DFTCalc: a tool for efficient fault tree analysis

Effective risk management is a key to ensure that our nuclear power plants, medical equipment, and power grids are dependable; and it is often required by law. Fault Tree Analysis (FTA) is a widely used methodology here, computing important dependability measures like system reliability. This paper presents DFTCalc, a powerful tool for FTA, providing (1) efficient fault tree modelling via compact representations; (2) effective analysis, allowing a wide range of dependability properties to be analysed (3) efficient analysis, via state-of-the-art stochastic techniques; and (4) a flexible and extensible framework, where gates can easily be changed or added. Technically, DFTCalc is realised via stochastic model checking, an innovative technique offering a wide plethora of powerful analysis techniques, including aggressive compression techniques to keep the underlying state space small

Confluence reduction for Markov automata

Markov automata are a novel formalism for specifying systems exhibiting nondeterminism, probabilistic choices and Markovian rates. Recently, the process algebra MAPA was introduced to efficiently model such systems. As always, the state space explosion threatens the analysability of the models generated by such specifications. We therefore introduce confluence reduction for Markov automata, a powerful reduction technique to keep these models small. We define the notion of confluence directly on Markov automata, and discuss how to syntactically detect confluence on the MAPA language as well. That way, Markov automata generated by MAPA specifications can be reduced on-the-fly while preserving divergence-sensitive branching bisimulation. Three case studies demonstrate the significance of our approach, with reductions in analysis time up to an order of magnitude

Compositional Verification and Optimization of Interactive Markov Chains

Interactive Markov chains (IMC) are compositional behavioural models extending labelled transition systems and continuous-time Markov chains. We provide a framework and algorithms for compositional verification and optimization of IMC with respect to time-bounded properties. Firstly, we give a specification formalism for IMC. Secondly, given a time-bounded property, an IMC component and the assumption that its unknown environment satisfies a given specification, we synthesize a scheduler for the component optimizing the probability that the property is satisfied in any such environment

Modelling and analysis of Markov reward automata

Costs and rewards are important ingredients for many types of systems, modelling critical aspects like energy consumption, task completion, repair costs, and memory usage. This paper introduces Markov reward automata, an extension of Markov automata that allows the modelling of systems incorporating rewards (or costs) in addition to nondeterminism, discrete probabilistic choice and continuous stochastic timing. Rewards come in two flavours: action rewards, acquired instantaneously when taking a transition; and state rewards, acquired while residing in a state. We present algorithms to optimise three reward functions: the expected cumulative reward until a goal is reached, the expected cumulative reward until a certain time bound, and the long-run average reward. We have implemented these algorithms in the SCOOP/IMCA tool chain and show their feasibility via several case studies

A tutorial on interactive Markov chains

Interactive Markov chains (IMCs) constitute a powerful sto- chastic model that extends both continuous-time Markov chains and labelled transition systems. IMCs enable a wide range of modelling and analysis techniques and serve as a semantic model for many industrial and scientific formalisms, such as AADL, GSPNs and many more. Applications cover various engineering contexts ranging from industrial system-on-chip manufacturing to satellite designs. We present a survey of the state-of-the-art in modelling and analysis of IMCs.\ud We cover a set of techniques that can be utilised for compositional modelling, state space generation and reduction, and model checking. The significance of the presented material and corresponding tools is highlighted through multiple case studies

DFTCalc: reliability centered maintenance via fault tree analysis (tool paper)

Reliability, availability, maintenance and safety (RAMS) analysis is essential in the evaluation of safety critical systems like nuclear power plants and the railway infrastructure. A widely used methodology within RAMS analysis are fault trees, representing failure propagations throughout a system. We present DFTCalc, a tool-set to conduct quantitative analysis on dynamic fault trees including the effect of a maintenance strategy on the system dependability

Stretching and squeezing of sessile dielectric drops by the optical radiation pressure

We study numerically the deformation of sessile dielectric drops immersed in a second fluid when submitted to the optical radiation pressure of a continuous Gaussian laser wave. Both drop stretching and drop squeezing are investigated at steady state where capillary effects balance the optical radiation pressure. A boundary integral method is implemented to solve the axisymmetric Stokes flow in the two fluids. In the stretching case, we find that the drop shape goes from prolate to near-conical for increasing optical radiation pressure whatever the drop to beam radius ratio and the refractive index contrast between the two fluids. The semi-angle of the cone at equilibrium decreases with the drop to beam radius ratio and is weakly influenced by the index contrast. Above a threshold value of the radiation pressure, these "optical cones" become unstable and a disruption is observed. Conversely, when optically squeezed, the drop shifts from an oblate to a concave shape leading to the formation of a stable "optical torus". These findings extend the electrohydrodynamics approach of drop deformation to the much less investigated "optical domain" and reveal the openings offered by laser waves to actively manipulate droplets at the micrometer scale

Distributed Synthesis in Continuous Time

We introduce a formalism modelling communication of distributed agents strictly in continuous-time. Within this framework, we study the problem of synthesising local strategies for individual agents such that a specified set of goal states is reached, or reached with at least a given probability. The flow of time is modelled explicitly based on continuous-time randomness, with two natural implications: First, the non-determinism stemming from interleaving disappears. Second, when we restrict to a subclass of non-urgent models, the quantitative value problem for two players can be solved in EXPTIME. Indeed, the explicit continuous time enables players to communicate their states by delaying synchronisation (which is unrestricted for non-urgent models). In general, the problems are undecidable already for two players in the quantitative case and three players in the qualitative case. The qualitative undecidability is shown by a reduction to decentralized POMDPs for which we provide the strongest (and rather surprising) undecidability result so far

Combined selective plane illumination microscopy (SPIM) and full-field optical coherence tomography (FF-OCT) forin vivoimaging

Selective plane illumination microscopy (SPIM), also known as light sheet fluorescence microscopy, provides high specificity through fluorescence labeling. However, it lacks complementary structural information from the surrounding context, which is essential for the comprehensive analysis of biological samples. Here, we present a high-resolution, multimodal imaging system that integrates SPIM with full-field optical coherence tomography (FF-OCT), without requiring modifications to the existing SPIM setup. Both SPIM and FF-OCT offer low phototoxicity and intrinsic optical sectioning, making them well-suited for in vivo imaging. Their shared detection path enables seamless and efficient co-registration of fluorescence and structural data. We demonstrate the functionality of this combined system by performing in vivo imaging of zebrafish larvae

The impact of perceived injustice on appraisals of physical activity:An examination of the mediating role of attention bias to pain in a chronic low back pain sample

The current study examined the relationship between perceived injustice and attentional bias (AB) toward pain among individuals with chronic low back pain asked to perform and appraise the pain and difficulty of a standardized set of common physical activities. A pictorial dot-probe task assessed AB toward pain stimuli (i.e., pain faces cueing pain), after which participants performed the physical tasks. Participants also rated face stimuli in terms of pain, sadness, and anger expression. As hypothesized, perceived injustice was positively associated with AB toward pain stimuli; additionally, perceived injustice and AB were both positively associated with appraisals of pain and difficulty. Counter to expectations, AB did not mediate the relationship between perceived injustice and task appraisals, suggesting that AB is insufficient to explain this relationship. Exploratory analyses indicated that participants with higher levels of perceived injustice rated stimulus faces as sadder and angrier; no such differences emerged for pain ratings. This is the first study to examine the association between perceived injustice and AB toward pain, as well as perceived injustice and in-vivo appraisals of common physical activity. Results extend existing literature and suggest that attentional and potential interpretive bias should be considered in future research. Perspective This article identifies significant associations between perceived injustice, biased attention to pain, and appraisals of common physical activities among individuals with chronic low back pain. These findings suggest targets for intervention as well as directions for future research regarding individuals with high perceptions of injustice related to pain