Sampling Distribution Research Articles

Study of Damage Mechanism and Evolution Model of Concrete under Freeze–Thaw Cycles

Researching the mechanical characteristics of concrete subjected to the freeze–thaw cycle is crucial for building engineering in cold climates. As a result, uniaxial compression tests were performed on concrete samples exposed to various freeze–thaw (F–T) cycles, and the measurements of the pore size distribution, porosity, and P-wave velocity of the saturated concrete samples were obtained, both before and after being exposed to the F–T cycles. Concrete’s F–T damage mechanism and damage evolution model were thoroughly examined. Using rock structure and moisture analysis test equipment to observe the T2 spectrum, the results showed that the F–T cycles can cause the internal structure of the samples to deteriorate. Porosity and F–T cycles have a positive correlation, although P-wave velocity has a negative correlation with the F–T cycles. As the F–T cycles increased, the specimens’ peak strength and elastic modulus steadily declined, while the peak strain clearly exhibited an increasing trend. A microscopic F–T damage model that takes into account the pore size distribution was developed, based on the relative changes in the pore structure distribution (PSD), before and after the F–T cycles. The concrete sample damage evolution law under various F–T cycles was examined using the following metrics: total energy, pore size distribution, static and dynamic elastic moduli, porosity, and P-wave velocity. Uniaxial compressive strength (UCS) and peak strain tests were used to evaluate the accuracy of the pore size distribution damage model, as well as that of five other widely used damage models.

Smart Hydrogel Based on Derivatives of Natural α-Amino Acids for Efficient Removal of Metal Ions from Wastewater.

A novel class of hydrogels, rich in a variety of functional groups capable of interacting/complexing with metal ions was successfully synthesized. This was achieved by using acryloyl derivatives of natural α-amino acids, specifically ornithine and cystine. The δ-amino group of ornithine was modified with an acryloyl group to facilitate its attachment to the polymer chain. Additionally, N,N'-bisacryloylcystine, derived from cystine, was employed as the cross-linker. The hydrogel was obtained through a process of free radical polymerization. This hydrogel, composed only from derivatives of natural amino acids, has proven to be a competitive sorbent and has been effectively used to remove heavy metal pollutants, mainly lead, copper, and silver ions, from aqueous media. The maximum sorption capacities were ca. 155 mg·g-1, 90 mg·g-1, and 215 mg·g-1, respectively for Pb(II), Cu(II), and Ag(I). The material was characterized by effective regeneration, maintaining the sorption capacity at around 80%, 85%, and 90% for Cu(II), Ag(I), and Pb(II), respectively, even after five cycles. The properties of sorption materials, such as sorption kinetics and the effect of pH on sorption, as well as the influence of the concentration of the examined metal ions on the swelling ratio and morphology of the gel, were investigated. The EDS technique was employed to investigate the composition and element distribution in the dry gel samples. Additionally, IR spectroscopy was used to identify the functional groups responsible for binding the studied metal ions, providing insights into their specific interactions with the hydrogel.

A size-distinguishing miniature electromagnetic tomography sensor for small object detection

Electromagnetic tomography (EMT) is an emerging imaging technique that presents the property distribution of conductive or magnetic materials based on signals from the coil array on the EMT sensor. However, conventional EMT researches generally involve large-size EMT sensors, which are ineffective in detecting and discerning the size of small objects due to limited spatial resolution, sensitivity and relatively high noise level. As such, this paper presents a novel miniature EMT sensor, which incorporates 8 small coils around the circumference for imaging and 2 larger horizontal coils that generate a vertical field for target size distinction. Moreover, a novel equivalent theory is proposed to approximate the effect of the horizontal coils by the cumulative effect of the 8 small coils. An EMT testing system is established with the proposed sensor array and the multi-channel instrument developed in our lab. Experiments based on multiple sample distributions and different reconstruction algorithms validate the ability of the sensor to detect and distinguish the size of small objects of different materials. Furthermore, the equivalent theory was validated through the experiments.

Identification of DNA methylation-regulated WEE1 with potential implications in prognosis and immunotherapy for low-grade glioma.

WEE1 is a critical kinase in the DNA damage response pathway and has been shown to be effective in treating serous uterine cancer. However, its role in gliomas, specifically low-grade glioma (LGG), remains unclear. The impact of DNA methylation on WEE1 expression and its correlation with the immune landscape in gliomas also need further investigation. This study used data from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Gene Expression Omnibus (GEO) and utilized various bioinformatics tools to analyze gene expression, survival, gene correlation, immune score, immune infiltration, genomic alterations, tumor mutation burden, microsatellite instability, clinical characteristics of glioma patients, WEE1 DNA methylation, prognostic analysis, single-cell gene expression distribution in glioma tissue samples, and immunotherapy response prediction based on WEE1 expression. WEE1 was upregulated in LGG and glioblastoma (GBM), but it had a more significant prognostic impact in LGG compared to other cancers. High WEE1 expression was associated with poorer prognosis in LGG, particularly when combined with wild-type IDH. The WEE1 inhibitor MK-1775 effectively inhibited the proliferation and migration of LGG cell lines, which were more sensitive to WEE1 inhibition. DNA methylation negatively regulated WEE1, and high DNA hypermethylation of WEE1 was associated with better prognosis in LGG than in GBM. Combining WEE1 inhibition and DNA methyltransferase inhibition showed a synergistic effect. Additionally, downregulation of WEE1 had favorable predictive value in immunotherapy response. Co-expression network analysis identified key genes involved in WEE1-mediated regulation of immune landscape, differentiation, and metastasis in LGG. Our study shows that WEE1 is a promising indicator for targeted therapy and prognosis evaluation. Notably, significant differences were observed in the role of WEE1 between LGG and GBM. Further investigation into WEE1 inhibition, either in combination with DNA methyltransferase inhibition or immunotherapy, is warranted in the context of LGG.

Retrieving ground-level PM2.5 concentrations in China (2013–2021) with a numerical-model-informed testbed to mitigate sample-imbalance-induced biases

Abstract. Ground-level PM2.5 data derived from satellites with machine learning are crucial for health and climate assessments. However, uncertainties persist due to the absence of spatially covered observations. To address this, we propose a novel testbed using nontraditional numerical simulations to evaluate PM2.5 estimation across the entire spatial domain. The testbed emulates the general machine-learning approach by training the model with grids corresponding to ground monitoring sites and subsequently testing its predictive accuracy for other locations. Our approach enables comprehensive evaluation of various machine-learning methods' performance in estimating PM2.5 across the spatial domain for the first time. Unexpected results are shown in the application in China, with larger absolute PM2.5 biases found in densely populated regions with abundant ground observations across all benchmark models due to the higher baseline concentration, though the relative error (approximately 20 %) is smaller compared to that in rural areas (over 50 %). The imbalance in training samples, mostly from urban areas with high emissions, is the main reason, leading to significant overestimation due to the lack of monitors in downwind areas where PM2.5 is transported from urban areas with varying vertical profiles. Our proposed testbed also provides an efficient strategy for optimizing model structure or training samples to enhance satellite-retrieval model performance. Integration of spatiotemporal features, especially with conventional neural network (CNN)-based deep-learning approaches like the residual neural network (ResNet) model, has successfully mitigated PM2.5 overestimation (by 5–30 µg m−3) and the corresponding exposure (by 3 million people ⋅ µg m−3) in the downwind area over 9 years (2013–2021) compared to the traditional approach. Furthermore, the incorporation of 600 strategically positioned ground monitoring sites identified through the testbed is essential for achieving a more balanced distribution of training samples, thereby ensuring precise PM2.5 estimation and facilitating the assessment of the associated impacts in China. In addition to presenting the retrieved surface PM2.5 concentrations in China from 2013 to 2021, this study provides a testbed dataset derived from physical modeling simulations which can serve to evaluate the performance of data-driven methodologies, such as machine learning, in estimating spatial PM2.5 concentrations for the community (Li et al., 2024a; https://doi.org/10.5281/zenodo.11122294).

Adaptive sampling strategy for Fourier single-pixel imaging

Fourier single-pixel imaging is a technique that utilizes Fourier bases as illumination patterns to capture images. While the use of orthogonal bases enhances the imaging quality compared with other methods of single pixel imaging, the slow imaging speed has hindered its broader application. Acquiring only low-frequency Fourier coefficients enables capturing a significant portion of image information at low sampling ratio, thereby enhancing imaging speed. Nevertheless, truncating frequencies results in a loss of image detail and a decrease in picture quality. To address this challenge, this paper introduces an adaptive sampling strategy for Fourier single-pixel imaging, allowing for customized sampling distributions for each specific object. The sampling distribution is dynamically generated based on the acquired Fourier coefficients during the acquisition process, eliminating the need for prior information. Through extensive simulations and experimental analysis, this approach has demonstrated its ability to capture more complex image details at equivalent sampling ratios.

A novel wind power forecast diffusion model based on prior knowledge

AbstractTo improve the forecast accuracy of wind power, diffusion model based on prior knowledge (DMPK) is proposed. Different from the traditional diffusion model (DM), where the noise perturbation in the diffusion or generation process is random, the noise added in DMPK is modified aiming to the characteristics of wind power signals. The distribution of wind power forecast errors is not a standard Gaussian. Wind power forecast errors are related to forecast methods, weather conditions, and other factors, containing both random signals and certain regularity. This paper adapts the Gaussian distribution to fit the historical forecast error to represent the prior knowledge of wind power. Then, the sampling distribution is derived from its relationship with the fitted prior distribution to replace the standard Gaussian in DM. Taking the prior knowledge into account during the process of noise sampling, the data in the forward process of DMPK can be guided by the distribution of historical errors for diffusion, while the generated result by the reverse process is more consistent with the actual wind power signal. Finally, the superiority of the proposed method is verified by using the wind power data from two real‐world wind farms.

Multi-source fully test-time adaptation

Deep neural networks have significantly advanced various fields. However, these models often encounter difficulties in achieving effective generalization when the distribution of test samples varies from that of the training samples. Recently, some fully test-time adaptation methods have been proposed to adapt the trained model with the unlabeled test samples before prediction to enhance the test performance. Despite achieving remarkable results, these methods only involve one trained model, which could only provide certain side information for the test samples. In real-world scenarios, there could be multiple available trained models that are beneficial to the test samples and are complementary to each other. Consequently, to better utilize these trained models, in this paper, we propose the problem of multi-source fully test-time adaptation to adapt multiple trained models to the test samples. To address this problem, we introduce a simple yet effective method utilizing a weighted aggregation scheme and introduce two unsupervised losses. The former could adaptively assign a higher weight to a more relevant model, while the latter could jointly adapt models with online unlabeled samples. Extensive experiments on three image classification datasets show that the proposed method achieves better results than baseline methods, demonstrating the superiority in adapting to multiple models.

Learning a multi-cluster memory prototype for unsupervised video anomaly detection

In recent years, there has been rapid development in video anomaly detection (VAD). The previous methods ignored the differences between normal videos and only emphasized learning the commonalities of normal videos. In order to improve the performance of anomaly detection, we delve into the spatial distribution of normal video features and utilize their differences for clustering, leading to more minor reconstruction errors for normal videos and more significant reconstruction errors for abnormal videos. To achieve this goal, we introduce a Multi-Cluster Memory Prototype framework (MCMP) for VAD, which explores the coarse-grained and fine-grained information from video snippet features simultaneously to learn a memory prototype, thereby significantly improving the ability to discriminate abnormal events in complex scenes. First, a video feature clustering method that employs contrastive learning is introduced to group samples sharing similar fine-grained features. Second, the memory mechanism is used to capture the feature distribution of normal samples. Lastly, the Gaussian filter feature transformation method is introduced to make normal and abnormal features more distinguishable. The frame level AUC of MCMP on ShanghaiTech and UCF-Crime benchmark datasets has increased by 1.26% and 0.45% compared to state-of-the-art methods. Our code is publicly available athttps://github.com/WuIkun5658/MCMP.

Fuzzy system reliability analysis based on new aging indexes

The concept of aging plays an important role in reliability analysis. The aim of this paper is providing the novel method to definition aging indexes based on fuzzy random variables and using the concept of α-pessimistic in both parametric and non-parametric approaches. In parametric approach, the concepts of survival function, hazard rate function and mean residual function were extended for fuzzy random variables. After that, we obtain and analyse these indexes for fuzzy exponential and fuzzy two-parameter Weibull distributions as two frequently used lifetime distribution in reliability theory. Moreover, with the assumption that the distribution of fuzzy observations is unknown (non-parametric approach), first an estimate for the fuzzy distribution function is presented, and then an estimate for the aging indices of the system is investigated. To do this, the concept of fuzzy empirical distribution function is investigated as an estimator of cumulative distribution function. Moreover, some properties of investigated estimator have been established.

Rank tests for the Latin square design

A rank test for Latin square data is derived. Forms are given for when there are no ties, for when ties occur and mid-ranks are used, and for when ties occur in general. These adjustments are akin to those for the Kruskal-Wallis, Friedman, and Durbin tests. Since substantial block effects may affect ranking, alignment is recommended. The ANOVA F test on the ranks, the rank transform test, is a competitor test. We designate this test, where the data are aligned before ranking, the FART test. The proposed test, implemented as a permutation test, is designated the permutation aligned RL test: the PARL. We also give an approximation to the sampling distribution using the χ 2 distribution, and call this test the CARL. An indicative simulation study assuming the usual parametric model and testing the null hypothesis that the treatment parameter is zero shows that the CARL, PARL, FART, and the F test on the raw data all control their type I errors well and have comparable powers.

UB-FineNet: Urban building fine-grained classification network for open-access satellite images

Fine classification of city-scale buildings using satellite imagery is a crucial research area with significant implications for urban planning, infrastructure development, and population distribution analysis. However, the task faces great challenges due to low-resolution overhead images acquired from high-altitude space-borne platforms and the long-tailed sample distribution of fine-grained urban building categories, leading to a severe class imbalance problem. To address these issues, we propose a deep network approach to the fine-grained classification of urban buildings using open-access satellite images. A Denoising Diffusion Probabilistic Model (DDPM) based super-resolution method is first introduced to enhance the spatial resolution of satellite images, which benefits from domain-adaptive knowledge distillation. Then, a new fine-grained classification network with Category Information Balancing Module (CIBM) and Contrastive Supervision (CS) technique is proposed to mitigate the problem of class imbalance and improve the classification robustness and accuracy. Experiments on Hong Kong data set with 11 distinct building types revealed promising classification results with a mean Top-1 accuracy of 60.45%, which is on par with street-view image based approaches. A comprehensive ablation study demonstrates that the CIBM and CS modules improve Top-1 accuracy by 2.6% and 3.5%, respectively, over the baseline approach. In addition, these modules can be easily integrated into other classification networks, achieving similar performance improvements. This research advances urban analysis by providing an effective solution for detailed classification of buildings in complex mega-city environments using only open-access satellite imagery. The proposed technique can serve as a valuable tool for urban planners, aiding in the understanding of economic, industrial, and population distribution within cities and regions, ultimately facilitating informed decision-making in urban development and infrastructure planning. Data and code will be publicly available at https://github.com/ZhiyiHe1997/UB-FineNet.

On the plastic deformation mechanism of Al0.6CoCrFeNi high entropy alloy: In-situ EBSD study and crystal plasticity modeling

In this study, in-situ tensile tests with electron back-scattered diffraction (EBSD) were conducted to reveal the microstructural effects on the plastic deformation behavior of dual-phase Al0.6CoCrFeNi high entropy alloys (HEAs). The in-situ EBSD results demonstrated a uniform distribution of dislocation density in the fine grain (FG) samples, whereas high dislocation density was predominantly localized near body-centered cubic (BCC) phases in the coarse grain (CG) samples. The difference in mechanical properties between face-centered cubic (FCC) and BCC phases caused stress concentration and cracking at the boundaries during tension. The FCC phase within FG samples exhibited excellent plasticity, effectively impeding the propagation and coalescence of microcracks emanating from the BCC phase into longer cracks. For CG samples, brittle cracks formed in BCC phases are easy to merge, thus evolving into long cracks. Crystal plasticity finite element analysis revealed that as the strain increases, stress distribution in FG samples tended to become more homogeneous, whereas for CG samples high stress concentration consistently occurred within BCC phases. The deformation characteristics of FG were attributed to the synergistic effect generated by the FCC phase with heterogeneous grain size and the BCC phase characterized by small grain size.

Overlap Analysis in Progressive Hybrid Censoring: A Focus on Adaptive Type-II and Lomax Distribution

This article explores the adaptive type-II progressive hybrid censoring scheme, introduced by Ng et al. (2009), which is used to make inferences about three measures of overlap: Matusita's measure ($\rho $), Morisita's measure ($\lambda $), and Weitzman's measure ($\Delta $) for two Lomax distributions with different parameters. The article derives the bias and variance of these overlap measures' estimators. If sample sizes are limited, the precision or bias of these estimators is difficult to determine because there are no closed-form expressions for their variances and exact sampling distributions, so Monte Carlo simulations are used. Also, confidence intervals for these measures are constructed using both the bootstrap method and Taylor approximation. To demonstrate the practical significance of the proposed estimators, an illustrative application is provided by analyzing real data.

Nanospray Desorption Electrospray Ionization Mass Spectrometry Imaging (nano-DESI MSI): A Tutorial Review.

Nanospray desorption electrospray ionization (nano-DESI) is a liquid-based ambient mass spectrometry imaging (MSI) technique that enables visualization of analyte distributions in biological samples down to cellular-level spatial resolution. Since its inception, significant advancements have been made to the nano-DESI experimental platform to facilitate molecular imaging with high throughput, deep molecular coverage, and spatial resolution better than 10 μm. The molecular selectivity of nano-DESI MSI has been enhanced using new data acquisition strategies, the development of separation and online derivatization approaches for isobar separation and isomer-selective imaging, and the optimization of the working solvent composition to improve analyte extraction and ionization efficiency. Furthermore, nano-DESI MSI research has underscored the importance of matrix effects and established normalization methods for accurately measuring concentration gradients in complex biological samples. This tutorial offers a comprehensive guide to nano-DESI experiments, detailing fundamental principles and data acquisition and processing methods and discussing essential operational parameters.

Enabling Invar to Ti6Al4V transitions through copper in functionally graded laser powder bed fused components

This study examines the microstructural evolution, elemental distribution, phase composition, and hardness variations in multi-material samples fabricated using Laser Powder Bed Fusion (LPBF) with Invar and Ti6Al4V with compositional gradients mediated by copper. A range of compositional transitions from Invar to Ti6Al4V with different copper fractions are reported. Defects such as gas and keyhole pores were detected in well-deep keyhole mode areas, while lack of fusion defects were observed in samples with pure Cu interlayers due to poor wettability and processing parameter variations. Crack formation between interlayer gradient zones and Invar regions was linked to FeNi and CuNi intermetallic phases. Intermetallic compounds (e.g., FeNi, CuNi, TiFe, TiNi) significantly influenced the microstructure and mechanical performance. Vickers hardness testing shows varying hardness across different builds, with the highest hardness being observed in the gradient zone enabled by pure copper (approximately 620 HV). This study also underscores the need for tailored component design linking build processing parameters and compositional transitions in multi-material LPBF to achieve desired properties and component structural behaviour.

Metagenomics uncovers microbiome and resistome in soil and reindeer faeces from Ny-Ålesund (Svalbard, High Arctic)

Research on the microbiome and resistome in polar environments, such as the Arctic, is crucial for understanding the emergence and spread of antibiotic resistance genes (ARGs) in the environment. In this study, soil and reindeer faeces samples collected from Ny-Ålesund (Svalbard, High Arctic) were examined to analyze the microbiome, ARGs, and biocide/metal resistance genes (BMRGs). The dominant phyla in both soil and faeces were Pseudomonadota, Actinomycetota, and Bacteroidota. A total of 2618 predicted Open Reading Frames (ORFs) containing antibiotic resistance genes (ARGs) were detected. These ARGs belong to 162 different genes across 17 antibiotic classes, with rifamycin and multidrug resistance genes being the most prevalent. We focused on investigating antibiotic resistance mechanisms in the Ny-Ålesund environment by analyzing the resistance genes and their biological pathways. Procrustes analysis demonstrated a significant correlation between bacterial communities and ARG/BMRG profiles in soil and faeces samples. Correlation analysis revealed that Pseudomonadota contributed most to multidrug and triclosan resistance, while Actinomycetota were predominant contributors to rifamycin and aminoglycoside resistance. The geochemical factors, SiO42− and NH4+, were found to significantly influence the microbial composition and ARG distribution in the soil samples. Analysis of ARGs, BMRGs, virulence factors (VFs), and pathogens identified potential health risks associated with certain bacteria, such as Cryobacterium and Pseudomonas, due to the presence of different genetic elements. This study provided valuable insights into the molecular mechanisms and geochemical factors contributing to antibiotic resistance and enhanced our understanding of the evolution of antibiotic resistance genes in the environment.

Clustering with empty clusters

Cluster analysis is widely used in various scientific and practical fields related to data analysis. It is an important tool for solving problems in such areas as machine learning, image processing, text recognition, etc. The absence of observations is not always the absence of information, therefore it is assumed that the presence of gaps in the data, the presence of “empty” clusters, also carries information about the object of study, as well as real observations. In this study, it is assumed that we do not observe not only a variable, but a whole set of objects forming a separate cluster. Thus, it is assumed that the missing in data is not the fact of the missing of a cluster of objects as such, but potentially existing objects that are absent from our selection. An algorithm is proposed to determine potential "empty" clusters for one-dimensional and two-dimensional data sets, taking into account their size and location in the feature space, depending on the initial distribution of samples. A method is implemented to fill in these gaps and estimate the displacement of the centroids of the initial clustering when taking into account an empty cluster. The application of this approach to rid the data of outliers is demonstrated.

Lipoprotein(a) levels distribution in an adult sample from the middle ural region

Lipoprotein(a) levels distribution in an adult sample from the middle ural region

Methodologic procedure of beer competitions eliminating the problematic practices and based on the probabilistic approach

In beer competitions, there are some common problematic practices which ruin the final results, i.e. inappropriate distribution of samples into individual groups, inappropriate scoring of samples, comparison of results obtained from different sub-groups selecting better samples, inappropriate method of handling tied scores, and inappropriate numerical evaluation. To avoid these, the presented study aims to propose a methodologic procedure which eliminates these problematic practices and is based on the probabilistic approach. Further, it wishes to test and evaluate it by the Monte Carlo simulation. The procedure is based on a sensory evaluation of beer samples by assessors through ranking tests. Beer samples and assessors are randomly divided into groups in the lowest round of the competition. Data evaluation, which determines advancing samples or winners, is based on the application of the probability theory, namely the Bayesian theorem, so that the best-evaluated samples could be identified. This procedure achieves a higher probabilitiy of accurately recognised best-evaluated samples in comparison to the procedures involving the aforementioned problematic practices.