Traditional Risk Assessment Methods Research Articles

Research on Risk and Early Warning System of Inter‐Basin Multi‐Reservoir Water Transfer‐Supply Operation with Consideration of Uncertain Factors

AbstractAccording to traditional risk assessment methods it is impossible to have a quantitative analysis of the nonlinear relationship between uncertain factors of inter‐basin reservoir regulation. So, fuzzy mathematics and a mixed Copula function are used in this paper to determine existing water supply indexes. The joint probability distribution of runoff forecasting and water demand is simulated and it also makes a quantitative analysis of future flows. The information entropy principle is applied to determine early risk warning index and signals, and emergency water shortage measures are established for an inter‐basin water transfer‐supply operation. Then the early warning decision system of reservoir optimization water supply dispatching is built up. A practical example computation shows that the information of water supply and water shortage can be described effectively with such an early risk warning system regarding donor and recipient reservoirs. The early risk warning method is applied for the first time, which simultaneously takes into account water inflow, water utilization and water shortage regarding donor and recipient reservoirs. Copyright © 2018 John Wiley & Sons, Ltd.

School is out on noisy reefs: the effect of boat noise on predator learning and survival of juvenile coral reef fishes.

Noise produced by anthropogenic activities is increasing in many marine ecosystems. We investigated the effect of playback of boat noise on fish cognition. We focused on noise from small motorboats, since its occurrence can dominate soundscapes in coastal communities, the number of noise-producing vessels is increasing rapidly and their proximity to marine life has the potential to cause deleterious effects. Cognition-or the ability of individuals to learn and remember information-is crucial, given that most species rely on learning to achieve fitness-promoting tasks, such as finding food, choosing mates and recognizing predators. The caveat with cognition is its latent effect: the individual that fails to learn an important piece of information will live normally until the moment where it needs the information to make a fitness-related decision. Such latent effects can easily be overlooked by traditional risk assessment methods. Here, we conducted three experiments to assess the effect of boat noise playbacks on the ability of fish to learn to recognize predation threats, using a common, conserved learning paradigm. We found that fish that were trained to recognize a novel predator while being exposed to 'reef + boat noise' playbacks failed to subsequently respond to the predator, while their 'reef noise' counterparts responded appropriately. We repeated the training, giving the fish three opportunities to learn three common reef predators, and released the fish in the wild. Those trained in the presence of 'reef + boat noise' playbacks survived 40% less than the 'reef noise' controls over our 72 h monitoring period, a performance equal to that of predator-naive fish. Our last experiment indicated that these results were likely due to failed learning, as opposed to stress effects from the sound exposure. Neither playbacks nor real boat noise affected survival in the absence of predator training. Our results indicate that boat noise has the potential to cause latent effects on learning long after the stressor has gone.

Advancing chemical risk assessment decision-making with population variability data: challenges and opportunities.

Characterizing population variability, including identifying susceptible populations and quantifying their increased susceptibility, is an important aspect of chemical risk assessment, but one that is challenging with traditional experimental models and risk assessment methods. New models and methods to address population variability can be used to advance the human health assessments of chemicals in three key areas. First, with respect to hazard identification, evaluating toxicity using population-based in vitro and in vivo models can potentially reduce both false positive and false negative signals. Second, with respect to evaluating mechanisms of toxicity, enhanced ability to do genetic mapping using these models allows for the identification of key biological pathways and mechanisms that may be involved in toxicity and/or susceptibility. Third, with respect to dose-response assessment, population-based toxicity data can serve as a surrogate for human variability, and thus be used to quantitatively estimate the degree of human toxicokinetic/toxicodynamic variability and thereby increase confidence in setting health-protective exposure limits. A number of case studies have been published that demonstrate the potential opportunities for improving risk assessment and decision-making, and include studies using Collaborative Cross and Diversity Outbred mice, as well as populations of human cell lines from the 1000 Genomes project. Key challenges include the need to apply more sophisticated computational and statistical models analyzing population-based toxicity data, and the need to integrate these more complex analyses into risk assessments and decision-making.

Risk Assessment of Processes and Products in Industrial Biotechnology.

Risk assessment has been used extensively as the main approach to prevent accidents in the chemical and process industry. Industrial biotechnology has many of the same hazards as chemical technology, but also encounters biological hazards related to biological agents. Employees in the biotechnology industry are susceptible to health risks because of different types of exposure to harmful agents. The external environment may also be affected by these agents in cases of accidental release. This chapter first presents several traditional risk assessment methods that may be used in industrial biotechnology after comparing differences between industrial biotechnology and chemical technology. Hazard identification in industrial biotechnology is then discussed, for biological as well as traditional hazards. Furthermore, risk assessment of occupational health and safety related to biological hazards is examined using exposure analysis and risk characterization. A two-stage risk assessment method is recommended to assess environmental and ecological risks in industrial biotechnology. Risk analysis of traditional accidents (fire, explosions, and toxic releases) in industrial biotechnology is also described. Graphical Abstract.

Nanomaterials, and Occupational Health and Safety—A Literature Review About Control Banding and a Semi-Quantitative Method Proposed for Hazard Assessment.

In recent decades, the control banding (CB) approach has been recognised as a hazard assessment methodology because of its increased importance in the occupational safety, health and hygiene (OSHH) industry. According to the American Industrial Hygiene Association, this approach originates from the pharmaceutical industry in the United Kingdom. The aim of the CB approach is to protect more than 90% (or approximately 2.7 billion) of the world’s workers who do not have access to OSHH professionals and traditional quantitative risk assessment methods. In other words, CB is a qualitative or semi-quantitative tool designed to prevent occupational accidents by controlling worker exposures to potentially hazardous chemicals in the absence of comprehensive toxicological and exposure data. These criteria correspond very precisely to the development and production of engineered nanomaterials (ENMs). Considering the significant lack of scientific knowledge about work-related health risks because of ENMs, CB is, in general, appropriate for these issues. Currently, CB can be adapted to the specificities of ENMs; hundreds of nanotechnology products containing ENMs are already on the market. In this context, this qualitative or semi-quantitative approach appears to be relevant for characterising and quantifying the degree of physico-chemical and biological reactivities of ENMs, leading towards better control of human health effects and the safe handling of ENMs in workplaces. The need to greater understand the CB approach is important to further manage the risks related to handling hazardous substances, such as ENMs, without established occupational exposure limits. In recent years, this topic has garnered much interest, including discussions in many technical papers. Several CB models have been developed, and many countries have created their own nano-specific CB instruments. The aims of this research were to perform a literature review about CBs, to classify the main approaches that were developed worldwide, and then to suggest an original methodology based on the characterisation of the hazard. For this research, our team conducted a systematic literature review over the past 20 years. This approach is important in understanding the conceptual basis for CB and the model’s overall effectiveness. These considerations will lead to the proposal of an original hazard assessment method based on physico-chemical and biological characteristics. Such a method should help the entire industry better understand the ability of the CB approach to limit workers’ exposure, while identifying the strengths and weaknesses of the approach. Developing this practice method will help to provide relevant recommendations to workers who handle hazardous chemicals such as ENMs and to the general population.

Determining critical infrastructure risks using social network analysis

PurposeThe purpose of this paper is to develop a novel risk analysis method named fuzzy critical risk analysis (FCRA) for assessing the infrastructure risks from a risk-community network perspective. The basis of this new FCRA method is the integration of existing risk magnitude analysis with the novel risk impact propagation analysis performed in specific infrastructure systems to assess the criticality of risk within specific social-infrastructure interrelated network boundary.Design/methodology/approachThe FCRA uses a number of scientific methods such as failure mode effect and criticality analysis (FMECA), social network analysis (SNA) and fuzzy-set theory to facilitate the building of risk evaluation associated with the infrastructure and the community. The proposed FCRA approach has been developed by integrating the fuzzy-based social network analysis (FSNA) method with conventional fuzzy FMECA method to analyse the most critical risk based on risk decision factors and risk impact propagation generated by various stakeholder perceptions.FindingsThe application of FSNA is considered to be highly relevant for investigating the risk impact propagation mechanism based on various stakeholder perceptions within the infrastructure risk interrelation and community networks. Although conventional FMECA methods have the potential for resulting in a reasonable risk ranking based on its magnitude value within the traditional risk assessment method, the lack of considering the domino effect of the infrastructure risk impact, the various degrees of community dependencies and the uncertainty of various stakeholder perceptions made such methods grossly ineffective in the decision-making of risk prevention (and mitigation) and resilience context.Research limitations/implicationsThe validation of the model is currently based on a hypothetical case which in the future should be applied empirically based on a real case study.Practical implicationsEffective functioning of the infrastructure systems for seamless operation of the society is highly crucial. Yet, extreme events resulted in failure scenarios often undermine the efficient operations and consequently affect the community at multiple levels. Current risk analysis methodologies lack to address issues related to diverse impacts on communities and propagation of risks impact within the infrastructure system based on multi-stakeholders’ perspectives. The FCRA developed in this research has been validated in a hypothetical case of infrastructure context. The proposed method will potentially assist the decision-making regarding risk governance, managing the vulnerability of the infrastructure and increasing both the infrastructure and community resilience.Social implicationsThe new approach developed in this research addresses several infrastructure risks assessment challenges by taking into consideration of not only the risk events associated with the infrastructure systems but also the dependencies of various type communities and cascading effect of risks within the specific risk-community networks. Such a risk-community network analysis provides a good basis for community-based risk management in the context of mitigation of disaster risks and building better community resilient.Originality/valueThe novelty of proposed FCRA method is realized due to its ability for improving the estimation accuracy and decision-making based on multi-stakeholder perceptions. The process of assessment of the most critical risks in the hypothetical case project demonstrated an eminent performance of FCRA method as compared to the results in conventional risk analysis method. This research contributes to the literature in several ways. First, based on a comprehensive literature review, this work established a benchmark for development of a new risk analysis method within the infrastructure and community networks. Second, this study validates the effectiveness of the model by integrating fuzzy-based FMECA with FSNA. The approach is considered useful from a methodological advancement when prioritizing similar or competing risk criticality values.

Limitations of current risk assessment methods to foresee emerging risks: Towards a new methodology?

The objective of this work-in-progress is to investigate the potentialities but also the limitations of traditional risks analysis tools especially in the context of emerging technologies and develop a method facilitating the early detection of scenarios of accidents. This is certainly a challenge particularly for new industrial fields since, in this case, very little or no lesson from past accidents is available. It is believed that such situations cannot be conveniently treated using traditional risk assessment methods (HAZOP, FMEA, …) and typical examples are given. The reason is that those methods rely heavily on past accidents and are therefore “trapped” in them so that they are largely “inductive”. In terms of foreseeing the future, the shortcomings of inductive methods are recalled. The possibility to imagine the future with very little clues is then discussed on the ground of theoretical consideration and a way to do so is proposed (abduction, serendipity). Then on the basis of the observation of how the experts work and how discoveries are made, a potential new methodology is outlined.

Comment on Cox’s “Uncertain Causation, Regulation, and the Courts”

Comment on Cox’s “Uncertain Causation, Regulation, and the Courts”

Establishment of Method for Health Risk Assessment of Pollutants from Fixed Sources

A health risk assessment method of pollutants from fixed sources was developed by applying AERMOD model in the health risk assessment. The method could directly forecast the health risks of toxic pollutants from source by some exposure pathway. Using the established method, in combination with the data of sources and traditional health risk assessment method as well as the measured data of PAHs in inhalation particle matter (PM₁₀) in Lanzhou, the health risk of polycyclic aromatic hydrocarbons (PAHs) and benzo [a] pyrene (BaP) in PM₁₀ from the three fire power plants and the health risk of PAHs and BaP in PM₁₀ at the receptor point by inhalation exposure in heating and non-heating seasons was calculated, respectively. Then the contribution rates of the health risk caused by the three fire power plants to the health risk at the receptor point were calculated. The results showed that the contribution rates were not associated with sex and age, but were associated with time period and risk types. The contribution rates in the non-heating seasons were greater than those in heating seasons, and the contribution rates of the carcinogenic risk index were greater than those of the cancer risk value. The reliability of the established method was validated by comparing with the traditional method. This method was applicable to health risk assessment of toxic pollutants from all fixed sources and environmental risk assessment of environmental impact assessment.

Quantifying Hazard Exposure Using Real-Time Location Data of Construction Workforce and Equipment

AbstractTraditional risk-assessment methods typically evaluate accident probability, exposure frequency or duration, and consequence severity, respectively, and finally determine a quantitative risk score or a qualitative risk level. The assessment process is prospective and subjective. Changing and complex construction situations impede their practical and efficient application. To assess safety risk with regard to changing site conditions in an objective and efficient way, this study proposes a quantitative model for hazard exposure assessment based on real-time location data of construction workforce and equipment, collected by a location-based proximity warning system. In contrast with traditional risk-assessment methods, the model proposed in this study combines accident probability and hazard exposure duration into a single quantitative concept—hazard exposure amount. This concept takes into account the space factor as well as the time factor of construction activities. For ease of understanding, a ...

Security risk assessment of cyber physical power system based on rough set and gene expression programming

Risk assessment is essential for the safe and reliable operation of cyber physical power system. Traditional security risk assessment methods do not take integration of cyber system and physical system of power grid into account. In order to solve this problem, security risk assessment algorithm of cyber physical power system based on rough set and gene expression programming is proposed. Firstly, fast attribution reduction based on binary search algorithm is presented. Secondly, security risk assessment function for cyber physical power system is mined based on gene expression programming. Lastly, security risk levels of cyber physical power system are predicted and analyzed by the above function model. Experimental results show that security risk assessment function model based on the proposed algorithm has high efficiency of function mining, accuracy of security risk level prediction and strong practicality.

Risk Prevention and Deduction in Software Development Using Fuzzy Membership Function

Evidence indicates that risks in IT projects which are not effectively managed and lack of identification and management during the life cycle of a project can contribute to their failures. Traditional risk assessment methods usually model risks with objective probabilities based on the expected frequency of repeatable events. Meanwhile, managers prefer to linguistically represent likelihoods because of the uncertainty and vagueness of risk factors. The objective of this paper is to identify risk mitigation strategies in software development projects from the perspectives of software practitioners and determine the effectiveness of these strategies. We explore the use of fuzzy methods to overcome the problems associated with probabilistic modelling through a set of questionnaire surveys which was conducted among 3000 IT practitioners using Tukey-B test, Kendall's test and Post Hoc Tukey HSD test. We apply Fuzzy Membership Function (Fuzzy-MBF) as an appropriate mechanism in dealing with the subjectivity in the assessment of risk factors in different stages of a software development life cycle. The proposed Fuzzy-MBF offers a quantitative evaluation of risk factors and provides a systemic evaluation of risk and visualization of results.

Rethinking risk assessment for emerging technology first-in-human trials

Recent progress in synthetic biology (SynBio) has enabled the development of novel therapeutic opportunities for the treatment of human disease. In the near future, first-in-human trials (FIH) will be indicated. FIH trials mark a key milestone in the translation of medical SynBio applications into clinical practice. Fostered by uncertainty of possible adverse events for trial participants, a variety of ethical concerns emerge with regards to SynBio FIH trials, including 'risk' minimization. These concerns are associated with any FIH trial, however, due to the novelty of the approach, they become more pronounced for medical applications of emerging technologies (emTech) like SynBio. To minimize potential harm for trial participants, scholars, guidelines, regulations and policy makers alike suggest using 'risk assessment' as evaluation tool for such trials. Conversely, in the context of emTech FIH trials, we believe it to be at least questionable to contextualize uncertainty of potential adverse events as 'risk' and apply traditional risk assessment methods. Hence, this issue needs to be discussed to enable alterations of the evaluation process before the translational phase of SynBio applications begins. In this paper, we will take the opportunity to start the debate and highlight how a misunderstanding of the concept of risk, and the possibilities and limitations of risk assessment, respectively, might impair decision-making by the relevant regulatory authorities and research ethics committees, and discuss possible solutions to tackle the issue.

Harmful algal bloom characterization at ultra-high spatial and temporal resolution using small unmanned aircraft systems.

Harmful algal blooms (HABs) degrade water quality and produce toxins. The spatial distribution of HAbs may change rapidly due to variations wind, water currents, and population dynamics. Risk assessments, based on traditional sampling methods, are hampered by the sparseness of water sample data points, and delays between sampling and the availability of results. There is a need for local risk assessment and risk management at the spatial and temporal resolution relevant to local human and animal interactions at specific sites and times. Small, unmanned aircraft systems can gather color-infrared reflectance data at appropriate spatial and temporal resolutions, with full control over data collection timing, and short intervals between data gathering and result availability. Data can be interpreted qualitatively, or by generating a blue normalized difference vegetation index (BNDVI) that is correlated with cyanobacterial biomass densities at the water surface, as estimated using a buoyant packed cell volume (BPCV). Correlations between BNDVI and BPCV follow a logarithmic model, with r2-values under field conditions from 0.77 to 0.87. These methods provide valuable information that is complimentary to risk assessment data derived from traditional risk assessment methods, and could help to improve risk management at the local level.

Risk Evaluation of a UHV Power Transmission Construction Project Based on a Cloud Model and FCE Method for Sustainability

In order to achieve the sustainable development of energy, Ultra High Voltage (UHV) power transmission construction projects are being established in China currently. Their high-tech nature, the massive amount of money involved, and the need for multi-agent collaboration as well as complex construction environments bring many challenges and risks. Risk management, therefore, is critical to reduce the risks and realize sustainable development of projects. Unfortunately, many traditional risk assessment methods may not perform well due to the great uncertainty and randomness inherent in UHV power construction projects. This paper, therefore, proposes a risk evaluation index system and a hybrid risk evaluation model to evaluate the risk of UHV projects and find out the key risk factors. This model based on a cloud model and fuzzy comprehensive evaluation (FCE) method combines the superiority of the cloud model for reflecting randomness and discreteness with the advantages of the fuzzy comprehensive evaluation method in handling uncertain and vague issues. For the sake of proving our framework, an empirical study of “Zhejiang-Fuzhou” UHV power transmission construction project is presented. As key contributions, we find the risk of this project lies at a “middle” to “high” level and closer to a “middle” level; the “management risk” and “social risk” are identified as the most important risk factors requiring more attention; and some risk control recommendations are proposed. This article demonstrates the value of our approach in risk identification, which seeks to improve the risk control level and the sustainable development of UHV power transmission construction projects.

Method for Evaluation of Railway Dynamic Safety Based on 5M Model and Neural Network Algorithm

Traditional risk assessment methods, mainly static analysis, can’t anticipate the development trend and consequences of accidents. This paper firstly presents 5M safety model, combined with the characteristics of the rail transit safety assessment, including complexity, dynamic, ambiguity, etc. By using neural network algorithm, this paper proposes a method of railway safety dynamic assessment. Finally, the method is validated by comparing the output value of the model and the veritable value. The result indicated that it has advantages of real time and predictability.

Global warming and changes in risk of concurrent climate extremes: Insights from the 2014 California drought

AbstractGlobal warming and the associated rise in extreme temperatures substantially increase the chance of concurrent droughts and heat waves. The 2014 California drought is an archetype of an event characterized by not only low precipitation but also extreme high temperatures. From the raging wildfires, to record low storage levels and snowpack conditions, the impacts of this event can be felt throughout California. Wintertime water shortages worry decision‐makers the most because it is the season to build up water supplies for the rest of the year. Here we show that the traditional univariate risk assessment methods based on precipitation condition may substantially underestimate the risk of extreme events such as the 2014 California drought because of ignoring the effects of temperature. We argue that a multivariate viewpoint is necessary for assessing risk of extreme events, especially in a warming climate. This study discusses a methodology for assessing the risk of concurrent extremes such as droughts and extreme temperatures.

An advanced risk analysis approach for container port safety evaluation

Risk analysis in seaports plays an increasingly important role in ensuring port operation reliability, maritime transportation safety and supply chain distribution resilience. However, the task is not straightforward given the challenges, including that port safety is affected by multiple factors related to design, installation, operation and maintenance and that traditional risk assessment methods such as quantitative risk analysis cannot sufficiently address uncertainty in failure data. This paper develops an advanced Failure Mode and Effects Analysis (FMEA) approach through incorporating Fuzzy Rule-Based Bayesian Networks (FRBN) to evaluate the criticality of the hazardous events (HEs) in a container terminal. The rational use of the Degrees of Belief (DoB) in a fuzzy rule base (FRB) facilitates the implementation of the new method in Container Terminal Risk Evaluation (CTRE) in practice. Compared to conventional FMEA methods, the new approach integrates FRB and BN in a complementary manner, in which the former provides a realistic and flexible way to describe input failure information while the latter allows easy updating of risk estimation results and facilitates real-time safety evaluation and dynamic risk-based decision support in container terminals. The proposed approach can also be tailored for wider application in other engineering and management systems, especially when instant risk ranking is required by the stakeholders to measure, predict and improve their system safety and reliability performance.

Assess on E-Commerce Transaction Based on Web Technology

In this paper, an overview and security threats of e-commerce are described.Combining traditional risk assessment method fore-commerce transactions,the assessmentindicatorsneed to be considered in risk assessment of e-commerce transactionsare analyzed, and quantitative calculation method oftransaction riskis given based on cloudcomputing.Moreover,how to detect andassess the riskof e-commercetransactionenvironmentare made simple discussionand assessment. Experimental resultsshow that the algorithmcan effectively reducetransaction risk andprotect the legitimaterights and interestsof users.

Optimization method to branch-and-bound large SBO state spaces under dynamic probabilistic risk assessment via use of LENDIT scales and S2R2 sets

Traditional probabilistic risk assessment (PRA) methods have been developed to evaluate risk associated with complex systems; however, PRA methods lack the capability to evaluate complex dynamic systems. In these systems, time and energy scales associated with transient events may vary as a function of transition times and energies to arrive at a different physical state. Dynamic PRA (DPRA) methods provide a more rigorous analysis of complex dynamic systems. Unfortunately DPRA methods introduce issues associated with combinatorial explosion of states. In order to address this combinatorial complexity, a branch-and-bound optimization technique is applied to the DPRA formalism to control the combinatorial state explosion. In addition, a new characteristic scaling metric (LENDIT – length, energy, number, distribution, information and time) is proposed as linear constraints that are used to guide the branch-and-bound algorithm to limit the number of possible states to be analyzed. The LENDIT characterization is divided into four groups or sets – ‘state, system, resource and response’ (S2R2) – describing reactor operations (normal and off-normal). In this paper we introduce the branch-and-bound DPRA approach and the application of LENDIT scales and S2R2 sets to a station blackout (SBO) transient.