Strong Consistency of M Estimator in Linear Model for φ-mixing Samples

The strong consistency of M estimator of regression parameter in linear model for φ-mixing samples is discussed by using the classic Rosenthal type inequality. We get the strong consistency of M estimator under lower moment condition, which generalizes and improves the corresponding ones for independent sequences.

Strong Consistency for the Kernal Estimates of the Random Window Width of the Density Function and its Derivatives Under Φ-Mixing Samples

In the paper,we study the strong uniform consistency for the kernal estimates of random window w■th of density function and its derivatives under the condition that the sequence{X_n}of the ■ are the identically Φ-mixing random variabks.

Attention-relation network for mobile phone screen defect classification via a few samples

How to use a few defect samples to complete the defect classification is a key challenge in the production of mobile phone screens.An attention-relation network for the mobile phone screen defect classification is proposed in this paper.The architecture of the attention-relation network contains two modules:a feature extract module and a feature metric module.Different from other few-shot models,an attention mechanism is applied to metric learning in our model to measure the distance between features,so as to pay attention to the correlation between features and suppress unwanted information.Besides,we combine dilated convolution and skip connection to extract more feature information for follow-up processing.We validate attention-relation network on the mobile phone screen defect dataset.The experimental results show that the classification accuracy of the attentionrelation network is 0.9486 under the 5-way 1-shot training strategy and 0.9039 under the 5-way 5-shot setting.It achieves the excellent effect of classification for mobile phone screen defects and outperforms with dominant advantages.

Research on the Encapsulation Device for Lunar Samples

The encapsulation of lunar samples is a core research area in the third phase of the Chinese Lunar Exploration Program.The seal assembly,opening and closing mechanism(OCM),and locking mechanism are the core components of the encapsulation device of the lunar samples,and the requirements of a tight seal,lightweight,and low power make the design of these core components difficult.In this study,a combined sealing assembly,OCM,and locking mechanism were investigated for the device.The sealing architecture consists of rubber and an Ag-In alloy,and a theory was built to analyze the seal.Experiments of the electroplate Au coating on the knife-edge revealed that the hermetic seal can be significantly improved.The driving principle for coaxial double-helical pairs was investigated and used to design the OCM.Moreover,a locking mechanism was created using an electric initiating explosive device with orifice damping.By optimizing the design,the output parameters were adjusted to meet the requirements of the lunar explorer.The experimental results showed that the helium leak rate of the test pieces were not more than 5×10^(-11) Pa·m^(3)·s^(-1),the minimum power of the OCM was 0.3 W,and the total weight of the principle prototype was 2.9 kg.The explosive driven locking mechanism has low impact.This investigation solved the difficulties in achieving tight seal,light weight,and low power for the lunar explorer,and the results can also be used to explore other extraterrestrial objects in the future.

Effectiveness of Histopathological Examination of Ultrasound-guided Puncture Biopsy Samples for Diagnosis of Extrapulmonary Tuberculosis

Objective To evaluate the diagnostic value of histopathological examination of ultrasound-guided puncture biopsy samples in extrapulmonary tuberculosis(EPTB).Methods This study was conducted at the Shanghai Public Health Clinical Center.A total of 115patients underwent ultrasound-guided puncture biopsy,followed by MGIT 960 culture(culture),smear,Gene Xpert MTB/RIF(Xpert),and histopathological examination.These assays were performed to evaluate their effectiveness in diagnosing EPTB in comparison to two different diagnostic criteria:liquid culture and composite reference standard(CRS).Results When CRS was used as the reference standard,the sensitivity and specificity of culture,smear,Xpert,and histopathological examination were(44.83%,89.29%),(51.72%,89.29%),(70.11%,96.43%),and(85.06%,82.14%),respectively.Based on liquid culture tests,the sensitivity and specificity of smear,Xpert,and pathological examination were(66.67%,72.60%),(83.33%,63.01%),and(92.86%,45.21%),respectively.Histopathological examination showed the highest sensitivity but lowest specificity.Further,we found that the combination of Xpert and histopathological examination showed a sensitivity of 90.80%and a specificity of 89.29%.Conclusion Ultrasound-guided puncture sampling is safe and effective for the diagnosis of EPTB.Compared with culture,smear,and Xpert,histopathological examination showed higher sensitivity but lower specificity.The combination of histopathology with Xpert showed the best performance characteristics.

Exploring device physics of perovskite solar cell via machine learning with limited samples

Perovskite solar cells(PsCs)have developed tremendously over the past decade.However,the key factors influencing the power conversion efficiency(PCE)of PSCs remain incompletely understood,due to the complexity and coupling of these structural and compositional parameters.In this research,we demon-strate an effective approach to optimize PSCs performance via machine learning(ML).To address chal-lenges posed by limited samples,we propose a feature mask(FM)method,which augments training samples through feature transformation rather than synthetic data.Using this approach,squeeze-and-excitation residual network(SEResNet)model achieves an accuracy with a root-mean-square-error(RMSE)of 0.833%and a Pearson's correlation coefficient(r)of 0.980.Furthermore,we employ the permu-tation importance(PI)algorithm to investigate key features for PCE.Subsequently,we predict PCE through high-throughput screenings,in which we study the relationship between PCE and chemical com-positions.After that,we conduct experiments to validate the consistency between predicted results by ML and experimental results.In this work,ML demonstrates the capability to predict device performance,extract key parameters from complex systems,and accelerate the transition from laboratory findings to commercialapplications.

A Fault Detection Method for Electric Vehicle Battery System Based on Bayesian Optimization SVDD Considering a Few Faulty Samples

Accurate and reliable fault detection is essential for the safe operation of electric vehicles.Support vector data description(SVDD)has been widely used in the field of fault detection.However,constructing the hypersphere boundary only describes the distribution of unlabeled samples,while the distribution of faulty samples cannot be effectively described and easilymisses detecting faulty data due to the imbalance of sample distribution.Meanwhile,selecting parameters is critical to the detection performance,and empirical parameterization is generally timeconsuming and laborious and may not result in finding the optimal parameters.Therefore,this paper proposes a semi-supervised data-driven method based on which the SVDD algorithm is improved and achieves excellent fault detection performance.By incorporating faulty samples into the underlying SVDD model,training deals better with the problem of missing detection of faulty samples caused by the imbalance in the distribution of abnormal samples,and the hypersphere boundary ismodified to classify the samplesmore accurately.The Bayesian Optimization NSVDD(BO-NSVDD)model was constructed to quickly and accurately optimize hyperparameter combinations.In the experiments,electric vehicle operation data with four common fault types are used to evaluate the performance with other five models,and the results show that the BO-NSVDD model presents superior detection performance for each type of fault data,especially in the imperceptible early and minor faults,which has seen very obvious advantages.Finally,the strong robustness of the proposed method is verified by adding different intensities of noise in the dataset.

Mechanical behavior of 2G NPR bolt anchored rock samples under static disturbance loading

The deep mining of coal resources is accompanied by severe environmental challenges and various potential engineering hazards.The implementation of NPR(negative Poisson's ratio)bolts are capable of controlling large deformations in the surrounding rock effectively.This paper focuses on studying the mechanical properties of the NPR bolt under static disturbance load.The deep nonlinear mechanical experimental system was used to study the mechanical behavior of rock samples with different anchored types(unanchored/PR anchored/2G NPR anchored)under static disturbance load.The whole process of rock samples was taken by high-speed camera to obtain the real-time failure characteristics under static disturbance load.At the same time,the acoustic emission signal was collected to obtain the key characteristic parameters of acoustic emission such as acoustic emission count,energy,and frequency.The deformation at the failure of the samples was calculated and analyzed by digital speckle software.The findings indicate that the failure mode of rock is influenced by different types of anchoring.The peak failure strength of 2G NPR bolt anchored rock samples exhibits an increase of 6.5%when compared to the unanchored rock samples.The cumulative count and cumulative energy of acoustic emission exhibit a decrease of 62.16%and 62.90%,respectively.The maximum deformation of bearing capacity exhibits an increase of 59.27%,while the failure time demonstrates a delay of 42.86%.The peak failure strength of the 2G NPR bolt anchored ones under static disturbance load exhibits an increase of 5.94%when compared to the rock anchored by PR(Poisson's ratio)bolt.The cumulative count and cumulative energy of acoustic emission exhibit a decrease of 47.16%and 43.86%,respectively.The maximum deformation of the bearing capacity exhibits an increase of 50.43%,and the failure time demonstrates a delay of 32%.After anchoring by 2G NPR bolt,anchoring support effectively reduces the risk of damage caused by static disturbance load.These results demonstrate that the support effect of 2G NPR bolt materials surpasses that of PR bolt.

φ-mixing序列强大数律的一个结果

本文讨论了“φ-mixing序列的强大数律，给出了一个较为一般的结论，从而补充了陈希孺、吴月华关于这个问题的结果．

Selection of Test Samples for Monitoring Veterinary Drug Residue in Bred Animals

[Objective] This study aimed to compare the residue situation of cimaterol,a kind of forbidden veterinary drug in hair, urine and flesh of pig, so as to provide theoretical basis for monitoring veterinary drug residue in bred animals. [Method]Total three different concentrations of cimaterol were administered to pigs, and the residue amounts of cimaterol in pig hair, urine and flesh were monitored at different raising stage. [Result] During the administration period, the residue amount of cimaterol was highest in urine, so urine is the suitable sample for rapid detection of cimaterol in manufacturing enterprises. Cimaterol could be accumulated in pig hair,where cimaterol was metabolized slowly. Thus, pig hair can be used as the test sample for tracing illegal use of veterinary drugs and for vivo detection. Flesh can be used as test sample for direct judgment whether cimaterol residue exceeds the relevant standard. [Conclusion] This study will provide certain theoretical basis for drug monitor in animal husbandry.

和差变换与φ-mixing的稳定性

设{x_i·i≥1}为φ—mixing.本文利用和差变换公式,在一定条件下,证明了S_a/a_n(?).

非同分布φ-mixing序列的完全收敛性

设为随机变量序列，文章在较弱的混合速度且非同分布的条件 下证明了其完全 收敛性的一个结果.

基于NVAE和OB-Mix的小样本数据增强方法

由于深度学习模型对海量标注数据的依赖性较高,导致目前许多前沿性目标检测理论难以适用于工业检测领域。为此,提出一种基于NVAE图像生成和OB-Mix数据增强的小样本数据扩充方法。具体方法是通过NVAE构建检测目标的数据分布模型,再通过采样潜变量的方式生成与真实目标图像属于同一分布的全新目标图像。在得到生成目标图像后,提出了OB-Mix数据增强策略,将生成目标图像与背景图像进行随机位置融合以构建出新的图像数据,从而提高网络的定位能力及泛化能力。方法在仅使用474张标注图像以及400张无检测目标的背景图像情况下,使YOLOv5的检测精确率达到95.86%,相比于不使用该方法的结果提高了17.60个百分点。

Comparison of results analyzed by Chinese and European laboratories for FOREGS geochemical baselines mapping samples

Geochemical subsoil data obtained from China and European laboratories have been compared in this study. 787 C horizon subsoil samples from FOREGS （Forum of European Geological Surveys） geochemical baselines mapping project were sent to China＇s IGGE （Institute of Geophysical and Geochemical Exploration） laboratory and composited to 190 samples according to the 160 kin x 160 km GNT （Global Terrestrial Network） cells. In addition to the FOREGS elemental analysis package, Au, Pt, Pd, B, Ge, Br, CI, Se, N, Li and F were also analyzed by using the IGGE＇s 76 element analytical scheme. Geochemical data statistics, scatter plotting, and geochemical map compilation tech- niques have been employed to investigate differences between FOREGS and IGGE analytical results. The results of two datasets, the IGGE＇s analysis data for composited samples, and the FOREGS average data of samples in each GNT cell, agree extremely well lor about 23 elements, viz： SiO2, St, Al2O3, Zr, Ba, Fe2O3, Ti, Rb, Mn, Gd, CaO, Ga, MgO, P, Pb, Na2O, Y, Th, As, U Sc, Cr, and Co. There are slight differences between-laboratory biases shown as proportional errors between the datasets for Ni, K2O, Tb, Tl, Cu, S, Sin, La, Ce, Pr, Nd, Eu, Ho, Er, Tin, Yb, Lu, Ta, Nb, HE and Dy. For Cd, Cs, Be, Sb, In, Mo, I, Sn, and Te, the correlation of the two datasets and the similarity of the geochemical maps are fairly good, but obvious biases exist between the two datasets at values near detection limits.

Application of Modified Silica Coated Magnetite Nanoparticles for Removal of Iodine from Water Samples

The adsorption of iodine onto silica coated magnetite nanoparticles(im-SCMNPs) that modified with imidazole was investigated for removal of high concentrations of iodine from wastewater. Modified silica magnetite nanoparticles showed high efficiency in removing iodine from wastewater samples. The optimum pH for iodine removal was 7.0-8.0. The adsorption capacity was evaluated using both the Langmuir and Freundlich adsorption isotherm models. The size of the produced magnetite nanoparticles was determined by X-ray diffraction analysis and scanning electron microscopy. Synthesized magnetite nanoparticles showed the high adsorption capacity and would be a good method to increase adsorption efficiency for the removal of iodine in a wastewater treatment process. The Langmuir adsorption capacity(qmax) was found to be 140.84 mg/g of the adsorbent.

Novel method for the determination of five carbamate pesticides in water samples by dispersive liquid-liquid microextraction combined with high performance liquid chromatography

A novel method for the determination of five carbamate pesticides （metolcarb, carbofuran, carbaryl, isoprocard and diethofencard） in water samples was developed by dispersive liquid-liquid microextraction （DLLME） coupled with high performance liquid chromatography-diode array detector （HPLC-DAD）. Some experimental parameters that influence the extraction efficiency were studied and optimized to obtain the best extraction results. Under the optimum conditions for the method, the calibration curve was linear in the concentration range from 5 to 1000 ng mL^-1 for all the five carbamate pesticides, with the correlation coefficients （r^2） varying from 0.9984 to 0.9994. Good enrichment factors were achieved ranging from 80 to 177- fold, depending on the compound. The limits of detection （LODs） （S/N = 3） were ranged from 0.1 to 0.5 ng mL^-1. The method has been successfully applied to the analysis of the pesticide residues in environmental water samples.

Investigation on Fe,Mn,Zn,Cu,Pb and Cd fractions in the natural surface coating samples and surficial sediments in the Songhua River,China

Natural surface coating samples （NSCSs） from the surface of shingles and surficial sediments （SSs） in the Songhua River, China were employed to investigate the relationship between NSCSs and SSs in fractions of heavy metals （Fe, Mn, Zn, Cu, Pb, and Cd） using the modified sequential extraction procedure （MSEP）. The results show that the differences between NSCSs and SSs in Fe fi＇actions were insignificant and Fe was dominantly present as residual phase （76.22% for NSCSs and 80.88% for SSs） and Fe-oxides phase （20.33% for NSCSs and 16.15% for SSs）. Significant variation of Mn distribution patterns between NSCSs and SSs was observed with Mn in NSCSs mainly present in Mn-oxides phase （48.27%） and that in SSs present as residual phase （45.44%）. Zn, Cu, Pb and Cd were found dominantly in residual fractions （〉48%）, and next in solid oxides/hydroxides for Zn, Pb and Cd and in easily oxidizable solids/compounds form for Cu, respectively. The heavy metal distribution patterns implied that Fe/Mn oxides both in NSCSs and SSs were more important sinks for binding and adsorption of Zn, Pb and Cd than organic matter （OM）, and inversely, higher affinity of Cu to OM than Fe/Mn oxides in NSCSs and SSs was obtained. Meanwhile, it was found that the distributions of heavy metals in NSCSs and SSs were similar to each other and the pseudo-total concentrations of Zn, Cu, Pb and Cd in NSCSs were greater than those in SSs, highlighting the more importance for NSCSs than SSs in controlling behaviours of heavy metals in aquatic environments.

Inductively Coupled Plasma(ICP) Mass Spectrometry(MS) Hyphenated with Atomic Emission Spectrometry(AES) for Simultaneous Determination of Major, Minor and Micro Amounts of Elements in Geochemical Samples

Introduction Geological resource survey demands for determining various constituents including major, minor, micro, trace and ultra-trace levels of elements for preparing the map of resource distribution of our country. As a powerful and popularly used technique for multi-element analysis, inductively coupled plasma（ICP） atomic emission spectrometry （AES） has been applied to this field for a period of time. However, ICP spectrometric determination of those micro, trace and ultratrace elements needs enrichment procedures for improving the detection limit, which is unacceptable in case a great mass of samples should be analyzed as that in the task of geological resource survey. On the other hand, although ICP mass spectrometry （MS） is considered the most powerful method for trace elements determination, it is difficult for ICP-MS to be used to determine the trace and major analytes simultaneously in a spectrum.

Data processing of small samples based on grey distance information approach

Data processing of small samples is an important and valuable research problem in the electronic equipment test. Because it is difficult and complex to determine the probability distribution of small samples, it is difficult to use the traditional probability theory to process the samples and assess the degree of uncertainty. Using the grey relational theory and the norm theory, the grey distance information approach, which is based on the grey distance information quantity of a sample and the average grey distance information quantity of the samples, is proposed in this article. The definitions of the grey distance information quantity of a sample and the average grey distance information quantity of the samples, with their characteristics and algorithms, are introduced. The correlative problems, including the algorithm of estimated value, the standard deviation, and the acceptance and rejection criteria of the samples and estimated results, are also proposed. Moreover, the information whitening ratio is introduced to select the weight algorithm and to compare the different samples. Several examples are given to demonstrate the application of the proposed approach. The examples show that the proposed approach, which has no demand for the probability distribution of small samples, is feasible and effective.

CONVERGENCE RATES IN THE STRONG LAWS OF NONSTATIONARYρ~*-MIXING RANDOM FIELDS

By using a Rosenthal type inequality established in this paper, the complete convergence and almost sure summability on the convergence rates with respect to the strong law of large numbers are discussed for *-mixing random fields.