Intelligent situation awareness support system for safety-critical environments

University of Technology, Sydney. Faculty of Engineering and Information Technology.In today’s safety-critical systems such as process and manufacturing plants, operators are often moved to a control room far away from the physical environment, and increasing amounts of information are passed to them via automated systems, they therefore need a greater level of support to control and maintain the facilities in a safe condition. This is especially important when operators confront abnormal situations in which the information flow is quite high and poor decisions may lead to serious consequences. Therefore, they need to be supported from a cognitive perspective to reduce their workload, stress, and consequent error rate. Of the various cognitive activities, a correct understanding of the situation, that is situation awareness (SA), has been found to be a crucial factor in improving performance and reducing error. However, existing system safety researches focus mainly on technical issues and often neglect SA. This research reviews the role of SA in accidents of safety-critical environments and introduces a clear definition for abnormal situations based on risk indicators. It then relies on mental models that embody stored long-term knowledge about the systems, and develops an abnormal situations modelling (ASM) method, that exploits the specific capabilities of Bayesian networks (BNs). In this sense, it is assumed that the operator’s mental model can be modelled using BNs as a representation of static cause–effect relationships between objects in the situation. Following this, the research presents an innovative cognition-driven decision support system called the situation awareness support system (SASS) to manage abnormal situations in safety-critical environments in which the effect of situational complexity on human decision-makers is a concern. The SASS consists of five major components: (1) a knowledge–base that contains the abnormal situation models of the intended environment developed by the ASM method, (2) a situation data collection component that provides the current state of the observable variables based on online conditions and monitoring systems, (3) a situation assessment component that uses risk indicators and a fuzzy logic system to generate the assessment result, (4) a situation recovery component that provides a basis for decision-making to reduce the risk level of situations to an acceptable level, and (5) a human-computer interface. The performance of the SASS is demonstrated by three cases investigated by the US Chemical Safety Board in which poor operators’ SA has created industrial disasters in recent US history. The results of performance demonstrate that the SASS provides a useful graphical, mathematically consistent system for dealing with incomplete and uncertain information to help operators maintain the risk of dynamic situations at an acceptable level. The SASS is partially evaluated by a sensitivity analysis, which is carried out to validate the BN-based situation models, and a multi-perspective evaluation approach is proposed based on SA measures to determine the degree to which the SASS improves not degrades the operator’s SA. The approach consists of three SA metrics: the Situation Awareness Global Assessment Technique, the Situation Awareness Rating Technique, and the NASA Task Load Index. The first two metrics are used for direct objective and subjective measurement of SA, while the third is used to estimate the workload of operators. The approach is applied in a safety-critical environment, and ten operators participate in two 40-minute simulation trials using a virtual plant user interface, both with and without the support of the SASS. The results indicate that the SASS improves operators’ SA, and specifically has benefits for SA levels 2 and 3. No significant correlations between the participants’ SA scores have been found. In addition, it is concluded that the SASS reduces the workload of operators, although further investigations in different environments with a larger number of participants have been suggested

A Situation Analysis Decision Support System Based on Dynamic Object Oriented Bayesian Networks

This paper proposes a situation analysis decision support system (SADSS) for safety of safety-critical systems where the operators are stressed by the task of understanding what is going on in the situation. The proposed SADSS is developed based on a new model-driven engineering approach for hazardous situations modeling based on dynamic object oriented Bayesian networks to reduce the complexity of the decision-making process by aiding operators’ cognitive activities. The SADSS includes four major elements: a situation data collection based on observable variables such as sensors, a situation knowledgebase which consists of dynamic object oriented Bayesian networks to model hazardous situations, a situation analysis which shows the current state of hazardous situations based on risk concept and possible near future state, and a humancomputer interface. Finally two evaluation methods for partial and full validation of SADSS are presented

A Primer on COVID-19 for Clinicians: Clinical Manifestation and Natural Course

Context: COVID-19 is a new pandemic in the world and data in the various aspect of this disease are evolving. In this review, the authors try to cover different aspects of clinical manifestations and the natural course of the disease. Evidence acquisition: For data gathering, the authors searched through MEDLINE, Cochrane library, google scholar and Scopus. The key phrases for search were "clinical presentation of COVID-19", "clinical features of COVID-19", "natural course of COVID-19", "neurologic manifestation of COVID-19", "cardiovascular manifestation of COVID-19" and "gastrointestinal manifestation of COVID-19". Results: After screening of titles and abstracts, the authors finally enrolled 55 articles. Then the full texts of the selected articles were read carefully to determine eligibility and extracting relevant information. Conclusion: The most common presentations of COVID-19 patients were fever, non-producing cough and dyspnea but a considerable amount of patients may seek heath care without these complaints. Asymptomatic patients and patients with only gastrointestinal and neurologic symptoms remain a significant challenge for medical practitioners

A Primer on COVID-19 for Clinicians: Clinical Manifestation and Natural Course

Context: COVID-19 is a new pandemic in the world and data in the various aspect of this disease are evolving. In this review, the authors try to cover different aspects of clinical manifestations and the natural course of the disease. Evidence acquisition: For data gathering, the authors searched through MEDLINE, Cochrane library, google scholar and Scopus. The key phrases for search were "clinical presentation of COVID-19", "clinical features of COVID-19", "natural course of COVID-19", "neurologic manifestation of COVID-19", "cardiovascular manifestation of COVID-19" and "gastrointestinal manifestation of COVID-19". Results: After screening of titles and abstracts, the authors finally enrolled 55 articles. Then the full texts of the selected articles were read carefully to determine eligibility and extracting relevant information. Conclusion: The most common presentations of COVID-19 patients were fever, non-producing cough and dyspnea but a considerable amount of patients may seek heath care without these complaints. Asymptomatic patients and patients with only gastrointestinal and neurologic symptoms remain a significant challenge for medical practitioners

Supporting situation awareness using neural network and expert system

Situation awareness (SA) is a critical factor for human decision making and performance in dynamic environments. Actually SA is a mental model of the current state of the environment and includes many types of complex systems such as safety supervisory systems. The current paper employs two focus areas including neural network and expert system for maintaining SA in a safety supervisory system. The neural network components provide adaptive mechanisms for perception, and the expert system offers the ability to support comprehension and projection

Texas LPG fire: Domino effects triggered by natural hazards

© 2018 Institution of Chemical Engineers On February 2007, a massive fire in a propane de-asphalting unit in an oil refinery in Texas, USA happened due to liquid propane release from a cracked pipe in a control station injuring four people, damaging extensive equipment, causing significant business interruption, and resulting in more than $50 million losses. The accident was triggered by a natural hazard: freezing of piping at a control station caused an inlet pipe elbow to crack, which in turn, led to the release of high-pressure liquid propane which was rapidly ignited. In addition, there were two near-miss events due to potential domino effects. In fact, the accident could reasonably have resulted in much more severe consequences due to the exposure of large butane storage spheres and chlorine containers, increasing the possibility of a catastrophic domino effect. This paper develops a Natech (natural hazard triggering technological disasters) risk assessment methodology that relies upon Bayesian network capabilities and takes into account the potential Natech domino effects. The methodology is implemented in the intended refinery and mathematically graphically represents the dynamic cause–effect relations between units involved in the scenario, and handles uncertainties among the interactions. In addition, the methodology can provide a risk value for the entire scenario that can be used further for risk-based decision making

E-commerce development risk evaluation using MCDM techniques

© Springer International Publishing Switzerland 2016. Electronic commerce (EC) development takes place in a complex and dynamic environment that includes high levels of risk and uncertainty. This paper proposes a new method for assessing the risks associated with EC development using multi-criteria decision-making techniques A model based on the analytic hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS) is proposed to assist EC project managers and decision makers in formalizing the types of thinking that are required in assessing the current risk environment of their EC development in a more systematic manner than previously. The solution includes the use of AHP for analyzing the problem structure and determining the weights of risk factors. The TOPSIS technique helps to obtain a final ranking among projects, and the results of an evaluation show the usefulness performance of the method

A fuzzy virtual machine workload prediction method for cloud environments

© 2017 IEEE. Due to the dynamic nature of cloud environments, the workload of virtual machines (VMs) fluctuates leading to imbalanced loads and utilization of virtual and physical cloud resources. It is, therefore, essential that cloud providers accurately forecast VM performance and resource utilization so they can appropriately manage their assets to deliver better quality cloud services on demand. Current workload and resource prediction methods forecast the workload or CPU utilization pattern of the given web-based applications based on their historical data. This gives cloud providers an indication of the required number of resources (VMs or CPUs) for these applications to optimize resource allocation for software as a service (SaaS) or platform as a service (PaaS), reducing their service costs. However, historical data cannot be used as the only data source for VM workload predictions as it may not be available in every situation. Nor can historical data provide information about sudden and unexpected peaks in user demand. To solve these issues, we have developed a fuzzy workload prediction method that monitors both historical and current VM CPU utilization and workload to predict VMs that are likely to be performing poorly. This model can also predict the utilization of physical machine (PM) resources for virtual resource discovery

Handling Uncertainty in Social Lending Credit Risk Prediction with a Choquet Fuzzy Integral Model

As one of the main business models in the financial technology field, peer-to-peer (P2P) lending has disrupted traditional financial services by providing an online platform for lending money that has remarkably reduced financial costs. However, the inherent uncertainty in P2P loans can result in huge financial losses for P2P platforms. Therefore, accurate risk prediction is critical to the success of P2P lending platforms. Indeed, even a small improvement in credit risk prediction would be of benefit to P2P lending platforms. This paper proposes an innovative credit risk prediction framework that fuses base classifiers based on a Choquet fuzzy integral. Choquet integral fusion improves creditworthiness evaluations by synthesizing the prediction results of multiple classifiers and finding the largest consistency between outcomes among conflicting and consistent results. The proposed model was validated through experimental analysis on a real- world dataset from a well-known P2P lending marketplace. The empirical results indicate that the combination of multiple classifiers based on fuzzy Choquet integrals outperforms the best base classifiers used in credit risk prediction to date. In addition, the proposed methodology is superior to some conventional combination techniques

Decision making on adoption of cloud computing in e-commerce using fuzzy TOPSIS

© 2017 IEEE. Cloud computing promises enhanced scalability, flexibility, and cost-efficiency. In practice, however, there are many uncertainties about the usage of cloud computing resources in the e-commerce context. As e-commerce is dependent on a reliable and secure online store, it is important for decision makers to adopt an optimal cloud computing mode (Such as SaaS, PaaS and IaaS). This study assesses the factors associated with cloud-based e-commerce based on TOE (technological, organizational, and environmental) framework using multi-criteria decision-making technique (Fuzzy TOPSIS). The results show that Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) approach proposes software-as-a-service (SaaS) as the best choice for e-commerce business