Search Results (617)

Increasing train speeds and axle loads result in greater demands on the reliability of predictions for vertical bridge accelerations in high-speed rail traffic. Complex multibody models, e.g., in the form of coupling beam models, allow for the explicit consideration of track–structure interaction, thereby improving prediction quality at the cost of significant computational and modelling effort. In this contribution, a computationally efficient alternative for the consideration of track-bridge interaction effects is developed that transfers the majority of the beneficial effects of the coupling beam to simple Euler–Bernoulli beam models without explicit coupling beam modelling. In this regard, the load-distributing effect of the ballast superstructure is extracted from train passage simulations of a track beam on a rigid base, whose coupling with the base via discrete spring and damper elements represents the elastic and dissipative properties of the ballast superstructure. The support force distributions determined under passing axle loads are assembled into train-specific load models and transferred to Euler–Bernoulli beam models. In addition, a probabilistic virtual bridge parameter field derived from real bridge data is developed to systematically evaluate the suitability of this novel load distribution model across a wide range of structural parameters. Compared to the load distribution model specified in standards, which specifies a distribution of axle loads into three individual loads, the load distribution model presented here shows significantly better agreement with the coupling beam model across all examined ballast superstructure and structural parameters, while consistently providing a conservative estimation of the structural response. The novel approach thus offers a physically sound method for efficiently accounting for the beneficial effects of the ballast superstructure in dynamic analyses. Full article

Carbon fiber-reinforced aluminum laminate (CARALL) has excellent properties such as strong impact resistance and fracture toughness. Lamb waves are expected to provide an efficient means for non-destructive testing of CARALL. However, the complex propagation characteristics of Lamb waves in CARALL with both anisotropic and isotropic materials between layers make it difficult to accurately identify and locate the damage. We proposed an integrated probabilistic imaging method to detect the damage localization considering Lamb waves propagation characteristics. First, the relationship between the propagation velocity and direction of Lamb waves was analyzed. It was found that, unlike CFRP laminates, where Lamb waves propagate fastest along the transverse direction, Lamb waves in CARALL exhibit the highest velocity along the 45° direction. Subsequently, a damage localization algorithm considering anisotropy and integrated probability imaging was proposed. Experimental and numerical results demonstrate that the damage localization algorithm proposed in this work accurately predicted the location of damage in CARALL, and the area of damage could be characterized by the direct wave correlation coefficient of Lamb waves. This study provides a useful framework for non-destructive testing of CARALL. Full article

This work presents a probabilistic methodology for the analysis and design of detonation sequences in non-electric blasting, considering both the statistical dispersion of pyrotechnic delays and the dependencies between detonation time intervals. These intervals are modeled as correlated multivariate normal random variables, allowing the estimation of blast success probability based on the simultaneous fulfillment of the desired firing order and the minimum time interval between consecutive detonations. The main contribution of this work is a general matrix formulation based on block matrices that provides a unified framework for representing and analyzing different non-electric initiation network configurations. The proposed formulation enables the direct computation of the mean vector and covariance matrix of detonation intervals from the statistical properties of connectors and detonators, while remaining readily scalable to complex layouts involving multiple rows and branching connection chains. Numerical examples demonstrate the strong influence of delay dispersion and network topology on blast reliability and illustrate the applicability of the proposed methodology to the probabilistic analysis and optimization of complex firing sequences. Full article

In this article, we propose a first-order integer-valued autoregressive (INAR(1)) model based on binomial thinning, in which the innovation sequence follows a zero–one–two-modified Poisson (ZOTMPo) distribution. The proposed model accommodates overdispersion and allows for inflation or deflation at low-count values, particularly at zero, one, and two, which are commonly observed in public health count time series. We derive the main probabilistic properties of the model and estimate the unknown parameters using the conditional maximum likelihood (CML) method. A Monte Carlo simulation study is conducted to evaluate the finite-sample performance of the estimators. Furthermore, we establish the information-theoretic properties of the model, specifically deriving the Shannon entropy and conditional entropy bounds to quantify the dynamical complexity and predictability of the stochastic process. The practical utility of the model is illustrated using two real-world datasets on dengue fever incidence and Escherichia coli (E. coli) enteritis. Model performance is assessed using standard information criteria and forecast accuracy measures, as well as the Euclidean distance between observed and fitted probabilities for zero, one, and two. Diagnostic checks, including analysis of residual autocorrelation, cumulative periodograms, and jump process behavior, provide further confirmation of the fitted model’s adequacy. The results indicate that the proposed ZOTMPo–INAR(1) model provides an effective framework for modeling overdispersed count time series with a modified low-count structure. Full article

Urban pluvial flood resilience relies not only on engineering defenses but also on effective behavioral coordination among local governments, property management companies (PMCs), and residents. Yet even when risks are recognized, preparedness often fails because costs are immediate and certain, whereas benefits are delayed and probabilistic. This study addresses this dilemma by integrating prospect theory into a tripartite evolutionary game model. We position PMCs as pivotal intermediaries whose maintenance decisions critically influence whether physical infrastructure can be translated into operational resilience. Simulation results show that actors initially remain inactive under uncertainty. Stable collaboration emerges only when government regulation, PMC maintenance, and resident participation reinforce one another. Equal-magnitude policy comparisons reveal an asymmetric policy effect: sufficiently strong penalties induce tripartite cooperation, whereas equivalent subsidies primarily improve PMC maintenance without sustaining resident engagement. Excessive post-disaster relief further weakens ex ante incentives by creating moral hazard. These findings advance community flood governance toward psychologically informed, actor-specific policy mixes, emphasizing clear responsibility allocation, targeted incentives, and conditional relief, to strengthen community-level climate adaptation and adaptive resilience under uncertainty. Full article

Health care empowerment is essential for self-care, informed decision-making, and appropriate use of health services among university students. This cross-sectional pilot study examined the association between selected social determinants of health and health care empowerment among university students in Peru. A sample of 336 undergraduate and graduate students from six universities completed an online survey that included the Empowerment Scale for Health Care Empowerment in University Students (ECSU). The instrument demonstrated adequate psychometric properties (Cronbach’s α = 0.859; McDonald’s ω = 0.835). Descriptive analyses and ordinal logistic regression were conducted to identify factors associated with health care empowerment and its dimensions. Income level, family type, and housing situation were associated with overall empowerment. Lower income levels and extended family structures were associated with lower empowerment levels, while university type and educational level were associated with specific dimensions related to decision-making and the ability to empower others. The explanatory capacity of the models was modest (pseudo-R² = 5.0–12.8%). These findings provide preliminary evidence on the relationship between social determinants and health care empowerment in university students. However, results should be interpreted with caution due to the cross-sectional design and convenience sampling. Future studies using probabilistic sampling and longitudinal designs are needed to confirm these associations. Full article

Radar-based Quantitative Precipitation Estimation (QPE) in semi-arid Mediterranean climates is critically challenged by systematic underestimation of shallow precipitation, yet gauge–radar merging frameworks tailored to such environments remain poorly evaluated. This study develops and assesses a merging pipeline for Cyprus, combining X-band polarimetric observations from the Paphos and Larnaca operational radar network with accumulations from a 50-station rain gauge network across 11 rainfall events spanning the 2024 wet season (January and November–December 2024). Four approaches were evaluated: raw radar mosaic, global mean field bias (MFB) correction, spatially varying local inverse distance weighting (IDW) bias correction assessed through leave-one-out cross-validation (LOOCV), and a Random Forest (RF) machine-learning retrieval trained on polarimetric, geometric, and orographic predictors and evaluated through leave-one-event-out cross-validation (LOEO-CV). Raw radar exhibited severe and highly variable underestimation, with station-level bias factors ranging from 1.4 to 200×. Global MFB correction removed systematic offset but, as a single spatially uniform scalar, could not improve spatial correspondence; it was beneficial only where the bias field was spatially coherent. Local IDW correction provided cross-validated reduction in RMSE for most events (commonly 40–53%), but this improvement reflected removal of mean bias rather than recovery of spatial pattern: only 17 January 2024 combined RMSE reduction (24.07 mm to 11.31 mm) with genuine spatial skill (leave-one-out r = 0.850, bias-field coherence r = 0.742), while several events improved in RMSE yet retained near-zero spatial correlation, and 30 and 31 January degraded outright. These results characterise the limits of distance-weighted (IDW) interpolation specifically; whether geostatistical estimators incorporating topographic external drift can restore spatial skill where the present gauge network constrains the bias field remains to be tested. When re-evaluated on the same rainy matched-pair set (N = 2378), the Random Forest reduced 10 min RMSE by only 2.6% relative to the best classical Z-R estimator (from 10.38 mm to 10.10 mm) and reduced the systematic bias from −5.06 mm to −4.25 mm, but did not improve point-to-point spatial correspondence (r ≈ 0), indicating that this mean-regression Random Forest provides effective bias-correction skill without spatial-correspondence skill, leaving the fundamental representativeness gap between CAPPI sampling and gauge point measurements unresolved. Three pre-conditions for local bias correction skill are identified as empirical diagnostics under the sample conditions of this study: a minimum of approximately 40 contributing gauges, a spatially coherent bias field, and a moderate bias range. A formal bootstrap or resampling-based uncertainty estimate for these indicators was not attempted, because eleven events constitute too small a sample for stable resampling statistics; the per-event relationships between the number of contributing gauges, the bias-factor range, the bias-field spatial autocorrelation, and the LOOCV error are therefore presented as the empirical basis for these diagnostic indicators, which should be refined and tested for statistical robustness as longer event records become available. These findings demonstrate that the suitability of spatial merging can be diagnosed from network and bias field properties prior to correction, and that machine-learning retrieval offers complementary value through systematic bias removal where spatial interpolation fails. Probabilistic merging frameworks, denser gauge networks, and ML approaches that explicitly target spatial correspondence are identified as priority developments for eastern Mediterranean QPE. Full article

Purpose: This study conducts a systematic literature review on the intersection of real estate exposure and geotechnical hazards, focusing specifically on seismic and subsurface risks. The objective is to synthesize key thematic trends, methodologies, and governance frameworks that inform risk-informed planning in seismically vulnerable urban areas. Design/methodology/approach: A Boolean search query was implemented on Lens.org, identifying 55 peer-reviewed articles published between January 2020 and May 2025. Inclusion criteria required explicit focus on property exposure to seismic or ground instability risks. Thematic analysis was conducted based on title and abstract data, supported by a Python (version 3.11)-generated word cloud to inductively identify five core clusters: (1) seismic assessment and earthquake risk, (2) building vulnerability and structural performance, (3) subsurface hazards and ground instability, (4) urban areas, heritage, and social vulnerability, and (5) risk mitigation, planning, and resilience frameworks. Findings: The review reveals a shift from hazard-centric, engineering-based models toward integrated, multi-scalar frameworks that embed risk within socio-economic, spatial, and institutional contexts. While consensus exists on the importance of probabilistic modeling, retrofitting, and GIS-based tools, divergences persist around behavioral valuation, policy uptake, and equity in implementation. Heritage cities and informal settlements emerge as under-addressed but critically vulnerable domains. Originality/value: This study systematically maps interdisciplinary research on real estate exposure to seismic and subsurface risks post-2020. By bridging engineering, planning, behavioral economics, and disaster governance, the review provides a unique synthesis relevant for academics, urban planners, and policymakers seeking to design equitable and resilient urban futures. The five-cluster thematic taxonomy introduced in this review represents an original synthesis that bridges engineering vulnerability assessment, behavioral economics, heritage preservation, and resilience governance. Unlike previous reviews that have typically focused on single disciplinary perspectives, this taxonomy integrates multi-scalar approaches spanning asset-level diagnostics to national exposure modeling, providing a comprehensive framework for understanding real estate exposure to seismic and subsurface risks. Full article

In Multi-Criteria Group Decision-Making (MCGDM), the assignment of weights to decision-makers is a crucial but methodologically delicate step, especially when the group includes both human experts and artificial experts such as intelligent agents, Artificial Intelligences (AIs) or Large Language Models (LLMs). Existing weighting strategies are often either difficult to interpret or poorly suited to heterogeneous groups of evaluators. In this paper, we investigate a fuzzy rule-based approach to expert weighting, building on a previously introduced methodological framework and focusing here on its application-oriented validation. The proposed method models expert weighting as a Fuzzy Rule-Based System (FRBS) in which the relevant properties of the experts are represented by linguistic variables and combined through interpretable IF–THEN rules. In this way, weighting policies can be expressed transparently and adapted to the requirements of the decision domain. The framework produces normalised weights in the interval $[0,1]$, which can then be incorporated into standard MCGDM aggregation procedures. To assess the operational behaviour of the approach, we consider an application involving the weighting of four open-source LLMs (apertus:8b, gemma4:e4b, mistral-small3.2:24b, and nemotron-cascade-2:30b) over three multilingual criteria (English, Italian, Portuguese) and two resource-side criteria (VRAM, open-sourceness), each modelled by three trapezoidal fuzzy sets and combined into a five-class output partition; the underlying dataset is built from 10 independent repetitions of 100 questions per model. Under a language-focused rule base of five IF–THEN rules, the four experts receive sharply separated normalised weights $(0.003,0.149,0.301,0.548)$—a top-to-bottom ratio above 180—whereas a combined linguistic/resource-aware rule base of five rules flattens the distribution to $(0.227,0.360,0.222,0.191)$ and selects a different winner, demonstrating that policy changes are encoded explicitly in the output. A 100-run Kendall’s $\tau$ perturbation analysis confirms that the induced rankings remain stable under moderate input noise, particularly for the language-focused policy, while substituting the Product t-norm with Gödel or Lukasiewicz leaves the language-focused ranking invariant but induces rank reversals in the more discriminative resource-aware policy. A comparison against three independent baselines (Markov Logic Networks, ProbLog, TOPSIS) shows that ProbLog reproduces the FRBS ordering in both case studies, MLN compresses the normalised scores under its global probabilistic interaction, and TOPSIS diverges whenever conditional IF–THEN preferences must be encoded. A worked end-to-end aggregation example with three alternatives, three criteria, and four experts further shows that the FRBS weights propagate into a clear selection of the best alternative, with aggregated scores $(S_1,S_2,S_3)=(8.88,6.78,6.21)$. These results confirm both the practical usability of the method and its suitability for contexts in which multiple, potentially competing, objectives must be balanced explicitly. Overall, the paper provides an application-oriented study of an FRBS-based weighting scheme for artificial experts, highlighting its interpretability, adaptability, and potential relevance for contemporary MCGDM settings. Full article

Housing financialization has transformed residential property from a basic social necessity into a speculative asset, intensifying socio-spatial inequalities in the Global South. In Iran, a bank-dominated financial regime and high spatial inequality severely restrict access to housing finance, particularly for low-income households and climate-induced migrants in mid-sized cities. While the spatial consequences of financialization have been extensively studied in major metropolises, neighborhood-scale exclusion mechanisms in secondary cities remain significantly understudied. This study addresses this gap by employing a hybrid methodology that integrates the Bayesian Best–Worst Method (BWM) with Geographic Information Systems (GISs) and Fuzzy Overlay analysis to quantify and spatially visualize barriers to housing finance in Birjand, Iran. Eleven indicators were weighted through probabilistic expert elicitation (n = 10) using Markov Chain Monte Carlo (MCMC) sampling, with excellent convergence confirmed by Gelman–Rubin diagnostics (R-hat ≈ 1.00). Results identify land use as the dominant barrier (posterior mean = 0.256; 95% CrI [0.244, 0.270]), followed by homeownership rate and education level (≈0.132 each). Spatial modeling reveals a pronounced north–south monetary–spatial divide: southern districts concentrate financial services and formal tenure, while northern peripheries, predominantly inhabited by low-income and climate-migrant populations, constitute financial deserts. These findings demonstrate that spatial barriers to housing finance are structurally embedded in land-use classification and asset-based gatekeeping. The study offers a replicable probabilistic–spatial framework for diagnosing financial exclusion in Global South mid-sized cities and provides evidence-based guidance for spatially targeted policy interventions aligned with SDGs 1, 10, and 11. Full article

Modeling bivariate count data with complex dispersion and dependence structures remains a significant challenge in statistical data analysis. This article introduces two new bivariate count distributions derived from the generalized shifted inverse trinomial distribution. The proposed models, denoted by BGIT-I and BGIT-II, are constructed using convolution and trivariate reduction methods. They provide flexible joint frameworks for modeling correlated count data while accommodating different marginal dispersion patterns. BGIT-I allows negative, near-zero, and positive dependence, whereas BGIT-II induces non-negative dependence through a common component. The proposed models have simple, tractable probability generating functions, which facilitate the derivation of probabilistic properties and motivate a probability-generating-function-based estimation approach alongside maximum-likelihood estimation. The finite-sample performance of the estimators is further examined through a Monte Carlo simulation study. The practical utility of the proposed models is illustrated using two real bivariate count data sets involving shunter accidents and patient counts in critical care and emergency room settings. The results show that the proposed BGIT models provide competitive alternatives for modeling bivariate count data with different dispersion and dependence characteristics. Full article

Driven by the low-carbon transformation in the construction industry, Moso bamboo has emerged as a promising green material. However, a critical gap exists in current structural design theories: they predominantly rely on homogeneous assumptions, failing to capture the inherent spatial variability and coupled strength–stiffness degradation of bamboo. Experimental results reveal a distinct longitudinal gradient, where the top section (5–8 m) exhibits approximately 15% higher average tensile strength and 12% higher average elastic modulus compared to the bottom section (1–3 m). This oversight severely compromises the accuracy of structural reliability evaluations. To address this, this study pioneers a high-precision digital representation method by developing a novel three-dimensional (3D) anisotropic bivariate coupled random field model. Experimental and stochastic finite element simulations demonstrate that the model accurately replicates spatial variations, maintaining relative errors for primary statistical indicators strictly below 1% while robustly capturing the bivariate coupling characteristics. Crucially, by integrating non-parametric probability box (P-box) theory, the macroscopic tensile resistance of full-scale bamboo members is rigorously bounded within a definitive statistical interval. Furthermore, the extracted Interval Skewness Ratio generally remains greater than 1.0 (averaging 1.38), providing robust quantitative proof of an asymmetric structural degradation governed by the brittle weakest-link failure mechanism. By effectively eliminating non-physical sample generation associated with traditional univariate models, this research makes a significant contribution to the field, providing a rigorous digital twin framework and theoretical foundation for the advanced non-probabilistic safety design of modern bamboo structures. Full article

Electric vehicle (EV) charging has a large, spatially clustered, schedulable load whose vehicle-to-grid flexibility can be sold back to the power system. That flexibility has grid value only when the committed quantity can be reliably delivered under uncertainty. Open forecasting benchmarks operators rely on report-only point predictions. The dispatch models that turn forecasts into firm commitments assume a constant round-trip efficiency, so the committed flexibility is systematically over-scheduled. This study contributes two complementary modules, validated separately on public data. The first is a calibrated probabilistic charging forecaster that provides, to our knowledge, the first prediction intervals with reported empirical coverage on the UrbanEV benchmark. It is a gradient-boosted quantile-regression model that combines each zone’s own-history lags with adjacency-weighted neighbor-mean features and exogenous price and calendar inputs. It is calibrated by conformalized quantile regression and scored over thirty zones across a 120-day hourly window. The second is a deliverable-flexibility envelope whose returnable-energy bounds are set by measured, state-of-charge- and rate-dependent vehicle-to-grid (V2G) discharge efficiency rather than a constant round-trip number. These bounds are fit to the measured discharge traces of three V2G-capable vehicles in the Esser bidirectional-charging dataset. Chosen as a lightweight, reproducible baseline, the forecaster keeps its prediction intervals within a five-percentage-point coverage tolerance at both the 80% and 90% nominal levels. Measured coverage is 0.823 and 0.911. It also improves on the continuous ranked probability score of its conformalized-point counterpart at matched point accuracy. This calibration holds across the hyperparameter neighborhood and under data deficiency. On the delivery side, a leave-one-vehicle oracle shows the efficiency-aware envelope short-delivers less than the constant-average-efficiency aggregator on held-out vehicles. Its residual shortfall is 1.21% against the aggregator’s 2.03% at the conservative operating point. The margin widens as commitments grow more aggressive and discharges reach the lowest states of charge. Each of these two measured properties, calibrated demand-side uncertainty and state-dependent discharge physics, imposes a material, separately validated constraint on how much contracted EV flexibility can be delivered, a constraint the point-forecasting frontier leaves unaddressed. Full article

Noisy, expensive, gradient-free optimisers—simulated annealing chief among them—almost never report how confident one should be in the configuration they return, and reinforcement-learning hyperparameter tuning, where the noise is large and the budget tight, is the setting where this silence hurts most. The contribution of this paper is a mechanism for uncertainty quantification, not a faster optimiser: we equip simulated annealing with a calibrated credible interval over the value of the recovered configuration, and we are explicit that this comes at an optimisation cost that only some landscapes repay. We introduce Kalman-Annealing (KA), a minimal modification of simulated annealing in which a one-dimensional Kalman filter—the canonical probabilistic numerical method—is interleaved with the Metropolis acceptance step. The filter denoises each return before acceptance, and a short terminal refinement of the best visited state converts the run into a calibrated credible interval over the value of the recovered hyperparameter. A single analytical identity, $Q_t=c{T}_t^2$, couples the filter process noise to the cooling schedule and absorbs the only free parameter of the filter into one already present in the metaheuristic. Under standard cooling assumptions the credible intervals are calibrated and the posterior variance contracts at a rate compatible with simulated-annealing convergence. On synthetic benchmarks (a noisy five-dimensional quadratic and the noisy Branin function, 200 seeds each) and on hyperparameter tuning of REINFORCE on three classic-control tasks (10 seeds each), the empirical $90\%$ coverage of KA’s credible intervals lies within sampling error of the nominal level—a property none of the baselines provides—and the optimiser overhead is close to four orders of magnitude below that of Gaussian-process Bayesian optimisation. The interval cannot be extracted for free from an unmodified SA run: an interval built from the trailing evaluations of the vanilla trajectory fails to calibrate in every reading we test, and the repair that does calibrate is exactly KA’s terminal-refinement phase grafted onto the unfiltered chain, at the same cost in diverted evaluations. Honest scoreboard: On simple regret, KA is at best on par with vanilla simulated annealing on the unimodal synthetic (the nominal advantage does not survive correction for multiple comparisons) and loses to the SA family, to CMA-ES and to Gaussian-process Bayesian optimisation on the multi-modal and ill-conditioned synthetics and on the informative reinforcement-learning tasks, under both REINFORCE and PPO. We trace this gap quantitatively to the filter acting as a low-pass, with a mean Kalman gain near one half, on the favourable-tail observations that drive SA’s basin escape, and we delineate the operating regime in which the calibrated-uncertainty contribution of KA is worth its optimisation cost. Full article

Deep reinforcement learning (DRL) agents implicitly memorize the structure of their training environment, allowing an attacker to reconstruct it by querying the action outputs of a deployed model and causing environment-privacy leakage. To address this, this study proposes Entropy-Driven Action Randomization (EDAR), a deployment-time action randomization defense. Inspired by the exponential mechanism from differential privacy, EDAR replaces deterministic greedy action selection with probabilistic action sampling based on a normalized Q-value utility function, without altering the trained policy. A state-adaptive dynamic privacy budget is further designed, guided by the Shannon entropy of the action distribution. A simulated-annealing-based policy inversion attack is used to quantify privacy leakage, measured by the normalized recovery rate (NRR). Experiments on GridWorld environments ranging from 7 × 7 to 13 × 13 show that, at a privacy level comparable to a strong fixed budget, the proposed dynamic budget raises the reward retention from 6.2% to 78.9% while keeping a comparable NRR reduction. These results indicate that adapting the privacy budget to per-state policy determinism yields a markedly better privacy–utility trade-off than existing training-perturbation-based methods. We clarify that the differential privacy property invoked here is a per-query, mechanism-level guarantee of indistinguishability between neighboring observation states under a fixed trained model. The protection of the environment structure itself is established empirically through the policy inversion attack. Experiments are conducted on discrete GridWorld navigation tasks; the conclusions are scoped to small value-based navigation settings. Full article