Sampling Distribution Research Articles

Assessing Variable Importance for Best Subset Selection.

One of the primary issues that arises in statistical modeling pertains to the assessment of the relative importance of each variable in the model. A variety of techniques have been proposed to quantify variable importance for regression models. However, in the context of best subset selection, fewer satisfactory methods are available. With this motivation, we here develop a variable importance measure expressly for this setting. We investigate and illustrate the properties of this measure, introduce algorithms for the efficient computation of its values, and propose a procedure for calculating p-values based on its sampling distributions. We present multiple simulation studies to examine the properties of the proposed methods, along with an application to demonstrate their practical utility.

Solving the imbalanced dataset problem in surveillance image blur classification

Surveillance videos taken in unconstrained environments can be tampered with due to different environmental factors and malicious human activities. They often blur the video content and introduce difficulty in identifying the events in the scene. The problem is particularly acute for smart surveillance systems that need to make real-time decisions based on the video. Automatic detection and classification of the blur anomalies in the video are crucial to these systems. Traditional learning-based classification methods often face imbalance problems in the sample numbers and distributions among the data classes in the dataset that severely affect their training and hence the classification performance. In this paper, a new learning-based approach for surveillance image blur classification is proposed. The imbalanced dataset problem is tackled both at the data and algorithm levels. At the data level, two synthesizers are developed to generate the required negative surveillance images to balance the sample numbers for all classes. At the algorithm level, an attention-based structure making use of the special feature of the minority class is proposed to improve the classification accuracy. Our experiment results show that the proposed approach significantly outperforms state-of-the-art methods for blur classification while keeping the model size small for edge applications.

Accelerated formation of M2C carbides by proton irradiation inhibits molten salt corrosion in Ni-based alloy

The effect of displacement damage on the corrosion behavior in a molten FLiBe salt environment was investigated. The element distribution and microstructure around the grain boundaries (GBs) after corrosion were characterized. The results show a decrease in corrosion thickness with increasing irradiation dose and the presence of intergranular corrosion. Nanoscales M2C carbides were observed to be distributed, with a denser and thicker distribution in samples with higher irradiation doses. Their distribution depth is related to the Cr depletion region, inhibiting Cr diffusion toward the GBs and surface. Furthermore, the nucleation mechanism of M2C carbides along the GBs and in irradiated regions was revealed, attributed to the combined effects of thermal influences, element preferential dissolution due to corrosion, and irradiation-induced segregation.

Periodically correlated time series and the Variable Bandpass Periodic Block Bootstrap.

This research introduces a novel approach to resampling periodically correlated time series using bandpass filters for frequency separation called the Variable Bandpass Periodic Block Bootstrap and then examines the significant advantages of this new method. While bootstrapping allows estimation of a statistic's sampling distribution by resampling the original data with replacement, and block bootstrapping is a model-free resampling strategy for correlated time series data, both fail to preserve correlations in periodically correlated time series. Existing extensions of the block bootstrap help preserve the correlation structures of periodically correlated processes but suffer from flaws and inefficiencies. Analyses of time series data containing cyclic, seasonal, or periodically correlated principal components often seen in annual, daily, or other cyclostationary processes benefit from separating these components. The Variable Bandpass Periodic Block Bootstrap uses bandpass filters to separate a periodically correlated component from interference such as noise at other uncorrelated frequencies. A simulation study is presented, demonstrating near universal improvements obtained from the Variable Bandpass Periodic Block Bootstrap when compared with prior block bootstrapping methods for periodically correlated time series.

PSVII-25 Contributions to bacterial diversity of the piglet pioneer microbiome

Abstract The establishment of the gastrointestinal tract microbiome of the neonate, or pioneer microbiome, can predetermine the health and performance of the newborn. Previous work has established that both maternal and environmental sources are capable of shifting the pioneer microbial composition. Despite this, proportional quantifications of the maternal and environmental factors that influence the bacterial diversity of the piglet pioneer microbiome have yet to be evaluated. Therefore, the objective of this study was to determine the contributions from both maternal and environmental origins on piglet pioneer bacterial diversity. Sterile swab samples were collected from farrowing crates prior to (EMPTYCRATE) and after gilts were housed (FULLCRATE; n = 5; Landrace x Yorkshire x Duroc), gilt vaginas at farrowing (BIRTHCANAL), and piglet rectums on d 0 (pre-suckling), 3, and 10 post-farrowing and at weaning (21.6 ± 1.0 d post-farrowing). Colostrum (COLOSTRUM) from a representative number of teats was also collected from each gilt at farrowing into sterile collection cups. All samples were stored at -80°C until subsequent analyses. Gene sequencing for microbiome analysis targeted the V4 hypervariable region of the 16S rRNA bacterial gene. Sequence quality was assessed utilizing DADA2, and taxon analysis and diversity metrics were processed in the qiime2 pipeline. Alpha diversity (within sample) metrics were analyzed with Kruskal-Wallis, and beta diversity (between sample) metrics were assessed with PERMANOVA. Alpha diversity analyses included Pielou’s evenness index (i.e., within sample distribution), Shannon’s diversity index (i.e., within sample richness and evenness), observed OTUs (i.e., within sample richness), and Faith’s diversity index (i.e. within sample richness and phylogeny). Pielou’s evenness index (P = 0.027) and Shannon’s diversity index (P = 0.086) indicated differences between EMPTYCRATE and FULLCRATE. However, no differences were detected between the environmental sources for observed OTUs or Faith’s diversity index. Piglet samples by day were different for observed OTUs (P = 0.002), Faith’s diversity index (P = 0.0002), and Shannon’s diversity index (P = 0.073), which all consider bacterial richness within the samples. Specifically, the piglet d 3 samples were the least for these three metrics; however, there were no differences in Pielou’s evenness index for all piglet by day samples. There were no differences for all alpha diversity metrics between BIRTHCANAL and COLOSTRUM. Beta diversity metrics included unweighted UniFrac (i.e., between sample bacterial presence or absence) and weighted UniFrac (i.e., between sample bacterial presence or absence and abundance). Evaluation between the maternal contributions BIRTHCANAL and COLOSTRUM indicated differences for both unweighted UniFrac (P = 0.011) and weighted UniFrac (P = 0.007). Bacterial diversity between the environmental origins EMPTYCRATE and FULLCRATE were also different for unweighted UniFrac (P = 0.012) and weighted UniFrac (P = 0.038). There were no differences in beta diversity between all piglet by day samples. These results indicate that both maternal and environmental sources contribute to changes in piglet pioneer microbial diversity during early neonatal life.

Detailed particle breakage of crushable granular materials under cyclic shearing conditions using dyed gypsum technique

Particle breakage of granular materials under cyclic shearing is related to a variety of engineering problems in geotechnical and transportation engineering. However, there is limited understanding regarding the detailed evolution law of breakage under cyclic shearing. To this end, a series of cyclic simple shear tests were conducted on artificially dyed gypsum particles. It was observed that, in contrast to the particle size distribution of the whole sample, the fractional particle size distributions of gap-graded samples followed a unified breakage evolution path as those of the uniformly graded ones and tended towards fractional fractal distributions. These results have inspired the introduction of the fractional breakage index. Moreover, the breakage-plastic work relationship was further extended to describe the breakage of fractional particles, incorporating the effect of the number of cycles on the plastic work distribution. Finally, based on the concept of breakage-packing, a predictive model for plastic work-breakage-deformation of crushable granular materials under cyclic shearing was proposed. These results have the potential in understanding the detailed particle breakage evolution and establishing a predictive framework for the breakage-induced deformation of crushable granular materials.

DMMGNet: A Discrimination Mapping and Memory Bank Mean Guidance-based Network for High-performance Few-shot Industrial Anomaly Detection

For deep learning-based industrial anomaly detection, it is still challenging to get adequate images for model training and achieve cold start for cross-product migration, restricting their practical application in real industrial production. Herein, an innovative few-shot anomaly detection network DMMGNet based on discrimination mapping and memory bank mean guidance strategies are demonstrated, which is trained by a new two-branch data augmentation technique. By separating the features stored in memory bank from the features used for training, the two-branch data augmentation method can significantly improve the robustness of few-shot model training and reduce the redundance of memory bank. In the elaborately designed discrimination mapping module, new negative samples are generated by adding dynamic Gaussian noise to normal samples along the channel dimension in feature space to solve the problem of sample imbalance. Meanwhile, the discrimination mapping module also helps to map the feature distribution of positive samples to the target domain more efficiently and reduce the deviation of feature domain, conducive to a more precise separation of positive and negative samples. In addition, a novel mean guidance approach with an optimized loss function is developed to guide the positive sample feature mapping by specifying the local feature space center to form a clear feature domain contour and enhance the detection accuracy. The multiple experimental results validate that our DMMGNet outperforms the most advanced anomaly detection counterparts on image-level AUROC, showing an increase by 0.3-3% on both MVTec AD and MPDD benchmarks under several few-shot scenarios.

Impacts of DROSHA (rs10719) and DICER (rs3742330) Variants on Breast Cancer Risk and Their Distribution in Blood and Tissue Samples of Egyptian Patients.

MicroRNAs (miRNAs) are small, noncoding RNAs that regulate gene expression and play critical roles in tumorigenesis. Genetic variants in miRNA processing genes, DROSHA and DICER, have been implicated in cancer susceptibility and progression in various populations. However, their role in Egyptian patients with breast cancer (BC) remains unexplored. This study aims to investigate the association of DROSHA rs10719 and DICER rs3742330 polymorphisms with BC risk and clinical outcomes. This case-control study included 209 BC patients and 106 healthy controls. Genotyping was performed using TaqMan assays in blood, tumor tissue, and adjacent non-cancerous tissue samples. Associations were analyzed using logistic regression and Fisher's exact test. The DROSHA rs10719 AA genotype was associated with a 3.2-fold increased risk (95%CI = 1.23-9.36, p < 0.001), and the DICER rs3742330 GG genotype was associated with a 3.51-fold increased risk (95%CI = 1.5-8.25, p = 0.001) of BC. Minor allele frequencies were 0.42 for rs10719 A and 0.37 for rs3742330 G alleles. The risk alleles were significantly more prevalent in tumor tissue than adjacent normal tissue (rs10719 A: 40.8% vs. 0%; rs3742330 G: 42.7% vs. 0%; p < 0.001). However, no significant associations were observed with clinicopathological features or survival outcomes over a median follow-up of 17 months. In conclusion, DROSHA rs10719 and DICER rs3742330 polymorphisms are associated with increased BC risk and more prevalent in tumor tissue among our cohort, suggesting a potential role in miRNA dysregulation during breast tumorigenesis. These findings highlight the importance of miRNA processing gene variants in BC susceptibility and warrant further validation in larger cohorts and different ethnic populations.

Evaluation of HPV-DNA Prevalence and Genotype Distribution and Cytological Examination Results in Cervical Sample

Evaluation of HPV-DNA Prevalence and Genotype Distribution and Cytological Examination Results in Cervical Sample

GENOMIC CHARACTERIZATION AND DRUG RESISTANCE PROFILES OF MYCOBACTERIUM TUBERCULOSIS LINEAGE 2 IN KAZAKHSTAN: PRELIMINARY RESULTS

Tuberculosis remains a highly contagious infection, with an estimated annual transmission of about 10 million people globally, as reported in the latest Global Tuberculosis Report by the World Health Organization (WHO). The primary objective of this research is to determine the geographical distribution and drug resistance profiles of Mycobacterium tuberculosis lineage 2, which is predominant in Kazakhstan. A total of over 200 clinical isolates of M. tuberculosis complex (MTBC) were collected from regional phthisiopulmonology centers across all regions of Kazakhstan between 2012 and 2024. Clinical isolates were genotyped using a PCR assay targeting the dnaA-dnaN:IS6110 region, specific for the modern Beijing genotype. Subsequently, selected samples were sequenced using the Illumina MiSeq platform. The TB-Profiler prediction tool was utilized to identify resistance against anti-tubercular drugs and to genotype the lineages. A total of 102 clinical isolates belonging to the L2 modern Beijing genotype were selected for whole-genome sequencing (WGS). The L2.2.1 genetic cluster was identified as the only sublineage present, according to Coll et al.'s classification. More than half of the specimens were found to be multidrug-resistant (n=60, 59%). Among the analyzed samples, monoresistance to isoniazid (IHN) was detected in 17 (16%) cases, while 14 (14%) cases were drug-susceptible TB. Additionally, 7 (7%) cases were identified as Pre-XDR (resistant to rifampicin, isoniazid, and either fluoroquinolones or second-line injectable drugs), 3 (3%) cases were monoresistant to streptomycin, and 1 (1%) case exhibited resistance to rifampicin only. Regional distribution of samples is as follows: Aktau and Kostanay are 14% (n=13/102), Kyzylorda (n=12/102), Taldykourgan (n=11/102), Pavlodar, Karaganda, and Atyrau are at 6% (6/102); Semey is 10% (n=9/102), Almaty and Taraz at 4%, and Shymkent city is 1% (n=1/102). This ongoing research is focused on resistance patterns, genotypes, and transmission dynamics among different regions of Kazakhstan using next-generation sequencing, followed by bioinformatics analysis and interpretation of the data.

Exploring the influence of Thai private universities human resource management practices on employee retention and performance

The article examines employees' perceptions regarding human resource management practices in Thai private universities. It also analyses the effects of these practices on employee job satisfaction, quality service performance and retention within the context of Thai private universities. The study employed a cross-sectional survey research approach to ensure the inclusion of a representative sample from small and medium sized private universities within the population under investigation. The study includes 12,318 employees of the 38 Thai private universities: 6,458 from small sized private universities and 5,860 from medium size private universities. 20 private universities comprised of 14 (70%) small sized and 6 (30%) will form the sample of the study. From this sample distribution, 91 (72.8) academic staff from 14 small sized private universities and 34 (27.2%) from medium sized private universities making 125 academic staff and 20 HR managers as the sample size of the investigation. The study utilized a maximum variation deliberate (MVD) sampling method for the selection of 125 academic staff and 20 HR managers. Closed-ended questionnaires and semi-structured questionnaires were employed for the purpose of data collection. The validity of the instrument was assessed by a panel of experts to establish content validity, utilizing Lawshe's template with a ratio of 0.99. Furthermore, the homogeneity of the instrument was determined using Cronbach Alpha resulting in a reliability index of .81. The data was collected primarily via an online survey. The data underwent analysis utilizing frequency counts and percentages, chi-square at a significance level of p.05. This was done to ascertain the statistical significance of HRMP on employee retention and performance. The findings demonstrate a conspicuous and uniform association between employee responses across the institutions under scrutiny, underscoring a statistically significant connection between human resource management practices and job satisfaction, quality service performance and retention of employees. Therefore, it is essential to acknowledge that the implementation of HRM practices holds significant importance for small and medium private universities. This is primarily due to its ability to effectively address the expenses incurred from employee turnover, which can arise because of insufficient job satisfaction leading to poor job performance and retention.

Capacity estimation of lithium-ion battery based on soft dynamic time warping, stratified random sampling and pruned residual neural networks

Accurate battery capacity estimation is crucial for ensuring battery management systems' safe and reliable operation. Although deep learning algorithms have been widely applied in the field of image recognition, their application in battery diagnosis is relatively limited. Besides, obtaining complete cycling data during charging/discharging processes can be challenging in practical applications. This paper proposes a battery capacity estimation method based on partial charge curves and pruned residual neural networks. To leverage the aging information inherent in partial charge curves, the partial segment battery data is transformed into images using wavelet denoising and soft dynamic time warping algorithms. Subsequently, we present a stratified random sampling method to address the issue of imbalanced sample distributions in battery dataset partitioning, and employ residual neural networks to mitigate performance degradation caused by stacking multiple network layers. To validate the effectiveness of the proposed method, experiments are conducted on three types of battery data. During the experimental phase, the root mean square error and mean absolute error of estimated battery capacity are both below 0.79% and 0.54%. Furthermore, a hybrid-pruning algorithm is proposed to enable the deployment of the model on the battery management system, which reduces the model size and computational complexity without compromising accuracy. Compared to other pruning algorithms, the proposed hybrid-pruning algorithm outperforms diagnostic accuracy and computational efficiency after fine-tuning, reducing the model size by 77.40% and creating a more compact structure.

Multi-source domain adaptation network for partial discharge severity assessment in gas-insulated switchgear

Abstract Gas-insulated switchgear (GIS) partial discharge (PD) severity assessment is critical for ensuring the reliable operation of GIS systems. However, existing assessment methods often overlook the long-term dependencies of historical data and fail to adequately address challenges related to limited on-site samples and variations in sample distribution. To overcome these challenges, we propose a novel multi-source domain adaptation network (MSDAN) specifically designed for on-site GIS PD severity assessment, which is the first model developed considering distribution differences in GIS PD severity assessment for different defect types. Our approach begins with the development of a feature extractor that captures both discernible PD features and long-term dependencies. We then introduce a multi-source domain adaptation strategy to mitigate distribution disparities across PD severity samples from different defect types, ensuring effective domain alignment. Additionally, we incorporate an adaptive weighted classification mechanism to accurately assess PD severity by considering the varying contributions of different defect types to the target domain task. Experimental results demonstrate that MSDAN achieves a remarkable accuracy of 95.38% in on-site GIS PD severity assessment, outperforming other benchmark models in both accuracy and robustness. This highlights the potential of MSDAN as a robust solution for real-world GIS PD severity assessment.

Local Bootstrap for Network Data

SUMMARY In network analysis, we frequently need to conduct inference for network parameters based on one observed network. Since the sampling distribution of the statistic is often unknown, we need to rely on the bootstrap. However, due to the complex dependence structure among vertices, existing bootstrap methods often yield unsatisfactory performance, especially under small or moderate sample sizes. To this end, we propose a new network bootstrap procedure, termed local bootstrap, to estimate the standard errors of network statistics. We propose to resample the observed vertices along with their neighbor sets, and reconstruct the edges between the resampled vertices by drawing from the set of edges connecting their neighbor sets. We justify the proposed method theoretically with desirable asymptotic properties for statistics such as motif density, and demonstrate its excellent numerical performance in small and moderate sample sizes. Our method includes several existing methods, such as the empirical graphon bootstrap, as special cases. We investigate the advantages of the proposed methods over the existing methods through the lens of edge randomness, vertex heterogeneity, neighbor set size, which shed some light on the complex issue of network bootstrapping.

The adaptive mean estimation of heavy tailed distribution under random censoring

We propose an alternative estimator of the heavy tailed mean under censored data relies with the threshold t which divides it into two parts for x ≤ t and x &gt; t respectively. In the first parts we present an empirical distribution function under random censoring (rc). Then, we check the convergence of Lyapunov condition to ensuring the asymptotic normality of this parts. For the second parts we use the Peaks-Over-Threshold (POT) method of the tail distribution and fitting to the generalized Pareto distribution (GPD). Intentionally to give an alternative estimator of the tail expected value for the heavy tailed distribution when x &gt; t under random censoring relies the KIB estimators of the GPD parameters. Ditto, we ensuring the asymptotic normality property of the KIB estimators with bias reduced and we present the asymptotic normality of the second parts when x &gt; t. An asymptotically normally distributed estimate for the heavy tailed mean estimation with infinite variance under censored data is introduced by combining the two parts. Finally, numerical examples are presented at the end of the paper to demonstrate the reliability of this estimation procedure and to better illustrate the results of this paper.

Extropy and entropy estimation based on progressive Type-I interval censoring

This paper proposes nonparametric estimates for the two information measures extropy and entropy when a progressively Type-I interval censored data is available. Different nonparametric approaches are used for deriving the estimates, including: moments of the empirical cumulative distribution function and linear regression. The performance of the proposed estimates is studied under various censoring schemes via simulation studies. Furthermore, different real data sets are analyzed for illustrative purposes.The estimates based on linear approximation J^2 and H^2 outperform the other estimate in the majority of studied cases.

Assessing model selection techniques for distributions use in hydrological extremes in the presence of trimming and subsampling

A suitable probability distribution is required to quantify and estimate hydraulic structure design for risk evaluation and management. The inability of model selection criteria to differentiate, in some cases, among candidate distributions used in the analysis of hydrological extremes is often criticised. This study verifies, with the aid of model selection techniques, the potential utility of trimming and subsampling in distinguishing between candidate distributions, which might not be feasible using the traditional goodness of fit method alone, when samples available are small. The performance of the proposed method is evaluated through its application to real and simulated yearly peak rainfall datasets. The proposed approach is then compared with several standard model selection techniques. Results show that the model selection techniques with the aid of subsampling are effective in identifying the true parent distribution for the untrimmed samples given a two-parameter distribution; contrarily, they are inefficient where a distribution with a three-parameter is the parent distribution. However, as trimming is introduced, all model selection methods recognise the true parent distribution for a three-parameter distribution. Overall, utilising trimming and subsampling with the aid of model selection methods yields promising outcomes in the analysis of hydrological extreme frequencies. Drawing from the results of numerical simulation and examination of observed data, the use of trimming and subsampling can be a viable tool in differentiating among candidate distributions used in the investigation of hydrological extreme frequencies.

GANs for Image Security Applications: A Literature Review

Generative Adversarial Networks (GANs) have earned significant attention in various domains due to their generative model’s compelling ability to generate realistic examples probably drawn from sample distribution. Image security indicates the process of protecting digital images from unauthorized access, modification, or distribution. This requires a guarantee of image privacy, integrity, and authenticity to prohibit them from being exploited by malicious attacks. GANs can also be utilized for improving image security by exploiting its generation ability in encryption, steganography, and privacy-preserving tech-niques. This paper reviews GANs-based image security techniques providing a systematic overview of current literature and comparing the role of GANs in image encryption, image steganography, and priva-cy preserving from multiple dimensions. Additionally, it outlines future research directions to further explore the potential of GANs in addressing privacy and image security concerns

SFNet: Stellar Feature Network with CWT for Stellar Spectra Recognition

Stellar spectral classification is crucial in astronomical data analysis. However, existing studies are often limited by the uneven distribution of stellar samples, posing challenges in practical applications. Even when balancing stellar categories and their numbers, there is room for improvement in classification accuracy. This study introduces a Continuous Wavelet Transform using the Super Morlet wavelet to convert stellar spectra into wavelet images. A novel neural network, the Stellar Feature Network, is proposed for classifying these images. Stellar spectra from Large Sky Area Multi-Object Fiber Spectroscopic Telescope DR9, encompassing five equal categories (B, A, F, G, K), were used. Comparative experiments validate the effectiveness of the proposed methods and network, achieving significant improvements in classification accuracy.

Estimating Addiction-Related Brain Connectivity by Prior-Embedding Graph Generative Adversarial Networks.

The study of nicotine addiction mechanism is of great significance in both nicotine withdrawal and brain science. The detection of addiction-related brain connectivity using functional magnetic resonance imaging (fMRI) is a critical step in study of this mechanism. However, it is challenging to accurately estimate addiction-related brain connectivity due to the low-signal-to-noise ratio of fMRI and the issue of small sample size. In this work, a prior-embedding graph generative adversarial network (PG-GAN) is proposed to capture addiction-related brain connectivity accurately. By designing a dual-generator-based scheme, the addiction-related connectivity generator is employed to learn the feature map of addiction connection, while the reconstruction generator is used for sample reconstruction. Moreover, a bidirectional mapping mechanism is designed to maintain the consistency of sample distribution in the latent space so that addiction-related brain connectivity can be estimated more accurately. The proposed model utilizes prior knowledge embeddings to reduce the search space so that the model can better understand the latent distribution for the issue of small sample size. Experimental results demonstrate the effectiveness of the proposed PG-GAN.