Modified DS np Chart Using Generalized Multiple Dependent State Sampling under Time Truncated Life Test

This study presents the design of a modified attributed control chart based on a double sampling(DS)np chart applied in combination with generalized multiple dependent state(GMDS)sampling to monitor the mean life of the product based on the time truncated life test employing theWeibull distribution.The control chart developed supports the examination of the mean lifespan variation for a particular product in the process of manufacturing.Three control limit levels are used:the warning control limit,inner control limit,and outer control limit.Together,they enhance the capability for variation detection.A genetic algorithm can be used for optimization during the in-control process,whereby the optimal parameters can be established for the proposed control chart.The control chart performance is assessed using the average run length,while the influence of the model parameters upon the control chart solution is assessed via sensitivity analysis based on an orthogonal experimental design withmultiple linear regression.A comparative study was conducted based on the out-of-control average run length,in which the developed control chart offered greater sensitivity in the detection of process shifts while making use of smaller samples on average than is the case for existing control charts.Finally,to exhibit the utility of the developed control chart,this paper presents its application using simulated data with parameters drawn from the real set of data.

Stability and accuracy of central difference method for real-time dynamic substructure testing considering mass participation coefficient

For real-time dynamic substructure testing(RTDST),the influence of the inertia force of fluid specimens on the stability and accuracy of the integration algorithms has never been investigated.Therefore,this study proposes to investigate the stability and accuracy of the central difference method(CDM)for RTDST considering the specimen mass participation coefficient.First,the theory of the CDM for RTDST is presented.Next,the stability and accuracy of the CDM for RTDST considering the specimen mass participation coefficient are investigated.Finally,numerical simulations and experimental tests are conducted for verifying the effectiveness of the method.The study indicates that the stability of the algorithm is affected by the mass participation coefficient of the specimen,and the stability limit first increases and then decreases as the mass participation coefficient increases.In most cases,the mass participation coefficient will increase the stability limit of the algorithm,but in specific circumstances,the algorithm may lose its stability.The stability and accuracy of the CDM considering the mass participation coefficient are verified by numerical simulations and experimental tests on a three-story frame structure with a tuned liquid damper.

Scoring and Consequential Validity Evidence of Computer-and Paper-Based Writing Tests in Times of Change

Little is known about how the assessment modality,i.e.,computer-based(CB)and paper-based(PB)tests,affects language teachers’scorings,perceptions,and preferences and,therefore,the validity and fairness of classroom writing assessments.The present mixed-methods study used Shaw and Weir’s(2007)sociocognitive writing test validation framework to examine the scoring and consequential validity evidence of CB and PB writing tests in EFL classroom assessment in higher education.Original handwritten and word-processed texts of 38 EFL university students were transcribed to their opposite format and assessed by three language lecturers(N=456 texts,152 per teacher)to examine the scoring validity of CB and PB tests.The teachers’perceptions of text quality and preferences for assessment modality accounted for the consequential validity evidence of both tests.Findings revealed that the assessment modality impacted teachers’scorings,perceptions,and preferences.The teachers awarded higher scores to original and transcribed handwritten texts,particularly text organization and language use.The teachers’perceptions of text quality differed from their ratings,and physical,psychological,and experiential characteristics influenced their preferences for assessment modality.The results have implications for the validity and fairness of CB and PB writing tests and teachers’assessment practices.

4种植物源性成分多重real-time PCR检测方法的建立及其在食用淀粉中的应用

建立一种可同时快速检测红薯、木薯、马铃薯、玉米源性成分的多重实时聚合酶链式反应(real-time polymerase chain reaction,real-time PCR)方法。分别以红薯g3pdh基因、木薯g3pdh基因、马铃薯UGPase基因、玉米zSSIIb基因为靶基因设计特异性引物和TaqMan探针,以18S rRNA基因为内参基因,建立多重real-time PCR方法,开展方法学验证,并对不同掺入比例模拟样品和实际淀粉样品进行检测。结果显示,该方法具有高通量、特异性强、灵敏度高等优点。与15种非目标源性均无交叉反应;对目标DNA的检测灵敏度可达到3×10^(-3) ng/μL,且具有良好的线性关系和扩增效率;对淀粉样品的检出限可达0.1%,对50份实际样品进行检测,结果与参比方法一致,说明建立的多重real-time PCR法可用于食用淀粉种类掺假鉴别检测。

Intraprocedural gastric juice analysis as compared to rapid urease test for real-time detection of Helicobacter pylori

BACKGROUND Endofaster is an innovative technology that can be combined with upper gastrointestinal endoscopy(UGE)to perform gastric juice analysis and real-time detection of Helicobacter pylori(H.pylori).AIM To assess the diagnostic performance of this technology and its impact on the management of H.pylori in the real-life clinical setting.METHODS Patients undergoing routine UGE were prospectively recruited.Biopsies were taken to assess gastric histology according to the updated Sydney system and for rapid urease test(RUT).Gastric juice sampling and analysis was performed using the Endofaster,and the diagnosis of H.pylori was based on real-time ammonium measurements.Histological detection of H.pylori served as the diagnostic gold standard for comparing Endofaster-based H.pylori diagnosis with RUT-based H.pylori detection.RESULTS A total of 198 patients were prospectively enrolled in an H.pylori diagnostic study by Endofasterbased gastric juice analysis(EGJA)during the UGE.Biopsies for RUT and histological assessment were performed on 161 patients(82 men and 79 women,mean age 54.8±19.2 years).H.pylori infection was detected by histology in 47(29.2%)patients.Overall,the sensitivity,specificity,accuracy,positive predictive value,and negative predictive value(NPV)for H.pylori diagnosis by EGJA were 91.5%,93.0%,92.6%,84.3%,and 96.4%,respectively.In patients on treatment with proton pump inhibitors,diagnostic sensitivity was reduced by 27.3%,while specificity and NPV were unaffected.EGJA and RUT were comparable in diagnostic performance and highly concordant in H.pylori detection(κ-value=0.85).CONCLUSION Endofaster allows for rapid and highly accurate detection of H.pylori during gastroscopy.This may guide taking additional biopsies for antibiotic susceptibility testing during the same procedure and then selecting an individually tailored eradication regimen.

ICON/MIGHTI与TIMED/SABER探测温度数据的对比

ICON卫星为临近空间环境特性研究、建模和预报提供了新数据.通过对ICON/MIGHTI与TIMED/SABER在90~105 km高度探测温度数据的比较,计算两者的年平均温度偏差和均方根误差,同时分析月平均温度偏差在不同月份中随高度和纬度的分布情况,为MIGHTI和SABER温度探测数据在临近空间大气建模和预报应用提供参考依据.结果表明,MIGHTI和SABER的温度垂直廓线变化趋势基本吻合,数值上有所差异.在12°S-42°N范围内,MIGHTI探测温度与SABER相比,在90~93 km时偏低,偏差最大值约2.5 K,在93~105 km偏高,偏差的绝对值最大约10 K.在不同季节,白天的温度偏差通常高于夜晚.SABER和MIGHTI的月平均温度偏差随季节和纬度的变化显著,夏季时的月平均温度偏差最大,且温度的均方根误差最大.

昆明烟区土壤线虫数量与环境因子的Mantel Test分析

为进一步明确昆明烟区影响线虫的环境因素,于2022年采集云南昆明烟区8个县(市、区)64个乡镇的320个植烟土壤样品,调查与测定样品采集地的海拔、土壤线虫数量以及土壤理化性质(pH值及有机质、水解性氮、速效钾和有效磷含量),并探讨土壤线虫数量与植烟土壤环境因子之间的潜在关系。结果表明,昆明烟区土壤线虫数量一定程度上受到海拔及土壤理化性质的影响,海拔与有机质、水解性氮和速效钾呈显著正相关,pH值与有效磷呈极显著负相关,相关系数为-0.850;土壤线虫数量与土壤有效磷呈正相关,与海拔、pH值、有机质、水解性氮、速效钾呈负相关,其中与海拔、有机质和水解性氮的相关系数较大,分别为-0.751、-0.651和-0.568。通过Mantel test分析确定海拔、有机质和水解性氮含量是影响昆明烟区土壤线虫数量的重要因子。本结果可为有效防治土壤线虫提供科学依据。

Fast Remaining Capacity Estimation for Lithium-ion Batteries Based on Short-time Pulse Test and Gaussian Process Regression

It remains challenging to effectively estimate the remaining capacity of the secondary lithium-ion batteries that have been widely adopted for consumer electronics,energy storage,and electric vehicles.Herein,by integrating regular real-time current short pulse tests with data-driven Gaussian process regression algorithm,an efficient battery estimation has been successfully developed and validated for batteries with capacity ranging from 100%of the state of health(SOH)to below 50%,reaching an average accuracy as high as 95%.Interestingly,the proposed pulse test strategy for battery capacity measurement could reduce test time by more than 80%compared with regular long charge/discharge tests.The short-term features of the current pulse test were selected for an optimal training process.Data at different voltage stages and state of charge(SOC)are collected and explored to find the most suitable estimation model.In particular,we explore the validity of five different machine-learning methods for estimating capacity driven by pulse features,whereas Gaussian process regression with Matern kernel performs the best,providing guidance for future exploration.The new strategy of combining short pulse tests with machine-learning algorithms could further open window for efficiently forecasting lithium-ion battery remaining capacity.

TimeGAN-Informer长时机场能见度预测

能见度的预测对机场的业务决策、保障飞机的安全起降具有重要的意义。针对现有能见度预测模型预测时间较短的问题,提出一种基于TimeGAN Informer(Time Generative Adversarial Network-Informer)的机场能见度预测方法。利用2018—2022年气象和污染物数据,通过相关系数法和递归特征消除法提取出能见度的主要影响因素,使用TimeGAN时间序列生成对抗网络对数据进行扩充,并将Informer长时间序列预测模型应用于能见度预测。结果显示:当预测步长为1 d、2 d、3 d时,TimeGAN Informer的绝对误差(Mean Absolute Error,MAE)分别为2.42、3.13、3.57,比Informer分别降低了0.29、0.27、0.28,比长短时记忆网络(Long Short-Term Memory,LSTM)分别降低了0.28、0.49、0.63;均方根误差(Root Mean Square Error,RMSE)分别为3.03、3.7、4.09,比Informer分别降低了0.38、0.22、0.24,比长短时记忆网络(LSTM)分别降低了0.3、0.5、1.04;百分误差小于30%的分别占测试样本集的78.07%、70.68%、63.84%。尽管随着步长的增加预测效果变差,但在预测步长为3 d时,多数样本的预测误差仍小于30%,实现了对机场区域较为准确的长时能见度预测。

Failure characterization of fully grouted rock bolts under triaxial testing

Confining stresses serve as a pivotal determinant in shaping the behavior of grouted rock bolts.Nonetheless,prior investigations have oversimplified the three-dimensional stress state,primarily assuming hydrostatic stress conditions.Under these conditions,it is assumed that the intermediate principal stress(σ_(2))equals the minimum principal stress(σ_(3)).This assumption overlooks the potential variations in magnitudes of in situ stress conditions along all three directions near an underground opening where a rock bolt is installed.In this study,a series of push tests was meticulously conducted under triaxial conditions.These tests involved applying non-uniform confining stresses(σ_(2)≠σ_(3))to cubic specimens,aiming to unveil the previously overlooked influence of intermediate principal stresses on the strength properties of rock bolts.The results show that as the confining stresses increase from zero to higher levels,the pre-failure behavior changes from linear to nonlinear forms,resulting in an increase in initial stiffness from 2.08 kN/mm to 32.51 kN/mm.The load-displacement curves further illuminate distinct post-failure behavior at elevated levels of confining stresses,characterized by enhanced stiffness.Notably,the peak load capacity ranged from 27.9 kN to 46.5 kN as confining stresses advanced from σ_(2)=σ_(3)=0 to σ_(2)=20 MPa and σ_(3)=10 MPa.Additionally,the outcomes highlight an influence of confining stress on the lateral deformation of samples.Lower levels of confinement prompt overall dilation in lateral deformation,while higher confinements maintain a state of shrinkage.Furthermore,diverse failure modes have been identified,intricately tied to the arrangement of confining stresses.Lower confinements tend to induce a splitting mode of failure,whereas higher loads bring about a shift towards a pure interfacial shear-off and shear-crushed failure mechanism.

Unbound^(28)O,the heaviest oxygen isotope observed:a cutting-edge probe for testing nuclear models

The beyond-dripline oxygen isotopes^(27,28)O were recently observed at RIKEN,and were found to be unbound decaying into^(24)O by emitting neutrons.The unbound feature of the heaviest oxygen isotope,^(28)O,provides an excellent test for stateof-the-art nuclear models.The atomic nucleus is a self-organized quantum manybody system comprising specific numbers of protons Z and neutrons N.

基于TimeGAN数据增强的复杂过程故障分类方法

针对传统基于重构的故障分类方法在故障样本稀疏或失衡情况下效果不佳、故障子空间区分能力弱的问题,提出基于TimeGAN数据增强的复杂过程故障分类方法.针对小子样故障,使用TimeGAN对历史故障数据进行数据增强,生成与历史数据分布相似的虚拟故障样本;采用马氏距离评估虚拟样本的质量,剔除不可信样本,构造平衡的故障样本集.将故障样本映射到高维核空间,并在核空间中提取故障子空间.设计故障分类策略并定义4种故障分类性能评估指标以定量衡量算法的分类性能.Tennessee Eastman应用结果表明,所提数据增强方法可以有效扩充故障样本,进而提高故障重构率.与WGAN-GP和SMOTE方法进行对比,发现基于TimeGAN数据增强的故障分类方法具有更好的分类性能.

基于MDTimeGAN的序列数据生成方法

非侵入式负荷分解是能源管理领域的一个热门研究课题,其在各种工业和商业场景中都得到广泛应用。针对负荷分解数据集中存在的样本不平衡问题,提出一种基于多判别器时间序列生成对抗网络(MDTimeGAN)的序列数据生成方法。通过对原始序列提取时域、频域、时频域以及自相关特征,并在TimeGAN模型基础上采用4种不同的判别器对时间序列的多维度特征进行判别,从而提高对原始数据的判别能力,提升数据质量。在3种公开数据集上进行横向和纵向对比实验,结果表明,与对比模型相比,MDTimeGAN模型生成的数据能够更好地覆盖原始数据的分布,在数据分布方面保持良好的性能,生成数据符合时间序列数据的特点。

Brain Time Stack图像融合技术在CT中的应用

目的:分析Brain Time Stack图像融合技术在CT中的应用。方法:选取2021年3月—2022年9月衡水市第四人民医院收治的50例CT检查患者作为研究对象。所有患者进行CT检查并进行Brain Time Stack后处理。比较四组不同部位CT值、标准差(SD)、信噪比(SNR)。比较四组图像主观质量评分。分析不同部位CT值、SD、SNR与图像主观质量评分的相关性。结果:B组的延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显低于A组;C组的延髓、脑室、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值高于A组;D组延髓、额叶灰质、颞肌肌肉CT值明显低于A组,脑室、额叶白质、小脑外侧CT值明显高于A组;C组延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显高于B组;D组延髓、脑室、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显高于B组;D组延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显低于C组;D组脑室CT值明显高于C组,差异有统计学意义(P<0.05)。B组、C组、D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SD值明显低于A组;C组延髓、脑室、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SD值均明显高于B组;C组额叶灰质SD明显低于B组;D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、肌肉SD均明显低于B组、C组,差异有统计学意义(P<0.05)。B组、C组、D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SNR均明显高于A组;C组、D组延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SNR值明显高于B组;C组、D组脑室SNR明显低于B组;D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SNR明显高于C组,差异有统计学意义(P<0.05)。D组图像主观质量评分最高,差异有统计学意义(P<0.05)。延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧及颞肌肌肉SD与主观质量评分呈明显负相关,SNR与主观质量评分间呈明显正相关,差异有统计学意义(P<0.05)。结论:利用Brain Time Stack图像融合技术对头部CT扫描检查图像处理,动脉期结合前一期及后一期的图像数据在处理后具有更好的质量和更少的噪音。

Association of daily sitting time and leisure-time physical activity with body fat among U.S.adults

Background:Prolonged sitting and reduced physical activity lead to low energy expenditures.However,little is known about the joint impact of daily sitting time and physical activity on body fat distribution.We investigated the independent and joint associations of daily sitting time and physical activity with body fat among adults.Methods:This was a cross-sectional analysis of U.S.nationally representative data from the National Health and Nutrition Examination Survey2011-2018 among adults aged 20 years or older.Daily sitting time and leisure-time physical activity(LTPA)were self-reported using the Global Physical Activity Questionnaire.Body fat(total and trunk fat percentage)was determined via dual X-ray absorptiometry.Results:Among 10,808 adults,about 54.6%spent 6 h/day or more sitting;more than one-half reported no LTPA(inactive)or less than 150 min/week LTPA(insufficiently active)with only 43.3%reported 150 min/week or more LTPA(active)in the past week.After fully adjusting for sociodemographic data,lifestyle behaviors,and chronic conditions,prolonged sitting time and low levels of LTPA were associated with higher total and trunk fat percentages in both sexes.When stratifying by LTPA,the association between daily sitting time and body fat appeared to be stronger in those who were inactive/insuufficiently active.In the joint analyses,inactive/insuufficiently active adults who reported sitting more than 8 h/day had the highest total(female:3.99%(95%confidence interval(95%CI):3.09%-4.88%);male:3.79%(95%CI:2.75%-4.82%))and trunk body fat percentages(female:4.21%(95%CI:3.09%-5.32%);male:4.07%(95%CI:2.95%-5.19%))when compared with those who were active and sitting less than 4 h/day.Conclusion:Prolonged daily sitting time was associated with increased body fat among U.S.adults.The higher body fat associated with 6 h/day sitting may not be offset by achieving recommended levels of physical activity.

Electrochemical biosensors for point-of-care testing

Point-of-care testing(POCT)is the practice of diagnosing and monitoring diseases where the patient is located,as opposed to traditional treatment conducted solely in a medical laboratory or other clinical setting.POCT has been less common in the recent past due to a lack of portable medical devices capable of facilitating effective medical testing.However,recent growth has occurred in this field due to advances in diagnostic technologies,device miniaturization,and progress in wearable electronics.Among these developments,electrochemical sensors have attracted interest in the POCT field due to their high sensitivity,compact size,and affordability.They are used in various applications,from disease diagnosis to health status monitoring.In this paper we explore recent advancements in electrochemical sensors,the methods of fabricating them,and the various types of sensing mechanisms that can be used.Furthermore,we delve into methods for immobilizing specific biorecognition elements,including enzymes,antibodies,and aptamers,onto electrode surfaces and how these sensors are used in real-world POCT settings.

Time series prediction of reservoir bank landslide failure probability considering the spatial variability of soil properties

Historically,landslides have been the primary type of geological disaster worldwide.Generally,the stability of reservoir banks is primarily affected by rainfall and reservoir water level fluctuations.Moreover,the stability of reservoir banks changes with the long-term dynamics of external disastercausing factors.Thus,assessing the time-varying reliability of reservoir landslides remains a challenge.In this paper,a machine learning(ML)based approach is proposed to analyze the long-term reliability of reservoir bank landslides in spatially variable soils through time series prediction.This study systematically investigated the prediction performances of three ML algorithms,i.e.multilayer perceptron(MLP),convolutional neural network(CNN),and long short-term memory(LSTM).Additionally,the effects of the data quantity and data ratio on the predictive power of deep learning models are considered.The results show that all three ML models can accurately depict the changes in the time-varying failure probability of reservoir landslides.The CNN model outperforms both the MLP and LSTM models in predicting the failure probability.Furthermore,selecting the right data ratio can improve the prediction accuracy of the failure probability obtained by ML models.

Prescribed Performance Tracking Control of Time-Delay Nonlinear Systems With Output Constraints

The problem of prescribed performance tracking control for unknown time-delay nonlinear systems subject to output constraints is dealt with in this paper. In contrast with related works, only the most fundamental requirements, i.e., boundedness and the local Lipschitz condition, are assumed for the allowable time delays. Moreover, we focus on the case where the reference is unknown beforehand, which renders the standard prescribed performance control designs under output constraints infeasible. To conquer these challenges, a novel robust prescribed performance control approach is put forward in this paper.Herein, a reverse tuning function is skillfully constructed and automatically generates a performance envelop for the tracking error. In addition, a unified performance analysis framework based on proof by contradiction and the barrier function is established to reveal the inherent robustness of the control system against the time delays. It turns out that the system output tracks the reference with a preassigned settling time and good accuracy,without constraint violations. A comparative simulation on a two-stage chemical reactor is carried out to illustrate the above theoretical findings.

Aquifer Test求群孔抽水试验水文地质参数

在水文地质条件复杂的地区,往往采用大型的群孔抽水试验充分揭露含水层特征,获取水文地质参数。用Aquifer Test软件求解水文地质参数,则方便实用、简捷高效,可快速输出拟合图形和求参结果。Aquifer Test提供了丰富的求参方案,求参必须结合当地当时的水文地质条件实际情况考虑,选择适合的方案和时空数据求取水文地质参数。

Defect Detection Model Using Time Series Data Augmentation and Transformation

Time-series data provide important information in many fields,and their processing and analysis have been the focus of much research.However,detecting anomalies is very difficult due to data imbalance,temporal dependence,and noise.Therefore,methodologies for data augmentation and conversion of time series data into images for analysis have been studied.This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to noise.The method of data augmentation is set as the addition of noise.It involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the model.In addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into images.It enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the data.For anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat maps.It allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies occur.The performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to images.Additionally,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training time.The proposed method can provide an important springboard for research in the field of anomaly detection using time series data.Besides,it helps solve problems such as analyzing complex patterns in data lightweight.