Improving proactive decision making with object trend displays

Operators of dynamic systems often use time-series data to support their diagnostic and proactive decision-making. Those data have traditionally been displayed in the form of separate trend charts, for example, line graphs of pressure and temperature over time. Configural object displays are a widely advocated approach to the visual integration of information yet have been applied only rarely to time-series data. One example was the 'time tunnel' format but its benefits were equivocal, seemingly compromised by its graphical complexity. There is then the need to investigate other graphical forms for object displays of time series data. This research will require a microworld representing a knowledge-rich task domain accessible to multiple participants (the nuclear power plant simulation used with the time tunnel display studies required participants to have 20 hours of experience with the system). We report a design for such a microworld that adopts the domain of financial control of a business where decisions need to be made about the pricing of products to optimize returns in a changing and sometimes volatile market. Alternative visual displays of the essential time series data for this domain are possible and whilst decision making is knowledge rich, involving reasoning about high level relationships, pilot tests showed that it is accessible to participants with only moderate training

Direct Perception Biases With Maritime Navigation Displays of Collision Risk

Maritime navigation systems need to assist judgments about safe separations and avoidance manoeuvres. Collision Danger Sectors (CDS) are a proposed visualisation for radar displays intended to support the direct perception of impending traffic separation violations. We report an evaluation of the CDS display format in a laboratory study of navigation decision-making involving judgments of safe separations. Contrary to prediction, the CDS display did not bias decision making towards neglecting rules about track crossing. The CDS display did encourage excessive avoidance manoeuvres at higher angles of approach. A marked increase in response time with angle of approach was also evident, indicating a need for decision-aiding

A Semantic-driven adaptive architecture for large scale P2P networks

The increasing amount of online information demands for effective, scalable and accurate mechanisms to manage and search this information. Distributed semantic-enabled architectures, which enforce semantic web technologies for resource discovery, could satisfy these requirements. In this work a semantic-driven adaptive architecture is presented, aiming to improve existing resource discovery processes. The P2P network is organised in a two-layered super-peer architecture. The network formation of super-peers is a conceptual representation of the network’s knowledge, which is shaped from the information provided by the nodes using collective intelligence methods. The main focus of the paper is on the creation of a dynamic hierarchical semantic-driven P2P topology using the network’s collective intelligence. The unmanageable amounts of data are therefore transformed into a repository of semantic knowledge, transforming the network into an ontology of conceptually related entities of information collected from the resources located in the peers. Appropriate experiments have been undertaken through a case study, by simulating the proposed architecture and evaluating the results

A knowledge-driven architecture for efficient resource discovery in P2P networks

As shared electronic data increases, it has become more difficult to manage it successfully and the demand for scalable and efficient mechanisms for managing and retrieving data effectively becomes essential. In this paper a more effective P2P architecture is presented, aiming to improve existing resource discovery processes. The proposed architecture is organised as a hierarchical super-peer structure, where super-peers of the network represent network's knowledge that is formalised dynamically using its peers' resources. The main focus of this paper is the creation of an adaptive hierarchical concept-based P2P topology using collective intelligence methods. In that process, unmanageable data is transformed into a structured knowledge based repository of semantic resources. Therefore, the network takes the form of an ontology of conceptually related entities of resource information, as provided by the peers. This knowledge driven approach has benefits over traditional load driven architectures, as the user query context is usually the main driver for managing the performance of the network, and in a way the network can be characterised as proactive rather than reactive. A number of experiments have been undertaken and results demonstrate the advantages of the proposed concept-based architecture over other popular architectures

A Semantic-driven adaptive architecture for large scale P2P networks

The increasing amount of online information demands for effective, scalable and accurate mechanisms to manage and search this information. Distributed semantic-enabled architectures, which enforce semantic web technologies for resource discovery, could satisfy these requirements. In this work a semantic-driven adaptive architecture is presented, aiming to improve existing resource discovery processes. The P2P network is organised in a two-layered super-peer architecture. The network formation of super-peers is a conceptual representation of the network’s knowledge, which is shaped from the information provided by the nodes using collective intelligence methods. The main focus of the paper is on the creation of a dynamic hierarchical semantic-driven P2P topology using the network’s collective intelligence. The unmanageable amounts of data are therefore transformed into a repository of semantic knowledge, transforming the network into an ontology of conceptually related entities of information collected from the resources located in the peers. Appropriate experiments have been undertaken through a case study, by simulating the proposed architecture and evaluating the results

Personalised Treatment in Aortic Stenosis: A Patient-Tailored Transcatheter Aortic Valve Implantation Approach

Transcatheter aortic valve replacement (TAVI) has become a game changer in the management of severe aortic stenosis shifting the concept from inoperable or high-risk patients to intermediate or low surgical-risk individuals. Among devices available nowadays, there is no clear evidence that one device is better than the other or that one device is suitable for all patients. The selection of the optimal TAVI valve for every patient represents a challenging process for clinicians, given a large number of currently available devices. Consequently, understanding the advantages and disadvantages of each valve and personalising the valve selection based on patient-specific clinical and anatomical characteristics is paramount. This review article aims to both analyse the available devices in the presence of specific clinical and anatomic features and offer guidance to select the most suitable valve for a given patient

Safety and efficacy of non-steroidal anti-inflammatory drugs to reduce ileus after colorectal surgery

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