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In this article, the authors focus on the introduction of a hybrid method for risk-based fault detection (FD) using dynamic principal component analysis (DPCA) and failure method and effect analysis (FMEA) based Bayesian networks (BNs). The FD problem has garnered great interest in industrial application, yet methods for integrating process risk into the detection procedure are still scarce. It is, however, critical to assess the risk each possible process fault holds to differentiate between non-safety-critical and safety-critical abnormalities and thus minimize alarm rates. The proposed method utilizes a BN established through FMEA analysis of the supervised process and the results of dynamical principal component analysis to estimate a modified risk priority number (RPN) of different process states. The RPN is used parallel to the FD procedure, incorporating the results of both to differentiate between process abnormalities and highlight critical issues. The method is showcased using an industrial benchmark problem as well as the model of a reactor utilized in the emerging liquid organic hydrogen carrier (LOHC) technology.

Rockfall represents a sudden and highly destructive geological hazard, posing significant risks to mountainous communities and infrastructure. This study presents an integrated dynamic risk assessment for the Jiaohua perilous rock zone in Kaizhou District, Chongqing, China, by fusing multi-source data including field investigation, UAV photogrammetry, and 3D numerical simulation. Kinematic analysis identified a critical slope angle of 57° for rockfall initiation, enabling the classification of two primary susceptibility zones. High-precision 3D simulations using RAMMS: ROCKFALL were conducted on six identified hazardous rock masses (#WY1–#WY6). The simulations delineated two distinct rockfall mechanisms: #WY1–#WY3 sources generate high-energy, short-duration impacts, achieving kinetic energies up to 1.88 × 10⁴ kJ within 10–15 s, posing a direct threat to the residential area below. Conversely, rockfalls from #WY4–#WY6 involve longer travel paths with considerable energy attenuation, yet residual kinetic energy remains capable of causing zonal damage. The simulated kinetic energies were translated into quantitative impact force estimates, forming the basis for differentiated mitigation strategies. These include active reinforcement and high-strength interception for short-range, high-energy events, and multi-level buffering with trajectory control for long-runout cases. This integrated methodology offers a scientifically grounded framework for precise hazard prevention and serves as a valuable reference for rockfall risk management in analogous geological settings, particularly within the Three Gorges Reservoir area.

Traditional information security risk assessment (RA) methodologies and standards, adopted by information security management systems and frameworks as a foundation stone towards robust environments, face many difficulties in modern environments where the threat landscape changes rapidly and new vulnerabilities are being discovered. In order to overcome this problem, dynamic risk assessment (DRA) models have been proposed to continuously and dynamically assess risks to organisational operations in (near) real time. The aim of this work is to analyse the current state of DRA models that have been proposed for cybersecurity, through a systematic literature review. The screening process led us to study 50 DRA models, categorised based on the respective primary analysis methods they used. The study provides insights into the key characteristics of these models, including the maturity level of the examined models, the domain or application area in which these models flourish, and the information they utilise in order to produce results. The aim of this work is to answer critical research questions regarding the development of dynamic risk assessment methodologies and provide insights on the already developed methods as well as future research directions.

The current approach for patients with differentiated thyroid carcinoma should be individualized according to the risk of recurrence, and this stratification could be used to identify the risk of persistent/recurrent disease in three scenarios: preoperatively, immediately postoperatively, and during long-term follow-up. The initial risk of recurrence will tailor the management of the patient in the preoperative and immediate postoperative settings, while the dynamic risk, which considers the responses to treatment, could guide the decision-making process for remnant ablation and long-term management.This review provides a summary of the existing information regarding the dynamic risk of recurrence and recommended management for patients with differentiated thyroid cancer. The application of this approach is essential to avoid unnecessary treatments for most patients who will have a favorable prognosis. On the other hand, it allows specific therapeutic interventions for those patients at high risk of recurrence. In the future, analysis of tumor biology and prospective studies will surely improve the accuracy of recurrence risk prediction.

The operational risks of equipment in coalbed methane (CBM) gathering stations exhibit dynamic characteristics. To address this, a dynamic risk assessment method based on Dynamic Bayesian Networks (DBNs) is proposed for CBM station equipment. Additionally, a comprehensive safety management evaluation model is established for gathering station equipment. This approach enables accurate risk assessment and effective implementation of safety management in CBM gathering stations. This method primarily consists of three core components: risk factor identification, dynamic risk analysis, and comprehensive safety management evaluation. First, the Bow-tie model is applied to comprehensively identify risk factors associated with station equipment. Next, a DBN is constructed based on the identified risks, and Markov theory is employed to determine the state transition matrix. Finally, a Comprehensive Safety Management (CSM) evaluation model for gathering station equipment is established. The feasibility of the proposed method is validated through case study applications. The results indicate that during the operation of equipment at CBM gathering stations, priority should be given to strengthening maintenance for medium-hole and enhancing prevention and emergency measures for jet fires. Temperature-controlled spiral-wound heat exchangers, skid-mounted circulating pumps, and pipelines have been identified as critical factors affecting accident occurrence at CBM gathering stations. Enhanced daily inspection and maintenance of this equipment should be implemented. Furthermore, compared to other safety evaluation indicators, the Emergency Preparedness and Response indicator has the most significant impact on the operational safety of CBM gathering station equipment. It requires high-priority attention, thorough implementation of relevant measures, and continuous improvement through targeted actions.

Because deep foundation pits and tunnels are deformation-sensitive structures, the safety of these projects is generally affected by coupled risks. In deep foundation pit construction, if the existing tunnel structure adjacent to the deposit is damaged, it can produce a severe group disaster. It is necessary to identify an efficient risk analysis model to study the dynamic coupled risk of deep foundation pit projects adjacent to existing underpass tunnels and to analyze the risk evolution law to achieve effective real-time safety control. This study proposes a coupled risk analysis model using the N–K model and dynamic Bayesian network to construct deep foundation pits in adjacent existing underpass tunnels. The model is predicated on association rules to explore the interrelationship between risk factors to build a dynamic Bayesian network structure. In addition, the N–K model is utilized to quantify coupled risks under such complex working conditions and to optimize the dynamic Bayesian network structure. The developed model clarifies the risk coupling mechanism of deep foundation pit construction adjacent to an existing underpass tunnel, finds the critical points in the risk transfer process, and conducts dynamic risk prediction and accident causation diagnosis for the coupled risk to realize the dynamic control of the coupled risk in the adjacent existing underpass tunnel construction. Taking the Nanning underground comprehensive utilization project as an example, the validity and applicability of the proposed approach were tested. The results showed that the model is feasible and has application potential, providing effective decision support for safety control while constructing deep foundation pits adjacent to existing tunnels.

After the Fukushima Daiichi Accident, the safety features such as accident tolerant fuel (ATF) and diverse and flexible coping strategies (FLEX) for existing nuclear fleets are being investigated by the US Department of Energy under the Light Water Reactor Sustainability Program. This research is being conducted to quantify the risk-benefit of these safety features. Dynamic probabilistic risk assessment (DPRA)-based response-surface approach has been presented to quantify the FLEX and ATF benefits by estimating the risk associated with each option. ATFs with multilayered silicon carbide (SiC), iron-chromium-aluminum, and chromium-coated zirconium cladding were considered in this study. While these ATF candidates perform better than the current zirconium cladding (Zr), they may introduce additional failure modes in some operating conditions. The fuel failure analysis modules (FAMs) were developed to investigate ATF performance. The dynamic risk assessments were performed using RAVEN, a DPRA tool, coupled with RELAP5 and FAMs. A cumulative distribution function-based index provided a mean of comparing the benefits of safety enhancements. For medium break loss of coolant accidents, FLEX operational timing window for each fuel type was estimated. Among these ATF candidates, SiC-type ATF was the most beneficial candidate for an increased safety margin than Zr-based fuel and was found to complement FLEX strategies in terms of risk and coping time.

Modern medical devices connected to public and private networks require additional layers of communication and management to effectively and securely treat remote patients. Wearable medical devices, for example, can detect position, movement, and vital signs; such data help improve the quality of care for patients, even when they are not close to a medical doctor or caregiver. In healthcare environments, these devices are called Medical Internet-of-Things (MIoT), which have security as a critical requirement. To protect users, traditional risk assessment (RA) methods can be periodically carried out to identify potential security risks. However, such methods are not suitable to manage sophisticated cyber-attacks happening in near real-time. That is the reason why dynamic RA (DRA) approaches are emerging to tackle the inherent risks to patients employing MIoT as wearable devices. This paper presents a systematic literature review of RA in MIoT that analyses the current trends and existing approaches in this field. From our review, we first observe the significant ways to mitigate the impact of unauthorised intrusions and protect end-users from the leakage of personal data and ensure uninterrupted device usage. Second, we identify the important research directions for DRA that must address the challenges posed by dynamic infrastructures and uncertain attack surfaces in order to better protect users and thwart cyber-attacks before they harm personal (e.g., patients’ home) and institutional (e.g., hospital or health clinic) networks.

Urban flood disasters demand dynamic assessment of population risk, yet most evacuation models fail to capture the multifaceted, time‐varying nature of such events. To address this gap, this study develops the modular urban flood‐evacuation (MUFE) framework, an integrated approach to dynamic urban flood risk assessment. MUFE explicitly models pedestrian and vehicle behaviors under inundation and their interactions with evolving hydrodynamic conditions. The framework is tailored for island cities facing compounded threats from storm surges and sea‐level rise, and is demonstrated on Haidian Island, Haikou, China. The research couples multi‐scenario rainfall‐tide hydrodynamic simulations (via InfoWorks ICM) with an agent‐based model implemented in the open‐source Mesa framework. Mesa enables the explicit representation of pedestrian and vehicle agents operating under perceive‐decide‐act cycles and local information constraints. This integration enables joint analysis of hydrodynamic hazard fields—shaped by dynamic tidal fluctuations—and the behavioral responses of pedestrians and vehicles. The framework is modular, allowing alternative hydrodynamic or data‐driven flood models to be coupled for transparent comparison. Simulation results show that pedestrian hazard exposure varies markedly across space and time, shaped by adaptive evacuation behavior, shelter availability, and tide dynamics. Small vehicles are more susceptible to instability—even under shallow depths—while larger vehicles are more resilient but not immune under severe conditions. These patterns underscore the need for differentiated evacuation planning and targeted infrastructure resilience measures in island cities.