Surgical patients constitute a group of individuals who are commonlyunderdiagnosed and undertreated, where nutritional impairment can be either apreexisting finding or a result of the hypercatabolic and hypermetabolicstate. The purpose of this study was to assess the prevalence of malnutrition,according to the GLIM criteria, and its association with clinical andnutritional factors, in individuals admitted to a surgical unit of a generalhospital. A cross-sectional, retrospective study was conducted, involving patients inthe preoperative period due to gastrointestinal diseases. Demographic,clinical, and nutritional data were collected from adult and elderlypatients admitted to a surgical unit between March and December 2019.Nutritional risk was assessed using the Nutritional Risk Screening tool(NRS-2002). The prevalence of malnutrition was found using the GLIMcriteria. Binary logistic regression modeling was performed to determine theassociation between the diagnosis of malnutrition using the GLIM method andclinical and nutritional variables. The majority of the sample presented nutritional risk (50.2%) according tothe NRS-2002. The prevalence of malnutrition according to the GLIM criteriawas 32.3%, with severe malnutrition being predominant (21.2%) in all agegroups. There was an association between malnutrition and nutritional riskdetected by the NRS-2002 . There was apredominance of patients undergoing cancer surgery (64%) and these patientswere more likely to be diagnosed with malnutrition , after statistical adjustment. An important prevalence of nutritional risk assessed by the NRS-2002 and ofmalnutrition assessed by the GLIM method was identified, especially in itssevere form. In addition, preoperative patients with nutritional risk, asdetected using the NRS-2002 nutritional screening tool, and candidates foroncologic surgery are more likely to be diagnosed as malnourished using theGLIM criteria. Inaddition to failures in the recognition and treatment of malnutrition, themultiplicity of terminologies points to both the frail setting and the need toestablish a single definitionThis new proposal to unify the diagnosis of malnutrition is of fundamental importancein the early identification of compromised nutritional status, which is a predictorof worse clinical outcomes for surgical patients. However, establishing thediagnosis of malnutrition does not in itself improve the clinical evolution of thepatient, since the nutritional diagnosis represents the link between nutritionalassessment and intervention. Therefore, this study proposes to provide not onlyrecent and relevant data for recognizing cases of malnutrition using a currentmethod in surgical patients but is also important with regard to the undertaking ofadequate nutritional interventions and, consequently, reduction of morbidity,mortality, and hospital costs in this population. Thus, this study aimed to evaluatethe prevalence of malnutrition, according to the GLIM criteria, and its associationwith clinical and nutritional factors in individuals admitted to the surgical unitof a general hospital.This is a cross-sectional, retrospective study, conducted from the analysis ofclinical records of individuals in the preoperative period, admitted between Marchand December 2019 to an inpatient surgical unit of a reference General Hospital ingeneral surgery, located in the city of Salvador, Bahia, Brazil. This surgical unitadmits patients who are the candidates for surgery, predominantly forgastrointestinal diseases , including the surgicaltreatment of diseases that affect the digestive system , inaddition to abdominal trauma.The study population was selected following the established inclusion and exclusioncriteria. All individuals admitted to a general surgery unit, aged 20 years orolder, of both sexes, affected by diseases of the gastrointestinal tract andcandidates for surgery, whose nutritional risk screening had been performed within72 h, participated in the study and underwent anthropometric assessment within 48 hof admission. Individuals hospitalized in other units, under the age of 20 years,admitted in the postoperative period, bedridden individuals, pregnant and lactatingindividuals, those with severe infection or sepsis, and those with incompleteinformation or incompatible with the established period, were excluded from thisstudy.Thus, 331 individuals were included in the study. When calculating the a posteriorisample power (1 − β) for the sample of 331 individuals at a significance level (α)of 0.05 and precision of 2%, a final power to detect malnutrition of 87% wascalculated.Data were collected between August and October 2020, recording demographicinformation , clinical information , and nutritional information .After data collection, the variables were categorized. Subjects were groupedaccording to age into adults (20-59 years), elderly 1 (60-69 years), and elderly 2(70 years or older). All individuals were screened using the same Nutritional RiskScreening instrument, the Nutritional Risk Screening (NRS-2002)As for the evaluation of the clinical variable immunological depletion, this wasperformed using the equation of the total lymphocyte count (CTL), equal to the ratiobetween the percentage of lymphocytes multiplied by the absolute value ofleukocytes, divided by one hundred .Findings were interpreted using the following cutoff points The length of hospital stay was determined using the interval between the date ofadmission to the unit and hospital discharge, transfer to another unit, or death. Itwas categorized as ≤15 days or >15 days), according to the median of days ofhospitalization.Regarding nutritional status, individuals were classified as malnourished orwell-nourished. For the diagnosis of malnutrition, the criteria established by theGLIM were adopted, through the association between a phenotype criterion and anetiological criterion, as described in In verifying the phenotypic criterion, the body mass index (BMI) was calculated bydividing the patient’s weight by their height squared . For the classification of adult individuals, the cutoff pointsestablished by the World Health Organization (WHO) were used Among the etiological criteria of the GLIM method, the burden of disease/inflammationwas used, based on the adequacy of CRP. The cutoff points were established byWilliamson and SnyderFor the classification of malnutrition severity, the cutoff points defined by theGLIM based on the BMI phenotypic criterion were used, being either phase 1 ormoderate (BMI: <20 kg/m² if <70 years and BMI: <22 kg/m² if >70 years)or stage 2 or severe (BMI: <18.5 kg/m² if <70 years and BMI: <20 kg/m² if>70 years)Data were statistically analyzed using SPSS , version 20.0.0. Categorical variables were presented as absolute andrelative frequencies and, after the Shapiro-Wilk normality test, continuousvariables were described using medians and interquartile ranges. The validity of thefindings was evaluated using the chi-square test for categorical variables and theMann-Whitney U test for continuous variables. A p-value <0.05 was used toindicate statistical significance for all tests.Binary logistic regression models (bivariate and multivariate) were used to assessthe association between the diagnosis of malnutrition using the GLIM method andclinical , and nutritional variables .Models were adjusted for sex, age, and diabetes diagnosis. A 95% confidence interval(CI) was adopted for the estimated odds ratio (OR).This study did not involve direct patient exposure, presenting minimal risks ofinvasion of privacy or unnecessary exposure. The data collected were secondary,meaning that the requirement for free and informed consent of the participants waswaived. This study was approved by the Research Ethics Committee of the Universidadedo Estado da Bahia, under protocol number 4.222.186, CAAE 35945220.2.0000.0057.The main characteristics of the 331 individuals evaluated are shown in The length of hospital stay ranged from 9 to 29 days, and the median was higher (18days) in malnourished patients compared to well-nourished patients (17 days)(p=0.450).Median weight and CTL were significantly lower in the group of malnourishedindividuals in the sample. In addition, among the chronic and acute injuriesidentified in the study, only systemic arterial hypertension was significantlydifferent in prevalence between the groups.As can be seen in Patients who were the candidates for oncologic surgery were twice as likely to havemalnutrition according to the GLIM, after adjusting the analysis for sex, age,diagnosis of diabetes mellitus, nutritional risk, immunological depletion, andlength of hospital stay (p=0.014).Although a tendency was observed for immunological depletion and length of hospitalstay to increase the chances of malnutrition diagnosis using the GLIM method, theresults were not statistically significant.This study identified a high prevalence of nutritional risk using the NRS-2002 tooland malnutrition using the GLIM method in surgical patients at the largest publichospital in the northeast region of Brazil, highlighting that severe malnutritionwas predominant regardless of age group. In addition, malnutrition was found to beassociated with nutritional risk as detected using the NRS-2002 nutritionalscreening tool and with oncologic surgery, after statistical adjustment.,It is widely accepted that malnutrition negatively affects patient outcomes, aspreoperative nutritional status is an important determinant of postoperativeoutcomesIn this study, it was found that almost one-third of the patients in the preoperativeperiod (32.3%) were diagnosed with some degree of malnutrition as they met thecombination of the adopted GLIM criteria . Thisprevalence was higher than that described by Henrique et al.Similar to this study, an observational and cross-sectional survey carried out withcancer patients in Australia detected a prevalence of malnutrition similar to thatfound (using an unspecified combination of GLIM criteria), showing that 35% ofpatients were identified as malnourished,It should be noted that in the sample evaluated, abdominal surgeries predominated,whether oncological or not, since the patients were affected by diseases in thegastrointestinal tract, explaining the relative prevalence of malnutrition. Weightloss and malnutrition are believed to be prevalent among individuals withgastrointestinal, hepatobiliary, and pancreatic diseases, as a result of theinvolvement of the digestive organs and their metabolic functions. However, both thefrequency and severity of malnutrition in these patients are not well-described inthe literatureNutritional risk is possibly reversible, so its early recognition can play animportant role in preventing the development of malnutrition and improving clinicaloutcomesA meta-analysis of 3,527 patients undergoing abdominal surgery in Asia and Europeshowed a strong correlation between preoperative nutritional risk and increasedrates of complications, mortality, and length of hospital stayPatients identified to be at nutritional risk in this study were more likely to bediagnosed with malnutrition using the GLIM criteria. A study carried out in Romaniawith 3,198 adult patients, admitted to the gastroenterology sector, identified thatpatients with nutritional risk according to the NRS-2002 and cancer patients aremore prone to the development of malnutritionIt is estimated that about 30-90% of cancer patients suffer from malnutrition It is indisputable that cancer negatively affects the nutritional status ofindividuals, since it triggers enormous changes and metabolic responses to the stateof persistent inflammation,Studies show that the immune system is intensely affected by malnutrition, sometimespresenting an insufficient response to bacteria, viruses, and fungi. About 20.5% ofmalnourished patients express low levels of CTL,Finally, there was no statistically significant association between length of stayand diagnosis of malnutrition using the GLIM method in this study. However, themedian number of days of hospitalization was high when compared to other studiescarried out with surgical patients, which can be explained by the high demand forsurgeries at the reference hospital in general surgery where the study was carriedout,There are still few studies validating the use of the GLIM criteria and theirapplication to surgical patients. Furthermore, depending on the combination of thephenotype and etiological criteria used, different rates of malnutrition prevalencecan be verified in the same populationAmong the limitations found, we highlight the fact that this was a retrospectivestudy where the available data did not cover all the criteria listed by the GLIM fora broader and comparative analysis with other studies. In addition, long-termoutcomes such as late complications, readmissions, or deaths were not evaluated. Onthe other hand, the strengths of this study include that data collection wasperformed by a single professional, in a standardized way, and the study populationincluded only surgical patients, predominantly with diseases of the gastrointestinaltract. In addition, this study can contribute to the data on malnutrition accordingto the GLIM criteria in this population, information that is still scarce.This study identified a high prevalence of nutritional risk using the NRS-2002 tooland malnutrition using the GLIM method in surgical patients, highlighting thepredominance of severe malnutrition among the evaluated individuals. In addition,preoperative patients with nutritional risk, as detected using the NRS-2002nutritional screening tool, and candidates for oncologic surgery are more likely tobe diagnosed as malnourished using the GLIM criteria.It is, therefore, recommended that further studies be carried out in surgicalpatients, reinforcing the use of this new robust method of diagnosing malnutrition,in order to promote more effective nutritional interventions and contribute to thereduction of morbidity and mortality, and hospital costs in this population.


To solve the emotional differences between different regions of the video frame and make use of the interrelationship between different regions, a region dual attention-based video emotion recognition method (RDAM) is proposed. RDAM takes as input video frame sequences and learns a discriminatory video emotion representation that can make full use of the emotional differences of different regions and the interrelationship between regions. Specifically, we construct two parallel attention modules: one is the regional location attention module, which generates a weight value for each feature region to identify the relative importance of different regions. Based on the weight, the emotion feature that can perceive the emotional sensitive region is generated. The other is the regional relationship attention module, which generates a region relation matrix that represents the interrelationship of different regions of a video frame. Based on the region relation matrix, the emotion feature that can perceive interrelationship between different regions is generated. The outputs of these two attention modules are fused to produce the emotional features of video frames. Then, the features of video frame sequences are fused by attention-based fusion network, and the final emotion feature of the video is produced. The experimental results on the video emotion recognition data sets show that the proposed method outperforms the other related works. Emotion plays a very important role in people's daily life. Emotional intelligence is to be considered an important part of human intelligence. With the continuous improvement of people's demand for intelligence, emotional intelligence, which is an important part of human intelligence has attracted extensive attention in the field of artificial intelligence . AlthougThe traditional video emotion recognition method is based on manual features. It learns the mapping relationship between emotion features and emotion types through pattern classifier and rule reasoning. De Silva and Ng  construcThe attention mechanism allows people to focus on useful information while ignoring irrelevant signals and noise. In recent years, it has been widely used in the field of computer vision. Some researchers , 7 use tHowever, due to the diversity of human emotional expression, the emotional expression is also different in different regions of the same video frame. Human emotional expression is not limited to the face. Poses and gestures may also contain rich emotional clues and the emotional information contained in different parts are also different. In addition, the video frame may also contain contextual information such as scenes and objects, which also contain different emotional clues. When we judge the emotion of a video frame, we often focus on the areas that contain rich emotional clues. As an example, take a look at the images in There are not only emotional differences but also mutual relations between different regions of the video. The relation of some regions is close, which will make it easier to recognize human emotions in video frames. For example, in The existing emotion recognition methods extract the emotional features of the whole video frame, which may contain faces, different parts of the human, scenes, and objects. These features are finally transformed into a unified emotional representation through full connection operation, which cannot effectively express the relationship between different regions. Therefore, how to establish the relationship between different regions is another challenge for video emotion recognition.This paper proposes a video emotion feature extraction method based on the regional dual attention mechanism. It consists of two parallel attention modules: one is the regional location attention module, which generates a weight for each region to identify the relative importance of different regions. The emotion features are recalculated based on the generated weight to generate the features of perceptible emotion sensitive regions. The other is the regional relationship attention module, which generates a regional relationship matrix representing the relationship between any two regions. The emotional features are recalculated according to the regional relationship matrix, and the emotional features which can perceive the interrelationship between regions are obtained. Finally, the outputs of the two attention modules are fused to further improve the representation of emotional features.A video frames feature fusion method is proposed, which fuses features of the video frames when the emotional features of each video frame are generated by the region dual attention module. It also uses the attention mechanism to obtain the emotional weight of video frames and fuses the features of video frames according to their emotional weight to generate the emotional features of the video.To overcome the above two challenges, this paper proposes a region dual attention-based video emotion recognition method (RDAM). It takes as input the video frames to learn a discriminative video emotion representation that can solve the emotional differences of different regions and make full use of the relationship between different regions. The main contributions of this paper are as follows:We have carried out experimental verification on video emotion recognition data sets. The experimental results show that the region dual attention mechanism can focus on emotion sensitive regions and make full use of the relationship between regions, to improve the accuracy of video emotion recognition.Human emotion is often expressed through multiple modes. The human face, audio, human body, action, and environment are all contain emotional clues. Integrating emotional clues of multiple modes can further improve the performance of emotion recognition. The experiments of the paper  show thaSome researchers model the temporal evolution features of emotion by converse 3d (C3d), long short-term memory (LSTM), or recurrent neural network (RNN). Ye et al.  extract To establish the relationship between different modes, Mo et al.  propose Due to the sparsity of emotional expression in the video, people will focus on some key frames in the perception process of a specific video. These key frames can provide more emotional clues and obtain better emotion recognition performance. Kayaoglu and Eroglu Erdem  select kThe attention mechanism in this paper not only focuses on key frames in the video but also focuses on the emotion-significant regions in the video frame. The methods , 22 mainIn this section, the region dual attention-based video emotion recognition method (RDAM) will be introduced in detail. Firstly, the system architecture of the RDAM will be introduced, and then each module of the RDAM will be introduced in detail.Different regions of video frames contain different emotional cues and play different roles in judging video emotion. In addition, the emotional cues between different regions are often related to each other. Modeling the relationship between different regions will help human emotion recognition. These two aspects are often ignored by the existing video emotion recognition methods. To solve these two problems, a video emotion recognition method based on region dual attention is proposed. It can effectively aggregate the emotional features of different regions and establish the emotional relationship between different regions, so as to improve the accuracy of video emotion recognition.V={I1, I2,…, IK}, where K is the number of video frames of the V and In is the nth video frame. RDAM takes as input the video frame sequence {I1, I2,…, IK} and generates a distinguishing video emotion representation that can solve the emotional differences of different regions and make full use of the relationship between different regions.Given a video As shown in ResNet-50 CNN structure  is used The emotional features of perceptible emotion sensitive regions and perceptible regional relationships are fused and then pass through a small fully connected network to obtain the emotional features of the video frames. The video frames sequence feature fusion network also adopts an attention module to obtain the emotional weight of the video frame sequences, and video frame sequences are fused according to the emotional weight to generate the final emotional feature of the video. Next, we will introduce the regional location attention module, regional relationship attention module, two attention modules fusion network, and video frames sequence feature fusion in detail.Each region of high-level features can be regarded as the response of a specific region of the video frame. The responses of all regions of the video frame together constitute the feature of the video frame. However, due to the different emotional sensitivity in different regions, humans often focus on the sensitive regions when judging the emotion of a video frame. For example, given a video frame containing humans, we often recognize human emotion by focusing on the emotional sensitive regions (such as faces) of the human. The emotion recognition method of focusing on local regions is often ignored by deep-learning-based emotion recognition. The emotional sensitivity of different regions is different. To make full use of the emotional sensitive region, meanwhile, the robustness of other regions of the video frame can also be used. This paper proposes a regional location attention module to model the emotional differences of different regions to further improve the ability of emotional feature representation.M ∈ ℜC×H×W are obtained. M is divided into H ∗ W regions. Thus, each video frame In can be expressed as {fnl}l=1,2,…,L, where L = H ∗ W represents the number of regional features, and each regional feature can be expressed as a C-dimensional feature vector, and l represents the region number, and it can be expressed as follows:i=1,2,…, H, j=1,2,…, W.After the feature extraction module extracts the features using conv1 to res5c of ResNet-50 CNN, the emotional features of the video frame M, it is first input into a convolution layer to generate a feature map X, where X is a H × W matrix. The regional location attention matrix is calculated with the softmax layer, and it can be expressed as follows:xij indicates the relative importance of the region 〈i, j〉. The greater the value xij, the more important the region is.The regional location attention module generates a regional emotion score for each region. Given an intermediate layer representation M is recalculated according to the matrix, and the formula can be expressed as follows:After obtaining the regional location attention score matrix, the emotional feature When the emotion features are recalculated according to the regional location attention score matrix, the emotion features that can perceptible emotion sensitive regions are obtained.Distinctive feature representation is very necessary for video emotion recognition. Building the regional relationship attention module to model the relationship between different regions can further improve the ability of regional feature fusion. Thus, the ability of video emotion expression can be enhanced. Next, we will introduce the regional relationship attention network in detail.M extracted by ResNet-50 CNN is input into two convolution layers, and two mappings B and C are obtained, where B and C are both H × W matrices. Their dimensions are readjusted to N × 1 and 1 × N, respectively, where the value of N is H × W. B multiplied by C is S; then the regional relationship attention matrix is calculated by a softmax layer. The formula can be expressed as follows:sij indicates the influence of the area j on the area i. It reflects the importance of emotion recognition when the two regions are fused. The higher the value sij, the higher the performance improvement when two regions are fused.As shown in i, j〉, its feature recalculated according to the regional relationship attention matrix can be expressed as follows:l represents the l − th region determined by the value of i and j. Mijr is the recalculated feature of region 〈i, j〉, which can perceptible regions correlation, according to the region relationship matrix. It reflects the relationship between different regions, and the complementarity between different regions can be guaranteed. The coordination and consistency between different regions are ensured and make the expression of emotional features more effective.After obtaining the regional relationship attention matrix, the emotion features can be recalculated according to the matrix. For each regional feature, it is fused with other regions according to the relationship between the region and other regions. The fused features fully reflect the relationship between different regions. For regional feature 〈Mnp be the emotion feature of the video frame In output by the regional location attention network, and fnp represents the transformed feature of Mnp; fnp can be expressed byWnp and bnp are weight parameters. Similarly, let Mnr be the emotion feature of the video frame In output by the regional relationship attention network, and fnr represents the transformed feature of Mnr, fnr can be expressed byWnr and bnr are weight parameters.To make full use of the emotional features of different regions, the features obtained by two parallel attention modules are fused. Specifically, first, the outputs of the two attention modules are transformed by a convolution layer. Let fn are obtained through a full connection layer. It should be pointed out that the regional dual attention module is very simple. It can be directly inserted into the existing full convolution network pipeline without adding too many parameters and can effectively enhance the feature representation.Then, the corresponding elements of the two features are summed, and the fusion features fn of the video frame In, the video frame sequence feature fusion network fuses features of the video frame sequence {I1, I2,…, IK} to generate the emotional features of the video Vmmc5. The network also uses an attention module to obtain the emotional sensitive region of the video frame sequence. Let matrix F be the feature set of K video frame sequences.After obtaining the emotional features F passes through the attention network of the video frame sequences, the emotional feature representation of video V can be obtained:αi is the weight value of the i − th video frame, which represents the emotional weight of the video frame. It can be obtained by learning a linear mapping Wig and can be calculated by the following formula:Wig is a small fully connected network. In our experiments, two continuous full connection layers are used, and the last full connection layer contains only one neuron.After FV is the emotional feature of video V, and then the emotional feature is passed through a full connection layer and supervised by a softmax loss function.The features of different video frames are aggregated according to the emotion weight to obtain the final emotion representation of the video. The aggregation method is as follows:We conduct experiments on five publicly available video emotion recognition data sets, namely the MHED data set , the HEIThe Ekman-6 data set contains 1,637 videos that are manually annotated by 10 annotators according to Ekman's theory  on 6 basSFEW data set contains over 2,000 minutes of video data that are annotated with valence and arousal values. Similar to , we applIn this subsection, we perform detailed ablation studies on the MHED data set and the HEIV data set to understand the contributions of the proposed model components.To evaluate the regional dual attention module, video frames of the MEHD data set are first extracted, and then the faces of video frames are extracted. Video frames sequence and faces sequence are constructed and are called MEHD-I and MEHD-F, respectively. This subsection will evaluate the regional dual attention network on the video frame sequence MEHD-I and face sequence MEHD-F.The regional dual attention network is implemented based on ResNet-50 network to solve the emotional differences of different regions and make full use of the relationship between different regions, to further improve the performance of video emotion recognition. To verify the performance of the regional dual attention module, experiments with different settings were carried out  is proposed to effectively solve the emotional differences between different regions and make full use of the relationship between different regions. Specifically, RDAM is composed of two parallel attention modules: one is the regional location attention module, which generates a score matrix representing the weights of different regions and then recalculates the emotional features according to the score matrix to obtain the features of perceptible emotional sensitive regions. The other is the regional relationship attention module, which generates a regional relationship matrix representing the relationship between any two regions, and then the emotional features are recalculated according to the regional relationship matrix to obtain the emotional features that can perceptible regions correlation. The two features are fused to obtain the emotional features of video frames. Finally, the attention-based video frame sequence feature fusion network is used to fuse the video frame sequence, and the emotional feature of the video is obtained. A series of experiments are carried out on four public video emotion recognition data sets. The experimental results show that RDAM can effectively solve the emotional differences between different regions and make full use of the relationship between different regions, to improve the performance of emotion recognition. In addition, it is very important to reduce the computational complexity and enhance the robustness of the model. In the next step, we will study how to reduce the computational complexity and improve the robustness while ensuring performance.


Video gaming is a recreational activity with yearly increasing popularity. It is mostly a sedentary behavior combined with repetitive movements of the upper limbs. If performed excessively, these movements may promote strain injuries and a sedentary lifestyle is one of the contributing factors to musculoskeletal disorders. Therefore, a systematic review was conducted to evaluate if video gaming negatively affects the musculoskeletal system of video gamers.PubMed, Web of Science and The Cochrane Library were systematically searched in order to identify relevant peer reviewed original articles in English published between 2000 and 2021. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was used for the analysis. Studies were included when they contained investigations of changes of the musculoskeletal system due to video gaming in healthy individuals. Studies with participants older than 60 years or solely psychological, social or cardiovascular outcomes were excluded. An adapted version of the Newcastle–Ottawa Scale was used for the risk of bias analysis.n = 4), shoulder (n = 4) and back (n = 3). Ten studies reported odds ratios (OR) for the dependence of the appearance of musculoskeletal disorders on video game playtime. In eight studies OR were significantly increased (1.3—5.2).Sixteen observational studies involving a total of 62,987 participants met the inclusion criteria. A majority (11) of the studies reported statistical negative musculoskeletal changes due to video game playtime. Four studies did not report changes and one study found no effect of video game playtime on the musculoskeletal system. Out of the eleven studies, which demonstrated a negative impact of video game playtime on the musculoskeletal system, the most reported painful body parts were the neck  seemed to be a predictor for the appearance of musculoskeletal disorders. Due to their great popularity across multiple generations, specific and tailored prevention and health promotion programs for video gamers need to be developed to counteract this important public health issue.The online version contains supplementary material available at 10.1186/s12891-022-05614-0. Video gaming is one of the most popular recreational activities in the world . In 2021Video gaming is a screen-based activity, comparable to watching TV, working at the computer or using a smartphone. These kinds of activities are characterized by long, continuous sitting periods and physical inactivity . Such seMSDs are a huge financial burden for healthcare systems in European countries. In 2016 Germany reported a loss of gross value added of EUR 30.4 billion because of musculoskeletal and connective tissue disorders . For thiThe aim of this systematic review is to give an overview and review evidence of the extent to which video gaming influences the musculoskeletal system of gamers. For this purpose, in this systematic review it was investigated (1) whether video gaming negatively affects the musculoskeletal system of gamers, (2) to what extent the factor playtime influences musculoskeletal changes and (3) if there are certain body parts or specific tissues particularly affected. This is especially important if the imbalances in video gamer’s health-behavior and special needs are to be identified. Moreover, these findings could be used for group specific prevention and rehabilitation recommendations to counteract these behaviors and improve the health of video gamers.The protocol for this review was registered and published in the PROSPERO database (CRD42021220167) and was reported in accordance with the Preferred Reporting Items for Reviews and Meta-Analysis (PRISMA) .Studies had to be peer-reviewed, published between 2000 and 2021, and written in English. Participants in the studies had to be active video gamers.All studies in which changes were investigated in the musculoskeletal system because of video gaming were of interest. This included issues of bones, cartilages, ligaments, tendons, muscles and tissues. Of special interest were negative changes in all human tissues and movements, for example range of motion, muscle tensions, pain syndromes and injuries. Reports about changes in the posture, muscle-nerve-connectivity or pain were included, as well as studies of the assessment of harms like cross-sectional, case–control or longitudinal studies. In addition, experimental studies like randomized controlled trials (RCTs) were also included.Studies were excluded if they only contained psychological, social, or cardiovascular outcomes. Other reviews, meta-analysis and case reports were excluded for this review. Moreover, studies on overweight or obese people were excluded. Studies with elderly people (over 60 years) were excluded, along with studies which were restricted to specific musculoskeletal diseases or conditions or psychological, cognitive or social disorders.Systematic electronic literature searches were undertaken of PubMed, Web of Science and Cochrane Library databases. Additionally, Google Scholar and reference lists of included studies were searched unsystematically. The first systematic electronic literature search was conducted on 08 October 2020 using PubMed and was updated on 01 December 2020 and 26 June 2021. For Web of Science the first search was conducted on 20 October 2020 and was updated on 02 December 2020 and 26 June 2021. The Cochrane Library was searched on 15 December 2020 and this was updated on 26 June 2021. Unsystematic searches of Google Scholar were conducted on 08 and 09 December 2020. Literature search strategies used medical subject headings (MeSH) and text words related to video gaming and musculoskeletal, injury or pain  for cross sectional studies , 31 was It was anticipated that the identified studies in this systematic review would be heterogeneous in the study design. Therefore, the study results were synthesized narrative without any further statistical analysis. The narrative synthesis is based on the guidelines of the Centre for Reviews and Dissemination .In Fig. n = 7), back (n = 6), shoulders (n = 5) and hands (n = 5). As the evaluation method, in eleven of the studies self-designed non-standardized questionnaires were used [In Table ere used , 41–46. ere used , 40 and ere used , a cliniere used  or only ere used .Table 1SThe risk of bias in all studies was assessed by an adapted version of the NOS .The first research question can be confirmed, since in eleven out of 16 studies a negative impact of video game playtime on the musculoskeletal system was demonstrated. Seven of these studies were rated with a low risk of bias. The remaining four studies were rated with a moderate risk of bias. Hence, the overall bias of these eleven studies can be rated as low to medium. Consequently, the results confirm that video gaming negatively affects the musculoskeletal system of video gamers.The second research question, whether excessive video game playtimes can be determined as a decisive factor in the development of MSDs, can also be confirmed. In eight studies a significant increase was reported of OR in individuals with high video game play times. More precisely, the probability of developing a MSD was significantly increased in subjects with video game playtimes of about two to three hours per day or more.From the results the third research question can also be validated. Video gaming negatively affects different body parts. Out of the eleven studies, in which a negative impact of video gaming on the musculoskeletal system was demonstrated, the most negatively affected body parts caused by video gaming were the neck, shoulder and back. Consequently, video gamers who have excessive playtimes seem to be more vulnerable to developing pain in the neck, shoulder or back area.According to the results video game playtimes had a negative impact on the musculoskeletal system of video gamers. Moreover, this effect was amplified in individuals who had higher video game play times. However, this effect was not amplified in multivariate models (see Table n = 3), esports athletes (n = 1) and gaming room visitors (n = 1) were observed. These populations were domiciled in ten different countries. Hence, cultural, social and psychological differences between the populations might have led to a variation of the reported diseases, pain or risk factors [The majority of the included populations were school aged kids and young adults in eleven of the 16 studies. In the remaining studies college students  seemed to be a predictor for the appearance of MSDs. In particular, the neck, shoulder and back were the most negatively affected body regions. The combination of continuous sitting, physical inactivity and repetitive movements of the upper limbs might be the reasons for that. Additionally, other screen-based activities could even amplify this association. Due to the great popularity of video gaming across multiple generations, specific and tailored prevention and health promotion programs for video gamers need to be developed to counteract this immense public health issue. Not only casual video gamers, but also and especially esports athletes should be aware of this if they are to counteract the negative effects of long video game playtimes. For this purpose, it seems reasonable that esports professionals should serve as role models for casual video gamers. In particular, possible correlations between MSDs and different game genres should be observed. Hence, future research should be focused on prevention and health promotion programs for this target group in order to provide a basis for recommendations. Furthermore, studies are needed in which more details of the requirements and burdens are demonstrated on the human body while video gaming.Additional file 1. Search strategy.Additional file 2. Adapted Newcastle-Ottawa Quality Assessment Scale.Additional file 3. OR models of individual studies.


Global encoding of visual features in video captioning is important for improving the description accuracy. In this paper, we propose a video captioning method that combines Vision Transformer (ViT) and reinforcement learning. Firstly, Resnet-152 and ResNeXt-101 are used to extract features from videos. Secondly, the encoding block of the ViT network is applied to encode video features. Thirdly, the encoded features are fed into a Long Short-Term Memory (LSTM) network to generate a video content description. Finally, the accuracy of video content description is further improved by fine-tuning reinforcement learning. We conducted experiments on the benchmark dataset MSR-VTT used for video captioning. The results show that compared with the current mainstream methods, the model in this paper has improved by 2.9%, 1.4%, 0.9% and 4.8% under the four evaluation indicators of LEU-4, METEOR, ROUGE-L and CIDEr-D, respectively. Generating video content description by manual annotation is time-consuming and inapplicable for large volumes of videos. With the continuous and rapid development of information technology, short video sharing platforms represented by Tiktok, Kwai and micro-blog have emerged. These platforms produce a large number of videos with various views and tendencies in the short term. There may be some videos that have a negative impact on users, and bring great hidden dangers if these videos contain blood, pornography, violence and other harmful information without timely review. Therefore, it is of great significance to adopt automatic means to timely review video content and reject undesirable videos in real time.At present, video captioning methods are mainly based on template matching and deep learning . The temVideo captioning methods based on deep learning are inspired by the encoder-decoder framework used in machine translation research, and many similar video captioning models have been designed. For example, The most existing video captioning methods utilize deep convolutional neural networks or 3D convolutional neural networks as encoders to extract visual representation vectors. It decodes its visual representation vector as the input of Recurrent Neural Network (RNN) to generate serialized natural language expression. For example, Although the use of feature sequences extracted by RNN to establish a language description model is successful, 3D convolutional networks can extract both spatial and temporal features of videos, which can further strengthen the features of static visual semantic objects and dynamic visual events in videos and improve the robustness of the model. For example, In summary, although the video captioning method based on template matching is simple and straightforward, these methods depend too much on the preset templates and rules. It leads to monotonous captioning sentences and poor flexibility. Although the video captioning method based on deep learning can effectively solve the above problems, the common video captioning method usually directly takes the final output state of encoding as the input in the decoding stage, and loses a large number of intermediate hidden states.(1) Use an encoder composed of Transformer Encoder blocks to encode video features in a global view, thereby reducing the loss of intermediate hidden layer information.(2) Introduce the Policy Gradient reinforcement learning method to improve the accuracy of the model.(3) Conduct Experiments on the MSR-VTT dataset to demonstrate the effectiveness of the video captioning method proposed in this paper.In recent years, inspired by the successful application of Transformer in vision tasks in natural language processing, a large number of vision tasks also use Visual Transformer (ViT) as a model encoder to verify the efficiency of the model, such as semantic segmentation, image editing and entity segmentation. Therefore, the paper proposes a video content description method integrating vision transformer and reinforcement learning. It utilizes the transformer encoder provided by ViT as the feature encoder, and globally encodes the video features combined with the hidden state in the middle of the encoder. It solves the problem that the traditional encoder loses the information of the middle-hidden layer and cannot globally encode the video features. The main work of this paper is following:As shown in Video data is composed of objects, scenes, people and other static elements in the spatial domain, and its structure is composed of multiple continuous video frames. There are changes in motion trajectories between frames, which contain rich temporal motion information . Hence, In deep learning methods, we usually extract richer features by continuously stacking the number of network layers. However, the accuracy of the model gradually begins to saturate and rapidly decline as neural networks deepen. It will lead to the disappearance of the gradient and the degradation of accuracy. In response to this problem, He Kaiming et al. designed ResNet network and introduced deep residual learning module . It fitsri and dynamic feature ei for each frame respectively. The sum result xi of the two features is used as the overall feature of the video. The sequence of visual features is Before extracting the video features, we first segment the video data into ri represents the result of static feature extraction, ei is the result of temporal motion feature extraction, fr and fe represent static and dynamic feature extraction functions respectively, i-th video frame, dvisiual is the dimension of the video feature. We set the size of dvisiual to 4,096 dimensions. C, H and W are the number of channels, height, and width of the video frame. Their values are 3, 224, and 224.The video caption generation phase consists of the encoding and decoding of the video features, as shown in The vision transformer (ViT) model proposed in 2021 can encode image features with the global field of view, and tackle the problem that convolution networks are highly sensitive to the high-frequency information in the image. Inspired by literature , we use The encoder of the benchmark model is made up of a stack of 12 single Vision Transformer encoding blocks. Each block consists of Multi-Head Attention (MHA) and MultiLayer Perceptron (MLP) Block, as shown in x-th time step shown in C. First, we use MHA function to calculate the normalized characteristic C of the previous time step. Then, apply the MLP function to calculate the output of the coding block. Finally, the N features are normalized. The result is expressed as the final feature of the encoder, and its output size is 1,024 dimensions, which is calculated as In the zl is the output of the multilayer perceptron, xl is the output of the encoder at time l, which is the result of the global feature encoding. N is the total time step length.Taking into account the timing relationship between video frames, the benchmark model in this paper uses a multi-layer Long Short-Term Memory neural network (LSTM) to construct the decoder. The LSTM consists of input gate, forgetting gate, output gate and memory unit. The network structure of the LSTM unit is shown in t time model be 1,024-dimensional vector xt, the hidden layer feature corresponding to the input feature be ht−1, and the cell memory unit of the LSTM network be ct. Then the activation function it of the LSTM unit. Similarly, the forgetting feature ft and the output feature ot can be obtained as As shown in it represents the inputs, ft is the forgetting feature, ot is the output gate, gt is the input modulation gate, W and b are the parameters to be optimized. We use the sigmoid activation function and the tanh activation shown in The detailed process of the video feature decoding is shown in X is shown in Furthermore, in order to enable mapping between the decoded result and the text, we preprocess the captioning text tags corresponding to the video. First, we use the word embedding method to encode each word in the caption into a 512-dimensional vector corpus corresponding to the softmax layer. We represent the string [END] as the end of the sentence. The end marker not only prompts the model to switch coding and decoding in the training stage, but also can be used as a marker to describe the completion of generation in the test stage.t, the model first encodes the video features through the transformer encoder block, and then sends it to the multi-layer LSTM network to decode and generate the caption In summary, at time step In order to improve the accuracy of video captioning model, a reinforcement learning method is introduced to learn the strategy gradient st represents the status of the Environment at time t, at is the word generated at time t. Finally, the model calculates the loss function gradient and optimizes the model parameters according to the loss value and the reward value as follows:When optimizing the model with reinforcement learning, let the sequence of words, status values and rewards generated by a video be N is the number of samples. In order to improve the readability and fluency of the generated captions, we refer to the literature  to optimize model parameters and improve the captioning performance. Multiple experiments on the MSR-VTT data set demonstrate the effectiveness of the proposed method measured by the evaluation indicators of METEOR, BLEU, ROUGE-L and CIDEr.In the video captioning task, the collection and labeling of training data often consumes a lot of manpower and material resources. Therefore, in the future work, the zero-shot and few-shot learning techniques can be used to achieve adequate optimization of the model with less training data.10.7717/peerj-cs.916/supp-1Supplemental Information 1L is the number of transformer encoder blocks in the encoder of the model, sw is the word sequence generated by the model, and r(*) is the reinforcement learning reward functionThe model includes three parts: feature extraction, video captioning generation, reward mechanism (Policy Gradient). Click here for additional data file.10.7717/peerj-cs.916/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj-cs.916/supp-3Supplemental Information 3Specifically, the encoding block first inputs the features into Layer Norm and Multi-Head Attention, and then sends them to the MLP block.Click here for additional data file.10.7717/peerj-cs.916/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj-cs.916/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj-cs.916/supp-6Supplemental Information 6Click here for additional data file.


The older person population is rapidly increasing globally, including sub-Saharan Africa (SSA). Concurrently, malnutrition is also increasing among older persons in SSA, with a dearth of empirical evidence on nutritional status and associated factors among the older persons in the region to inform effective interventions to promote healthy ageing.This review assessed the nutritional status and associated factors among older persons in SSA.PubMed, Google Scholar, and Cochrane Library electronic databases were searched for published articles from 2010 to 2021 using keywords and Boolean logic. Also, we carried out a free web-based search to retrieve other relevant evidence that assesses the older persons’ nutritional status. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow chart was used to appraise the research articles that responded to the study’s research questions.Twenty studies met the inclusion criteria, from which data were extracted as findings. The malnutrition prevalence was between 6 to 54% among older persons in SSA. We found that the prevalence of malnutrition vary and could be as high as 28.4% in a low socio-economic status area. Of these studies, twelve (12) provided data on undernutrition with prevalence ranging from 2.9 to 41%;10 provided data on overweight (8.1–54.1%) and 5 on obesity (2.7–44.7%). Seventeen of the studies evaluated factors associated with malnutrition; 4 studies revealed the association between socioeconomic status and malnutrition, 7 studies reported a significant association between dietary habits and malnutrition. Four studies showed an association between educational status and malnutrition. Disease conditions associated with malnutrition were reported in four of the studies.This review shows that malnutrition is a problem among older persons in SSA coupled with many risk factors which should be given critical attention. We recommend nutrition education for older persons as well as the development of nutrition interventions for this vulnerable group. The older population is on the rise globally, including sub-Saharan Africa (SSA) , 47. BesAccording to the American Society for Parenteral and Enteral Nutrition 2014), malnutrition is an imbalance between nutrient requirement and intake that negatively affects the individual’s growth. Such effects may manifest as under-nutrition, overnutrition, or micronutrient deficiencies 14, malnu, 31, 51.Globally, malnutrition among older persons is rising, with undernutrition and overweight/obesity causing significant morbidity and mortality . For ins2 equivalent to a mid-upper arm circumference (MUAC) of about 23 cm  , 62. The Adepoju , the preA comparison study in two countries, the Republic of Congo and the CAR, showed that while undernutrition was higher among the older persons in CAR than in Congo, the reverse was overweight and obesity . It has Seventeen (17) studies also reported on the various factors associated with malnutrition; four studies revealed the association between socio-economic status and malnutrition, whereas seven studies were on dietary habits. Also, four studies showed a significant association between educational status and malnutrition. Disease conditions related to malnutrition were assessed in four of the studies. The review revealed a clear association between socio-economic status and malnutrition of the older persons in SSA. Most African adults experience poverty, poor health care access and services, and poor diet in old age . For insAlso, we found a significant association between the educational status of older persons and malnutrition. Affirmatively, according to Alphas et al. , the levFurthermore, our review found that consumption of an unbalanced diet (poor diet), decline to eat by the older persons and poor dietary patterns influenced malnutrition among the older persons in SSA. The primary outcome of this habit is underweight, overweight or obesity. Steyn and Mchiza  support Also, In comparison, women showed a higher prevalence of overweight than men, and in urban than rural areas, which also progress with advancing age . ChangesIn addition, we found that older persons with oral problems were more likely to suffer from undernutrition. According to Adebusoye , oral prThis study has provided insight into the nutritional status and associated factors of older persons in SSA. However, there are a few limitations of the study. Firstly, our literature search was restricted to studies published in English for practical reasons. We may have missed some relevant literature. Also, due to restrictions of some databases, we focused our search on only three databases and freehand search; we may have missed other relevant literature in our review.Malnutrition among older persons in SSA is high, with a higher prevalence of undernutrition, overweight and obesity. A higher prevalence of undernutrition implies that older persons in SSA are at risk of developing osteoporosis and a weak immune system accompanying complications. Also, the higher prevalence of overweight and obesity among the older persons in SSA implies that the older person population in the continent are at risk of developing CVDs and diabetes. The consequences of both undernutrition, overweight, and obesity may affect the QoL of older persons if critical attention is not given to their nutritional needs. Older persons with low socio-economic status, poor dietary habits, and those experiencing disease conditions are more likely to be malnourished. Suppose adequate measures are not implemented to improve older persons’ socio-economic status and dietary habits. In that case, they are likely to experience morbidity and mortality, which may burden the countries’ health care systems and the economy in SSA. Also, SSA’s efforts to attain sustainable development goal three, to ensure healthy lives and promote well-being for all ages, may be hindered.This scoping review established that older people even though with increased risk of malnutrition, and most of them being malnourished , 57, 59,Based on our findings, further exploratory studies among individual SSA countries would likely yield key evidence on the current case of malnutrition among older persons. With a suggestive growing prevalence of malnutrition among older persons with low socio-economic statuses, the role of social support and social interventions for older persons have to be explored by researchers in SSA.Governments and stakeholders in SSA should implement effective social and health promotive interventions to address the nutritional health needs of older persons. These interventions should pay attention to their socio-cultural perspectives and socio-economic statuses. As the world focuses on attaining the Sustainable Development Goals, governments and stakeholders in SSA should enact policies to address older person care and nutritional health needs.The authors declare that they have no known conflicting personal or financial interests regarding the conduct of this study that could have affected the findings reported.


Gmax) of saturated sand-silt mixed soil materials, a series of tests were conducted using the bender element apparatus, and the influences of fines content (FC), relative density (Dr), and effective confining pressure . The decrease rate between the Gmax and e differs when the FC. The Gmax of the mixed soil materials can be evaluated by the Hardin model when the FC is determined. The best-fitting parameter A of the Hardin model first decreases and then increases as FC increases. The revised Hardin model, considering the influence of FC, e, can be used to evaluate the Gmax for different types of sand-silt mixed soil materials. The error between the evaluated and tested Gmax is less than 10%.To study the small strain shear modulus ( Gmax, which is denoted as the shear modulus at small strain , is often used as an important mechanical index in soil material stability evaluation and numerical simulation [Gmax, regardless of the angle of repose and packing fraction.Small strain shear modulus mulation . Simultamulation  establismulation . In thisGmax is influenced by various factors, such as void ratio (e) [Cu) [Gmax is proved to be incredibly sensitive to e and effective confining pressure  not only extensively exist in natural strata such as colluvium deposits, alluvial deposits, and glacial tills, but also have many applications in man-made constructions, including land reclamation, dams, and subgrades [FC on Gmax. For instance, Iwasaki and Tatsuoka [Gmax of Iruma Sand with the same Cu decreased with FC up to 15%. Yang and Liu [FC of up to 30%. Choo and Burns [Vs) decreased with increasing FC under the same relative density (Dr). Wichtmann et al. [Cu and FC and established the empirical formulas for parameters A, n, and c with FC and Cu. Cheng et al. [Gmax of silty sand initially decreases and then increases with increasing plastic FC from 0 to 50%. Thevanayagam and Liang [e∗) instead of e in Equation (1). However, the relationships that many scholars have attempted to establish are discrepant and dependent on special sand and fines materials in specific areas. Therefore, the Gmax behavior of mixed soil materials still deserves further investigation.atio (e) , particlatio (e) ,7,8, uni(e) [Cu) , fines c(e) [Cu) ,11,12, f(e) [Cu) ,14, stre(e) [Cu) ,16, etc.ean sand . The facubgrades ,19. TherTatsuoka  first re and Liu  also obsnd Burns  performen et al.  conductend Liang  and Goudnd Liang ,26 adoptGmax test methods, such as the resonant column method and the bender element method. The bender element method is widely applied to the test Gmax, because of its simple principle, convenient operation, and non-destructive inspection [Gmax of the saturated sand-silt mixed soil materials with a wider range of FC in this study than that in previous studies. The influences of FC, Dr, and Gmax evaluation model of various mixed soil materials was finally proposed.There exist various kinds of spection ,28. A seVs) and associated Gmax was implemented using a pair of piezoceramic bender elements (BEs) installed in the GCTS HCA-300  dynamic hollow cylinder-TSH testing system to excite the sample in the form of a sinusoidal wave. The test apparatus is shown in The measurement of shear wave velocity  and maximum void ratio (emax) versus FC of the mixtures is shown in FC ≤ 30%, the values of emin and emax decrease with the increasing FC, and when FC > 30%, the values of emin and emax increase with the increasing FC, which indicates the presence of a threshold (around FC = 30%) for the effect of FC on emin and emax.The tested sand-silt mixed soil materials were taken from the tidal flat sediment in Nantong Gulf in China. The mixed materials with a particle size of less than 0.075 mm obtained by sieving are regarded as pure fines, and the rest of the particles are regarded as clean sand. The clean sand is a fine-grained, angular siliceous sand, and the pure fines is a non-plastic, sub-angular fines. B > 0.95 [The tested specimen is a solid cylinder with a 100 mm diameter and a 200 mm height, and the moist tamping method was introduced to prepare the specimens . All speB > 0.95 , the speFC, Dr, and Gmax of the mixed materials. FC = 0, 10, 20, 30, 50, 70, and 100% were considered, and three specimens were prepared at different Dr  at a fixed FC. The Gmax was measured and subjected to Gmax. The typical signal wave of the bender element test of S11 specimen :A is the parameter influenced by the property of the soil material type. Pa is the standard atmospheric pressure (≈100 kPa). n is the empirical constant, and the value is between 0.4 and 0.6 for the pluvial sand, and many researchers have observed that the value can be taken as 0.5 [F(e) is the function of e, which is decreased with the increase of e. Hardin and Black 15 described the F(e) as Equation (4):c is the parameter influenced by the shape of the soil particle, and the values are 2.17 and 2.97 for the angular and rounded sand particles, respectively. The shape of Nantong sand particles is angular, and the value of c is 2.17 as a result. The reasonability of Equation (4) is verified by many scholars, such as Iwasaki et al. [Gmax of the Nantong sand-silt mixed soil materials can be described by Equation (5):The acknowledged empirical relationship among the n as 0.5 ,36. F(e)i et al. , Yamashii et al. . AccordiGmax~F(e)~Pa is shown in Gmax~F(e)~Pa is at the top when the FC is 0 (pure sand). The data of the Gmax~F(e)~Pa is at the bottom when the FC is 30% (pure sand). The data of the Gmax~F(e)~Pa is above the evaluation of the Hardin model when the FC is 100% (pure silt). The results mean that the influence of the FC on the relationship between Gmax~F(e)~Pa is not monotonic. The fitted results of the Gmax~F(e)~Pa data under various FC via the Hardin model are improper.The normalized relationship between the Gmax/F(e) and (Pa)0.5 of the sand-silt mixed soil materials under various FC is shown in Gmax/F(e) increases almost linearly with the increased (Pa)0.5 when the FC is given. The Gmax of the mixed soil materials under a given FC can be evaluated via the Hardin model. The data points (black square) and evaluation lines of the Gmax of the mixed soil materials by the Hardin model are shown in R2 of mixture under various FC is almost all greater than 0.9, which means the Gmax evaluation of the Hardin model is effective. In addition, the relationship between the parameters A and FC is not monotonous.The relationship between the FC on the mechanical behavior. The mixed materials are composed of sand and fines particles of various sizes, and the contact state of the sand and fines particles influences the mechanical behavior. The sand particles contact each other and constitute the skeleton of the mixed materials when the FC is low. The fines fill the voids left by the mixed materials. The mechanical behavior of the mixed materials is dependent on the framework of the sand particles, and the materials under such a particle contact state are called sand-like soil materials. The fines particles contact each other and constitute the skeleton of the mixed materials when the FC is high. The sand particles are suspended in the fines. The mechanical behavior of the mixed materials is dependent on the framework of the fines particles, and the mixed materials under such a particle contact state is called silt-like soil materials. As a result, there is a threshold fines content (FCth) for the mixed materials [FC is smaller than the FCth. The mixtures are called silt-like soil materials when the FC is larger than the FCth. The FCth is a critical parameter which can distinguish the particle contact state and influence the mechanical behavior of the mixed materials. Rahman et al. [FCth.Thevanayagam and Martin  proposedaterials . The mixn et al.  proposedα and β are the fitted parameters and the values are suggested as 0.50 and 0.13, respectively. FCth used in the study is about 35% which can be calculated according to the data of A and FC is shown in A decreases with the increasing FC when the FC is smaller than FCth. Parameter A increases with the increasing FC when the FC is larger than FCth. As a result, parameter A can be described by Equation (6):The relationship between parameter m and n are the fitted parameters. AFC=0)( is the parameter A fitted by Hardin model of pure sand. AFC=100%):As a result, the revised Hardin model, which considered the influence of Gmax and the evaluated Gmax by the revised Hardin model of the various mixed soil materials in the study is shown in Gmax and evaluated Gmax by the revised Hardin model of the data of Payan et al. is shown in Gmax of the mixed soil materials.The relationship between the tested Gmax of the saturated sand-silt mixed soil materials, and the influence of FC, relative density Dr and Gmax of the materials based on the test data. The Gmax of the mixed materials increases linearly with the increased Gmax and FC is related to the Dr when the Gmax decreases with the increased e and the decrease rate is influenced by the FC when the Gmax of the mixed materials when the FC is given. The parameter A decreases and then increases with the increased FC. The revised Hardin model, which is based on the threshold fines content (FCth) can be used to evaluate the Gmax of the mixed materials under various FC, e, and A series of bender element tests were conducted to study the Gmax between wet and dry soil materials. In engineering practice, the soil materials are mostly wet and especially saturated below the groundwater level. Thus, this study focuses on the Gmax of wet soil materials, and the difference between wet and saturated soils will be investigated in the near future.It should be noted that there may be differences in


The 2020 COVID-19 pandemic has greatly accelerated the adoption of online learning and teaching in many colleges and universities. Video, as a key integral part of online education, largely influences student learning experiences. Though many guidelines on designing educational videos have been reported, the quantitative data showing the impacts of video length on students’ academic performance in a credit-bearing course is limited, particularly for an online-flipped college engineering course. The forced pandemic lockdown enables a suitable environment to address this research gap. In this paper, we present the first step to examine the impact of short videos on students’ academic performance in such circumstances. Our results indicate that short videos can greatly improve student engagement by 24.7% in terms of video viewing time, and the final exam score by 9.0%, both compared to the long-video group. The quantitative Likert questionnaire also indicates students’ preference for short videos over long videos. We believe this study has important implications for course design for future online-flipped engineering courses. Remote online learning and teaching (RO-L&T) has long been considered as a promising education practice in the future, thanks to its wide availability and timing convenience. The 2020 COVID-19 pandemic has greatly accelerated the usage of RO-L&T in many universities  difference between the short-video group and long-video group. The second hypothesis tests the quantitative exam scores between the two groups.0 For the online-flipped engineering drawing course, the student video engagement time of the short-video group and long-video group are identical.H1 For the online-flipped engineering drawing course, the student video engagement time of the short-video group is longer than that of the long-video group.H0 For the online-flipped engineering drawing course, student exam scores of the short-video group and long-video group are identical.H1 For the online-flipped engineering drawing course, student exam scores of the short-video group are better than that of the long-video group.HIntroduction to Engineering Drawing course at Suzhou City University. The practice of flipped classes was designed according to Bishop and Verleger . To rule out the influences of prior knowledge, the repeating students are not included in this study. Descriptive statistics of selected background information of the participating student group are shown in Table The intrinsic learning skill of students that are developed from early studies may also influence the results of this research, but that skill can be well-evaluated based on the Gaokao score  indicates that both groups share similar learning skills.We pre-recorded weekly lecture videos via the video recording function that comes with the online virtual platform, with a normal length , and they were directly used for the long-video group. The teaching style is that a teacher shows slides while lecturing. The short video was created by trimming the long-video into the videos with average 8-mins long , which has been proved to the optimal video length .The five-point Likert questionnaire contains 9 questions, each followed by a scale: U-test, due to the rejection of the normality test. The significance level was set to 0.05 for checking the hypotheses. The Likert survey data were compared using a one-way ANOVA test.All statistical analyses were performed using Matlab (MathWorks Inc). Cronbach’s alpha test was used to examine the reliability of the Likert scale survey. Both groups’ exam scores and video viewing time were compared using the Mann–Whitney By far, video viewing time (engagement time) has been considered as the most significant indicator of student engagement in online education  for the average score comparison was obtained. This result demonstrates that short videos can result in better academic performances, indicating the validation of our hypothesis 2.The Mann–Whitney Based on the above quantitative results, several findings and contributions that are different from previous studies can be discussed and summarized below.First, compared to those studies on non-credit MOOC courses  , and subjective attitudes .Although RO-L&T has been developed for a while, students may still be limited by their internet skills to adapt to the flipped RO-L&T. As students’ negative subjective attitudes toward the learning content (engineering drawing) and learning format (recorded video and flipped classroom) may also affect the experimental results. Therefore, we conducted a data survey on students’ internet skills and subjective attitudes through three questions (Q1-Q3) before the class to exclude the influence of subjective factors and large individual differences. As shown in Fig. p > 0.05) and (2) retain the video information . However, the objective scores of the final exams showed that significant information retaining differences existed between the two groups. This may suggest that our students all have a good learning attitude (in line with Q2) and wish to stay focused for the entire video length and retain all the information.Students’ concentration and self-perceived information retention for videos of different lengths may influence the objective feedback on the experimental results . One interesting finding is that both groups reported a similar level of ability to (1) stay focused for the entire video length , though no statistical difference was identified. This result was also in line with subjective reports when asked if the videos were too long or not, and the long-video group reported a much higher value . However, for the question asking if the videos are too short, the short-video group reported a similar value compared to the long-video group .Although we divided the videos into short (8 min) and long (55 min), short and long are relative concepts that also depend on the perception of different students. One unique advantage of using recorded videos is that the students can frequently rewind or pause the video, which can help student process and understand the difficult content and aid the test. This will help eliminate potential result bias caused by difficulties in understanding long videos. The long-video group also reported a higher frequency of video rewinding than that of the short-video group , this study is the first step to assessing the impact of short videos on students’ performance in an online-flipped college engineering course during the pandemic. The strict lockdown enabled us to conduct this credit-bearing course in the RO-L&T environment, which is impossible otherwise. Several new findings can be summarized. First, the engagement time for the long-video group has significantly increased compared to previous MOOC studies. The median engagement time is less than 4 min for 12–40 min long videos in MOOC but increased to 24.7 min for 55-min-long videos in our studies. This suggests that students may be strongly motivated to control themselves for studying towards their degrees and thereby leaves more room for instructors to edit the video length to meet the specific needs better instead of strictly limiting the video length to 6 min that as suggested by MOOC studies , preferred viewing device used (computer or phone), viewing location , viewing time (day or night), audio playback method (headphones or speakers), preferred video speed  and multitasking . In the following study, we plan to add more qualitative research, such as designing a scale to measure learning activities for improving the videos. In addition to the length of the videos, we will consider quizzes, assignments, multimedia, and other influencing factors when designing videos in the future. As the video platform develops, we may focus on monitoring pre-screen student behavior, absences, and poor interactions, as well as quantitative research on other possible group factors . Moreover, to enhance external validity and further longitudinal depth, we intend to replicate our experiment in additional engineering courses for follow-up studies.“Tell students and they forget, teach students and they may remember, involve students and they learn,” is the core of the flipped classroom concept. Rapid advances in technology have made better implementation of flipped RO-L&T possible. Such an environment is a positive experience for both the instructor and the students while increasing students’ engagement. The evolving engineering courses could benefit from providing length-optimized videos to students under the flipped course structure. In this way, class time can be focused on discussing problems encountered in the videos and on learning how to “think like an engineer”. Flipped RO-L&T may initially be accepted by teachers and students, and the opportunity to improve the instructional model should not be overlooked.In particular, educators need to use research-proven methods that can be replicated from one course, such as engineering drawing, to another. We offer several recommendations based on this study that we hope these will help instructors improve flipped RO-L&T. First, it is recommended that teachers should make the videos as short as possible, but one complete knowledge point should be contained in one video. Second, all pre-class video preparations are recommended to be linked to student in-class activities, which will help increase students’ motivation and engagement. For example, ask targeted questions related to the video content to provoke active thinking or discussion among students. Third, during the class, according to cognitive load theory, it is recommended to intentionally pause after reviewing each point in the video, to give students time to digest the contents. For example, give three to five minutes of break to allow students to share the things they are unclear about. Fourth, based on our research finding that students are likely to watch the videos repeatedly after class, it is recommended that use one short video to cover one question or knowledge point, rather than one large block of video covering all the questions and points.We have presented one study to evaluate the impact of short videos on students’ performance in an online-flipped college engineering course during the COVID-19 pandemic. This credit-bearing course is completely delivered in a remote online learning and teaching environment. Our findings demonstrate that short videos can significantly improve students’ engagement by 24.7 %  and their final exam scores by 7.4%, compared to the long-video group, which can be attributed to the reduced cognitive load. We also discovered that the student engagement time is longer than in MOOC studies, leaving more room for instructors to construct videos in credited course teaching. We are hopeful for future developments and have made some recommendations, including video design, students’ motivation and engagement, knowledge retention, and classroom activities. We envision that our studies could provide useful information for future curriculum design in remote online-flipped courses.


To analyze the correlation between preoperative nutritional status, frailty, sarcopenia, body composition, and anthropometry in geriatric inpatients undergoing major pancreatic and biliary surgery.This is a cross-sectional study of the database from December 2020 to September 2022 in the department of hepatopancreatobiliary surgery, Beijing Hospital. Basal data, anthropometry, and body composition were recorded. NRS 2002, GLIM, FFP 2001, and AWGS 2019 criteria were performed. The incidence, overlap, and correlation of malnutrition, frailty, sarcopenia, and other nutrition-related variables were investigated. Group comparisons were implemented by stratification of age and malignancy. The present study adhered to the STROBE guidelines for cross-sectional study.p-values were below 0.002. Albumin, prealbumin, CC, GS, 6MTW, ASMI, and FFMI showed a significantly negative correlation with the diagnoses of the four tools. Participants with frailty or sarcopenia were significantly more likely to suffer from malnutrition than their control groups with a 5.037 and 3.267 times higher risk, respectively . Summarizing from stratification analysis, most body composition and function variables were worsen in the ≥70 years group than in the younger group, and malignant patients tended to experience more intake reduction and weight loss than the benign group, which affected the nutrition diagnosis.A total of 140 consecutive cases were included. The prevalence of nutritional risk, malnutrition, frailty, and sarcopenia was 70.0, 67.1, 20.7, and 36.4%, respectively. The overlaps of malnutrition with sarcopenia, malnutrition with frailty, and sarcopenia with frailty were 36.4, 19.3, and 15.0%. There is a positive correlation between every two of the four diagnostic tools, and all six Elderly inpatients undergoing major pancreatic and biliary surgery possessed high prevalence and overlap rates of malnutrition, frailty, and sarcopenia. Body composition and function deteriorated obviously with aging. The geriatric syndrome refers to a range of multifactorial health conditions representing the accumulation of multiple system impairments in older adults. Malnutrition, frailty, and sarcopenia are three common geriatric syndromes, which can substantially lead to poor outcomes, such as disability, dysfunction, falls, and perioperative complications, and thereby increase the length of hospital stay (LOS) and the cost of hospitalization, and result in long-term care or even mortality –5.Malnutrition or undernutrition refers to deficiencies in nutritional intake resulting in altered body composition, and approximately 1/3 of Chinese geriatric inpatients experience malnutrition . In the Frailty, sarcopenia, and malnutrition have independent diagnostic criteria, but share many components, such as weight loss, muscle mass, or strength loss, and often coexist or overlap in elderly inpatients . In a reThis study is a cross-sectional study analyzing the daily database of the Department of hepatopancreatobiliary surgery, Beijing Hospital. From December 2020 to September 2022, 205 consecutive patients undergoing major pancreatic and biliary surgery were screened, and then, 140 elderly patients were recruited in this study.The inclusion criteria of this study are as follows: (1) age ≥60 years old, which is the age cut-off of older adults defined by the Nation Health Commission of China ; (2) maj2 was defined as underweight, 18.5 kg/m2 ≤ BMI < 24 kg/m2 was normal weight, 24 kg/m2 ≤ BMI < 28 kg/m2 was considered overweight, and BMI ≥ 28 kg/m2 was considered obesity , co-morbidities, and serum examination . According to the standard of the guidelines for prevention and control of overweight and obesity in Chinese adults, a BMI < 18.5 kg/m obesity .A diet survey was conducted after admission. We recorded the change of diet before and after the diagnosis of the original disease, and calculated the contents composition, containing protein, carbohydrate, fat, and total energy.Anthropometry was done 1 to 2 days after admission, including calf circumference (CC) and grip strength (GS), both of which, we used the average value of the left and right sides. To assess the functional status, 15-foot and 6-meter timed walk speed (6MTW) was conducted to get the walking speed. Bioelectrical impedance analysis (BIA) was applied with the InBody 720 bioimpedance body composition analyzer . Appendicular skeletal muscle mass index (ASMI) was calculated, which was the sum of the lean muscle mass of the upper and lower extremities adjusted with height. Also, the fat-free mass index (FFMI) was recorded. Visceral fat area (VFA), waist-hip ratio (WHR), and body fat percentage (BTP) were included to reflect fat metabolism.We used Nutritional Risk Screening 2002 (NRS 2002) for nutritional screening for each patient within 24 h after admission, which was recommended by the European Society of Parenteral Enteral Nutrition (ESPEN) . NRS20022 if age < 70 years, BMI < 20 kg/m2 if age ≥ 70 years; (3) reduced muscle mass: in our study, we used AMMI and FFMI assessed by BIA. AMMI < 7 kg/m2 or FFMI < 17 kg/m2 in men were considered patients with reduced muscle mass, and AMMI < 5.7 kg/m2 or FFMI < 15 kg/m2 in women were considered positive. Etiologic criteria include: (1) Reduced food intake: ≤50% of needs from 1 to 2 weeks, or any reduction for >2 weeks; (2) Disease burden or inflammation: in this study, most of the patients were suffering from malignancies and the co-morbidities were also taken into account. If at least one criterion was fulfilled in each section, malnutrition can be diagnosed.The Global Leadership Initiative on Malnutrition (GLIM) criteria were implemented for malnutrition diagnosis and grading among patients with nutritional risk determined by NRS2002 . The fra2 if age < 70 years or BMI < 17.8 kg/m2 if age ≥ 70 years) or severe muscle deficit were defined as severe malnutrition. 5–10% Unintentional weight loss (WT) within the past 6 months or 10–20% WT beyond 6 months or low BMI (17.0 ≤ BMI < 20.0 kg/m2 if age < 70 years or 17.8 ≤ BMI < 22.0 kg/m2 if age ≥ 70 years) or Mild-to-Moderate muscle deficit were the grading criteria for moderate malnutrition.The grading of malnutrition also followed the GLIM criteria. Unintentional weight loss (WT) > 10% within the past 6 months or WT > 20% beyond 6 months or low BMI  . For patThe Fried Frailty Phenotype (FFP) is a recommended assessment tool for frailty in geriatric patients by Chinese expert group consensus . FFP crit-test. Measurement data that did not correspond to normal distribution were presented as median with interquartile range (IQR) and analyzed by Mann–Whitney U test. Categorical data were presented as counts and percentages, and compared by chi-square (χ2) test. Correlations were analyzed by Spearman’s correlation coefficient analysis according to the classification of variables. Multivariate analysis was performed by binary logistic regression to identify potential associated factors of malnutrition. A p-value < 0.05 were declared as statistically significant. All figures including flowchart, overlap bubble chart, and correlation heatmap were designed and drawn by Microsoft Office (Version 2016), and the regression analysis figure was drawn by GraphPad Prism version 7.0.0 for Windows .The sample size was calculated by PASS software 11.0 . The confidence level was set at 0.8. According to our former study, the prevalence of malnutrition was 56.6% and we set the proportion at 60% . The tolA total of 140 participants were included with a mean age of 70.0 ± 7.3 years. 58.6% (82/140) were male. 75% (105/140) of cases were malignancies, of which, 71 cases were pancreatic duct adenocarcinoma. The details of the history and blood test at admission are shown in 2. 83 cases (59.3%) experienced weight loss to varying degrees, in which, 66 cases exceeded 5%. According to NRS 2002, 70.0% (n = 98) of cases were at risk of nutrition. 94 cases (67.1%) were malnutrition and 49 cases (35.0%) were severe malnutrition according to GLIM criteria. Based on FFP criteria, 53.6% (n = 75) participants were pre-frailty, and 20.7% (n = 29) were frailty. According to the AWGS 2019 consensus, at the step of finding cases, 52.9% (n = 74) cases were at risk of sarcopenia determined by reduced calf circumference, among which, in the second step, 36.4% (n = 31) participants were diagnosed as sarcopenia, 24.2% (n = 34) fulfilled the criteria of severe sarcopenia. We also reported every diagnostic criterion in each tool in p = 0,024) and older age  in the fulfill-three-criteria group.A diet survey showed 71 cases had a decline in the intake of total energy, protein, and fat before and after the diagnosis of the disease. In The patients were stratified by age and divided into the <70 years group and ≥70 years group. In p = 008), which was a sensitive variable to indicate recent nutrition changes. Both nutritional risk and severe nutritional risk were significantly higher in the malignant group, but no difference was found in the prevalence of GLIM-defined malnutrition. The malignant group possessed higher rates of frailty and sarcopenia.When the patients were divided into malignant and benign groups, the results were completely different from the results in the groups stratified by age as above . The maip-values were below 0.05; the part on the lower right of the blue area shows a positive correlation between variables of serum test, body composition, and anthropometry. In serum tests, hemoglobin, albumin, and prealbumin show a significant correlation with body composition and anthropometry. Body composition  are well correlated with anthropometry  with statistical significance.r = −0.248, p = 0.018).The first column shows the correlation between age and other variables. The prevalence of nutritional risk, malnutrition, frailty, and sarcopenia were all positively correlated with age with significance, and all body composition and anthropometry variables were negatively correlated with age. Meanwhile, in the second column, nutritional risk, malnutrition, and frailty were proved to positively correlate with malignant diseases with statistical significance. However, a significant negative correlation was only found in prealbumin in all body composition and anthropometry variables   .With increasing global aging problems, aging-related debilitating disorders, so-called geriatric syndrome, are becoming the hotspots of geriatric research. All frailty, sarcopenia, and malnutrition are components of geriatric syndrome and are closely interrelated and interdependent. Surgical patients suffer from a double attack of disease and aging. In our study, the prevalence of nutritional risk, and malnutrition are 70.0 and 67.1%, respectively, which are higher than in elderly patients with other gastrointestinal diseases . The preDiagnosis of malnutrition, frailty, and sarcopenia depend on the diagnostic tools, different tools might lead to different prevalence . In a reAccording to the guidelines of ESPEN, malnutrition, sarcopenia, and frailty were treated as parallel definitions . The lin2, and nearly 40% of patients suffered from overweight and obesity, in which, nearly 20% were sarcopenia (From the perspective of body composition in the criteria, BMI may not be sensitive enough to be used in the surgical population, only 5.7% of cases were lower than 18.5 kg/mrcopenia . FFMI anrcopenia . ASMI warcopenia , 33. So In this study, we did stratification analyses by age and malignancy. Like reports from other centers, it was no doubt that nutritional status became worse with aging and malignant diagnosis . HoweverAs we know, few researchers have been reported to study the effect of two of the three conditions in older adults, but the three conditions are rarely studied together . This stElderly inpatients undergoing major pancreatic and biliary surgery had a high prevalence and overlap rates of malnutrition, frailty, and sarcopenia. Body composition and function deteriorated obviously with aging. Patients with malignant diseases often suffer from short-term nutrition changes like intake reduction. Simple and effective biomarker needs to be explored and validated. Rational preoperative prehabilitation containing nutrition support and exercise should be considered in this population to reduce postoperative complications and mortality.xujingyong@bjhmoh.cn.The data supporting this study’s findings are available from the corresponding author upon reasonable request. Requests to access the datasets should be directed to JX, The studies involving human participants were reviewed and approved by the Ethics Committee of Beijing Hospital. The patients/participants provided their written informed consent to participate in this study.JX and JW: conception, design, and administrative support. JX, LL, JS, and JW: provision of study materials or patients. JX, YH, PL, LW, LD, and BC: collection, assembly of data, data analysis, and interpretation. LW, YH, and JX: manuscript writing. LW, PL, YH, BC, LD, LL, JS, JW, and JX: final approval of manuscript. All authors contributed to the article and approved the submitted version.