2,539 research outputs found

    Estimating Impact and Frequency of Risks to Safety and Mission Critical Systems Using CVSS

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    Many safety and mission critical systems depend on the correct and secure operation of both supportive and core software systems. E.g., both the safety of personnel and the effective execution of core missions on an oil platform depend on the correct recording storing, transfer and interpretation of data, such as that for the Logging While Drilling (LWD) and Measurement While Drilling (MWD) subsystems. Here, data is recorded on site, packaged and then transferred to an on-shore operational centre. Today, the data is transferred on dedicated communication channels to ensure a secure and safe transfer, free from deliberately and accidental faults. However, as the cost control is ever more important some of the transfer will be over remotely accessible infrastructure in the future. Thus, communication will be prone to known security vulnerabilities exploitable by outsiders. This paper presents a model that estimates risk level of known vulnerabilities as a combination of frequency and impact estimates derived from the Common Vulnerability Scoring System (CVSS). The model is implemented as a Bayesian Belief Network (BBN)

    A Bayesian Belief Network to Infer Incentive Mechanisms to Reduce Antibiotic Use in Livestock Production

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    Efficient policy intervention to reduce antibiotic use in livestock production requires knowledge about the rationale underlying antibiotic usage. Animal health status and management quality are considered the two most important factors that influence farmers’ decision-making concerning antibiotic use. Information on these two factors is therefore crucial in designing incentive mechanisms. In this paper, a Bayesian belief network (BBN) is built to represent the knowledge on how these factors can directly and indirectly determine antibiotic use and the possible impact on economic incentives. Since both factors are not directly observable (i.e. latent), they are inferred from measurable variables (i.e. manifest variables) which are influenced by these factors. Using farm accounting data and registration data on antibiotic use and veterinary services in specialized finisher pig production farms, a confirmatory factor analysis was carried out to construct these factors. The BBN is then parameterized through regression analysis on the constructed factors and manifest variables. Using the BBN, possible incentive mechanisms through prices and management training are discussed.Livestock Production/Industries,

    Use of a Bayesian belief network to predict the impacts of commercializing non-timber forest products on livelihoods

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    Commercialization of non-timber forest products (NTFPs) has been widely promoted as a means of sustainably developing tropical forest resources, in a way that promotes forest conservation while supporting rural livelihoods. However, in practice, NTFP commercialization has often failed to deliver the expected benefits. Progress in analyzing the causes of such failure has been hindered by the lack of a suitable framework for the analysis of NTFP case studies, and by the lack of predictive theory. We address these needs by developing a probabilistic model based on a livelihood framework, enabling the impact of NTFP commercialization on livelihoods to be predicted. The framework considers five types of capital asset needed to support livelihoods: natural, human, social, physical, and financial. Commercialization of NTFPs is represented in the model as the conversion of one form of capital asset into another, which is influenced by a variety of socio-economic, environmental, and political factors. Impacts on livelihoods are determined by the availability of the five types of assets following commercialization. The model, implemented as a Bayesian Belief Network, was tested using data from participatory research into 19 NTFP case studies undertaken in Mexico and Bolivia. The model provides a novel tool for diagnosing the causes of success and failure in NTFP commercialization, and can be used to explore the potential impacts of policy options and other interventions on livelihoods. The potential value of this approach for the development of NTFP theory is discussed

    Joining the dots: hydrology, freshwater ecosystem values and adaptation options

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    AbstractThe objective of this research was to investigate and test the necessary steps in developing an adaptation planning framework for freshwater biodiversity. We used Tasmania as a test case to demonstrate how downscaled climate model outputs could be integrated with spatially resolved hydrological models and freshwater biodiversity data. This enabled us to scope adaptation actions at local, regional and state scales for Tasmania, and to explore how priorities might be set.To achieve this integration we quantified how different climate change scenarios could affect the risks to biodiversity and ecosystem values (‘biodiversity assets’) in freshwaters, the scope and types of adaptation actions, and assessed the strengths and weaknesses of the policy and planning instruments in responding to climate change.We concluded that downscaled climate modelling, linked with modelling of catchment and hydrological processes, refines projections for climate-driven risks to aquatic environments. Spatial and temporal hazards and risks can now be compared at a variety of scales, as well as comparisons between biodiversity assets (e.g. relative risk to riparian vegetation v. in-stream biota). Uncertainties can be identified and built into adaptation processes. Notwithstanding this progress, we identified a number of issues that need to be addressed in order to increase confidence in this process.The main issues for improved and timely modelling are: frameworks for using and downscaling outputs from improved global climate models as they become available; better data on thermal tolerances of freshwater biota; and, improved methods for predicting key water temperature variables from air temperature and other biophysical predictors. Improvements are also needed in updating and maintaining high quality biodiversity data sets, and better spatially explicit information on the contributions of groundwater to surface waters and rates of recharge.The list of adaptation options available is extensive, but the key challenge is to organise these options so that stakeholders are not overwhelmed. Scenario modelling that incorporates explicit tools for comparing costs, benefits, feasibility and social acceptability should help with setting priorities but require further development.A review of current Australian policies revealed a variety of responses driven by both water reform and climate change agendas. Many agencies are actively revising their policies to accommodate adaptation. However, we note that much of the reform of the water sector in the last 10–15 years has aimed to improve certainty for non-environmental water uses. Under the National Water Initiative, governments have agreed that entitlement holders should bear the risks of reduced volumes or reliability of their water allocations as a result of changes in climate. The key opportunity for adaptive uptake of climate adaptations is by developing and periodically reviewing water management planning tools. Pathways need to be developed for integrating the traditional evolution of planning and policy with the needs for climate change adaptation for aquatic ecosystems. Formal mechanisms for the uptake of knowledge about identified risks into policy and legislative instruments remain under-developed. An even bigger challenge is to integrate multiple adaptation strategies (sometimes at different scales) to achieve specific adaptation objectives within regions or catchments—especially where a mix of water management and non-water management is required

    Freshwater ecosystem services in mining regions : modelling options for policy development support

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    The ecosystem services (ES) approach offers an integrated perspective of social-ecological systems, suitable for holistic assessments of mining impacts. Yet for ES models to be policy-relevant, methodological consensus in mining contexts is needed. We review articles assessing ES in mining areas focusing on freshwater components and policy support potential. Twenty-six articles were analysed concerning (i) methodological complexity (data types, number of parameters, processes and ecosystem-human integration level) and (ii) potential applicability for policy development (communication of uncertainties, scenario simulation, stakeholder participation and management recommendations). Articles illustrate mining impacts on ES through valuation exercises mostly. However, the lack of ground-and surface-water measurements, as well as insufficient representation of the connectivity among soil, water and humans, leave room for improvements. Inclusion of mining-specific environmental stressors models, increasing resolution of topographies, determination of baseline ES patterns and inclusion of multi-stakeholder perspectives are advantageous for policy support. We argue that achieving more holistic assessments exhorts practitioners to aim for high social-ecological connectivity using mechanistic models where possible and using inductive methods only where necessary. Due to data constraints, cause-effect networks might be the most feasible and best solution. Thus, a policy-oriented framework is proposed, in which data science is directed to environmental modelling for analysis of mining impacts on water ES

    A model-based approach to System of Systems risk management

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    The failure of many System of Systems (SoS) enterprises can be attributed to the inappropriate application of traditional Systems Engineering (SE) processes within the SoS domain, because of the mistaken belief that a SoS can be regarded as a single large, or complex, system. SoS Engineering (SoSE) is a sub-discipline of SE; Risk Management and Modelling and Simulation (M&S) are key areas within SoSE, both of which also lie within the traditional SE domain. Risk Management of SoS requires a different approach to that currently taken for individual systems; if risk is managed for each component system then it cannot be assumed that the aggregated affect will be to mitigate risk at the SoS level. A literature review was undertaken examining three themes: (1) SoS Engineering (SoSE), (2) M&S and (3) Risk. Theme 1 of the literature provided insight into the activities comprising SoSE and its difference from traditional SE with risk management identified as a key activity. The second theme discussed the application of M&S to SoS, providing an output, which supported the identification of appropriate techniques and concluding that, the inherent complexity of a SoS required the use of M&S in order to support SoSE activities. Current risk management approaches were reviewed in theme 3 as well as the management of SoS risk. Although some specific examples of the management of SoS risk were found, no mature, general approach was identified, indicating a gap in current knowledge. However, it was noted most of these examples were underpinned by M&S approaches. It was therefore concluded a general approach SoS risk management utilising M&S methods would be of benefit. In order to fill the gap identified in current knowledge, this research proposed a new model based approach to Risk Management where risk identification was supported by a framework, which combined SoS system of interest dimensions with holistic risk types, where the resulting risks and contributing factors are captured in a causal network. Analysis of the causal network using a model technique selection tool, developed as part of this research, allowed the causal network to be simplified through the replacement of groups of elements within the network by appropriate supporting models. The Bayesian Belief Network (BBN) was identified as a suitable method to represent SoS risk. Supporting models run in Monte Carlo Simulations allowed data to be generated from which the risk BBNs could learn, thereby providing a more quantitative approach to SoS risk management. A method was developed which provided context to the BBN risk output through comparison with worst and best-case risk probabilities. The model based approach to Risk Management was applied to two very different case studies: Close Air Support mission planning and the Wheat Supply Chain, UK National Food Security risks, demonstrating its effectiveness and adaptability. The research established that the SoS SoI is essential for effective SoS risk identification and analysis of risk transfer, effective SoS modelling requires a range of techniques where suitability is determined by the problem context, the responsibility for SoS Risk Management is related to the overall SoS classification and the model based approach to SoS risk management was effective for both application case studies

    Towards a real-time Structural Health Monitoring of railway bridges

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    More than 350,000 railway bridges are present on the European railway network, making them a key infrastructure of the whole railway network. Railway bridges are continuously exposed to changing environmental threats, such as wind, floods and traffic load, which can affect safety and reliability of the bridge. Furthermore, a problem on a bridge can affect the whole railway network by increasing the vulnerability of the geographic area, served by the railway network. In this paper a Bayesian Belief Network (BBN) method is presented in order to move from visual inspection towards a real time Structural Health Monitoring (SHM) of the bridge. It is proposed that the health state of a steel truss bridge is continuously monitored by taking account of the health state of each bridge element. In this way, levels of bridge deterioration can be identified before they become critical, the risk of direct and indirect economic losses can be reduced by defining optimal bridge maintenance works, and the reliability of the bridge can be improved by identifying possible hidden vulnerabilities among different bridge elements

    Incorporating stakeholders' values into environmental decision support : A Bayesian Belief Network approach

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    Participatory modelling increases the transparency of environmental planning and management processes and enhances the mutual understanding among different parties. We present a sequential probabilistic approach to involve stakeholders' views in the formal decision support process. A continuous Bayesian Belief Network (BBN) model is used to estimate population parameters for stakeholder groups, based on samples of individual value judgements. The approach allows quantification and visualization of the variability in views among and within stakeholder groups. Discrete BBN is populated with these parameters, to summarize and visualize the information and to link it to a larger decision analytic influence diagram (ID). As part of ID, the resulting discrete BBN element serves as a distribution-form decision criteria in probabilistic evaluation of alternative management strategies, to help find a solution that represents the optimal compromise in the presence of potentially conflicting objectives. We demonstrate our idea using example data from the field of marine spatial planning. However, this approach is applicable to many types of management cases. We suggest that by advancing the mutual understanding and concrete participation this approach can further facilitate the stakeholder involvement also during the various stages of the environmental management process. (C) 2019 The Authors. Published by Elsevier B.V.Peer reviewe

    Developing a risk analysis and decision making strategy for an offshore wind farm

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    The renewables sector and particularly offshore wind energy is a fast developing industry over the last few years. Especially activities related to the Installation,Operation and Maintenance (O&M) of offshore wind turbines becomes a challenging task with inherent risks. This paper assesses the risks related to the above stages of a wind farm lifecycle using the FMECA (Failure Mode, Effects and Criticality Analysis) and HAZID (Hazard Identification) methods. The full-scale offshore installation and O&M tasks are considered together with the wind turbine main components. An integrated risk analysis methodology is presented addressing personnel Safety (S), Environmental impact (E), Asset integrity (A) and Operation (O). The above is supplemented by a cost analysis with the aid of BBN(Bayesian Belief Networks) method in order to assist the decision making process related to installation and O&M tasks. All major risks and critical wind turbinecomponents are identified as well as measures are suggested in order to prevent or mitigate them. Moreover, a thorough inspection and maintenance plan can be elaborated for the mentioned activities
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