Data harmonization is a key step widely used in multisite neuroimaging studies to remove inter-site heterogeneity of data distribution. However, data harmonization may even introduce additional inter-site differences in neuroimaging data if outliers are present in the data of one or more sites. It remains unclear how the presence of outliers could affect the effectiveness of data harmonization and consequently the results of analyses using harmonized data. To address this question, we generated a normal simulation dataset without outliers and a series of simulation datasets with outliers of varying properties  based on a real large-sample neuroimaging dataset. We first verified the effectiveness of the most commonly used ComBat harmonization method in the removal of inter-site heterogeneity using the normal simulation data, and then characterized the effects of outliers on the effectiveness of ComBat harmonization and on the results of association analyses between brain imaging-derived phenotypes and a simulated behavioral variable using the simulation datasets with outliers. We found that, although ComBat harmonization effectively removed the inter-site heterogeneity in multisite data and consequently improved the detection of the true brain-behavior relationships, the presence of outliers could damage severely the effectiveness of ComBat harmonization in the removal of data heterogeneity or even introduce extra heterogeneity in the data. Moreover, we found that the effects of outliers on the improvement of the detection of brain-behavior associations by ComBat harmonization were dependent on how such associations were assessed , and on the outlier location, quantity, and outlier score. These findings help us better understand the influences of outliers on data harmonization and highlight the importance of detecting and removing outliers prior to data harmonization in multisite neuroimaging studies. First, Using data from multiple sites is often inevitable to form a large sample size for neuroimaging studies. For example, almost all currently existing big neuroimaging datasets, including Adolescent Brain Cognitive Development ABCD; , EnhanciHowever, due to the differences in scanning equipment and techniques, multisite datasets often present differences in the distribution of data collected from different sites, also known as site effect or inter-site heterogeneity of data. The problem of inter-site heterogeneity in multisite studies needs to be attended to carefully as it is an important confounding factor for statistical analyses and will lead to unreliable results if mishandled or disregarded . SeveralAnother important issue in multisite neuroimaging data with large sample sizes is the prevalence of outliers, defined as observations that dramatically deviate from the majority of the data . There aThe issue caused by the presence of outliers in data analyses becomes more complicated in multisite neuroimaging studies due to the inter-site heterogeneity in the data. It is likely that outliers may also bias the results of data harmonization for the removal of inter-site heterogeneity and even exacerbate the heterogeneity of multisite data, thus leading to unexpectedly erroneous results. Therefore, outlier removal has been considered as a regular step of the standard workflow of data harmonization. Nonetheless, it is worth noting that ComBat has been suggested to be more robust to outliers even in small sample sizes compared with other data harmonization methods such as singular-value decomposition , distancTo address this question, in the present study we generated a series of multisite simulation datasets with or without outliers based on a real large neuroimaging dataset CHIMGEN; . We firs2.2.1.http://atlas.brainnetome.org/download.html). It should be noted that the original combined Brainnetome atlas contains 274 regions; however, one region was reduced to 0 voxels after downsampling the original atlas image (1.253 voxel size) to match with our current data (1.53 voxel size), and thus 273 regions in total remained for subsequent analyses. Third, we obtained a GMV for each brain region and subject, resulting in a dataset consisting of 273 IDPs and 3,013 subjects in total. Fourth, for each site, outliers were detected and removed from the data for each IDP and consequently 2,894 samples in total  remained for subsequent analyses. Here, outliers were defined as the values that were at least three times the interquartile range (IQR) below the lower quartile (Q1) or above the upper quartile (Q3) of the data distribution, as suggested by Tukey in 1977  following the normal distribution with the same mean and covariance as the real brain imaging dataset. The generated three sets of random values composed the simulated brain imaging data of three sites, with 1,000 samples in each site.A normal simulation dataset  was generated according to the statistical properties (mean and covariance) of a real brain imaging dataset – the grey matter volume (GMV) computed from the T1 weighted imaging data of the CHIMGEN database . The det in 1977 . Fifth, th IDP (IDP-187) as a representative IDP (because the IDP-187 showed correlations with many other IDPs), performed z-score normalization ([IDP-mean(IDP)]/std.(IDP)), and then added some Gaussian noises . The resultant variable served as a simulated behavioral variable without inter-site heterogeneity. In this way, this simulated behavioral variable would correlate with the IDP-187 as well as some other IDPs showing correlations with the IDP-187. This was performed for each site to generate the corresponding simulated behavioral variable for each site. Therefore, we generated 273 simulated IDPs and 1 simulated behavioral variable for each site.To generate a simulated behavioral variable that would be correlated with some of the 273 simulated IDPs, we selected the 1872.2.To generate the simulation datasets with outliers, we added some extra samples with extreme values  to the above generated normal simulation dataset. As aforementioned, in the present study, outliers were defined as the values that were at least three times the IQR below the lower quartile Q1 or above the upper quartile Q3. We further quantified the deviation of outliers for each IDP using outlier scores. Specifically, outlier scores were defined using the following mvnrnd with a mean vector of [Q3 + Outlier score2 times the correlation matrix of the normal real data. The simulated outliers were then added to the corresponding site of the normal simulation data. Here, we chose a relatively small standard deviation  for generating outliers so that we could easily control the outlier scores.where Q1 denotes the lower quartile, Q3 denotes the upper quartile, and IQR denotes interquartile. To generate simulated outliers for a given site, we used the MATLAB function To test how the presence of outliers in different scenarios affect the effectiveness of ComBat harmonization, the outliers were added to the normal simulation dataset in six different ways which varied in quantity , outlier score , and location of outliers : (A) Scenario “one-site unilateral”: outliers were added to the upper side of Q3 in Site 1; (B) Scenario “one-site bilateral”: the same number of outliers with the same outlier scores were added to both sides of the data distribution  in Site 1; (C) Scenario “one-site different”: the same number of outliers were added to both sides of the data distribution  but with different outlier scores  in Site 1; (D) Scenario “two-site unilateral”: the same number of outliers with the same outlier scores were added to the upper side of Q3 in both Sites 1 and 2; (E) Scenario “two-site bilateral”: the same number of outliers with the same outlier scores were added to the lower side of Q1 and the upper side of Q3 in both Sites 1 and 2; (F) Scenario “two-site different”: the same number of outliers were added to the upper side of Q3 in Site 1 and the lower side of Q1 in Site 2  but with the same outlier scores. For each of the six scenarios, the quantity of the added outliers varied in five different values  and the outlier scores varied in seven different values . Note that, to maintain the same sample size in all sites, for the first three scenarios  an equal number of normal values as the total number of the outliers added to Site 1 were also added to Site 2 and 3, and for the last three scenarios  an equal number of normal values as the number of the outliers added to Site 1/2 were also added to Site 3. Then, for each site, an equal number of simulated values (generated using the same approach described in Section 2.1) were also added to the simulated behavioral variable to make sure that there were equal number of samples for the simulated IDPs and behavioral variable. Note that, no outliers, but only normal values, were added to the simulated behavioral data.2.3.ComBat was initially developed to eliminate batch effects in microarray gene expression data  and is nTaking voxel-wise grey matter volume (GMV) for example, ComBat algorithm models the raw GMV using the following i, Subject j, and Voxel v, v, v, i for Voxel v, respectively, and where As the overall differences in GMV between different voxels may lead to errors in the estimation of the prior distribution of site effect, the ComBat algorithm first standardizes the data to have comparable overall mean and variance across different voxels. The equation for the standardization isi, Subject j, and Voxel v. The data are eventually adjusted using 2.4.r) between each IDP and the behavioral variable before and after ComBat harmonization using the subjects within each site  and using the subjects across all sites . We plotted these IDP-behavior correlation coefficients as violin-box plots and also calculated the intraclass correlation coefficients  for each single site and the combined site.For the normal simulation data, heterogeneity among sites was evaluated in three ways (1) We plotted a density curve of all IDP values for each subject to show differences in IDP values across sites, (2) To visualize the IDP data distribution of all subjects for the three sites using a scatter plot in a two-dimensional space, we reduced the dimensionality of the IDP data (273 IDPs) to the first two principal components (PCs) using principal components analysis (PCA), (3) To test whether some significant IDP-behavior correlations that were not detected due to data heterogeneity among sites could be recovered after ComBat harmonization, we calculated the Pearson correlation coefficient  before and after ComBat harmonization under all six outlier scenarios compared with those where outliers were absent. Furthermore, to quantify the effect of outlier location  on the IDP-behavior relationships, we calculated the ICCs of the 35 IDP-behavior correlation coefficients  among the six outlier scenarios for each site (within-site correlations) and the combined site (across-site correlations).Based on the normal simulation dataset and the simulation datasets with outliers, the impact of outliers on the effectiveness of removal of inter-site heterogeneity by the ComBat harmonization algorithm was evaluated in two ways: (1) whether the site effects were successfully removed after ComBat harmonization when outliers were present in the data, and (2) whether the estimation of the associations between IDPs and behavioral variables were affected by the presence of outliers after ComBat harmonization. For the evaluation of (1), we visualized the IDP values of all data samples of all sites using the first and the second PCs of PCA under all six outlier scenarios , and with varying outlier quantities and outlier scores, before and after ComBat harmonization. We also calculated the mean and variance of a representative IDP (IDP-187) of normal samples under all outlier quantities and outlier scores. As for the evaluation of (2), we assessed the changes in the correlation coefficients between IDPs and behavioral variables (measured by both Pearson correlation coefficient 3.3.1.r) between all IDPs and the behavioral variable of each site  and the combined site (Site 1 + 2 + 3) in r ≈ 0.2) compared with the within-site correlations (r ≈ 0.4). The ICC values of the IDP-behavior correlation coefficients between different sites and between each site and the combined site also showed that the IDP-behavior correlation coefficients changed greatly (ICCs ranged between 0.0580 and 0.0598) when calculated across subjects of different sites  compared with those when calculated across subjects within sites (ICCs ranged between 0.8909 and 0.9458). Moreover, 13 high IDP-behavior correlations (r ≥ 0.6) were identified in all three sites for the within-site correlations, but none reached 0.60 for the across-site correlations.r ≥ 0.6) were identified for the across-site correlations after ComBat harmonization which included all 13 high IDP-behavior correlations (r ≥ 0.6) identified in all three sites for the within-site correlations.3.2.The 2-D data visualization of all subjects from different sites using the first two PCs of all IDPs under all six outlier scenarios with a fixed outlier quantity  and outlier score (10) before and after ComBat harmonization are shown in y-axis) changed with the outlier scores (x-axis) and with the outlier quantities (lines in different colors) both before ComBat harmonization (black dashed lines) and after ComBat harmonization (colored solid lines) in the six outlier scenarios for each site and the combined site.As depicted in As depicted in r) in ρ) in As depicted in ρ) did not change appreciably with the increase of the outlier scores in the sites with outliers for the within-site correlations (Site 1 in panels A-C and Site 1&2 in panels D-F).When using Spearman rank correlation to measure the IDP-behavior relationships , similarAs for the quantification of the effect of outlier location  on the IDP-behavior relationships, the ICCs of the 35 IDP-behavior correlation coefficients between the “unilateral” scenario and the “bilateral” scenario, calculated for the scenarios of “one-site outliers”  and for the scenarios of “two-site outliers”  separately, are summarized in 4.In the present study, we tested whether and how the presence of outliers could affect the effectiveness of ComBat harmonization in the removal of inter-site heterogeneity using simulated multisite neuroimaging data. Importantly, the simulated dataset was generated based on a real large neuroimaging dataset, and different outlier properties such as the outlier location , the outlier quantity  and the outlier score (how far the outliers deviate from the majority of the data) were modulated in the simulated data. With this design, we were able to test how these outlier properties could affect the effectiveness of ComBat harmonization using simulation data while maintaining a good representation of real data scenarios. We found that, although ComBat harmonization effectively removed the inter-site heterogeneity in multisite data which further improved greatly the detection of the true relationships between IDPs and behavioral variables in the absence of outliers, the presence of outliers could damage severely the performance of ComBat harmonization in the removal of data heterogeneity; instead, the presence of outliers might even introduce additional inter-site differences in the data. Such outlier effects were dependent on the quantity and deviation level of outliers – the effectiveness of ComBat harmonization was severely biased when the outlier quantity was greater than 5% or the outlier score exceeded 10; only moderate effects were observed otherwise. Moreover, we found that the degree of the impact of the outliers on the improvement of the detection of IDP-behavior relationships was dependent on how the IDP-behavior relationships were assessed  – the degree of the impact was generally larger when Pearson correlation was used than when Spearman correlation was used, and was also dependent on outlier quantity and outlier score.4.1.Although we have demonstrated the effectiveness of ComBat harmonization in the removal of inter-site heterogeneity in the absence of outliers, the results of our present study clearly showed that such effectiveness could be much reduced or even eliminated by the presence of outliers. We found that, after ComBat harmonization, the data distributions of the sites with outliers were no longer aligned with those of the sites without outliers in terms of the space covered by data samples ; instead, the distribution of data majority became much denser for the sites with outliers. This is because the means and variances of multiple sites are supposed to be similar after ComBat harmonization, and the exaggerated variance caused by outliers would result in a decrease in the variance of normal values. In other words, due to the presence of outliers, the data variance would be overestimated and thus the variance of the data majority  would shrink after ComBat harmonization even if there had been no differences in the mean or variance of non-outlier samples between this site and other sites before harmonization.We also manipulated the location, the quantity, and the deviation level of outliers to test their impact on the data distribution  of each site after ComBat harmonization. We found that the mean of normal samples was affected to an increasing extent, as the outlier quantity and outlier score increased. As for outlier location, when outliers were added to only one side of the data distribution, the mean of normal samples would shift opposite to where outliers were added. In the scenarios where outliers were added to both sides of the data distribution with different outlier scores, the mean of normal samples would shift opposite to where the outlier score was greater. We also found that the variances of normal samples decreased with the increase of outlier score and outlier quantity in the sites with outliers, resulting in a denser distribution of normal samples due to the existence of outliers.4.2.As the presence of outliers reduces the effectiveness of ComBat harmonization in the removal of inter-site heterogeneity, they would thereby lead to erroneous assessment of IDP-behavior relationships, which were confirmed by the results of our present study. Furthermore, we found that the degree of the confounding effect of the outliers on IDP-behavior correlation coefficients was dependent on the outlier quantity and outlier score , 7 but wAlthough what we were mainly interested in here was the impacts of these outlier properties  on the assessment of across-site correlations between IDPs and behavioral variables in a mega-analysis after ComBat harmonization, we also examined the impacts on the within-site correlations to see whether and how ComBat harmonization could affect the assessment of IDP-behavior correlations in the presence of outliers within each site. For within-site correlations, the assessment of IDP-behavior relationships was unaffected for the sites without outliers which is expected given that it has been demonstrated using the normal simulation data in the present study that ComBat harmonization did not affect the correlation coefficients in the absence of outliers. However, the assessment of IDP-behavior relationships was affected by both outlier quantities and outlier scores for the sites with outliers in all six outlier scenarios especially when Pearson correlation was used – the Pearson correlation coefficients decreased gradually when the number of outliers and/or the outlier scores increased. Moreover, the impact of the outliers on the assessment of IDP-behavior relationships was also dependent on how such relationships were assessed. Pearson correlation coefficients were affected more badly than Spearman rank correlation coefficients. As the data samples were replaced with their rankings before the calculations of the correlation coefficient, the resultant correlation coefficient was more robust to the change of the actual IDP values caused by the change of outlier scores, although it still decreased with the increase of the number of outliers.Compared with the impact of the outlier locations  on multisite data distribution, the IDP-behavior correlation coefficients measured either by Pearson correlation or Spearman rank correlation did not seem to be affected much by the outlier locations, except that the impact on the Spearman correlation coefficients seems smaller when the outliers were present only in one site than when they were present in two sites, especially when a relatively large number of outliers (≥5%) were added to different sides of data distribution on two sites. This is because a larger number of outliers, which was twice of the outliers added on single site, were added to two sites, and thus may disturb more badly the rankings and consequently the estimation of correlation coefficients.Now that the presence of outliers could reduce the effectiveness of ComBat harmonization in the removal of inter-site heterogeneity of data distribution and in the detection of IDP-behavior correlations, we recommend, consistent with the workflow commonly used in most multisite studies, that outliers should be removed before ComBat harmonization. However, there might also be cases where outliers represent genuine biological variability  and thus are of interest to be included in the study, it is possible to “harmonize” the outliers using the ComBat parameters generated from the data excluding outliers using the following procedure: remove the outliers first before ComBat harmonization, then estimate the batch-specific overall mean and variance parameters only using the normal data, and finally “harmonize” all data (including outliers) using the above-estimated parameters.4.3.There are a few limitations in the present study. First, although we were trying to be comprehensive by creating six outlier scenarios and modulating both outlier quantity and outlier score in each scenario, outliers in real data may be more complicated than all the scenarios simulated in the present study, and thus the impact of outliers on the effectiveness of ComBat harmonization may be more complex than what we demonstrated here. Second, we only focused on the ComBat harmonization method in the present study and thus how the presence of outliers affects the effectiveness of other harmonization methods remains unclear and needs to be addressed in the future. Third, we only demonstrated the effects of outliers on the effectiveness of ComBat harmonization using simulated data rather than real data because a meticulous quality control procedure was used during data acquisition of the CHIMGEN database and the quantity and deviation level of the outliers were very low in this particular real dataset , thus resulting in a minimal effect of outliers on the effectiveness of ComBat harmonization. Other real data with large and severe outliers may be tested for the effects of outliers on ComBat harmonization in future studies.4.4.In summary, our present study systematically characterized the impact of outliers on the effectiveness of ComBat harmonization in the removal of inter-site heterogeneity of data distribution in multisite neuroimaging studies, and demonstrated that the presence of outliers may affect or even eliminate the effectiveness of ComBat harmonization and thus lead to erroneous results for the across-site analyses of brain-behavior associations. The findings deepen our understanding of the mechanisms by which outliers may affect the performance of ComBat harmonization and the results of subsequent brain-behavior association analyses according to their location, quantity, and deviation level, and highlight that the influence of outliers is not negligible and they must be detected and removed carefully prior to ComBat harmonization in multisite neuroimaging studies.The simulation data supporting the conclusions of this article will be made available by the authors, without undue reservation.QH and ML designed the study. QH, XX, and SW analyzed the data. QH and XX visualized results and drafted this article. QH, XX, and ML revised this article. WQ, CY, and ML advised the design and data analysis of this study. All authors contributed to the article and approved the submitted version.This study was funded by the National Natural Science Foundation of China (Grant nos: 81971694 and 81971599).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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Diagnosis of allergic diseases is a complex, multi-stage process. It often requires the use of various diagnostic tools. The in vitro diagnostics (IVD), which includes various laboratory tests, is one of the stages of this process. Standard laboratory tests include the measurement of the serum concentration of specific immunoglobulin E (sIgE) for selected allergens, full allergen extracts and/or single allergen components (molecules). The measurement of IgE sIgE to the allergen components is called molecular allergy diagnosis. During the standard laboratory diagnostic process, various models of immunochemical tests are used, which enable the measurement of sIgE for single allergens  or IgE specific for many different allergens  in one test. Currently, there are many different test kits available, validated for IVD, which differ in the method type and allergen profile. The aim of the manuscript is to present various technical aspects related to modern allergy diagnostics, especially in the area of molecular allergy diagnostics. Allergy and hypersensitivity are increasingly becoming serious global public health problems. These reactions are based on complex pathophysiological mechanisms leading to the dysfunction of various organs and even leading to a life-threatening condition. Both allergy and hypersensitivity can develop at any age and have a significant impact on the quality of life of patients and their families . The terThe currently used division of hypersensitivity reactions was proposed in 1963 by Gell and Coombs. This classification divides hypersensitivity reactions into four distinct groups based on the mechanism of tissue damage : Type I Allergy diagnosis is a complex and multi-stage process. It begins with a clinical interview, physical examination of the patient and additional tests. Additional tests used in the diagnosis of allergic diseases can be divided into in vivo tests  and in vitro tests (laboratory tests). Allergy tests validated for IVD can in principle only confirm or rule out Type I (IgE-mediated) hypersensitivity reactions. The in vitro tests include, among others, serological diagnostics based on the measurement of serum levels of allergen-specific immunoglobulins IgE (sIgE) ,4.The possibilities of modern serological diagnostics of allergy are very extensive. The IgE specific for both whole allergen extracts and individual allergen components can be measured in the patient’s blood serum. It is possible to determine sIgE both for single allergens and for mixes of various allergens . Currently available serological tests can also be used to measure the serum sIgE level for a single allergen , as well as for many different allergens in one test  ,6.This type of laboratory test can be performed with test kits from various manufacturers that have been validated for in vitro diagnostics (IVD) and have certificates required by dedicated legal regulations in force in a specific country. The manufacturers of the test kits guarantee that the results of laboratory tests measured using the analytical reagents produced by them are reliable and repeatable. The test kits are provided with instructions for use, which also define the conditions for the storage of reagents and the collection of biological material for testing and storage. The manual also specifies possible factors interfering with the analytical procedure and the patient’s preparation for a specific laboratory test, including the possible need to discontinue various drugs or other clinically significant parameters that may interfere with the analytical procedure.Routinely, under standard conditions, the measurement of IgE is measured in the serum of venous blood. Blood for the test should be collected in the morning, although in the case of IgE it is not an obligatory recommendation, as this parameter is not subject to significant daily fluctuations. In these types of laboratory tests, there is also no need to stop treatment with medicines normally used to treat allergies, because they do not interfere with the analytical procedures used. The patient does not have to be fasting, although it is definitely better to fast before each laboratory test, even if it is not absolutely required. It may be important to refrain from ingestion dietary supplements, mainly vitamins , which may significantly interfere with some laboratory procedures and may cause false-negative or false-positive results. Vitamins should be stopped at least 7 days before taking blood for laboratory testing. It is important that each laboratory prepares appropriate guidelines for the patient on how to prepare for the collection of biological material for a specific laboratory test .The technique of all currently available serum sIgE assays is based on the Enzyme-Linked Immunosorbent Assay (ELISA) method, which involves the binding of IgE antibodies from the patient’s serum to the allergens for which these antibodies are specific .The allergy laboratory tests use standardized allergens, extracts or single allergen components (native or recombinant), which can be suspended in the liquid phase or bound in the solid phase . If the patient’s serum contains IgE antibodies specific to the allergen used in the test, the allergen/allergen-specific IgE complexes are formed. The complexes are bound to the solid phase and unbound serum components are washed out of the reaction tube with a washing solution . These complexes are then visualized using antibodies specific to human IgE  labeled with the enzyme and a substrate compatible with the labeling enzyme used (chromogen or fluorochrome). The intensity of the reaction is expressed as color intensity or fluorescence intensity (depending on the marker used) and is proportional to the concentration of specific IgE in the tested serum. If appropriate calibration standards are used, the method can be quantitative. Quantitative or semi-quantitative methods are usually used to detect sIgE ,9,10.The solid-phase immunoassays based on the ELISA model for routine immunochemical diagnostics, including the measurement of specific IgE levels, were available as early as the 1970s. Initially, radioimmunoassays  were used. RAST tests consisted of immobilizing allergen extracts on activated paper discs to bind specific IgE from the patient’s serum. Paper discs were the solid phase of the RAST test. Radiolabels were used to detect allergen/sIgE immune complexes in RAST tests. Currently, radioactive tracers are not used, mainly due to their harmfulness. They have been effectively replaced by colorimetric markers, and above all by fluorescent markers (chemiluminescent and electrochemiluminescent techniques) ,10. The materials of the solid phase of testing have also changed. In addition to paper discs, which are rarely used anymore, various types of polymers and multidimensional cellulose matrices (cellulose sponges) are more often used. There are also reaction models based on allergens suspended in the liquid phase. In this assay model, the first step of the reaction  takes place in the liquid phase. In the next step, the formed allergen/sIgE complexes are immobilized on a solid phase matrix. Matrices made of glass fibers or nanoparticles with paramagnetic properties are most often used ,12.Qualitative, semi-quantitative and quantitative tests are used to measure the concentration of specific IgE in allergy diagnostics. Quantitative immunoassays for sIgE antibodies require the inclusion of a standard curve. Calibrators for both specific and tIgE measurements should be traceable to the World Health Organization (WHO) International Reference Preparation for Human IgE, 75/502 .Traditional immunological  diagnosis of allergies is based on the determination of IgE antibodies specific for extracts of native allergens. This type of allergy diagnosis is called allergy diagnosis based on extracts. Allergen extract is a mixture of allergenic and non-allergenic proteins derived from a specific allergenic source . Extracts from natural allergen sources are often heterogeneous and may contain many non-allergenic molecules. Extracts of the same allergen from different lots may differ in the composition and amount of allergenic proteins. The detailed composition of full extracts is not clearly possible to determine. Extracts from the same allergen source from different manufacturers may have different component compositions. The composition of extracts from food allergens also depends on the method and conditions of production of a particular food, cultivation and storage of plants or animal husbandry technology. In addition, the presence of bioactive molecules  in natural extracts may affect the stability of the allergen extract. Contamination of the natural allergen extract with proteins from other sources is also possible ,17,18,19Escherichia coli (E. coli), or yeast cells, are most commonly used for the production of recombinant allergens. This technology makes it possible to produce unlimited amounts of perfectly purified allergens with precisely defined molecular structures. The downside of recombination is that proteins produced in this way do not undergo post-translational modifications . This may change their spatial structure and immunological specificity. Such molecules may not bind antibodies produced by immunization with natural proteins. In turn, the advantage of recombinant allergenic components is that as they are devoid of carbohydrate groups, they do not bind clinically insignificant antibodies specific for cross-reactive carbohydrate determinants (anti-CCD). This prevents false-positive results with sIgE tests [Molecular diagnosis of allergy is based on the detection in the blood serum of IgE specific against single allergen components (molecules) from specific allergen source. An allergenic component is a single protein or, less commonly, a carbohydrate moiety that can induce an allergic immune response ,20,21,22gE tests ,14,15,16Traditional in vitro allergy diagnosis based on allergen extracts makes it possible to determine the source of sensitizing allergens, but it does not allow for the identification of cross-reactions or estimation of the severity of a possible reaction after contact with this allergen. This is especially important for patients allergic to food allergens. Molecular diagnosis of allergy enables accurate identification of the allergen molecule to which the patient is allergic. This gives the opportunity to precisely identify a harmful allergen, determine possible cross-reactions, predict the severity of a possible reaction after contact with this allergen, including severe anaphylactic reactions, or estimate the expected effectiveness of allergen immunotherapy. It also helps to determine the dietary management of a patient with food allergy ,20,21.Molecular diagnosis of allergies is a method aimed at determining the individual allergic profile of the patient. It is treated as a type of personalized medicine. Currently, this type of diagnostics can be performed in two ways—by measuring the concentration of IgE specific for single allergen molecules (single-component diagnostics) or by simultaneously measuring the concentration of sIgE for many allergenic components in one test  ,6.Monocomponent molecular allergy diagnostics is a measurement of the serum concentration of IgE specific for a selected allergen molecule. Validated kits from various manufacturers are available in routine laboratory practice. They differ in analytical sensitivity and specificity. These differences result from the technical and methodical construction of the test. These are optional solid or liquid phase tests. Solid phase tests may differ in the material from which the solid phase is made . Native or recombinant allergen components may be used in these tests. There may be a different type of detection antibody, a different label, and a different way of detecting the result of the reaction. Each test kit is dedicated to performing the test on a compatible immunochemistry analyzer. The result obtained in singleplex studies is always a quantitative result. These techniques require a relatively large volume of serum per allergen per test . Singleplex tests are also relatively expensive per single sIgE result [Multicomponent molecular allergy diagnostics is based on the determination of specific IgE concentration for many different allergen components in one test. Such a diagnostic model is possible thanks to the use of multi-parameter tests for in vitro diagnostics, the construction of which is based on allergen matrices and nanotechnology. Allergen matrices used in this type of analysis are a powerful diagnostic tool that enables simultaneous analysis of IgE specific for many different allergens in biological material during one analytical process. Microarray technology allows for the simultaneous analysis of thousands of parameters in a single experiment. Microspots of capture molecules, e.g., allergens, are immobilized in precisely defined places on a solid substrate  and then exposed to samples of the test biological material. If there are appropriate analytes in the tested biological material, they are bound to molecules coated on the matrix. The immune complexes that are the product of the reaction are then detected by appropriate immunochemical techniques, and signal detection can be based on fluorescence, chemiluminescence, mass spectrometry, densitometry or electrochemistry .It is worth noting that any test in which sIgE is determined for more than one allergen at the same time is a multiparameter test. Using currently available allergy diagnostic multiplex platforms, it is possible to determine the concentration or level of allergen-specific IgE for several to several hundred allergen molecules during a single analysis. These are solid phase tests giving quantitative or semi-quantitative results. Several component or component-extract panels are available, requiring about 100–200 µL of blood serum, which are usually targeted at a specific allergen source  and panels in which we simultaneously determine sIgE for several hundred allergen components, or both extracts and allergen components, aimed at determining the individual allergy profile of the tested patient.The laboratory analytical platforms enabling the simultaneous detection of IgE specific for several hundred allergens are usually used in the diagnosis of multi-sensitized patients. These tests contain allergen components from several dozen different allergen sources. They can be used to detect either sIgE only for allergen components  or sIgE for allergen components, allergen extracts and the total concentration of IgE in the blood serum . Currently, two multiplex platforms, ISAC and ALEX tests, are available for broad-profile allergy molecular diagnostics. The ISAC and ALEX tests differ in the allergen profile, immunochemical technology, laboratory procedure, method of reporting results and the volume of serum needed to perform the test.The ISAC test is a solid phase enzyme immunoassay based on biochip technology. A biochip (microarray) is a solid matrix  on which specific biological molecules have been precisely applied in a specific location. This technology makes it possible to simultaneously detect the presence of many biological components in a small amount of test material (e. g. blood serum), using a small volume of reagent. In the ISAC test, allergen components applied in triplets on a glass plate (slide) bind IgE antibodies specific to them from the patient’s blood serum. Specific IgE from the test serum are captured by the allergens coated on the slide and bound to the solid surface of the test. The formed allergen/sIgE immune complexes are then detected with fluorochrome labeled anti-human IgE antibodies. Each ISAC slide contains four microarrays for four patients. The fluorescence intensity is measured with a biochip scanner that is compatible with the ISAC test slides and the fluorochrome used. The manufacturer of the ISAC test recommends the use of laser devices for confocal scanning, in particular the CapitalBio LuxScan 10 k microarray scanner . The microarray images are then analyzed using Phadia Microarray Image (MIA) software.® software (ThermoFisher Scientific Inc.) and the AllerGenius® expert system  [The ISACE112i platform is a semi-quantitative test and sIgE antibody results are reported in ISAC Standardized Units (ISU-E) and are divided into four categories based on level: <0.3 ISU-E (undetectable); 0.3–0.9 ISU-E (low); 1–14.9 ISU-E (medium/high); >15 ISU-E (very high). The test requires 30 µL of serum or heparinized plasma. The patient does not have to stop taking any medications and does not have to fast . The lab, Italy) ,26.The first multi-parameter allergen chips of this type (ISAC test) were made available in 2001. Since 2006/2007, the ISAC test has been widely available for IVD ,28. The The ALEX test is a solid-phase enzyme immunoassay based on technology using nanoparticles (nanospheres) as allergen carriers. It is the newest multiplex in vitro allergy test currently available. The use of nanoparticle technology as carriers of allergens makes the ALEX test a test with an easily modifiable allergen profile. This makes it possible to replace one allergen molecule with another in subsequent generations of the test. Thanks to this functionality, the allergen profile of the ALEX test dynamically responds to new clinical requirements and the discovery of new relevant allergen molecules .Technologies that use nanoparticles are nowadays often used in various branches of medicine, both therapy and diagnosis. Drugs and diagnostic tests using this technology are part of the trend of personalized medicine. The nanoparticles are defined as particles with at least one cross-sectional dimension in the range of 1 to 100 nm. They can be formed naturally (as a result of erosion of rocks or organic compounds), or they can be by-products of human activity, or they can be intentionally created nanoparticles of a defined size and structure . Engineering nanoparticles can be made of various materials: gold, silver, platinum, carbon, cobalt, copper, silicon and other materials ,31,32.8 m−1 for nanoparticles with a diameter of 10 nm), thanks to which large amounts of various substances can be placed on a small nanoparticle surface. This ratio is greater the smaller the diameter of the nanoparticle. In addition, the small dimensions of nanoparticles make it possible to place a large number of them on a small surface area of the solid phase. Depending on the material from which they were made, nanoparticles have the ability to bind various biological molecules  on their surface, thanks to which they become their carriers. The process of producing particles with desired physical, chemical or biological properties is called functionalization of nanoparticles. Functionalization affects the ability of nanoparticles to interact with specific chemical or biological substances [A characteristic feature of nanoparticles is a high ratio of surface area to particle volume , can be immobilized in a solid phase . This enables the construction of comprehensive solid phase assays for IVD. With the use of nanoparticles, complex test platforms are constructed, which are used for simultaneous multi-analysis of a small volume of biological material . Functionalizing biomolecules  are applied to the surface of the nanoparticles before immobilizing them in the solid phase. Such a strategy allows the use of the most optimal conditions necessary for the application of functionalizing agents to the surface of the nanosphere. Thanks to this property, the surface of the nanoparticle is used to the maximum, and the concentration of functional biomolecules  on the nanosphere is very high. Tests constructed in this way are usually characterized by very high analytical sensitivity. From the point of view of the possibility of obtaining the highest possible analytical sensitivity of the test, it is also important that the nanoparticles are coated with various functionalizing factors  independently of each other. This allows for adjusting the conditions of functionalization of nanospheres to the specific requirements of specific functional biomolecules. Thanks to such a strategy, optimal use of the surface of the nanoparticle is obtained in relation to each applied functionalizing agent. For example, in the context of tests for the analysis of sIgE, the conjugation of allergens with nanospheres is performed using protocols optimized for specific allergens taking into account their biochemical properties. In addition, the availability of relevant epitopes is controlled, which increases the detection efficiency of sIgE. During the construction of the test, previously functionalized nanospheres are bound to the solid phase matrix. Thanks to this strategy, tests with very high analytical sensitivity are obtained for each analyte independently. In addition, the small dimensions of the nanoparticles make it possible to obtain a high density of these carriers on a small area of the test matrix, which also increases the efficiency of the test. In relation to tests for the detection of sIgE, this means the possibility of constructing analytical platforms on which even several hundred different allergens are bound to the matrix. Such multiplex matrices allow for the detection of IgE specific for several hundred different allergens simultaneously (in one test) and in a small volume of blood serum. In conclusion, the analytical tests constructed in the technology of nanoparticles are characterized by very high efficiency and high analytical sensitivity independently for each measured analyte ,31,32,33The first generation of the ALEX test was introduced for IVD in 2017 and enabled the simultaneous measurement of sIgE levels for 282 allergens, including 126 allergen components and 156 allergen extracts, and total IgE (tIgE) concentration. The current generation of the ALEX test  has been available since 2019. This edition offers simultaneous measurement of sIgE concentrations for 295 allergens, including 178 molecules and 117 extracts, from most families of inhalant allergens and cross-reactive food allergens . An impoIt is widely believed that one of the main added values of the ALEX2 test is the ability to simultaneously measure the concentration of sIgE for whole allergen extracts, as well as for allergen particles from many food and airborne sources and tIgE. This gives the possibility of comprehensive, broad-profile in vitro diagnostics of IgE-mediated hypersensitivity reactions .Another unique feature of the ALEX2 multiplex assay is that an inhibitor of antibodies against cross-reacting carbohydrate determinants (CCD) was used in the analytical procedure. CCDs are carbohydrate residues of glycoproteins, absent in mammalian proteins, that can induce the synthesis of CCD-sIgE antibodies. Anti-CCD antibodies specifically bind to CCDs that may be present on allergen particles of natural origin but are unable to induce mast cell degranulation. In most cases, they do not contribute to the clinical symptoms of allergic disease and do not give positive results in skin tests. In general, anti-CCD IgE antibodies are considered clinically insignificant  found, for example, in tick saliva), but may cause false positive results of sIgE in in vitro allergy tests. This significantly hinders the clinical interpretation of the laboratory test result, which is not adequate to the clinical manifestation of allergy observed in the patient. It is estimated that this problem may affect up to 30% of patients. The source of CCDs stimulating the synthesis of specific anti-CCD antibodies in the IgE class are most often allergens of plant pollens and venoms of Hymenoptera insects and some allergens of dust mites. Blocking anti-CCD antibodies in the standard ALEX2 assay procedure significantly reduces the number of false positive sIgE results ,37,38,39Two methods are most commonly used to solve the problem of anti-CCD antibodies. One of these strategies is the use of recombinant allergens in the test. Recombinant allergens do not undergo natural processes of post-translational processing, e.g., glycosylation. Therefore, they do not expose carbohydrate determinants in their molecules (they are CCD-free). The second strategy is the binding of anti-CCD antibodies, which may be present in the test serum, before the actual analytical procedure (pretreatment) or during the first incubation of the test, using an anti-CCD blocker/inhibitor. For this purpose, the test serum is mixed with an excess of glycoconjugates competing for the binding of anti-CCD antibodies  with the appropriate volume ratio (according to the procedure). The blocking reagent usually contains a mixture of CCD molecules of natural or synthetic origin that bind specific antibodies to each other. In the initial phase of the test, anti-CCD IgE antibodies are bound to free CCDs from the CCD-blocker/inhibitor (anti-CCD CCD/IgE complexes are formed) and then they are removed from the test plate in the subsequent washing steps provided for in the analytical procedure. The CCDs inhibitor must be engineered to block only anti-CCD antibodies and not affect other sIgE antibodies. Therefore, although theoretically it would be possible to use any glycoproteins of plant origin as an anti-CCD-blocker/inhibitor, in practice, synthetic CCDs turned out to be a much better solution ,41. A syAfter the analytical procedure is completed, the ALEX2 cartridge is scanned using a dedicated image analyzer (ImageXplorer) and the resulting image is analyzed by dedicated analytical software (MADx Raptor Software). The software calculates the concentration of sIgE bound by the individual allergens on the ALEX2 assay matrix and evaluates the level of tIgE relative to the control value . The ALEAllergy diagnosis based on allergen molecules  is a precise tool for in vitro allergy diagnosis. The development of tools for MA significantly improved the quality of allergy diagnosis, giving the opportunity to distinguish true allergies from cross-reactions, selection of specific allergen immunotherapy and risk stratification in food allergy. Extensive, multi-parameter platforms for molecular diagnostics of allergies are precise tools for determining the individual profile of patients’ sensitization. These multiplexes are specific diagnostic tools in line with the current trend of personalized medicine ,44. CurrMolecular diagnosis of allergies is undoubtedly a very valuable tool necessary in the diagnostic and therapeutic process of allergic patients, especially in difficult, life-threatening conditions or requiring an individualized dietary approach. However, it is still an insufficient tool in particularly difficult cases of allergies, e.g., to very rare allergens or in multi-allergic patients requiring personalized therapeutic strategies. It seems that in the future it will be necessary to use even more advanced diagnostic tools. In vitro diagnostics based on sIgE for epitopes of allergen molecules seem to be a promising tool for the future of allergological diagnostics ,48.According to Alessandri et al. , an idea


In the existing rolling bearing performance degradation assessment methods, the input signal is usually mixed with a large amount of noise and is easily disturbed by the transfer path. The time information is usually ignored when the model processes the input signal, which affects the effect of bearing performance degradation assessment. To solve the above problems, an end-to-end performance degradation assessment model of railway axle box bearing based on a deep residual shrinkage network and a deep long short-term memory network (DRSN-LSTM) is proposed. The proposed model uses DRSN to extract local abstract features from the signal and denoises the signal to obtain the denoised feature vector, then uses deep LSTM to extract the time-series information of the signal. The healthy time-series signal of the rolling bearing is input into the DRSN-LSTM reconstruction model for training. Time-domain, frequency-domain, and time–frequency-domain features are extracted from the signal both before and after reconstruction to form a multi-domain features vector. The mean square error of the two feature vectors is used as the degradation indicator to implement the performance degradation assessment. Artificially induced defects and rolling bearings life accelerated fatigue test data verify that the proposed model is more sensitive to early failures than mathematical models, shallow networks or other deep learning models. The result is similar to the development trend of bearing failures. Rolling bearings are key components of mechanical rotating equipment, and their operating condition can determine the overall equipment performance life . The effIn general, bearings in mechanical equipment will undergo a performance degradation from normal to degradation and failure. Monitoring the operating status of bearings and analyzing their vibration signals can effectively detect early, weak faults and reveal their performance degradation trends, which can be utilized to develop focused equipment maintenance strategies. Therefore, the performance degradation assessment of rolling bearings is the premise and key point of implementing condition-based maintenance .The bearing performance degradation assessment process can be divided into: Feature extraction, model establishment, and assessment analysis with indicators construction. In these three steps, a lot of research work has been done by domestic and foreign scholars. Liu et al.  combinedAlthough good results in equipment performance degradation assessment can be achieved with the above methods, there are still some problems: (1) A series of feature engineering of signals has to be implemented such as feature selection, feature downscaling and feature fusion in the feature extraction process. Feature engineering requires a lot of effort and time in designing the preprocessing process and data conversion, which requires certain expert knowledge to judge the quality of feature extraction; (2) signals collected from sensors are inevitably mixed with a large amount of noise and transmission path interference, and the direct feature extraction of signals with noise and interference will influence the effect of performance degradation assessment; (3) most of the performance degradation assessment models are simple linear mathematical models or shallow networks, which have limited the ability to learn and extract features, and the volume of data that are input into model is quite low, which cannot meet the data analysis requirements in the background of big data; (4) the timing information of the bearing signal is usually ignored after inputting into the model. Especially for the performance degradation model of signal reconstruction , the timIn recent years, with the rise of artificial intelligence, deep learning has been widely used in the field of equipment fault diagnosis . The mosConsidering the above advantages, a few scholars have started to use deep learning methods for equipment state performance assessment. Xu et al.  proposedIn summary, an end-to-end rolling bearing performance degradation assessment model based on DRSN-LSTM is proposed in this paper, in which DRSN is used to extract local abstract features and implement denoising operation from signals to obtain the denoising feature vector. The signal timing information is extracted using deep LSTM and then the original signal is reconstructed using two nonlinear layers. In order to comprehensively encompass the state information of the rolling bearing signal, the time-domain, frequency domain, and time-frequency domain features before and after signal reconstruction are extracted to form a multi-domain feature vector. Finally, the mean square error of two feature vectors is used as the degradation indicator, and the effectiveness of the proposed method for detecting early bearing faults and portraying the monotonicity and consistency of the performance degradation process is confirmed using the bearing fatigue test data.The Deep Residual Shrinkage Network (DRSN) , is imprj′-th weight of the i-th convolution kernel in the l-th layer, j’-th bias of the i-th convolution kernel in the l-th layer. The local field of perception of the j-th convolution kernel in the l-th layer is k is the width of the convolution kernel.I is the input feature size. O is the output feature size. K is the convolutional kernel size. S is the convolutional step size of the convolutional kernel. P is the size of padding.In the convolution layer, the convolution kernel implements the convolution operation with the input data to obtain the feature map. The convolution layer can be used to extract the required features by changing the parameters of the convolution kernel. The convolution operation of the convolution layer is shown in Equation (1). Different features of the data can be learned by using different convolution kernels during feature extraction. The formula for calculating the feature map output during the convolution operation is shown in Equation (2).Since most of the problems, in reality, are nonlinear. In nonlinear problems, errors inevitably arise if modeled with linear functions. To enhance the nonlinear modeling capability, nonlinear activation functions have been introduced to CNN. The activation function layer enables CNN to learn complex and nonlinear features from the data. The activation function layer is generally placed between the convolutional layer and the pooling layer. The commonly used activation functions are the sigmoid function, the tanh function, and the linear rectification function (ReLU). ReLU is used as the activation function in DRSN.The mathematical formula of ReLU is:BN can change the distribution of feature value in the layer, pulling it back to the standard normal distribution. The feature value will be distributed in an interval where the activation function is more sensitive to the input. A small change in the input can correspond to a larger fluctuation in the loss function, resulting in a larger gradient, avoiding the problem of gradient disappearance and speeding up the training convergence. The formula of BN can be expressed as:x is input, y is output, In signal processing, the soft-threshold segmentation algorithm implements signal denoising by converting the near-zero feature values of the signal to zero. The soft-threshold function can be expressed as:cZ is the node feature in channel C. DRSN implements feature extraction with simultaneous denoising by introducing a soft thresholding function to construct RSBU which is the most important part of DRSN. The detailed structure of the RSBU is shown in i, j, and c represent the width, length, and the number of channels of the feature map x, respectively; x.In Input From the above, it can be seen that the threshold module can automatically set the threshold value during the training process of the network.The traditional Deep Neural Networks (DNN) have the advantage of nonlinear data processing, but they are not sensitive to processing time-series data problems. The Recurrent Neural Network (RNN) can process time-series data through chain interconnection between its own neurons.LSTM  introducIn the training and testing of LSTM, the value of each hidden layer is determined using the current moment input and the previous moment hidden layer value. The memory unit structure of LSTM consists of three gate controllers, which are the input gate, the forgetting gate and the output gate. Three gated recurrent memory units implement the forgetting and updating of the memory state information, during which the key information is ensured to be retained by memory and the minor information is forgotten, ensuring the flow and storage of memory state information among hidden layers. The process keeps recurrent until the input of all temporal data is completed. The memory cell structure of LSTM is shown in Immediate memory state information of the current moment Current moment memory state information Output gate Output of the current hidden layer of the LSTM t, respectively; t − 1 and t, respectively; In Equations (11)–(16), The flow of the proposed performance degradation assessment method is shown in This model is a data reconstruction model. The encoder part consists of 20 DRSNs and 1 LSTM. The decoder consists of two fully connected layers, and the structural parameters of the DRSN-LSTM reconstruction model are shown in In this paper, the training process of the DRSN-LSTM data reconstruction model is to first randomly select data from the bearing health state signal dataset as model input, and intercept the signal into segmentation. This paper divides the signal into 20 segments. Each segment of the signal is denoised and downscaled separately using DRSN, which can encode a 20 × L signal vector into a 20 × L/8 temporal feature vector. In order to retain the spatial and temporal information of the vector, the 20 × L/8 timing feature vector is encoded using LSTM as 20 × L/32. The encoded vectors are flattened and input to two linear layers for signal reconstruction, and setting loss function to optimize the distance between the reconstructed signal m is the vector length of the multi-domain feature extraction vector In the testing process, similar to training, data are selected one by one from the testing dataset as the input of the trained data reconstruction model. DRSN and LSTM encode input signals to obtain multiple sets of low-dimensional coding vectors that retain temporal information, and the flattened coding vectors are decoded with two-layer linear model to obtain the reconstructed signals. In order to fully obtain the information of signal features, the input signal and the reconstructed signal are extracted with multi-domain features, including 12 groups of time-domain features, such as mean value, standard deviation, square root amplitude, root mean square value, cliffs, peak value, margin, waveform factor and pulse indicator; 12 groups of frequency-domain features, such as spectral mean value, spectral root mean square value, frequency center of gravity, average energy, spectral concentration, main band position change, root mean square frequency and standard deviation frequency. The signal is decomposed with a three-layer db5 wavelet packet to obtain eight sets of time-frequency domain features with eight frequency sub-band energy ratios. Time-domain features, frequency-domain features, and time–frequency-domain features form a 32-dimensional input signal feature extraction vector The defects data are collected from a pure rolling test platform of a high-speed train. The train moving mechanism consists of two groups of bogie systems. Each group of bogies has two groups of wheels and axle box bearings. The train was driven by a drive motor. In Selecting 50 samples from failure-free bearings signal in the healthy state as the train dataset, the length of each group of samples was 10,240, and also collecting 50 samples from failure-free signal and three different fault degree signals as test dataset, and the data length was also 10,240. The train samples were input into the DRSN-LSTM reconstruction model, and the network loss value was constructed with the mean square error of the input signal and reconstructed signal.As shown in In this paper, the XJTU-SY rolling bearing accelerated full-life dataset has been used to verify the effectiveness of the proposed method in performance degradation assessment under actual working conditions. The test rig consisted of an AC motor, motor speed controller, support bearings, rotating shaft, acceleration sensor, test bearing and hydraulic pressurization system, etc., as shown in The time-domain waveform of the whole life-circle vibration signal is shown in The developed model was implemented using Pytorch-1.10.0. Experiments were conducted on a laptop with an i7-11800H CPU and a NVIDIA RTX3060 GPU. The optimal hyperparameters were finally determined by changing them several times to debug the program. So, the iterations of network (Epoch) were 300, the number of batches (Batch Size) was 2, and the learning rate was 0.001. The gradient descent optimizer for network training was Adam, and the loss function was the mean square error loss function.The first 400 sets of health state data of the bearing fatigue test full life bearing 1 were input into the model for training. The length of each data was intercepted L = 10,240. Four hundred sets of data were disrupted for each training to prevent the model from overfitting due to sample sorting. The network loss value was constructed using the mean square error between the input signal and the reconstructed signal. As shown in The bearing 1 full 0-life data, i.e., 2538 sets of data, were all input one by one into the trained DRSN-LSTM reconstruction model. The length of data was also 10,240. The input signal was reconstructed using the DRSN-LSTM reconstruction model to output the reconstructed signal. The input signal and reconstructed signal were extracted using multi-domain feature extraction to obtain the input signal feature extraction vector and reconstructed signal feature extraction vector, respectively. The mean square error between two vectors was calculated to obtain the bearing performance degradation assessment graph shown in As can be observed from In The mean square error indicator dropped slightly from the 767th sample to the 808th sample, which indicated that, previously, the early failure of the bearing appeared during a short period of grinding flat. From the 808th to the 2046th sample, the mean square error indicator generally showed an increasing trend, which indicated that at this time the bearing is in the moderate failure stage. From the 2046th to the 2403rd sample, the mean square error indicator was generally decreasing, and the bearing was worn out again at this stage. During the period from sample No. 415 to sample No. 2403, the trend of the mean square error indicator of testing data increased, then decreased, and then increased and decreased again, which indicated that the bearing to be tested in its stage had the phenomenon of “gradual aggravation of failure—failure point wears out—deterioration again—deterioration wears out”. In the 2403rd to 2538th samples, the mean square error indicator degradation curve showed an upward trend in general, although there was a certain fluctuation in the middle stage, which indicated that the bearing had occurred the phenomenon of “wears out—deterioration” again. Because the time of its multiple fluctuation phenomenon was greatly shortened, it indicated that the bearing was in the stage of extreme deterioration to failure.To verify the superiority of the proposed method, the performance degradation of the same bearing data is assessed using multiple comparison methods under the same model hyperparameters, and the result curves are shown in In order to study the influence of DRSN model on bearing performance degradation assessment results, the CNN-LSTM model was used to build the signal reconstruction model. The mean square error between reconstructed signal and input signal after multi-domain feature extraction was used as the performance degradation indicator. The result curves are shown in The DRSN-LSTM model was used to reconstruct the signal, in the model testing stage, without feature extraction, the mean square error between reconstructed signal and input signal was directly used as the performance degradation indicator. The curve shown in In this paper, the early failure points detected for bearing 1 are 540 points ahead of the 955th sample early failure point detected using the multivariate state estimation model in the literature . This isTo further test the feasibility of the DRSN-LSTM reconstruction model, the envelope spectrum spectral peak factor was used as the optimization indicator for adaptive resonance demodulation of the 414th and 415th sample signals. The envelope spectrum analysis was implemented after resonance demodulation, and the results are shown in Based on the advantages of the deep learning model, a rolling bearing performance degradation assessment method incorporating noise reduction features and timing information is proposed to solve the problem that the input signal of the traditional performance degradation assessment model is mixed with a large amount of noise and transmission path interference, and its timing information is usually ignored when the model processes the input signal, which leads to an unsatisfactory bearing performance assessment. The fatigue test data show that.(1)The reconstruction model of rolling bearing performance degradation assessment proposed in this paper removes the noise in the signal while retaining its time information when reconstructing the signal, so as to better reflects the useful components of the original signal and improve the validity of the performance degradation assessment results.(2)The reconstruction model for rolling bearing performance degradation assessment proposed in this paper is an end-to-end data-driven model, in which the model automatically learns spatial and temporal features in the signal. The tedious feature engineering processing and expert knowledge experience of traditional methods are not required.(3)The proposed method is more sensitive to early faults than mathematical models, shallow networks or other deep learning models, more similar to bearing fault trends, and has the advantages of better consistency and monotonicity.


Risk control in complex transport construction is complicated due to the dangerous nature of high variation and unpredictability. Most of the current research analysis focuses on the health, safety, and environment (HSE) risk assessment and employee performance evaluation, which neglects the impact of HSE risks on employee performance. Consequently, this research develops a framework to evaluate employee performance and identify key factors affecting performance. The employee performance indicators and HSE indicators are established by reviewing related literature. Using data from questionnaires, an artificial neural network- (ANN-) based model of employee activity effectiveness is then developed to evaluate employee performance. Sensitivity analysis is implemented to determine the key factors affecting employee performance. The Hong Kong-Zhuhai-Macau Bridge, a large-scale cross-sea channel project, is taken as a case study for validation. The model results show that the employees are satisfied with the effect of HSE management in general, but the psychological stress they perceive becomes large. The indicators of risk control and employee participation positively impact employee performance, while job satisfaction has a negative impact on performance. These findings indicate that operators should pay more attention to employees' psychological perception of work and form a standardized process management and control plan to prevent cumbersome processes from increasing employees' workload. This study helps construction systems and managers to identify the areas of strengths and weaknesses in their HSE management. The research only focuses on the impact of HSE risks on managers' performance in the complex transport construction project. In the future, further engineering projects and employee performance in composite scenarios can be investigated to improve the overall productivity. The transport industry has proven to be an indispensable component in guaranteeing the smooth running of socioeconomic activities by providing public services for social production and people's lives. Complex transportation construction projects (such as the Hong Kong-Zhuhai-Macau Bridge) face great challenges in risk control and safety management due to the dynamic complexity , sudden Health, safety, and environment (HSE), as a systematic and integrative management system developed in the 1980s, aims to assure safe production, reduce risks, prevent accidents, and achieve sustainable development . IntegraIn the field of HSE risk assessment, scholars have done considerable effort using many mature methods and new technologies. Risk assessment techniques used in the field of HSE generally calculate the risk value depending on the probability and severity factors . In the Minimizing human-caused risks is crucial to project construction and operation management due to the hazardous nature of complex transportation construction. The performance evaluation is of great importance considering that it can help reduce human-caused risks . PerformThe rest of this paper is organized as follows. Some relevant research and methods will be reviewed in the next part. This is followed by methods of the employee HSE performance evaluation. Next, the results and discussions are presented in succession. Finally, the paper is summarized.In recent years, complex transport projects have been constructed in massive numbers as an essential tool to support society travel. Complex transport projects involve multiple disciplines such as roads, bridges, railroads, and tunnels. A very high level of long-term safety risk exists in construction since it requires the collaboration of thousands of workers and sophisticated technologies. Its construction process is generally a substantial, expensive, and one-time construction project with complicated and varied construction sites. In addition to complex engineering systems, the construction usually concerns complex hydrogeological conditions, fluctuating working environments, considerable employee mobility, and lengthy construction cycles. All of these factors increase the risk of safety incidents and work-related injuries during all phases of construction , 26. TheHealth, safety, and environment, usually referred to as HSE, was proposed initially by the International Organization for Standardization Technical Committee in 1976. It aims to assure safe production, decrease accidents, and achieve sustainable development . The IntHSE system has played an important role in risk and safety management in construction industry in past decades. Many researchers have studied the HSE implementation and evaluation to improve safety in construction. Saksvik and Nytro  presenteNowadays, much attention has also been given to evaluating employee performance and identifying the HSE factors that affect performance because of the fact that more than 80% of incidents are attributable to illegal operations, low-security awareness, and poor technical skills of employees . In otheUnderstanding and structuring employee performance factors are very essential tasks for organizations to encourage productive behaviors of employees as well as discourage those that are unproductive. Three factors are used frequently to reflect the employee performance, whether positive or negative. These factors are job satisfaction , 40, 41,Job satisfaction has been defined in many forms. The traditional definition of job satisfaction focuses on the feelings that individuals have about their job experience . Job satHSE awareness refers to personnel that have a duty to be aware of both their own present status and the status of the defenses in their company . HSE awaWork stress is an adverse physical and emotional response that occurs when the workload is excessive , 48. WorIn this study, we use an intelligent methodology to complete the evaluation of employee performance. The whole methodology consists of three parts: (1) to establish employee performance indicators and HSE indicators; (2) to construct an ANN model to explore the relationship between employee performance indicators and HSE indicators, whose output is used as part of the employee activity effectiveness; and (3) to perform a sensitivity analysis to determine the key factors affecting performance. The flowchart of the proposed methodology is presented in This paper utilizes an intelligent methodology for employee performance evaluation in a complex transport construction project. Based on the literature review, three indicators reflecting employee performance are established . The HSE indicators are selected to observe employees' responses. A total of fifteen items are derived, which include important elements in international standard practices and evaluation models developed by Zohar , McDonalIn designing the questionnaire, we tried to cover fifteen HSE indicators and three employee performance indicators derived from the references. The preliminary questionnaire is distributed to a six-member group of experts. The group of experts consists of four HSE experts and two managers from HSE administrative, safety and environmental department, and other research institutions. All of the experts consulted have more than ten years of experience in safety engineering, civil engineering, or structural engineering. r is the margin of error or precision; σ is the standard deviation assumed to be 0.5; and Zα1−(1/2) is the standard normal statistic corresponding to the (1 − α) level.As a complex transportation construction project, the Hong Kong-Zhuhai-Macau Bridge covers several types of work, including bridges, island tunnels, and transportation engineering. As the project involves numerous aspects , with various types of risks, most of the grassroots personnel are responsible for the construction of the project and lack a thorough understanding of the HSE management system. In order to ensure the reliability and accuracy of the information obtained, we set the respondents as HSE managers of different departments, such as the Safety and Environmental Protection or Operation and Management Department, because the managers are relatively fixed and they were more knowledgeable about the HSE management implementation process. The respondents were trained during the survey process to accurately capture the content of the questionnaire and improve the quality of questionnaire filling. There were a total of 149 HSE management personnel working on the project, and 120 of them filled out the questionnaires based on the respondents' willingness. The margin of error determined according to the following statistical method is 0.089 . The final 96 questionnaires are adopted after removing the ineffective questionnaires. Despite the small dataset, the ANN-based model can still be applied because there are no strict criteria on the dataset size of ANNs. For example, in the previous studies of engineering management that used questionnaires as a data source for ANN, the dataset sizes were 250 in Patel and Jha , 115 in n represents the number of problems; si represents the standard deviation of the problem; and St represents the total standard deviation. Cronbach's alpha ranges from 0 to 1. Generally, alpha greater than 0.8 reflects a high internal consistency of the questionnaire; alpha in the range of 0.6–0.8 reflects a better internal consistency; a smaller value of alpha than 0.6 indicates that the internal consistency is poor [The demographic profile of the effective respondents is presented in  is poor .ne is the number of experts who choose “essential” and N is the total number of experts.This paper analyzes the validity of the questionnaire through a content validity ratio (CVR). Experts are invited to evaluate the question to the evaluation of the indicator as “not essential,” “useful but not necessary,” or “essential.” If more than 50% of the experts believe that the problem is essential, the item has certain content validity. CVR can be calculated using the following equation :(3)CVR=nResearch has shown that performance can be calculated by judging the effectiveness of activities, such as accomplishing goals and adopting strategies. The US National Audit Office defines performance as the effectiveness of strategies used to solve or mitigate targeted problems. Brown and Pyers  believe Determine the input and output variables: As stated previously, the three indicators of employee performance  take turns as the output variable; the other two indicators of employee performance and fifteen HSE indicators are used as input variables in the development of the model , 64.Select architecture: The neural network chosen is a multilayered feed-forward network with backpropagation (BP) because it is efficient for performing any linear or multivariate arbitrary nonlinear computation. The training is carried out using the MATLAB Neural Network (NN) toolbox. One hidden layer may be enough, especially when it is not overladen with too many hidden nodes . So, oneTraining and testing of data: The number of test sets and training sets is determined empirically. Out of a total of 96, 86 (90%) and 10 (10%) datasets are used for training and testing of the network in this paper, respectively. In the previous studies, Azadeh et al.  used datDetermine the number of neurons in a hidden layer: A fewer number of hidden-layer neurons result in underfitting problems, while a greater number of neurons lead to overfitting problems. According to a rule of thumb, the number of neurons in the hidden layer should be less than twice the number of neurons in the input layer . TherefoSelect training parameters: Three transfer functions universally applied in ANN are log-sigmoid (logsig) function, tan-sigmoid (tansig) function, and linear transfer function (purelin). The combination of logsig-purelin function and tansig-purelin function is chosen as the transfer function of the model considering the nonlinear question involved in this paper. Seventeen commonly used training functions are selected to train the neural network in this paper . The exp∗ 2 (transfer function) ∗ 3 (employee performance indicators)) are constructed to acquire the optimal neural network based on the minimum principle of MAPE. As shown in Finalization of the ANN model: A total of 102 neural networks (17 (training function) An ANN is applied to explore the relationship between fifteen HSE indicators and employee performance indicators. The following steps are considered for the development of the ANN-based model:After finding the optimal neural network, the model output is computed and the employee activity effectiveness is measured according to the following process.Pi)actual and the model output (Pi)model for all dataset were calculated as follows [The datasets were put into the optimal neural network model for job satisfaction, and the errors between the actual value Ek has the largest error Ei and therefore:Assume that Ek′ which is the most remarkable difference between the maximum job satisfaction and general job satisfaction is defined as Ek:The value of Di between the general job satisfaction and the maximum job satisfaction of each kind of data can be calculated by the following formula:Therefore, the difference Pi of data i is the ratio of the actual job satisfaction of the employees to the maximum job satisfaction of employees. The actual job satisfaction is Pi)actualmodel, we use SPSS to compare the effectiveness of the entire variable (μF) and the effectiveness after deleting the variable (μi) to perform a paired-sample bilateral t-test [P value gives the probability of observing the test results under the null hypothesis. If the P value is less than 0.05, the null hypothesis is accepted, which means that the deleted variables' effect on the employee HSE activity effectiveness is not significant. On the contrary, it means that the deleted variable has a significant impact on the effectiveness. If the effect is significant, then the paired sample unilateral t-test is employed to determine whether the effect is positive or negative . If the null hypothesis is accepted, it means that the corresponding indicator has a better effect, that is, the factor has a positive impact on the effectiveness because its deletion reduces the effectiveness; if the null hypothesis is rejected, it means that the factor harms the effectiveness of employee HSE activities.Different factors have different contributions to employees' performance. Identifying indicators that have a significant impact is beneficial to improve management performance, which can be achieved by sensitivity analysis. Sensitivity analysis identifies the degree to which each of the input variables contributes to each of the output variables , 38. To l t-test , 22. TheAfter descriptive statistical analysis of the questionnaire, the effectiveness of employee activities is calculated through the constructed model. Then, sensitivity analysis is used to determine indicators that have a significant impact on effectiveness, which allows managers to determine the key factors affecting employee performance in complex transport construction projects.The final questionnaire designed was distributed to 120 managers of the project and the contractors in different positions, and 96 available copies obtained finally with poor-quality questionnaires were excluded. After standardizing and unifying the questionnaire's data, Cronbach's alpha and CVR are calculated to verify the questionnaire's reliability, as shown in As discussed in the previous section, employee performance is divided into three components: job satisfaction, HSE awareness, and work stress, and employee performance is quantified through employee activity effectiveness. We calculate the three performance indicators' effectiveness and the total employee activity effectiveness according to formulas equations –9) as s as s9) aAlthough the three employee performance indicators show different levels of effectiveness, the employee activity effectiveness of 80.2% of employees is not less than 0.6; that is, the performance of most employees is above the qualified level, indicating that employees psychologically identify with the implementation of the HSE management system in the construction of complex transport construction projects.To verify the relationship between job satisfaction effectiveness, work stress effectiveness, and HSE awareness effectiveness, the Pearson correlation coefficient is used, as shown in To understand the employees' reasons for unqualified activity effectiveness, their perception of the HSE management is reported statistically . The resIn particular, almost 66.7% of employees' work stress effectiveness is lower than 0.6, implying that employees are under greater pressure when implementing the HSE management system. To find out the cause of greater pressure, the employee perception characteristics with the effect of unqualified work stress were proposed . The rest-test results are shown in To identify the key indicators affecting employee performance, sensitivity analysis is employed by recalculating the effectiveness after deleting input variables successively. The paired sample t-test results indicate that the p values of the risk control, employee participation, and job satisfaction are 0.375, 0.514, and 0.057, respectively, which are greater than the significance level of 0.05 so that they have a considerable impact on employee activity effectiveness. HSE organization, responsibilities, and authority have a little impact on effectiveness. The remaining variables have little impact on effectiveness. Therefore, when applying the HSE management system to complex transport construction projects, full attention should be paid to risk control, employee participation, and employee job satisfaction.The bilateral t-test results show that the risk control system and employee participation have a positive impact on the effectiveness of employee activities, as the effectiveness is reduced after removing the risk control or employee participation variables (μF > μi). This implies that the HSE management system's introduction made the project more stringent for risk control and increased employees' sense of participation. However, job satisfaction has a negative impact on the effectiveness because the removal of job satisfaction variables increases the effectiveness (μF < μi). This variation reflects the lack of sufficient attention to job satisfaction in the project [Besides, the unilateral  project . Job sat project . As a reThe findings of the performance evaluation revealed that 80.2% of the employees performed at a qualified level, showing that the majority of them thought the application of HSE management systems in complicated transport construction projects was effective. According to the responses provided by these employees in the survey, the implementation of HSE management has increased the efficiency of project risk control and safety management. A precise control of the entire process is achieved in HSE management through the risk source identification, risk evaluation, and control. It was discovered that 19.8% of employees had unqualified performance, frequently as a result of a perceived deficiency in ongoing self-auditing and adequate training and education. In fact, in the dynamic work environment of the construction industry, the attitude of construction personnel towards safety directly influences their behavior on site. There is a need for regular training and occasional spot checks on safety practices and risk prevention to reduce the occurrence of personnel-related accidents. Additionally, a large cause of unqualified employee performance is the increased work pressure, as reflected by the fact that nearly 66.7% of employees have a work stress effectiveness below 0.6. These stressed employees may consider the workload or work difficulty during the construction of the project to be high. Considering the project background, the construction of the project faces complex conditions such as typhoons, rainstorms, thunder and lightning, high temperatures, and sea streams, causing operational difficulties, and danger factors are higher than those of traditional transport construction projects. These could be the causes of the increased work pressure.Sensitivity analysis identified three factors  as key indicators that affect employee performance. As one of the most important factors of project management, risk control is regarded as an influential factor affecting system performance in industries such as gas refineries and power plants , 35. TheEmployee participation, which is another significant factor according to the sensitivity analysis, has a positive impact on effectiveness in the case study. This is similar to the study by Alazzaz and Whyte , which sInterestingly, job satisfaction has a negative impact on performance in this project, which is inconsistent with the results of existing studies where job satisfaction has a positive impact on system performance and has been implemented appropriately in the petrochemical industry . Since jThis paper develops an intelligent framework to assess employee performance on HSE management systems and to identify key influencing factors in complex transport projects. As shown in The results obtained in this study have several implications for policymakers. Construction managers should be aware that the most crucial elements in ensuring safe construction are those that relate to employees' operational and safety knowledge. HSE management systems' more rigid management guidelines and regulated operational procedures affect employees' traditional work practices while also adding to their workload. Therefore, only a relatively good safety management process is not enough, and the management of transport construction projects should also pay attention to the psychological state of employees, which can affect the operational effectiveness. Several actions should be made in this regard. To reduce the risk of overwork, employees' physical and mental stress should be periodically assessed. Additional means, such as psychological help and provision of personnel benefits and financial incentives, can also be used to increase employee motivation. Another important aspect is to strengthen employees' psychological acceptance of HSE management. As the management process implementers, employees must be made aware of the value and significance of the HSE management process. Moreover, it is important to understand the difficulty and cumbersome degree of the implementation process for employees and allow them to make suggestions on the system process. This facilitates the execution of the HSE management system and lessens the employees' unfavorable psychological reactions. Construction projects should introduce information technology equipment and facilities to assist in process management, reduce redundant workload, and improve management efficiency.The results of this study help organizations to improve employee performance by managing key elements. To better implement HSE management in transport construction projects, organizations have a basic understanding of the impact of different factors through this paper. The findings of this study evaluation assist managers in developing a thorough grasp of the elements that influence worker performance and in enhancing their focus on the psychological well-being of workers. The proposed methodology can be applied to other similar construction projects to identify the most important and critical HSE elements of the construction process.In this study, an integrated method is proposed to study the impact of HSE risk indicators on employee performance in a complex transport construction project, which is a supplement to the risk management knowledge system. Based on the questionnaire survey of HSE management professionals, an employee activity effectiveness model with an ANN is established to evaluate employee performance. Then, sensitivity analysis is applied to identify critical factors affecting performance for further reference. The results indicate that most employees' performance reaches a qualified level when implementing the HSE management system in the complex transportation construction project. The performance of 19.8% of employees is unqualified because they perceive that the project is not adequate for regular assessment of the objectives and new employee training, and the on-site inspection is infrequent. Sensitivity analysis finds that the indicators of risk control and employee participation positively impact employee performance, indicating that risk control is paid enough attention during the construction process, and the employees can actively participate in HSE management. However, job satisfaction has a negative impact on performance, which means that employees' perceptions of work are not being paid enough attention.This evaluation's results provide managers and stakeholders with elements that HSE management needs to focus on in complex transport construction projects. Aspects like work pressure, incentive mechanism, and the usage of information technology are crucial for researchers and managers in terms of enhancing employee performance and effectiveness. Understanding the physical and mental loads and stresses of employees can help develop more accurate strategies to enhance health and safety conditions for employees . ManagerHowever, this study still has some limitations. On the one hand, the number of questionnaires collected is relatively fewer because the subject of this article focuses on the HSE full-time manager of the Hong Kong-Zhuhai-Macau Bridge. In future research, grassroots employees can be used as survey objects to study the implementation of the HSE management system. On the other hand, it is important to further explore the obstacles while using the HSE management system for a construction project with a single scene.