The massively and rapidly spreading disinformation on social network platforms poses a serious threat to public safety and social governance. Therefore, early and accurate detection of rumors in social networks is of vital importance before they spread on a large scale. Considering the small-world property of social networks, the source tweet-word graph is decomposed from the global graph of rumors, and a rumor detection method based on graph attention network of source tweet-word graph is proposed to fully learn the structure of rumor propagation and the deep representation of text contents. Specifically, the proposed model can adequately capture the contextual semantic association representation of source tweets during the propagation and extract semantic features. For the data sparseness of the early stage of information dissemination, text attention mechanism based on opinion similarity can aggregate and capture more tweet propagation structure features to help improve the efficiency of early detection of rumors. Through the analysis of the experimental results on real public datasets, the rumor detection performance of the proposed method is better than that of other baseline methods. Especially in the early rumor detection tasks, the proposed method can detect rumors with an accuracy of nearly 90% in the early stage of information dissemination. And it still has good robustness with noise interference. The vigorous development and iteration of network technology and electronic devices have made social networks an indispensable part of people’s daily lives. The emergence of social network platforms such as Twitter, Yelp, and Reddit has greatly facilitated people to quickly obtain and exchange information through virtual networks. By the end of 2020, the number of registered users of Twitter has exceeded 1 billion, the average number of monthly active users had exceeded 330 million, and the total number of tweets sent daily exceeded 500 million. Twitter has become the second-largest social media platform in the world. As one of the most popular social networking platforms, Twitter gives people a lot of information every day and is considered an important news source, which means that information usually spreads faster than traditional media [Therefore, it is very necessary and beneficial for society to detect the large amount of false information spread on social media as early as possible, which can prevent rumors from harming public safety and misleading citizens. It is necessary to develop an effective method that can identify different types of rumors with higher accuracy in the early stage of information dissemination. Furthermore, misclassifying and blocking the spread of fake news or information can be counterproductive, such an inappropriate action will affect the freedom and fairness of information sharing on social platforms .Most of the current work mainly considers the text content features, user features, and retweet propagation features of rumors, and extracts such features to realize rumor detection. However, these methods are often limited in extracting features, mainly due to the following reasons. Firstly, the dissemination of information in social networks often takes the form of short texts, and it is difficult to achieve accurate rumor detection with text content features extracted from a single short text . SecondlTo solve the above problems, in the current paper, rumors are considered a claim that may or may not be true at the time that it is posted on Twitter. Rumor detection on Twitter specifies whether the sets of incoming tweets are rumors or not . NumericIn this study, a method for rumor early detection based on graph attention network is proposed to learn the contextual semantic association representation of source tweets and named STWA, which can jointly learn the source tweet contextual semantic association representation of rumors as well as the source tweet propagation structure features. This study evaluates the performance of the proposed method STWA on the rumor datasets. Through the analysis of the experimental results, the rumor detection performance of the proposed method is better than that of the baselines, especially the performance on the task of early detection of rumors is better than the existing methods. The academic contributions of this study are as follows:In this paper, Considering the small-world property of social networks, this research decomposes the source tweet-word graph based on the global graph in the data processing work. The model can capture the propagation structure features and contextual semantic association representations of source tweets more effectively in this decomposition graph, which contributes to feature extraction efficiency and achieves higher rumor detection accuracy.This study proposes a text aggregation attention mechanism based on opinion similarity. Adding the calculation of edge connection weights based on opinion similarity in the model can make the model further learn the structure of the propagation graph and obtain more propagation structure features. Therefore, it can resist more influence of the interference of noise in the early detection task and achieve more efficient rumor detection in the early stage of information dissemination.The present paper is organized as follows: Related works are reviewed in Section 2. A problem statement and detailed explanation of the main aspects of the proposed method STWA are presented in Section 3. A quantitative evaluation of the proposed model is carried out in Section 4. Section 5 concludes and briefly analyzes the direction of future work.The present paper proposes a rumor detection method based on graph attention neural network. The current related work in this field is mainly based on traditional machine learning and deep learning for feature extraction. These features include content-based, user-based, and propagation-based features to complete the classification tasks in rumor detection and verification.In current, most of the methods for early rumor detection are based on traditional machine learning, which considers starting from the text content features, user features, and communication structure features in the dataset, and extracting such features to realize rumor detection. Combining different types of features, Castillo et al.  made greFor the task of early rumor detection, the above two works try to use statistical text features to capture the features of text contents of source tweets or retweets to achieve early detection of rumors. Further, to obtain the structural features of rumor propagation. Ma et al.  proposedHowever, methods to manually extract features are time-consuming and labor-intensive, and these features are dataset-dependent and sometimes impossible to extract. Therefore, some deep learning models that can automatically extract rumor features are proposed.In recent years, deep learning has achieved some success in many fields, such as artificial intelligence including natural language processing (NLP). More scholars have begun to pay attention to the application of deep learning in rumor detection tasks. Many research results have demonstrated that the ability of these methods to extract language features is significantly enhanced, which can improve the performance of the model .Ajao et al.  providedTo complete the task of early rumor detection, some scholars have considered using deep learning models to automatically extract relevant features from source tweets. Ma et al.  proposedRecent studies have demonstrated the high efficiency of using deep learning models on graph structures to solve problems in NLP . CompareFew methods based on text and propagation structure take into account the learning of semantic association representations between source tweets and retweets in combination with the propagation graph topology. What’s more, in the early stage of information dissemination, the problem of network data sparseness is still a major challenge for early rumor detection. This paper argues that the implicit features of rumor text content are not effectively extracted, especially the contextual semantic association features of source tweets, which may help improve the early detection accuracy of rumors.Therefore, in this study, a graph attention network-based method is used to model the Twitter information dissemination structure. the proposing method in this paper aims to establish a rumor detection method to capture the text content features and propagation structure features of source tweets as many as possible and achieve high detection accuracy in the early stage of rumor propagation. What’s more, with the increase in noisy data, the model still has good robustness in the case of sparse data. Specifically, Considering the small-world properties of social networks, a global graph of the tweet propagation process is built in this paper, as shown in In this section, we first illustrate how to construct the Twitter global graph and the source tweet-word graph, and make a preliminary statement on the rumor detection problem. After that, the overall framework of the proposed method STWA and the details of two modules of the source tweet-word graph attention network and the text attention mechanism based on opinion similarity included in the framework are described in detail. In this study. The two main challenges that need to be addressed to develop an effective rumor early detection model in this study are as follows: (1) How to capture the semantic association representation between a particular source tweet and the retweet text during the propagation process. (2) In the case of sparse data in the early stage of Twitter network propagation, how to ensure that the model learns the explicit and implicit representations of all Twitter text content features as fully as possible. For solving the above two problems, a global graph that meets the requirements of this paper is first constructed.G = , where V and E represent nodes and edges in the graph. Node V represents the source tweet or retweet corresponding to the user node and the words it contains, which is constructed based on the propagation process of all tweets in the dataset. This study defines each participating user in the propagation as a node, and the global graph includes all the nodes in the propagation process of the source tweet. However, each node in the Twitter global graph has different importance for learning node embeddings for rumor detection and suffers from data sparseness in the early stage of information propagation. To more accurately learn the semantic association representation between the source tweet text and the retweeted text, the source tweet-word propagation graph can be obtained by decomposing the global graph. The propagation graph of different source tweet Sti is shown in This paper considers the small-world properties of social networks and constructs a Twitter global propagation graph based on the rumor propagation structure, as shown in Specifically, this study defines each user who participates in the dissemination of source tweets as a node, the user who sends out the source tweet is defined as a source node, and each user node contains the tweet text and related words corresponding to the user. In E: Connected edges between source tweets and words Esw, Connected edges between tweets corresponding to node V with opinion similarity Esr. Esw denotes the relationship of the source tweet to the words it contains. Esr denotes indicates that the tweets of different users have opinion similarity or their tweets contain co-occurring words.In the decomposed source tweet-word graph, there are two forms of edge G = , Where V = {S, O}, E = {Esw, Esr} denote nodes and edges in the global graph, respectively. T represents all tweets corresponding to nodes V, Sti denotes the collection of the i-th source tweet and its retweets, i.e. T = {St1, St2,…,Stn}, where n is the number of source tweets. W denotes the set of words contained in the tweet, i.e. W = {w1, w2,…,wm}, where m is the total number of words in the set of words. O denotes the set of tweets with opinion similarity corresponding to nodes V. Esw, Esr represents the edge of the source tweet and the words it contains, the edge with co-occurring words or opinion similarity between the source tweet and the retweet, respectively.In this study, given a constructed source tweet-word graph y is a rumor or not. In this paper, for obtaining an effective classifier, the proposed model will learn the function p to determine the label probability of the set of tweets Sti. c and θ represent the class labels and model parameters to be learned, respectively, and the studied model is constructed based on the graph attention network.Generally speaking, rumor detection is transformed into a binary classification task to determine whether news or information circulating on social media is a rumor. A classifier can be formalized as a function that determines whether Sti’s Source Tweet-Word Graph. (b) Processing layer, which includes (1) the source tweet-word graph attention network, which utilizes the attention mechanism of graph attention network [In this subsection, the overall framework of the proposed method STWA is described. As shown in  network  to captu network  in the pThe STWA will make full use of the source tweet text content to learn the contextual semantic association representation in the small-world network propagation process. Afterward, the text attention mechanism based on opinion similarity is used to fuse all textual content and contextual semantic association representations in different source tweets to achieve the purpose of early rumor detection.To capture the semantic association between source tweets and retweets. This paper considers the small-world property in Twitter networks and is inspired by graph attention networks. The multi-head attention mechanism in the graph attention network is used to model and analyze the source node and its neighbor nodes with a large aggregation coefficient, and give a higher weight to the neighbor nodes that have a shorter propagation path than the source node. Word embeddings are then generated through a graph neural network to learn semantic association representations in the context of the source tweet.Esw of the source tweet and the words it contains, and the edge Esr with co-occurring words or opinion similarity between the source tweet and the retweet. The weight of the edge Esw can be obtained by computing the term frequency-inverse document frequency (TF-IDF) [i and node j are calculated as follows:Therefore, in this study, the source tweet-word graph is modeled based on the decomposition of global graph. Construct edge (TF-IDF)  of the wt denotes the elapsed time for the retweet i related to the word j. Therefore, the length of elapsed forwarding time can be used to judge the connection strength between node i and node j in the small-world network. The TF−IDF values of source tweet i and word j are calculated as follows [τ| represents the total number of tweets. |{k: ωj∈tk}| denotes the number of tweets that contain word j. The PMI value [i and retweets j in PMI is calculated as:p(i) and p(j) can be calculated by referring to work [Where  follows :IDFj=lMI value  of the w to work .Sti, the tweet word W corresponding to each node is defined as xwi is the word embedding representation of word wi. The T is denoted as xti is the average value of the word representations contained in its corresponding tweet ti. In particular, the xt1 representation is computed from the source tweet St1. Its calculation formula is [In the spread graph of the source tweet rmula is :xti=1|V in the source tweet-word propagation graph are denoted as ei,j of a node pair  in a given propagation graph is as follows [Next, define the nodes  follows :ei,j=fαi,j [Then, the attention is randomly masked and the structural information of source tweet-word graph is introduced into the model. Normalize them with the softmax function to obtain the coefficients αi,j :αi,j=si and their corresponding coefficients in the propagation graph to update the embedded representation of node i and perform K transformations. The final output representation is as follows:fk), ωk Represents the weight matrix corresponding to the input linear transformation [Then aggregate the neighbor representations of node ormation .Xtw of node V in the source tweet-word propagation graph, after feeding the node representation into the propagation graph attention network, the node embedding Define the representation In addition to obtaining the contextual semantic relationship between source tweets and retweets in the source tweet-word graph. In order to learn text semantic association representation for tweets corresponding to user nodes with weak link strength in the rumor dataset, this study applies a word-level aggregated attention mechanism in the processing layer.Esr in global graph, which can help to obtain the opinions to further learn contextual semantic association representations between tweets.The network in the early stage of information dissemination is often sparse. Therefore, it can be considered to increase the embedding representation in the global graph for more accurate node embedding learning. In this study, in order to determine whether the tweet opinions between the retweets and the corresponding user nodes of the source tweet are similar. Opinion similarity is introduced to determine the weight of edge Esr weight is calculated by formula  value is calculated as follows:Therefore, a word attention network is established by obtaining node embeddings based on opinion features, and the edge  formula . The PMIOi represents the number of opinion words containing node i, #O represents the total number of tweet words, and Oi,j) is a measure of whether the test set is a rumor and the classification is correct. the F1 score is the harmonic mean of precision and recall. Therefore, for a fair comparison with the baselines, in this study, the micro-average accuracy Acc. and F1 score can be used to evaluate the proposed method STWA.The experimental results can be significantly observed in Tables Further observations show that traditional machine learning-based methods  perform poorly, mainly because they use features based on hand-crafted statistics of tweets, and both methods are insufficient to capture the propagation structure features related to tweet text. Notably, SVM-TK outperforms DTC mainly because it exploits additional temporal or structural features in the feature set.As for deep learning-based methods , they have better performance than machine learning-based methods. The results in BU-RvNN, and TD-RvNN show that it is effective to study and model the propagation structure and temporal information of rumors. The results of PPC show that both user features and text features are important for rumor detection, and Rumor2vec, which is better than other baselines, shows that the method of jointly learning alliance graph and text content representation has achieved good results. The proposed method STWA outperforms all other baselines on datasets. Compared with the sub-optimal baseline models PPC and Rumor2vec, SWTA learns rumor representations only from textual content without requiring any user-profiles, proving the main motivation of this work—Semantic association between contextual texts in rumor propagation plays an important role in the early detection of rumors.Early detection of rumors has always been one of the most difficult problems in this field. The original intention of STWA is to detect rumors at an early stage of their propagation and improve the accuracy of early detection. To achieve the task, this paper refers to work  and workThe experimental results are shown in Figs It can be observed from From In this subsection, to evaluate the importance of the source tweet-word graph attention network for the STWA model, we conduct ablation experiments to verify the rumor detection performance of the model in the absence of the source tweet-word graph attention network. Learning text content representations in global graph for rumor detection using only a model with global graph attention mechanism in the validation context. The experimental results are shown in scatter plot 6, w/o STWA represents a model that removes the source tweet-word graph attention network.From the experimental results in User nodes in the Twitter network have small-world properties with large aggregation coefficients and short propagation paths. To learn more features from the source tweet text and its propagation structure to achieve early and accurate detection of rumors. This paper constructs a global graph based on source tweet propagation structure and the decomposed source tweet-word graph and proposes a novel method STWA, which is a rumor detection method based on the graph attention network mechanism to capture as much as possible the global semantic relational representation of the tweet text content. Compared with previous rumor detection work based on text content and propagation structure, the method proposed in this paper focuses more on the early data-sparse problem of information dissemination and the learning of the semantic association representations between the source tweet text and the retweet text during the propagation process. The model can learn as many explicit and implicit representations of tweet text content as possible.Experimental results on two public Twitter social network datasets show that the proposed rumor detection framework STWA has better rumor detection performance than existing baselines, especially in early rumor detection tasks. The method in this paper still has good robustness and stable performance in the case of sparse data.In future work, on the one hand, the user profile information in the social network can contribute to the analysis of user node confidence. The user profiles in the dataset can be added to the model to further improve performance. On the other hand, it can be considered to achieve multimodal rumor detection tasks through semantic feature extraction of videos or pictures.S1 File(ZIP)Click here for additional data file.


Background: Modern lifestyles mean that people are more likely to suffer from some form of cancer. As anticancer peptides can effectively kill cancer cells and play an important role in fighting cancer, they have been a subject of increasing research interest.Methods: This study presents a useful tool to identify the anticancer peptides based on a multi-kernel CNN and attention model, called ACP-MCAM. This model can automatically learn adaptive embedding and the context sequence features of ACP. In addition, to obtain better interpretability and integrity, we visualized the model.Results: Benchmarking comparison shows that ACP-MCAM significantly outperforms several state-of-the-art models. Different encoding schemes have different impacts on the performance of the model. We also studied tmethod parameter optimization.Conclusion: The ACP-MCAM can integrate multi-kernel CNN and self-attention mechanism, which outperforms the previous model in identifying anticancer peptides. It is expected that the work will provide new research ideas for anticancer peptide prediction in the future. In addition, this work will promote the development of the interdisciplinary field of artificial intelligence and biomedicine. Anticancer peptide  (ACP) isSome research methods have used machine learning and deep learning methods to build anticancer peptide prediction models . Tyagi e1) The paper proposes a model with strong anti-cancer peptide recognition ability based on learnable adaptive embedding and amino acid structure features. It is a self-attention mechanism that can automatically learn the context sequence features of ACP, learns the contribution of each amino acid node in the entire anti-cancer peptide sequence, automatically captures the global information in the ACP sequence, and can capture the contributions of protein cluster formed by 3–5 amino acid nodes in the anti-cancer peptide sequence to improve the ability to identify the anti-cancer peptide model.2) This article comprehensively evaluates the different feature projects of anti-cancer peptides, constructs a deep learning model with strong performance, and integrates the advantages of multiple deep learning models to improve predictive performance. To improve the interpretability of the model prediction results, this article visualizes model prediction characteristics to improve the interpretability of the model prediction results.3) The methods for identifying various functional peptides of the same type from the same functional peptides are relatively similar. It is difficult to identify anti-cancer peptides from multiple peptide sequences. Most of the existing methods recognize anti-cancer peptides from a single type of peptide sequence, so research on new deep learning models is needed. The new deep learning model studies the impact of different coding schemes on the performance of the model, examining the method of parameter optimization to build a better deep learning model and obtain the best model for identifying anticancer peptides from different functional peptides.Existing models have some problems, such as low recognition accuracy, insufficient generalization ability, and there is a lack of large-scale evaluation of features and prediction models. Almost all the existing anticancer peptide prediction studies use sequence features to construct anticancer peptide prediction models, which show that the anticancer peptide prediction methods based on sequence information are effective. But most research to date has not considered the combination of protein structure and sequence data characteristics or the feature space used was not comprehensive enough. Most of them use single machine learning methods, such as support vector machines, and seldom use the attention mechanism . To solvDatasets Section. Second, the anticancer peptide predicting model of multi-kernel CNN and the attention mechanism is explained in detail in Model Overview Section. Third, the performance evaluation index, loss function, experimental process, and results of the model are presented in Experiments and Results Section. Finally, the results and the prospects for future work are discussed and summarized in Conclusion Section.This article first examines the peptide datasets used, which are introduced in This chapter introduces the structural frame of the ACP-MCAM model and the datasets of anti-cancer peptides in detail.The datasets used in this experiment come from the literature . The samThis chapter details the ACP-MCAM model structural frame used to predict the anticancer peptides.The architecture of the ACP-MCAM model is shown in K = 50–300 dimensions). Since the embedding layer maps each amino acid into a K-dimensional vector. If there are n amino acids in all of the anti-cancer peptide sequences. All of the peptide sequences can be represented by an The core idea of embedding is to map all of the amino acids in the anticancer peptide sequence into a dense vector in a low-dimensional space  (2) can output a unique position code for each time step. Moreover, the distance between any two time steps between anticancer peptide sequences with different lengths is the same.ith dimension of the position pth amino acid.Among them, position embedding (PE) indicates the code containing the specific position information of the anticancer peptide after being coded. P represents the position of the amino acid in the sequence, The basic module of the encoder layer is the encoder model of the transformer . Each enSuppose we now want to know the attention score of the first amino acid node “G" in the anticancer peptide sequence. We calculate the respective attention score by the embedding vector of the amino acid node “G" and the embedding vectors of all of the other amino acid nodes in the anticancer peptide sequence. These scores determine the attention weight of the amino acid “G" node embedding vector when we encode the node “G".In the first step, the query, key, and value vectors are obtained by multiplying the anticancer peptide embedding vector with three parameter matrices. These three parameter matrices are the parameters that the model needs to learn. The second step is to multiply Q by the transpose of K to obtain an initial attention score. Then divide each score by The output vector of the encoder layer is the feature of anticancer peptides. The main job of the task-output layer is to convert the output vector to binary classification. The task-output layer mainly contains several important modules: linear connection layer, residual network layer, normalization layer, and activation function.The linear connection layer is a fully connected neural network. It obtains the output of the specified dimension through the linear change of the previous step and plays the role of transforming the dimension. The final dimension corresponds to the number of output categories.The residual network layer is implemented in the form of skip layer connections, and the input unit is directly added to the output unit. The residual network can solve the degradation problem of the deep neural network well. The residual network converges faster than the same number of layers.The normalization layer is a standard network layer required by the deep network model. As the number of network layers increases, the output value will become too large or too small. It may cause abnormal and the model may converge very slowly. The normalization layer is used to normalize the output value. Then the output value can be in a reasonable range.The main role of the activation function is to provide the nonlinear modeling capability of the network. To avoid the pure linear combination, we add an activation function  after the output of each layer. ReLU can keep the gradient undecayed when x > 0, thereby alleviating the problem of gradient disappearance. The output mean of tanh is 0, and its convergence speed is fast, which can reduce the number of iterations. Using different combinations of activation functions can make the network achieve better results.Focal loss (FL) is mainly a strategy proposed by Kaiming to solve the classification problem of indistinguishable samples, that is, to set weights according to the contribution of difficult and easy samples to the loss. The formula is as follows:In this section, the performance of the ACP-MCAM model is evaluated in several evaluation metrics. We compare it with other models and discuss the results. The experimental parameters are then discussed to verify the effectiveness of the model.The following evaluation indicators were used to evaluate our model. Including recall, precision, accuracy (ACC), Matthew correlation coefficient (MCC), and the area under the ROC curve (AUC) . The speAmong them, true positive (TP) and false negative (FN) represent the number of true anticancer peptides that are predicted correctly and incorrectly. True negative (TN) and false positive (FP) represent the number of non-anticancer peptides that are predicted correctly and incorrectly. Accuracy is the percentage of correctly classified samples in all samples. The sensitivity (SE) and specificity (SP) index measure the predictive ability of predictors for positive and negative samples, respectively. Precision represents the prediction success rate of positive samples. The recall represents the proportion of predicting positive samples in all true positive samples. The F1 score is the coordinated average value of accuracy and recall. The higher the selected value, the better the performance of the model. The other two indicators, AUC and MCC measure the overall performance of the predictor. AUC sorts all samples obtained from model evaluation by score. By calculating the area enclosed by the ROC curve, the AUC value can be obtained.To verify the predictive performance of anticancer peptide ACP-MCAM, we compared it with several existing models, including iACP, ACPred-As shown in In order to verify the superiority of the proposed ACP-MCAM model, we used an independent test dataset to compare its performance with several existing predictions. As shown in Several important parameters may affect the performance of our models, such as the learning rate and the kernel of multi-kernel CNN. Learning rate is an important parameter of deep learning. Through the adjustment of learning rate, we can see whether the objective function can quickly converge to the minimum value and fall into the local optimal value. An appropriate learning rate can make the objective function converge to the optimal value quickly.In this section, we will perform a sensitivity analysis on these parameters. In our model, the number of training epochs is set to 50. The output dimension is 64. We train our model by modifying the learning rate. Since the CNN kernel represents several amino acids on an anti-cancer peptide sequence sharing the same parameters during the process of convolution. Therefore, the different combination of kernels means that the sequence of the anticancer peptide is affected by different combinations of several amino acids. Modifying the combination of the kernels may improve the effect of the model. Therefore, the combination of the kernel is also a very important parameter. As shown in We compared our model to the ACP164 data set for ablation experiments. It can be seen that the experiment is mainly to compare three models: the embedding attention model, CNN attention model, and Multi-kernel CNN attention model. From The results in Principal Component Analysis (PCA)  is a comThe figure shows that in the ACP dataset, the positive examples (represented by train:1 and purple dots) and negative examples (represented by train:0 and blue dots) in the training dataset are mixed in the initial stage because they are initialized randomly. The same is in the test dataset , which indicates that the model has no distinguishing ability before training. As the training epoch number increases, positive and negative samples are gradually separated from the sample points. We can observe that in the training dataset and the test dataset, the embedding vectors of the ACP samples almost belong to the same cluster, and after training, the positive and negative examples have similar distributions, which indicates that the model has indeed learned feature of the positive and negative samples. This shows that the model in this paper can learn the common features and distinguishing features of positive and negative cases.In addition, there are many ACPs in the negative cluster, but few non-ACPs in the positive cluster, which explains the reason why the performance of SP is better than SE to some extent. We speculate that those ACPs predicted to be negative samples have characteristics that our method cannot capture. Therefore, the unique physical and chemical properties of these indistinguishable samples should be further studied in the future.A very important point in deep learning is how to extract features from data. The quality of the extracted features will largely affect the effectiveness of the model. The advantage of natural language processing (NLP) is that it can effectively extract word embedding and sequence information from sequence data, and use it for subsequent specific tasks. Our method can automatically learn useful information from the amino acid sequence data of anti-cancer peptides and perform feature representation on node features and sequence features.In this work, we proposed a new predictive model called ACP-MCAM. This is a powerful bioinformatics tool. The model can predict anti-cancer peptides based on a convolutional neural network and self-attention mechanism network, which can extract effective amino acid nodes and anti-cancer peptide sequence information. The advantage of ACP-MCAM is that it can effectively use the position information and the information of the amino acid node cluster. The ACP-MCAM model mainly includes the following modules: embedding layer, multi-kernel convolutional neural network layer, position coding layer, attention encoding layer, and task output layer. The experimental results of 10-fold cross-validation and independent testing show that this predictor can effectively distinguish anti-cancer peptides from non-anti-cancer peptides. Moreover, we used the model to predict neuropeptides and antifungal peptides and achieved good prediction results. The excellent predictive ability of this model will accelerate its application in cancer treatment.Our model has achieved good prediction performance, but there are still some shortcomings to be overcome. First of all, the prediction performance of the model fluctuates greatly, and different parameters have a greater impact on the prediction results. The main reason is that 500 pieces of data in the training set and 164 pieces of data in the test set are too small for deep learning to train all parameters. This is where we will strive to improve in the future. In future work, we will expand more datasets and try more computing techniques, such as pre-training strategies for automatic feature extraction, to achieve more accurate and better predictions. Second, in our current case study, we only made computer model predictions based on the original database and did not verify it in silicon experiments. In the future, we will plan to cooperate with biologists to conduct wet laboratory experiments to verify the predicted results.


The novel algorithm takes into account the kinematic constraints of the vehicle and the AOA means of measurement. We evaluate the performance of the algorithm in five scenarios involving stationary and mobile beacons and we find the estimation error approaches the lower bound for the estimator. The simulations show the vehicle maneuvers to locations that allow for minimum estimation uncertainty, even when beacon placement is not conducive to accurate estimation.We consider the problem of optimal maneuvering, where an autonomous vehicle, an unmanned aerial vehicle (UAV) for example, must maneuver to maximize or minimize an objective function. We consider a vehicle navigating in a Global Navigation Satellite System (GNSS)-denied environment that self-localizes in two dimensions using angle-of-arrival (AOA) measurements from stationary beacons at known locations. The objective of the vehicle is to travel along the path that minimizes its position and heading estimation error. This article presents an informative path planning (IPP) algorithm that (i) uses the determinant of the self-localization estimation error covariance matrix of an unscented Kalman filter as the objective function; (ii) applies an  Localization is the process of using acquired information to determine a vehicle’s position in a global frame . Accuratangle-based sensing provides information on both position and orientation, enabling estimation of both position and orientation (pose) in two dimensions, unlike range-based sensing  ,20. DiffIn terrain monitoring, the aim is to minimize map uncertainty, which depends on the map representation and available sensing data . One forIn 3D building image reconstruction applications, IPP is about planning the path of an imaging drone to acquire images that can be used to accurately reconstruct 3D models of buildings. Schmid et al.   based on admissible vehicle movements rather than considering existing waypoints.A waypoint selection algorithm is used to generate potential waypoints for an IPP scheme to optimize over and determine an informative path. The waypoints must be generated such that they capture enough of the environment that informative trajectories can be determined, but sparse enough that the algorithms can be run in real time. A lattice grid search can be used to generate potential waypoints to perform measurements in 2D ,21 and 3The path optimization problem is generally nonconvex and hence metaheuristic and numerical optimization algorithms are commonly used. Among these algorithms were greedy search (one-step look-ahead), reinforcement learning (RL), gradient descent, graph search methods, neural networks, and evolutionary algorithms such as the covariance matrix adaptation evolution strategy (CMA-ES).Greedy search algorithms have been applied to IPP  and haveRL algorithms have a high initial computational complexity due to their learning-based nature. However, the execution time of RL algorithms increases linearly with budget and number of iterations rather than the exponential increase of a recursive greedy (RG) algorithm. Thus, for large maps, RL algorithms are more efficient than RG algorithms .Methods of gradient descent use first or second-order finite difference approximations to determine the direction and hence choose the waypoint that minimizes the objective function ,28,31,38state lattice is a sampling of the state space in the form of a graph whose vertices are a discretized set of all reachable system states and whose edges are feasible controls [target is of interest, and can be generated by calculating the (i) a priori target state distribution, (ii) sampling the distribution by taking the sigma points of the distribution to generate lattice states, (iii) connecting the matching sigma points to create candidate target trajectories [Graph search methods are applicable whenever candidate trajectories can be represented as a graph. Hernandez  divided controls . A statecontrols . In the ectories . For effectories  and algeectories  can thenNeural networks have been used to solve the optimal maneuvering problem ; howeverThe covariance matrix adaptation evolution strategy (CMA-ES)  is a gloThe objective function is defined such that the utility or information gain along the vehicle trajectory is maximized, or equivalently, the uncertainty along the trajectory is minimized . Not allFisher information matrix (FIM), resulting in the minimization of the volume of the estimation error ellipsoid [The D-optimality criterion maximizes the determinant of the llipsoid . The D-ollipsoid  and a mollipsoid ,45,48. Tllipsoid . For IPPllipsoid .The A-optimality criterion maximizes the decrease in the error covariance matrix trace rather than the inverse of the determinant of the covariance matrix because the latter is computationally expensive . The A-oAn advantage of the D-optimality criterion over the A-optimality criterion is that the former does not depend on the scale of the estimated variables . This adOur proposed approach is a waypoint selection algorithm where an exhaustive search is used to determine the optimal path. The novelty in our approach is the waypoint selection algorithm; it is based on constant-speed vehicle kinematics and allows for the trajectory to be determined over a planning horizon. The waypoints are selected based on the admissible motion of the vehicle, producing achievable locations at each time step of the planning horizon, with an accuracy defined by the process noise and estimation error. The end result is an informative path planned to maximize the effectiveness of the self-localization algorithm. One of the novelties of this work is the context. Target localization is the focus of many research works, however, the ability of a system to perform self-localization is inherently implied due to the abundant use of GNSS localization systems. For operations in GNSS-denied environments, the ability to self-localize is of the utmost importance. Furthermore, the ability to plan a path to maximize the informativeness of the sensor measurements is a process that is ignored in many scenarios as they assume perfect knowledge of vehicle position. The informative path planning algorithm presented in this article is a precursor to a framework wherein a vehicle can maneuver to minimize its estimation uncertainty to then enhance any target localization operation (or indeed any other application that requires accurate location and heading estimation) subsequently enacted.The waypoints generated by a sampling method ,22 may nOur proposed approach shares similarities with other waypoint selection algorithms that are dependent on the dynamics or the kinematics of a vehicle ,39. The We compare the proposed algorithm to the RIG-tree algorithm developed by Hollinger and Sukhatme . Their a studies ,17. It cv and the control input is the rotational rate of the vehicle We consider a two-dimensional optimal maneuvering problem in an environment with T is the sample period. Let n denote the number of states, and the k. In Equation  is a linear estimator that is applicable to nonlinear systems [unscented transform, which (i) deterministically approximates the state vector with sigma points; (ii) transforms the sigma points using the nonlinear state function; (iii) uses the mean and covariance of the transformed sigma points to predict the future mean and covariance of the state vector. Let a posteriori state estimate at time step k. At time step a priori state estimate for time step a priori error covariance matrix for a priori measurement estimate produced by applying the unscented transform to a priori innovation covariance matrix. a priori cross correlation matrix. a posteriori state estimate and The Kalman filter produces estimates of the states of a linear dynamic system using a series of noisy measurements observed over time . The sta systems . The keyBayesian FIM indicates how much information the data contains about the estimated variables. The Bayesian FIM is based on the log-likelihood of the joint probability a priori information [ith element of jth element of Z. The posterior Cramér-Rao lower bound (PCRLB [Fisher information is a way of measuring the information that observable random variables y p(x|θ) . In the ed L. Since Lormation ,59,60, id x have abk, thenk, the measurement at time The error covariance matrix of the UKF can be approximated by the PCRLB . Thereforion see . Denote  k, thenJk=|Pk+1|k, k, and T is the sample period. The optimal maneuvering scheme detailed in Forward speed constraint Given a fixed forward speed V,Above, Turn-rate constraint Physical limitations of any real-world vehicle constrain the vehicle to a maximum turn rate, denoted by k and Proximity constraints to the beacons Proximity constraints arise from the need to prevent numerical instability which occurs when the vehicle gets too close to a beacon. ith beacon, and In Equation , the rotϕk+1=ϕk+Tk, the possible vehicle positions at time The optimal maneuvering algorithm takes the constraints above into account when determining the desired heading. Satisfying the speed and turn-rate constraints in Equations  and 15)15) restrEquation . This prm points, i.e.,m is chosen based on a trade-off between decision resolution and computational efficiency. Multi-step planning provides a larger search area by determining waypoints several time steps in advance; we call these steps look-ahead steps. For each point on the arc at time l the number of look-ahead steps, then m points on each arc and l steps of looking ahead amount to a search area of l.An optimal waypoint is determined by evaluating the objective function Equation  at each ints see , and l×ml is that the UKF state estimate cannot be updated when predicting future costs as future measurements are unknown and the accuracy of objective function prediction drops as l increases due to the compounding of unmodeled disturbances. The optimal branch is defined as the set of waypoints from the first to the lth look-ahead step that minimizes the value of Another consideration that favors a small f Jk see . DenotedEquation  becomes Equation , they arm and l are used; (ii) mobile beacons are used; (iii) communication with some of the beacons is disrupted; (iv) stationary beacons are positioned far away from the initial position of the vehicle; (v) a less informative beacon formation is used. In every scenario, there were three beacons in the environment. The waypoint selection component of the RIG algorithm [We present simulations to evaluate the performance of the optimal maneuvering algorithm detailed in lgorithm  was usedIn each scenario, 100 simulation runs were performed. The actual objective function was calculated by first calculating the true error covariance matrix using the data from all simulation runs l and m values. Analyzing the rows in l values. Analyzing the columns of l is increased  is generally more beneficial for reducing the objective function value and the settling time than increasing the value of m. We compared our algorithm to the RIG-tree [The RIG-tree  informatFuture work involves equipping the vehicle with the ability to predict the trajectory of mobile beacons in order to determine improved maneuvers when loitering in the presence of mobile beacons. The optimal maneuvering framework that is presented can be extended to form a multi-objective optimization problem that balances the information gathering action with achieving other mission objectives. Another avenue of future work is to conduct experiments to validate our algorithm on real hardware. An updated simulation environment will be developed to benchmark the performance of the algorithm on hardware. The simulation would require changes to the model and sensor characteristics to more closely align with the physical system.


Overcrowding occurs when the identified need for emergency services outweighs the available resources in the emergency department (ED). Literature shows that ED overcrowding impacts the overall quality of the entire hospital production system, as confirmed by the recent COVID-19 pandemic. This study aims to identify the most relevant variables that cause ED overcrowding using the input-process-output model with the aim of providing managers and policy makers with useful hints for how to effectively redesign ED operations.A mixed-method approach is used, blending qualitative inquiry with quantitative investigation in order to: i) identifying and operationalizing the main components of the model that can be addressed by hospital operation management teams and ii) testing and measuring how these components can influence ED LOS.With a dashboard of indicators developed following the input-process-output model, the analysis identifies the most significant variables that have an impact on ED overcrowding: the type (age and complexity) and volume of patients (input), the actual ED structural capacity (in terms of both people and technology) and the ED physician-to-nurse ratio (process), and the hospital discharging process (output).The present paper represents an original contribution regarding two different aspects. First, this study combines different research methodologies with the aim of capturing relevant information that by relying on just one research method, may otherwise be missed. Second, this study adopts a hospitalwide approach, adding to our understanding of ED overcrowding, which has thus far focused mainly on single aspects of ED operations. In recent years, both practitioners and academics have recognized the strategic role of operations management in healthcare delivery organizations. A better management of the flows of patients and materials throughout different healthcare production units is critical in dealing with the current challenges of healthcare systems and improving the overall quality of the care provided.In this context, the emergency department (ED) plays a relevant role as, together with the outpatient departments, is one of the two entry points to the hospital production system .“a situation in which the identified need for emergency services outstrips available resources in the ED. This situation occurs in hospital EDs when there are more patients than staffed ED treatment beds and wait times exceed a reasonable period” [. ED overcrowding has a relevant impact on several aspects of the overall quality of the care provided by hospitals [Even the case-based evidence drawn from the recent pandemic shows that the ED has been crucial in the management of COVID-19 patients , 3: well period”  (p.174).ospitals –11, suchBy applying operations management principles to unscheduled emergency patient flow, Asplin et al.  developeThis conceptual model provides a general framework with which to study the causes and consequences of ED crowding, but it is too broad and vague to be applied as an ED operations management tool, requiring the development of measurements for each component .To date, the literature has focused mainly on a few specific variables of the three components of this framework, providing piecemeal solutions to ED crowding rather than integrated solutions . AccordiTo fill this gap, this paper aims to achieve two different goals: (i) the operationalization of the input-process-output model through the development of an integrated, balanced dashboard of indicators that are useful for measuring the performance of emergency patient flow management and (ii) the understanding of the most relevant explicatory variables of ED LOS to provide managers and policy makers with an evidence-based road map for the redesign of ED patient flows.In particular, the study adopts an operations management hospitalwide perspective, focusing on unscheduled patient flow management from the very first hospital access of the patient to final discharge, with the goal of investigating the whole patient journey throughout the different hospital production units to capture the interdependencies between the ED and units involved . In the literature background” section, the main evidence on the variables explaining ED LOS within each component are traced. Then, this paper adopts a mixed-method approach with a three-step sequential exploratory design. Qualitative analysis informs the identification and measurement of the possible explicatory variables of ED crowding for each component through focus groups with ED operation managers of the Italian National Health System (INHS); then, the relevance of each variable is tested with a regression model to identify the most important causes of ED crowding from each model component. Then, the results are discussed in a final focus group with the same ED operation management experts. Finally, this work draws some implications for both policy makers and hospital managers.Assuming a hospital perspective for the analysis of ED LOS through the input-process-outcome model, crowding in the ED can occur due to the type and volume of patients waiting to be seen (input), delays in assessing or treating those patients already in the ED (process), or impediments to patients leaving the ED once their treatment has been completed (output) . In prev. [Regarding input, some studies have outlined the relevance of patient characteristics as important explicatory variables of ED crowding. In particular, different authors have focused on patient age as a possible explanatory variable of ED overcrowding. George et al.  examined. . Once in. . Increas. . Likewis. . Further.  reported. However, few studies have analysed the impact of patient codes on ED operations and crowding.Second, patients arriving in the ED are assigned a priority code based on the severity of the disease; this assessment process is known as “triage”. The role of the ED should be focused on providing care to patients with sudden deterioration or sudden and potentially severe manifestations of an acute illness or injury. Nonetheless, in reality, a larger variety of patients seek care in the ED . HoweverFinally, in the analysis of the input component, it is important to consider that EDs play an important role as a “safety net” for vulnerable populations in the community. We refer here to categories such as uninsured or underinsured patients, homeless patients, and psychiatric patients for whom an ED is often the only place where they can receive assistance and care , 21.In conclusion, the latest contributions outline that the characteristics of patient arrivals, mainly age, complexity, and level of urgency, have a relevant impact on ED crowding .For the throughput component, ED processes are plagued by several problems. Some studies have shown that shortages of nurses, junior medical staff and specialty doctors have a strong impact on crowding . Particu, such as poor physical design and a shortage of physical space, equipment and computers, difficulties in accessing medical notes, tests, results and ancillary services, and time spent discharging or arranging follow-up appointments, are all thought to contribute to ED crowding [Numerous other factorscrowding . For exacrowding  proved tcrowding . One Frecrowding . The setcrowding . The adocrowding .Finally, several studies have focused on the output component, indicating the inability to transfer patients to inpatient beds as the main cause of ED patient flow problems. Several studies have shown a direct link between innovation in discharge management practice and ED wait time. For example, Villa and colleagues  found a In general, the better management of patients in the ward alleviates the pressure placed on the ED. For example, the removal of urethral catheters at midnight prior to discharge, compared to at 6 am the day of discharge, was found to be associated with a 163.6% decrease in the number of patients discharged later in the same day , while tThe literature offers interesting empirical evidence on ED crowding. Despite the growing awareness that ED problems are caused by drawbacks in the overall logistics system for hospital patient flows , 27, theTo achieve these goals, this study adopts a mixed-method (MM) approach , which iIn the first phase, a qualitative approach is used to identify and operationalize the key drivers that can be addressed by hospital operation management teams to deal with the ED LOS issue. Specifically, the authors organize two focus groups with professionals from 10 (ten) Italian hospitals to derive those variables that can be identified as the key drivers that must be addressed when dealing with ED operation management inside the hospital. The focus groups last three hours each, taking place in May and October 2019.Participants are employees of Italian hospitals belonging to a professional network coordinated by a consortium of universities that promote the adoption of operations management concepts to patient flow in the INHS through research activities and executive education programs. The INHS is a publicly funded system that guarantees universal coverage; it is structured along three tiers: the central government at the top, 21 regional governments in the middle, and approximately 200 local health authorities (LHA) with their own hospitals and other healthcare facilities at the bottom. Each citizen is assigned to an LHA but can freely choose to receive care from any other LHA, public independent trusts (nearly 100) or all private hospitals accredited by the National Health System (NHS). The boards of network hospitals are invited to select a team of professionals involved in ED operations management who have taken part in the focus groups. For the hospital board, participation in this research program is seen as valuable, as the information produced during the different research stages can provide useful insights that can be put into practice by participant teams in their own ED hospital settings.The main characteristics of the ten hospitals are summarized in Table Table During the first round, the authors present the available literature on the input-process-output model discussed in the background session to the professionals and asked them to identify i) the variable for each input-process-output component that can be addressed by the ED OM team to redesign ED operations and ii) the possible indicators to be used to operationalize each variable, considering the specific data/information to be gathered for calculation. Participants use action priority maps to identify the components and a specific, measurable, achievable, reasonable, and time-bound (SMART) classification to rank the indicators that emerged for each component.The results of this first round are presented by the authors during the second focus group, aiming to let the professionals revise, elaborate, or validate their previous responses to set the framework to be tested with regression analysis. Participants are asked to offer their own interpretations of those variables/indicators that are not recognized as significant during the first round and to confirm the poor relevance according to ED OM inside their hospital.The results of the qualitative phase inform the following quantitative phase 2 of the research, the purpose of which is to test the framework and identify the most important causes of ED overcrowding.The ten hospitals in this study offer their availability to provide the data and information needed to calculate the indicators selected in phase 1. In the quantitative part of the research design, we have built a regression model on ED accesses and hospital discharges, using the Principal Components Analysis (PCA) to avoid problems of collinearity among the dependent variables.Data provided by the ten hospitals were plugged in a regression model in order to estimate the relevance of various groups of variables in explaining patients LOS in EDs. As dependent variable the ED LOS has been chosen since, in scientific literature, it is the most widely used parameter to measure ED overcrowding.All the variables considered relevant by the experts during the focus groups were included in the model; however, since many of these variables present a high degree of correlation, thus prospecting possible problems related to collinearity, a PCA was used in order to obtain scores which properly summarize the variability within each group. PCA was applied in order to reduce the number of variables to be included in our regression analysis while, at the same time, keeping a proper level of explained variability for each group .2). The contribution of any given source of LOS to overall explained variability can be interpreted as the expected marginal impact of the factor when such expectation is made over all possible sequences of elimination. The idea is to specify a regression model to explain LOS by what we consider contributing factors and subsequently decompose the R-squared index isolating the effect of each factor. This methodology presents a significant advantage, because it allows to consider groups of factors as a single entity without affecting their total contribution. Formally we consider the following regression model:After that, we applied the Shapley Value Decomposition (SVD) to estimate the share of LOS explained variability that could be ascribed to each of the groups previously defined. The SVD is a regression-based decomposition used in cooperative game theory as well as in the analysis of the determinants of inequality. This decomposition is easily employed in any context in which it is necessary to estimate the contribution of a set of variables (considered jointly or as single entities) on a given index , the ED administrative hospital database, and information on the ED organizational model.Specifically, hospitals provide two types of data: (i) hospital discharge data, which trace information about activities and procedures delivered to the patient within the hospital, and (ii) emergency department data, which trace information about the different timings of the patient within the ED from his or her very arrival to his or her final discharge in another setting that can be, alternatively, the patient’s home, hospital ward, outpatient department or another institution. A formal request (available upon request) is sent to the ten hospitals included in the study specifying, for each of the two databases, the exact variables needed and the procedures used to respect patients’ privacy. Data cover the period from January 1st to December 31st, 2018.description of patient flow logistics in the ED,admission process of ED patients to other hospital settings through the ED,role and procedures for bed management,ED personnel, andinformation and communication technology (ICT) system adopted to support ED management.To collect the information on the ED organization model, a semistructured interview (available upon request) is administered to the teams of the ten hospitals, aiming to collect information on five different thematic areas:To evaluate whether other hospital variables may influence ED performance, some variables are included in the model to measure general hospital characteristics: i) type of ownership (private vs. public), ii) geographical context , iii) number of beds, iv) production case mix , v) type of organization model , and vi) number of operating rooms.n = 560,178),Subsample #1, which excluded patients who have been through the observational unit , andSubsample #2, which includes only cases admitted to the hospital .Subsample #3, which includes patients who have not been admitted to inpatient facilities and have not been through an observational unit , whether to admit him or her to the wards , andseparate analysis for ED patients who have been admitted to the hospital’s wards and those who have been discharged in other settings (including patient home).The aim of running the model on these three different subsamples is as follows:The statistical model clearly shows that some dimensions do have a relevant and significant impact on overall ED length of stay. As explained in the methodology section, to avoid possible problems of collinearity, we group those factors that actually represent a single phenomenon.Table If we focus on PCA scores obtained from context-related indicators, we can see that coefficients associated with the second and third components are significant across the samples analysed. These components, according to the results presented in Table Considering the PCA scores obtained from input-related indicators, we can see that the coefficients associated with all components are significant across the samples analysed. Again, referring to Table Considering the PCA scores obtained from ED endowment-related indicators (process dimension), we can see that coefficients associated with the first two components are significantly different from zero in Subsample #1, the coefficient of the second component is significantly different from zero in Subsample #2, and all coefficients are significant in Subsample #3. We interpret these components as proxies for skill mix (pc1), number of nurses (pc2) and ED endowment (pc3). Considering Subsamples #1 and #3, we find a positive (negative) correlation between ED LOS and skill mix (number of nurses). In addition, in Subsample #3, ED endowment is negatively correlated with ED LOS. Finally, in Subsample #2, only skill mix is positively correlates with ED LOS.Considering the PCA scores obtained from ED flow separation-related indicators (process dimension), we can see that coefficients associated with all components are significant across the samples analysed. We interpret these components as proxies for minor codes (pc1) and the presence of fast-track/see-and-treat procedures (pc2). These components are positively correlated with ED LOS across each sample analysed.Considering the PCA scores obtained from admission management-related indicators, we can see that the coefficients associated with all components are significant across the samples analysed. We interpret these components as proxies for hospital admissions (pc1), medical patients (pc2), surgical patients (pc3), bed managers (pc4) and out/in (pc5). In line with our expectations, we find a positive influence of hospital admissions, whereas we find evidence of a negative correlation between surgical patients (pc3) and bed managers in terms of ED LOS and medical patients (pc2) and out/in across Subsamples #1 and #2.Finally, considering the PCA scores obtained from emergency-related indicators (output dimension), we outline the strong negative association with pc1 that represents the presence of an emergency ward.If we focus on the Shapley values, we can see that across the three different subsamples, the most important group of variables is that considering the output dimension connected to admission management; the share of explained variability, in fact, amounts to 30.31%, 60.43% and 15.94% for Subsamples #1 (NO OU model), #2 (ADM model) and #3 (NO OU – NO ADM model), respectively.When looking at the most important PCA scores across subsamples, we can confirm the relevance of hospital admissions across all the samples analysed  and the presence of a bed manager in Subsamples #1 and #2 .According to the SVD analysis, the second most relevant group of variables in explaining ED LOS is represented by the process dimension connected with ED endowment, which explains 13.05% of overall variability. This dimension is also relevant for each of the subsamples analysed, where the share of explained variability amounts to 30.63%, 13.57% and 37.74% for Subsamples #1 , #2  and #3 , respectively.Thus, the variable ED endowment is particularly relevant only for Subsample #3, suggesting that the availability of technology and equipment is not significant for more urgent and complex cases.Within this dimension, the PCA scores correlated with the number of nurses are those that explain most of the variability in the overall population and in other subsamples, except that for Subsample #2 (those patients who require admission).The third most relevant group of variables in explaining ED LOS is represented by the input dimension, which explains 9.96% of overall variability. This dimension is also relevant for Subsamples #1 and #3, where the share of explained variability amounts to 16.46% and 26.21%, respectively. Within this dimension, the PCA scores correlated with admissions, case mix and presence of elderly patients are all almost always equally relevant.The three dimensions discussed above  explain, for each of the subsamples considered, 77%, 78% and 80% of overall ED LOS variability. The other groups of variables considered have a more limited relevance. In particular, the presence of separated pathways for minor codes does not play a relevant role. This result is probably due to two different factors: (i) it is a dichotomous variable (Yes/No), and (ii) most of the hospitals included in the sample have implemented this type of organizational model.Finally, in all samples, the hospital dimension plays a marginal role in explaining the dependent variable.The findings of this study summarized in the previous paragraph help the authors accomplish two different goals: (i) the operationalization of the input-process-output model through the creation of a balanced dashboard of indicators that are useful for measuring the performance of emergency patient flow management, which can be adopted across sites and times, and (ii) providing managers and policy makers with an evidence-based road map for the redesign of ED patient flows capable of considering all the interdependencies between ED and all other hospital production units.The use of a three-step mixed-method approach helped to strengthen the relevance of the results. The semistructured interviews helped gathering information about some organizational variables (e.g. the presence of a beds management function) that turned to be statistically relevant in explaining ED LOS. The results of our logistical model show clear and robust trends that offer both managers and policy makers powerful hints for redesigning ED operations in all three main components: (i) input, (ii) process and (iii) output. Finally, during the final focus group the participants discussed these results with the purpose of contributing in identifying possible OM strategies to be adopted in the hospital to optimize ED patient flow.Table Regarding input, our study confirms a recent trend in the literature that outlines that ED crowding is significantly influenced by the typology of patients, such as age, clinical complexity, presence of comorbidities or level of urgency. In particular, our study proves that crowding worsens as the mean age of patients in the ED increases.Furthermore, it has been proven that ED performance is also influenced by the trend in the number of arrivals, an aspect not typically considered in the recent literature on ED crowding. In other words, the probability of waiting is significantly higher during those hours or days where we record a higher number of arrivals.Looking at ED processes, in the cases analysed, there is, for sure, a capacity problem; the model, in particular, shows that the number of nurses assigned to each single shift has a relevant impact on ED crowding.Another interesting finding is represented, in contrast, by the data of the positive correlation between ED LOS and the physician-to-nurse ratio. This circumstance seems to suggest that those EDs where there is a higher presence of doctors tend to anticipate therapeutic and diagnostic activities in the ED, which in turn increases overall length of stay. This consideration opens up a broader discussion about the role that an ED should play in the overall hospital production system: (i) either the mere stabilization of the patient and sending him or her to hospital wards or (ii) the management of the initial phase of the diagnostic and medical treatment of the patient’s pathway.Finally, looking at the output, our results are consistent with those of other studies that show how ED problems are not only ED problems; in contrast, such problems represent a broader hospital issue linked to the balance between the number of hospital admissions and discharges. Our model, in particular, outlines the statistical relevance of the following variables: (i) the number of hospital admissions ; (ii) the daily difference in the number of discharges and hospital admissions; (iii) the presence, within the hospital, of a bed management function; and (iv) the presence of an emergency ward to accommodate patients admitted to the hospital through the ED.The present study, combining qualitative and statistical evidence, provides robust evidence to allow for the sketching of an evidence-based road map for the redesign of ED patient flow logistics around the three broad dimensions considered.Looking at the input component of the model, our study confirms the evidence stemming from a growing body of scientific literature that patients’ characteristics do have an impact on ED crowding. This element calls for the implementation of the well-known operational principles of flow separation through the creation of dedicated pathways for fragile, complex and elderly patients. This scenario requires the creation of appropriate scores that are capable of detecting these patients from their very first access to the ED.Furthermore, the present study supports a quite intuitive circumstance: ED crowding is significantly influenced by the daily variability of patient arrivals. This variability is a typical natural variability, that is, a variability that cannot be eliminated but only more efficiently managed through, for example, a scheduling system that allocates nurses and physicians in a more productive manner. We know, for example, by looking at our data and the findings of other studies , that thFor the process component, the novel and interesting result about the relevance of skill mix proves the fact that one-size-fits-all solutions do not exist; in contrast, solutions need to be shaped around the specific role and mission of each ED. In those EDs characterized by higher physician-to-nurse ratios and more inclined to anticipate medical treatment before admission to the ward, the overall length of stay is higher.This study also proves that part of ED crowding is linked to a problem of capacity; in particular, the model shows the relevance of the variable “number of nurses per shift”. There are two main strategies through which to overcome this problem: (i) hiring more nurses (solution that has an economic impact) or (ii) adopting organizational and logistical solutions to save nursing time through, for example, shifting some typically nursing tasks to other roles such as scribes or social workers.Finally, as for the output component, the study provides some clear indications about the possible strategies through which to solve the coordination problems between the ED and hospital wards: (i) create an emergency ward or an admissions unit as a buffer area between the ED and hospital wards; (ii) set-up an office in charge of coordinating bed management  with responsibilities such as the assessment of bed availability in real time and the triage and admission of patients; and (iii) streamline the discharge process through, for example, a different organization of the activities in the ward or the constitution of a discharge room to alleviate the problem of a lack of beds, especially during the morning hours when the pressure from the ED is more intense.This study, performed just before the surge of the current COVID-19 pandemic, has shown that Italian EDs were operating at maximum capacity and that they were highly exposed to variability in demand in terms of both the gross number of arrivals and the level of severity of coming patients. This circumstance has turned out to be dramatically true during the current pandemic, where, in many areas of the country, EDs have not been capable of dealing with the surge in the number of arrivals due to the pandemic.The hope is that once the pandemic is over, healthcare managers and policy makers will be granted the resources, time, lucidity and foresight to redesign ED operations for the long term along the lines of the evidence-based suggestions indicated by the results discussed in this work.As previously stated, the present study focuses on internal hospital operations with the goal of identifying a balanced dashboard to control ED patient flow logistics and defining a possible coherent plan for change. Future research and efforts should focus on the coordination of hospital operations with other production units upstream  and downstream .