Dynamic Risk Assessment Research Articles

Dynamic assessment method for human factor risk of manned deep submergence operation system based on SPAR-H and SD

Due to the complex and changeable contextual environment, the human factor risk of the manned deep submergence operating system shows dynamic characteristics. Compared with the traditional static human reliability analysis (HRA) method, dynamic HRA method can better simulate the dynamic characteristics and the nonlinear information feedback mechanism of the operating system. This paper proposed a dynamic risk assessment model based on System Dynamics and SPAR-H. The cognitive load was introduced into the Performance Shaping Factor (PSF) network to make it more suitable for the task and environment of manned deep submergence. In addition, in order to measure the compensation effect of PSF on the work efficiency of oceanauts, eight compensation functions were constructed between PSFs and work efficiency. Finally, key risk tasks and sensitive PSFs were identified. Taking the 12 h manned deep diving mission as an example, the dynamic quantification of human error probability and work efficiency of oceanauts was simulated. The results indicate that the dynamic simulation results of the constructed risk assessment model are consistent with the actual situation, and can effectively predict the changes of dynamic risk.

Flammable gas leakage risk assessment for methanol to hydrogen refueling stations and liquid hydrogen refueling stations

Due to its excellent combustion properties and environmental performance, the development of hydrogen energy has become an international trend in recent years. However, hydrogen refueling stations, which are the infrastructure for hydrogen energy development, contain flammable gases such as hydrogen and methanol, which are highly susceptible to explosion and may threaten people's health and property safety. In this paper, a dynamic risk assessment method based on Dynamic Bayesian Network (DBN) is established for combustible gas leakage accidents in hydrogen refueling stations, and the methanol to hydrogen refueling stations and liquid hydrogen refueling stations are taken as research objects to quantitatively analyze their leakage risks and accident consequences. It is found that the leakage accident probability and consequences of methanol to hydrogen refueling stations show periodicity with a period of three months, while there is no periodicity for liquid hydrogen refueling stations. The probabilities of leakage accident and serious consequences in methanol to hydrogen refueling stations show a sudden increase within a cycle, on the other hand, those in liquid hydrogen refueling stations tend to increase linearly over three years with time. In addition, based on the diagnostic reasoning function of DBN, this paper identifies the two most critical hazard sources for flammable gas leakage accident in hydrogen refueling stations: human factors and corrosion factors, and provides guidance for relevant enterprises to reduce the risk.

A Three-Stage Dynamic Risk Model for Metro Shield Tunnel Construction

The complex construction process of the metro shield method often leads to safety accidents. The various construction stages of shield tunnel construction comprise different construction activities and are accompanied by different safety risk factors. However, traditional risk assessment often evaluates the risk factors as a whole before shield tunnel construction and does not evaluate the risk factors dynamically by construction stages and by construction activities. To fill this gap, this paper aims to construct a dynamic Bayesian-based safety risk assessment model for shield tunnel construction from the perspective of changing construction stages and activities. First, safety risk factors were identified using the work breakdown structure-risk breakdown structure (WBS-RBS) method. Then, a three-stage dynamic assessment model of safety risks was constructed to depict the shield launch, shield tunnel, and shield reach. The dynamic Bayesian network (DBN) was improved to address the model with triangular fuzzy numbers and the leaky noisy-or-gate extension model. Finally, a case study was conducted. The model proposed in this paper is able to reveal the dynamic evolution of safety risks triggered by different construction activities. It offers a new simulated model for the prevention of safety accidents in the construction of metro shields.

Instability Risk Assessment for Deep Excavation of Soil–Rock Combinations Containing Groundwater

Dynamic risk assessment is a pivotal tool for enhancing construction safety and minimizing the potential for partial failure during deep and extensive excavation projects. To enhance the efficacy of dynamic risk assessment in deep excavation, this study introduces a novel risk assessment model designed to evaluate instability risk in extensive excavations. It comprises a risk factor selection model for identifying the most pertinent factors and an instability risk assessment model for gauging the extent of instability risk throughout the construction process. Then, the model was deployed in the construction of Anshan Road Station of the Qingdao Metro. To pinpoint the factors with the most pronounced impact on excavation instability, a risk factor selection model was employed, yielding a comprehensive risk evaluation index system. For real-time assessment of risk, the monitoring data were used as the primary source of evidence. A comprehensive comparative analysis involving actual data and predictions from conventional RBF and back propagation neural networks was performed. The outcome of this analysis underscored the superior accuracy and predictive capabilities of the assessment model. The instability risk assessment model offers the ability to dynamically evaluate the instability risk associated with extensive excavations featuring a combination of soil and rock. It can serve as a valuable methodological tool, furnishing essential support for the systematic prevention and mitigation of excavation instability disasters.

Dynamic risk assessment in patients with differentiated thyroid cancer.

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.

Reclassification of Ascertain (ASTX727-02) Myelodysplastic Syndrome (MDS) Patients: Outcomes Including Clinical Response, Overall Survival (OS), and Leukemia Free Survival (LFS) Based on IPSS-R and IPSS-M Scoring Systems

Background: Oral decitabine/cedazuridine (ASTX727) is a fixed dose combination of decitabine (35 mg) and the cytidine deaminase inhibitor cedazuridine (100 mg) given once daily X 5 days on a 28-day cycle producing pharmacokinetic (PK) exposure equivalent to the standard intravenous (IV) decitabine regimen of 20 mg/m 2 daily X 5 days on a 28-day cycle. This was demonstrated in the pivotal ASCERTAIN study (Garcia-Manero, et al, ASH 2019) as it provided a PK bridge to existing decitabine data and demonstrated median OS was 31.7 mo. (Savona,et al. Intl. MDSF International Congress on MDS, 2021). Subjects were initially classified by the International Prognosis Scoring System (IPSS) for historical reasons, however IPSS-R and IPSS-M are enhancements to IPSS that provide dynamic risk assessment for predicting clinical outcomes in MDS. Here, the objective was to re-classify the MDS subjects enrolled on the ASCERTAIN study by IPSS-R and IPSS-M and measure the impact of informing patient outcomes based on re-calculated risk assessment. Methods: One hundred thirty-three subjects with MDS/CMML (chronic myelomonocytic leukemia) were enrolled in ASCERTAIN and were randomly assigned either IV decitabine for cycle 1 and oral decitabine/cedazuridine for cycle 2 or the opposite treatment sequence. All subjects continuing beyond cycle 2 received oral decitabine/cedazuridine for all subsequent cycles until treatment discontinuation for disease progression, toxicity, patient's decision, or HSCT. Whole blood collected prior to treatment was used for DNA isolation and molecular abnormalities identified using next-generation sequencing (NGS) hematologic malignancy panel of 179 genes including all genes commonly mutated in MDS. In the initial analysis of clinical outcomes, subjects were classified by IPSS with response assessment by IWG 2006 IPSS low and Int-1 risk levels were categorized as lower-risk MDS (LR-MDS), whereas IPSS Int-2 and high-risk categories were categorized as higher-risk MDS (HR-MDS). Subjects with sufficient data based on (e.g., available heme parameters, cytogenetics, NGS etc.) were reclassified by IPSS-R and IPSS-M. Subjects with IPSS-R score of ≤3.5 or IPSS-M of either very low, low, or moderate low were categorized as LR-MDS. Similarly subjects with IPSS-R score of >3.5 or an IPSS-M categorization of either moderate high, high, or very high were categorized as HR-MDS. Reclassified subjects were reassessed for CR (Complete Response), OS, and LFS and Harrell's concordance index (c-index) was used to describe the level of agreement between each scale and outcomes. Results: Based on the available data, the number of MDS subjects in the different risk classifications were the following: IPSS: 117, IPSS-R: 104, and IPSS-M: 105. Thirteen and 12 subjects on the IPSS could not be reclassified in the IPSS-R and IPSS-M, respectively, including 5 Int-1 and 2 low-risk MDS cases. CMML subjects were excluded from these analyses. Re-classification generally resulted in the upgrade of the subjects from LR-MDS to HR-MDS (Fig. 1). Thirty-one (26.5%) subjects from IPSS were reallocated in the IPSS-R to different risk categories; 3 (9.7%) were downgraded and 28 (90.3%) were upgraded. Similarly on reclassification with IPSS-M 34 (32.4%) of the patients reclassified; 5 (14.8%) were downgraded and 29 (85.3%) were upgraded. For IPSS LR-MDS, 23.2% of patients achieved CR, and 22.9% in the IPSS HR-MDS. Similarly, 21.6% patients achieved CR in the IPSS-R LR-MDS, and 23.9% in the IPSS-R HR-MDS; 26.3% patients achieved CR in the IPSS-M LR-MDS, and 20.9% in the IPSS-M HR-MDS. The c-index for the IPSS was .64 (OS) and .67 (LFS), IPSS-R was .70 (OS) and .71 (LFS), and .75 (OS) and .78 (LFS) for the IPSS-M (Table1). Conclusion: Reclassification from IPSS to IPSS-R or IPSS-M upgraded multiple subjects from a LR to a HR category, describing the ASCERTAIN patient population as a majority higher risk population with worse prognosis than previously assumed based on the IPSS. The efficacy as measured by the CR rates did not change when the LR and HR categories were defined by the different risk stratification systems. In contrast, the c-index improves with migration from IPSS to IPSS-R to IPSS-M, indicating an increased discriminatory ability of IPSS-M score in comparison to IPSS and IPSS-R, to predict patient outcomes.

Predicting the Clinical Course of Treatment with Midostaurin in Patients with Advanced Systemic Mastocytosis

Introduction: Advanced systemic mastocytosis (AdvSM) is characterized by presence of the KIT D816V mutation and variably other somatic mutations in the mast cell but also non-mast cell lineages, e.g. monocytes or eosinophils, in ≥80-90% of patients. Midostaurin is an approved multikinase/KIT inhibitor for treatment of AdvSM. The intricate landscape in assessment of response, resistance or progression, the occurrence of adverse events with its impact on dose modification and the recent approval of the specific KIT D816V inhibitor avapritinib contribute to complex decision-making. We here sought to characterize the most valuable baseline and on-treatment parameters for prediction of response and survival in midostaurin-treated patients with AdvSM. Methods: On basis of the ´German Registry on Disorders of Eosinophils and Mast Cells' (GREM), this retrospective-prospective analysis included 79 patients with AdvSM. Treatment was initiated between 2009 and 2022 accumulating into a 239 patient-years overall follow-up period. In addition to common serological, morphological, and molecular response parameters, we specifically examined the recurrent observation of an early flare-up in alkaline phosphatase (AP) levels, which was defined as a ≥25% AP increase within three months after treatment initiation, followed by a subsequent decline below baseline within six months. Results: Midostaurin was used in first-line (1L) or second and further lines (2L+; median 1 prior treatment line, range 0-4) in 63 (80%) and 16 (20%) patients, respectively. In 1L/2L+, median overall survival (OS) was 2.6/2.4 years. At baseline, the presence of additional somatic mutations in SRSF2 (n=36, 46%, HR 2.0 [1.2-3.6], P=0.014), ASXL1 (n=15, 19%, HR 2.0 [1.0-3.9], P=0.043), and EZH2 (n=6, 8%, HR 4.3 [1.7-11.3], P=0.003) significantly stratified patients according to their hazard ratio-weighted risk (median OS, 5.8 vs. 2.9 vs. 1.0 years; P<0.001). In 25 evaluable patients, the variant allele frequency in 72 additional somatic mutations remained stable (mean change 8%, absolute change >5% in 16/72 (22%) mutations). New mutations were identified in 9/25 (36%) patients during follow-up (Figure). Durable responses (complete, partial, clinical improvement) according to modified Valent criteria, IWG-MRT-ECNM criteria or Pure Pathological Response (PPR) criteria over a median duration between 12 and 24 months were achieved in 60%, 29% and 13% of patients, respectively. Within the first 12 months, duration of response was associated with an OS benefit ( P<0.001). An AP flare-up was observed in 22/79 (28%) patients, was paralleled by contemporaneous decrease of serum tryptase (median -35%, range -82 - 191%) and resulted into improved OS (median 5.8 vs. 2.2 years, P=0.007). Multivariable analysis identified the AP flare-up ( P=0.029), a ≥25% reduction of bone marrow (BM) mast cell (MC) infiltration ( P=0.046) and a ≥25% reduction of the KIT D816V expressed allele burden (EAB; P=0.034) at any time within the first 6 months as independent markers for improved OS (Table). Dynamic risk assessment of the independent parameters during follow-up allowed to abrogate a negative baseline risk-categorization. The initial dose was 200 mg/d (100 mg BID, including patients with a prespecified rapid dose escalation), 150 mg/d or 100 mg/d in 63/79 (80%), 2/79 (3%) and 14/79 (18%) patients, respectively. In the combined 150 mg and 100 mg cohorts, a dose increase to 200 mg/day was achieved in 9/16 (56%) patients. The 200 mg could be maintained at month 3, 6, 12, 24, and 36 in 50/63 (79%), 30/48 (63%), 23/34 (68%), 15/22 (68%) and 9/9 (100%) patients, respectively. Overall, 96 adverse events (AE) led to dose-adjustment (including temporary or permanent discontinuation). Hematologic and nonhematologic toxicities accounted for 24 (25%) and 36 (38%) of AEs with neutropenia (n=8, 8%) and nausea/emesis (n=22, 22%) being the most common AEs leading to dose-adjustment. Of note, the various dose levels had no impact on response or survival. Conclusions: Baseline mutations in SRSF2, ASXL1 and EZH2 conferred resistance to midostaurin. The various dose levels had no impact on response or survival. The on-treatment parameters AP flare-up, and a ≥25% reduction of BM MC infiltration and of KIT D816V EAB abrogated a negative baseline risk-categorization. No new safety signals occurred.

Risk-based path planning for preventing collisions and groundings of maritime autonomous surface ships

In this paper, we propose and demonstrate a method for risk-based path planning, generating risk-acceptable trajectories for an autonomous surface ship. This objective is achieved in two steps. The first step uses a dynamic probabilistic risk assessment method called K-shortest-paths Probabilistic Risk Assessment (KPRA), alongside risk models and dynamic system simulation, whose output is a list of paths ranked with respect to risk. Multiple risk influencing factors and accident types may be considered in this step. In this work, collision and powered grounding accidents, and their risk-influencing factors and indicators, are considered. The second step is to perform risk evaluation on the ranked scenarios, comparing them to predefined risk acceptance criteria and defining which scenarios have acceptable risk. Evaluating multiple paths allows operators to choose how strict the autonomous ship’s decision-making should be regarding risk, avoiding the problem of minimization algorithms being too conservative in terms of risk. One of the risk-acceptable paths is then chosen as a local plan to be acted by the autonomous ship. This paper has two main scientific contributions. First, an online Bayesian belief network risk model for navigation accident probability is designed and integrated to improve KPRA. Second, we propose a modified guidance, navigation, and control framework for autonomous ships, to include risk-based planning. We implement the modified framework into a real autonomous surface vehicle (ASV) named Grethe and demonstrate the method’s capabilities in field trials, where the ASV uses the method to generate local plans that avoid possible collision and grounding accidents.

Dynamic risk analysis of evolving scenarios in oil and gas separator

Risk assessment models in the oil and gas (O&G) industry has begun to transform from time-static models like fault tree, event tree or bowtie to dynamic risk assessment (DRA) models, as the latter is able to better capture the real-time-dependent risk behavior of safety barriers. Currently, most existing works on DRA in O&G industry mainly rely on event-driven data, such as failures or accident data from similar systems for risk updating. These data can be collected only when accidents or near-misses have occurred, which limits the prediction capability of the DRA model. To address this drawback, a DRA model is developed in this paper that uses condition-monitoring data for risk updating. A significant advantage of using condition-monitoring data is that the risk can be updated before accidents or failures occur, giving the operation team more time to take preventive actions.The developed approach comprises of an offline and an online phase. In the offline phase, a conventional risk assessment is performed based on fault tree models to calculate the risks at the beginning of the operation. Through the offline analysis, we can also identify the most critical safety barriers by examining their contribution to the risk indexes. Critical safety barriers are selected for condition-monitoring. In the online phase, the condition-monitoring data are used to update the reliability of the safety barriers in real-time based on a sequential Markov Chain Monte Carlo (MCMC) algorithm using a Bayesian framework. The updated reliabilities of the safety barriers are, then, used in the offline risk assessment model for a DRA.The developed approach is applied for a DRA of liquid carryover from an oil and gas separator to downstream equipment, using real-time data from the separator's safety barriers. The results show that the developed method provides a more accurate representation of the system's performance, enabling early detection of potential failures and reducing uncertainty in risk estimates. The proposed DRA model demonstrated its effectiveness in predicting failures of safety barriers in real-time, giving operational teams ample opportunity to take corrective action. This leads to improved decision-making in the O&G industry, enabling timely response to changes in risk levels. The use of condition-monitoring data enhances the accuracy of risk estimations, representing a crucial advance in DRA applications in the O&G sector.

SEAG: A novel dynamic security risk assessment method for industrial control systems with consideration of social engineering

The development of information and communication technology and its wide application in industrial control systems (ICSs) has brought a growing number of security risks to ICSs. Quantifying and dynamically assessing the security risks of ICSs is of great significance to protect ICSs from cyber attacks. Current risk assessment methods, however, do not take into account social engineering (SE) attacks and the potential cyber-to-physical risks associated with cyber attacks. To address these issues, we propose a novel method for the dynamic security risk assessment of ICSs, called SEAG. Specifically, the scheme first extends and modifies three metrics in the common vulnerability scoring system to four metrics for objectively calculating the exploit probability of SE. Then, we construct the attack graph by relying on the knowledge graph that integrates three kinds of knowledge including SE knowledge, common vulnerability knowledge, and control system knowledge. In addition, we combine real time attack data that causes system performance loss with the industrial protocol function code attack detected by the intrusion detection system to accurately quantify the potential cyber-to-physical risks associated with cyber attacks. This method allows us to dynamically assess the security risks of ICSs in real time. Finally, the method is verified by one simulation testbed, which shows the effectiveness and accuracy of the proposed method for dynamic quantitative evaluating security risks of ICSs.

Dynamic risk assessment of chemical process systems using the System-Theoretic accident model and process approach (STAMP) in combination with cascading failure propagation model (CFPM)

To maintain continuous production, chemical plant operators may ignore faults or handle faults online rather than shutting down process systems. However, interaction and interdependence links between components in a digitalized process system are substantial. Thus, faults will be propagated to downstream nodes, potentially leading to risk accumulation and major accidents. However, limited attention has been paid to this type of risk. To model the risk accumulation process, a dynamic risk assessment method is proposed by integrating the system-theoretic accident model and process approach (STAMP) and the cascading failure propagation model (CFPM). Firstly, STAMP is used to model and analyze the system safety of a process system. Two CFPMs are then proposed to measure risk accumulation under two different engineering situations. The proposed method is applied to the Chevron Richmond refinery crude unit and its associated upstream process. The results show that the proposed approach can effectively quantify the process of risk accumulation. This method can generate a real-time dynamic risk profile to support auxiliary decision-making.

Spatio-temporal variation and dynamic risk assessment of drought and flood disaster (DFD) in China

It is important to understand the variations of Drought-Flood-Disaster (DFD) from the past to future for disaster management decision-making under the backdrop of global warming. This study explored the spatio-temporal variations of DFD risks in China from the past (2000–2020) to future (2020–2100) based on the natural and socio-economic datasets. The main findings include: (1) The spatial pattern of disaster vulnerability decreased from 2000 to 2100. The overall vulnerability distribution showed a spatial pattern of high east and low west, and its eastern distribution presented high north and low south. (2) The drought hazard danger degree showed a trend of decreasing first and then increasing with obvious spatial heterogeneity. The flood hazard danger degree had a decreasing spatial distribution from southeast to northwest, with an overall gradually increasing trend. (3) The DFD risks showed a spatial pattern of high east and low west, and were more closely related to disaster vulnerability. Overall, the flood and drought risks had good consistency at the highest level (VI), mainly distributed in the North China Plain, Northeast Plain, eastern Sichuan, and southern Guangdong. This level means that the most severe damage to life and property will occur in these regions. (4) The DFD showed the direction of “north(east)-south(west)”, and the main axis of the ellipse moved north eventually. The DFD hot spots were mainly distributed in North China and the middle and lower reaches of the Yangtze River. The DFD cold spots were mainly distributed in the southwest, Qinghai-Tibet region, and Gansu province.

Life-cycle dynamic analysis and probabilistic seismic risk assessment of cross-fault hydraulic tunnels considering AAR-induced deterioration

The alkali-aggregate reaction (AAR) triggered the deterioration of the machinistic property of concrete materials is among the major negative factor that results in the reduction of structural serviceability and performance for concrete structures, especially for those hydraulic engineering in contact with water for service life-cycle. Hydraulic tunnels, being a vital component of hydraulic engineering, are frequently employed in various lifeline projects around the world. Once the AAR effect renders the hydraulic tunnel inoperable, the government and society will suffer enormous economic losses, delaying the achievement of carbon neutrality. However, current specification for seismic design does not account for the degradation of structural performance parameters induced by AAR effects, which cannot fully reveal the earthquake performance of AAR-affected tunnels during use. Having realized this challenge, this work presents a mechanical model of the AAR effect integrating the concrete elastic-plastic damage model to conduct the inelastic dynamic response and random earthquake risk assessment of AAR-affected cross-fault hydraulic tunnels with design life-cycle. To this end, a study of the AAR-affected hydraulic tunnels, which consider the fluid-structure-surrounding rock coupling systems throughout the service period, is undertaken to associate the structural reaction with the predefined evaluation index directly. Subsequently, The nonlinear dynamic assessment of cross-fault hydraulic tunnel is conducted by using increased dynamic method to generate several data. Besides, this research constructs the three-dimensional (3D) time-dependent fragility surfaces considering the AAR effect and seismic intensity level. The results of this study underscore this point that the AAR effect possesses the capability to aggravate the response of relative displacement and extend the cumulative damage cracking area of the cross-fault hydraulic tunnels (CFHTs) with the increase in service time. Similarly, the 3D time-varying fragility surfaces show that the longer the service time of AAR-affected cross-fault hydraulic tunnels, the more likely they are to collapse. The above phenomena demonstrate that the AAR effect can be recommended for the utilization in estimating the likelihood of the erosion of the building during service life-cycle and future seismic design code.

Dynamic risk assessment method for road transport of hazardous chemicals based on BP neural network algorithm

To achieve a dynamic comprehensive risk assessment of road transport of hazardous chemicals, this paper first collected 213 road transport accidents involving hazardous chemicals that occurred between 2015 and 2020 to build a case library. Then, it established a risk assessment system (including 9 primary indicators and 28 secondary indicators) based on the frequencies of reasons for accident cases and explored the quantification methods for indicators based on their definitions. Subsequently, this paper calculated the weights of indicators based on the backpropagation neural network algorithm and the data in the case library and formulated a model for dynamic risk assessment of road transport of hazardous chemicals. The model takes into account the impact of comprehensive indicators such as drivers, vehicles, goods, roads, and the environment. The accuracy of the model calculation results is 93%. Eventually, the study verified the model using simulation data in real scenarios. The validation results show that the model could achieve a dynamic comprehensive assessment of risks in road transport of hazardous chemicals.

Static and dynamic fuzzy assessment methods for the collapse risk of mountain tunnel construction

The static risk assessment method has difficulty tracking variations of the risk level, which is not conducive to the dynamic control of construction. Tunnel collapse during the construction of mountain tunnels has a dynamic evolution law and contains great risk of harm, and the corresponding dynamic risk assessment is extremely important. This study proposes a static and dynamic fuzzy uncertainty assessment method for the collapse risk of mountain tunnels. First, 150 tunnel collapse accidents were investigated and analysed, and the static and dynamic risk assessment index system of mountain tunnel construction collapse was established. Second, the DEMATEL method is processed by applying fuzzy logic, the subjective weight of each index is calculated, and the interaction between the indexes is analysed. Finally, the traditional VIKOR method is improved upon, and the weight of each assessment index is coupled and analysed. A static and dynamic uncertainty assessment model of the construction collapse risk of multiple construction sections is constructed. This method has been successfully applied to the risk assessment of tunnel collapse, and the assessment results are consistent with the actual construction situation. This study provides a new method for the static and dynamic assessment of mountain tunnel collapse risk.

Machine Learning Provides Individualized Prediction of Outcomes after First Complete Remission in Adult AML Patients - Results from the Harmony Platform

Background: Acute myeloid leukemia (AML) is a heterogeneous disease with high disease-related mortality and allogeneic hematopoietic stem cell transplantation (allo-HSCT) remains the only curative option for a large proportion of patients. However, allo-HSCT has a significant transplant-related mortality (TRM), so a careful evaluation between risk of disease relapse and TRM is needed for the challenging decision of whom to transplant in first complete remission (CR1). The European LeukemiaNet (ELN) provides a 3-tier static risk classification which is already a useful guidance, although dynamic risk assessment using measurable residual disease (MRD) information should also be taken into account. However, the intermediate-risk group is characterized by a wide heterogeneity and there are also some favorable-risk patients who require allo-HSCT because of early relapses after CR1. Therefore, more precise prediction tools are warranted to provide additional information valuable for clinical decision-making. Aims: To develop and validate a model that provides individualized outcome estimations in adult AML patients achieving CR1 with intensive treatment approaches. Methods: From the HARMONY Platform including 7467 AML patients at the time of the analysis, we selected patients aged 18-70 years old who received intensive treatment, achieved CR and were not consolidated with allo-HSCT in CR1. Patients who were refractory to first line chemotherapy or who had a relapse-free survival (RFS) of less than two months after achieving CR were excluded. A Bayesian Additive Regression Trees (BART) nonparametric machine learning model was used to predict the individualized likelihood of RFS, cumulative incidence of relapse (CIR) and overall survival (OS) in 1863 AML patients who were also selected based on the availability of comprehensive mutational information by next-generation sequencing analysis. Genomic aberrations that were present in at least 10 patients were included in the model. In order to test the accuracy of the model, a 10-fold cross-validation was applied using the area under receiver operating curve (AUC) at predefined time points (1, 2, 3, 4 and 5 years). Subsequently, the model was validated using an external publicly available database (Tazi et al, Nat. Commun. 2022) which included 722 patients based on identical inclusion criteria. Results: The study population of 1863 adult AML patients in CR1 included 51.2% of males and median age was 50 years. Regarding ELN2022 classification, 52% of patients were classified as favorable, 30% as intermediate and 18% as adverse risk. Most frequently mutated genes were NPM1 (35.9%), DNMT3A (25.6%) and NRAS (21.6%). The HARMONY model was able to estimate individualized likelihood of RFS, CIR and OS for all patients, providing confidence intervals for all estimations. Moreover, we could predict outcomes with increased accuracy over ELN2022 at all predefined time points. At 5 years, the AUC value was significantly better for the HARMONY model than ELN2022 in predicting RFS (0.729 vs 0.679, p=0.003), CIR (0.729 vs 0.680, p=0.002) and OS (0.733 vs 0.675, p<0.001) ( figure 1A). Validation in an external independent AML cohort confirmed superiority of the HARMONY model in predicting RFS (5-year AUC 0.714 vs 0.609, p <0.001), CIR (0.713 vs 0.610, p <0.001) and OS (0.737 vs 0.638, p <0.001) as compared to the ELN2022 risk stratification ( figure 1B), despite the fact that there was a different distribution of risk categories (28% favorable, 45% intermediate and 27% adverse risk). Conclusions: Analysis of large well-defined AML cohorts allows the development of increasingly precise predictive models for clinically relevant scenarios. The HARMONY machine learning model provides individualized outcome prediction for patients aged 18-70 years in CR1 consolidated without allo-HSCT and might be used in the future for clinical decision-making. The model can be accessed online via an interactive web calculator () and, while it has been validated using a large independent AML cohort, future studies are required focusing on prospective validation. Furthermore, the incorporation of additional variables such as MRD and the inclusion of patients treated with targeted therapies and non-intensive approaches will provide more accurate risk predictions.

Glofitamab (Glofit) Plus R-CHOP Has a Favorable Safety Profile and Induces High Response Rates in Patients with Previously Untreated (1L) Large B-Cell Lymphoma (LBCL) Defined As High Risk By Circulating Tumor DNA (ctDNA) Dynamics: Preliminary Safety and Efficacy Results

Background: Up to one-third of patients (pts) with 1L LBCL treated with R-CHOP do not achieve long-term remission or cure depending on disease stage. Outcome prediction beyond the International Prognostic Index (IPI) score is difficult due to the lack of reproducibility of common biomarkers. ctDNA offers potential as a sensitive biomarker to identify newly diagnosed pts with suboptimal chemoimmunotherapy responses. Reduction of ctDNA levels after 1-2 therapy cycles in pts with 1L diffuse LBCL (DLBCL) is associated with improved survival (Kurtz et al. J Clin Oncol 2018). Whether early intervention can improve outcomes of pts with elevated on-treatment ctDNA levels is unknown. Glofitamab, a CD20xCD3 bispecific antibody, engages and redirects T cells to eliminate B cells and has received FDA and EMA approval as monotherapy for relapsed/refractory DLBCL after ≥2 prior lines of therapy. Glofit + R-CHOP has favorable safety and efficacy in 1L DLBCL (Topp et al. ASH 2022). We report initial safety and efficacy from a Phase II open-label prospective trial (NCT04980222) evaluating glofit + R-CHOP in pts with 1L LBCL defined as high risk by ctDNA dynamics. Methods: Eligible pts had 1L CD20+ LBCL, Eastern Cooperative Oncology Group performance status 0-2, and IPI 0-5 (IPI 0 for pts with bulky disease; IPI 2-5 for USA pts). During ctDNA screening, pts received R-CHOP on Day (D)1 of Cycle (C)1 and C2 (21-day cycles). In pts defined as high risk by ctDNA (<2-log [100×-fold] reduction in plasma ctDNA after 1 R-CHOP cycle), R-CHOP was continued to C6, and glofitamab was administered as step-up dosing in C3 (D8, 2.5mg; D15, 10mg), at target dose (30mg) on D8 of C4-6, and on D1 of C7-10 (21-day cycles). Hospitalization for the first dose of glofitamab to assess cytokine release syndrome (CRS) was at the investigator's discretion; granulocyte colony-stimulating factors were advised for neutropenia prophylaxis. Real-time ctDNA analysis was performed centrally (AVENIO Oncology Assay for Non-Hodgkin's Lymphoma; Stokowski et al. ASH 2022). The primary endpoint was complete response (CR) at end of treatment (EOT). Responses were investigator-assessed after C2 and at EOT by positron emission tomography using Lugano criteria (Cheson et al. J Clin Oncol 2014). CRS was graded by ASTCT criteria (Lee et al. Biol Blood Marrow Transplant 2019); other adverse events (AEs) were assessed by CTCAE v5.0. Results: At data cut-off (May 25, 2023), of 121 pts screened, 24 (19.8%) were defined as high risk by ctDNA (low risk, 47 [38.8%] pts; missing/not evaluable, 49 [40.5%] pts) and received ≥1 dose of study drug from C3D1 (safety population). Of the 24 high-risk pts, 15 reached the EOT assessment (14 pts completed treatment; 1 pt discontinued due to progressive disease [PD]), and 9 pts were ongoing on treatment. Of 24 pts, 2 pts had double-hit and 4 pts had triple-hit cytogenetics; median age was 61.5 years (range: 39-77); 15 (62.5%) pts had IPI 1-2 (IPI 1: 12.5%, IPI 2: 50.0%) and 9 (37.5%) pts had IPI 3-5 (IPI 3: 25.0%, IPI 4: 8.3%, IPI 5: 4.2%). In the safety population, 20 (83.3%) pts had AEs of any Grade (Gr); 15 (62.5%) pts had Gr 3/4 AEs, including neutropenia (11 [45.8%] pts); febrile neutropenia was not observed ( Table). The most common (>20%) treatment-emergent AEs (any Gr) were neutropenia (13 [54.2%] pts) and diarrhea (6 [25.0%] pts). No Gr 5 AEs were reported. Nine (37.5%) pts experienced 17 serious AEs (14 glofitamab-related) including 7 CRS events.Gr ≥3 glofitamab-related AEs occurred in 8 (33.3%) pts. CRS events occurred in 5 (20.8%) pts: 4 (16.7%) pts had Gr 1 fever, 1 (4.2%) pt had Gr 2 fever and hypotension. CRS events occurred early and resolved at data cut-off; treatment (tocilizumab and dexamethasone) was administered for the Gr 2 CRS event only. One (4.2%) pt had a non-immunotoxic Gr 3 neurologic AE (presyncope, glofitamab-unrelated) that resolved by data cut-off. One pt died due to PD during treatment follow-up after discontinuing glofitamab. The interim CR rate was 46.7% (95% confidence interval [CI]: 21.3-73.4); CR at EOT was 80.0% (95% CI: 51.9-95.7) and the interim and EOT overall response rate were both 93.3% (95% CI: 68.1-99.8) ( Figure) . Conclusions: Glofit + R-CHOP in 1L LBCL defined as high risk by ctDNA, had a manageable safety profile and induced high response rates at the interim analysis and at EOT. Dynamic on-treatment risk assessment using ctDNA offers potential to identify high-risk pts with LBCL, independent of baseline characteristics.

Dynamic risk assessment methodology of operation process for deepwater oil and gas equipment

Dynamic risk assessment methodology of operation process for deepwater oil and gas equipment

Dynamic probabilistic risk assessment with K-shortest-paths planning for generating discrete dynamic event trees

Conventional risk assessment methods are often not well suited for fast-changing dynamic and complex systems since the results from the analysis may be averages or valid for a short time before the system’s state changes. A response to this problem is dynamic probabilistic risk assessment (DPRA), which considers the ever-changing nature of such systems and how their dynamic behavior affects the likelihood of future accident scenarios. Performing DPRA is difficult for complex systems — i.e., systems with many interconnected subsystems and components, for example, autonomous systems. There is a combinatorial “explosion” when considering how component failures affect one another and the overall system performance, known in the DPRA literature as the “state explosion problem”, causing DPRA methods to have poor computational performance for large-scale systems. Although solutions to state explosion alleviate the average-case performance, most DPRA methods remain computationally expensive, with exponential worst-case time complexities. In this paper, a method for solving DPRA problems with K-Shortest-Paths planning algorithms is proposed. The method, called KPRA, consists in framing a subset of DPRA problems as relaxed versions of the K-Shortest-Paths (KSP) planning problem, allowing these DPRA problems to be solved by a graph search algorithm called K*, which has a theoretical log-linear worst-case complexity. Therefore, in theory, KPRA with K* solves DPRA problems with computational performance better than exponential. KPRA was implemented and applied to a case study of DPRA for an autonomous ship for validation and comparison with conventional DPRA methods. The case study consists of two ships, one of them autonomous, in a crossing encounter with a possible collision risk. The task is to find the most critical situations for the autonomous ship, i.e., the scenarios where its collision risk is the highest. KPRA’s performance in solving the case study is compared with two conventional DPRA methods. The results show that the KPRA implementation in this work can solve the case study, i.e., produce an output equivalent to the other methods, with a polynomial worst-case computational complexity, i.e., more efficiently than the other methods with exponential complexities.

Dynamic risk evaluation of hydrogen station leakage based on fuzzy dynamic Bayesian network

This paper introduces a dynamic risk assessment method for hydrogen leakage at hydrogen stations, employing fuzzy dynamic Bayesian networks. To begin, we utilize the Bow-Tie model for an in-depth analysis and consolidation of the primary risk factors contributing to hydrogen leak explosions. Subsequently, we establish a dynamic Bayesian network model for hydrogen leakage at hydrogen stations, adhering to the mapping rules derived from the Bow-Tie model. In order to reduce the impact of subjectivity, our model derives event prior probabilities through expert scoring and fuzzy set theory. Furthermore, the inclusion of a time factor is followed by the application of the Leaky Noisy-or gate model to enhance and calculate conditional probabilities, leading to the generation of time-series change curves for hydrogen leak probability and the probabilities associated with each accident consequence. The research findings yield valuable insights for risk assessment, accident prevention, and emergency management at hydrogen stations.