Risk Assessment Platform Research Articles

MEXICAN PINK ABALONE (Haliotis corrugata WOOD, 1828) FISHERY MANAGEMENT STRATEGY EVALUATION: A DATA-LIMITED APPROACH

Management procedures (MP) were evaluated to address alternative management of the pink abalone (Haliotis corrugata) fishery in the Mexican Pacific. This assessment utilized the Method Evaluation and Risk Assessment (MERA) platform within the context of management strategy evaluation (MSE). This species has been a crucial component of the abalone fishery in Mexico, with a history dating back to the 19th century. Since 1996, fishery authorities and fishermen have noted a decline in the stock biomass. In response, they implemented various strategies and efforts to address the situation. However, despite these measures, the stock has yet to recover satisfactorily. Eight MP were evaluated, and two corresponded to the status quo (current catch and effort). The simulation results suggest that relying solely on effort-based MP falls short of meeting the management objectives set in the reference points (Biomass and Yield). In contrast, alternative strategies involving different quota allocation strategies (e.g., based on the depletion level) demonstrated superior performance and a higher likelihood of meeting management objectives. Despite incorporating fishery information, the existing management procedures could have performed better in the simulation. Therefore, it is crucial to assess and implement alternative management strategies that are more likely to succeed

In Vivo Exposure Pathways of Ambient Magnetite Nanoparticles Revealed by Machine Learning-Aided Single-Particle Mass Spectrometry.

Nanosized ultrafine particles (UFPs) from natural and anthropogenic sources are widespread and pose serious health risks when inhaled by humans. However, tracing the inhaled UFPs in vivo is extremely difficult, and the distribution, translocation, and metabolism of UFPs remain unclear. Here, we report a label-free, machine learning-aided single-particle inductively coupled plasma mass spectrometry (spICP-MS) approach for tracing the exposure pathways of airborne magnetite nanoparticles (MNPs), including external emission sources, and distribution and translocation in vivo using a mouse model. Our results provide quantitative analysis of different metabolic pathways in mice exposed to MNPs, revealing that the spleen serves as the primary site for MNP metabolism (84.4%), followed by the liver (11.4%). The translocation of inhaled UFPs across different organs alters their particle size. This work provides novel insights into the in vivo fate of UFPs as well as a versatile and powerful platform for nanotoxicology and risk assessment.

A Cloud-Based WEB Platform for Fall Risk Assessment Using a Therapist-Centered User Interface Which Enables Patients’ Tracking Remotely

A Cloud-Based WEB Platform for Fall Risk Assessment Using a Therapist-Centered User Interface Which Enables Patients’ Tracking Remotely

Enhancing Forest Fire Risk Assessment: An Ontology-Based Approach with Improved Continuous Apriori Algorithm

Forest fires are sudden and difficult to extinguish, so early risk assessment is crucial. However, there are currently a lack of suitable knowledge-mining algorithms for forest fire risk assessment. This article proposes an improved continuous Apriori algorithm to mining forest fire rules by introducing prior knowledge to classify input data and enhance its ability to process continuous data. Meanwhile, it constructs an ontology to provide a standardized expression platform for forest fire risk assessment. The improved continuous Apriori algorithm cooperates with ontology and applies the mining rules to the forest fire risk assessment results. The proposed method is validated using the forest fire data from the Bejaia region in Algeria. The results show that the improved continuous Apriori algorithm is superior to the raw Apriori algorithm and can mine the rules ignored by the raw Apriori algorithm. Compared to the raw Apriori algorithm, the number of generated rules increased by 191.67%. The method presented here can be used to enhance forest fire risk assessments and contribute to the generation and sharing of forest-fire-related knowledge, thereby alleviating the problem of insufficient knowledge in forest fire risk assessment.

Microfluidic human physiomimetic liver model as a screening platform for drug induced liver injury

The pre-clinical animal models often fail to predict intrinsic and idiosyncratic drug induced liver injury (DILI), thus contributing to drug failures in clinical trials, black box warnings and withdrawal of marketed drugs. This suggests a critical need for human-relevant in vitro models to predict diverse DILI phenotypes. In this study, a porcine liver extracellular matrix (ECM) based biomaterial ink with high printing fidelity, biocompatibility and tunable rheological and mechanical properties is formulated for supporting both parenchymal and non-parenchymal cells. Further, we applied 3D printing and microfluidic technology to bioengineer a human physiomimetic liver acinus model (HPLAM), recapitulating the radial hepatic cord-like structure with functional sinusoidal microvasculature network, biochemical and biophysical properties of native liver acinus. Intriguingly, the human derived hepatic cells incorporated HPLAM cultured under physiologically relevant microenvironment, acts as metabolic biofactories manifesting enhanced hepatic functionality, secretome levels and biomarkers expression over several weeks. We also report that the matured HPLAM reproduces dose- and time-dependent hepatotoxic response of human clinical relevance to drugs typically recognized for inducing diverse DILI phenotypes as compared to conventional static culture. Overall, the developed HPLAM emulates in vivo like functions and may provide a useful platform for DILI risk assessment to better determine safety and human risk.

A Computer Vision-Based System to Help Health Professionals to Apply Tests for Fall Risk Assessment.

The increase in life expectancy, and the consequent growth of the elderly population, represents a major challenge to guarantee adequate health and social care. The proposed system aims to provide a tool that automates the evaluation of gait and balance, essential to prevent falls in older people. Through an RGB-D camera, it is possible to capture and digitally represent certain parameters that describe how users carry out certain human motions and poses. Such individual motions and poses are actually related to items included in many well-known gait and balance evaluation tests. According to that information, therapists, who would not need to be present during the execution of the exercises, evaluate the results of such tests and could issue a diagnosis by storing and analyzing the sequences provided by the developed system. The system was validated in a laboratory scenario, and subsequently a trial was carried out in a nursing home with six residents. Results demonstrate the usefulness of the proposed system and the ease of objectively evaluating the main items of clinical tests by using the parameters calculated from information acquired with the RGB-D sensor. In addition, it lays the future foundations for creating a Cloud-based platform for remote fall risk assessment and its integration with a mobile assistant robot, and for designing Artificial Intelligence models that can detect patterns and identify pathologies for enabling therapists to prevent falls in users under risk.

Architecture and Development Framework for a Web-Based Risk Assessment and Management Platform Developed on WordPress to Address Opioid Overdose.

The number of overdose-related fatalities continues to reach historic levels across Canada, despite ongoing efforts by authorities. To reduce mortality, a clinical trajectory ranging from preventative measures to crisis intervention, skill training to treatment, and risk assessment to risk management needs to be supported. The web-based Risk Assessment and Management Platform (RAMP) was developed to realize this concept and to empower people who use drugs through an integrated tool that allows them to better understand and manage their risk of overdose. This paper outlines the architecture and development of RAMP, which is built on the WordPress platform. WordPress components are mapped onto a 3-tier architecture that consists of presentation, application, and database layers. The architecture facilitates the development of a modular software that includes several features that are independent in functionality but interact with each other in an integrated platform. The relatively low coupling and high coherence of the features may reduce the cost of maintenance and increase flexibility of future developments. RAMP's architecture comprises a user interface, conceptual framework, and backend layers. The RAMP front end effectively uses some of the WordPress' features such as HTML5, CSS, and JavaScript to create a mobile, friendly, and scalable user interface. The RAMP backend uses several standard and custom WordPress plug-ins to support risk assessment and monitoring, with the goal of mitigating the impacts and eliminating risks together. A rule-based decision support system has been hard-coded to suggest relevant modules and goals to complement each user's lifestyle and goals based on their risk assessment. Finally, the backend uses the MySQL database management system and communicates with the RAMP framework layer via the data access layer to facilitate a timely and secure handling of information. Overall, RAMP is a modular system developed to identify and manage the risk of opioid overdose in the population of people who use drugs. Its modular design uses the WordPress architecture to efficiently communicate between layers and provide a base for external plug-ins. There is potential for the current system to adopt and address other related fields such as suicide, anxiety, and trauma. Broader implementation will support this concept and lead to the next level of functionality.

Global multi-hazard risk assessment in a changing climate

Natural hazards pose significant risks to people and assets in many regions of the world. Quantifying associated risks is crucial for many applications such as adaptation option appraisal and insurance pricing. However, traditional risk assessment approaches have focused on the impacts of single hazards, ignoring the effects of multi-hazard risks and potentially leading to underestimations or overestimations of risks. In this work, we present a framework for modelling multi-hazard risks globally in a consistent way, considering hazards, exposures, vulnerabilities, and assumptions on recovery. We illustrate the approach using river floods and tropical cyclones impacting people and physical assets on a global scale in a changing climate. To ensure physical consistency, we combine single hazard models that were driven by the same climate model realizations. Our results show that incorporating common physical drivers and recovery considerably alters the multi-hazard risk. We finally demonstrate how our framework can accommodate more than two hazards and integrate diverse assumptions about recovery processes based on a national case study. This framework is implemented in the open-source climate risk assessment platform CLIMADA and can be applied to various hazards and exposures, providing a more comprehensive approach to risk management than conventional methods.

Design and Implementation of an Inpatient Fall Risk Management Information System.

Falls had been identified as one of the nursing-sensitive indicators for nursing care in hospitals. With technological progress, health information systems make it possible for health care professionals to manage patient care better. However, there is a dearth of research on health information systems used to manage inpatient falls. This study aimed to design and implement a novel hospital-based fall risk management information system (FRMIS) to prevent inpatient falls and improve nursing quality. This implementation was conducted at a large academic medical center in central China. We established a nurse-led multidisciplinary fall prevention team in January 2016. The hospital's fall risk management problems were summarized by interviewing fall-related stakeholders, observing fall prevention workflow and post-fall care process, and investigating patients' satisfaction. The FRMIS was developed using an iterative design process, involving collaboration among health care professionals, software developers, and system architects. We used process indicators and outcome indicators to evaluate the implementation effect. The FRMIS includes a fall risk assessment platform, a fall risk warning platform, a fall preventive strategies platform, fall incident reporting, and a tracking improvement platform. Since the implementation of the FRMIS, the inpatient fall rate was significantly lower than that before implementation (P<.05). In addition, the percentage of major fall-related injuries was significantly lower than that before implementation. The implementation rate of fall-related process indicators and the reporting rate of high risk of falls were significantly different before and after system implementation (P<.05). The FRMIS provides support to nursing staff in preventing falls among hospitalized patients while facilitating process control for nursing managers.

OpenStreetMap for multi-faceted climate risk assessments

Natural hazards pose significant risks to human lives, infrastructure, and ecosystems. Understanding risks along all these dimensions is critical for effective adaptation planning and risk management. However, climate risk assessments mostly focus on population, economic asset values, and road or building infrastructure, because publicly available data on more diverse exposures are scarce. The increasing availability of crowd-sourced geospatial data, notably from OpenStreetMap, opens up a novel means for assessing climate risk to a large range of physical assets. To this end, we present a stand-alone, lightweight, and highly flexible Python-based OpenStreetMap data extraction tool: OSM-flex. To demonstrate the potential and limitations of OpenStreetMap data for risk assessments, we couple OSM-flex to the open-source natural hazard risk assessment platform CLIMADA and compute winter storm risk and event impacts from winter storm Lothar across Switzerland to forests, UNESCO heritage sites, railways, healthcare facilities, and airports. Contrasting spatial patterns of risks on such less conventional exposure layers with more traditional risk metrics (asset damages and affected population) reveals that risk hot-spots are inhomogeneously and distinctly distributed. For instance, impacts on forestry are mostly expected in Western Switzerland in the Jura mountain chain, whereas economic asset damages are concentrated in the urbanized regions around Basel and Zurich and certain train lines may be most often affected in Central Switzerland and alpine valleys. This study aims to highlight the importance of conducting multi-faceted and high-resolution climate risk assessments and provides researchers, practitioners, and decision-makers with potential open-source software tools and data suggestions for doing so.

Abstract A079: Implementation of the FOREST cancer risk study at an HBCU: The family history and cancer risk study at Meharry Medical College

Abstract PURPOSE: Family Health History (FHH) is a key factor in assessing cancer risk, yet health providers often do not have adequate time or resources to collect FHH systematically. African Americans and other medically underserved populations suffer significantly higher cancer incidence and mortality. These populations would benefit if their cancer risk were better defined. The Family History and Cancer Risk Study (FOREST) aims to implement a patient-facing web-based FHH cancer risk assessment platform called MeTree in a clinic with a high percentage of underserved patients at Meharry Medical College (MMC) and a cohort at Vanderbilt University Medical Center. METHODS: Partnering with the MMC Community Engagement Core (CEC), we conducted two virtual studios with 12 Nashville minority community members. Topics of concern were medical data privacy, potential benefits, and proper informed consent. CEC recommendations informed our pre-implementation planning. Recruitment methods consisted of a staffed table outside of and, later, presence inside the MMC Family &amp; Community Med. Clinic waiting room. Patients could also request assistance to navigate MeTree with the study research coordinator (in person or virtually). RESULTS: Since October 2022, 76 potential participants have been invited to FOREST from MMC. 26 patients were interested, 18 were eligible, 16 patients consented, and 12 patients completed the MeTree FHH. Of note, 6 patients utilized in-person assistance from the research study coordinator. Overall, 2 of the 12 patients who completed MeTree were determined to be at high-risk for cancer. CONCLUSION: We observed a 30% enrollment rate and 43% completion rate. Also, 6 of 26 patients requested and received assistance. In-person recruitment had the highest volume of potential enrollments (16 interested), yet lower completion rates (5/16 = 31%) compared to the pamphlet QR code (5 interested with 3/5 completing = 60%). We realize that these numbers are not large enough to show statistical significance at this early stage, and we plan to implement FOREST in a higher volume clinic (Internal Medicine at Nashville General Hospital) to determine if this trend scales. Citation Format: Clasherrol Edwards, Dana R. Marshall, Leah Alexander, Justin D. Andujar, S.T. Bland, Jillian Duke, Sarah Jones, Jeffrey Leegon, Kate F. Mittendorf, Lori A. Orlando, Georgia L. Wiesner, Siddharth Pratap. Implementation of the FOREST cancer risk study at an HBCU: The family history and cancer risk study at Meharry Medical College [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr A079.

Application of the Method Evaluation and Risk Assessment Tool for a Small-Scale Grouper Fishery in Indonesia

Management strategy evaluation using the Method Evaluation and Risk Assessment (MERA) platform was used to evaluate management procedures (MPs) for improving the management of the leopard coral grouper (Plectropomus leopardus) fishery in Saleh Bay, Indonesia. This grouper is a valuable species currently under high fishing pressure. It is targeted by small-scale fisheries using a wide range of fishing methods; hence, management recommendations are needed to ensure sustainability. A suite of MPs for data-limited conditions were evaluated for their ability to achieve limit and target biomass reference points (B/BMSY = 0.5 and B/BMSY = 1, respectively), while maintaining a target yield of at least 0.5 MSY. The simulation results suggest that the currently implemented harvest control rules (HCRs) in Saleh Bay (size limit and spatial closure) may not be effective in achieving the management objective to attain the target biomass reference point due to relatively low compliance with the size limit regulation (320 mm total length) and the very small proportion of existing MPA no-take areas (~2.2%). This study recommends that the fisheries management authority explores the feasibility of implementing the total allowable catch (TAC) and seasonal closure in addition to the existing fishing regulations for P. leopardus in Saleh Bay.

A generalized framework for designing open-source natural hazard parametric insurance

Parametric insurance schemes allow for payouts to be triggered by real-time hydro-/meteorological parameters instead of waiting for damage assessments, which means they can be settled swiftly, giving people access to funds right after the event. In this work we propose a framework to design parametric insurance schemes and systematically quantify the basis risk: the difference between the parameter-based payout and the actual damage. We implement the framework in the open-source global risk assessment platform CLIMADA and illustrate it with two stylized parametric insurance case studies, targeting tropical cyclones in Mozambique and winter storms in France. The data used and the provided code base are globally-consistent, open-source, and readily available. The presented methods are therefore applicable in data-scarce areas and accessible to stakeholders from the public and private sector. Moreover, our approach can easily be adapted to other hazards and exposures worldwide. This improves the accessibility and transparency of such innovative insurance schemes.

Analysis of supply chain finance risk assessment based on numerical analysis algorithm

To promote the coordination and stability of supply chain finance and improve the financing environment of small and medium-sized enterprises, this paper designs a supply chain finance risk assessment and analysis platform. Combining the characteristics of a large amount of risk assessment data, a numerical analysis algorithm is introduced in the process of platform design, and the extrapolation method in the numerical analysis algorithm is used to calculate the risk assessment- related data. To make the calculation faster and the data more accurate, the central difference quotient extrapolation is used to accelerate and a downtime mechanism is introduced. Firstly, the approximation formula for the calculation is constructed, followed by the construction of a sequence of variable steps to obtain a sequence of approximations. Finally, the obtained approximate sequence values are used to construct an interpolating polynomial, and the constant term of the polynomial, which is the final risk factor, is obtained through continuous iteration. To verify the effectiveness of the numerical analysis-based algorithm in supply chain financial risk assessment, the simulation results show that the risk assessment accuracy of the numerical analysis-based supply chain financial risk assessment platform is as high as 99% and the time required is 17 seconds higher than other assessment models, which verifies that the numerical analysis algorithm can improve the accuracy and rapidity of risk assessment.

Large-scale risk assessment on snow avalanche hazard in alpine regions

Abstract. Snow avalanches are recurring natural hazards that affect the population and infrastructure in mountainous regions, such as in the recent avalanche winters of 2018 and 2019, when considerable damage was caused by avalanches throughout the Alps. Hazard decision makers need detailed information on the spatial distribution of avalanche hazards and risks to prioritize and apply appropriate adaptation strategies and mitigation measures and thus minimize impacts. Here, we present a novel risk assessment approach for assessing the spatial distribution of avalanche risk by combining large-scale hazard mapping with a state-of-the-art risk assessment tool, where risk is understood as the product of hazard, exposure and vulnerability. Hazard disposition is modeled using the large-scale hazard indication mapping method RAMMS::LSHIM (Rapid Mass Movement Simulation::Large-Scale Hazard Indication Mapping), and risks are assessed using the probabilistic Python-based risk assessment platform CLIMADA, developed at ETH Zürich. Avalanche hazard mapping for scenarios with a 30-, 100- and 300-year return period is based on a high-resolution terrain model, 3 d snow depth increase, automatically determined potential release areas and protection forest data. Avalanche hazard for 40 000 individual snow avalanches is expressed as avalanche intensity, measured as pressure. Exposure is represented by a detailed building layer indicating the spatial distribution of monetary assets. The vulnerability of buildings is defined by damage functions based on the software EconoMe, which is in operational use in Switzerland. The outputs of the hazard, exposure and vulnerability analyses are combined to quantify the risk in spatially explicit risk maps. The risk considers the probability and intensity of snow avalanche occurrence, as well as the concentration of vulnerable, exposed buildings. Uncertainty and sensitivity analyses were performed to capture inherent variability in the input parameters. This new risk assessment approach allows us to quantify avalanche risk over large areas and results in maps displaying the spatial distribution of risk at specific locations. Large-scale risk maps can assist decision makers in identifying areas where avalanche hazard mitigation and/or adaption is needed.

A generalized natural hazard risk modelling framework for infrastructure failure cascades

Critical infrastructures are more exposed than ever to natural hazards in a changing climate. To understand and manage risk, failure cascades across large, real-world infrastructure networks, and their impact on people, must be captured. Bridging established methods in both infrastructure and risk modelling communities, we develop an open-source modelling framework which integrates a network-based interdependent infrastructure system model into the globally consistent and spatially explicit natural hazard risk assessment platform CLIMADA. The model captures infrastructure damages, triggers failure cascades and estimates resulting basic service disruptions for the dependent population. It flexibly operates on large areas with publicly available hazard, exposure and vulnerability information, for any set of infrastructure networks, hazards and geographies of interest. In a validated case study for 2018’s Hurricane Michael across three US states, the model reproduced important failure dynamics among six infrastructure networks, and provided a novel spatial map where people were likely to experience disruptions in access to healthcare, loss of power and other vital services. Our generalized approach allows for a view on infrastructure risks and their social impacts also in areas where detailed information and risk assessments are traditionally scarce, informing humanitarian activities through hotspot analyses and policy frameworks alike.

A Smart Risk Assessment Tool for Decision Support during Ship Evacuation

In case of a ship emergency situation and during its evolvement that might result in an evacuation, the master and the bridge command team of a ship have to continuously assess risk. This is a very complex procedure, as crucial decisions concerning safety are made under time pressure. The use of a decision-support tool would have a positive effect on their performance, resulting in an improvement in the way ships are evacuated. The purpose of this paper is to present the PALAEMON smart risk assessment platform (SRAP). SRAP is a real-time risk assessment platform developed to assist the decision-making process of the master and bridge command team of a ship regarding the evacuation process. Its purpose is to provide decision support for the following aspects: (1) the decision to sound the general alarm (GA) following an accident, (2) monitoring the progress of the mustering process in order to take any additional actions, and (3) the decision to abandon the ship or not. SRAP dynamically assesses the risk to the safety of the passengers and crew members in the different phases of the evacuation process, so one model in the form Bayesian networks (BNs) was developed for each stage of the evacuation process. The results of a case study that was implemented reflect how various parameters such as injuries, congestion, and the functionality of the ship’s systems affect the outcome of each model.

Key Technologies of Marine Environment Safety

An increasing number of marine disasters and maritime accidents occurred frequently in coastal area of China which lead to giant loss of life and property in recent years. In response to those disasters and accidents, China had put enormous effort into ocean observation, disaster prediction and forecasting, risk assessment and informationization, which lead to the preliminary improvement on safety capability of marine environment. In this paper, China’s marine environment safety situation are analyzed for concluding the insufficient research on Multi-source Information Aggregation, Assistant Decision-Making, Emergency Dispatching and relevant informationization. Hence plenty of key technologies as Multi-source Information Aggregation and Fusion, Dynamic Risk Assessment, Scenario Deduction, Command Coordination and Information Platform Construction Technology are also analyzed for feasible implementation mean and possible development direction..

Abstract 785: A smart cytopathology risk-assessment platform for oral potentially malignant disorders and oral squamous cell carcinoma at the point-of-care

Abstract Background &amp; Objective: Oral squamous cell carcinoma (OSCC) affects over 400,000 individuals globally, every year. When diagnosed early, the 5-year survival rate for OSCC is 64%, but two-third of these lesions are diagnosed in later stages, leading to lower survival rates (&amp;lt;40%). Clinical diagnosis of these lesions is complicated by benign and Oral-Potentially-Malignant-Disorders (OPMD) like oral lichenoid conditions (OLC), mimicking OSCC in clinical presentation. Studies in literature demonstrate high rates of incorrect chair-side diagnosis for OSCC and OPMD, making their early and accurate detection challenging. This clinical gap prompts a strong need for clinical chair-side point-of-care (POC) platforms, to aid accurate screening of these lesions and prevent diagnostic delays. Methods: In this work we present a ‘smart diagnostic approach’ that combines three key capabilities into an integrated sensing modality as follows: i) powerful microfluidic engine that allows for cytology measurements to be completed outside of a sophisticated lab infrastructure, ii) cytomics platform that allows for single cell molecular imaging to be completed in a portable analyzer, iii) AI-linked diagnostic models for early disease detection using cyto-signatures. This platform has been clinically validated across a multi-site prospective clinical study. Results: Multiple parameters including cellular phenotypes, nuclear parameters, biomarker expressions were indexed. Further, combining these features allowed discrimination and stratification of these lesions with high accuracy (99.3%) and significance. Further examination using logistic regression and receiver operating characteristic curve analyses yielded significant lesion identifiers and AUC values towards positive discrimination of OLC and OSCC (0.824 and 0.95, respectively vs benign lesions), with high sensitivity and specificity. Conclusion: This rapid (&amp;lt;30 minutes) cytopathology POC solution has the potential to impact OSCC and OPMD screening accurately, characterizing subtle cellular changes to aid long-term monitoring of these lesions. Additionally, this platform has the capability to uncover new parameters that can further aid these assessments and improve confidence in clinicians’ decisions. Citation Format: Kritika Srinivasan Rajsri, Michael P. McRae, Glennon W. Simmons, Alexander Ross Kerr, Nadarajah Vigneswaran, Spencer W. Redding, Malvin Janal, Stella Kang, Leena Paloma, Nicolaos J. Christodoulides, John T. McDevitt. A smart cytopathology risk-assessment platform for oral potentially malignant disorders and oral squamous cell carcinoma at the point-of-care [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 785.

A dynamic probabilistic risk assessment platform for nuclear power plants under single and concurrent transients

ABSTRACT Dynamic probabilistic risk assessment (DPRA) of nuclear power plants (NPPs) has become one of the most critical research areas, especially in the aftermath of the 2011 Fukushima Daiichi nuclear accident. Uncertainty in NPP behavior is key when considering its safety under different operating conditions. Such uncertainty typically results from operation parameters, system conditions, and modeling assumptions. This study integrates the system dynamics (SD) modelling approach with an uncertainty analysis method to quantify the dynamic probabilistic risk in NPPs. To demonstrate the approach’s applicability, the average fuel temperature is used to estimate the probability of reactor core damage under different transients, representing perturbations in reactivity and steam valve coefficient. A Monte Carlo simulation is employed to investigate the effect of uncertainties associated with the different model parameters. A global sensitivity analysis demonstrates that the total delayed neutron fraction, the heat transfer coefficient from fuel to coolant, the coolant temperature coefficient of reactivity, and the fuel temperature coefficient of reactivity are the primary controllers of the plant response variability under the transients considered. In summary, the integration of SD modelling and uncertainty analysis presents an effective DPRA approach that overcomes the limitations of static counterparts while minimizing the computational resources required.