Maritime data integration and analysis: Recent progress and research challenges

The correlated exploitation of heterogeneous data sources offering very large historical as well as streaming data is important to increasing the accuracy of computations when analysing and predicting future states of moving entities. This is particularly critical in the maritime domain, where online tracking, early recognition of events, and real-time forecast of anticipated trajectories of vessels are crucial to safety and operations at sea. The objective of this paper is to review current research challenges and trends tied to the integration, management, analysis, and visualization of objects moving at sea as well as a few suggestions for a successful development of maritime forecasting and decision-support systems

Chromophore Ordering by Confinement into Carbon Nanotubes

International audienceWe report an experimental study on the confinement of oligothiophene derivatives into single-walled carbon nanotubes over a large range of diameter (from 0.68 to 1.93 nm). We evidence by means of Raman spectroscopy and transmission electron microscopy that the supramolecular organizations of the confined oligothiophenes depend on the nanocontainer size. The Raman Radial Breathing Mode frequency is shown to be monitored by both the number of confined molecules into a nanotube section and the competition between oligothiophene/oligothiophene and oligothiophene/tube wall interactions. We finally propose simple Raman criteria to characterize oligothiophene supramolecular organization at the nanoscale

Increasing maritime situation awareness via trajectory detection, enrichment and recognition of events

The research presented in this paper aims to show the deployment and use of advanced technologies towards processing surveillance data for the detection of events, contributing to maritime situation awareness via trajectories’ detection, synopses generation and semantic enrichment of trajectories. We first introduce the context of the maritime domain and then the main principles of the big data architecture developed so far within the European funded H2020 datAcron project. From the integration of large maritime trajectory datasets, to the generation of synopses and the detection of events, the main functions of the datAcron architecture are developed and discussed. The potential for detection and forecasting of complex events at sea is illustrated by preliminary experimental results

Context-dependent combination of sensor information in Dempster–Shafer theory for BDI

© 2016, The Author(s). There has been much interest in the belief–desire–intention (BDI) agent-based model for developing scalable intelligent systems, e.g. using the AgentSpeak framework. However, reasoning from sensor information in these large-scale systems remains a significant challenge. For example, agents may be faced with information from heterogeneous sources which is uncertain and incomplete, while the sources themselves may be unreliable or conflicting. In order to derive meaningful conclusions, it is important that such information be correctly modelled and combined. In this paper, we choose to model uncertain sensor information in Dempster–Shafer (DS) theory. Unfortunately, as in other uncertainty theories, simple combination strategies in DS theory are often too restrictive (losing valuable information) or too permissive (resulting in ignorance). For this reason, we investigate how a context-dependent strategy originally defined for possibility theory can be adapted to DS theory. In particular, we use the notion of largely partially maximal consistent subsets (LPMCSes) to characterise the context for when to use Dempster’s original rule of combination and for when to resort to an alternative. To guide this process, we identify existing measures of similarity and conflict for finding LPMCSes along with quality of information heuristics to ensure that LPMCSes are formed around high-quality information. We then propose an intelligent sensor model for integrating this information into the AgentSpeak framework which is responsible for applying evidence propagation to construct compatible information, for performing context-dependent combination and for deriving beliefs for revising an agent’s belief base. Finally, we present a power grid scenario inspired by a real-world case study to demonstrate our work

MARITIME DATA INTEGRATION AND ANALYSIS: RECENT PROGRESS AND RESEARCH CHALLENGES

The correlated exploitation of heterogeneous data sources offering very large historical as well as streaming data is important to increasing the accuracy of computations when analysing and predicting future states of moving entities. This is particularly critical in the maritime domain, where online tracking, early recognition of events, and real-time forecast of anticipated trajectories of vessels are crucial to safety and operations at sea. The objective of this paper is to review current research challenges and trends tied to the integration, management, analysis, and visualization of objects moving at sea as well as a few suggestions for a successful development of maritime forecasting and decision-support systems. Document type: Articl

Big data analytics for time critical maritime and aerial mobility forecasting

The correlated exploitation of heterogeneous data sources offering very large archival and streaming data is important to increase the accuracy of computations when analysing and predicting future states of moving entities. Aiming to significantly advance the capacities of systems to improve safety and effectiveness of critical operations involving a large number of moving entities in large geographical areas, this paper describes progress achieved towards time critical big data analytics solutions to user-defined challenges in the air-traffic management and maritime domains. Besides, this paper presents further research challenges concerning data integration and management, predictive analytics for trajectory and events forecasting, and visual analytics

MARITIME DATA INTEGRATION AND ANALYSIS: RECENT PROGRESS AND RESEARCH CHALLENGES

The correlated exploitation of heterogeneous data sources offering very large historical as well as streaming data is important to increasing the accuracy of computations when analysing and predicting future states of moving entities. This is particularly critical in the maritime domain, where online tracking, early recognition of events, and real-time forecast of anticipated trajectories of vessels are crucial to safety and operations at sea. The objective of this paper is to review current research challenges and trends tied to the integration, management, analysis, and visualization of objects moving at sea as well as a few suggestions for a successful development of maritime forecasting and decision-support systems. Document type: Articl

Visual exploration of movement and event data with interactive time masks

We introduce the concept of time mask, which is a type of temporal filter suitable for selection of multiple disjoint time intervals in which some query conditions fulfil. Such a filter can be applied to time-referenced objects, such as events and trajectories, for selecting those objects or segments of trajectories that fit in one of the selected time intervals. The selected subsets of objects or segments are dynamically summarized in various ways, and the summaries are represented visually on maps and/or other displays to enable exploration. The time mask filtering can be especially helpful in analysis of disparate data (e.g., event records, positions of moving objects, and time series of measurements), which may come from different sources. To detect relationships between such data, the analyst may set query conditions on the basis of one dataset and investigate the subsets of objects and values in the other datasets that co-occurred in time with these conditions. We describe the desired features of an interactive tool for time mask filtering and present a possible implementation of such a tool. By example of analysing two real world data collections related to aviation and maritime traffic, we show the way of using time masks in combination with other types of filters and demonstrate the utility of the time mask filtering

Fermi level shift in carbon nanotubes by dye confinement

International audienceDye confinement into carbon nanotube significantly affects the electronic charge density distribution of the final hybrid system. Using the electron-phonon coupling sensitivity of the Raman G-band, we quantify experimentally how charge transfer from thiophene oligomers to single walled carbon nanotube is modulated by the diameter of the nano-container and its metallic or semiconducting character. This charge transfer is shown to restore the electron-phonon coupling into defected metallic nanotubes. For sub-nanometer diameter tube, an electron transfer optically activated is observed when the excitation energy matches the HOMO-LUMO transition of the confined oligothiophene. This electron doping accounts for an important enhancement of the photoluminescence intensity up to a factor of nearly six for optimal confinement configuration. This electron transfer shifts the Fermi level, acting on the photoluminescence efficiency. Therefore, thiophene oligomer encapsulation allows modulating the electronic structure and then the optical properties of the hybrid system

Living with a Crucial Decision: A Qualitative Study of Parental Narratives Three Years after the Loss of Their Newborn in the NICU

BACKGROUND: The importance of involving parents in the end-of-life decision-making-process (EOL DMP) for their child in the neonatal intensive care unit (NICU) is recognised by ethical guidelines in numerous countries. However, studies exploring parents' opinions on the type of involvement report conflicting results. This study sought to explore parents' experience of the EOL DMP for their child in the NICU. METHODS: The study used a retrospective longitudinal design with a qualitative analysis of parental experience 3 years after the death of their child in four NICUs in France. 53 face-to-face interviews and 80 telephone interviews were conducted with 164 individuals. Semi-structured interviews were conducted to explore how parents perceived their role in the decision process, what they valued about physicians' attitudes in this situation and whether their long-term emotional well being varied according to their perceived role in the EOL DMP. FINDINGS: Qualitative analysis identified four types of perceived role in the DMP: shared, medical, informed parental decision, and no decision. Shared DM was the most appreciated by parents. Medical DM was experienced as positive only when it was associated with communication. Informed parental DM was associated with feelings of anxiousness and abandonment. The physicians' attitudes that were perceived as helpful in the long term were explicit sharing of responsibility, clear expression of staff preferences, and respectful care and language toward the child. INTERPRETATION: Parents find it valuable to express their opinion in the EOL DMP of their child. Nonetheless, they do need continuous emotional support and an explicit share of the responsibility for the decision. As involvement preferences and associated feelings can vary, parents should be able to decide what role they want to play. However, our study suggests that fully autonomous decisions should be misadvised in these types of tragic choices