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Fish-like robot is a special autonomous underwater vehicle with broad application prospects. Some previous studies concentrated on the hydrodynamics of free-swimming fish-like robots. But the hydrodynamic performance of fish-like robot swimming with a tilt angle in constrained space has not been well studied, and the influence of environmental wave and current on its is also still unclear. In this paper, the experiment devices, including a physical fish-like robot, a hydrodynamics measurement platform, and a six-axis force sensor, are used to study the effect of attack angle and environmental condition on the hydrodynamics of near-surface swimming fish-like robot. Nine attack angles, five oscillating amplitudes, and three environmental conditions are analyzed in the experiments. It shows that thrust force decreases when caudal fin passes above water surface, but the increased difference between gravity force and buoyancy force will compensate the decreased force generated by caudal fin when fish-like robot swims with certain dive angle. The extra reaction force generated by solid bottom boundary will promote the thrust force and vertical force. The surface water wave condition or surface water current condition also has obvious effects on hydrodynamic performance. This paper provides a new perspective to the research on the hydrodynamic performance of fish-like robot and will do favor in the development of fish-like robot.

Low-grade glioma (LGG) is one of the most common brain tumors and often develops into the worst glioblastoma (GBM). Pyroptosis is related to inflammation and immunization. It has been demonstrated to influence the progression of a variety of cancers. However, the value of pyrosis-related genes (PRGs) in LGG remains unclear. Public TCGA-LGG data are used to analyze the differential expression and genetic variation of PRGs in LGG. Subsequently, this paper identifies pyroptosis-related subtypes and constructs prognostic models. This paper analyzes the expression and function of selected CASP5 in LGG and constructs a ceRNA regulatory network. Final CASP5-related immune infiltration analysis and methylation analysis are performed. Most PRGs are differentially expressed and altered in LGG. Subtypes and prognostic models based on PRGs not only have good functions but also have a great connection with immune infiltration. Enrichment analysis of PRGs with prognostic value of LGG also shows functions correlated mainly with immunity and inflammation. CASP5 is significantly differentially expressed in different grades of gliomas and different prognoses. Despite fewer mutations, CASP5 has a clear correlation for both immune cells and immune checkpoint molecules in the LGG microenvironment. Its methylation may also have a role in the prognosis of LGG. This paper shows the association of pyrosis-related subtypes, prognostic models, and genes, with immune infiltration.

In recent years, soft pipeline robot, as a new concept, is proposed to adapt to tunnel. The soft pipeline robots are made of soft materials such as rubber or silicone. These materials have good elasticity, which enhance the adaptability of the soft pipeline robot. Therefore, the soft pipeline robot has better performance on deformability than rigid robot. However, the structure of tunnel is complex and varied that brought challenges on design structure of soft pipeline robot. In this paper, we propose soft pipeline robot with simple structure and easy fabrication, which can be realized straight, turning motion in a variety of tunnels with different diameters. The soft pipeline robot composed of two types of structure, which are expansion part and deformation part. Front and rear deformation part for bending and position fixation, and middle expansion part for elongation, so the pipeline soft robot can be moved in various structures of tunnels. Moreover, the locomotion ability and adaptability in tunnel are verified by simulating on software. The structure of chamber proposed in this paper can guide the design method of soft pipeline robot.

The heart is one of the most important organs of the human body, but in recent years heart disease has become one of the human health killers and this paper explores endomyocardial fibrosis, which is a common cardiomyopathy, commonly seen in infants and children, and refers to a diffuse elastic fibrous disease of the endocardium. The purpose of this paper is to explore the diagnostic imaging analysis and care of patients with endocardial heart machine fibrosis using wireless network intelligent medical technology, aiming to provide a new power basis for the treatment of the disease in related patients. This paper proposes a new endocardial segmentation algorithm that aims to process image information using image features, intervene in image noise reduction and smoothing, etc., and use image grayscale values to confirm cardiac cavity grayscale values as a basis for physicians to make certain judgments for the diagnosis of patients with endocardial machine fibrosis. The experimental results show that the atrial fibrillation group is distinctly higher compared to the sinus rhythm group, with values remaining between 25 and 39, which is a significant advantage compared to other methods.

In order to improve the nursing effect of emergency general surgery, this paper combines computer algorithms to carry out the intelligent management of general surgery nursing, and realizes the standardization of nursing information, the electronic nursing file, the precision of nursing workload, and the intelligentization of nursing quality control by means of informatization. This truly and objectively reflects the nursing operation and treatment situation, prevents the occurrence of some adverse events, and effectively reduces the workload of nursing care. Moreover, this paper uses a standardized software design method to define the software concept, and then conducts a detailed demand analysis of the nursing display function through detailed investigation, class work, discussion and analysis, and comparison decision-making methods. In addition, this paper compiles the software through strict coding standards, and finally designs test cases to test and improve the software. Through actual case studies, it can be seen that the computer-assisted emergency general surgery nursing method proposed in this paper has a certain progress compared with the traditional nursing method.

An intelligent health detection model is a new technology developed under an artificial intelligence environment, which is of great significance to the care of the elderly and other people who cannot take care of themselves. This paper comprehensively reviews the structural health monitoring method based on an intelligent algorithm, introduces the application model of neural networks in structural health monitoring in detail, and points out the shortcomings of using neural network technology alone. On the basis of previous work, the genetic algorithm and fuzzy theory were introduced as optimization tools, and a new neural network training algorithm was constructed by combining genetic algorithm, fuzzy theory, and neural network technology for structural health monitoring research. Aimed at the shortcoming of insufficient samples for training neural networks based on experimental data, this paper proposes to use the finite element method to construct a genetic fuzzy RBF neural network after corresponding processing of the first six-order bending modal frequencies of the structure, so as to realize the localization and detection of delamination damage of composite beams. Injury Assessment. The experimental results of this paper show that the finite element method proposed in this paper can effectively carry out damage localization and damage assessment; compared with the traditional algorithm, the localization accuracy of this algorithm is improved by 20%, and the damage assessment performance is improved by 10%.

Rough drawings provide artists with a simple and efficient way to express shapes and ideas. Artists frequently use sketches to highlight their envisioned curves, using several groups’ raw strokes. These rough sketches need enhancement to remove some subtle impurities and completely simplify curves over the sketched images. This research paper proposes using a fully convolutional network (FCNN) model to simplify rough raster drawings using deep learning. As input, the FCNN takes a sketch image of any size and automatically generates a high-quality simplified sketch image as output. Our model intuitively addresses the shortcomings in the rough sketch image, such as noises and unwanted background, as well as the low resolution of the rough sketch image. The FCNN model is trained by three raster image datasets, which are publicly available online. This paper demonstrates the efficiency and effectiveness of using deep learning in cleaning and improving the roughly drawn image in an automatic way. For evaluating the results, the mean squared error (MSE) metric was used. From experimental results, it was observed that an enhanced FCNN model reported better accuracy, reducing the prediction error by 0.08 percent for simplifying the rough sketch compared to the existing methods.

With the rapid development of society and science technology, human health issues have attracted much attention due to wearable devices’ ability to provide high-quality sports, health, and activity monitoring services. This paper proposes a method for feature extraction of wearable sensor data based on a convolutional neural network (CNN). First, it uses the Kalman filter to fuse the data to obtain a preliminary state estimation, and then it uses CNN to recognize human behavior, thereby obtaining the corresponding behavior set. Moreover, this paper conducts experiments on 5 datasets. The experimental results show that the method in this paper extracts data features at multiple scales while fully maintaining data independence, can effectively extract corresponding feature data, and has strong generalization ability, which can adapt to different learning tasks.

In order to further accurately predict gas emission of working face, this paper proposes a prediction model of gas emission of working face based on the combination of improved artificial bee colony algorithm and weighted least squares support vector machine (IABC-WLSSAVM). The research steps are as follows: Firstly, in order to obtain the sparse solution of LSSVM, a more reliable prediction model is realized by weighting the error value. Secondly, the chaotic sequence is introduced into the artificial bee colony algorithm to find a better initial honey source, which increases the diversity of the population, and combines the Levy flight to update the search step to avoid falling into the trap of local optimum. At the same time, the improved artificial bee colony algorithm is used to optimize the kernel width σ and regularization parameter λ of WLSSVM, which improves the prediction accuracy and convergence rate of WLSSVM. Finally, the quantitative analysis model of WLSSVM is reconstructed by using the optimized parameters, and the nine parameters of buried depth of coal seam, gas content of coal seam, coal thickness, interlayer lithology, production rate of working face, length of working face, inclination of coal seam, gas content of adjacent layer, and thickness of adjacent layer are used as the main influencing factors. After normalization, the nonlinear prediction model of gas emission is established. The simulation results based on the three indicators of determination coefficient, root mean square error, and average relative variance show that the IABC-WLSSVM prediction model proposed in this paper can not only overcome the local optimization to obtain the global optimal solution but also has faster convergence speed and higher prediction accuracy. This prediction model has obvious advantages compared with the other three improved prediction models in terms of fitting, accuracy, and generalization ability, which can provide a reliable theoretical basis for the prediction of gas emission in coal mining face under complex factors and propose a new idea for the application of artificial intelligence in the construction of intelligent mines. At the same time, the prediction model can also be applied to other fields.

Neurons refer to nerve cells. Each neuron is connected with thousands of other neurons to form a corresponding functional area and carry out complex communication with other functional areas. Its importance to the human body is self-evident. There are also many scholars studying the mechanism of apoptosis. This paper proposes a study of neuronal apoptosis in patients with liver cirrhosis and neuronal epileptiform discharge models based on multi-modal fusion deep learning, aiming to study the influencing factors of abnormal neuronal discharge in the brain. The method in this paper is to study multi-modal information fusion methods, perform Bayesian inference, and analyze multi-modal medical data. The function of these research methods is to obtain the relationship between the independence of information and the intersection of information among modalities. In the neuronal epileptiform discharge model, the mRNA expression level of the necroptotic signaling pathway related protein was detected, and the mechanism of neuronal necrosis in patients with liver cirrhosis was explored. Experiments show that the neuron recognition rate has been increased from 67.2% to 84.5%, and the time has been reduced, proving the effectiveness of deep learning.

With the continuous development of China’s cultural industry, people’s health has become one of the topics of the highest concern. Therefore, all the application models of physical health test data in the actual analysis have become the current research focus and trend direction of healthy constitution. This paper summarizes the significant problems in the analysis of physical health test data, through the comprehensive analysis and investigation of physical health test data, combined with the measurement of the test indicators, through the analysis and processing system of youth physical health data, the use process of national youth group physical health standard data management software, and decision tree intelligent algorithm in physical health. The research steps of test data analysis and application model summarize the application characteristics of physical health test data in the application process. Based on this, a decision tree intelligent algorithm is proposed, and the corresponding functions and optimization formulas of the algorithm are substituted. In the process of actual sample checking calculation, each weight range and corresponding errors are inferred and analyzed by combining examples. This paper summarizes the application model and optimization model of health test data analysis based on decision tree intelligent algorithm. Through the repeated test of the research data, the feasible area and application scope of the algorithm are obtained, and the practical optimization scheme and application ideas under the algorithm are obtained.

In chorus activities, the conductor leads chorus members to recreate music works. If you want to interpret music works perfectly with sound, emotion and emotional expression are particularly important. In this paper, a cloud HBD (health big data) integration system based on ensemble learning is designed to realize the high-efficiency and high-precision integration of HBD. An emotional speech database containing three emotions such as pleasure, calmness, and boredom is established, and the corpus problems such as emotional feature analysis and extraction needed for chorus emotion recognition research are solved. It also studies the classification and decision-making in emotional changes, and a DBN (deep belief network) chorus emotion recognition algorithm based on multiple emotional features is proposed. Feature DBN (Deep Belief Network) Chorus Emotion Recognition Algorithm This paper extracts various robust low-level features according to different features' ability to describe emotions and then feeds them into the DBN network to extract high-level feature descriptors. Then, the classification results of ELM (extreme learning machine) are voted and fused with the idea of ensemble learning, and the effectiveness of the algorithm is proved on three public datasets.

With the high development of sports, football has attracted more and more attention from the public. However, the hot competition has made football players undergo high-intensity training, and the risk of injury caused by training is also increasing. Lateral incision knee ACL injury is one of the most common types of injuries in football players, which has a serious impact on the athlete’s physiology and daily training. The comprehensive, multi-level, convenient and fast medical system brought by the medical Internet of Things has become the growing demand of the medical industry. Based on the medical Internet of Things, this paper studies the rehabilitation nursing of football players’ ACL injury by lateral cutting and running combined with optical microscope. In this paper, 36 male and female football players were selected for group experiments, and the landing and peak torque indexes of the experimental group and the control group of male and female athletes were analyzed under the observation of an optical microscope. Group (P

In this paper, the safety of Tripterygium wilfordii polyglycoside (TW) preparation was evaluated by combining literature research and evidence-based evaluation research, so as to provide evidence-based safety information of Tripterygium wilfordii polyglycoside preparation (nephroptosis) for government decision making and clinical application. In this paper, we propose a network structure inspired by the LSTM gate mechanism. All the research methods of the included references are evaluated by internationally recognized evaluation tools or standards. Prevalence was analyzed according to the type of intervention (e.g., time of administration) and route of administration. The results of this experiment provide methods and suggestions for the evaluation of traditional Chinese medicine nephroptosis in the future.

To a large extent, track and field sports require strong physical fitness of athletes, and athletes’ physical fitness determines their competition results. With the improvement of people’s living standards, athletes can get better nutritional supplements, but competition in track events has gradually become fierce, and physical fitness is extremely important for athletes. Physical training can improve athletes’ endurance, sports coordination, and sensitivity, but coaches should arrange the training intensity reasonably, not exceeding the athlete’s tolerance, to avoid problems such as overloading training causing athletes to be injured and sports age shortened. Traditional track and field training methods are no longer suitable for the physical development of modern athletes. This paper mainly studies the college track and field sports training teaching platform based on data mining technology. By using data mining technology, this paper constructs a track and field training platform in colleges and universities. Therefore, this paper designs a teaching platform for physical training in track and field events and puts the teaching platform into training teaching. It uses data mining technology to collect athletes’ sports characteristics and analyze athletes. The physical parameters and movement norms of the people develop a personalized training program for them.

Objective. The aim of this systematic review was to provide an overview of available scientific evidence regarding the comparative efficacy of computer-aided design (CAD) and computer-aided manufacturing (CAM) glass fiber posts with prefabricated and metal cast posts for the restoration of endodontically treated teeth (ETT). Methods. This systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Electronic and manual searches were performed using the PubMed, SciELO, Cochrane, ScienceDirect, Web of Science, and EBSCO databases. The reference lists of the selected papers were reviewed to identify relevant papers. There were no year restrictions, and eligible studies were those in English publications and describing in vitro studies evaluating intraradicular retainers (IRs) for (i) fracture resistance, (ii) bond strength, (iii) adaptation, and (iv) cement layer thickness. Literature reviews, systematic reviews, meta-analyses, case reports, in vitro studies with

The success or failure of badminton competition often depends on the training level of technical, physical, tactical, and psychological quality, as well as the competitive ability to comprehensively use these factors in the competition. Aiming at the improvement of the BP neural network algorithm, the improved BP neural network algorithm is used to analyze the multidimensional attributes of the collected data, and the simulation experiment is carried out to find out our competitive advantages and disadvantages. This paper presents a statistical study on the change of service area, the application of service receiving technology, and the treatment of the third stroke technology in the badminton competition. The innovative contribution of this paper is to explore the differences of double service decision-making of badminton at different levels and form a theoretical mechanism analysis system. The research shows that the relationship between active and passive attack and hitting quality has been dealt with in the game. In training, mastering the basic technical movements of badminton is conducive to improving the technical level of badminton.

With a large number of images provided by TV and other media flowing into the Internet and the reduction of technical barriers, images have not only become a daily practice for people to record their lives and communicate their behaviors but also become an important means for the public to express their discourse in the cyberspace. Therefore, it is of great significance to analyze the image propagation algorithm using artificial intelligence. This paper mainly studies the algorithm analysis and governance of local media image propagation in the era of artificial intelligence. In this paper, the media is the research object, with its daily dissemination of video works as the research text, in order to discover the ethical problems in its dissemination activities as the purpose, integrating disciplinary knowledge to analyze the ethical problems in this art form, and trying to find out the fundamental measures to solve the problem. The advantages and disadvantages of the video recommendation intelligent algorithm based on the BP neural network are analyzed. By comparing different algorithms, it can be seen that the video recommendation accuracy of the BP neural network algorithm based on swarm optimization (FEBP) is 15.8% higher than that of the traditional BP neural network algorithm. These intelligent algorithms are added into the image transmission system, in order to achieve the goal of improving the image transmission and recommendation effect.

Objective. This study aims to investigate the clinical efficacy of plasma exchange in treating acute-on-chronic liver failure (ACLF) through meta-analysis. Method. PubMed, Web of Science, Embase, China National Knowledge Infrastructure (CNKI), and Wanfang databases were searched using a computer for all relevant Chinese and English literature from 2000 to 2021 in each database. At the same time, a large number of related papers and materials were manually consulted. Randomized controlled trials of plasma exchange (PE, control group) and combined double plasma molecular absorption system (DPMAS + PE, observation group) for the treatment of ACLF were collected. Meta-analysis was performed with Stata16.0 software. Result. A total of 474 articles were retrieved, and 11 papers were finally included for research after screening. Meta-analysis results showed that the effective rate of treatment in the experimental group was significantly higher than that in the control group. At the same time, the observation group’s prothrombin activity (PTA) level was better than that of the control group after treatment. After treatment, there was no significant difference in prothrombin time (PT) and international normalized ratio (INR) between the two groups. In addition, after treatment, the alanine aminotransferase (ALT) level of the observation group was significantly lower than that of the control group. However, TBIL levels and albumin (ALB) levels did not change significantly between the two groups. Regarding blood routine indexes, there were no significant changes in creatinine (Cr) levels and platelet counts (PLT) in the two groups after treatment, but hemoglobin (HGB) levels in the observation group were significantly lower than those in the control group. Conclusion. DPMAS combined with plasma exchange therapy can improve liver function, coagulation function, and blood routine level of ACLF patients and increase the effective rate of treatment. It is an effective treatment for acute-on-chronic liver failure.

In order to improve the effectiveness, this article puts forward a research on the integration mechanism of explicit education and implicit education of psychological education in ideological and political education from the perspective of new media. Starting from the concept, characteristics, and types of new media, from the connotation, characteristics, theory, and practical significance of explicit education and recessive education, and from the perspective of new media, the integration mechanism of explicit education and recessive education in ideological and political education should be promoted. According to the integration principle of explicit education and implicit education in the new media perspective, this paper puts forward the integration strategy of explicit education and implicit education in the new media perspective. By using Marxist philosophy, instillation theory, and four learning theories, the paper uses the subjective sampling method to conduct a questionnaire survey on a university and puts forward some suggestions for the practical situation. The results show that the integration of explicit and implicit education into ideological and political education from the perspective of new media is effective and feasible, which can effectively improve the effectiveness of ideological and political education, promote the development of ideological and political education in university, and ensure the quality of ideological and political education in university.

Mental health essentially refers to a state of mind. It means that an individual can keep a good or normal state in behavior. It is not only related to the overall development of the individual body and mind but also affects the construction of university teachers. This paper mainly studies the career development anxiety of music teachers in colleges and universities in China, analyzes its causes, and puts forward corresponding countermeasures and suggestions to alleviate the pressure and promote the physical and mental harmony of music teachers and the common growth of a good social atmosphere. The purpose is to help music teachers in colleges and universities better manage their mental health. This paper collects the data of occupational stress and mental health of 164 music teachers in many colleges and universities through the method of a questionnaire survey and processes the data with the analysis formula of sample variance and standard deviation. The analysis shows that a considerable number of the respondents have career development anxiety.

With the acceleration of the informatization process, but because of the late start of the informatization construction of logistics management, the current digital system construction of logistics management has not been popularized, and the intelligent logistics integrated management evaluation system is also extremely lacking. In order to solve the lack of existing intelligent logistics comprehensive management evaluation system, this paper introduces the research of intelligent logistics comprehensive management evaluation system based on hospital data fusion technology. This paper analyzes and utilizes the Kalman filter and adaptive weighted data fusion technology in data fusion technology and then analyzes the evaluation index and system design principles of the intelligent logistics comprehensive management evaluation system and then designs the application layer from the application layer. Design the application layer from the application layer. Then design the framework of the intelligent logistics comprehensive management evaluation system at the network layer and the data layer. The system is finally tested, and the test results show that the evaluation accuracy of the system reaches 80%.

The problem of poor children’s psychological development has been widely concerned by governments all over the world. At present, the research results of poor children’s psychological development show that poor children mainly have cognitive, mental health, and behavioral problems. This paper studies the personality traits and their influencing factors of children from poor families from the perspective of psychological education. By giving the definition of children from poor families and poor families, this paper analyzes the psychological development of poor children, analyzes the personality traits and influencing factors of children from poor families from the perspective of psychological education, obtains the standard scores of 16 personality traits and their dimensional personality factors and personality factors, and uses the clustering method to realize the difference analysis of personality characteristics of various samples. From the perspective of quantitative psychological education, this paper analyzes the correlation between personality traits and influencing factors of children from poor families in order to provide basis and reference for the government and society to implement targeted poverty alleviation and cut off the intergenerational transmission of poverty.

In order to explore the reliable clinical characteristics of patients with pulmonary thromboembolism, this paper applies computer statistical analysis methods to the analysis of clinical characteristics of patients with pulmonary thromboembolism. Moreover, starting from the actual situation, this paper combines experiments to study the case, uses sample screening and sample processing to group samples freely, and conducts grouping reliability research through data statistics methods. After verifying the reliability of the grouping, this paper combines the samples to test the effectiveness of the computer statistical analysis method applied to the clinical characteristic analysis of patients with pulmonary thromboembolism and conducts numerical analysis in combination with the comparative analysis method. The results of the research show that the computer statistical method proposed in this paper has a good effect on the clinical characteristics of patients with pulmonary thromboembolism and meets the actual needs of clinical analysis.

For athletes of different sports, their muscle activities in different sports will show different special characteristics according to different sports. The purpose of this paper is to study the technology of multi-information fusion and to study the athletes of the long jump competition. The relevant research on the muscle specific ability test methods of the athletes in the long jump competition is of great significance to the study of the long jump. This paper proposes a long jumper’s special muscle ability experiment, using the now very popular multi-information fusion technology, to conduct in-depth research on the biomechanics of the long jumper’s special ability of the take-off muscle, which can provide an effective scientific basis for the special ability level and special strength of the long jumper’s take-off sports. The results of the study show that the hip joint of the take-off leg produces greater hip extensor torque during the take-off action of long jump, and the active contraction ability of the knee flexor group is very important for taking off and avoiding damage to the posterior femoral muscle group. The change range of the pressure center is between 51.26% and 74.35%, which has great application value in actual training.

Chronic obstructive pulmonary disease (COPD) is a respiratory disease that can be treated and prevented. The purpose of this paper is to conduct a meta-analysis of clinical rehabilitation nursing of COPD patients based on intelligent medical care by constructing a suitable model so as to make the research on clinical nursing of COPD patients more effective. This paper first introduced the intelligent medical system, analyzed the clinical rehabilitation nursing of COPD patients, established the SCNet model suitable for this paper, and then used statistical algorithms to carry out the meta dynamic analysis of the clinical rehabilitation nursing for COPD patients. Through the analysis of the current situation of medical equipment and the comparison of models and statistical analysis, the experimental results showed that the pulmonary function indexes of the pulmonary rehabilitation treatment group and the conventional treatment group were improved after treatment compared with before treatment. Although on specificity metrics SCNet did not perform the best, it was about 1% lower than the best baseline model. However, the comprehensive performance of the SCNet model on Acc, Sen, F1-Score, and AP indicators showed that the model SCNet proposed in this paper had certain advantages, which was helpful for the clinical rehabilitation care of patients with COPD and could better assist doctors in treatment.

With the continuous prominence of medical problems, the construction of a smart medical system has received more and more attention. With coronavirus disease 2019 in a state of regular prevention and control, the epidemic prevention and control in China had produced favorable effects. Therefore, a comprehensive evaluation of the emergency governance mechanism in China was crucial to epidemic prevention and control worldwide. This paper empirically analyzed the effects of epidemic prevention and control based on the dual perspective of epidemic governance and capital market, taking the daily outbreak data at the provincial level in China as the sample. The findings were as follows: (1) The accumulation of epidemic medical treatment experience in each province and city produced a positive emergency effect on preventing and controlling the dissemination of epidemic. The effective medical security mechanism was one of the most important mechanisms for the significant emergency effect, with the effective material security as a critical supplement. (2) Companies and corporations with stronger capabilities for emergency medical treatment received more positive responses from the capital market comparatively. Aiming at diversifying directions and measurements for the improvement of emergency governance, this paper provided theoretical bases for improving the emergency governance system against major public health emergencies by using the Chinese characteristics.

For athletes who are eager for success, it is difficult to obtain their own movement data due to field equipment, artificial errors, and other factors, which means that they cannot get professional movement guidance and posture correction from sports coaches, which is a disastrous problem. To solve this big problem, combined with the latest research results of deep learning in the field of computer technology, based on the related technology of human posture recognition, this paper uses convolution neural network and video processing technology to create an auxiliary evaluation system of sports movements, which can obtain accurate data and help people interact with each other, so as to help athletes better understand their body posture and movement data. The research results show that: (1) using OpenPose open-source library for pose recognition, joint angle data can be obtained through joint coordinates, and the key points of video human posture can be identified and calculated for easy analysis. (2) The movements of the human body in the video are evaluated. In this way, it is judged whether the action amplitude of the detected target conforms to the standard action data. (3) According to the standard motion database created in this paper, a formal motion auxiliary evaluation system is established; compared with the standard action, the smaller the Euclidean distance is, the more standard it is. The action with an Euclidean distance of 4.79583 is the best action of the tested person. (4) The efficiency of traditional methods is very low, and the correct recognition rate of the method based on BP neural network can be as high as 96.4%; the correct recognition rate of the attitude recognition method based on this paper can be as high as 98.7%, which is 2.3% higher than the previous method. Therefore, the method in this paper has great advantages. The research results of the sports action assistant evaluation system in this paper are good, which effectively solves the difficult problems that plague athletes and can be considered to have achieved certain success; the follow-up system test and operation work need further optimization and research by researchers.

With the continuous development of signal amplification technology and nanotechnology, more and more electrochemical sensors combining nanotechnology and signal amplification technology are applied in the field of analysis. In this paper, combined with the Internet of Things technology, the construction of gold nanobiosensors and signal characteristic processing are carried out. In this paper, a T-rich DNA probe is used as the recognition element, modified on the electrode surface, combined with DNA-modified nanogold particle amplification technology, and the electroactive substance peg amine is used as the signal molecule to develop a highly sensitive electrochemical biosensor for the detection of melamine. The sensor has good specificity and sensitivity, and the detection limit is as low as 0.5 NM. In addition, by combining sensors with the Internet of Things technology, melamine monitoring and signal characteristic processing can be carried out in real time. This model can easily achieve the purpose of accurate and quantitative analysis of melamine toxins and can be effective for food safety.

To explore the association of the biochemical markers after birth with BPD-PAH, factors independently predicting BPD-PAH risk were identified by multivariate logistic regression. Cut off values were determined by plotting receiver-operator curve (ROC), for the sake of dichotomizing continuous variables that showed independent relation with BPD-PAH risk. The results show that uric acid (UC) and blood urea nitrogen (BUN) contents markedly increased among infants experiencing BPD-PAH in comparison with those without BPD-PAH (11.6 vs. 9.7 mmol/L, P = 0.006 and 482.0 vs. 249.0 μ mol/L, P < 0.001 , separately). As shown by multivariate logistic regression, serum BUN levels ( OR = 1.143 ) and uric acid levels ( OR = 1.034 ) were important risk factors for BPD-PAH. Through a lot of experiments, the effectiveness and the advanced nature of the framework proposed in this paper are proved effectively. The framework proposed in this paper can provide some reference and thinking for follow-up research.

This paper aims to explore the application value of SonoVue contrast-enhanced ultrasonography based on deep unsupervised learning (DNS) in the diagnosis of nipple discharge. In this paper, a new model (ODNS) is proposed based on the unsupervised learning model and stack self-coding network. The ultrasonic images of 1,725 patients with breast lesions in the shared database are used as the test data of the model. The differences in accuracy (Acc), recall (RE), sensitivity (Sen), and running time between the two models before and after optimization and other algorithms are compared. A total of 48 female patients with nipple discharge are enrolled. The differences in SE, specificity (SP), positive predictive value (PPV), and negative predictive value (NPV) of conventional ultrasound and contrast-enhanced ultrasonography are analyzed based on pathological examination results. The results showed that when the number of network layers is 5, the classification accuracies of DNS and ODNS model data reached the highest values, which were 91.45% and 98.64%, respectively.

Respiratory failure is the most common clinical symptom, seriously endangering people’s health, which is mainly caused by a series of reasons, leading to serious respiratory dysfunction and severe pulmonary respiratory disorders, damaging the pulmonary airway function, and causing disability to maintain normal human gas exchange activities. At present, the main treatment of respiratory failure is to use the ventilator to help patients exchange gas and keep their breathing unobstructed. The nursing method is also around the artificial airway mechanical ventilation. The nursing of patients with respiratory failure is mainly respiratory nursing and psychological nursing. In this paper, the main research is to explore the way of severe nursing of respiratory failure. Before the nursing of respiratory failure, we should carry out nursing detection of the ventilator and test its performance, tightness, pipeline safety, and airway patency. Then carrying on the respiratory failure mechanical ventilation nursing and clarifying its mechanical ventilation nursing process is the main work of intensive care, at the same time, giving consideration to the psychological nursing of respiratory failure and implementing comprehensive nursing. In this paper, 50 patients were selected and divided into group A and group B; group A was given comprehensive nursing, and group B was given general nursing. The experimental results show that a series of situations in group A are more optimistic than those in group B. There was no doubt that the PH value of the two groups changed significantly 6 months after discharge. The pO2 value of group A was higher than that before discharge, while the pCO2 value of group B was unstable and increased. The pO2 value of group A was 55.52 before discharge and 62.36 six months after discharge. The pCO2 value of group A was 54.31 before discharge and 50.61 six months after discharge. The pCO2 value of group B was 55.23 before discharge and 57.34 six months after discharge.

Breast cancer is one of the cancers with the highest incidence among women. In the late stage, cancer cells may metastasize to a distance, causing multiple organ diseases, threatening the lives of patients. The detection of lymph node metastasis based on pathological images is a key indicator for the diagnosis and staging of breast cancer, and correct staging decisions are the prerequisite and basis for targeted treatment. At present, the detection of lymph node metastasis mainly relies on manual screening by pathologists, which is time-consuming and labor-intensive, and the diagnosis results are variable and subjective. The automatic staging method based on the panoramic image calculation of the sentinel lymph node of the breast proposed in this paper can provide a set of standardized, high-accuracy, and repeatable objective diagnosis results. However, it is very difficult to automatically detect and locate cancer metastasis areas in highly complex panoramic images of lymph nodes. This paper proposes a novel deep network training strategy based on the sliding window to train an automatic localization model of cancer metastasis area. The training strategy first trains the initial convolutional network in a small amount of data, extracts false-positive and false-negative image blocks, and uses manual screening combined with automatic network screening to reclassify the false-positive blocks to improve the class of negative categories. Using mammography, ultrasound, MRI, and 18F-FDG PET-CT examinations, the detection rate and diagnostic accuracy of primary cancers in the breast of patients with axillary lymph node metastasis as the first diagnosis were obtained. The detection rate and diagnostic accuracy of breast MRI for primary cancers in the breast are much higher than those of X-ray, ultrasound, and 18F-FDG PET-CT (all P values

Mental health issues are alarmingly on the rise among undergraduates, which have gradually become the focus of social attention. With the emergence of some abnormal events such as more and more undergraduates’ suspension, and even suicide due to mental health issues, the social attention to undergraduates’ mental health has reached a climax. According to the questionnaire of undergraduates’ mental health issues, this paper uses keyword extraction to analyze the management and plan of undergraduates’ mental health. Based on the classical TextRank algorithm, this paper proposes an improved TextRank algorithm based on upper approximation rough data-deduction. The experimental results show that the accurate rate, recall rate, and F1 of proposed algorithm have been significantly improved, and the experimental results also demonstrate that the proposed algorithm has good performance in running time and physical memory occupation.

This study aims to investigate the blood flow in bionic artificial blood vessels and to reduce the resistance to blood flow, the drag reduction characteristics of V-shaped groove drag reduction microstructures in artificial blood vessel structures were investigated in depth. By varying the parameters of incoming flow velocity, groove width, and groove depth, the effect of various variable conditions on the drag reduction effect of the grooves was investigated, and the flow field characteristics and drag reduction effect of the V-shaped groove microstructure in the artificial blood vessel were obtained. A detailed analysis of the effect of velocity and groove size on the drag reduction effect of the groove was also carried out to demonstrate the drag reduction mechanism of the V-shaped groove microstructure and to summarize the variation law of the drag reduction rate of the V-shaped groove. The results show that the resistance reduction rate of the V-shaped groove microstructure decreases with the increase of blood flow velocity, increases with the increase of groove width, and increases and then decreases with the increase of groove depth. The velocity range used in this paper is 0.3–0.6 m/s, the groove width varies from 0 to 0.3 mm, and the groove depth varies from 0 to 0.3 mm.

With the development of radiology and computer technology, diagnosis by medical imaging is heading toward precision and automation. Due to complex anatomy around the pancreatic tissue and high demands for clinical experience, the assisted pancreas segmentation system will greatly promote clinical efficiency. However, the existing segmentation model suffers from poor generalization among images from multiple hospitals. In this paper, we propose an end-to-end data-adaptive pancreas segmentation system to tackle the problems of lack of annotations and model generalizability. The system employs adversarial learning to transfer features from labeled domains to unlabeled domains, seeking a dynamic balance between domain discrimination and unsupervised segmentation. The image quality control toolbox is embedded in the system, which standardizes image quality in terms of intensity, field of view, and so on, to decrease heterogeneity among image domains. In addition, the system implements a data-adaptive process end-to-end without complex operations by doctors. The experiments are conducted on an annotated public dataset and an unannotated in-hospital dataset. The results indicate that after data adaptation, the segmentation performance measured by the dice similarity coefficient on unlabeled images improves from 58.79% to 75.43%, with a gain of 16.64%. Furthermore, the system preserves quantitatively structured information such as the pancreas’ size and volume, as well as objective and accurate visualized images, which assists clinicians in diagnosing and formulating treatment plans in a timely and accurate manner.

In this paper, we propose a method to evaluate the motor coordination of runners based on the analysis of amplitude and spatiotemporal dynamics of multichannel electromyography. A new diagnostic index for the coordination of runners was proposed, including the amplitude of electromyography, the spatiotemporal stability coefficient, and the symmetry coefficient of muscle force. The motor coordination of 13 professional runners was studied. Detailed anthropometric information was recorded about the professional runners. It has been found that professional athletes are characterized by the stability of movement repetition (more than 83%) and the high degree of symmetry of muscle efforts of the left and right legs (more than 81%) regardless of the changes in load during running at a speed of 8–12 km/hr. Scientific and technological means can support the scientific training of athletes. The end of the Winter Olympic Games has shown us the powerful power of a series of intelligent scientific equipment, including electro-magnetic gun, in sports training. We also look forward to the continuous innovation of these advanced technologies, which will contribute to the intelligent development of sports scientific research.

Heart rate (HR) estimation from multisensor PPG signals suffers from the dilemma of inconsistent computation results, due to the prevalence of bio-artifacts (BAs). Furthermore, advancements in edge computing have shown promising results from capturing and processing diversified types of sensing signals using the devices of Internet of Medical Things (IoMT). In this paper, an edge-enabled method is proposed to estimate HRs accurately and with low latency from multisensor PPG signals captured by bilateral IoMT devices. First, we design a real-world edge network with several resource-constrained devices, divided into collection edge nodes and computing edge nodes. Second, a self-iteration RR interval calculation method, at the collection edge nodes, is proposed leveraging the inherent frequency spectrum feature of PPG signals and preliminarily eliminating the influence of BAs on HR estimation. Meanwhile, this part also reduces the volume of sent data from IoMT devices to compute edge nodes. Afterward, at the computing edge nodes, a heart rate pool with an unsupervised abnormal detection method is proposed to estimate the average HR. Experimental results show that the proposed method outperforms traditional approaches which rely on a single PPG signal, attaining better results in terms of the consistency and accuracy for HR estimation. Furthermore, at the designed edge network, our proposed method processes a 30 s PPG signal to obtain an HR, consuming only 4.24 s of computation time. Hence, the proposed method is of significant value for the low-latency applications in the field of IoMT healthcare and fitness management.

Health digital GIS map provides a great solution for medical geographical distribution to efficiently explore diseases and health services. In Sudan, tuberculosis disease is expanding in different areas, which requires a digital GIS map to collect information about the patients and support medical institutions by geographical distribution based on health services, drug supply, and consumption. This paper developed a health digital GIS map to provide a fair geographical distribution of tuberculosis health centers and control the drug supply according to medical reports. The proposed approach extracts the unfair distribution of medicine, as some centers receive medicine but do not receive patients, while others receive a large number of patients but limited amounts of medicine. The analysis results show that there is a defect in some states representing the distribution of tuberculosis centers. In the Northern State, there are 15 tuberculosis centers distributed over all localities, serving about 84 tuberculosis-infected patients only.

In sports, fatigue management is vital as adequate rest builds strength and enhances performance, whereas inadequate rest exposes the body to prolonged fatigue (PF) or also known as overtraining. This paper presents PF identification and classification based on surface electromyography (EMG) signals. An experiment was performed on twenty participants to investigate the behaviour of surface EMG during the inception of PF. PF symptoms were induced in accord with a five-day Bruce Protocol treadmill test on four lower extremity muscles: the biceps femoris (BF), rectus femoris (RF), vastus medialis (VM), and vastus lateralis (VL). The results demonstrate that the experiment successfully induces soreness, unexplained lethargy, and performance decrement and also indicate that the progression of PF can be observed based on changes in frequency features (ΔFmed and ΔFmean) and time features (ΔRMS and ΔMAV) of surface EMG. This study also demonstrates the ability of wavelet index features in PF identification. Using a naïve Bayes (NB) classifier exhibits the highest accuracy based on time and frequency features with 98% in distinguishing PF on RF, 94% on BF, 9% on VL, and 97% on VM. Thus, this study has positively indicated that surface EMG can be used in identifying the inception of PF. The implication of the findings is significant in sports to prevent a greater risk of PF.

This paper aims to explore the construction of an individualized pulmonary artery stenosis model based on computed tomography (CT) images. The stenosis model is simulated using a porous medium, and the numerical simulation is carried out by computational fluid dynamics (CFD) method to discuss non-Newtonian effects on hemodynamics. The hemodynamic parameters and quantitative pulmonary pressure ratio (QPPR) of the right pulmonary artery stenosis are obtained. The change curves of hemodynamic parameters show that the effects of non-Newtonian fluid are more significant than those of Newtonian fluid. Under the non-Newtonian condition, pressure and velocity drop more and faster when blood flow enters into the stenosis region. There is a high wall shear stress in the stenosis downstream. The margin of error between the QPPR value of the non-Newtonian fluid simulation and the clinical measurement value is not more than 10%. This work provides the evidence that the simulation of non-Newtonian fluid is closer to the reality when a porous medium model is used in a stenosis model. This contributes to assessing the severity of pulmonary stenosis behavior and is essential to guide disease treatment.

Background. Though treatable, osteoporosis continues as a substantially underdiagnosed and undertreated condition. Bone mineral density (BMD) monitoring will definitely aid in the prediction and prevention of medical emergencies arising from osteoporosis. Although quantitative computed tomography (QCT) is one of the most widely accepted tools for measuring BMD, it lacks the contribution of bone architecture in predicting BMD, which is significant as aging progresses. This paper presents an innovative approach for the prediction of BMD incorporating bone architecture that involves no extra cost, time, and exposure to severe radiation. Methods. In this approach, the BMD is predicted using clinical CT scan images taken for other indications based on image processing and artificial neural network (ANN). The network used in this study is a standard backpropagation neural network having five input neurons with one hidden layer having 40 neurons with a tan-sigmoidal activation function. The Digital Imaging and Communications in Medicine (DICOM) image properties extracted from QCT of human skull and femur bone of rabbit that are closely associated with the BMD are used as input parameters of the ANN. The density value of the bone which is computed from the Hounsfield units of QCT scan image through phantom calibration is used as the target value for training the network. Results. The ANN model predicts the density values using the image properties from the clinical CT of the same rabbit femur bone and is compared with the density value computed from QCT scan. The correlation coefficient between predicted BMD and QCT density valued to 0.883. The proposed network can assist clinicians in identifying early stage of osteoporosis and devise suitable strategies to improve BMD with no additional cost.

This paper describes a robotic system that uses an actively controlled glass to help patients with tremors. End-point control is proposed for the upper part of a robotic device that becomes active in response to motion changes or vibrations of its holder. The device mechanisms, hardware, software, and sensory system are all integrated, presenting a novel robotic glass design. The control system consists of two degrees of freedom proportional–integral velocity regulators for direct current motors. These regulators are designed, implemented, and tuned to keep the robotic glass stable against changes in its position. In realizing direct current motor control, it is essential to take system constraints into consideration to develop regulators that can handle the nonlinear Coulomb friction and avoid operating in the saturation zone. This is crucial when designing, tuning, and implementing regulators for real-time applications. The computer simulations of the system, which involved developing and running all the control algorithms for real-time applications, are carried out in Matlab/Simulink. The proposed designs are validated by comparing system simulations to real-time physical experiments. The recorded results confirmed the outstanding performance of the proposed experimental platform mechanisms and the accurate control tracking, which provides fast and precise control responses to meet the high requirements of a fast end-point control application.

This paper aims to analyze the recurrence of indirect inguinal hernia in children after laparoscopic surgery and investigate the influencing factors that may lead to recurrence so as to guide the prevention and treatment of postoperative recurrence of this kind of disease in the future. The data of 260 children with indirect inguinal hernia treated by laparoscopic surgery and followed up in our hospital from July 2019 to July 2021 were selected. A self-designed questionnaire was used to collect the basic data. The recurrence was analyzed, and the influencing factors of recurrence were analyzed by univariate analysis and multivariate analysis. Among 400 children after indirect inguinal hernia laparoscopic surgery, an occurrence was observed in 15 children, and the recurrence rate was 5.77%. Univariate analysis showed that the age and course of disease were not correlated with recurrence after indirect inguinal hernia laparoscopic surgery ( P > 0.05 ). Being male, bilateral lesions, exact high ligation, loose hernia back wall peritoneum, deciduous ligature, incorrect ligation of the fascia of musculus obliquus externus abdominis, large inguinal hernia, circumferential wiring, and too early off-bed activity were the influencing factors of recurrence after indirect inguinal hernia laparoscopic surgery ( P < 0.05 ). Logistic multivariate regression analysis showed that being male, bilateral lesions, loose hernia back wall peritoneum, deciduous ligature, incorrect ligation of the fascia of musculus obliquus externus abdominis, large inguinal hernia, and too early off-bed activity were the influencing factors of recurrence after indirect inguinal hernia laparoscopic surgery (OR>1, P < 0.05 ). Exact high ligation and circumferential wiring were protective factors of recurrence after indirect inguinal hernia laparoscopic surgery (OR>1, P < 0.05 ). After indirect inguinal hernia laparoscopic surgery, recurrences were affected by many factors, such as gender, site of pathological changes, and loose hernia back wall peritoneum. For these children with risk factors, reasonable intervention should be taken to reduce recurrence; exact high ligation and circumferential wiring are the protective factors. If permitted, the children meeting related indications can be treated by high ligation or circumferential wiring to reduce the risk of recurrence after indirect inguinal hernia laparoscopic surgery.

Quality of life is a kind of sensory experience of people's physical health, social ability, and personal overall situation. Correct understanding and evaluation of the quality of life is conducive to human rational planning and control of life resources, promote physical and mental health, and improve the quality of life. In order to further explain the positive impact of physical exercise behavior on promoting physical and mental health, based on the bee colony optimization algorithm this paper analyzes the relationship between physical exercise behavior and quality of life and discusses the role of physical exercise behavior in promoting physical and mental health. The essential attributes and sociological significance of sports determine that sports play a unique role in providing people with social support. The quality of life score increases as the duration of the exercise increases. The theory and methods of defense and treatment advocated by sports thoughts will also have a positive effect on individuals avoiding and getting rid of mental illness. Teachers who have lower exercise time and frequency of exercise should also develop a step-by-step exercise program to strive to join them in the sports population.

Recruitment maneuver (RM) has become a routine supplementary maneuver for clinical rescue of severe ARDS with low tidal volume/pressure-limited mechanical ventilation. Recruitment of patients with ARDS mechanical ventilation can improve the lung compliance, promote the opening of collapsed alveoli, improve the ratio of ventilation to blood flow, reduce dead space, reduce shunt flow, and improve oxygenation function. In this paper, the patients were divided into lung recruitment group and conventional treatment group by the random number permutation table method. When the patient’s percutaneous oxygen saturation is less than or equal to 88%, the partial pressure of oxygen in the arterial blood gas is less than or equal to 55 mmHg, or the ventilator tube is disconnected during sputum suction or other accidents, a CPAP × 60 − second lung recruitment maneuver is required. Then adjust the ventilator parameters in the same way. In the process of lung recruitment, the changes in invasive continuous arterial blood pressure will also be observed. If the blood pressure dropped to ≤90/60 mmHg, one recruitment maneuver was terminated in advance. And both groups of patients used the Dräger- or PB840-imported multifunctional ventilator. The treatment of primary disease and predisposing factors, fluid management strategies, antibiotics and glucocorticoids, nutrition, and metabolic support in the two groups of patients in the study were the same. The PaO2/FiO2 value improved by 51% 10 minutes after recruitment, and the median increased from 111 (IQR, 73-265) before recruitment to 170 (IQR, 102-340) ( P < 0.01 ), the improvement of PaO2/FiO2 at 4 hours after recruitment and 12 hours after recruitment was 78% ( P < 0.05 ) and 39% ( P < 0.01 ), respectively, and the median PaO2/FiO2 at 4 hours after recruitment was 198 (IQR, 116-256). The median PaO2/FiO2 became 155 (IQR, 127-235) 12 hours after recruitment. Recruitment can reduce the accumulation of neutrophils in lung tissue, reduce the release of inflammatory factors, reduce pulmonary edema, and reduce pathological damage.

In order to investigate the effects of sevoflurane on the serum stress index level and prognosis of patients with hypertensive cerebral hemorrhage (HICH) during small bone window microsurgery, a total of 102 HICH patients are selected for analysis. MAP values in both groups decreased significantly at T1 and T2 ( P < 0.05 ), and the changes in MAP and HR indexes in the sevoflurane combined group were more stable than those in the control group. The time of postoperative awakening in the sevoflurane combined group decreases significantly than the control group ( P < 0.001 ). The levels of T-AOC and GSH-Px in both groups increase significantly after operation, and those in the sevoflurane combined group increase significantly than the control group ( P < 0.001 ). The levels of MDA and 8-OHDG in the sevoflurane combined group decrease significantly than the control group after operation ( P < 0.05 ). Spearman correlation coefficient analysis shows that the levels of T-AOC and GSH-Px are negatively correlated with the prognosis of HICH patients, while MDA and 8-OHDG are positively correlated with the prognosis of HICH patients ( P < 0.001 ). Sevoflurane interventional anesthesia has a high anesthetic effect in small bone window microsurgery, which has positive effects on controlling blood pressure of HICH patients, shortening postoperative recovery time and improving patients’ stress response and neurological function. This paper conducts an in-depth analysis of the prognosis of HICH patients, indicating that the prognosis of HICH patients is closely related to their serum stress indicators T-AOC, GSH-Px, MDA, and 8-OHDG, providing a new direction for follow-up clinical diagnosis and treatment of HICH patients and accurate prognosis assessment.

In this paper, clinical manifestations of inferior vena cava injuries and the progress of emergency treatment are presented. Inferior vena cava (IVC) is the large vein returning venous blood from the lower limbs and pelvic and abdominal cavities to the right atrium of the heart. The clinical manifestations of IVC injuries include shock, progressive hemorrhage, air embolism, retroperitoneal hematoma, active bleeding, and hemoperitoneum. The patients may be combined or not combined with injuries to other organs or even die. Routine examination methods for IVC injuries include general examination, color Doppler ultrasound, abdominal contrast-enhanced CT, magnetic resonance spectroscopic imaging (MRSI), and IVC angiography. These examinations are usually performed to confirm the diagnosis. Surgical treatment is the primary emergency treatment for this condition. Increasing the blood volume and symptomatic treatment are auxiliary treatments. The surgeries and repairs for IVC injuries are currently under investigation. Experimental results have verified the exceptional performance of the proposed scheme.

Recently, with the availability of fast and reliable Internet, the distance between a patient and a doctor is becoming unimportant. Physicians will be able to request the medical images of their patients regardless of the geographical area. However, a lot of challenges face such successful implementation. To facilitate remote diagnosis, patient electronic medical record (EMR), including medical images, that originates in one system needs to be exchanged either within the same organization or across different organizations. Steganography is the practice of concealing a secret message inside a cover medium. In this paper, steganography will be used to embed the patient’s personal information securely and imperceptibly in their medical images to enhance confidentiality in case of a distant diagnosis. The security of the medical data is improved to maintain confidentiality and integrity using IoT. The least significant bit of the approximate coefficient of integer wavelet transform is proposed. The distortion between the cover image and stego-image is obtained by measuring the mean square error and PSNR, and normalized cross-correlation is utilized to estimate the degree of closeness between the cover image and stego-image.

Mental health problems will seriously damage social harmony and family happiness. This paper proposes a study on mental health intelligence assessment based on multisource information fusion. Combining the data description of UPI and SCL-90 and the corresponding task requirement analysis, the multisource information fusion visualization based on the Circos diagram is designed and realized, and the parallel coordinate visualization is established. The MDS algorithm is used to project multidimensional survey objects into low-dimensional visualization space, and the actual UPI and SCL-90 questionnaire data for 278 students of a certain major at a university provided by the cooperative unit are used. The results show that the mental health problems of student A are serious, and the symptoms of depression and schizophrenia are prominent, so it is necessary to seek psychological experts for corresponding treatment in time. By contrast, student B's mental health problems are also serious, and the user judges that student B still needs further diagnosis and treatment. The correlation analysis results of multisource information fusion are roughly the same as the actual situation, which is convincing. Therefore, the multisource mental health information fusion of college students has a certain correlation and complementarity.