Chatbots are computer programs that present a conversation-like interface through which people can access information and services. The COVID-19 pandemic has driven a substantial increase in the use of chatbots to support and complement traditional health care systems. However, despite the uptake in their use, evidence to support the development and deployment of chatbots in public health remains limited. Recent reviews have focused on the use of chatbots during the COVID-19 pandemic and the use of conversational agents in health care more generally. This paper complements this research and addresses a gap in the literature by assessing the breadth and scope of research evidence for the use of chatbots across the domain of public health.This scoping review had 3 main objectives: (1) to identify the application domains in public health in which there is the most evidence for the development and use of chatbots; (2) to identify the types of chatbots that are being deployed in these domains; and (3) to ascertain the methods and methodologies by which chatbots are being evaluated in public health applications. This paper explored the implications for future research on the development and deployment of chatbots in public health in light of the analysis of the evidence for their use.Following the PRISMA-ScR  guidelines for scoping reviews, relevant studies were identified through searches conducted in the MEDLINE, PubMed, Scopus, Cochrane Central Register of Controlled Trials, IEEE Xplore, ACM Digital Library, and Open Grey databases from mid-June to August 2021. Studies were included if they used or evaluated chatbots for the purpose of prevention or intervention and for which the evidence showed a demonstrable health impact.Of the 1506 studies identified, 32 were included in the review. The results show a substantial increase in the interest of chatbots in the past few years, shortly before the pandemic. Half  of the research evaluated chatbots applied to mental health or COVID-19. The studies suggest promise in the application of chatbots, especially to easily automated and repetitive tasks, but overall, the evidence for the efficacy of chatbots for prevention and intervention across all domains is limited at present.More research is needed to fully understand the effectiveness of using chatbots in public health. Concerns with the clinical, legal, and ethical aspects of the use of chatbots for health care are well founded given the speed with which they have been adopted in practice. Future research on their use should address these concerns through the development of expertise and best practices specific to public health, including a greater focus on user experience. Sundar Pichai, the chief executive officer of Google, expressed in a recent interview his view that artificial intelligence (AI) will have a more profound impact on humanity than the advent of fire, the internet, or electricity . AlthougChatbots—software programs designed to interact in human-like conversation—are being applied increasingly to many aspects of our daily lives. Made to mimic natural language conversations to facilitate interaction between humans and computers, they are also referred to as “conversational agents,” “dialog assistants,” or “intelligent virtual assistants,” and they can support speech and text conversation. Notable early chatbots include ELIZA , PARRY , and ALICE  . ImproveThe ongoing COVID-19 pandemic has further driven the rapid uptake and deployment of chatbots , many maThe use of AI for symptom checking and triage at scale has now become the norm throughout much of the world, signaling a move away from human-centered health care  in a remIn the light of the huge growth in the deployment of chatbots to support public health provision, there is pressing need for research to help guide their strategic development and application . This paWhat does the evidence tell us about the use of chatbots in public health?In which fields of public health have chatbots been used the most frequently?What are the types of chatbots that have been used in public health?How have chatbots in public health been evaluated?What are the potential lessons to be learned from the evidence for the use of chatbots in public health?We carried out a scoping review of studies on the use of chatbots in public health. We followed the PRISMA-ScR  guidelines and methodological framework by Arksey and O'Malley  for scopThe use of AI and digital technologies and the roles in which they are deployed in health tend to blur the boundaries between population and clinical health—that is, chatbots that are used to service individual health needs are often equally as relevant to population-level health in application. In this respect, the synthesis between population-based prevention and clinical care at an individual level  becomes Our selection methodology was as follows. One of the authors screened the titles and abstracts of the studies identified through the database search, selecting the studies deemed to match the eligibility criteria. The second author then screened 50% of the same set of identified studies at random to validate the first author’s selection. The papers meeting the criteria for inclusion at the title- and abstract-screening stage were retrieved and reviewed independently by both authors, with subsequent discussion about discrepancies and resolution to end with an agreed upon list of included studies.Our inclusion criteria were for the studies that used or evaluated chatbots for the purpose of prevention or intervention and for which the evidence showed a demonstrable health impact. We included experimental studies where chatbots were trialed and showed health impacts. We also included feasibility studies for agents that are being rolled out, randomized controlled trials (RCTs) informing the feasibility of conversational agents that have obvious applicability for scalability and potential for population-level interventions, and comparative analyses of in-service chatbots. We chose not to distinguish between embodied conversational agents and text-based agents, including both these modalities, as well as chatbots with cartoon-based interfaces.We excluded thought experiments, design outlines and reflections on systems that have yet to be implemented, descriptions of proposed chatbots and conversational agents, prototypes of system architecture, surveys and predesign analyses, frameworks, commentaries, validation studies, technical papers that introduced agents explaining their architecture and design that have yet to be trialed, and papers exploring perceptions of digital agents or their acceptability or validity among users. We also excluded studies comparing the effect of differences in technical approaches  and studies that used “Wizard of Oz” protocols—a protocol used to test users’ reactions in which a human responds to users through an interface in which they think they are interacting with a computer. The review selection process is shown in In total, 32 studies met the inclusion criteria. These studies included RCTs (n=12), user analytics (n=8), user experience studies (n=3), an experimental pilot (n=1), a descriptive study (n=1), comparative analyses (n=2), a case control study (n=1), design processes (n=2), and feasibility studies (n=2). These studies were distributed across 11 application domains.Mental health and COVID-19 dominated the application domains. This result is possibly an artifact of the maturity of the research that has been conducted in mental health on the use of chatbots and the massive surge in the use of chatbots to help combat COVID-19. The graph in The timeline for the studies, illustrated in Similarly, one can see the rapid response to COVID-19 through the use of chatbots, reflecting both the practical requirements of using chatbots in triage and informational roles and the timeline of the pandemic.Studies that detailed any user-centered design methodology applied to the development of the chatbot were among the minority  -18. MostLayla, a trusted source of information in contraception and sexual health among a population at higher risk of unintended pregnancy. Layla was designed and developed through community-based participatory research, where the community that would benefit from the chatbot also had a say in its design. Layla demonstrates the potential of AI to empower community-led health interventions. Such approaches also raise important questions about the production of knowledge, a concern that AI more broadly is undergoing a reckoning with [One study that stands out is the work of Bonnevie and colleagues , who desing with .Two-thirds 21/32, 66%) of the chatbots in the included studies were developed on custom-developed platforms on the web 6% of the,20-26, fAll the included studies tested textual input chatbots, where the user is asked to type to send a message (free-text input) or select a short phrase from a list (single-choice selection input). Only 4 studies included chatbots that responded in speech ,25,37,38The majority  of the studies contained very little description of the technical implementation of the chatbot, which made it difficult to classify the chatbots from this perspective. Most  of the included papers included screenshots of the user interface. However, some only provided sketches of the interface, and often, the text detailing chatbot capabilities was not congruent with the picture accompanying the text . In such cases, we marked the chatbot as using a combination of input methods see .Surprisingly, there is no obvious correlation between application domains, chatbot purpose, and mode of communication (see The presentation of the chatbot persona see  was usuaThe included studies consisted of RCTs (n=12), user analytics (n=8), user experience studies (n=3), an experimental pilot (n=1), a descriptive study (n=1), comparative analyses (n=2), a case control study (n=1), design processes (n=2), and feasibility studies (n=2).For RCTs, the number of participants varied between 20 to 927, whereas user analytics studies considered data from between 129 and 36,070 users. Overall, the evidence found was positive, showing some beneficial effect, or mixed, showing little or no effect. Evidence was predominantly preliminary and context specific. Most  of the included studies established that the chatbots were usable but with some differences in the user experience and that they can provide some positive support across the different health domains.Moderate positive results were found across several studies in regard to knowledge and skill improvement ,39, reduStudies on the use of chatbots for mental health, in particular anxiety and depression, also seem to show potential, with users reporting positive outcomes on at least some of the measurements taken ,34,41. TChatbots were found to have improved medical service provision by reducing screening times  and triaThe evidence for the use of chatbots to support behavior change is mixed. One study found that any effect was limited to users who were already contemplating such change , and anoMixed findings were also reported regarding adherence. One study found that there was no effect on adherence to a blood pressure–monitoring schedule , whereasResearch on the use of chatbots in public health service provision is at an early stage. Although preliminary results do indicate positive effects in a number of application domains, reported findings are for the most part mixed. Moreover, varying user engagement with the chatbots (though not necessarily correlated with the effect ), the siThe majority  of the studies used quantitative methods and methodologies for the evaluation of chatbot design and their impact in relation to health outcomes. For the most part, qualitative methods were used to examine the acceptability of chatbots to patients and their self-reported experience in using them alongside other quantitative usability metrics . User exBy far the most prevalent means of assessing health impacts of chatbot-led interventions were RCTs (n=12). Studies that focused on the effectiveness of chatbots with regard to an assigned task, such as triage and symptom checking, lent themselves more easily to evaluation through user analytics (n=8). There was, however, limited evaluation of user experience (n=3), and chatbot development was rarely design-led—although there were notable exceptions, with 1 study identifying user principles in development ; 1 studyNo included studies reported direct observation  or in-depth interviews as evaluation methods. Given the recognized need for observational study in chatbots deployed for public health  and the Although research on the use of chatbots in public health is at an early stage, developments in technology and the exigencies of combatting COVID-19 have contributed to the huge upswing in their use, most notably in triage roles. Studies on the use of chatbots for mental health, in particular depression, also seem to show potential, with users reporting positive outcomes ,34,41. INotably, people seem more likely to share sensitive information in conversation with chatbots than with another person . SpeakinAlthough the COVID-19 pandemic has driven the use of chatbots in public health, of concern is the degree to which governments have accessed information under the rubric of security in the fight against the disease. For example, in South Korea, the implementation of integrated technological responses, including personalized communication chatbots and the use of personal data gathered for contact tracing , uses AIThe evidence cited in most of the included studies either measured the effect of the intervention or surface and self-reported user satisfaction. There was little qualitative experimental evidence that would offer more substantive understanding of human-chatbot interactions, such as from participant observations or in-depth interviews. In this respect, we should remember that chatbots are complex systems, and chatbot deployment in public health is a technology design activity , as much as it is a medical intervention (the design of the intervention and setting up measures for its effectiveness). As an interdisciplinary subject of study for both HCI and public health research, studies must meet the standards of both fields, which are at times contradictory . MethodsStudies in the existing research often do not provide sufficient information about the design of the chatbot being tested to be reproducible, including by RCT standards, as the chatbot description is not sufficient for an equivalent chatbot to be implemented. There are further confounding factors in the intervention design that are not directly chatbot related  or include aspects such as the chatbot’s programmed personality that affect people differently . As an eFew of the included studies discussed how they handled safeguarding issues, even if only at the design stage. Of those that did, the studies mentioned that they could not provide a person to support the chatbot , so the chatbot was programmed to message the user to contact official health authorities if they had an issue . This methodology is a particular concern when chatbots are used at scale or in sensitive situations such as mental health. In this respect, chatbots may be best suited as supplements to be used alongside existing medical practice rather than as replacements ,33.Although the use of NLP is a new territory in the health domain , it is aMost of the included papers contained screenshots of the chatbots. However, some of these were sketches of the interface rather than the final user interface, and most of the screenshots had insufficient description as to what the capabilities were. Although the technical descriptions of chatbots might constitute separate papers in their own right, these descriptions were outside the scope for our focus on evidence in public health. However, a previously published scoping review , focusinFuture research on chatbots would benefit from including more details as to how the chatbot is implemented and what type of NLP it uses and cross-referencing the equivalent technical paper describing the system implementation and technical contribution, if it is available.More broadly, in a rapidly developing technological field in which there is substantial investment from industry actors, there is a need for better reporting frameworks detailing the technologies and methods used for chatbot development. Similarly, given the huge range of chatbot deployments across a wide variety of public health domains, there is a need for standards of comparative criteria to facilitate a better evaluation and validation of these agents and the methods and approaches that they use to improving health and well-being. Finally, there is a need to understand and anticipate the ways in which these technologies might go wrong and ensure that adequate safeguarding frameworks are in place to protect and give voice to the users of these technologies.Given the immaturity of the research on chatbots, the huge investment in their development and use for health, and the dynamic nature of AI and HCI, our study does not capture the abundance of chatbots, commercial and otherwise, that have been developed across of the domains of public health application. There is a substantial lag between the production of academic knowledge on chatbot design and health impacts and the progression of the field.Research on the recent advances in AI that have allowed conversational agents more realistic interactions with humans is still in its infancy in the public health domain. Studies show potential, especially for easily automated and repetitive tasks, but at the same time, concerns with the clinical, legal, and ethical aspects of the use of conversational agents for health care are well founded given the speed with which they have been adopted in practice. There is still little evidence in the form of clinical trials and in-depth qualitative studies to support widespread chatbot use, which are particularly necessary in domains as sensitive as mental health. Most of the chatbots used in supporting areas such as counseling and therapeutic services are still experimental or in trial as pilots and prototypes. Where there is evidence, it is usually mixed or promising, but there is substantial variability in the effectiveness of the chatbots. This finding may in part be due to the large variability in chatbot design  but also the large variability in the users’ response to engaging with a chatbot.There is no doubting the extent to which the use of AI, including chatbots, will continue to grow in public health. The ethical dilemmas this growth presents are considerable, and we would do well to be wary of the enchantment of new technologies . Of para


Endoscopy is widely applied in the examination of gastric cancer. However, extensive knowledge and experience are required, owing to the need to examine the lesion while manipulating the endoscope. Various diagnostic support techniques have been reported for this examination. In our previous study, segmentation of invasive areas of gastric cancer was performed directly from endoscopic images and the detection sensitivity per case was 0.98. This method has challenges of false positives and computational costs because segmentation was applied to all healthy images that were captured during the examination. In this study, we propose a cascaded deep learning model to perform categorization of endoscopic images and identification of the invasive region to solve the above challenges. Endoscopic images are first classified as normal, showing early gastric cancer and showing advanced gastric cancer using a convolutional neural network. Segmentation on the extent of gastric cancer invasion is performed for the images classified as showing cancer using two separate U-Net models. In an experiment, 1208 endoscopic images collected from healthy subjects, 533 images collected from patients with early stage gastric cancer, and 637 images from patients with advanced gastric cancer were used for evaluation. The sensitivity and specificity of the proposed approach in the detection of gastric cancer via image classification were 97.0% and 99.4%, respectively. Furthermore, both detection sensitivity and specificity reached 100% in a case-based evaluation. The extent of invasion was also identified at an acceptable level, suggesting that the proposed method may be considered useful for the classification of endoscopic images and identification of the extent of cancer invasion. Gastric cancer is a malignant tumor that mainly affects the gastric mucosa and is the second-most-common cause of death among all cancers after lung cancer ,2. It isGastric examination is performed by endoscopy and gastrofluoroscopy using barium. Because endoscopy has superior sensitivity in detecting early stage gastric cancer and allows for tissue collection and treatment under observation, it has been widely adopted for gastric cancer screening and detailed diagnosis. In endoscopic diagnosis, a physician checks the endoscopic images while operating the endoscope and records still images at key points when abnormalities are detected. In addition, magnification and staining are often performed to classify diseases and diagnose the extent and depth of lesions .However, the procedure is highly complex, requiring multiple tasks to be performed during the examination. Hence, there is some concern that lesions may be missed. According to the results of a survey, the probability of missing a lesion during endoscopy was 22.2% and the accuracy of diagnosis depends, to a considerable extent, on the experience and skill of the physician. Therefore, technologies to assist examinations are strongly desired to reduce the burden on the physician and to improve diagnostic accuracy.In recent years, artificial intelligence technologies have made remarkable progress and deep learning technology has shown excellent performance in the field of image recognition ,5. Deep Various studies have been conducted on the automated detection of gastric cancer lesions in endoscopic images, the extraction of the extent of invasion, and the evaluation of cancer depth ,13,14,15To evaluate the depth of gastric cancer, Zhu et al. used a CNN to classify cancer as remaining within the mucosa or as having invaded the submucosa, the latter indicating an advanced cancer. The evaluation resulted in a sensitivity of 76.47% and a specificity of 95.56% using the original 993 endoscopic images . Hamada These techniques present independent methods to detect the presence and evaluate the depth of gastric carcinoma. In our previous study, semantic segmentation was applied directly to endoscopic images; however, the many healthy images collected during the examination were subject to processing, resulting in false positives and high computational cost. The actual detailed diagnoses were performed by first detecting abnormal areas during the endoscopic procedure and approaching the lesion. Hence, a more automated detection and classification scheme in line with the diagnostic flow is desirable. This cascaded processing has been applied to other medical images. For example, Krzysztof et al. proposed a method for segmenting brain tumors from head MR images that uses two types of U-Nets to perform tumor location identification and detailed multiclass segmentation . Jie et Methods, we propose a cascade-based gastric cancer detection and classification method. In the Results, we evaluate the detection characteristics by calculating the detection performance and activation maps. In the Discussion section, the detection performance and its comparison with previous studies are discussed. Finally, in the Conclusions, we describe the effectiveness and contribution of this study.The organization of this paper is as follows: In the For this study, endoscopic images were collected from healthy subjects, patients with early stage gastric cancer and patients with advanced gastric cancer. All images were collected at the endoscopy center of Fujita Health University Hospital during an examination. They comprised 1208 endoscopic images collected from 42 healthy subjects, 533 images from 93 patients with early gastric cancer, and 637 images from 50 patients with advanced gastric cancer. Images were obtained using upper endoscopes  and standard endoscopic video systems . The image matrix sizes ranged from 640 × 480 to 1440 × 1080 pixels; these were standard white-light images stored in JPEG format. This study was approved by an institutional review board and patients’ informed consent was obtained under the condition that all data were anonymized (No. HM17-226).Here, early gastric cancer was defined as lesions that remained in the mucosal or submucosal layer of the stomach, whereas advanced gastric cancer was defined as lesions that reached to the intrinsic muscular layer or deeper than that layer. For healthy subjects, we reassessed the cases diagnosed as normal by the endoscopists and characterized a case as “healthy” when there was no specific lesion, such as a polyp, tumor, or gastritis.Images stored by endoscope systems may contain text information, such as patient names, and the shape of the field of view differs depending on the model of the endoscope. Therefore, as shown in For images diagnosed as gastric cancer, labeled images of gastric cancer areas were created for the segmentation of gastric cancer areas. Using an in-house annotation tool, labeled images with the invasion areas of gastric cancer marked were created and confirmed by a board-certified endoscopist (T. S.).The number of images in the three categories differed. In addition, training a network using a small number of images taken at a certain angle of view may result in overfitting. In endoscopic examination, images of a single lesion are taken from various positions and distances and there is wide variation in the position and size of the lesion in the image, with sufficient data available. On the other hand, there is not much variation in the rotation of images due to the limitations of examination techniques. Therefore, to avoid bias and overfitting caused by the number of images and viewing angle, data augmentation was performed by image rotation . The ratThe proposed method involves image classification and segmentation by two CNNs, as shown in We introduced transfer learning to the training of the CNN described above, which involves transferring the processing ability acquired in solving one task to learning to solve another task. If the CNN of the original diversion is a well-trained model with a huge amount of data, so it may provide a higher capacity . In thisImages classified by the first-stage CNN were transferred to the next-stage CNN for segmentation according to their classification results. Images classified as healthy were judged as not including cancer areas and, thus, segmentation was not performed. Images classified as showing early and advanced gastric cancer were fed to a dedicated CNN for segmentation processing.We implemented U-Net as a CNN model to perform classification, given its excellent performance in medical image segmentation . The strHere, the morphology shown in images of early and advanced gastric cancer differs considerably. Therefore, two independent U-Nets were created to segment early and advanced gastric cancer. The Adam optimizer was used for training with a learning coefficient of 0.0001 and 50 training epochs.Healthy, NEGC, and NAGC indicate the numbers of images for healthy, early gastric cancer, and advanced gastric cancer, respectively, and CHealty, CEGC, and CAGC are the numbers of images successfully classified as healthy, early gastric cancer, and advanced gastric cancer, respectively.To confirm the effectiveness of the proposed method, we evaluated the output of the CNN models for image classification and segmentation. First, to evaluate the performance of the CNN for image classification in the first stage, a confusion matrix was created based on the CNN classification results. Based on the matrix, we calculated the accuracy, sensitivity, and specificity of the models. NThe above indices were evaluated on an image-by-image basis  and on a case-by-case basis . For the former, the results were tabulated when each image was classified into the class with the largest CNN output value. For the case-by-case evaluation, the output values of images collected from the same case were averaged for each class and the class with the highest average value was considered as the classification result.As for the visualization method of CNN output, class activation mapping (CAM) calculates what parts of an image influence the prediction based on the feature map during inference. Among various CAM methods, Grad-CAM calculates the activation map by calculating the gradient of the CNN feature map and can provide a stable activation map regardless of the model . In thisIn the second step of the segmentation process, the Dice coefficient (Di) and Jaccard coefficient (Ji) were used to evaluate the extent to which the invasive area output by U-Net was correctly extracted. Di and Ji were defined by the following equation to evaluate the degree of agreement between the label image output by U-Net and the correct label (Ground Truth) created by the endoscopist. process . In crosThe calculations of two CNNs were performed using software we developed in the Python programming language with an AMD Ryzen 9 3950X processor  with 128 GB of DDR4 memory. The training processes of the CNNs were accelerated by an NVIDIA Quadro RTX 8000 GPU (48 GB memory).The results of U-Net segmentation are shown in In this study, we proposed a cascaded deep learning model for endoscopic images, designed to classify the depth of normal and gastric cancer using a CNN model to perform image classification and then performed segmentation on the extent of gastric cancer invasion using U-Net.In the first stage of the image classification task, nine different CNN models were employed and their classification performances were compared. The results showed that the performance differences among the models were not large, being within 5% for most of the evaluation indices. Among the CNN models, DenseNet-121 achieved the highest classification performance for all items, with a detection sensitivity of 97% and a specificity of 99.4% for gastric cancer. A slight misclassification occurred in the image-based evaluation. In cases of early gastric cancer, images with extensive areas of invasion were classified as advanced gastric cancer. In healthy cases, some bubbles adhering to the gastric mucosa and some hyperemic areas produced by endoscopy were misclassified as advanced gastric cancer. Endoscopy involves collecting multiple images for a single patient. The classification results of the individual images were tabulated and a decision was made for each patient. Consequently, all cases were classified into the correct category, as shown in As a result of acquiring the regions of interest for image classification by Grad-CAM, the lesion and its surrounding area tended to be focused on for early and advanced gastric cancer. In healthy subjects, the results showed that the entire gastric mucosa was uniformly focused on. These results are similar to the process of diagnosis using images, which can be considered reasonable.In the second stage of the cascade model, the invasive extent was segmented by U-Net for images classified as early or advanced gastric cancer and the agreement between the invasive extent output by U-Net and the gold standard specified by the endoscopist was evaluated using the Dice and Jaccard coefficients. The Dice coefficient was 0.555 for early gastric cancer and 0.716 for advanced gastric cancer. This was due to the lack of morphological changes in the images of early gastric cancer and the fact that many of the images were taken from the side of the gastric wall rather than from the front, so the contours of the invasive area were not completely defined. Even with this underestimation, the extent of the invasion output, like the Grad-CAM output, provides a basis for image classification and can provide important information during endoscopy.To compare the detection performance of gastric cancer, Our previous study faced challenges about false positives and high computational cost because semantic segmentation was applied to all healthy images collected during the examination. The detection sensitivity and the number of false positives in the proposed cascade model were superior to those of our previous study and the computational cost of the segmentation process was eliminated because healthy images were cut off by the classification CNN. Although an accurate comparison is not possible because a different database was used, the proposed method with cascaded model had a better detection sensitivity than those in previous studies using an SSD (sensitivity of 99.2%) . The proA limitation of this study is that the endoscopic images were collected at a single facility. In the future, an evaluation of usefulness of the proposed approach should be conducted using images from multiple facilities. Regarding the evaluation of the extraction accuracy of the invasive area, the Dice and Jaccard coefficients were introduced. However, a subjective evaluation of whether the clinically important invasive area was extracted is required. Furthermore, we intend to develop endoscopy support software that incorporates the constructed model and confirm its usefulness in practical medicine.In this study, we proposed a cascade deep learning model to support the automated detection and classification of gastric cancer during endoscopy by combining image classification and segmentation to detect normal samples and gastric cancer, to evaluate the degree of depth, and to identify the extent of invasion. The evaluation results showed that the accuracy of first-stage image classification was high and the proposed method accurately classified normal images and gastric cancer and classified the depth of invasion. In addition, the extent of gastric cancer invasion was correctly extracted from several images. The number of false positives generated by the proposed method was significantly low, indicating that the method is effective for endoscopic screening.


Emotion is an important expression generated by human beings to external stimuli in the process of interaction with the external environment. It affects all aspects of our lives all the time. Accurate identification of human emotional states and further application in artificial intelligence can better improve and assist human life. Therefore, the research on emotion recognition has attracted the attention of many scholars in the field of artificial intelligence in recent years. Brain electrical signal conversion becomes critical, and it needs a brain electrical signal processing method to extract the effective signal to realize the human-computer interaction However, nonstationary nonlinear characteristics of EEG signals bring great challenge in characteristic signal extraction. At present, although there are many feature extraction methods, none of them can reflect the global feature of the signal. The following solutions are used to solve the above problems: (1) this paper proposed an ICA and sample entropy algorithm-based framework for feature extraction of EEG signals, which has not been applied for EEG and (2) simulation signals were used to verify the feasibility of this method, and experiments were carried out on two real-world data sets, to show the advantages of the new algorithm in feature extraction of EEG signals. In over the next two hundred years, the human exploration of the brain will never stop. Study on the brain structure and its related research appeared in particular cognitive function for nearly 10 years, with high performance due to the rapid development of computer technology and to the widespread use of noninvasive brain imaging technology, to become one of the difficult and frontier hot academic research directions. However, understanding and recognizing the neural mechanism behind the mysterious brain is still a basic and difficult problem in current brain science research . Among tWith the rapid development of the information age and the increasing improvement of human living standards, people are increasingly aware of the importance of emotions to their own health and social development –9. EmotiIn recent years, a large number of feature extraction methods of EEG signals have been proposed, but due to the large individual differences, the current research goal is to extract effective features to improve the accuracy of emotion recognition. In the process of signal acquisition, most studies adopted 32- and 64-electrode guide of EEG acquisition. This paper applied the EEG feature extraction and selection into channel emotion recognition, using as few channels as possible to extract effective feature information, to achieve considerable recognition accuracy. The research methods of emotion recognition have many directions. The common research method is to study the recognition and classification of emotions through some physiological characteristics, including facial expressions, behaviors, language, and intonation –15. Althk-nearest neighbor and support vector machine classifier to identify emotion [The traditional research on emotion recognition is to extract the energy features of EEG signal band or various entropy features to identify an emotion and then use  emotion , 21. Fin emotion . At the  emotion , 25.In this paper, the ICA model and feature extraction are innovatively combined to integrate the advantages of both.The existing methods are generally limited to the training of athletes. This paper not only deals with the training of athletes but also studies the selection of athletes.The contributions of this paper are concluded as follows:When people imagine unilateral hand movements, a region of the contralateral cortex becomes active in association with an image called an event in which the amplitude of a specific pitch decreases –28. At tQazzaz et al.  predicteK-nearest neighbor and support vector machine, emotions were divided into five types: surprise, anger, fear, disgust, and sadness. The highest average recognition rate reached 83.33% with support vector machine. In the process of feature selection, in order to construct a better model, it is necessary to select specific features from a certain number of feature sets. However, in the practical application of pattern recognition, research is faced with features of more dimensions extracted from a large number of data, including features with high correlation and low correlation and features without correlation. The more the number of features, the higher the dimension of the feature vector, the longer the analysis and calculation time, and the more complex the model will be; the recognition rate cannot be guaranteed to be high, so the selection of features is of great significance in the research.Feature extraction plays an extremely important role in the research of emotion recognition based on EEG signals. Only by selecting the right features that can effectively represent emotion can the following classification research be carried out. Experts have conducted a large number of correlation experiments on feature extraction based on EEG signals, and combining with psychology, physics, and neuroscience, they have identified EEG signatures that can be used to identify emotions. Anem et al.  extracteIn the process of EEG emotion recognition, feature extraction is extremely important and the quality of extracted features directly affects the accuracy of final recognition. The feature extraction methods of EEG mainly include linear time domain, frequency domain, time-frequency direction, nonlinear differential entropy permutation entropy, approximate entropy, and combination entropy. Throughout the experiment, we used EEG signals from the international open data set DEAP, since we use the signal of each guide containing data from 63 s (8064 sampling points) and 60 s and only one is used to stimulate the data according to the experiment; so before the feature extraction of EEG signals, we first need to obtain brain electrical signals to carry out the simple processing.Some researchers believed that the data fragments of the first 20 s were collected in an unstable state because the data of the last 40 s would be selected as the research object. ICA is the result of the different frequencies with a cosine function; this function to some extent can express the original signal information, and its essence is to convert the chaotic signal into frequency phase A sinusoidal signal amplitude that has a fixed rule. Because previous studies have shown that EEG emotion recognition can signal the rhythmicity of interest rate, in the process of EEG signal processing, a more classical approach is to obtain the power spectrum of the signal through Fourier transform to calculate the energy value of each frequency band. The whole system of the method is given in X are obtained by linear mixing of K independent sources S, where A is called the mixing matrix:Independent component analysis (ICA) is a signal processing method that attempts to recover independent sources from a group of mixed observations. It is closely related to a method called blind source separation. Shortly after the discovery of the ICA, it was widely used in the processing and analysis of biomedical signals and images. FastICA algorithm is an efficient computing method for ICA estimation. It uses a fixed-point iterative scheme, which has been shown in independent experiments to be 10–100 times faster than ICA's traditional gradient descent method. Another advantage of the FastICA algorithm is that it can also be used to perform projection tracing, thus providing a general data analysis method that can be used both in an exploratory manner and for independent component estimation. Mixed observations X to obtain the unrelated columns [ICA unmixing first needs to whiten  columns as followW to maximize the non-Gaussianity of this direction:FastICA iterative algorithm: after whitening, it is necessary to find an optimal direction Its derivative under constraints is as follows:w-union can be obtained by solving the above equations:The optimal This kind of problem can be solved by Newton's method:So, formula  can be fTherefore, it can be approximated as follows:Using Newton's formula ,we can oThe recursive formula is obtained.G′s are as follows:The usual In related studies on EEG emotion recognition, some researchers use self-collected data sets. Because the process of obtaining self-collected data sets is complicated and the cost is high, the data set used in this study is DEAP as an open data set. DEAP is a multimodal emotion data set generated by video clips stimulation. The data set generates 40 channels of physiological signal data per experiment. In the studies related to emotion recognition, only the former is required. The EEG signal of 32 conduction data and the other 8 derivative data including ECG signal and respiratory band signal are not very relevant to emotion recognition, so those will not be described here. Next, the data format of an experimental flow path of acquisition equipment of the DEAP data set will be introduced in detail.The Biosemi Active Two system was used to collect EEG signals for DEAP data, and prestation software was used to play the video of the emotion generated by the selected stimulus. In order to eliminate the error caused by eye movement in the experimental results, the range of playing the video was 2/3 of the video screen. In addition, the system is relatively compatible with signal reception hardware and is smaller and lighter in weight. In the process of DEAP data set collection, 32 EEG-related electrodes among 128 electrodes were selected for the collection and the signal sampling frequency was 512 Hz. Due to the strong correlation between similar EEG signals, using all 128 electrodes would lead to overlapping of collected signals in this data set. The 32 electrodes are positioned in accordance with international standards and are evenly distributed throughout the brain, containing information from various brain regions.After preprocessing, the labeled EEG data of each subject were divided into a window length of 5 seconds. If the labeled continuous data segment was less than 5 seconds, the labeled EEG data segment of each subject was removed. Then, the labeled EEG data segment of each subject underwent fast-Fourier transform (FFT) in the form of nonoverlapping sliding window to carry out energy calculation. Finally, the average of the energy of data was taken as the subjects of the frequency band energy.The database collected 32 volunteers' EEG signals stimulated by 40 segments of the material. There were 40∗32 = 1280 emotion samples in total, among which 270 samples met the classification criteria. With the development, the innovation hot spots in the payment field are further evolving from payment mode innovation to payment medium innovation. New technologies such as code payment, biometric payment, and NFC are emerging, and their application scope is gradually expanding. According to the iResearch data statistics, in 2017, by virtue of the intelligent transformation of traditional POS machines, bank card orders accounted for 19.4% of the payment market size, Internet payment accounted for 16.8%, and mobile payment accounted for 63.7% which has become the absolute leader in new payment formats. In addition, the results of the frequency method of Beta rhythm mode based on cluster analysis are shown in Here, the features mentioned above in the time domain are extracted and the recognition results of single feature and combination feature are compared. It is then compared with time-frequency-based features separately. All the comparisons were performed on all 32 subjects who watched 40 movie clips, each of which generated data from all 32 channels (electrodes). Next, we verify the validity of the feature extraction algorithm using points, and the comparison of results is shown in In order to evaluate the difference in the performance of the EEG measures corresponding to each channel for attention recognition of auditory targets, based on the SVM auditory attention classifier, the experiment further discussed the EEG features corresponding to each channel as the input feature vector. As shown in The effectiveness of the migration method is further verified, and the effect of the migration method is vividly demonstrated. The feature distribution before and after the migration is visualized in this small pair. Here, T-SNE technology is used for feature dimension reduction and data visualization of extracted features. T-SNE can reduce high-dimensional data to 2-3 dimensions, and the features after dimension reduction can vividly show the distribution of features. This paper mainly studies feature extraction, channel selection, and classification model selection of emotion recognition based on EEG. At present, research studies on emotion recognition based on EEG signal have low recognition accuracy. In this paper, the method of feature extraction is improved and the ICA model with excellent performance in data mining is selected for classification. Compared with some existing studies, the recognition accuracy of emotion recognition by the feature extraction method proposed in this paper has been improved to some extent.Emotion recognition is studied in this paper, based on EEG signals, comparing only the electrical characteristics of the time domain and the time-frequency combination and not the electrical characteristics and other properties of the mixed emotion recognition results; in the process of emotion recognition, due to the individual difference being greater, the identification results of this paper are the emotion recognition results of each people, respectively. Therefore, in subsequent studies, we will explore the emotion recognition results of the combination of EEG signal features and other physiological signal features. For problems with large individual differences, we will try to use a deep learning algorithm to carry out relevant studies.


Background: Industry 4.0 technologies have been widely used in the railway industry, focusing mainly on maintenance and control tasks necessary in the railway infrastructure. Given the great potential that these technologies offer, the scientific community has come to use them in varied ways to solve a wide range of problems such as train failures, train station security, rail system control and communication in hard-to-reach areas, among others. For this reason, this paper aims to answer the following research questions: what are the main issues in the railway transport industry, what are the technologic strategies that are currently being used to solve these issues and what are the technologies from industry 4.0 that are used in the railway transport industry to solve the aforementioned issues? Methods: This study adopts a systematic literature review approach. We searched the Science Direct and Web of Science database inception from January 2017 to November 2021. Studies published in conferences or journals written in English or Spanish were included for initial process evaluation. The initial included papers were analyzed by authors and selected based on whether they helped answer the proposed research questions or not. Results: Of the recovered 515 articles, 109 were eligible, from which we could identify three main application domains in the railway industry: monitoring, decision and planification techniques, and communication and security. Regarding industry 4.0 technologies, we identified 9 different technologies applied in reviewed studies: Artificial Intelligence (AI), Internet of Things (IoT), Cloud Computing, Big Data, Cybersecurity, Modelling and Simulation, Smart Decision Support Systems (SDSS), Computer Vision and Virtual Reality (VR). This study is, to our knowledge, one of the first to show how industry 4.0 technologies are currently being used to tackle railway industry problems and current application trends in the scientific community, which is highly useful for the development of future studies and more advanced solutions. Funding: Colombian national organizations Minciencias and the Mining-Energy Planning Unit. The railway industry is one of the most important industries related to the economic growth and commutation transport in countries. As the world is experiencing major changes in industry processes due to new developments in our technology, the digitization of the railway industry must go hand in hand with these technological advances given its importance in the economic development of countries. The use of new technologies such as Artificial Intelligence (AI), Internet of Things (IoT) and Cloud Computing in industry processes have the power of transforming productivity, employment and other facets related to the human economy .At the present time, industry 4.0 technologies are the ultimate result of technical progress in information and communication technologies (ICTs) . These tThese technologies have been applied in many railway industry solutions that are mostly oriented to monitor and control the rail and train infrastructure. However, due to the large amount of research focused on the development of new tools based on industry 4.0 technologies in this field, it is imperative to understand which are the main problems that the scientific community is facing in the railway industry and how these technologies are implemented to solve these issues.What are the main issues in the railway transport industry?What are the technologic strategies that are currently being used to solve these issues?What are the industry 4.0 technologies used in the railway transport industry that are used to solve the aforementioned issues?Because of this, this paper aims to provide an overview of the research work reported in this field and related to industry 4.0 technologies by means of a Systematic Literature Review (SLR). This will give a better overview of the applied industry 4.0 technologies for specific problems to assist other researchers in the development of more advanced solutions for the railway industry. Therefore, the following research questions are considered for the present SLR:This paper is organized as follows: Studies published from January 2017 to November 2021.Studies written in English or Spanish.Studies published in conferences or journals reviewed by academic peers.This step aimed to stablish an inclusion and exclusion criteria for the extracted studies from the electronic databases and to select the ones that were more relevant to answer the proposed research questions. Therefore, we proposed the following for the inclusion criteria:Regarding the exclusion criteria, we analyzed the extracted articles in two steps. The first one was focused on analyzing each article by its title, abstract and keywords where we removed the ones that did not answer any of the three proposed research questions. Meanwhile, the second step was oriented to the full lecture of the articles. In the second step we found articles that, after a detailed lecture, did not give any pertinent information about industry 4.0 technologies in rail transportation that could lead towards the answer to the proposed research questions. Additionally, we excluded duplicate articles found in the multiple searches.For the search information phase, we used some of the most important digital libraries oriented to technology and engineering that are currently available. The selected information sources are described in These information sources allow the use of search algorithms composed of logical operators that are useful in extracting the desired information to perform the systematic review.This phase was focused on the definition of the keywords that could help us extract all the available information in the selected scientific databases with the goal of leading us towards the answers to the proposed research questions. In order to do this, we created three keywords’ groups that are described in The first keywords group was oriented to the railway transportation activity. The second group was used as a complementary keywords group that includes multiple general words related to technologies that are currently being used in railway transportation solutions. Lastly, the third group contains industry 4.0 technologies related keywords to complement the search.Afterwards, we created multiple search queries combining different groups using logical and Boolean operators. The search queries were made to find studies having at least one of the keywords belonging to the selected groups for that specific query. Therefore, we combined group 1 and group 2 for the first search query and group 1 and group 3 for the second one. These search queries are described in It is important to mention that some digital libraries have restrictions in the number of words used in the search algorithm. For example, Science Direct limits the number of words to 8.The use of these search algorithms let us recover a total of 515 articles from the selected electronic databases where 393 were from Science Direct and 122 from Web of Science. The protocol utilized in this systematic review  offers cThe search process was conducted using the words from the groups in The articles search process was limited to title, abstract and keywords in the platform of Science Direct. In the platform of Web of Science, it was limited to title and abstract. This is because we found that the words “train” and “transport” used in the search inside the abstract for Web of Science could lead to multiple results that do not necessarily lead to our topics of interest but that are useful in the title and keywords search for this engine. This search process was carried out by one researcher.Initially, we recovered 515 articles from electronic databases where 393 were from Science Direct and 122 from Web of Science. In Based on the eligibility criteria for Science Direct, we found multiple papers related to machine learning in the transport field because of the use of the word “train” . After that, we reviewed each article that passed the initial eligibility criteria with a detail lecture to detect the most important technologies used in these solutions. This was done by pairs of researchers, and a consensus was reached by discussion. In the process, we detected articles that did not use any technology related to industry 4.0 and, therefore, were excluded. This process left us 109 studies with 52 studies from Science Direct and 57 from Web of Science. In This phase presents the results of the Systematic Literature Review (SLR) in order to answer the aforementioned research questions based on the main studies selected. The selected studies are the result of the search and selection process that was described in In order to determine the main issues in the railway transport industry and technological solutions, the extracted and filtered studies were classified into multiple groups that included the following: (i) monitoring, (ii) communication and security and (iii) decision and planification techniques. From these groups, the monitoring domain has the majority of the studies (52%) followed by decision and planification techniques (35%) and communication and security (13%) see . The claPapers grouped in the monitoring domain are mostly focused on monitoring the railway infrastructure concerning technological assets, detecting irregularities in the rails and train equipment and environmental pollution monitoring, among others. It is important to mention that most of these papers aim to detect, as early as possible, any problem in the railway or train infrastructure in order to maintain the safety of train travel and reduce maintenance costs .In fact, one of the main issues in train logistics for freight transport is related to the maintenance costs of the railway infrastructure. Usually, the maintenance costs are elevated due to contact measurement techniques and human inspections that are still used .Hence, the use of real-time monitoring in the railway infrastructure is able to increase the reliability, availability, maintainability and security in the rail system .The technological solutions included in the monitoring domain vary greatly in their end goal. For example, some of the technological solutions belonging to this group are focused on helping the train driver make better decisions based on collected train travel information, while others are more focused on monitoring the train equipment in order to prevent mechanical system faults. Therefore, the collected papers in this domain can be categorized into the following subdomains: rail monitoring (79%), Driver Advisory Systems (DASs) (7%) and train monitoring (14%) see .Rail monitoring: Papers classified in this subdomain aimed to use technologies from industry 4.0 to check the state of the rail and its components to detect possible rail faults, decreasing maintenance costs and preventing railway accidents, among others. In fact, railroad system infrastructure is composed of a superstructure and a substructure [tructure . The suptructure  proposedtructure  with a SDriver Advisory Systems (DAS’s): Studies classified in this domain proposed algorithms oriented to support train drivers along the travel of the train. For example, authors in [thors in  proposedTrain monitoring: Papers included in this subdomain are oriented to collecting train information in order to process it and gain insights about train performance, train pollution or train faults in the equipment. Usually, proposed studies in this subcategory collect and process train information in real time to improve daily operations and profit. Authors in [thors in  proposedIt is necessary to highlight that most of the selected studies in this SLR can be categorized in more than one subdomain due to their multiple advantages of use in the train transport industry. Some of the most representative examples of railway system solutions based on industry 4.0 that are grouped into the monitoring domain are described below:Papers classified under communication and security domain were focused on the development of Industry 4.0 solutions for the improvement of security protocols of trains, train stations and railroads. Also, this subdomain focuses on papers that improve travel connectivity of trains. Generally, poor train travel connectivity is an important field of research due to its complexity that includes geographical location-based solutions, solutions for high-speed trains, underground railroads, among others. Technological solutions comprised in this domain can be classified in multiple subdomains such as rail safety (57%), security system (36%) and travel connectivity (7%) see .Railway safety: Papers included in this subdomain use industry 4.0 technologies to improve safety protocols in railroads and train stations using, mostly, a real-time risk status approach. For instance, an artificial neural network (ANN) in conjunction with a fuzzy system architecture was used to develop a smart risk-management system that could monitor overcrowding in train station areas [on areas   has been growing in railway security systems such as in [ch as in . The maiTravel connectivity: Papers included in this subdomain aim to provide high data connectivity rates to passengers in trains while they are traveling. In fact, authors in [thors in  proposedThe last domain used to classify the previously filtered articles was the decision and planification techniques domain. Unlike the other subdomains, this one centers on the optimization of rail processes  and various rail predictions . In fact, this domain can be subclassified as: rail transport optimization (74%), rail transport insights (16%) and energy optimization (10%) see .Rail transport optimization: Studies classified in this subdomain proposed algorithms based on technologies of industry 4.0 in order to optimize railway traffic flow. The majority of the studies classified in this subdomain aimed to solve the following problems: prediction of train arrival times, minimization of train delay times, train rerouting process, train scheduling problem and the improvement of train dispatching systems. In fact, most of the studies focus on the prediction and minimization of train delays, which is an important research field. This is because trains are usually operated under a planned schedule timetable and sometimes the planned schedule cannot be accomplished due to external factors such as repair work, accidents or weather conditions. Therefore, the timetable has to be updated in order to decrease delay times in the train and prevent the delay propagation to other trains [r trains . An examr trains , where ar trains  to predir trains ,123.Rail transport insights: This subdomain focuses on using railway collected information in conjunction with technologies such as big data, IoT or cloud computing to get insights that could improve railway processes. For example, in [mple, in  a decisiEnergy optimization: This is a global issue that has been addressed by many researchers and governments in different fields. The identification of drivers and barriers to climate change mitigation in each study field is important since climate change cannot be tackled solely by optimizing the used resources in mechanical or technological components, and climate change affects transportation worldwide [orldwide . The traorldwide , authorsThe mentioned subdomains are described below:From the identified domains in the SLR, the main issues in the railway transport industry are related to the development of monitoring tools. These must be able to get real-time data from physical elements such as trains, rail-track infrastructure and its components . The development of these tools allows the early detection of damaged components improving the safety of rail tracks, decreasing maintenance costs and give proper advice to train drivers with the analyzed real-time data.Another detected main issue is the minimization of train delays, which has been highly studied by researchers from multiple point of views such as traffic control, schedule adjustments and travel information systems . This isLastly, communication and security of the rail infrastructure is a growing study field among researchers. The scientific community has managed to use industry 4.0 technologies to develop protocols that can be applied to rail processes and infrastructure. These protocols are oriented to the train communication in hard-to-reach areas or are aimed at preserving the integrity of physical and digital spaces that are only accessible to authorized personnel.It is important to highlight that many of these solutions can be applied to passenger and freight transportation problems. For example, there are proposed solutions oriented to tackling specific problems in each of these transportation modes. For example, in the freight transportation there are rail track systems exclusive only for this mode. Therefore, merging problems can be present when multiple freight trains are in congested areas or monitoring systems must be developed specifically for these trains. Solutions such as a train scheduling algorithm for only freight trains , monitorTechnological strategies in this research refer to the combination of technologies and methods that appeared in the reviewed articles for this SLR for each domain. It is important to clarify that this is a general perspective of what could be seen in the reviewed articles. In terms of the technology strategies that are currently being used to tackle the main issues described in Many of the monitoring domain solutions that were selected in this SLR use IoT technologies to capture real-time data. Then, the captured data is sent to processing algorithms (which in some cases are stored in the cloud) in order to clean it. Later, we identified that the core of the proposed solution implemented by the authors utilized algorithms based on AI, SDSS, CV and modelling and simulation. Finally, alerts are sent to the personnel to attend the incoming requirement if needed .Some of the identified algorithms for these industry 4.0 technologies are the following: Breadth First Search (BFS) algorithm or Genetic Algorithms (GA) for DAS systems ,55, PrinThis is an emerging domain in the railway industry from which we obtained a few articles. From the analyzed studies, we could not identify a specific strategy for the solutions, but most of them used IoT devices in their architecture. Many of these solutions use Machine Learning (ML) or Deep Learning (DL) models in conjunction with sensors, cameras, optical fibers or even GPS to improve security and communication protocols in train travels , restricLastly, the solutions grouped in this domain present two principal strategies. The first one is aimed at obtaining insights from railway data that were previously stored in databases while the second one focuses on the use of real-time data in the proposed solution. It is crucial to mention that the identified papers include different data preprocessing steps for each solution. In addition, in the case of real-time data, these are obtained by using IoT devices installed in trains, railroads or train stations such as the strategy introduced in the monitoring domain. Later, these data can be used in the proposed algorithms where they mainly focus on the optimization of railway processes via data analytics or the optimization of the railway traffic flow by using AI or modelling and simulation solutions.Specifically, the identified industry 4.0 technologies used in this domain are AI, IoT, modelling and simulation, Big Data, SDSS, VR and Cloud Computing. Within these technologies we found the following algorithms for rail transport optimization: GA , GaussiaIn the studied solutions in this SLR, we could identify 9 technologies related to industry 4.0 and they can be classified as follows: (i) Artificial Intelligence (AI), (ii) Cloud Computing, (iii) Big Data, (iv) Internet of Things (IoT), (v) Cybersecurity, (vi) Simulation, (vii) Smart decision support systems (SDSS), (viii) Computer Vision (CV) and (ix) Virtual Reality (VR). The identified studies belonging to these technologies are compiled in It is important to highlight that the selected papers can be classified in multiple types of industry 4.0 technologies. For example, we identified that solutions oriented to the railway industry are usually validated with computational simulation or real data. In order to capture real data, they use machine sensors installed in the train or the rail track and, for example, big data procedures to process it.As shown in Interestingly, there are some AI applications in this field that proposed strategies based on images when the problem is not directly an image problem. For example, authors in  proposedThe second most-cited technology in the train transport field is the internet of things. This technology is commonly used for designing monitoring systems for the railway infrastructure . The IoTOn the other hand, modelling and simulation solutions have an important role in train transportation technologies. Usually, solutions created for this field are tested with real data through the use of realistic software simulators that allow them to tune the proposed methods and get conclusions about the research. In , authorsSmart decision support systems are used in the train transport processes as a way to help operators’ and managers’ tasks via processed information collected from the railway system. DAS’s are a good example of support systems in the railway industry. They are used to provide optimized and detailed advice to train drivers. In , authorsThe technologies that are not cited as often are Big Data, Computer Vision (CV), Cybersecurity and Cloud Computing. Big Data can be used in the railway transportation systems as a way of using Big Data parallel architectures to process the large amount of data collected from dynamic large-scale railway networks . It can Finally, we have the least-cited technologies in the selected studies, which are Cybersecurity and Cloud Computing solutions. In this field, we found that cybersecurity is becoming an important technology to apply in the railway infrastructure due to the incremental use of IoT applications where a security access control scheme is of vital importance to preserve access only to authorized personnel . MeanwhiThe presented study was performed by extracting papers from two scientific digital library databases to synthesize the growing body of literature. However, this is one of the limitations of this study. It is highly possible that there are papers from other scientific digital library sources that can complement our findings and give a better overview of the actual research trends in this field. This can also be related to the selected keywords for the search algorithm that may have some bias for particular search interests. Nevertheless, other search algorithms based on the same or different keywords could be created and have the possibility of recovering works that were not considered in this review.Another limitation of this study, as we mentioned in the results sections, is that many of the reviewed articles can be classified into multiple industry 4.0 technology groups. This is because the solutions are not limited to the use of only one type of technology; they can be combined to get a better and optimized approach to solve a problem. Therefore, authors classified the reviewed articles into the identified technology groups shown in In this paper, we present a systematic literature review on the studies related to industry 4.0 technologies applied in the railway industry. With this, we aimed to recognize the most important problems in the railway industry and how these have been solved with industry 4.0 technologies. We started our SLR by establishing three research questions: what are the main issues in the railway transport industry, what are the technology strategies that are currently being used to solve these issues and what are the technologies from industry 4.0 used in the railway transport industry that are used to solve the aforementioned issues? To answer these questions, we reviewed the past 5 years’ studies oriented to solve rail transportation problems using industry 4.0 technologies by proposing multiple query algorithms that were used in two digital libraries named Science Direct and Web of Science from which we extracted the primary studies. Then, we filtered through them using inclusion and exclusion criteria to select the relevant studies to answer our research questions.After reviewing the selected 109 primary studies, we were able to identify three main application domains: monitoring domain, decision and planification techniques domain, and communication and security domain. In addition, we presented multiple subdomains in which we tried to categorize the extracted primary studies based on a set of existing subproblems in the railway industry. Therefore, the monitoring domain has multiple subdomains that are rail monitoring, Driver Advisory Systems (DAS’s) and train monitoring; the decision and planification techniques domain has studies that are mostly oriented to rail transport optimization, rail transport insights and energy optimization; and lastly, the communication and security domain focuses its works on railway safety, security systems and travel connectivity problems.Additionally, we identified nine industry 4.0 technologies that make contributions to tackle the existing problems in the railway industry: Artificial Intelligence (AI), Internet of Things (IoT), Cloud Computing, Big Data, Cybersecurity, Modelling and Simulation, Smart Decision Support Systems (SDSS), Computer Vision and Virtual Reality (VR). The identified technologies are not exclusive in their usage to propose a solution. In fact, multiple works show how some of these are related  and the combination can lead to better results or innovative solutions. This is not something that happens only in the rail industry for industry 4.0 technologies. The same effect can be seen in other fields such as the Smart Manufacturing Systems (SMS) in which the integration of these 4.0 technologies can be applied to the creation of semi-autonomous industrial systems ,135.Finally, additional studies should be performed by including other digital library sources in order to identify other approaches in which industry 4.0 technologies are involved in the rail transportation field. This can contribute to the number of identified articles and variety of approaches by subdomain in which fewer articles were classified . In addition, other SLRs can be performed in this field to identify approaches comprising the environmental envelope and construction of railway infrastructure systems. Topics such as the development of more sustainable systems or the mitigation of environmental impacts have been addressed in some of the reviewed articles but require further exploration.


It has been estimated that women spend a disproportionate amount of their time undertaking the world's unpaid work, about 11 billion hours a day and globally women undertake three times more care and domestic work than men. This disparity is more aggravated in low- and middle-income countries (LMICs). The double burden of both unpaid and paid work also has a more detrimental effect on women with studies from USA and Sweden showing links with increased depression in women  data in 2016, less than 30% of researchers in STEM are women. Furthermore, many studies have shown that women that do pursue STEM fields, publish less, are paid less for their research, and do not progress as far in their careers, as their male counterparts . They found that the Hungarian respondents perceived the lowest necessity of these drugs, followed by the Czech and Slovak respondents with the Polish respondents believing the highest necessity of these drugs. However, fears and concerns around these drugs did not differ amongst these countries .The first contribution by Lin et al., investigated health of women with and without dysmenorrhea, regarding stroke and the related medical care costs using the National Health Insurance Research Database in China. Using data from 1997 to 2013 for women between 15 and 44 years, 66,048 women with dysmenorrhea and 66,048 women without dysmenorrhea, the authors showed that women with dysmenorrhea had a higher stroke risk . They also found that the dysmenorrhea cohort had a higher portion of transient cerebral ischemia stroke, which however was associated with least cost of care (US$157 ± 254) .The next contribution in this series by Tuczyńska, Matthews-Kozanecka, et al., conducted a targeted scoping literature review of the accessibility to healthcare services in different regions globally during the COVID-19 pandemic. The review included 21 articles and found that there was a decrease in accessibility to health services, including a decrease in planned surgeries, doctor appointments, patient admission to hospital or emergency rooms and access to medicines during this period, as reported by most studies, though an expansion of online consultations was also noted. Some of the studies included also noted an increase in mortality rate .The third contribution by Sharma et al., conducted a systematic review to consolidate and synthesize the economic evidence of screening programs for cardiovascular diseases (CVD) and diabetes in LMICs. The review included 15 articles and found that numerous innovative screening programs have been piloted. However, based on the available resources and context, the cost-effectiveness may vary for any such program where in only certain circumstances they could be made universal or otherwise targeted just for the high-risk populations .The fourth contribution by Tuczyńska, Staszewski et al., conducted a mini scoping review on the quality of healthcare services available in European countries during the COVID-19 pandemic. The review included 12 articles from studies in the Catalonia, Belgium, France, Germany, Italy, Netherlands, Poland, Sweden, and the United Kingdom. The authors found that patients in the United Kingdom felt that the quality of services was good during the pandemic, though this was not seen in the other regions, where the patients' felt that the quality of care declined. Though the development of telemedicine was considered as a positive impact of the pandemic .The final piece of this series by We know that investing in women's health improves their rights, reduces gender inequities, while generating health, economic, social, and environmental gains , 7. For TS had prepared the manuscript draft while SC has revised it for important intellectual content. Both approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The aim of this study was to assess the effect of fluid administration by emergency life-saving technicians (ELST) on the prognosis of traffic accident patients by using a propensity score (PS)-matching method.The study included traffic accident patients registered in the JTDB database from January 2016 to December 2017. The main outcome was hospital mortality, and the secondary outcome was cardiopulmonary arrest on hospital arrival (CPAOA). To reduce potential confounding effects in the comparisons between two groups, we estimated a propensity score (PS) by fitting a logistic regression model that was adjusted for 17 variables before the implementation of fluid administration by ELST at the scene.p = 0.582). However, the fluid group showed statistically better outcome for CPAOA than the no fluid group in the multiple logistic regression model .During the study period, 10,908 traffic accident patients were registered in the JTDB database, and we included 3502 patients in this study. Of these patients, 142 were administered fluid by ELST and 3360 were not administered fluid by ELST. After PS matching, 141 patients were selected from each group. In the PS-matched model, fluid administration by ELST at the scene was not associated with discharge to death (crude OR: 0.859 ; In this study, fluid administration to traffic accident patients by ELST was associated not with hospital mortality but with a lower proportion of CPAOA. Death due to hemorrhage is an important issue in trauma care around the world. Indeed, it was reported that approximately 40% of trauma deaths occurring within 24 h were due to hemorrhage . In the In Japan, the regulations for emergency life-saving technicians (ELST) were amended in 2014 to allow ELST to secure intravenous lines and administer lactated Ringer’s solution to patients in shock or with crush syndrome . HoweverThe emergency medical service (EMS) in Japan is a public service provided by the local government, and all ambulance records are reported to the Fire and Disaster Management Agency (FDMA). The Japanese Trauma Data Bank (JTDB) is the nationwide trauma registry in Japan, and about 300,000 trauma patients were registered by 2017. The aim of this study was to assess the effect of fluid administration by ELST on the prognosis of traffic accident patients by linking population-based ambulance records and the nationwide trauma registry in Japan based on a propensity score (PS)-matching method.This was a retrospective observational study using ambulance records in Japan and data registered in the JTDB database. The study period was 2 years from January 2016 to December 2017. We included traffic accident patients among the trauma patients registered in the JTDB. We excluded patients with an Injury Severity Score (ISS) of < 16, patients in cardiopulmonary arrest (CPA) at the time of ambulance arrival at the scene, and patients with insufficient data on date, vital signs at hospital arrival, and prognosis in this study. Patients in CPA at the time of ambulance arrival at the scene were defined as patients for whom the EMS personnel at the scene provided pre-hospital care for CPA, such as chest compressions, airway clearance, and administration of adrenaline. This study was approved by the ethics committee of Osaka University Graduate School of Medicine . Because the JTDB data and ambulance records that we were provided from the FDMA were anonymized, the requirement of informed consent was waived. This article was written based on the STROBE statement to assess the reporting of cohort and cross-sectional studies .The EMS system and ambulance record in Japan were previously described in detail . The EMSAmbulance records are collected annually for statistical and administrative purposes in all prefectures of Japan via a standardized electronic form. EMS personnel submit the ambulance record to the local fire station. All emergency patients who required EMS for transport by ambulance to a particular institution were captured. Then, all ambulance records were collected in the FDMA under the Ministry of Internal Affairs and Communications in Japan. The data used in this study were provided from the FDMA after all personal identifiers were removed.The JTDB was established in 2003 by the Japanese Association for the Surgery and Trauma (Trauma Surgery Committee) and the Japanese Association for Acute Medicine  , 16 and The JTDB captures data from trauma patients that includes age, sex, type of patient, Abbreviated Injury Score , ISS, vital signs on hospital arrival, date and time course from hospital arrival to discharge, medical treatments such as interventional radiology and CT scanning, and complications in accordance with regular forms with coding items  vs 26.2% [37/141]), and there was no significant relationship between fluid administration by ELST at the scene and discharge to death . Fluid administration by ELST at the scene was also not associated with discharge to death in the multivariate logistic regression model .Table p = 0.735). In the multivariate logistic regression model, the fluid group tended to have a better prognosis than the no fluid group, but the difference was not statistically significant . However, in the PS-matched model, the fluid group had a smaller proportion of CPAOA than the no fluid group . The fluid group had a statistically better prognosis than the no fluid group in the multiple logistic regression model .Table p = 0.832) or in those without severe head injury . However, the proportion of CPAOA tended to be lower in the fluid group among patients without severe head injury, although this was not significant in the multivariate regression model .Table In this study, we found that fluid administration by ELST to traffic accident victims at the scene decreased the proportion of CPAOA but did not improve the mortality of these patients. In addition, both the time interval from ambulance call to hospital arrival and that between departure from the scene and hospital arrival were significantly longer in the group with fluid administration by ELST than those in the group without fluid administration by ELST. This study, which found that fluid administration by ELST improved the status of patients on hospital arrival, may be useful in examining the treatment for trauma patients by EMS personnel in the prehospital situation.First, fluid administration by ELST was shown to be neither beneficial nor harmful to traffic accident patients in this study. In a subgroup analysis divided by severe head injury, fluid administration by ELST was neither beneficial nor harmful in either the patients with severe head injury or in those without severe head injury. There are several studies on intravenous access and fluid administration in prehospital settings. Some studies reported that fluid administration by EMS personnel to trauma patients had no effect on mortality , 23, 24,Second, the proportion of CPAOA was lower in the group with fluid administration by ELST than that in the group without fluid administration in this study. In addition, fluid administration by ELST tended to be associated with a lower proportion of CPAOA among the patients without severe head injury. These results may be influenced by fluid administration by ELST and the maintenance of circulation in severe trauma patients. Seamon et al. reported that prehospital procedures were the only predictor of hospital mortality in a study of trauma patients undergoing emergency department thoracotomy, for which they compared ambulance versus non-ambulance transport methods such as a police car or private vehicle . AlthougThere are several limitations to this study. First, this study was limited to traffic accident patients to allow merging of ambulance records and trauma registry data. Therefore, although all of the traffic accident patients suffered blunt trauma, some of these patients may have also sustained penetrating trauma from car parts or a guardrail destroyed in the traffic accident. We also did not include patients with penetrating trauma such as gunshot or stab wounds. Therefore, we could not evaluate the effect of fluid administration in the prehospital setting on patients with penetrating trauma. Second, because ELST in Japan can only administer lactated Ringer’s solution, we could not evaluate the effect of the administration of other solutions such as normal saline. Third, it is unclear whether the cause of CPAOA was due to exsanguinating hemorrhage. In fact, about 40% of the patients included in this study suffered head trauma, so it is possible that this condition was the cause of CPAOA. Finally, this was an observational study, and therefore, we could not adjust for the effects of unknown confounding factors.In this study, we revealed that fluid administration by ELST to traffic accident patients at the scene was associated with a low proportion of CPAOA but was not associated with improved hospital mortality.