Search Results (242)

This study presents the results of preliminary documentation and radon tracer investigations conducted at the “Aurelia” Mine in Złotoryja. Measurements of ²²²Rn activity concentrations were carried out between 17 March and 26 August 2023, while terrestrial laser scanning (TLS) for mapping purposes was performed on 16 November 2024. The radon data exhibited a consistently right-skewed distribution, with skewness coefficients ranging from 0.9 to 8.2 and substantial standard deviations, indicating significant data dispersion. Outliers and extreme outliers were identified as key factors influencing average radon activity concentrations from April through August, whereas data from March displayed homogeneity, with no detected anomalies. The average ²²²Rn activity concentrations recorded from March to July ranged from 51.4 Bq/m³ to 65.9 Bq/m³. In contrast, July and August showed elevated average values (75.8 Bq/m³ and 5784.8 Bq/m³, respectively) due to the presence of outliers and extreme values. Upon removal of these anomalies, the adjusted means were 73.8 Bq/m³ and 1003.6 Bq/m³, respectively, resulting in reduced skewness and improved representativeness. These findings suggest that the annual average radon concentrations at the “Aurelia” Mine remain compliant with the regulatory threshold of 300 Bq/m³ set by the Atomic Law Act, with exceedances likely related to atypical or rare geophysical phenomena requiring further statistical validation. August exhibited a significant occurrence of outliers and extreme outliers in ²²²Rn activity concentration data, particularly concentrated between the 13th and 17th days of the month. This anomaly is hypothesized to be associated with geological processes, notably mining-induced seismic events within the LGOM (Legnica–Głogów Copper District) region. It is proposed that periodic transitions between tensional and compressional phases within the rock mass, triggered by mining activity, may lead to abrupt increases in radon exhalation, potentially occurring before or after seismic events with a magnitude exceeding 2.5. Although the presented data provide preliminary evidence supporting the influence of tectonic kinematic changes on subsurface radon dynamics, further systematic observations are required to confirm this relationship. At the current stage, the hypothesis remains speculative but may contribute to the broader understanding of radon behavior in geologically active underground environments. Complementing the geochemical analysis, TLS enabled detailed geological mapping and 3D spatial modeling of the mine’s underground tourist infrastructure. The resulting simplified linked data model—integrating radon activity concentrations, geological structures, and spatial parameters—provides a foundational framework for developing a comprehensive GIS database. This integrative approach highlights the feasibility of combining tracer studies with spatial and cartographic data to improve radon risk assessment models and ensure regulatory compliance in underground occupational settings. Full article

This study presents the results of a spatiotemporal analysis of indoor radon concentration dynamics at the Al-Farabi Kazakh National University (Almaty, Republic of Kazakhstan), located near the Almaty tectonic fault. The research is based on a 2.5-year monitoring campaign of radon levels using the RAMON-02A radiometer. The radon activity concentration ranged from 1.29 ± 0.19 to 149 ± 22 Bq/m³. The distribution of radon concentrations was found to follow a lognormal law, with a skewness coefficient of 1.55 and kurtosis of 4.7. The mean values were 28.7 ± 4.2 Bq/m³ (arithmetic mean) and 24.5 ± 3.6 Bq/m³ (geometric mean). Distinct seasonal and monthly variations were observed: the lowest concentrations were recorded during the summer months (August—20.8 ± 3.1 Bq/m³), while the highest were observed in spring and winter (May—34.0 ± 4.9 Bq/m³, December—34.2 ± 4.9 Bq/m³). The springtime increase in radon levels is attributed to thermobaric effects, limited ventilation, and precipitation, which contributes to soil sealing. Autocorrelation analysis revealed diurnal, seasonal, and annual fluctuations, as well as quasi-periodic variations of approximately 150 days, presumably linked to geophysical processes. Correlation analysis indicated a weak positive relationship between radon concentration and air temperature during winter and spring (≈0.2), and a pronounced negative correlation with atmospheric pressure in winter (−0.57). The influence of humidity was found to be minor and seasonally variable. Full article

Land resources are the basis of human production and life, and they face many challenges in the process of urbanization, such as the prominent contradiction between land supply and demand and the inefficient use of land, which in turn restricts the socio-economic development and the promotion of urbanization. This paper takes China’s ten largest urban agglomerations as its research object and constructs a land resource drag effect model based on the C-D production function. The geographical weighted regression method is used to calculate the coefficient of the land drag effect. Combining kernel density analysis and spatial autocorrelation analysis, the paper reveals the temporal and spatial evolution patterns of the drag effect and discusses the impact of land resources on new urbanization and its temporal and spatial differentiation characteristics. The study shows that during the period of 2006–2022, China’s new-type urbanization as a whole rises, but the development of each urban agglomeration has significant differences, showing a spatial pattern of “east high, west low”; the drag effect of land resources shows a decreasing trend, but regional differences are obvious, showing a distribution of “east strong, west weak”; the kernel density curve of drag effect of land shows a “right-skewed-left-skewed” change, with the overall level weakening and the degree of concentration increasing; the drag effect of land resources shows significant positive global autocorrelation, and there are spatial proximity effect and spillover effect in space. The findings provide a theoretical basis for land resource utilization and spatial development in China’s new-type urbanization process. Therefore, it is necessary to implement differentiated land resource allocation and urban planning policies according to different types of urban spatial agglomeration and to give full play to the cooperative linkage effect of urban agglomerations in reducing the drag effect of land resources. Full article

(1) Objective: Volatile organic compounds (VOCs) monitoring in industrial parks is crucial for environmental regulation and public health protection. However, current techniques face challenges related to cost and real-time performance. This study aims to develop a dynamic calibration framework for accurate real-time conversion of VOCs volume fractions (nmol mol⁻¹) to mass concentrations (μg m⁻³) in industrial environments, addressing the limitations of conventional monitoring methods such as high costs and delayed response times. (2) Methods: By innovatively integrating photoionization detector (PID) with machine learning, we developed a robust conversion model utilizing PID signals, meteorological data, and a random forest’s (RF) algorithm. The system’s performance was rigorously evaluated against standard gas chromatography-flame ionization detectors (GC-FID) measurements. (3) Results: The proposed framework demonstrated superior performance, achieving a coefficient of determination (R²) of 0.81, root mean squared error (RMSE) of 48.23 μg m⁻³, symmetric mean absolute percentage error (SMAPE) of 62.47%, and a normalized RMSE (RMSE_norm) of 2.07%, outperforming conventional methods. This framework not only achieved minute-level response times but also reduced costs to just 10% of those associated with GC-FID methods. Additionally, the model exhibited strong cross-site robustness with R² values ranging from 0.68 to 0.69, although its accuracy was somewhat reduced for high-concentration samples (>1500 μg m⁻³), where the mean absolute percentage error (MAPE) was 17.8%. The inclusion of SMAPE and RMSE_norm provides a more nuanced understanding of the model’s performance, particularly in the context of skewed or heteroscedastic data distributions, thereby offering a more comprehensive assessment of the framework’s effectiveness. (4) Conclusions: The framework’s innovative combination of PID’s real-time capability and RF’s nonlinear modeling achieves accurate mass concentration conversion (R² = 0.81) while maintaining a 95% faster response and 90% cost reduction compared to GC-FID systems. Compared with traditional single-coefficient PID calibration, this framework significantly improves accuracy and adaptability under dynamic industrial conditions. Future work will apply transfer learning to improve high-concentration detection for pollution tracing and environmental governance in industrial parks. Full article

This study analyzes the volatility of Bitcoin using stochastic volatility models fitted to one-minute transaction data for the BTC/USDT pair between 1 April 2023, and 31 March 2024. Bernstein polynomial terms were introduced to accommodate intraday and intraweek seasonality, and flexible return distributions were used to capture distributional characteristics. Seven return distributions—normal, Student-t, skew-t, Laplace, asymmetric Laplace (AL), variance gamma, and skew variance gamma—were considered. We further incorporated explanatory variables derived from the trading volume and price changes to assess the effects of order flow. Our results reveal structural market changes, including a clear regime shift around October 2023, when the asymmetric Laplace distribution became the dominant model. Regression coefficients suggest a weakening of the volume–volatility relationship after September and the presence of non-persistent leverage effects. These findings highlight the need for flexible, distribution-aware modeling in 24/7 digital asset markets, with implications for market monitoring, volatility forecasting, and crypto risk management. Full article

Rapid and accurate identification of maize architecture characteristics is important for understanding both yield potential and crop breeding experiments. Most canopy architecture indicators cannot fully reflect the vertical leaf distribution in field environments. We conducted field experiments on sixty maize cultivars under four planting densities at three different sites, and herein introduce two novel indicators, “kurtosis and skewness,” based on the manually measured leaf area index (LAI) of maize at five different canopy heights. Then, we constructed the LAI, plant height (PH), kurtosis, and skewness estimation models based on unmanned aerial vehicle multispectral, RGB, and laser detecting and ranging data, and further assessed the canopy architecture and estimated yield. The results showed that the fitting coefficient of determination (R²) of cumulative LAI values reached above 0.97, and the R² of the four indicators’ estimation models based on multi-source data were all above 0.79. A high LAI, along with greater kurtosis and skewness, optimal PH levels, and strong stay-green ability, are essential characteristics of high-yield maize. Moreover, the four indicators demonstrated high accuracy in estimating yield, with the R² values based on measured canopy indicators at the four planting densities being 0.792, 0.779, 0.796, and 0.865, respectively. Similarly, the R² values for estimated yield based on estimated canopy indicators were 0.636, 0.688, 0.716, and 0.775, respectively. These findings provide novel insight into maize architecture characteristics that have potential application prospects for efficient estimation of maize yield and the breeding of ideal canopy architecture. Full article

Characterizing complex heterogeneous structures in high-dimensional data remains a significant challenge. Traditional approaches often rely on summary statistics such as histograms, skewness, or kurtosis, which—despite their simplicity—are insufficient for capturing nuanced patterns of heterogeneity. Motivated by a brain tumor study, we consider data in the form of point clouds, where each observation consists of a variable number of points. Our goal is to detect differences in the heterogeneity structures across distinct groups of observations. To this end, we employ the Dirichlet Diffusion Tree (DDT) to characterize the latent heterogeneity structure of each observation. We further extend the DDT framework by introducing a regression component that links covariates to the hyperparameters of the latent trees. We develop a Markov chain Monte Carlo algorithm for posterior inference, which alternatively updates the latent tree structures and the regression coefficients. The effectiveness of our proposed method is evaluated by a simulation study and a real-world application in brain tumor imaging. Full article

To enhance the structural safety in wind-sand regions, this study employs the Euler-Lagrange numerical method to investigate the wind pressure characteristics of typical low-rise auxiliary buildings in a strong wind-blown sand environment. The results reveal that sand particle motion dissipates wind energy, leading to a slight reduction in average wind speed, while the increase in small-scale vortex energy enhances fluctuating wind speed. In the sand-laden wind field, the average wind pressure coefficient shows no significant change, whereas the fluctuating wind pressure coefficient increases markedly, particularly in the windward region of the building. Analysis of the skewness and kurtosis of wind pressure reveals that the non-Gaussian characteristics of wind pressure are amplified in the sand-laden wind, thereby elevating the risk of damage to the building envelope. Consequently, it is recommended that the design fluctuating wind load for envelopes and components of low-rise buildings in wind-sand regions be increased by 10% to enhance structural resilience. Full article

Downy mildew caused by Plasmopara viticola is an important disease in grape production, particularly in the highly susceptible, widely cultivated Vitis vinifera L. Breeding for disease resistance is an effective solution, and V. vinifera intraspecific crosses can yield progeny with both disease resistance and high quality. To assess the potential of intraspecific recurrent selection in V. vinifera (IRSV) in improving grapevine resistance to downy mildew and to analyze the pattern of disease resistance inheritance, the disease-resistant variety Ecolly was selected as one of the parents and crossed with Cabernet Sauvignon, Marselan, and Dunkelfelder, respectively, creating three reciprocal combinations, resulting in 1657 hybrid F1 progenies. The primary results are as follows: (1) significant differences in disease resistance among grape varieties and, significant differences in disease resistance between different vintages of the same variety were found; (2) the leaf downy mildew resistance levels of F1 progeny of different hybrid combinations conformed to a skewed normal distribution and showed some maternal dominance; (3) the degree of leaf bulbous elevation was negatively correlated with the level of leaf downy mildew resistance, and the correlation coefficient with the level of field resistance was higher; (4) five progenies with higher levels of both field and in vitro disease resistance were obtained. Intraspecific hybridization can improve the disease resistance of offspring through super-parent genetic effects, and Ecolly can be used as breeding material for recurrent hybridization to obtain highly resistant varieties. Full article

This study proposes a method for estimating the kinetic coefficient of friction (COF) for asphalt pavements by improving and applying Persson’s friction theory. The method utilizes the power spectral density (PSD) of the top surface topography instead of the full PSD to better reflect the actual contact conditions. This approach avoids including deeper roughness components that do not contribute to real rubber–pavement contact due to surface skewness. The key aspect of the method is determining an appropriate cutting plane to isolate the top surface. Four cutting strategies were evaluated. Results show that the cutting plane defined at 0.5 times the root mean square (RMS) height exhibits the highest robustness across all pavement types, with the estimated COF closely matching the measured values for all four tested surfaces. This study presents an improved method for estimating the kinetic coefficient of friction (COF) of asphalt pavements by employing the power spectral density (PSD) of the top surface roughness, rather than the total surface profile. This refinement is based on Persson’s friction theory and aims to exclude the influence of deep surface irregularities that do not make actual contact with the rubber interface. The core of the method lies in defining an appropriate cutting plane to isolate the topographical features that contribute most to frictional interactions. Four cutting strategies were investigated. Among them, the cutting plane positioned at 0.5 times the root mean square (RMS) height demonstrated the best overall applicability. COF estimates derived from this method showed strong consistency with experimentally measured values across all four tested asphalt pavement surfaces, indicating its robustness and practical potential. Full article

The velocity distribution at the deflector jet outlet significantly influences the pressure characteristics of the pilot stage, thereby affecting the dynamic performance of the servo valve. Conventional mathematical models fail to account for the influence of dynamic velocity distribution on pilot stage pressure characteristics, resulting in significant deviations from actual situations. As the deflector shifts, the secondary jet velocity distribution transitions from a symmetric to an asymmetric dynamic profile, altering the pressure within the receiving chambers. To address this, a dynamic skewed velocity distribution model is proposed to more accurately capture the pressure characteristics. The relationship between the skewness coefficient and deflector displacement is established, and the pressure calculation method for the receiving chambers is refined accordingly. A comparative analysis shows that the proposed model aligns most closely with computational fluid dynamics results, achieving a 98% match in velocity distribution and a maximum pressure error of 1.43%. This represents an improvement of 84.98% over the normal model and 82.35% over the uniform model, confirming the superior accuracy of the dynamic skewed model in pilot stage pressure calculation. Full article

As urban traffic density and congestion increase, effective urban traffic management becomes increasingly challenging, negatively impacting travel times and the overall efficiency of transportation systems. In this paper, a hierarchical Stackelberg model is presented to address both priority for emergency vehicles (EVs) and fairness for other vehicles. This model involves the Traffic Management Center (TMC) as the top-level authority, with emergency vehicles as the first-level leaders and regular vehicles (RVs) as the second-level followers. The multilevel decision-making structure enables real-time adjustments to prioritize critical traffic and ensure equitable treatment for regular traffic. Simulations were conducted under various traffic scenarios, including normal conditions, emergency vehicle priority, and peak traffic congestion. According to the results, the hierarchical Stackelberg model outperforms traditional models in terms of reducing average travel time, waiting time, and congestion. The model also incorporates fairness metrics such as Gini coefficients and skewness to ensure that regular vehicles are not disproportionately affected by emergency vehicle priority. According to these findings, the hierarchical Stackelberg model improves both traffic efficiency and fairness in complex urban environments, positioning it as a promising solution. Full article

This study investigates formation homogeneous regions in regional flood frequency analysis (RFFA) and compares two RFFA methods, the quantile regression technique (QRT) and the index flood method (IFM). A total of 201 gauged stations from southeast Australia were adopted in this study. Multivariate statistical techniques were applied to form candidate regions. Also, regions are formed in the L-moments space (such as the L coefficient of variation (LCV) and L coefficient of skewness (LCS) of annual maximum flood data). Hosking and Wallis test statistics were used to find discordant sites and for testing the homogeneity of the assumed regions. No homogeneous regions were found in southeast Australia based on catchment characteristics data; however, homogeneous regions can be formed in the space of L-moments. It was found that regions formed in the L-moments space have little link with the catchment characteristics data space. The QRT provides more accurate flood quantile estimates than the IFM. Full article

In this paper, we introduce a new distribution for modeling bimodal data supported on non-negative real numbers and particularly suited with an excess of very small values. This family of distributions is derived by multiplying the exponential distribution by a fourth-degree polynomial, resulting in a model that better fits the shape of the second mode of the empirical distribution of the data. We study the general density of this new family of distributions, along with its properties, moments, and skewness and kurtosis coefficients. A simulation study is performed to estimate parameters by the maximum likelihood method. Additionally, we present two applications to real-world datasets, demonstrating that the new distribution provides a better fit than the bimodal exponential distribution. Full article

Induction motors have a simple structure, have low manufacturing costs and are widely used. However, various vibration effects with mechanical or electromagnetic origins are also very common. To analyze the impact of rotor skewing on electromagnetic vibrations in induction motors, this paper investigated the skew factor of skewed rotor slots and proposes an electromagnetic force wave analysis method. The method aimed to optimize the skew angle parameters for vibration amplitude reduction, with its effectiveness verified through simulations and experiments. Taking a 7.5 kW four-pole induction motor with 36 stator slots and 28 rotor slots as the research object, the suppression law of different skew parameters on force waves generated by stator harmonics was obtained. Results show that when the rotor is skewed by an angle equivalent to three stator teeth pitch, electromagnetic forces of different orders are attenuated by approximately 5% on average. Physical rotors with skew angles of 0°, 10°, 12.8°, 14°, and 20° were manufactured for experimental validation, while considering the influence of rotor skewing on starting torque and maximum torque. The study concludes that the amplitude of tooth harmonics varies with the skew coefficient, consistent with the skew factor analysis. By analyzing motor vibration with the skew coefficient, the amplitude relationship of electromagnetic vibration under different optimization parameters can be determined, thereby selecting reasonable skew parameters for rotor optimization. Full article