Risk analysis in software design

Models, modelling and modellers: an application to risk analysis

The SAFE FOODS Risk Analysis Framework suitable for GMOs? A case study

To benefit from cloud computing and the advantages it offers, obstacles regarding the usage and acceptance of clouds have to be cleared. For cloud providers, one way to obtain customers' confidence is to establish security mechanisms when using clouds. The ISO 27001 standard provides general concepts for establishing information security in an organization. Risk analysis is an essential part in the ISO 27001 standard for achieving information security. This standard, however, contains ambiguous descriptions. In addition, it does not stipulate any method to identify assets, threats, and vulnerabilities. In this paper, the authors present a method for cloud computing systems to perform risk analysis according to the ISO 27001. The authors' structured method is tailored to SMEs. It relies upon patterns to describe context and structure of a cloud computing system, elicit security requirements, identify threats, and select controls, which ease the effort for these activities. The authors' method guides companies through the process of risk analysis in a structured manner. Furthermore, the authors provide a model-based tool for supporting the ISO 27001 standard certification. The authors' tool consists of various plug-ins for conducting different steps of their method.

Global trade in ornamental fish from an Australian perspective: The case for revised import risk analysis and management strategies

A key feature of risk analysis is that risk assessment and risk management should be functionally separated. However, the usefulness of a risk assessment may be limited if the output is not designed to help with risk management decisions. The COMRISK project investigated the communication between risk assessors and risk managers. The overall goal of the project was to identify current practices and challenges in communication between risk assessors and risk managers during the risk analysis process, and thus increase and improve the understanding and the quality of the communication between them. Specific actions to achieve this aim included reviewing of historical food safety cases, analysing risk assessment requests, identifying communication guiding documents, including legislation and agreements, conducting semi-structured interviews with risk assessors and risk managers, and identifying tools for facilitating the communication between risk assessors and risk managers. It was concluded that the usefulness of a risk assessment is strongly dependent on well-defined and mutually recognised risk questions and that scarce or poor communication between risk assessors and risk managers is one of the major reasons when an output from risk assessment fails to support risk management. The communication between risk assessors and risk managers preceding the onset of the risk assessment, when the risk assessment requests with its risk questions are defined, is especially identified as one of the critical points to ensure a risk assessment that is fit for purpose. However, difficulties in understanding were also reported for the communication between risk assessors and risk managers during and after the risk assessment. Lack of communication is seldom a result of formal constraints or agreements nor can it be explained by a wish of the risk assessors or risk managers. Instead, perceived constraints or traditions appear to be possible underlying factors leading to scarce or poor communication between risk assessors and risk managers. It is essential that both risk assessors and risk managers acknowledge the crucial importance of communication between them while at the same time respect their different roles in a risk analysis.According to respondents, the best solution to facilitate the framing of the risk assessment questions isan open dialogue between risk assessors and risk managers to agree on the goal of the assessment and to build trust. Further, the interviewresults indicate that a formal systematic process may facilitate communication during the risk analysis. Where there is uncertainty, e.g due to data gaps or issues related to the methodology and models, it should be acknowledged and described properly by risk assessors to risk managers. Training of risk assessors and risk managers may improve the possibility of a timely and fit-for-purpose output.Such a training should give a deeper insight in the risk management process, give a better understanding of the risk managers role, and especially raise the awareness of the importance of the communication between risk assessors and risk managers.To improve the risk analysis process, it is also important that the risk assessor gets feedback regarding how risk assessments have met the needs of the risk managers.The present study also found that aspects of risk communication studied in this project are not extensively discussed in the guidance documents for risk analysis. More research is needed to identify the barriers for a fit for purpose communication.

Preface. 1 LNG Properties and Overview of Hazards. 1.1 LNG Properties. 1.2 Hazards of LNG with Respect to Public Risk. 1.3 Risk Analysis Requires Adequate Modeling. 1.4 Flammability. 1.5 Regulations in Siting Onshore LNG Import Terminals. 1.6 Regulation for Siting Offshore LNG Import Terminals. 1.7 Controversial Claims of LNG Opponents. 2 LNG Incidents and Marine History. 2.1 LNG Ship Design History. 2.2 Designs and Issues First Commercial LNG Ships. 2.3 LNG Trade History. 2.4 LNG Accident History. 2.5 Summary of LNG History and Relevant Technical Developments. 3 Current LNG Carriers. 3.1 Design Requirements. 3.2 Membrane Tanks. 3.3 Moss Spheres. 4 Risk Analysis and Risk Reduction. 4.1 Background. 4.2 Risk Analysis Process. 4.3 Frequency: Data Sources and Analysis. 4.4 Frequency: Predictive Methods. 4.5 Consequence Modeling. 4.6 Ignition Probability. 4.7 Risk Results. 4.8 Special Issues Terrorism. 4.9 Risk Reduction and Mitigation Measures for LNG. 5 LNG Discharge on Water. 5.1 Type 1 Above Water Breaches at Sea. 5.2 Type 2 At Waterline Breaches at Sea. 5.3 Type 3 Below Waterline Breaches at Sea. 5.4 Discharges from Ship's Pipework. 5.5 Cascading Failures at Sea. 5.6 Initial Discharge Rate. 5.7 Time-Dependent Discharge (Blowdown). 5.8 Vacuum Breaking and Glug-Glug Effects. 6 Risk Analysis for Onshore Terminals and Transport. 6.1 Typical Basis for LNG Receiving Terminal. 6.2 Features of LNG Receiving Terminals. 6.3 Standards for Receiving Terminal Design. 6.4 U.S. Guidelines and Regulations for Receiving Terminals. 6.5 European Regulations for LNG Receiving Terminals. 6.6 Empirical Formula for Required Land Area of Terminal. 6.7 Leak in Loading Arm or in Storage Tank. 6.8 Rollover. 6.9 LNG Land Transport Risk. 6.10 Offshore LNG Terminals. 7 LNG Pool Modeling. 7.1 Flashing and Droplet Evaporation in Jet Flow. 7.2 Pool Spread and Evaporation Modeling. 7.3 Rapid Phase Transition Explosions. 7.4 Aerosol Drop Size. 7.5 Heat Balance Terms to LNG Pool. 7.6 Nomenclature. 8 Vapor Cloud Dispersion Modeling. 8.1 Atmospheric Transport Processes. 8.2 Model Types. 8.3 LNG Dispersion Test Series. 8.4 Factors Affecting Plume Length. 8.5 Effect of Wind, Currents, and Waves on LNG Plume. 8.6 Comparison of Dispersion Model Predictions. 8.7 Descriptions of Dispersion Test Series. 8.8 Vapor Intrusion Indoors. 8.9 Theoretical Basis for Suppression of Turbulence. 9 LNG Pool Fire Modeling. 9.1 Types of Fires from LNG Facilities. 9.2 The Challenge for Pool Fire Modeling. 9.3 Pool Fire Characteristics. 9.4 Summary of LNG Fire Experiments. 9.5 Burning Rate Data and Correlations From Fire Tests. 9.6 Point Source Fire Model. 9.7 Solid Flame Models: Flame Length Correlations. 9.8 Flame Tilt Correlations. 9.9 Flame Drag Near Pools. 9.10 Sep Correlations and Smoke Shielding. 9.11 Atmospheric Transmissivity. 9.12 Trench Fires. 9.13 View Factors. 9.14 CFD Modeling. 9.15 Comparison of Model Predictions. 9.16 Fire Engulfment of LNG Carrier. 10 Other LNG Hazards. 10.1 Fire and Explosion Scenarios. 10.2 Jet Fires. 10.3 Flash Fires. 10.4 BLEVEs, Fireballs. 10.5 LNG Vapor Cloud Explosions. 10.6 Asphyxiation and Cryogenic Hazard from LNG Spills. 11 Fire Effects. 11.1 Fire Radiation Effects on Individuals. 11.2 Effects of Thermal Radiation on Property. 12 Research Needs. 12.1 Uncertainties. 12.2 Recommendations of GAO Survey. 12.3 LNG Model Evaluation Protocols (MEPs). 12.4 Special Topics. 12.5 Conclusions. References. Index.

About the Authors

In the context of the socio-economic development of the world economy, as well as improvements in the field of automation of risk analysis and assessment processes intelligent methods are used more often. The article explores the possibility of creating a risk analysis decision support system using modern intelligent methods. Creating a decision support system in this area is a complex process, but it is also important and valuable in the context of the transition to a digital economy. The possibility of creating an algorithm for dynamic risk analysis under uncertainty taking into account the obtained unstructured information is described. The modern intelligent methods applied to the development of decision support system in the socioeconomic sphere are investigated. We propose using the methods of cognitive visualization in the transformation of the results of risk analysis into a graphical form, which simplifies the evaluation and analysis of the received information.

The paradigm shift in the pharmaceutical industry to continuous manufacturing, which has recently progressed from conceptual demonstration to pilot production, has stimulated the development and application of process systems engineering (PSE) tools for implementing efficient and robust control strategies. In this study, a systematic framework for process control design and risk analysis for continuous pharmaceutical solid-dosage manufacturing is proposed, consisting of system identification with state-space models; control design and analysis metrics; hierarchical three-layer control structures; risk mapping, assessment and planning (Risk MAP) strategies; and control performance indicators. The framework is applied to a feeding-blending system, wherein the major source of variance in the product quality arises. It can be demonstrated that the variance in the feeding-blending system can be mitigated and managed through the proposed systematic framework for control design and risk analysis. The process analytical technology (PAT) tool for mass fraction measurement of active pharmaceutical ingredient (API) and its relative standard deviation (RSD) were indispensable to achieve an efficient control design at the advanced layers. Specifically, the improvements in control performance by implementing advanced model-based control strategy are found to be limited by model-plant mismatch and the sampling time of the PAT tools.

Emerging of cloud computing with flexibility, improve accessing data, and cost-saving makes this technology accessible and growing fast. As a result of the emergence of cloud computing bring interest to industries to used cloud computing. Although cloud computing brings so many benefits to customers, the previous study reveals that cloud computing penetration in the Healthcare area is still low. With effective cloud risk assessment methodology will gain the confidence to cloud users in this technology. Study in cloud risk assessment methodology still infant and the complexity in identifying security risk still debating. This paper explores the risk assessment process by highlighting the method in the risk evaluation process. Risk evaluation is an essential phase in the risk assessment process. It compares the result from the risk analysis process and determines whether to accept or tolerate the risk criteria to decide on the risk analysis. In this study, the Nominal Group Technique (NGT) is introduced to compare risk analysis results in the earlier phase. Since risk evaluation based on organizational objectives, external and internal context and stakeholders' views, NGT is promising for effective results. This study not only contributing to the prioritizing list of risks and threats in a systematical manner but indirectly NGT process makes stakeholders aware of the current cloud security risk situation in the organization. Equal opportunity expressing views in this focus group discussion is hope can generate a brilliant solution in risk assessment results.

This article describes the development of an intelligent system for railway risk analysis using fuzzy reasoning approach (FRA) and fuzzy analytical hierarchy decision making process (Fuzzy-AHP), which is specially designed and developed for the railways. In the system, FRA is employed to estimate the risk level (RL) of each failure event in terms of failure probability (FP) and consequent severity (CS). This allows imprecision or approximate information in risk analysis process. Fuzzy-AHP technique is incorporated into the risk model to use its advantage in determining the relative importance of the risk contributors, i.e. weight factor (WF) so that the risk assessment can be progressed from component level to the subsystem level and finally to system level. This risk assessment system can evaluate both qualitative and quantitative risk data and information associated with a railway system efficiently and effectively, which will provide railway risk analysts, managers and engineers with a method and tool to improve their safety management of railway systems and set safety standards. A case study on rolling stock asset risk analysis is used to illustrate the application of the intelligent system. (6 pages)

Millions of people have no access to healthy and safe food due to food insecurity that may occur at household, regional or national levels. National policies, insufficient agricultural development and low levels of education may lead to food insecurity. Furthermore, food quality and food safety is a major benchmark of economic development. Food borne risks to human health can arise from biological, chemical or physical hazards. Risk analysis is a key discipline for reducing food borne diseases and food insecurity, and for strengthening food safety systems. Food safety risk analysis is used for assessing and managing risks associated with food hazards. The risk analysis process contains three elements: risk assessment, risk management and risk communication. This paper focuses on risk communication and summarizes the results and findings of the case study for official food safety communicators. The preliminary descriptive study intends to present the reaction of Hungarian consumers to a crisis that affected a wide range of society. In 2007, the guar gum scandal caused panic among consumers when dioxin-contaminated guar gum entered the food chain. This food contamination scare followed previous occasions such as production of intoxicated paprika powder, re-labelling of overdue meat packages and the Bovine spongiform encephalopathy (BSE) crisis in Hungary. To analyze risk perception and consumer behavior, a primary research was conducted with a sample size of 1,577.

This chapter presents an example outlining the process and results of a software security risk analysis. Unlike other types of security risk analyses, a software security analysis focuses on the design and implementation of the online application rather than the network and physical environment in which the application is deployed. An example is used to illustrate the benefits of a software security risk analysis and demonstrate how software security extends and complements conventional security and business risk analyses.

The dynamics which the business environment and business processes develop in modern conditions require minimizing threats from both external and internal factors. In this article, we present a different point of view—the ability to overcome major problems through the usage of intelligent risk analysis. It combines the established stages of the risk management process according to ISO 31000 (risk management) and the capabilities of artificial intelligence. The contribution of this paper is in the mechanism of acquiring added value through the usage of artificial intelligence. This is the way, the risk analysis of business processes can process extremely large amounts of data in real time; to automate complex and time-consuming risk management processes; to respond optimally and adequately to current threats. The purpose of this paper is to offer a new look at risk analysis methodology. Examining the process and going through all stages of management, it reveals the possibilities of the concept/technology to predict and react intelligently to respond long before their occurrence; to help turn threats into opportunities. In order to prove the applicability of the methodology, we consider various cases and follow their development, with and without the participation of artificial intelligence. All this is aimed at reducing the frequency and severity of risk events in the management of business processes.KeywordsRisk managementBusiness processesArtificial intelligenceRoadmapIntelligent risk assessment

Life-cycle cost analysis (LCCA) is an important tool in transportation asset management. Transportation agencies have used both deterministic and probabilistic LCCA approaches. Probabilistic approaches allow decision makers to evaluate the risk of an investment by using uncertain input variables, assumptions, or estimates. The incorporation of fuzzy logic–based models into the risk analysis process is explored to enhance further the traditional probabilistic LCCA method. It proposes a fuzzy logic approach for determining the timing of pavement maintenance, rehabilitation, and reconstruction (MR&R) treatments in a probabilistic LCCA model for selecting pavement MR&R strategies. Instead of using predefined service life for initial construction and future rehabilitations, the proposed approach uses performance curves and fuzzy logic triggering models to determine the most effective timing of MR&R activities. The new approach is compared with the deterministic and traditional probabilistic approaches in a simple case study. The case study demonstrates that the fuzzy logic–based risk analysis model for LCCA can effectively produce results that are at least comparable to those of the benchmark methods while effectively considering some of the uncertainty inherent to the process.