6,576 research outputs found

    Design-Time Quantification of Integrity in Cyber-Physical-Systems

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    In a software system it is possible to quantify the amount of information that is leaked or corrupted by analysing the flows of information present in the source code. In a cyber-physical system, information flows are not only present at the digital level, but also at a physical level, and to and fro the two levels. In this work, we provide a methodology to formally analyse a Cyber-Physical System composite model (combining physics and control) using an information flow-theoretic approach. We use this approach to quantify the level of vulnerability of a system with respect to attackers with different capabilities. We illustrate our approach by means of a water distribution case study

    Synergizing Roadway Infrastructure Investment with Digital Infrastructure for Infrastructure-Based Connected Vehicle Applications: Review of Current Status and Future Directions

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    The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The safety, mobility, environmental and economic benefits of Connected and Autonomous Vehicles (CAVs) are potentially dramatic. However, realization of these benefits largely hinges on the timely upgrading of the existing transportation system. CAVs must be enabled to send and receive data to and from other vehicles and drivers (V2V communication) and to and from infrastructure (V2I communication). Further, infrastructure and the transportation agencies that manage it must be able to collect, process, distribute and archive these data quickly, reliably, and securely. This paper focuses on current digital roadway infrastructure initiatives and highlights the importance of including digital infrastructure investment alongside more traditional infrastructure investment to keep up with the auto industry's push towards this real time communication and data processing capability. Agencies responsible for transportation infrastructure construction and management must collaborate, establishing national and international platforms to guide the planning, deployment and management of digital infrastructure in their jurisdictions. This will help create standardized interoperable national and international systems so that CAV technology is not deployed in a haphazard and uncoordinated manner

    Incorporating Cardiac Substructures Into Radiation Therapy For Improved Cardiac Sparing

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    Growing evidence suggests that radiation therapy (RT) doses to the heart and cardiac substructures (CS) are strongly linked to cardiac toxicities, though only the heart is considered clinically. This work aimed to utilize the superior soft-tissue contrast of magnetic resonance (MR) to segment CS, quantify uncertainties in their position, assess their effect on treatment planning and an MR-guided environment. Automatic substructure segmentation of 12 CS was completed using a novel hybrid MR/computed tomography (CT) atlas method and was improved upon using a 3-dimensional neural network (U-Net) from deep learning. Intra-fraction motion due to respiration was then quantified. The inter-fraction setup uncertainties utilizing a novel MR-linear accelerator were also quantified. Treatment planning comparisons were performed with and without substructure inclusions and methods to reduce radiation dose to sensitive CS were evaluated. Lastly, these described technologies (deep learning U-Net) were translated to an MR-linear accelerator and a segmentation pipeline was created. Automatic segmentations from the hybrid MR/CT atlas was able to generate accurate segmentations for the chambers and great vessels (Dice similarity coefficient (DSC) \u3e 0.75) but coronary artery segmentations were unsuccessful (DSC\u3c0.3). After implementing deep learning, DSC for the chambers and great vessels was ≥0.85 along with an improvement in the coronary arteries (DSC\u3e0.5). Similar accuracy was achieved when implementing deep learning for MR-guided RT. On average, automatic segmentations required ~10 minutes to generate per patient and deep learning only required 14 seconds. The inclusion of CS in the treatment planning process did not yield statistically significant changes in plan complexity, PTV, or OAR dose. Automatic segmentation results from deep learning pose major efficiency and accuracy gains for CS segmentation offering high potential for rapid implementation into radiation therapy planning for improved cardiac sparing. Introducing CS into RT planning for MR-guided RT presented an opportunity for more effective sparing with limited increase in plan complexity

    X-Ray Emission from the Pre-Planetary Nebula Henize 3-1475

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    We report the first detection of X-ray emission in a pre-planetary nebula, Hen 3-1475. Pre-planetary nebulae are rare objects in the short transition stage between the Asymptotic Giant Branch and planetary nebula evolutionary phases, and Hen 3-1475, characterised by a remarkable S-shaped chain of optical knots, is one of the most noteworthy members of this class. Observations with the Advanced CCD Imaging Spectrometer (ACIS) onboard the Chandra X-Ray observatory show the presence of compact emission coincident with the brightest optical knot in this bipolar object, which is displaced from the central star by 2.7 arcsec along the polar axis. Model fits to the X-ray spectrum indicate an X-ray temperature and luminosity, respectively, of (4.3-5.7) 10^6 K and (4+/-1.4) 10^{31} (D/5 kpc)^2 erg s^{-1}, respectively. Our 3-sigma upper limit on the luminosity of compact X-ray emission from the central star in Hen 3-1475 is ~5 10^{31} (D/5 kpc)^2 erg s^{-1}. The detection of X-rays in Hen 3-1475 is consistent with models in which fast collimated post-AGB outflows are crucial to the shaping of planetary nebulae; we discuss such models in the context of our observations.Comment: 2 figure

    Salerno: A Military Fiasco

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