128 research outputs found
Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles
The damaging effects of cyberattacks to an industry like the Cooperative Connected and Automated Mobility (CCAM) can be tremendous. From the least important to the worst ones, one can mention for example the damage in the reputation of vehicle manufacturers, the increased denial of customers to adopt CCAM, the loss of working hours (having direct impact on the European GDP), material damages, increased environmental pollution due e.g., to traffic jams or malicious modifications in sensors’ firmware, and ultimately, the great danger for human lives, either they are drivers, passengers or pedestrians.
Connected vehicles will soon become a reality on our roads, bringing along new services and capabilities, but also technical challenges and security threats. To overcome these risks, the CARAMEL project has developed several anti-hacking solutions for the new generation of vehicles.
CARAMEL (Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles), a research project co-funded by the European Union under the Horizon 2020 framework programme, is a project consortium with 15 organizations from 8 European countries together with 3 Korean partners. The project applies a proactive approach based on Artificial Intelligence and Machine Learning techniques to detect and prevent potential cybersecurity threats to autonomous and connected vehicles. This approach has been addressed based on four fundamental pillars, namely: Autonomous Mobility, Connected Mobility, Electromobility, and Remote Control Vehicle. This book presents theory and results from each of these technical directions
Translating computational modelling tools for clinical practice in congenital heart disease
Increasingly large numbers of medical centres worldwide are equipped with the means to acquire 3D images of patients by utilising magnetic resonance (MR) or computed tomography (CT) scanners. The interpretation of patient 3D image data has significant implications on clinical decision-making and treatment planning. In their raw form, MR and CT images have become critical in routine practice. However, in congenital heart disease (CHD), lesions are often anatomically and physiologically complex. In many cases, 3D imaging alone can fail to provide conclusive information for the clinical team. In the past 20-30 years, several image-derived modelling applications have shown major advancements. Tools such as computational fluid dynamics (CFD) and virtual reality (VR) have successfully demonstrated valuable uses in the management of CHD. However, due to current software limitations, these applications have remained largely isolated to research settings, and have yet to become part of clinical practice. The overall aim of this project was to explore new routes for making conventional computational modelling software more accessible for CHD clinics. The first objective was to create an automatic and fast pipeline for performing vascular CFD simulations. By leveraging machine learning, a solution was built using synthetically generated aortic anatomies, and was seen to be able to predict 3D aortic pressure and velocity flow fields with comparable accuracy to conventional CFD. The second objective was to design a virtual reality (VR) application tailored for supporting the surgical planning and teaching of CHD. The solution was a Unity-based application which included numerous specialised tools, such as mesh-editing features and online networking for group learning. Overall, the outcomes of this ongoing project showed strong indications that the integration of VR and CFD into clinical settings is possible, and has potential for extending 3D imaging and supporting the diagnosis, management and teaching of CHD
Child Obesity and Nutrition Promotion Intervention
Childhood obesity continues to be a global problem, with several regions showing increasing rates and others having one in every three children overweight despite an apparent halt or downward trend. Children are exposed to nutritional, social, and obesogenic environmental risks from different settings, and this affects their lifelong health. There is a consensus that high-quality multifaceted smart and cost-effective interventions enable children to grow with a healthy set of habits that have lifelong benefits to their wellbeing. The literature has shown that dietary approaches play key roles in improving children’s health, not only on a nutritional level but also in diet quality and patterns. An association between the nutritional strategy and other lifestyle components promotes a more comprehensive approach and should be envisioned in intervention studies. This Special Issue entitled “Child Obesity and Nutrition Promotion Intervention” combines original research manuscripts or reviews of the scientific literature concerning classic or innovative approaches to tackle this public health issue. It presents several nutritional interventions alongside lifestyle health factors, and outcome indicators of effectiveness and sustainability from traditional to ground-breaking methods to exploit both qualitative and quantitative approaches in tackling child obesity
Review of Particle Physics
The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,143
new measurements from 709 papers, we list, evaluate, and average measured properties of gauge bosons and the
recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical
particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search
limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs
Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology,
Particle Detectors, Colliders, Probability and Statistics. Among the 120 reviews are many that are new or heavily
revised, including a new review on Machine Learning, and one on Spectroscopy of Light Meson Resonances.
The Review is divided into two volumes. Volume 1 includes the Summary Tables and 97 review articles. Volume
2 consists of the Particle Listings and contains also 23 reviews that address specific aspects of the data presented
in the Listings.
The complete Review (both volumes) is published online on the website of the Particle Data Group (pdg.lbl.gov)
and in a journal. Volume 1 is available in print as the PDG Book. A Particle Physics Booklet with the Summary
Tables and essential tables, figures, and equations from selected review articles is available in print, as a web version
optimized for use on phones, and as an Android app.United States Department of Energy (DOE) DE-AC02-05CH11231government of Japan (Ministry of Education, Culture, Sports, Science and Technology)Istituto Nazionale di Fisica Nucleare (INFN)Physical Society of Japan (JPS)European Laboratory for Particle Physics (CERN)United States Department of Energy (DOE
Obstructions in Security-Aware Business Processes
This Open Access book explores the dilemma-like stalemate between security and regulatory compliance in business processes on the one hand and business continuity and governance on the other. The growing number of regulations, e.g., on information security, data protection, or privacy, implemented in increasingly digitized businesses can have an obstructive effect on the automated execution of business processes. Such security-related obstructions can particularly occur when an access control-based implementation of regulations blocks the execution of business processes. By handling obstructions, security in business processes is supposed to be improved. For this, the book presents a framework that allows the comprehensive analysis, detection, and handling of obstructions in a security-sensitive way. Thereby, methods based on common organizational security policies, process models, and logs are proposed. The Petri net-based modeling and related semantic and language-based research, as well as the analysis of event data and machine learning methods finally lead to the development of algorithms and experiments that can detect and resolve obstructions and are reproducible with the provided software
Development of a GPU-accelerated flow simulation method for wind turbine applications
A new and novel GPU accelerated method has been developed for solving the Navier-Stokes equations for bodies of arbitrary geometry in both 2D and 3D. The present method utilises the vortex particles to discretize the governing equations in the Lagrangian frame. Those particles act as vorticity carriers which translate in accordance with the local velocity field. Vorticity information is thus propagated from the vorticity source to the rest of the flow domain in mimicking the advection and diffusion processes of the real flow.
In the high-fidelity method, vorticity generation can take place around the bodies. The no-slip condition produces a boundary flux which is subsequently diffused to the neighbouring particles. The new method has been successfully validated by simulating the flow field of an impulsively started cylinder. The calculated drag curve matches well with the theoretical prediction and other numerical results in the literature. To extend the applicability of the code to wind-turbine applications, a simplified re-meshing strategy is adopted which is found to produce small numerical inaccuracies.
In the engineering method, a simplified hybrid approach has been developed which decouples the advection and diffusion processes. The viscous effects are ignored on the bodies and are recovered in the wake. For this purpose, the Laplace equation that resulted from the irrotational assumption of the flow has been solved using the boundary element method. The solution produces a dipole distribution that is subsequently converted to viscous particles by employing the Hess’ equivalence principle. In addition, an accurate interpolation scheme has been developed to evaluate the dipole gradient across the distorted wake geometry.
To reduce the simulation time, the fast multipole method has been implemented on the GPU in 2D and 3D. To parallelize the implementation, a novel data construction algorithm has been proposed. Furthermore, an analytical expression for the velocity strain has been derived.
The new developed methods have been applied to problems involving aerofoils and vertical axis wind turbines. Comparisons with experimental data have shown that the new techniques are accurate and can be used with confidence for a wide variety of wind turbine applications
Deep Learning-Based Machinery Fault Diagnostics
This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis
Computational Fluid Dynamics 2020
This book presents a collection of works published in a recent Special Issue (SI) entitled “Computational Fluid Dynamics”. These works address the development and validation of existent numerical solvers for fluid flow problems and their related applications. They present complex nonlinear, non-Newtonian fluid flow problems that are (in some cases) coupled with heat transfer, phase change, nanofluidic, and magnetohydrodynamics (MHD) phenomena. The applications are wide and range from aerodynamic drag and pressure waves to geometrical blade modification on aerodynamics characteristics of high-pressure gas turbines, hydromagnetic flow arising in porous regions, optimal design of isothermal sloshing vessels to evaluation of (hybrid) nanofluid properties, their control using MHD, and their effect on different modes of heat transfer. Recent advances in numerical, theoretical, and experimental methodologies, as well as new physics, new methodological developments, and their limitations are presented within the current book. Among others, in the presented works, special attention is paid to validating and improving the accuracy of the presented methodologies. This book brings together a collection of inter/multidisciplinary works on many engineering applications in a coherent manner
Computation of optical properties of chromophores in different environments using QM/MM methods
Die theoretische Beschreibung der Wechselwirkung zwischen Molekülen und Licht kann herausfordernd sein, insbesondere dann, wenn es sich um flexible Farbstoffe in einer komplexen und dynamischen Umgebung handelt.
Obgleich quantenmechanische (QM) Methoden den angeregten Zustand eines Moleküls beschreiben können, sind sie zu rechenaufwändig, um strukturelle Fluktuationen simulieren zu können. Darüber hinaus ist die mögliche Systemgröße, die beschrieben werden kann, durch die Rechenkosten begrenzt. Aus diesem Grund kommen für die Untersuchung von Farbstoffen in Proteinumgebung semiempirische und Multiskalenansätze ins Spiel.
Die semiempirische Time-Dependent Long-range Corrected Density Functional Tight Binding (TD-LC-DFTB2) Methode wurde als effiziente Alternative zu ab initio Methoden oder der Dichtefunktionaltheorie in Bezug auf Geometrien im angeregten Zustand und Anregungsenergien getestet. Sie wurde in QM/MM Simulationen angewandt, in denen sie einen angeregten Fluorophor beschrieb, dessen Umgebung von einem klassischen Kraftfeld beschrieben wurde. Diese neue Strategie für die Untersuchung von Fluoreszenz wurde sorgfältig anhand von Literaturergebnissen bewertet, indem die Ergebnisse sowohl mit experimentellen als auch mit theoretischen Studien, die auf anderen Ansätzen basieren, verglichen wurden. Es wurde herausgefunden, dass TD-LC-DFTB2 im Allgemeinen Geometrien und Anregungsenergien von ausreichender Qualität liefert, aber es wurden auch einige Schwächen entdeckt.
Außerdem wurde ein optischer Glukosesensor untersucht, der aus dem Glukosebindeprotein und einem angefügten Fluorophor besteht. Mit Hilfe von klassischen Molekulardynamiksimulationen (MD Simulationen) konnten Zusammenhänge zwischen der Anwesenheit von Glukose, den Proteinkonformationen und dem Aufenthaltsort des Farbstoffs gefunden werden. Daraus ergab sich ein starker Hinweis auf die Funktionsweise des Sensors.
Schließlich wurde der Energietransfer in einem Pigment-Protein-Komplex untersucht. Der Fenna-Matthews-Olson-Komplex von Photosynthese betreibenden grünen Schwefelbakterien beinhaltet mehrere Bakteriochlorophyll a -- Pigmente in seinem Proteingerüst. Diese leiten die im Chlorosome gesammelte Anregungsenergie mit erstaunlicher Effizienz zum Reaktionszentrum weiter. Es wird Vorarbeit für eine Simulation der Exzitonenpropagation durch den Komplex gezeigt. Anregungsenergien und die Kopplungen zwischen den Pigmenten, das heißt die Elemente des exzitonischen Hamiltonoperators, wurden mit TD-LC-DFTB2 für Strukturen aus klassischen MD Simulationen berechnet. Dadurch wurde ein Eindruck zu deren Entwicklung über die Zeit und den Einfluss der Proteinumgebung gewonnen. Weiterhin wurden diese Daten genutzt, um neuronale Netze zu trainieren, die Anregungsenergien und Kopplungen noch schneller als TD-LC-DFTB2 vorhersagen können
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