An increasing number of patients in hospital have diabetes, with most of them cared for by non-specialist staff. The effect of diabetes education for staff on patient outcomes, as well as the most effective method of staff education is unclear. Therefore, the aim of this study was to compare diabetes outcomes in medical wards where nursing staff were offered one face-to-face (F2F) session followed by access to online education (online), F2F education only, or standard care (control).We conducted a pilot cluster randomised controlled trial involving 16-weeks baseline/rollout followed by a 28-week post-intervention period across three medical wards (clusters) in a Sydney Teaching Hospital. The online ward provided an online competency-based diabetes education program and 1-h F2F teaching from a diabetes nurse educator (DNE), the F2F ward provided four separate 1-h teaching sessions by a DNE, with no additional sessions in the control ward. The primary outcome was length of stay (LOS); secondary outcomes included good diabetes days (GDD), hypoglycaemia and medication errors. Poisson and binary logistic regression were used to compare clusters.p < 0.001]. Among the 827/881 patients, there was no difference in LOS change between online [Median(IQR) 5(2–8) to 4(2–7) days], F2F [7(4–14) to 5(3–13) days] or control wards [5(3–9) to 5(3–7) days]. GDD improved only in the online ward 4.7(2.7–7.0) to 6.0(2.3–7.0) days; p = 0.038. Total patients with hypoglycaemia and appropriately treated hypoglycaemia increased in the online ward.Staff attendance/completion of ≥ 2 topics was greater with online than F2F education [39/48 (81%) vs 10/33 (30%); The inclusion of online education increased diabetes training uptake among nursing staff. GDD and appropriate hypoglycaemia management increased in the online education wards.ACTRN12617000762358.Prospectively registered on the Australia New Zealand Clinical Trials Registry (ANZCTR) on 24/05/2017: The online version contains supplementary material available at 10.1186/s12902-022-00975-y. The increasing prevalence of diabetes places a huge burden on health systems, with one in every 4 or 5 patients in hospital having diabetes –3. PeoplHowever, healthcare practitioners face difficulty in accessing face to face education due to understaffing, workplace demands, personal commitments, expense and variation in individual learning needs . PreviouThe purpose of this pilot study was to evaluate uptake of an online competency-based program among ward-based nursing staff, followed by a comparison of reach and clinical outcomes in a pilot cluster randomised controlled trial.What is Diabetes” and “Managing Diabetes in Hospital” over a 6-week period. Following each module, an embedded evaluation score graded 1–5  was completed regarding increase in overall competency in diabetes patient care, increase in overall confidence with managing patients with diabetes, and an increase in familiarity with diabetes guidelines in relation to managing patients with diabetes. Formative evaluation included three focus groups among participants undertaking the online modules which were recorded, transcribed verbatim and de-identified. Thematic analysis was used to identify and group themes [The online competency based program used was an Australian  adaptatip themes .While no formative evaluation was conducted for the F2F teaching ward, feedback was taken into account from previous F2F teaching sessions on the wards and discussions with the nurse unit manager. Based on this feedback, the F2F teaching was arranged to be conducted on the ward itself rather than a lecture theatre, and the time of the session was chosen by the nurse unit manager to allow for the maximum number of nursing staff to be available to attend the session.This was a pilot cluster randomised controlled trial (CRCT) (ACTRN12617000762358) on three medical wards in the same hospital in Sydney, Australia. Each medical ward was a cluster, and randomised to receive either (1) one hour of protected time on three occasions with access to complete online education modules followed up by 1-h of face to face education for a total of four hours (online), (2) face-to-face (F2F) education for four 1-h sessions with protected time for a total of four hours, or (3) staff diabetes education as usual (control)  . GlucoseThe Role of Nursing in the Continuum of Inpatient Diabetes Care developed by the American Association of Clinical Endocrinologists (AACE), at the end of the rollout phase. Staff had ongoing access to the online modules. On the F2F ward, nursing staff were provided with three separate one-hour face to face sessions over three months, developed by the National Association of Diabetes Centres , plus the hour long AACE session identical to the online ward. The control ward had no scheduled diabetes education sessions in the intervention period.Nursing staff on the online ward were given access to the online modules with advice to complete at least two modules in three hours of protected time in an office with a computer away from the clinical environment, during work hours, as shown in Fig. The primary outcome measure was the difference between wards in the change in length of stay. Secondary outcomes are shown in Fig. 1 including good diabetes days (GDD) based on the  definition [P < 0.05 was considered statistically significant.As a pilot study, no power calculations were undertaken and the duration of the study (and ergo the sample size of 400) was defined pragmatically. We predicted that it would take three months to undertake the study but roll out was delayed for administrative reasons, so this actually took 6 months. An intention to treat (ITT) analysis of the inpatient data was undertaken by a blinded statistician in the UK (DY). Analysis of variance (ANOVA) was used to detect differences in baseline characteristics between clusters (wards) and between the baseline/rollout and intervention period. Poisson regression and binary logistic regression analysis were used to analyse the change in primary and secondary outcomes including of lengths of stay (LOS) and good diabetes days, adjusting for clustering and baseline differences. STATA MP15.1  was used for all analyses. Tests are 2-tailed and Perceptions and ExperienceFunctionality of program/webpageContextual and Clinical RelevancePerceived Access: Barriers and FacilitatorsBefore the CRCT, formative evaluation was undertaken on a separate ward that was similar in nursing staff numbers and patient bed numbers, to ensure acceptability of the online program. Overall, 18/33 ward nursing staff registered for the online program, of whom nine completed two topics, one completed one topic and eight nurses started but did not complete any topics. Thirteen ward staff members voluntarily participated in three focus groups convened between 13 December 2016 and 2 February 2017, lasting between 27–32 min. Four themes emerged in the formative evaluation as shown in Supplementary Table Staff self-reported an increase in overall competency in diabetes patient care mean score of 4.4/5, an increase in overall confidence with managing patients with diabetes mean score of 4.3/5, and an increase in familiarity with diabetes guidelines in relation to managing patients with diabetes mean score of 4.3/5. Adjustments to the program were made following the formative evaluation (Suppl table n = 43/48) nurses completed one diabetes topic compared to 45% (n = 15/33) on the F2F ward (p < 0.01). Two diabetes topics were completed by 81% (n = 39/48) on the online ward compared to 30% (n = 10/33) on the F2F ward (p < 0.001). Of the 43 nurses completing at least one topic in the online ward, only nine (19%) attended the face to face teaching session on that ward.Uptake of diabetes education was greater on the online ward compared to the F2F ward; 90% , less diet/more medication treated patients (p = 0.012), more hyperglycaemia (p < 0.001) and more patients with macrovascular disease (p < 0.001). The F2F ward had less patients admitted for a diabetes related reason in the post-intervention period (p = 0.044).Of the 881 eligible patients, 827 were included in the trial as shown in the Consort diagram , along with increased hypos treated appropriately by ward staff  and total hypos , while severe hypos was borderline significant . In the control ward alone, there was a reduction of the proportion of hypos being appropriately treated by ward staff from 88 to 68% in the post-intervention phase (p = 0.028), and an increase in the number of total medication errors in the control ward from 9 to 20% (p = 0.0028).Table This pilot study is the first randomised trial to evaluate the impact of non-specialist nursing staff education on patient outcomes, and directly compare uptake of online learning with traditional F2F education. The study demonstrated a significantly greater uptake of online learning compared to F2F learning, accompanied by a self-reported improvement in confidence and competency in diabetes patient care as well as an increased familiarity with diabetes guidelines. The primary outcome of the pilot CRCT of length of stay did not differ between the wards. However, there was an increase in the number of GDD/week in the online ward only.Computer based and online learning tools for staff education have been in place for a number of years, with increasing accessibility with smart phones and handheld devices . StudiesIn this study, the face to face sessions were conducted on the study ward at a time agreed with the ward nurse unit manager to make it easily accessible for staff to attend. In spite of this ease of access, staff not being on shift on the specified days and some staff not being able to attend the sessions due to clinical needs on the wards meant the attendance rate for the four education sessions was < 50%. This is in contrast to the 90% completion of one module and 81% completion of two modules in the online ward. Even in the online ward, < 20% attended the single face to face teaching session provided. Formative thematic analysis showed that main themes as seen in Suppl Table Length of stay (LOS) was the primary outcome of the study but this was not affected by staff education in this study. This may not have been too surprising as LOS is affected by a multitude of factors beyond diabetes such as acuity of the co-morbidity, social circumstances, availability of the destination and overall efficiency of the inpatient care . Hence, On the other hand, good diabetes days/week (GDD/week) is a direct measure of diabetes control in hospital and has been used to describe glycaemic control in hospital in the UK National Diabetes Inpatient Audit . With ovHypos are a major problem in people with diabetes in hospital and have been linked to greater length of stay in hospital as well as in-hospital mortality , 26. HowOne of the ways to improve diabetes care in hospital has been use of technology including networked blood glucose meters with a dashboard or the use of Flash Glucose continuous glucose monitoring systems . The useThe lack of difference in medication errors in the online or F2F wards may be linked to the fact that the education was limited to nursing staff and not medical staff, who are the ones prescribing the medications including insulin. The fact that medical doctors and teams cover more than one ward, especially when providing out of hours cover, made it too difficult to randomise them into groups in this study. The lack of difference in LOS in the same way, may be because often the decision to discharge is led by the medical team.The strengths of this study are that this is the first RCT assessing the effect of nursing staff education on patient outcomes with such a large number of patients over 6 months. The RCT was implemented robustly, with unbiased randomisation, blinding of the investigators and of the statistician. Also, the online learning modules were modified following formative evaluation from staff, before the CRCT. The main limitations of this study are that the baseline planned phase of 2 weeks had to be extended to 3 weeks’ baseline and 13 weeks’ rollout phase to allow more time for both the face to face education sessions to be organized for the ward nursing staff, as well as more protected time to be allowed for the online ward-based nursing staff. This delay in implementation is perhaps a sign that nursing education, whether it be online or face to face, was a low priority even in this CRCT setting. Another limitation is that we did not have the demographic data for the nursing staff involved in the study. However, the wards were chosen for the study as they were all medical wards of a similar size with a similar number of nursing staff and patient beds, and they were randomised after the start of the trial. We were also unable to include patient reported outcomes or patient satisfaction data from these wards.In conclusion, the addition of access to online learning as well as face to face to education, significantly increased uptake of diabetes education among hospital non-specialist nursing staff. Although length of stay was not reduced, glycaemic control and hypoglycaemia treatment were improved. The findings of this pilot study suggest that there would likely be further benefits if online learning was rolled out to include medical staff, which, with social distancing and meeting restrictions amidst the Covid-19 pandemic, may offer a better alternative to traditional methods of diabetes education. Further randomised clinical trials to confirm these findings are required.Additional file 1.


The quality of boxing video is affected by many factors. For example, it needs to be compressed and encoded before transmission. In the process of transmission, it will encounter network conditions such as packet loss and jitter, which will affect the video quality. Combined with the proposed nine characteristic parameters affecting video quality, this paper proposes an architecture of video quality evaluation system. Aiming at the compression damage and transmission damage of leisure sports video, a video quality evaluation algorithm based on BP neural network (BPNN) is proposed. A specific Wushu video quality evaluation algorithm system is implemented. The system takes the result of feature engineering of 9 feature parameters of boxing video as the input and the subjective quality score of video as the training output. The mapping relationship is established by BPNN algorithm, and the objective evaluation quality of boxing video is finally obtained. The results show that using the neural network analysis model, the characteristic parameters of compression damage and transmission damage used in this paper can get better evaluation results. Compared with the comparison algorithm, the accuracy of the video quality evaluation method proposed in this paper has been greatly improved. The subjective characteristics of users are evaluated quantitatively and added to the objective video quality evaluation model in this paper, so as to make the video evaluation more accurate and closer to users. With the booming development of communication technology in the information age, the network has become an essential part of society. Sparring sports information can be transmitted in the network in various forms such as voice, image, and video. And, in today's era, with more and more information and better network services, it is natural that casual sports video information plays a more important role and video services have become an important business for network service providers because casual sports videos carry more information and the presentation form that people are more willing to accept –4.The video of casual sports is played in real time in streaming form through network transmission. Since the video is played in streaming form, users do not need to wait until the whole video is downloaded and can play it while downloading. Nowadays, the video service is a very important part of the process of receiving information about sparring sports for sparring athletes and fans and has become an indispensable part of the life of sparring sports fans , 6. For As people's interest in sports, especially sparring, increases, sparring video quality has become a key concern for users, including the following two points: first, the quality of sparring video images is required; just SD quality video can no longer meet the needs of users; HD, ultra HD, and even Blu-ray quality sparring videos are increasingly appearing in today's video services; second, the smoothness of sparring video playback, for any real-time online video service and frequent and long playback lags seriously affects the users' experience. So, ensuring video clarity while enabling smooth playback of casual sports videos is also a key concern for users. Therefore, how to effectively evaluate the quality of casual sports videos to provide a basis for improving the quality of casual sports video services and user experience is of great practical significance .Aiming at the compression damage and transmission damage of leisure sports video, this paper creatively proposes a video quality evaluation algorithm based on BP neural network (BPNN). A specific Wushu video quality evaluation algorithm system is implemented. The system takes the feature engineering results of 9 feature parameters of boxing video as the input and the subjective quality score of video as the training output. The mapping relationship is established through BPNN algorithm, and finally the objective evaluation of boxing video quality is obtained. In this paper, the subjective characteristics of users are quantitatively evaluated and added to the objective video quality evaluation model to make the video evaluation more accurate and closer to users.The structure of this paper is mainly arranged as follows: Chapter 1 introduces the research background, significance, and structure of this paper on video quality evaluation of sparring sports. Chapter 2 introduces the related research on video quality evaluation in recent years and the main research content of this paper. Chapter 3 analyzes the damage suffered by the sparring sports video during compression and transmission, extracts the feature parameters for evaluating the quality of sparring sports video based on the damage analysis, and analyzes the correlation between these feature parameters and the quality score. Chapter 4 proposes a BPNN-based quality evaluation algorithm model for sparring sports video, discusses the specific implementation method of the model, and deploys the implementation in practice. Chapter 5 introduces the evaluation criteria of the quality evaluation algorithm and verifies the superiority of the algorithm using experimental simulations. Chapter 6 summarizes the full text and summarizes the results of this paper, while proposing the shortcomings of this paper and the research directions for the next work.Since the last few years, studies on the quality evaluation of casual sports videos disseminated through the Internet have received increasing attention, and many institutions and laboratories have been involved in related studies , 10. TheVideo quality evaluation of casual sports has received much attention and has been rapidly developed in recent years , 21. TheAlthough there are many methods for video quality evaluation, there is no uniform standard yet. So, video quality evaluation is still an important issue. In addition, the current research has achieved some results, which can meet the demand for the quality of casual sports video in daily life. However, because there are more degradation factors causing the video quality and the video needs to consider the feature information in the time domain, it is much more difficult compared with the picture quality evaluation. Therefore, the performance of the current algorithm is not particularly good, and researchers still have a long way to go in this area. This paper makes a study of the video quality evaluation of sparring sports, taking into account today's trends. In this paper, the following results are proposed for the video quality evaluation of sparring sports: the video quality impairment of sparring sports is studied, and compression impairment and transmission impairment are analyzed. Three feature parameters, namely, quantization parameter, ambiguity degree, and the number of jump macroblocks, are proposed for compression impairment, and six feature parameters, namely, strain degree, block effect degree, aggregation block effect degree, initial buffering delay, average duration of jamming, and jamming frequency, are proposed for transmission impairment, and the correlation between each of the proposed feature parameters and the quality of scattered sports video is also analyzed. A model for evaluating the quality of scattering sports video is established. The feature parameters are extracted from the sparring sports video and the BPNN network model is used to evaluate the video quality. Meanwhile, the concrete implementation of this scattering sports video quality evaluation model is given from a practical point of view, and each specific process of the system implementation is given, and finally the superiority of the model is demonstrated by comparing the algorithm with other scattering sports video quality evaluation methods.In video quality evaluation, compression impairment is a factor that must be considered, and the degree of compression impairment can greatly affect the video source quality. In this section, two coding parameters, quantization parameter and number of jump macroblocks, are extracted as features, as well as the evaluation of fuzzy distortion to obtain the fuzziness as a feature parameter, while evaluating the video compression quality impairment.The distortion of the video in the compression process mainly lies in the quantization step, and the quantization parameter is an important parameter to determine the degree of quantization. The smaller the quantization parameter, the smaller the quantization step (QStep), and the larger the amount of compressed video data, but at the same time the smaller the degree of distortion after decoding, while if the quantization parameter is larger, there will be a larger quantization step. For a frame, there are three components of YUV, which are quantized separately with different quantization parameters and value ranges. For the coding of luminance component, there are 52 quantization steps, so the quantization parameters are from 0 to 51; for the coding of chrominance component, there are 40 quantization steps, and the quantization parameters are from 0 to 39.I′ is the filtered image point information and is a G5 × 5 Gaussian filter matrix:The blurriness of the video is often an important factor affecting the video quality, and this can be caused by a variety of reasons, such as intraframe prediction and motion compensation for interframe prediction. Therefore, in this paper, the blurriness is extracted as another feature to evaluate the video quality. In this paper, we use the method of measuring the blurring degree of a video by the loss of video details after filtering the video. First, this paper uses a Gaussian filter to filter the video. The Gaussian filtering process is as follows.w and h are the width and height of a frame, respectively:Ultimately, the blurriness of a frame is defined as follows, where When H.264 is encoded, interframe prediction and intraframe prediction are performed. For macroblocks within the flat region of nonkey frames, they are often defined as jump macroblocks. In order to improve the coding efficiency, jump macroblocks are encoded without any element information and their decoding is completely predicted by the previous I macroblocks and motion vectors. However, sometimes the I macroblock and motion vector cannot fully recover the jump macroblock before encoding, which will cause the compression distortion of the video. Based on this, this paper proposes the concept of the number of jump macroblocks as another characteristic parameter to evaluate the compression impairment. The larger the number of jump macroblocks, the higher the compression rate, but the more serious the distortion, while the smaller the number of jump macroblocks, the more the pixel information in the encoded video, and the better the decoded video quality.Since it is difficult to obtain the network information of each node in the transmission process at the terminal in practical applications, this paper proposes two air-domain impairment characteristic parameters: strain impairment and block effect impairment and three time-domain impairment characteristic parameters: initial buffer duration, motion jump frequency, and motion jump average duration; thus, the video quality can be evaluated.Stretching refers to the distortion generated by the boundary of adjacent strips after decoding. The minimum object of this parameter is the strip of the video frame image, not the macroblock. Striping is a concept in video coding and decoding. A frame image usually has multiple strips and many macroblocks in a strip, and the purpose of setting strips is to prevent excessive error transmission during decoding and prediction. Therefore, the strain between strips is usually reflected on the edges in the horizontal direction, because strips are usually lined up horizontally side by side.Si, Si−1, and Si+1 are adjacent to each other up and down, Ii represents the brightness value of the pixel where the strip Si is located at . Then, the degree of strain is expressed asAssuming that strips When calculating the overall strain effect of a frame, it is sufficient to average the degree of strain of the individual strips; i.e.,The degree of strain is for strips, while block effect damage is for macroblocks. The traditional block effect calculation method tends to count the block effect degree of all macroblocks and then average it into one frame. However, this approach is usually only applicable to a single image, and, for video, there are multiple frames per second, so the calculation will consume a lot of time. Therefore, this paper detects the possible damaged macroblocks and finally calculates the block effect degree of a frame by the possible damaged macroblock area.For two frames with the same average block effect level, if the damaged macroblocks are more concentrated in one frame, the quality of this frame is poorer. Therefore, the greater quality damage caused by multiple adjacent macroblocks with block effect is called “block effect set” in this paper. In this paper, we determine the quality of the whole frame according to the most serious block effect areas in a frame and cluster the block effect macroblocks to evaluate the combined block effect degree of each cluster.Ii denotes the luminance signal of the first i frame and w and h denote the width and height of the image frame, respectively. The calculation result M reflects the motion of the image sequence, and a larger value indicates a more intense motion; a smaller value indicates a less significant difference between frames. When the value is less than a threshold m, it can be assumed that a motion jump may have occurred here. However, the repetition of the view may not be perceived by the human eye. Therefore, the algorithm requires that the value of M must be less than the threshold m for n consecutive frames and the best result is obtained when the value m is 0.3, which is n taken as follows. N is the total number of frames in the video sequence and T is the length of the video sequence:When users watch videos, they often prefer to watch videos with poor clarity and to ensure the continuity of video playback. Therefore, the video is particularly important for the assessment of motion jump impairment. For motion jump impairment, whether it is frame repetition or frame freezing or initial buffering, the user feels the video lag. Therefore, in order to evaluate the time-domain impairment from the pixel domain, this paper proposes an algorithm to detect video jams. First, the pixel luminance difference between two adjacent frames is calculated to indicate the motion of the image sequence.Based on the above formula, it has been possible to determine the occurrence of video playback jams. Considering the subjective perceptual characteristics of human eyes, the time-domain impairment is subdivided into three characteristic parameters for the time-domain impairment, which are initial playback delay, motion jump frequency, and average motion jump duration. These three parameters can be updated by real-time statistics each time the network lag is discerned.Combining the feature parameters proposed in Chapter 3, a BPNN-based video quality evaluation system model for sparring sports is proposed, and the system structure block diagram is shown in Firstly, the video received by users is read in real time and then decoded by H.264 decoder. Here, the JM decoder is chosen, which is from the official source code of H.264 and has good support features. The quantization parameters, the number of jump macroblocks, and the frame sequence can be obtained directly from the decoder.From the frame sequence, the strain parameter, block effect parameter, and aggregated block effect degree can be extracted by the formula in Chapter 3. Meanwhile, according to the motion jump detection algorithm proposed in Chapter 3, the occurrence of each motion jump can be detected from the frame sequence, and the duration of each occurrence and the position in the video where it occurs can be counted, so that the three parameters of initial buffering delay, jamming frequency, and average duration of jamming can be calculated.After all the input parameters of the BPNN are obtained through data processing, the video quality scores in the training set are then trained to derive the weights of each neuron in the neural network. In this way, the predicted video quality scores can be obtained by extracting the above nine feature parameters from the video, processing them, and inputting them into the trained BPNN during testing and application.The basic idea of BPNN is the gradient descent method, which uses the gradient search technique to minimize the mean squared error between the actual output value and the desired output value of the network. A typical three-layer BPNN is shown in The basic BPNN algorithm consists of two processes: forward propagation of the signal and backward propagation of the error. That is, the error output is calculated in the direction from the input to the output, while the adjustment of the weights and thresholds is performed from the output to the input. In forward propagation, the input signal acts on the output node through the implied layer, and, after nonlinear transformation, the output signal is generated, and if the actual output does not match the desired output, it is transferred to the backward propagation process of the error. Backpropagation is to pass the output error back to the input layer through the hidden layer, apportion the error to all units of each layer, and use the error signal obtained from each layer as the basis for adjusting the weight of each unit. By adjusting the connection strength of the input nodes to the hidden layer nodes and the connection strength of the hidden layer nodes to the output nodes and the threshold value, the error decreases in the gradient direction, and, after repeated learning and training, the network parameters (weights and thresholds) corresponding to the minimum error are determined and the training is stopped. At this point, the trained neural network is able to process the input information of similar samples and output the information with the smallest error after nonlinear transformation by itself.(1)Xi denote the input features of the training set samples.Let the input parameters (2)Zi denote the prediction results obtained after the eigenvalues of the training set samples are passed through BPNN.Let the output parameters (3)Oi denote the standard result of the training set samples.Let the standard output (4)W denote the coefficient matrix between the input layer output and the hidden layer input and the weight matrix V denote the coefficient matrix between the hidden layer output and the output layer input. As the model output obtained from each training set sample is compared with the standard output for learning, these two matrices are corrected by the gradient descent method. The matrices are corrected once for each sample until the input of the next sample, and finally the learning process of the network model meets the requirements.Let the weight matrix (5)g(x) is the transfer function between the input and output of the hidden layer, and f(x) is the transfer function between the input and output of the output layer. Since the input features have been processed by the feature engineering related methods before the input layer, the input of the input layer is often equal to the output of the input layer. And, for BPNN, g(x) and f(x) can use the same sigmoid function, as shown in(6)ai as the hidden layer threshold, which is used to correct the input of the hidden layer; set bi as the output layer threshold, which is used to correct the input of the output layer.Set (7)Si be the input of the hidden layer and Ri the input of the output layer. The number of nodes in the input layer is equal to the number of input features, which is set as n , the number of nodes in the hidden layer is set as m, and the number of nodes in the output layer is set as L . The total number of input samples in the training set is P; Xpi is the i input component of the p sample. Wik denotes the transfer weight from the first i node in the input layer to the first k node in the hidden layer. Vkj denotes the transfer weight from the first k node of the hidden layer to the first j node of the output layer.Let The important parameters and description of the learning process are presented as follows, based on the three-layer BPNN block diagram in p sample input be the following:Let the Then, the input of the hidden layer isThe matrix form is written asThe output of the hidden layer isOutput layer input isThe output layer output isP samples, there is a global mean square error function that represents the measure of deviation of the sample as a whole between the sample output Zp and the standard output Op for each sample. The mean squared error function is denoted by E:Ep denotes the first p sample error and Opt is the t expected output of the p sample.For all η represents the learning efficiency and generally takes a value between . If the value of η is larger, the convergence speed increases, but it may lead to oscillation or even divergence; if the value of η is smaller, the training can converge steadily, but the learning speed is slower η.According to the derivation of gradient descent method, the output layer weight adjustment formula isBased on the derivation of gradient descent method, the implied layer weight adjustment formula isAmong them,W and V. After the two matrices are corrected, one iteration ends. This step is followed by the next sample input, and the correction of the weight matrices continues. Continuing in this order, the objective quality score of the output gradually approximates the subjective quality score and finally reaches the desired error accuracy, which represents the end of the BPNN learning process. With this trained BPNN model, the quality score of the video can be obtained from the feature parameters of the video, which is an objective assessment of the video quality.In summary, video quality evaluation using BPNN is divided into two main parts. The first part inputs the relevant feature parameters of the video to get a set of outputs  and then compares this set of outputs with the desired outputs  to derive the errors. The second part uses the gradient descent method to correct the two weight matrices When people make subjective evaluations, they are able to give good or bad video image quality without the original video as a reference, even if most of the video images have never been seen before, based on their own a priori knowledge. This is mainly because people have seen videos of various qualities before. For neural networks, the prerequisite for supervised learning is the need for a priori knowledge of video quality, i.e., the need for a subjective quality assessment value of the video. In this paper, there are 24 original HD reference videos in the experimental database, which are from various sports topics on YouTube, including sports interviews, boxing commercials, and sports news. Each original video in the database corresponds to 180 distorted video sequences, which are recorded by terminals under different network conditions and contain real network transmission impairments, mainly varying degrees of airspace impairments and time-domain “lag.” In addition, the distortion videos also contain compression distortion for each compression parameter. The video set was given 3280 quality scores by 54 evaluators, and the video quality of the 320 video sequences was obtained by combining these quality scores, with the final quality score ranging from 1 to 100 indicating the worst to the best, with one decimal place for minimum precision. All evaluators watched the videos using the web page and scored them immediately after viewing.The parameters of the final training neural network are as follows: the number of hidden layer nodes is 47, the momentum term is 0.95, and the initial value of the learning rate is 0.001, but it can be adjusted dynamically during the training process, the slope factor of the sigmoid function is −4.5, and the final minimum error is 0.0006. The test sample and the training set are one to four. The fitting diagram of subjective evaluation results and objective evaluation results of training samples and test samples has been given. As shown in Figures Among them, the iterative training was stopped on the training set when the fit reached 98.47%, which indicates that there is a strong correlation between the selected feature parameters and the subjective quality scores and also avoids the overfitting of the training. The fit of the test set is 92.04%, which indicates that the quality evaluation results of this model are closer to the subjective quality scores.In addition, the Standard Error (RE), Pearson's Correlation Coefficient (PCC), and Deviation Rate (DR) between the objective and subjective evaluation results of the proposed method are calculated and compared with Pulse Coupled Neural Network (PCNN), nonsubsampled shear wave transform (NSSWT), and orthogonal matching pursuit (OMP) methods, proving that the method in this paper has better performance in terms of accuracy and stability and the results are shown in Figures 5In this paper, we analyze the comparison for the missing feature parameter changes and the comparison for the added feature parameter changes, as shown in Figures In this paper, we also try other regression algorithms such as Linear Regression (LR), Gradient Boosting Regression (GBR), and Support Vector Machine (SVM) regression models. The comparison of the fitting results of each regression algorithm is shown in It can be seen that, among these four regression algorithms, the accuracy of BP neural network is the best. The effect of SVM and neural network is very close, and after several times of debugging the parameters of the objective function and kernel function, there is a slight difference between the optimal case and the result of neural network, so this paper finally uses BP neural network algorithm to fit the features to derive the video quality score.In this paper, we discuss the background and significance of video quality evaluation according to the development status of video and introduce the development status and trend of video quality evaluation methods. This paper analyzes in detail the compression damage and transmission damage suffered by the video and proposes nine parameters: quantization parameters, blurring degree, number of jumping macroblocks, strain degree, block effect, aggregated block effect degree, initial buffering time, average playback pause time, and playback pause frequency. Combining the proposed nine characteristic parameters affecting the video, this paper proposes a video quality evaluation system architecture. This architecture uses a BP neural network algorithm to fit the feature parameters, which can eventually output the predicted video quality scores. Finally, the proposed video quality evaluation method is experimentally simulated by the implemented system in this paper, and the accuracy of this paper is improved by 14.28% compared to the comparison algorithm.The research work on video quality evaluation in this paper is only a small part in this field. At the same time, the research on video quality evaluation in this paper may have many areas for improvement and can be used as a direction for future research. The video quality evaluation method proposed in this paper is based on compression impairment and transmission impairment. However, in the actual network service system, the video quality may be affected by many subjective factors such as user terminal, user viewing environment, and user's subjective expectation for video. Therefore, a future research direction of this paper is to evaluate the video quality based on compression impairment and transmission impairment. Therefore, one of the future research directions of this paper is to quantitatively evaluate the subjective characteristics of users and add them to the objective video quality evaluation model in this paper, so that the evaluation of videos can be more accurate and more relevant to users.


In wireless sensor networks, the reliability of communication can be greatly improved by applying low-density parity-check (LDPC) codes. Algorithms based on progressive-edge-growth (PEG) pattern and quasi-cyclic (QC) pattern are the mainstream approaches to constructing LDPC codes with good performance. However, these algorithms are not guaranteed to remove all short cycles to achieve the desired girth, and their excellent inputs are difficult to obtain. Herein, we propose an algorithm, which must be able to construct LDPC codes with the girth desired. In addition, the optimal input to the proposed algorithm is easy to find. Theoretical and experimental evidence of this study shows that the LDPC codes we construct have better decoding performance and less power consumption than the PEG-based and QC-based codes. The wireless sensor network (WSN) ,2 is a dLDPC code was proposed by Gallager in 1962 . It attrThe girth is the low bound of the lengths of all cycles in an LDPC code, and a larger girth indicates that the code evades more short cycles. Algorithms based on progressive-edge-growth (PEG) pattern ,19,20 anTo solve these problems, we put forward an algorithm for constructing LDPC codes with arbitrary girth, and we call the algorithm the girth-cycle-embedding (GCE) algorithm. The algorithm requires the girth as the only input, and the code is constructed by embedding girth-member cycles into it. Our algorithm can overcome the above disadvantages through theoretical analysis, and produces LDPC codes with better performance than codes from PEG-based and QC-based algorithms in practice.The rest of the paper is organized as follows: In In LDPC codes, a cycle is a path which alternately passes through check nodes and variable nodes  and endsIn view of the harm of short cycles, many LDPC code construction algorithms are designed along the lines of maximizing the girth of LDPC codes, i.e., trying to avoid generating or to eliminate short cycles, and improving the connectivity of the short cycles when they cannot be avoided or removed. Next, we will introduce two main types of construction algorithms.There are many construction algorithms of LDPC codes, among which PEG-based and QC-based algorithms are the two main kinds of methods.In the PEG-based algorithms, all the variable nodes are assigned targeted degrees by a variable-node degree distribution ,28. Thenlgorithm  the checlgorithm , it selelgorithm  has harslgorithm  which isp positions and p is called the shift of this circulant permutation matrix. Eliminating short cycles can be realized by giving suitable shift p for every circulant permutation matrix. In [As for the QC-based algorithms, a matrix (LDPC code) is divided into many square matrices of the same size. Each of these square matrices is either a zero matrix or a circulant permutation matrix, which is obtained by cyclically right-shifting an identity matrix by trix. In , a Hill-trix. In , the Hiltrix. In  which isThrough the reviews of the PEG-based and QC-based algorithms above, we know that they remain the mainstream approaches to constructing LDPC codes because of many advantages. Despite these strengths, they consistently suffer from several shortcomings which are difficult to overcome.Firstly, we found that all the PEG-based algorithms have an over-reliance on variable-node degree distribution ,28. It iFor structured LDPC codes generated by the QC-based algorithms, removing short cycles can be realized via changing the shifts. However, the computational cost needed grows exponentially with the increase of cycle length. Intuitively speaking, it is hard enough to discover and eliminate all eight-member cycles. Next, constructing a superb base matrix after determining its size is also a high-complexity question. Specifically, a Variants ,35 of PEAs specified above, neither PEG-based nor QC-based algorithms can fully decide the girth so that the LDPC codes they constructed still suffer the loss of performance caused by short cycles. Furthermore, in these algorithms, the quality of LDPC codes is closely related to the inputs, i.e., variable-node degree distribution and base matrix, whereas excellent inputs are rarely available. In order to solve these problems, we put forward a new algorithm for LDPC code construction. In the algorithm, the expected girth is designed as the only input, and an LDPC code is constructed by way of embedding girth-member cycles into it. The algorithm overcomes the disadvantages mentioned above and produces LDPC codes with better performance than ones from PEG-based and QC-based algorithms. We call the algorithm we proposed GCE algorithm.g which can be expressed as m check nodes are split into two sets, n variable nodes are divided into Algorithm 11:2:fordo3:    4:if the highest layer of then    5:        6:output:        7:end if    8:end for9:output: failureIn the GCE algorithm, we denote the girth by ort tree ,23 spreaIn After illustrating the Initialize node sets and form the first cycle.x variable nodes from Initialize the node sets: Exhaust check nodes inh:Set a constant h, execute x is even or x is odd. If h check nodes from h check nodes and If h, execute If If Exhaust variable nodes inIf If Increase the degrees of variable nodes.Execute Algorithm 2 Girth-cycle-embedding algorithm.1:g-member cycleForm the first 2:while check nodes have not been exhausted do3:ifthen    4:ifthen        5:           6:else        7:           8:end if        9:else    10:        11:end if    12:g-member cycles    Connect 13:end while14:while variable nodes have not been exhausted do15:    16:g-member cycles    Connect 17:end while18:whiledo19:    Connect 20:end whileg are avoided by setting the input of g, their harm is significantly reduced by improving their connectivity in steps 2–4. Moreover, the only uncertain input in the GCE algorithm is the girth g, which greatly lowers the external influence on the quality of LDPC codes. The advantages above are shown directly in the experiments. Of course, GCE remains some shortcomings, for example leading to too small average variable-node degree and weakening the performance if g is too large or too small. However, compared with obtaining an excellent variable-node degree distribution for the PEG-based algorithms and a superb base matrix for the QC-based algorithms, the complexity of selecting an optimal g in GCE algorithm is low enough.Except for step 1, steps 2–4 are executed with the help of For an algorithm, it needs to consider space complexity and time complexity. Generally, the mplexity . We calcmplexity ,20,21,22g was set to 12.In this section, we executed three experiments to verify the advantages of the GCE algorithm. Before the experiments, we have constructed six matrices (LDPC codes) with code rate lgorithm , improvelgorithm  and genelgorithm . The varlgorithm ,32 and plgorithm  and the lgorithm  were utiDecoding with LDPC codes can correct errors in messages and thus guarantee the communication reliability of WSNs. For the comparison of decoding performance of finite-length LDPC codes, we chose to perform decoding simulations instead of running some analysis algorithms ,40 whichlgorithm ; 2. imprlgorithm ; 3. genelgorithm ; 4. Hilllgorithm ; 5. imprlgorithm ; and 6. We observe that matrix 6 achieves the optimal performance when Since the power of sensor nodes is limited, LDPC codes that achieve the same decoding effect but consume less energy are needed. Power consumption for decoding can be measured by the iteration number of BP decoder. A good LDPC code can effectively reduce the iteration number of BP decoder and such save energy. Therefore, we performed an experiment to calculate the average iteration numbers corresponding to the PEG-based codes, QC-based codes and the code from GCE algorithm, and present the results in g 6∼18, and An LDPC code construction algorithm is easy to wield, meaning that the user can easily get the optimal input to the algorithm. In LDPC code is a good candidate for channel coding of WSN and can be constructed by PEG-based and QC-based algorithms. The aim of the present research was to discuss the ways to overcome the shortcomings of PEG-based and QC-based algorithms. We solve the problems by proposing GCE algorithm for constructing LDPC codes of any desired girth to avoid generating short cycles. The experimental result shows that the LDPC codes we construct have better decoding performance than the PEG-based and QC-based codes, especially in the error-floor region. LDPC codes constructed by GCE can effectively reduce the iteration number of decoding, thus reducing the power consumption of WSNs. In addition, the optimal input to GCE algorithm is easy to find.


Current alcohol public health policy in Australia is not uniform but is generally focused on restricting access and early prevention of problematic alcohol use. Semi-urban and rural populations are at greater risk of disease and other poor health outcomes due to a variety of factors. Little is known about problematic drinking patterns over time in semi-urban and rural populations. This study aims to assess patterns of problematic drinking defined as both long-term risky and heavy episodic drinking over time by age, sex, and mental health status among urban, semi-urban and rural populations). Four waves (2004 to 2016) of the Australian NDSHS  were analyzed to assess problematic drinking of participants over 18 years of age. We used regression models and predictive margins to identify trends in problematic drinking over time based on age, sex, and mental health status. Our results show young adults across all regions, males, and mentally well individuals in urban areas have reductions in the risk of problematic drinking over time. Middle-aged adults across all regions, females, and those with varying mental health presentations in rural areas have some increases in risk of problematic drinking over time. The general conclusion is that targeted alcohol-related public health policy may need to change and focus on females, middle-aged individuals, and those living in rural areas. Programs to support problematic drinking in people with mental health disorders may also need to be a priority. Alcohol consumption in the 21st century is a multifaceted issue that poses social, psychological, health, and anthropological problems ,4.Changes in alcohol consumption and cultural attitudes towards consumption have changed over time. Increased wealth in both developed and developing countries contributes to increased alcohol consumption ,6, in adIn Australia, reported time trends indicated that per capita consumption of high-risk (defined as 2 standard drinks or more on average per day) drinkers increased from 20.7 L in 2001 to 21.5 L . In someSome studies have assessed problematic drinking time trends by age and sex. Problematic drinking peaks between the ages of 24–26, a phenomenon that is more prominent amongst males than females . Long-teThe complexities of alcohol dependence become aggravated when one has a mental illness. When an individual has both a mental health issue and substance use issue, this condition is known as a dual diagnosis . AlthougWe know little of the associations between mental health outcomes and problematic drinking trends over time in semi-urban and rural populations. Given these knowledge gaps, we sought to examine time trends of problematic drinking outcomes in urban, semi-urban, and rural areas using the Australian National Drug Strategy Household Survey (NDSHS). In particular, we aimed to both examine trends in alcohol-related use (heavy episodic drinking and long-term risky drinking) for sex, age groups based on location, and assess comorbidity trends for mental health status and alcohol-related use (heavy episodic drinking and long-term risky drinking) based on location and over time.n = 55,280 males and n = 69,317 female) were included. Ethical approval was granted from the La Trobe university human ethics committee (HREC #: 19124).The present study used five waves of the cross-sectional NDSHS (from 2004 to 2016), an Australia-wide health survey that collects self-reported drug and alcohol data from different sub-population groups. Overall, a total of 124,597 participants aged between 14 and 99 years of age Likely to be well (10–15)(b)Likely to have a mild mental disorder (16–21)(c)Likely to have a moderate mental disorder (22–29)(d)Likely to have a severe mental disorder (30–50)We considered variables for psychological well-being, location, age and sex for inclusion. For psychological well-being, the NDSHS utilizes the Kessler Psychological Distress Scale (K10) to determine the level of psychological well-being. The K10 is a brief screening scale for non-specific psychological distress. However, the focus of the scale is on anxiety and depression. It has been used in many Australian population health surveys since the late 1990s. Detailed descriptions of the K10 can be found in the relevant article . Each quAn open-ended and standalone question was used to capture age (in years). Based on these, participants were grouped into the following age groups: (18–24), (25–29), (30–39), (40–49), (50–59), (60–69) and 70+. A standalone question requested one’s postcode, which was matched with its region and coded the participant as either living in an urban, semi-urban, or rural area.Data are presented as proportions for each wave (2004 to 2016). For each outcome of long-term risky and heavy episodic drinking, we used logistic regression methods. In each regression model, we assessed associations between sex, age, and psychological well-being stratified by wave. For each outcome, separate models were fitted for urban, semi-urban, and rural populations. Study year was interacted with these variables to assess change over time. Pairwise comparisons were made for each year and time trends were statistically assessed using trend analyses based on predicted marginal change . Statistical significance was set at 0.05. The results are presented as odds ratios (OR) and 95% confidence intervals (Cis). Statistical analyses were performed using Stata release 14.1 .n = 80,458), semi-urban areas ) and also, remote areas .Characteristics of the population are displayed in Time-trend figures are displayed as online supplements.p = 0.03, p = 0.04), but this trend reduced to 2004 levels thereafter.Between 2004 and 2016, there was a consistent reduction in the likelihood to engage in heavy episodic drinking in urban areas. Between 2004 and 2010, there was a significant increase in risk in semi-urban areas  and then returned to 2004 levels. In rural areas, an increased risk up to 2013 occurred, with a reduction to 2004 levels (p = 0.01).In urban areas, there was a consistent reduction in the likelihood to engage in long-term risky drinking over time . For semi-urban areas, males were at greater risk overall than females. Despite some oscillations in risk between the study years for both females and males, there was neither a statistically significant change nor a statistically significant trend over time. The likelihood to engage in heavy episodic drinking over time significantly increased for females residing in rural areas up to 2013 (p = 0.02). Despite oscillating risk for men, there were non-significant between the study years.For males residing in urban areas, there was a consistent reduction in the likelihood to engage in heavy episodic drinking over time  and declined thereafter. Semi-urban areas females had significant increases in risk up to 2010 (p = 0.002), followed by a decline in 2013 and a slight increase thereafter. Risk for both males and females residing in rural areas were similar over time. Despite oscillating risk for males between 2004 and 2016, these proved to be non-significant.In all areas, males were at a greater risk overall than females. For males, in urban areas there was a consistent reduction in the likelihood to engage in long-term risky drinking .For those living in urban areas, over time, the 18–24-, 25–29, and 30–39-year-old groups (p = 0.01). Between 2004 and 2013, the 40–49-year-old group had significant increases in risk (p = 0.01). By 2016, this increase had leveled out. The 50–59-year-old group had a consistent increase in risk over time (p = 0.000). The risk for heavy risky drinking remained relatively unchanged for the 60–69-year-old group between 2004 and 2007. However, between 2007 and 2016, there was a significant increase in risk (p = 0.03).In rural areas, there was a consistent reduction in risk of heavy episodic drinking for the 18–24-year age group (p = 0.04). Although it returned to 2004 levels, this increase was non-significant.For those living in urban areas, most age groups displayed a reduction in risk of long-term risky drinking behaviors , Table 2p = 0.02). For the 40–49-year-old group, risk had increased up to 2010 (p = 0.03) but leveled out thereafter. Despite oscillating risk being evident in the other age groups, these remained non-significant. In rural areas, over time, there was a consistent reduction in risk for the 18 to 24 age groups and 30–39 age groups , respectively. Between 2007 and 2013, there was a significant increase in risk in the 40–49-year age group (p = 0.04). Similarly, there was a consistent increase in risk for the 50–59-year-old group (p = 0.04). Between 2007 and 2013, there was a significant increase in risk for the 60–69-year age group (p = 0.04), but by 2016, these increases had leveled out. Between 2007 and 2016 there was a significant increase in risk for the 70 + year age group (p = 0.01).For semi-urban areas, the likelihood to engage in long-term risky drinking for the 18–24-year-old group remained steady until 2010, before there was a significant reduction thereafter  then declined thereafter (p = 0.01). Despite oscillating risk between 2004 and 2016 for those who were likely to be mildly, moderately, and severely mentally ill, these changes remained non-significant. In rural areas, for those who reported being mentally well, risk increased significantly up to 2013 (p = 0.01) and then declined thereafter. For those likely to have a moderate mental disorder, there was a significant and substantial increase in risk between 2013 and 2016 (p = 0.03). Significant and substantial increases in risk were also identified for those likely to have a severe mental illness up to 2010 (p = 0.001), which declined thereafter.Overall, from urban areas, those reporting both mild and moderate mental well-being were associated with an increased likelihood of heavy episodic drinking. Over time, there was a consistent reduction in the likelihood to engage in heavy episodic drinking for those who were psychologically well  but declined thereafter. Despite oscillating risk for those who were likely to have a mild, moderately, and severely mentally ill, these remained non-significant. For those residing in rural areas, risk of long-term risky drinking significantly increased for those who were mentally well (p = 0.003), but by 2016, these increases had leveled out. Despite oscillating risk for those likely to have a mild and moderate mental disorder, these were non-significant. The risk for those likely to have a severe mental disorder declined between 2007 and 2013.There was a consistent reduction in risk of long-term risky drinking for those who were mentally well residing in urban areas (Our study suggests differences in problematic drinking behaviors over time among urban, semi-urban and rural areas. Patterns exist with problematic drinking to varying degrees based on age group, psychological well-being and sex.Our findings are consistent with others identifying middle-aged females as an emerging at-risk group as they are not reducing their drinking over time ,18,27. AMiddle-aged groups, especially those residing in rural areas also had increasing risk of problematic drinking over time. The middle-aged group, known as baby boomers, failing to reduce problematic drinking over time is consistent with other Australian time-trend data . As the Our findings highlight decreasing risk of problematic drinking over time for those who are mentally well in urban areas. Further out from the urban area, mentally well and also those likely to be moderately mentally ill had increased risk in problematic drinking over time. Our results suggest these associations with problematic drinking appear to become prominent for those who are likely to be moderately mentally ill, especially for heavy episodic drinking in rural areas. More studies are needed to replicate our findings as few have assessed these associations over time. In terms of mental health, rural life is markedly different from metropolitan life in a number of areas including: susceptibility to severe economic hardship isolation and poor job opportunities . This coPrevious research has mainly been cross-sectional, with only a small amount of trend research in this field . AlthougAlthough the issues are complex, more studies are needed to focus on the environmental and behavioral determinants of these trends to enable a better understanding of the contribution of socially disadvantaged, varying ethnicities, and culturally diverse communities on these ever-changing trends over time.Problematic drinking is not only an individual burden but also a burden on extended family, the community, local and national economies ,40. PastWe have a number of strengths in our study. The large sample size at the different time periods enabled sufficient power to detect multiple interactions between key variables. However, limitations of the data source can question this robustness and should be considered when making future inferences, given the lack of data to suggest causality. It is worth noting the limitation of social desirability bias, the inclination to describe one in a more positive light. People who consider themselves to be healthy are generally more likely to respond to a health survey than those who have a chronic condition; respondents with a chronic condition either ignore the survey or respond in a positive way . AdditioIn summary, our findings suggest that from the period of 2004 to 2016, females, middle-aged individuals, and those with varying psychological well-being presentations in rural areas are emerging at-risk groups of problematic drinking. More studies are needed to replicate our findings in order to contribute to the development of targeted interventions to reduce this increasing risk.


The public security image detection system is an important way to assist the police in the investigation. In today's cloud computing environment, the processing power of cloud computing is gradually improving. In order to explore its application in the investigation system, this paper constructs a public security video image investigation system based on cloud computing environment. This paper uses cloud computing technology to improve the processing capacity of the system. It then combines the storage capabilities of the backend server implementation technology and the working principle of Hadoop to construct a basic model. Then combined with the storage capability of the backend server implementation technology and the working principle of Hadoop and CP-ABE encryption, decryption, and reconstruction, a basic model is constructed. This paper also designs public security video surveillance system vehicle detection and test experiments, cloud storage encryption algorithm experiments, and computational storage requirements analysis experiments. It optimizes the system based on the results of the experiment and finally compares it with the traditional investigation system. The experimental results show that the public security video image detection system based on cloud computing can improve the accuracy by 5%–25% compared with the traditional detection system. And the public security video image detection system based on cloud computing can increase the efficiency by 2%–17% compared with the traditional detection system. In order to maintain long-term social stability, the Ministry of Public Security launched the National Ministry of Public Security's strong police demonstration city construction project to strengthen urban construction and science and technology. Major cities across the country have launched large-scale scientific and safe urban governance programs. At two meetings in 2017, the government proposed to strengthen the integration of police operations and the Internet in order to improve the efficiency of public security operations.In recent years, with the active advancement of the construction of the “Skynet” video surveillance project, it has played an increasingly important role in maintaining social order, preventing street crimes, ensuring public safety, and responding to emergencies. Incidents caused by its insufficient investigative capabilities are also increasing, which also highlights the lack of domestic video image investigative capabilities .Mobile users usually have a high demand for localized and location-based information services . Deng etThe innovation of this paper lies in the use of cloud computing technology, backend server implementation technology, and Hadoop working principles, combined with pedestrian-related knowledge elements in the video data of the video image detection system and public safety video image information. It preprocesses the image to improve the accuracy and efficiency of image acquisition.Matter, energy, information, our food, clothing, shelter, and transportation are inseparable from the support of matter. The development of all things in human life is due to the existence of energy. Cloud computing is now used in various fields, especially in developers with more convenient and flexible applications , 15. DemAt present, cloud computing early warning has spread to all aspects of our lives: when enjoying the fast shopping on Taobao, cloud computing technology has provided a powerful impetus for hundreds of millions of transactions; when using 12306 to purchase tickets, cloud computing technology  escortedAs shown in the figure, there are three business models for cloud servers: IaaS model    is a tecLarge-scale company-level projects are more flexible, expandable, and easy to maintain after being built using J2EE technology.(1) Keep Existing IT Assets. Because of the fierce market competition, companies must always adapt to new market demands. It is important to use the existing project organization structure to realize new market demand functions and to avoid overthrowing the existing structure and reformulating the project structure plan.(2) Efficient Development. J2EE enables project developers to focus more time and energy on developing business logic. Some general, repetitive, and cumbersome background basic logic is handed over to the corresponding middleware vendors to complete, which shortens the development time.(3) Unlimited Operating Environment. J2EE makes it possible to develop certain programs that need to run in a heterogeneous environment. J2EE-based application projects do not need to rely on any specific software system or specific hardware system.(4) Scalability. In order to meet the increasing number of customers using the company's platform, companies need to choose a server-side platform with high scalability. Company projects based on the J2EE platform have a wide choice of server-side platforms due to the characteristics of J2EE's unlimited operating environment.(5) Stable Availability. The server-side platform must meet the needs of 7 × 24 hours operation due to the characteristics of Internet applications. If the server shuts down unexpectedly, it will cause catastrophic loss of user data.In the intelligent video surveillance system, the accuracy of the video image recognition algorithm is largely affected by the video image quality of the network video stream. The transmission bandwidth, real-time performance, and definition of video images are high, so high requirements are put forward for the transmission of video streams. So this system uses RTMP protocol to transmit real-time data. The server uses OpenCV's built-in FFMPEG to analyze RTMP protocol data, and after decoding, it is analyzed by the image recognition algorithm, and the result is fed back to the web server. After the web server gets the result, it pushes the result to the user.In distributed storage and computing, Chen et al.  used a mThe Hadoop platform can provide three ways of working. Stand-alone mode : all configuration items are empty, and the system runs in stand-alone mode for program development and debugging; pseudodistributed mode: on the basis of single-machine mode, some simple functions such as memory checking, input, and output of distributed operation are added; in fact, all DataNodes run on the same PC and run the Hadoop system in a single node mode; fully distributed mode: it is to form a real Hadoop cluster and run it, install Hadoop on different computers, and connect different computers. It can be clearly seen from the above that, in order to realize the storage and processing of large-scale video data and to realize real-time monitoring and calling of video conditions in various places and fields, we need to build a distributed cluster in a fully distributed mode.HDFS  reads anThe powerful video image detection function  helps prVideo image investigation presents objective facts in front of investigators in the form of videos and photos. Investigators can use the intuitiveness of video and images to perform the same identification, tracking, and discovery of clues to related events.In order to better describe the pedestrian status information in different surveillance scenarios, the comprehensive analysis of pedestrians in the video big data environment can be realized. It needs to abstractly describe the scenes involved in the analysis of pedestrian behavior in video surveillance and some information of pedestrians based on the knowledge metamodel . The bas(1) Environmental Knowledge Element. The environment, also called weather, is the change in the state of the atmosphere within a certain time interval. The state of the weather will affect the apparent state of things to a certain extent. For example, the behavior habits and behaviors of pedestrians are different under clear weather and rainy and snowy weather. At the same time, the weather state and light intensity will also affect the actual state and imaging conditions of objectively existing things in the monitoring scene and then affect the accuracy of the video processing algorithm. Therefore, it is necessary to define environmental knowledge elements.(2) Surveillance Camera Knowledge Element. The video surveillance system mainly includes front-end camera part, transmission part, control part, display part, power supply system, and antitheft alarm part.(3) Scene Knowledge Element. For a specific monitoring scene, fixed things such as trees, buildings, and street lights in the scene are called objectively existing things. First, the background image of the scene is acquired based on the surveillance camera, and then the segmentation state of the background image is obtained according to the image segmentation algorithm in the operator library. It combines the scene segmentation state with the foreground extracted from the recorded video, which can effectively eliminate the influence of noise on the video processing accuracy and processing speed. That is, in the process of video image processing, only the scene area where pedestrians may exist is considered, which can improve the accuracy and extraction speed of foreground pedestrian primitives.(4) Event Knowledge Element. According to the survey results, the social security emergency management based on the video surveillance system mainly focuses on the following events in the surveillance scenario: border intrusion incidents, cross-line detection incidents, wandering detection incidents, population statistics, crowd gatherings, stampede incidents, violent incidents, crowd fights, and other incidents.(5) Behavioral Knowledge Elements. The behavioral knowledge element is mainly based on the pedestrian behavior image library, and the common feature representation is based on the knowledge discovery for different behavior categories in the common sense knowledge.(6) Video Knowledge Element. Video knowledge element is mainly used to record the main information of video clips. There are many tools for recording video, such as mobile phones, video recorders, tablets, computers, and surveillance cameras.Public safety video image information  is to maPublic safety video image information has the following characteristics.First, the purpose is to safeguard the public interest. Obtaining video image information is a means, and maintaining public safety is the goal. Public safety involves factors that affect social safety such as theft and robbery and factors that affect traffic safety such as speeding and retrograde. Video image information is used in various fields of public security, which improves the government's management and service efficiency. Factors that affect food safety include illegal production and processing, forest fires, etc., that affect fire safety, and even factors that involve environmental monitoring and other fields.Second, the collection area is a public space. There is no need for a specific majority of people to enter freely, and the space is open. Video image acquisition equipment is installed in public areas such as streets, stations, squares, and vital parts, which is completely open. There are also public places in specific areas such as communities or campuses, libraries, bank business halls, shopping malls, subways, and buses, which are semiopen. It does not include personal spaces such as homes and dormitories.Third, the content is comprehensive. Different from ordinary government information disclosure in the form of text, pictures, and so on, a video image is a collection of data information. As long as it enters the video shooting range, all behaviors and activities of the parties will be objectively and comprehensively recorded. The content contains various information such as appearance, sound, and action, which provides the viewer with more direct sensory stimulation in visual and auditory sense, and is complex and real-time. For example, the whole law enforcement process of the seizure and seizure of law enforcement officers captured by the law enforcement recorder is a collection of a series of information, including the appearance, voice, and actions of the law enforcement officers and the parties.Fourth, the collection equipment is diversified. With reference to the State Council's guidance on the implementation of the whole-process recording system for law enforcement, a variety of video image capture devices are listed. The acquisition equipment is mainly fixed video equipment, supplemented by mobile equipment such as body cameras and cameras. With the deep integration of technology, new types of equipment such as drones adapt to meet the needs of different scenarios, and video equipment becomes more intelligent. The video collection methods range from a single collection method of fixed video equipment to a variety of collection methods such as body enforcement recorders and drones.Fifth, the management subject is statutory. The main body includes both the main body that records the video and the main body that obtains the video from other places according to law, mainly including the public security department, the national security department, the transportation management department, the fire safety management department, and other administrative bodies. In practice, public security organs are used as competent departments to build video and image information network sharing platforms in many places. Its grasp of the video information is more comprehensive, and the disputes are mostly concentrated in the department, so this paper focuses on the discussion of the public security organ's video image information disclosure scope.(1)f is regarded as the product of illuminance and reflection [p is the illuminance and s is the reflection component.First, the original image flection . Here, p(2)First, it performs a logarithmic transformation on both sides of formula  to separ(3)It performs Fourier transform on both sides of formula , convert(4)Using a filter processing with a transfer function of, let(5)It performs the inverse Fourier transform  of the f(6)The pair performs an exponential operation to obtain an enhanced image.Homomorphic filtering  is a comIt can be seen from the above algorithm flow that the most important thing in quasi-identical filtering is the selection of filters. The form of the quasi-identical filter used in this paper is as follows:sI and sg are adjustment parameters , and z is a constant, which controls the clarity of the filter function. T is the distance between point  and the center frequency point , and T0 is the variance of the Gaussian function [Among them, function .(8)Tx,y=As shown in F and convolution of the original image to obtain low-frequency information.It uses a low-pass filter  F F is the Gaussian convolution function.φ is a constant matrix, which satisfiesAmong them, The MSR algorithm ensures the enhancement of details and the fidelity of colors. The formula is as follows:Wn represents SSR of different scales for weighting l is the color channel, and generally K = 3 .where Smooth linear filtering  is a gen(1) General Threshold. The general threshold value is the best threshold value line by line proposed in 1994, and it is also the most commonly used and simplest threshold value. The selection criteria are as follows:(2) Bayesian Shrinkage Threshold. In the Bayesian framework, the Bayesian shrinkage threshold is achieved by minimizing the Bayesian risk.θK2 is the variance of the noise, and θa is the standard deviation of the wavelet coefficients in the subband, which can be estimated using the following formula:Among them, Among them,So the noise model of the image isx0 is the observed image, x is the image without noise pollution, and y is Gaussian noise with 0 noise and θ2 variance.Among them, In this experiment, two Linux clusters installed on the rack are used. One is composed of 16 1 GHz Pentium II nodes (each node has 512 MB of memory). Each node runs the Red Hat Linux kernel version 2.4.20-8, and the Java runtime version is J2SDK1.5.006-b05. The other is an Itanium 2 server with 20 dual-core 1.3 GHz. The nodes in the cluster are connected through a 100 Mbps Ethernet switch. The kernel version of each node running Red Hat Linux is 2.4.0-2, the Java runtime version is J2SDK1.5.003-b07, and the two clusters are connected through 100 Mbps bandwidth.In the following experiment, NonD represents a general maintenance program that does not consider dependencies, such as ProActive and SmartFrog; SRL means maintaining the service in accordance with the shell script sequence; CG-0 means calling a maintenance plan that depends on perception; CG-1 represents the group maintenance plan with feedback; and REQ represents the fixed request rate from the service component.In order to verify the influence of propagation dependency and deployment dependency, the experiment set the fixed request rate of the request distribution service to 6 requests per second. At 15 seconds, the maintenance program is entered into the running license plate retrieval service. This experiment tested three solutions: NonD, SRL, and CG-0. The next experiment will test the vehicle detection service and set the fixed request rate to 10 requests per second. The result is shown in n solutions.As can be seen in the figure, before the maintenance process started, the system was running well, and when the maintenance started in 15 s, the system throughput of the three different schemes all dropped to zero. Although the figure shows that the maintenance process of NonD ended the earliest, the system did not work properly, and finally, the maintenance of the license plate extraction service failed. This is because NonD did not consider the AND dependency between the license plate extraction service and the shape analysis service. Compared with CG-0, although the SRL program successfully completed the system maintenance, it took too much time to affect the availability of the system, and CG-0 achieved relatively high efficiency. Similarly, two different solutions, CG-0 and CG-1, were launched at 15 s. The CG-0 scheme spreads the maintenance tasks to the node virtual machines related to the vehicle detection service, and the entire propagation process takes 62.97 s. However, it can be found from the figure that the amount of swallowing leaves of CG-0 in the execution process is close to zero, and about 57.9% of the requests are lost at the same time. Compared with CG-0, the CG-1 scheme optimizes the dependencies through grouping dependencies and obtains better experimental results. It maintains target services by setting different priorities. As can be seen from The user's private key will be associated with any number of attributes represented as strings. When a party encrypts a message, it specifies the associated access structure on the attribute. If a user's attributes are passed through the ciphertext's access structure, the user can only decrypt that ciphertext and handle more complex access controls, such as numeric ranges, by transforming the access tree into a smaller access tree.Traditionally, this type of access control was implemented by using trusted servers to store data locally. Increasingly, however, data is stored in a distributed fashion across many servers. The downside is that it is increasingly difficult to keep data secure using traditional methods. When data is stored in multiple locations, the chances of one of them being compromised are greatly increased.In order to verify that the video encryption algorithm in this paper can meet the real-time requirements, the six basic video sequences are packetized, encrypted, decrypted, and reconstructed. The efficient access policy update method proposed in this paper is to subpackage the video based on data dependency coding and use CP-ABE to encrypt the key data group and upload it to the cloud storage server. When updating the access control policy, the video data owner only needs to retrieve the key data packets from the cloud to decrypt and reencrypt. The update process only involves critical data packages, not critical data packages. It is not affected and can greatly reduce the computational burden and network resource consumption brought by the update process to the video owner and effectively improve the update efficiency of the CP-ABE access control strategy. For the same coded video, if the GOP value in video coding changes, the proportion of key data in the total video data will also change accordingly. It can be seen from the figure that the larger the GOP value, the lower the proportion of key data in the total data. The efficient access control strategy update method proposed in this paper can effectively reduce the amount of data involved in the update process. The more the update times, the more obvious the effect.The owner of the encoded video data updates the access control policy and needs to download the encrypted encoded video from the cloud. After adjusting the access control policy locally, the encrypted encoded video needs to be reuploaded to the cloud server. Although video files are compressed, the amount of data is generally still large. If the data owner frequently updates the access control strategy, the upload and download process will consume a lot of network resources. The efficient access control policy update method in this chapter can effectively reduce the amount of data encrypted and decrypted by the owner and greatly reduce the consumption of network resources.H.264 compression is currently the most effective compression technology. Compared with the 4Part2 standard of M.JPEG and MPEG, the H.264 encoder can reduce the size of digital video files by 80% and 50%, respectively, without affecting the image quality. This means that H.264 files require less bandwidth and storage space.Tables 3Approximate bit rate/8 (the number of bits in a byte) × 3600 s = KB per hour/1000 = MB/d, hour.MB/hour × working hours per day/1000 = GB/day.GB/day × required storage time = storage demand.Approximate bit rate/8 (the number of bits in each byte) × 3600 s = KB per hour/1000 = MB/d, hour.MB/hour × working hours per day/1000 = GB/day.GB/day × required storage time = storage demand.Note. The formula does not consider exercise flow, which is an important factor that affects the amount of storage required.Image size × frame/second × 3600 s = Kilobyte(1④)/1000 = Megabyte(MB)/d, hour.MB/hour × working hours per day/1000 = Gigabyte(GB)/day.GB/ × required storage duration = required storage.In order to study the advantages and disadvantages of the design of the public security video image detection system based on cloud computing and the traditional detection system, this paper designs a comparison experiment of the accuracy and efficiency of the public security video image detection system based on cloud computing. The experimental results are shown in It can be seen from the figure that the accuracy of the public security video image detection system based on cloud computing can reach 80%–95%, while the accuracy of the traditional detection system is only 70%–75%. This new type of investigation system has improved the accuracy by 5%–25% compared with the traditional investigation system. And the efficiency of the public security video image detection system based on cloud computing can reach 82%-92, while the efficiency of the traditional detection system is only 75%–80%. Compared with the traditional detection system, the efficiency of the public safety video image detection system based on cloud computing has increased by nearly 2%–17%. This shows that the public security video image detection system based on cloud computing is much higher in accuracy and efficiency than the traditional ones.This paper mainly studies the construction of a public security video image detection system platform in a cloud computing environment. This paper uses the processing power of cloud computing technology, the backend storage capability of the backend server implementation technology, and the working principle of Hadoop, combined with pedestrian-related knowledge elements in the video data of the video image detection system and public safety video image information. It preprocesses the image and finally builds a public security video image detection system based on cloud computing.


The ileoanal pouch (IPAA) provides patients with ulcerative colitis (UC) that have not responded to medical therapy an option to retain bowel continuity and defecate without the need for a long-term stoma. Despite good functional outcomes, some pouches fail, requiring permanent diversion, pouchectomy, or a redo pouch. The incidence of pouch failure ranges between 2 and 15% in the literature. We conducted a systematic review and meta-analysis aiming to define the prevalence of pouch failure in patients with UC who have undergone IPAA using population-based studies.We searched Embase, Embase classic and PubMed from 1978 to 31st of May 2021 to identify cross-sectional studies that reported the prevalence of pouch failure in adults (≥ 18 years of age) who underwent IPAA for UC.Twenty-six studies comprising 23,389 patients were analysed. With < 5 years of follow-up, the prevalence of pouch failure was 5% (95%CI 3–10%). With ≥ 5 but < 10 years of follow-up, the prevalence was 5% (95%CI 4–7%). This increased to 9% (95%CI 7–16%) with ≥ 10 years of follow-up. The overall prevalence of pouch failure was 6% (95%CI 5–8%).The overall prevalence of pouch failure in patients over the age of 18 who have undergone restorative proctocolectomy in UC is 6%. These data are important for counselling patients considering this operation. Importantly, for those patients with UC being considered for a pouch, their disease course has often resulted in both physical and psychological morbidity and hence providing accurate expectations for these patients is vital.The online version contains supplementary material available at 10.1007/s00384-021-04067-6. Restorative proctocolectomy with ileal pouch-anal anastomosis (IPAA) is considered to be the gold standard surgical treatment for ulcerative colitis (UC) that is refractory to medical therapy . SeveralDespite good functional outcomes, serious complications such as pelvic sepsis, strictures, anastomotic leaks, de novo Crohn’s disease, pouchitis and persistent pouch dysfunction can occur. These are known risk factors for pouch failure, defined as the need for pouch resection, permanent diversion, or a redo pouch. Several risk factors for pouch failure have been documented in the literature, the commonest being pelvic sepsis . ChronicTherefore, we conducted a systematic review and meta-analysis aiming to define the prevalence of pouch failure in patients with UC exclusively who have undergone IPAA using population-based studies.We searched Embase, Embase classic and PubMed from 1978 to 31st of May 2021 to identify population-based studies  that reported the prevalence of pouch failure in adults (≥ 90% of the population), age 18 years and above who underwent IPAA for UC exclusively. Pouch failure was defined as the need for permanent diversion and/or pouchectomy and/or a redo pouch. Short-term outcomes have been reported extensively in the literature . TherefoData were extracted by two independent reviewers  using Microsoft Excel spreadsheets. The extracted data is provided in Supplementary Table We combined the proportion of patients with pouch failure in each study to give a pooled prevalence for each study. We then performed a random effects model to pool the data and provide an estimate of the prevalence of pouch failure.I2 statistic. All statistics were performed using R with the package “meta.”We assessed the quality of case–control studies using the Newcastle–Ottawa scale, with a total possible score of 9 . Sensitivity analysis was performed using the one-study remove method to detect the impact of each study on the combined effect. Publication bias was assessed by funnel plot inspection and Egger’s test. Heterogeneity was assessed using the This review is presented in line with the PRISMA guidelines and was registered priori on Prospero (CRD42021259505).Two thousand six hundred and twenty-two studies were identified in the search. Twenty-six cohort studies (5 prospective cohort studies and 21 retrospective cohort studies) met the inclusion criteria and were included in the meta-analysis , 12–33. I2 = 89%, P < 0.01), ≥ 5 but < 10 , ≥ 10 years  and overall prevalence of pouch failure ). Using Egger’s test, no funnel plot asymmetry was observed when assessing studies with < 5 years and ≥ 5 but < 10 years of follow-up  . With ≥ 5 but < 10 years of follow-up, the prevalence was 5% (95%CI 4–7%). This increased to 9% (95%CI 7–16%) with ≥ 10 years of follow-up. The overall prevalence of pouch failure was 6% (95%CI 5–8%) Fig. . However0%. With Using the Newcastle–Ottawa scale and AHRQ standards, 19 studies were classified as poor while 7 were deemed to be good (Table n = 72). Other reported causes of pouch failure include pouchitis (n = 33), strictures (n = 20), Crohn’s disease (n = 45), surgical complications (n = 6) and pelvis sepsis (n = 1). Four articles reported failure rates at multiple time points Supplementary file2 (XLSX 16 KB)Supplementary file3 (DOCX 141 KB)Below is the link to the electronic supplementary material.