Search Results (99)

The correct estimation of the population variance plays a vital role in the sampling procedure in surveys, especially when simple random sampling techniques are used. In this work, we propose a new generalized statistical inference in order to estimate the population variance using auxiliary information. We can use the relationship between the study variable and the auxiliary variable to construct a novel generalized class of estimators that is better performing in terms of minimum mean squared error (MSE) and has a higher percentage of relative efficiency than the traditional estimators. The proposed methodology is based on the existing methods of inference with the introduction of modifications to cover the known population parameters of additional auxiliary variables, like the mean, the coefficient of variation, skewness, or kurtosis. Theoretical properties such as bias and mean squared error are obtained with regard to the first-order approximation. The performance of the proposed class of estimators is checked by comparing with that of the classical variance estimators in different population conditions based on real-life data sets and a simulation study. The numerical findings have indicated that the suggested class of estimators is more effective compared to classical methods, especially in cases where there is a very high linear correlation between the auxiliary and the study variables. Also, the estimators are robust, as confirmed using various sample sizes and population structures. The research has made a significant contribution to the development of statistical procedures in survey sampling because the practical and efficient tools provided in the study were useful in estimating the variance. The results have been of great importance when applied by researchers and practitioners active in large-scale surveys. Subsequently, in the case of efficient utilization of auxiliary information, it is feasible to have more accurate and cost-effective statistical inference. Full article

This study addresses the condition monitoring of rolling bearings by applying robust linear regression to statistically derived features from vibration data. Four datasets of acceleration signals were collected under varying operating conditions: aligned and misaligned bearings at rotational speeds of 1000 rpm and 1500 rpm. From each signal, key statistical indicators were extracted, including root mean square (RMS), skewness, kurtosis and crest factor, to capture signal characteristics that were relevant to fault detection. To follow-up, we applied the Kolmogorov–Smirnov test to assess data normality and the results confirmed significant deviations from a Gaussian distribution, motivating the use of robust regression techniques for further investigations. The regression model created incorporated rotational speed and alignment conditions as predictors of acceleration and the results indicated that while the coefficient associated with misalignment suggested a possible increase in acceleration (~1.115 units), statistical testing (p = 0.5233) indicated that neither speed nor alignment had a significant influence on the measured vibration levels within the dataset. The findings suggest that under the tested conditions, misalignment does not manifest as a strong linear change in acceleration magnitude, and the study underscores the importance of robust modeling techniques and feature selection in the condition monitoring of rotating machinery. Full article

Background/Objectives: Germ Cell Neoplasia In Situ (GCNIS) is considered the precursor lesion for the majority of testicular germ cell tumors (TGCTs). The aim of this study was to evaluate whether first-order radiomics features derived from volumetric diffusion tensor imaging (DTI) metrics—specifically apparent diffusion coefficient (ADC) and fractional anisotropy (FA) histogram parameters—can detect GCNIS. Methods: This study included 15 men with TGCTs and 10 controls. All participants underwent scrotal MRI, including DTI. Volumetric ADC and FA histogram metrics were calculated for the following tissues: group 1, TGCT; group 2: testicular parenchyma adjacent to tumor, histologically positive for GCNIS; and group 3, normal testis. Non-parametric statistics were used to assess differences in ADC and FA histogram parameters among the three groups. Pearson’s correlation analysis was followed by ordinal regression analysis to identify key predictive histogram parameters. Results: Widespread distributional differences (p < 0.05) were observed for many ADC and FA variables, with both TGCTs and GCNIS showing significant divergence from normal testes. Among the ADC statistics, the 10th percentile and skewness (p = 0.042), range (p = 0.023), interquartile range (p = 0.021), total energy (p = 0.033), entropy and kurtosis (p = 0.027) proved the most significant predictors for tissue classification. FA_energy (p = 0.039) was the most significant fingerprint of the carcinogenesis among the FA metrics. These parameters correctly characterized 88.8% of TGCTs, 87.5% of GCNIS tissues and 100% of normal testes. Conclusion: Radiomics features derived from volumetric ADC and FA histograms have promising potential to differentiate TGCTs, GCNIS, and normal testicular tissue, aiding early detection and characterization of pre-cancerous lesions. 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

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

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

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

The transition stage to pre-primary school represents a key event, in which the support received from family and teachers is essential in facilitating the transition and in ensuring an effective adjustment to the school environment. Our study aimed to investigate the impact of mathematical prerequisites on cognitive development, compared to the impact of reading and writing prerequisites, from the perspective of parents and early childhood educators. Thus, we quantified the impact of reading–writing and math prerequisites on children’s transition from kindergarten to school, analyzing the challenges, opportunities, and possibilities that arise. The sample consisted of 685 parents and 188 teachers, using the preschool prerequisites screening standardized questionnaire developed by the company Cognitrom, a questionnaire-survey, and the focus group method. Initially, the fidelity of the research instrument was assessed by calculating Cronbach’s alpha coefficient. The data distribution was tested using the skewness and kurtosis coefficients. Subsequently, descriptive analyses were carried out in order to provide an overview of the data collected by performing a multiple linear regression analysis. In addition, the Phi coefficient and V Cramer’s V coefficient were used to analyze the association between the research variables. By corroborating the obtained results, we can state that, from the parents’ and early childhood teachers’ perspective, math prerequisites have a greater influence on children’s cognitive development in the transition process from kindergarten to school compared to reading–writing prerequisites, confirming the general hypothesis. Full article

Objectives: This study aims to investigate cardiovascular disease markers in patients with schizophrenia and to contribute to the early indication of asymptomatic cardiovascular diseases in these patients. In our study, there are three groups: schizophrenia with negative symptoms (SCH-N), schizophrenia in remission (SCH-R), and a healthy control group (HC). In these groups, there were compared parameters such as lipid panel, Atherogenic Index (AIP), Triglyceride-glucose (TyG) index, Castelli Risk Index-1 (CRI-I), Castelli Risk Index-2 (CRI-II), and Atherogenic Coefficient (AC), which are associated with the risk of cardiovascular disease. Methods: The participants of the study were from the HC group and schizophrenia patients aged between 18 and 65 who were followed up at the Psychiatry Clinic of Karaman Hospital. This cross-sectional case–control study consists of the SCH-N (n:20), the SCH-R (n:23), and the HC (n:21) groups. Those with cardiovascular, endocrine, and inflammatory diseases, those with alcohol and substance addiction, those using drugs other than psychiatric drugs, and those lacking informed consent were excluded from the study. Patients in active psychotic episodes were also excluded from the study due to communication difficulties. All data were analyzed using SPSS 25.0 package program in a computer environment. The conformity of continuous data to normal distribution was evaluated with normality test value, q-q plot, skewness, and kurtosis. For significant results in the ANOVA test, pairwise comparisons were conducted using the post hoc Bonferroni correction when variances were homogeneously distributed. Similarly, for significant results in the Kruskal–Wallis Test, pairwise comparisons were performed using the Dunn–Bonferroni test. In this study, values less than p < 0.05 were considered statistically significant. Results: When all groups were compared, the increase in the TGs, TyG index, AIP, CRI-I, CRI-II, and AC values in the SCH-R group compared to the HC group was found to be statistically significant (p < 0.001, p < 0.001, p < 0.001, p < 0.001, p = 0.015, p < 0.001; sequentially). Conclusions: This study revealed that cardiovascular risk markers in schizophrenia patients showed significant differences. In particular, the elevation in parameters such as TGs, TyG index, AIP, CRI-I, CRI-II, and AC indicates that schizophrenia patients have an increased risk for cardiovascular diseases. Therefore, it is recommended that schizophrenia patients be closely monitored for cardiovascular risk factors and to intervene early. Full article

This article proposes a method for diagnosing the main systems of bucket elevators in order to ensure their reliable operation. This method employs diagnostic indices of vibration velocity and vibration acceleration, which were deemed useful based on tests performed on four bucket elevators operating in a research laboratory and in a power plant. This article also analyzes other indicators, such as the coefficient of variation, skewness, kurtosis, crest factor, and quantile peak factor, and demonstrates the usefulness of kurtosis for diagnostic evaluation. Additionally, it proposes using the quantile peak factor as an alternative to the crest factor. This study estimates the statistical distributions of diagnostic signals and presents the results in the form of histograms. This is followed by the detection of outliers in all measurement series. Based on the results of the performed tests and their analysis, recommendations are made for diagnosing bucket elevators. Full article

Background: The discovery of novel antibody–drug conjugates for low-expression human epidermal growth factor receptor 2 (HER2-low) breast cancer highlights the inadequacy of the conventional binary classification of HER2 status as either negative or positive. Identification of HER2-low breast cancer is crucial for selecting patients who may benefit from targeted therapies. This study aims to determine whether qualitative and quantitative magnetic resonance imaging (MRI) features can effectively reflect low-HER2-expression breast cancer. Methods: Pre-treatment breast MRI images from 232 patients with pathologically confirmed breast cancer were retrospectively analyzed. Both clinicopathologic and MRI features were recorded. Qualitative MRI features included Breast Imaging Reporting and Data System (BI-RADS) descriptors from dynamic contrast-enhanced MRI (DCE-MRI), as well as intratumoral T2 hyperintensity and peritumoral edema observed in T2-weighted imaging (T2WI). Quantitative features were derived from diffusion kurtosis imaging (DKI) using multiple b-values and included statistics such as mean, median, 5th and 95th percentiles, skewness, kurtosis, and entropy from apparent diffusion coefficient (ADC), D_app, and K_app histograms. Differences in clinicopathologic, qualitative, and quantitative MRI features were compared across groups, with multivariable logistic regression used to identify significant independent predictors of HER2-low breast cancer. The discriminative power of MRI features was assessed using receiver operating characteristic (ROC) curves. Results: HER2 status was categorized as HER2-zero (n = 60), HER2-low (n = 91), and HER2-overexpressed (n = 81). Clinically, estrogen receptor (ER), progesterone receptor (PR), hormone receptor (HR), and Ki-67 levels significantly differed between the HER2-low group and others (all p < 0.001). In MRI analyses, intratumoral T2 hyperintensity was more prevalent in HER2-low cases (p = 0.009, p = 0.008). Mass lesions were more common in the HER2-zero group than in the HER2-low group (p = 0.038), and mass shape (p < 0.001) and margin (p < 0.001) significantly varied between the HER2 groups, with mass shape emerging as an independent predictive factor (HER2-low vs. HER2-zero: p = 0.010, HER2-low vs. HER2-over: p = 0.012). Qualitative MRI features demonstrated an area under the curve (AUC) of 0.763 (95% confidence interval [CI]: 0.667–0.859) for distinguishing HER2-low from HER2-zero status. Quantitative features showed distinct differences between HER2-low and HER2-overexpression groups, particularly in non-mass enhancement (NME) lesions. Combined variables achieved the highest predictive accuracy for HER2-low status, with an AUC of 0.802 (95% CI: 0.701–0.903). Conclusions: Qualitative and quantitative MRI features offer valuable insights into low-HER2-expression breast cancer. While qualitative features are more effective for mass lesions, quantitative features are more suitable for NME lesions. These findings provide a more accessible and cost-effective approach to noninvasively identifying patients who may benefit from targeted therapy. Full article

This article investigates the effects of roof opening and closure conditions on the mean and fluctuating wind pressure coefficient of the roof surface through rigid model wind tunnel tests and further explores the non-Gaussian characteristics of wind pressure (skewness, kurtosis, and wind pressure probability density) under the two conditions. Then, based on the non-Gaussian characteristics under two working conditions, this paper constructs a Hermite moment model to solve the peak factor of the roof surface to evaluate the impact of roof opening and closure on the most unfavorable extreme wind pressure. The research results show that under the two working conditions of roof opening and closure, the windward leading edge’s mean and fluctuating wind pressure coefficients change most significantly, leading to an increase in the degree of flow separation at the windward leading edge. This causes the skewness, kurtosis, and probability density function of the wind pressure at the windward leading edge of the roof to deviate significantly from the standard Gaussian distribution, exhibiting strong non-Gaussian characteristics. Meanwhile, based on the Hermite moment model, it is found that the peak factor of most measuring points is concentrated between 3.5 and 5.0 under both roof opening and closure conditions, significantly higher than the recommended value of 2.5 in GB 50009-2012. In addition, under roof opening, the most unfavorable negative pressure coefficient is −4.54, and the absolute value of its most unfavorable negative pressure extreme is 1.3% higher than the roof opening closure condition. Full article