Towards optimising distributed data streaming graphs using parallel streams

Modern scientific collaborations have opened up the op-portunity of solving complex problems that involve multi-disciplinary expertise and large-scale computational experi-ments. These experiments usually involve large amounts of data that are located in distributed data repositories running various software systems, and managed by different organi-sations. A common strategy to make the experiments more manageable is executing the processing steps as a work-flow. In this paper, we look into the implementation of fine-grained data-flow between computational elements in a scientific workflow as streams. We model the distributed computation as a directed acyclic graph where the nodes rep-resent the processing elements that incrementally implement specific subtasks. The processing elements are connected in a pipelined streaming manner, which allows task executions to overlap. We further optimise the execution by splitting pipelines across processes and by introducing extra parallel streams. We identify performance metrics and design a mea-surement tool to evaluate each enactment. We conducted ex-periments to evaluate our optimisation strategies with a real world problem in the Life Sciences—EURExpress-II. The paper presents our distributed data-handling model, the op-timisation and instrumentation strategies and the evaluation experiments. We demonstrate linear speed up and argue that this use of data-streaming to enable both overlapped pipeline and parallelised enactment is a generally applicable optimisation strategy

From innovation to commercialization through networks and agglomerations: Analysis of sources of innovation, innovation capabilities and performance of Dutch SMEs

This study claims that policy makers may not be sufficiently aware of the importance of maintaining an appropriate balance between exploration and exploitation networks for small and medium-sized enterprises (SMEs). On the basis of the open innovation model, policy makers are also increasingly stimulating SMEs to develop their exploration skills. In the Netherlands, a government subsidy called the 'innovation voucher programme' was introduced to stimulate SMEs to develop innovation in cooperation with knowledge institutes. Yet, although many studies show that SMEs tend to have a higher R&D productivity than larger firms, and innovative SMEs are more likely to make external networks with other SMEs or institutions such as universities, there is still little examination of the successfulness of SME's innovation activities. The growing policy attention for the role of SMEs in innovation prompts the questions how innovation in SMEs can be facilitated, and which factors contribute to the success (or failure) of their innovation efforts. This study explores the innovation strategy of innovative Dutch SMEs by means of their sources of innovation, innovation capabilities, innovation performance, and commercialization sources. By means of structural equation modelling of a sample of 243 Dutch SMEs, this study shows that exploring (technology) opportunity together with institutions such as universities and private research establishments is important for successful innovation in SMEs. But, in addition, our model shows that contacts with competitors are also important for successful innovation performance. Our finding that openness of open innovation also applies to the commercialization phase is too often neglected by researchers and policy makers. © 2012 The Author(s)

Onboarding Employees Through Shared Values

How employees are on-boarded and welcomed into their new positions sets the tone for the culture of a place, the people, and the values of a new institution and can have a significant impact on employees sense of belonging. At Ohio University, the Division of Student Affairs employs approximately 400 staff and annually welcomes around 40-50 new employees a year, 25 of which are College Student Personnel (CSP) graduate students. New employees are welcomed with an ethic of care through a five day onboarding process rooted in Bolman and Deal’s human resource frame. This process includes departmental specific welcomes and trainings, along with division-wide team building, sexual misconduct training, social justice training, and suicide prevention training. This session outlined the correlation with Bolman and Deal’s human resource frame and NASPA/ ACPA competencies as well as lessons learned from implementing such a process, specifically include how these trainings intersect with the learning and development of new professionals

Managing Dynamic Enterprise and Urgent Workloads on Clouds Using Layered Queuing and Historical Performance Models

The automatic allocation of enterprise workload to resources can be enhanced by being able to make what-if response time predictions whilst different allocations are being considered. We experimentally investigate an historical and a layered queuing performance model and show how they can provide a good level of support for a dynamic-urgent cloud environment. Using this we define, implement and experimentally investigate the effectiveness of a prediction-based cloud workload and resource management algorithm. Based on these experimental analyses we: i.) comparatively evaluate the layered queuing and historical techniques; ii.) evaluate the effectiveness of the management algorithm in different operating scenarios; and iii.) provide guidance on using prediction-based workload and resource management

Prognostic value of pathological personality traits for treatment outcome in anxiety and depressive disorders: the Leiden routine outcome monitoring study.

Previous studies have failed to take baseline severity into account when assessing the effects of pathological personality traits (PPT) on treatment outcome. This study assessed the prognostic value of PPT (Dimensional Assessment of Personality Pathology-Short Form) on treatment outcome (Brief Symptom Inventory [BSI-posttreatment]) among patients with depressive and/or anxiety disorders ( N = 5689). Baseline symptom level (BSI-pretreatment) was taken into account as a mediator or moderator variable. Results showed significant effects of PPT on outcome, of which Emotional Dysregulation demonstrated the largest association ( β = 0.43, p < 0.001). When including baseline BSI score as a mediator variable, a direct effect ( β = 0.11, p < 0.001) remained approximately one-third of the total effect. The effects of Emotional Dysregulation (interaction effect β = 0.061, p < 0.001) and Inhibition (interaction effect β = 0.062, p < 0.001), but not Compulsivity or Dissocial Behavior, were moderated by the baseline symptom level. PPT predicts higher symptom levels, both before and after treatment, but yields relatively small direct effects on symptom decline when the effect of pretreatment severity is taken into account.Stress and Psychopatholog