Model-based Analysis Research Articles

A Systematic Literature Review of Modelling Approaches to Evaluate the Cost Effectiveness of PET/CT for Therapy Response Monitoring in Oncology.

This systematic literature review addresses model-based cost-effectiveness studies for therapy response monitoring with positron emission tomography (PET) generally combined with low-dose computed tomography (CT) for various cancer types. Given the known heterogeneity in therapy response events, studies should consider patient-level modelling rather than cohort-based modelling because of its flexibility in handling these events and the time to events. This review aims to identify the modelling methods used and includes a systematic assessment of the assumptions made in the current literature. This study was conducted and reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Information sources included electronic bibliographic databases, reference lists of review articles and contact with experts in the fields of nuclear medicine, health technology assessment and health economics. Eligibility criteria included peer-reviewed scientific publications and published grey literature. Literature searches, screening and critical appraisal were conducted by two reviewers independently. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) were used to assess the methodological quality. The Bias in Economic Evaluation (ECOBIAS) checklist was used to determine the risk of bias in the included publications. The search results included 2959 publications. The number of publications included for data extraction and synthesis was ten, representing eight unique studies. These studies addressed patients with lymphoma, advanced head and neck cancers, brain tumours, non-small cell lung cancer and cervical cancer. All studies addressed response to chemotherapy. No study evaluated response to immunotherapy. Most studies positioned PET/CT as an add-on modality and one study positioned PET/CT as a replacement for conventional imaging (X-ray and contrast-enhanced CT). Three studies reported decision-tree structures, four studies reported cohort-level state-transition models and one study reported a partitioned survival model. No patient-level models were reported. The simulation horizons adopted ranged from 1 year to lifetime. Most studies reported a probabilistic analysis, whereas two studies reported a deterministic analysis only. Two studies conducted a value of information analysis. Multiple studies did not adequately discuss model-specific aspects of bias. Most importantly and regularly observed were a high risk of structural assumptions bias, limited simulation horizon bias and wrong model bias. Model-based cost-effectiveness analysis for therapy response monitoring with PET/CT was based on cohorts of patients instead of individual patients in the current literature. Therefore, the heterogeneity in therapy response events was commonly not addressed appropriately. Further research should include more advanced and patient-level modelling approaches to accurately represent the complex context of clinical practice and, therefore, to be meaningful to support decision making. This review is registered in PROSPERO, the international prospective register of systematic reviews funded by the National Institute for Health Research, with CRD42023402581.

A rapid response process for evaluating causes of extreme temperature events in the United States: The 2023 Texas/Louisiana heat wave as a prototype

Abstract As climate attribution studies have become more common, routine processes are now being established for attribution analysis following extreme events. This study describes the prototype process being developed through a collaboration across National Oceanic and Atmospheric Administration (NOAA), including monitoring tools as well as observational and model-based analysis of causal factors. The prolonged period of extreme heat in summer 2023 over Texas, Louisiana and adjacent areas provided a proving ground for this emerging capability. This event posed unique challenges to the initial process. The extreme heat lasted for most of the summer while most heat wave metrics have been designed for 3–7 d events. The eastern portion of the affected area also occurred within the so-called summer-time daytime warming hole where the warming trend in maximum temperatures has been mitigated wholly or in part by increased precipitation. The extreme temperature coincided with a strong—but not record—precipitation deficit over the region. Both observations and climate model simulations illustrate that the temperatures for a given precipitation deficit have warmed in recent decades. In other words, meteorological droughts today are hotter than their historical analogs providing a stronger attribution to anthropogenic forcing than for temperature alone. These findings were summarized in a prototype plain language report that was distributed to key stakeholders. Based on their feedback, the monitoring and assessment tools will continue to be refined, and the project is exploring other climate model large ensembles to increase the robustness of attribution for future events.

Localized shear and distributed strain accumulation as competing shear accommodation mechanisms in crustal shear zones: constraining their dictating factors

Abstract. Understanding the underlying mechanisms of strain localization in the Earth’s lithosphere is crucial for explaining the mechanics of tectonic plate boundaries and various failure-assisted geophysical phenomena, such as earthquakes. Geological field observations suggest that shear zones are the most important lithospheric structures demonstrating intense shear localization at plate boundaries, accommodating a major portion of tectonic deformations. Despite extensive studies over the past several decades, the factors governing how shear zones accommodate bulk shear, whether via distributed strain (i.e. the development of macroscopic S (schistosity) foliations normal to the principal shortening strain axis) or via localized shearing (i.e. the formation of shear-parallel C bands, where C refers to the French “cisaillement” (shear)), remain largely unexplored. This study aims to address this gap in knowledge by providing observational evidence of varying S and C development in crustal shear zones from two geological terrains in eastern India. These field observations are complemented by 2D viscoplastic numerical simulations within a strain-softening rheological framework to constrain the factors controlling two competing shear accommodation mechanisms: distributed strain accumulation and shear band formation. The model-based analysis recognizes the bulk shear rate (γ˙b), initial viscosity (ηv), and initial cohesion (Ci) of a shear zone as the most critical factors determining the dominance of one mechanism over the other. For a given Ci value, low γ˙b and ηv values facilitate the formation of S foliation (uniformly distributed strain), which transitions to a C-dominated shear accommodation mechanism as ηv increases. However, increasing γ˙b facilitates shear accommodation through a combination of the two mechanisms, leading to S–C structures. The article finally discusses the conditions under which shear zones can significantly intensify rates of localized shear, producing rapid slip events, such as frictional melting and seismic activities.

Large language model-based optical network log analysis using LLaMA2 with instruction tuning

The optical network encompasses numerous devices and links, generating a significant volume of logs. Analyzing these logs is significant for network optimization, failure diagnosis, and health monitoring. However, the large-scale and diverse formats of optical network logs present several challenges, including the high cost and difficulty of manual processing, insufficient semantic understanding in existing analysis methods, and the strict requirements for data security and privacy. Generative artificial intelligence (GAI) with powerful language understanding and generation capabilities has the potential to address these challenges. Large language models (LLMs) as a concrete realization of GAI are well-suited for analyzing DCI logs, replacing human experts and enhancing accuracy. Additionally, LLMs enable intelligent interactions with network administrators, automating tasks and improving operational efficiency. Moreover, fine-tuning with open-source LLMs protects data privacy and enhances log analysis accuracy. Therefore, we introduce LLMs and propose a log analysis method with instruction tuning using LLaMA2 for log parsing, anomaly detection and classification, anomaly analysis, and report generation. Real log data extracted from the field-deployed network was used to design and construct instruction tuning datasets. We utilized the dataset for instruction tuning and demonstrated and evaluated the effectiveness of the proposed scheme. The results indicate that this scheme improves the performance of log analysis tasks, especially a 14% improvement in exact match rate for log parsing, a 13% improvement in F1-score for anomaly detection and classification, and a 23% improvement in usability for anomaly analysis, compared with the best baselines.

Hybrid heterogeneous prognosis of drill-bit lives through model-based spindle power analysis and direct tool inspection

Hybrid heterogeneous prognosis of drill-bit lives through model-based spindle power analysis and direct tool inspection

ZMAP toolset: model-based analysis of large-scale proteomic data via a variance stabilizing z-transformation

Isobaric labeling-based mass spectrometry (ILMS) has been widely used to quantify, on a proteome-wide scale, the relative protein abundance in different biological conditions. However, large-scale ILMS data sets typically involve multiple runs of mass spectrometry, bringing great computational difficulty to the integration of ILMS samples. We present zMAP, a toolset that makes ILMS intensities comparable across mass spectrometry runs by modeling the associated mean-variance dependence and accordingly applying a variance stabilizing z-transformation. The practical utility of zMAP is demonstrated in several case studies involving the dynamics of cell differentiation and the heterogeneity across cancer patients.

Comparing Recurrence Rates and the Cost-Effectiveness of Arthroscopic Labral Repair and Nonoperative Management for Primary Anterior Shoulder Dislocations in Young Patients: A Decision-Analytic Markov Model-Based Analysis.

Value-based decision-making regarding nonoperative management versus early surgical stabilization for first-time anterior shoulder instability (ASI) events remains understudied. To perform (1) a systematic review of the current literature and (2) a Markov model-based cost-effectiveness analysis comparing an initial trial of nonoperative management to arthroscopic Bankart repair (ABR) for first-time ASI. Economic and decision analysis; Level of evidence, 3. A Markov chain Monte Carlo probabilistic model was developed to evaluate the outcomes and costs of 1000 simulated patients (mean age, 20 years; range, 12-26 years) with first-time ASI undergoing nonoperative management versus ABR. Utility values, recurrence rates, and transition probabilities were derived from the published literature. Costs were determined based on the typical patient undergoing each treatment strategy at the authors' institution. Outcome measures included costs, quality-adjusted life-years (QALYs), and the incremental cost-effectiveness ratio (ICER). The Markov model with Monte Carlo microsimulation demonstrated mean (± standard deviation) 10-year costs for nonoperative management and ABR of $38,649 ± $10,521 and $43,052 ± $9352, respectively. Total QALYs acquired over the 10-year time horizon were 7.67 ± 0.43 and 8.44 ± 0.46 for nonoperative management and ABR, respectively. The ICER comparing ABR with nonoperative management was found to be just $5725/QALY, which falls substantially below the $50,000 willingness-to-pay (WTP) threshold. The mean numbers of recurrences were 2.55 ± 0.31 and 1.17 ± 0.18 for patients initially assigned to the nonoperative and ABR treatment groups, respectively. Of 1000 samples run over 1000 trials, ABR was the optimal strategy in 98.7% of cases, with nonoperative management the optimal strategy in 1.3% of cases. ABR reduces the risk for recurrent dislocations and is more cost-effective despite higher upfront costs when compared with nonoperative management for first-time ASI in the young patient. While all these factors are important to consider in surgical decision-making, ultimate treatment decisions should be made on an individual basis and occur through a shared decision-making process.

Immunogenicity dynamics and covariate effects after satralizumab administration predicted with a hidden Markov model.

Immunogenicity is the propensity of a therapeutic protein to generate an immune response to itself. While reporting of antidrug antibodies (ADAs) is increasing, model-based analysis of such data is seldom performed. Model-based characterization of factors affecting the emergence and dissipation of ADAs may inform drug development and/or improve understanding in clinical practice. This analysis aimed to predict ADA dynamics, including the potential influence of individual covariates, following subcutaneous satralizumab administration. Satralizumab is a humanized IgG2 monoclonal recycling IL-6 receptor antagonist antibody approved for treating neuromyelitis optica spectrum disorder (NMOSD). Longitudinal pharmacokinetic (PK) and ADA data from 154 NMOSD patients in two pivotal Phase 3 studies (NCT02028884, NCT02073279) and PK data from one Phase 1 study (SA-001JP) in 72 healthy volunteers were available for this analysis. An existing population PK model was adapted to derive steady-state concentration without ADA for each patient. A mixed hidden Markov model (mHMM) was developed whereby three different states were identified: one absorbing Markov state for non-ADA developer, and two dynamic and inter-connected Markov states-transient ADA negative and positive. Satralizumab exposure and body mass index impacted transition probabilities and, therefore, the likelihood of developing ADAs. In conclusion, the mHMM model was able to describe the time course of ADA development and identify patterns of ADA development in NMOSD patients following treatment with satralizumab, which may allow for the formulation of strategies to reduce the emergence or limit the impact of ADA in the clinical setting.

A clinically feasible algorithm for the parallel detection of glioma-associated copy number variation markers based on shallow whole genome sequencing.

Molecular features are incorporated into the integrated diagnostic system for adult diffuse gliomas. Of these, copy number variation (CNV) markers, including both arm-level (1p/19q codeletion, +7/-10 signature) and gene-level (EGFR gene amplification, CDKN2A/B homozygous deletion) changes, have revolutionized the diagnostic paradigm by updating the subtyping and grading schemes. Shallow whole genome sequencing (sWGS) has been widely used for CNV detection due to its cost-effectiveness and versatility. However, the parallel detection of glioma-associated CNV markers using sWGS has not been optimized in a clinical setting. Herein, we established a model-based approach to classify the CNV status of glioma-associated diagnostic markers with a single test. To enhance its clinical utility, we carried out hypothesis testing model-based analysis through the estimation of copy ratio fluctuation level, which was implemented individually and independently and, thus, avoided the necessity for normal controls. Besides, the customization of required minimal tumor fraction (TF) was evaluated and recommended for each glioma-associated marker to ensure robust classification. As a result, with 1× sequencing depth and 0.05 TF, arm-level CNVs could be reliably detected with at least 99.5% sensitivity and specificity. For EGFR gene amplification and CDKN2A/B homozygous deletion, the corresponding TF limits were 0.15 and 0.45 to ensure the evaluation metrics were both higher than 97%. Furthermore, we applied the algorithm to an independent glioma cohort and observed the expected sample distribution and prognostic stratification patterns. In conclusion, we provide a clinically applicable algorithm to classify the CNV status of glioma-associated markers in parallel.

Use of a PK/PD Model to Select Cetagliptin Dosages for Patients with Type 2 Diabetes in Phase 3 Trials.

Cetagliptin is a novel dipeptidyl peptidase-4 (DPP-4) inhibitor developed for the treatment of patients with type 2 diabetes (T2D). Several phase 1 studies have been conducted in China. Modelling and simulation were used to obtain cetagliptin dose for phase 3 trials in T2D patients. A pharmacokinetic (PK)/pharmacodynamic (PD) model and model-based analysis of the relationship between hemoglobin A1c (HbA1c) and dosage was explored to guide dose selection of cetagliptin for phase 3 trials. The PK/PD data were derived from four phase 1 clinical studies, and sitagliptin 100 mg was employed as a positive control in studies 1, 3, and 4. The PK profiles of cetagliptin were well described by a two-compartment model with first-order absorption, saturated efflux, and first-order elimination. The final PD model was a sigmoid maximum inhibitory efficacy (Emax) model with the Hill coefficient. The final model accurately captured cetagliptin PK/PD, demonstrated by goodness-of-fit plots. Based on weighted average inhibition (WAI), the relationship between HbA1c and dose was well displayed. Cetagliptin 50 mg once daily or above as monotherapy or as add-on therapy appeared more effective in HbA1c reduction than sitagliptin 100 mg. Cetagliptin 50 mg or 100 mg once daily was selected as the dose for phase 3 trials of cetagliptin in T2D patients. The PK/PD model supports dose selection of cetagliptin for phase 3 trials. A model‑informed approach can be used to replace a dose-finding trial and accelerate cetagliptin's development.

Calibration and surrogate model-based sensitivity analysis of crystal plasticity finite element models

Calibration and surrogate model-based sensitivity analysis of crystal plasticity finite element models

Assessment of Five-Year Relative Survival of Patients With Endometrial Cancer: A Period Analysis.

Endometrial cancer is one of the most common female cancers globally and in China. Although timely assessment of 5-year relative survival is crucial for guiding secondary prevention and early screening programs for endometrial cancer patients, those kinds of data are scarce in China. We aimed to provide a timely and accurate assessment of 5-year relative survival for patients with endometrial cancer from eastern China. Overall, 945 patients diagnosed with endometrial cancer during 2004 - 2018 from four cancer registries with high-quality data from Taizhou, eastern China were included. Period analysis was used to calculate 5-year relative survival for overall and the stratification by age at diagnosis and region. Model-based period analysis was used to predict the 5-year relative survival for the upcoming period of 2019 - 2023. We found that 5-year relative survival during 2014 - 2018 reached 86.4% for overall, while urban areas had higher survival compared to rural areas (91.3% vs. 85.3%). Furthermore, there was a clear age gradient, decreasing from 89.3% for age < 55 years to 80.5% for age > 74 years. Predicted 5-year relative survival for the upcoming period 2019 - 2023 could reach 88.4%. We provide, a timely and accurate assessment of 5-year relative survival for patients with endometrial cancer from Taizhou, eastern China, reaching 86.4% for overall. Our finding has important implications for the overall evaluation of early detection and screening programs for patients with endometrial cancer in eastern China.

Modeling the transport and mixing of suspended sediment in ecological flows with submerged vegetation: A random displacement model-based analysis

Aquatic vegetation in rivers influences the flow structure, impacting the sediment transport in the river and further changing the ecosystem and geomorphologic evolution of the river. In this paper, an improved random displacement model (RDM) is proposed to investigate the concentration of suspended sediment (CSS) in flows with flexible submerged vegetation. Considering the effect of sediment resuspension on sediment transport, a probability model of sediment resuspension is embedded into the RDM so that the simulation process is more consistent with the natural cases. For the streamwise development of flow with flexible submerged vegetation, the flow is divided into a flow adjustment region and a fully developed region in the longitudinal direction. The vertical distributions of the flow velocity and turbulence diffusion coefficient were analyzed in these two regions, and they were incorporated into the RDM to simulate the along-flow development of the CSS. The simulated concentrations were compared with the experimental data. The mean relative errors (MREs) were below 15%, the root mean square errors (RMSEs) were below 0.20, and the Nash–Sutcliffe efficiencies (NSEs) ranged from 0.71 to 0.99, indicating that the improved RDM is plausible and reliable. In addition, the along-flow distribution of the sediment transport rate (STR) was analyzed. The rate exhibited a decreasing trend during the flow adjustment region and tended to stabilize during the fully developed region. This study provides a new way of thinking for research on sediment transport in rivers with aquatic vegetation, which is of great significance for achieving the sustainable development of river ecosystems and the optimal design of river channels.

Dispatch of Decentralized Energy Systems using Artificial Neural Networks: A Comparative Analysis with Emphasis on Training Methods

Due to the availability of flexibility, Decentralized Energy Systems (DES) play a central role in integrating renewable energies. To efficiently utilize renewable energy, dispatchable components must be operated to bridge the time gap between inflexible supply and energy demand. Due to the large number of energy converters, energy storage systems, and flexible consumers, there are many ways to achieve this. Conventional rule-based dispatch strategies often reach their limits here, and optimized dispatch strategies (e.g., model predictive control or the optimal dispatch) are usually based on very good forecasts. Reinforcement learning, particularly the application of Artificial Neural Networks (ANN), offers the possibility to learn complex decision-making processes. Since long training times are required for this, an efficient training framework is needed. The present paper proposes different training methods to learn an ANN-based dispatch strategy. In Method I, the ANN attempts to learn the solution to the corresponding optimal dispatch problem. In method II, the energy system model is simulated during the training to compute the observation state and operating costs resulting from the ANN-based dispatch. Method III uses the fast executable Method I solution as a warm-up solution for the computationally expensive training with Method II. In the present paper, a model-based analysis compares the different ANN-based dispatch strategies with rule-based dispatch strategies, model predictive dispatch (MPC), and optimal dispatch regarding their computational efficiency and the resulting operating costs. The dispatch strategies are compared based on three case studies with different system topologies for which training and test data are applied.Training method I proved to be non-competitive. However, training methods II and III significantly outperformed rule-based dispatch strategies across all case studies for both training and test data sets. Notably, methods II and III also surpassed MPC-based dispatch strategies under high and medium uncertainty forecasts for the training data in the first two case studies. In contrast, MPC-based dispatch was superior in the third case study, likely due to the higher system’s complexity and in the test data set due to the methodological advantage of being optimized for each specific data set. The effectiveness of training method III depends on the performance of the warm-up training with method I: warm-up is beneficial only if this results in an already promising dispatch (as seen in case study two). Otherwise, training method II proves more effective, as observed in case studies one and three.

In silico data-based comparison of the accuracy and error source of various methods for noninvasively estimating central aortic blood pressure

Background and objectivesThe higher clinical significance of central aortic blood pressure (CABP) compared to peripheral blood pressures has been extensively demonstrated. Accordingly, many methods for noninvasively estimating CABP have been proposed. However, there still lacks a systematic comparison of existing methods, especially in terms of how they differ in the ability to tolerate individual differences or measurement errors. The present study was designed to address this gap. MethodsA large-scale ‘virtual subject’ dataset (n = 600) was created using a computational model of the cardiovascular system, and applied to examine several classical CABP estimation methods, including the direct method, generalized transfer function (GTF) method, n-point moving average (NPMA) method, second systolic pressure of periphery (SBP2) method, physical model-based wave analysis (MBWA) method, and suprasystolic cuff-based waveform reconstruction (SCWR) method. The errors of CABP estimation were analyzed and compared among methods with respect to the magnitude/distribution, correlations with physiological/hemodynamic factors, and sensitivities to noninvasive measurement errors. ResultsThe errors of CABP estimation exhibited evident inter-method differences in terms of the mean and standard deviation (SD). Relatively, the estimation errors of the methods adopting pre-trained algorithms (i.e., the GTF and SCWR methods) were overall smaller and less sensitive to variations in physiological/hemodynamic conditions and random errors in noninvasive measurement of brachial arterial blood pressure (used for calibrating peripheral pulse wave). The performances of all the methods worsened following the introduction of random errors to peripheral pulse wave (used for deriving CABP), as characterized by the enlarged SD and/or increased mean of the estimation errors. Notably, the GTF and SCWR methods did not exhibit a better capability of tolerating pulse wave errors in comparison with other methods. ConclusionsClassical noninvasive methods for estimating CABP were found to differ considerably in both the accuracy and error source, which provided theoretical evidence for understanding the specific advantages and disadvantages of each method. Knowledge about the method-specific error source and sensitivities of errors to different physiological/hemodynamic factors may contribute as theoretical references for interpreting clinical observations and exploring factors underlying large estimation errors, or provide guidance for optimizing existing methods or developing new methods.

Model-Based Safety Analysis for Autonomous Driving

Model-Based Safety Analysis for Autonomous Driving

Model-based safety analysis of requirement specifications

Model-based design primarily aims to establish a communication framework throughout a system’s design. Moreover, models with formal semantics allow verification based on rigorous methods, including the analysis of system safety. However, building formal models is a tedious manual process and cannot be easily applied to real problems.A key gap that hinders automation of model development is that there is no systematic way to connect system requirements with the activity of model-based design. In this article, we introduce a workflow to tackle this gap and ultimately automate the analysis of system safety using formal methods.We extend our previous work on boilerplate-based specification of system requirements with ontological semantics towards specifying FDIR (Failure, Detection, Isolation, Recovery) requirements. The workflow is centered around the automated generation of a model skeleton in SLIM, a component-based formal modeling language, from a set of ontology-based requirement specifications. Our approach has been implemented into a dedicated tool, which not only provides visualization of the ontology relations, but also supports traceability of the analysis findings back to the requirements specification. Finally, we provide results on the safety analysis of a real star-tracker system based on a SLIM model derived by minimally changing the auto-generated model skeleton.

Alternative Scenarios for Projected Prevalence of Stroke and Post-Stroke Dementia to 2046 in Ireland: A Model-Based Analysis

Abstract Background Understanding future population need is key for informing stroke service planning. This study aims to evaluate alternative scenarios for future trends in stroke age-specific incidence and case-fatality and estimate impact on projected stroke and post-stroke dementia prevalence in Ireland. Methods We used a probabilistic Markov model to project and track incidence and prevalence of stroke and post-stroke dementia in the Irish population aged 40-89 years to 2046. Using systematic review and observational evidence, we extrapolated trends in stroke age-specific incidence and case-fatality from 1990-2019 to 2016-2046. We defined optimistic, realistic and pessimistic scenarios, including scenarios where trends decelerated over time. Results The stable (pessimistic) scenario indicates a projected 95,278 stroke survivors in 2046 (95% uncertainty interval, UI = 91,404-99,487) (30.1 per 1000 pop), an increase of 53% from 2022 to 2046. The most optimistic scenario would involve a 3.2% increase from 2022 to 2046, to a projected 61,000 survivors. More realistic scenarios based on lower rates of incidence/case-fatality decline, or rates of decline slowing over time, imply an increase ranging between 22.9% and 37.2%. There will be a projected 19,430 with post-stroke dementia in 2046 (95% UI 17,182-21,904), an increase of 69.2% from 2022. In the most optimistic scenario, the increase would be 22.6%, with realistic scenarios implying an increase ranging between 38.9% and 53.2%. Conclusion Future stroke healthcare needs will vary substantially depending on epidemiological trends. A large increase in post-stroke dementia is projected across scenarios. Further work is needed to identify optimal policies to promote continuing decline in stroke incidence.

Enhancing water management and urban flood resilience using Hazard Capacity Factor Design (HCFD) model: Case study of Eco-Delta city, Busan

This study evaluated future climate scenarios and the changes in urban flood resistance capacity in Busan Eco-Delta City using the hazard capacity factor design (HCFD) model. It analyzed the flood reduction effects of both gray and green infrastructure. Despite existing flood safety systems, there is a growing need to enhance urban flood resilience due to increasing heavy rainfall, unpredictable precipitation, and frequent typhoons driven by climate change. The HCFD model predicted urban flood volumes of Busan Eco-Delta City and analyzed the effectiveness of gray and green infrastructure in flood control. A stormwater management model (SWMM) simulated urban flood resistance capacity under the SSP1–2.6 climate change scenario. Results indicate that for an anticipated 500 mm rainfall over 3 h, green infrastructure can mitigate floods by 9 % to 17.6 %, while gray infrastructure can reduce flooding by 24 % to 32.1 %. The integration of gray and green infrastructure leads to an overall flood mitigation ranging from 47.1 % to 63.3 %. A notable contribution of this research is its predictive analysis of future flood scenarios using model-based scenario analysis and decision support algorithms, offering valuable insights into changes in urban flood resistance capacity and strategies for effective flood control decision-making.

Association between betrayal trauma and typologies of anger and aggression

Anger and aggression are related forms of externalizing phenomena that are distinct in that anger is a form of emotion, whereas aggression is a behavior. Both anger and aggression (as well as their combination) function as responses to environmental conditions, one of the most common of which is trauma. Recent research has highlighted the association between trauma high in betrayal and externalizing problems, although this research has not been examined with respect to typologies of anger and aggression specifically. In this study, we examined the association between different forms of trauma and typologies of anger and aggression in a sample of women and men recruited online (N = 364) using model-based cluster analysis and multinomial logistic regression. Results suggest that trauma with a high degree of betrayal was associated with a typology of implicit anger characterized by intense anger but controlled aggression. Findings suggest the importance of understanding the impact of betrayal on anger and aggression.