Enhancing Workflow with a Semantic Description of Scientific Intent

Peer reviewedPreprin

DPN -- Dependability Priority Numbers

This paper proposes a novel model-based approach to combine the quantitative dependability (safety, reliability, availability, maintainability and IT security) analysis and trade-off analysis. The proposed approach is called DPN (Dependability Priority Numbers) and allows the comparison of different actual dependability characteristics of a systems with its target values and evaluates them regarding trade-off analysis criteria. Therefore, the target values of system dependability characteristics are taken as requirements, while the actual value of a specific system design are provided by quantitative and qualitative dependability analysis (FHA, FMEA, FMEDA, of CFT-based FTA). The DPN approach evaluates the fulfillment of individual target requirements and perform trade-offs between analysis objectives. We present the workflow and meta-model of the DPN approach, and illustrate our approach using a case study on a brake warning contact system. Hence, we demonstrate how the model-based DPNs improve system dependability by selecting the project crucial dependable design alternatives or measures

GRIDCC: Real-time workflow system

The Grid is a concept which allows the sharing of resources between distributed communities, allowing each to progress towards potentially different goals. As adoption of the Grid increases so are the activities that people wish to conduct through it. The GRIDCC project is a European Union funded project addressing the issues of integrating instruments into the Grid. This increases the requirement of workflows and Quality of Service upon these workflows as many of these instruments have real-time requirements. In this paper we present the workflow management service within the GRIDCC project which is tasked with optimising the workflows and ensuring that they meet the pre-defined QoS requirements specified upon them

e-Social Science and Evidence-Based Policy Assessment : Challenges and Solutions

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Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.

Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence-based treatment planning applications, such as deep learning-based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence-based treatment planning are discussed for future works