With the continuous improvement of people’s health awareness and the continuous progress of scientific research, consumers have higher requirements for the quality of drinking. Compared with high-sugar-concentrated juice, consumers are more willing to accept healthy and original Not From Concentrated (NFC) juice and packaged drinking water. At the same time, drinking category detection can be used for vending machine self-checkout. However, the current drinking category systems rely on special equipment, which require professional operation, and also rely on signals that are not widely used, such as radar. This paper introduces a novel drinking category detection method based on wireless signals and artificial neural network (ANN). Unlike past work, our design relies on WiFi signals that are widely used in life. The intuition is that when the wireless signals propagate through the detected target, the signals arrive at the receiver through multiple paths and different drinking categories will result in distinct multipath propagation, which can be leveraged to detect the drinking category. We capture the WiFi signals of detected drinking using wireless devices; then, we calculate channel state information (CSI), perform noise removal and feature extraction, and apply ANN for drinking category detection. Results demonstrate that our design has high accuracy in detecting drinking category. With the development of people’s living standards and the gradual enhancement of health awareness, the consumption demand for beverages is also rising. At the same time, it is beginning to show a diversified trend. People no longer focus on the function of thirst quenching, and prefer natural, low sugar, and healthy drinks ,2. In adAccording to research from the United States, drinking one or two glasses of beer a day can help bones be healthier . For womCurrently, many industries are solving this problem with some instruments. For example, drinking categories can be detected and drinking concentrations analyzed by conventional viscosity testing instruments ,8,9,10. In recent years, non-contact drinking category detection methods based on wireless signals such as radio frequency (RF) signals ,12,13, UThis paper introduces a novel drinking category detection method based on wireless signals and an artificial neural network that leverages WiFi signals to identify drinking categories, which are inexpensive and ubiquitous with WiFi devices. The intuition of our design is that when the WiFi signals propagate in different drinking, the signals will have different attenuation , which iOur design employs a transmitter and a receiver, which are commercial off-the-shelf (COTS) devices, for transmitting and receiving wireless signals, respectively. The wireless signals are transmitted from the transmitter to the target to be tested and then propagated to the receiver through multiple paths. Different drinking categories have different multipath profiles, also known as CSI measurements, which can be leveraged to detect drinking categories.One challenge is the noise in the CSI measurements, resulting in lower detection accuracy for drinking categories. Since WiFi devices are susceptible to interference, CSI measurements are noisy even in static environments without human activity. Moreover, because conventional denoising methods are only suitable for low-density noise, and the noise density in CSI measurements is higher, conventional denoising methods do not perform well in removing noises. To overcome this challenge, our design leverages a principal component analysis (PCA)-based CSI denoising method.Another challenging problem is the need to extract effective features from the received wireless signals for drinking category detection. Different materials will change the propagation paths of wireless signals, resulting in different multipath effects. The detection method based on wireless signals detects the drinking categories by capturing multipath profiles of different drinks. Therefore, feature extraction is the basis of this method, and the effective features will improve the overall performance of our design. To remove the limitations, we extract fifteen time–frequency domain statistical features and then use ANN to detect and classify drinking categories.Summary of Results: We built a model of our design using COST devices as a transceiver and evaluated it with six common drinking categories. Our experimental results show that our design can achieve high detection accuracy.Contributions: Our design makes the following contributions:It presents a novel drinking category detection method based on wireless signals and an artificial neural network. As a result, our design has high detection accuracy and high classification precision.It demonstrates that ANN performs well in drinking category detection compared with traditional machine learning methods.The beverage is a liquid for human or livestock drinking; it is a product with quantitative packaging for direct drinking or mixing or brewing with water in a certain proportion, where the ethanol content (mass content) does not exceed 0.5%. According to the general classification rules for beverages, beverages can be divided into 11 categories: packaged drinking water, fruit and vegetable juice, carbonated beverages, protein beverages, special purpose beverages, flavor beverages, tea beverages, coffee beverages, plant beverages, solid beverages, and other beverages.Carbonated beverages (soft drinks) refer to drinks filled with carbon dioxide gas under certain conditions, generally including Coke, Sprite, soda, etc.Fruit and vegetable juice drinks refer to fruit and vegetable juice obtained directly from refrigerated or fresh vegetables and fruits without the addition of any foreign substances, and are made from fruit and vegetable juice with water, sugar, acid, or spices. Generally includes fruit juice, fresh juice, vegetable juice, mixed fruit and vegetable juice, etc.Energy drinks  refer to a beverage that regulates human function to a certain degree by changing the composition and nutritional content percentage of the drink. According to energy drink categorization based on relevant references , they arTea drinks refer to tea products made by soaking the tea in water, extracting, filtering, or clarifying, and/or by adding water, sugar, sour, food flavors, and fruit juices into the tea soup. Generally includes green tea, black tea, oolong tea, wheat tea, herbal tea, fruit tea, etc.Milk beverages refer to the products made from fresh milk or dairy products after fermentation or without fermentation, generally including milk, yogurt, milk tea, etc.Coffee drinks are made from roasted coffee beans. Generally includes coffee.The classification includes the following:In this paper, we investigate six kinds of drinks in all.Wireless sensing technology refers to the non-contact sensing technology for people and the environment through universal wireless signals, such as electromagnetic waves , light wTaking the RF signal as an example, the principle of wireless sensing is that the wireless signal generated by the transmitter has physical phenomena such as direct reflection and scattering in the propagation process, thus forming multiple propagation paths, as shown in Compared with current sensing technology ,21,22,23CSI describes how the WiFi signals propagate from the transmitter to the receiver , reflectn transmit antennas and m receive antennas, the system will receive m × n × s subcarriers at the receiver, where s is the number of subcarriers in each channel. In addition, the measured channel frequency response f and t are frequency and time, respectively.When the deployed device has  formula :(1)H and CSI.The WiFi signals propagate through the target to the receiver via reflection, refraction, attenuation, etc., resulting in wireless signals distortion, which is known as the multipath effect. RSS is sensitive to the environment and is vulnerable to multipath propagation, which affects detection accuracy . FurtherCSI describes how the physical environment affects the wireless signals . FurtherCompared with RSS, CSI can obtain more fine-grained information and higher accuracy ; so, ourThe WiFi-signal-based drinking category detection method relies on similar CIR measurements. To analyze whether WiFi signals can detect drinking categories, we plot the CSI magnitude images for different drinks in the same environment and the same drinks collected multiple times, as shown in Data Collection. Our design uses two wireless devices to collect CSI measurements at the receiver end of the wireless link, one as a transmitter and the other as a receiver. Current CSI-based detection methods collect CSI measurements using the PicoScenes tool [nes tool ,29, whicnes tool , 117 subNoise Removal. For the fluctuation of the collected CSI measurements due to the interference of factors such as internal CSI reference levels, transmission rates, and transmit power levels, CSI measurements frequently contain noise in the time domain and frequency domain. Therefore, the collected CSI measurements need to be denoised for further feature extraction and drinking category detection. In this paper, our design uses Principal Component Analysis (PCA) to remove noise [ve noise ,32,33,34n is the CSI stream size and Generally, the denoising steps of PCA include preprocessing, correlation estimation, eigendecomposition, and movement signal reconstruction. First, 1-second data are intercepted for each CSI stream and the average is calculated as a constant offset for each CSI stream, which is the average CSI amplitude. After that, the static path components in each CSI stream are removed by subtracting the corresponding offset from each stream. Next, the remaining CSI streams are formed into a matrix of The first principal component mponents ; so, we We plot the CSI measurements of different drinking categories before and after denoising, as shown in Time domain statistics features ,36 are eWe plotted the box plots of the feature values, as shown in To analyze the necessity of the 15 features, we have verified the effectiveness of the features based on the F-test, and the results of the F-test are represented in a heat map, as shown in Our design uses a fully connected, feed-forward artificial neural network for drinking category detection, as shown in The extracted feature length determines the number of nodes within the input layer, and the drinking categories affect the number of nodes within the output layer. The detection model learns how to relate CSI measurements to different drinking categories based on the training data. Once the model has learned the mapping, it can easily be used to test the CSI measurements. We use the back propagation of the Stochastic Gradient Descent (SGD) method and the cross-entropy loss function to train the detection model, see Appendices Wireless devices setup. We employ a TL-WR886N router as the transmitter and an IWL 5300 NIC with a mini PC as the receiver to collect CSI. In our design, shown in Drinking categories. In our design, we chose six common drinks as test targets, including Coke, freshly squeezed watermelon juice, RedBull energy drink, black tea, milk, and instant coffee. All drinks were purchased from the supermarket; so, the density and materials of the same drinks to be tested were kept fixed. During the data collection, the volume of all drinks to be tested was fixed at 300 mL, and the collection environment and container were kept constant.Model Parameters. Our design utilizes Model Evaluation. In drinking category detection model, we consider the detection performance impact of different hidden layer numbers starting from two to ten and different numbers of neurons in the hidden layer from 100 to 1000. In addition, we compare our detection model with current common learning methods and we use cross-validation to evaluate our design.Evaluation metrics. Four evaluation metrics, including accuracy, precision, recall, and f1-score, are used to evaluate the effectiveness of the proposed method in the experiment. When Our method achieves about 87.9% accuracy for detecting the drinking categories. The results show that this method can successfully achieve drinking category detection, which promotes its actual implementation in further development.Our system is novel and intelligent compared with current drinking category detection methods. The system’s novelty and intelligence are represented in the fact that it does not need any support of professional devices and it can be achieved using commercial devices. However, our design only provides a prototype framework; more drinking categories can be detected and additional intelligent functions can be developed in the future.The main findings of evaluations are as follows:In all detection models, we fixed the training datasets and test datasets. Besides, we used the evaluation metrics above to evaluate the detection performance, which is shown in In the parameter adjustment of neural network, the number of hidden layer nodes and the number of layers are also used closely, which can fully adjust the effect of neural network. The activation function and model complexity control play a major role. In the process of neural network training, parameter adjustment is through continuous attempt and running, and it is a common optimization method to adjust the four parameters in the neural network model. These parameters need to be combined continuously to achieve the best model. The most typical parameters are the number of hidden layers, the number of nodes in each hidden layer, and the loss function. Thus, in the experiment, we evaluate the three parameters.The results show that a seven-layer ANN will be the better choice of our design.More network layers can better help the network capture relationships, but can also lead to overfitting. Therefore, to evaluate the impact of hidden layers on the method performance, we increase the number of hidden layers from two layers to ten layers. The convolution kernel structure is the same in the experiment and the results of evaluation metrics are shown in As can be seen from The results demonstrate that 500 is an excellent choice for ANN nodes at each layer.When the number of nodes in the network is too large, the information processing ability is enhanced, causing the limited amount of data included in the training dataset to not be enough to train all the neurons in the hidden layer, and it is difficult to obtain the expected effect. In order to properly choose the number of nodes for each layer in ANN, we choose the number of nodes in each layer from 100 to 1000 to compare the performance improvements. It should be noted that the network structure employed in the models is the same. As can be seen from Experimental results show that using MAE as loss function has higher detection accuracy.The loss function is a measure of the performance of the prediction model. No loss function can be applied to all types of data. In order to select the loss function suitable for the drinking category detection model, we selected the loss function including MAE, Mean squared error performance function (MSE), Sum absolute error performance function (SAE), Sum squared error performance function (SSE), and Cross-entropy performance function (CE) to compare their performance. The network structure used in the model is the same. As can be seen from The results show that the ANN detection model has a significant performance improvement compared with other detection models.To evaluate the drinking category detection performance, we compare ANN with three commonly used learning methods including SVM (Supported Vector machine), RF (Random Forest), and KNN (K-Nearest Neighbor). The results are shown in From There is still potential for improvement in terms of the performance of our design, and we will discuss various points below.Feasibility. Since our design requires a fixed transceiver, changes in device deployment may require the re-collection of fingerprints in real-world scenarios. We believe this will not be a problem as we can solve it using transfer learning. For the drinking category detection model used in this paper, in addition to the above six drinking categories, other types of drinking can also be added to the training dataset, allowing our design to identify more drinking categories. However, the increase in the variety of drinks may affect the detection performance;, we can extract other features that are better suited for drinking category detection, which is beyond the scope of this paper and will be the subject of future research.Depending on particular hardware cards. In order to collect CSI measurements, specific NICs in the Linux system, which includes IWL 5300 NICs and Atheros NICs, must be used. However, both wireless transmitters and NICs are commercial devices, and they are quite inexpensive—for example, the IWL 5300 network card, costs around USD 3. With the growth of smart houses, wireless transmitters may become widespread. Furthermore, as CSI-based detection applications proliferate and mature, CSI will be accessible to upper layers via most NICs in the foreseeable future.Target. The purpose of this paper is to detect drinking categories, and the drinking to be tested is a single category. We have not detected mixed drinking categories. Besides, there is research on adulterants identification based on wireless signals [ signals ,14,47,48Impact factors. Our design assumes no human activity in the current environment when detecting the drinking category, which is the assumption of most current wireless-signal-based detection methods. When the environment is noisy or there is human activity, the CSI measurements received at the receiver are mixed signals [ signals ,27 of th signals  and Venk signals , the noi signals . In the Material identification techniques play an important role in industry ,52, techInstrument-based methods. This method utilizes the differences in chemical properties of different drinking, applies instruments to analyze them, and then detects the target [e target ,8,9,10. Wireless-signal-based methods. This method utilizes the propagation characteristics of wireless signals through the target drinking to detect the drinking category. Currently, there are several popular wireless-signal-based liquid detection methods: based on RF signals [ signals ,12,13,53 signals ,54, rada signals ,55. For  signals  identifi signals  identifiOptical-based methods. This method analyzes the optical spectra of different liquids by obtaining information about the optical absorption or reflection from the liquid to detect the target [e target ,57,58. Fe target  utilizesANN is used to detect the target. A large number of experiments are performed to demonstrate the effectiveness of the method, including model parameters comparison and currently used model comparison. Experimental results demonstrate the effectiveness of our design, which can achieve about 88% accuracy in multiclass classification. We believe that combined with multicategory detection and considering more interference factors and more drinking categories in current drinking category detection systems, the system can be more intelligent, which is beyond the scope of this paper and will be our future work.This paper presents a novel drinking category detection method based on wireless signals and an artificial neural network, which identifies which category  contains the detected target. A convolution kernel is first used to extract features automatically; then,


This sentiment analysis helped to compute metric variables. The I-score represents the extent of portrayals of Muslims in a particular news story. The next step is to calculate the canonical correlation between video transcripts and their respective responses, explaining the relationship between news portrayal and hate speech. This study provides empirical evidence of how news stories can promote Islamophobic sentiments and eventually atrocities towards Muslim communities. It also provides the implicit impact of reporting news stories that may impact hate speech and crime against specific communities.Islamophobia is a sentiment against the Muslim community; recently, atrocities towards Muslim communities witnessed this sentiment globally. This research investigates the correlation between how news stories covered by mainstream news channels impede the hate speech/Islamophobic sentiment. To examine the objective mentioned above, we shortlisted thirteen mainstream news channels and the ten most widely reported Islamophobic incidents across the globe for experimentation. Transcripts of the news stories are scraped along with their comments, likes, dislikes, and recommended videos as the users’ responses. We used a word embedding technique for sentiment analysis,  According to the Merriam-Webster dictionary, the term “phobia” is an exaggerated, inexplicable, and illogical fear toward a particular entity or object or their classes . It is hAl Jazeera, a leading news channel, summarized recent violent attacks against the Islamic community in different parts of the world, following a rise in frequency. Their summary, along with work by , mentionIslamophobia is an evolving Social Science comparative phenomenon. Yet no generally accepted Islamophobia concept allows for systematic comparative and casual study . Anti-MuIn today’s digitalized world, a person receives information at a higher pace about any incidents and develops mass sentiments about specific communities. This research paper has examined how reporting certain events develops public sentiments about the Muslim community. For this purpose, we have taken fourteen mainstream news channels reporting about incidents in the near past. We captured their YouTube stories transcripts and their public response. . This research activity’s objective is (a) the correlation of hate speech between the news story’s contents and its response to specific news channels. (b) Identification of news channels, information and their respective audience with a relatively higher number of hate words (promoting Islamophobia). The third objective is to understand the media’s impact on public sentiments toward Muslims.corpus is English ; therefore, it is not multilingual sentiment analysis. We have used a bag of words models and specialized scales for sentiment analysis for hate words. Finally, canonical correlation is measured among incidents reported by various research news channels. The contribution of this research work is to simulate a state-of-the-art method for corpus collection from online videos and their respective responses from social media platforms. Furthermore, it also provides a method for sentiment analysis from multilingual data. It also provides empirical evidence regarding news portrayals’ impact upon hate speech.This research activity’s essence is the sentiment analysis of YouTube videos’ transcripts and their responses . Data is gathered from YouTube using Data Miner’s customized recipe. To clean different activities for cleaning data has been carried out discussed in the subsection of methodology. Our corpus was collected; organized and canonical correlation was performed. Finally, “Discussion” concludes the article with a comprehensive discussion.“Literature Review” provides a literature review about the approaches used for data collection, sentiment analysis. “Methodology” provides a detailed overview of how The media have a significant role in shaping domestic and geopolitical landscapes. Approaches to determining the relation between media portrayals of Muslim communities with Islamophobic sentiments emergence utilized text analysis more than any other approach (up to 63% out of 38 studies). Text analysis provides sentiment analysis, along with many statistical measures, its utility in said domain more than any other approach. Our approach to understanding the correlation between portrayals for Muslim communities and Islamophobic sentiments in localities/demographics. These Islamophobic sentiments eventually cause atrocities towards Muslim communities. Muslim communities’ fear and hostility are also caused by Islamophobic sentiments (and media has a role in such sentiment development).An approach  harvesteText analysis approaches for sentiment analysis  for poliThe objective of the research paper is to understand the role of anti-Muslim sentiment from news story portrayals of the Muslim community in mainstream media.corpus creation step is crucial, i.e., representation for statistical or Machine learning modeling for several tasks, including sentimental analysis. Since our study is related to sentiment analysis, a generic overview of the text mining approach for sentiment analysis is as follows:Carrying out a text mining-based project began with formulating a question followed by data collection and its cleaning, finally, its visualization . The Corpus creation is a multistep process, begins with the decision of violent incidents globally that happened very recently. The list of recently happened events under consideration is shown in Although some incidents in the list are not related to essentially Islamophobia, their respective response is Islamophobians. The respective news channels from which their information is captured are given in corpus creation's very first step is getting social media data using the R “vosonSML” library , or someone can use incognito/private mode to search for videos.We manipulate the YouTube recommended algorithm to identify how other recommended video titles show Islamophobia related or not. The critical aspect of the YouTube recommendation algorithm somehow clusters videos of similar types. The algorithm for video recommendation is influenced by many factors such as user history for YouTube, locality, subscriptions of channels, videos, Another aspect of YouTube is three sorts of responses: Likes, Dislikes, and Comments . Although it’s not a valid argument, we can consider it Dislike as hate and Likes as appreciation. But how we can differentiate the Likes and Dislikes meant to be hate and gratitude is just an individual understanding prospect. Therefore, we have taken both with their meaning as the ratio of Dislikes/Likes videos. The user comments being the most contributing modality is scrapped using vosonSML (R-Library) and classified using Multilingual-Lexicon based sentence classification.The final dataset consists of 38 videos, 1,018,500 comments, and 496 recommended video titles.The most significant resource is YouTube, and other resources are the website from where we have captured 40,800 Islamophobic and hate speech keywords for lexicon development. Another resource is lexicon development key words resource—. Some tools are used R . These libraries and web scrappers as “scrapper.io” and video transcripts captured manually scrapped, and finally, dataset architecture is given in Some examples of keywords: Islamic terrorism, Islamic fanatics, Islamic fundamentalism, Islamic extremists, Islamic radicals, Islamic fascists, Islamic fundamentalism, Islamists, jihadist, Islamism, militant Islam, radical Islam, political Islam, fanatical Islam, Islamo fascists, militant Muslims, and the Muslim terrorists.Comments are classified using an R-Library tubeR , and the classification is carried out collectively on the entire repository and particular video. This illustrates the extent of hate speech in a specific video. It also enables cluster analyses of certain incidents that trigger Islamophobic sentiment. Moreover, there is some sort of similarity among the entire sentiment.We have scraped video transcripts manually and performed sentiment analysis using the lexicon-based technique. A lexicon is prepared using terminologies widely used for hate speech or Islamophobic terms used for the Muslim community. We captured these terminologies from three different resources. However, sentences are not as structured as the standard text sentence (since these videos are transcripts of spoken words). These videos, however, are in the English language, therefore unilingual lexicon analysis.i.e., Like/Dislike and Dislike/Like with their alternative meaning . These ratios provide input for overall analysis.Both Likes and Dislikes are user responses, having significant meaning (most YouTube users tend to click on the Like/Dislike button instead of commenting on a sentence). However, it is challenging to cultivate its true meaning in which users have used these buttons. For example, a violent incident against the Muslim community news story liked by the user meant to appreciate the event or its reporting and Dislike meant to protest the event or its reporting vice versa is also possible. Moreover, these buttons might be clicked accidentally or unnoticed act. Therefore, we devised a ratio to get public sentiment out of it, YouTube video recommendations algorithm manipulation for capturing recommended videos. Video titles are also classified to learn the video under consideration is recommended Islamophobic or hate speech videos. The recommended videos are scraped by using a scraping tool, Webscrapper.io. To avoid YouTube video recommender influenced by user history and location, we have used chrome, which was newly installed without any user login. To verify these results, one should do the same. Those titles were not only considered for learning either the video is clustered with hate speech videos are Islamophobic videos  but also for current video types.i.e., products, facilities, societies, events, topics, and their attributes. The method used for the classification of textual data is as follows.Sentiment analysis has a broader range of applications from business, social, political, medical, and scientific research. Sentimental analysis  or opinion meaning (the term used by academia) has many forms, such as opinion extraction, sentiment mining, or subject analysis . SentimecorpusTitles of videos in Titles of recommended videosSentences of video transcriptsCommentsSentiment analysis refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information . Some of these actions include removing, non-English words, removing uniform resource locators (URLs), slang word translation, removing extra letters from words, and Stemming. The third step is sentiment identification, sentiment word identification (SWI), which is used by many applications for opinion mining. The following subsection provides the approach employed.A summary of the extraction process, as described in the following paragraph. The first step of data extraction from the number of websites for Step 1. Compilation of datai.e., Titles, transcripts, recommended video titles, and comments, and we have organized all these textual data based on video ID for simplicity and identification.For the sake of sentiment analysis, we have used textual data, Step 2 PreprocessingPreprocessing of the text includes several activities, as shown in Step 4. Sentiment identificationi.e., polarity words. Their technique employs polarity identification without seed or any labeling showed significant results. The detail of this process, as given below.Sentiment Identification is the key step for the identification of polarity. We have employed the sentiment identification technique , i.e., p1. Word polarity vectorThe word polarity is either positive or negative and neutral. For each word in the document, the polarity is computed as followsFor each word The next step is to compute document polarity by computing cosine polarity for each document, as given below Finally, document polarity of the sentence provides an estimation of the polarity of the entire transcript of the video and entire comments. The polarity is calculated using a sentiment analysis scheme as described in The final computation modules are described in Experimentation was carried out using lexicon-based on if is the frequency is the frequency of polar term and where s transcripts as given in These I-Scores provide information about how some news channels portrayals impact Muslim communities across the globe with their direct impact, based on cumulative IAnother measure that describes how YouTube video groups its video following its recommendations. Although many other factors impact YouTube video recommendations, we have used a chrome web browser without any login/user account. In addition, deleting all temporary files from the local machine and deleting history before scrapping videos from each channel minimize other influencing factors the recommendation. We have compared the cumulative I-Score for video transcripts and recommended video titles, as shown in However, the cumulative I-Score for comments is not compared because it depends on respective videos. Furthermore, these videos depend on incidents; therefore, relevant canonical correlation is performed to understand the entire content.i.e., it avoids Type I error or inflation of experimentation. Second, by considering all the variables simultaneously in a single analysis, CCA honors  , 2005 isA honors . The ecoi.e., Like, Dislike, and Comments, along with another construct, i.e., recommended videos. The YouTube video recommender is somehow an aggregated viewer response as the viewer tends to watch similar videos simultaneously. These four responses are compiled into a single spreadsheet for further analysis, as shown in This research’s primary objective is to learn the impact of news story coverage on Islamophobia on viewers’ point of view from their response. Therefore, we compile three types of user’s responses, i.e., Microsoft Excel, using as a data analysis tool.These responses struct utilized further to learn the correlation between user response and news story covered. The correlation analysis is carried out concerning incidents and concerning specific news channels. These correlation analyses are carried out in a simple spreadsheet, i.e., Words of news channels, and Set B has comments, number of views, number of likes, and number of dislikes, i.e., user’s response, and our objective is to learn canonical correlation analysis CCA.The dataset is bisected into set A contains Transcripts, Titles, recommended titles, i.e., metric form, to carry out canonical correlation, as shown in The next step is converting all textual data into a normalized form, The next step is computing correlation weights for correlation analysis as follows CS1 represents the correlation coefficient between video transcript and comments, CS2 is between video transcript and Likes/Dislikes, CS3 is between video transcript, and CS4 is between video transcript and its recommended videos. These four coefficients represent the correlation between all four constructs, as shown in The fourth step is to compute Pearson correlation; for CCA, the entire process is depicted in The respective results of CCA for each news channel are shown in following i.e., scrapping data from YouTube videos transcripts (News Stories) and their respective responses, i.e., comments, likes, dislikes, and the number of views. Another construct we considered is YouTube video recommendation (their titles are only under consideration). This dataset underwent different steps to eventually, all variables achieved homogeneous data types for final canonical correlation analysis. The reader carefully examines the encounter in many positive values of the correlation coefficient. This positivity of the coefficient reflects the relationship between news stories portrayals and Islamophobic sentiment; this phenomenon can also be visualized from Our approach is determined to furnish empirical evidence to demonstrate the relationship between news stories’ portrayal of Muslim communities. We have compiled ten news channel stories about ten global incidents. We collected a dataset from scratch, This study provides a systematic approach for computational social sciences research problems as it provides a mechanism for empirical evidence for qualitative research questions. Since Islamophobic sentiment is a qualitative research area for diverse academic interests, including computational social sciences, it provides a comprehensive systematic approach to a social media analysis project. This research project portrays a multistep project for social media analysis for diverse research questions for several academic disciplines.i.e., historical influences, social and cultural differences, and even personal differences, Islamophobic sentiments arousal in certain parts of the world have their own particular social, economic, cultural, and geopolitical phenomenon, i.e., India, Myanmar, and China. However, the role of the news cannot be neglected.Although many other significant factors are affecting Islamophobic sentiment, This research aims to understand the correlation between Islamophobic sentiments with portrayals of Muslims across the globe. This study is neither targeting any community nor any institute.10.7717/peerj-cs.838/supp-1Supplemental Information 1The dataset was scraped from Youtube.Click here for additional data file.


The Mini Nutritional Assessment Short-Form (MNA-SF) is the recommended screening tool for older persons. Data on interrater reliability in clinical routine are rare. Thus, we wanted to quantify the interrater reliability of the MNA-SF in hospital.This observational cross-sectional study was undertaken retrospectively. The study population comprised 105 participants. Risk of malnutrition was measured twice with the routine MNA-SF performed by nurses (within 24 h after admission) and a dedicated dietician (one to three days after the first MNA-SF). The MNA-SF score was analyzed for interrater reliability between nurse and dietician.p < 0.05) showing a fair agreement.Participants’ mean age was 82.4 (±7.1) years and 71 (68%) were women. The mean total MNA-SF score was 7.4 (±2.4) assessed by dietician and 7.8 (±2.3) assessed by nurse. The intra-class correlation coefficient between the total MNA-SF scores was 0.74 , indicating moderate reliability. For the MNA-SF nutritional status, Cohens Kappa was 0.37 (Multiple misclassifications were observed between malnutrition and risk of malnutrition. Because mean scores were near the border between malnutrition and risk of malnutrition, we recommend to consider the total MNA-SF score in addition to the three risk groups to assess nutritional risk in geriatric hospital patients. We noticed the lack of evidence regarding the interrater reliability of screening for risk of malnutrition using MNA-SF in routine geriatric hospital setting. Thus, we wanted to quantify the interrater reliability between the screening results for risk of malnutrition with MNA-SF performed by the nurse and the dietician, differentiated by the total MNA-SF score and the categories of MNA-SF nutritional status. Moreover, we investigated if single items of the MNA-SF are especially vulnerable for errors.The prevalence of malnutrion in geriatric hospital patients is high. Malnutrition has serious consequences for health outcomes . Malnourhttps://www.drks.de/drks_web/setLocale_EN.do).This observational cross-sectional study was undertaken retrospectively between July 2020 and October 2020 in the acute care geriatric hospital department of the university hospital Marien Hospital Herne, Germany. The MNA-SF is part of the routine geriatric assessment in the geriatric department since many years. The study population comprised 105 older participants. Participants were consecutively admitted to the geriatric acute care ward. Inclusion criteria were admission to the acute care geriatric hospital department, 60 years or older, and two screening results for malnutrition with MNA-SF performed by nurse and dietician. Exclusion criteria were severe cognitive impairment and palliative situation. Data were extracted from medical records. The study protocol had been approved by the ethical committee of Ruhr University Bochum (no 20-7077-BR approved on 29.10.2020). The study is registered at German Clinical trial register  patients’ date of birth, sex, height, and weight. Body weight was assessed in light clothing on a seat scale with an accuracy of 0.1 kg, and height was measured to the nearest 0.5 cm with a stadiometer during hospitalization. If the measurement of weight and/or height was not possible, weight and/or height were estimated based on the patient’s report. Activities of daily living were determined using the Barthel-Index (BI) . The poiRisk of malnutrition was measured according to the MNA-SF , which iThe MNA-SF consists of six items (A–F) with a scoring up to 3 points per item: (A) Has food intake declined over the past 3 months due to loss of appetite, digestive problems, chewing or swallowing difficulties? (B) Involuntary weight loss during the last 3 months? (C) Mobility? (D) Has the patient suffered psychological stress or acute disease in the past 3 months? (E) Neuropsychological problems? (F1) Body Mass Index (BMI) (F2) Calf circumference (CC) in cm. BMI (F1) was read off a BMI disc (nurse) or calculated (dietician). If height or weight could not be captured, calf circumference (CC) in cm was measured and categorized as CC < 31 or ≥31 instead, as recommend by the MNA-SF. The screening result of the MNA-SF is the calculated total MNA-SF score 0–14). The total MNA-SF score is then transferred to the corresponding MNA-SF nutritional status . Participants were divided into three groups according to the MNA-SF classification . Participant’s age was calculated for the day of hospital admission. If the assessment with MoCA could not be fully recorded for non-cognitive reasons, the result was extrapolated according to the questions answered.The statistical analysis was completed using SPSS statistical software . Means and standard deviations (SDs) were used for continuous data. Categorical variables are demonstrated as In the present work, the MNA-SF score was analyzed for interrater reliability between routine screening for risk of malnutrition with MNA-SF of the nurse and of the dietician in two analyses: interrater reliability for the total MNA-SF score  and for the MNA-SF nutritional status .A Bland-Altman plot was used to graphically demonstrate the consistency of the total MNA-SF score. The Bland-Altman plot compares graphically the two total MNA-SF scores values 0–14). It also shows possible relationships between measurement inaccuracies  with the level of the true value  . Therefo. It alsop < 0.05 was considered as the limit of significance.Cohens Kappa was used to assess the interrater reliability between the categorical MNA-SF nutritional status of the nurse and the dietician. It was calculated for the MNA-SF nutritional status  and for each single item (A–F). A Kappa of 1 indicates perfect agreement, whereas a kappa of 0 indicates agreement equivalent to chance. A The study population comprised 105 older participants aged between 62 and 97 years (mean age 82.4 ± 7.1 years) with 68% being women. Reasons for hospital admission in this multimorbid and frail cohort were predominantly falls, fractures, infections, cardiovascular and neurodegenerative disease. Participants had a mean MoCA of 17.7 (±5.9) and a mean BI of 47.4 (±16.7). All baseline characteristics of study participants are summarized in Table The mean total MNA-SF score was 7.4 (±2.4) assessed by dietician with a range from 2 to 12. The mean total MNA-SF score was 7.8 (±2.3) assessed by nurse with a range from 2 to 13. The mean difference of the total MNA-SF score (nurse - dietician) was 0.3 (±2.1) ranging from −6 to 6. The highest frequency of differences was 0 in 21.9% of patients, followed by 1 with 21.0% of patients  and −3.8 (=0.3 − 2.1*1.96). The Bland-Altman plot shows no systematic bias  between total difference and mean value of the two total MNA-SF scores  showing a fair agreement between MNA-SF nutritional status assessed by dietician and nurse [According to MNA-SF nutritional status assessed by the dietician, 3 2.9%) had a normal nutritional status, 51 (48.6%) were at risk of malnutrition, and 51 (48.6%) were malnourished. Nurses categorized 3 (2.9%) having a normal nutritional status , 62 (59.0%) were at risk of malnutrition , 40 (38.1%) malnourished  . In contrast to other studies –10, our p < 0.05)), the present study demonstrates, that severe misclassification with possible clinical consequences happened rarely. Only one participant, who was classified as malnourished by the dietician was rated as normal nutritional status in routine assessment. The opposite misclassification, which was also seen in one other patient, would barely be harmful for the patient, because it led to nutritional counseling first, where this kind of misclassification would be discovered. Even if oral nutritional supplements were given without further counseling, this would not harm any patient, but lead to avoidable costs. Multiple misclassifications were observed between malnutrition and risk of malnutrition. Their clinical relevance depends on the routine management of patients at risk of malnutrition and is likewise probably low. If there is a clinical suspicion of categorical misclassification, the total MNA-SF score should also be taken into account for nutritional risk grading .In our study, the mean total MNA-SF scores assessed by dietician (7.4) and nurse 7.8) revealed a minor difference of 0.3 showing a moderate interrater reliability. The mean scores, however, are exactly on the border between the two categories “risk of malnutrition”  revealed and “malp < 0.05) and 0.21 (p < 0.05). Respectively, pointing out that it possibly would increase accuracy if moderate and severe decrease of food intake would be defined more precisely, as done in other screening tools. To the contrary, the weight loss item is clearly defined but frequently classified as unknown in the routine assessment. In contrast to the nurse in charge, the dedicated dietician often has gathered more information about the nutritional situation of the patient, including current weight and weight loss. The psychological stress/acute disease item should have always been rated as “yes” in this study, since hospitalized patients always suffer an acute disease. About 13% of this item was rated with “no” exclusively by the nurse. Thus, we conclude that nurses need more structured training on correctly performing the screening. In addition, it could be hypothesized that the accuracy of the screening for risk of malnutrition with MNA-SF could be improved by performing it on the second or third day of hospital stay, after obtaining more information about the patient, after body weight has been measured and food intake has been observed for more than one meal. Therefore, we recommend a re-screening after a few days for patients with missing or uncertain data to prevent only relying on the subjective information of the patient.On the level of individual items of the MNA-SF, the first two questions about food intake and weight loss demonstrated the lowest level of agreement, with a Cohens Kappa of 0.23  malnutrition in each category. Moreover, we recommend performing the MNA-SF with sufficient information about the patient, even if screening is postponed to the second or third day of the hospital stay. Overall, nurses need to receive a structured trained in performing the screening for the risk of malnutrition using MNA-SF.


Combined pulmonary fibrosis and emphysema (CPFE) is a novel clinical entity with a poor prognosis. This study aimed to develop a clinical nomogram model to predict the 1-, 2- and 3-year mortality of patients with CPFE by using the machine learning approach, and to validate the predictive ability of the interstitial lung disease-gender-age-lung physiology (ILD-GAP) model in CPFE.The data of CPFE patients from January 2015 to October 2021 who met the inclusion criteria were retrospectively collected. We utilized LASSO regression and multivariable Cox regression analysis to identify the variables associated with the prognosis of CPFE and generate a nomogram. The Harrell's C index, the calibration curve and the area under the receiver operating characteristic (ROC) curve (AUC) were used to evaluate the performance of the nomogram. Then, we performed likelihood ratio test, net reclassification improvement (NRI), integrated discrimination improvement (IDI) and decision curve analysis (DCA) to compare the performance of the nomogram with that of the ILD-GAP model.A total of 184 patients with CPFE were enrolled. During the follow-up, 90 patients died. After screening out, diffusing lung capacity for carbon monoxide (DLCO), right ventricular diameter (RVD), C-reactive protein (CRP), and globulin were found to be associated with the prognosis of CPFE. The nomogram was then developed by incorporating the above five variables, and it showed a good performance, with a Harrell's C index of 0.757 and an AUC of 0.800 (95% CI 0.736–0.863). Moreover, the calibration plot of the nomogram showed good concordance between the prediction probabilities and the actual observations. The nomogram also improved the discrimination ability of the ILD-GAP model compared to that of the ILD-GAP model alone, and this was substantiated by the likelihood ratio test, NRI and IDI. The significant clinical utility of the nomogram was demonstrated by DCA.Age, DLCO, RVD, CRP and globulin were identified as being significantly associated with the prognosis of CPFE in our cohort. The nomogram incorporating the 5 variables showed good performance in predicting the mortality of CPFE. In addition, although the nomogram was superior to the ILD-GAP model in the present cohort, further validation is needed to determine the clinical utility of the nomogram. Pulmonary interstitial fibrosis and emphysema are two distinct clinical entities with different pathogeneses and pathophysiologic manifestations. However, an increasing number of studies consider that the two phenotypes can coexist within one patient , 2. CottThe study of prognostic prediction of CPFE remains challenging because of the heterogeneity in disease-specific variables and the lack of awareness for this clinical entity –9. UnforIn this study, we investigated the prognostic factors of CPFE and established a comprehensive nomogram to predict the mortality of CPFE in a Chinese population. Furthermore, we also evaluated the prognostic predictive performance of the ILD-GAP model in CPFE.This study was a retrospective cohort study that included 184 confirmed CPFE patients who were admitted to the First Affiliated Hospital of Zhengzhou University between January 2015 and October 2021.Patients were diagnosed with CPFE according to the criteria suggested by Cottin et al. , namely,CTD was defined according to the criteria recommended by the American Rheumatism Association and the American College of Rheumatology –19, inclThe exclusion criteria were as follows: (1) patients who met the criteria for the diagnosis of CPFE, but CPFE was secondary to other etiologies, including pneumoconiosis (asbestosis or siderosis); (2) patients with incomplete data; and (3) patients younger than 18 years old.The ethical approval of this study was granted by the Ethics Committee of Scientific Research and Clinical Trials of the First Affiliated Hospital of Zhengzhou University  prior to the data collection. Since the data were deidentified and aggregated, written consent was waived.Data were collected from electronic medical records at the initial diagnosis. The collected data included demographic characteristics, systematic classification, pulmonary function test results, echocardiography results, high-resolution computed tomography (HRCT) images and laboratory test results.The demographic characteristics included age, sex, body mass index, smoking history, complications (lung cancer and pulmonary hypertension) and treatment. Pulmonary hypertension (PH) was defined according to the echocardiographic criteria for high probability of PH recommended by the European Society of Cardiology and the European Respiratory Society (ESC/ERS): the peak tricuspid regurgitation velocity (TRV) > 3.4 m/s; or the TRV is 2.9–3.4 m/s within the signs assessing the right ventricular (RV) size, the pressure overload, the pattern of blood flow velocity out of the RV, the diameter of the pulmonary artery and an estimate of right atrial pressure .The systematic classifications included idiopathic CPFE and CTD-CPFE, using the classification criteria of CTD as the inclusion standard.1), forced vital capacity (FVC), total lung capacity (TLC), peak expiratory flow (PEF), maximal midexpiratory flow rate , DLCO and the ratio of FEV1 to FVC (FEV1/FVC).The variables of the pulmonary function test that we collected included the percentage of the predicted values (%Predicted) for forced expiratory volume in the first second , RVD  , 22, lefHRCT scans were examined by two independent chest radiologists, and final conclusions on the findings were reached by consensus. The collected HRCT images included fine reticular opacity, ground-glass opacity, pseudoplaque, flocculent shadow , parenchymal band, honeycomb shadow, traction bronchiectasis, local pleural thickening and mediastinal lymphadenopathy. The detailed description of these images refers to the Fleischner terminology .The collected laboratory examination data included leukocyte count, erythrocyte count, haemoglobin count, platelet count, red cell distribution width, platelet distribution width, aspartate transaminase, alanine aminotransferase, γ glutamyl transferase, alkaline phosphatase, total protein, albumin (ALB), globulin, total bilirubin, direct bilirubin, indirect bilirubin, urea nitrogen, creatinine, uric acid, glomerular filtration rate, total cholesterol, total triglycerides, high-density lipoprotein, low-density lipoprotein, B-type natriuretic peptide, CRP, procalcitonin, erythrocyte sedimentation rate, complement component C3, complement component C4, immunoglobulin A, immunoglobulin M, immunoglobulin G, Krebs von den Lungen-6 (KL-6), partial pressure of carbon dioxide, partial pressure of arterial oxygen, blood oxygen saturation, lactate, alpha-fetoprotein, carcinoembryonic antigen, carbohydrate antigen 125, cytokeratin-19-fragment (CYFRA21-1), neuron-specific enolase, carbohydrate antigen 199, carbohydrate antigen 153, carbohydrate antigen 724 and serum ferritin.The study endpoint was all-cause mortality during follow-up until January 2022. Follow-up information was obtained from patients or their families via telephone interviews.P values less than 0.05 were entered into the forward stepwise regression multivariable Cox analysis. A nomogram was constructed based on the results of multivariate Cox regression analysis and by using the “rms” package of R. For clinical use of the model, the risk scores of each patient were calculated based on the nomogram. The performance of the nomogram was assessed by discrimination and calibration [P < 0.05 was considered to be statistically significant.Missing data were processed by multiple imputations. Imputation for missing variables was considered if missing values were less than 20%. A t test or corrected t test was used to compare the continuous variables of normal distribution between the two groups, which were presented as the mean ± standard deviation (mean ± SD). Continuous nonnormally distributed data were compared using the Mann–Whitney U test and presented as the median and interquartile range . Categorical variables of the two groups were compared by the χ2 test and presented as frequencies (percentages). LASSO regression analysis was used for data dimension reduction and variable selection. The penalty value  was selected by tenfold cross-validation, and the best subset of the variables was selected by using the “glmnet” package of R. The significance of each variable in the best subset was evaluated by univariable Cox regression analysis. The variables with ibration . The disibration . The preP < 0.05). Compared with patients who were alive, those who died were more likely to have lower FVC, TLC, DLCO, LVEF and higher RVD . Additionally, the deceased patients were more likely to have mediastinal lymphadenopathy, higher levels of serum KL-6 and CYFRA21-1 .A total of 204 patients with confirmed CPFE were screened in the present study according to the above defined criteria. After excluding the patients with CPFE secondary to pneumonoconiosis (n = 3), patients with incomplete data (n = 6), those younger than 18 years old (n = 1) and those lost to follow-up (n = 10), 184 patients were included in this study, as presented in Fig. P < 0.001) (Table P < 0.001)  in the univariate analysis were entered into a multivariate Cox model, and the results showed that the 5 variables of age, DLCO, RVD, CRP and globulin affected all-cause mortality significantly   Table . However1) Table . Signifi5) Table . The nom5) Table .Fig. 2InThe predictive performance of the nomogram was good in our study cohort. The C-index value was 0.757, and the mean Harrell's C index in the validation cohort constructed by 1000 bootstrap resamples was 0.853. The AUC of the nomogram was 0.800 (95% CI 0.736–0.863) Fig. A. The caP < 0.001; Table P < 0.001), but there was no significant difference when the inclusion of the ILD-GAP model in nomogram (P = 0.160) (Table P = 0.013), 0.362  and 0.173 , respectively, and the IDIs of the nomogram and the ILD-GAP model for 1-, 2- and 3-year mortality were 0.145 , 0.142  and 0.084 , respectively  in the univariable Cox regression analysis. However, PH was not selected in the LASSO regression analysis. The probable reason is that RVD, which is essentially a marker of right ventricular dilation, may be collinear with PH given that right ventricular dilation is often a consequence of PH. With the progression of PH, the pulmonary artery systolic pressure will decrease with the occurrence of right heart failure, while right ventricular dilation is irreversible.Age has been reported as a risk factor for the prognosis of CPFE and many other lung diseases because older individuals typically have more comorbidities and poorer health status . CPFE haCRP and globulin are the serological indicators commonly tested in clinic, and their levels are acceptably correlated with the severity of infection and immune status . Our stu1 and FEV1/FVC were mortality predictors of pulmonary emphysema [A nomogram can provide an individualized, evidence-based and highly accurate risk estimation, thus facilitating decision-making by physicians and policy makers , 43. Themphysema , while tmphysema .Although our study is based on real-world data and has relatively complete information of the patients, there are still some limitations. First, this retrospective cross-sectional analysis was based on data from a single institution and therefore may suffer from selection bias. Therefore, more prospective and longitudinal studies are required to further validate the reliability of the nomogram. Second, the nomogram lacked specific genetic markers. However, our study screened 95 clinical characteristics and then selected the most significant variables associated with the prognosis of CPFE. These variables are comprehensive and easily available, thus facilitating decision-making by physicians. Third, quantitative indicators of fibrosis or emphysema were not included in the study, but the more objective indicators, lung function parameters, were included because of the risk of collinearity , 49. FouIn this study, age, DLCO, RVD, CRP, and globulin were identified as significant predictive factors of prognosis for CPFE patients. Then, we established a nomogram incorporating the 5 variables to predict the mortality of CPFE, and the nomogram showed good performance. In addition, the nomogram was superior to the ILD-GAP model in terms of performance in the present cohort. Finally, although our nomogram can facilitate individualized therapy design, further validation is still needed to determine the clinical utility of the nomogram.


Marxist philosophy has always been attached to the practice. In the new age, Marxist philosophy needs to solve many problems, such as ecological destruction, environmental pollution, international conflicts, and technical innovation, and improve the integration of the Marxist philosophy system with China's national conditions. The premise of change is practice, and the premise of practice is dissemination. Promoting the dissemination of Marxist philosophy is the cornerstone of solving the blind spot in the process of Marxist philosophy popularization. Because of the development of Internet technology, in order to ensure the validity of the uploaded videos related to Marxist philosophy on the platform, combining the research on human visual perception and the advantages of the long-term recurrent convolutional network (LRCN) model in video content recognition, an attention mechanism-based LRCN model is proposed, which simulates the attention characteristics of the human brain in the deep learning model, considers the video content globally, and makes the attention of the model fall in the effective area of the whole video. The experiment uses HMDB51, UCF101, and YouTube-8M data sets, and the results show that the LRCN model based on the attention mechanism proposed in this paper can effectively improve the accuracy of video content recognition, and it can converge quickly during training to improve the efficiency of model training. Marxist philosophy is the inevitable result of social development in the middle of the nineteenth century . It is aPhilosophy is not equal to science. Philosophy may represent the views and theories of thinkers of a certain class, but it is not necessarily the true understanding of the objective world , 6. HoweMarxist philosophy not only treats capitalist society with a critical attitude but also treats socialist society with a critical spirit, believing that socialist society is also a society in need of constant reform. Marxist philosophy not only treats other philosophical trends of thought critically but also treats its own theory in a critical spirit and carries on self-criticism . MarxistWe-media has profoundly changed the communication system of Marxist philosophy. The emergence of we-media has enriched the communication form and mode of Marxism, enabling Marxist philosophy to spread through videos, images, and texts and further enhancing the influence of Marxist philosophy. In the practice teaching of Marxist philosophy, we-media can also be used to expand teaching content, enrich teaching means, and enhance the learning interest of the educated. Making full use of the advantages of we-media can further expand the communication channels of Marxist philosophy, especially the application of we-media such as Weibo and WeChat, closely combine the knowledge related to Marxist philosophy with social practical problems, and realize the dissemination of Marxist philosophy through the fan effect. At the same time, we should clearly see that the dissemination of traditional Marxist philosophy still has its own advantages, and we should combine traditional education methods with modern communication methods to jointly enhance the influence of Marxist philosophy.The interactive advantages of we-media enhance the pertinence of Marxist philosophy communication. The main advantage of we-media lies in that it breaks the limitation of time and space and can realize interactive communication between people. One-to-one, one-to-many, many-to-many, and many-to-one communication can be realized through the we-media platform, which can effectively motivate ordinary people to participate in the dissemination of Marxist philosophy. Supported by big data and cloud computing, data analysis can specify different audiences' needs at different levels and make personalized communication plans according to different audiences' needs. In particular, people at different cultural levels can be divided more scientifically, which can reduce the inconvenience caused by knowledge communication.We-media age has created a good external communication environment, and it prompts the interaction channel between educators and learners of Marxist philosophy to be more smooth, more accurate, and more timely. At the same time, under the background of the information age, the dissemination of all kinds of information shows explosive growth, especially in the dissemination process of Marxist philosophy; all kinds of information sources are wide; and the quality of information is also different.Marxist philosophy is inclusive and can be organically combined with the governing thought of any country in the world to continuously enrich and develop Marxist philosophy. It can be seen that Marxist philosophy has the characteristics of learning, guidance, publicity, and communication, but in fact, how to expand the dissemination of Marxist philosophy in the public environment is the issue we study. The age of we-media is the inevitable result of the development of science and technology, with distinct characteristics of the ages. We-media has become an important way for people to exchange information and has a profound impact on information transmission in various fields , 13. In In the new media age, the quality of information release is related to the feelings of information receivers. To improve the quality of information release, information publishers often have to make preparations in information selection. In particular, publishing some information on the platform that can cause a big impact on people's ideological field requires the information publisher to prepare for it , 15. WheHowever, the rich and diverse video content of Marxist philosophy poses a great challenge to its supervision and management. Because of the breakthrough of deep learning in intelligent recognition, video content recognition technology based on deep learning has gradually become the main technology in video content recognition and analysis , 19. TheThe contribution of this paper is that an attention-mechanism-based long-term recurrent convolutional network (LRCN) model is proposed for illegal Marxist philosophy video recognition. The rest of the paper is structured as follows. In In recent years, with the deepening of deep learning, breakthroughs have been made in machine translation, speech recognition, computer vision, and other fields. With the help of the study of attention characteristics of the human brain, the introduction of attention mechanism into deep learning has become a research hotspot in the field of deep learning. Similar to the human visual system, the human eye does not focus on the whole image, but instead focuses on more interesting or important areas. Although it needs to be viewed as a whole, it still scans the image in a certain order, moving from one area to another. Therefore, it is helpful to understand and analyze video content by simulating the attention characteristics of the human brain in the video content recognition method based on deep learning.Video data is a kind of three-dimensional data, which contains not only spatial information but also temporal information. Although convolutional neural networks (CNN) can extract robust spatial features and represent the spatial information of the video, they cannot deal with the sequence problem, extract the temporal features of the video, and represent the temporal information of the video. The recognition method based on the three-dimensional convolution kernel and the recognition method based on the two-channel CNN have improved the recognition effect compared with the recognition method based on the single frame . TherefoLRCN model consists of two parts: spatial feature extraction and temporal feature extraction . The CNNDue to the attention characteristic of the human brain, when faced with complex and diverse video content, especially the video content related to Marxist philosophy, the manual detection method always has high accuracy compared with other video content recognition methods. Studies have shown that one of the important properties of human visual perception is that people do not process the whole image at the beginning. Instead, they selectively focus their attention on certain visual areas and combine information from different areas to construct an internal representation of the whole image, guiding human eyes to move in the image and make decisions. LRCN model adopts the structure of CNN and LSTM networks, which cannot only capture the spatial information of video content but also capture the temporal information of video content for a long time. However, the LRCN model only takes a single video frame as input at each moment, without considering the influence of the complete video sequence on video content recognition.vi represents the feature vector of the i-th video frame.Venugopalan uses a codec structure to automatically generate video descriptions . The encThe model takes all video feature vectors as inputs and pays attention to the whole video sequence information at any time, but this average way makes the video lose temporal structure information, and all video frame sequences are treated equally, which makes it impossible to distinguish useful information from interference information in the whole video. For example, a video of Marxist philosophy may contain preparatory activities, Marxist philosophy, and some words, while only the content of Marxist philosophy is directly related to the theme of teaching. Therefore, when recognizing the content of the Marxist philosophy video, the model should pay more attention to the video frame sequence of teaching.Then the attention mechanism is introduced into the model to make the model pay attention to the whole video frame sequences, especially the video frame sequences related to the teaching of Marxist philosophy, which is helpful for recognition of Marxist philosophy video content.CNN extracts robust spatial features and obtains fixed-size feature vectors. LSTM networks recognize video content according to input feature vectors. Therefore, CNN is regarded as an encoding network and LSTM networks as an decoding network, and an attention mechanism is introduced in the decoding process to learn attention weight. So that the model's attention falls on the effective area of the whole video, and the interference of irrelevant information to the Marxist philosophy video content recognition is eliminated, so as to extract the temporal feature with attention to identify the Marxist philosophy video content.wit is the weight to be learned and ∑i=1Kwit=1. wit reflects the tightness of the feature vector of the i-th frame and the set of feature vectors of the whole video at time t. If the content of the video frame is more related to the content of the whole video, the weight of attention will be larger.In the decoding process, feature vectors of whole video frame sequences are weighted as the input of the LSTM network, which is shown as follows:wit is related to the hidden unit state of LSTM networks at the last moment and the feature vector at the current moment, and the correlation score of wit is shown as follows:ht−1 is the state of the hidden unit at time t − 1, 𝕎 and 𝔸 are the weight matrix to be learned, b is the bias parameter, and tanh  is the activation function [The learning of function .The LRCN model based on the attention mechanism makes the LSTM networks selectively pay attention to the effective information in the video and reduce the interference of invalid information by learning the attention weight. The structure of the LRCN model based on the attention mechanism is shown in Video preprocessing. At first, the data set is divided into a training set and a test set. Then, FFmpeg is used to decode the raw video stream into a sequence of video frames . Each tySpatial feature extraction. In the LRCN model based on the attention mechanism, the video frame sequence is first input into CNN to extract the spatial feature vector. Since the feature of the fully connected layer is a high-level semantic feature, the original spatial structure information of the video content has been lost. To ensure that the subsequent LSTM networks contain more original spatial information of video content when extracting temporal features, this paper extracts the pooled feature vector corresponding to the feature graph of the last convolutional layer in the CNN as the spatial feature vector.Attention-based temporal feature extraction. Since the sequence of video frames contains the temporal information of the video, the spatial feature vectors of the video are weighted and then input into the LSTM networks. The attention weight of each moment in the LSTM networks is calculated by the LSTM unit state of the last moment and the input of the current moment, and the attention-based temporal feature is extracted.Video content prediction of illegal Marxist philosophy. LSTM network takes the continuous video spatial feature vectors as input, and the extracted temporal features contain not only the spatial information of video content but also the temporal information of video content. The attention-based temporal features extracted by LSTM networks at every moment are input to the classifier for prediction, and the corresponding probability output is obtained. The category corresponding to the maximum value in the output probability is selected as the input video category and compared with the preset feature library, so as to recognize the illegal Marxist philosophy video content.In the LRCN model based on the attention mechanism, the process of video content recognition mainly includes four steps: video preprocessing, spatial feature extraction, attention-based temporal feature extraction, and video content prediction of illegal Marxist philosophy.In this paper, HMDB51, UCF101, and YouTube-8M are selected as data sets used in the experiment . The conThe CNN adopts the GoogLeNet model and is pretrained on ImageNet 2012 image recognition library. ImageNet data set is currently the largest image recognition database, containing 14 million images and covering the common things in life categories. ImageNet 2012 classification data set is a subset of the ImageNet data set, selecting 1,000 categories. The video features extracted from the pretrained GoogLeNet model can effectively represent the spatial information of the video content. When training the LRCN model based on the attention mechanism, the parameters of the GoogLeNet model do not participate in the training, and the feature map of the last convolutional layer is extracted and pooled as the spatial feature of the video. The feature size is 2,048 dimensions.The number of LSTM network layers is set to 1; the number of input units is 2,048; and the number of hidden units is 512. According to different data sets, the number of output units is 51 or 101. Adam optimization algorithm is used to train the LRCN model based on the attention mechanism. The initial learning rate is set to 0.001, and the maximum number of iterations is set to 20,000.VWF is a new method of human action recognition using video and Wi-Fi cluesWSL-CVCR is a weak supervised learning framework and a new component-based video content representation methodACSL is an attention-based consistent semantic learning methodThis paper compares the recognition accuracy of the LRCN model based on the attention mechanism with different video content recognition methods such as VWF , WSL-CVCAs can be seen from The HMDB51 data set contains 51 different categories of videos, each of which contains 30 test videos. The UCF101 data set contains 101 different categories of videos, with a total of 3,783 test videos, while YouTube-8M contains more videos in different categories. To further analyze the recognition effect of the LRCN model based on the attention mechanism on each category of video content, we select 10 categories of video content from each data set for the targeted test. This paper compares the recognition accuracy of the four models in different categories of video content in HMDB51, UCF101, and YouTube-8M data sets, and the comparison results are shown in Figures 3The video content of the HMDB51 data set includes five categories: facial-related actions, facial actions related to object interaction, body-movement-related actions, body movements with objects, and human-interaction-related actions. As can be seen from the statistical results in The video content of the UCF101 data set covers five categories: human movement, human-object interaction, human-human interaction, playing musical instruments, and sports. As can be seen from the statistical results in In particular, to verify whether the videos of Marxist philosophy on the platform are illegal, we use the video lesson and teacher entities related to YouTube-8M to conduct iterative training on the proposed model, so as to verify the accuracy. As can be seen from In this paper, we analyze the dissemination of Marxist philosophical content in the public environment. Based on the research of human visual perception, we propose to simulate the attention characteristics of the human brain in the deep learning model and make the model's attention fall on the effective area of the whole video in the process of illegal Marxist philosophy video content recognition, so as to eliminate the interference of irrelevant information on video content recognition. The LRCN model based on the attention mechanism takes weighted video spatial features as input of the LSTM network and focuses on the video frames related to the video theme in the long recognition process. Through training and testing on HMDB51, UCF101, and YouTube-8M data sets, the recognition accuracy of the LRCN model based on attention mechanism in various categories of video is analyzed. However, it is necessary to enhance the affinity and pertinence of Marxist philosophy communication in the public environment.The affinity of Marxist philosophy can enhance the attraction of Marxist philosophy, while the pertinence of Marxist philosophy can enhance the effectiveness of Marxist philosophy, solve various problems in practice, and enhance the identity of the educated. Therefore, in order to spread Marxist philosophy more widely and meaningfully, we must enhance the affinity and pertinence of the spread.Enhancing the affinity and pertinence of the dissemination of Marxist philosophy reveals the essence of Marxist philosophy. Marxist philosophy itself is an important science with humanity, which plays an important role in promoting national economic and social development. Therefore, it is necessary to strengthen the dissemination of Marxist philosophy. To realize the goal of educating people, in the process of disseminating Marxist philosophy theory, it is necessary to transfer the humanistic care and the spirit of educating and loving. Disseminators should further highlight the humanistic nature of Marxist philosophy by enhancing affinity and pertinence, form a harmonious and close dissemination atmosphere, and constantly stimulate the enthusiasm and initiative of learning.Enhancing the affinity and pertinence of Marxist philosophy can better understand the dialectical relationship between subject and object. The dissemination of Marxist philosophy emphasizes the equal relationship between subject and object, and the status between subject and object is interactive and equal in the dissemination of Marxist philosophy. Especially in the age of we-media, it provides an interactive platform for the dissemination of Marxist philosophy, eliminating factors such as rank and inferiority in the dissemination process. In practical interaction, if the disseminator and receiver of Marxist philosophy can have more affinity, it is bound to improve the dissemination effect of Marxist philosophy and also help realize the infiltration of theory and the output and establishment of value in the process of interaction between subject and object. To further understand the dialectical relationship between subject and object of Marxist philosophy, it is necessary to formulate practical dissemination programs according to the different characteristics and personality differences of the receivers, inspire the receivers with sincerity so that the receivers can be respected as far as possible in the whole dissemination process, and dare to interact with the disseminators, so as to imperceptibly disseminates Marxist philosophy theory in a warm and harmonious atmosphere.Enhancing the affinity and pertinence of the dissemination of Marxist philosophy is the inevitable requirement of Marxist humanism. Under the background of the we-media age, people's values are being impacted by diversified social trends of thought, especially under the background of the market economy, people's thoughts and behaviors are changing from following traditional ideas to following the direction of fairness and equality. However, Marxist philosophy is just the value guidance to correct, solve, and adapt to the development of the times. Affinity and pertinence of Marxist philosophy are better able to respect and care for the educated, solve problems in their real lives, gradually enhance the recognition and participation in Marxist philosophy, and sublimate their values and ideal beliefs through continuous interaction with Marxist philosophy.


To evaluate their morphological characteristics, eight variables were measured, while two standardized tests were used to evaluate their situational precision. The results indicate that a statistically significant difference between the groups was noticeable for nine variables in total, seven in morphology  and two in situational precision , and that the achieved difference ranges from a medium to a large effect. For only one variable , significant difference was not determined between the groups. It was also determined (by Cohen’s criterion) that handball players with higher longitudinal dimensionality achieve better results for specific precision. Therefore, when selecting young handball players, the aforementioned dimensions should be taken into consideration as predictors of success.The aim of this cross-sectional study was to determine the differences in the morphological characteristics and situational precision among younger and older groups of handball players. The sample of participants consisted of 30 handball players, members of the younger category of the Al-Ahli handball club (Bahrein), divided into two groups: older (U16,  Current research indicates that the morphological characteristics of athletes are significantly associated with success in sport ,2,3,4,5.Handball is a demanding contact sport which in its structure contains elements of movement such as running, jumping, sprinting, swinging, hitting, blocking and pushing an opponent . ConsequCurrent research carried out on handball players of various age categories and levels of competition indicates that handball requires higher values of body height and body mass . Even thMoreover, one of the most important skills for success in handball is the ability to throw  and for Therefore, this study aim to determine the differences in the morphological characteristics and situational precision between older and younger male groups of handball players.n = 18, 15.60 ± 0.30 yrs.) and a younger group of boys born in 2003 . The sample included participants who were psychologically and physically healthy, who had no pronounced aberrations concerning the locomotor apparatus. In addition, the inclusion criteria for the participants were that they have actively been taking part in handball for at least a period of 3 years, that they train at least 4 times a week, and that are competing in the appropriate age category. All of the participants, as well as their parents, were well acquainted with the procedure and purpose of the testing, and the parents gave their consent on the condition that they could withdraw their children from the study at any point (which none of them did). All procedures performed in studies involving human participants were in accordance with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.The sample of participants consisted of 30 handball players, all members of younger categories of the Al-Ahli club (Bahrein), divided into two groups: an older group of boys born in 2002 (Skinfold thicknesses (mm) were measured at six sites: triceps skinfold, forearm skinfold, thigh skinfold, calf skinfold, chest skinfold, and abdominal skinfold thickness .Standardized anthropometric instruments were used to measure the morphological characteristics. The measurements were taken according to a predetermined International Biological Program (IBP) . To evalp < 0.05. What was then calculated was Eta squared (η2), as an indicator of effect size. Cohen’s classification indicates the size of the effect whereby 0.01 is a small effect, 0.06 is a medium effect and 0.14 is a large effect [The data analysis was carried out using the IBM SPSS Statistics 26 software . The means and standard deviation were calculated for each variable. To evaluate the normality of the distribution, the Shapiro-Wilk’s test was used. Differences in the morphological characteristics and situational precision between the groups were determined using a univariate analysis of variance (ANOVA), while the level of significance was set at e effect .p = 0.021), while the remaining variables have normal distribution (p > 0.05) and, along with a visual overview of the histogram and Q-Q plot, it was determined that the data were normal, and that a parametric technique could be used.The normal distribution of the data was confirmed by the Shapiro-Wilk test . One varWhen we view the average values (Mean) between the younger and older group of handball players , it can p > 0.05) between the older and younger group of handball players, while a significant difference between the groups was noted for the remaining nine variables (p = 0.003–0.049), which also goes for the seven morphological variables (p = 0.004–0.49), and two variables of situational precision, the standing shot (p = 0.003) and the jump shot (p = 0.17).The ANOVA determined that only the values of body mass did not show a significant difference (2 = 0.131), while all the other variables have a large effect size (η2 = 0.142–0.275).Based on Eta squared and Cohen’s criterion , we can This study showed that there are differences between the morphological characteristics of the two studied groups of young handball players, members of the Al-Ahli handball club, and also between their results for situational precision. Statistically significant differences with greater values for the older boys were noted for all the variables which refer to the longitudinal dimensionality of the skeleton, which is congruent with the results of Vuleta et al. , who alsWhen we look at the results of tests which measured the specific precision of young handball players  we can see that there were statistically significant differences on both tests with better results scored by the older group of boys. Šibila and Pori  point ouIn order to compare the precision of young Bahraini handball players, which along with the strength of the throw is the most important component of an effective throw in handball , there i


Some members of the phylum Campylobacterota (formerly Epsilonproteobacteria), including Helicobacter pylori and Campylobacter jejuni, have been classified by WHO as “high priority pathogens” for research and development of new antimicrobials due to the rapid emergence and dissemination of resistance mechanisms against first-line antibiotics and the alarming increase of multidrug-resistant strains worldwide. Notably, these clinically relevant pathogens express a variety of TCRS and orphan response regulators, sometimes unique among its phylum, that control transcription, translation, energy metabolism and redox homeostasis, as well as the expression of relevant enzymes and virulence factors. In the present mini-review, we describe the signalling mechanisms and functional diversity of TCRS in H. pylori and C. jejuni, and provide an overview of the most recent findings in the use of these microbial molecules as potential novel therapeutic targets for the development of new antibiotics.Two-component regulatory systems (TCRS) are ubiquitous signal transduction mechanisms evolved by bacteria for sensing and adapting to the constant changes that occur in their environment. Typically consisting of two types of proteins, a membrane sensor kinase and an effector cytosolic response regulator, the TCRS modulate  Campylobacterota, previously known as Epsilonproteobacteria  are ubiquitous signal transduction mechanisms among bacteria that enable microbial cells to perceive both environmental changes and internal information and subsequently trigger a rapid physiological response in order to better adapt to the new conditions. Typically, a TCRS comprises two different proteins that act in concert, a membrane-anchored histidine kinase (HK) and a cognate soluble response regulator (RR), which mostly acts as a DNA binding transcription factor Figure 1unctions . Notablyunctions .Campylobacterota remain poorly understood, significant advances in the comprehension of signalling transduction mechanisms and regulatory functions of certain TCRS have been achieved. As we detail below, strains of H. pylori and C. jejuni express a variety of TCRS and atypical RRs that control the on/off switch of a plethora of genes and operons involved in relevant physiological processes and virulence mechanisms  and HP1021  and the lactate produced by the commensal microbiota of lower and upper intestinal tracks, respectively , whose function is stimulated by the FlgSR TCRS  and University of Zaragoza (2018/0420).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.