This paper deals with dependencies in accident sequences with multiple Human Failure Events (HFEs). When these are present, the probability of some HFEs may require modification, given knowledge of preceding failures. HFE dependence can have significant impact on Probabilistic Safety Assessment results, because the joint probability of the HFEs may increase by orders of magnitude. State-of-the-art dependence analysis in Human Reliability Analysis (HRA) relies on simplistic approaches with a largely subjective basis. This paper addresses empirical evidence of HRA dependencies from operational events, contributing to ongoing research to strengthen the technical and empirical basis of dependence analysis. This first-of-a-kind analysis of six events identifies three groups of coupling factors: work practice-related, task-related, and knowledge-related. Task-related factors have the strongest influence on dependence because they manifest as specific performance drivers for the multiple tasks. The evaluation of a widely adopted dependence analysis Decision Tree (DT) suggests that the method is practical and adequate for most analysis cases. The analysis identifies some cases for which the DT assessment of independence does not seem appropriate based on the event narrative, e.g. when the DT assessment yields independence (zero dependence) because of an intervening success. As a result, a recommendation is formulated to analysts to question the plausibility of the assessment of independence. The evaluation methodology applied to the DT may also be used to assess more recent modelling developments, also addressing the important need for a larger set of events for broader coverage of situations of interest for HRA.

This work investigates data-based discrete Bayesian Belief Networks (BBNs) as surrogate energy system models for result analysis and interactive analyses, such as what-if analyses. A simplified version of the Swiss TIMES (STEM) model, referred to as STEM-lite, is used for demonstration. A method to optimize the BBN model is devised, based on performance metrics related to the accuracy of the BBN predictions, calculated over data records unseen by the BBN in the training phase. Further validation of the BBN on a set of seven scenarios yielded an average relative error below 2 %, suggesting adequate performance as surrogate model. The application of the surrogate BBN model is demonstrated to highlight its benefits, which include enabling interactive analysis (supported by the visualization of key variables, their relationships and interactions), fast and intuitive uncertainty propagation, and support for goal-driven analysis (backward reasoning from outcomes to the inputs that produce these outcomes). The surrogate BBN presented here was developed to elaborate the methods for constructing, validating, and using BBN models for energy systems analysis and to demonstrate the benefits of such a model; at this stage, this model is not intended for energy systems and economics policy discussions. For practical applications, future work is needed to reduce the number of data records to construct the BBN, to introduce the option to treat the time dependence of the input variables, and to allow for larger BBN models (involving more variables) that reflect the increasing complexity of energy systems.

Main control room simulators are an important information source for improving the empirical basis of human reliability analysis (HRA). Both quantitative and qualitative observations provide valuable insight on the operators’ ability to successfully perform their tasks. However, relevant portions of this evidence are not used in typical approaches for estimation of human error probabilities (HEP), which are based on failure counts. With small sample sizes and the operator performance levels representative for nuclear power plants, very little information enters the probability estimates. This paper presents a novel approach to formalize the collection and use of reliability-relevant evidence from simulators. The methodology treats task reliability with the plant-centred orientation characteristic of the systems or Probabilistic Safety Assessment (PSA) perspective as well as a human-centred view of task performance based in human factors engineering. These are combined to form a Task reliability index (TRI) able to distinguish finer performance variations and consider indications of potential failures. Bayesian updating of HEP distributions is suggested by translating the TRI data into pseudo-failures and trials, preserving the convenient coupling of Beta-Binomial distributions in the Bayesian analysis. The approach is demonstrated using data from the International HRA Empirical Study and is shown to produce results that are comparable to those obtained in the empirical study, which were based on comprehensive expert analyses.

To enhance the empirical basis of probability estimates of human failure events in human reliability analysis, a framework for data collection and analysis has recently been proposed based on a task reliability index (TRI). In this framework, the TRI is obtained by combining two component measures of performance, addressing plant outcomes and task performance. These component measures are not fully operationalized at this time: this paper explores their potential operationalization, using an existing set of data collected in a nuclear power plant simulator. The aims are (1) to examine how the TRI could be operationalized based on the existing data set (2) to determine what other data would be needed to support a TRI-based data collection effort. Simulation records were scrutinized; the plant outcomes and operator behaviours were assessed in terms of (sub-)indices corresponding to the component measures. The insights obtained from this application were used to establish some assessment rules for the indices. Some practical issues of preparation and analysis for future simulation data collection are also identified.

With the ongoing efforts to collect new data for Human Reliability Analysis (HRA) (in particular, from nuclear power plant control room simulators), it becomes important that the coming data will be processed traceably, addressing its underlying variability, eventually in combination with expert judgment. In this direction, this work presents a two-stage Bayesian model to integrate expert-elicited probability estimates and empirical evidence from simulator data in the quantification of HEP values and of the associated variability distributions. The general aim is to provide a data aggregation framework able to mathematically combine diverse information sources throughout the HEP estimation process, in a systematic and reproducible way, contributing to strengthening the empirical basis of future HRA methods. The Bayesian model can be used to produce reference values and bounds for HRA methods as well as to improve the quality of plant-specific HEP estimates for use in Probabilistic Safety Assessment applications. The model is first verified with artificial data and then applied to quantify the HEP of human failure events from literature. Model sensitivity to biases in expert estimates is also investigated.

La maladie de Lapeyronie (MLP) retentit sur la vie sexuelle des hommes, via une courbure du pénis, une réduction de sa taille et/ou un effet en sablier. Le Peyronie's Disease Questionnaire (PDQ) est un outil conçu pour évaluer quantitativement le retentissement de la MLP. À ce jour, il était validé en anglais et en espagnol. L'objectif de cette étude est de le valider en français. Cette étude prospective a été approuvée par le Comité de protection des personnes (N°ID-RCB : 2023-A00219-36) et par l'auteure du questionnaire. Après réalisation d'une double traduction du PDQ de l'anglais vers le français, suivie d'une contre-traduction par quatre traducteurs experts maîtrisant leur langue d'origine, une conciliation a permis la création de la version finale française testée auprès de la population d'étude. Pour chaque item il était demandé aux participants de répondre à deux sous-questions a) et b). Le pourcentage de réponses « J'ai bien compris la question » à la sous-question a) de chaque item constituait le critère de validation de la traduction. Une fois les étapes de traductions et contre-traductions effectuées, la version française du PDQ a été soumise aux 30 participants de l'étude pilote, d'âge moyen 59 (± 12) ans. La majorité des hommes était en couple (n = 22, 73 %) et près de la moitié (43 %) retraités (Tableau 1). Concernant les réponses aux sous-questions du PDQ, pour chaque item, il a été retrouvé un taux de compréhension supérieur à 95 % et pour 12/15 items une compréhension de 100 %. Pour les items restants, 3 hommes ont déclaré ne pas se sentir concernés par les questions. Aucune question traduite de l'anglais en français n'a été incomprise (compréhension de la phrase, de certains mots ou du sens de la question) ou n'a dérangé (blessée ou gênée) les sujets de l'étude (Tableau 2). Cette étude pilote montre que ce travail de traduction a abouti à une version définitive du PDQ valide et compréhensible par tous les participants, quel que soit leur âge ou leur niveau d'enseignement. Cette version en français du PDQ mériterait d'être validée sur d'autres populations francophones en multicentrique.

Human operations play a vital role for the resilience of power grids. While past research concentrated on performance in cascading failures, this work proposes a system model that focuses on assessing the impact of the performance of operators in grid operations and restorations. The operator model developed for this purpose addresses human performance and variability by accounting for stochastic durations, potential errors and alternative responses, and the probabilities of these outcomes. Discrete event simulation is the assessment framework, with the model implemented in MATLAB Simulink in conjunction with a dynamic power system model. A re-energization cell provided by a Swiss generation system operator is used for a case study demonstration of the simulation model, which produces distributions of the total restoration duration and restoration success rates while identifying the parts of restoration plans that could be modified to enhance restoration performance. The overall outcomes of the case study suggest that the human element can be treated practically in grid simulations in order to produce findings that will increase grid resilience.

The present paper develops a Bayesian Belief Network (BBN) for quantification of aggravating actions, as outcomes of inappropriate decisions, to be integrated in probabilistic safety assessment (PSA) models (i.e., the socalled errors of commission, EOCs). The BBN connects analyst ratings on influencing factors to the error forcing impact of a specific scenario, supporting the CESA-Q method (the Quantification module of the Commission Error Search and Assessment method). While contributing to the quantification of EOCs, this paper presents a novel process for the quantification of the BBN parameters (the Conditional Probability Distributions, CPDs), striving for traceable integration of expert knowledge and (scarce) data, in the form of retrospective analyses of operational events involving EOCs. The process combines the functional interpolation method for populating CPDs and Bayesian updates to adjust the BBN response to the available evidence. A first, prior BBN is developed, then sequentially updated to adjust to two data sets. This allows some intermediate validation and puts forwards the steps for future BBN updates as new EOC events (or new analyst assessments) become available.

Le partage de la charge contraceptive dans le couple est une demande sociétale. La plupart des hommes seraient prêts à assumer cette responsabilité. La contraception thermique masculine est une des modalités de contraception masculine envisagée. Un anneau de remontée testiculaire appelé Androswitch® a été développé à cette fin. Nous avons essayé d’évaluer les raisons de ce choix, l’observance et la tolérance de cet appareillage. Nous avons réalisé une enquête en ligne auprès de 75 hommes utilisant ce dispositif. 38 individus (50,6 %) ont répondu au questionnaire. Au total, 78 % des répondants étaient en couple, depuis moins de 2 ans dans 45 % des cas. La tranche d’âge majoritaire était 30–34 ans (41 %). La raison principale du choix de ce mode de contraception était le partage de la charge mentale dans le couple (89 %). Seul un tiers des hommes n’avait aucune difficulté à porter le dispositif 15 h par jour, et 17 % avaient beaucoup de difficultés à le faire. 19 % avaient dû l’arrêter au moins temporairement dans les 3 derniers mois. 80 % des hommes ont présenté des effets indésirables, notamment cutanés (74,3 %). Le niveau de gène était supérieur à 3/10 dans 38,5 % des cas. La Fig. 1 résume les effets indésirables observés, et leur fréquence d’apparition. Cette méthode de contraception est encore en cours d’expérimentation, et ce dispositif n’ayant pas de marquage CE ne doit donc pas être recommandé en pratique courante. Les faibles effectifs de cette enquête ne permettent pas de tirer de conclusion définitive, mais au vu de cette enquête, il semble que le profil de tolérance et d’observance ne soit pas optimal.

Besides robustness, a crucial aspect of power grid resilience is the postdisruption restoration of transmission capacity. Conventionally, grid repair planning is initiated when damage assessment is complete. With the current communication bandwidth and the role of drones in inspection, damage assessment is an increasingly dynamic process. Early damage estimates can serve preliminary repair planning. Subsequent replanning is then performed as updated damage assessments come in, thus mitigating the impact of restoration uncertainties. The present work examines the gains from starting grid recovery using preliminary damage estimates and replanning repair. A receding horizon approach, model predictive control (MPC), is applied to the IEEE-39 bus system. The benefits are expressed by the integral loss of service (ILOS), measuring the power demand not served over time. In the baseline, repair planning is not performed before definitive repair estimates are delivered. In this study, MPC reduces the maximum ILOS by up to 57%. In terms of computation, three prediction steps are sufficient for the receding horizon to decrease the maximum ILOS by at least 37%.

The Human Reliability Analysis (HRA) of inappropriate actions (Errors Of Commission, EOCs) still suffers from technical gaps, especially for actions with decision-related motivations. Traditional, factor-based HRA methods often fail to address these motivations. Holistic analyses frameworks have been developed (namely, ATHEANA and MERMOS), but these rely on strong analyst expertise and require large efforts to make analyses traceable. This paper presents the application of the factor framework underlying the quantification module of the Commission Error Search and Assessment (CESA-Q) method. The framework is applied to fourteen operational events from the period 2000-2016, not used for the factor framework development. This gives the chance to confirm the validity of the CESA-Q factor framework to represent adequately the diverse situations influencing inappropriate decisions in real operational events. In the majority of the events, the triggering condition for the inappropriate decision is the information available to the operators, typically the procedural guidance, Human-Machine Interface, experience and training. In these cases, the dominant influencing factors (positively and negatively) identified through the CESA-Q analysis relate to verification of appropriateness of the decision. For another set of events, the inappropriate decision was driven by the prospect of other benefits (e.g. simplifying the plant control).

Current Human Reliability Analysis models express error probabilities as a function of task types and operational context, without explicitly modelling the influence of different crew behavioral characteristics on the error probability. The influence of such variability is treated only implicitly, by variability and uncertainty distributions with bounds primarily obtained by expert judgment. This paper presents a methodology to empirically incorporate crew performance variability in error probability quantification, from simulator data. Crew behaviors are represented by a set of “behavioral patterns” that emerge in the observation of operating crews (e.g. in information sharing or in adhering to procedural guidance). The paper demonstrates the use of a Bayesian hierarchical model to explicitly capture the performance variability emerging from data. The methodology is applied to a case study from literature. Numerical demonstrations are performed in order to compare the proposed approach to the existing quantification models used in HRA for treating simulator data.

An approach to manage human performance related risks in petrochemical sector is to use human reliability analyses (HRA) techniques. However, the focus of HRAs on individuals and on decomposed tasks overlooks the likelihood that collective actions and behaviors might lead to system failures. This study introduces an alternative approach, referred to as Human Performance Integrity (HPI) index, to assess human performance conditions on the whole, as they relate to safety, in petrochemical facilities. Additionally, the approach is used to rate installations in terms of their defenses against safety‐relevant human failures. The HPI index is built upon the notions of Cognitive Reliability and Error Analysis Method. By means of a 42‐question survey, data was collected on the factors that improve or reduce human performance in 11 oil refineries. Data was used to assess the facilities' safety performance. Results were compared against information obtained from relevant investigation reports, as well as and an independent evaluation of the facilities carried out by certified auditors. Findings support the use of HPI index from novice and experienced scholars and/or practitioners as a quick and effortless, yet sound and efficient manner to assess safety and reliability performance of oil refineries from a human factors perspective.

Les biopsies guidées par l’IRM améliorent le diagnostic des cancers de prostate (CaP). Cependant peu de données existent dans la littérature concernant le risque de cancer et la conduite à tenir après des biopsies ciblées (BC) négatives. L’objectif principal de cette étude était de définir le risque de CaP significatif (CaPs) après une première série de BC négatives au cours du suivi. Il s’agit d’une étude rétrospective incluant des hommes ayant eu des BC négatives entre 2014 et 2020. Les biopsies étaient ciblées sur une lésion IRM PIRADS ≥ 3 avec le système de fusion d’image (KOELIS). L’ensemble des données démographiques, des paramètres pré-biopsiques et de l’évolution sont décrites. Le taux de cancer significatif (ISUP ≥ 2) diagnostiqué dans le suivi constituait le critère de jugement principal. Par la suite, une analyse statistique des données démographiques, biologiques, d’imagerie a été réalisée pour déterminer si des variables étaient associées au risque de cancer significatif. Parmi les 3076 hommes ayant eu des BC, 394 patients avaient des BC négatives et ont été inclus dans la cohorte. L’âge médian était de 65 ans, 111 patients (28,17 %) ont eu une nouvelle IRM. Au total, 326 patients n’avaient pas de preuve de la maladie à 13 mois de suivi médian. Parmi les 68 patients (17,26 %) qui ont eu une deuxième série de BC au cours du suivi médian de 24 mois, 14 (3,55 %) avait un CaPs (Fig. 1). En analyse univariée, aucun facteur clinicobiologique, pré/post-biopsique n’était associé à un risque de cancer lors de la deuxième biopsie (Tableau 1). Cependant un score PIRADS 5 sur la première IRM et confirmé ≥ 4 sur l’IRM post-BC était associée à un risque de CaPs de 36,4 % (4/11) (Tableau 2). Les résultats de cette étude montrent que le risque de cancer de prostate significatif est faible dans les 2 ans suivant des biopsies ciblées négatives. La réalisation d’une nouvelle IRM avant de nouvelles biopsies pourrait permettre de sélectionner les sujets à risque de cancer de prostate significatif. Ces résultats doivent être confirmés dans une étude prospective.

Evacuation modelling has developed over time from simple engineering equations that do not consider behavioral tendencies to more sophisticated models with the potential to represent evacuation behaviors and decisions. This paper aims to lay the foundations for a more realistic representation of human factors in evacuation models, which is needed to ensure the adequacy of the infrastructure, decision processes and safety of evacuation. To provide a clearer picture of the empirical knowledge and modelling for evacuation studies, a generalized timeline is introduced. Recent behavioral evidence from empirical studies in the fields of both pedestrian evacuation and vehicular evacuations are reviewed to investigate the impact of various factors on the evacuee behavior over different phases. The consensus perspective on key behaviors that emerges is then used to review and consolidate the recent advances in evacuation modelling, in particular with respect to the formulations and techniques for representing these behaviors. Within each of these discussions, we pointed to current limitations and make corresponding suggestions on future research directions.

La torsion du cordon spermatique (TCS) est une urgence chirurgicale qui nécessite un diagnostic et une prise en charge rapides pour éviter l’orchidectomie. La durée des symptômes est un facteur prédictif de la viabilité testiculaire, cependant il est difficile dans de nombreux cas d’identifier la durée exacte. L’objectif de cette étude était d’identifier des facteurs biologiques préopératoires permettant d’évaluer la viabilité testiculaire après une TCS. Une étude multicentrique rétrospective nationale (TorsAFUF) a été réalisée chez les patients pris en charge pour une douleur testiculaire aiguë dans 14 centres hospitaliers. Les patients présentant une TT objectivée au bloc opératoire et ayant des données complètes ont été inclus. Les paramètres biologiques préopératoires ont été recueillis (hémoglobine, plaquettes, leucocytes, neutrophiles, lymphocytes, rapport neutrophiles/lymphocytes, rapport plaquettes/lymphocytes). La décision de préserver ou de retirer le testicule était laissée à la discrétion du chirurgien. Une régression logistique a été réalisée pour rechercher les facteurs biologiques à une orchidectomie sur l’ensemble de la série. Compte tenu de la répartition des variables d’intérêt nous avons effectué une transformation logarithmique. Nous avons inclus 750 patients dans l’analyse finale dont 12 % (n = 91) ont eu une orchidectomie. L’âge médian était de 20,5 ans (17–26). Le délai médian entre le début des symptômes et l’exploration chirurgicale était de 6 h. Le rapport médian neutrophiles/lymphocytes (NLR) et le rapport plaquettes/lymphocytes (PLR) étaient de 3,9 (2–7) et 127 (90–177), respectivement. En analyse multivariée, seul le NLR préopératoire et la durée des symptômes était prédictif d’une orchidectomie (OR : 1,63 et 1,04 ; respectivement). L’aire sous la courbe ROC de notre modèle (délai et NLR) pour la prédiction d’une orchidectomie était de 0,90. Dans cette série rétrospective, le NLR semble être un marqueur biologique préopératoire puissant du risque d’orchidectomie chez les patients pris en charge pour une TCT.