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Background In the PROSELICA, a randomized controlled trial (RCT) comparing cabazitaxel 20 mg/m2 (C20) versus 25 mg/m2 (C25) in metastatic castration‐resistant prostate cancer (mCRPC), one‐variable‐at‐a‐time subgroup analysis suggested possible heterogeneity in treatment effect (HTE) of C25 versus C20 among study participants. Novel predictive HTE analysis approaches may provide an in‐depth understanding of such results. Methods We analyzed patient‐level data from 1200 patients with mCRPC who were randomized in the PROSELICA trial. Outcomes included overall survival (OS) and progression‐free survival (PFS). Using baseline characteristics, patients were stratified into quartiles based on either quantitative baseline risk of poor outcome (risk modeling) or predicted individualized treatment effect (ITE) using a causal survival forest algorithm (effect modeling). Treatment effects were measured as differences in restricted mean survival time (RMST). Results For risk modeling, the OS effect of C25 increased with risk quartiles: −0.07 months (95% CI, −1.60 to 1.46) in the lowest risk quartile and 1.67 months (95% CI, 0.25 to 3.10) in the highest risk quartile. For effect modeling, the OS effect ranged from −0.17 months (95% CI, −3.01 to 2.68) in the lowest ITE quartile to 0.57 months (95% CI, −2.27 to 3.41) in the highest ITE quartile. Both approaches demonstrated greater C25 benefit in patients with extensive previous treatment and baseline disease burden. PFS effects remained consistent across all quartiles. Conclusions The OS effect of C25 versus C20 may vary based on baseline characteristics in post‐docetaxel mCRPC. Patients with extensive treatment history and disease burden may benefit more from C25.

This study creates and refines a risk–effectiveness–integrated dynamic simulation framework that brings together risk and effectiveness factors affecting qualified workforce allocation in multi-project contexts, specifically in the construction of industrial production facilities. Based on a case study of three overlapping projects in West Java, Indonesia, this study examines the requirements for an expert workforce across the Engineering, Procurement, and Construction (EPC) phases. Conventional mitigation measures generally assume that a qualified expert workforce is immediately available. However, hiring the right personnel with specific qualifications for a project takes time. To fill this gap, this paper presents a system dynamics-based model that explicitly integrates quantified project risks and execution effectiveness to determine expert workforce requirements at the multi-project level. This aspect is often addressed implicitly in the existing workforce planning approaches. This mixed-methods strategy includes a literature review, variable validation, simulation modeling, and case analysis. The results show that workforce planning based on integrated risk and effectiveness factors significantly improves project delivery by anticipating expert workforce shortages and reducing the need for reactive solutions. Model validation using real project data demonstrates that the simulated expert workforce demand reproduces both the average behavior and variability observed in real-world practice, satisfying quantitative behavioral validation criteria across projects and the EPC phases. The model contributes to sustainability by enhancing long-term workforce resilience, reducing resource waste, and supporting more efficient industrial project delivery.

Preparing a city for the impact of global warming is becoming of major importance. Adopting climate-proof policies and strategies in response to climate change has become a fundamental element for city planning. To this end, this research considers a multidisciplinary approach, at the local scale, able to connect urban planning and architecture, as a vital base for considering a coastal cities’ ability to control the consequences of climate change, specifically floods. So far, there is a scarcity of research connecting sea ground and land surveys, and this study could become a foundational reference for coastline settlement management using BIM. We found in BIM (Building Information Modeling) a possible tool for managing coastal risk, since it can combine crowdsourced data for geometric and information modeling of the city. The proposed BIM model includes a topography used for 3D thematic maps, a riverbed model, and a waterway model. This model aims to facilitate coordination across separate actors and interests since the urban area model is always updatable and improvable. Focusing on a case study of Lisbon, we developed risk-based 3D maps of the area close to the shoreline of the Tagus River.

Recent efforts to assess coastal compound surge and rainfall‐driven flooding hazard from tropical (TCs) and extratropical cyclones (ETCs) in a warming climate have intensified. However, challenges persist in gaining actionable insights into the changing magnitude and spatial variability of these hazards. We employ a physics‐based hydrodynamic framework to numerically simulate compound flooding from TCs and ETCs in both current and future climates, focusing on the western side of Buzzards Bay in Massachusetts. Our approach leverages hydrodynamic models driven by extensive sets of synthetic TCs downscaled from CMIP6 climate models. We also perform a far less extensive analysis of ETCs using a previously produced event set, dynamically downscaled using the WRF model driven by a single CMIP5 model. This methodology quantifies how climate change may reshape the compound flooding hazard landscape in the study area. Our findings reveal a significant increase in TC‐induced compound flooding hazard due to evolving climatology and sea level rise (SLR). Although compound flooding induced by ETCs increases mostly in coastal areas due to SLR, inland areas exhibit almost no change, and some even show a decline in rainfall‐driven flooding from high‐frequency ETC events toward the end of the century compared to the current climate. Our methodology is transferable to vulnerable coastal regions, serving as a tool for adaptive measures in populated areas. It equips decision‐makers and stakeholders with the means to mitigate the destructive impacts of compound flooding arising from both current and future TCs, and shows how the same methodology might be applied to ETCs. Plain Language Summary During storms in coastal areas, strong winds can cause surge‐driven flooding, and simultaneously, intense rainfall may lead to inland heavy rainfall‐driven flooding. Sometimes, these two flooding sources coincide, forming compound surge‐ and rainfall‐driven flooding, which is more destructive than either hazard alone. To assess the hazard of such destructive compound flooding, we use physics‐based models to quantify the frequency and magnitude of these hazards. Additionally, we evaluate how climate change and factors such as SLR may affect the frequency and magnitude of such events in coastal areas. Through these detailed and granular hazard assessments, regions facing increased flooding threats can develop strategies to more effectively mitigate damages posed by compound flooding during extreme storms. Key Points A newly developed model simulates the interplay of surge and rainfall flooding from tropical and extratropical cyclones in a warming climate Tropical cyclone‐induced compound flooding hazard increases with evolving storm climatology and rising sea levels in a warming climate Extratropical cyclone‐induced flooding increases in coastal areas with sea‐level rise, while staying minimal inland in a warming climate

Urbanization replaces the extant natural resource base (e.g., forests, wetlands) with an infrastructure that is capable of supporting humans. One ecological consequence of urbanization is higher concentrations of nitrogen (N) and phosphorous (P) in streams, lakes, and estuaries. When received in excess, N and P are considered pollutants. Continuing urbanization will change the relative distribution of extant natural resources. Characteristics of the landscape can shape its response to disturbances such as urbanization. Changes in landscape characteristics across a region create a geographic pattern of vulnerability to increased N and P as a result of urbanization. We linked nutrient-export risk and urbanization models in order to identify areas most vulnerable to increases in nutrient-export risk due to projected urbanization. A risk-based model of N and P export exceeding specified thresholds was developed based on the extant distribution of forest, agriculture, and urban land cover. An empirical model of urbanization was used to increase urban land cover at the expense of forest and agriculture. The modeled (future) land cover was input into the N and P export risk model, and the \"before\" and \"after\" estimates of N and P export were compared to identify the areas most vulnerable to change. Increase in N and P export had to be equal to or greater than the accumulated uncertainties in the nutrient-export risk and urbanization models for an area to be considered vulnerable. The areas most vulnerable to increased N and P export were not spatially coincident with the areas of greatest urbanization. Vulnerability also depended on the geographic distribution of forest and agriculture. In general, the areas most vulnerable to increased N exports were where the modeled urbanization rate was at least 20% and the amount of forest was about 6 times greater than the amount of agriculture. For P, the most vulnerable areas were where the modeled urbanization rate was at least 20% and the amount of forest was about 2 times greater than the amount of agriculture. Vulnerability to increased N and P export was the result of two interacting spatial patterns, urbanization and the extant distribution of land cover. It could not be predicted from either alone.

Environmental, Social, and Governance (ESG) ratings are widely used for investment and regulatory decision-making, yet they often suffer from symbolic compliance and information asymmetry. To address these limitations, this study introduces a hybrid ESG risk assessment model that integrates court ruling data with traditional ESG ratings to detect latent sustainability risks. Using a dataset of 213 ESG-related U.S. court rulings from January 2023 to May 2025, we apply natural language processing (TF-IDF, Legal-BERT) and explainable AI (SHAP) techniques to extract structured features from legal texts. We construct and compare classification models—including Random Forest, XGBoost, and a Legal-BERT-based hybrid model—to predict firms’ litigation risk. The hybrid model significantly outperforms the baseline ESG-only model in all key metrics: F1-score (0.81), precision (0.79), recall (0.84), and AUC-ROC (0.87). SHAP analysis reveals that legal features such as regulatory sanctions and governance violations are the most influential predictors. This study demonstrates the empirical value of integrating adjudicated legal evidence into ESG modeling and offers a transparent, verifiable framework to enhance ESG risk evaluation and reduce information asymmetry in sustainability assessments.

PurposeGiven the expansion of cities and urbanization, developing efficient and reliable transportation infrastructure, especially urban tunnels, is essential. Failure to maintain such complex construction facilities with intelligent equipment systems could result in human losses and impose huge costs on governments. Therefore, it is necessary to have practical maintenance plans and operational safety monitoring for urban tunnels, which leads to their long lifespan, increases users’ safety and reduces operation risks.Design/methodology/approachHence, this research aims to evaluate the maintenance risks of urban tunnel lighting systems (UTLS) using a hybrid risk-based maintenance (RBM) approach. In this vein, three rounds of a fuzzy Delphi survey were conducted to consolidate the specific operation criteria and maintenance risk factors to the circumstances of Iran and UTLS. Furthermore, the fuzzy DEMATEL method was applied to determine the cause-and-effect relationships among the identified critical operation criteria. The identified risks associated with maintenance in UTLS were then analyzed and ranked using a combination of fuzzy ANP-VIKOR techniques.FindingsThe ranking of the various risks revealed that the “poor performance of switchboards in power supply due to faults in switchboard equipment” risk was ranked first, followed by the “poor performance of panels in the power supply due to unfavorable environmental conditions,” “The poor performance of panels in the power supply due to problems with switches (key failure)” and “The poor performance of panels in power supply due to burning fuses due to unauthorized current” risks. The findings of this study indicate that this hybrid maintenance method, developed as a risk-based network, provides reliability for maintaining urban tunnel lighting systems (UTLS).Originality/valueIt is anticipated that the findings of this research will considerably contribute to improving UTLS maintenance management while enhancing different stakeholders’ understanding of the most critical risks in maintenance, particularly toward the UTLS in Iran. An RBM management program can result in preparing and formulating policies, comprehensive guidelines or regulations for the maintenance of urban tunnels that are recommended for future research.

Earthquake-induced pulse-like ground motions are often observed in near-source conditions due to forward-directivity. Recent worldwide earthquakes have emphasised the severe damage potential of such pulse-like ground motions. This paper introduces a framework to quantify the impact of directivity-induced pulse-like ground motions on the direct economic losses of building portfolios. To this aim, a simulation-based probabilistic risk modelling framework is implemented for various synthetic building portfolios located either in the fault-parallel or fault-normal orientations with respect to a case-study strike–slip fault. Three low-to-mid-rise building typologies representative of distinct vulnerability classes in the Mediterranean region are considered: non-ductile moment-resisting reinforced concrete (RC) frames with masonry infills, mainly designed to only sustain gravity loads (i.e. pre-code frames); moment-resisting RC infilled frames designed considering seismic provisions for high ductility capacity (i.e. special-code frames); special-code steel moment-resisting frames. Monte Carlo-based probabilistic seismic hazard analysis is first performed, considering the relevant modifications to account for the pulse-occurrence probability and the resulting spectral amplification. Hazard curves for sites/buildings located at different distances from the fault are obtained, discussing the spatial distribution of the hazard amplification. A set of pulse-like ground motions and a set of one-to-one spectrally-equivalent ordinary records are used to perform non-linear dynamic analysis and derive fragility relationships for each considered building typology. A vulnerability model is finally built by combining the derived fragility relationships with a (building-level) damage-to-loss model. The results are presented in terms of intensity-based and expected annual loss for synthetic portfolios of different sizes and distribution of building types. It is shown that, for particularly short-period structures (e.g. infilled RC frames), the influence of near-source directivity can be reasonably neglected in the fragility derivation while kept in place in the hazard component. Overall, near-source directivity effects are significant when estimating losses of individual buildings or small portfolios located very close to a fault. Nevertheless, the impact of pulse-like ground motions on losses for larger portfolios can be considered minimal and can be neglected in most of the practical large-scale seismic risk assessment applications.

Imaging features for radiomic analyses are commonly calculated from the entire gross tumour volume (GTVentire). However, tumours are biologically complex and the consideration of different tumour regions in radiomic models may lead to an improved outcome prediction. Therefore, we investigated the prognostic value of radiomic analyses based on different tumour sub-volumes using computed tomography imaging of patients with locally advanced head and neck squamous cell carcinoma. The GTVentire was cropped by different margins to define the rim and the corresponding core sub-volumes of the tumour. Subsequently, the best performing tumour rim sub-volume was extended into surrounding tissue with different margins. Radiomic risk models were developed and validated using a retrospective cohort consisting of 291 patients in one of the six Partner Sites of the German Cancer Consortium Radiation Oncology Group treated between 2005 and 2013. The validation concordance index (C-index) averaged over all applied learning algorithms and feature selection methods using the GTVentire achieved a moderate prognostic performance for loco-regional tumour control (C-index: 0.61 ± 0.04 (mean ± std)). The models based on the 5 mm tumour rim and on the 3 mm extended rim sub-volume showed higher median performances (C-index: 0.65 ± 0.02 and 0.64 ± 0.05, respectively), while models based on the corresponding tumour core volumes performed less (C-index: 0.59 ± 0.01). The difference in C-index between the 5 mm tumour rim and the corresponding core volume showed a statistical trend (p = 0.10). After additional prospective validation, the consideration of tumour sub-volumes may be a promising way to improve prognostic radiomic risk models.

Effective enforcement of laws and regulations hinges heavily on robust inspection policies. While data-driven approaches to testing the effectiveness of these policies are gaining popularity, they suffer significant drawbacks, particularly a lack of explainability and generalizability. This paper proposes an approach to crafting inspection policies that combines data-driven insights with behavioral theories to create an agent-based simulation model that we call a theory-infused phenomenological agent-based model (TIP-ABM). Moreover, this approach outlines a systematic process for combining theories and data to construct a phenomenological ABM, beginning with defining macro-level empirical phenomena. Illustrated through a case study of the Dutch inland shipping sector, the proposed methodology enhances explainability by illuminating inspectors’ tacit knowledge while iterating between statistical data and underlying theories. The broader generalizability of the proposed approach beyond the inland shipping context requires further research.