Search Results (861)

The removal efficiency of wet scrubbers is governed by complex nonlinear interactions among operating parameters such as liquid level, airflow velocity, and dust concentration, making accurate real-time prediction challenging, which in turn leads to operational instability, increased energy consumption, and excessive emissions. To address this bottleneck, we first introduce multi-gene genetic programming (MGGP) to develop interpretable models quantifying multi-parameter coupling and predicting removal efficiency for PM₁, PM_2.5, PM₁₀, and TSP. Key input variables, including liquid level height, inlet airflow velocity, system pressure, and inlet dust concentration, were identified via correlation analysis. Explicit mathematical models were derived. Global sensitivity analysis using the elementary effect test (EET) identified inlet airflow velocity as most influential. Uncertainty quantification via quantile regression (QR) confirmed the model’s reliability with narrow prediction intervals and high coverage probabilities. MGGP offers a favorable balance of accuracy, generalization, and interpretability compared to extreme gradient boosting (XGBoost) and multiple nonlinear regression (MNR). Its explicit form quantifies parameter interactions, enabling efficient on-site monitoring with low computational cost. This study provides an interpretable prediction tool for intelligent wet scrubber operation, supporting cleaner production and refined control in complex industrial processes. Full article

This study examines the impact of family multidimensional poverty on cognitive function among older adults in China using the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS). Filling a critical gap in the existing literature, we construct a multidimensional poverty index (MPI) based on the Alkire-Foster methodology to evaluate cognitive decline within the context of China’s post-poverty-eradication landscape. Utilizing quantile regression analysis, our findings demonstrate that multidimensional poverty exerts a significant, negative effect on cognitive function, which is more pronounced among individuals at lower cognitive quantiles, consistent with the cumulative disadvantage theory. Furthermore, we identify substantial urban–rural and regional disparities, revealing unique socio-economic inequalities. By linking multidimensional poverty to elderly cognitive health through psychosocial pathways, this study provides empirical evidence that reducing multidimensional deprivation among older adults is integral to achieving both SDG1 and SDG3 in China’s post-eradication context, demonstrating that income-based metrics alone are insufficient to capture the full burden of poverty on elderly cognitive health. Full article

Increasing the carbon sink function of forests is critical for achieving carbon (C) neutrality in the context of global climate change. Past studies have focused on the estimation of forest biomass or C storage, while those on forest C sink potential remain limited. In particular, there remain few systematic investigations to define the forest C sink, to characterize the synergistic influencing factors, and to develop related quantitative analysis methods. The development of scientific C enhancement strategies requires the construction of C density-age models integrating multiple stand factors. These models allow accurate quantification of the gap (∆C) between actual and maximum C sequestration capacity. This study used permanent sample plot data to develop and validate a novel multi-model assessment approach for quantifying the C sink potential of Larix olgensis plantations in Heilongjiang Province, China, and to translate the results into precise management tools. An Average-Level Model (ALM) was established to define baseline C sequestration. Three innovative potential assessment models were then proposed: (1) the Empirical Upper Boundary Model (PLM1); (2) the Dummy Variable Model (PLM2); and (3) the Quantile Regression Model (PLM3). These models define the maximum C sequestration capacity from distinct perspectives. PLM1 (R² = 0.7910) characterized the theoretical upper limit of C sink potential (79.86 Mg·ha⁻¹), making it suitable for macro-strategic goal setting, though it is somewhat dependent on extreme data points. PLM2 (R² = 0.7943) achieved the best fit, and when combined with measurable stand conditions (site class index [SCI] > 16 m, stand density index [SDI] > 800 trees·ha⁻¹), it provides clear guidance for management practices. Although PLM3 showed a lower goodness-of-fit (R² = 0.1056), it provided reasonable parameter estimates and robust predictions, offering a reliable upper-bound reference for C sink project planning and risk control. At a stand age of 60 years (yr), the C sink enhancement potentials (“∆” C) corresponding to the three models were 15.73, 14.48, and 13.26 Mg·ha⁻¹, representing increases of 24.53%, 22.58%, and 20.68%, respectively, over the average level (64.13 Mg·ha⁻¹); the peak C sequestration rates of the models were 104.3%, 82.7%, and 60.5% higher than that of the ALM, with peak times occurring earlier at 9, 7, and 11 yr, respectively, underscoring the importance of the early management. The multi-model assessment approach developed here facilitates “precision carbon enhancement” by quantifying C sink potential across its theoretical, achievable, and robust upper-bound dimensions. This quantification provides both mechanistic insights into C sequestration processes and a critical link between theoretical understanding and practical forest management. This work holds significant value for advancing forestry C sinks in service of national strategies. Full article

Parabens, a prevalent class of endocrine-disrupting chemicals (EDCs), are ubiquitous in consumer products; however, their role in linking pediatric allergic phenotypes remains poorly understood. This case-control study analyzed paraben levels in urine and indoor dust as proxies for internal and external exposures and investigated their associations with allergic rhinitis only (AR Only), asthma only (AS Only), and comorbidities (AR&AS) among children in Shanghai. The concentrations for each of four paraben compounds were quantitatively measured, and multi-pollutant frameworks—including Bayesian Kernel Machine Regression (BKMR) and Weighted Quantile Sum (WQS) regression—were employed to characterize the mixture exposure and risk. Propylparaben (PrP) was detectable in 100% of urine samples and over 90% of dust samples, and the concentrations ranked the highest out of the four compounds in both samples. Benzylparaben (BzP) was detected in >70% of urine samples and over 50% of dust samples at relatively lower levels. Urinary PrP exhibited significantly positive associations with all phenotypes (OR in 2.18–2.92) and BzP with the AR&AS Comorbidity (OR = 3.55, 95% CI: 1.32–9.55). Dust-borne PrP was associated with AR Only (OR = 2.26, 95% CI: 1.16–4.43), indicating a potential “Portal of Entry” effect via direct nasal deposition. According to BKMR and WQS analyses, urinary PrP and BzP emerged as two primary risk drivers. Using interaction analysis, an additive synergistic effect was observed between urinary PrP and BzP with parental history of allergy, suggesting heightened vulnerability to paraben exposure in genetically predisposed subgroups. In conclusion, children with respiratory allergies were associated with higher exposure to PrP and BzP and exhibited higher susceptibility in those with a parental history of allergy. Full article

Accurate soil moisture assessment is essential for effective agricultural management in the southern US, where water availability has a significant impact on crop productivity. This study evaluates the Soil Moisture Active Passive (SMAP) Level-4 daily soil moisture product using in situ measurements from Natural Resources Conservation Service (NRCS) Soil Climate Analysis Network (SCAN) stations and the US. Climate Reference Network (USCRN) across diverse agroecosystems in Texas from 2016 to 2024. SMAP’s performance was examined across ten climate zones and six major land cover types, including urban regions, pastureland, grassland, rangeland, shrubland, and deciduous forests. Statistical metrics, including the coefficient of determination (R²), Root Mean Square Error (RMSE), Bias, and unbiased RMSE (ubRMSE) were used to evaluate the agreement between SMAP-derived and in situ soil moisture measurements. Results show that SMAP effectively captures seasonal soil moisture dynamics but exhibits spatially variable accuracy. The highest agreement was observed at Panther Junction (R² = 0.57, RMSE = 2.29%), followed by Austin (R² = 0.57, RMSE = 9.95%). While a weaker coefficient of determination was observed at PVAMU (R² = 0.28, RMSE = 11.28%) and Kingsville (R² = 0.11, RMSE = 7.33%), likely due to heterogeneity in land cover, and urbanized landscapes in these stations. Applying the quantile mapping bias correction methods significantly reduced RMSE and improved the accuracy of SMAP soil moisture data at some in situ measurement stations. The results highlight the importance of station-specific calibration and the integration of satellite and ground-based measurements to improve soil moisture monitoring for agriculture and drought management in Texas and similar regions. Full article

Medium-term 5G/NB-IoT planning is made difficult by simultaneous uncertainty in device adoption and per-device traffic behavior because deterministic point forecasts do not quantify overload risk or support reliability-based capacity decisions. A diffusion-to-dimensioning workflow is proposed in which S-curve adoption modeling, bounded usage priors, scenario stress testing, and Monte Carlo uncertainty propagation are combined to generate predictive demand distributions, exceedance curves, and quantile-based capacity rules. The framework is applied to a Great Britain case study for 2025–2029 using smart meter deployment data and an M2M-based proxy for asset-tracking adoption. Analysis shows that planning-year upper-tail outcomes are driven primarily by asset-tracking usage uncertainty rather than by proxy scale alone. A ±30% perturbation of the AT adoption anchor changes $Q_{0.95}$ by approximately ±29.8%, whereas stressed AT usage increases $Q_{0.95}$ by 74.4%. Plausible positive dependence among key AT operational inputs further raises $Q_{0.95}$ by 18.3–22.5%. Limited hold-out evaluation provides strong out-of-sample support for the smart meter adoption stage and plausibility-only support for the shorter AT proxy. The framework is intended for medium-term, data-lean planning settings and is designed to support transparent risk-based capacity decisions rather than deterministic point sizing. Full article

Soil organic carbon (SOC) is a key indicator reflecting soil quality and management level. Understanding its spatiotemporal dynamics in cropland is necessary for sustainable land management. Revealing the relationship patterns between SOC (Sampling resolution: 1 km²; analysis resolution: 4 km²) and environmental factors in one period allows inferring SOC distribution in unsampled years, partly compensating for temporal data gaps. This study introduces a season-based crop rotation system (Winter wheat in the first season and summer corn in the next) as independent variables in a machine learning model innovatively, enriching variable selection in SOC inference and improving understanding of SOC accumulation. The Beijing–Tianjin–Hebei (BTH) region, characterized by a typical winter wheat–summer corn rotation system, was selected for analysis. The results show that in 2000, the average SOC was relatively low compared with global levels. Climatic variables were negatively correlated with SOC below the 0.8 quantile but positive above it, which corresponds to the upper 20% of the observed range of each climatic variable. Winter-wheat growth is more important on SOC distribution than summer-corn growth (two annual peaks of NDVI and EVI), showing a positive correlation with SOC, while corn showed a weak correlation and became negative above the 0.8 quantile. In the inferred results, the differences between observed and inferred mean values and their confidence intervals were approximately 0.1. This research provides a reference method for evaluating regional-scale SOC distribution patterns under data-limited conditions by integrating environmental factors and crop rotation characteristics. Full article

This research investigates the influence of economic, energy, and institutional variables on sustainable economic growth for Pacific Asian countries using Adjusted Net Savings (ANS) as a more refined measure of sustainable development. Using an unbalanced panel dataset for the period 1996 to 2021, second-generation panel data analysis is conducted to capture both long-run and distributional relationships, addressing potential concerns about cross-sectional dependence. The results indicate the presence of long-run relationships that are stable for both sustainable development itself and for its defining factors. Foreign direct investments (FDI) are found to have the most significant influence on sustainable development for all quantile values, underlining their central importance to long-run capital accumulation efforts. Renewable energy consumption helps increase sustainability outcomes for countries with lower savings performance values, while renewable energy production is found to have a modest but positive influence for each quantile of the distribution of outcomes. Natural resource wealth is seen to have non-linear effects on outcomes, with countries with lower savings values being adversely affected, while countries with higher savings values are beneficially affected. The presence of institutional factors is an enabler for countries with lower values of sustainable development performance. Full article

Environmental monitoring systems require robust uncertainty quantification for effective decision-making in complex ecological processes. Harmful algal blooms represent a critical challenge where prediction uncertainty directly impacts resource allocation and response timing, yet current remote sensing-based prediction systems provide only deterministic classifications without confidence measures. This gap between algorithmic predictions and actionable risk assessment limits operational utility for stakeholders managing water quality under varying risk tolerances. This study developed a transferable probabilistic forecasting framework integrating Sentinel-2 multispectral imagery with quantile regression and ensemble machine learning to generate continuous confidence indicators for cyanobacteria density prediction, demonstrated through its application to Lake Okeechobee, Florida. The methodology combines spectral indices extracted from Sentinel-2 data with XGBoost for quantile regression at 0.05, 0.50, and 0.95 probability levels, and LightGBM for multi-horizon temporal forecasting. Sentinel-2’s 13 spectral bands spanning visible to shortwave infrared wavelengths, combined with its 5-day revisit frequency provide a spectrally rich and temporally dense input space that is well-suited to gradient boosting methods such as XGBoost, which can exploit complex nonlinear interactions among spectral features to distinguish cyanobacterial signatures from background water constituents. LightGBM achieved mean absolute percentage errors of 2.9% for 10-day forecasts and 5.7% for 20-day forecasts, outperforming conventional regression models. The framework generates 90% prediction intervals that enable reliable risk classifications for operational bloom management. This approach bridges the gap between satellite-based algal bloom detection and actionable decision-making by quantifying predictive uncertainty, representing a shift from binary classifications to probability-based environmental monitoring systems that accommodate varying stakeholder risk tolerances in water quality management applications. Full article

We introduce the one-parameter bounded p-exponential distribution on $(0, p+1)$, which includes the uniform model as a special case and converges pointwise to the exponential law as $p\to \infty$. Closed-form expressions are derived for the CDF and PDF, the survival function, an explicit increasing-failure-rate hazard function, the quantile function (enabling inversion-based simulation), moments, and entropy, along with a constructive scaled beta or Kumaraswamy representation. We also establish stochastic ordering with respect to p in stop-loss and increasing convex order, formalizing how dispersion varies with the parameter while preserving the mean scale. Inference is discussed under parameter-dependent support, a non-regular setting, and we develop and compare several estimation procedures, including a likelihood-based boundary MLE, a variance-matching method-of-moments estimator, and Bayesian estimation under a gamma prior implemented via numerical quadrature or MCMC. Monte Carlo simulation studies evaluate finite-sample performance and interval behavior, and two real-world applications in survival and reliability analysis illustrate competitive goodness-of-fit relative to standard benchmark models. Full article

As mineral exploration increasingly targets deeper and more geologically complex terrains, the need for reliable predictive models becomes critical to mitigating exploration risk and improving cost efficiency. Correspondingly, the effectiveness of deep mineral exploration strategies depends substantially on the effectiveness and precision of three-dimensional mineral prospectivity mapping (3D MPM) models. However, the inherent spatial non-stationarity—where ore grade variability changes across geological domains—and the strongly skewed distribution of high-grade samples present a dual challenge. Conventional methods, which primarily rely on mean-based regression, often struggle to adequately address this dual challenge, limiting their predictive performance in complex geological settings. To address these issues, this paper proposes a pinball-loss-guided, global–local fusion Transformer model within a unified framework for 3D MPM. It leverages a multi-head self-attention mechanism with global–local fusion to capture long-range dependencies and global geological contexts, while incorporating local feature extraction modules to adaptively model spatially varying mineralization controls, jointly optimized through a pinball loss function to address mineralization distribution skewness. The proposed framework was first rigorously evaluated using the Xiadian gold deposit as a case study. Bootstrap analysis of the ablation experiments confirmed its predictive performance in terms of quantile-specific accuracy and prediction interval (PI) calibration. Ten rounds of random data splits provided further confirmation of the model’s stability. Subsequently, the validated model was applied to prospectivity mapping in unexplored regions, leading to the delineation of several high-potential exploration targets. Finally, comparative analyses with state-of-the-art machine learning methods were conducted, which further validated the competitive fitting capability of the proposed framework. Full article

Credit default prediction from firm-level accounting statements is central to risk management, yet the underlying financial data are highly sensitive and often siloed across banks, auditors, and platforms. Federated learning (FL) offers a practical route to collaborative modeling without centralizing raw records, but standard FL optimization can converge slowly under severe client heterogeneity, heavy-tailed accounting features, and label imbalance typical of default events. This paper proposes FedQuAD, a novel fast-converging FL algorithm that couples (i) quasi-Newton curvature aggregation on the server with a lightweight limited-memory update to accelerate global progress, (ii) a proximal variance-reduced local solver that stabilizes client drift under non-IID accounting distributions, and (iii) federated robust standardization of tabular financial ratios via secure aggregated quantile statistics to mitigate scale instability and outliers. FedQuAD is communication-efficient by design: It transmits compact gradient and curvature sketches and adapts local computation to each client’s stochasticity and drift. We provide convergence guarantees for strongly convex default-risk objectives (logistic and calibrated GLM losses) under bounded heterogeneity, and extend the analysis to nonconvex deep tabular models via expected stationarity bounds. Experiments on public credit-risk benchmarks with simulated cross-silo (institutional) partitions demonstrate that FedQuAD reaches target AUC and calibration error with substantially fewer communication rounds than representative baselines while maintaining privacy constraints compatible with secure aggregation and optional client-level differential privacy accounting. Full article

Parameter estimation for biochemical reaction networks is computationally demanding, especially for systems with oscillatory nonlinear dynamics, where standard iterative optimization strategies, including genetic algorithms, often struggle with prohibitive computational costs. We introduce an efficient parameter estimation framework that combines a real-coded genetic algorithm with a novel adaptive simulation termination strategy. This strategy defines a time-dependent termination boundary based on population quantiles, which is permissive during early transients and becomes progressively stricter as simulations advance, explicitly accounting for the temporal structure of oscillatory behavior. Crucially, this mechanism facilitates the efficient identification and early simulation termination of poor parameter candidates, thus avoiding the computational expense of full-horizon simulations. The framework further integrates global exploration with the modified Powell method for rapid local refinement. Numerical experiments on two benchmark oscillatory models—the Lotka–Volterra and Goodwin oscillators—demonstrate that the framework reduces computational cost by approximately 30–50% compared to a baseline GA without this strategy. For the parameter-sensitive Goodwin model, the framework efficiently identifies candidates evolving toward damped oscillations caused by subtle parameter variations. Sensitivity analysis also confirms robustness across diverse hyperparameter settings, indicating that adaptive simulation termination provides a practical acceleration mechanism for inverse problems in systems biology where iterative objective function evaluation dominates runtime. Full article

Environmental pressure in Nigeria persistently escalates despite several development efforts, prompting questions about the structural factors contributing to the nation’s ecological vulnerability. Considering this, the study employs a time-series research design that synthesizes collective theoretical perspectives to elucidate the interplay between income inequality (GINI), institutional quality (INST), trade in services (TO), and population density (POPd) in shaping Nigeria’s ecological footprint (ECF), utilizing data for the aforementioned variables from 1960 to 2024. The analysis shows time-varying dynamics across pollution regimes using Quantile-on-Quantile Regression (QQR) and Wavelet Quantile Correlation (WQC). The result reveals notable asymmetries across the ECF distribution. GINI and POPd intensify ecological pressure mainly at higher ECF quantiles. While INST serves as a key mitigating factor of ECF, particularly in a long-term pollution scenario. TO exhibits a regime-dependent effect, aligning with the Pollution Haven expectation in poor environments. These findings suggest that environmental outcomes in emerging economies are shaped by structural inequality and institutional strength. Highlighting the necessity of building institutional capacity to decouple inequality that drives ecological degradation. Thus, connecting national strategies with the Sustainable Development Goals (SDGs) 1, 8, 10, 11, 12, 13, 15, 16, and 17. These provide actionable insights into an inclusive and resilient environment. Full article

Traffic incident impact prediction remains challenging for intelligent transportation systems due to complex spatiotemporal dependencies. This study analyzes 38,430 real-world traffic incidents from Istanbul (2022–2024) to predict normalized traffic deviation $\Delta Traffic(\%)$ using machine learning with rigorous temporal validation. Three models—Random Forest (RF), XGBoost, and LightGBM—were evaluated using rolling-origin cross-validation (2022 training, 2023 testing; 2022–2023 training, 2024 testing) to prevent temporal leakage, employing a strictly operational 13-feature set that excludes information unavailable at incident onset (t₀). LightGBM achieved MAE = 26.81 ± 1.94% and R² = 0.506 ± 0.042 (mean ± std across folds) with 95% bootstrap confidence intervals of [27.54%, 28.81%] for MAE on the 2024 test set, significantly outperforming historical baselines (R² = 0.100 ± 0.054, p < 0.001, Bonferroni-corrected). Feature ablation studies revealed that temporal features contribute 65.2% of predictive power, while incident type contributes only 1.3%. Distributional robustness analysis confirms conclusions are stable across distributional treatments (log, winsorised, quantile), with feature importance rank correlations ρ = 1.000 between all treatment pairs. This work provides empirical evidence for context-aware traffic management systems and demonstrates the importance of proper temporal validation in transportation forecasting. Full article