Quantitative Framework Research Articles

Meeting report: DMPK optimisation strategies and quantitative translational PKPD frameworks to predict human PK and efficacious dose of targeted protein degraders

Meeting report: DMPK optimisation strategies and quantitative translational PKPD frameworks to predict human PK and efficacious dose of targeted protein degraders

Assessing the effectiveness of PM2.5 pollution control from the perspective of interprovincial transport and PM2.5 mitigation costs across China

Due to the transboundary nature of air pollutants, a province's efforts to improve air quality can reduce PM2.5 concentration in the surrounding area. The inter-provincial PM2.5 pollution transport could bring great challenges to related environmental management work, such as financial fund allocation and subsidy policy formulation. Herein, we examined the transport characteristics of PM2.5 pollution across provinces in 2013 and 2020 via chemical transport modeling and then monetized inter-provincial contributions of PM2.5 improvement based on pollutant emission control costs. We found that approximately 60% of the PM2.5 pollution was from local sources, while the remaining 40% originated from outside provinces. Furthermore, about 1011 billion RMB of provincial air pollutant abatement costs contributed to the PM2.5 concentration decline in other provinces during 2013–2020, accounting for 41.2% of the total abatement costs. Provinces with lower unit improvement costs for PM2.5, such as Jiangsu, Hebei, and Shandong, were major contributors, while Guangdong, Guangxi, and Fujian, bearing higher unit costs, were among the main beneficiaries. Our study identifies provinces that contribute to air quality improvement in other provinces, have high economic efficiency, and provide a quantitative framework for determining inter-provincial compensations. This study also reveals the uneven distribution of pollution abatement costs (PM2.5 improvement/abatement costs) due to transboundary PM2.5 transport, calling for adopting inter-provincial economic compensation policies. Such mechanisms ensure equitable cost-sharing and effective regional air quality management.

Development of a method to assess synergy and competition for water use among water-energy-food nexus in the Yellow River basin: Water quantity-quality dimensions

Water, as an indispensable component of the water-energy-food (WEF) nexus, plays a pivotal role in shaping its stability and safety. The competition for water between the food and energy systems is accentuated in the concept of the nexus. However, existing quantitative frameworks and assessment mechanisms have limitations as they often neglect to consider water quality in water competition within the WEF nexus. Therefore, a WEF nexus synergy and competition assessment method was proposed by coupling water footprint theory and the Lotka-Volterra model in this paper. This method establishes two scenarios based on the water footprint perspective, namely, water quantity and water quality-quantity. It effectively addresses the deficiencies in quantitative analysis of WEF system trade-offs and synergies. To illustrate the application of the method, this study conducted a case study in the Yellow River Basin (YRB) of China, evaluating the complex competitive and synergistic mechanisms of water use in the energy and food industries. Compared to the water quantity scenario, the results reveals that the inclusion of gray water footprint intensifies the competition for water in the food and energy industries in the YRB from 2000 to 2020, particularly in resource-based and economically developed cities. The spatial distribution characteristics of water resource competition and synergy in the YRB’s WEF system align with WEF endowment and economic technology levels. To address water conflicts in the WEF nexus and enhance nexus security, this study proposes a regulatory pathway for WEF system synergistic security based on two aspects (water competition and synergy characteristics and resource endowment). It contributes to a more comprehensive understanding of water competition and synergy in the WEF nexus and provides valuable insights for resource management.

Quantitative assessment of static voltage stability for power system with high-penetration wind power based on energy function

As the scale of power systems expands, the penetration of renewable energy sources and power electronic devices are accelerating. With the growing complexity of power system, there is an urgent need for in-depth research on the impact of reactive power on static voltage stability. This paper proposes a quantitative assessment approach of static voltage stability for the power system with high-penetration wind power based on the energy function. A quantitative assessment framework for voltage stability indices based on power flow analysis has been established. The classic static voltage stability index L based on reactive power has been comprehensively analyzed. Additionally, combining the energy function and power flow analysis, a new quantitative index for voltage stability based on bus reactive power injection has been proposed. Finally, the effectiveness of this index has been validated by case studies. With the increasing penetration of wind power, the trend of the change of the assessment index of voltage margin is analyzed.

Quantitative risk assessment for the introduction of bluetongue virus into mainland Europe by long-distance wind dispersal of Culicoides spp.: A case study from Sardinia.

Europe faces regular introductions and reintroductions of bluetongue virus (BTV) serotypes, most recently exemplified by the incursion of serotype 3 in the Netherlands. Although the long-distance wind dispersal of the disease vector, Culicoides spp., is recognized as a virus introduction pathway, it remains understudied in risk assessments. A Quantitative Risk Assessment framework was developed to estimate the risk of BTV-3 incursion into mainland Europe from Sardinia, where the virus has been present since 2018. We used an atmospheric transport model (HYbrid Single-Particle Lagrangian Integrated Trajectory) to infer the probability of airborne dispersion of the insect vector. Epidemiological disease parameters quantified the virus prevalence in vector population in Sardinia and its potential first transmission after introduction in a new area. When assuming a 24h maximal flight duration, the risk of BTV introduction from Sardinia is limited to the Mediterranean Basin, mainly affecting the southwestern area of the Italian Peninsula, Sicily, Malta, and Corsica. The risk extends to the northern and central parts of Italy, Balearic archipelago, and mainland France and Spain, mostly when maximal flight duration is longer than 24h. Additional knowledge on vector flight conditions and Obsoletus complex-specific parameters could improve the robustness of the model. Providing both spatial and temporal insights into BTV introduction risks, our framework is a key tool to guide global surveillance and preparedness against epizootics.

An evaluation of tradeoffs in restoring ephemeral vs. perennial habitats to conserve animal populations

IntroductionHabitat loss and degradation pose significant threats to global fish and wildlife populations, prompting substantial investments in habitat creation and restoration efforts. Not all habitats provide equal benefits, leading to challenges in prioritizing restoration actions. For example, juvenile anadromous salmonids require high quality rearing aquatic habitats to achieve the physiological requirements needed to successfully migrate to the ocean. However, there are profound disagreements among anadromous salmon restoration managers whether it is best to focus efforts on restoring in-channel habitats that are available for the entire rearing period or floodplain habitats that, while facilitating greater growth and survival than in-channel habitats, are only available for a few weeks at a time and are typically only activated every two-to-three years.MethodsWe used an existing fall-run Chinook salmon decision-support model to evaluate under what conditions floodplain restoration would provide greater benefits than in-channel habitat restoration. The simulations included a wide range of floodplain inundation frequencies and durations and floodplain benefits in the form of increased survival and growth relative to in-channel habitats.ResultsThe simulations results indicated that in-channel habitat restoration was always the best habitat restoration action when there was no existing in-channel habitat despite simulating a wide range of flood frequency, duration, and growth and survival benefits. Floodplain restoration was generally best when there was sufficient in-channel habitat available to successfully rear most of the juveniles produced by the returning adult salmon.DiscussionWe hypothesize that in-channel and floodplain habitats have different roles in salmon population maintenance with in-channel habitats regulating the overall population size and floodplains acting as recurrent resource pulses. Our study provides a quantitative framework to evaluate the benefit of these two habitat types and provides generalizable rulesets that can be used by managers when implementing habitat restoration strategies for species that inhabit both in-channel and floodplain habitats.

A comprehensive framework for assessing the spatial drivers of flood disasters using an optimal Parameter-based geographical Detector–machine learning coupled model

Flood disasters pose serious threats to human life and property worldwide. Exploring the spatial drivers of flood disasters on a macroscopic scale is of great significance for mitigating their impacts. This study proposes a comprehensive framework for integrating driving-factor optimization and interpretability, while considering spatial heterogeneity. In this framework, the Optimal Parameter-based Geographic Detector (OPGD), Recursive Feature Estimation (RFE), and Light Gradient Boosting Machine (LGBM) models were utilized to construct the OPGD–RFE–LGBM coupled model to identify the essential driving factors and simulate the spatial distribution of flood disasters. The SHapley Additive ExPlanation (SHAP) interpreter was employed to quantitatively explain the driving mechanisms behind the spatial distribution of flood disasters. Yunnan Province, a typical mountainous and plateau area in Southwest China, was selected to implement the proposed framework and conduct a case study. For this purpose, a flood disaster inventory of 7332 historical events was prepared, and 22 potential driving factors related to precipitation, surface environment, and human activity were initially selected. Results revealed that flood disasters in Yunnan Province exhibit high spatial heterogeneity, with geomorphic zoning accounting for 66.1% of the spatial variation in historical flood disasters. The OPGD–RFE–LGBM coupled model offers clear advantages over a single LGBM in identifying essential driving factors and quantitatively analyzing their impacts. Moreover, the simulation performance shows a slight improvement (a 6% average decrease in RMSE and an average increase of 1% in R2) even with reduced factor data. Factor explanatory analysis indicated that the combination of the essential driving factor sets varied across different subregions; nevertheless, precipitation-related factors, such as precipitation intensity index (SDII), wet days (R10MM), and 5-day maximum precipitation (RX5day), were the main driving factors controlling flood disasters. This study provides a quantitative analytical framework for the spatial drivers of flood disasters at large scales with significant heterogeneity, offering a reference for disaster management authorities in developing macro-strategies for disaster prevention.

Evaluation of secretome biomarkers in glioblastoma cancer stem cells: A bioinformatics analysis.

Glioblastoma (GBM) is a malignant brain tumor that frequently occurs alongside other central nervous system (CNS) conditions. The secretome of GBM cells contains a diverse array of proteins released into the extracellular space, influencing the tumor microenvironment. These proteins can serve as potential biomarkers for GBM due to their involvement in key biological processes, exploring the secretome biomarkers in GBM research represents a cutting-edge strategy with significant potential for advancing diagnostic precision, treatment monitoring, and ultimately improving outcomes for patients with this challenging brain cancer. This study was aimed to investigate the roles of secretome biomarkers and their pathwayes in GBM through bioinformatics analysis. Using data from the Gene Expression Omnibus and the Cancer Genome Atlas datasets-where both healthy and cancerous samples were analyzed-we used a quantitative analytical framework to identify differentially expressed genes (DEGs) and cell signaling pathways that might be related to GBM. Then, we performed gene ontology studies and hub protein identifications to estimate the roles of these DEGs after finding disease-gene connection networks and signaling pathways. Using the GEPIA Proportional Hazard Model and the Kaplan-Meier estimator, we widened our analysis to identify the important genes that may play a role in both progression and the survival of patients with GBM. In total, 890 DEGs, including 475 and 415 upregulated and downregulated were identified, respectively. Our results revealed that SQLE, DHCR7, delta-1 phospholipase C (PLCD1), and MINPP1 genes are highly expressed, and the Enolase 2 (ENO2) and hexokinase-1 (HK1) genes are low expressions. Hence, our findings suggest novel mechanisms that affect the occurrence of GBM development, growth, and/or establishment and may also serve as secretory biomarkers for GBM prognosis and possible targets for therapy. So, continued research in this field may uncover new avenues for therapeutic interventions and contribute to the ongoing efforts to combat GBM effectively.

Simulation of a scintillator-based fast ion loss detector for steady-state operation in Wendelstein 7-X (invited).

A quantitative theoretical framework has been created to model neutral beam injection and fast ion losses in the Wendelstein 7-X (W7-X) stellarator, including a novel method to develop synthetic diagnostics for fast ion loss detectors (FILDs) of many types, such as scintillating and Faraday Cup FILDs. This is the first time that this has been done in stellarator geometry with this level of fidelity, providing a way for fast ion losses to be predicted more precisely in future stellarator experiments and in W7-X. Simulations of the signal seen by a Faraday Cup FILD have been completed for multiple W7-X plasmas and show close agreement with the measured signals. This method is now applied to an actively water-cooled, scintillator-based FILD, which is currently in development to measure the fast ion loss distribution in W7-X in greater detail. The design makes use of a double slit to measure energy-and-pitch-angle-resolved losses of both co-going and counter-going fast ions. The diagnostic, which can be inserted to different radial positions, has been designed to withstand steady-state heat fluxes of up to 120 kW/m2 along with additional transient heat loads of 100 kW/m2 lasting for up to 20s at a time. Simulations of W7-X standard magnetic configuration show up to 8 × 1013 (s-1 cm-2) ion fluxes onto the sensor from each neutral beam source and no signal from the counter-going slit. These simulations will help inform experimental proposals for future W7-X campaigns after installation of this diagnostic.

Observable Atrial and Ventricular Fibrillation Episode Durations Are Conformant With a Power Law Based on System Size and Spatial Synchronization.

Atrial fibrillation (AF) and ventricular fibrillation (VF) episodes exhibit varying durations, with some spontaneously ending quickly while others persist. A quantitative framework to explain episode durations remains elusive. We hypothesized that observable self-terminating AF and VF episode lengths, whereby durations are known, would conform with a power law based on the ratio of system size and correlation length ([Formula: see text]. Using data from computer simulations (2-dimensional sheet and 3-dimensional left-atrial), human ischemic VF recordings (256-electrode sock, n=12 patients), and human AF recordings (64-electrode basket-catheter, n=9 patients; 16-electrode high definition-grid catheter, n=42 patients), conformance with a power law was assessed using the Akaike information criterion, Bayesian information criterion, coefficient of determination (R2, significance=P<0.05) and maximum likelihood estimation. We analyzed fibrillatory episode durations and [Formula: see text], computed by taking the ratio between system size ([Formula: see text], chamber/simulation size) and correlation length (xi, estimated from pairwise correlation coefficients over electrode/node distance). In all computer models, the relationship between episode durations and [Formula: see text] was conformant with a power law (Aliev-Panfilov R2: 0.90, P<0.001; Courtemanche R2: 0.91, P<0.001; Luo-Rudy R2: 0.61, P<0.001). Observable clinical AF/VF durations were also conformant with a power law relationship (VF R2: 0.86, P<0.001; AF basket R2: 0.91, P<0.001; AF grid R2: 0.92, P<0.001). [Formula: see text] also differentiated between self-terminating and sustained episodes of AF and VF (P<0.001; all systems), as well as paroxysmal versus persistent AF (P<0.001). In comparison, other electrogram metrics showed no statistically significant differences (dominant frequency, Shannon Entropy, mean voltage, peak-peak voltage; P>0.05). Observable fibrillation episode durations are conformant with a power law based on system size and correlation length.

Structural Color Out of the Blue: A Quantitative Framework for the Self-Assembly Kinetics of Cholesteric Cellulosic Mesophases.

Cholesteric mesophases based on cellulose ethers, such as ethyl cellulose and hydroxypropyl cellulose, have been studied widely for their remarkable ability to display macroscopic structural color. However, the typical time scales involved in the multiscale self-assembly of cholesteric liquid crystals, from individual nanoscale helical arrangements to discrete microscopic domains, and their dependence on the gel's viscoelastic properties remain underexplored. Here, we establish a quantitative relationship between the kinetics of structural color formation after shear deformation and cholesteric order development at the nano- and microscales. Utilizing rheology in tandem with static and time-resolved reflectivity measurements, we underscore the strong influence of polymer diffusivity and chain elasticity on self-assembly kinetics in cholesteric cellulose ether gels. We show that our phenomenological model can be employed to assess the structure-property relationships of multiple polysaccharide systems, elucidating key design guidelines for the development and processing of structurally colored cholesteric mesophases.

Feasibility of the Northern Sea Route: Impact of Sea Ice Thickness Uncertainty on Navigation

With the accelerated melting of the Arctic sea ice, the opening of the Northern Sea Route (NSR) of the Arctic is becoming increasingly accessible. The purpose of this paper was to examine the impact of uncertainty in sea ice thickness (SIT) data on the opening of the NSR and to quantify the extent of this impact, which is essential to the regularized operation of polar shipping. A quantitative assessment framework was proposed to examine the influence of SIT data uncertainty on navigational uncertainty through three aspects: the navigational spatiotemporal windows, the distribution of safe sailing speeds, and the uncertainty in optimal route planning by employing four distinct SIT datasets. Furthermore, the sensitivity of navigational spatiotemporal windows, the distribution of safe sailing speeds, and route planning to variations in SIT were also evaluated. Results show that experiment results based on CS2SMOS exhibit a more aggressive profile, while results based on CPOM are more conservative. The difference in SIT data has a significant impact on the proportion of operations subject to special consideration areas, such as safety speed, sailing time, and distance in NEP. A 0.1 m discrepancy in sea ice thickness data results in an approximate 0.067 change in the proportion of operations within special consideration areas. This discrepancy also leads to an approximate speed change of 0.89 knots, a navigation duration change of approximately 4 days, and a distance change of 152 km within specified limits.

Temporal assessment of operational resilience of transmission network and adaptation measures for a high-impact long duration cyclonic windstorm

The exposure to High-Impact Low-Probability (HILP) events can have significant impact on the performance of transmission networks. Under such conditions, the power system components must be resilient and robust to meet the uninterrupted load demand. This paper proposes a quantitative framework for temporal assessment of operational resilience of transmission network and suggests suitable adaptation measures when the system is subjected to high impact cyclonic windstorm lasting for a long duration. The fragility curves of transmission lines are correlated with wind profiles during cyclone and failure probability each transmission line is determined using the Monte Carlo Simulation (MCS) The operational resilience of transmission networks is quantified by computing Total Transfer Capability (TTC), Available Transfer Capability (ATC), Existing Transmission Uses (ETU), Total Reliability Margin (TRM) and unserved load. To improve the operational resilience, adaptation measures with modifications in the robustness of the structural strength is proposed and investigated on standard IEEE 57 bus system and IEEE 118 bus system. Sensitivity analysis is performed to understand how changes in the percentage increase of robustness affect the overall system performance. The findings give valuable insights for evaluating the operational resilience of transmission line infrastructure during such extreme weather events.

Quantifying and spatializing the island landsenses ecological condition using a two-dimensional model

Achieving the harmonious development of the “social-ecological” system is a crucial aspect of the global sustainable development goals. Landsenses ecology is an emerging concept that emphasizes sustainable land use planning and comprehensive management. Islands serve as ideal study areas setting for studying the landsenses ecology due to their vulnerability as ecosystems exposed to significant human activities. To assess the condition and spatial distribution of landsenses ecology, we established a quantitative evaluation framework that encompassed eight factors across two perceptions - physical and psychological. Our study focused on two islands situated in southeast China, which, despite their proximity, demonstrate remarkable differences in terms of natural and economic characteristics. The results revealed a significant spatial heterogeneity in the landsenses ecological condition on both islands. Nan'ao island (NAI) exhibited higher values for physical (0.5) and psychological (0.243) perceptions compared to Dongshan island (DSI), which was primarily attributed to varying natural environments and topographic factors. Notably, elevation and slope demonstrated strong correlations with landsenses ecological condition, acting as critical determinants of island development. Finally, using beaches as an example, we examined the impact of landsenses ecological conditions about the tourist resources of the islands. This study contributes to the development of scientifically informed urban and tourism planning processes by providing decision-makers with valuable insights.

Brain clocks capture diversity and disparity in aging and dementia.

Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of multimodal diversity (geographical, socioeconomic, sociodemographic, sex, neurodegeneration) on the brain age gap (BAG) is unknown. Here, we analyzed datasets from 5,306 participants across 15 countries (7 Latin American countries -LAC, 8 non-LAC). Based on higher-order interactions in brain signals, we developed a BAG deep learning architecture for functional magnetic resonance imaging (fMRI=2,953) and electroencephalography (EEG=2,353). The datasets comprised healthy controls, and individuals with mild cognitive impairment, Alzheimer's disease, and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (fMRI: MDE=5.60, RMSE=11.91; EEG: MDE=5.34, RMSE=9.82) compared to non-LAC, associated with frontoposterior networks. Structural socioeconomic inequality and other disparity-related factors (pollution, health disparities) were influential predictors of increased brain age gaps, especially in LAC (R2=0.37, F2=0.59, RMSE=6.9). A gradient of increasing BAG from controls to mild cognitive impairment to Alzheimer's disease was found. In LAC, we observed larger BAGs in females in control and Alzheimer's disease groups compared to respective males. Results were not explained by variations in signal quality, demographics, or acquisition methods. Findings provide a quantitative framework capturing the multimodal diversity of accelerated brain aging.

Identification and threshold analysis of strong winds and heavy rain disaster factors based on frequent-pattern mining

Meteorological disasters pose a significant challenge to megacities, and quantitatively identifying the influence of meteorological conditions on disasters is an urgent issue. Compared to other traditional datasets, disaster reporting data offers significant informational value for urban meteorological risk response and assessment due to its high credibility and timeliness. This study is based on meteorological disaster reporting data for Shanghai over the past 10 years and focuses on the identification and threshold analysis of meteorological disaster risk factors for disaster-affected entities, offering a novel perspective on analyzing disaster-causing risk under the combined influence of meteorological conditions. The findings suggest that disaster reporting data can be used for meteorological risk assessment. There were noticeable differences in the disaster thresholds for affected entities in central urban areas (city center) versus suburban areas (suburbs). The combined effects of strong winds and heavy rain were often significant contributors to the occurrence of disasters. Furthermore, several distinct disaster characteristics were identified through frequent pattern (FP)-growth mining. The results of this study provide technical support for early warning and disaster management under specific weather conditions by constructing a quantitative framework for understanding the impact of hazardous weather.

"Modeling Diffusive Search by Non-Adaptive Sperm: Empirical and Computational Insights".

During fertilization, mammalian sperm undergo a winnowing selection process that reduces the candidate pool of potential fertilizers from ~106-1011 cells to 101-102 cells (depending on the species). Classical sperm competition theory addresses the positive or 'stabilizing' selection that acts on sperm phenotypes within populations of organisms but does not strictly address the developmental consequences of sperm traits among individual organisms that are under purifying selection during fertilization. It is the latter that is of utmost concern for improving assisted reproductive technologies (ART) because 'low fitness' sperm may be inadvertently used for fertilization during interventions that rely heavily on artificial sperm selection, such as intracytoplasmic sperm injection (ICSI). Importantly, some form of sperm selection is used in nearly all forms of ART (e.g., differential centrifugation, swim-up, or hyaluronan binding assays, etc.). To date, there is no unifying quantitative framework (i.e., theory of sperm selection) that synthesizes causal mechanisms of selection with observed natural variation in individual sperm traits. In this report, we reframe the physiological function of sperm as a collective diffusive search process and develop multi-scale computational models to explore the causal dynamics that constrain sperm 'fitness' during fertilization. Several experimentally useful concepts are developed, including a probabilistic measure of sperm 'fitness' as well as an information theoretic measure of the magnitude of sperm selection, each of which are assessed under systematic increases in microenvironmental selective pressure acting on sperm motility patterns.

Perspective: the evolution of hormones and person perception-a quantitative genetic framework.

Evolutionary biology provides a unifying theory for testing hypotheses about the relationship between hormones and person perception. Person perception usually receives attention from the perspective of sexual selection. However, because person perception is one trait in a suite regulated by hormones, univariate approaches are insufficient. In this Perspectives article, quantitative genetics is presented as an important but underutilized framework for testing evolutionary hypotheses within this literature. We note tacit assumptions within the current literature on psychiatric genetics, which imperil the interpretation of findings thus far. As regulators of a diverse manifold of traits, hormones mediate tradeoffs among an array of functions. Hormonal pleiotropy also provides the basis of correlational selection, a process whereby selection on one trait in a hormone-mediated suite generates selection on the others. This architecture provides the basis for conflicts between sexual and natural selection within hormone-mediated suites. Due to its role in person perception, psychiatric disorders, and reproductive physiology, the sex hormone estrogen is highlighted as an exemplar here. The implications of this framework for the evolution of person perception are discussed. Empirical quantification of selection on traits within hormone-mediated suites remains an important gap in this literature with great potential to illuminate the fundamental nature of psychiatric disorders.

An algorithmic framework for synthetic cost-aware decision making in molecular design.

Small molecules exhibiting desirable property profiles are often discovered through an iterative process of designing, synthesizing and testing sets of molecules. The selection of molecules to synthesize from all possible candidates is a complex decision-making process that typically relies on expert chemist intuition. Here we propose a quantitative decision-making framework, SPARROW, that prioritizes molecules for evaluation by balancing expected information gain and synthetic cost. SPARROW integrates molecular design, property prediction and retrosynthetic planning to balance the utility of testing a molecule with the cost of batch synthesis. We demonstrate, through three case studies, that the developed algorithm captures the non-additive costs inherent to batch synthesis, leverages common reaction steps and intermediates, and scales to hundreds of molecules.

A novel framework for quantitative resilience assessment in complex engineering systems during early and late design stages

Recently, resilience assessment has evolved and grown in prominence, yet most studies are carried out on operational stage when sufficient knowledge on the processes is available, overlooking the design stage, a time frame that is more suitable for making a resilient system. To this end, this work aims at developing a novel quantitative resilience assessment framework for engineering systems with two different approaches that practically analyse resilience at the early and late design stages when detailed information on the system’s safety and resilience capabilities may be deficient. In the early design stage, system resilience attributes are identified, and expert judgment is used to assess their quality. In the late design stage, attributes are derived from revealed information such as detailed emergency response and safety barrier data. In both stages, dynamic Bayesian Network (DBN) is used to quantify resilience based on the acquired information. Since the green hydrogen technology is relatively novice, the application of the proposed framework is demonstrated in a resilience assessment of a green hydrogen plant undergoing hydrogen release scenarios. The proposed framework can be used as an effective tool for early design improvements as well as enhancing process safety in the late design stage of hydrogen plants or any other complex engineering system.