École Nationale de l’Aviation Civile

Abstract. This article, the second in the series, presents kinetic and photochemical data evaluated by the IUPAC Subcommittee on Gas Kinetic Data Evaluation for Atmospheric Chemistry. It covers the gas phase and photochemical reactions of Organic species, which were last published in 1999, and were updated on the IUPAC website in late 2002, and subsequently during the preparation of this article. The article consists of a summary table of the recommended rate coefficients, containing the recommended kinetic parameters for the evaluated reactions, and eight appendices containing the data sheets, which provide information upon which the recommendations are made.

The new Scientific Decade 2013-2022 of IAHS, entitled Panta RheiEverything Flows, is dedicated to research activities on change in hydrology and society. The purpose of Panta Rhei is to reach an improved interpretation of the processes governing the water cycle by focusing on their changing dynamics in connection with rapidly changing human systems. The practical aim is to improve our capability to make predictions of water resources dynamics to support sustainable societal development in a changing environment. The concept implies a focus on hydrological systems as a changing interface between environment and society, whose dynamics are essential to determine water security, human safety and development, and to set priorities for environmental management. The Scientific Decade 2013-2022 will devise innovative theoretical blueprints for the representation of processes including change and will focus on advanced monitoring and data analysis techniques. Interdisciplinarity will be sought by increased efforts to connect with the socio-economic sciences and geosciences in general. This paper presents a summary of the Science Plan of Panta Rhei, its targets, research questions and expected outcomes.

We present a novel way of extracting features from short texts, based on the activation values of an inner layer of a deep convolutional neural network. We use the extracted features in multimodal sentiment analysis of short video clips representing one sentence each. We use the combined feature vectors of textual, visual, and audio modalities to train a classifier based on multiple kernel learning, which is known to be good at heterogeneous data. We obtain 14% performance improvement over the state of the art and present a parallelizable decision-level data fusion method, which is much faster, though slightly less accurate.

The aim of the paper is to study the capabilities of the extended finite element method (XFEM) to achieve accurate computations in non-smooth situations such as crack problems. Although the XFEM method ensures a weaker error than classical finite element methods, the rate of convergence is not improved when the mesh parameter h is going to zero because of the presence of a singularity. The difficulty can be overcome by modifying the enrichment of the finite element basis with the asymptotic crack tip displacement solutions as well as with the Heaviside function. Numerical simulations show that the modified XFEM method achieves an optimal rate of convergence (i.e. like in a standard finite element method for a smooth problem). Copyright © 2005 John Wiley & Sons, Ltd.

An improved understanding of how the brain allocates mental resources as a function of task difficulty is critical for enhancing human performance. Functional near infrared spectroscopy (fNIRS) is a field-deployable optical brain monitoring technology that provides a direct measure of cerebral blood flow in response to cognitive activity. We found that fNIRS was sensitive to variations in task difficulty in both real-life (flight simulator) and laboratory settings (tests measuring executive functions), showing increased concentration of oxygenated hemoglobin (HbO2) and decreased concentration of deoxygenated hemoglobin (HHb) in the prefrontal cortex as the tasks became more complex. Intensity of prefrontal activation (HbO2 concentration) was not clearly correlated to task performance. Rather, activation intensity shed insight on the level of mental effort, i.e., how hard an individual was working to accomplish a task. When combined with performance, fNIRS provided an estimate of the participants' neural efficiency, and this efficiency was consistent across levels of difficulty of the same task. Overall, our data support the suitability of fNIRS to assess the mental effort related to human operations and represents a promising tool for the measurement of neural efficiency in other contexts such as training programs or the clinical setting.

stationary output vector . 149 6.3.

Adaptive Automation (AA) is a promising approach to keep the task workload demand within appropriate levels in order to avoid both the under- and overload conditions, hence enhancing the overall performance and safety of the human-machine system. The main issue on the use of AA is how to trigger the AA solutions without affecting the operative task. In this regard, passive Brain-Computer Interface (pBCI) systems are a good candidate to activate automation, since they are able to gather information about the covert behaviour (e.g. mental workload) of a subject by analysing its neurophysiological signals (i.e. brain activity), and without interfering with the ongoing operational activity. We proposed a pBCI system able to trigger AA solutions integrated in a realistic Air Traffic Management (ATM) research simulator developed and hosted at ENAC (École Nationale de l’Aviation Civile of Toulouse, France). Twelve Air Traffic Controller (ATCO) students have been involved in the experiment and they have been asked to perform ATM scenarios with and without the support of the AA solutions. Results demonstrated the effectiveness of the proposed pBCI system, since it enabled the AA mostly during the high-demanding conditions (i.e. overload situations) inducing a reduction of the mental workload under which the ATCOs were operating. On the contrary, as desired, the AA was not activated when workload level was under the threshold, to prevent too low demanding conditions that could bring the operator’s workload level towards potentially dangerous conditions of underload.

Abstract The most famous lattice problem is the Shortest Vector Problem (SVP), which has many applications in cryptology. The best approximation algorithms known for SVP in high dimension rely on a subroutine for exact SVP in low dimension. In this paper, we assess the practicality of the best (theoretical) algorithm known for exact SVP in low dimension: the sieve algorithm proposed by Ajtai, Kumar and Sivakumar (AKS) in 2001. AKS is a randomized algorithm of time and space complexity 2 O ( n ) , which is theoretically much lower than the super-exponential complexity of all alternative SVP algorithms. Surprisingly, no implementation and no practical analysis of AKS has ever been reported. It was in fact widely believed that AKS was impractical: for instance, Schnorr claimed in 2003 that the constant hidden in the 2 O ( n ) complexity was at least 30. In this paper, we show that AKS can actually be made practical: we present a heuristic variant of AKS whose running time is polynomial-time operations, and whose space requirement is polynomially many bits. Our implementation can experimentally find shortest lattice vectors up to dimension 50, but is slower than classical alternative SVP algorithms in these dimensions.

We trace the history of revenue management in an effort to illustrate a successful e–commerce model of dynamic, automated sales. Our discourse begins with a brief overview of electronic distribution as practiced in the airline industry, emphasizing the fundamental role of central reservation and revenue management systems. Methods for controlling the sale of inventory are then introduced along with related techniques for optimization and forecasting. Research contributions and areas of significant research potential are given special attention. We conclude by looking at how revenue management is practiced outside of the airline industry, its relationship to dynamic pricing, and future directions for the discipline.

This article presents a new tracking technique for sine-BOC(n,n) (or Manchester encoded) ranging signals, which is most likely to be a part of the new European Global Navigation Satellite System (GNSS), Galileo, signal plan. When traditional sine-BOC(n,n) tracking is considered, although offering excellent performance compared with current signals, it has the main drawback of potentially giving biased measurements. The new method presented herein allows the removal of this threat while maintaining the same level of performance. An adapted version of this technique can also be used for acquisition purposes

Cognitive workload (CWL) is a fundamental concept in the assessment and monitoring of human performance during cognitive tasks. Numerous studies have attempted to objectively and continuously measure the CWL using neuroimaging techniques. Although the electroencephalogram (EEG) is a widely used technique, the impact of CWL on the spectral power of brain frequencies has shown inconsistent results. The present review aimed to synthesize the results of the literature and quantitatively assess which brain frequency is the most sensitive to CWL. A systematic literature search following PRISMA recommendations highlighted three main frequency bands used to measure CWL: theta (4-8 Hz), alpha (8-12 Hz), and beta (12-30 Hz). Three meta-analyses were conducted to quantitatively examine the effect of CWL on these frequencies. A total of 45 effect sizes from 24 studies involving 723 participants were computed. CWL was associated with significant effects on theta (g = 0.68, CI [0.41, 0.95]), alpha (g = -0.25, CI [-0.45, 0.04]), and beta (g = 0.50, CI [0.21, 0.79]) power. Our results suggests that theta, especially the frontal theta, is the best index of CWL. Alpha and beta power were also significantly impacted by CWL; however, their association seemed less straightforward. These results are critically analyzed considering the literature on cerebral oscillations. We conclude by emphasizing the need to investigate the interaction between CWL and other factors that may influence spectral power (e.g., emotional load), and to combine this measure with other methods of analysis of the central and peripheral nervous system (e.g., functional connectivity, heart rate).

Ozonation of municipal wastewater effluent has been considered in recent years as an enhanced wastewater treatment technology to abate trace organic contaminants (micropollutants).

In this paper we give optimal constants in Talagrand’s concentration inequalities for maxima of empirical processes associated to independent and eventually nonidentically distributed random variables. Our approach is based on the entropy method introduced by Ledoux.

This book is an accessible introduction to data-driven storytelling, resulting from discussions between data visualization researchers and data journalists. This book will be the first to define the topic, present compelling examples and existing resources, as well as identify challenges and new opportunities for research.

Abstract We present the generalized space‐time cube , a descriptive model for visualizations of temporal data. Visualizations are described as operations on the cube, which transform the cube's 3D shape into readable 2D visualizations. Operations include extracting subparts of the cube, flattening it across space or time or transforming the cubes geometry and content. We introduce a taxonomy of elementary space‐time cube operations and explain how these operations can be combined and parameterized. The generalized space‐time cube has two properties: (1) it is purely conceptual without the need to be implemented, and (2) it applies to all datasets that can be represented in two dimensions plus time (e.g. geo‐spatial, videos, networks, multivariate data). The proper choice of space‐time cube operations depends on many factors, for example, density or sparsity of a cube. Hence, we propose a characterization of structures within space‐time cubes, which allows us to discuss strengths and limitations of operations. We finally review interactive systems that support multiple operations, allowing a user to customize his view on the data. With this framework, we hope to facilitate the description, criticism and comparison of temporal data visualizations, as well as encourage the exploration of new techniques and systems. This paper is an extension of Bach et al .'s (2014) work.

In this paper, we present a novel approach for constructing bundled layouts of general graphs. As layout cues for bundles, we use medial axes, or skeletons, of edges which are similar in terms of position information. We combine edge clustering, distance fields, and 2D skeletonization to construct progressively bundled layouts for general graphs by iteratively attracting edges towards the centerlines of level sets of their distance fields. Apart from clustering, our entire pipeline is image-based with an efficient implementation in graphics hardware. Besides speed and implementation simplicity, our method allows explicit control of the emphasis on structure of the bundled layout, i.e. the creation of strongly branching (organic-like) or smooth bundles. We demonstrate our method on several large real-world graphs.

Abstract We present a fast and simple method to compute bundled layouts of general graphs. For this, we first transform a given graph drawing into a density map using kernel density estimation. Next, we apply an image sharpening technique which progressively merges local height maxima by moving the convolved graph edges into the height gradient flow. Our technique can be easily and efficiently implemented using standard graphics acceleration techniques and produces graph bundlings of similar appearance and quality to state‐of‐the‐art methods at a fraction of the cost. Additionally, we show how to create bundled layouts constrained by obstacles and use shading to convey information on the bundling quality. We demonstrate our method on several large graphs.

With the technological advances, there is an increasing attention on micro-UAVs in the military area as well as in the civilian domain. They are used as swarm (several UAVs) forming a UAS (Unmanned Aircraft System) since they are relatively cheap and offer better performance than one aircraft. The UAVs, in a UAS, have to exchange information with each other and with the control station in order to create a clear vision of the swarm situation and the task performance. This exchange is made possible by the application of an ad hoc network between UAVs which is a challenging issue because of the node mobility, the network topology change, and the operation communication requirements in term of quality of service (delay, throughput or loss rate for instance). This paper presents a realistic mobility model designed for UAV ad hoc networks. since evaluating the performances of ad hoc protocols is an important step in order to predict possible problems that can affect the system in the real environment. This mobility model behavior is compared to the well-known mobility model behavior Random-Way Point. It is also compared to real movements traces using several metrics.

We isolated Gram-positive alkane-degraders from soil and a tricking-bed reactor, and show using polymerase chain reaction (PCR) with degenerate alkane hydroxylase primers and Southern blots that most Rhodococcus isolates contain three to five quite divergent homologues of the Pseudomonas putida GPo1 alkB gene. Two Mycobacterium isolates each contain one homologue, however there is no evidence for the presence of alkB homologues in the remaining strains.

Recently, it has been reported that inconsistent range-measurement or, equivalently, mismatches in prescribed interagent distances, may prevent popular gradient controllers from guiding rigid formations of mobile agents to converge to their desired shape and, even worse, from standing still at any location. In this paper, instead of treating mismatches as the source of poor performance, we take them as design parameters and show that by introducing such a pair of parameters per distance constraint, distributed controller achieving simultaneously both formation and motion control can be designed that not only encompasses the popular gradient control, but more importantly allows us to achieve constant collective translation, rotation, or their combination, while guaranteeing asymptotically that no distortion in the formation shape occurs. Such motion control results are then applied to 1) the alignment of formations' orientations and 2) enclosing and tracking a moving target. Besides rigorous mathematical proof, experiments using mobile robots are demonstrated to show the satisfying performances of the proposed formation-motion distributed controller.