Search Results (1,213)

Evaluating urban drainage system efficacy is critical for design and renovation. Existing probabilistic models often rely on exponential distributions, which are inadequate for specific climatic regions (coefficient of variation of rainfall characteristics does not equal 1). This study proposes a Gamma distribution-based probabilistic model, integrating B.J. Adams’ rainfall-runoff transformation theory to accurately characterize rainfall properties (volume, duration, intensity, interevent time) and assess drainage system performance. A systematic, criteria-based framework is provided to determine where the Gamma model should be preferred. The model enhances estimation accuracy by incorporating both the mean and standard deviation of meteorological data, providing a reliable tool for engineering design. Full article

In subhumid Mediterranean agroecosystems, runoff drives soil erosion by controlling particle detachment and transport, with its generation and connectivity strongly influenced by land use. In areas affected by land abandonment and reforestation, terracing modifies hillslope morphology and flow pathways, thereby altering soil redistribution patterns. Fallout ¹³⁷Cs has been widely used to assess medium term soil redistribution, and in situ gamma ray spectrometry using scintillation detectors provides an alternative for improving spatial coverage, yet the influence of factors specific to the site on measurements remains insufficiently explored. This study investigates how ¹³⁷Cs counts obtained in situ with a LaBr₃ detector can be used to interpret soil redistribution patterns in two paired catchments that experienced land abandonment since the mid-1960s. Following abandonment, catchment A underwent natural revegetation, whereas catchment B was terraced for reforestation, allowing the effects of water erosion and terracing on soil mobilisation to be analyzed through the spatial distribution of ¹³⁷Cs. By linking ¹³⁷Cs counts with catchment physiography, land use, flow pathways, and NDVI, the study aims to identify the main controls on soil redistribution in both catchments. ¹³⁷Cs counts were significantly higher in catchment A (156.8 ± 108.2 counts) than in catchment B (53.2 ± 68.1), with coefficients of variation of 69% and 128%, respectively. The in situ ¹³⁷Cs measurements provide reliable indicators of soil redistribution patterns controlled not only by runoff but also by anthropogenic modifications of hillslope morphology that alter flow pathways and hydrological connectivity following terracing. The paired catchment approach, combined with in situ ¹³⁷Cs measurements, provides valuable insights into the key controls on soil redistribution, which is essential for effective land management. Full article

Camellia japonica is a widely cultivated woody ornamental plant. However, current studies mostly focus on the onset of flowering, neglecting the overall flowering time distribution patterns of the blooming process. In this study, we used uniform 5-year-old potted cuttings of C. japonica ‘Jinjiang Mudan’ to evaluate six candidate distribution models, including normal, log-normal, skew-normal, gamma, Weibull, and exponential, to model flowering time distribution. These candidates were compared to obtain an optimal distribution model using three-fold cross-validation, six evaluation indicators, and three goodness-of-fit tests in the control. The optimal distribution model was used to compare and analyze the different effects of the control, shading, and exogenous hormone treatments. The results showed that the skew-normal distribution model emerged as the most suitable distribution model among the six candidates and captured the flowering time distribution characteristics effectively in all treatments. Shading treatments were found to delay and extend the flowering period, with moderate treatments (50% and 70% shading) demonstrating better performance, extending the flowering period by approximately 40%. In terms of exogenous hormone treatments, BG (a mixture of the 6-BA and GA₃) concentrations could prolong and delay the flowering period. Lower concentrations (100 and 250 mg L⁻¹) of 6-BA and GA₃ were effective in extending the flowering period, with BA250 exhibiting the most pronounced effect, delaying flowering onset by approximately 12% and extending the flowering period by approximately 17%. Considering that this study is based on single-location and single-season trials, these findings provide a valuable methodological resource for quantifying and predicting flowering time distribution in C. japonica, other ornamentals, and crops. Full article

Porous, crystalline gamma-Al₂O₃ coatings with a thickness of (6 ± 0.5) μm and a uniform distribution of rare earth (RE) dopants are synthesized by plasma electrolytic oxidation of aluminum at a current density of 150 mA/cm² in a boric acid and borax (BB) solution containing added RE oxide particles (Ho₂O₃, Tb₄O₇, and Pr₆O₁₁) at concentrations of 1, 2, and 4 g/L. The concentration of RE oxide particles in the BB solution determines the amount of RE elements incorporated into the coatings but does not significantly affect their surface morphology, crystal structure, or light absorption properties. The coatings exhibit high absorption in the middle/near-ultraviolet region, characteristic of Al₂O₃. Typical 4f-4f transitions of Ho³⁺, Tb³⁺, and Pr³⁺ are observed in the photoluminescence spectra. Photocatalytic evaluations using methyl orange degradation under simulated solar irradiation show that RE doping significantly enhances photocatalytic efficiency. Peak degradation efficiencies are achieved at a concentration of 4 g/L for all RE oxides. After 8 h of irradiation, maximum degradation reaches 88%, 92%, and 85% with pseudo-first-order rate constants (k_app) of about 0.274 h⁻¹, 0.339 h⁻¹, and 0.232 h⁻¹ for coatings synthesized in BB with 4 g/L Ho₂O₃, Tb₄O₇, or Pr₆O₁₁, respectively. In comparison, the pristine Al₂O₃ coating achieves only about 50% degradation (k_app ≈ 0.087 h⁻¹). Photoluminescence indicates that RE³⁺ ions serve as effective charge-carrier traps, suppressing electron–hole pair recombination. RE-doped Al₂O₃ coatings demonstrate exceptional structural stability and reusability over six cycles, highlighting their potential for sustainable wastewater remediation. Full article

Functionally graded AA2024/B₄C metal matrix composites were fabricated via mechanical alloying and hot pressing to investigate structure–property–radiation shielding relationships. Single-layer, two-layer, and three-layer architectures with varying B₄C contents were systematically produced. Microstructural homogeneity and phase constitution were examined using SEM/EDS and XRD, while thermal stability was evaluated by thermogravimetric analysis. Density and porosity measurements were conducted to assess the influence of reinforcement distribution and functional grading on densification behavior. Gamma radiation shielding performance was experimentally evaluated using a ¹⁵²Eu source and an HPGe detector over a wide photon energy range. Key shielding parameters, including linear and mass attenuation coefficients, half-value layer, tenth-value layer, mean free path, and radiation protection efficiency, were determined. The results reveal that functional grading significantly enhances radiation attenuation compared to monolithic composites. The three-layer AA2024/B₄C composite exhibited the highest attenuation coefficients and the lowest HVL, TVL, and MFP values at all investigated energies, achieving nearly 100% improvement in shielding efficiency relative to unreinforced AA2024. These findings demonstrate that controlled B₄C distribution and layered composite architecture provide a synergistic improvement in thermal stability, physical integrity, and radiation shielding performance, positioning functionally graded AA2024/B₄C composites as efficient lightweight materials for advanced radiation shielding applications. These results indicate that the developed functionally graded AA2024/B₄C composites are promising candidates for advanced radiation shielding applications in nuclear facilities, aerospace structures, and medical radiation protection systems, where lightweight and high-performance materials are critically required. Full article

Time headway is a key parameter for describing car-following behavior and microscopic traffic flow characteristics, and it is important for traffic safety analysis, road design, and optimizing intelligent-driving strategies. Existing research offers limited insight into the heterogeneity of time headway under different vehicle types and lane conditions. It is particularly important to investigate how time headway distributions differ across lane–vehicle-type combinations on highways, as these differences can affect safety evaluation and operational performance. This study is based on drone-captured vehicle trajectories from the publicly available HighD dataset. We select 378,751 vehicle–frame trajectory records; these records are used to construct valid follower–leader pairs and derive time headway (THW) samples for distribution fitting. Eight subsets are formed by combining two lane positions (inner vs. outer) and four follower–leader vehicle-type pairs (car–car, car–truck, truck–car, truck–truck). Six candidate distributions (Lognormal, Log-logistic, Burr, Weibull, Gamma, and Logistic) are fitted using maximum likelihood estimation, and their fit is evaluated using Kolmogorov–Smirnov, Anderson–Darling, and Chi-square tests, which are fused via an entropy-weighted composite score for model ranking. Results show pronounced heterogeneity across lane–vehicle-type subsets: Inner-lane samples exhibit smaller and more concentrated time gaps, whereas outer-lane samples show larger mean gaps, stronger dispersion, and heavier upper tails. Overall, Lognormal(3P) is selected as the top-ranked model in 5 of 8 subsets (62.5%), while Burr(4P) (car–truck, outer lane), Gamma(3P) (truck–car, outer lane), and Weibull(3P) (truck–truck, inner lane) are optimal in the remaining subsets. These findings indicate that lane position and vehicle-type pairing materially affect THW distributional characteristics, providing quantitative guidance for lane- and vehicle-aware traffic modeling, safety-oriented assessment, and intelligent-driving strategy design. Full article

This paper proposes a novel multi-relay deep learning-assisted differential chaos shift keying (MR-DL-DCSK) communication system to enhance the capabilities of existing chaos-based cooperative communication systems. Channel quality significantly affects transmission reliability. However, existing channel quality evaluation methods require channel state information (CSI). To address this limitation, a deep neural network (DNN) classifier is employed at the receiver in this paper to perform joint channel quality assessment and symbol demodulation. We propose a channel quality-aware relay coordination strategy: at the relay stage, all relays assess their channel qualities using the DNN-output probability distribution, and relays with lower channel quality align their decoded bits with the bits from the relay with the highest channel quality before forwarding; at the destination stage, the destination selects the signal with the highest channel quality probability for final demodulation. This joint detection approach enables reliable demodulation without requiring explicit CSI, while the channel quality-aware relay coordination mechanism ensures that signals from the most reliable links are prioritized for final decision. Comprehensive simulation results demonstrate that the proposed multi-relay DL-DCSK system achieves superior bit error rate performance. Furthermore, the system exhibits excellent generalization capability when tested on vehicle-to-vehicle (V2V) communication channels modeled by the double-generalized gamma distribution, validating its practical applicability in diverse wireless environments. Full article

Background/Objectives: Previous research demonstrates higher sodium retention with increasing levels of dietary salt in some populations. Our objective was to determine whole-body sodium retention and sodium distribution on high and low salt diets using rodent models. Methods: Whole body retention of orally dosed Na-22, a gamma emitter, was measured in female growing and adult Sprague-Dawley rats on high (3.1% by wt. of diet) and low salt (0.13% by wt. of diet) diets. In a second study, whole-body sodium retention was compared between destructive inductively coupled plasma optical emission spectroscopy (ICP-OES) and neutron activation analysis (NAA) in adult male and female C57BL/6 mice. Results: Whole body retention of Na-22 was not different due to the age of rats on a high salt diet, but rats fed the high salt diet excreted Na-22 much more rapidly than rats fed a low salt diet. In mice, neither sodium retention nor tissue distribution was affected by dietary salt. Bland–Altman analysis indicated overall agreement between NAA and ICP-OES measurements, with observed systematic positive bias. Conclusions: Dietary salt had little effect on retention in normotensive rodents and should be studied in hypertensive models. Full article

Environmental and financial datasets often display complex distributional characteristics, including heavy tails, high skewness and the presence of extreme observations. Traditional probability models such as the exponential, gamma or log-normal distributions may not adequately capture these behaviours particularly when modelling extreme events such as rainfall, pollution levels, stock returns or loss severities. By integrating the characteristics of Pareto and exponential distributions into an exponentiated framework that can describe datasets arising from environmental and finance fields, this study presents a novel three-parameter exponentiated Pareto exponential distributions using the exponentiated Pareto family of distributions with classical exponential distribution as the baseline model. This novel model extends the classical exponential distribution with the addition of extra shape parameters which simultaneously regulate the centre and tail behaviours of the new model. The statistical and mathematical characteristics of the proposed distribution are determined and studied. The maximum likelihood estimate approach is used in a conducted simulation exercise, and the estimator’s efficiency is evaluated as seen from the results. The practical applicability of the model is illustrated with four real-life datasets utilising model adequacy and goodness-of-fit measurements such as log–likelihood, Akaike information criteria and Bayesian information criteria. The data reveal that the proposed model gives a better fit than the models chosen as comparators, making the EPE distribution useful and robust in environmental and financial fields of study. Full article

Timber sale prices are influenced by multiple tract, product, and transaction characteristics. This study evaluates the effects of species composition, product class, sale method, harvest type, timber quality, and average tree diameter on timber stumpage prices using timber sale records from Alabama between 1 January 2010 and 31 December 2019. Prices were modeled on a per weight unit basis using a generalized linear model with a Gamma distribution and logarithmic link. Results indicate that larger average diameters were consistently associated with higher prices across most product classes. Harvest type also influenced prices, with salvage operations yielding prices approximately 8.3% lower than thinning operations. Timber quality had a moderate effect: good-to-excellent quality timber sold for about 4.8% higher prices than poor-to-fair quality timber. Sale method was an important determinant of price outcomes. Negotiated sales generated significantly lower prices than sealed-bid sales, averaging approximately 17% lower overall. However, interaction analysis revealed that negotiated sales produced higher prices for mixed hardwood sawtimber, likely reflecting the diverse end uses of these products. Regional effects were also evident, with higher prices observed in the southwestern portion of the state, likely due to proximity to the Port of Mobile and associated export markets. These findings highlight the importance of both tract and transaction characteristics in determining timber prices and provide guidance for landowners and forest managers when selecting sale strategies and management practices. Full article

The jet structure plays an important role in both the prompt and afterglow emission phases of gamma-ray bursts (GRBs). Whether GRB jets are better described by uniform (top-hat) or structured models remains an open question. We use the afterglowpy Python package to numerically model the late X-ray afterglow light curves of a large sample of long and short GRBs, and apply the Bayesian Information Criterion (BIC) to compare the performance of top-hat and Gaussian structured jet models. Within our adopted modeling framework, we find that the top-hat model is preferred by the BIC for ∼78.9% (150/190) of long GRBs and 70% (7/10) of short GRBs. GRB 180205A and GRB 140515A exhibit $\Delta$BIC < 2 for all three model comparisons, indicating that top-hat, Gaussian, and power-law jets provide equivalent fits to their afterglow light curves. This large-sample analysis suggests that uniform jets may be more common than structured jets in the observed GRB population, although this conclusion is subject to the limitations of our model assumptions and the BIC-based model selection criterion. Furthermore, we find that the best-fit distributions of observer angle ${\theta }_{\mathrm{obs}}$, electron energy fraction ${ϵ}_e$, and isotropic equivalent energy $E_0$ differ significantly between the top-hat and Gaussian jet models, with ${\theta }_{\mathrm{obs}}$ showing the most pronounced distinction. Full article

Neutron gamma density (NGD) plays an increasingly important role in petroleum exploration and development. However, current NGD logging fails to obtain reliable results in anomalous mineral formations (such as anhydrite, halite and coal). To address these issues, the application of NGD logging in anomalous minerals has been studied in this paper. Studies have shown that, compared to the standard formations (dolomite, limestone and sandstone), halite, anhydrite and coal have additional influence on inelastic gamma rays, epithermal neutron distribution, and thermal neutron distribution. This causes additional errors when the gamma and neutron information is used for density calculation. In addition, since the influence mechanisms of different minerals on NGD logging are different, it is necessary to determine the mineral type before conducting NGD correction. Compared to other minerals, halite can be easily distinguished by its very high sigma (thermal neutron capture cross-section) and low apparent density; anhydrite by its high sigma, high density and low neutron porosity; and coal by its very low density and zero neutron porosity. Furthermore, for a given anomalous mineral, the density error of NGD logging has a clear linear relationship with the apparent density, which can be used for density correction. By using the corresponding correction algorithm, the density error of NGD logging can be controlled within 0.025 g/cm³ in anomalous mineral formations. This study can provide guidance for the application of NGD technology in mineral exploration. Full article

Commercial Microwave Links (CMLs) have emerged as one of the most widely utilized opportunistic sensors for rainfall monitoring. However, rainfall retrieval using microwave links continues to face significant challenges in terms of accuracy, particularly for shorter path lengths. In recent years, a statistical approach has been demonstrated to effectively enhance retrieval accuracy. Concurrently, studies have shown that the selection of localized parameters can further optimize CML retrieval results. In this study, we evaluate and calibrate the probabilistic–statistical retrieval method proposed in a previous study for the Chinese region. Following their framework, we replace the global parameters with a Gamma rainfall distribution derived from local rain gauge observations, making the method more suitable for local climatic conditions. To validate the effectiveness of the improved method, we deployed three experimental microwave links with path lengths ranging from 420 m to 3.50 km and simultaneously recorded path attenuation along with rainfall data from surrounding rain gauges. The results show that the coefficient of determination and correlation coefficient between the proposed method and rain gauge observations reach 0.85 and 0.86, respectively, indicating a significant improvement over traditional models. The calibrated method performs particularly well during high-intensity rainfall events, demonstrating the importance of parameter localization for improving retrieval accuracy. Full article

A new odd exponential-type (NOT-Exp) distribution provides a flexible and analytically tractable framework for modeling lifetime data exhibiting non-constant hazard behaviors, including increasing, decreasing, bathtub-shaped, and unimodal forms, which are commonly observed in real-world reliability and survival studies. In this work, a comprehensive inferential methodology is developed for the NOT-Exp model under a unified progressive Type-II hybrid censoring, allowing several traditional censoring designs to be treated as special cases within a single unified structure. The main advantages of the proposed model lie in its ability to capture complex risk dynamics while maintaining mathematical simplicity, making it particularly suitable for censored lifetime data. Classical inference is conducted via maximum likelihood estimation, along with two asymptotic confidence interval constructions based on normal and log-normal approximations for both model parameters and reliability characteristics. In addition, a Bayesian estimation framework is introduced using independent gamma priors and Markov chain Monte Carlo techniques to obtain posterior estimates, credible intervals, and highest posterior density regions. Extensive simulations demonstrate the accuracy, stability, and robustness of the proposed estimators under varying sample sizes, censoring intensities, and prior specifications. Applications to airborne toxicological variation data and bank customer waiting times highlight the practical importance of the methodology, where the NOT-Exp model consistently outperforms twelve competing lifetime distributions according to standard goodness-of-fit criteria. These results confirm that the suggested design gives a strong and versatile tool for analyzing complex censored lifetime data across environmental and service-system applications. Full article

Fractional calculus and distribution theory share a common conceptual origin in the symbolic interpretation of differentiation and integration. Despite this connection, most developments in fractional calculus have traditionally been formulated within the framework of ordinary functions, while the systematic use of distributions remains limited. In this work, a novel distributional framework is developed by constructing a fractional Taylor representation of the product of Euler gamma and Riemann zeta functions in terms of fractional derivatives of the Dirac delta distribution. The proposed formulation enables the derivation of new fractional identities via Laplace transformation and facilitates the analytical solution of fractional differential equations containing such functions. Closed-form solutions are obtained in both classical and generalized distributional senses, allowing the extension of solutions from the positive real axis to the entire real line. Furthermore, the framework is applied to fractional operators of Erdélyi–Kober type, yielding new integral and derivative transforms. Fractional differential and integral equations with singular terms arise naturally in several engineering models involving memory effects, impulsive responses, and anomalous transport phenomena. However, the presence of nonremovable singularities—such as those associated with Euler gamma and Riemann zeta functions—significantly restricts the applicability of classical analytical methods. Overall, the proposed distributional framework bridges the gap between abstract fractional calculus and practical engineering models by enabling analytical solutions of fractional systems with singular memory kernels that were previously inaccessible using classical methods. Full article