Classification Of Events Research Articles

Methodological framework for early detection of dengue outbreaks in Tanzania

Abstract Background Dengue fever is a major public health problem because it is a widespread and rapidly transmitted mosquito-borne viral disease. We developed a framework for early detection of dengue outbreaks in Tanzania, starting with detection of suspected and/or confirmed cases, followed by site-specific risk characterization, both useful for planning and prioritizing outbreak interventions or epidemic preparedness. Methods Our initial focus was on the identification and categorization of indicators that are specifically tailored to the early detection and classification of outbreak events. The framework was then evaluated using (i) available cases with syndromic and laboratory-confirmed disease information from ProMED emails using decision tree analysis, and (ii) available historical dengue epidemic data at the local level from 2019 with 6,795 confirmed cases using negative binomial regression analysis adjusted for month and area. Results The laboratory-confirmed diagnosis (dengue yes or no) was consistent with the results of the suspected case classification algorithm for clinically defined syndromic cases. There was strong evidence of an increase in dengue cases with higher site-specific risk (rate ratio = 2.51 (95% CI = [1.76, 3.58])) when regressing confirmed dengue cases in 2019 as the dependent variable and site-specific risk as the independent variable. Conclusions The rapid dengue outbreak risk assessment developed may be useful in controlling the epidemic in Tanzania. The suspected case classification algorithm can be a very useful tool to better assess whether a potential dengue case is present and thus requires laboratory confirmation. The framework can be used to rapidly predict the risk of dengue outbreaks, which is useful for planning and prioritizing interventions or for epidemic preparedness. Key messages • Framework for a novel rapid risk assessment for early detection of dengue outbreaks in Tanzania. • Framework relevant to epidemic preparedness.

Hypnotic use and the risk of cardiovascular diseases in insomnia patients

Abstract Background Insomnia, commonly treated with hypnotic agents, is an independent risk factor of cardiovascular diseases. Previous research demonstrated the detrimental effect of hypnotics on the prognosis of cardiovascular patients. Purpose We aimed to examine the association between hypnotic agents and cardiovascular outcomes in general individuals with insomnia. Methods In a propensity score matched cohort of UK Biobank (UKB) participants with insomnia, Cox proportional hazard model was used to estimate the association between regular use of hypnotic agents and predetermined cardiovascular outcomes including incident coronary heart diseases (CHD), heart failure (HF), stroke, and cardiovascular death. Inverse probability of treatment weighting and competing risk models were further performed during sensitivity analysis. Drug-target Mendelian randomization (MR) analyses were employed for further evaluation of hypnotics-related genes and cardiovascular disease association. The genome-wide association data of CHD, HF, and stroke were there large-scale genome-wide association analyses. Tissue-specified expression quantitative trait loci (eQTL) data was derived from BrainSeq phase II. Results During a median follow-up of 14.3 years, the matched cohort documented a total of 1,019 CHD cases, 406 HF cases, 285 stroke cases, and 197 cardiovascular deaths. No significant association was detected between Z-meds and CHD, stroke, and cardiovascular mortality. Benzodiazepine use was significantly associated with the increased risk of CHD, HF, and cardiovascular mortality (Figure 1). The inverse probability of treatment weighting and competing risk models didn’t alter the above associations. Moreover, drug-target MR analyses corroborated the safety of Z-meds in the general population regarding cardiovascular health. However, GABRG1—a drug target for benzodiazepines—was associated with heightened risks for heart failure, and ischemic stroke (Figure 2). Conclusions Our findings suggested the heterogeneous associations between different categories of hypnotics and incident cardiovascular events in individuals with insomnia. Z-meds are safe regarding the risk of cardiovascular outcomes in the UKB population with insomnia.Figure 1Figure 2

A real scale application of a novel set of spatial and similarity features for detection and classification of natural seismic sources from Distributed Acoustic Sensing data

Summary Distributed Acoustic Sensing (DAS) turns a fiber optic into a very dense network of equally-distributed seismic sensors. We focused on the high-density sampling of the seismic wavefield, expressed in strain rates, measured by DAS. Classical approaches used to identify seismic signals rely on the recorded features at one station, but it is difficult to include spatial information in case of dense seismic station networks. This work aims at introducing new spatial and similarity features for seismic event classification suitable to analyze DAS observations. We propose a processing chain based on the XGBoost algorithm and the use of specifically designed spatio-temporal and similarity features for the event classification, and Markov Random Field for the spatial clustering. The methodology is designated to be applied on a continuous stream of DAS observations. We tested our processing chain to detect earthquakes and quarry blasts recorded in the region by permanent seismic networks and included in the RENASS catalog. These events are part of a strain-rate seismic survey carried out during a 3 weeks campaign of DAS measurements along à 91 km fiber optic cable deployed in the central Pyrenees mountains (France). Despite the high anthropogenic activities along the fiber optic path, the proposed method succeeded in detecting earthquakes of magnitude >0.4 and quarry blasts of magnitude >1.0 while limiting the number of false alarms. This performance is particularly noteworthy for low-magnitude events, where detection is accomplished despite a lower signal-to-noise ratio compared to traditional seismometers. The methodology opens the door to real time detection and classification of seismic events measured with long-distance fiber optic systems.

Multisource Event Classification in ϕ-OTDR Through Sample Feature Synthesis

Multisource Event Classification in ϕ-OTDR Through Sample Feature Synthesis

Domain knowledge-powered attention for air traffic management hazardous events classification

The automated classification of hazardous events in Air Traffic Management (ATM) involves concise texts with specialized terminology. The problem is currently addressed by machine learning text classification methods; however, some methods overlook domain knowledge, while others require it excessively. This research aims to identify a process for integrating low-cost, text-based domain knowledge into classification to address the issue of domain shift. To achieve this, the Wide and Deep Bidirectional Encoder Representations from Transformers (WD-BERT) model is proposed, featuring a unique knowledge-powered attention mechanism. WD-BERT's Wide Attention module assesses the compatibility between input text and domain knowledge to determine class probabilities, while its Deep Attention module extracts contextual features guided by domain knowledge. Additionally, a text mining method is employed to extract domain knowledge from ATM regulation texts. The model is trained and evaluated using a dataset derived from the China ATM Hazard Source Database. It achieves a multi-label classification accuracy of 80.24% and an F1-micro score of 0.88, outperforming comparative models with state-of-the-art performance. Breakdown analyses of the model indicate that the carefully designed attention mechanism endows WD-BERT with the ability to integrate domain knowledge, allowing it to excel in contexts involving complex sentences, multiple terms, and domain shifts. The innovation of this study lies in the proposed domain knowledge-powered attention mechanism, which allows simply organized domain knowledge to effectively guide ATM hazardous events classification, alleviating the impact of domain shift. This method can also be applied to other vertical domains without knowledge graphs.

Addressing the second victim phenomenon among community pharmacists and its impact on clinical pharmacy practice: a consensus study.

The second victim phenomenon, denoting the harmful effects of patient safety incidents on healthcare practitioners, remains insufficiently examined within the pharmacy workforce. This study aimed to investigate the second victim phenomenon in community pharmacies, focusing on its triggers, impacts on pharmacists' well-being, and effects on pharmaceutical care and safety. This consensus study with the Nominal Group Technique involved 27 community pharmacists in three equal groups. The final ranks of the statements scored by participants from 5 to 1 were recalculated using the Van Breda method, combining three distinct data sets with higher values for a higher impact on the output evaluated. Statistics were applied to ascertain event distribution and investigate the potential relationships between event categories and outcomes for patients and pharmacists. "Patient-centric anxiety" (6.8) was the top mental health issue, followed by "Personal responsibility and resilience" and "Future concerns and career aspirations" (6.0 each). The dominant support was "Colleague/Peer support" (5.3). The most frequent patient safety incidents were "Inadequate pharmaceutical service" (8.0) and "Wrong drug dispensed" (7.8). Most errors (63%) were dispensing failures, primarily wrong drug dispensed (44.4%). Of these, 50% were near misses, 25.0% caused no harm, and 16.7% had serious consequences. Field notes suggest contributing factors like inadequate supervision, crowding, and storage issues. This study revealed the second victim phenomenon among pharmacists, which potentially stems from breaches in practice standards. The impact on the quality and safety of pharmaceutical care and its influence on pharmacists' well-being should be studied in further studies.

Analysis of the radiation accidents prevalence in nuclear medicine in the Russian Federation

Radiation events (accidents) appearance is an integral part of the use of ionizing radiation sources in medicine in general and nuclear medicine in particular. To minimize the negative impact on patients, workers, and public due to such events, it is necessary to have reliable information about real prevalence of the radiation events (accidents). The current work presents the analysis of the radiation accidents with medical ionizing radiation sources registered in the “Data bank of radiation accidents and incidents” of the Rospotrebnadzor Information and Analytical Centre for Radiation Safety and the results of workers questionnaires conducted in 25 nuclear medicine departments (about 30% of all nuclear medicine departments in the Russian Federation). The results of the analysis showed that the most common registered radiation accidents in the “Data bank of radiation accidents and incidents” are identification of passengers with high external dose rate as well as identification of waste contaminated by medical radionuclides. The results of the questionnaire showed that the most common radiation accidents (events) in nuclear medicine are contamination of work clothes or work surfaces with radionuclides, or patient fluids containing radionuclides; conducting examination without proper referral; extravasation of radiopharmaceutical. Existing systems of identification and registration of radiation accidents do not allow to identify radiation events (accidents) specific to nuclear medicine. The further research aimed at developing a classification of radiation events (accidents) in medicine and methods for responding to such events are feasible.

Social Event Classification Based on Multimodal Masked Transformer Network

The key to multimodal social event classification is to fully and accurately utilize the features of both image and text modalities. However, most existing methods have the following limitations: (1) they simply concatenate the image features and text features of the event, and (2) there is irrelevant contextual information between different modalities, which leads to mutual interference. Therefore, it is not enough to only consider the relationship between the modalities of multimodal data, but also the irrelevant contextual information (i.e., regions or words) between the modalities. To overcome these limitations, a novel social event classification method based on multimodal masked transformer network (MMTN) is proposed. A better representation of text and image is learned through an image-text encoding network. Then, the obtained image and text representations are input into the multimodal masked transformer network to fuse the multimodal information, and the relationship between the modalities of multimodal information is modeled by calculating the similarity between the multimodal information, masking the irrelevant context between the modalities. Extensive experiments on two benchmark datasets show that the proposed multimodal masked transformer network model achieves state-of-the-art performance.

Instrumentation and automatic evaluation of event indicators alongside railway lines: a first attempt to comply with article 90 of the French mobility legislation on transport

According to the Article 90 of the French Mobility Orientation Law (LOM) No. 2019-1428 (December 24, 2019), the Testing and Measurement Laboratory (LEM) of the Régie Autonome Des Transports Parisiens (RATP) 1 has been at the forefront in determining and evaluating indicators of annoyance due to event-related noise from railway transport infrastructure. Six measurement points located near tramway, metro, and suburban trains transport infrastructures were equipped with specific instrumentation allowing for measuring and automatically detecting railway and non-railway sound events (road traffic, alert sirens, and other sound activities), this multiplicity of noise sources necessitated a comprehensive approach to data collection and analysis, ensuring accurate assessment of the impact of railway transport infrastructure on surrounding area. Measurements were carried out continuously for 15 days. A post-processing system was also developed to analyze over a dozen noise indicators for each category of sound events. This article presents the technical solutions developed and deployed by the laboratory in a few months to address the regulatory challenges posed by the experimental phase of Article 90 of the LOM law.

Aircraft automatic detection on Station7hz interface using signal processing and artificial intelligence - ACOUDET - First method

The global project is to design a automatic noise event detection and classification system in real time using signal processing and artificial intelligence (AI). The first phase focuses on transport noise (aircrafts, trains, road vehicles, etc.). Secondly, the plan is to extend detection to construction site noise and any natural or life sources. This paper presents the first method developed to detect audible aircrafts pass-by. It focused on the development of a prototype detection algorithm, its results in situ conditions, and insights on future developments to create a robust aircraft and train noise detection system. The data collection method, aircraft audio signal particularities and a first detection method are described. The robustness of the detections is evaluated and limitation induced by real time detection are discussed. Thanks to the proprietary monitoring system Station-7hz. it has been possible to detect aircrafts in different types of sound environments in a straightforward manner, showing better results at night due to a quieter environment. Future developments based on a hybrid algorithm include AI models for audibility assessment and unwanted event filtering during aircraft pass-by. Aircraft's presence is detected via radar data and the detection of maximum acoustic levels correlated with aircraft parameters.

Magnetic single-anastomosis side-to-side duodeno-ileostomy for revision of sleeve gastrectomy in adults with severe obesity: 1-year outcomes.

Uncomplicated surgical approaches that minimize anastomotic complications while improving revisional metabolic/bariatric surgical (MBS) outcomes are needed. This prospective single-center study assessed the feasibility, safety, and efficacy of the novel linear magnetic anastomosis system (LMAS [3cm]) in performing a side-to-side duodeno-ileostomy (MagDI) bipartition to revise clinically suboptimal primary sleeve gastrectomy (SG). Patients with severe obesity with/without type 2 diabetes (T2D) with suboptimal weight loss, regain, and/or T2D recurrence post SG underwent revisional MagDI. A distal and proximal magnet were delivered endoscopically to the ileum and duodenum and aligned via laparoscopic assistance. Gradual magnet fusion formed a DI bipartition. technical feasibility, safety (Clavien-Dindo [CD] severe adverse event classification) at 1year. Secondary endpoints: MBS weight and T2D reduction. July 29, 2022-March 28, 2023, 24 patients (95.8% female, mean age 44.9±1.5years, and body mass index [BMI] 39.4±1.3kg/m2) underwent MagDI. Feasibility was attained via correct magnet placement (mean operative time 63.5±3.3min), patent anastomoses created, and magnet passage per anus in 100.0% of patients. There were 4 CD-III mild or moderate severe AEs, 0.0% associated with the LMAS or MagDI: 0.0% anastomotic leakage, obstruction, bleeding, infection, reintervention, or death. Mean BMI reduction was 2.1kg/m2 (p<0.05); total weight loss 5.3%, excess weight loss 16.4%; and the patient with T2D improved. The single-anastomosis MagDI procedure using the novel 3-cm LMAS to revise clinically suboptimal SG was technically straightforward, incurred no major complications, mitigated weight regain, and renewed clinically meaningful weight loss. NCT05322122.

A Machine learning-based framework and open-source software for Non Intrusive Water Monitoring

Recent research highlights the potential of consumption-based feedback for water conservation, emphasizing the need for Non Intrusive Water Monitoring (NIWM). However, existing studies often rely on small datasets, a pre-selected class of models, and inaccessible software. Here, we introduce PyNIWM, a machine learning-based open-source Python framework for NIWM. PyNIWM enables water end-use classification via (i) data characterization and feature engineering, (ii) water end-use event classification with four machine learning classifiers, and (iii) performance assessment. We demonstrate PyNIWM on a real-world dataset containing around 800,000 labelled end-use events from 762 homes across the USA and Canada. The four PyNIWM classifiers achieve F1 scores above 0.85, indicating high suitability for water end-use classification. However, a tradeoff between accuracy and computational cost exists. Finally, data balancing through oversampling enhances classification of low-represented end-use classes, but does not improve overall classification. We release PyNIWM as an open-source software, aiming for collaborative and reproducible research.

Influence of magnetosphere disturbances on particle fluxes measured by ground-based detectors

This study examines how the Earth's surface particle fluxes are modulated by the interplanetary magnetic field (IMF) carried by coronal mass ejections (ICME). Our findings underscore the role of magnetic reconnection in allowing low-energy galactic cosmic rays (GCRs) to penetrate the magnetosphere, leading to enhanced secondary particle fluxes through reduced cutoff rigidity —a phenomenon known as the magnetospheric effect (ME). In contrast, the Forbush decrease (FD) driven by the scalar magnetic field strength results in significant particle flux reductions. On May 10–11, 2024, the FD, directly linked to the enormous geomagnetic storm (GMS), was complicated by the simultaneous registration of secondary particles from the solar energetic particle (SEP) event, which was energetic enough to generate secondary particles in space and on the ground, leading to increases in detector count rates, known as ground-level enhancements (GLEs). Using new experimental facilities, we reveal that secondary particles during ME events release up to 10 MeV energy (maximum energy of approximately 10 MeV), whereas, during FD and GLE events, the energy release extends to 100 MeV (maximum energy of approximately 100 MeV). These insights contribute to refining event classification schemes and predictive models of space weather.

Initial Experience with Embolization using a Shear-Thinning Conformable Hydrogel Embolic

A new conformable embolic agent, brand name Obsidio (Boston Scientific Inc), is indicated for embolizing hypervascular tumors and treating peripheral vessel bleeding. It is a non-Newtonian hydrogel that becomes less viscous when shear force is applied. This retrospective study examines the safety and effectiveness of the shear-thinning embolic in a single-academic-center experience. Technical and clinical success were assessed. Adverse events were defined per the SIR Adverse Events Classification. Twenty-seven patients were treated with the shear-thinning embolic in 39 vessels over 28 procedures. Technical success was achieved in 37/39 vessels (95%), with clinical success in 26/28 procedures (93%). There was one treatment-related severe adverse event (3.6%): left gastric artery embolization resulting in ischemia and subtotal gastrectomy (Grade 3). Two patients had mild adverse events (7.1%). Initial experience shows promising effectiveness of this shear-thinning embolic, though because of differences between this embolic and conventional liquid embolics, operator training is required for safe use.

End-to-end deep learning on classification of neutral-to-ground voltage in secondary distribution systems

Neutral-to-ground voltage (NTGV) abnormalities in secondary distribution systems (SDS) pose significant power quality (PQ) challenges, including safety hazards, power losses, and equipment damage. Despite their importance, these abnormalities remain relatively understudied. Accurate classification of NTGV events is crucial for effective mitigation strategies. Existing research primarily relies on machine learning (ML) models trained on manually extracted features from simulated or real-world signals. This paper introduces a novel end-to-end deep learning approach that leverages Gate Recurrent Units (GRU) to bypass manual feature extraction, directly utilizing real-world signals from three NTGV event categories: ground fault, lightning strike, and normal conditions. This is first time that GRU has been used for NTGV classification using raw data. The model's generalizability is assessed through 5-fold cross-validation. A comparative analysis with baseline models and traditional ML techniques demonstrates the proposed model's superior performance and computational efficiency due to its ability to directly process raw data.

Retrospective Review of Falls in Medical Inpatients Following Introduction of a Falls Proforma at Wexford General Hospital

Abstract Background Inpatient falls are the largest category of preventable inpatient adverse events in hospitals coming at both great financial and safety cost to a healthcare system. Approximately 30% of inpatient falls result in injury, 4-6% resulting in serious injury with older patients have the highest risk of falling and injury. The aim of this retrospective audit cycle was to improve standards of falls assessments performed by junior doctors. Methods We initially conducted a retrospective review of 7 medical inpatient falls in July 2023. Standards were compared against NICE Guidelines on Assessment and Prevention of Falls in older people 2013 and NICE guidelines on Assessment and early management of head injury 2023. 15 key elements were analysed: doctor identifier, date and time, history of fall, confusion, pain, loss of consciousness, amnesia, seizure, vomiting, medication review, hip fracture, wrist fracture, skull fracture, Glasgow Coma Scale and neurological deficit. One point was given for each of the 15 key elements noted in the falls review. Following introduction of a falls proforma, we re-audited 18 falls in February 2024 to analyse differences in scores. Results Prior to proforma introduction, 7 falls reviewed had an average score of 3 points. 4 falls were reviewed by SHOs, 1 each by an SpR and Intern and 1 NCHD review was unidentifiable. Mean age was 72. Following proforma introduction, 18 falls in 13 patients were analysed. 2 patients did not have a falls review, 15 were reviewed by SHOs, 1 by an Intern. Average age was 81. Proforma was used in 7 cases with an average score of 12.7, and no proforma was used in 11 cases with an average score of 5. Conclusion These findings demonstrate improved quality of falls reviews using proformas in line with guidelines compared to those without. More education is needed to incorporate proformas into standard practice.

Learning incremental audio–visual representation for continual multimodal understanding

Deep learning methods have demonstrated remarkable success in processing static datasets for various video tasks. However, when confronted with continuous data streams, these approaches often encounter the challenge of catastrophic forgetting. This phenomenon leads to a significant decline in overall performance when learning new classes incrementally. Moreover, existing methods tend to overlook the correlation between audio and visual modalities in video incremental learning, despite their joint significance in scene comprehension. How to continuously learn from new classes while maintaining the knowledge of old videos with limited storage and computing resources is becoming imperative in the field of multimodal learning. In this paper, we introduce CavRL, a pioneering benchmark for audio–visual representation learning under class incremental scenarios. To mitigate catastrophic forgetting, we propose a rehearsal-based training approach that leverages a small exemplar set from previous classes. Our approach constrains the memory buffer within strict storage limits, optimizing exemplar selection by learning correlative audio–visual representations. Additionally, we employ a distillation method to mitigate forgetting in a self-supervised manner. Evaluations on two prevalent multimodal tasks: audio–visual event classification and audio–visual speaker recognition, which demonstrate that CavRL outperforms existing state-of-the-art incremental learning methods across various settings. We anticipate that CavRL will significantly advance research in continual multimodal learning.

Time to use the right classification to predict the severity of checkpoint inhibitor-induced liver injury, as assessed for causality using the updated RUCAM.

While immune checkpoint inhibitors (ICIs) are revolutionising cancer therapy, checkpoint inhibitor-induced liver injury is a significant immune-related side effect of this immunotherapy. This study focuses on the severity classifications and characteristics of patients with checkpoint inhibitor-induced hepatitis. A retrospective analysis of patients with severe Checkpoint Inhibitor-induced hepatitis grade 3 and 4 according to the recommended Common Terminology Criteria for Adverse Events (CTCAE) classification was conducted. Data on clinicobiological characteristics, treatment and outcomes were collected from 3 university hospitals, and causality was assessed by using the updated Roussel Uclaf Causality Assessment Method. The severity of hepatitis was assessed using the Model for End-stage Liver Disease score, the Drug-Induced Liver Injury Network, and the Drug-Induced Liver Injury International Expert Working Group classifications. We retrospectively included 100 patients presenting various hepatitis patterns with a median time to onset of 20 days after checkpoint inhibitors. Severity grading varied significantly among the classifications used. A lower incidence of severe cases was observed when using the Drug-Induced Liver Injury classifications instead of the recommended CCTCAE classification, and this was correlated with outcomes. This retrospective study challenges the efficacy of the CTCAE classification in defining the severity of Checkpoint Inhibitor-induced hepatitis and suggests that the traditional hepatology-focused scores may be more relevant. The CTCAE classification is inconsistent and gives equal weight to jaundice and elevated transaminases, which leads to steroid overtreatment and limits the rechallenge of ICIs.

Impact of in situ simulation training on quality of postnatal stabilization and resuscitation—a before-and-after, non-controlled quality improvement study

This study aimed to evaluate the impact of in situ simulation-based training on quality indicators of patient care at a level IV neonatal intensive care unit. A before-and-after, non-controlled quality improvement study was performed at the Division of Neonatology, Medical University of Graz. The educational intervention comprised a period of 4 months, with structured in situ simulation training delivered regularly for neonatal providers and nurses in interprofessional teams. The primary study outcome was the quality of non-technical skills and team interaction during actual postnatal stabilization and resuscitation. This was assessed using video recording during two 2-month observational phases before (pre-training) and after the educational intervention (post-training). Delivery room video recordings were assessed by two external, blinded neonatologists using the Anaesthetists’ Non-Technical Skills (ANTS) score. Furthermore, we collected clinical patient data from video-recorded neonates during the pre- and post-training periods, and training participants’ individual knowledge of neonatal resuscitation guidelines was assessed using a before- and after-questionnaire. A total of 48 healthcare professionals participated in 41 in situ simulation trainings. The level of non-technical skills and team interaction was already high in the pre-training period, and it did not further improve afterwards. Nonetheless, we observed a significant increase in the teamwork event “evaluation of plans” (0.5 [IQR 0.0–1.0] versus 1.0 [1.0–2.0], p = 0.049). Following the educational intervention, training participants’ knowledge of neonatal resuscitation guidelines significantly improved, although there were no differences in secondary clinical outcome parameters.Conclusion: We have successfully implemented a neonatal in situ simulation training programme. The observed improvement in one teamwork event category in the post-training period demonstrates the effectiveness of the training curriculum, while also showing the potential of in situ simulation training for improving postnatal care and, ultimately, patient outcome. What is Known:• In situ simulation-based training is conducted in the real healthcare environment, thus promoting experiential learning which is closely aligned with providers’ actual work.• In situ simulation-based training may offer an additional benefit for patient outcomes in comparison to other instructional methodologies.What is New:• This observational study investigated translational patient outcomes in preterm neonates before and after delivery of high-frequency in situ simulation-based training at a level IV neonatal intensive care unit.• There was a significant increase in the frequency of one major teamwork event following the delivery of in situ simulation-based training, indicating a notable improvement in the non-technical skills domain, which is closely linked to actual team performance.

A Demonstration of the Risk-Informed NEI 18-04 Design Evaluation Model for the Modular High-Temperature Gas Reactor

Several advanced reactor designs are now under active development in the United States and elsewhere, promising sustainable solutions to the growing world energy needs. The designs currently being considered are quite diverse and different from the more established light water reactor technology that has dominated the operating commercial nuclear landscape. While advanced reactor concepts were first explored in the dawn of the nuclear age, they are now being reconsidered under the light of modern needs, and specifically, for their flexible operating conditions and inherent safety characteristics. In response, the U.S. Nuclear Regulatory Commission staff is moving forward with development of 10 CFR Part 53 rulemaking, which is a more risk-informed, technology-agnostic framework for licensing and regulating such new designs. The nuclear industry response to this regulatory initiative resulted in the technical report by the Nuclear Energy Institute, NEI 18-04 Revision 1, which provides an implementation roadmap of the risk-informed approach when defining the safety case for a new plant design. The implementation of this safety case may be a nontrivial exercise for an actual reactor design. This paper provides a demonstration of performing such an analysis for a representative advanced reactor. Public information from the General Atomics high-temperature gas reactor design was considered in this demonstration. The analysis workflow was facilitated with the FPoliSolutions’ proprietary Risk-Informed System Engineering (RISE) digital platform, a product that was presented in previous publications. RISE is one application of FPoli’s enterprise digital platform, which was created to facilitate orchestration of complex workflows leveraging recent technologies developed at national laboratories, such as Idaho National Laboratory’s RAVEN and EMRALD frameworks. The analysis described in the paper includes the selection and classifications of events, the integration of probabilistic risk analysis artifacts, and event modeling simulations for consequence evaluations. The results are then used for system, structures, and components safety classification and a synthesis of the safety case for the design in line with the frequency-consequence targets presented in NEI 18-04. The purpose of the analysis, as framed in RISE, is to readily produce outputs and views that can aid users and regulators in making risk-informed decisions to demonstrate their plant safety case.