Universal scaling of strange particle pTp_{\rm T} spectra in pp collisions

As a complementary study to that performed on the transverse momentum ($p_{\rm T}$) spectra of charged pions, kaons and protons in proton-proton (pp) collisions at LHC energies 0.9, 2.76 and 7 TeV, we present a scaling behaviour in the $p_{\rm T}$ spectra of strange particles ($K_{S}^{0}$, $\rm \Lambda$, $\rm \Xi$ and $\phi$) at these three energies. This scaling behaviour is exhibited when the spectra are expressed in a suitable scaling variable $z=p_{\rm T}/K$, where the scaling parameter $K$ is determined by the quality factor method and increases with the center of mass energy ($\sqrt{s}$). The rates at which $K$ increases with $\mathrm{ln}\sqrt{s}$ for these strange particles are found to be identical within errors. In the framework of the colour string percolation model, we argue that these strange particles are produced through the decay of clusters that are formed by the colour strings overlapping. We observe that the strange mesons and baryons are produced from clusters with different size distributions, while the strange mesons (baryons) $K_{S}^{0}$ and $\phi$ ($\rm \Lambda$ and $\rm \Xi$) originate from clusters with the same size distributions. The cluster's size distributions for strange mesons are more dispersed than those for strange baryons. The scaling behaviour of the $p_{\rm T}$ spectra for these strange particles can be explained by the colour string percolation model in a quantitative way.Comment: 8 pages, 10 figures, accepted by EPJ

Exploring the role of the transactive memory system in virtual team resilience: Evidence from online medical teams

The capacity to resist and recover from challenges and adversities (i.e., resilience capacity) is critical for a virtual team to survive. However, our knowledge of what influence the development of resilient virtual teams have yet to be fully developed. Drawing on the transactive memory system (TMS) theory, we propose that TMS will enhance a virtual team resilience capacity. Applying discontinuous growth modeling, results of an empirical study involving 1974 online medical teams from a popular online healthcare platform in China provide available evidence. We found inconsistent effects of the three dimensions of TMS on online medical team resilience capacity. Specifically, specialization shows no significant impact. Credibility can enhance online medical team resilience capacity for both process and outcome performance. For coordination, voice centralization positively affects online medical team resilience capacity for process performance. These findings advance virtual team resilience literature and inform practitioners about how to build resilient virtual teams

When worlds collide: combining Ordnance Survey and Open Street Map data

The context of this paper is the progress of national and international spatial data infrastructures such as the UK Location Programme and INSPIRE, contrasted against crowd-sourced geospatial databases such as Open Street Map. While initiatives such as INSPIRE tend towards a top-down process of harmonised data models and services using ISO & OGC standards, the OSM approach is one of tagged data with attribute tags agreed through consensus, but a tag set that can change with time (with inherent related issues of data quality). There is a danger that should the more formal approaches simply ignore the crowd sourced initiatives then they will miss an opportunity to evolve to better meet growing demands for geographic information. In any case both formal and informal data will increasingly coexist begging the question of how an end user gains maximum benefit from both. Ordnance Survey as the national mapping agency of Great Britain provides authoritative datasets with published data specifications driven by a combination of user need and the history of national mapping with a remit to ensure real-world feature changes are reflected in the OS large-scale data within 6 months. OSM in contrast relies on the availability of local mapping enthusiasts to capture changes but through its more informal structure can capture a broader range of features of interest to different sub-communities such as cyclists or horse riders. This research has been carried out to understand the issues of data integration between crowd sourced information and authoritative data. The aim of the research was to look into the mid-term and long-term effects of crowd sourcing technologies for understanding their effects on the change intelligence operations of national mapping agencies (NMAs) in the future. Mobile phones, with more computing power than the desktop machine of 5 years ago and incorporating built-in GPS receivers and cameras have become widespread and give people a multi-sensor capability. This combined with CCTV, sensor webs, RFID etc. offers the potential to make data capture pervasive and ubiquitous. All key sectors of modern economies will be affected by the developments in crowd sourcing of information. The synergies created by new technologies will create the conditions for exciting new developments in geospatial data integration. This has an impact in the spatial data collection domain especially in collecting vernacular and crowd-sourced information. Individual users will be able to use these technologies to collect location data and make it available for multiple applications without needing prior geospatial skills. The basic question behind our research is how do we combine data from authoritative OS data sets with feature-rich, informal OSM data, recognising the variable coverage of OSM while capturing the best of both worlds? There have been previous studies (Al-Bakri and Fairbairn, 2010) focussing on geometric accuracy assessment of crowd-sourced data(OSM) with OS data. Another important context is the rapid developments in Open Source GIS. The availability of free and open source GIS has made possible for large number of government organizations and SMEs to make use of GIS tools in their work. The Open Source Geospatial Foundation (OSGeo) is an excellent example of community initiative to support and promote the collaborative development of open geospatial technologies. OSGeo’s key mission is to promote the use of open source software in the geospatial industry and to encourage the implementation of open standards and standards based interoperability in its projects