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.

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.

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.

Fear of missing out: A brief overview of origin, theoretical underpinnings and relationship with mental health

Fear of missing out(FoMO)is a unique term introduced in 2004 to describe a phenomenon observed on social networking sites.FoMO includes two processes;firstly,perception of missing out,followed up with a compulsive behavior to maintain these social connections.We are interested in understanding the complex construct of FoMO and its relations to the need to belong and form stable interpersonal relationships.It is associated with a range of negative life experiences and feelings,due to it being considered a problematic attachment to social media.We have provided a general review of the literature and have summarized the findings in relation to mental health,social functioning,sleep,academic performance and productivity,neuro-developmental disorders,and physical well-being.We have also discussed the treatment options available for FoMo based on cognitive behavior therapy.It imperative that new findings on FoMO are communicated to the clinical community as it has diagnostic implications and could be a confounding variable in those who do not respond to treatment as usual.

Missed Opportunities for Prevention of Mother-to-Child Transmission of HIV (PMTCT) in Ibadan, Southwest Nigeria

Background: Nigeria has the largest paediatric HIV-infected population in the world. Missed opportunities for prevention of mother-to-child transmission of HIV (PMTCT) compromise efforts at eliminating new pediatric HIV infections. Methods: Six hundred children, aged < 15 years, presenting to the pediatric units of the University College Hospital (UCH), Ibadan Southwest Nigeria between June to December 2007 were studied. The demographics, HIV status and socioeconomic status of mothers and their children were studied. A 4-step hierarchy was used to assess the missed opportunities for PMTCT. Step 1: utilization of a health facility for antenatal care and delivery;Step 2: maternal HIV status determination during pregnancy;Step 3: provision of antiretroviral medication to HIV-infected mother and baby;and Step 4: avoidance of mixed feeding in HIV-exposed children. The rates of missed opportunities for PMTCT services at different steps in the PMTCT cascade, perinatal transmission rates, and associated factors were reported. Results: There were 599 mothers and 600 children (one set of twins), 60 (10%) were HIV infected and 56 (93.3%) of these were adjudged perinatally infected. Of 78 HIV-infected women, 7 (9.0%) accessed all interventions in the PMTCT cascade and 71 (91.0%) had missed opportunities for PMTCT. Missed opportunities for PMTCT occurred 42.9% in cascade Step 1, 64.2% in Step 2, 52.6% in step 3 and 73.7% in Step 4. All mother-baby pairs who accessed complete PMTCT interventions received care at a teaching hospital. Among infants with perinatal HIV infection, 53 (94.6%) were born to mothers who had missed opportunities for PMTCT. Most women with missed opportunities attended antenatal care outside the teaching hospital setting and belonged to low socioeconomic status. Conclusion: It is imperative to expand PMTCT access to women who receive antenatal care outside the teaching hospitals and to those of low socioeconomic status.

Missed opportunities for hepatitis C treatment at a tertiary care hospital in South Australia

BACKGROUND Hepatitis C is a global epidemic and an estimated 230000 Australians were living with chronic hepatitis C in 2016.Through effective public health policy and state commitment,Australia has utilised the advent of direct acting antiviral(DAA)therapy to transform the therapeutic landscape for hepatitis C virus(HCV).However,treatment rates are falling and novel public health approaches are required to maintain momentum for HCV elimination.Contemporary discourse in cascades of care have focused on expanding testing capabilities but less attention has been given to linking previously diagnosed patients back to care.Our simple and focused study rests on the premise that hospital admissions are an excellent opportunity to identify and refer previously diagnosed patients for HCV treatment.AIM To assess whether inpatients with HCV are appropriately referred on for treatment.METHODS We conducted a retrospective single centre cohort study that examined all patients with HCV presenting to The Queen Elizabeth Hospital(QEH)inpatient service between January 1 and December 31,2017.QEH is a tertiary care hospital in South Australia.The main inclusion criteria were patients with active HCV infection who were eligible for DAA therapy.Our study cohort was identified using a comprehensive list of diagnosis based on international classification of diseases-10 AM codes for chronic viral hepatitis.Patients were excluded from the analysis if they had previously received DAA therapy or spontaneously cleared HCV.Patients presenting with decompensated liver cirrhosis or other systemic medical conditions conferring poor short-term prognosis were also excluded from the analysis.The primary outcome of our study was referral of patients for HCV treatment.Secondary outcomes included assessment of factors predicting treatment referral.RESULTS There were 309 inpatients identified with hepatitis C as a principal or additional diagnosis between January 1 and December 31,2017.Of these patients,148 had active HCV infection without prior treatment or spontaneous clearance.Overall,131 patients were deemed eligible for DAA treatment and included in the main analysis.Mean patient age was 47.75±1.08 years,and 69%of the cohort were male and 13%identified as Aboriginal or Torres Strait Islander.Liver cirrhosis was a complication of hepatitis C in 7%of the study cohort.Only 10 patients were newly diagnosed with HCV infection during the study period with the remainder having been diagnosed prior to the study.CONCLUSION Under 25%of hepatitis C patients presenting to an Australian tertiary hospital were appropriately referred for treatment.Advanced age,cirrhosis and admission under medical specialties were predictors of treatment referral.

Evaluating Methods for Dealing with Missing Outcomes in Discrete-Time Event History Analysis: A Simulation Study

Background: In discrete-time event history analysis, subjects are measured once each time period until they experience the event, prematurely drop out, or when the study concludes. This implies measuring event status of a subject in each time period determines whether (s)he should be measured in subsequent time periods. For that reason, intermittent missing event status causes a problem because, unlike other repeated measurement designs, it does not make sense to simply ignore the corresponding missing event status from the analysis (as long as the dropout is ignorable). Method: We used Monte Carlo simulation to evaluate and compare various alternatives, including event occurrence recall, event (non-)occurrence, case deletion, period deletion, and single and multiple imputation methods, to deal with missing event status. Moreover, we showed the methods’ performance in the analysis of an empirical example on relapse to drug use. Result: The strategies assuming event (non-)occurrence and the recall strategy had the worst performance because of a substantial parameter bias and a sharp decrease in coverage rate. Deletion methods suffered from either loss of power or undercoverage issues resulting from a biased standard error. Single imputation recovered the bias issue but showed an undercoverage estimate. Multiple imputations performed reasonably with a negligible standard error bias leading to a gradual decrease in power. Conclusion: On the basis of the simulation results and real example, we provide practical guidance to researches in terms of the best ways to deal with missing event history data.

The impact of out-of-pocket expenditures on missed appointments at HIV r care and treatment centers in Northern Tanzania

Background:Missed clinic appointments negatively impact clinic patient flow and health outcomes of people living with HIV(PLHIV).PLHIV likelihood of missing clinic appointments is associated with direct and indirect expenditures made while accessing HIV care.The objective of this study was to examine the relationship between out-of-pocket(OOP)health expenditures and the likelihood of missing appointments.Method:Totally 618 PLHIV older than 18 years attending two HIV care and treatment centres(CTC)in Northern Tanzania were enrolled in the study.Clinic attendance and clinical characteristics were abstracted from medical records.Information on OOP health expenditures,demographics,and socio-economic factors were self-reported by the participants.We used a hurdle model.The first part of the hurdle model assessed the marginal effect of a one Tanzanian Shillings(TZS)increase in OOP health expenditure on the probability of having a missed appointment and the second part assessed the probability of having missed appointments for those who had missed an appointment over the study period.Results:Among these 618 participants,242(39%)had at least one missed clinic appointment in the past year.OOP expenditure was not significantly associated with the number of missed clinic appointments.The median amount of OOP paid was 5100 TZS per visit,about 7%of the median monthly income.Participants who were separated from their partners(adjusted odds ratio[AOR]=1.83,95%confidence interval[CZ]:1.11-8.03)and those aged above 50 years(AOR=2.85,95%CI:1.01-8.03)were significantly associated with missing an appointment.For those who had at least one missed appointment over the study period,the probability of missing a clinic appointment was significantly associated with seeking care in a public CTC(P=0.49,95%CI:0.88-0.09)and aged between>25-35 years(P=0.90,95%CI:0.11-1.69).Conclusion:Interventions focused on improving compliance to clinic appointments should target public CTCs,PLHIV aged between>25-35 years,above 50 years of age and those who are separated from their partners.

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.

大学生错失焦虑、自我损耗与手机成瘾的关系:基于变量中心和个体中心的分析

目的:探讨大学生错失焦虑、自我损耗与手机成瘾的关系。方法:采用错失焦虑量表、自我损耗量表和手机成瘾量表,对1586名大学生进行团体施测,采用变量中心和个体中心的方法进行分析。结果:1相关性分析结果显示,错失焦虑与手机成瘾、自我损耗显著正相关(r=0.59,0.44;P<0.01);自我损耗与手机成瘾显著正相关(r=0.62,P<0.01);2中介效应检验表明,错失焦虑不仅直接预测大学生手机成瘾(β=0.59,P<0.01),还通过自我损耗的中介作用间接预测手机成瘾(β=0.39,P<0.01),中介效应占比33.90%;3对错失焦虑—自我损耗得分进行潜在剖面分析,可分为3种类别:低焦虑低损耗型(35.56%)、中等焦虑中等损耗型(51.14%)、高焦虑高损耗型(13.30%);3种潜在类别在手机成瘾上的得分具有显著差异(F=461.20,P<0.001)。结论:自我损耗在大学生错失焦虑与手机成瘾之间起部分中介作用;错失焦虑—自我损耗存在显著的群体异质性,具有明显的分类特征且与手机成瘾关系密切。

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.