Around 4.9 billion Internet users worldwide watch billions of hours of online video every day. As a result, streaming is by far the predominant type of traffic in communication networks. According to Google statistics, three out of five video views come from mobile devices. Thus, in view of the continuous technological advances in end devices and increasing mobile use, datasets for mobile streaming are indispensable in research but only sparsely dealt with in literature so far. With this public dataset, we provide 1,081 hours of time-synchronous video measurements at network, transport, and application layer with the native YouTube streaming client on mobile devices. The dataset includes 80 network scenarios with 171 different individual bandwidth settings measured in 5,181 runs with limited bandwidth, 1,939 runs with emulated 3 G/4 G traces, and 4,022 runs with pre-defined bandwidth changes. This corresponds to 332 GB video payload. We present the most relevant quality indicators for scientific use, i.e., initial playback delay, streaming video quality, adaptive video quality changes, video rebuffering events, and streaming phases.  However, datasets on Internet traffic dealing with mobile streaming of videos from major streaming platforms are currently sparsely treated in literature. Thus, the study of current mobile traffic and prediction of future traffic for accurate network management is a challenging task without a data basis.The nationwide rollout of new mobile communication technologies requires in-depth traffic analysis, usage studies, and network management. With more than 4.9 billion Internet users worldwide5. Statistically speaking, every person in the world watches an average of 7.55 minutes on YouTube every day5. This makes YouTube the second most visited website in the world7, with mobile access accounting for two-thirds of the platform’s video views in the second quarter of 202111. In fact, YouTube generates over a fifth  of all global mobile Internet traffic12. But in literature are only a few datasets available that document the use of YouTube in mobile environments. The available archives mainly concentrate exclusively on data collection via the desktop version of YouTube15.Overall, approximately 1.24 billion monthly users watch nearly 1 billion hours of online video on YouTube every day17. Thus, it is not representative for mobile usage. Although YouTube follows the Dynamic Adaptive Streaming over HTTP (DASH) standard18 on both platforms, it uses different settings for adaptive streaming16. Ramos et al.19 show that, for example, other buffer threshold values are used for the mobile app. Furthermore, YouTube mobile is more aggressive with throttling factors at higher encoding rates. Other studies show a far greater use of the Quick UDP Internet Connections (QUIC) protocol20 for mobile applications than for desktops21. The few public datasets on YouTube that contain measurements from mobile clients22 provide network or application traces only. Focusing solely on one type of trace offers only a limited view of the streaming process, since there are interactions between the network and the application, especially during adaptive video streaming.The desktop version used in modern web browsers behaves differently from Android or iOS versions, as different libraries, application types, and operating systems come into play23 aims to close this gap by providing measurements that were obtained simultaneously at the network, transport, and application level. The data was generated using YouTube’s native Android application over 29 months between January 2018 and May 2020. We provide 1,081.18 hours of time-synchronized video measurements, resulting in 45 days of continuous video with the native YouTube streaming app on mobile devices. The dataset includes 11,142 measurement runs conducted with 171 different bandwidth limitations used in 80 different network scenarios. The measured data corresponds to a total of 332 GB of video payload with TCP and UDP/QUIC traffic. Thus, this dataset stands out from related work in particular with the combination of a large number of different network scenarios and videos to understand, model, or predict current or future networks.Our dataset24, which enables remote control and monitoring of the native Android application by YouTube. The dataset was extensively post-processed and annotated. We present the most relevant quality indicators for scientific use, namely initial playback delay, streaming video quality, adaptive video quality changes, and video rebuffering events.At the application, we extracted a wide range of adaptive streaming parameters from YouTube’s mobile client, among others, streaming quality, buffer level, and frame playout information. We recorded available and generally accepted parameters in the network such as packet length, number of flows, transport protocol, and transport layer protocol ports. This was made possible by our freely available wrapper app25 to enable controlled measurements so that throughput, packet error rate, and streaming quality can be controlled during the measurements. Thus, the mobile use can be documented as comprehensively as possible with a focus on application-layer and quality indicators, in addition to the technical network and streaming parameters.The idea behind the dataset is on synchronous measurements at the network, transport, and application layer in strictly controlled environments. The main goal of the measurement setup is to ensure to replicate real world circumstances as closely as possible for a mobile user. This allows for the comparison between network and application information. The measurement environment follows the guidelines of the DASH Industry ForumDatasets regarding applications of video streaming as well as adjacent areas have been published in recent years. To integrate this work in the broad landscape of related work, the following section and Table 26. The datasets include, among others, DASH, H264 and H265, mobile video quality, or QoE datasets. For video streaming only, and in particular, several datasets already exist for the YouTube platform dealing with watch histories27, video application information, key-frame distribution and object names for search engines28. For viewing activity, in particular, Lall et al. published a dataset recently for Netflix29. It includes 1060 users and more than 1.7 M watched episodes and movies. However, none of these works take the video itself into consideration.In general, datasets exist for audiovisual and subjective measurements and studies uploaded at the Qualinet databaseet al. in 2018 for DASH videos30. The authors present a dataset with multi codec DASH videos for ten different videos, 19 bitrates and four codecs. Other works and datasets focus on video segmentation information32. Furthermore, Wang et al. studies YouTube’s user generated content for video compression research and published a dataset for that purpose in 201933. In addition, real streaming data and video meta-data is discussed by Baccour et al. for Facebook Live34. The authors published a data overview of Facebook live videos, viewers, and broadcasters of more than 1.5 M live streams. For YouTube streaming in particular, datasets exist for very specific metrics like the initial delay35 or aggregated application and network data to be used in machine learning and for quality prediction36.This is done for example by Zabrovskiy et al. published an initial mobile web browser and native app dataset for YouTube16 and Alcock and Nelson studied the application flow control in YouTube video streams13. These works were extended in 2015 with a raw network trace dataset of measurements with the mobile YouTube app in 201522.Back in 2011, Rao et al. published a small dataset with application and network data for the native YouTube app with three different videos and eight different bandwidth and quality settings in 201821. However, because of the small video and network setting diversity, the usability for streaming quality prediction or streaming modeling is limited. Thus, in general, recent and sufficiently large raw full packet trace information or minimally processed data is missing to date.More recently, Karagkioules In contrast, in the dataset presented in this work, the full network packet trace containing network and transport layer information is available together with all application data, and thus, the complete streaming behavior. In total 246 different videos are included with 171 different individual bandwidth limitations and more than 1,000 h of total video playtime. In particular, the large number of different videos and bandwidth limitations is, to the best of our knowledge, not available so far in literature.et al. published an initial dataset with uncompressed video data to study gaming video quality in 201837. This work has recently been extended by Zhao et al.38. In their work, the authors provide a test dataset of gaming video content together with a performance analysis of existing coding tools. Besides gaming, 360° video is one hot topic in recent multimedia data transmission research. There, several works with interesting datasets study content and sensor data39, head movement40, and head together with eye movement41 to optimize 360° videos or reduce traffic requirements based on different metrics. In addition, Nguyen and Yan presented a saliency dataset for 360° videos in 201942. The goal of their dataset is to give other researchers the opportunity to create attention models, head movement predictions, or video tile preparations for 360° videos based on 24 videos and more than 50,000 saliency maps.The importance of datasets is increasing in many application areas. In recent years, gaming is also becoming more and more relevant in the context of multimedia and video data transmission. Barman The procedure for creating our dataset is based on the design and deployment of a testbed and the definition of the used measurement procedure. In addition, post-processing steps to enrich the dataset with additional streaming information is presented in this section. Both, the raw data and the post-processed information is included in the published dataset.To ensure that real-world scenarios are replicated as precisely as possible, a client-server-based measurement setup was developed to record both network and application data. The full setup is presented in Fig. The management server is an entity that validates and organizes measurements and does not take an active part in the measurement process. Instead, its responsibilities include the validation, post-processing, and storage of data. Examples for validation steps are scans for empty or erroneous files, the correct enforcement of bandwidth limitations, and extensive logging of events occurring during the measurement process.tc43 is used for traffic shaping and control in the Linux kernel. To perform actions, such as starting measurements or logging application data, the measurement control unit is connected to the measurement device, a smartphone, via the Android Debug Bridge (adb)44. This way can be used to directly connect to Android devices via USB and does not interfere with measurements. Finally, the measurement control unit provides a wireless connection with variable and controlled QoS parameters for the measurement device itself. The interface of the wireless access point is the monitoring point for the complete network traffic containing network and transport layer data. All upstream and downstream traffic is captured using tcpdump45 within the Linux kernel ringbuffer between the wireless network interface and the connected device. Data is stored locally on the measurement control unit before it is offloaded to the management server for further processing.The measurement control unit is responsible for the actual measurement process and is connected to the management server. It is equipped with a sufficiently powerful processor, an i7-4770 processor with 8 × 3.40 GHz and 16 GB RAM to avoid bottlenecks during the actual measurements. It is connected to the Internet via the German national research and education network (Deutsches Forschungsnetz) via fixed connection to guarantee that measurements are not impacted by physical bandwidth limitations. To control uplink and downlink bandwidths, the state-of-the-art Linux command line tool 24. Seufert et al.17 have published a detailed description of this wrapper app and the measurement process used in this work.The measurement device is connected to the Internet via the provided 2.4 GHz WiFi access point. To exclude unintended bottlenecks at the smartphone, for the measurement setup, a clean, new Google Pixel XL with Android SDK version 28 released in 2020 is used. The display resolution of 2560 × 1440 pixel  does not limit the video playback, and the 2.15 GHz and 1.6 GHz quad-core Qualcomm Snapdragon 821 processor and 4 GB RAM are sufficient for the playback of videos. Please note that playback related decisions are triggered by the YouTube app and not by the phone. Thus, it is not expected to achieve other results with other Android-based smartphones if the available resources are sufficient. An in-depth study of the performance with Apple’s iOS is subject of future work. However, we expect no large differences. Furthermore, no additional applications are running at the smartphone and the battery is kept at sufficient health during all measurements. The chosen app and OS version is available in the dataset. During a measurement run, a video is played and relevant application layer metrics are logged directly by the device. To achieve this, a specially developed wrapper app has been developed to monitor the native Android YouTube app exactly as it is distributed through Google Play Store. The source code of our tool is freely available on Githubsetup video (ID FiO0iLzTyVg) is played for 10 s. This is done to ensure that all network, transport, and application data of the desired video can be logged and the bandwidth setting is applied correctly. During this setup video, the player can adapt the requested playback quality towards the initial bandwidth setting. This avoids unwanted playback behavior that is not a result of the defined scenario but of the switch to the initial bandwidth limitation. Afterwards, a YouTube video is selected for the measurement based on a predefined list of video URLs.The steps of a single measurement run are defined as follows. Every measurement is started by the measurement unit. In a first step it checks for available connections to the management server, the Internet, and the measurement device via adb. Upon success, a WiFi access point is opened to provide Internet connectivity to the measurement device. Subsequently, the network scenario is defined. Either no bandwidth limitation can be set for the complete measurement, which results in approximately 400 Mbit/s downlink bandwidth (and thus no impairment when streaming videos between 144p and 1080p) or a predefined bandwidth setting schedule can be used. In the latter case, bandwidth limitations are planned for the complete measurement by dynamically applying different limits based on either synthetic traffic limitations or real-world bandwidth measurements of 3 G and 4 G mobile networks. Afterwards, prior to the actual playback of the desired video, a stats-for-nerds data provided by the native YouTube app once a second. In these stats-for-nerds information, the complete application behavior like buffer filling status, played video, or the number of already played frames is logged. This data is transmitted immediately to the measurement control unit and written to a file via the USB-connection to not interfere with the WiFi connection used for the measurement. After video playback is finished, all network, transport, and application data points are sent from the measurement control unit to the management server for validation and further post-processing steps. The network and transport data include the timestamp, the source and destination IP address, source and destination port as well as packet lengths for all TCP and UDP packets observed during the measurement period. The application data include a timestamp, the currently played out video and audio quality, the frames per second, buffer status information, the number of dropped and already played out frames as well as the video ID. Additional information include the current App version, OS version, number of connections, and the battery status during the measurement. An overview of all logged network and application layer metrics is summarized in Table Beginning with the measurement start, after connectivity to all components has been established, all network and transport data transmitted and received at the WiFi access point is logged. This includes especially the uplink and downlink video data from the measurement device. Furthermore, as already mentioned during the testbed description, the application data is logged directly at the measurement device by parsing and storing the The post-processing of the data presented in this work is done by three major steps according to the overview in Fig. cleaned data is the second part of the dataset.Second, to simplify a streaming behavior study, flows related to YouTube streaming are identified based on their IP-addresses. Application data is cleaned by removing advertising data points related to the playback of commercials. This player health which means whether the video player receives enough video data to fill or keep the current playback buffer health or if the buffer fill level is declining. This additional information is included in the third part of the dataset. To receive a more general idea about the post-processing in this work, these three steps are explained in detail in the following.In the last post-processing step, data is processed to further simplify usage and extend the usage potential. In this last step, streaming phases are defined describing the current In the data integrity and cleaning step, all invalid measurements are discarded. Therefore, the following tests are performed.tcpdump capture crashes or if the application data logging is faulty.First, all measurements where either the raw network file or the application stats-for-nerds file does not contain data are deleted. This may occur if the YouTube app was not opened correctly during measurement pre-start. Furthermore, inconsistent measurement runs for which network traffic is measured longer than application data or vice versa are deleted. This occurs if the bw_settings.csv.In this step, it is determined whether the bandwidth was set correctly. Therefore, bandwidth limit changes logged during the measurement process by the testbed are compared with the predefined bandwidth setting. Furthermore, all network data are analyzed to determine if the network throughput exceeded the possible bandwidth limitation. All error-prone measurements are discarded. For the remaining measurements, the bandwidth information is logged with the timestamp and the set bandwidth limitation in the The timestamps of all measurements are checked for plausibility. Since both sources, network and application data, contain timestamps which are supposed to give a complete view together for the measurement duration, the overlay of the timestamps is validated. There is no exact congruence because the network layer first establishes a connection and starts downloading video data before the application logs are filled. Similarly, no more data is downloaded at the end of the video when the rest of the content is already in the buffer, but the application logs still show video playback.46, relevant network and transport layer traffic information listed in Table all_network_traffic.csv in the dataset. All raw application data presented in Table stats_for_nerds.csv.Since the raw network data from the packet capture file contains the encrypted payload from which no further information can be extracted this data is further processed. By means of tsharkstats-for-nerds file are cleaned.In the second post-processing step, relevant video streaming data is separated from cross-traffic. Furthermore, error-prone values in the application setup video described earlier as well as advertising played during the measurement process is excluded. Since this dataset presents YouTube streaming data, all measurement runs where the correct video is never played because of too long advertising are discarded.The video data extraction is simple for the application data since the video ID is available for each measurement point. Thus, only the application information for the measured video ID is kept while other video IDs, especially the stats-for-nerds logs are checked for valid ranges. During this process, negative buffer health values are found frequently in the proximity of a video quality change. Via manual validation in the application, this inconsistency is confirmed to be a logging issue of the YouTube application itself. If the quality of a video changes, the buffer health level may change suddenly since the adaptive streaming algorithm switches the input for its buffer health prediction from the buffered content in the previous quality to the buffered content in the upcoming quality. This can lead to very low or even negative buffer health values in the logs. However, these negative values occur regardless of changing to a higher or lower video quality. For example, the playback may be uninterrupted, and quality changes to a higher level. The negative buffer health values occur when the client decides to request a new quality, but neither has the client changed to the new quality nor downloaded enough content in this quality to allow for a quality change without playback interruption. After this step, all valid data points from the stats-for-nerds files without advertising and setup video are added as application_data.csv to the dataset.In this step, all values in the googlevideo.com36. These flows are identified as video flows and separated from other traffic. However, to filter not only cross traffic, but also the traffic of the setup video at the beginning of each measurement run and specific advertisements, another approach is used in addition. Firstly, from the application data information the start and end time of the correct video is determined for each measurement run. Next, from the network data, all flows which are active within that time window are considered candidates for the video stream. Candidate flows are marked and listed with the complete traffic in descending order by traffic volume. Afterwards, the candidate flows are added as streaming flows to the dataset until the traffic volume accumulates at least 90% of the total traffic during the video stream. This is valid since video flows are identified as dominant flows in YouTube streaming measurements47. With this method, cross-traffic like loading of video comments or video recommendations, transmission of DNS requests, and other background processes are excluded. Furthermore, including only the largest flow would not be sufficient because YouTube may change the connection to a different server during a video stream in case of, for example, data transmission issues, quality changes, or video rebuffering. The remaining traffic is added as video_traffic.csv to the dataset.For the network and transport data, there are two possible ways of separating video data from cross-traffic. For many measurement runs, it is possible to extract the video flows by following IP-port-tuples based on the DNS resolution for 48. Please note, information about the start up delay and an explanation of the calculation procedure with our dataset is already published in the work35 and thus not included here. Furthermore, the streaming phase indication is introduced that describes whether the player is currently in good condition and receives enough data to constantly keep the buffer filled or not. Therefore, the application_data.csv file is enriched with additional information. The stalling column indicates that the playback of the stream is interrupted because of a buffer underrun. Since playback is never paused during the measurements, this information is achieved by comparing consecutive playedFrames values. If no frames are played out between two log entries, the stream is assumed to stall.In the third and final post-processing step, the dataset is extended by critical quality metrics during video streaming that are not included so far: quality changes and stallings qc and qcTo are added to list all quality changes. If the fmt value in the application data log changes from the current to the next timestamp, the video quality changed. Then, the qc value is set to 1 and the qcTo value is set to the target video quality code.The columns filling if the available playback time in the buffer is filling, steady if the buffer level remains constant, depletion if it decreases or stalling if no frames can be played out because of a buffer underrun.Moreover, we provide an estimate about the current video phase based on a video phase detection algorithm. Each video is in one of four phases during an ongoing streaming session: filling and depletion respectively, since in the beginning and the end of playback the video is always in these phases. Afterwards, all other logs with a buffer level below 1.2 s are set to stalling. This shows good results in practice since the player can only play out completely downloaded video segments. For that reason, in most cases some playback time is left and the buffer does not drop to zero. The threshold of 1.2 s was chosen by looking at the maximum buffer level during a stalling event in the data. The remaining logs are listed as steady if the buffer level does not change more than 0.3 s between two logged values and the overall buffer is larger than 5 s. We choose the slope boundary of 0.3 s by looking at the occurring slopes in unlimited bandwidth scenarios where a steady phase can be determined manually. This ensures that small changes in the buffer health do not prevent the algorithm from detecting a steady phase and too small buffer levels are not set to steady since a buffer health level of less than 5 s is not enough to guarantee a smooth video playback experience if the bandwidth fluctuates. All other values are set to filling if the buffer level is increasing or depletion if it is decreasing. Please note: if the quality of a video changes, the buffer health level changes suddenly based on the pre-loaded data for the upcoming video quality. This can lead to small or even negative buffer health values in the logs as described in the data cleaning above. This is a logging inconsistency as the drop may occur before the video quality is changed for the user. To correct false assignments of a stalling phase in this case, it is checked whether it is possible to have played out all the pre-loaded data from the previously observed log value. If this is not the case, the value is set to the previously selected phase value.All values in the first and last 5 s are assigned as steady the entire period is set to steady. Furthermore, the same procedure is applied for short jumps out of the stalling phase. This is in accordance with Gutterman et al.49. A technical description of the phase detection algorithm is out of scope at this point. Instead, a detailed description by a pseudocode is included in the dataset.After this correction, the phase detection values are smoothed to prevent frequent jumps out of and back into steady state. The minimum duration of a steady phase is 15 s. If within 10 s after a steady phase another data point is labeled as 23. Each measurement contains five files as described above. The complete network trace with all network and transport layer information is available in the all_network_traffic.csv file in each folder, the extracted video traffic captured from the network is available in the video_traffic.csv. The complete application log, as it is available in the stats-for-nerds information directly from the YouTube app, is logged in the stats_for_nerds.csv, while the extracted video-only application data together with the extended dataset information as described in post-processing step 3 is available in the application_data.csv. To provide information about the network conditions during each measurement, the bw_setting.csv lists the network bandwidth limitation at specific timestamps for each measurement. If the available bandwidth is changed during the measurement, this bandwidth is kept until another change is performed and logged in the file. Furthermore, the data records include a pseudocode of the phase detection algorithm, several evaluation and visualization scripts, a large general overview csv file, and the post-processing scripts to process own measurements similarly to this dataset and reproduce each step for interested readers. In the following, detailed statistics about the data records in general are provided followed by observed details and special characteristics during different bandwidth limitation settings.The dataset presented in this work contains 11,142 individual video measurementsvideo_catalog.csv) in our materials folder. This file includes all video IDs, duration, available resolutions per video, fps, and the video genres. The application data contain 19,242 total quality changes in 6,929 different measurement runs, 35,558 total dropped frames, and 8,652 total stallings in 3,734 different measurement runs. A total video payload of 332.75 GB is downloaded contained in close to 375 M video packets. The dataset contains 242,973 total video flows while 1,288 measurement runs contain only TCP traffic as transport protocol and 8,996 contain only QUIC traffic logged as UDP in the dataset. The remaining 858 contain both, TCP and QUIC traffic.The general data overview is split in three main categories: application, network, and bandwidth information as summarized in Table bwlist.txt file in the dataset. The maximal bandwidth is 400 Mbit/s which is the maximal possible network bandwidth in the university network in Würzburg, Germany. The minimum is set to 17 kbit/s from a 3 G bandwidth trace. A total of 92,508 bandwidth changes measured during video playback. The bandwidth limitation scenarios can be split in three categories: 5,181 measurement runs are conducted with constant bandwidth limits during the complete measurement run, 4,022 runs have pre-planned bandwidth settings that include: incrementally increasing or decreasing bandwidth in predefined time intervals, abrupt bandwidth drops to trigger quality changes or stalling, and fluctuating bandwidth settings between specific predefined settings. The remaining 1,939 measurement runs are conducted with emulated real bandwidth traces received from real 3 G50 or 4 G51 network traces. In these measurements, we update the bandwidth limitation every 5 s to a new value from one network trace. We have chosen the bandwidth settings to study three main situations during the YouTube streaming: (1) understanding the streaming procedure in general, (2) gaining knowledge about scenarios with playback issues and limited bandwidth, and (3) study the streaming process under conditions as similar as possible to reality. For that reason, we have chosen the bandwidth settings as follows: For our first goal, we selected constant bandwidth settings to monitor streaming in very regular, and for high bandwidth limitations, good conditions. With these settings, one can understand the streaming process in general and get many baseline details for streaming that helps, for example, in streaming issue prediction. Furthermore, we increased the steps of bandwidth limitations for larger limits since more than 10 Mbit/s was usually sufficient for a good streaming experience. For lower bandwidth limitations, smaller changes affected the playback behavior more severe and was thus, measured in smaller steps. As a second goal, we wanted to generate playback issues for the app. Therefore, we tried to trigger video re-buffering events with abruptly changing bandwidth to achieve a better understanding of this condition. With more slowly changing bandwidth settings, we wanted to trigger among others, quality changes, buffer level changes, or in general changing conditions in the app. Last, the goal was to test the behavior in realistic conditions with the emulated 3 G and 4 G scenarios. This helped us to understand whether stalling or varying playback quality is really an issue in real networks. This understanding can help to react on decreasing buffer situations early and improve the buffering behavior in general to avoid stalling and increase the user perceived quality. In the following, one example measurement for each of the bandwidth setting options is presented with additional information. A visualization is presented in Fig. The complete dataset is measured with 171 different individual bandwidth limitations used in 80 different network scenarios. A general overview of all bandwidth scenarios is included in the In the dataset, 5,181 video runs were measured with constant bandwidth limitations. In these scenarios, one bandwidth limitation is set and kept for the complete measurement run. The constant bandwidth limitations include 0.2 Mbit/s up to 2.0 Mbit/s in steps of 0.1 Mbit/s, 2.0 Mbit/s up to 3.0 Mbit/s in steps of 0.2 Mbit/s, 3.0 up to 6.5 Mbit/s in steps of 0.5 Mbit/s, and 1,024 kbit/s, 7.0 Mbit/s, 8.0 Mbit/s, 9.0 Mbit/s, 25.0 Mbit/s, and 400.0 Mbit/s. The results of these measurements show 5,533 quality changes that is equal to 1.07 per measurement run on average at application layer. Since in the complete dataset 1.73 quality changes per measurement run are detected on average, the quality change probability is lower. Furthermore, like expected, fewer quality changes are detected with increasing bandwidth limitations. For all measurements with bandwidth limitations larger than 1.0 Mbit/s, only 0.677 quality changes on average per measurement run are detected, for measurements with more than 5.0 Mbit/s, it is only 0.210. Similar data is measured for stalling occurrences. A total of 1,328 stallings are measured in all constant bandwidth limitation scenarios. Thus, on average 0.256 stallings are detected per video measurement run with constant bandwidth limitations compared to 0.776 for the complete dataset. With increasing bandwidth, again like expected, the average number of stallings per measurement run is decreasing with only 0.073 on average for measurements with more than 1.0 Mbit/s.N2sCbtodGMI. The video playback starts with a fast and constant buffer filling in the first 55 s shown by the brown line. There, the complete throughput limit is used shown by the black line that is constantly close to the limit. This part is labeled with (1) in green as filling phase. In this phase, more data are downloaded than played out and the buffer level is increasing. Afterwards, in the steady phase (2) marked in blue, the buffer level is kept at a constant level between 120 s and 125 s. The complete bandwidth is not required in this phase shown by the throughput spikes up to the bandwidth limit. At the end of the video, the buffer level is decreasing in the depletion phase (3) shown in red. In this case, the complete video is already downloaded and no more data is required since no download at all is detected. However, such depletion phases are also detected if the available bandwidth is lower than the required throughput to download the current video playback quality.For that reason, these scenarios are valuable to study and understand the general streaming behavior without the influence of bandwidth fluctuations. Especially the downloading and buffering behavior is included and can be studied in detail. This is shown in the left subfigure of Fig. bwlist.txt in the dataset). After the bandwidth dropped, it either keeps at a low level or it increases again. The resulting data from these scenarios include 10,028 total quality changes or an average of 2.493 per video and 6,689 total stallings which is equal to 1.663 per measurement run on average. Furthermore, more than 35,000 frames are recorded as dropped for all measurement runs with variable predefined bandwidth settings which suggests issues during playback, buffering, or video download.To study the buffer depletion phases in more detail and especially the resulting potential quality changes or video stalling events, the bandwidth limit must not be kept at the same single level. Thus, 40,022 video streams are measured with 20 pre-planned, fluctuating bandwidth settings to trigger these scenarios.In these bandwidth scenarios, the bandwidth limit changes from for example 800 kbit/s, 1.0 Mbit/s, 3.0 Mbit/s or 5.0 Mbit/s down to 1.0 Mbit/s or less in several steps (please find all bandwidth settings as an overview in the 2d1CVrCvdzbY is shown in the middle plot of Fig. The measured bandwidth changes result in download rates lower than the currently played out video bitrate and are used to study the buffering, and especially buffer depletion phases that lead to quality changes. Furthermore, it is possible to investigate stalling avoidance mechanisms of YouTube mobile streaming. An example scenario for a measurement of video ID The influence of this behavior is visible in the buffer level progress. After a fast increase at the beginning of the measurement, the increase is slowed down with the bandwidth drops. At a bandwidth limit of 1.5 Mbit/s, the increase is stopped and the buffer starts to decrease slowly after the drop to 1.0 Mbit/s. At 100 s playtime, a drop in the downlink bandwidth is detected which is assumed to occur due to a quality change triggered at network layer. This quality change from 720p to 360p is afterwards detected at 130 s marked by the red dashed line. It is assumed that the already pre-buffered old quality is played out before the actual quality change is visible in the application data information. Afterwards, the buffer is filling again up to 120 s at 300 s measurement time. At 437 s, a large drop in the buffer filling level is detected while in addition directly afterwards, the buffer is filling again. We assume that this behavior is another quality change triggered at network layer. The buffer level is already updated to the new quality in the application file but the quality information keeps the old quality. This is changed at 460 s, where the actual quality change from 360p to 720p is performed shown by the green dashed line. Please note that in this case, at 437 s, 120 s video is pre-buffered for quality 360p. With the quality change to 720p at 460 s, only 23 s of the remaining video is played out and much data is discarded. Afterwards, since the bandwidth is still not sufficient to keep the buffer at a constant level for 720p quality, another quality change down to 360p is triggered at 534 s and again up to 720p at 555 s.Since a constant bandwidth limit or pre-planned fluctuating bandwidth limitations are inappropriate to study the streaming behavior in real mobile networks, 1,939 measurement runs are conducted with real 3 G and 4 G bandwidth traces. The current bandwidth limitation is updated according to values from real 3 G and 4 G traces every 5 s as trade-off between very frequent bandwidth changes leading to possible computational or update overhead and sufficient accuracy. Smaller values are closer to real bandwidth settings while larger values smooth unwanted behavior or measurement errors more. To compare the behavior during real bandwidth scenarios and constant bandwidth limitations for the same video at the same time, the bandwidth from the traces is applied at the beginning of the measurements while, for example, after 200 s, a constant bandwidth limit is set. During these measurements, 3,681 total quality changes or 1.898 quality changes on average per measurement run are detected. Thus, it is shown that real bandwidth scenarios also trigger many quality changes. However, only 635 stallings that is equal to 0.327 stallings per measurement run are detected on average. Thus, this number is much lower compared to the predefined bandwidth setting and shows that the player can adapt well towards changing bandwidth settings in real mobile networks.An example measurement with a bandwidth setting of a real 3 G trace is plotted on the right side of Fig. 25. In doing so, we consider the recommendations for test cases for DASH-264/AVC HD and the recommendations for network emulation. In addition, the designed testbed relies on well-tested, peer-reviewed and freely available tools like the YouTube wrapper app17, which is freely available on Github24 or the state-of-the-art Linux command line tools tc and tcpdump to ensure consistent network emulation and data capture.In order to ensure the technical validity of the collected data, the developed testbed and measurement procedure follows the guidelines of the DASH Industry ForumFinally, the dataset was extensively post-processed and cleaned to ensure all included measurement repetitions contain valid data points. This data cleaning contains the deletion of empty or erroneous measurements, invalid logs, and advertising or other cross-traffic as described in the data post-processing section and visualized in Fig. 23. It contains all evaluation and post-processing scripts in the materials folder. The actual data representations are available in the dataset folder containing consecutively numbered subfolders. Each subfolder includes an all_network_traffic.csv file with the complete network and transport layer traffic, a video_traffic.csv file for the complete network and transport layer traffic of the video only, a stats_for_nerds.csv file with the complete application traffic during each measurement, a application_data.csv file with all application information for the video only, and a bw_settings.csv file with timestamps and bandwidth limitations for bandwidth changes.The complete dataset is available as zip file at figshareget_statistics.py file reads all data and summarizes important information like video sizes, quality change and stalling information, buffer filling, and playtime data in the data_overview.csv file. Furthermore, the plot_data.py script requires the path to one measurement representation folder and whether TCP or UDP is used as transmission protocol. The script plots the bandwidth limit, the throughput, and the buffer filling status as shown in Fig. 17. Measurement results can then be evaluated with our post-processing scripts according to Fig. process_stats_for_nerds.py file receives the measured raw stats-for-nerds data and outputs a csv file for all application data in a readable format. Furthermore, the script pcap_extraction.py takes the raw pcap files including the complete network trace and exports a csv file with important information like timestamp, IP address, port, and packet payload size from the measurement. The script also extracts all video traffic together with its uplink requests as it is used in36. If only the video traffic should be extracted, the video_only_traffic.py separates video and cross-traffic. Since the dataset is extended by different streaming phases, the pseudocode phase_detection_complete.pdf describes the phase detection that extends the dataset together with an included table summarizing and explaining all relevant parameters in more detail. Please find the readme.txt in the dataset for additional explanation. In the following, further dataset usage possibilities are outlined to, for example, extend, understand, or verify related work.The materials folder includes the following scripts: two evaluation scripts written in Python are added to simplify the work with the dataset. The 53, streaming traffic separation55, streaming modeling and quality impairment detection56, and machine learning approaches to predict or assess streaming quality58. However, most of these works have one in common: the lack of a publicly available dataset. With this work, we close that gap and give other researchers the opportunity to improve and extend their research. Furthermore, with the presented dataset and the available application information together with the network and transport layer data, it is possible to model streaming behavior at different layers. The dataset improves potential packet level, request based, application layer studies. Furthermore, it is possible to study streaming traffic generation and behavior. With these insights, predictions of streaming impairments based on network and application data can be made. This can be used by streaming platforms or network operators to optimize data transmission, resource management, or general user satisfaction because of service improvement.Because of the popularity in public and the large data generation in current networks, streaming studies and analysis - especially with YouTube streaming - are an important and hot topic in research. The broad range of research topics with YouTube streaming include, among others, general network traffic and streaming studies for various applications


The auxetic structures considered in this paper are three-dimensional periodic bar-and-joint frameworks. We start with the specific purpose of obtaining an auxetic design with underlying periodic graph of low valency. Adapting a general methodology, we produce an initial framework with valency seven and one degree of freedom. Then, we describe a saturation process, whereby edge orbits are added up to valency 16, with no alteration of the deformation path. This is reflected in a large dimension for the space of periodic self-stresses. The saturated version has higher crystallographic symmetry and allows a precise description of the deformation trajectory. Reducing saturation by adequate removal of edge orbits results in vast numbers of distinct auxetic designs which obey the same kinematics. Obviously, preservation of periodicity requires that such additions or removals be performed on entire orbits of edges, that is, equivalence classes of edges under periodicity. The case of interest for us will be when these operations do not alter locally the kinematics of the structure. Thus, in more technical language, we are concerned with spaces of periodic self-stresses in a local deformation neighbourhood of a given periodic framework . The tergeometric auxetics. In materials science, auxetic behaviour refers to lateral widening upon stretching and geometric auxetics gives a precise, autonomous mathematical definition to this type of structural deformation 32. We do not undertake here a more detailed investigation of the resulting polytope. Preliminary computations produced millions of vertices. Using a formula established in [The relevant combinatorial notion governing the removal possibilities is that of polytope –23. IndeIn the following section, we discuss a saturation scenario in dimension two, which is less involved computationally and refers to some well-known designs.. 8n = 4 vertex orbits and m = 8 edge orbits. The framework has 1 d.f. and allows a saturation with 12 edge orbits.We consider a periodic framework in dimension two with The initial configuration and its saturation are displayed in All vertex coordinates are integers and the rigidity matrix for the saturated framework is 12 × 12, with rank 8. Thus, the vertices of the matroid basis polytope can be computed by elementary routines. The result is a polytope of dimension 11, with 282 vertices. In . 9auxetic periodic design, established in [cognate periodic frameworks based on a shared local deformation space.The present study highlights two aspects of geometric auxetics: first, that the general methodology for ished in , can adesaturation, permitted by certain flexible bar-and-joint structures, which allow particular additions of bars without altering their local kinematic behaviour. In the periodic context, we require the initial framework to have a smooth local deformation space and no self-stress. Then, a bar orbit can be added when it produces a self-stress in the new structure. Cognates are obtained by reversing a saturation, that is, by eliminating the added self-stress through a sequence of bar orbit removals. We have shown that the possible reversals correspond to the vertices of a matroid basis polytope (defined by the rigidity matrix of the saturated framework).The phenomenon of cognates is explained by a process of large numbers of cognates, even after equivalence under symmetries. For auxetic design , this is quite relevant, since unexpected possibilities and relations are brought to light.It is apparent from our examples that saturation and reversal may result in


A high-power permanent magnet speed regulator is applied to a cooling water pump for conserving energy during the steel production in Magang (Group) Holding Co., Ltd. The designed setup of high-power permanent magnet speed regulator with a mobile base is shown in this manuscript, and the magnetic eddy under the different meshing area between driving and driven shafts has been simulated. And estimation indicates that the magnet speed regulator-controlled cooling water pump can save electric energy by 22%, about 1,756,400 kW·h per year, compared to the traditional valve-controlled pump, and the waste heat generated by this setup is below 5 ten-thousandths of the shaft power. Meanwhile, the permanent magnet speed regulator has a much lower vibration because of this non-contact way between the driving and driven shafts. The new technology for permanent magnet adjustable speed is used for the motor to control its speed and save its energy, which is benefit to the emission reduction. Therefore, more and more attention has been paid by the researchers in the industrial field.Compared with the traditional gear box, the permanent magnet speed regulator, which is based on the magnetic eddy due to the relative motion between the permanent magnet and the conductor, has several advantages such as higher energy efficient, stronger reliability, easier installation, lower cost, and soft start of motor4. In recent years, researches on the permanent magnet speed regulator were carried out not only in the items of the model and simulation for the basic analysis6, but also in the application with structure improvement in industry8. The virtual equivalent line method was developed to solve the end-effect on the magnetic field distribution in the air gap region of permanent magnet speed regulator. Through calculation of the static air gap flux density, it was found that the end-effect compensation function calculated based on the model was highly consistent with the result calculated by finite element method9. According to model for 3D transient eddy field in some research, the substitution of the copper disc by an aluminum disc could improve the speed governing stability of the permanent magnetic coupler10. A fast and accurate 3D modeling method was proposed to evaluate the electromagnetic performance of machines with axial flux permanent magnet under no-load conditions. The computed results of local field density, electromotive force, and cogging torque for the disk speed regulator were in very good agreement with the experimental measurements11. A novel flux-weakening control strategy with fast transient current response is designed to facilitate the flux-weakening control application on electric vehicle, and the simulation and experimental results exhibited that the proposed strategy could achieve the fast torque response and also have the capacity to reduce the torque fluctuation of the stable state12. Structure of 250 kW permanent magnet governor was advanced to improve the heat conduction, guaranteeing the steady as well as reliable operation of this equipment13. Halbach array, a kind of special permanent magnet array, was tried in the permanent magnet coupler, and both the simulation and test showed higher efficiency in the axial speed regulator15. In addition, Halbach array was also used in the separators to improve the separation efficiency16. Relatively systematic researches on permanent magnet speed regulator were also shown in the references19.Development of the disc-type permanent magnet speed regulator never stops, since it was proposed in 1990sIn this manuscript, 450 kW permanent magnet speed regulator used for cooling water pump in steel production is shown. N–S pole array structure is used in this speed regulator, and the magnetic field and current density in the conductor driving draft induced by the magnet driven draft are simulated based on finite element modeling (FEM) analysis. Under the motor, an automatic mobile base controlled by programmable logic controller (PLC) is used novelty to adjust the meshing area and consequentially magnetic eddy, changing the output power of the motor to keep the water flow in pipe. Measurement and calculation shows that the magnet speed regulator-controlled cooling water pump in this research can save electric energy by 22%, about 1,756,400 kW·h per year, compared to the traditional valve-controlled pump. Therefore, this kind of permanent magnet speed regulator is in a compact structure . Furthermore, an obviously reduction of vibration is observed when the permanent magnet speed regulator is used. Meanwhile, waste heat in the setup is very low based on the theory of thermal radiation.21, the permanent magnet speed regulator, which is used to adjust the high-power cooling water pump during the steel production, is advanced to increase transmission efficiency and heat dissipation efficiency. The structure of the advanced permanent magnet speed regulator with the coordinate system is shown in Fig. Based on former research and applicationAs seen in Fig. hg is defined as the distance from circumscribed circle of the permanent magnet array to the inner surface of the conductor cylinder. The key sizes remarked in figure are shown in Table As seen in Fig. lt) of the driving unit, which is adjusted by mobile base, can reach to the long size of the permanent magnet in Y axis (lm). The r–θ coordinate system is also introduced into XOZ plane of the Cartesian coordinate system, reducing difficulty of the calculation in simulation. According to Ampere molecular current hypothesis and Biot–Savart law23, magnetic intensity induced by the permanent magnet array has been simulated, as shown in Fig. Insert depth  is shown in Fig. As seen in formula , current19. And then the latter will increase the power to keep the set rotative velocity of the motor.As seen in Figs. The 450 kW permanent magnet speed regulator used for cooled water pump, as shown in Fig. The basic parameters of motor and load (water pump) are shown in Table P is proportional to the product of flow rate Q and torque H, namelyAccording to the fluid mechanics, the shaft power n, which are given asAnd there are different relations between the parameters and the rotative velocity Therefore,Rs that is given asPmax is the shaft power under the maximal rotative velocity.Table Q1 decreases to Q2 due to reduced valve in the traditional valve-adjusted mode, the running condition will shift to B from A since the network resistance in pipe increases. The flow is decreased by reducing the valve, however, the rotative velocity is always kept. Therefore, the shaft power cannot be reduced.Figure n2 from n1 and the flow is adjusted to Q2, the pipe also keeps a lower pressure HB. Thus, the shaft power is reduced, and the saving power ΔP is proportional to Q2(H2 − HB) as seen in Fig. 3·h−1), rated lift (59 m), and minimal torque (0.45 MPa) of two motors for the cooling water in Magang (Group) Holding Co., Ltd., the rated torque is 0.58 MPa. And the saving rate is (1 − 0.45 ÷ 0.58) × 100% ≈ 22.4%. Then the two motors can save power Ps = 2 × 1.732 × 6000 V × 51.78A × 0.86 × 8472 h ÷ 1000 × 22.4% ≈ 1,756,400 kW·h, and the fee about 833,000 Chinese Yuan  per year.On the other hand, the rotative velocity of the motor will be reduced by decreasing the insert depth of the driving unit in the magnet speed regulator. When the rotative velocity of the motor reduce to Vibration contrast before and after using the speed regulator is also shown in Table According to the measurement, motor vibration has a remarkable reduction after using the magnetic speed regulator, because there is no solid contact between the driving shaft (conductor) and driven shaft (magnet array), which removes the vibration amplification effect of rigid connection. Meanwhile, there is a relatively higher tolerance against the coaxiality between the driving and driven shafts also because of their non-contact state. On the other hand, a seriously accurate axis alignment whose error is below 0.05 mm is necessary in the traditional valve-controlled pump.2, if the cooling fin is supposed as a gray body with an emissivity of 0.85. Area of cooling fin is about 1.3 m2, and the waste heat generated by this magnetic speed regulator can be estimated as 185.9 W which is below 5 ten-thousandths of the shaft power of 450 kW.In addition, temperature of cooling fin was 43.4 ℃, as measured in Fig. The designed and applied permanent magnet speed regulator for a cooling water pump can save large electric energy during the process of steel production by adjusting the motor power actively. An alternating N–S pole array is used as the driven shaft and a conductor used as driving shaft in the permanent magnet speed regulator. A mobile base controlled by PLC is used to adjust the meshing area and consequentially magnetic eddy, changing the output power of the motor. According to the calculation, the magnet speed regulator can save electric energy by 22% in cooling water pump, about 1,756,400 kW·h per year, compared to the traditional valve-controlled pump. Furthermore, lower vibration and much lower waste heat are generated in the permanent magnet speed regulator.


Panoramic videos are shot by an omnidirectional camera or a collection of cameras, and can display a view in every direction. They can provide viewers with an immersive feeling. The study of super-resolution of panoramic videos has attracted much attention, and many methods have been proposed, especially deep learning-based methods. However, due to complex architectures of all the methods, they always result in a large number of hyperparameters. To address this issue, we propose the first lightweight super-resolution method with self-calibrated convolution for panoramic videos. A new deformable convolution module is designed first, with self-calibration convolution, which can learn more accurate offset and enhance feature alignment. Moreover, we present a new residual dense block for feature reconstruction, which can significantly reduce the parameters while maintaining performance. The performance of the proposed method is compared to those of the state-of-the-art methods, and is verified on the MiG panoramic video dataset. Video super-resolution (VSR) is a classic problem in computer vision, and aims to recover high-resolution videos from low-resolution ones. VSR technology has been widely used in various areas for high-definition displays, such as network videos, digital TV, and surveillance drones. The panoramic video is one class of videos that are real 360-degree omnidirectional sequences, and its pixels are usually arranged in a spherical shape that can provide an immersive experience for users. The panoramic video is the product of a combination of multiple video technologies. Such a video allows the audience to see a wider field of view and a more realistic field of view experience. Because of its 3D stereoscopic characteristics compared with ordinary videos, it is widely used in entertainment, news, the military, and other fields.In recent years, due to the emergence of deep learning, various methods for video super-resolution based on deep learning have been proposed. For instance, in SOFVSR , an optiAlthough the methods mentioned above can achieve good performance for general videos, they may degrade for panoramic videos. Because panoramic videos usually have ultrahigh spatial resolution, they can provide viewers with a strong sense of immersion in the virtual environment. Moreover, the higher the resolution of the camera, the more realistic effect of the panoramic video is. The high resolution requires great hardware performance from camera equipment, and the cost will be largely increased. For this problem, Liu et al.  first exIn order to balance the performance and the computational cost, we propose a novel lightweight super-resolution framework for panoramic videos. As is known, the alignment between video frames is significant for super-resolution. If more interframe information can be exploited for alignment, it is beneficial to the subsequent reconstruction. Thus, we present a new pooled, self-calibrated convolution (PSCC) for frame alignment, which significantly reduces the complexity of the deformable convolution and achieves accurate alignment in a gradual manner. Moreover, in the reconstruction operation, we design a new lightweight residual dense block to further reduce the complexity of the model. Our method achieves a balance between algorithm performance and complexity.The main contributions of this work are listed as follows.We propose the first lightweight panoramic video super-resolution (LWPVSR) method for panoramic video super-resolution, which can achieve a good balance between performance and complexity. To the best of our knowledge, this is the first proposition of a lightweight panoramic VSR framework.Moreover, we present a new pooled, self-calibrated convolution for frame alignment. The self-calibrated convolution is introduced to make the learned offset more accurate in a progressive manner and reduce the complexity of the proposed network.Finally, we design a new significantly lighter residual dense block (LWRDB) for feature reconstruction, which achieves the purpose of reducing the complexity of the model while maintaining the performance of our method. Many experimental results verify the advantage of the proposed LWPVSR method against state-of-the-art methods.The rest of this paper is organized as follows. Some related works on super-resolution of panoramic videos are introduced in Most of video super-resolution methods  method. The proposed LWPVSR method mainly consists of the four main modules: the feature extraction module, the feature alignment module, the reconstruction module, and the dual network module, as shown below.N is the temporal radius  in , as showFirst, the input features  shuffle  and the  shuffle  is imporIn our proposed method, the final super-resolution result is obtained by adding the output of the reconstruction module to the result of the upsampled target frame. It is expressed as follows,M and N denote the width and height of one frame, respectively, h is the height of the patch.In order to show the important content in the equatorial region in the panorama video, we introduce a weighted mean square error (WMSE) loss, which is defined as follows,In order to explain the weight change in each frame more intuitively, we show it visually in The loss function in our architecture is composed of two parts. One is from the main branch—Equation . The parIt is noted that compared with ordinary videos, the information of panoramic video is distributed on a sphere instead of a plane. The panoramic video, which is essentially a spherical video, cannot directly use the storage structure and encoding algorithm designed for ordinary videos. The current mainstream solution is to use the mapping relationship to project the spherical video onto the plane and compress the obtained plane video, and the equirectangular projection (ERP) is widely used. In this case, the important content is usually displayed in the equatorial region, and the less content is at the poles. Moreover, because the information of the panoramic video is distributed in a spherical shape, the features in the same dimensionality are more uneven, and the video is more prone to be distorted. Addressing the particularity that more content distributed at the equator and less content at the poles, we used the weighted loss function, as shown in Equation . It aimsIn this section, we compare the proposed LWPVSR method with eight state-of-the-art super-resolution algorithms for panoramic video super-resolution tasks.The MiG dataset  is utiliThe peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) are usually used as indicators to measure the performance of all the video super-resolution algorithms. In order to make a fair comparison, similar to other works, all indices are calculated on the Y channel for all the algorithms. Different from ordinary videos, we also use the two video quality metrics  in  to measuWe implemented all the models in the PyTorch framework and used two NVIDIA Titan XP GPUs for training. The training schemes and parameters of other methods are listed below.(1)SR360 : The bat(2)VSRnet : The batet as in .(3)FRVSR : The Ada(4)VESPVN : The ini(5)TDAN : The bat(6)SOFVSR : The bat(7)EDVR : The bat(8)OVSR : The batIn our method, the feature extraction module is composed of three residual blocks, each residual block consists of two layers of convolution, and the number of channels is set to 64. The reconstruction module includes five LWRDB blocks, each block is composed of six convolutional layers, and the number of channels is 64. In our experiment, we convert the video frames from the RGB space to the YCbCr space and then use the Y channel as the input to our network. Unless stated otherwise, the network takes three consecutive video frames as inputs. The input patch size is 64 × 64, and the batch size is set to 32. Moreover, we also employ data enhancement techniques as in other methods, including reflection, random cropping, and rotation. Furthermore, we defined the ratio for channel split by experience. If the ratio is larger than 0.5, it means that more features do not participate in the subsequent calculations but are directly cascaded to the subsequent feature maps. Then the following convolutional layers will be meaningless. If the ratio is smaller than 0.5, the model parameters will increase and it results in a higher computational cost. Therefore, the ratio equaling to 0.5 is a balanced choice. During training, we optimize the network by using the Adam optimizer with We also implemented nine other state-of-the-art VSR algorithms for performance comparison. They include bicubic, SR360 , VSRnet It can be seen that our LWPVSR method obtains the highest PSNR and SSIM results, and the amount of its parameters is relatively small. Our LWPVSR method performs much better than EDVR in terms of PSNR/WS-PSNR and SSIM/WS-SSIM, and the the former has significantly fewer parameters than the latter . That is, LWPVSR is nearly 1/10 size of EDVR. It is because the proposed PSCC module in our LWPVSR plays an important role, and decreases the PCD module in EDVR by man parameters but maintains the performance. In addition, compared with FRVSR, our model parameters are 2.7 M smaller, and the PSNR of model is 1.65 dB higher than FRVSR. SR360, VSRnet, VESPCN, TDAN, and SOFVSR20 are relatively lightweight video super-resolution architectures, with model parameters below 2.0 M. However, the performance of all of them is significantly lower than that of the proposed method. Moreover, the PSNR and WS-PSNR results of all the methods on other video clips are shown in In order to demonstrate the relation between performance and parameters more clearly, the visualized diagram is also shown in In this subsection, we qualitatively compare our method with the other methods on video sequences Clip_001, Clip_003, Clip_004 and Clip_009, as shown in It can be seen that our LWPVSR method has achieved much better performance than other methods, including EDVR with 20.60 M parameters, and they have superior visual results in all these figures. For example, in In this subsection, we analyze the contribution of each module in our network, mainly including PSCC and LWRDB, as shown in In this paper, a lightweight and efficient panoramic video super-resolution method was designed from the perspective of lightweight networks. This method adopts deformable convolution to align the nearest neighbor features with the target feature, in order to further enhance the alignment effect step. In particular, we introduced self-calibrated convolution to gradually implement the alignment operation in a recursive manner. Moreover, we also proposed a lighter and more efficient LWRDB module based on the RDB module. Various experimental results verified the effectiveness of the proposed method. Compared with mainstream video super-resolution algorithms, our proposed method achieves a better balance between performance and algorithm complexity.In the future, we will design more effective strategies, such as the attention strategy  for the


Leymus racemosus, a wild relative of wheat, was introduced into two elite wheat cultivars, ROELFS and MUNAL. Using BNI-isogenic-lines could become a cost-effective, farmer-friendly, and globally scalable technology that incentivizes more sustainable and environmentally friendly agronomic practices. We studied how BNI-trait improves N-uptake, and N-use, both with ammonium and nitrate fertilization, analysing representative indicators of soil nitrification inhibition, and plant metabolism. Synthesizing BNI molecules did not mean a metabolic cost since Control and BNI-isogenic-lines from ROELFS and MUNAL presented similar agronomic performance and plant development. In the soil, ROELFS-BNI and MUNAL-BNI plants decreased ammonia-oxidizing bacteria (AOB) abundance by 60% and 45% respectively, delaying ammonium oxidation without reducing the total abundance of bacteria or archaea. Interestingly, BNI-trait presented a synergistic effect with SNIs since made it also possible to decrease the AOA abundance. ROELFS-BNI and MUNAL-BNI plants showed a reduced leaf nitrate reductase (NR) activity as a consequence of lower soil Synthetic nitrification inhibitors (SNI) and biological nitrification inhibitors (BNI) are promising tools to limit nitrogen (N) pollution derived from agriculture. Modern wheat cultivars lack sufficient capacity to exude BNIs, but, fortunately, the chromosome region (Lr#n-SA) controlling BNI production in  The latter is the main greenhouse gas generated in upland agriculture derived from the use of N fertilizers , nitrite  that return to the atmosphere (Nitrogen (N) availability is the major nutrient condition limiting crop growth . Hence,  by 2050 . Unfortutilizers . It is emissions . N2O is rocesses . Nitrifibacteria . During  nitrite . The formosphere .2O emissions  is at the forefront among crops in terms of BNI-production capacity  when applying ammonium-based fertilizers. Currently, the most commonly used SNIs worldwide are nitrapyrin (2-chloro-6-(tri-chloromethyl)-pyridine), dicyandiamide (DCD), and 3,4-dimethylpyrazole phosphate (DMPP) . The addmissions . Neverthmissions  or soil missions . Althougmissions . Limitedmissions . Fortunamissions . Brachiacapacity . BNIs arcapacity ; both ofcapacity .-1 by 2050  than in soils with MUNAL-Control , in Mexico, with two wheat lines outfitted with BNI-trait, MUNAL and ROELFS. CIMMYT holds its main wheat research station in Mexico at CENEB, in part because this location is agro-ecologically representative of Mega-environment one (ME1). The region ME1 comprises a group of locations where 40% of wheat is produced in developing countries . Therefo22.1-1 single application as ammonium sulphate ((NH4)2SO4), and the subplots were the four genotypes from ROELFS and MUNAL isogenic-lines . The experiment was also fertilized with 69 kg P2O5 ha-1 using triple super phosphate, applied pre-planting as a broadcast, and then incorporated. The experiment was set up within the optimum planting date, with a density of 250 seed m-2. The experiment received five irrigations through the crop cycle when available soil water reached 50% and all weeds, diseases, and insects were controlled. The experimental unit was four beds 75-cm apart and 5-m long. The harvest area was the 2 central beds and the central 3 meters and it was done using a Wintersteiger experimental plot combine. The total grain protein content was taken as 5.7 times the total N content  at the Yaqui Valley, near Ciudad Obregón  in one growing season 2019/2020. Soil characteristics are described in  content , which w2.2Triticum aestivum) genetic stocks comprising two isogenic-lines for BNI-trait  were tested in this study  with a day/night cycle regimen of 14/10 h, average temperature of 25/18 °C, and relative humidity of 50/60%. Four elite wheat (is study . To germ4)2SO4) 2SO4)  were addC (g) is the soil gravimetric water content, Db (Mg m-3) is the bulk density; Dp (Mg m-3) is the particle density. Db was determined in the laboratory, resulting in a value of 1.31 Mg m-3, while Dp was assumed at 2.65 Mg m-3. After 14 days of soil activation, pots were divided into 4 groups (one per Control and BNI-isogenic-lines of ROELFS and MUNAL respectively). Later, 4 seedlings of their corresponding Control and BNI-isogenic-line were placed in each pot, and the soil was watered for 15 days to maintain the WFPS up to 45% and to ensure nitrifying conditions.where th day, 4 replicates per Control and BNI-isogenic-lines were harvested as T0 and the remaining pots were fertilized. Within each isogenic-line, 3 N-fertilization treatments (4 replicates per treatment) were established, as follows: 1) fertilization with ammonium sulphate (AS); 2) fertilization with ammonium sulphate + DMPP (AS+D); and 3) fertilization with potassium nitrate (KN). Nitrogen was applied in an equivalent dose to 195 kg N ha−1, which was achieved by adding 1726 mg 4)2SO4, alone or mixed with DMPP (EuroChem Agro Iberia S.L.). DMPP content represented 0.8% of applied N. To achieve a homogeneous distribution of the nitrogen in the soil, the fertilizer was dissolved in deionised water and added to the corresponding pots by pipetting. All the treatments were watered every two days to maintain the 45% WFPS up to 30 days after fertilization, when the plants were between Z51 and Z53 stages  and denitrification  were amplified as described by The abundance of nitrifying and denitrifying genes in soil was quantified through quantitative polymerase chain reaction (qPCR). Dry soil (0.25 g) was used to extract the DNA, using the PowerSoil DNA Isolation Kit (Quiagen) with the modifications described in Soil mineral N was determined as  content . The NO2.2.2Biomass production was given as dry weight (DW) per plant. To do so, one plant per pot was dried at 80°C in a circulation oven for 72 h until a constant DW was reached.® water in a ball miller (Retsch MM 500) for 3 min at a frequency of 27 s-1. Homogenates were incubated at 80 °C for 5 min and, afterward, centrifuged at 16,000 g for 20 min. Later, supernatants were recovered and stored at -20°C until metabolite quantification. Leaf in vivo method (Nitrate reductase (NR) activity was determined in leaves by the modified o method . The NR o method . Once lehloride) . The abs2.3p-values< 0.05 were considered to be statistically significant differences.The data obtained either in the field or in the greenhouse experiments were analysed by the Mann–Whitney U test to compare the absence or the presence of BNI-trait in both ROELFS and MUNAL BNI-isogenic-lines. Additionally, for 33.1-2 were also akin between all BNI-isogenic-lines, with no differences in 1000-grain weight. However, there were some differences in the grain protein content, since it was 9% higher in ROELFS-BNI compared to its Control line; on the contrary, the MUNAL-BNI was 11% lower than its Control line.Similar grain yield was found in Control and BNI wheat plants from ROELFS and MUNAL BNI-isogenic-lines Table 1.3.23.2.1As it was expected, ROELFS-Control and ROELFS-BNI isogenic-lines did not present differences before the addition of N sources neither in the measured parameters of the soil  nor in those of the leaf  Figure 13.2.2amoA abundance reduced in soil by 60% and 45%, respectively compared to their Control-isogenic-lines  the most noticeable changes occurred for MUNAL line. MUNAL BNI-lines showed 43% lower nirK abundance  of L. racemosus expressed the BNI trait with different abilities  capacity of bilities . ROELFS-bilities ; howeverbilities . In the bilities Table 1.bilities . Therefo-1 for the microcosm’s experiment, and 250 kg N ha-1 for the field experiment) since as reported in +-ATPase. This activity is entangled in the intracellular pH homeostasis through the release of protons to the rhizosphere  , but itsachiaria  where noThe inhibitory effect of ROELFS-BNI and MUNAL-BNI plants on soil ammonia-nitrifying microorganisms is reflected in the soil mineral N. The soil lization . Accordil plants . InteresnirK, nirS, nosZI, nosZII) after ammonium fertilization  and by the Basque Government . AB-L and LU held grants from the Basque Government (PRE-2020-2-0142 and PRE-2020-1-0127).The authors thank for the technical and human support of greenhouse provided by SGIker .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.


Nutritional status is an independent predictor of outcome in cirrhosis patients. Nutritional Risk Screening 2002 (NRS2002), Royal Free Hospital-Nutritional Prioritizing Tool (RFH-NPT), and Liver Disease Undernutrition Screening Tool (LDUST) were employed to detect cirrhosis with malnutrition risk in this work. Meanwhile, their diagnostic performances were compared to find the best screening method. This work aimed to establish the sarcopenia cut-off value of the transversal psoas thickness index (TPTI), and identify the risk factors for malnutrition. Cirrhosis patients who were admitted to Heibei Gerneral hospital from April 2021 to October 2021 and underwent abdominal CT examination were enrolled. 78 patients were assessed by NRS2002, RFH-NPT, and LDUST. The Global Leadership Initiative for Malnutrition (GLIM) criteria were selected as the gold standard for the diagnosis of malnutrition. Meanwhile the cut-off value of sarcopenia was established based on the TPTI of malnourished patients. Logistic regression analysis was adopted to assess the influencing factors of malnutrition risk and malnutrition. The prevalence of malnutrition was 42.31%. The prevalence of malnutrition risk was 32.1%, 61.5%, and 62.8% with NRS2002, RFH-NPT, and LDUST, respectively. NRS2002 presented the best specificity compared with the other methods, while RFH-NPT showed the highest sensitivity. The optimal gender-specific TPTI cut-off value for diagnosing sarcopenia was determined as TPTI < 14.56 mm/m  and TPTI < 8.34 mm/m . In the multivariate analysis, ascites was associated with malnutrition risk, while sarcopenia showed a significant risk for malnutrition. NRS2002 and RFH-NPT were superior to LDUST at detecting the malnutrition in cirrhosis patients diagnosed according to GLIM criteria. The gender-specific TPTI cut-off value was TPTI < 14.56 mm/m  and TPTI < 8.34 mm/m . Malnutrition risk should be screened for patients with ascites as soon as possible. In addition, it was important to evaluate malnutrition in sarcopenia patients in time. Early detection and diagnosis are vitally associate to prognosis of cirrhosis patients2.Malnutrition is a common complication of cirrhosis. It can prolong the hospital stays, especially the time in intensive care unit (ICU), accelerate the decompensation, and increase mortalityHowever, nutrition assessment and nutrition screening are challenges to this group of patients in China. It is caused by multiple factors, including the absence of gold standard for nutrition assessment, unavailable validated screening tools, and few studies research on the difference of screening tools.5. Multiple scientific nutrition societies have established the GLIM criteria as a global diagnostic reference for malnutrition6. As far as we know, the GLIM criteria for cirrhosis have been investigated in some studies.The prevalence of malnutrition in cirrhosis ranges from 5 to 99%, depending on the severity of liver cirrhosis and the diagnostic criteria7 is most widely used in the world due to some limitations in the application of cirrhosis because of the absence of ascites parameters. RFH-NPT8 andLDUST9 are established for patients with liver cirrhosis, while their data in China are few.Various malnutrition risk screening tools have been proposed with different advantages and disadvantages, but their results vary among different studies. NRS200210. TPTI is an easy, reliable, and repeatable parameter for assessing the sarcopenia since it is computed tomography (CT)-based and just needs to measure the psoas diameter11. However, data on TPTI in cirrhosis are still lacking in China and the cut-off value of sarcopenia is rare.Cirrhosis is complicated by sarcopeniaIn this work, the GLIM was undertake as the gold standard to diagnose malnutrition and three malnutrition risk screening tools  were evaluated. This work aimed to compare the differences in these 3 tools and identify their superiorities in live cirrhosis. Meanwhile, the best cut-off value of sarcopenia was identified by comparing the TPTI in nutrition group and malnutrition group.This work included 78 cirrhosis patients who were admitted to Heibei General Hospital from April 2021 to October 2021 and underwent abdominal CT examination. All patients met the diagnostic criteria of the ‘Chinese Guidelines on the Management of Liver Cirrhosis’. Patients with any of below conditions had to be excluded: severe cardiopulmonary disease, cancer, inflammatory bowel disease, and other metabolic disorders. In addition patients over 80 and under 18 years old were excluded.This work was approved by the ethics committee of the Heibei General Hospital (2021-267) and was performed in accordance with the Declaration of Helsinki. Written informed consents were obtained from all the participants prior to the enrollment.The data included gender, age, height, body weight, body mass index (BMI), routine blood examination, biochemical parameters and other laboratory tests. The body weight was adjusted according to water content retention. The weight was reduced by 5%,10%, and 15% in mild, moderate, and severe ascites, respectively and it was reduced by 5% in patients who had combined peripheral edema. The classical nutritional markers included calf circumference, arm circumference, and arm muscle circumference.12  was defined as the largest antero-posterior diameter of the right psoas muscle at the L3 level and recorded in millimeters(mm), and the longest transverse diameter which was perpendicular to APMT was defined as the psoas muscle transversal psoas muscle thickness (TPMT), TPTI = TPMT / height (mm/m)The NRS2002, RFH-NPT, and LDUST were used for malnutrition risk screening, and the GLIM criteria were selected for the assessment of malnutrition. Patients who were assessed to be at risk of malnutrition were included in the risk group. According to the GLIM criteria, the 78 patients were divided into a malnutrition group and a nutrition group. All tools were performed by an experienced clinical physician.13. Nutritional impairment ranged from 1 to 3 according to the weight loss over 5% in 3 to 1 months or the food intake reduced < 50%, 50–75% and > 75% compared with the values in previous week. It can be recorded as 3 points if the BMI < 18.5 kg/m2. The severity of disease was assessed as 1, 2, and 3 points, respectively, in different situations. Cirrhosis was recorded as 1 point. The total score of three parts classified patients into a no risk (< 3 points) and a malnutrition risk (≥ 3 points) group.The NRS2002 contained nutritional impairment scores, severity of disease scores, and an age adjustment in which over 70 years adds one point14. First, the alcoholic hepatitis patients or those who needed tube feeding were assessed as high risk. Those who failed to meet the conditions above needed to assess ascites or edema and its impacts on food intake and weight loss. If ascites or edema was found, 1 point was assigned. The food intake impact on ascites or edema was recorded as 0 to 2 points, which corresponded to no influence, occasional influent, and influent. If the food intake was reduced by half in 5 days, 2 points were given, or otherwise 0 points were assigned. The weight loss in the past 3–6 months ranged from 0 to 2 points, which meant for no, being unable to evaluate, and yes, respectively. Those who did not have fluid overload were estimated using BMI, unplanned weight loss, and dietary intake. BMI was recorded as 0 to 2 points according to > 20 kg/m2, 18.5–20 kg/m2 and  < 18.5 kg/m2, respectively. The unplanned weight loss in the past 3–6 months ranged from 0 to 2 points according to < 5%, 5–10%, and  > 10%, respectively. It should assess whether the patient is in acute exacerbation and has or may have no nutritional intake for more than 5 days. It was recorded as 2 points if yes. Patients with a score of 2–7, were at high risk, and they were at low risk with a score of 0 and moderate risk with a score of 1.RFH-NPT was performed in three steps15. 5 or more points were included in grade A, which was identified as no risk, while 2–5 points in grade B and  < 2 points in grade C were identified as malnutrition risk.The LDUST included six questions. Food intake, weight loss, muscle loss, swelling or fluid, and daily activities were determined as grades A, B and C6. Phenotypic criteria included weight loss, low BMI, and reduced muscle mass, while etiologic criterion contained reduced food intake or assimilation and inflammation. All the cirrhosis patients were considered to present chronic disease-related inflammation, so they all met the etiologic criterion. Weight loss which went down 5% in 6 months or 10% over 6 months was assessed in this work. Meanwhile, the low BMI (BMI < 18.5 kg/m2 for patients < 70 years old or BMI < 20 kg/m2 for patients > 70 years old) was assessed. If the weight and BMI were in normal ranges, their calf circumference can be assessed to check whether the muscle mass was reduced. According to a Japanese standard, reduced muscle mass was assessed by calf circumference ≤ 30 cm  and ≤ 29 cm  since there was no sarcopenia standard in China16. The patients who met one phenotypic criterion and one etiologic criterion were diagnosed with malnutrition in the GLIM criteria.There were two components in the GLIM criteria: phenotypic criterion and etiologic criterionThe TPTI of the malnutrition group based on the GLIM criteria was employed to establish the cut-off value of sarcopenia in cirrhosis.Data were analyzed using SPSS 25 and Graphpad Prism 8.3.0. Data that which conformed to the normal distribution were expressed as the mean ± standard deviation (SD). The comparison between the two groups was performed by using the t test. Non-normally distributed data were represented by Median (P25 ~ P75) and the comparison between two groups was performed using the Mann–Whitney U test. The count data was expressed as percentages (%), and the two groups were compared by the Chi-square test. Correlation and consistency were assessed by spearman correlation coefficient analysis and Kappa consistency test, respectively. Receiver operating characteristic (ROC) curves were adopted to evaluate the ability of the three screening tools to distinguish the malnourished patients. The GLIM criteria were selected as the reference, ROC curves were generated for TPTI, and the best cut-off values to diagnose sarcopenia were determined using the Youden Index. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were estimated to test the quality of screening tools to predict malnutrition. Logistic regression analysis was employed to assess the influencing factors of malnutrition risk, malnutrition, and sarcopenia. P value less than 0.05 meant that the difference was significant.The characteristics of the 78 included subjects were detailed in Table In this work, NRS2002 exhibited the highest correlation with the GLIM criteria based on spearman correlation coefficient , followed by RFH-NPT and GLIM criteria  , followed by RFH-NPT  Fig. , Table 42 = 17.313, P < 0.001). Regardless of gender, the rate of sarcopenia in the malnutrition group was significantly higher than that in the nutrition group (P < 0.01). The incidence showed no differences in Child-A, Child-B, and Child-C, and gender.Using GLIM criteria as the reference, the cut-off values were calculated and derived from ROC curves and the Youden index Fig. . The TPTThe logistic regression analyses revealed that albumin, prealbumin, lactate dehydrogenase, alkaline phosphatase, ascites, and sarcopenia were influencing factors for malnutrition risk. Among them, only ascites was an independent risk factor Table . TPTI an18.There are various malnutrition methods to assess cirrhosis, such as SGA and RFH-SGA. These two methods are time-consuming and poorly applied in China. In 2018, the global nutrition societies established the GLIM criteria to form a global consensus on the diagnosis of malnutrition. After that, the use of GLIM criteria was in many crowds, such as cancer or older adults20. Meanwhile, there are increasingly more studies selecting the GLIM standard as the gold standard in patients with liver cirrhosis21.At present, few studies have examined on the application of the GLIM criteria for malnutrition in cirrhosis. In addition, some studies have found that compared with SGA and RFH-GA, GLIM can better predict the mortality of patients with chronic liver disease22. With the use of GLIM criteria, 42.3% of cirrhosis patients were identified to be complicated with malnutrition in this work, which is similar to data from Boulhosa RSSB and Diego 22.Boulhosa RSSB and Diego were the first batch researchers to focus on this fieldThe assessment using the GLIM criteria is performed in two steps. First, whether patients were at risk of malnutrition should be assessed using nutritional screening tools. Second, the GLIM criteria are adopted to assess the malnutrition risk patients with malnutrition. This work aimed to determine which nutritional screening tools could obtain findings consistent findings with the results of the GLIM criteria, so that they could be applied in the first step of Glim in the follow-up clinical work. This work validated three screening tools in predicting malnutrition when diagnosed based on the GLIM criteria.NRS2002, RFH-NPT, and LDUST were validated and effective methods for nutritional screening of patients with liver cirrhosis. The GLIM group recommend the NRS2002, but this work also found that the RFH-NPT showed the same correlation and consistency as NRS2002.24.This work confirmed that NRS2002 and RFH-NPT were the optimal tools applied in patients with the best specificity and sensitivity, respectively. This conduction is in line with previous studies that revealed superiorities of NRS2002 and RFH-NPT in specificity and sensitivity2 as a reference. In terms of food intake, the NRS2002 pays more attention to changes in a short time (within 1 week), while the GLIM expands the range to changes within two weeks. Besides, the NRS2002 assesses the weight loss within 1–3 months, while the GLIM only evaluates weight loss within 6 months of malnutrition grading. There are so many similarities between them that the good consistency between them is easy to understand.Compared with NRS2002, GLIM takes BMI, liver cirrhosis, and food intake as evaluation items. The difference is that the BMI of GLIM is more detailed according to patients’ ages, unlike NRS2002, where is the BMI of all patients is 18.5 kg/mRFH-NPT focuses on fluid retention, and patients are divided into two groups according to different results, and receive different assessment steps for BMI and weight loss are carried out. GLIM is proposed for patients with various diseases. It is not included in the item of fluid retention, but in the assessment of muscle loss. For patients with liver cirrhosis, the assessment of muscle loss is more objective and accurate than fluid retention.LSUST is a subjective evaluation form for patients. Many Chinese patients will ignore their weight, muscle loss, and diet changes, so the consistency between LSUST and GLIM is not as good as that of the other two methods.NRS2002 score needs over 3 when it is considered as malnutrition risk, RFH-NPT score reaches 1 point, while LDUST needs two of the six subjective questions to get B or C. NRS2002 is relatively more rigid to confirm the diagnose malnutrition risk. Therefore, patients who were evaluated as having no malnutrition risk by NRS2002 can be more accurately diagnosed as having no malnutrition. RFH-NPT considers patients with ascites or peripheral edema as malnutrition risk, while LDUST needs to merge the fluid overload with another entry. NRS2002 does not involve fluid retention. This is the reason that RFH-NPT shows the highest sensitivity. Among the three methods, NRS2002 and RFH-NPT can be better applied to screen the malnutrition risk in cirrhosis, while LDUST is more subjective and can be used for self-screening management in a short time, so it is applicable to clinical practice.26, TPTI can be measured by abdominal CT but requires no special measurement software. This work found the cut-off value of sarcopenia as TPTI < 14.56 mm/m  and < 8.34 mm/m , which was higher than the value mentioned in a foreign study27, which may be related to the distinct way to get thecut off values. Psternosrto27 obtained his threshold by the mortality of patients in a follow-up period, but the malnutrition of patients was adopted in this work. The average TPTI of deaths was lower than that of malnourished patients. The threshold in men is higher than that in women, which is similar all over the world, because of the influence of gender hormones that men have more muscle groups. Even after adjustment for height, the amount of psoas muscle still shows a significant gender difference28.TPTI is a simple and rapid method of diagnosing sarcopenia. In contrast with the L3 skeletal muscle index which is commonly used abroad30. In addition, rest energy expenditure increases significantly in these patients31. The nutrition assessment for most ascites patients with increased weight is often ignored by clinicians. According to the literature, Xiaoyu Wang and their colleagues used RFH-NPT to assess 135 cirrhosis patients and found that ascites increased the malnutrition risk, which was similar to the findings of this work32. Malnutrition can accelerate the progression of ascites and significantly increase the probability of refractory ascites34. However, ascites was not proven to be a risk factor for malnutrition in this work, which may be because of the small sample size and the use of GLIM criteria as a standard for malnutrition assessment.Because nutritional status is an intervention that can impact prognosis, it is expect to identify the risk factors for different nutritional stages and to take patients with risk factors seriously. The data in this work suggested that the malnutrition risk of patients with ascites was 5.665 times as that of patients without ascites. Patients with ascites are more likely to suffer from anorexia and induced intake due to severe gastrointestinal symptoms and early satiety37. The research of Hsu CS found that the prevalence of sarcopenia increased in men and patients with ascites, liver failure, and kidney failure38. A similar conclusion was reached by this work, in which prealbumin was an independent factor.The impact factors of sarcopenia have been studied for many years. Studies have shown that Child–Pugh, arm circumference, arm muscle circumference, age, myostatin, albumin, and prealbumin are the impact factorsOn the other hand, there were some limitations in this work. First, it may be subject to population bias due to the small sample size and single center study may cause population bias. Second, the effects of nutritional support therapy on the prognosis of patients with malnutrition or sarcopenia have not been studied.As far as we know, this work was the first study in China to compare the NRS2002, RFH-NPT, and LDUST with GLIM criteria in patients with cirrhosis. In the light of GLIM criteria, the prevalence of malnutrition was 42.3%, which confirmed that NRS2002 and RFH-NPT could better screen for malnutrition risk in clinical application in patients with cirrhosis. Moreover, TPTI < 14.56 mm/m  and < 8.34 mm/m  were proven to be the cut-off values of male and female patients with sarcopenia, respectively. In conclusion, this work verified that clinicians should attach importance to the patients with ascites and sarcopenia. The datasets generated or analyzed in this work were available from the corresponding author on reasonable request.Supplementary Information.