Cloud service localisation

The essence of cloud computing is the provision of software and hardware services to a range of users in dierent locations. The aim of cloud service localisation is to facilitate the internationalisation and localisation of cloud services by allowing their adaption to dierent locales. We address the lingual localisation by providing service-level language translation techniques to adopt services to dierent languages and regulatory localisation by providing standards-based mappings to achieve regulatory compliance with regionally varying laws, standards and regulations. The aim is to support and enforce the explicit modelling of aspects particularly relevant to localisation and runtime support consisting of tools and middleware services to automating the deployment based on models of locales, driven by the two localisation dimensions. We focus here on an ontology-based conceptual information model that integrates locale specication in a coherent way

Improving Dental Experiences by Using Virtual Reality Distraction: A Simulation Study

Dental anxiety creates significant problems for both patients and the dental profession. Some distraction interventions are already used by healthcare professionals to help patients cope with unpleasant procedures. The present study is novel because it a) builds on evidence that natural scenery is beneficial for patients, and b) uses a Virtual Reality (VR) representation of nature to distract participants. Extending previous work that has investigated pain and anxiety during treatment, c) we also consider the longer term effects in terms of more positive memories of the treatment, building on a cognitive theory of memory (Elaborated Intrusions). Participants (n = 69) took part in a simulated dental experience and were randomly assigned to one of three VR conditions (active vs. passive vs. control). In addition, participants were distinguished into high and low dentally anxious according to a median split resulting in a 362 between-subjects design. VR distraction in a simulated dental context affected memories a week later. The VR distraction had effects not only on concurrent experiences, such as perceived control, but longitudinally upon the vividness of memories after the dental experience had ended. Participants with higher dental anxiety (for whom the dental procedures were presumably more aversive) showed a greater reduction in memory vividness than lower dental-anxiety participants. This study thus suggests that VR distractions can be considered as a relevant intervention for cycles of care in which people’s previous experiences affect their behaviour for future events

Charm-quark mass from weighted finite energy QCD sum rules

The running charm-quark mass in the $\bar{MS}$ scheme is determined from weighted finite energy QCD sum rules (FESR) involving the vector current correlator. Only the short distance expansion of this correlator is used, together with integration kernels (weights) involving positive powers of $s$, the squared energy. The optimal kernels are found to be a simple {\it pinched} kernel, and polynomials of the Legendre type. The former kernel reduces potential duality violations near the real axis in the complex s-plane, and the latter allows to extend the analysis to energy regions beyond the end point of the data. These kernels, together with the high energy expansion of the correlator, weigh the experimental and theoretical information differently from e.g. inverse moments FESR. Current, state of the art results for the vector correlator up to four-loop order in perturbative QCD are used in the FESR, together with the latest experimental data. The integration in the complex s-plane is performed using three different methods, fixed order perturbation theory (FOPT), contour improved perturbation theory (CIPT), and a fixed renormalization scale $\mu$ (FMUPT). The final result is $\bar{m}_c (3\, {GeV}) = 1008\,\pm\, 26\, {MeV}$, in a wide region of stability against changes in the integration radius $s_0$ in the complex s-plane.Comment: A short discussion on convergence issues has been added at the end of the pape

Study of moments of event shapes in e+e- annihilation using JADE data

Data from e+e- annihilation into hadrons collected by the JADE experiment at centre-of-mass energies between 14 GeV and 44 GeV were used to study moments of event shape distributions. The data were compared with Monte Carlo models and with predictions from QCD NLO order calculations. The strong coupling constant measured from the moments is alpha_S(M_Z) = 0.1286 +/- 0.0007 (stat) +/- 0.0011 (expt) +/- 0.0022 (had) +/- 0.0068 (theo), alpha_S(M_Z) = 0.1286 +/- 0.0072 (total error), consistent with the world average. However, systematic deficiencies in the QCD NLO order predictions are visible for some of the higher moments.Comment: JADE note 147 submitted as contributed paper to ICHEP 2004, corrected statistical error of 6 observable average and several typo

Measurement of the Strong Coupling Constant alpha_S from the Four-Jet Rate in e+e- Annihilation using JADE data

Data from e+e- annihilation into hadrons collected by the JADE experiment at centre-of-mass energies between 14 GeV and 44 GeV were used to study the four-jet rate as a function of the Durham algorithm's resolution parameter y_cut. The four-jet rate was compared to a QCD NLO order calculations including NLLA resummation of large logarithms. The strong coupling constant measured from the four-jet rate is alpha_S(M_Z) = 0.1169 +/- 0.0004 (stat) +/- 0.0012 (expt) +/- 0.0021 (had) +/- 0.0007 (theo), alpha_S(M_Z) = 0.1169 +/- 0.0026 (total error) in agreement with the world average.Comment: JADE note 146 submitted as contributed paper to ICHEP 200

Sustainability Learning in Natural Resource Use and Management

Premi a l'excel·lència investigadora. Àmbit de les Ciències Socials. 2008We contribute to the normative discussion on sustainability learning and provide a theoretical integrative framework intended to underlie the main components and interrelations of what learning is required for social learning to become sustainability learning. We demonstrate how this framework has been operationalized in a participatory modeling interface to support processes of natural resource integrated assessment and management. The key modeling components of our view are: structure (S), energy and resources (E), information and knowledge (I), social-ecological change (C), and the size, thresholds, and connections of different social-ecological systems. Our approach attempts to overcome many of the cultural dualisms that exist in the way social and ecological systems are perceived and affect many of the most common definitions of sustainability. Our approach also emphasizes the issue of limits within a total socialecological system and takes a multiscale, agent-based perspective. Sustainability learning is different from social learning insofar as not all of the outcomes of social learning processes necessarily improve what we consider as essential for the long-term sustainability of social-ecological systems, namely, the co-adaptive systemic capacity of agents to anticipate and deal with the unintended, undesired, and irreversible negative effects of development. Hence, the main difference of sustainability learning from social learning is the content of what is learned and the criteria used to assess such content; these are necessarily related to increasing the capacity of agents to manage, in an integrative and organic way, the total social-ecological system of which they form a part. The concept of sustainability learning and the SEIC social-ecological framework can be useful to assess and communicate the effectiveness of multiple agents to halt or reverse the destructive trends affecting the life-support systems upon which all humans depend.Synthesis, part of a Special Feature on Social Learning in Water Resources Managemen