Search Results (448)

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

The Offset Elliptical Normal (OEN) mixture model represents the seasonal–diurnal surface wind vector for wind engineering design applications. This study upgrades the parameterization of OEN by accounting for changes in format of the global database of surface observations, improving performance by eliminating manual supervision and extending the scope of the model to include skewness. The previous coordinate transformation of binned speed and direction, used to evaluate the joint probability distributions of the wind vector, is replaced by direct kernel density estimation. The slow process of sequentially adding additional components is replaced by initializing all components together using fuzzy clustering. The supervised process of sequencing each mixture component through time is replaced by a fully automated unsupervised process using pattern matching. Previously reported departures from normal in the tails of the fuzzy-demodulated OEN orthogonal vectors are investigated by directly fitting the bivariate skew generalized t distribution, showing that the small observed skew is likely real but that the observed kurtosis is an artefact of the demodulation process, leading to a new Offset Skew Normal mixture model. The supplied open-source R scripts fully automate parametrization for locations in the NCEI Integrated Surface Hourly global database of wind observations. Full article

Standard asymptotic inference for proportional hazards models is conventionally performed by calculating a standard error for the estimate and comparing the estimate divided by the standard error to a standard normal distribution. In this paper, we compare various standard error estimates, including based on the inverse observed information, the inverse expected inverse information, and the jackknife. Furthermore, correction for continuity is compared to omitting this correction. We find that correction for continuity represents an important improvement in the quality of approximation, and furthermore note that the usual naive standard error yields a distribution closer to normality, as measured by skewness and kurtosis, than any of the other standard errors investigated. Full article

Potentially toxic elements such as cadmium (Cd) in agricultural soils represent a global concern due to their toxicity and potential accumulation in the food chain. However, our understanding of cadmium’s complex sources and the mechanisms controlling its spatial distribution across diverse edaphic and geological contexts remains limited, particularly in underexplored agricultural regions. Our study aimed to assess the total accumulated Cd content in soils under avocado cultivation and its association with edaphic, geochemical, and geomorphological variables. To this end, we considered the total concentrations of other metals and explored their associations to gain a better understanding of Cd’s spatial distribution. We analyzed 26 physicochemical properties, the total concentrations of 22 elements (including heavy and trace metals such as As, Ba, Cr, Cu, Hg, Ni, Pb, Sb, Se, Sr, Tl, V, and Zn and major elements such as Al, Ca, Fe, K, Mg, and Na), and six geospatial variables in 410 soil samples collected from various avocado-growing regions in Peru in order to identity potential associations that could help explain the spatial patterns of Cd. For data analysis, we applied (1) univariate statistics (skewness, kurtosis); (2) multivariate methods such as Spearman correlations and principal component analysis (PCA); (3) spatial modeling using the Geodetector tool; and (4) non-parametric testing (Kruskal–Wallis test with Dunn’s post hoc test). Our results indicated (1) the presence of hotspots with Cd concentrations exceeding 3 mg·kg⁻¹, displaying a leptokurtic distribution (skewness = 7.3); (2) dominant accumulation mechanisms involving co-adsorption and cation competition (Na⁺, Ca²⁺), as well as geogenic co-accumulation with Zn and Pb; and (3) significantly higher Cd concentrations in Leptosols derived from Cretaceous intermediate igneous rocks (diorites/tonalites), averaging 1.33 mg kg⁻¹ compared to 0.20 mg·kg⁻¹ in alluvial soils (p < 0.0001). The factors with the greatest explanatory power (q > 15%, Geodetector) were the Zn content, parent material, geological age, and soil taxonomic classification. These findings provide edaphogenetic insights that can inform soil cadmium (Cd) management strategies, including recommendations to avoid establishing new plantations in areas with a high risk of Cd accumulation. Such approaches can enhance the efficiency of mitigation programs and reduce the risks to export markets. Full article

Competing risk models are essential in survival analysis for studying systems with multiple mutually exclusive failure events. This study investigates the application of competing risk models in the presence of progressively Type-II censored data for the Dagum distribution, a flexible distribution suited for modeling data with heavy tails and varying skewness and kurtosis. The methodology includes maximum likelihood estimation of the unknown parameters, with a focus on the special case of a common shape parameter, which allows for a closed-form expression of the relative risks. A hypothesis test is developed to assess the validity of this assumption, and both asymptotic and bootstrap confidence intervals are constructed. The performance of the proposed methods is evaluated through Monte Carlo simulations, and their applicability is demonstrated with a real-world example. Full article

Regarding the issue of constructing Fourier transform infrared (FTIR) spectral characteristics of hot jet of aero-engines, this paper presented a construction algorithm for the FTIR spectral characteristics of an aero-engine hot jet, which integrated staged refined processing and statistical feature fusion. First, a remote-sensing Fourier transform infrared spectrometer was employed to collect data on the hot jets of two distinct types of aero-engines, thereby establishing a measured spectral dataset. Subsequently, a multi-dimensional feature extraction vector construction algorithm was proposed, encompassing a peak feature extraction algorithm based on staged refined processing and a high-order statistical feature extraction algorithm. The peak feature extraction algorithm based on staged refined processing consisted of four steps: “coarse detection—local optimization—dynamic screening—intelligent merging”. It adopted an adaptive threshold for the initial coarse detection of peaks, enhanced the positioning accuracy through local gradient optimization, dynamically screened the local strongest peak according to intensity information, and resolved the problem of overlapping peak resolution via an intelligent merging strategy based on the physical characteristics of spectral lines, achieving high-precision and high-robustness peak feature extraction. The high-order statistical feature extraction algorithm realized the extraction of the intensity distribution information and waveform symmetry information of the spectral signal by fusing the kurtosis and skewness statistics. Compared with the traditional feature construction algorithms, the multi-dimensional feature vector construction algorithm proposed in this paper possessed a higher-dimensional comprehensive representation capability. In the experiment, we selected the GMM classifier of the unsupervised clustering algorithm. The classification accuracy of the features extracted by the algorithm in this paper on this classifier reached 82.42%, thereby validating the effectiveness of the algorithm presented in this paper. Full article

This work proposes a novel extension for a new extended Weibull distribution. Some statistical properties of the proposed distribution are studied including quantile, moments, skewness, and kurtosis. The hazard rate function of the new distribution has certain elastic qualities, allowing it to take increasing, upside-down bathtub, and modified upside-down bathtub shapes commonly observed in medical contexts. Different methods of estimation are studied using complete data. Two real data sets from the medical field are analyzed to demonstrate that the proposed model has adaptability in practice. In comparison to some well-known distributions, the suggested distribution fits the tested data better based on both parametric and non-parametric statistical criteria. A simulation study is presented to compare the obtained estimates based on mean square error and average absolute bias. Full article

Background: Radiotherapy for brain tumors can induce cognitive decline, yet most studies examine white matter (WM) damage six months post-treatment, overlooking early microstructural changes. This study investigated whether early WM changes, as measured by diffusion tensor imaging (DTI) histogram and texture features, can predict later cognitive deficits. Methods: Nineteen adults with brain metastases underwent DTI before and immediately after radiotherapy. Ten features—eight histogram-based and two texture-based—were extracted from normal-appearing WM of major DTI indices. Changes (Δ) in these features, if any, were analyzed via multiple linear regression, correlating them with cognitive performance at four months after therapy. Results: Out of 40 features, four exhibited significant post-radiotherapy changes. These were the mean (AD_mean) and skewness (AD_skewness) of axial diffusivity and the kurtosis of mean diffusivity (MD_kurtosis) and radial diffusivity (RD_kurtosis). Regression identified ΔAD_mean (β = −3.303 × 10⁴, p = 0.002) as negatively and ΔAD_skewness (β = 4.642, p = 0.006) and ΔRD_kurtosis (β = −1.505, p = 0.027) as positively associated with semantic fluency. Conclusions: Early WM microstructural disruptions—particularly axonal damage and heterogeneous injury—correlate with declines in semantic fluency. DTI histogram and texture features may be promising as early non-invasive biomarkers for cognitive risk following radiotherapy. 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

This study analyzes the impact of different types of random noise applied in Denoising Autoencoder (DAE) training on fault diagnosis performance, with the aim of improving noise removal for vibration time series data. While conventional studies typically train DAEs using Gaussian random noise, such noise does not fully reflect the complex noise patterns observed in real-world industrial environments. Therefore, this study proposes a novel approach that uses high-frequency noise components extracted from actual vibration data as training noise for the DAE. Both Gaussian and high-frequency noise were used to train separate DAE models, and statistical features (mean, RMS, standard deviation, kurtosis, skewness) were extracted from the denoised signals. The fault diagnosis rates were calculated using One-Class Support Vector Machines (OC-SVM) for performance comparison. As a result, the model trained with high-frequency noise achieved a 0.0293 higher average F1-score than the Gaussian-based model. Notably, the fault detection accuracy using the kurtosis feature improved significantly from 26.22% to 99.5%. Furthermore, the proposed method outperformed the conventional denoising technique based on the Wavelet Transform, demonstrating superior noise reduction capability. These findings demonstrate that incorporating real high-frequency components from vibration data into the DAE training process is effective in enhancing both noise removal and fault diagnosis performance. Full article

Fractal theory of particle size distribution (PSD) is a widely used approach in soil science. However, fractal studies on sandstone PSDs are scarce in sedimentology and geology. Taking littoral sandstones in the Carboniferous Donghetang Formation of the Hade Oilfield as an example, fractal dimensions of 115 fine sandstone and 150 silty sandstone PSDs are calculated and compared with particle size compositions and traditional statistical parameters in this paper. The results show that fractal dimension values in fine sandstones, 1.69–2.17 averaged at 1.99, are usually lower than that in silty sandstones, 2.12–2.73 averaged at 2.37. Fractal dimension and sandy content of littoral sandstones show a strong negative linear relationship. Significant logarithmic correlations are implied between fractal dimension and silty and clayey contents of littoral sandstones, which is different from linear relations in soil PSDs. The relationships between fractal dimension and mean, sorting, and skewness of silty sandstone PSDs are better than those of fine sandstones. Fractal dimension and kurtosis of silty sandstones and fine sandstones exhibit weak negative and positive linear relationships, respectively. Fractal dimension values in lower-shoreface facies, 2.05–2.47 averaged at 2.33, are generally higher than that in upper-shoreface facies, 1.79–2.30 averaged at 2.11. Fractal dimension values in bar and beach microfacies are commonly lower than those in trough microfacies. Combined with additional sedimentary information from various clastic deposits, the fractal dimension can serve as a new depositional environment indicator. Full article

This study examines the impact that fat-tailed distributions of the spread residuals have on the optimal orders for pairs trading of stocks and cryptocurrencies. Using daily data from selected pairs, the spread dynamics has been modeled through a mean-reverting Ornstein–Uhlenbeck process and investigates how deviations from normality affect strategy design and profitability. Specifically, we compared four fat-tailed distributions—Lévy stable, generalized hyperbolic, Johnson’s S_U, and non-centered Student’s t—and showed how they modify optimal entry and exit thresholds, and performance metrics. The main findings reveal that the proposed pairs trading strategy correctly captures some key stylized facts of residual spreads such as large jumps, skewness, and excess Kurtosis. Interestingly, we considered regime-switching behaviors to account for structural changes in market dynamics, providing empirical evidence that optimal trading rules are regime-dependent and significantly influenced by the residual distribution’s tail behavior. Unlike conventional approaches, we optimized the entry signal and link heavy tails not only to volatility clustering but also to the nonlinearity in switching regimes. These findings suggest the need to account for distributional properties and dynamic regimes when designing robust pairs trading strategies, providing a more realistic and effective framework of these strategies in highly volatile and non-normal markets. Full article

In this study, we introduce a novel distribution related to the gamma distribution, referred to as the generalized incomplete gamma distribution. This new family is defined through a stochastic representation involving a linear transformation of a random variable following a distribution derived from the upper incomplete gamma function. As a result, the proposed distribution exhibits a probability density function that effectively captures data exhibiting asymmetry and both mild and high levels of kurtosis, providing greater flexibility compared to the conventional gamma distribution. We analyze the probability density function and explore fundamental properties, including moments, skewness, and kurtosis coefficients. Parameter estimation is conducted via the maximum likelihood method, and a Monte Carlo simulation study is performed to assess the asymptotic properties of the maximum likelihood estimators. To illustrate the applicability of the proposed distribution, we present two case studies involving real-world datasets related to mineral concentration and the length of odontoblasts in guinea pigs, demonstrating that the proposed distribution provides a superior fit compared to the gamma, inverse Gaussian, and slash-type distributions. Full article

Objectives: The aim of our study is to evaluate whether texture analysis of 68Ga-DOTATOC PET/CT images can predict clinical outcome in patients with neuroendocrine tumors (NET). Methods: Forty-seven NET patients who had undergone 68Ga-DOTATOC PET/CT were studied. Primary tumors were localized in the gastroenteropancreatic (n = 35), bronchopulmonary (n = 8), and other (n = 4) districts. NET lesions were segmented using an automated contouring program and subjected to texture analysis, thus obtaining the conventional parameters SUVmax and SUVmean, volumetric parameters of the primary lesion, such as Receptor-Expressing Tumor Volume (RETV) and Total Lesion Receptor Expression (TLRE), volumetric parameters of the lesions in the whole-body, such as wbRETV and wbTLRE, and texture features such as Coefficient of Variation (CoV), HISTO Skewness, HISTO Kurtosis, HISTO Entropy-log₁₀, GLCM Entropy-log₁₀, GLCM Dissimilarity, and NGLDM Coarseness. Patients were subjected to a mean follow-up period of 17 months, and survival analysis was performed using the Kaplan–Meier method and log-rank tests. Results: Forty-seven primary lesions were analyzed. Survival analysis was performed, including clinical variables along with conventional, volumetric, and texture imaging features. At univariate analysis, overall survival (OS) was predicted by age (p = 0.0079), grading (p = 0.0130), SUVmax (p = 0.0017), SUVmean (p = 0.0011), CoV (p = 0.0037), HISTO Entropy-log₁₀ (p = 0.0039), GLCM Entropy-log₁₀ (p = 0.0044), and GLCM Dissimilarity (p = 0.0063). At multivariate analysis, only GLCM Entropy-log₁₀ was retained in the model (χ² = 7.7120, p = 0.0055). Kaplan–Meier curves showed that patients with GLCM Entropy-log₁₀ >1.28 had a significantly better OS than patients with GLCM Entropy-log₁₀ ≤1.28 (χ² = 10.6063, p = 0.0011). Conclusions: Texture analysis of 68Ga-DOTATOC PET/CT images, by revealing the heterogeneity of somatostatin receptor expression, can predict the clinical outcome of NET patients. Full article

Chromium–nickel austenitic stainless steels are widely used in various industries after their initial hardness and strength are increased. Apart from low-temperature thermal–chemical diffusion, the mechanical properties can be improved by surface cold working (SCW). A cheap and reliable form of static SCW is diamond burnishing (DB), which drastically improves the surface integrity (SI) and hence the operational behavior of the processed component. To be maximally effective, the DB parameters must be optimized according to a relevant criterion, depending on the desired effect. For high fatigue strength and/or high wear resistance, complex experimental tests are necessary, which require significant time and financial resources. This study presents a cost-effective optimization approach based on the DB process–SI–operating behavior correlations. Using these correlations, in addition to the correlations between appropriately selected SI characteristics, the proposed approach relies on the control of only three easy-to-measure roughness parameters, namely the arithmetic average roughness, skewness, and kurtosis, which, in turn, depend on the governing factors of the DB process. Full article