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One of the most central questions in network science is: which nodes are most important? Often this question is answered using structural properties such as high connectedness or centrality in the network. However, static structural connectedness does not necessarily translate to dynamical importance. To demonstrate this, we simulate the kinetic Ising spin model on generated networks and one real-world weighted network. The dynamic impact of nodes is assessed by causally intervening on node state probabilities and measuring the effect on the systemic dynamics. The results show that structural features such as network centrality or connectedness are actually poor predictors of the dynamical impact of a node on the rest of the network. A solution is offered in the form of an information theoretical measure named integrated mutual information. The metric is able to accurately predict the dynamically most important node (”driver” node) in networks based on observational data of non-intervened dynamics. We conclude that the driver node(s) in networks are not necessarily the most well-connected or central nodes. Indeed, the common assumption of network structural features being proportional to dynamical importance is false. Consequently, great care should be taken when deriving dynamical importance from network data alone. These results highlight the need for novel inference methods that take both structure and dynamics into account.

The outbreak of the COVID-19 pandemic has led to a resurgence of protests. Various societal conditions of social systems, such as economic stability, demographic ageing, and political elites, are often associated to the emergence of civil resistance movements. Several qualitative and quantitative models have been developed to analyse the relationship between societal conditions and the emergence of protests. The existing models use the underlying assumptions that these conditions operate in similar time-scales. However, the analysis of social systems also shows the importance of considering explicitly the inherent time-scales particularly slow-fast dynamics. The sudden and dramatic disruptive force of the pandemic has yield fine-grained data sets that can be used to better grasp the different dynamics of this social phenom. This paper proposes an integrated approach to explore the relationship between societal conditions and the emergence of protests in the context of the COVID-19 pandemic. First, a literature based causal-loop-diagram is constructed to conceptualise the emergence of civil resistance as a result of intertwined dynamics. Based on the derived factors in the literature study, a data set is constructed to enable this analysis. Furthermore, by means of statistical and computational modelling we conduct a quantitative analysis in which we compare the emergence of protests for 27 countries during the pandemic. Also based on the factors found in literature we have constructed a system dynamics model that explicitly models the development of societal strains and social mobilisation in order to provide a better quantitative explanation of the emergence of protests. We found that while fast-changing factors are better estimators for ‘when’ civil resistance emerges, slow-changing factors are better estimators of ‘how’ civil resistance manifests itself in terms of the relative intensity of the protests in specific countries.

Background The efficacy of smartphone apps for improving medication adherence in type 2 diabetes is not well studied in Asian populations. Objective This study aimed to determine the feasibility, acceptability, and clinical outcomes of using a smartphone app to improve medication adherence in a multiethnic Asian population with type 2 diabetes. Methods We block randomized 51 nonadherent and digitally literate patients with type 2 diabetes between the ages of 21 and 75 years into two treatment arms (control: usual care; intervention: usual care+Medisafe app) and followed them up for 12 weeks. Recruitment occurred at a public tertiary diabetes specialist outpatient center in Singapore. The intervention group received email reminders to complete online surveys monthly, while the control group only received an email reminder(s) at the end of the study. Barriers to medication adherence and self-appraisal of diabetes were assessed using the Adherence Starts with Knowledge-12 (ASK-12) and Appraisal of Diabetes Scale (ADS) questionnaires at baseline and poststudy in both groups. Perception toward medication adherence and app usage, attitude, and satisfaction were assessed in the intervention group during and after the follow-up period. Sociodemographic data were collected at baseline. Clinical data (ie, hemoglobin A1c, body mass index, low-density lipoprotein, high-density lipoprotein, and total cholesterol levels) were extracted from patients’ electronic medical records. Results A total of 51 (intervention group: 25 [49%]; control group: 26 [51%]) participants were randomized, of which 41 (intervention group: 22 [88.0%]; control group: 19 [73.1%]) completed the poststudy survey. The baseline-adjusted poststudy ASK-12 score was significantly lower in the intervention group than in the control group (mean difference: 4.7, P=.01). No changes were observed in the clinical outcomes. The average 12-week medication adherence rate of participants tracked by the app was between 38.3% and 100% in the intervention group. The majority (>80%) of the participants agreed that the app was easy to use and made them more adherent to their medication. Conclusions Our feasibility study showed that among medication-nonadherent patients with type 2 diabetes, a smartphone app intervention was acceptable, improved awareness of medication adherence, and reduced self-reported barriers to medication adherence, but did not improve clinical outcomes in a developed Asian setting.

The existence of slums or informal settlements is common to most cities of developing countries. Its role as single housing delivery mechanism has seriously challenged the popular notion held by policy makers, planners and architects. Today informality is a paradigm of city making and economic growth in Africa, Asia and Latin America. This paper discusses the role of computer simulation models to understand the emergence and growth of slums in developing countries. We have identified the key factors influencing the growth of slums and formulated a standardized set of criteria for evaluating slum models. The review of existing computer simulation models designed to understand slum formation and expansion enabled us to define model requirements and to identify new research questions with respect to exploring the dynamics of slums.

In order to perform clinical investigations on integrated biomedical data sets and to predict virological and epidemiological outcome, medical experts require an IT-based collaborative environment that provides them a user-friendly space for building and executing their complex studies and workflows on largely available and high-quality data repositories. In this paper, the authors introduce such a novel collaborative working environment a so-called virtual laboratory for clinicians and medical researchers, which allows users to interactively access and browse several biomedical research databases and re-use relevant data sets within own designed experiments. Firstly, technical details on the integration of relevant data resources into the virtual laboratory infrastructure and specifically developed user interfaces are briefly explained. The second part describes research possibilities for medical scientists including potential application fields and benefits as using the virtual laboratory functionalities for a particular exemplary study.

The complete cascade from genome, proteome, metabolome, and physiome, to health forms multiscale, multiscience systems and crosses many orders of magnitude in temporal and spatial scales. The interactions between these systems create exquisite multitiered networks, with each component in nonlinear contact with many interaction partners. Understanding, quantifying, and handling this complexity is one of the biggest scientific challenges of our time. In this paper we argue that computer science in general, and Grid computing in particular, provide the language needed to study and understand these systems, and discuss a case study in decision support for HIV drug resistance treatment within the European ViroLab project.

Combining simulations of different scale in one application is non-trivial issue. This paper proposes solution that supports complex time interactions that can appear between elements of such applications. We show that High Level Architecture, especially its time management service can be efficiently used to distribute and communicate multiscale components. Grid HLA Management System (which was presented in our previous work [10]) is used to run HLA-based distributed simulation system on the Grid. The example application is build from simulation modules taken from Multiscale Multiphysics Scientific Environment (MUSE)[8], which is sequential simulation system designed for calculating behaviour of dense stellar systems like globular clusters and galactic nuclei.

Grid benchmarking for improved computational resource selection can shed a light for improving the performance of computationally intensive applications. In this paper we report on a number of experiments with a biomedical parallel application to investigate the levels of performance offered by hardware resources distributed across a pan-European computational Grid network. We provide a number of performance measurements based on the iteration time per processor and communication delay between processors, for a blood flow simulation benchmark based on the lattice Boltzmann method. We have found that the performance results obtained from real application benchmarking are much more useful for running our biomedical application on a highly distributed grid infrastructure than the regular resource information provided by standard Grid information services to resource brokers. © Springer-Verlag Berlin Heidelberg 2005. 3514 534 543 Sponsors: Intel Corporation IBM Corporation Microsoft Research SGI Silicon Graphics Inc. Emory University Conference code: 65644 Cited By :8

In a Problem Solving Environment (PSE), a scientific workflow management system (SWMS) provides a meta environment for managing activities and data in scientific experiments, for prototyping experimental computing systems and for orchestrating the runtime system behaviour. A Grid infrastructure makes data and computing intensive experiments feasible in PSEs but also requires the management of workflow to support dynamics of the flow execution. A dynamic SWMS includes a human user in the runtime loop of a flow execution, and allows an engine to flexibly orchestrate a workflow according to the human decision and the runtime states of the environment. In this paper, we present our research in an ongoing project: Virtual Laboratory for e-Science (VL-e). An agent based solution is proposed to enhance an existing Grid enabled Problem Solving Environment framework called VLAM-G. The intelligence for problem solving strategies and for workflow orchestration is encapsulated as knowledge in two types of agents: study managers and scenario conductors.

Scientific discoveries are often the result of methodical execution of many interrelated scientific workflows, where workflows and datasets published by one set of users can be used by other users to perform subsequent analyses, leading to implicit or explicit collaboration. In this paper, we describe a data model for "collaborative provenance" that extends common workflow provenance models by introducing attributes for characterizing the nature of user collaborations as well as their strength (or weight). In addition, through the implementation of a real-world bioinformatics use case scenario and an associated collaborative provenance database, we demonstrate and evaluate the effectiveness of our model in understanding and analyzing user collaboration in scientific discoveries driven by scientific workflows. Key Words: Collaborative e-Science, user collaborations, scientific workflow systems, provenance, workflow runs, data publication, querying.

In this paper we present a High Level Architecture (HLA) component model, particularly suitable for distributed multiscale simulations. We also present a preliminary implementation of HLA components and the CompoHLA environment that supports setting up and managing multiscale simulations built in the described model. We propose to integrate solutions from High Level Architecture (such as advanced time and data management) with possibilities given by component technologies (such as reusability and composability) and the Grid (such as joining geographically distributed communities of scientists). This approach will allow users working on multiscale applications to more easily exchange and join the simulations already created. The particular focus of this paper is on the design of a HLA component. We show how to insert simulation logic into a component and make it possible to steer from outside its connections with other components. Its functionality is shown through example of multiscale simulation of dense stellar system.

The complete cascade from genome, proteome, metabolome, and physiome, to health forms multiscale, multiscience systems and crosses many orders of magnitude in temporal and spatial scales. The interactions between these systems create exquisite multitiered networks, with each component in nonlinear contact with many interaction partners. Understanding, quantifying, and handling this complexity is one of the biggest scientific challenges of our time. In this paper we argue that computer science in general, and Grid computing in particular, provide the language needed to study and understand these systems, and discuss a case study in decision support for HIV drug resistance treatment within the European ViroLab project.

The input workload of an irregular application must be evenly distributed amongits threads to enable cutting-edge performance. To address this need in OpenMP, several loopscheduling strategies were proposed. While having this ever-increasing number of strategies at dis-posal is helpful, it has become a non-trivial task to select the best one for a particular application.Nevertheless, this challenge becomes easier to be tackled when existing scheduling strategies areextensively evaluated. Therefore, in this paper, we present a performance and scalability eval-uation of the recently-proposed loop scheduling strategy named Smart Round-Robin (SRR). Todeliver a comprehensive analysis, we coupled a kernel benchmarking technique with several rigorousstatistical tools, and considered OpenMP’s Static and Dynamic loop schedulers as our baselines.Our results unveiled that SRR performs better on irregular applications with symmetric workloadsand coarse-grained parallelization, achieving up to 1.9x and 1.5x speedup over OpenMP’s Staticand Dynamic schedulers on synthetic kernels, respectively. On a N-Body Simulations applicationkernel, SRR delivered 2.48x better performance in contrast to OpenMP’s Dynamic scheduler.

International audience; The power consumption of the High Performance Computing (HPC) systems is an increasing concern as large-scale systems grow in size and, consequently, consume more energy. In response to this challenge, we have develop and evaluate new energy-aware load balancers to reduce the average power demand and save energy of parallel systems when scientific applications with imbalanced load are executed. Our load balancers combine dynamic load balancing with DVFS techniques in order to reduce the clock frequency of underloaded computing cores which experience some residual imbalance even after tasks are remapped. The results show that our load balancers present power reductions of 7.5% in average with the fine-grained variant that performs per-core DVFS, and of 18.75% with the coarse-grained variant that performs per-chip DVFS over real applications.