23,451 research outputs found
FRAM for systemic accident analysis: a matrix representation of functional resonance
Due to the inherent complexity of nowadays Air Traffic Management (ATM) system, standard methods looking at an event as a linear sequence of failures might become inappropriate. For this purpose, adopting a systemic perspective, the Functional Resonance Analysis Method (FRAM) originally developed by Hollnagel, helps identifying non-linear combinations of events and interrelationships.
This paper aims to enhance the strength of FRAM-based accident analyses, discussing the Resilience Analysis Matrix (RAM), a user-friendly tool that supports the analyst during the analysis, in order to reduce the complexity of representation of FRAM. The RAM offers a two dimensional representation which highlights systematically connections among couplings, and thus even highly connected group of couplings. As an illustrative case study, this paper develops a systemic accident analysis for the runway incursion happened in February 1991 at LAX airport, involving SkyWest Flight 5569 and USAir Flight 1493. FRAM confirms itself a powerful method to characterize the variability of the operational scenario, identifying the dynamic couplings with a critical role during the event and helping discussing the systemic effects of variability at different level of analysis
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A survey on online monitoring approaches of computer-based systems
This report surveys forms of online data collection that are in current use (as well as being the subject of research to adapt them to changing technology and demands), and can be used as inputs to assessment of dependability and resilience, although they are not primarily meant for this use
XBioSiP: A Methodology for Approximate Bio-Signal Processing at the Edge
Bio-signals exhibit high redundancy, and the algorithms for their processing
are inherently error resilient. This property can be leveraged to improve the
energy-efficiency of IoT-Edge (wearables) through the emerging trend of
approximate computing. This paper presents XBioSiP, a novel methodology for
approximate bio-signal processing that employs two quality evaluation stages,
during the pre-processing and bio-signal processing stages, to determine the
approximation parameters. It thereby achieves high energy savings while
satisfying the user-determined quality constraint. Our methodology achieves, up
to 19x and 22x reduction in the energy consumption of a QRS peak detection
algorithm for 0% and <1% loss in peak detection accuracy, respectively.Comment: Accepted for publication at the Design Automation Conference 2019
(DAC'19), Las Vegas, Nevada, US
Time series genome-centric analysis unveils bacterial response to operational disturbance in activated sludge
Understanding ecosystem response to disturbances and identifying the most critical traits for the maintenance of ecosystem functioning are important goals for microbial community ecology. In this study, we used 16S rRNA amplicon sequencing and metagenomics to investigate the assembly of bacterial populations in a full-scale municipal activated sludge wastewater treatment plant over a period of 3 years, including a 9-month period of disturbance characterized by short-term plant shutdowns. Following the reconstruction of 173 metagenome-assembled genomes, we assessed the functional potential, the number of rRNA gene operons, and the in situ growth rate of microorganisms present throughout the time series. Operational disturbances caused a significant decrease in bacteria with a single copy of the rRNA (rrn) operon. Despite moderate differences in resource availability, replication rates were distributed uniformly throughout time, with no differences between disturbed and stable periods. We suggest that the length of the growth lag phase, rather than the growth rate, is the primary driver of selection under disturbed conditions. Thus, the system could maintain its function in the face of disturbance by recruiting bacteria with the capacity to rapidly resume growth under unsteady operating conditions.Fil: PĂ©rez, MarĂa Victoria. Agua y Saneamientos Argentinos S.a.; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Instituto de Investigaciones en IngenierĂa GenĂ©tica y BiologĂa Molecular "Dr. HĂ©ctor N. Torres"; ArgentinaFil: Guerrero, Leandro DemiĂĄn. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Instituto de Investigaciones en IngenierĂa GenĂ©tica y BiologĂa Molecular "Dr. HĂ©ctor N. Torres"; ArgentinaFil: Orellana, Esteban. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Instituto de Investigaciones en IngenierĂa GenĂ©tica y BiologĂa Molecular "Dr. HĂ©ctor N. Torres"; ArgentinaFil: Figuerola, Eva Lucia Margarita. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Instituto de Investigaciones en IngenierĂa GenĂ©tica y BiologĂa Molecular "Dr. HĂ©ctor N. Torres"; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de FisiologĂa, BiologĂa Molecular y Celular; ArgentinaFil: Erijman, Leonardo. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Instituto de Investigaciones en IngenierĂa GenĂ©tica y BiologĂa Molecular "Dr. HĂ©ctor N. Torres"; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de FisiologĂa, BiologĂa Molecular y Celular; Argentin
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