To ensure the quality of the new-born screening (NBS), our laboratory reviewed the analytical procedure to detect subjective steps that may represent a risk to the patient. Two subjective activities were identified in the extra-analytical phases: the classification of dried blood spots (DBS) according to their quality and the assignment of haemoglobin patterns. To keep these activities under control, inter-rater studies were implemented. This study aimed to evaluate the inter-rater reliability and the effectiveness of the measures taken to improve the agreement between observers, to assure NBS results’ quality.Dried blood spots specimens were used for the inter-rater studies. Ten studies were performed to assess DBS quality classification, and four to assess the assignment of haemoglobin patterns. Krippendorff’s alpha test was used to estimate inter-rater reliability. Causes were investigated when alpha values were below 0.80.For both activities, the reliability obtained in the first studies was inadequate. After investigation, we detected that the criterion to classify a DBS as scant was not consolidated, and also a lack of consensus on whether or not to report Bart’s haemoglobin depending on its percentage. Alpha estimates became higher once the training was reinforced and a consensus about the appropriate criteria to be applied was reached.Inter-rater reliability assessment helped us to ensure the quality of subjective activities that could add variability to NBS results. Furthermore, the evolution of the alpha value over time allowed us to verify the effectiveness of the measures adopted. It is estimated that 70 percent of clinical decisions are based on laboratory tests’ results (e.g. caused by drugs or body creams among others), or the analytical performance of technology, but also to failures in other aspects of the NBS program that depend on the decisions taken by the personnel involved in the different phases of the procedure.New-born screening (NBS) allows the early detection of congenital diseases and it is intended to initiate early treatment to reduce morbidity and mortality. Laboratories receive dried blood spots (DBS) for testing, that is a specimen of blood collected onto a particular filter paper on which printed circles indicate the area to be filled. The panel of screened diseases varies depending on the country. The Catalan NBS program (in Spain) includes the screening of amino acidemias, organic acidemias, fatty acid oxidation disorders, congenital hypothyroidism, cystic fibrosis, sickle cell disease, and severe combined immunodeficiency disease. As in any other screening program, the existence of false positive and negative results is assumed. These false results may be due not only to the biological variability of the measurands, the analytical interferences  for NBS. Fifty DBS were used to perform ten inter-rater studies to assess the agreement on the classification of the specimen’s quality in the period between 2015 and 2020. Of the selected DBS, 22 had an acceptable quality and 28 were unacceptable . Twenty-five DBS were used to conduct four inter-rater studies to assess the agreement on the assignment of Hb patterns between 2017 and 2020. For these studies, the following cases were selected: 2 unaffected, 15 affected  and 8 carriers of Hb variants.Specific data about the samples included in each study and the number of observers who participated are shown in The inter-rater studies performed to assess the agreement on the classification of DBS specimen’s quality were carried out by visual inspection before the punching of the spot. Specimens were classified as acceptable, scant, or non-optimal according to the following criteria:Acceptable: Blood filled all spots and they were evenly saturated.Unacceptable:Scant: Blood transferred to the back side of the paper but it was not enough to perform all tests.Non-optimal: a) Insufficient: Blood did not transfer to the back side of the paper; b) Compressed: Blood filled the spots but they were not saturated; c) Over-impregnated: Blood applied to both sides of the paper; d) Multispotted: Multiple small bloodspots to make one larger.The inter-rater studies conducted to assess the agreement on the assignment of Hb patterns were carried out by visual inspection of the electrophoreticgrams obtained by capillary electrophoresis. All tests were run on Capillarys 2 Neonat Fast . The criteria followed for reporting the pattern was:i.e. a report of FA pattern indicates that the concentration of HbF is higher than that of HbA, which is the normal pattern in new-borns).Hb fractions were sorted by decreasing concentration  were coded as X.Rare Hb variants . AlpFor both activities, the results showed that the inter-rater reliability of the first studies performed was inadequate . The firi.e. the blood did not transfer to the back side of the paper); and, a DBS that was acceptable to perform some tests but was not enough to complete all screening tests (i.e. few spots filled with blood and evenly saturated). No differences between observers in the classification of other unacceptable DBS specimens  were identified. Secondly, in the assignment of Hb patterns, disagreement among the observers in deciding whether or not to report the presence of Bart’s Hb depending on the quantity detected was observed. No discrepancies in the assignment of other pathological Hb patterns or the normal Hb pattern (i.e. FA) were found.When investigating the causes behind these low alpha values, the lack of unified criteria between the personnel in certain aspects was revealed. Firstly, in the classification of the DBS quality, disagreement between observers in classifying two types of unacceptable specimens was observed: a DBS that had insufficient blood on the spots  or false negative results (e.g. compressed bloodspots) can be obtained. However, the final results report is not released until a repeat screening on an additional requested specimen is performed, as long as the new specimen has an acceptable quality. The root-cause analysis performed after the seventh inter-rater study showed the need to enhance training in the classification of DBS specimens for the new staff, to provide them with the expertise required.The root-cause analysis performed to identify the causes of the poor reliability in the first inter-rater study in the classification of DBS quality revealed the need to re-educate the technical staff to clarify the differences between a scant sample and an insufficient sample. Based on this classification, the decision of accepting or rejecting the specimen is taken, leading to different consequences. On the one hand, the rejection of a DBS specimen that is acceptable to perform a portion of tests can lead to a delay in the detection of disease, since it implies requesting a new sample for testing. On the other hand, acceptance of a DBS specimen with insufficient blood may lead to a false negative result for some measurands (The root-cause analysis performed after the first inter-rater study for the assignment of Hb patterns revealed discrepancies between the laboratory specialists when reporting Bart’s Hb. Some participants reported Bart’s Hb only when the fraction detected was higher than 15%. Other participants reported always Bart’s Hb regardless of the percentage. Based on the fact that low percentages do not require further testing or referral to a haematologist, for the next inter-rater studies, a consensus on reporting Bart’s Hb only when the fraction was higher than 15% was reached (The evolution of the alpha value over time allowed us to verify the effectiveness of the corrective measures implemented. For both activities, a better level of agreement between observers was achieved in the subsequent studies. Despite the rapid increase of the alpha value observed in the second inter-rater studies performed, continuous training and education of the laboratory personnel was required to sustain a high level of agreement. Thus, we established to conduct inter-rater studies at least once a year; and, additionally, when recruiting new staff or after a long-term leave.Other authors have assessed the inter-rater reliability in order to implement actions to minimize the effect of the human factor in laboratory results and have even informed clinicians about the degree of reliability attained (In conclusion we can say that conducting inter-rater exercises is essential to control subjective activities that can lead to an error or delay in diagnosis. Their implementation in our laboratory helped us to detect controversial points in the decision making and to focus corrective measures to standardize important criteria. The assessment of the inter-rater reliability allowed us to verify if the measures taken were and remain effective over time, thus boosting the quality assurance of NBS. Moreover, standardization of subjective activities helped us to accomplish the quality assurance requirements of test results described in the ISO 15189:2012 standard.


In recent years, network traffic contains a lot of feature information. If there are too many redundant features, the computational cost of the algorithm will be greatly increased. This paper proposes an anomalous network traffic detection method based on Elevated Harris Hawks optimization. This method is easier to identify redundant features in anomalous network traffic, reduces computational overhead, and improves the performance of anomalous traffic detection methods. By enhancing the random jump distance function, escape energy function, and designing a unique fitness function, there is a unique anomalous traffic detection method built using the algorithm and the neural network for anomalous traffic detection. This method is tested on three public network traffic datasets, namely the UNSW-NB15, NSL-KDD, and CICIDS2018. The experimental results show that the proposed method does not only significantly reduce the number of features in the dataset and computational overhead, but also gives better indicators for every test. The network environment has gotten more complex in recent years as the internet advances. While people use the Internet’s convenience, several types of cyber-attacks come with it. This is still needed to be addressed to network security issues. This issue affects the performance of anomalous network traffic detection systems. Machine Learning has been introduced to detect anomalous network traffic detection systems because of the proliferation of network traffic. Moreover, network traffic includes a large number of feature information and carries various redundant features. Redundant features are features that can be difficult to detect and include features that are highly correlated with the rest of the detection targets and also features that have low correlations with the detection target. If there are too many redundant features in the network traffic data, it will add time complexity and space complexity to the algorithm, reduce the detection capability of the anomalous network traffic detection system and increase the computational overhead of the algorithm. It is a very difficult field for anomalous network traffic detection to select the best subset of features from the original data that benefit from the detection methods.Because of its high computational overhead and its low search efficiency, the random search algorithm used in traditional feature selection is difficult to meet the demand for real-time performance in anomalous traffic detection systems. Because of their faster computational speed, meta-heuristic search algorithms are becoming another alternative to feature selection algorithms by increasing detection accuracy and efficiency . The curBased on the above research, this paper proposes an approach to anomalous traffic detection using EHHO-GRU. This method is the first to use the relatively novel Harris Hawks Optimisation (HHO) algorithm as a feature selector that extracts a wide range of features from anomalous network traffic data and designs a unique fitness function to optimize the convergence speed of the overall abnormal traffic detection method. The HHO feature selector is compared to the optimized Elevated Harris Hawks Optimisation-Gated Recurrent Unit(EHHO) feature selector proposed in this paper. For the first time, the EHHO feature selector is combined with a GRU neural network to train and test the feature subset data solved from multiple common datasets. The dimensionality of the feature is significantly reduced, the computational overhead is reduced and the detection index of the neural network for abnormal traffic is improved.Recurrent Neural Network (RNN)  is a deeTherefore, Long-Short Term Memory Neural Network (LSTM)  was propt, which is jointly determined by the hidden layer unit state t, where rt is the reset gate at time step t and t. The hidden layer unit state t is also used as the input for the next time step.To solve the RNN gradient problem and the high training cost of LSTM at the same time, Gated Recurrent Unit (GRU) was proposed by Cho et al.  Comparedh denotes the candidate hidden layer state at time step t, ⊙ means that two variables are multiplied according to their corresponding elements, and its equation is: In the GRU hidden layer unit of h, and the reset gate will ignore all hidden layer states The update gate Harris Hawks Optimization (HHO) is a meta-heuristic algorithm proposed by Heidari et al.  in 2019.HHO uses mathematical equations to simulate the prey mechanism of Harris hawks in different situations. In the Harris Hawks Optimization, the prey gradually approaches the optimal solution with the number of iterations, and the candidate solution is the Harris Hawk. The overall algorithm includes two parts: the exploration and exploitation phases. Exploration versus exploitation: exploring unknown actions to gain more information versus exploiting the information already collected.lb, ub]. And randomly search for prey under two strategies, update the position with q as the probability during iteration, and its mathematical expression is: In this phase, all Harris hawks are considered candidate solutions. In each iteration, the fitness value is computed for all these possible solutions based on the intended prey. Exploitation refers to a local search around the area acquired during the exploration phase. In the exploration phase, Harris Hawks first waits, after which it monitors and evaluates the search space . t denotes the current iteration round, and T denotes the maximum number of iterations. When Among them, E indicates the escaping energy of the prey. r is used to denote the probability that the prey escapes successfully. When r < 0.5, the prey escapes successfully; When In this phase, the exploitation phase is accomplished using four approaches at parameter sets. These approaches are based on the position identified in the exploration phase. Unfortunately, prey often escapes ahead of the Harris Hawk. Therefore, according to the escape behavior of the prey and its pursuit strategy, Harris Hawk has evolved four attack strategies, and the HHO algorithm also uses four strategies to simulate the four attacks of Harris Hawk. In this paper, Soft BesiegeWhen t, t, J = 2(1 − rand), which is the random jump strength of the rabbit throughout the escaping procedure, where rand is the a random number inside .In the equation, Hard BesiegeWhen Soft besiege with progressive rapid divesr < 0.5 and D is the space dimension, and S is a 1 × D random vector, that is, S = rand; LF(D) is the Levy function:u and v are random numbers uniformly distributed in , When Hard besiege with progressive rapid divesr < 0.5 and E is insufficient, so the Harris hawk adopts a hard besiege with progressive rapid dives, forming a hard besiege before the raid, and then shrinks them and the prey average distance. The mathematical expression for its position update is:When E and factor r to configure four attack mechanisms between Harris hawk and prey to solve the optimization problem.The HHO uses escape energy t denotes the current iteration round, and T denotes the maximum number of iterations.The structure of the traditional Harris Hawks Optimization itself has certain defects, and the search process is prone to the problems of falling into local optimum and low convergence accuracy. The escape energy equation E is used to adjust the exploration and exploitation phases and four attack strategies. The higher the value of E, the more HHO tends to perform exploration, and vice versa for exploitation. However, in traditional HHO, E decreases linearly with the number of iterations, which will cause the algorithm to be biased towards local search and easily fall into local optimum. Therefore, an improved energy escape function E is proposed in this paper, and a special exponential function is introduced to adjust the exploration and exploitation phases, so it makes the optimization algorithm more inclined to perform a global search. Its corresponding energy equation is: J in the HHO cannot well simulate the trend of the prey changing with the decay of the energy for the position update equation. Therefore, this paper proposes a jumping distance equation J that changes with the escape energy to better simulate the position of the prey, and make the optimization algorithm better explore the space of different regions. The improved jump distance equation is: t denotes the current iteration round, and T denotes the maximum number of iterations. This elevated HHO algorithm will be referred to as EHHO hereinafter. The pseudo code of the HHO algorithm is shown in Algorithm 1, and the pseudo code of EHHO is shown in Algorithm 2.The prey jumping distance Algorithm 1: Pseudo code of standard Harris Hawks Optimisation.Input: The population size N and maximum number of iterations TOutput: The location of rabbit and its fitness valueInitialize the random population returnAlgorithm 2: Pseudo code of Elevated Harris Hawks Optimisation.Input: The population size N and maximum number of iterations TOutput: The location of rabbit and its fitness valueInitialize the random population returnThe selection of the fitness function directly affects the convergence speed of the optimization algorithm and whether it can find the optimal solution, because the optimization algorithm basically does not use external information in the process of optimization, and only uses the fitness function as the basis to use the adaptation of each individual in the population degree to search. Because the complexity of the fitness function is the main component of the complexity of the entire anomalous network traffic detection method, the design of the fitness function should be as simple as possible to minimize the time complexity of the calculation. Accordingly, this paper designs the fitness function of the HHO as follows: Among them, Equation  that theData preprocessing. Delete the feature columns with more than half of the total missing values in the malware detection dataset, map discrete data into feature columns with one-hot encoding, and process the missing values in the dataset with 0 padding; in binary classification, set the class label to normal and anomalies are coded 0 and 1 respectively; the data set is processed in a normalized way to reduce the influence of the data dimension, and the features that are completely irrelevant to the label column in the data set are deleted in the way of mutual information feature selection.Optimization algorithm initialization. Set the initialization parameters of the EHHO algorithm population, the number of iterations, and the problem dimension, and determine the fitness function. Initialize the population and perform binary discretization of the feature dimensions in feasible solutions.Calculate the fitness. Taking the fitness function as the optimization direction, calculating the fitness value of the feasible solution after iteration, taking the feasible solution with the best fitness in the population as the prey, and record the optimal individual information.Determine whether the optimization cycle is terminated, if the termination condition is met, go to step (5); otherwise, go back to step (3).Output the optimal feature subset and send it to the GRU classifier, and end the algorithm process.The overall process is shown in In this paper, the specific experimental environment is shown in In this paper, NSL-KDD , UNSW-NBNSL-KDD datasetThe NSL-KDD dataset is a simplified and improved version of the KDDCUP99 dataset. The NSL-KDD dataset ensures that the intrusion detection model is free from bias and is more suitable for deep learning for anomalous traffic monitoring. The classification labels in the multiclassification test are shown in UNSW-NB15 datasetThe UNSW-NB15 dataset is a new public dataset introduced by the Cyber Security Experimentation Team at the Australian Cyber Security Centre. Cyber security researchers often use the UNSW-NB15 dataset to address issues identified in the NSL-KDD dataset and the KDDCUP99 dataset. The dataset is generated in a hybrid manner and includes both normal and attack traffic from real-time network traffic, making it a comprehensive dataset of network attack traffic. There was a total of 49 features in the dataset to describe a piece of data. There are a total of nine types of abnormal attack traffic marked as 1 and one type of normal traffic marked as 0 in this dataset, the details of which are shown in CICIDS2018 datasetThe CICIDS2018 dataset is a collaborative project between the Communications Security Establishment (CSE) and the Canadian Institute for Cybersecurity Research (CIC). Previous partial datasets were highly anonymous, did not reflect current trends, or they lacked certain statistical characteristics for which perfect datasets existed. The Canadian research team has therefore devised a systematic approach to generating datasets to analyze, test, and evaluate intrusion detection systems, with a focus on network-based anomaly detectors. The dataset includes seven different attack scenarios: Brute-force, Heartbleed, Botnet, DoS, DDoS, Web attacks, and more. The data is made up of an attack machine consisting of 50 hosts, with 5 sectors of target machines, including 420 machines and 30 servers. The dataset includes network traffic and system logs for each machine captured, as well as 80 features extracted from the captured traffic using CICFlowMeter-V3. As the dataset is too large to contain ten datasets and is limited by the experimental environment, a portion of the data from each dataset is selected to form a new dataset for the experiment, and 10% of each dataset is extracted hierarchically for generalizability. There are seven types of abnormal attack traffic marked as 1 and one type of normal traffic marked as 0 in this dataset, and the detailed composition is shown in Data normalisationxA min-max normalization process is used to compress the data into the  interval. One advantage is that it improves the speed of convergence of the model, and another is that it improves the accuracy of convergence, which is given by:Character feature unique heat codeIn feature engineering, data will appear as category-based features, including character-based features and discontinuous features. In order to solve the problem that the classifier does not handle attribute data well, this paper uses the pre-processing part of the Keras framework to process these category-based data with unique thermal coding.Dataset labelsSome datasets commonly used in the field of anomalous traffic detection will have more than one label, due to the characteristics of the data commonly used in this field, which, in addition to labeling the different attack method category labels, also typically use 0: for normal network traffic, 1: for abnormal network traffic, to act as labels for the network data. Some datasets  inherently have two label columns, while in others , only the attack category label column exists. In order to better evaluate the generalisability of the anomalous traffic detection model proposed in this paper, this paper tests and evaluates each dataset separately for multiple and dual classifications.The hyperparameters of the anomalous traffic detection model proposed in this paper consist of two main parts, one is the hyperparameters of the EHHO feature selection part for iteration, and the other is the hyperparameters part of the two-layer GRU neural network classifier. The detailed parameters are shown in In the feature selection part of this paper, four novel nature-inspired algorithms with good optimization search results - the Whale Optimisation Algorithm , the GenThe performance of the neural network classification model is influenced by the parameters selected.The model evaluation criteria  are defiTP (True Positive): True positives, which are actually intrusion samples and the number of successfully detected as intrusion samples; FN : False negatives, which are actually intrusion samples but have not been correctly detected, that is, false negatives Number of samples; FP : false positives, which are actually normal samples but detected as intrusion samples, that is, the number of false positive samples; TN (True Negative): true negatives, which are actually normal samples and are not falsely reported the number of samples.Accuracy: This metric counts the ratio of the number of correctly classified samples to the entire test set. The higher the accuracy, the better the performance of the neural network model : This metric is also known as Recall, and its equation is the same as Recall. False Positive Rate (FPR): This metric counts the ratio of the number of attack samples predicted to be normal samples to the actual total number of attacks. The lower the FPR, the better the performance of the neural network model . In order to address this situation which would lead to the neural network not being able to correctly classify the test set after training, this paper will first perform a binary classification test on the NSL-KDD dataset by setting the new label to 0 except for the one with the classification label of normal, and setting the abnormal traffic of the rest of the labels to 1. After this treatment, the test set contains labeled data that did not appear in the training set, which can better detect the detection model’s Generalisability. The number of iterations of the feature subset solving algorithm was 1500 and the number of iterations of the GRU neural network was 3000, and the detailed experimental results of the binary classification are shown in The histogram is shown in According to the table above, it can be seen that after the neural network has completed training on the training set, the test set contains 14 labeled data that are not available in the training set, resulting in a GRU neural network without feature selection The accuracy of the detection model with the subset of features solved by the optimization algorithm and then fed into the neural network for training was higher than that of the neural network without feature selection, except for the Particle Swarm Optimisation (PSO) + GRU model, which was less accurate than the original version. The accuracy of the neural network classification was still higher than that of the model without feature selection, even though the feature dimensionality was sharply reduced to one-eighth of the original version. The EHHO+GRU model with the highest accuracy was 2.5% more accurate than the HHO+GRU model, and 4.13% more accurate than the model without feature selection, while sharply reducing 35 redundant features and having a higher recall and f1-score than the GRU model. And the multi-classification tests for the NSL-KDD dataset are shown in As can be seen in After eliminating some redundant data columns such as dates, the UNSW-NB15 dataset is described in this paper using 42 features, which are labeled as 0 and nine different 1 types. Therefore both binary and multiclassification performance tests are conducted on this dataset in this paper, where the results of the binary classification experiments are shown in This is because the training data in the UNSW-NB15 dataset is less than one-half of the test data, which reduces the classification performance of the normal GRU neural network on the test set. The remaining five detection models using the optimization algorithm for feature selection showed substantial improvements in all four evaluation metrics, with the HHO+GRU model achieving 89.33% classification accuracy using a subset of 11-dimensional features, an improvement of 15.25%, and 15.04% improvement in f1-score, while the elevated EHHO+GRU model achieved the highest accuracy despite using 20-dimensional features, but achieved the highest accuracy, recall and f1-score.The paper next conducts a multi-category experiment on this dataset, the results of which are shown in the table below, where the data imbalance problem is particularly severe due to the under-representation of Analysis, Backdoor, Shellcode, and Worms. The statistical tables are shown in As can be seen from the above table, the overall accuracy of the model using all 42-dimensional features is only the lowest at 74.01%, with a false positive rate of 37.86% for normal tags, which is particularly serious as an abnormal traffic detection model, meaning that a large amount of abnormal traffic is labeled as normal traffic. The overall accuracy of the detection models with optimized algorithm feature selection exceeds 85%, with the elevated EHHO+GRU model using a 22-dimensional feature subset having the highest overall accuracy, as well as the lowest false alarm rate for the normal tag and the highest tpr for the Fuzzers, DoS, and Reconnaissance tags, this shows that the proposed EHHO+GRU anomaly traffic monitoring model has the best overall performance in the UNSW-NB15 dataset for both binary and multi-classification performance evaluation.This experimental dataset was experimented with batch processing due to the large amount of data, Batch size = 64, the number of iterations of the optimization algorithm As can be seen from the table, the GRU model using all 80-dimensional features has the highest accuracy, but also has the highest false alarm rate. The training of data with 80-dimensional features is very slow and requires a lot of computational resources, while the detection model after feature selection can reduce the feature dimensionality to as low as one in sixteen, with a slight decrease in accuracy. In contrast, the EHHO+GRU model in this paper reduces the feature dimensionality to one-eighth, with a 0.37% decrease in accuracy, and fpr also only increased by 0.9%.The Harris Hawks optimization algorithm has been a relatively new meta-heuristic optimization algorithm in recent years. The experimental results of binary and multi-classification of NSL-KDD and UNSW-NB15 datasets show that the model has better performance, which not only greatly reduces the feature dimension of the dataset, but also reduces the learning time of the neural network, and can also improve the accuracy, precision, recall, and f1-score of the anomalous network traffic detection system. The EHHO-GRU method is used to select features and dimensionality reduction even reaches 90% in the CICIDS2018 data set. However, the method is not tested on the full CICIDS2018 dataset owing to its size and can’t address the data imbalance problem that occurred in the UNSW-NB15 dataset, causing a very low accuracy in classifying a relatively small proportion of labels. Since the study focuses on metrics such as the accuracy of the detection method on the test set, EHHO’s testing is relatively simple. From there, in the future work I will focus on improving the generality of the proposal and testing it in a real experimental environment.


Flavobacterium (65 genomes) and Pseudomonas (100 genomes), Staphylococcus (500 genomes)) were used. The initial functional pathway assignment rate of the three test genera were 27.7% (Flavobacterium), 49.5% (Pseudomonas) and 30.1% (Staphylococcus). PPA-GCN achieved excellence performance of 84.8% (Flavobacterium), 77.0% (Pseudomonas) and 71.0% (Staphylococcus) for assignment rate. At the same time, PPA-GCN was proved to have strong fault tolerance. The framework provides novel insights into assignment for metabolism pathways and is likely to inform future deep learning applications for interpreting functional annotations and extends to all prokaryotic genera with sufficient genomes.With the rapid development of sequencing technology, completed genomes of microbes have explosively emerged. For a newly sequenced prokaryotic genome, gene functional annotation and metabolism pathway assignment are important foundations for all subsequent research work. However, the assignment rate for gene metabolism pathways is lower than 48% on the whole. It is even lower for newly sequenced prokaryotic genomes, which has become a bottleneck for subsequent research. Thus, the development of a high-precision metabolic pathway assignment framework is urgently needed. Here, we developed PPA-GCN, a prokaryotic pathways assignment framework based on graph convolutional network, to assist functional pathway assignments using KEGG information and genomic characteristics. In the framework, genomic gene synteny information was used to construct a network, and ideas of self-supervised learning were inspired to enhance the framework’s learning ability. Our framework is applicable to the genera of microbe with sufficient whole genome sequences. To evaluate the assignment rate, genomes from three different genera ( With the rapid development of sequencing technology, the number of newly released prokaryotic genomes has exploded, providing an important foundation for subsequent research work . FunctioHere, we propose PPA-GCN, a framework based on graph convolutional network (GCN) that uses genomic gene synteny information within specific genus, from which the graph topological pattern and gene node characteristics can be learned, to disseminate node attributes in the network and provide assistance to the assignment of metabolic pathways. Synteny is defined as two or more pairs of homologous genes occupying the same chromosomal segment, where homologous loci are defined based on the similarity of function of the products of the corresponding genes . AnalyziThe study of gene location in the genome is one of the classic fields of genetics ., 2004. In recent years, deep learning has been widely used in the field of life science, for example, for identifying and interpreting the contextual features of transcription factors , generattr, Vte, E), where Vtr is the set of nodes that assigned function pathway, Vte is the set of nodes that unassigned function pathway, V = {Vtr, Vte}. E is the set of edges and the edge represents two genes belonging to different nodes are connected in the genome. A label set L = {l1, l2...lk} is formed according to the KEGG secondary class. The relationship between the node set and the label set is represented by a matrix YNxK. Yij = 1, if there is a gene in node i has assigned to label j. Our goal is to assign the possible pathway labels to those nodes that have no labels.Given an undirected graph G =  . ThroughPi that an edge is connected to a specific node i was calculated. Finally, the probability that there are k edges between node i and node j was defined as:N is the total number of nodes in the gene synteny graph and degree(i) is the degree of node i, C is the combination symbol.The adjacency probability is defined as the probability that two nodes form a certain number of connections in the network. First, the degree of each node in the gene synteny network (the number of connections by which a node is directly connected to surrounding nodes) was calculated. Then, the probability N*N adjacency probability matrix, because there are no connections between most nodes, the node2vec algorithm was used to densify the adjacency probability matrix.After the adjacency probabilities of all nodes had been formed into an X and adjacency matrix A, the graph convolution operation  is defined as:I is the identity matrix, W is the matrix of trainable weights in the neural network, X is the feature matrix before the update, H is the feature matrix after the update, and σ is the activation function (ReLU). The graph convolution operation iteratively calculates the weighted average of the node attributes of the neighbors of the current node to obtain the new feature matrix of the node. In this framework, the features of unlabeled nodes  and the features of nearby labeled nodes  are mixed to be propagated through the synteny network diagram. If two nodes have the same neighbor structure and neighbor features, their embedded feature matrix H will be exactly the same.Given an undirected graph with node feature matrix Python’s PyTorch Geometric Module was used to implement PPA-GCN. Multiple graph convolutional layers can be stacked to enable learning on a larger domain structure. After testing, a three-layer stack was found to perform the best. The two-class cross entropy was used as the loss function because of the multilabel nature of the problem.The original input was fed into the framework, and 50 epochs of random sampling verification training were performed with the test set. The nodes with an average cross-validation accuracy rate of less than 30% are removed from the training set, and nodes and labels with a assignment stability of 90% in the test set  are added to the training set. After many iterations, when the number of nodes in the training set reached more than 90% of the total number of nodes in the gene synteny network, the training was considered to have reached a stable state, and the final assignment results were output.The degree is defined as the number of all edge connections of a node in a graph, describing the first-order connection degree of the node. The degree distribution is an overall description of the nodes in a network, that is, the probability distribution or statistical distribution of the node degrees.i with degree ki, the local clustering coefficient is defined as:Li is the number of connections among the ki neighbors of node i. The overall aggregation coefficient of the network is characterized as the average value of the aggregation coefficients of all nodes.The clustering coefficient is used to describe the degree of clumping among the vertices of a network. Specifically, it is the degree of interconnection among the adjacent nodes of a node, describing the second-order connection degree of the node. For node https://www.ncbi.nlm.nih.gov/genome/browse#!/overview/). The datasets include Flavobacterium , Pseudomonas  and Staphylococcus . Staphylococcus has a closed pan-genome. The 500 genomes selected for this study contain 1,332,382 genes grouped into a gene synteny network of 10,074 nodes. Flavobacterium and Pseudomonas have open pan-genomes. The 65 Flavobacterium genomes and 100 Pseudomonas genomes selected for this study contain 243,834 and 550,752 genes grouped into 51,247 and 79,941 nodes, respectively.All training genomes were downloaded from the National Center for Biotechnology Information (NCBI) database in June 2021  assigned to metabolic pathways, Flavobacterium had 67,529 genes  assigned to metabolic pathways, and Pseudomonas had 272,388 genes  assigned to metabolic pathways (Flavobacterium), 15.5% (Pseudomonas) and 13.1% (Staphylococcus).KEGG internal annotation tool KofamKOALA  was used to assign genes to functional pathways. The pathway labels belonging to the global and overview maps category were removed. pathways . The oriFlavobacterium (30 genomes), Pseudomonas (50 genomes) and Staphylococcus (200 genomes).In order to verify the performance of the model, the new genome data of the three genera were downloaded from the National Center for Biotechnology Information (NCBI) database in October 2021 (newly released genomes were downloaded first). The datasets include Pathway label assignment is essentially a multilabel classification problem. Hence, some commonly used evaluation indicators for binary classification problems are not suitable for PPA-GCN. We use six indicators to measure the effectiveness of the framework:PRA is the accuracy at the node level and is defined as the proportion of genes with at least one label assigned correctly.The TLPR is the accuracy at the label level and is defined as the number of correctly assigned labels divided by the total number of labels.WPRA of that node, and divide by the total number of nodes to obtain the overall WPRA:N is the total number of nodes, I is the number of labels for node i, Ti is the order of the correct label probabilities assigned for node i , and k is the k-th ranked probability label that was assigned correctly.When a label is predicted for a node, we assign weights in accordance with the assignment probability, sum the WPRA of each label of a node to obtain the M is the confusion matrix of the assignment results.The kappa coefficient is often used for testing consistency, that is, whether the assignment effect of the model is consistent with the actual classification effect. Its value is between -1 and 1. When the value is greater than 0.6, it is considered substantial, and when it is greater than 0.8, it is considered almost perfect. The calculation of the kappa coefficient is based on the confusion matrix:The Hamming distance is measure of the distance between the assigned and real labels, with a value between 0 and 1. A distance of 0 means that the assigned results are exactly the same as the real results, and a distance of 1 means that the model’s results are completely opposite to the desired results. This indicator is calculated as the number of erroneously assigned labels divided by the total number of labels.This coefficient is an indicator for comparing the similarity of two finite sets, defined as the size of the intersection of two label sets (the true label set and the assigned label set) divided by the size of the union. When this coefficient is 1, the assigned results are completely consistent with the actual situation; when the coefficient is 0, the assigned results are completely inconsistent with the actual situation.Flavobacterium), 77.0% (Pseudomonas) and 71.0% (Staphylococcus) on the three prokaryotic bacterial genera  of 84.8%  of incorrectly labeled samples were added in each epoch independently, and in the other, incorrectly labeled samples were added accumulatively. The PRA without the addition of incorrect labels was taken as the standard, and the PRA after the addition of incorrect labels was divided by the standard PRA to serve as the performance indicator. The results  show thaA graph neural network can achieve excellent prediction accuracy, but it is difficult to give practical meaning to features. To evaluate the importance of the selected features, the PRAs before and after feature removal were compared . There aStaphylococcus has a closed pan-genome with an average node scale of 132.3, that is, an average of approximately 132 genes grouped into one node. Flavobacterium and Pseudomonas have open pan-genomes with average node scales of only 4.8 and 6.9, respectively. The node scale was one of the major observed differences between the labeled (training set) and unlabeled (test set) node sets in the gene synteny network. PPA-GCN showed no significant difference in performance when the node scale information was removed from the input  are not significantly different between the initial training set and the test set; in contrast, the initial training set networks of the open pan-genomes (Flavobacterium and Pseudomonas) are more closely connected than the test set networks, and the overall networks exhibit some level of inhomogeneity. These findings show that the self-supervised inspiration can effectively adapt to gene synteny networks with different topologies.We speculate that PPA-GCN’s performance could be significantly improved because labeled nodes spread node attributes in a certain pattern, ultimately causing the entire gene synteny network to present a genus-specific information pattern. The question of whether this kind of propagation can be universally applied to different types of gene synteny networks or is suitable only for network structures with a more “uniform” topology should be considered. Labeled and unlabeled nodes were extracted to construct training and test networks, respectively, and the topological structures of the two new networks were compared. Because PPA-GCN iteratively extracts information from the first- and second-order neighbors of nodes, the tightness of the first- and second-order connections in the network, as measured in terms of the degree distribution and clustering coefficient, need to be considered. The results  show thaSynteny has been used to filter, organize and process local similarities between genome sequences of related organisms to build a coherent global chromosomal context . Each geStaphylococcus requires more iterations to reach a steady state than Flavobacterium or Pseudomonas. This suggests that the information pattern of a closed pan-genome is relatively conservative and cannot be easily extended, while the information pattern of an open pan-genome is easier to spread. PPA-GCN could provide insights for judging genome types in accordance with the number of iterations needed for self-supervised learning when analyzing the genome of an unknown species.Whether different types of genomes would affect training results should be considered. In addition to the node scale, the run number of self-supervised iterations needed to reach convergence can also reflect differences between different types of genomes. There are several nodes with a “super connection number” in the gene synteny network of each genus. Further analysis revealed that these hyperlinked nodes have certain similarities in function. A large proportion of such nodes is assigned to mobile genetic elements (MGEs), which have the potential to disrupt the synteny of the involved genomes and are considered to cause gradual changes (sometimes mutations) in biological genes and promote biological evolution .We investigated whether the insertion of MGEs into the genomes is random and has an impact on the pattern of functional labels. Two sets of experiments were designed. In the first set, all MGE nodes were removed from the gene synteny network to verify whether the insertion of the MGEs disrupted the information pattern of the original gene synteny networks. In the second set, all MGE nodes were added to the training set as negative samples to verify whether the intervention of the MGEs affected the distribution of functional labels. The results show  that wheFlavobacterium, Pseudomonas and Staphylococcus) were used to evaluate the assignment rate of the framework, and on all of them, good performance and strong fault tolerance were achieved. These results support the broad application of PPA-GCN to prokaryotic genomic research. For example, it can provide support for the mechanism research of pathogenic bacteria and the design of synthetic biology elements, modules and pathways.In present, PPA-GCN is the first deep learning framework that uses genomic structure information to directly assist metabolic pathway assignments of prokaryotic genomes against KEGG information. Datasets representing three genera (Although all bacterial genome had been fragmented and shuffled by the endless genomic reconstruction and horizontal gene transfer, the localized genome structure was conserved within specific genus of bacteria. Gene synteny structure is intrinsic and stable under genus level and PPA-GCN relies on it. PPA-GCN captures the graph structure and node attributes from the gene synteny information through a graph convolutional network. To maximize the given pathway information of genomes of a genus, PPA-GCN obtains and mines as many possibilities for label assignment through the network as possible. Then PPA-GCN constructs the adjacency probability matrix to evaluate all possibilities, improving the certainty of all assigned labels. The idea of self-supervised learning is adopted to expand the training set and reinforce the training process.PPA-GCN has the potential for further improvement. The runtime and memory usage of PPA-GCN will be optimized . At presPPA-GCN exhibits good performance and shows promise to help guide experimental verification and provide considerable additional space for downstream analysis. PPA-GCN could be applied to more genera of prokaryotes with sufficient whole genome sequences and used to build a database of consensus sequences from the perspective of functional pathway assignment, that could describe the differences in prokaryotes of various genera. In short, we present a deep learning framework with great potential to explain the relationship between gene synteny and KEGG pathway information in prokaryotes, which can provide novel insights into functional pathways assignments and is likely to inform future deep learning applications for interpreting functional annotations.


After COVID-19 restrictions, hybrid solutions were established that combined smart working and work in presence. Workplace conditions significantly impact employees' lives, particularly in terms of meeting their needs and promoting their wellbeing. Based on a socio-ecological and multilevel methodology, the UP150 concept (Proactive Office 150) represents a possible innovative solution to meet employees' needs and valorize flexible work. It encourages physical exercise and active breaks during the typical workday by using particular architectural modifications, a dedicated App, and physical activity professionals as wellness coaches. The present study is the last step of the preliminary actions planned to check the benefits of the UP150 concept and aims to explore the workers' perceptions after experiencing this project.The qualitative analysis of a preliminary survey (concerning information about the company structure and workers' habits) performed before conducting a randomized controlled trial intervention study and the analysis of the semi-structured interviews after 8 weeks of a UP150 experience served as datasets for this study and have been examined and discussed.In the preliminary survey, the young (under 40) and generally active (57% of the workers) reported being motivated to exercise but inhibited by a lack of time and a heavy workload. After 8 weeks at a modified workplace designed in accordance with the motive behind the UP150, the workers displayed noticeable positive perceptions and appreciation.The qualitative analysis confirmed and supported the effectiveness of the UP150 concept that previous research had already found in quantitative parameters related to employees' motor efficiency, psychophysical status, and amount of physical activity. Participants reported beneficial perceived effects on their wellness and psychophysical status following a UP150 experience. Moreover, the concept improved social relationships and increased motivation. In conclusion, the UP150 concept efficiently fostered a positive perception of physical exercise and directed the employees toward the assumption of healthy behaviors fitting the physical literacy paradigm. The COVID-19 pandemic has dramatically altered our society and human behavior. For example, the restrictions led to a change in working habits, and innovative solutions, such as smart working, have been promoted or encouraged.The working environment is of utmost importance for people's productivity and, above all, their wellbeing. Indeed, action plans such as the Luxembourg Declaration on Workplace Health Promotion in the European Union ENWHP,  or the dThe literature evinced that smart working increased the workers' sense of autonomy and independence. Research by the Nomisma observatory reported that 56% of the Italian workers interviewed would like to keep working part-time smart work after the lockdown Nomisma, .However, even before the pandemic that negatively affected the working social context  or favor (affordances) behaviors. Constraints and affordances can favor personal adaptations that benefit good wellness practices. They grant a certain degree of autonomy whenever physical exercises are directly selected and tailored according to the individual psychophysical characteristics and conditions , the diagnostic tool designed to screen the physical efficiency of participants in the upcoming UP150 intervention research.As a first step , step #1evaluates motor efficiency  through submaximal exercises. They contemplate skills involved in injury prevention  and promotion of lifespan functional autonomy . The submaximal tests are proposed at a moderate intensity of the perceived exertion scale, and their good validity scores and reliability have already been demonstrated  and a control group . Five participants withdrew during experimentation and were, therefore, excluded from the study. Analyses were performed on participants who completed the protocol . EG worked for 8 weeks in the new worksite environment as designed according to the UP150 concept, alternating 3 days on-site in the office with 2 days at home in smart working. The UP150 office had been equipped with stations for active pausing or exercising during the usual workflow, which permitted strict interaction with the UP150 App by pairing fitness equipment with working and personal devices  calculated by a system point based on activity duration and individual perceived effort . Assessment tools for one's psychophysical efficiency based on the perception of effort  increased, confirming the efficacy of the UP150 concept in acting as a proactive environment to improve the employees' physical activity, mental health, and wellness. A synthesis of the outcomes of the intervention study related to the participant's motor efficiency, psychophysical status, and increase in the amount of physical activity , 2.What differences do you perceive in your health status at the end of the UP150 intervention?; (ii) Which factor among architectural changes, wellness coaches, and the training  influenced you most? Which of these do you consider irrelevant? Is it possible to improve some of them?; (iii) How did you experience the integration of physical activity in the standard workflow? Underline the positive and negative aspects you perceived; and (iv) Did you perceive any change in relationships between colleagues and bosses within the working context?After 8 weeks of intervention, 20 participants  in the intervention group of the study by Invernizzi et al.  were intpre-labels were extrapolated. Next, pre-labels expressing similar concepts were grouped into labels. After that, subsequent axial coding was completed to group labels into categories that were quantified according to how often they appeared in the interviews. Labels with a frequency lower than 50% were defined as “sporadic,” in the 51–70% range as “typical,” and higher than 71% as “general.” The category presenting the highest percentage frequency was identified as the core category. This coding process was performed by two evaluators with an inter-rater consensus and successively discussed with the research team. The analysis of labels and categories was accomplished by the MAXQDA software . The outcomes of the analysis of the interviews through grounded analysis are presented in the results.The answers were grouped to evaluate their respective frequencies. The semi-structured interviews were recorded and analyzed using the grounded theory method .The preliminary survey results accomplished before the UP150 intervention study depicted detailed information about the software company's employees and their habits related to healthy lifestyles.(“Which definition better describes how you take care of yourself?”) specifically investigated beliefs about nutrition and physical activity habits. As a result, most of the employees (57%) considered themselves physically active and cared about the nutritional aspects of their food. In comparison, a considerable part of the remaining respondents (39%) answered that they would like to be more active and attentive to nutrition. Still, they lack the discipline to successfully adopt and keep such a good habit , tiredness from daily work , lack of consistency (14%), and laziness (11%) as the main barriers to exercising .I feel better while working in a UP150 context, days are lighter,” and “I perceived physical and mood improvements.” In addition, while direct feedback on the overall UP150 experience was the only topic of the first question, the participants also indirectly demonstrated their satisfaction by highlighting many additional positive features in answering the other questions, which appreciation can be adduced to a junction of several further aspects.The qualitative analysis of the semi-structured interviews conducted using the grounded theory approach allowed us to define the most recurrent subjects (categories and labels) and the connections among them . A core The concept encourages me to move more”), which positively affected psychophysical wellness. In addition, the employees reported a significant change in their daily working routine in favor of a healthy-oriented lifestyle thanks to the improved predisposition to move . Moreover, the element most likely affecting motivation appeared to be the presence of the wellness coach at the workplace . Instead of having the wellness coaches available daily by remote video calling and just two times a week at the office, the employees would have appreciated an ulterior, more frequent attendance of them at the worksite and during worktime (“I would have desired the wellness coaches to be more frequently available at work and during worktime”).One of them was motivation: participants reported that the intervention considerably increased their enthusiasm toward physical exercise . They also traced the individual physical activity performed and easily monitored the personal improvements over time . Furthermore, the app, connected to the workplace setting, promoted frequent active breaks during the workflow, improving the workers' motivation and psychophysical wellness (“I appreciated active breaks as they rested me from working”). Similarly, the workplace setting has been valued by the employees, who reported they appreciated the equipment disposition and the worksite design.The UP150 App and the architectural changes of the workplace were also suggested to have effects on motivation to exercise. Indeed, the App made the employees feel motivated to reach the targets, both at work and outside of work time . Specifically, having joint active breaks or exercising helped to create more interactions and, consequently, improve communication and working relationships among colleagues .Regarding socio-relational aspects, several participants conveyed that those architectural solutions improved interpersonal relationships within the business context  and generally active (57%) workers reported being motivated to exercise but needed more time to do it. They were often discouraged from practicing because of the tiered workload. It is a common problem in desk workers  to improve physical activity and engagement. As the socio-ecological model expresses, individual behaviors can modify when environmental interventions embrace different areas of social influence . Nevertheless, coaches did not limit themselves to supporting the employees in exercising and adapting practices to individual needs; they had the function of sustaining employees in the transition from the standard workplace conception to a new thought about practicing physical activity in a formal environment, such as the workplace. In addition, the wellness coaches had a meaningful educational role: they guided the employees in managing the active breaks by autonomously handling exercise and training loads based on perceived exertion, thereby acquiring competence in self-monitoring  enabled autonomy, competence, and relationships. They appropriately responded to one of the occurrences that emerged from the preliminary survey , impedinI have found a place for new routines throughout my working day.” Further habits and adaptations to the modified environment originated from the new workplace concept . These healthy habits fit the reference theory on which this study is based, i.e., physical literacy. Hence, the UP150 concept created a framework also permitting the workplace to become an educational place, promoting healthy habits that workers can preserve until they are elderly, and allowing workers to manage their working time and workloads adequately.Including exercises in the workflow exploited the workers' routines: “The socio-ecologic approach positively affected the intrapersonal sphere: participants reported improvements in the company's social climate. These improvements have been attributed to the active-break strategy and the advancement of active relationship occasions. Indeed, in the socio-ecologic model, the social environment is decisive in advancing physical exercise: watching other people being active or practicing physical activity at an organizational level is one of the factors contributing to physical exercise promotion . This could have affected the results, making it necessary to recommend extending further research to companies with different objectives and organizational charts. In addition, the results were from 8 weeks of intervention, a relatively short time. Even if the results howed a clear positive trend in changing behaviors and perceptions, longer interventions might strengthen the effectiveness of the UP150 concept. Longitudinal studies in different workplaces must confirm and generalize the positive effects on behavior and perception and their long-term preservation.The qualitative analysis of the perception of workers' UP150 experience in the present study confirmed and supported the efficacy of the new-concept worksite office previously investigated by Invernizzi et al.  as effecThe raw data supporting the conclusions of this article will be made available by request to the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee of the University of Milan . The patients/participants provided their written informed consent to participate in this study.GS and PI contributed to conception and design of the study. CD'A supervised methodology. MR and GS organized the database. CD'A and GS performed the formal analysis. GS, PI, and RS wrote the first draft of the manuscript. PI and RS supervised the study. All authors contributed to manuscript revision, read, and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer NL declared a shared parent affiliation with the authors GS, RS, MR, and PI to the handling editor at the time of review.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.


With the strengthening of global anti-terrorist measures, it is increasingly important to conduct security checks in public places to detect concealed objects carried by the human body. Research in recent years has shown that deep learning is helpful for detecting concealed objects in passive terahertz images. However, previous studies have failed to achieve superior accuracy and performance for real-time labeling. Our research aims to propose a novel method for accurate and real-time detection of concealed objects in terahertz images. To reach this goal we trained and tested a promising detector based on deep residual networks using human image data collected by passive terahertz devices. Specifically, we replaced the backbone network of the SSD (Single Shot MultiBox Detector) algorithm with a more representative residual network to reduce the difficulty of network training. Aiming at the problems of repeated detection and missed detection of small targets, a feature fusion-based terahertz image target detection algorithm was proposed. Furthermore, we introduced a hybrid attention mechanism in SSD to improve the algorithm’s ability to acquire object details and location information. Finally, the Focal Loss function was introduced to improve the robustness of the model. Experimental results show that the accuracy of the SSD algorithm is improved from 95.04 to 99.92%. Compared with other current mainstream models, such as Faster RCNN, YOLO, and RetinaNet, the proposed method can maintain high detection accuracy at a faster speed. This proposed method based on SSD achieves a mean average precision of 99.92%, an F1 score of 0.98, and a prediction speed of 17 FPS on the validation subset. This proposed method based on SSD-ResNet-50 can provide a technical reference for the application and development of deep learning technology in terahertz smart security systems. In the future, it can be widely used in some public scenarios with real-time security inspection requirements. In recent years, the global counter-terrorism situation has become increasingly severe. Security inspections in public places have gradually attracted widespread attention from various countries. In crowded public places, in addition to the security checks on the packages people carry, it is also important to conduct security checks on the human body to detect dangerous hidden objects. However, the existing security inspection systems have some deficiencies more or less. In this case, the terahertz security inspection system using electromagnetic waves as the detection method is playing an increasingly important role.1. Terahertz imaging can detect not only metallic objects, but also non-metallic contraband such as explosives, ceramic knives, drugs, and glass knives3. Terahertz waves can penetrate insulating materials such as clothing, plastics, and ceramics6, so they can be used to detect objects hidden under human clothing. Compared with microwaves, the terahertz waves have shorter wavelengths and higher imaging accuracy. Terahertz waves have much longer wavelengths compared to X-rays and visible light. Therefore, the resolution and signal-to-noise ratio of human security imaging based on terahertz systems are quite different from X-ray imaging and visible light imaging. Such characteristics make it difficult for terahertz imaging to automatically detect hazardous objects using existing object detection algorithms. Terahertz imaging systems are divided into active and passive working modes. In the application of detecting concealed objects in the human body, passive terahertz imaging systems occupy a major position8. Passive terahertz imaging systems do not emit electromagnetic waves and will not cause harm to the human body. The current speed of passive terahertz imaging is as high as 10 frames per second, which puts forward higher requirements for the speed and accuracy of detection. At present, most of the images acquired by the terahertz systems are checked manually one by one, which not only consumes a lot of manpower, but also may lead to missed inspections when people mark images for a long time. As a result, the advancement of automatic identification technology for passive terahertz images is critical for the widespread adoption of intelligent security inspection scenarios.Terahertz waves refer to electromagnetic waves with frequencies between 0.1 and 10 THz, which have similar characteristics to microwaves and infrared9, Otsu method10, region growing method, etc. The target segmentation-based algorithm is fast and easy to implement, but it can’t detect complex targets. The feature matching-based detection algorithms construct a target feature descriptor to match known prior feature information. Common features include Haar features11, gradient histograms12 (HOG), dense SIFT features13, etc. Feature-based detection methods rely on manual feature extraction. However, since artificially constructed features are usually limited to specific object types, satisfactory object detection results cannot be achieved. In recent years, with the development of deep learning, the application of deep convolutional neural networks in optical image target detection has achieved good results, and its detection accuracy is far superior to traditional methods15. Low-resolution passive terahertz images contain very little information, making it very difficult to extract features from images manually. The CNN network can automatically extract features from passive terahertz images. Although large-scale CNN networks have good detection and classification effects, they cannot meet real-time performance due to large parameters and complex structures. Extensive research shows that there is still space for improvement in the accuracy and speed of current CNN-based terahertz image target detection algorithms18. Hence, there is an urgent need to propose a method with sufficient accuracy and robustness for object recognition in passive terahertz images. Nevertheless, to the best of the author’s knowledge, there are only a handful of studies in the area.It is well known that the security screening of terahertz imaging systems is designed to detect and identify the threat of dangerous goods carried by the human body. More and more terahertz passive imaging systems have been studied in recent years, but theories and methods for identifying or locating dangerous objects from terahertz images are still in their infancy. Compared with optical and active terahertz imaging, passive terahertz images have a low signal-to-noise ratio and are easily affected by background variations. These characteristics make the use of passive terahertz imaging systems for security inspections a huge challenge. Traditional object detection algorithms of passive terahertz images mainly include segmentation-based detection algorithms and feature matching-based detection algorithms. The segmentation-based detection algorithm uses the grayscale information in the passive terahertz image to divide the image into two regions, the target and the background, and then detects the target. At present, the commonly used segmentation algorithms include maximum entropy segmentation method19 in the SSD algorithm, the improved algorithm uses the ResNet-50 network20 as the feature extraction network.Aiming at the network degradation problem of the VGGNet network21, which is used to detect objects of different scales. Through upsampling, deep features and shallow features are fused to construct feature representations with rich semantic information, making the fused features more descriptive.For the problem of poor detection performance of small objects, we extract multi-level features to form a feature pyramid networkThe proposed algorithm introduces the spatial-channel attention mechanism into the SSD network to enhance the semantic information of high-level feature maps. Therefore, the ability of the algorithm to obtain the details and position information of the object can be improved, thereby reducing the missed detection rate and false detection rate, and improving the detection accuracy of small objects.22 to improve the imbalance of positive and negative samples and hard and easy samples. By increasing the weight of the hard samples in the loss function, the robustness of the proposed algorithm can be improved.This paper introduces the Focal LossThere are two main problems in the direct use of deep learning algorithms in the field of passive terahertz image object recognition, one is that the accuracy of object detection is not high, and the other is that the speed of object detection is not fast enough. In response to these two issues, we chose the single-stage detection algorithm SSD, which has relatively high accuracy and speed in the natural light object detection algorithm, as the basic model. This is the first application of the SSD algorithm in the field of passive terahertz image object detection. We investigate the potential of the residual network ResNet-50 in terms of accuracy and real-time performance in detecting concealed objects in passive terahertz images. The method described in this paper aims at selecting the most efficient CNN architecture and further exploring the ways of its modification and optimization to ensure superior real-time classification potential for detecting concealed objects. The significance and originality of this research lie in the first exploration of the application of the SSD model in the field of passive terahertz image detection and several important improvements are proposed for image characteristics. The ultimate goal of this research is to develop a fast and accurate detection algorithm for passive terahertz images and promote its practical application in intelligent security inspection scenarios. We propose the following improvements to further improve the accuracy and speed of the algorithm in detecting concealed objects. To summarize, our main contributions are as follows:Compared with the unoptimized SSD algorithm, this method can improve the detection accuracy and at the same time meet the detection speed requirements in intelligent security inspection scenarios. Specifically, our proposed algorithm achieves a mean average precision of 99.92%, and the detection speed of the algorithm can reach 17 FPS, which can meet the real-time requirements. This is an excellent outcome that could not have been obtained with earlier methods. The remainder of the paper is organized as follows: The related works on terahertz image object detection and the basic introduction to SSD algorithm are introduced in “Related work”. In the following section, the proposed improved SSD algorithm is presented in detail. Section “Experimental results and discussion” shows and analyzes the experimental results of our method after its deployment. The conclusion of the paper is formed in the last section.This section introduces the related works, including the introduction of common object detection methods based on CNN models, the basic introduction of the SSD model, and related work on terahertz image object detection.23, including Fast R-CNN24, Faster R-CNN25, Mask R-CNN26, RFCN27, etc. These two-stage algorithms greatly improve the detection accuracy, but the two-stage detection results in a slow detection speed, which cannot meet the real-time requirements. The other category is one-stage detection algorithms represented by YOLO28 (You Only Look Once) and SSD29. The YOLO algorithm uses the idea of regression to greatly improve the detection speed, but its recognition accuracy of object location is poor and the recall rate is low. The SSD algorithm combines the regression method in the YOLO algorithm, not only follows the anchor mechanism in Faster R-CNN but also extracts multi-scale feature maps for classification and prediction, which greatly improves the precision and speed of object detection.In recent years, deep learning has made breakthroughs in object visual detection. At present, object detection algorithms based on deep learning models are mainly divided into two categories: one is various two-stage algorithms based on R-CNN29, which combines the anchor mechanism in Faster R-CNN and the regression idea in YOLO. The SSD algorithm not only has a clear speed advantage over Faster R-CNN, but also has a clear accuracy advantage over YOLO. It is based on the VGGNet-16 backbone and fuses feature maps of different scales for feature extraction. Its network structure is divided into two parts: one is the feature extraction network, and the other is the classification and regression layer. Specifically, the feature extraction network can extract the feature information of the object in the image, and at the same time can improve the ability of the network to perceive the object. The classification and regression layers are capable of classifying and regressing each candidate box to detect objects in the terahertz images. The structure of the original SSD network is shown in Fig. The SSD algorithm is an object detection algorithm proposed by Wei Liu et al.As we all know, the SSD algorithm no longer uses fully connected layers, which improves computational efficiency. Moreover, the SSD algorithm uniformly modifies the size after the input image, and there is no mandatory requirement for the size of the input image. Besides, SSD directly uses convolution to calculate candidate boxes and predict classification in one step, which can simplify the process. However, the SSD algorithm also has some disadvantages. To begin with, the maximum size of feature maps used for prediction has a resolution of 3838. If the size of small objects in the input image is small, the detailed information contained in the lower layers can easily be lost after the pooling layer. Besides, as the feature map size of additional feature extraction networks continues to decrease, the feature extraction and representation capabilities of shallow feature layers are limited. While the feature output layer of deep features is mainly responsible for large objects, resulting in poor detection performance of SSD for small objects. Finally, the convolutional layer of SSD algorithm feature extraction cannot take into account the features of adjacent feature layers of different scales. This results in insufficient feature extraction capability for small object features in complex environments during object feature extraction. All in all, the detection performance of the original SSD algorithm for small objects in complex environments needs to be improved. To meet the need of detecting hidden objects in passive terahertz images, some improvements to its network structure are also required.31. According to the characteristics of terahertz images, Zhang et al. adopted the Otsu threshold segmentation algorithm and applied the contour tracking method to extract contours, which can realize object detection in human terahertz images32. Aiming at the difficulty of detecting edge objects in human terahertz images, Jiang et al. proposed an automatic recognition algorithm for human edge objects33. Santiago Lopez Tapia et al. proposed a method combining image processing and statistical machine learning techniques to solve the problem of object localization detection in terahertz images34. Niu et al. proposed a terahertz human image processing and recognition method based on the principle of saliency and sparse coding, which can realize automatic recognition of hidden objects in the human body35.In recent years, passive terahertz imaging technology has shown significant development prospects in the field of security inspection. It is of great significance to detect dangerous objects hidden in the human body in passive terahertz security images. At present, some scholars have carried out research on passive terahertz human body security image object detection algorithmsIn these methods, the objects were first separated from the image and then classified and recognized in a complex and slow process. Moreover, the above traditional object detection methods have certain defects and poor generalization ability. The performance of these algorithms is often affected by the complexity of the image background. The simpler the image background, the higher the object detection efficiency and the better the detection performance. On the contrary, once the image background becomes complex, the efficiency and performance of object detection will decrease accordingly.36  based on deep learning technology can solve the defects of traditional methods well. CNN can not only complete feature extraction, but also has good robustness and strong feature expression ability. These properties enable it to accurately localize detection objects in both simple and complex environments. Liu Chen et al. introduced Focal Loss22 on the basis of Faster R-CNN and detected concealed objects in active millimeter-wave images without using transfer learning16. Hong Xiao et al. proposed the R-PCNN algorithm. The algorithm adds traditional image preprocessing methods to the front end, which improves the speed and accuracy of object detection and recognition17. Xi Yang et al. exploited the spatiotemporal information of terahertz image sequences and used CNN to achieve automatic object detection and recognition18. Extensive studies show that there is still space for improvement in the accuracy and speed of current CNN-based terahertz image object detection algorithms.The convolutional neural network CNNAccording to the characteristics of terahertz images, we propose a novel method for object detection in terahertz human security images. This method enables accurate real-time detection of concealed objects in terahertz images. The improved SSD framework is shown in Fig. The overall flowchart of our proposed algorithm is shown in Fig. This study conforms to the ethical guidelines of the Declaration of Helsinki revised in 2013. The study was approved by the ethics committee of the Aerospace Information Research Institute, Chinese Academy of Sciences. All experiments were performed by relevant guidelines and regulations. We confirmed that informed consent had been obtained from all subjects. All images were deidentified before inclusion in this study.20, which successfully solved the above two problems by introducing a batch normalization (BN) layer and residual block. The BN layer normalizes the feature maps of the middle layer to speed up the network convergence and improve the accuracy. The residual block fits the residual map by adding a skip connection layer between the network layers. It can directly transfer the optimal weight of the front layer to the back layer, so as to achieve the effect of eliminating network degradation. The comparison between the ordinary structure and the residual structure is shown in Fig. H(x) is called the desired map for stacking several network layers, x is the entry of the current stacking block, and the use of the ReLU activation function shortens the learning cycle. Assuming that n nonlinear layers can be approximately expressed as a residual function, then the stacked network layers are fitted to another map The VGGNet-16 network is a simple stack of traditional CNN structures with many parameters. As the number of network layers deepens, there are not only the phenomenon of gradient disappearance and gradient explosion, but also the problem of network degradation. In response to the problems existing in the traditional CNN structure, the team of Kaiming He of MSRA proposed a convolutional neural network called Deep Residual Network(ResNet)The detection of concealed objects in passive terahertz images is affected by complex backgrounds and similar interference. Research has proved that deepening the network structure is effective for improving feature detection. However, VGGNet-16 is a relatively shallow network, which is insufficient to extract features of hidden objects. Therefore, we replace the feature extraction network with ResNet-50, a network with deeper and richer semantic information. ResNet-50 has a total of 16 Bottlenecks, each of which contains 3 convolutional layers. Together with the input layer and the final fully connected layer, a 50-layer residual network is formed. The comparison of network parameters and complexity between VGGNet-16 and ResNet-50 is shown in Table 37. The depth of each convolutional layer of ResNet-50 is higher than that of VGGNet-16, which enhances the network learning ability and further improves the feature extraction ability of concealed objects in images. ResNet-50 introduces a residual module in the convolutional layer, which solves the problem of training degradation caused by the deepening of the network. The network has high convolution calculation efficiency and reduces the redundancy of the algorithm. Taking into account factors such as accuracy and detection speed, the novel CNN network based on the ResNet-50 network is more accurate than VGGNet-16. By using ResNet-50 as the backbone network, the data features can be better extracted for classification and loss computation. The improved algorithm uses ResNet-50 as the extraction network, removes the fully connected layer in ResNet-50, adds several additional convolutional layers to extract features, and obtains feature maps of different scales. In this way, the limitation of the fully connected layer on the size of the input image can be solved. The network can obtain feature maps of different sizes to construct the detection part of the feature maps of different sizes in the network. The size of the feature map of the feature extraction layer of the improved algorithm is 3838, 1919, 1010, 55, 33 and 11, and the dimensions are 128, 256, 512, 256, 128 and 64 respectively. The shallow convolution layers extract relatively small features, while deep convolution layers can extract richer information. Therefore, shallow convolutional feature maps and deep convolutional feature maps are used to detect small and large objects, respectively. Replacing the original front-end network VGGNet-16 with the ResNet-50 network can improve the accuracy on the basis of the original SSD algorithm, but there are still problems such as poor real-time performance, false detection, and repeated detection of small objects. Therefore, feature fusion techniques are used to improve the SSD algorithm.Compared to AexNet, GoogleNet, and VGGNet-16, ResNet-50 is known for its ability and performance to achieve lower error rates and higher accuracy output than other architecturesThe SSD algorithm takes full advantage of multiple convolutional layers to detect objects. It has better robustness to the scale changes of objects, but the disadvantage is that it is easy to miss small objects. The network structure of the SSD algorithm is shown in Fig. The improved algorithm fuses adjacent low-level high-resolution feature maps with clear and detailed features and high-level low-resolution feature maps with rich semantic information. Feature fusion can enhance the ability of the low-level feature layer to fully express the detailed features of small objects, and improve the detection effect of the SSD algorithm on small objects. The feature fusion structure proposed in this paper is shown in Fig.  21, and transfers the feature information of feature maps of different scales from top to bottom. Feature fusion can provide feature representations with rich semantic information, improving the descriptiveness of fused features.As we all know, the update of the parameters of the previous convolutional layer will cause the data distribution of the later input layer to change, resulting in a large difference in the data distribution of the convolutional feature layer. Therefore, there will be large differences between feature maps, and feature connection operations cannot be performed directly. When the network layer changes slightly, the features are accumulated and amplified through the fusion layer, resulting in slow convergence of the algorithm. Therefore, a BN layer is added after each feature layer for normalization. BN layers can reduce the effects of a slow training process due to increased model complexity. The improved SSD algorithm adopts the method of multi-level fusion of different scales, combined with the idea of FPN38 is a little universal module that saves computational resources and parameters. For a given feature map, attention weights are sequentially derived along both spatial and channel dimensions, and then the features are adaptively adjusted by multiplying with the original feature map. The implementation of the attention mechanism includes two modules: channel attention and spatial attention. The structure of the CBAM attention mechanism module is shown in Fig. The feature maps extracted by the feature extraction network not only have object features but also have similar interference features. The concealed objects on the human terahertz image and other similar objects are given the same importance on the feature maps, which is not conducive to the detection of hidden objects in complex backgrounds. Therefore, to improve the object recognition ability of feature maps for specific regions and specific channels, and reduce the negative interference of complex backgrounds and similar objects, the attention mechanism is applied in both channel and space dimensions. Mechanism of Attention CBAMGiven an intermediate feature map r is the reduction ratio. After applying the shared network to each descriptor, the channel attention module merges the output feature vectors using element-wise summation. Briefly, the channel attention module is computed as:The structure of the channel attention module is shown in Fig. The spatial attention module utilizes the spatial relationship of features to generate a spatial attention map. Figure The channel information of the feature maps is aggregated using two pooling operations to generate 2D maps: The improved algorithm is based on SSD-ResNet-50 and adds CBAM modules after 6 feature maps of different sizes. By adjusting the size of the feature maps, each feature map can remain the same size as before the output after the feature weighting of the CBAM module. The attention mechanism further enhances the semantic information of high-level feature maps, reduces the target missed detection rate, and improves the robustness of the algorithm.c and l are the category confidence and position offset information of the prediction box, respectively. x is the matching result between the previous frame and different categories, if it matches, the result is g represents the offset between the ground-truth bounding box and the prior bounding boxes, and N denotes the number of the prior bounding boxes.The loss function of the original SSD network consists of a weighted sum of the confidence loss and the position loss. The specific expression is calculated as:22 to replace the confidence loss function in the original loss function. Its specific expression can be summarized as:The problem of sample imbalance can be solved by adjusting the positive and negative sample ratio parameters in the input network. To solve the problem of sample imbalance in the SSD algorithm, we use the Focal Loss functionThe Focal Loss is visualized for several values of In this section, the image dataset is introduced first, and then the evaluation metrics are described. The results of the ablation experiments demonstrate the effectiveness of the improved points proposed by this method. Extensive comparison experiments between the improved SSD algorithm and other methods are conducted in succession. In addition, we give an intuitive discussion about the visual detection effect of this algorithm and depict the ROC curve.In the research field of terahertz concealed object detection, few passive image datasets are currently publicly available. The passive terahertz dataset we used in this paper was constructed by a team led by researcher Chao Li from the Aerospace Information Research Institute, Chinese Academy of Sciences. The dataset was collected by the 0.2 THz passive imaging device shown in Fig. Since passive terahertz image object detection in the security field is still a relatively “niche” field in computer vision, there is no standard evaluation protocol defined. We evaluate our proposed improved SSD algorithm using mean precision (mAP), F1 score, ROC curve, AUC value, and FPS. The following is a comprehensive overview of the indicators.Object detection performance can be evaluated with Average Precision (AP) and mean Average Precision (mAP) between ground truth and predicted bounding box (IOU). AP is defined as the area enclosed by the precision and recall curves and the axes. The precision rate is the accuracy rate, which measures the classification ability of the object detection algorithm to detect objects. The higher the accuracy rate, the stronger the model’s ability to classify the object. The recall rate measures the detection ability of the model to the object. The higher the recall rate, the stronger the model’s ability to distinguish the object. In the object detection algorithm, the precision-recall curve (PR curve) of each category can be obtained. The area enclosed by the curve and the abscissa axis is the average precision AP value. The mAP can be obtained by averaging the APs of all classes. Under the same confidence, the larger the mAP of the model, the better the detection performance of the model. The precision and recall rate are calculated as follows:True positive (TP) means that the model predicts a positive sample and the actual prediction is correct; false positive (FP) means that the model predicts a positive sample and detects it incorrectly, and false negative (FN) means that the model predicts a negative sample and detects it incorrectly. These evaluation metrics are illustrated in Fig. Additionally, the F1 score is a composite metric that combines precision and recall. The advantage of the F1 score is that it provides a single measure of quality that is easier to understand. Its calculation formula is as follows:40, commonly used to visualize the discriminant ability of a binary classifier, is a plot of the true positive rate  versus the false positive rate  at various given cut points. A typical ROC curve is a plot of FPR on the x-axis versus TPR on the y-axis. The area under the curve (AUC) maps the entire ROC curve into a single number that reflects the overall performance of the classifier over all thresholds. Typically, the value of AUC is between 0.5 and 1. The larger the value, the better the discriminative ability of the classifier. The formulas for calculating TPR and FPR are as follows:A receiver operating characteristic (ROC) curveThe detection speed is mainly used to evaluate the real-time performance of the algorithm. If the inference speed cannot meet the real-time requirements, our model cannot be used in the real environment. FPS (frames per second) is currently a common metric for evaluating model speed. In two-dimensional image detection, it is defined as the number of pictures that can be processed per second. The larger the value, the shorter the detection time and the faster the speed.The experimental environment of this paper is: in terms of hardware, we adopt the Inter(R)Core(TM)i5-10400F CPU processor and the graphics card of GeForce RTX 3090 Ti. In terms of software, we choose the Ubuntu 20.04 operating system, Pytorch deep learning framework, and Python 3.6 programming language.Batch_size is the number of images sent to training at one time. A larger batch_size can make model training more stable, but it will increase the amount of computation. Considering the computing power of the graphics card we have, we chose 16 as the batch_size. The learning rate is the speed at which the model is iteratively trained, and setting the learning rate correctly can make the loss curve smoother. The initial learning rate was set to 2e-4 and the learning rate decay weight was 0.001. The number of training iterations is 6000, the parameter update method is the gradient descent method with momentum (momentum SGD), and the momentum factor is set to 0.9. The resulting model was saved after every 500 iterations in the training process. After training, the object detection accuracy is used as the criterion for selecting the optimal model. The model is validated using the test set, and the experimental results are analyzed. The change of the loss function during the training process is shown in Figure This section carries out the ablation study of the improved SSD algorithm to prove its superiority over the baseline model. We analyze the impact of improving feature extraction network, adding feature fusion network, adding attention mechanism, and improving loss function on recognition accuracy. The results of the ablation experiments are shown in Table Model-1 replaces the backbone network from VGGNet-16 to ResNet-50, and the mAP is 3.61Furthermore, the comparison of mAP values obtained by different models in the first 6000 iterations is shown in Fig. We evaluated the improved SSD algorithm to other state-of-the-art detection approaches, including Faster RCNN, YOLO v5, RetinaNet, and the baseline model SSD, to verify its comprehensive performance. Table As shown in Table We employed the passive terahertz human security image dataset given in the “Datasets” section above for detection experiments, and the visual detection results are shown in Fig. The ROC curve of the proposed model is shown in Fig. To further improve the detection accuracy and speed of concealed objects in terahertz human security images, we proposed an improved SSD algorithm to promote its algorithm performance in this paper. First, the ResNet-50 network was used to replace the original VGGNet-16 network to overcome the VGG network degradation problem. Afterward, we designed a feature fusion module to fuse deep features and shallow features to construct features rich in semantic information, which is beneficial to the detection of small objects. In the next parts, the hybrid attention mechanism was introduced in the SSD network to improve the network’s attention to concealed objects. Finally, we introduced the Focal Loss function in the loss function to improve the robustness of the algorithm. The results of ablation experiments illustrate the efficacy of the changes described in this paper. At the same time, we also compare the proposed model with other mainstream detection methods on the terahertz human security image dataset. The results showed that our method achieves significantly improved detection accuracy in comparison with the original algorithm when the speed is only slightly reduced. The detection accuracy of the proposed method is as high as 99.9%, and the detection speed is about 17 FPS. Therefore, it can meet the real-time detection needs of security inspection scenarios.Our approach can be improved in the future as follows: (1) The training dataset can be augmented to improve the generalization ability of the model. (2) The model can be further lightweight to reduce the amount of calculation and improve the detection speed. (3) More emphasis should be placed on the model’s interpretability in order to make the model easier to interpret and understand. It also makes the black box of the CNN-based model more transparent and reasonable. We believe that the application of Explainable AI will help to reduce the uncertainty of deep learning models. The insights gained from this research facilitate the application of deep learning techniques in smart security screening scenarios. It is believed that the method proposed in this paper will have important application value in the terahertz intelligent security systems.