2011), we interpreted the results as indicating two distinct kinds of in extremis situations and classified the fire fighters and law enforcement as being in a “protectors” role, and the military being in a “vanquisher” role. Instead of routinely looking at all in extremis occupations as one population with identical roles, research into these differences needs to be explored further. Results thus seem relevant to all professional first-responders facing life-threatening situations, because each group may benefit from different criteria for hiring, recruiting and training of personnel. The results may also be of interest to individuals facing tense, ambiguous, albeit less acute, circumstances.

Hepatic progenitor cells (HPCs) have regenerative properties that could aid the development of treatments for severe liver disease. To study how pressure influences HPC fate, a hydrostatic pressure-controlled cell culture chamber was developed. The design incorporates custom LabView scripting for enhanced pressure regulation and data acquisition. Pressure can be controlled within ±0.2mmHg. Continuous airflow permits gas exchange, and CO2 is maintained at 5%±0.2%. Applied pressures range from 5 to 20 mmHg, reflecting interstitial pressure conditions in healthy and diseased livers, respectively. Bipotential Murine Oval Liver (BMOL) cells, an HPC-like cell line, were cultured in the chamber to test for maintenance of cell viability, adequate CO2 regulation, and maintenance of adequate media volume over 24 hours. Cultured cells were exposed to 5 or 19 mmHg. After 24 hours, media pH was measured, viable cells were counted (Trypan Blue, n=3), and plates were weighed to assess fluid loss. The number of live cells cultured under pressure vs. control conditions was not statistically different (p>.05). The pH remained constant at 7.0 for all conditions, suggesting adequate gas exchange. Evaporation of media was minimal at 3.97%. Results indicate that the pressure chamber provides appropriate environmental conditions for future studies on HPC pressure sensitivity.

This chapter proposes the narrative network analysis methodology for application in the examination of online communities. The narrative network analysis provides a basis for systematic examination of online communities that has been missing from the literature. The chapter describes three online communities and their characteristics to demonstrate the possibilities of the methodology. From these descriptions a proposed model of the communities is presented, and then an abbreviated narrative network analysis is developed. The network analysis demonstrates how an ethnographically informed model may be tested in a systematic manner with the narrative network analysis techniques. The chapter then concludes with a number of questions for future research in this area that have been proposed by other authors. These unanswered questions are likely candidates for future research using this promising methodology.

Background: During early development, neural circuits fire spontaneously, generating activity episodes with complex spatiotemporal patterns. Recordings of spontaneous activity have been made in many parts of the nervous system over the last 25 years, reporting developmental changes in activity patterns and the effects of various genetic perturbations.Results: We present a curated repository of multielectrode array recordings of spontaneous activity in developing mouse and ferret retina. The data have been annotated with minimal metadata and converted into HDF5. This paper describes the structure of the data, along with examples of reproducible research using these data files. We also demonstrate how these data can be analysed in the CARMEN workflow system. This article is written as a literate programming document; all programs and data described here are freely available.Conclusions: 1. We hope this repository will lead to novel analysis of spontaneous activity recorded in different laboratories. 2. We encourage published data to be added to the repository. 3. This repository serves as an example of how multielectrode array recordings can be stored for long-term reuse.

The emerging transformation from a product oriented economy to a service oriented economy based on Cloud environments envisions new scenarios where actual QoS (Quality of Service) mechanisms need to be redesigned. In such scenarios new models to negotiate and manage Service Level Agreements (SLAs) are necessary. An SLA is a formal contract which defines acceptable service levels to be provided by the Service Provider to its customers in measurable terms. SLAs are an essential component in building Cloud systems where commitments and assurances are specified, implemented, monitored and possibly negotiable. This is meant to guarantee that consumers’ service quality expectations can be achieved. In fact, the level of customer satisfaction is crucial in Cloud environments, making SLAs one of the most important and active research topics. This paper presents an SLA implementation for negotiation, monitoring and renegotiation of agreements for Cloud services based on the CMAC (Condition Monitoring on A Cloud) platform. CMAC offers condition monitoring services in cloud computing environments to detect events on assets as well as data storage services.

This paper extends our understanding of knowledge creation in virtual communities of practice by examining crowdsourcing activities that enable knowledge creation in these social structures. An interpretive methodology, narrative networks analysis, is used to systematically study the narratives of discussion forums in a virtual community. The virtual community studied is voluntary for the participants, and open to anyone. Through the analysis of the narrative, a model of knowledge creation is developed that identifies types of evidentiary knowledge contributions, as well as conversation mitigators that help or hinder knowledge creation within the community. Knowledge is a primary attraction of a virtual community for many of its members, and this study aims to understand how knowledge is shared and created in such voluntary communities of practice. The model highlights elements that enhance and impair knowledge creation in this type of crowdsourced environment.

The emerging transformation from a product oriented economy to a service oriented economy based on Cloud environments envisions new scenarios where actual QoS mechanisms need to be redesigned. In such scenarios new models to negotiate and manage Service Level Agreements (SLAs) are necessary. An SLA is a formal contract which defines acceptable service levels to be provided by the Service Provider to its customers in measurable terms. This is meant to guarantee that consumers’ service quality expectation can be achieved. In fact, the level of customer satisfaction is crucial in Cloud environments, making SLAs one of the most important and active research topics. The aim of this paper is to explore the possibility of integrating an SLA approach for Cloud services based on the CMAC (Condition Monitoring on A Cloud) platform which offers condition monitoring services in cloud computing environments to detect events on assets as well as data storage services.