Model-based dependability analysis : state-of-the-art, challenges and future outlook

Abstract: Over the past two decades, the study of model-based dependability analysis has gathered significant research interest. Different approaches have been developed to automate and address various limitations of classical dependability techniques to contend with the increasing complexity and challenges of modern safety-critical system. Two leading paradigms have emerged, one which constructs predictive system failure models from component failure models compositionally using the topology of the system. The other utilizes design models - typically state automata - to explore system behaviour through fault injection. This paper reviews a number of prominent techniques under these two paradigms, and provides an insight into their working mechanism, applicability, strengths and challenges, as well as recent developments within these fields. We also discuss the emerging trends on integrated approaches and advanced analysis capabilities. Lastly, we outline the future outlook for model-based dependability analysis

Compositional dependability analysis of dynamic systems with uncertainty

Over the past two decades, research has focused on simplifying dependability analysis by looking at how we can synthesise dependability information from system models automatically. This has led to the field of model-based safety assessment (MBSA), which has attracted a significant amount of interest from industry, academia, and government agencies. Different model-based safety analysis methods, such as Hierarchically Performed Hazard Origin & Propagation Studies (HiP-HOPS), are increasingly applied by industry for dependability analysis of safety-critical systems. Such systems may feature multiple modes of operation where the behaviour of the systems and the interactions between system components can change according to what modes of operation the systems are in.MBSA techniques usually combine different classical safety analysis approaches to allow the analysts to perform safety analyses automatically or semi-automatically. For example, HiP-HOPS is a state-of-the-art MBSA approach which enhances an architectural model of a system with logical failure annotations to allow safety studies such as Fault Tree Analysis (FTA) and Failure Modes and Effects Analysis (FMEA). In this way it shows how the failure of a single component or combinations of failures of different components can lead to system failure. As systems are getting more complex and their behaviour becomes more dynamic, capturing this dynamic behaviour and the many possible interactions between the components is necessary to develop an accurate failure model.One of the ways of modelling this dynamic behaviour is with a state-transition diagram. Introducing a dynamic model compatible with the existing architectural information of systems can provide significant benefits in terms of accurate representation and expressiveness when analysing the dynamic behaviour of modern large-scale and complex safety-critical systems. Thus the first key contribution of this thesis is a methodology to enable MBSA techniques to model dynamic behaviour of systems. This thesis demonstrates the use of this methodology using the HiP-HOPS tool as an example, and thus extends HiP-HOPS with state-transition annotations. This extension allows HiP-HOPS to model more complex dynamic scenarios and perform compositional dynamic dependability analysis of complex systems by generating Pandora temporal fault trees (TFTs). As TFTs capture state, the techniques used for solving classical FTs are not suitable to solve them. They require a state space solution for quantification of probability. This thesis therefore proposes two methodologies based on Petri Nets and Bayesian Networks to provide state space solutions to Pandora TFTs.Uncertainty is another important (yet incomplete) area of MBSA: typical MBSA approaches are not capable of performing quantitative analysis under uncertainty. Therefore, in addition to the above contributions, this thesis proposes a fuzzy set theory based methodology to quantify Pandora temporal fault trees with uncertainty in failure data of components.The proposed methodologies are applied to a case study to demonstrate how they can be used in practice. Finally, the overall contributions of the thesis are evaluated by discussing the results produced and from these conclusions about the potential benefits of the new techniques are drawn

A fuzzy Bayesian network approach for risk analysis in process industries

YesFault tree analysis is a widely used method of risk assessment in process industries. However, the classical fault tree approach has its own limitations such as the inability to deal with uncertain failure data and to consider statistical dependence among the failure events. In this paper, we propose a comprehensive framework for the risk assessment in process industries under the conditions of uncertainty and statistical dependency of events. The proposed approach makes the use of expert knowledge and fuzzy set theory for handling the uncertainty in the failure data and employs the Bayesian network modeling for capturing dependency among the events and for a robust probabilistic reasoning in the conditions of uncertainty. The effectiveness of the approach was demonstrated by performing risk assessment in an ethylene transportation line unit in an ethylene oxide (EO) production plant

A safety analysis approach to clinical workflows : application and evaluation

Clinical workflows are safety critical workflows as they have the potential to cause harm or death to patients. Their safety needs to be considered as early as possible in the development process. Effective safety analysis methods are required to ensure the safety of these high-risk workflows, because errors that may happen through routine workflow could propagate within the workflow to result in harmful failures of the system’s output. This paper shows how to apply an approach for safety analysis of clinic al workflows to analyse the safety of the workflow within a radiology department and evaluates the approach in terms of usability and benefits. The outcomes of using this approach include identification of the root causes of hazardous workflow failures that may put patients’ lives at risk. We show that the approach is applicable to this area of healthcare and is able to present added value through the detailed information on possible failures, of both their causes and effects; therefore, it has the potential to improve the safety of radiology and other clinical workflows

A synthesis of logic and bio-inspired techniques in the design of dependable systems

Much of the development of model-based design and dependability analysis in the design of dependable systems, including software intensive systems, can be attributed to the application of advances in formal logic and its application to fault forecasting and verification of systems. In parallel, work on bio-inspired technologies has shown potential for the evolutionary design of engineering systems via automated exploration of potentially large design spaces. We have not yet seen the emergence of a design paradigm that effectively combines these two techniques, schematically founded on the two pillars of formal logic and biology, from the early stages of, and throughout, the design lifecycle. Such a design paradigm would apply these techniques synergistically and systematically to enable optimal refinement of new designs which can be driven effectively by dependability requirements. The paper sketches such a model-centric paradigm for the design of dependable systems, presented in the scope of the HiP-HOPS tool and technique, that brings these technologies together to realise their combined potential benefits. The paper begins by identifying current challenges in model-based safety assessment and then overviews the use of meta-heuristics at various stages of the design lifecycle covering topics that span from allocation of dependability requirements, through dependability analysis, to multi-objective optimisation of system architectures and maintenance schedules

Molecular Mechanism of Rapamycin Resistance in Cancer Cells

The mammalian target of rapamycin (mTOR) acts as the central regulator of multiple cellular processes including cell growth, proliferation, and survival by integrating signals via nutrients, growth factors, hormones, and energy sensing. In cancer cells, the mTOR pathway is highly dysregulated providing survival signals to the cells for their uncontrolled growth. Hence, mTOR has evolved to be a potential therapeutic target for cancer treatment for the past two decades. Application of micro-molar doses of Rapamycin in vitro has been found to successfully inhibit mTOR complex 1 (mTORC1) by blocking the phosphorylation of its downstream substrates- a) ribosomal protein p70 S6 kinase (S6K) and b) eukaryotic initiation factor 4E (eIF4E)-binding protein 1 (4E-BP1). Previous studies have shown that Rapamycin acts as a cytostatic drug and causes G1 cell cycle arrest in the presence of transforming growth factor- β (TGF-β). However, in the absence of TGF-β signal, the drug induces cell-death. The rationale behind the cytotoxic effect of Rapamycin in the absence of TGF-β is that without TGF-β, the cells do not arrest in G1 and progress into S phase where they lack further survival signals from mTORC1 which has been inhibited by Rapamycin – therefore, the cells undergo apoptosis. Of significance, we have found that cancer cells with mutated RB and CDKN2A are not susceptible to cell-death upon Rapamycin treatment in the absence of TGF-β. The gene products of RB and CDKN2A (pRb and p14ARF respectively) suppress E2F family transcription factors that promote cell cycle progression from G1 into S. Restoration of wild type RB or inhibition of E2F activity in cancer cells led to Rapamycin sensitivity. These data provide evidence that the combination of mutant RB and mutant CDKN2A in cancer cells leads to Rapamycin resistance, which has implications for precision medicine approaches to anti-cancer therapies

Climate change and inclusive growth in Africa

Africa’s pursuit of inclusive and sustainable economic growth is impeded by many challenges, including climate change, whose effect is most apparent in the continent’s tropical regions. To this end, this study investigates the impact of climate change on achieving pro-poor economic growth in Africa. Predicated on poverty-inequality-climate analysis, the Augmented Mean Group (AMG) estimator is used to analyse data from 1996 to 2020 covering 51 African countries. The results reveal that climate change significantly impedes inclusive growth. Furthermore, evidence of a long-lasting negative effect of climate change on inclusive growth, which could be attributed to a lack of coping mechanisms among the poor and vulnerable groups, is found. Finally, the findings show a marginal impact of institutional quality and government spending on inclusive growth in the face of climate change. The study recommends more climate mitigation efforts and enhanced adaptation mechanisms, especially for the poor, as they are most vulnerable to the adverse effects of climate change

An overview of fault tree analysis and its application in model based dependability analysis

YesFault Tree Analysis (FTA) is a well-established and well-understood technique, widely used for dependability evaluation of a wide range of systems. Although many extensions of fault trees have been proposed, they suffer from a variety of shortcomings. In particular, even where software tool support exists, these analyses require a lot of manual effort. Over the past two decades, research has focused on simplifying dependability analysis by looking at how we can synthesise dependability information from system models automatically. This has led to the field of model-based dependability analysis (MBDA). Different tools and techniques have been developed as part of MBDA to automate the generation of dependability analysis artefacts such as fault trees. Firstly, this paper reviews the standard fault tree with its limitations. Secondly, different extensions of standard fault trees are reviewed. Thirdly, this paper reviews a number of prominent MBDA techniques where fault trees are used as a means for system dependability analysis and provides an insight into their working mechanism, applicability, strengths and challenges. Finally, the future outlook for MBDA is outlined, which includes the prospect of developing expert and intelligent systems for dependability analysis of complex open systems under the conditions of uncertainty