Search Results (1,287)

This paper introduces Exponentiated Inverse Exponential Distribution (EIED), a novel probability model developed within the power inverse exponential distribution framework. A distinctive feature of EIED is its highly flexible hazard rate function, which can exhibit increasing, decreasing, and reverse bathtub (upside-down bathtub) shapes, making it suitable for modeling diverse lifetime phenomena in reliability engineering, survival analysis, and risk assessment. We derived comprehensive statistical properties of the distribution, including the reliability and hazard functions, moments, characteristic and quantile functions, moment generating function, mean deviations, Lorenz and Bonferroni curves, and various entropy measures. The identifiability of the model parameters was rigorously established, and maximum likelihood estimation was employed for parameter inference. Through extensive simulation studies, we demonstrate the robustness of the estimation procedure across different parameter configurations. The practical utility of EIED was validated through applications to real-world datasets, where it showed superior performance compared to existing distributions. The proposed model offers enhanced flexibility for modeling complex lifetime data with varying hazard patterns, particularly in scenarios involving early failure periods, wear-in phases, and wear-out behaviors. Full article

The increasing penetration of renewable energy sources and the consequent rise in forecast uncertainty have underscored the need for robust operational strategies in transmission power systems. This paper introduces a risk-averse, data-driven distributionally robust optimization framework that integrates unit commitment and power flow constraints to enhance both reliability and operational security. Leveraging advanced forecasting techniques implemented via gradient boosting and enriched with cyclical and lag-based time features, the proposed methodology forecasts renewable generation and demand profiles. Uncertainty is quantified through a quantile-based analysis of forecasting residuals, which forms the basis for constructing data-driven ambiguity sets using Wasserstein balls. The framework incorporates comprehensive network constraints, power flow equations, unit commitment dynamics, and battery storage operational constraints, thereby capturing the intricacies of modern transmission systems. A worst-case net demand and renewable generation scenario is computed to further bolster the system’s risk-averse characteristics. The proposed method demonstrates the integration of data preprocessing, forecasting model training, uncertainty quantification, and robust optimization in a unified environment. Simulation results on a representative IEEE 24-bus network reveal that the proposed method effectively balances economic efficiency with risk mitigation, ensuring reliable operation under adverse conditions. This work contributes a novel, integrated approach to enhance the reliability of transmission power systems in the face of increasing uncertainty. Full article

The management of municipal solid waste (MSW) plays a crucial role in advancing sustainable development and circular economy goals across the European Union. In Italy, despite improvements in separate collection, significant regional disparities in MSW performance and costs persist. This study assesses the eco-efficiency of MSW services in 5516 Italian municipalities to uncover performance gaps and their underlying drivers. Eco-efficiency is measured using a Data Envelopment Analysis (DEA) model based on the Generalized Directional Distance Function (GDDF). This model incorporates per capita cost as an input, sorted waste as a desirable output, and residual waste as an undesirable output. A second-stage quantile regression is then utilized to explore how contextual factors influence eco-efficiency across various performance levels. The results reveal significant territorial disparities, with only 0.13% of municipalities achieving full eco-efficiency. Paradoxically, higher levels of separate waste collection—typically a policy goal—are associated with increased costs, especially in more efficient municipalities, suggesting a trade-off between environmental performance and economic sustainability. Similarly, population density negatively affects eco-efficiency but may facilitate economies of scale in collection systems. These findings highlight a tension between achieving optimal sorting rates and maintaining cost-effectiveness. Policy interventions should consider these trade-offs, prioritizing basic performance in lagging areas while promoting cost-control strategies in high-performing municipalities. Full article

This study proposes a hybrid EGARCH-Informer framework for forecasting volatility and calibrating tail risk in financial time series. The econometric layer (EGARCH) captures asymmetric and persistent volatility dynamics, while the attention layer (Informer) models long-range temporal dependence with sparse attention. The framework produces horizon-specific forecasts (H = 1 and H = 5) that are mapped to VaR and CVaR at α = 0.95 and 0.99. Evaluation covers pointwise accuracy (MAE, RMSE) and risk coverage calibration (CVaR bias and Kupiec’s unconditional coverage), complemented by Conditional Coverage (CC) and Dynamic Quantile (DQ) diagnostics, and distributional robustness via a Student-t mapping of VaR/CVaR. Across four U.S. equity indices (SPX, IXIC, DJI, SOX), the hybrid matches GARCH at the short horizon and yields systematic error gains at the longer horizon while maintaining higher calibration quality than deep learning baselines. MAE and RMSE values remain near 0.0002 at H = 1, with relative improvements of 2–6% at H = 5. CVaR bias stays tightly bounded; DQ rarely rejects, and CC is stricter but consistent with clustered exceedances, and the Student-t results keep the median hit rates near nominal with small, mildly conservative CVaR biases. These findings confirm the hybrid model’s robustness and transferability across market conditions. Full article

Forecasting pedestrian congestion in urban back streets is challenging due to “shadow areas” where CCTV coverage is absent and trajectory data cannot be directly collected. To address these gaps, we propose the Peak-aware Graph-attention Temporal Fusion Transformer (PGTFT), a lightweight hybrid model that extends the Temporal Fusion Transformer by integrating a non-parametric attention-based Graph Convolutional Network, a peak-aware Gated Residual Network, and a Peak-weighted Quantile Loss. The model leverages both physical connectivity and functional similarity between roads through a fused adjacency matrix, while enhancing sensitivity to high-congestion events. Using real-world trajectory data from 38 CCTVs in Anyang, South Korea, experiments show that PGTFT outperforms LSTM, TFT, and GCN-TFT across different sparsity settings. Under sparse 5 m neighbor conditions, the model achieved the lowest MAE (0.059) and RMSE (0.102), while under denser 30 m settings it maintained superior accuracy with standard quantile loss. Importantly, PGTFT requires only 1.54 million parameters—about half the size of conventional Transformer–GCN hybrids—while delivering equal or better predictive performance. These results demonstrate that PGTFT is both parameter-efficient and robust, offering strong potential for deployment in smart city monitoring, emergency response, and transportation planning, as well as a practical approach to addressing data sparsity in urban sensing systems. Full article

This paper addresses the challenges of fault diagnosis in gas turbines (GTs) utilized in oil and gas pipeline systems by proposing a novel multiparameter analysis framework that integrates dynamic, quantile-based Statistical Process Control (SPC) with prior domain knowledge. The proposed approach initially employs a dynamic quantile SPC model to establish adaptive control limits, effectively handling the non-stationarity and non-normality of gas turbine operational data. By analyzing parameter variations under typical operating conditions and incorporating expert insights, a multiparameter fault analysis matrix and corresponding weighting factors are constructed to facilitate fault diagnosis with prior knowledge. Furthermore, a fault probability model based on parameter change rates and weighting factors is developed to quantify the likelihood of different fault modes. An operating condition clustering and correction mechanism enables the dynamic adjustment of control limits, thereby preventing misdiagnoses caused by varying operational states. The validity of the proposed method is demonstrated using real data from a domestic pipeline gas turbine, validated by real domestic pipeline GT data, outperforming existing models, with a fault accuracy up to 10%. The approach efficiently estimates fault probabilities and accurately detects both sudden and gradual faults, significantly enhancing intelligent fault diagnosis capabilities for gas turbines. Full article

Fluctuations in precipitation usually affect the ecological environment and human socioeconomics through events such as floods and droughts, resulting in substantial economic losses. The high-resolution models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) are vital for simulating precipitation patterns in China; however, significant uncertainties still exist. This study utilized the quantile mapping (QM) method to correct biases in nine high-resolution Earth System Models (ESMs) and then comprehensively evaluated their precipitation simulation capabilities over the Chinese mainland from 1985 to 2014. Based on the selected models, the Bayesian Model Averaging (BMA) method was used to integrate them to obtain the spatial–temporal variation in precipitation over the Chinese mainland. The results showed that the simulation performance of nine models for precipitation from 1985 to 2014 was significantly improved after the bias correction. Six out of the nine high-resolution ESMs were selected to generate the BMA ensemble model. For the BMA model, the precipitation trend and the locations of significant points were more closely aligned with the observational data in the summer than in other seasons. It overestimated precipitation in the spring and winter, while it underestimated it in the summer and autumn. Additionally, both the BMA model and the worst multi-model ensemble (WMME) model exhibited a negative mean bias in the summer, while they displayed a positive mean bias in the winter. And the BMA model outperformed the WMME model in terms of mean bias and bias range in both the summer and winter. Moreover, high-resolution models delivered precipitation simulations that more closely aligned with observational data compared to low-resolution models. Full article

This paper introduces a new class of probability distributions, termed the generated log exponentiated polynomial (GLEP) family, designed to enhance flexibility in modeling complex real financial data. The proposed family is constructed through a novel cumulative distribution function that combines logarithmic and exponentiated polynomial structures, allowing for rich distributional shapes and tail behaviors. We present comprehensive mathematical properties, including useful series expansions for the density, cumulative, and quantile functions, which facilitate the derivation of moments, generating functions, and order statistics. Characterization results based on the reverse hazard function and conditional expectations are established. The model parameters are estimated using various frequentist methods, including Maximum Likelihood Estimation (MLE), Cramer–von Mises (CVM), Anderson–Darling (ADE), Right Tail Anderson–Darling (RTADE), and Left Tail Anderson–Darling (LEADE), with a comparative simulation study assessing their performance. Risk analysis is conducted using actuarial key risk indicators (KRIs) such as Value-at-Risk (VaR), Tail Value-at-Risk (TVaR), Tail Variance (TV), Tail Mean Variance (TMV), and excess function (EL), demonstrating the model’s applicability in financial and insurance contexts. The practical utility of the GLEP family is illustrated through applications to real and simulated datasets, including house price dynamics and insurance claim sizes. Peaks Over Random Threshold Value-at-Risk (PORT-VaR) analysis is applied to U.K. motor insurance claims and U.S. house prices datasets. Some recommendations are provided. Finally, a comparative study is presented to prove the superiority of the new family. Full article

Enhancing green total factor energy efficiency (GTFEE) is crucial for achieving sustainable development. Against this backdrop, this study aims to investigate the impact of fintech on GTFEE, using annual data from 240 Chinese cities between 2011 and 2021. Methodologically, we employ the SBM–Malmquist–Luenberger model to measure GTFEE and assess the role of fintech. The results demonstrate that fintech significantly promotes GTFEE, a finding that remains robust after addressing endogeneity issues and replacing key variables. Further mechanism analysis reveals that fintech facilitates GTFEE by alleviating financing constraints and stimulating technological innovation. Moreover, the effect is particularly pronounced in eastern regions, non-resource-based cities, service-oriented cities, and larger urban areas. Importantly, quantile regression results confirm that fintech exerts a stronger positive impact at higher quantiles of the GTFEE distribution. These findings offer both theoretical insights and practical policy implications for advancing energy efficiency through fintech development. Full article

This paper develops a nonlinear quantile structural equation model via the Bayesian approach, aiming to more accurately analyze the relationships between latent variables, with special attention paid to the issue of non-ignorable missing data in the model. The model not only incorporates quantile regression to examine the relationships between latent variables at different quantile levels but also features a specially designed mechanism for handling missing data. The non-ignorable missing mechanism is specified through a logistic regression model, and a combined method of Gibbs sampling and Metropolis–Hastings sampling is adopted for missing value imputation, while simultaneously estimating unknown parameters, latent variables, and parameters in the missing data model. To verify the effectiveness of the proposed method, simulation studies are conducted under conditions of different sample sizes and missing rates. The results of these simulation studies indicate that the developed method performs excellently in handling complex data structures and missing data. Furthermore, this paper demonstrates the practical application value of the nonlinear quantile structural equation model through a case study on the growth of listed companies, providing researchers in related fields with a new analytical tool. Full article

This study examines the global mobility of researchers in the smart city domain from 2000 to 2020, using inter-country and intercity affiliation data from the Web of Science. Employing network analysis and spatial econometric models, the paper maps the structural reconfiguration of scientific labor circulation. The results show that the international mobility network is dense yet asymmetric, dominated by a small set of high-frequency corridors such as China–United States, which intensified markedly over the two decades. While early networks were fragmented and polycentric, the later period reveals a multipolar configuration with significant growth in South–South and intra-European exchanges. At the city level, Beijing, Shanghai, Wuhan, and Nanjing emerged as central nodes, reflecting the consolidation of East Asian hubs within the global knowledge system. Mesoscale community detection highlights the coexistence of territorially embedded ecosystems and transregional corridors sustained by thematic and reputational affinities. Growth decomposition indicates that high-income countries benefit from both talent retention and international inflows, while upper-middle-income countries rely heavily on inbound mobility. Spatial regression and quantile models confirm that economic growth and baseline scientific visibility remain robust drivers of urban smart city performance. In contrast, mobility effects are context-dependent and heterogeneous across city positions. Together, these findings demonstrate that researcher mobility is not only a vector of knowledge exchange but also a mechanism that reinforces spatial hierarchies and reshapes the geography of global smart city innovation. Full article

In order to ensure the earthquake safety of existing buildings, retrofitting applications come to the fore in terms of being fast and cost-effective. Among these applications, fiber-reinforced polymer (FRP) composites are widely preferred thanks to their advantages such as high strength, corrosion resistance, applicability without changing the cross-section and easy assembly. This study presents a data augmentation, modeling, and comparison-based approach to predict the fire resistance (FR) of FRP-strengthened reinforced concrete beams. The aim of this study was to explore the role of data augmentation in enhancing prediction accuracy and to find out which augmentation method provides the best prediction performance. The study utilizes an experimental dataset taken from the existing literature. The dataset contains inputs such as varying geometric dimensions and FRP-strengthening levels. Since the original dataset used in the study consisted of 49 rows, the data size was increased using augmentation methods to enhance accuracy in model training. In this study, Gaussian noise, Regression Mixup, SMOGN, Residual-based, Polynomial + Noise, PCA-based, Adversarial-like, Quantile-based, Feature Mixup, and Conditional Sampling data augmentation methods were applied to the original dataset. Using each of them, individual augmented datasets were generated. Each augmented dataset was firstly trained using eXtreme Gradient Boosting (XGBoost) with 10-fold cross-validation. After selecting the best-performing augmentation method (Adversarial-like) based on XGBoost results, the best-performing augmented dataset was later evaluated in HyperNetExplorer, a more advanced NAS tool that can find the best performing hyperparameter optimized ANN for the dataset. ANNs achieving R² = 0.99, MSE = 22.6 on the holdout set were discovered in this stage. This whole process is unique for the FR prediction of structural elements in terms of the data augmentation and training pipeline introduced in this study. Full article

The COVID-19 pandemic and the implementation of strict lockdown measures have significantly impacted various dimensions of the global economy. This study examines the impact of COVID-19 and lockdown stringency on exchange rate volatility in India using three core variables, i.e., COVID-19 cases, the lockdown stringency index, and exchange rate volatility. To achieve the above objectives, we have employed advanced econometric techniques, such as wavelet coherence and a hybrid non-parametric quantile causality framework, on the dataset spanning from 30 December 2020 to 24 January 2022. Robustness is assessed using Troster–Granger causality in quantiles and Breitung–Candelon Spectral Causality tests. The wavelet coherence analysis indicates that the initial outbreak of COVID-19 increased the exchange rate volatility, while the enforcement of stringent lockdowns in the later phases helped reduce this volatility. Similarly, the hybrid quantile causality results indicate that both COVID-19 cases and lockdown measures possess predictive power over exchange rate fluctuations. The robustness checks confirm these findings and establish a causal relationship between the pandemic, policy responses, and currency market behaviour. This study helps clarify the complex, nonlinear dynamics between pandemic-related variables and exchange rate volatility in emerging markets. Based on the aforementioned result, it is recommended that policymakers implement targeted lockdown strategies coupled with timely monetary interventions (such as foreign exchange reserve management or interest rate adjustments) to mitigate volatility and maintain currency stability during future pandemic-induced shocks. Full article

Survival analysis is a fundamental tool for modeling time-to-event data in healthcare, engineering, and finance, where censored observations pose significant challenges. While traditional methods like the Beran estimator offer nonparametric solutions, they often struggle with the complex data structures and heavy censoring. This paper introduces three novel survival models, iSurvM (imprecise Survival model based on Mean likelihood functions), iSurvQ (imprecise Survival model based on Quantiles of likelihood functions), and iSurvJ (imprecise Survival model based on Joint learning), that combine imprecise probability theory with attention mechanisms to handle censored data without parametric assumptions. The first idea behind the models is to represent censored observations by interval-valued probability distributions for each instance over time intervals between event moments. The second idea is to employ the kernel-based Nadaraya–Watson regression with trainable attention weights for computing the imprecise probability distribution over time intervals for the entire dataset. The third idea is to consider three decision strategies for training, which correspond to the proposed three models. Experiments on synthetic and real datasets demonstrate that the proposed models, especially iSurvJ, consistently outperform the Beran estimator from accuracy and computational complexity points of view. Codes implementing the proposed models are publicly available. Full article

Grassland fire risk perception constitutes a fundamental element of fire risk assessment and underpins the evaluation of response capacities in grassland regions. This study examines Qinghai Province, the fourth-largest pastoral region in China, as a case study to develop an evaluation index system for assessing residents’ perceptions of grassland fire risk. Using micro-level survey data, the study quantifies these perceptions and applies a quantile regression model to investigate influencing factors. The results indicate that: (1) the average grassland fire risk perception index among residents in Qinghai Province’s grassland areas is 0.509, with response behaviors contributing the most and response attitudes contributing the least; (2) Residents in agricultural areas perceive higher risks than those in semi-agricultural/semi-pastoral or purely pastoral areas, and individuals in regions with moderate dependency ratios and moderate fire-susceptibility conditions demonstrate the highest performance, whereas those in pastoral and high-susceptibility zones exhibit signs of “risk desensitization”; (3) risk communication and information dissemination are the primary drivers of enhanced perception, followed by climate variables, whereas individual characteristics of residents attributes exert no significant effect. It is recommended to monitor the impacts of climate change on fire risk patterns, update risk information dynamically, address deficits in residents’ cognition and capabilities, strengthen behavioral guidance and capacity-building initiatives, and foster a transition from passive acceptance to active engagement, thereby enhancing both cognitive and behavioral responses to grassland fires. Full article