Errata regarding missing Ethical Statements in previously published articles:Part 2

Ethical statements were not included in the published version of the following articles that appeared in previous issues of Journal of Integrative Agriculture.The appropriate statements provided by the Authors are included below.

Errata regarding missing Ethical Statements in previously published articles:Part 5

Ethical statements were not included in the published version of the following articles that appeared in previous issues of Journal of Integrative Agriculture.The appropriate statements provided by the Authors are included below.

Errata regarding missing Ethical Statements in previously published articles:Part 3

Ethical statements were not included in the published version of the following articles that appeared in previous issues of Journal of Integrative Agriculture.The appropriate statements provided by the Authors are included below.

Errata regarding missing Ethical Statements in previously published articles:Part 1

Ethical statements were not included in the published version of the following articles that appeared in previous issues of Journal of Integrative Agriculture.The appropriate statements provided by the Authors are included below.

Errata regarding missing Ethical Statements in previously published articles:Part 4

Ethical statements were not included in the published version of the following articles that appeared in previous issues of Journal of Integrative Agriculture.The appropriate statements provided by the Authors are included below.

A Practical Approach for Missing Wireless Sensor Networks Data Recovery

In wireless sensor networks(WSNs),the performance of related applications is highly dependent on the quality of data collected.Unfortunately,missing data is almost inevitable in the process of data acquisition and transmission.Existing methods often rely on prior information such as low-rank characteristics or spatiotemporal correlation when recovering missing WSNs data.However,in realistic application scenarios,it is very difficult to obtain these prior information from incomplete data sets.Therefore,we aim to recover the missing WSNs data effectively while getting rid of the perplexity of prior information.By designing the corresponding measurement matrix that can capture the position of missing data and sparse representation matrix,a compressive sensing(CS)based missing data recovery model is established.Then,we design a comparison standard to select the best sparse representation basis and introduce average cross-correlation to examine the rationality of the established model.Furthermore,an improved fast matching pursuit algorithm is proposed to solve the model.Simulation results show that the proposed method can effectively recover the missing WSNs data.

Missing Value Imputation for Radar-Derived Time-Series Tracks of Aerial Targets Based on Improved Self-Attention-Based Network

The frequent missing values in radar-derived time-series tracks of aerial targets(RTT-AT)lead to significant challenges in subsequent data-driven tasks.However,the majority of imputation research focuses on random missing(RM)that differs significantly from common missing patterns of RTT-AT.The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation.Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss.In this paper,a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed.Our model consists of two probabilistic sparse diagonal masking self-attention(PSDMSA)units and a weight fusion unit.It learns missing values by combining the representations outputted by the two units,aiming to minimize the difference between the missing values and their actual values.The PSDMSA units effectively capture temporal dependencies and attribute correlations between time steps,improving imputation quality.The weight fusion unit automatically updates the weights of the output representations from the two units to obtain a more accurate final representation.The experimental results indicate that,despite varying missing rates in the two missing patterns,our model consistently outperforms other methods in imputation performance and exhibits a low frequency of deviations in estimates for specific missing entries.Compared to the state-of-the-art autoregressive deep learning imputation model Bidirectional Recurrent Imputation for Time Series(BRITS),our proposed model reduces mean absolute error(MAE)by 31%~50%.Additionally,the model attains a training speed that is 4 to 8 times faster when compared to both BRITS and a standard Transformer model when trained on the same dataset.Finally,the findings from the ablation experiments demonstrate that the PSDMSA,the weight fusion unit,cascade network design,and imputation loss enhance imputation performance and confirm the efficacy of our design.

Optimal Estimation of High-Dimensional Covariance Matrices with Missing and Noisy Data

The estimation of covariance matrices is very important in many fields, such as statistics. In real applications, data are frequently influenced by high dimensions and noise. However, most relevant studies are based on complete data. This paper studies the optimal estimation of high-dimensional covariance matrices based on missing and noisy sample under the norm. First, the model with sub-Gaussian additive noise is presented. The generalized sample covariance is then modified to define a hard thresholding estimator , and the minimax upper bound is derived. After that, the minimax lower bound is derived, and it is concluded that the estimator presented in this article is rate-optimal. Finally, numerical simulation analysis is performed. The result shows that for missing samples with sub-Gaussian noise, if the true covariance matrix is sparse, the hard thresholding estimator outperforms the traditional estimate method.

Cross Validation Based Model Averaging for Varying-Coefficient Models with Response Missing at Random

In this paper, a model averaging method is proposed for varying-coefficient models with response missing at random by establishing a weight selection criterion based on cross-validation. Under certain regularity conditions, it is proved that the proposed method is asymptotically optimal in the sense of achieving the minimum squared error.

How Clever Miss Rabbit Was!

One day in autumn,Miss Rabbit went out to look for food.She found a big pumpkin very soon.She was so happy that she decided to carryit home.However,it was too heavy for her to carry.And soon she got tired.Just then,Mr.Panda came over on his bike.Miss Rabbit saw the wheels of the bike and came up with a good idea.

Comparison of two statistical methods for handling missing values of quantitative data in Bayesian N-of-1 trials: a simulation study

Background:Missing data are frequently occurred in clinical studies.Due to the development of precision medicine,there is an increased interest in N-of-1 trial.Bayesian models are one of main statistical methods for analyzing the data of N-of-1 trials.This simulation study aimed to compare two statistical methods for handling missing values of quantitative data in Bayesian N-of-1 trials.Methods:The simulated data of N-of-1 trials with different coefficients of autocorrelation,effect sizes and missing ratios are obtained by SAS 9.1 system.The missing values are filled with mean filling and regression filling respectively in the condition of different coefficients of autocorrelation,effect sizes and missing ratios by SPSS 25.0 software.Bayesian models are built to estimate the posterior means by Winbugs 14 software.Results:When the missing ratio is relatively small,e.g.5%,missing values have relatively little effect on the results.Therapeutic effects may be underestimated when the coefficient of autocorrelation increases and no filling is used.However,it may be overestimated when mean or regression filling is used,and the results after mean filling are closer to the actual effect than regression filling.In the case of moderate missing ratio,the estimated effect after mean filling is closer to the actual effect compared to regression filling.When a large missing ratio(20%)occurs,data missing can lead to significantly underestimate the effect.In this case,the estimated effect after regression filling is closer to the actual effect compared to mean filling.Conclusion:Data missing can affect the estimated therapeutic effects using Bayesian models in N-of-1 trials.The present study suggests that mean filling can be used under situation of missing ratio≤10%.Otherwise,regression filling may be preferable.

Efficient and robust missing key tag identification for large-scale RFID systems

Radio Frequency Identification(RFID)technology has been widely used to identify missing items.In many applications,rapidly pinpointing key tags that are attached to favorable or valuable items is critical.To realize this goal,interference from ordinary tags should be avoided,while key tags should be efficiently verified.Despite many previous studies,how to rapidly and dynamically filter out ordinary tags when the ratio of ordinary tags changes has not been addressed.Moreover,how to efficiently verify missing key tags in groups rather than one by one has not been explored,especially with varying missing rates.In this paper,we propose an Efficient and Robust missing Key tag Identification(ERKI)protocol that consists of a filtering mechanism and a verification mechanism.Specifically,the filtering mechanism adopts the Bloom filter to quickly filter out ordinary tags and uses the labeling vector to optimize the Bloom filter's performance when the key tag ratio is high.Furthermore,the verification mechanism can dynamically verify key tags according to the missing rates,in which an appropriate number of key tags is mapped to a slot and verified at once.Moreover,we theoretically analyze the parameters of the ERKI protocol to minimize its execution time.Extensive numerical results show that ERKI can accelerate the execution time by more than 2.14compared with state-of-the-art solutions.

Missing Value Imputation Model Based on Adversarial Autoencoder Using Spatiotemporal Feature Extraction

Recently,the importance of data analysis has increased significantly due to the rapid data increase.In particular,vehicle communication data,considered a significant challenge in Intelligent Transportation Systems(ITS),has spatiotemporal characteristics and many missing values.High missing values in data lead to the decreased predictive performance of models.Existing missing value imputation models ignore the topology of transportation net-works due to the structural connection of road networks,although physical distances are close in spatiotemporal image data.Additionally,the learning process of missing value imputation models requires complete data,but there are limitations in securing complete vehicle communication data.This study proposes a missing value imputation model based on adversarial autoencoder using spatiotemporal feature extraction to address these issues.The proposed method replaces missing values by reflecting spatiotemporal characteristics of transportation data using temporal convolution and spatial convolution.Experimental results show that the proposed model has the lowest error rate of 5.92%,demonstrating excellent predictive accuracy.Through this,it is possible to solve the data sparsity problem and improve traffic safety by showing superior predictive performance.

Ultra-High Dimensional Feature Selection and Mean Estimation under Missing at Random

Next Generation Sequencing (NGS) provides an effective basis for estimating the survival time of cancer patients, but it also poses the problem of high data dimensionality, in addition to the fact that some patients drop out of the study, making the data missing, so a method for estimating the mean of the response variable with missing values for the ultra-high dimensional datasets is needed. In this paper, we propose a two-stage ultra-high dimensional variable screening method, RF-SIS, based on random forest regression, which effectively solves the problem of estimating missing values due to excessive data dimension. After the dimension reduction process by applying RF-SIS, mean interpolation is executed on the missing responses. The results of the simulated data show that compared with the estimation method of directly deleting missing observations, the estimation results of RF-SIS-MI have significant advantages in terms of the proportion of intervals covered, the average length of intervals, and the average absolute deviation.

Missed Opportunities for Diagnosing Bacilliferous Pulmonary Tuberculosis by Optical Microscopy versus GeneXpert MTB/RIF in Endemic Areas

Objective: To assess the missed opportunities from the diagnosis of bacilliferous pulmonary tuberculosis by optical microscopy compared to GeneXpert MTB/RIF between 2015 and 2019. Methods: This is a retrospective analysis of the diagnostic results of bacilliferous pulmonary tuberculosis in patients suspected of pulmonary tuberculosis at their first episode during the period. GeneXpert MTB/RIF (GeneXpert) and optical microscopy (OM) after Ziehl-Neelsen stained smear were performed on each patient’s sputum or gastric tubing fluid sample. Results: Among 341 patients suspected of pulmonary tuberculosis, 229 patients were declared bacilliferous tuberculosis by the two tests (67%), 220 patients by GeneXpert and 95 patients by OM, i.e. 64.5% versus 28% (p i.e. 58.5% of the positive cases detected by the two tests (134/229 patients) and 39.3% of the patients suspected of tuberculosis (134/341 patients). On the other hand, among 95 patients declared positive by OM, the GeneXpert ignored 9 (9.5%), i.e. 4% of all the positive cases detected by the two diagnostic tests (9/229 patients) and 3% of the patients suspected of tuberculosis (9/341 patients). The differences observed between the results of the two tests were statistically significant at the 5% threshold (p Conclusion: This study reveals missed diagnostic opportunities for bacilliferous pulmonary mycobacteriosis, statistically significant with optical microscopy than GeneXpert. The GeneXpert/optical microscopy couple could be a good contribution to the strategies for the elimination of pulmonary tuberculosis in sub-Saharan Africa.

Smoothed Empirical Likelihood Inference for Nonlinear Quantile Regression Models with Missing Response

In this paper, three smoothed empirical log-likelihood ratio functions for the parameters of nonlinear models with missing response are suggested. Under some regular conditions, the corresponding Wilks phenomena are obtained and the confidence regions for the parameter can be constructed easily.

中元古界非叠层石灰岩中的MISS:以北京延庆千沟剖面高于庄组第三段为例

前寒武纪碳酸盐岩多以叠层石碳酸盐岩序列为特征。燕山地区中元古界高于庄组,其中的第三段(张家裕亚组)则为一个以灰岩为主、贫乏叠层石的碳酸盐岩沉积序列,该序列被定义为前寒武纪非叠层石碳酸盐岩序列。该非叠层石碳酸盐岩沉积序列,尤其以燕山西部的延庆千沟剖面最为典型。根据沉积相序列及其所反映的旋回性,可以将该剖面的高于庄组第三段划分为3个三级层序。在这些三级层序的海侵体系域和早期高水位体系域中,中薄层隐晶质泥晶灰岩(均一石灰岩)和灰黑色薄层泥灰岩组成若干潮下型米级旋回;而在隐晶质泥晶灰岩层面上,普遍发育各种奇形怪状的沉积构造。这些沉积构造包括穹窿状构造、规则网状和杂乱的帐篷脊、变余波痕等,构成一个潮下相灰岩层面上的特别的微生物形成的沉积构造(Microbial Induced Sedimentary Structure,MISS)组合。因此,延庆千沟剖面的高于庄组第三段,特别的岩石类型和沉积构造成为前寒武纪碳酸盐岩沉积中非叠层石碳酸盐岩沉积序列的典型代表,尤其是那些奇形怪状的MISS所代表的沉积学特点表明:在前寒武纪,即使在叠层石生长的黄金时段,也发育非叠层石碳酸盐岩沉积序列。因此,这些现象将特别有助于对前寒武纪非叠层石生态系所造成的另一类席底生境的深入理解,也有助于复杂多变的碳酸盐岩世界。

基于Near Miss的成山头水域交通冲突风险可视化

针对成山头水域商船与渔船交通冲突频发的现状,对船舶交通管理系统(Vessel Traffic Service,VTS)存储的船舶全程航行动态大数据进行挖掘;在交通冲突和黑点理论的基础上,基于船舶会遇提出采用Near Miss分布来定量描述水域风险的方法。在Atlas VTS的基础上二次开发基于Near Miss的分析软件,根据Near Miss的时空特征实现水域风险可视化。以成山头水域2013年典型时段的船舶交通流数据为例,对该水域商船与渔船交通冲突风险进行可视化分析。结果表明:基于Near Miss分布的水域风险可视化方法可定量挖掘水域交通冲突风险的时空特征,有助于优化监管措施以降低潜在水域风险。

山西黎城中元古界常州沟组微生物成因构造(MISS)及其地质意义

在山西黎城县西井镇彭庄至大井盘公路旁剖面以及黄崖洞景区发育大量的微生物成因构造(MISS),多发育在砂岩表面。由于构造形态奇特,被误以为各种遗迹化石或是不规则的泥裂构造。依据其形态特征、成因构造和前人分类方案,将研究区此类构造分为3种类型,即微生物席生长构造、微生物席破坏构造和微生物席腐烂构造,以及9个不同形态构造,即小瘤状突起、圆顶大瘤状突起、不规则侧向生长脊、大脊状生长构造、纺锤状脱水裂痕、多边形网状脱水裂痕、曲形脱水裂痕、微生物席砂片、砂火山构造,对9种不同形态的MISS进行了详细的论述。对研究区微生物成因构造(MISS)的成因进行了探讨,与豫西鲁山地区、贺兰山苏峪口地区、北京南口地区、河北兴隆地区发育的MISS及地层特征进行类比,认为MISS对华北地台中元古代地层对比以及古环境研究有重要的指导意义。

地铁工程Near-miss知识库构建

针对目前地铁施工的安全管理问题,引入Near-miss(未遂事件或险兆事件)概念,构建出地铁工程的Near-miss知识库.首先,分析了地铁工程Near-miss知识的5种来源,并通过对地铁工程事故数据库的分析,得到地铁工程Near-miss知识.然后,从人员身份、机械状态、材料状态、环境状况和相互位置等5个方面对地铁工程Near-miss知识进行框架表示,并根据其特点,研究地铁工程Near-miss知识库的逻辑结构.最后,采用具有成熟管理技术的关系数据库,将Near-miss知识存储于关系数据库表中,实现了知识库和关系数据库的耦合.一个完善的地铁工程Near-miss知识库的构建,将有助于促进地铁施工安全管理能力的提高,减少地铁事故的发生.