Roux-en-Y gastric bypass (RYGB) results in extraordinary weight loss and glycemic control outcomes for patients with obesity; however, the effect of gastric pouch size is still unclear, and the reported results are contradictory. Additionally, long-term data on type 2 diabetes (T2D) patients with low body mass index (BMI) are sparse. This study was to assess the effect of 6-year outcomes in Chinese patients with T2D and a BMI < 35 kg/m2 who underwent RYGB with gastric pouches of different sizes.A retrospective cohort study was performed. There were 42 patients in the large gastric pouch group (L) and 53 patients in the small gastric pouch group (S). Baseline demographic history, pre- and postoperative BMI, waist circumference, and glucose- and lipid metabolism-related indicators were compared.Assessments were completed in 100%, 100%, 93.6%, and 89.4% of patients at baseline, 1 year, 3 years, and 6 years, respectively. At 6 years, the changes in BMI and fasting plasma glucose were greater in the S group (-4.25 ± 0.51 kg/m2 and -4.58 ± 0.73 mmol/l) than in the L group (-2.06 ± 0.48 kg/m2 and -2.64 ± 0.61 mmol/l). The independent predictors of complete remission of T2D were preoperative BMI and the size of the gastric pouch. A large gastric pouch was associated with a higher risk for marginal ulcers.A small gastric pouch results in better weight loss and glycemic control. High preoperative BMI and a small gastric pouch are associated with better T2D remission rates. A large gastric pouch leads to a higher incidence of marginal ulcers. In 1967, Mason completed the world’s first gastric bypass surgery . Roux-enBased on the characteristics of these patients, we initially performed RYGB using a large gastric pouch and a short Roux limb and biliopancreatic limb. Over time, we found that some of the patients had relatively poor glycemic control and a high incidence of postoperative ulcers. Based on improvements in surgical knowledge and technology, we performed subsequent gastric bypass surgeries using a small gastric pouch. Controversy regarding the size of the gastric pouch has persisted over the past 30 years. Scholars have performed many studies on the postoperative efficacy of different gastric pouch sizes –11. MuchThus, we performed this retrospective study to compare the long-term efficacy of RYGB with different gastric pouch sizes in Chinese T2D populations with a BMI < 35 kg/m2.The study protocol was approved by the local institutional review board. In brief, the study was a two-group, retrospective, comparative study of RYGB in 95 patients, in which the effects of a small gastric pouch were compared with those of a large gastric pouch. The eligibility criteria were as follows: a diagnosis of T2D based on the 1999 World Health Organization standard , age betRYGB with a large gastric pouch has been described in a previous study . In brieAll patients were routinely administered proton pump inhibitors and multivitamins after surgery. For long-term follow-up, All patients received the same dietary guidance and rehabilitation exercises after surgery, and all data collection and verification were managed by a dedicated person. We collected clinical information, including BMI, waist circumference, high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol (TC), triglyceride (TG), glycosylated hemoglobin (GHbA1c), and fasting plasma glucose (FPG), for every patient. The percent excess weight loss (%EWL) was calculated with the following formula: %EWL = [/] × 100. The ideal BMI is 24kg/m2 in Chinese patients according WHO guidelines. The comorbidities of interest in the present study were bleeding, gallstone diseases, intestinal obstruction, marginal ulcer, dumping syndrome and anemia. Diabetes complete remission was defined as an HbA1c level < 6% and/or an FPG level<5.6 mmol/l and 12 months without active pharmacological intervention . HomeostAll continuous variables conforming to a normal distribution were described as the mean ± standard deviation (SD), and percentages and frequencies are used to describe categorical variables. Normality of the data was verified using the Kolmogorov–Smirnov test. Statistical significance was indicated by P <0.05, and a paired t test was used to compare different time points within the group. Independent samples t tests were used for comparisons between the large gastric pouch group and the small gastric pouch group at 6 years postsurgery. The chi-square test was used for categorical variables. Analysis of variance was used to evaluate the differences in the two groups at postsurgery. Logistic regression analysis was applied to explore factors associated with T2D remission and marginal ulcers. SPSS version 26.0  was used to perform the statistical analyses.The baseline characteristics of the 95 patients who underwent different surgeries are shown in BMI, waist circumference and lipid profiles at 6 years postsurgery are presented in At 6 years postsurgery, the FPG, GHbA1c and HOMA-IR values in the small gastric pouch group were 5.51 ± 1.21 mmol/l, 6.10 ± 0.96% and 2.43 ± 1.13, respectively, while in the large gastric pouch group, these values were 6.25 ± 1.38 mmol/l, 7.81 ± 0.77% and 5.06 ± 1.36 ; significant differences between the two groups were evident Table 2.Adverse events reported at 6 years are shown in Bariatric surgery has been widely performed worldwide over the past 60 years, and RYGB is considered to be the most effective treatment for obesity and obesity-related complications. However, the specific mechanism of RYGB is still unclear, especially the role played by the gastric pouch. At present, the existing literature simply illustrates the relationship between gastric pouch size and short-term weight loss outcome, and these results are inconsistent , 13.The results of this study indicate that RYGB with a small gastric pouch achieves better weight loss than RYGB with a large gastric pouch among T2D patients with a BMI<35 kg/m2. Our current results demonstrated that the %EWL of the two groups was significantly different at 1, 3, and 6 years postoperation . At the 6-year follow-up, there were significant differences in the changes in BMI and waist circumference between the two groups, and the small gastric pouch had significantly better outcomes than the large gastric pouch (P= 0.021 and P= 0.018). At 6 years postsurgery, the small gastric pouch group had significantly greater changes in BMI, %EWL, and waist circumference  than the large gastric pouch group  (P<0.05) Figure 3Our research suggests that during the entire 6-year follow-up, the small gastric pouch allowed better glycemic control than the large gastric pouch –25. A poThe results of the multivariate regression analysis showed that a higher preoperative BMI and a small gastric pouch result in successful T2D remission, indicating that the size of the gastric pouch plays an important role in T2D remission. Similarly, the ABCD Diabetes Surgery Score  developed by Lee et al. also includes preoperative BMI . ObesityOur results support the hypothesis that with the same Roux limb and biliopancreatic limb length, a small gastric pouch is associated with better outcomes than a large gastric pouch in terms of weight loss and glycemic control. Furthermore, our study shows that a large gastric pouch does not yield better weight loss and glycemic control and is associated with a higher incidence of marginal ulcers. Although the condition of these patients improved after regular proton pump inhibitor treatment, it is important to note that a smaller gastric pouch size is especially important for the low-BMI T2D population.A number of limitations need to be noted regarding the present study. First, we did not specifically measure the exact size of the gastric pouch. Currently, there is no gold standard for the measurement of gastric pouch size. Most of the assessments of the gastric pouch are carried out by gastroscopy or computed tomography, and some scholars indirectly determine the size of the gastric pouch with a 40 French orogastric calibration tube or based on the total length of the stapler , 33, 34.Overall, the small gastric pouch achieved better weight loss and glycemic control than the large gastric pouch at 6 years postoperation. A large gastric pouch resulted in a high prevalence of marginal ulcers (23.7% at 6 years postsurgery). Multiple regression analysis revealed that preoperative BMI and gastric pouch size could predict complete remission of T2D.Small gastric pouch has better weight loss and glycemic control than large gastric pouch.Small gastric pouch has better improvement of insulin resistance than large gastric pouch.High preoperative BMI and a small gastric pouch are associated with better T2DM remission rates.A large gastric pouch can lead to a higher incidence of marginal ulcers.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by institutional review board of Third Xiangya Hospital. The patients/participants provided their written informed consent to participate in this study.XG, formal analysis and writing-original draft. SD, data curation and methodology. GW, formal analysis. WL, writing-original draft. ZS, methodology. ZHS, data curation. SZ, supervision. LZ and PL, performed surgery, revised the draft, and conceptualization. All authors contributed to the article and approved the submitted version.This study was funded by the Wisdom Accumulation and Talent Cultivation Project of the Third Xiangya Hospital of Central South University [YX202106].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.


Introduction YouTube is one of the top-searched online video streaming platforms. However, the content of YouTube may not match the standards required for clinical skills learning. Therefore, we investigated the quality of top-viewed YouTube videos related to three basic surgical procedures that need to be performed by general surgery residents in their first year of training in our institute.Methods We searched YouTube for the top 10 viewed demonstration videos related to ultrasound-guided abscess drainage, chest tube insertion, and central line insertion. For the eligible videos, we calculated the likes ratio, view ratio, and video power index. The videos’ quality was assessed using LAParoscopic surgery Video Educational GuidelineS (LAP-VEGaS) scores. The videos were categorized into high-quality (LAP-VEGaS score ≥ 11) and low-quality videos (LAP-VEGaS score < 11). The different descriptive  and calculated parameters stated above were compared between the two video quality-based groups.ResultsThe selected videos were uploaded between July 2008 and March 2022. Their mean view counts were 460391.13±373760.19. Their mean video duration was 8.12±4.26 minutes. Their mean likes and dislikes were 2578.38±2977.43 and 144.10±129.80, respectively. The mean like ratio and the mean view ratio were 93.42±13.53 and 317.76±827.79, respectively. The mean video power index was 310.67±827.96. The mean LAP-VEGaS scores for ultrasound-guided abscess drainage, chest tube insertion, and central line insertion-related videos were 6.80, 11.10, and 11.20, respectively. The numbers of likes and dislikes were significantly higher for high-quality videos. Conversely, the view counts, the view ratio, and the video power index were significantly lower for high-quality videos.Conclusion Top-viewed videos related to general surgery procedural demonstrations are of low quality. The video view counts, popularity, and likes-dislikes are highly unreliable indicators of surgical video’s usefulness. There is a need for regulatory mechanisms to screen the YouTube content suitable for general surgery residents learning. The residents should therefore be cautious while making inferences based on YouTube videos. Video-based learning has emerged as one of the preferred teaching-learning modalities in medical education -3. The vRapp et al.  found Yoth December 2022, we conducted three separate searches on YouTube using the keywords: “ultrasound guided abscess drainage,” “chest tube insertion,” and “central line insertion.” The search results were arranged in decreasing order of view counts. Two faculty members with more than five-year experience in postgraduate teaching of general surgery reviewed the search results. In separate searches, they selected the videos demonstrating the procedural steps of abscess drainage, chest tube insertion, and central line insertion. Any videos without demonstration of procedural steps, non-surgical videos, slideshows or non-video presentations, lectures, webinars, commercial videos, and those unrelated to the searched topics were excluded. A list of the top 10 videos based on view counts was reached for each search separately. We accessed the selected videos’ view counts, age from the upload date, total duration, the number of likes and dislikes, and the nature of the video presenter . From this information, the likes ratio , view ratio (number of views/days since upload), and the video power index (like ratio*view ratio/100) were calculated. In addition, we used the scores calculated using the tool described under LAParoscopic surgery Video Educational GuidelineS (LAP-VEGaS) to assess the quality of reviewed videos [On 4The characteristics of the top 10 selected videos in each of the three categories are shown in Table Based on LAP-VEGaS scores, there were 14 low-quality and 16 high-quality videos. The comparison of different variables among the two video quality-based groups has been shown in Table The current analysis’ findings point towards the heterogeneity of video-based learning material available on YouTube. The standard deviations for various parameters were wide, suggesting a large value disparity among the reviewed videos. The content may be appropriate for one procedural skill or theme but may not be the same for others. This disparity is evident from the mean LAP-VEGaS score of ultrasound-guided abscess drainage-related video content, which was too low. Moreover, the content quality of other themes was just borderline. Therefore, the criteria for screening videos based on top view counts may not provide reliable results. The analysis also suggests that it would be hard to differentiate high-quality surgical videos from lower-quality ones merely based on popularity. The lower-quality videos gathered much larger views and had a higher video power index. As video-based learning is becoming popular, first-year general surgery residents tend to search for basic surgical content on YouTube. Without regulations, this would result in a constant habit of learning from a source without proven authenticity. Furthermore, unlike the published research and techniques, YouTube videos do not undergo peer review. Moreover, considering their access to the non-medical audience, the popularity indices may reflect something other than the true nature of the information provided in the video. For example, some videos might attract non-medical viewers because of their non-procedural content, which may not help in learning clinical skills. Thus, the curriculum should establish certain norms to prevent trainee residents from gaining knowledge from unregulated YouTube content. Meanwhile, faculty members in surgical disciplines should use online video platforms to present well-structured and high-quality surgical videos, providing better alternatives for trainee residents. Such a strategy would result in more reliable skills learning.There have been some studies in the past that have assessed the quality of YouTube content related to general and advanced surgical procedures. Savran et al.  investigAs far as the videos' likes are concerned, again, the conclusions about the video's educational value are doubtful. It has been suggested the number of likes reflects subjective preference towards the video, which may not be uniform . Kohler There were some limitations of the current analysis. First, we assessed the quality of surgical videos uploaded on YouTube only. Other video platforms may have a variable impact compared to our findings. However, the freely accessible and widely popular nature of YouTube makes the current analysis worthy. Second, the content was analyzed by general surgery teaching faculty members. While there is a risk of bias in the assessment, we feel the assessment would remain standardized for all videos analyzed in this study. Third, we assessed only the top 10 viewed relevant videos for each category in our analysis. High-quality videos with lower view counts may have been missed. However, that again points towards the heterogeneity of content and its parameters on YouTube. Lastly, we used one particular score for the analysis of videos. Some other tools may quantify videos according to varying attributes. Nevertheless, the LAP-VEGaS tool used in the current analysis is comprehensive and widely used. Thus, variations compared to other tools are likely minor.The current study findings suggest that most top-viewed videos related to general surgery procedural demonstrations are of low quality. Moreover, the quality varies with the content searched, and some search themes may generate unreliable results over others. In addition, the video view counts, popularity, and likes-dislikes are highly unreliable indicators of surgical video’s usefulness. Therefore, there is a need for regulatory mechanisms to screen the YouTube content suitable for general surgery residents learning. The residents should therefore be cautious while making inferences based on YouTube videos. In addition, further efforts from surgical disciplines’ faculty members would be needed to create well-structured videos appropriate for educational purposes.


With the massive expansion of videos on the internet, searching through millions of them has become quite challenging. Smartphones, recording devices, and file sharing are all examples of ways to capture massive amounts of real time video. In smart cities, there are many surveillance cameras, which has created a massive volume of video data whose indexing, retrieval, and administration is a difficult problem. Exploring such results takes time and degrades the user experience. In this case, video summarization is extremely useful. Video summarization allows for the efficient storing, retrieval, and browsing of huge amounts of information from video without sacrificing key features. This article presents a classification and analysis of video summarization approaches, with a focus on real-time video summarization (RVS) domain techniques that can be used to summarize videos. The current study will be useful in integrating essential research findings and data for quick reference, laying the preliminaries, and investigating prospective research directions. A variety of practical uses, including aberrant detection in a video surveillance system, have made successful use of video summarization in smart cities. Analyzing video content to extract valuable or intriguing information is difficult and time-consuming. Many videos are uploaded to YouTube, IMDB, tourism sites, Flickr, and other video-sharing sites every minute. Every minute, 300 h of video are posted to the YouTube channel and about one billion hours of video are watched every day . Millennial video cameras are installed in smart cities including public spaces, public transportation, banks, airfields, and other locations, resulting in a tremendous amount of data that is difficult to analyze in real time. There will be hundreds of suggestions for each search topic; navigating through these lengthy videos to find the essential video takes time, and also challenging to efficiently obtain this much data in a short amount of time.Secondly, due to the abundance of videos, users must rely on metadata such as title, image, description, and remark to locate the video they want to see. This metadata, too, isn't a trustworthy predictor of the accompanying video's semantic content, leaving consumers with no choice but to skim it to get a general idea of what's going on. To address these concerns, work is underway to construct a video summary that summarizes the entire video in a short period. A video comprises a series of video frames. If a video summary has a high recall, precision, and low redundancy rate, it is deemed to be good  One of the most challenging parts of video summary is subjectivity; because various annotators may have different perspectives, different people may select different important shots for the same video  Jitter effects caused by the camera wearer's movement, are more difficult to summarize. Use cases for smart cities include sophisticated traffic monitoring, legal parking management, speed detection. Additional problems with lifelogging video summaries include accurate feature tracking, uniform sampling, and large data streams with very narrow limits  Collecting summarization labels takes time, and a little dataset will not be sufficient. Because the available datasets only contain videos of specific sorts, the model performs poorly on videos from other categories. To deal with this, we can use unsupervised, semi-supervised, or multi-task learning. Computed hardware and development complexity are well-known issues  The Internet of Things is rapidly evolving, and it is quickly displacing traditional sensing in existing systems. Vision sensors in IoT have lately been popular due to their widespread use in smart cities and industries for a variety of applications, including security. Various mechanisms can be offered for analyzing observed personnel in the industry for aberrant activity and generating alerts  Deep neural networks, which require a significant amount of labeled data to train, are used in modern video summarizing approaches. Existing datasets for video summarization, on the other hand, are small-scale, which makes deep models readily overfit.The rest of the paper follows the structure as; Section 2 gives an introduction to the video summarization and its types. The scope and approach of RVS are described in Section 3. Section 4 covers the systematic review of real time video summarization. Section 5 is the prominent study for RVS. The next sections include a conclusion and references.Video data is today's most generated data. A group of representative video frames  or video fragments  sewed in progressive sequence to generate a shorter video is commonly used to create the produced summary. Video summarization aims to provide an overview of the content that highlights the video's most instructive and relevant elements.To identify which parts of videos are to be removed, video summarization algorithms must rely on video content. There are three main steps to video summarization  may differ for different users and application domains, a general framework for a video summarizing will not work for everyone. There are many approaches to video summarization as indicated in Summary based video summarization can be further classified into static, dynamic, hierarchical, multi-view, image, and text summaries  or audio and the most relevant video shots are chosen. The goal of video skimming techniques is to choose pictures or scenes from the full video and compile them into a relevant summary. Zhong et al.  considers multiple points of view at the same time and creates a representative summary of all of them. Smart IIoT-based architecture with an embedded vision for detecting incredulous articles, and exchanging traffic volume statistics are proposed by the author  techniques and does not include any audio or visual descriptions. Text summaries are cost-effective in terms of storage and calculation, but they are unable to communicate all of the information since they lack the audio and visual components of the video sequence.It is broadly divided into 5 categories listed Video summarization is domain-specific, query-based, semantically based, event-based, and feature-based.2.2.2.1. Domain-specific Kaushal et al. , which is then clustered using k-means, and a sequential keyframe-generating procedure follows.2.2.2.3. Semantic-based These are summaries that are generated based on the video's content and are mostly based on objects, actions, activities, and events with a high level of interpretation based on domain expertise  and User-based information (derived from a user's direct input). For Contextual Hussein et al.  or an online  in memory, such approaches can be made to use less memory. This situation is particularly interesting because online methods are computationally less expensive than their batch counterparts when batch processing a video is too resource-intensive on a device, when an application needs access to the historical summary, or for unboundedly long video streams.Unlike offline options, generating a video summary online comes with several challenges and is significantly more difficult due to insufficient video information. The video summary's quality may be harmed by the short delay, progressive generation, and absence of the complete video sequence information   research papers include Springer, ACM, IEEE, Science Direct, Wiley, Taylor, and Francis. The only title was considered in the title. Consequently, the main aim of this phase was to define and collect all the relevant research papers needed to perform a review.This method isolates the outdated, redundant, and unsuitable studies based on the 'Exclusion-Inclusion' selection criterion. It performs the filtration process by either selecting or rejecting studies that directly within the specified problem domain promote or address at least one research question (RQ). The following conditions for Inclusion-Exclusion are followed.Inclusion criteria:The research is focused on the field of real-time video summarization.The research gives a well-described summary method or real time video summarization.Studies carried out over the last 15 years i.e. from April 2006-till date.Exclusion criteria:Studies that lack adequate empirical or comparative analyses.Studies including video summarization detection on languages other than English .Books and works of gray literature.A research study that is duplicated.The study's entire text is unavailable.The selected studies were analyzed during this process to determine the importance and intensity of the studies chosen. A quality review has already been ensured because only high-quality, high-impact journals from reputable digital libraries were evaluated.This step summarized and extracted information from the selected study based on mapping to one or more RQs. Information such as authors, year of publication, datasets used, methods, strengths, and weaknesses were the details from the extracted research studies. All of this information was then placed in a table that was used to synthesize data.The purpose of this step is to summarize and interpret the information obtained. In this review, the extracted data is summarized in tabular form and presented using different visual methods like graphs, charts, etc. The search terms found were input as a search query and applied to the selected digital libraries resulting in 15 articles.In this section, a brief idea about all the selected studies has been given in reverse chronological order in the form of a table. The state-of-the-art is presented in The distribution of RVS literature along with the citation of each paper is shown in The rapid rise of video data and lack of time has necessitated the deployment of effective and advanced video summarizing , while others require offline processing. Because the source frame rate and resolution determine the processing time for each frame, real-time algorithms cannot be overly complicated.New hardware solutions now enable quicker processing speeds. However, depending on the application, there are still certain limits. Real-time feedback and processed images from sensors are required for a variety of real-time applications, including traffic monitoring, military target tracking, observation, and monitoring, real-time video games, and other programs. Real-time video enhancement, which aims to improve the visual quality of live videos, can be used in video communication, augmented reality, and robotics applications. A real-time video summarization system can process videos online, eliminating the possibility of backlogs.Offline processing allows for more complicated and computationally intensive algorithms, yielding better results than real-time processing. Processing an already recorded video sequence or image offline is called post-processing  because bad QoS can lead to customer defection, among other reasons. This article has covered real time video summarization. Compared to different summarization approaches, real time video summarization is a less explored issue. Users often want tailored video summaries that reflect their specific video interests in less time and space. Many computer vision problems have been solved using deep learning approaches. Video summarization systems will have to progress in the future to generate user-specific compressed video summaries with excellent efficiency in less time. GPU computation has grown in prominence due to its ability to solve various computer vision issues. Because of the inherent parallel processing and the large video dataset that must be dealt with, real time video summarization can be expedited by utilizing GPUs.Due to its value and importance in many domains, video summarization research is developing rapidly. This paper thoroughly analyzes current real-time video summarization (RVS) methodologies and discovers less research in the RVS. For training their systems, most approaches employed datasets from OVP, YouTube, and SumMe/TVSum. RVS is in demand today and is used in many applications. Working in real-time and preventing problems before they develop benefits the bottom line by lowering risk and improving accuracy. A real-time video summarizing system might process videos in real time, effectively removing the backlog risk. New algorithms for more accurate keyframe extraction, better trash frame elimination, and more intriguing content inclusion could be implemented to improve the situation even further. Smartness is utilizing data and technology to improve decisions and quality of life. Despite advancements in Internet technologies, dynamic real-time data distribution to responders continues to be a concern. Even if the devices collect data in real time from various sources, the device is only helpful if the processed data in the form of a video summary is precisely generated or covers all keyframes. Due to this, it will be challenging to handle emergencies and critical circumstances. Because of the widespread use of live video applications, real-time video improvement is in high demand in smart cities. Still, present techniques need to meet the stringent requirements for speed and reliability.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.RG: research work. PS: supervision. All authors contributed to the article and approved the submitted version.


Ovotesticular disorder of sexual development (DSD) is the rarest of DSDs with an incidence of 1:20000  and magnetic resonance imaging (MRI) were suggestive of a vaginal pouch. The patient underwent endo-evaluation followed by robot-assisted excision of the vaginal pouch. Endo-evaluation showed two orifices in the posterior urethra. The posterior orifice was leading into a blind-ending rudimentary uterus and the true urethra was lying anteriorly. The DaVinci Xi Robotic Surgical System was used and the entire pouch was dissected free of the surrounding tissues using monopolar scissors. The pouch was transected just a few millimetres from its junction with the urethra. The urethra was then closed with V-loc 4-0 suture. The patient was discharged on postoperative day 2 and the catheter was removed on day 21.Follow-up VCUG at 6 weeks did not show any residual pouch. There was no complaint of post-void dribbling or UTI at 30 months of follow-up.Robot-assisted laparoscopy should be considered as an alternative to laparoscopy for the primary treatment of a large symptomatic vaginal pouch.


In the postnatal period, extensive peripheral arteriovenous malformations (AVM) are associated with high morbidity, especially when localized in the liver. Their urgent treatment is always a challenging problem in neonates and infants. We analyzed four consecutive children aged three days to three years who underwent eight liquid embolization procedures with ethylene-vinyl alcohol copolymer. The AVM were situated on the thoracic wall, in the liver, and on the lower leg. In three cases, the malformations showed total regression. The tibial AVM degenerated widely. If impaired beforehand, cardiac or hepatic function normalized after the interventions. There were no embolization-associated complications such as nontarget embolization or tissue ischemia. We conclude that application of ethylene-vinyl alcohol copolymer seems to be a safe therapeutic option and can be used in neonates and infants with peripheral AVM in consideration of the agent's characteristics. Nevertheless, there are still hardly any data concerning young children. Vascular anomalies in children are rare congenital malformations. According to the International Society for the Study of Vascular Anomalies (ISSVA) , 2, theyAVM tend to grow progressively. There are various clinical symptoms and manifestations of AVM depending on their dimension, localization, and shunt volume affecting the circulatory system. The previously published Schobinger score provides a clinical staging of AVM. These four clinical stages are quiescence , expansion , local destruction , and decompensation  , 6, 7. IIf associated with severe systemic symptoms, extensive AVM require urgent treatment. The therapy of AVM in young children is always a challenging problem because of the patients' small size and the restricted adaptation of their circulatory system. The therapeutic strategy depends on the localization, size, and the clinical manifestation. Small AVM may be resected surgically, whereas resection is not suitable for extensive lesions due to higher perfusion by multiple feeding arteries and hence the higher risk for perioperative bleeding and recrudescence. Selective ligation or embolization of feeding arteries is not promising, for it leads to further angiogenesis without AVM involution. Conservative and medical treatment options have also been found inappropriate , 9.Percutaneous and transvascular embolization are considered to be first line therapy in advanced state AVM regarding the Schobinger score , 7, 10. EVOH is originally used in interventional neuroradiology. Besides, there are several published studies of successful embolization of peripheral AVM with EVOH primarily in adults , 9, 12 aThe aim of our retrospective study was to present our experience in urgent AVM embolization using EVOH in neonates and infants in different localizations.We performed 8 liquid embolization procedures in 4 consecutive children, two of them male. The parents gave written informed consent the day before the intervention.Two AVM were located on the thoracic wall, one was a pretibial AVM; and there was one newborn with prenatal diagnosis of huge intrahepatic AVM. The patients' characteristics are summarized in Patient 1 had undergone a surgical correction of atrioventricular septal defect at the age of 5 months. A thoracic AVM showed increasing size  with increasing flowing shunt. During follow-up, the arteriovenous shunt was associated with progressive cardiac output and increasing mitral regurgitation, and cardiomegaly occurred. Neither anticongestive treatment with propranolol nor coil occlusion of feeding arteries deriving from the subclavian artery with 5 coils  were successful to induce significant reduction of the AVM perfusion, since further feeding arteries were also established from the intercostal arteries. Liquid embolization was then indicated and successfully performed.Patient 2 presented with a growing AVM (9 × 8 × 2.5 cm) of the thoracic wall. At the age of 6 months, he was admitted to the emergency department with a severe superficial bleeding due to accidental skin erosions covering the AVM. Emergency hemostasis was achieved by Neodym-Yag laser treatment and compression dressings. After stabilization and healing of the superficial skin erosions, we decided to subject the child to transcatheter embolization using EVOH.Patient 3 had a growing pretibial AVM (7 × 4 × 4.2 cm) on the right leg with tight consistency. Embolization was indicated because of imminent rupture due to hyperperfusion of the AVM.In patient 4, AVM and cardiomegaly were diagnosed prenatally. The major part of the AVM was located in the liver segments 2 and 3. Giant veins drained into the lower vena cava. After birth, the newborn was critically ill because of severe high-output heart failure and pulmonary hypertension. Highly urgent embolization therapy was indicated.All procedures were performed by percutaneous transarterial catheterization via the right femoral artery. After puncture, 4 French (Fr) introducers were inserted in each case. 4 Fr guiding catheters were used for angiography and guiding of microcatheters. In the case of the pretibial AVM, the sheath was introduced antegrade into the femoral artery. As antithrombotic therapy, heparin was administered in a dose of 100 IU per kilogram bodyweight.EVOH  was used for liquid embolization. This agent is an ethylene-vinyl alcohol copolymer, which is blended with radiopaque tantalum and dissolved in dimethyl sulfoxide (DMSO). In contact with other aqueous fluids such as water or blood, EVOH precipitates by polymerization and hardens from the surface to the inside. It hence builds an occlusive cast, when intravascularly applied. EVOH is available in 3 concentrations with increasing viscosity: Onyx©-18, -20, and -34, containing 6%, 6.5%, and 8% EVOH, respectively. The lower the concentration, the deeper it penetrates into the small peripheral capillary vessels.The interventional embolization treatment with EVOH was performed applying the previously published plug-and-push-technique , 10, 14:In the patients with thoracic AVM (patients 1 and 2), the procedures were conducted under analgosedation with propofol, midazolam, and ketamine. The embolization was performed via the feeding arteries originating from the subclavian and intercostal arteries  was intubated, ventilated, and in deep analgosedation. The feeding arteries were embolized via the anterior tibial artery. During the first embolization, there was a temporary hypoperfusion caused by extending of the EVOH plug into the anterior tibial artery. Immediate balloon dilatation was necessary with a 2 mm balloon. In a further catheterization, 1 microcoil  was implanted into a residual AVM artery. There was a transient vascular spasm of this artery during this procedure which was self-limiting within a few minutes. Arterial perfusion of the lower leg quickly normalized in both cases.The newborn with the large, prenatally diagnosed hepatic AVM (patient 4) had to be intubated and ventilated immediately postnatally, due to severe high-output cardiac failure and pulmonary hypertension. After interdisciplinary consultation, we decided to embolize the large hepatic AVM with EVOH. Two interventional procedures were necessary within the first 16 days of life. After angiography of the AVM including selective visualization of the feeding arteries via the arteria hepatica communis and the celiac trunc, catheterization and embolization of the feeding arteries were performed. Finally, a right hepatic arterial branch and a left phrenic arterial branch were occluded using 10 detachable microcoils . The diameters of the coils were 2 mm (3 coils), 4 mm (5 coils), and 5 mm (2 coils), respectively  with hypotension and increased shock parameters, acidosis, and coagulopathy after the first procedure. At the time of the procedure, the newborn was already in very unstable hemodynamic conditions due to the high-output cardiac failure and suprasystemic pulmonary hypertension. Postinterventionally, intensive care therapy was needed with administration of catecholamines, buffering, and transfusion of fresh frozen plasma and thrombocytes. Weaning from ventilation was feasible on day seven. Before the second procedure, the newborn was electively reintubated and ventilated, and glucocorticoids were administered to avoid possible allergic or hyperinflammatory reaction. At the end of the intervention, electrical cardioversion was necessary because of atrial flutter. SIRS did not recur. The child remained stable and was extubated on day 3 after intervention. The mean pulmonary pressure immediately decreased from 48 to 36 mmHg during the procedure and normalized within the next 3 weeks. Hepatic, renal, and cardiac parameters normalized as well see . Within As a side effect in all patients, there was an uncomfortable smell regressing within about 48 hours, typically caused by the sulfur-containing DMSO.Embolization is the first-line therapy of AVM. In the last years, liquid embolic agents have gained in importance. There are several published data considering EVOH as suitable for embolization therapy: apart from AVM, EVOH is applied in the treatment of different vascular entities such as gastrointestinal or bronchial hemorrhage , endoleaBecause AVM belong to the group of high-flow malformations, they are attended by high morbidity. Primarily hepatic AVM are associated with a poor prognosis and high mortality, because of severe organ manifestations such as high-output cardiac failure, pulmonary hypertension, and hepatic failure shortly after birth , 20. DueIn comparison to the published cases of Alexander et al.  and HazeSurgical resection of hepatic AVM bears a high risk for hemorrhage , which cRadiopacity of EVOH due to added tantalum powder allows a good controllability during fluoroscopically guided injectionThree different available EVOH concentrations feature a variable penetration depth into the AVM nidus. Thus, an extensive occlusion of the malformation is possibleBy polymerizing and building a coherent cast, EVOH reduces the risk of nontarget embolization by detaching particlesThere are remarkable advantages of Onyx© compared to other liquid agents :RadiopaThere may rarely be severe adverse effects of EVOH treatment such as nontarget embolization with resulting organ ischemia or asystole . AnotherEspecially extremity arteries of young children are small and therefore damageable. Vascular spasms or injuries  can result from extended interventional procedures.In cases of subcutaneous AVM, there can be a dark discoloration due to superficially injected EVOH. This tattoo effect is caused by the containing black tantalum. The discoloring usually vanishes within some months.DMSO (most commonly used for cryopreservation of stem cells) is the essential solvent of EVOH to avoid its premature precipitation. It causes an intensive garlic-like smell that volatilizes within 48 to 72 hours after injection. Cardiovascular adverse effects of intravenously administered DMSO can appear in 1-14% . ArteriaWe performed 8 transcatheter embolizations in four consecutive children with an age range of 3 days to 3 years with considerable success. In three cases, mechanical occlusive devices (coils) were additionally implanted.Three children had subcutaneous AVM, one had an intrahepatic AVM. In both cases of thoracic wall AVM, liquid embolization with EVOH resulted in complete involution without any severe complication. The tibial AVM could be diminished, restriction of motility or hypoperfusion of the leg as well as rupture and critical bleeding could be avoided. The typical garlic-like smell and slight tattoo effects were the only temporary side effects.Significant complication was noted in the neonate with the hepatic AVM. The child was in a critical general condition immediately after birth, high-output cardiac failure, and multiorgan dysfunction already existed preinterventionally. Whether the postintervention reaction was, at least partially, related to the DMSO or Onyx© is unclear. Nevertheless, the child recovered after the second procedure with intensive care management, which resulted in a normalized hepatic and cardiac function in long-term follow-up. Extended hepatic surgery and liver transplantation could be avoided.In summary, our case series confirms previously published data considering EVOH as suitable for treatment of AVM. It is possible to probe deep into the nidus with microcatheters so that it can be occluded widely and persistently. As well on the basis of our experience, exclusive occlusion of main AVM vessels is not a promising therapy. Beyond that, we showed its feasibility in infants as well as in neonates within the first days of life. Catheter size and small peripheral vessels frequently limit interventional procedures. Using microcatheters and guiding wires adapted from the neuroradiology, AVM embolizations are possible via transarterial catheters with small lumina (4 Fr) appropriate for small femoral vessels and thus for patients of the above age group. In contrast to still performed procedures in cases of hepatic AVM, surgical ligation of main hepatic arteries or liver resection can be avoided by this minimally invasive technique. It is thus possible to spare not affected liver tissue and to achieve normalization of biochemical hepatic markers and of the liver function.We also demonstrated its practicability in cases of emergency interventions in critically ill children suffering from high-output cardiac failure. Postinterventional cardiac reconvalescence occurred.Furthermore, rupture of superficial AVM attended by acute hemorrhage could be prevented.The main limitation of our study is the limited number of patients included which can be attributed to the low incidence of AVM as well as to the rare indication for interventional therapy in the first three years of life.Percutaneous embolization therapy with EVOH can be performed safely and effectively in the treatment of peripheral AVM in neonates and infants. An additional occlusion with mechanical devices such as detachable microcoils may be helpful. Selective vessel and nidus embolization in neonatal hepatic AVM can prevent acute heart failure; chronic hepatic and other organ dysfunction or even liver transplantation can be avoided. Further follow-up studies with larger a population are necessary to confirm our results.


Aiming at the problems that the existing video captioning models pay attention to incomplete information and the generation of expression text is not accurate enough, a video captioning model that integrates image, audio, and motion optical flow is proposed. A variety of large-scale dataset pretraining models are used to extract video frame features, motion information, audio features, and video sequence features. An embedded layer structure based on self-attention mechanism is designed to embed single-mode features and learn single-mode feature parameters. Then, two schemes of joint representation and cooperative representation are used to fuse the multimodal features of the feature vectors output by the embedded layer, so that the model can pay attention to different targets in the video and their interactive relationships, which effectively improves the performance of the video captioning model. The experiment is carried out on large datasets MSR-VTT and LSMDC. Under the metrics BLEU4, METEOR, ROUGEL, and CIDEr, the MSR-VTT benchmark dataset obtained scores of 0.443, 0.327, 0.619, and 0.521, respectively. The result shows that the proposed method can effectively improve the performance of the video captioning model, and the evaluation indexes are improved compared with comparison models. With the rapid development of the mobile Internet and the rapid popularization of intelligent devices, from “Internet + Plus” to “AI + Plus,” the process of human informatization has entered a new stage. As a new type of user-generated content, short video has widely appeared on various social platforms. While people interact through video, some unhealthy videos such as terrorist violence and pornography take advantage of it, which seriously endanger the physical and mental health of young people. Therefore, the audit of short video content is of great significance. Most of the existing video auditing methods use manual means, but manual auditing has poor real-time performance and low efficiency. Using the deep learning for video content auditing can not only improve the accuracy of the audit but also support the video screen, text, and speech for multidimensional audit. Therefore, how to obtain the main information from short videos and convert it into natural language, analyze, and understand it has become a hot research topic in the field of text expression of video content. Video captioning is a cross-modal, cross-disciplinary research, and has been a challenging research topic in the computer and multimedia fields , 2. The The early work of video captioning is based on the fixed template structure –5, whichAccording to different video modal information, various models pretrained by large-scale datasets are used to extract static, dynamic, and audio information in video, which improves the accuracy of the text expression of the video content.An embedded layer based on self-attention mechanism is designed to embed the single-mode eigenvector and learn the network parameters needed in sentence generation. The complementary information between different modes can be fitted better when encoding.Two schemes of joint representation and collaborative representation are used for cross-modal feature fusion, and it is verified that the collaborative representation strategy has better experimental results in this model when fusing multiple complementary video modal information.Video is a recording carrier based on static pictures, but it is a higher expression than static pictures. With the movement of the object and the camera, the composition structure and plot focus of the video screen will change accordingly, and the perspective relationship of the objects in the video will also change. This change is called the motion feature of the video; the optical flow graph of video has apparent feature invariance and contains coherent motion trajectory information. The accuracy of optical flow at the boundary and small displacement has a strong correlation to the capture of video motion information. The audio signal carried by the video is also of great significance to the video. Video dubbing can explain the main points and themes of the video in the form of sound. For example, applause and whistle can only be captured from audio information. Aiming at the problem of low accuracy of video captioning based on a single visual feature, and the high correlation and complementarity between different modal information, this article uses the fusion of multiple complementary modal information to train the video captioning model. Firstly, each frame of video in the dataset is converted into a single JPEG image, and the audio information of each extracted video is stored as an audio file in wav format. Then, the representation information of static image, audio, motion, optical flow, and other modes is extracted from the extracted modal data to cross-modal information fusion, and to generate a richer and more accurate video captioning. The main work of the research is as follows:The video captioning aims at analyzing, understanding, and expressing the content displayed in the video through the use of natural language. At present, the mainstream methods of video captioning are based on the “coding decoding” architecture, which can be divided into three types: methods based on visual feature mean/maximum, video sequence memory modeling, and three-dimensional convolution features.The method based on the mean/maximum value of visual features extracts the visual features by employing the mean value or the maximum value, and then encodes the features and decodes them to generate the natural language text. Venugopalan et al.  adopted The method based on video sequence memory modeling effectively solves the problem that time-series features in video clips are difficult to capture. Literature  adopted M3 − inv3 model that jointly models visual information and language information by extracting the 2D and 3D features of the video frame, which better solved the problem of the long-term dependence of multimodal information and semantic dislocation in LSTM. Literature [The method based on 3D convolution features can mine the static and temporal dynamic features of video at the same time by encoding the spatiotemporal features of video. In literature , the proterature  proposesterature  presentsterature  used semThe proposed model does not fully utilize the extracted single-mode representation information and take into account the audio information carried by the video to the model. Therefore, considering the complementarity between single-mode feature parameter learning and video multimodal representation information, the semantic attributes expressed by each mode are obtained by extracting the representation information of multiple modes of video, so as to improve the performance of the video captioning model.hv, hI3  D, haudio} and maps the information of multiple modes together to a single multimodal vector space Vmulti through collaborative representation. The decoder receives Vmulti for decoding, predicts the hidden state of the current time, outputs the probability distribution vector of each time step in turn, and uses the greedy search algorithm to take the word with the highest probability at each time step at the decoding time as the predicted output result. The word probability model of time T is shown inht is the current hidden state, Yt−1 is the result of the last time step, Vmulti is a unified multimodal vector space, softmax is a normalized exponential function, mapping the result to  as a probability value, and Pt(Yt) represents the probability distribution of each word in the current time step. When all probability distributions are calculated, the greedy search algorithm is used to extract the word with the highest probability in each time step at the decoding time as the prediction output until the output is 〈eos〉 and the decoding is completed.The structure of the cross-modal video captioning model based on multilayer attention is shown in Video data differ from picture data in that video is multiframe snapshot, which makes video more suitable for describing continuous actions or pictures. Meanwhile, the video is attached with corresponding real-time sound information, so that the video can record an event more stereoscopically and vividly. Compared to a single image, the video contains not only spatial features but also temporal features, as well as audio and motion features. Because the continuous frame structure in the video conveys a wealth of information, it is difficult to determine in the context of the more significant content to accurately describe. For this reason, we use multimodal features of video to express video content text.For the static feature extraction of video frames, a channel attention framework unit squeeze and excitation (SE) proposed in literature  is addedFor the extraction of dual-stream 3D features of video, the two-stream inflated 3D convolution network (I3D) proposed by the DeepMind team is adopted . This neFor the extraction of audio information in the video, FFmpeg is used to extract the Mel frequency cepstral coefficient (MFCC) of the voice signal.The attention mechanism in deep learning draws lessons from the human visual system. For example, the human visual system tends to focus on the key information that assist judgment in the image and ignore the irrelevant information . TherefoX is given, and its characteristic channel is C′. After a series of convolution and other operations, a characteristic diagram with the number of characteristic channels of C is obtained. Then, the three operations of the SE module are used to recalibrate the previously obtained feature map U.In the SE module, there are three key operations: squeeze, excitation, and reweight. In U along the spatial dimension. The two-dimensional information of each characteristic channel is compressed into a real number ZC, which has a global receptive field to a certain extent. ZC represents the global information of the response on the characteristic channel. Formally, the statistic ZC is generated by reducing the space dimension (H∗W) of the characteristic graph U, so the cth element of z is calculated in the following:The first is the squeeze operation. This operation compresses the feature map C channel into c/r channels, and the dimension reduction factor r is a super parameter. The second FC layer is used to restore the original dimensions of the feature map. Finally, a weight coefficient S is obtained, which is calculated as in the following equation:σ represents the sigmoid function and δ represents the ReLU function, w1 ∈ Rc/r×c, w2 ∈ Rc×c/rIn order to take advantage of the information gathered in the squeeze operation, the second important operation exception is carried out. The exception is similar to the design of the gate in the cyclic neural network structure. This operation aims at capturing the channel dependence completely. The excitation operation is implemented with two fully connected (FC) structures to reduce the model complexity and to improve the model generalization ability. The first FC layer reduces the U channel by channel, and the recalibration of the original feature on the channel dimension of the feature map is completed to obtain the final attention feature Fscale refers to the channel multiplication between the attention weight sc and the feature map uc.Finally, the reweight operation is performed. The weight output from the previous operation is weighted to the previously obtained feature map The SE module finally performs an attention or gating operation in the channel dimension. This attention mechanism allows the mode to pay more attention to the channel features with the largest amount of information and suppress the unimportant channel features.∗1 full connection layer is adopted at both ends of the ReLU activation function of the SE module [For the video frame feature extraction part, the SE (squeeze and excitation) module is added to the improved ResNet152 network structure and used as the video frame feature to extract the network. As shown in E module . The adv∗2048.Each video in the dataset is preprocessed into fixed frames, and 80 of them are taken as sample frames at equal intervals. Then, these sample frames are sent to the SE-ResNet model pretrained on the large-scale image dataset ImageNet to extract the feature information of the frames and obtain a high-dimensional feature vector of 80N to ensure that the network output is the same as the 2D convolution. Therefore, the convolution kernel and pooling increase the time dimension, and other nonlinear layer structures remain unchanged. The network connection details are shown in Two-stream inflating 3D convolution network  I3D is o∗224 fixed frame and sent it to the I3D model pretrained on the large-scale image dataset ImageNet and the video dataset kinetics to extract the dynamic features of the video to obtain a feature vector.Each video in the dataset is preprocessed into a 224fHz is the actual linear frequency and fmel is the Mel frequency standard.At present, the commonly used speech feature extraction methods include linear prediction cepstral coefficient extraction method , linear The cepstrum parameter feature of the Mel filter plays an important role in speech feature extraction. Its calculation is simple, and its discrimination ability is prominent. The feature parameter extraction principle of MFCC is shown in x(k) represents the time domain signal; xi(k) is the data of the i-th frame; and K represents the kth spectral line in the frequency domain.First, the audio signal extracted from the dataset is preprocessed, such as pre-emphasis, framing, and windowing, and then, the corresponding discrete Fourier transform is performed on the single frame signal after framing to obtain the frequency-domain data, as shown in the following equation:W Mel frequency filters, and the spectrum, Mel filter banks, and frequency envelope are extracted. The frequency-domain response of the filter Hw(k) is in the following equation:HW(k)=1; f(w) is the center frequency of the filter.Secondly, the frequency-domain data obtained above are filtered by i is the i-th frame and k is the k-th spectral line in the frequency domain.Then, the logarithm of the processed energy spectrum is taken so that the amplitude multiplication in the Fourier transform is converted into addition to obtain the logarithmic energy, which is calculated in the following equation:W is the w-th Mel filter, i is the i-th frame, and n is the spectral line obtained after DCT.Finally, it is substituted into the discrete cosine transform (DCT) to obtain the MFCC coefficient, which is calculated in the following equation:∗20 high-dimensional audio feature matrix.The audio extracted from each video in the dataset is divided into 1120 frames, and the MFCC signal of 20 dimensions is extracted from each frame and stored as an 1120The information fusion of different modes is a key point in multimodal research, which integrates the information extracted from different modes into a stable multimodal representation. There are two multimodal feature fusion strategies : joint rx1, ....., xm are obtained, the characteristic X=f is obtained by splicing and fusing. When the splicing vector dimension is high, principal component analysis (PCA) dimensionality reduction operation is carried out to form a multidimensional feature vector space F=PCA(X).Joint representation is shown in f(x1) ~ f(xn), where ∼ represents a collaborative relationship. The goal of network optimization is to optimize the cooperative relationship.Collaborative representation is shown in X by fusing multiple input modes X, which is more suitable for multimode as input.As shown in In this experiment, the server CPU is 48-core Intel(R) Xeon(R) Gold 5118, the running memory is 128G, the GPU card is NVIDIA Tesla V100, the video memory is 32G, and the operating system is Ubuntu18.04. NVIDIA CUDA 11.3, cuDNN V8.2.1 deep learning acceleration library, and PyTorch deep learning framework supporting GPU acceleration are installed.x-axis is the video category, a total of 20 categories, and the y-axis is the total number of videos under each category.MSR-VTT  is a larThe large-scale movie description challenge LSMDC dataset is based on the joint presentation of MPII Movie Description Dataset (MPII-MD)  and MontFor model performance evaluation, four algorithms widely used in the field of video caption, namely, consensus-based image description evaluation (CIDEr) , Metric ∗256 pixels before the model reads each frame. When extracting features, perform 15° random rotation on each frame of the image, which is needed firstly, and then perform random clipping to obtain an image with the size of 224∗224 pixels. Summarize and count the text vocabulary after the word segmentation, and then form a vocabulary list that consists of the words that are larger than the low-frequency threshold, and remove the vocabulary below the low-frequency threshold. Finally, select the vocabulary threshold as 5 and get 16860 words.Scaling the extracted original frame size to 256α=0.9, β=0.999, ε=10−8. The initial learning rate of the model is 0.001, and the learning rate decay rate is 0.8. The model is trained with a learning rate decay of 0.8 for 50 consecutive rounds without loss, and the negative log-likelihood loss function is used to measure the distance between the labeled statements of the dataset and the generated statements of the model, and the batch size is set to 128. The single-mode embedding layer adopts a two-layer LSTM network, and the numbers of LSTM layers of the fusion feature encoder and decoder are set to 1, 2, and 3.In the training phase of the model, Adam's algorithm  is used During the training of the model, the average loss value is kept every 50 rounds. The curve of the loss value is shown in Vf+VI3  D, Vf+Vaudio and three-mode Vf+VI3  D+Vaudio video captioning models were trained for static frame feature Vf, motion feature VI3  D, and MFCC feature Vaudio of the video. On the basis of each mode combination, the number of layers of LSTM network is set as 1 layer, 2 layers, and 3 layers for the single-mode embedded module and encoder module, and the model training experiments are carried out, respectively. The model comparison experiment is carried out under the MSR-VTT dataset, and the experimental results are shown in x1, ....., xm to obtain the multimode representation X=f, which is more suitable for multimode as input.To verify the validity of the model and the impact of specific parameters on the model, two-mode First, three modal features are fed into the embedded layer structure to learn the parameters that related to the single mode. Then, the multimodal information is fused through the joint representation and fed into the encoder-decoder. The ablation results show that the performance of the model is improved by fused audio information compared with single-mode and dual-mode cases. Compared with the single-mode fusion score evaluation indexes BLEU4, METEOR, ROUGEL, and CIDEr increased by 0.137, 0.072, 0.102, and 0.130, respectively. Compared with dual-mode fused score evaluation index, BLEU4, METEOR, ROUGEL, and CIDEr are improved by 0.084, 0.113, 0.016, and 0.124, respectively.This study is compared with the top five model structures in the second MSR-VTT challenge, and the results are shown in This article also compares the results with the representative research work in the field of video captioning, as shown in As can be seen from Tables This article also conducts experiments based on the latest large-scale movie description challenge (LSMDC) dataset. The model extracts multiple modal information of the video and uses it to train the model so that the model can obtain more complementary and diversified characterization information, making the model more robust and adaptable to multiple types of video clips. The text description is more specific and accurate, which further proves that the different modal information of the video has a high degree of correlation and complementarity.In the split test set from the MSR-VTT dataset, different categories of video were selected. In this article, a single-mode embedding multimode fusion video captioning model is proposed. Through a variety of efficient pretraining models, various modal representations contained in the video are effectively extracted, and static frame information, dynamic 3D, optical flow information, and audio information are complementary. The embedded layer based on self-attention is designed to learn the characteristic parameters of a single mode, which can enhance the complementarity of each mode better. Provide comprehensive and plentiful representation information for video generation text. And make the model generate more rich and accurate natural language. The above methods are verified by a series of ablation experiments and comparative experiments on MSR-VTT and LSMDC datasets. The experimental results show that the performance of the proposed model is significantly better than other models, and the generated text is more accurate.In the later research work, we will make further improvements on the method of cross-modal information fusion and the complementarity of modal information. Combined with the attention mechanism to do further improvement work, make the alignment of various modal information with the text more accurate, and make the model obtain more accurate and rich representation information, so as to generate high-quality text and ensure the lightweight of the model.


To support efforts to increase social prescribing and reduce levels of physical inactivity, parkrun UK and the Royal College of General Practitioners together developed the parkrun practice initiative to link general practices to local parkruns . General practice staff are encouraged to take part in parkrun events themselves and to encourage patients to participate.To provide insights for primary care clinicians about parkrun participants (parkrunners), especially those with characteristics of patients who might be signposted to physical activity.Secondary analysis of an online survey of parkrunners in the UK.Responders were ranked into 13 categories using mean parkrun finish time, allowing the following definitions: front runners; median runners; slower runners; runners/walkers; and walkers. Measures included demographics, health conditions, motives for first participating, and perceived impact on health and wellbeing.The survey included 45 662 parkrunners. More than 9% of all participants and 45% of walkers were found to have at least one long-term health condition, including arthritis, obesity, depression, hypertension, chronic pain, anxiety, type 2 diabetes, and asthma. Walkers were less likely to be motivated by fitness or competition, and were more likely to be motivated by physical health. Despite these differences, perceived improvements to wellbeing were broadly similar for all parkrunners, regardless of their finishing time.Parkrunners are a diverse population in terms of their physical health. Information provided by this study could be combined with other research on the barriers to participation and successful brief interventions to help address the key issues of primary care clinicians’ knowledge and confidence about social prescribing to increase patients’ physical activity levels. Parkrun , a charity that puts on free, weekly, timed 5 km events across 23 countries was suggested by the WHO as a good example of such an initiative.3,7,8Previous research showed that up to 70–80% of GPs do not speak to their patients about physical activity,The NHS Long Term Plan9), the parkrun practice initiative was created jointly by parkrun UK and the Royal College of General Practitioners, to support these efforts by linking primary care with a physical activity opportunity (parkrun).10–12 However, there is a lack of understanding among both patients and clinicians about what parkrun is and the appropriateness for some patients of participating in it.Given the need to increase social prescribing and reduce physical inactivity ;Index of Multiple Deprivation (IMD) derived from postcode where IMD quartile 1 is the most deprived and IMD quartile 4 the least deprived;Over the last 4 weeks, how often have you done at least 30 minutes of moderate exercise (enough to raise your breathing rate)? [Answer: less than once per week/about once per week/about twice per week/about three times per week/four or more times per week/rather not say/don’t know]. Those selecting ‘less than once per week’ were classified as ‘inactive’; andresponse to the following question asked at registration: their mean time for completing the parkruns.Responders provided their name, unique parkrun ID number , date of birth, and home parkrun, which allowed their survey data to be matched to parkrun databases. This provided the following information:front runners: those with mean 5 km times <20 min;median runners: runners in the category containing the median runner (the 22 832th runner with a time of 29 min 20 s), that is, those with mean 5 km times between 27.5 and 30 min;slower runners: those with mean 5 km times between 42.5 and 45 min;runners/walkers: those likely to have combined running and walking with mean 5 km times between 45 and 50 min; and14walkers: those with mean 5 km times ≥50 min or a mean speed of 6 km/h(1.67 m/s).The following definitions were used in this study:The remaining times were split into 11 categories 2.5 min apart.Data were validated using Excel (version 16.46) using statistical descriptors. Data for motives and impact were coded in Excel (version 16.49) and all statistics analysed using SPSS (version 26).2 test with effect size calculated using Cramér’s V.15 Continuous data were compared with walkers using the Kruskal–Wallis test with effect size defined as r = z-score/√n where n is the number of valid cases. Effect sizes were defined as small (<0.25), moderate (0.25 to 0.45), and large (>0.45). Statistical significance was set to P<0.001.Data within each time range were reported as frequencies or medians (since the variation within each time range was non-parametric). Categorical data for each time range were compared with walkers using the χThe survey was sent to all those who had been registered with parkrun since 2004 (whether their participation had lapsed or not) and those who had never done a parkrun (around 43% of those registered), which may account for the relatively low response rate of 100 866 survey returns (around 4.4% of registrants and 7.7% of participants). The following responders were removed: 37 039 who consented to view the survey but did not answer any questions; 1786 who had registered with parkrun but had not yet participated; 1349 who did not consent; 681 who self-identified exclusively as volunteers; and 12 who provided invalid or malicious responses. This left 59 999 responses, of which approximately 75% were matched to parkrun data, resulting in 45 662 participants with matched mean 5 km times from the parkrun database.13 The latter was primarily because the survey was restricted to those aged ≥16 years.Responders were normally distributed about a median of 27.5 to 30 min but with a tail of slower runners, runners/walkers, and walkers . Of the Those faster than median runners showed significant demographic differences from walkers with large effect sizes. Slower runners and runners/walkers were statistically similar to walkers and were more likely to be female, older, from deprived communities, and less active at registration.Supplementary Figure S1 illustrates responders’ motives for first participating in parkrun paired, where possible, with impact measures (see Supplementary Table S2 for details). The graphs are ranked in order of most to least selected motive for the full sample.The three most selected motives were ‘to contribute to my fitness’ (57.0%), ‘to improve my physical health’ (37.2%), and ‘to gain a sense of personal achievement’ (27.2%); these had large proportions of people reporting improvements of 90.1%, 85.4%, and 91.4%, respectively.Fewer slower runners, runner/walkers, and walkers selected ‘to contribute to my fitness’, while more selected ‘to improve my physical health’.‘To manage my weight’ was selected by 19.6% of the sample and was more likely to be selected by slower runners (33.8%), runners/walkers (33.0%), and walkers (32.7%), with improvement for approximately 55% of runners with times slower than the median.‘To improve or manage my health condition, disability, or illness’ was selected by 17.4% of those with a health condition and was more likely to be selected by walkers (31.5%). A total of 66.8% of all responders reported improvements to ‘your ability to manage your health condition, disability, or illness’, with no statistical differences between walkers and other participants.Few selected as a motive ‘to improve my mental health’ (12.7%), ‘to feel part of a community’ (11.3%), or ‘to improve my happiness’ (6.5%). However, large proportions of responders reported improvements in these areas: 69.5%, 71.1%, and 79.6%, respectively. There were few statistical differences between walkers and other responders.Few responders selected ‘to spend time outdoors’ (10.0%) or ‘to be active in a safe environment’ (3.9%), although the former was statistically more likely to be selected by walkers and the latter by runners slower than the median. ‘The amount of time you spend outdoors’ was improved for 74.8%, while ‘your ability to be active in a safe environment’ was improved for 60.0% of participants. There were higher values for walkers at 81.8% and 71.3%, respectively.More than 20% of slower runners, runners/walkers, and walkers were more likely to select ‘my friends, family, or colleagues encouraged me to’ and, while more walkers selected ‘a health professional advised me to’, this was only 1.8% compared with 0.3% for the full sample. (It should be noted that the survey was carried out as parkrun practice was being set up.) Finally, 51.9% of the full sample improved ‘your overall lifestyle choices ’, with little difference between walkers and other responders.In a survey of 45 662 parkrunners, slower runners tended to be older, and were more likely to be female, from a deprived community, and inactive at registration. More than 9% of the full sample were found to have at least one long-term health condition lasting ≥12 months: this rose to 45% for walkers. While slower runners, walkers/runners, and walkers represented 4.3% of participants, they reported 19.8% of health conditions: these conditions included arthritis, anxiety, asthma, depression, chronic pain, fibromyalgia, hypertension, and obesity.Slower runners, runners/walkers, and walkers were less likely to be motivated by fitness or competition than other parkrunners and more likely to be motivated by physical health, weight management, the management of their health condition(s), to spend time outdoors, and to be active in a safe environment. Despite these differences, perceived improvements to wellbeing were broadly similar, regardless of the responder’s finishing time.The analysis is drawn from a large survey, allowing statistically significant differences to be found between categories of runner/walker. Any survey is biased by the responders who answer it: in this case, responders might be considered ‘keen’ parkrunners with fewer health conditions than the general population, and they may be more likely to report improvements. Sex is a confounding factor in the analysis so that, for runners slower than the median, motives and impact may reflect the views of females rather than males. Other confounding factors are age, IMD, activity level at registration, and parkrun participation.16–19 the current study has shown that walking can confer similar health benefits to running. Fleming et al11 found that parkrun practices suggested to patients that participation could be through jogging or walking. This study shows that those with health conditions may already be participating in this way. The health benefits of parkrun have previously been studied,20–24 and this work confirms a 2015 study,20 which found that large proportions of participants improved wellbeing measures, with non-runners more likely to improve than runners. Another study involving the parkrun survey24 found that volunteering could also improve wellbeing and suggested that parkrun could deliver some of the components of 5 Steps to Mental Wellbeing promoted by the NHS.25 The results of the current study show that this is also true of running or walking.As with previous studies,1,5,6,12 When discussing potential benefits with patients, messages may include obvious impacts such as improvements to fitness and physical health. However, while few responders chose mental health, happiness, or feeling part of a community as a motive to join parkrun, seven to eight out of 10 responders reported improvements in these areas, with little difference between walkers and other runners. These areas may be equally important to those new to activity as well as to those who are already active but who might benefit from improved mental health. The information provided in this study should be combined with other research investigating the barriers to participation in parkrun.26 A toolkit could be provided via parkrun practice10 that incorporates the latest knowledge about delivering brief physical activity interventions in primary care27 to help address the key issues of clinicians’ knowledge and confidence.This article provides the rationale to general practice staff for signposting to parkrun, by outlining the broad range of people that take part as walkers and runners, what motivates them, and the impact to them of participation. Faster runners are very different from the slowest, although they still perceive some of the same wellbeing benefits. Forty-five per cent of walkers reported long-term health conditions, some of which are those for which GPs say they would prescribe physical activity: depression, anxiety, hypertension, obesity, and being overweight.