Micro-failure process and failure mechanism of brittle rock under uniaxial compression using continuous real-time wave velocity measurement

In this study,the micro-failure process and failure mechanism of a typical brittle rock under uniaxial compression are investigated via continuous real-time measurement of wave velocities.The experimental results indicate that the evolutions of wave velocities became progressively anisotropic under uniaxial loading due to the direction-dependent development of micro-damage.A wave velocity model considering the inner anisotropic crack evolution is proposed to accurately describe the variations of wave velocities during uniaxial compression testing.Based on which,the effective elastic parameters are inferred by a transverse isotropic constitutive model,and the evolutions of the crack density are inversed using a self-consistent damage model.It is found that the propagation of axial cracks dominates the failure process of brittle rock under uniaxial loading and oblique shear cracks develop with the appearance of macrocrack.

A New High-throughput Real-time PCR Assay for the Screening of Multiple Antimicrobial Resistance Genes in Broiler Fecal Samples from China

Objective Antimicrobial resistance(AMR)has become a global concern and is especially severe in China.To effectively and reliably provide AMR data,we developed a new high-throughput real-time PCR assay based on microfluidic dynamic technology,and screened multiple AMR genes in broiler fecal samples.Methods A high-throughput real-time PCR system with an new designed integrated fluidic circuit assay were performed AMR gene detection.A total of 273 broiler fecal samples collected from two geographically separated farms were screened AMR genes.Results The new assay with limits of detection ranging from 40.9 to 8,000 copies/reaction.The sensitivity rate,specificity rate,positive predictive value,negative predictive value and correct indices were 99.30%,98.08%,95.31%,99.79%,and 0.9755,respectively.Utilizing this assay,we demonstrate that AMR genes are widely spread,with positive detection rates ranging from 0 to 97.07%in 273 broiler fecal samples.bla CTX-M,bla TEM,mcr-1,fex A,cfr,optr A,and int I1 showed over 80%prevalence.The dissemination of AMR genes was distinct between the two farms.Conclusions We successfully established a new high-throughput real-time PCR assay applicable to AMR gene surveillance from fecal samples.The widespread existence of AMR genes detected in broiler farms highlights the current and severe problem of AMR.

ROBUST FILTERS WITH SAMPLED-DATA ESTIMATION COVARANCE CONSTRAINT FOR UNCERTAIN CONTINUOUS-TIME SYSTEMS

This paper was concerned with the problem of robust sampled data state estimation for uncertain continuous time systems. A sampled data estimation covariance is given by taking intersample behaviour into account. The primary purpose of this paper is to design robust discrete time Kalman filters such that the sampled data estimation covariance is not more than a prespecified value, and therefore the error variances achieve the desired constraints. It is shown that the addressed problem can be converted into a similar problem for a fictitious discrete time system. The existence conditions and the explicit expression of desired filters were both derived. Finally, a simple example was presented to demonstrate the effectiveness of the proposed design procedure.

Semi-implantable device based on multiplexed microfilament electrode cluster for continuous monitoring of physiological ions

Modern medicine is increasingly interested in advanced sensors to detect and analyze biochemical indicators.Ion sensors based on potentiometric methods are a promising platform for monitoring physiological ions in biological subjects.Current semi-implantable devices are mainly based on single-parameter detection.Miniaturized semi-implantable electrodes for multiparameter sensing have more restrictions on the electrode size due to biocompatibility considerations,but reducing the electrode surface area could potentially limit electrode sensitivity.This study developed a semi-implantable device system comprising a multiplexed microfilament electrode cluster(MMEC)and a printed circuit board for real-time monitoring of intra-tissue K^(+),Ca^(2+),and Na^(+)concentrations.The electrode surface area was less important for the potentiometric sensing mechanism,suggesting the feasibility of using a tiny fiber-like electrode for potentiometric sensing.The MMEC device exhibited a broad linear response(K^(+):2–32 mmol/L;Ca^(2+):0.5–4 mmol/L;Na^(+):10–160 mmol/L),high sensitivity(about 20–45 mV/decade),temporal stability(>2weeks),and good selectivity(>80%)for the above ions.In vitro detection and in vivo subcutaneous and brain experiment results showed that the MMEC system exhibits good multi-ion monitoring performance in several complex environments.This work provides a platform for the continuous real-time monitoring of ion fluctuations in different situations and has implications for developing smart sensors to monitor human health.

Approximate Continuous Aggregation via Time Window Based Compression and Sampling in WSNs

In many applications continuous aggregation of sensed data is usually required. The existing aggregation schemes usually compute every aggregation result in a continuous aggregation either by a complete aggregation procedure or by partial data update at each epoch. To further reduce the energy cost, we propose a sampling-based approach with time window based linear regression for approximate continuous aggregation. We analyze the approximation error of the aggregation results and discuss the determinations of parameters in our approach. Simulation results verify the effectiveness of our approach.

APPROXIMATE SAMPLING THEOREM FOR BIVARIATE CONTINUOUS FUNCTION

An approximate solution of the refinement equation was given by its mask, and the approximate sampling theorem for bivariate continuous function was proved by applying the approximate solution . The approximate sampling function defined uniquely by the mask of the refinement equation is the approximate solution of the equation , a piece-wise linear function , and posseses an explicit computation formula . Therefore the mask of the refinement equation is selected according to one' s requirement, so that one may controll the decay speed of the approximate sampling function .

Development of Wearable Semi-invasive Blood Sampling Devices for Continuous Glucose Monitoring: A Survey

Semi-invasive blood sampling devices mimic the way female mosquitoes extract blood from a host. They generally consist of a microneedle, a microactuator for needle insertion, a blood extraction mechanism and a blood glucose sensor. These devices have great potential to overcome the major disadvantages of several current blood glucose monitoring methods. Over last two decades, extensive research has been made in all of these related fields. More recently, several wearable devices for semi-invasive blood sampling have been developed. This review aims at summarizing the current state-of-the-art development and utilization of such wearable devices for continuous monitoring of blood glucose levels, with a special attention on design considerations, fabrication technologies and testing methods.

DETECTION OF INCIPIENT LOCALIZED GEAR FAULTS IN GEARBOX BY COMPLEX CONTINUOUS WAVELET TRANSFORM

As far as the vibration signal processing is concerned, composition ofvibration signal resulting from incipient localized faults in gearbox is too weak to be detected bytraditional detecting technology available now. The method, which includes two steps: vibrationsignal from gearbox is first processed by synchronous average sampling technique and then it isanalyzed by complex continuous wavelet transform to diagnose gear fault, is introduced. Twodifferent kinds of faults in the gearbox, i.e. shaft eccentricity and initial crack in tooth fillet,are detected and distinguished from each other successfully.

How relevant are engineering samples in the management of personal dust exposure?

A directive, legislated by the South African Department of Minerals and Energy(DME) in 1997, was introduced to reduce the dust exposures of continuous miner(CM) operators to below 5 mg/m3, when measured at the operator's cab position. The focus of this paper is to review the effectiveness of observing this rule for almost two decades and discuss industry perceptions arising from the application of this rule. The results have demonstrated that the engineering sample cannot predict the personal shift dust exposure of a CM operator. Therefore, it is recommended that the engineering sampling, as currently practiced,should be reviewed with the objective to discontinue and replace it by the approved PDM3700 realtime dust monitor.

Two-Phase Rate Adaptation Strategy for Improving Real-Time Video QoE in Mobile Networks

With the popularity of smart handheld devices, mobile streaming video has multiplied the global network traffic in recent years. A huge concern of users' quality of experience(Qo E) has made rate adaptation methods very attractive. In this paper, we propose a two-phase rate adaptation strategy to improve users' real-time video Qo E. First, to measure and assess video Qo E, we provide a continuous Qo E prediction engine modeled by RNN recurrent neural network. Different from traditional Qo E models which consider the Qo E-aware factors separately or incompletely, our RNN-Qo E model accounts for three descriptive factors(video quality, rebuffering, and rate change) and reflects the impact of cognitive memory and recency. Besides, the video playing is separated into the initial startup phase and the steady playback phase, and we takes different optimization goals for each phase: the former aims at shortening the startup delay while the latter ameliorates the video quality and the rebufferings. Simulation results have shown that RNN-Qo E can follow the subjective Qo E quite well, and the proposed strategy can effectively reduce the occurrence of rebufferings caused by the mismatch between the requested video rates and the fluctuated throughput and attains standout performance on real-time Qo E compared with classical rate adaption methods.

ROBUST CONTROL WITH COVARIANCE CONSTRAINT FOR UNCERTAIN SAMPLED-DATA FEEDBACK CONTROL SYSTEMS

The problem of robust controller design with covariance constraint for uncertain sampled data feedback control systems was considered in this paper. The goal of this problem is to design controllers such that the closed loop system meets the prespecified covariance constraint. This problem can be reduced to a controller design problem for an equivalent uncertain discrete time system. Sufficient conditions were given for the existence of the desired controllers. The analytical expression of the set of desired controllers was also presented. An illustrative example was given to show the applicability of the proposed design procedure.

Statistical Comparison of Eight Alternative Methods for the Analysis of Paired Sample Data with Applications

This paper presents and statistically compared eight alternative methods that could possibly be used in the analysis of matched or paired sample data, including situations in which the data being analyzed satisfy the usual assumptions of normality and continuity necessary for the use of parametric tests as well as when the data are numeric and non-numeric measurements on as low as the ordinal scale. It is shown that only the modified sign tests based on only the raw observations or their assigned ranks may be used with non numeric measurement on the ordinal scale. If the ordinary sign test, the Wilcoxon signed rank sum test and the modified sign tests can be equally used in data analysis, then it is shown that the modified sign tests are more efficient and hence more powerful than the ordinary sign tests because the two test statistics are intrinsically and structurally modified for the possible presence of tied observations between the sampled populations for both using raw and simulated data. Of all the non-parametric methods presented, the modified Wilcoxon’s signed rank sum test when applicable is the most efficient and powerful, following in this order by the modified sign test by ranks and the modified sign test based on only raw scores for raw data but simulation, modified sign test by ranks is the most efficient and powerful, following in this order by modified wilcoxon’s signed rank sum test and modified sign test. Each of the non-parametric methods presented can be easily modified and re-specified for use with one sample data by simply re-designating the observations from one of the sampled populations to correspond with a hypothesized value of some measure of central tendency. The methods are illustrated with some raw data as well as simulated data and their relative performances compared.

Data partitioning based on sampling for power load streams

A novel data streams partitioning method is proposed to resolve problems of range-aggregation continuous queries over parallel streams for power industry.The first step of this method is to parallel sample the data,which is implemented as an extended reservoir-sampling algorithm.A skip factor based on the change ratio of data-values is introduced to describe the distribution characteristics of data-values adaptively.The second step of this method is to partition the fluxes of data streams averagely,which is implemented with two alternative equal-depth histogram generating algorithms that fit the different cases：one for incremental maintenance based on heuristics and the other for periodical updates to generate an approximate partition vector.The experimental results on actual data prove that the method is efficient,practical and suitable for time-varying data streams processing.

A Perspective of Laser Sampling for Inductively Coupled Plasma-Atomic Emission Spectro metry for Rock and Mineral Analysis

The development of laser sampling for optical emission spectrometry is reviewed . Advantages and limitations of pulsed laser sampling are compared with those of continuous laser sampling . A novel method of laser sampling of liquid samples for inductively coupled plasma -atomic emission spectrometry has been proposed , and its analytical performance investigated.Experimental results showed that,as a method of sample introduction , laser vaporization of liquid samples enjoyed certain advantages , e.g.,much higher sensitivity, much lower detection limit and reduced sample volume , over solution nebulization . A perspective of the application of laser sampling-inductively coupled plasma - actomic emission spectrometry for rock and mineral analysis is estimated as well.

Average Sample-path Optimality for Continuous-time Markov Decision Processes in Polish Spaces

In this paper we study the average sample-path cost （ASPC） problem for continuous-time Markov decision processes in Polish spaces. To the best of our knowledge, this paper is a first attempt to study the ASPC criterion on continuous-time MDPs with Polish state and action spaces. The corresponding transition rates are allowed to be unbounded, and the cost rates may have neither upper nor lower bounds. Under some mild hypotheses, we prove the existence of （ε〉 0）-ASPC optimal stationary policies based on two different approaches： one is the ＂optimality equation＂ approach and the other is the ＂two optimality inequalities＂ approach.

基于CWT-sCARS的土壤铜含量高光谱反演

光谱变量的有效程度与土壤铜含量的反演精度密切相关。基于原始反射率以及不同分解尺度下的小波系数,本研究采用连续小波变换(CWT)算法、稳定性竞争自适应重加权采样(sCARS)算法和随机森林(RF)算法对土壤铜含量进行了反演与验证。研究结果表明:连续小波变换可以有效提高光谱特征与土壤铜含量之间的相关性,不同分解尺度对应的最大相关系数中,最大值位于Scale 8分解尺度下1343 nm处,相关系数为0.60;使用sCARS算法可以显著减少特征变量的数量,结合CWT变换和sCARS算法可以显著减轻数据冗余,提高土壤Cu含量的反演精度。该研究可为利用高光谱遥感技术,快速、高精度反演土壤Cu含量提供重要参考。

雾化性能预测驱动的航空发动机燃油喷嘴选配

为减少航空发动机燃油喷嘴装配中的反复拆卸重装,提高一次装配成功率,提出了一种基于雾化性能预判的关键零部件选配方法。首先,基于喷嘴历史装配数据构建喷嘴几何精度-雾化性能实例库;然后,考虑到样本空间大小和喷嘴几何精度波动较大、一致性差的影响,采用自适应综合过采样方法对样本空间进行扩充,同时利用改进的K-means聚类算法对连续属性离散化处理;最后,通过关联规则挖掘算法建立几何精度与雾化性能之间的关联关系,并利用规则适应度评价方法量化每条规则的准确性,基于这些关联规则集构建喷嘴雾化性能预判模型,用于指导喷嘴装配。研究结果表明,利用某双油路离心喷嘴的旋流器和副喷口的装配数据进行验证,与决策树、支持向量机和人工神经网络等方法进行比较,本方法提出的喷嘴雾化性能预判模型的预测效果最好,预测精度高达98.33%。可以对不同零件组合后的喷嘴雾化性能进行有效预判,进而减少无效装配,提高喷嘴的装配效率。

零故障样本下小波知识驱动的工业机器人故障检测

针对零故障样本问题,现有方法大多从迁移学习、样本生成等开展研究,然而该类方法依赖相近故障样本,难以保证训练样本与真实故障信号在数据分布上保持对齐,导致模型泛化性不足。针对上述问题,提出了基于连续小波变换知识库和ViT网络的故障检测方法。采用了多种母小波函数构建连续小波变换知识库,从不同时-频角度对机械装备监测数据进行分析;设计了一种基于多模态时-频特征的对比损失函数,实现了ViT的有效训练;开发了基于余弦相似性分析的故障检测算法,检测机械装备各类异常状态。使用工业机器人实验平台对方法进行验证。结果表明,所提方法能够在零故障样本下构建高性能的特征提取网络,并能对各类故障状态进行准确检测。

Maximum Likelihood Estimator of the Parameter for a Continuous One-Parameter Exponential Family Under the Optimal Ranked Set Sampling

This paper studies a maximum likelihood estimator(MLE) of the parameter for a continuous one-parameter exponential family under ranked set sampling(RSS). The authors first find the optimal RSS according to the character of the family, viz, arrange the RSS based on quasi complete and sufficient statistic of independent and identically distributed(iid) samples. Then under this RSS, some sufficient conditions for the existence and uniqueness of the MLE, which are easily used in practice,are obtained. Using these conditions, the existence and uniqueness of the MLEs of the parameters for some usual distributions in this family are proved. Numerical simulations for these distributions fully support the result from the above two step optimizations of the sampling and the estimation method.