Hasil untuk "Hazardous substances and their disposal"

Hongjun Wang, Wei Liu, Weibo Gu et al.

Regulating the importance ratio is critical for the training stability of Group Relative Policy Optimization (GRPO) based frameworks. However, prevailing ratio control methods, such as hard clipping, suffer from non-differentiable boundaries and vanishing gradient regions, failing to maintain gradient fidelity. Furthermore, these methods lack a hazard-aware mechanism to adaptively suppress extreme deviations, leaving the optimization process vulnerable to abrupt policy shifts. To address these challenges, we propose Modulated Hazard-aware Policy Optimization (MHPO), a novel framework designed for robust and stable reinforcement learning. The proposed MHPO introduces a Log-Fidelity Modulator (LFM) to map unbounded importance ratios into a bounded, differentiable domain. This mechanism effectively prevents high-variance outlier tokens from destabilizing the loss landscape while ensuring global gradient stability. Complementarily, a Decoupled Hazard Penalty (DHP) integrates cumulative hazard functions from survival analysis to independently regulate positive and negative policy shifts. By shaping the optimization landscape with hazard-aware penalties, the proposed MHPO achieves fine-grained regulation of asymmetric policy shifts simultaneously mitigating mode collapse from over-expansion and preventing policy erosion from catastrophic contraction within a stabilized trust region. Extensive evaluations on diverse reasoning benchmarks across both text-based and vision-language tasks demonstrate that MHPO consistently outperforms existing methods, achieving superior performance while significantly enhancing training stability.

Davide Zaccagnino, Didier Sornette

Epistemic uncertainty in probabilistic seismic hazard assessment (PSHA) is commonly addressed through a logic-tree framework that combines weighted alternative models to characterize the range of plausible hazard outcomes. Implicit in this approach is a critical assumption: that the available model class provides an adequate representation of the underlying physics governing fault networks. Yet current formulations remain highly simplified, neglecting nonlinear interactions, diverse fault slip modes, multi-scale coupling, and the emergent dynamics that govern the nucleation and evolution of large earthquakes. As a result, the standard treatment of epistemic uncertainty may introduce systematic hazard bias and substantially underestimate forecast uncertainty. To formalize this limitation, we introduce SHARP (Seismic Hazard Assessment and Risks with Physics), a new framework that shifts the focus from selecting among imperfect models to quantifying their collective distance from physical and observational constraints. Central to SHARP is the Model Adequacy Distance (MAD), a quantitative metric of model inadequacy. MAD combines (i) a moment-weighted scoring function scaling with seismic moment to reflect the disproportionate social and economic impact of large events and (ii) compatibility measures derived from geodetic observations and statistical properties of seismicity. We illustrate the approach with an application to the frequency-magnitude distribution of Southern California seismicity. SHARP establishes a rigorous foundation for moving beyond conventional epistemic uncertainty toward a physics-grounded framework for seismic hazard and risk assessment.

Phong K. Thai, Jeffrey T. McDonough, Trent A. Key et al.

Per- and polyfluoroalkyl substances (PFAS) retained within hardscape have been observed to leach over time in numerous laboratory studies. The aim of this study was to measure the release of key PFAS in rainfall runoff from a concrete pad impacted by historical AFFF use at the field-scale. Rainfall simulations were conducted on a 5 m2 (1 ×5 m) plot for 3 consecutive days. Runoff water was collected every 2 minutes and analyzed for 5 PFAS commonly associated with AFFF. Surface samples of the concrete were collected from the plot after the rainfall runoff experiment. Perfluorooctane sulfonate (PFOS) exhibited the highest concentrations within the concrete (up to ∼4000 μg kg−1) and runoff water (up to 500 μg L−1), followed by 6:2 fluorotelomer sulfonate (6:2 FTS). PFAS concentrations in runoff water were higher in the first sample and then decreased in the consecutive samples of each rainfall simulation. It is estimated that the percentage of the total PFAS mass within the surface of the concrete contributing to runoff samples ranged from 0.006 % (PFOS) to 0.031 % (PFHxA) per rainfall event. This suggests low but sustained PFAS leaching from AFFF-impacted concrete into runoff water. Our findings confirmed that concrete impacted by legacy use of AFFF is a likely secondary source of PFAS in runoff water and highlight some similarities and differences between laboratory- and field-scale rainfall simulations.

Sunkalp Chandra

This study evaluates the performance of several machine learning models for predicting hazardous near-Earth objects (NEOs) through a binary classification framework, including data scaling, power transformation, and cross-validation. Six classifiers were compared, namely Random Forest Classifier (RFC), Gradient Boosting Classifier (GBC), Support Vector Classifier (SVC), Linear Discriminant Analysis (LDA), Logistic Regression (LR), and K-Nearest Neighbors (KNN). RFC and GBC performed the best, both with an impressive F2-score of 0.987 and 0.986, respectively, with very small variability. SVC followed, with a lower but reasonable score of 0.896. LDA and LR had a moderate performance with scores of around 0.749 and 0.748, respectively, while KNN had a poor performance with a score of 0.691 due to difficulty in handling complex data patterns. RFC and GBC also presented great confusion matrices with a negligible number of false positives and false negatives, which resulted in outstanding accuracy rates of 99.7% and 99.6%, respectively. These findings highlight the power of ensemble methods for high precision and recall and further point out the importance of tailored model selection with regard to dataset characteristics and chosen evaluation metrics. Future research could focus on the optimization of hyperparameters with advanced features engineering to further the accuracy and robustness of the model on NEO hazard predictions.

Yangxinyu Xie, Bowen Jiang, Tanwi Mallick et al.

Large language models (LLMs) are a transformational capability at the frontier of artificial intelligence and machine learning that can support decision-makers in addressing pressing societal challenges such as extreme natural hazard events. As generalized models, LLMs often struggle to provide context-specific information, particularly in areas requiring specialized knowledge. In this work we propose a retrieval-augmented generation (RAG)-based multi-agent LLM system to support analysis and decision-making in the context of natural hazards and extreme weather events. As a proof of concept, we present WildfireGPT, a specialized system focused on wildfire hazards. The architecture employs a user-centered, multi-agent design to deliver tailored risk insights across diverse stakeholder groups. By integrating natural hazard and extreme weather projection data, observational datasets, and scientific literature through an RAG framework, the system ensures both the accuracy and contextual relevance of the information it provides. Evaluation across ten expert-led case studies demonstrates that WildfireGPT significantly outperforms existing LLM-based solutions for decision support.

Jinqing Li, Jun Ma

Survival analysis can sometimes involve individuals who will not experience the event of interest, forming what is known as the cured group. Identifying such individuals is not always possible beforehand, as they provide only right-censored data. Ignoring the presence of the cured group can introduce bias in the final model. This paper presents a method for estimating a semiparametric additive hazards model that accounts for the cured fraction. Unlike regression coefficients in a hazard ratio model, those in an additive hazard model measure hazard differences. The proposed method uses a primal-dual interior point algorithm to obtain constrained maximum penalized likelihood estimates of the model parameters, including the regression coefficients and the baseline hazard, subject to certain non-negativity constraints.

Narayanaswamy Balakrishnan, María Jaenada, Leandro Pardo

Accelerated life tests (ALTs) play a crucial role in reliability analyses, providing lifetime estimates of highly reliable products. Among ALTs, step-stress design increases the stress level at predefined times, while maintaining a constant stress level between successive changes. This approach accelerates the occurrence of failures, reducing experimental duration and cost. While many studies assume a specific form for the lifetime distribution, in certain applications instead a general form satisfying certain properties should be preferred. Proportional hazard model assumes that applied stresses act multiplicatively on the hazard rate, so the hazards function may be divided into two factors, with one representing the effect of the stress, and the other representing the baseline hazard. In this work we examine two particular forms of baseline hazards, namely, linear and quadratic. Moreover, certain experiments may face practical constraints making continuous monitoring of devices infeasible. Instead, devices under test are inspected at predetermined intervals, leading to interval-censoring data. On the other hand, recent works have shown an appealing trade-off between the efficiency and robustness of divergence-based estimators. This paper introduces the step-stress ALT model under proportional hazards and presents a robust family of minimum density power divergence estimators (MDPDEs) for estimating device reliability and related lifetime characteristics such as mean lifetime and distributional quantiles. The asymptotic distributions of these estimates are derived, providing approximate confidence intervals. Empirical evaluations through Monte Carlo simulations demonstrate their performance in terms of robustness and efficiency. Finally, an illustrative example is provided to demonstrate the usefulness of the model and associated methods developed.

Lorenzo Tedesco, Francesco Finazzi

The Proportional Hazards (PH) model is one of the most widely used models in survival analysis, typically assuming a log-linear relationship between covariates and the hazard function. However, in the context of spatial survival data, where the time-to-event variable is associated with a spatial location within a given domain, this assumption is often unrealistic in capturing spatial effects. Thus, this paper proposes modeling the location effect through a nonparametric function of spatial location. The function is approximated using finite element methods on a triangulated mesh to accommodate irregular domains. Estimation is carried out within the classical partial likelihood framework, with smoothness of the spatial effect enforced through differential penalization. Using sieve methods, we establish the consistency and asymptotic normality of the parametric component. Simulations and two empirical applications demonstrate superior performance compared to existing approaches.

Hamid Chekaveh, Mohsen Shakeri, Hossain Hassani

Background: In Iran, from the academic year 2021-2022, it was decided to reopen all schools. It is of great importance that the activities of schools be accompanied by principles, solutions, and requirements; so, concerns about students and school staff contracting this disease will be reduced. The present investigates the experiences of elementary school principals in Roodan city, in Hormozgan Province, Iran, about health requirements of school activity during post-corona era. Methods: This was a qualitative and phenomenological study. Data were analyzed by Smith's method after theoretical saturation of data obtained from an in-depth semi-structured interview. The interview contained 12 principals of elementary schools in Roodan city in the academic year of 2021-2022, who were selected through purposive sampling. Results: The experiences of elementary school principals about school activities were obtained in the form of 3 main themes including executive measures (provision of disinfectants, improvement of school infrastructure, absence of sick students and teachers in school, safe distance, creating a happy and safe environment for students to attend, and seeking support from benefactors and parents counseling sessions), educational and promotion-interventional measures (holding classes and remedial measures, teaching health issues to students, informing parents, environmental and virtual information, and counseling sessions), and supervisory measures (supervision of students, teachers, principals, and assistants of the school). Conclusions: The results of examining the experiences of elementary school principals about reopening of schools in the post-corona period showed that there is a need for many facilities and measures. Moreover, capable principals face various challenges and need to work with organizations, officials, students, and families.

Esmaeil Fakharian, Zahra Sehat, Mojtaba Sehat et al.

Background: Today, global attention has been directed towards differences in the health of different Socioeconomy of society (SES) groups. Trauma is one of the categories where SES determinants are not well understood, especially in developing countries. This study aims to determine the annual incidence of traumabased on SES in people over 15 years old in Kashan. Methods: This was a population-based cross-sectional studyusing a household survey .Data were collected through stratified-cluster sampling during 2018- 2019 for over 15-year in Kashan. The researchers conducted univariate and multivariate analyses to evaluate trauma during and the past year rgarding SES of individuals using Principal Components Analysis (PCA). Results: The incidence of trauma was 70.6 (62. 6-78. 7) in 1000 annually, the risk of trauma in low SES was 1.06 (0.82-1.38), in moderate SES , 0.87 (0.69-1.10), and in high SES, it was 1.13 (0.84-1.52). Among different SES groups, mechanisms of injury were different (P-value = 0.09); also, the annual incidence of trauma in different SES groups was different based on the place of trauma (P-value = 0.02), the number of injuries (P-value = 0.00), treatment (P-value = 0.02), and the time to return to work (P-value = 0.00). Conclusions: Annual incidence of trauma in different SES groups was different based on the place of trauma, the number of injuries, treatment, and time to return to work. The relationship between SES status and incidence of trauma is important to provide preventive services.

J. A. Christen, F. J. Rubio

The hazard function represents one of the main quantities of interest in the analysis of survival data. We propose a general approach for parametrically modelling the dynamics of the hazard function using systems of autonomous ordinary differential equations (ODEs). This modelling approach can be used to provide qualitative and quantitative analyses of the evolution of the hazard function over time. Our proposal capitalises on the extensive literature of ODEs which, in particular, allow for establishing basic rules or laws on the dynamics of the hazard function via the use of autonomous ODEs. We show how to implement the proposed modelling framework in cases where there is an analytic solution to the system of ODEs or where an ODE solver is required to obtain a numerical solution. We focus on the use of a Bayesian modelling approach, but the proposed methodology can also be coupled with maximum likelihood estimation. A simulation study is presented to illustrate the performance of these models and the interplay of sample size and censoring. Two case studies using real data are presented to illustrate the use of the proposed approach and to highlight the interpretability of the corresponding models. We conclude with a discussion on potential extensions of our work and strategies to include covariates into our framework. Although we focus on examples on Medical Statistics, the proposed framework is applicable in any context where the interest lies on estimating and interpreting the dynamics hazard function.

Lorenzo Tedesco, Jad Beyhum, Ingrid Van Keilegom

We consider instrumental variable estimation of the proportional hazards model of Cox (1972). The instrument and the endogenous variable are discrete but there can be (possibly continuous) exogenous covariables. By making a rank invariance assumption, we can reformulate the proportional hazards model into a semiparametric version of the instrumental variable quantile regression model of Chernozhukov and Hansen (2005). A naïve estimation approach based on conditional moment conditions generated by the model would lead to a highly nonconvex and nonsmooth objective function. To overcome this problem, we propose a new presmoothing methodology. First, we estimate the model nonparametrically - and show that this nonparametric estimator has a closed-form solution in the leading case of interest of randomized experiments with one-sided noncompliance. Second, we use the nonparametric estimator to generate ``proxy'' observations for which exogeneity holds. Third, we apply the usual partial likelihood estimator to the ``proxy'' data. While the paper focuses on the proportional hazards model, our presmoothing approach could be applied to estimate other semiparametric formulations of the instrumental variable quantile regression model. Our estimation procedure allows for random right-censoring. We show asymptotic normality of the resulting estimator. The approach is illustrated via simulation studies and an empirical application to the Illinois

Om M. Khare, Shubham Gandhi, Aditya M. Rahalkar et al.

Effective detection of road hazards plays a pivotal role in road infrastructure maintenance and ensuring road safety. This research paper provides a comprehensive evaluation of YOLOv8, an object detection model, in the context of detecting road hazards such as potholes, Sewer Covers, and Man Holes. A comparative analysis with previous iterations, YOLOv5 and YOLOv7, is conducted, emphasizing the importance of computational efficiency in various applications. The paper delves into the architecture of YOLOv8 and explores image preprocessing techniques aimed at enhancing detection accuracy across diverse conditions, including variations in lighting, road types, hazard sizes, and types. Furthermore, hyperparameter tuning experiments are performed to optimize model performance through adjustments in learning rates, batch sizes, anchor box sizes, and augmentation strategies. Model evaluation is based on Mean Average Precision (mAP), a widely accepted metric for object detection performance. The research assesses the robustness and generalization capabilities of the models through mAP scores calculated across the diverse test scenarios, underlining the significance of YOLOv8 in road hazard detection and infrastructure maintenance.

Chen Huang, Biao Jin

Niu Jiale

In this manuscript, we study stochastic comparisons of the second-order statistics from dependent or independent observations with modified proportional hazard rates models. First, we establish the usual stochastic order of the second-order statistics from dependent and heterogeneous observations. Second, sufficient conditions are provided in the hazard rate order of the second-order statistics from independent observations. Then, we investigate the hazard rate order of the second-order statistics arising from two sets of independent multiple-outlier modified proportional hazard rates observations. Finally, some numerical examples are given to illustrate the theoretical findings.

Jussi Leinonen, Ulrich Hamann, Ioannis V. Sideris et al.

Predictions of thunderstorm-related hazards are needed in several sectors, including first responders, infrastructure management and aviation. To address this need, we present a deep learning model that can be adapted to different hazard types. The model can utilize multiple data sources; we use data from weather radar, lightning detection, satellite visible/infrared imagery, numerical weather prediction and digital elevation models. We demonstrate the ability of the model to predict lightning, hail and heavy precipitation probabilistically on a 1 km resolution grid, with a temporal resolution of 5 min and lead times up to 60 min. Shapley values quantify the importance of the different data sources, showing that the weather radar products are the most important predictors for all three hazard types.

Martin Dumav

This paper considers dynamic moral hazard settings, in which the consequences of the agent's actions are not precisely understood. In a new continuous-time moral hazard model with drift ambiguity, the agent's unobservable action translates to drift set that describe the evolution of output. The agent and the principal have imprecise information about the technology, and both seek robust performance from a contract in relation to their respective worst-case scenarios. We show that the optimal long-term contract aligns the parties' pessimistic expectations and broadly features compressing of the high-powered incentives. Methodologically, we provide a tractable way to formulate and characterize optimal long-run contracts with drift ambiguity. Substantively, our results provide some insights into the formal link between robustness and simplicity of dynamic contracts, in particular high-powered incentives become less effective in the presence of ambiguity.

Carles Corral, Marc Berenguer. Daniel Sempere-Torres, Laura Poletti et al.

The anticipation of flash flood events is crucial to issue warnings to mitigate their impact. This work presents a comparison of two early warning systems for real-time flash flood hazard forecasting at regional scale. The two systems are based in a gridded drainage network and they use weather radar precipitation inputs to assess the hazard level in different points of the study area, considering the return period (in years) as the indicator of the flash flood hazard. The essential difference between the systems is that one is a rainfall-based system (ERICHA), using the upstream basin-aggregated rainfall as the variable to determine the hazard level, while the other (Flood-PROOFS) is a system based on a distributed rainfall-runoff model to compute the streamflows at pixel scale. The comparison has been done for three rainfall events in the autumn of 2014 that resulted in severe flooding in the Liguria region (Northwest of Italy). The results obtained by the two systems show many similarities, particularly for larger catchments and for large return periods (extreme floods).

Nil Kamal Hazra, Neeraj Misra

The relative ageing is an important notion which is useful to measure how a system ages relative to another one. Among all existing stochastic orders, there are two important orders describing the relative ageing of two systems, namely, ageing faster orders in the cumulative hazard and the cumulative reversed hazard rate functions. In this paper, we give some sufficient conditions under which one coherent system ages faster than another one with respect to the aforementioned stochastic orders. Further, we show that the proposed sufficient conditions are satisfied for $k$-out-of-$n$ systems. Moreover, some numerical examples are given to illustrate the developed results.

Boris Tsirelson

If the hazard rate $ \frac{ F'(x) }{ 1-F(x) } $ is increasing (in $x$), then $ \mathbb E\, ( X_{n:n} - X_{n-1:n} ) $ is decreasing (in $n$), and moreover, completely monotone.