Kinetically driven helix formation during the homopolymer collapse process

Using Langevin simulations, we find that simple 'generic' bead-and-spring homopolymer chains in a sufficiently bad solvent spontaneously develop helical order during the process of collapsing from an initially stretched conformation. The helix formation is initiated by the unstable modes of the straight chain, which drive the system towards a long-lived metastable transient state. The effect is most pronounced if hydrodynamic interactions are screened.Comment: 4 pages, 4 figure

INSIDES – A new Virtual Prototyping Platform of Human Machine Interactions Systems for Automotive and Aerospace Applications

International audienceHuman Machine Interactions Systems are decisive for the acceptance and the safety of new cockpits in the automotive as well as in the aerospace industries. A new design and simulation platform called INSIDES will be presented where virtual cockpit prototypes are being built based on 3D CAD geometry e.g. from CATIA and integrated with logical interaction data derived from UML specifications. This new development platform enables the continuous validation and check of new interaction concepts by involving usability engineers in the very early stage of the development cycle. Since the simulation work is being done in the context of the entire aircraft cockpit/car interior with all instruments, control commands as well displays devices a better validation of the HMI systems can be achieved

Intelligent Geospatial Maritime Risk Analytics Using The Discrete Global Grid System

Each year, accidents involving ships result in significant loss of life, environmental pollution and economic losses. The promotion of navigation safety through risk reduction requires methods to assess the spatial distribution of the relative likelihood of occurrence. Yet, such methods necessitate the integration of large volumes of heterogenous datasets which are not well suited to traditional data structures. This paper proposes the use of the Discrete Global Grid System (DGGS) as an efficient and advantageous structure to integrate vessel traffic, metocean, bathymetric, infrastructure and other relevant maritime datasets to predict the occurrence of ship groundings. Massive and heterogenous datasets are well suited for machine learning algorithms and this paper develops a spatial maritime risk model based on a DGGS utilising such an approach. A Random Forest algorithm is developed to predict the frequency and spatial distribution of groundings while achieving an R2 of 0.55 and a mean squared error of 0.002. The resulting risk maps are useful for decision-makers in planning the allocation of mitigation measures, targeted to regions with the highest risk. Further work is identified to expand the applications and insights which could be achieved through establishing a DGGS as a global maritime spatial data structure

Geospatial data analysis for global maritime risk assessment using the discrete global grid system

The effective management of the safety of navigation by coastguards is challenged by the complexity in quantifying and describing the relative risk of accidents occurrence. The discovery of patterns in observation data is reliant on the collection and analysis of significant volumes of relevant heterogenous spatial datasets. Conventional approaches of risk mapping which aggregate vessel traffic and incident data into Cartesian grids can result in misrepresentation due to inherent inadequacies in this spatial data format. In this paper, we explore how the Discrete Global Grid System (DGGS) overcomes these limitations through the development of global maps of incident rates at multiple resolutions. The results demonstrate hot spots of relative high risk across different regions and clearly show that DGGS is more suited to global analysis than conventional grids. This work contributes to a greater understanding of both the disposition of maritime risk and the advantages of adopting DGGS in supporting big data analysis

Spatial Modeling of Maritime Risk Using Machine Learning

Managing navigational safety is a key responsibility of coastal states. Predicting and measuring these risks has a high complexity due to their infrequent occurrence, multitude of causes, and large study areas. As a result, maritime risk models are generally limited in scale to small regions, generalized across diverse environments, or rely on the use of expert judgement. Therefore, such an approach has limited scalability and may incorrectly characterize the risk. Within this article a novel method for undertaking spatial modeling of maritime risk is proposed through machine learning. This enables navigational safety to be characterized while leveraging the significant volumes of relevant data available. The method comprises two key components: aggregation of historical accident data, vessel traffic, and other exploratory features into a spatial grid; and the implementation of several classification algorithms that predicts annual accident occurrence for various vessel types. This approach is applied to characterize the risk of collisions and groundings in the United Kingdom. The results vary between hazard types and vessel types but show remarkable capability at characterizing maritime risk, with accuracies and area under curve scores in excess of 90% in most implementations. Furthermore, the ensemble tree-based algorithms of XGBoost and Random Forest consistently outperformed other machine learning algorithms that were tested. The resultant potential risk maps provide decisionmakers with actionable intelligence in order to target risk mitigation measures in regions with the greatest requirement

From Conventional to Machine Learning Methods for Maritime Risk Assessment

Within the last thirty years, the range and complexity of methodologies proposed to assess maritime risk have increased significantly. Techniques such as expert judgement, incident analysis, geometric models, domain analysis and Bayesian Networks amongst many others have become dominant within both the literature and industry. On top of this, advances in machine learning algorithms and big data have opened opportunities for new methods which might overcome some limitations of conventional approaches. Yet, determining the suitability or validity of one technique over another is challenging as it requires a systematic multicriteria approach to compare the inputs, assumptions, methodologies and results of each method. Within this paper, such an approach is proposed and tested within an isolated waterway in order to justify the proposed advantages of a machine learning approach to maritime risk assessment and should serve as inspiration for future work

Influence de l'échelle de rugosité sur le frottement dans les contacts lubrifiés

La réduction du frottement, de la consommation d'huile et la limitation des émissions de gaz à effet de serre sont les principaux objectifs de l'industrie automobile afin d'améliorer l'efficacité environnementale des moteurs de véhicules. Cette efficience énergétique passe par une fonctionnalisation de la surface de la chemise du tribosystème segment piston chemise, responsable d’environ 45% des pertes par frottement dans le moteur. La surface rodée et le segment du piston constituent donc un système tribologique qui influe sur la longévité du moteur. En général, la surface rodée est obtenue par une succession de procédé d’abrasion (rectification, rodage, polissage…) permettant de répondre aux fonctionnalités requises relatives à leur durabilité et leur fiabilité. Ces procédés utilisent des grains abrasifs de différentes tailles allant du plus grossier au plus fin générant ainsi une texture de surface multi-échelle. La texture ainsi générée affecte de manière significative la performance du triplet piston segment chemise (SPC), bien que les effets de la topographie n’est pas clairement comprise. Dans cette étude, une caractérisation avancée de la surface est utilisée pour étudier les effets d’échelle de rugosité dans les contacts lubrifiés. La topographie de surface est basée sur la décomposition de la surface en deux éléments: la rugosité superficielle (reliée au frottement et à l’usure) et les vallées (servant de réservoirs). Les résultats montrent une dépendance entre l’exposant d’Hölder et le frottement ainsi qu’une échelle critique inversant l’influence de la profondeur des vallées sur le frottement

Ground-state properties of tubelike flexible polymers

In this work we investigate structural properties of native states of a simple model for short flexible homopolymers, where the steric influence of monomeric side chains is effectively introduced by a thickness constraint. This geometric constraint is implemented through the concept of the global radius of curvature and affects the conformational topology of ground-state structures. A systematic analysis allows for a thickness-dependent classification of the dominant ground-state topologies. It turns out that helical structures, strands, rings, and coils are natural, intrinsic geometries of such tubelike objects