As an important method for uncertainty modeling, Dempster–Shafer (DS) evidence theory has been widely used in practical applications. However, the results turned out to be almost counter-intuitive when fusing the different sources of highly conflicting evidence with Dempster’s combination rule. In previous researches, most of them were mainly dependent on the conflict measurement method between the evidence represented by the evidence distance. However, it is inaccurate to characterize the evidence conflict only through the evidence distance. To address this issue, we comprehensively consider the impacts of the evidence distance and evidence angle on conflicts in this paper, and propose a new method based on the mutual support degree between the evidence to characterize the evidence conflict. First, the Hellinger distance measurement method is proposed to measure the distance between the evidence, and the sine value of the Pignistic vector angle is used to characterize the angle between the evidence. The evidence distance indicates the dissimilarity between the evidence, and the evidence angle represents the inconsistency between the evidence. Next, two methods are combined to get a new method for measuring the mutual support degree between the evidence. Afterward, the weight of each evidence is determined by using the mutual support degree between the evidence. Then, the weights of each evidence are utilized to modify the original evidence to achieve the weighted average evidence. Finally, Dempster’s combination rule is used for fusion. Some numerical examples are given to illustrate the effectiveness and reasonability for the proposed method. In practical applications, most information acquisition is done by sensors. Due to the complexity of the target, the data provided by a single sensor may not be sufficient to obtain all of the information desired for data fusion, providing all the information of target estimation with multiple sensors is, therefore, often required. However, the data derived from multiple sensors could be uncertain or even conflicting. How to deal with uncertain information effectively has been paid much attention. Dempster–Shafer (DS) evidence theory is a powerful tool to represent and deal with uncertain information. It has been widely used in practical problems related to uncertainty modeling and reasoning, such as information fusion ,2,3,4, fk to describe the degree of conflict between the evidence [k only expresses the inconsistency between the evidence and cannot accurately reflect the evidence conflict. Murphy proposed to calculate the arithmetic average of n evidence, and then used Dempster’s combination rule for fusion [DS evidence theory, also called theories of belief functions, was firstly proposed by Dempster in 1967  and furtevidence . Howeverr fusion . Howeverr fusion , and user fusion  proposedr fusion  defined r fusion  used JouSmets  proposedIn this paper, we propose a novel method to measure the evidence conflict in vector space, taking into account both the impacts of the evidence distance and evidence angle on conflicts. The evidence distance describes the dissimilarity between the evidence, whereas the evidence angle represents the inconsistency among the evidence. These two measures are mutually complementary in a sense. The Hellinger distance measurement method is proposed to measure the evidence distance, and the sine value of the Pignistic vector angle between the evidence is used to characterize the angle among the evidence. Through combining the evidence distance and evidence angle, a new method to measure the mutual support degree between the evidence is proposed. Afterward, the credibility degree is determined by using the mutual support degree among the evidence, and the weight factor of each evidence is expressed by the credibility degree. The weighting factor of each evidence can be used to modify the original evidence. Based on that, the weighted average evidence can be obtained, and then we use Dempster’s combination rule to combine the weighted average evidence. The rationality and effectiveness of our proposed method are illustrated through numerical examples.The rest of this paper is organized as follows. We review the basic concepts of evidence theory in DS evidence theory is an effective mathematical tool to deal with information fusion. It can not only effectively distinguish “unknown” and “uncertain” information, but also strengthen the complementary relationship between the information and improve the accuracy of decision-making. It has great advantages in the modeling of uncertain information, and represents and combines the uncertain information. In this section, we mainly introduce the basic concepts knowledge of DS evidence theory.Definition 1(Frame of discernment).Suppose that where Definition 2(Basic probability assignment (BPA)).Suppose that Definition 3(Dempster’s rule of combination).Suppose that the basic probability assignment function of the body of evidence from two independent sources are where In the data fusion process, Dempster’s combination rule ignores the conflicting information between the evidence and only emphasizes the consistency between the evidence. When combining the highly conflicting evidence with Dempster’s combination rule, the counter-intuitive result is often obtained. To solve the problem, it is necessary to determine how to quantitatively describe the conflict information. Many researchers put forward to use evidence distance to describe the dissimilarity between the evidence, so as to determine the degree of conflict between the evidence. In this paper, we proposed to use the Hellinger distance to measure the dissimilarity between the evidence.Distance is a measure that describes the difference between two sets of vectors. The more similar the set of the vectors is, the lower the difference between them. Csiszar  and SlivDefinition 4(Hellinger Distance).Hellinger distance is a distance measurement method that is used to measure the dissimilarity between probability distributions, which is defined as follows :Suppose that (1)Non-negativity: (2)Symmetry: (3)Triangle inequality: Supposing that Hellinger distance satisfies the following properties:It can be seen that the Hellinger distance satisfies the measurable distance conditions. A detailed proof process of the Hellinger distance properties can be found in Ref. . DiffereThe essence of DS evidence theory is the generalization of probability theory. Hellinger distance is a distance measure that is defined in probability distribution space. So, the above Hellinger distance measurement method can be combined with DS evidence theory, and the Hellinger distance can be used to measure the dissimilarity between the evidence, so as to characterize the degree of conflict between the evidence. In this paper, we define a new dissimilarity measurement method in DS evidence theory. The definition is as follows:Definition 5(Hellinger Distance between the evidence).Suppose that where (1)Non-negativity: (2)Symmetry: (3)Triangle inequality: Hellinger distance can effectively measure the dissimilarity between the evidence. According to Equation (7), the smaller the Hellinger distance is, the smaller the dissimilarity is, and the less conflicting the evidence is. When two different bodies of evidence are completely in conflict, the Hellinger distance will be almost equal to one. However, when these two bodies of evidence are identical, the Hellinger distance will be close to zero. From the properties of Hellinger distance, the related properties of the Hellinger distance between the evidence can be inferred. Proof. Since According to Equation (8), it can be seen that non-negativity property and symmetry property are satisfied by the requirements. The triangle inequality of Equation (6) is proved as follows:According to Equation (8), we can get:Because Therefore That is That proof Only if Inequality (10) is held, Inequality (9) is necessary to be held. The problem is transformed into proving that Inequality (10) is held.Simplify Inequality (10) can be obtained:Taking the logarithm, it can be obtained that:In this section, we need to use the information entropy inequality as follows:According to the information entropy inequality, we can obtain thatSoThereforeThat isTherefore, Inequality (9) is held. The triangle inequality of the Hellinger distance is held.A few simple examples are given to illustrate the related properties and specific calculation processes of the Hellinger distance between the evidence. □Example 1.Suppose that there are two independent bodies of evidence As can be seen from Example 1, two bodies of evidence According to the results, when two bodies of evidence have the same basic probability assignment, the Hellinger distance between two bodies of evidence is zero. These two bodies of evidence are similar, which is consistent with the intuitive analysis results.Example 2.Suppose that there are two independent bodies of evidence The specific calculation process of the Hellinger distance between the two bodies of evidence Example 3.Suppose that there are three independent bodies of evidence It can be seen from Example 3 that the two bodies of evidence It can be noticed that Example 4.Suppose that there are two independent bodies of evidence When the parameter As can be seen from Example 5.Suppose that there are three independent bodies of evidence As is shown in Example 5, we can see that the two bodies of evidence In Example 5, we note that mere use of the evidence distance cannot accurately measure the degree of conflict between the evidence. In the evidence conflict measurement study, the idea of defining conflict measure as a distance was proposed by many researchers. Burger  considerIn this section, we introduce the Pignistic vector angle measure to describe the evidence conflict. Zhao , from thn elements, there are n-dimensional vector, where the converted n-dimensional vector is obtained from the Pignistic probability function introduced in Definition 6.For a frame of discernment Definition 6(Pignistic probability transformation function).Suppose that where Definition 7(Measure the angle between the evidence).In the frame of discernment The evidence from the conflict measurement function of the Pignistic vector angle can effectively measure the inconsistency between the evidence. According to Equation (14), the larger the sine value between the evidence is, the greater the inconsistency between the evidence is. If two bodies of evidence are completely in conflict, the sine value between the wo bodies of evidence is equal to one. Some simple examples are used to illustrate the calculation process of the sine value of the Pignistic vector angle between the evidence is as follows:Example 6.Suppose that there are two independent bodies of evidence As can be seen from Example 6, the two bodies of evidence First, the Pignistic probability transformation was performed for each body of evidence.As can be seen from the results, when two bodies of evidence are the same, the sine value of the Pignistic vector angle between the evidence is zero, which the two bodies of evidence are consistent.Example 7.Suppose that there are two independent bodies of evidence When the parameter As shown in Example 8.Suppose there are three independent bodies of evidence As shown in Example 8, we can see that there exists conflict among the three bodies of evidence. Therefore, the degree of inconsistency between the evidence is not 0. The sine value of the Pignistic vector angle between two bodies of evidence From the results, it can be seen that the degree of inconsistency between the evidence is 0, which contradicts the intuitive analysis. Note that, in some cases, the Pignistic vector angle cannot effectively measure the conflict between the evidence.In this example, when we use the Hellinger distance to measure the dissimilarity between the evidence, the following results can be achieved.It can be seen that the degree of dissimilarity between the evidence is not 0. This shows that there is a certain conflict between the evidence, which is consistent with the intuitive analysis.This example shows that only use the sine value of the Pignistic vector angle between the evidence cannot effectively measure the degree of conflict between the evidence. It is necessary to combine another criterion to jointly describe the conflict degree between the evidence. In fact, the conflict measurement method based on evidence distance and evidence vector angle is complementary. They separately capture the different aspects of conflict between the evidence. If the conflict among the evidence is only characterized by using the dissimilarity between the evidence, it cannot reflect the impact of the inconsistency between the evidence on the conflict. On the contrary, if only use the inconsistency between the evidence to represent the conflict, the effect of the dissimilarity between the evidence on the conflict is not taken into account. So, considering the impact of the dissimilarity and inconsistency between the evidence on conflict, we propose to comprehensively measure the mutual support degree between the evidence by using the evidence distance and evidence angle. After that, we use the mutual support degree of the evidence to characterize the degree of conflict between the evidence.In DS evidence theory, the essence of Dempster’s combination rule is the orthogonal sum of evidence. Obviously, in Dempster’s mind, the conflict is related to the degree of orthogonality between the evidence. In other words, the consistency between the evidence would correspond to collinearity of the belief function, while the inconsistency between the evidence would correspond to orthogonality. It follows that the degree of conflict is related to both evidence distance (measuring dissimilarity between evidence) and evidence angle (measuring inconsistency between evidence). The evidence distance and evidence angle are enough to describe the degree of conflict between the evidence. Synthesizing the evidence distance and evidence angle, we propose to use the mutual support degree between the evidence to characterize the degree of conflict between the evidence in order to capture the two main aspects that affect the evidence conflicts. The mutual support degree between the evidence is defined by fusing the dissimilarity and inconsistency between the evidence.Definition 8.In the frame of discernment Non-negativity: Symmetry: The proposed method satisfies the two following important properties: The larger the mutual support degree between the evidence is, the less conflict between the evidence is. When two bodies of evidence are completely in conflict, the mutual support degree between the evidence is close to zero. In this paper, we use the mutual support degree between the evidence to determine the weighting factor of each body of evidence.The following examples are given to illustrate the effectiveness of the proposed method for conflict measurement. In Example 5, we only use the dissimilarity measurement method to characterize the evidence conflict, while the results are inconsistent with the intuitive analysis. The proposed measurement method is used to calculate the mutual support degree between the evidence in Example 5, which is shown as follows.First, the dissimilarity and inconsistency between the two bodies of evidence According to Equation (15), we can achieve the mutual support degree between the two bodies of evidence The mutual support degree between In Example 8, we only use the inconsistency measurement method to describe the evidence conflict, while the results are inconsistent with the intuitive analysis. The proposed method is used to calculate the mutual support degree between the evidence in Example 8, which is shown as follows.First, the dissimilarity and inconsistency between the two bodies of evidence According to Equation (15), the mutual support degree between the two bodies of evidence The mutual support degree between the evidence is different. This example further illustrates the effectiveness of our proposed method.Example 9.Suppose there are two independent bodies of evidence When the parameter As can be seen from Several above examples show that our proposed method takes into account the different aspects that affect the evidence conflict, and can accurately measure the degree of conflict between the evidence.In the process of multi-sensor data fusion, due to the different information collection ability and accuracy of different sensors, the evidence provided by sensors is not all of the same reliability. In fact, the evidence with a high degree of conflict will lead to unreasonable fusion results. Therefore, we need to determine the credibility of each body of evidence before fusion. In this paper, we use the mutual support degree between the evidence to characterize the credibility of each evidence. Firstly, the dissimilarity and inconsistency between the evidence are measured by using the Hellinger distance and the sine value of the Pignistic vector angle, and then the mutual support degree between the evidence is obtained based on the dissimilarity and inconsistency between the evidence. When the evidence is more highly supported by another evidence, the more credible the evidence is. Secondly, the mutual support degree between the evidence is used to measure the credibility of each evidence. After that, the credibility degree of the evidence is used as the weighting factor for the evidence, and the original evidence is modified by using the weighting factor to obtain the weighted average evidence. Finally, we use Dempster’s combination rule to combine the weighted average evidence. The implementation process of the proposed method is shown as below.Step 1: According to Equations (7) and (14), the dissimilarity and inconsistency between the two bodies of evidence Step 2: According to the dissimilarity and inconsistency between the two bodies of evidence Step 3: The average support degree of the body of evidence Step 4: The credibility degree of the body of evidence Step 5: The greater the credibility of the evidence is, the more important the evidence is. Therefore, the credibility degree of the body of evidence Step 6: On the basis of the weighting factor of the evidence Step 7: The weighted average evidence is fused n − 1 times through Dempster’s combination rule, and then the final combination result can be obtained.The flowchart of our proposed method is shown in The following examples illustrate the implementation process of our proposed method and its advantages.Example 10.Suppose that there are two independent bodies of evidence As can be seen in Example 10, the evidence Finally, the weighted average evidence is fused by using Dempster’s combination rule, and the fusing results are shown in As can be seen from Suppose that the basic probability distribution of evidence The same method is used to combine the change evidence, and the fusion results are shown in As can be seen from Example 11.Suppose that in the same frame of discernment (1) The Hellinger distance and sine value of the Pignistic vector angle between the evidence are calculated as follows:(2) Calculate the mutual support degree among the evidence, and construct the support degree matrix as follows:(3) Calculate the average support degree of each body of evidence as follows:(4) Compute the credibility degree of each body of evidence as follows:Then, the weighting factor of each body of evidence is:(5) According to the weight factor of each body of evidence, the weighted average evidence is calculated as follows:(6) Combine the weighted average evidence through Dempster’s combination rule twice, and the fusing results are shown in It can be seen from As shown in The above two examples show that when dealing with high conflicting evidence, our proposed method can accurately evaluate the degree of conflict between the evidence, and have high decision-making efficiency and good robustness.In order to verify and analyze the effectiveness and rationality of the proposed method, we illustrate through a numerical example and a simplified fault diagnosis problem in this section, a present test and comparative analysis with some of the approaches that are currently available in the literature.Assume that there is a multi-sensor target recognition problem, each sensor can display the relevant data of the target type being detected. Suppose there are three types of target As can be seen from As shown in Murphy’s  method, Murphy’s  method, Murphy’s  method, Murphy’s  method, Murphy’s  method, In industrial production, mechanical systems will have various types of fault. In order to quickly repair the machine fault, we need to accurately determine the type of machine fault. Suppose that there are three different types of faults in some types of machine systems, which constitutes the frame of discernment In (1) The Hellinger distance and sine value of the Pignistic vector angle between the evidence are calculated as follows:(2) Calculate the mutual support degree between the evidence, and construct the support degree matrix as follows:(3) Calculate the average support degree of each body of evidence as follows:(4) Compute the credibility degree of each body of evidence as follows:(5) In fault diagnosis applications, we also need to calculate the static reliability of the evidence. The parameters related to static reliability are sufficiency index terature  are showThe static reliability of the evidence can be calculated by using these parameters. The calculation formula is as follows:(6) According to the static reliability and the credibility degree of each body of evidence, the final weight of each evidence is calculated as follows:(7) Normalize the final weight of the evidence and obtain the weight as shown below:(8) According to the weight factor of each body of evidence, the weighted average evidence is calculated as follows:(9) Combine the weighted average evidence through Dempster’s combination rule twice, and the fusing results are shown in In this paper, for comparative analysis, we use different methods for fusion. The fusion results obtained through different combination methods are shown in As shown in er, Ma’s  method, er, Ma’s  method, er, Ma’s  method, DS evidence theory is an effective tool for multi-sensor data fusion, which is widely applied in the field of information fusion. However, Dempster’s combination method cannot effectively manage conflicts between different information sources. When fusing the high conflicting evidence with Dempster’s combination rule, the anti-intuitive results will be obtained. How to deal with the high conflicting evidence effectively remains to be solved. In this paper, by considering both of the effects of the evidence distance and evidence angle on conflict, a novel method for conflicting evidence fusion based on the dissimilarity and inconsistency between the evidence is proposed. We use the Hellinger distance to measure the distance between the evidence, and then the dissimilarity between the evidence is described by the evidence distance. The sine value of the Pignistic vector angle is utilized to calculate the magnitude of the angle between the evidence, and the inconsistency between the evidence is represented by the evidence angle. On the basis of the dissimilarity and inconsistency between the evidence, a new method for the mutual support degree between the evidence is presented to characterize the degree of conflict between the evidence. Several examples are given to illustrate the applicability and effectiveness of the proposed method. Compared with other methods, the proposed method can accurately evaluate the degree of conflict between the evidence, obtain more reasonable fusion results, and have a faster speed in convergence, which can be applied to many practical problems.Considering the proposed method in this work can effectively deal with the conflicting evidence and overcome the problems existing in traditional methods, in future work, we intend to generalize this method to other uncertainty theories, such as fuzzy set theory and imprecise probabilities. In addition, we will also research the total uncertainty of evidence directly in the frame of discernment by using the idea of this method.


In primary care, the diagnosis of pneumonia is often based on history and clinical examination alone. However, a previous study showed that the general practitioner’s degree of suspicion correlates well with findings on chest X-ray, when the C-reactive protein (CRP) value is known.The present study aimed to investigate to what extent the physician’s degree of suspicion is affected by the CRP level when community-acquired pneumonia is suspected in primary care.n = 266) consulting their health care centre with symptoms of lower respiratory tract infection, where the physician suspected pneumonia, were included consecutively. Anamnestic information and findings from clinical examination were documented in a case report form. All patients were tested for CRP. The physicians rated their degree of suspicion as ‘unsure,’ ‘quite sure,’ and ‘sure’ before and after the CRP result.A prospective observational study was conducted at five primary health care centres in Sweden between October 2015 and December 2017. Adult patients . In 28% of the cases, there was no longer any suspicion of pneumonia after CRP.Our results indicate that CRP testing highly influences the physician’s degree of suspicion of pneumonia in primary care and that it seems to be of most value when not sure of the diagnosis. KEY MESSAGESTesting for CRP in the assessment of pneumonia has high impact on the physician’s degree of suspicion of pneumonia.CRP testing seems to be most valuable when the GP is not sure of the diagnosis and hence could contribute excluding the diagnosis of pneumonia.It is a challenge in primary care to correctly identify patients in need of antibiotics but also to avoid prescribing for self-limiting infections. Most patients consulting primary care for lower respiratory tract infections (LRTIs) have acute bronchitis, which is considered a self-limiting disease that should not be treated with antibiotics. However, some patients will have pneumonia, which is often of bacterial origin and may be lethal if not treated. In countries with a high antibiotic prescription rate, there are higher rates of antimicrobial resistance. Therefore, antibiotics should be used only for conditions where expected benefits exceed the risks, including the increase of antibiotic resistance on a society level ,2.Many studies have investigated different clinical findings and decision algorithms for the diagnosis of pneumonia, with varying results . It has In a previous study, we showed that there was a strong correlation between the physician’s degree of suspicion of pneumonia and CXR results when the CRP value was known. For example, when physicians were sure of the diagnosis of pneumonia after examination, CXR was positive in 88% of the cases compared to only 28% when unsure . HoweverThe present study aimed to evaluate the impact of CRP-result on the physician’s degree of suspicion of pneumonia in primary care.Four primary health care centres in the south of Sweden participated in this prospective observational study. All PHCCs were located less than 10 km from a hospital. Patients were included consecutively from 1 September 2015 to 31 December 2017. The chosen time interval of inclusion was adapted for another parallel stud to achieve the intended number of patients. Because of a slower inclusion rate than expected, one more health care centre was invited to participate from 1 December 2016.The physicians who included patients were either general practitioners (GPs), residents or interns. Consecutive patients ≥18 years, with lower respiratory tract symptoms for at least 24 h, who consulted one of the primary health care centres (PHCCs), and where the physician, after clinical examination, had some degree of suspicion of pneumonia, were included. The judgement of possible pneumonia was not specified any further, but was left to the physician. Exclusion criteria were living at a nursing home, self-reported pregnancy or diagnosis of chronic obstructive pulmonary disease (COPD) registered in the patient record.Based on anamnesis and physical examination of the patient, the physicians rated their degree of suspicion of pneumonia into one of the same three categories used in our previous study: ‘unsure,’ ‘quite sure’ or ‘sure’ . The susU-test was used to compare medians. Logistic regression was performed to explore the association of different variables with the doctor’s degree of suspicion, dichotomised. Odds ratios were calculated with 95% confidence intervals. When missing data, the case was left out of the analyses. CRP results were categorised into subgroups  to analyse the influence of CRP on the suspicion degree. As different devices for CRP analyses were used, the devices had different upper limits, ranging from 160 to 200 mg/L, therefore all values above 160 and below 5 mg/L were set to 160 and 5 mg/L, respectively.The demographic characteristics and clinical data are presented as proportions and mean values or median values in skewed data. Pearson Chi-square test was used for crude group comparisons. To compare non-parametric data between more than two groups, we used Kruskal–Wallis test. Mann–Whitney P values <0.05 were considered significant.The sample size was calculated for the above parallel study and was based on the results from the previous study, described in the introduction, using similar outcome measures . StatistA total of 266 patients were recruited. One patient was excluded due to missing information on suspicion degree; thus, 265 patients were eligible for analyses. A temperature of 38 °C or more was measured in 52% of the patients. Somewhat less than half of the participants had used an antipyretic before the consultation. Symptom duration ranged from 1-64 days. The characteristics of the study population are presented in p < 0.001). After CRP result, the physician was ‘sure’ of the diagnosis of pneumonia in 55 patients (median CRP 112 mg/L), ‘quite sure’ in 68 cases  and unsure in 64 cases . In 72 patients, there was no longer any suspicion of pneumonia according to the physician’s judgement after CRP testing .The distribution of the degree of suspicion of pneumonia to CRP level is presented in n = 159). Of these, as many as 45% were regarded as ‘no pneumonia’ after CRP-testing, whereas the physician remained ‘unsure’ of the diagnosis in 26%. When the physician was ‘quite sure’ before CRP-testing, there was equal distribution of degree of suspicion remaining as ‘quite sure’ and changing to ‘unsure’ and a tendency to shift to a higher suspicion degree ( 38%) after CRP testing. The higher degree of suspicion before CRP, the less likely the suspicion degree shifted to ‘no longer suspicion of pneumonia’ (5% in the ‘quite sure’ group and 0% in the ‘sure’ group). There was no difference regarding the change in degree of suspicion between residents and specialists.The physicians changed their degree of suspicion in 69% of the cases after CRP result. Of those, 40% shifted to a lower degree of suspicion, whereas 29% received a more substantial suspicion degree after CRP result. Overall, 28% of the patients were considered not having pneumonia after CRP analysis. The change in degree of suspicion before and after CRP is presented in Median CRP for changing to a lower degree of suspicion was 11 mg/L (interquartile 5 and 25 mg/L) compared to 97 mg/L (interquartile 64 and 133 mg/L) when shifting to a higher degree of suspicion.When a multiple logistic regression was performed on weak suspicion (including ‘unsure’) or strong suspicion (including ‘quite sure’ and ‘sure’), before CRP testing, as dependent and other findings as covariates, body temperature and abnormal chest sounds emerged as predictors of strong suspicion  than in the overall population in Sweden (10%) at the time of inclusion , possiblIt has been shown that GPs who use CRP testing in the diagnosis of pneumonia often overestimate the probability of radiographic pneumonia . HoweverA Dutch study suggested that patients with milder LRTI symptoms and CRP <20 mg/L are at low risk for pneumonia . This isOther factors, for example progression of symptoms over time, have also been shown to predict community-acquired pneumonia in primary care .In conclusion, although it is likely that other factors may affect the decision making, e.g. anamnestic details, clinical examination findings, or the impression of the patient’s general health condition, this study suggests that CRP result highly influences the degree of suspicion of pneumonia. Further CRP testing seems to be most valuable when the GP is not sure of the diagnosis and hence could contribute to exclude the diagnosis of pneumonia and thereby might keep the prescriptions of antibiotics restrictive and purposeful. It would be of further interest to investigate how, and to what extent, other clinical information, as mentioned above, affects the physician’s degree of suspicion to understand the essence of clinical decision making better. Further, it would be valuable with a study investigating the degree of suspicion to CRP and antibiotic prescription.


Seizure is one of the main symptoms of anti‐NMDAR encephalitis, but data of ictal electroencephalogram (EEG) patterns remain limited. In this study, we aimed to introduce a unique ictal pattern. This delta brush variant (DBV) was characterized as generalized delta rhythm with fast spike activity riding on it. We retrospectively evaluated the ictal pattern from six patients with anti‐NMDAR encephalitis, and patients were grouped based on the presence of DBV. DBV was found in two patients who were in the florid phase of the disease: (a) A 17‐year‐old girl experienced rhythmical jerking of bilateral limbs. Corresponding EEG patterns showed generalized DBV. Seizure terminated after intravenous injection of midazolam, but oral‐facial dyskinesia reappeared; and (b) a 24‐year‐old man suffered stiffening of the right limbs and oral‐facial dyskinesia. The EEG pattern showed frontal DBV with left prominence. Seizure was controlled, but oral‐facial dyskinesia remained after intravenous injection of midazolam. Compared with patients without DBV, patients in this group were more likely to have prolonged excessive delta brush (100% vs 25%) and hyperpyrexia (39.7℃ vs 38.2℃). Duration in ICU (36 days vs 18 days) and hospital (52 days vs 36 days) was relatively longer in DBV group, and no significant difference was found in terms of the mRS score (1 vs 0.5) and seizure relapse rate (0% vs 25%) during 3‐month follow‐up. DBV is a peculiar pattern in anti‐NMDAR encephalitis. An EEG‐based monitoring should be considered to avoid misleading this ictal EEG pattern to the electromyographic artifact. Delta brush variance is a novel ictal electroencephalogram pattern in anti‐NMDAR encephalitis.Delta brush variance is characterized as generalized delta rhythm with fast spike activity riding on it.Delta brush variance is derived from extreme delta brush in the florid phase of the disease.Cortex‐subcortical area interaction may contribute to the evolution between delta brush variance and extreme delta brush.1Anti‐N‐methyl‐D‐aspartate receptor (NMDAR) encephalitis is the most common form of autoimmune encephalitis due to specific antibody crosslinking the NMDAR, causing receptor capping and internalization.Seizure is common with prevalence of 70%‐90% in all anti‐NMDAR cases.Extreme delta brush is a unique EEG pattern in anti‐NMDA receptor encephalitis and is characterized as a RDA at 1‐3 Hz with superimposed bursts of rhythmic 20‐30 Hz beta frequency activity.22.11‐Ictal EEG patterns: based on the American Clinical Neurophysiology Society Research Criteria for Nonconvulsive Seizure,2‐Delta brush variant (DBV): a pattern that satisfied (a) the criteria of ictal EEG patterns, and (b) the key feature of EDB, namely a complex delta activity with superimposing fast activity or intermixed spikes.2.2Data were collected from the First Affiliated Hospital in Guangxi Medical University. This study involved human participants and was reviewed and approved by the hospital ethics committee. Patients from January 2012 to December 2019 who (a) had acute onset of neurological or psychiatric symptoms, (b) had confirmed serum and CSF NMDAR antibodies evaluated by specific staining against NMDAR isolated from rats’ hippocampus and cerebellum, and positive cell‐based assay with HEK293 cells transfected with NR1, and (c) underwent at least 4‐hour continuous EEG monitoring and presented ictal EEG patterns were included. The exclusion criteria included (a) patients diagnosed with epilepsy, cerebral infarction, cerebral trauma, cerebral tumor, and other nervous system disease prior to the onset of encephalitis, (b) patients with evidence of infectious encephalitis, for example, viral, bacteria, Mycobacterium tuberculosis, or fungi. Patients were grouped according to the presence of DBV.Clinical data were extracted retrospectively. Information regarding age, gender, semiology, neuroimaging findings, CSF findings, EEG features, immunotherapy, drug responses, and outcome at 3 months was collected. The continuous EEG recordings were reviewed independently by two internists (HQ and MM) who passed the National Examination of EEG Technology of China. The following variables related to EEG characteristics were collected: preictal and ictal electrographic patterns, and corresponding semiology.2.3Data were statistically analyzed using Stata 13. Between‐group difference of categorical and quantitative variables was compared using Fisher's exact test and Wilcoxon rank‐sum test respectively.3The initial dataset of anti‐NMDAR encephalitis contained 72 cases. Ictal patterns presented in 6 (8.3%) cases including four females and two males with mean age 21.3 years (range 17–30 years). DBV was performed in the following two cases.3.1A 17‐year‐old girl experiencing progressive psychosis, agitation, and oral‐facial dyskinesia for 4 days was admitted. The initial EEG results showed typical generalized EDB. Diffuse low‐density lesion involving the white matter of the bilateral temporal and occipital lobes was noted by computerized tomography (CT) scan. She was diagnosed as anti‐NMDAR encephalitis after NMDAR antibody was detected in her serum and CSF. Glucocorticoid with a dose of 500 mg/day was given but symptoms remained. Over the next 3 weeks, the patient developed hyperpyrexia, severe pneumonia, and pyothorax. On day 23, she began to have rhythmical jerking of bilateral limbs with cessation of choreic movement and oral‐facial dyskinesia. Corresponding EEG patterns showed generalized synchronous delta rhythm with fast spike activity riding on it Figure . Seizure3.2A 24‐year‐old man suffering psychosis and recurrent focal tonic seizures of right limbs for 7 days was admitted. Prolonged fever, right hypesthesia, and stiffness of right face were also reported. The initial EEG results presented generalized RDA. Magnetic resonance imaging (MRI) scan showed nothing remarkable. A NMDAR antibody with titer of 1:32 was detected in his CSF. Glucocorticoid with a dose of 1000 mg/day was given, but the patient developed aggravating impaired awareness and began to have oral‐facial dyskinesia. On day 6 in the hospital, focal status epilepticus with stiffening of the right arm and leg was recorded. The EEG pattern showed RDA with frontal fast spike activity, most prominent over the left region. Seizure was controlled but oral‐facial dyskinesia remained after intravenous injection of midazolam at a dose of 0.2 mg/kg Figure . The pat3.3About 75% seizure occurrences in non‐DBV group were non‐convulsive, and this percentage was higher than that in the DBV group. To detect factors causing DBV, we compared the preictal clinical data between groups. A relatively higher rate of prevalence of EDB (100% vs 25%) and lower GCS score (3.75 vs 6.62) was presented in the DBV group. And malignant hyperthermia was more likely to be accompanied with DBV (39.7℃ vs 38.2℃). Furthermore, a total of 3 (75%) patients in the non‐DBV group were sensitive to intravenous diazepam, showing better response than those in the DBV group Table .Compared with the non‐DBV group, patients with DBV had longer time in ICU (36 days vs 18 days) and hospital (52 days vs 36 days). We used mRS score to assess the recovery of encephalitis on the 3rd month, and no significance was found between groups (1 vs 0.5). All patients received two or more kinds of antiepileptic drugs. One female  signal to avoid misleading results. In general, the potentials generated in the muscles are of shorter duration than those generated in the brain. The frequency components are usually beyond 30‐50 Hz.EDB is one of the key features in anti‐NMDAR encephalitis and regarded as a potential marker for the disease with a prevalence of 30% in total patients.In fact, determining an ictal discharge is one of the most challenging issue for anti‐NMDAR encephalitis. Most paroxysmal attacks of psychomotor agitation need to be recorded by EEG to avoid mistakenly diagnosing as seizures, and most of paroxysmal rhythmic activities in anti‐NMDAR encephalitis are non‐epileptic.The semblable morphology of DBV and EDB indicated that dysfunction of NMDAR might lead to various EEG phenotypes. In a retrospective study, JeanninAnother finding of this study was that the presence of DBV had a similar mRS score and seizure relapse rate during a 3‐month follow‐up. Our observations, combined with those of other investigators, suggest that the relationship between EEG features and outcomes is debated.The main purpose of the study is to introduce a unique ictal EEG pattern to avoid misdiagnosis. However, several limitations should be noted in the interpretation of DBV. First, the relatively small sample makes it difficult to determine the overall features of DBV. This pattern tends to be accompanied with continuous EDB and prolonged hyperpyrexia. The potential necessary combination of these two factors may explain why such a pattern was not reported previously. As DBV is recorded in the acute phase of anti‐NMDAR encephalitis when choreic movement is more frequent than seizure, more EEG‐based studies are needed to analyze the incidence and clinical nature of DBV. Second, only 8.3% (6/72) anti‐NMDAR encephalitis patients had recorded seizures in our study, which was relatively lower than other reports.The authors declared no conflicts of interest with respect to the research, authorship, and publication of this article. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.File S1Click here for additional data file.File S2Click here for additional data file.


Failure Mode and Effects Analysis (FMEA) has been regarded as an effective analysis approach to identify and rank the potential failure modes in many applications. However, how to determine the weights of team members appropriately, with the impact factor of domain experts’ uncertainty in decision-making of FMEA, is still an open issue. In this paper, a new method to determine the weights of team members, which combines evidence theory, intuitionistic fuzzy sets (IFSs) and belief entropy, is proposed to analyze the failure modes. One of the advantages of the presented model is that the uncertainty of experts in the decision-making process is taken into consideration. The proposed method is data driven with objective and reasonable properties, which considers the risk of weights more completely. A numerical example is shown to illustrate the feasibility and availability of the proposed method. Failure Mode and Effects Analysis (FMEA) has received attention from many researchers ,4,5,6,7,Though FMEA has been used in practice for many years, how to determine the weights of risk factors and team members is still an open issue. In order to define the weights more reasonably, some scholars have proposed many methods. The intuitionistic fuzzy entropy is introduced by Lei and Wang  to deterIn recent years, the relative concept of intelligence has been paid great attention due to the simulation of human intelligence ,20. As aIn this paper, a hybrid weights determination of team members in the FMEA model is proposed based on the evidence distance  and the The rest of this paper is organized as follows. In In this section, some basic concepts which include evidence theory, intuitionistic fuzzy sets and belief entropy will be introduced.Evidence theory was developed by Shafer and was firstly proposed by Dempster , thus itN mutually exclusive and exhaustive elements, called the frame of discernment, which is symbolized by In evidence theory , there iposition .Definition 1.A basic probability assignment (BPA) is a function. The range is from fined by and it must satisfy the following conditions:The mass The basic probability assignment (BPA) function, plausibility function (PF), belief function (BF) and other trust quantization functions are described as follows. Each function has a clear definition with physical meaning and there are some corresponding relationships.Definition 2.Ω, the belief function Bel: Given a BPA m, for a proposition A⊆fined as The plausibility function Pl: Ω−A. The quantity of Bel(A) can be seen as a measure of people’s belief that the hypothesis A is true and can be viewed as a lower limit function on the probability of A. The plausibility Pl(A) can be interpreted as the degree that we absolutely believe in A and can be seen as an upper limit function on the probability of A.where Based on the classical evidence theory, the combination rules to aggregate the multiple sources are defined as follows.Definition 3.Ω, respectively, Assume that there are two bodies of evidence  follows ,49:wherewhere K (conflict coefficient) is called the degree of conflict which measures the degree of conflict between on rules ,51.Evidence distance is regarded as an effective method to measure the conflict of evidence. Here are some of the basic concepts:Definition 4.Ω, which contains N mutually exclusive and exhaustive hypotheses. Namely, it can be expressed as Assume that  follows :where where with To combine the multiple source of evaluation and better solve the combination issues of highly conflicting evidence, the weighted average method is proposed by Deng et al. .Since the intuitionistic fuzzy set (IFS) is proposed by Atanassov as a generalization of fuzzy sets in 1986, the aggregation of fuzzy sets and IFS theory have received a lot of attention in the past few years ,54. In aDefinition 5.An intuitionistic fuzzy set (IFS) A on the space X is defined by two functions, ion that where The degree of hesitancy of x is defined asThus, the membership grade of x in the IFS A can be expressed by the tuple With the development of IFS and evidence, the relationship between these two mathematical models has been investigated more and more. Here is a brief introduction.Assume that there exists an IFS sed that (12)mA={<A is defined as follows:Thus, the belief interval of proposition Definition 6.Assume that there exist two alternatives  follows :where In the classical information science, Shannon entropy has been used in many applications . Here arDefinition 7.Shannon entropy is defined as where N is the number of basic states, If the unit information is bit, then However, since Dempster–Shafer evidence theory has been widely used in many fields, the method to measure the uncertainty in evidence theory is still an issue worth exploring. To measure the uncertain information better, a belief entropy, named as Deng entropy, is presented to deal with uncertainty measure of BPA based on Shannon entropy . Here arDefinition 8.In frame of discernment X, the belief entropy is defined as where searched ,60.k cross-functional team members i potential failure modes j risk factor kth decision maker with respect to the jth risk factors for the ith potential failure modes. In addition, intuitionistic fuzzy numbers [In this section, a new method to determine the weights of team members based on the evidence theory, intuitionistic fuzzy sets and belief entropy is proposed to rank the failure modes. The function of Failure Mode and Effects Analysis (FMEA) team members is to assess the risk factors with linguistic variables, such as very low, low, medium, high, very high and so on. Assume that there are  numbers , which aAssume that the IFN Step 1: Determine the linguistic terms for each failure mode and transform them into IFNs. The specific judgement levels are divided into ten linguistic parts see in , which cStep 2: Evaluate the linguistic terms of relative importance for each risk factor and transform them into IFNs. Similarly, the specific judgement levels are divided into five parts see in , which cStep 3: Convert all IFNs into BPAs for all failure modes. In addition, the concrete form can be defined as follows:Step 4: Determine the weights of risk factors. For the three judgement model S, O and D, each model can be transformed into an IFS to represent the information value. Based on the weight calculation, which is proposed by Boran et al. , the weiStep 5: Determine the weights of team members e et al. .Assume two groups of BPAs The The Step 6: Determine the weights of team members  entropy . Based op is the total number of failure modes for each risk factor.Step 7: Calculate the total weights of team members Step 8: Calculate the weighted average of evidence considering the team members’ effect of FMEA model. For each failure mode Step 9: Calculate the weighted average of evidence considering the risk factors with team members. To consider the impact of different risk factors , the weighted average of evidence which can be denoted as Step 10: Calculate the belief intervals. After obtaining the weighted average of evidence in Step 9, the belief interval Step 11: Rank all kinds of failure modes. Based on the belief intervals, the risk of different failure model can be compared with others by using Equation . After tIn this section, an example is used to illustrate the complete procedures of the proposed method.The risk evaluation process has a great impact in many fields, such as multi-criteria decision-making (MCDM) ,63,64,65Thus, an FMEA team consisting of five functional team members identifies potential failure modes in the electronics manufacturing project and wants to prioritize them in terms of their risk factors such as S (Severity), O (Occurrence) and D (Detection). In addition, twelve failure modes are identified. For the difficulty of evaluating the risk factors, the FMEA team members in this numerical example are supposed to assess them employing the linguistic terms. The specific transforming process is shown in Step 1: The assessment information of the twelve failure modes on each risk factor, which was provided by the five team members, can be illustrated in Step 2: Evaluate the linguistic terms of relative importance for each risk factor and transform them into IFNs see in .Step 3: Convert those IFNs into BPAs for all failure modes. The specific transforming equation is shown in Equations –14)..14).Step 4: Determine the weights of risk factors. In this paper, the weights calculation of risk factors in this example is shown in Equations  and 28)28). In aStep 5: Determine the first weights of team members distance . The speStep 6: Determine the second weights of team members Step 7: Calculate the total weights of team members Step 8: Calculate the weighted average of evidence considering the team members’ effect of FMEA model. The results are shown in Step 9: Calculate the weighted average of evidence considering the risk factors with team members. By introducing the consideration of risk factors, the weighted average of evidence is calculated. In addition, the results are shown in Step 10: Calculate the belief intervals. With the Equations (41) and (42), the final results are also shown in Step 11: Rank all kinds of failure modes. After the process of comparison, the final ranking can be obtained see in .Here, we present some discussion about the proposed method.In the previous related research, many scholars have tried to enhance the effectiveness and availability based on intuitionistic fuzzy sets, evidence theory and so on. The ranking comparisons of all the related works are shown in The relatively importance of team members are different. In the method proposed by Liu et al. , the relThus, with the differences mentioned above, the aggregation approaches for all kinds of methods are different. As a comparison, the process of our proposed method to determine the weights of team members is particularly scientific and effective, with strong practical significance and good performance.FMEA has been regarded as an effective analysis approach to identify and rank the potential failure modes in many applications. However, the uncertainty of the experts’ decision process is not taken into consideration, which is regarded as an essential factor in decision-making. In this paper, the impact of experts factor uncertainty is modelled. A hybrid method to determine the weights of team members is proposed based on the belief entropy. The application in FMEA illustrates the efficiency of the proposed method.


The assessment of an instrument’s conceptual framework as prerequisite for conducting further analyses has been advocated for decades. Multidimensional instruments posit several components that are each expected to be homogeneous but distinct from each other. However, validity evidence supporting the proposed internal structure is often missing. This leaves researchers and practitioners who are interested in a certain instrument in a precarious situation: Before starting their own data collection, they do not know whether dimensions adequately discriminate from each other and thus whether they can have confidence in any interpretation of these dimensions. Adapting the Fornell–Larcker criterion, we propose estimating distinctiveness between dimensions by using nothing but the most commonly reported statistics: Cronbach’s alpha and the correlation matrix between the manifest composite scores of the dimensions in question. A simulation study demonstrates the usefulness of this “manifest Fornell–Larcker criterion” in providing an easily assessable method for vetting existing instruments, whereas a systematic literature review shows the necessity to do so even for instruments published in well-received journals. In psychology, we are often interested in concepts that cannot be observed directly. For instance, social psychology deals with “attitudes” or “intention,” work and organizational psychology is often interested in “motivation” or “commitment,” and educational psychology tries to figure out the effect of “teacher expectancy” on “verbal ability,” to name just a few. These concepts are theoretical in nature. They provide a degree of abstraction that permits us to generalize relationships . Since tHow to develop such “common tools” in the form of measurement instruments that allow for consequential decisions about individuals or systems has been the topic of methodological papers and standards for decades, for example, in the “Standards for Educational and Psychological Testing” , 2014.Following This article has five parts. First, we present a brief overview over evidence based on internal structure and its current reporting practice. Second, we compare different methods to assess the internal structure, more specifically the distinctiveness in multidimensional measurement instruments. Third, we propose how one of these methods, namely the Fornell–Larcker criterion , can be A prerequisite for the validational process is a detailed specification of the conceptual framework the instrument is intended to measure. Multidimensional instruments posit several components that are each expected to be homogeneous but also distinct from each other. Evidence based on internal structure reflects the degree to which the relationships among items and components conform to this conceptual framework .1 Otherwise, the components are not unique but capture phenomena also represented by other components, making any interpretation of differences between them likely a result of statistical discrepancies . In a similar vein, Besides the general recommendations for explorative and confirmatory factor analyses  = 1.19, p = 0.55). Importantly, these violations are not isolated incidents either. On average, if at least one pair of dimensions lacks distinctiveness, 56% of all non-redundant correlations violate mFL, suggesting a severe violation at large and emphasizing the necessity for such an auxiliary tool.As can also be seen in mFL or not . Likewise, the average number of items in a given dimension is not significantly related to whether this instrument does meet mFL or not , suggesting that the proposed method is indeed agnostic to the particular structure of the multidimensional instrument , thus indicating its utility.Importantly, our systematic review also shows that the number of non-redundant correlations between the dimensions  of a given instrument is not significantly related to whether this instrument does meet mFL is a useful and efficient way in helping to answer this question. More often than not, researchers and practitioners will find the necessary information within the journal article to a priori assess validity evidence based on the internal structure, specifically of the distinctiveness, by themselves. Alternatively, one can use the accompanying website https://hibobohi.github.io/, which computes both mFL with double correction and mFL with single correction. Again, we suggest estimating both criteria. When both point into the same direction (as they did for every instrument investigated in our systematic review), one can be sure that the result is correct. Only when the double correction does indicate violations and the single correction does not is it uncertain if there is any violation of distinctiveness.The systematic review illustrates that, even with peer-reviewed studies in well-received journals, one should pay close attention to whether the instrument in question validly measures what it is purported to measure. Estimating mFL criterion. The actual choice on how to proceed then depends on the researcher’s assessment and the emphasis she wants to place on interpreting dimensions separately. We would suggest to (a) contact the authors of the original study and request the raw data to conduct the required analyses on one’s own. After all, if the study is published in an APA journal, the authors had to sign the “APA Certification of Compliance with APA Ethical Principles”, which includes the principle on sharing data for reanalysis (statement 8.14). However, since However, the systematic review also shows that about 42% of the multidimensional instruments under investigation do not report sufficient information to calculate the mFL is no silver bullet. It can improve the decision-making process of researchers and practitioners interested in a certain multidimensional instrument, but only if some basic information is available. Therefore, and perhaps somewhat unconventional, we hope that the necessity of our method vanishes over time: Calculating mFL is an auxiliary tool to assess the internal structure of a multidimensional measurement instrument, something that could—and should—easily be provided by the authors introducing or using an instrument in the first place. Following mFL offers researchers and practitioners an easily assessable method for vetting existing measurement instruments and thus helps them choose better “common tools” (Thus, n tools”  to obtaiThis study re-analyzed the information provided in the research articles of the 2015 volumes of “Frontiers in Psychology,” “Journal of Personality Assessment,” and “Psychological Assessment”. The information can be found in the respective articles.FH, CB, TB, and JH all contributed to the conception of the overall method. TB and CB designed and computed the simulation study. FH, CB, and JH designed and carried out the systematic review. FH performed the statistical analysis and wrote the first draft of the manuscript. CB wrote sections of the manuscript. All authors contributed to manuscript revision, read and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Various aspects of speech production related to auditory–motor integration and learning have been examined through auditory feedback perturbation paradigms in which participants' acoustic speech output is experimentally altered and played back via earphones/headphones “in real time.” Scientific rigor requires high precision in determining and reporting the involved hardware and software latencies. Many reports in the literature, however, are not consistent with the minimum achievable latency for a given experimental setup. Here, we focus specifically on this methodological issue associated with implementing real-time auditory feedback perturbations, and we offer concrete suggestions for increased reproducibility in this particular line of work.Hardware and software latencies as well as total feedback loop latency were measured for formant perturbation studies with the Audapter software. Measurements were conducted for various audio interfaces, desktop and laptop computers, and audio drivers. An approach for lowering Audapter's software latency through nondefault parameter specification was also tested.Oft-overlooked hardware-specific latencies were not negligible for some of the tested audio interfaces (adding up to 15 ms). Total feedback loop latencies (including both hardware and software latency) were also generally larger than claimed in the literature. Nondefault parameter values can improve Audapter's own processing latency without negative impact on formant tracking.Audio interface selection and software parameter optimization substantially affect total feedback loop latency. Thus, the actual total latency (hardware plus software) needs to be correctly measured and described in all published reports. Future speech research with “real-time” auditory feedback perturbations should increase scientific rigor by minimizing this latency. During the last two decades, there has been a strong interest in various aspects of speech production related to auditory–motor interactions and auditory–motor learning  hardware audio interface. The relevant steps that “should” be taken into account for such a latency report include all three of (a) audio signal input to the interface (A/D conversion), (b) software signal processing, and (c) audio signal output from the interface (D/A conversion). Indeed, Audapter's own software processing latency (with default settings) already is 10–14 ms, and the input–output temporal asynchrony data that are available within the Audapter program refer exclusively to this software processing time . Thus, it is necessary to bring attention to the fact that—contrary to common assumptions—the additional hardware delays associated with the audio interface are not at all negligible  and need to be accurately taken into account and reported.In order to truly know the total system latency that accounts for both the hardware latencies and the Audapter (or other software) latency, the only option is to simultaneously record—and then measure the temporal asynchrony between—the (a) original microphone signal “before” it is digitized by the audio interface and (b) the processed signal to the headphones “after” it has been output by the audio interface. To date, there is no published documentation of such correctly measured latencies for setups where Audapter is linked with any of the commonly used audio interfaces. In this study, we therefore investigated for two different computers and five different ASIO-compatible audio interfaces (a) the devices' own intrinsic round-trip latency (RTL), (b) the Audapter processing time, and (c) the total system input–output latency when implementing a typical formant-shift perturbation. We also tested nondefault Audapter parameter values as a strategy to optimize feedback latency. The data provide realistic measures of the overall feedback delay in typical Audapter setups and demonstrate that some audio interfaces and software options perform significantly better in terms of avoiding unwanted feedback delays.We ran the Audapter software on two different computers (referred to as operating computers to distinguish them from a simultaneously used recording computer) in conjunction with the different audio interfaces in order to verify whether processing and overall latency differ depending on computing power. Given that Audapter is often used with laptop-based systems for portability, we first tested a Dell laptop (Latitude E5570) with Intel core i7-6820HQ processor, 16-GB RAM memory, and Windows 7 Professional operating system. The laptop ran on external power and in high-performance mode. The second operating computer was a Dell workstation (Precision Tower 7510) with Intel Xeon E5-2640 v4 processor, 32-GB RAM, and Windows 7 Professional operating system. During the tests, no other computer programs were open except for the Sophos antivirus program that ran in the background, as required by the University of Washington.We used an additional Dell laptop (Dell Inspiron 14R-N4010) as the recording computer. This recording computer's stereo line input was used to record on separate channels (a) the direct microphone signal  and (b) the final output signal . This recording laptop was also connected to external power and also operated in high-performance mode. Off-line, the latency between the recorded stereo channels was measured using Praat, a free software for analyzing speech sounds .For real-time processing, Audapter requires an ASIO-compatible audio interface. The interface most extensively tested by the developers of Audapter, the MOTU MicroBook , is now All audio interfaces were set up to use a sampling rate of 48 kHz and a buffer size (or frame size) of 96 or 128 samples, depending on which options were supported by the device. The MOTU MicroBook IIc and RME Babyface Pro support a buffer size of 96 samples ; thus, the smaller buffer size was used for these two interfaces. The other interfaces did not support a buffer size of 96 samples; only 64 and 128 samples. According to our tests, a buffer size of 64 samples is too short for the present purposes as the 1.3-ms frame length (64 buffer size/48 kHz before downsampling) resulted in unreliable formant tracking. Therefore, for those interfaces that did not support buffer size 96, we tested the next shortest buffer size of 128 samples. Given that the RME Babyface Pro supports both buffer sizes 96 and 128, an additional test was completed for this device, with the buffer size set to this greater value of 128 samples.In addition to each manufacturer's default ASIO driver for the operating system, we also installed ASIO4ALL, a hardware-independent low-latency ASIO driver. This allowed us to test whether there is any advantage to using the manufacturers' own default drivers.bRatioShift = 1 for a ratio shift, all 257 values of pertPhi = π/4 radians for a 45° shift in the F1-by-F2 vowel space, and all 257 values of pertAmp = 0.2 for a 20% shift in that direction (thus a 20% formant frequency upshift with a 45° direction in the vowel space). Consequently, both F1 and F2 received a 14% upshift in hertz, sin(π/4 radians) × 20%, which corresponds to 229 cents, 1200 × log2(1.14/1).Unless specified otherwise,downFact, was set to 3  for most of the tests with interfaces that used a buffer size of 96 samples, as suggested in the Audapter manual (downFact at 4 (downsampling 48–12 kHz). Audapter was operated with Audapter(“start”) and Audapter(“stop”) functions to start and stop the auditory perturbations. It was operated for 5 s before it was stopped. Audapter recordings were retrieved via the AudapterIO(“getdata”) command and saved into wav files, resampling to 44.1 kHz.The downsampling rate parameter, r manual . An excenDelay value. Given that only odd positive integers are accepted, we tested Audapter's processing latency with nDelay values of 1, 3, and 5, in addition to the default value of 7. With nDelay of 1, reliable formant tracking was not possible, so only the results from nDelay of 3 and nDelay of 5 are reported and compared with that of nDelay of 7. According to the manual  for formant tracking. Audapter then filters the (nDelay)th frame to perturb the formant frequencies by the desired amount (chosen by the user) and sends this altered audio signal to the output channel. Hence, in the software, the resulting temporal asynchrony between the input and output channel becomes 2 × nDelay − 1 − nDelay = nDelay − 1 frames. For the default nDelay value of 7, there would be a latency of six frames or 12 ms .www.oblique-audio.com) to measure RTL (input latency plus output latency) of the audio interfaces. This is accomplished by looping the audio interface's output back to its own input  and the perturbed feedback signal . To record the first of these two signals, we split the signal from the microphone  with an XLR splitter, such that both the tested USB audio interface and one audio channel of the recording laptop received the exact same microphone signal at the same time. To record the formant-shifted output, the signal that had been processed by Audapter and that was output by the audio interface was routed to a mixer  in order to be amplified and sent to the second channel of the recording laptop's stereo line-in input see . The twoA first, “indirect” method of determining the Audapter software processing latency was based on the fact that additional latencies due to mixer and audio cables were negligible (less than 0.05 ms). Thus, any part of the total feedback loop latency not caused by the interface hardware can be attributed to Audapter processing. Consequently, it was possible to subtract the measured hardware latencies from the overall feedback loop latencies to obtain an estimate of Audapter's signal processing time. As a second “direct” method of determining Audapter's software processing latency, we also compared the temporal asynchrony between the two channels  stored within Audapter's own recording on the operating computer. Using Praat to analyze the recordings from five trials, we measured the time difference between the two Audapter channels. These measures of the time offset between the unperturbed and perturbed signals stored by Audapter  can then be compared with the estimates obtained by subtracting hardware latency from total feedback loop latency .downFact at 3 downsamples the original audio input from a sampling rate of 48 kHz and a buffer size of 96 samples to a sampling rate of 16 kHz and a buffer size of 32 samples. However, some researchers have used downFact at 4 to downsample the original signal to a rate of 12 kHz and a buffer size of 24 samples  into a microphone  connected to the above described RME Babyface Pro audio interface. For each run of 90 trials, Audapter processed the incoming signal and tracked the formants at a given nDelay value. Off-line, F1 and F2 were calculated for each trial as the average value across the middle 40%–60% of the corresponding formant track.As mentioned above, we also aimed to examine whether Audapter's processing time can be shortened by lowering the downFact set to 3. As we had done in Windows 7, we ran this set of tests again with each of the three nDelay values 7, 5, and 3.As mentioned above, testing was generally completed with both the laptop and desktop operating computers running Audapter software within the Windows 7 Professional operating system. However, given that Microsoft will no longer provide support for Windows 7 starting in 2020, we decided to also do a test of Windows 7 versus Windows 10 on one of these operating computers. Therefore, after completing all the above described tests, we upgraded the Dell Latitude E5570 machine's operating system to Windows 10 Enterprise LTSC (Long-Term Service Channel), and we repeated a set of total latency measurements with the RME Babyface Pro interface and the Audapter parameter SD > 1.5 ms). Both results suggest significant compatibility issues between the ASIO4ALL driver and some audio interfaces. We will therefore focus our remaining description of the test results on those obtained with the manufacturers' own drivers.Hardware-related RTLs for all tested combinations of audio interfaces and operating computers are listed in It is also clear from The middle column in The most important observation in these results is that the total feedback loop latencies observed in this study  are substantially larger than claimed in most published work in which the Audapter software was used . It seems clear that the previously reported latencies took into account only the software-based signal processing latency  while ignoring the sometimes substantial additional delays associated with the audio interface's signal input and output components.With the appropriate measurement protocol, the overall best total feedback loop latency (19.3 ms) was observed when using the RME Babyface Pro with a buffer size of 96 samples. The second best performance (25.5 ms) was observed when the same device was used with a buffer size of 128 samples.The right-side column of These estimates closely match the processing latencies that one would predict based on the algorithm information provided in the Audapter manual . For a bAs one would expect, Audapter's software processing latency was not affected by selecting the manufacturer versus ASIO4ALL driver. Similarly, it was not affected by the use of any specific audio interface (when compared across interfaces operating with the same buffer size). And lastly, based on the two operating computers used in our testing, running Audapter on a more powerful computer (with faster processor and more RAM) also did not affect software processing latency.downFact at 3 versus at 4 are listed in Audapter offers the option of downsampling the input signal before processing in order to lower computational load. Results for our comparison of latencies obtained with nDelay parameter from its default value of 7 to either 5 or 3 can be used as a way to further minimize software processing latency  without sacrificing accuracy of the formant shift perturbation. nDelay of 5, and 8-ms processing time can be saved with nDelay of 3. With the latter value, Audapter processing latency was reduced from 14 or 12 ms  to only 6 or 4 ms. Used in combination with the RME Babyface Pro hardware, this strategy is capable of reducing the total feedback loop latency from 19.4 to 11.4 ms—thereby achieving a total feedback delay as short as that provided by the commercially produced VoiceOne processor (see above).We tested whether reducing Audapter's nDelay as low as 3 could degrade the accuracy of Audapter's formant tracking (thus affecting any applied formant perturbation). We therefore verified formant tracking accuracy for each of the three nDelay values of 7, 5, and 3. nDelay setting (top row), each individual trial's F1 and F2 values determined with each nDelay setting (middle row), and trial-by-trial differences in these formant values across the different nDelay settings (bottom row). Small differences in formant tracking can be observed, most notably when comparing the different measures on a per-trial basis  and the original unprocessed microphone signal . With default Audapter software settings (options for further optimization of these software settings are discussed below), our measurements of total feedback loop latency ranged from 19 to 39 ms across the different interfaces. These delays are considerably longer than most of the latencies previously reported in publications based on speech experiments with Audapter. Indeed, it seems clear that many prior reports of feedback latency in speech experiments with Audapter failed to capture the additional hardware-specific latency. This may happen if researchers compare the perturbed channel with an unperturbed channel that is also taken from the output of the audio interface see  rather tFourth, comparing the various audio interfaces tested here, we found that the RME Babyface Pro has the shortest latency. In addition, this RME Babyface Pro supports a buffer size of 96 samples, which shortens Audapter's software processing latency as compared with situations in which a buffer size of 128 samples needs to be used. Because of these advantages, our formant-shift tests showed a total feedback loop latency of 19.4 ms when using the RME Babyface Pro with Audapter . In contrast, when using Audapter with some of the other interfaces, latencies approached 30 ms or, when the ASIO4ALL driver was used, even 40 ms. It has been reported that a delay greater than 25 ms can already slow down the speech rate and a delay greater than 50 ms may induce disfluencies . In addiMOTU Ultralite AVB with the HSO setting of 16 had the second shortest latency after RME Babyface Pro. However, whether or not this HSO setting affects Audapter's performance is not known and must be examined. The manufacturer states that the host software may experience performance issues when the parameter is set too low. Hence, it is possible that the low HSO setting may compromise the quality of auditory feedback perturbations due to formant mistracking. We recommend that researchers interested in using this particular interface test it in depth before using it with the Audapter software. Furthermore, although it is theoretically possible to shorten the hardware latency by using rack-mounted systems  or internal PCI-e cards , various databases informing on the RTL of different interfaces indicate that any additional improvement would be marginal  as compared with the RME Babyface Pro tested here .nDelay parameter. Although the reasons for this discrepancy remain unknown, we strongly advise against using Audapter's own recordings for latency measures. They certainly do not take account of the audio interface's intrinsic latency and may not even capture the software processing latency accurately.Fifth, our results suggest caution when estimating Audapter's own signal processing latency. In the absence of additional latencies beyond the audio interface and Audapter software, this signal processing latency can be estimated by subtracting the hardware latency from the total latency. In our tests, this indirect estimate always slightly exceeded the latency measure obtained by measuring the temporal offset between the unperturbed and perturbed channels saved by Audapter and the prediction based on the software's downFact) as a strategy to lower computational load as suggested in the Audapter manual . Similarly, upgrading the Dell Latitude laptop's operating system from Windows 7 Professional to Windows 10 Enterprise LTSC did not affect total feedback loop latency. We also did not see any improvement in total latency when using a higher downsampling rate .Audapter is a software package that is considered to be the most comprehensive and flexible tool for perturbing auditory feedback. Most likely, it will continue to be heavily used in future studies of voice and speech motor control . It is cUndoubtedly, additional means of optimizing hardware and software components need to be investigated. For example, new technologies are available for the communication between audio interfaces and the operating computer . In fact, in our tests, the Presonus 192 Studio Mobile interface used a USB 3.0 connection, but it was considerably slower than the RME Babyface Pro interface, which used a USB 2.0 connection. Nevertheless, as manufacturers introduce interfaces with more advanced connections, the effect on device latency should be examined. Another potential influence may relate to the use of Windows versus Mac versus Linux as the computer operating system (depending on availability of native drivers for the audio interface), and it should be recalled that all tests reported here were conducted only with Windows computers.nDelay parameter to shorten latency in formant-shift experiments, the delayFrames parameter could be used to shorten latency for pitch-shift perturbations (Lastly, different types of perturbation in Audapter depend on different parameters, and each of these parameters may have its own specific effect on processing delay. For example, analogous to our above manipulation of the rbations . Investi


The best whole-food sources of these micronutrients available in part or all of the countries studied include beef liver, chicken liver, small dried fish, beef, and eggs. Investment is needed in many countries to collect data on micronutrient biomarkers and dietary intake. Strategic actions to improve child diets will require engagement and intervention across relevant systems to accelerate progress on improving the diets of infants and young children.Insufficient quantity and inadequate quality of foods in early life are key causes of all forms of malnutrition. Identification of nutrient and dietary gaps in the diets of infants and young children is essential to inform policies and programs designed to improve child diets. A Comprehensive Nutrient Gap Assessment was used to assess the public health significance of nutrient gaps during the complementary feeding period and to identify evidence gaps in 6 countries in Eastern and Southern Africa. Important gaps were identified in iron, vitamin A, zinc, and calcium and, to a lesser extent, vitamin B Approximately 1 in 5 of the 144 million children younger than 5 years worldwide who have stunted growth reside in the UNICEF Eastern and Southern Africa region. In 2019, 33% of children younger than 5 years in the region had stunted growth and 6% were wasted.Insufficient quantity and inadequate quality of foods during the complementary feeding period—between 6 and 23 months of age—are key causes of all forms of malnutrition, including micronutrient deficiencies, and have immediate and long-term consequences. In the short term, these include increased morbidity and mortality and delayed cognitive and motor development.Improving the diets of infants and young children in Eastern and Southern Africa would contribute to the prevention of malnutrition and is an important component of efforts to achieve the global nutrition targets of the World Health Assembly and Sustainable Development Goals. To make informed recommendations on how to improve diets, insight into specific problematic nutrients, along with foods and feeding practices, is essential. Evidence indicative of the burden of nutrient deficiencies or nutrient intake frequently is available in countries yet are often underused or misinterpreted in decision-making and program design, in part because relevant evidence often comes from disparate sources of varying quality, representativeness, and recency. The absence of guidance on what evidence types should be prioritized to estimate nutrient gaps  and how to collate and interpret such information, has resulted in limited understanding of the magnitude and significance of nutrient gaps in countries in Eastern and Southern Africa. As a result, specific reference to nutrient gaps is often omitted from policies and programs designed to improve child health and nutrition.Nutrition Reviews supplement issue—provides guidance on how to use various types of evidence  to assess the public health significance of nutrient gaps in a given population. The CONGA was used to assess nutrient gaps during the complementary feeding period in 6 countries in Eastern and Southern Africa: Ethiopia, Mozambique, South Africa, Tanzania, Uganda, and Zambia. After identifying micronutrient gaps, the most micronutrient-dense whole-food sources  available in part or all of the countries studied for these micronutrients were identified.A new methodology called Comprehensive Nutrient Gap Assessment (CONGA) was developed to fill this gap. This approach, detailed in an article by Beal et al1 (thiamine), niacin, vitamin B12, vitamin B6, folate, and vitamin C.The CONGA methodology used in this analysis is described in detail in the Beal et al article in this supplement.The analysis followed the 8-step method outlined in the CONGA methods paper.Collated evidence was reviewed to determine if the data met qualifying criteria for inclusion in CONGA. Data based on a sample size <50, representing <10% of the national population, specific to highly vulnerable populations , and collected >20 years ago were excluded from the analysis. Evidence specific to the age group of interest (children aged 6–23 months) was prioritized; however, evidence for other age groups that overlapped with the target group , were near to the target group , or not near but relevant  were also included where identified. Details on identified qualifying data points were inserted in country-specific spreadsheets. Each spreadsheet summarized the identified data points  and their associated metadata . When there was >1 data point for the same specific evidence type , the data point most similar to our age group of interest was included and the other estimate was recorded in a comment box to provide additional context rather than inserted as a stand-alone data point.Two experts (T.B. and J.M.W.) conducted CONGA steps 2–6 to determine gap ratings for all 11 nutrients in each of the 6 countries. In step 2, data points in the country-specific spreadsheets were reviewed and assigned an implied nutrient burden score per the CONGA methodology.In step 4, the implied burden score and evidence weight scores were used to calculate a quantitatively derived nutrient gap burden rating. The quantitative burden rating was calculated using data only from the aforementioned 5 core evidence types  that were collected in the last 10 years on age or sex groups similar to children 6–23 months of age. The data points excluded from this calculation were considered in step 5. A numeric score was calculated for each nutrient in each country by using the weighted mean of the implied burden score (where the evidence weights are the weight scores) and assigned a label of high, moderate, low, or negligible, per CONGA methodology.In step 5, the quantitative nutrient gap burden scores were reviewed alongside the totality of evidence for each nutrient, including data points that did not meet criteria for inclusion in the quantitative-burden score calculation and additional available information for each data point  to determine whether the final rating assigned to the nutrient gap should deviate at all from the quantitative-derived rating. A final qualitative rating of high, moderate, low, or negligible was assigned to each nutrient for each country, and any deviation from the quantitative burden score was documented and explained.The certainty of evidence was rated for each nutrient burden  in step 6 using established CONGA methodology criteria,In step 7, all coauthors, who are subject matter and contextual knowledge experts, reviewed the final qualitative nutrient gap burden and evidence certainty ratings produced in steps 5 and 6, respectively. Disagreements over final qualitative ratings were discussed and critically re-evaluated. Ratings were finalized only when consensus was achieved and documentation of additional considerations or deviations from quantitative burden scores was added. The 12, and calcium). This metric, called average share of requirements was calculated as the average proportion of daily requirements from complementary foods for these 6 micronutrients on the basis of a 100-g quantity (each micronutrient capped at 100% of daily requirements). The portion size of each food required to achieve an average of 33.3% of requirements —the equivalent of 100% of requirements for 2 micronutrients or 33.3% of requirements for all 6 micronutrients—was also calculated to demonstrate the ideal foods to fill ≥ 2 important micronutrient gaps simultaneously. Adjustments for differences in bioavailability between plant and animal source foods were made for iron and zinc. Additional details on the methodology used to calculate micronutrient density of and average share of requirements for identified foods and determine local availability can be found in other articles in this issue of Nutrition Reviews.,The most micronutrient-dense, regionally available, whole-food sources were determined for identified micronutrient gaps  and potential gaps  using national or US Department of Agriculturen = 39) followed by Zambia (n = 35), Uganda (n = 31), Mozambique (n = 29), Tanzania (n = 22), and South Africa (n = 21). Recency of data collection varied across countries: All eligible data points in Zambia and Mozambique were at least 6 and 7 years old, respectively, whereas Ethiopia, Tanzania, South Africa, and Uganda all had data from 2015 or 2016. All 6 countries had relevant nationally representative data available for several nutrients and evidence types. The variety of data sources differed by country: Qualifying data for Tanzania was almost exclusively from either the 2010 or 2015/16 DHS, whereas Ethiopia had a 2011 National Food Consumption Survey, a 2014/2015 National Micronutrient Survey, and a 2016 DHS survey. Relevance to the age group of interest varied by evidence type and data source; however, the majority of data reviewed were for children younger than 5 years or 6–59 months of age. Slightly more than 50% of all data points collated for CONGA (n = 93) met criteria for inclusion in the quantitative-burden score calculation.A total of 25 evidence sources were identified in CONGA step 1 across the 6 countries see . Ethiopi12 deficiency were available in Mozambique, Ethiopia, and Zambia, but none of the data points met qualifying criteria for the quantitative-burden rating (estimates were for WRA in Mozambique and Ethiopia and collected in 2008 in Zambia). Estimates of iodine deficiency  were available in Mozambique, Ethiopia, Tanzania, and Zambia, but none of the estimates were for children younger than 5 years (estimates were for WRA in 3 of the 4 countries and for children aged 5–14 years in Ethiopia). No biochemical data for any age groups were identified in the literature search for calcium, niacin, thiamin, vitamin C, or vitamin B6.Availability of data points for the 5 core evidence types outlined in the CONGA methodology varied. Evidence on the nutrient adequacy of individuals was available only from food consumption surveys in Uganda, Zambia, and Ethiopia; however, only the evidence from Ethiopia qualified for inclusion in the quantitative burden score, because the data from Uganda and Zambia were from 2008. No data points were identified for nutrient adequacy of households in any country for any nutrient.6, folate, and calcium were determined exclusively by estimates of adequacy of national food supplies. Nutrient-informative food-group estimates for individuals were available for vitamin A and iron in all countries. Nutrient-informative food-group estimates for households were available for iodine in all countries, as well. These data came largely from DHS surveys  and other national nutrition surveys. There were multiple data points categorized as “other” for most nutrients.Estimates on the nutrient adequacy of national food supplies were available for all nutrients in all countries. For several nutrients, this was the only qualifying data point for the quantitative nutrient gap burden score . The quantitative burden scores for niacin, vitamin C, vitamin Bn = 11) increased in severity after final review. Ratings changes applied somewhat evenly across many nutrients: Folate, iron, niacin, thiamine, vitamin C, and zinc had 2 changes each, whereas calcium and vitamin C each had 1. The During the final review of the quantitative burden score and criteria-based certainty-of-evidence ratings (CONGA step 5), 16 of the 66 nutrient gap burden ratings and 1 of the 66 certainty ratings were adjusted after critical assessment of all evidence available. Of the 16 nutrient gap burden rating changes, the majority Other identified nutrient gaps were for vitamin A and zinc, each with three high burden and three moderate burden ratings . Vitamin A deficiency has severe consequences, even with mild deficiency, including night blindness, increased susceptibility to infections, and death.12 and folate. Deficiency of either in infants and young children can have immediate and long-term consequences, including anemia, hindered brain development, and adult depression.,1, vitamin B6, and vitamin C in all 6 countries. Iodine deficiency was identified as low burden in all countries except Mozambique, where the burden was high. The Mozambique data on iodine, however, were from 2011 and the rating is based on low-certainty evidence.Other important nutrient gaps across the 6 countries in Eastern and Southern Africa were for vitamin Bn = 55 of 66 ratings) had a low certainty of evidence. Only 1 rating—vitamin A in Uganda—was given a high certainty rating. Nutrient gap ratings for iron received a moderate certainty-of-evidence rating in 5 countries. All ratings for vitamin B12, folate, calcium, niacin, thiamin, vitamin C, and vitamin B6 were based on low-certainty evidence.The majority of nutrient gap burden ratings , in terms of average share of requirements per 100-g portion, are small dried fish, chicken liver, beef liver, eggs, beef, and dark leafy greens. For example, 100 g of small dried fish will achieve an average of 88% of requirements across these 6 micronutrients for children aged 6–23 months. Small dried fish—common in Uganda, Tanzania, Mozambique, and Zambia, but not Ethiopia or South AfricaAvailable whole-food sources rich in the micronutrients with identified and potential gaps are listed in Identification of nutrient and dietary gaps during the complementary feeding period is essential to inform policies and programs designed to improve child health and nutrition.Although CONGA can help expand the evidence base on child diets (and the diets of other populations), there are important limitations to the process. For example, the literature search conducted in CONGA step 1 was not systematic, which may have resulted in omission of qualifying evidence from the review. Much of CONGA is standardized to minimize bias; however, there is purposeful flexibility for reviewers to override quantitative and criteria-based ratings based on qualitative assessment. Thus, the final ratings may reflect the subjective perspective of reviewers. The CONGA methodology addresses this by encouraging multiple reviewers to be engaged in the process and any changes made from quantitative and criteria-based ratings to be discussed, documented, and then validated by subject-matter and local experts.12 gap ratings, in part due to recent national nutrition or micronutrient surveys available in most countries in the region.6 deficiencies. Biochemical and functional marker data should be collected at the national level at least every 10 years  to help monitor programs and track progress on child diets. For nutrient adequacy of individuals, there were some data points available from national or subnational food consumption surveys; however, 2 of 3 of these surveys identifed were more than a decade old. In South Asia, no data points for this evidence category were identified in any country in the region.12, and folate.There was a notable lack of evidence to include in CONGA in the Eastern and Southern Africa region. For all 6 countries, only 25 evidence sources from which to pull relevant data points were identified. The vast majority of nutrient gap burden ratings were based on low-certainty evidence. In contrast, the majority of countries studied in South Asia had either a high or moderate certainty for iron, zinc, vitamin A, folate, and vitamin B12 and folate. These gaps are similar to those found in South Asia, where identified and potential nutrient gaps were found for iron, zinc, vitamin A, folate, vitamin B12, calcium, and vitamin C.Although the identified and potential nutrient gaps varied across countries, all countries included in this analysis had at least a moderate burden in iron, calcium, vitamin A, and zinc, and most had at least a moderate burden in vitamin B,This CONGA analysis helps broaden the understanding of priority and potential nutrient gaps in child diets and contributes to prioritization of strategic actions to improve the quality of children’s diets in these 6 countries as well as the Eastern and Southern Africa region. More research is required to understand the causes of these nutrient gaps, including both supply- and demand-side barriers. The analysis also identified nutrient-dense whole foods to overcome nutrient gaps during the complementary feeding period. More research is needed to understand the primary barriers to consuming these foods, like limited availability, accessibility, affordability, or desirability. Efforts to improve and maintain continued breastfeeding rates in the region should also be prioritized (only 72% of children are still breastfed during the second year of life in the regionnuaa142_Supplementary_DataClick here for additional data file.