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.

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.

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.

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.

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.

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.

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.

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.

数字低空空域栅格化的表征度量与最优标定

低空飞行空间范围小、目标速度慢、环境要素杂,传统的经纬度表征方式无法满足智联环境下的低空精细管理要求,为此本文研究了数字低空空域栅格化的表征度量与最优标定问题。首先,从“点-线-面”视角构建了多维度低空空域结构要素量化表征规则,提出了低空空域多层级栅格量化表征方法;然后,通过判定不同空域栅格的“点-线-面”位置关系,提出了基于栅格交集矩阵的低空空域拓扑关系度量方法;最后,综合考虑低空无人机碰撞指数、低空栅格利用指数等优化目标,以及节点与栅格匹配约束、空间位置约束、无人机与无人机/障碍物安全约束等限制条件,建立了面向多维性能的低空空域栅格粒度最优标定模型。针对城市低空典型飞行任务场景对所提方法的有效性及优化效果进行了验证分析。实验结果表明,针对任意设定的低空空域和无人机飞行任务,在可接受的无人机碰撞指数和栅格利用指数下,所提方法可对数字低空栅格粒度进行最优配置,从根源上有效确保低空飞行活动的安全和高效。研究成果对于支撑数字低空空域精细化管理和异质飞行器融合运行具有一定的理论价值和应用意义。

低空空域无人机运行安全保障技术研究综述

低空空域飞行活动日趋频繁,无人机类型多样且性能差异较大,飞行过程中易与建筑物或其他航空器发生飞行冲突。为实现低空空域的规范化、安全化、精细化和智慧化管控,需不断提升保障无人机运行安全的技术手段,例如冲突风险探测、安全风险评估等。文章从无人机的轨迹预测与意图识别、低空空域飞行冲突探测、低空空域风险评估及预警告警等3方面进行了研究。首先,总结了在无人机轨迹预测和意图识别方面的研究成果和面临的问题挑战;其次,基于低空空域冲突探测形式,分析不同探测方法的优缺点;随后,围绕低空空域风险评估及预警告警技术,分析当前研究需要解决的核心问题;最后,对无人机冲突探测和风险评估技术等方面进行了展望。

防空作战中多边形责任区预警机空域配置方法

针对防空作战中不规则多边形责任区预警机空域配置问题,基于预警机巡逻航线最短直飞距离和最小转弯直径所形成的实时探测区,通过寻优算法确定预警机责任子区和相应的预警机巡逻可用空域,再基于预警机稳定覆盖度评估准则,对其空域配置进一步寻优,得到预警机空域配置优化方案。通过算例仿真验证了此种方法在不规则多边形责任区中规划预警机空域配置的可行性和提升作战效能的有效性,其方法具有较强的实用价值和军事价值。

术前雌激素、孕激素水平与稽留流产清宫术后宫腔粘连发生的关系及其预测价值

目的探讨雌激素、孕激素水平与稽留流产(MA)患者清宫术后发生宫腔粘连(IUA)的关系及其预测价值。方法选取2019年1月至2022年1月在该院妇科就诊行清宫术的128例MA患者作为研究对象,根据术后6个月内宫腔检查情况分为未发生IUA组(MA组,97例)和IUA组(31例),并根据严重程度将IUA组分为轻、中和重度IUA组。比较IUA组和MA组,以及不同严重程度IUA组术前雌二醇(E 2)和孕酮(P)水平,采用多因素Logistic回归分析发生IUA的独立影响因素;分析E 2、P与IUA病情的相关性,采用受试者工作特征(ROC)曲线分析E 2、P水平对发生IUA及其病情严重程度的预测价值。结果IUA组E 2、P水平均明显低于MA组,差异均有统计学意义(P<0.05);多因素Logistic回归分析结果显示,E 2为MA清宫术后发生IUA的独立影响因素(P<0.05);术前E 2和P水平预测MA清宫术后发生IUA的曲线下面积分别为0.714和0.702。重度IUA组E 2、P水平均明显低于轻、中度IUA组,差异均有统计学意义(P<0.05),且E 2、P水平与IUA严重程度均呈负相关(P<0.05);术前E 2、P水平预测IUA轻/中度和重度的曲线下面积分别为0.845和0.923。结论E 2、P水平与MA清宫术后IUA的发生及严重程度均相关,对临床预测MA清宫术后IUA的发生及病情进展具有一定的参考价值。

北京市海淀区危重孕产妇救治网络运行现况分析

目的:探讨北京市海淀区危重孕产妇救治网络建设及运行现状。方法:对北京市海淀区危重孕产妇救治中心2020-2022年填报的《海淀区危重孕产妇转会诊指定医院接诊高危孕产妇和危重孕产妇个案登记表》等报表进行汇总分析。结果:2020-2022年海淀区高危孕产妇占比从16.16%升至27.34%(χ^(2)_(趋势)=923.56,P<0.001),危重救治网络高危双向转诊率由47.02%上升至59.21%(χ^(2)_(趋势)=26.03,P<0.001),危重孕产妇救治中心的月床位平均周转次数高于下级助产机构,产科医护人员配置相对紧张,下级助产机构危重孕产妇发生率由3.23‰降至1.45‰(χ^(2)_(趋势)=8.81,P=0.003),孕产妇死亡率也呈现下降趋势(χ^(2)_(趋势)=4.25,P=0.039)。结论:北京市海淀区危重孕产妇救治网络运行效果较为理想,降低了危重孕产妇发生率和孕产妇死亡率,下一步应加强区域资源配置,依托“云上妇幼”,加强信息反馈和人员能力提升。

基于场景理解的施工临边坠落险兆智能识别方法

为更及时、更有效地预防施工临边坠落事故的发生,并弥补现有智能预警方法在场景理解方面的不足,融合深度学习与语义推理,提出1种险兆识别方法。该方法通过neo4j构建险兆知识图谱,将引入轻量级视觉Transformer的YOLOx模型识别工人的险兆行为,设计描述空间关系的IoU计算方法并使用Cypher推理语言进行险兆推理。研究结果表明:施工临边坠落各要素识别的平均精度达91%以上,且IoU计算与险兆推理准确率均为100%,模型识别效果与险兆推理效果较好,该方法总体满足精度和速度的识别要求。研究结果可为实现施工临边坠落险兆行为的精准识别和预警提供参考。