2015 ESC Guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death the Task Force for the Management of Patients with Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death of the European Society of Cardiology (ESC) Endorsed by: Association for European Paediatric and Congenital Cardiology (AEPC)

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Maritime Pattern Extraction from AIS Data Using a Genetic Algorithm

The long term prediction of maritime vessels' destinations and arrival times is essential for making an effective logistics planning. As ships are influenced by various factors over a long period of time, the solution cannot be achieved by analyzing sailing patterns of each entity separately. Instead, an approach is required, that can extract maritime patterns for the area in question and represent it in a form suitable for querying all possible routes any vessel in that region can take. To tackle this problem we use a genetic algorithm (GA) to cluster vessel position data obtained from the publicly available Automatic Identification System (AIS). The resulting clusters are treated as route waypoints (WP), and by connecting them we get nodes and edges of a directed graph depicting maritime patterns. Since standard clustering algorithms have difficulties in handling data with varying density, and genetic algorithms are slow when handling large data volumes, in this paper we investigate how to enhance the genetic algorithm to allow fast and accurate waypoint identification. We also include a quad tree structure to preprocess data and reduce the input for the GA. When the route graph is created, we add post processing to remove inconsistencies caused by noise in the AIS data. Finally, we validate the results produced by the GA by comparing resulting patterns with known inland water routes for two Dutch provinces

Maritime pattern extraction and route reconstruction from incomplete AIS data

Effective barge scheduling in the logistic domain requires advanced information on the availability of the port terminals and the maritime traffic in their vicinity. To enable a long-term prediction of vessel arrival times, we investigate how to use the publicly available automatic identification system (AIS) data to identify maritime patterns and transform them into a directed graph that can be used to estimate the potential trajectories and destination points. To tackle this problem, we use a genetic algorithm (GA) to cluster vessel position data. Then, we show how to enhance the process to allow fast computation of incremental data coming from the sensors, including the importance of adding a quad tree structure for data preprocessing. Focusing on a real case implementation, characterized by partially incomplete and noisy AIS data, we show how the algorithm can handle routes intersecting the regions with missing data and the repercussions this has on the route graph. Finally, postprocessing is explained that handles graph pruning and filtering. We validate the results produced by the GA by comparing resulting patterns with known inland water routes for two Dutch provinces followed by the simulation using synthetic data to highlight the strengths and weaknesses of this approach

Intelligence amplification framework for enhancing scheduling processes

The scheduling process in a typical business environment consists of predominantly repetitive tasks that have to be completed in limited time and often containing some form of uncertainty. The intelligence amplification is a symbiotic relationship between a human and an intelligent agent. This partnership is organized to emphasize the strength of both entities, with the human taking the central role of the objective setter and supervisor, and the machine focusing on executing the repetitive tasks. The output efficiency and effectiveness increase as each partner can focus on its native tasks. We propose the intelligence amplification framework that is applicable in typical scheduling problems encountered in the business domain. Using this framework we build an artifact to enhance scheduling processes in synchromodal logistics, showing that a symbiotic decision maker performs better in terms of efficiency and effectiveness

Augmenting the Evaluation and Mapping of Progress in Scientific Research: A Human-Machine Symbiosis Perspective

In this paper we propose and demonstrate a software tool for symbiotic human-machine analysis, applicable for structured literature reviews (SLR). We present a seed-based search of bibliographic information, resulting in document clustering and graph visualization. Through a collaborative human-machine effort we show how to detect potential bridging articles and paradigm shifts. The overarching goal is to support the SLR process, especially for developing fields of science, as well as interdisciplinary fields, where similar concepts can be overlooked as they are associated with different keywords and belong to different groups, yet share common ideas. Finally, we demonstrate the application of the tool with two literature search and visualization examples

Collaborative Literature Search System: An Intelligence Amplification Method for Systematic Literature Search

In this paper, we present a method for systematic literature search based on the symbiotic partnership between the human researcher and intelligent agents. Using intelligence amplification, we leverage the calculation power of computers to quickly and thoroughly extract data, calculate measures, and visualize relationships between scientific documents with the ability of domain experts to perform qualitative analysis and creative reasoning. Thus, we create a foundation for a collaborative literature search system (CLSS) intended to aid researches in performing literature reviews, especially for interdisciplinary and evolving fields of science for which keyword-based literature searches result in large collections of documents beyond humans’ ability to process or the extensive use of filters to narrow the search output risks omitting relevant works. Within this article, we propose a method for CLSS and demonstrate its use on a concrete example of a literature search for a review of the literature on human-machine symbiosis