Search

Object: In this paper, eight male basketball players were tested for physical fitness using the method of sports biology. The content of the physical examination is the isokinetic muscle strength of the shoulder joint. This paper aims to investigate the effect of shoulder isokinetic muscle strength on athletic performance. Methods: This paper uses the principles of sports biology and the isokinetic muscle strength test system to measure the shoulder torque and fatigue index of eight basketball players. At the same time, this paper analyzes the obtained data using the method of mathematical statistics. Results: There were significant differences in the test data of eight male basketball players (P < 0.05). The fatigue of basketball players is mainly in the middle and late stages of sports. At average speed, the fatigue index of the shoulder is the lowest at 180°/s. Conclusion: Basketball players have stronger shoulder extensors than flexors. The balance of strength in the shoulder flexors is greater than the balance in the extensors. [ABSTRACT FROM AUTHOR]

In view of the shortcomings of the existing reversible watermarking algorithms for encrypted images, such as low embedding rate and incomplete separation, and at the same time ensuring the embedding rate and the quality of the watermarked image, this paper proposes a reversible watermarking algorithm for encrypted images with high embedding rate and separable effect. This paper proposes a reversible watermarking algorithm for encrypted images with high embedding rate and separable effect. First, the plaintext image is encrypted by using the encryption series generated by chaotic system and Zu Chongzhi's encryption algorithm; Second, analyze the PSNR value of the image under different interpolation conditions, generate the corresponding expected interpolation, and embed the watermark information into the image based on the expected interpolation; Finally, the original image can be recovered by using the encryption key, and the watermark information can be obtained by using the embedded key. The security and embedding ability of the algorithm are analyzed through simulation experiments. The results show that the introduction of ZUC stream cipher can expand the key space of the algorithm, have high security and achieve the separable effect. The embedding of watermark information has little impact on the ciphertext image, and the embedding rate is also higher than the contrast algorithm under the condition of ensuring the visual effect of the image. [ABSTRACT FROM AUTHOR]

The phonocardiogram (PCG) signal is sometimes affected by added parameters that reflect the presence of a specific pathology. The intensity or the energy of the signal is one of the most reliable parameters when studying cardiac severity. Yet, in a pathological electrophysiological and audio signal, the severity information does not fully remain in the intensity or energy, but in other variables. In this paper, we will discuss the ability of a time-frequency parameter to discriminate, separate, and monitor the pathological cardiac severity levels. We studied 14 PCG signal from eight pathologies, six of them contain clicks (reduce murmurs), and eight murmur PCG signals with four different cardiac severity levels. We then calculated the entropy of approximation coefficients (EAC) from a discrete wavelet transform (DWT) analysis, to differentiate the PCG signals with clicks from those with murmurs and to assess the cardiac severity evolution. Since the entropy EAC is also related to the signal's intensity (energy), we compared it to the energetic ratio (ER) evolution, a parameter widely used for PCG signals discrimination and classification, which revealed that the EAC provied better results for the paper' purposes. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Things (IoT) and Internet technology, the product of the combination of the two, the Industrial Internet, has also received extensive attention and there are more and more research achievements related to the Industrial Internet. In the industrial Internet system, the communication network system composed of sensors, communication nodes, controllers and other intelligent devices can realize efficient and convenient data interaction between people and machines, providing an important infrastructure and technical support for industrial big data analysis and intelligent production. However, in the current industrial Internet system, industrial equipment users generally have the problem of low computing energy efficiency, and the collected industrial data has a high-security risk in the transmission, processing and other processes. At the same time, the size and scale of the industrial Internet equipment group is huge, and the lack of rational resource allocation leads to excessive waste of computing resources in the system, which is also a prominent problem of the current industrial Internet system. In response to the above questions, this paper, on the basis of reading a large number of documents, integrates the improved DRL algorithm, End-Edge-Cloud architecture and blockchain to form a new industrial Internet architecture. The architecture realizes computing offload through the three-tier structure of terminal layer, edge layer and cloud layer, and guarantees the security of industrial data through the decentralized feature of blockchain, ultimately achieving the goal of reducing energy consumption, computing overhead and trusted computing. In the architecture proposed in this paper, the dynamic unloading of industrial data and computing tasks is achieved through a three-tier architecture. The MDP is used to build an optimization problem model, and the improved DRL algorithm is used to iteratively solve the optimal computing resource scheduling strategy. The main research contents of this paper include (1) Using MDP to model optimization problems; (2) Propose an industrial Internet system architecture that integrates and improves DRL, "end edge cloud" and blockchain; (3) The MDP problem is solved iteratively based on deep reinforcement learning. The simulation results show that the proposed architecture has more advantages than the existing six architectures in terms of computing cost, equipment energy consumption and total working time. [ABSTRACT FROM AUTHOR]

This paper studies the biomechanics of muscles after swimming. This paper uses exercise biomechanics and deep learning models to process surface EMG signals after muscle injury. In this way, the relationship between the EMG signal and muscle strength after muscle injury after swimming can be obtained. Studies have found a proportional relationship between muscle strength and the strength of EMG signals during muscle injury recovery after swimming. The conclusions of this study can provide us with some clinical experience in the recovery of muscle damage during physical exercise. [ABSTRACT FROM AUTHOR]

Gait analysis is one of the highly focused research areas inviting attention of academicians and practitioners. Being a prominent analytical tool to detect the abnormalities and onset of musculoskeletal disorders (MSDs), abundant literature is published in this area. Though a large number of gait analysis methods (GMSs) for motion analysis are available in the literature, very few studies are available to showcase the comparative analysis and applicability of various techniques. This study seeks to provide useful recapitulation of various gait measurement methods and interrogates the relative prevalence of various GMS based on the applications by various researchers. The paper summarizes the findings of a systematic review on gait measurement methods carried out using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. Fifty-three research papers published during 1998–2021 were included for critical review. The categories of gait measure ment systems were further compared based on certain criteria such as portability, cost, and accuracy, and ranked using analytic hierarchy process (AHP). The review helped to encapsulate various aspects of gait analysis, especially, the types of tasks, types of gait parameters (GPs), and the gait measurement systems adopted. The study provides suitable mappings between these factors and offers comparison between the gait measurement systems using a specially devised capability index. It further provides useful guidelines for selecting the gait systems based on the criteria. [ABSTRACT FROM AUTHOR]

Gait recognition based on acceleration sensor signals is more suitable for complex environments than other signal-based methods, which can be used for data acquisition for gait recognition. However, most traditional methods only consider the position matching of multiple accelerometers, ignoring the comfort of the collector. In recent years, support vector machine (SVM) has been widely used in portrait recognition, language, and video processing. It has a flexible and powerful processing ability for nonlinear sequence input, and is a sparse and robust classifier. In this paper, we propose a multi-feature SVM algorithm ME-SVM, which is classified after multi-feature fusion. Usually, in the research of action recognition based on acceleration sensors, researchers classify from a single eigenvalue, but this paper optimizes this and further improves the recognition performance. Simulation and experimental results show that the algorithm has high accuracy and robustness. [ABSTRACT FROM AUTHOR]

Analyzing eye movement data to evaluate learning status has become crucial in intelligent education. The eye movement scanning path can directly or indirectly reflect changes in thinking patterns and psychological states. By analyzing the scanning path, we can explore the commonality and differences in learners' eye movement behaviors and provide essential references for improving visual content and giving guidance. This paper first studies the time series representation and clustering of the learner's scanning path under the same task. Then, the three learning states of concentration, mind-wandering, and information wandering are evaluated through the clustering results. Specifically, the improved DBA algorithm (iDBA) is proposed to extract group eye movement patterns, combined with the dynamic time warping (DTW) algorithm to calculate the similarity of scanning paths and determine the clustering seeds, while the distance density clustering (DDC) algorithm is used for clustering. Experiments show that time series-based eye movement pattern mining can identify group viewing behaviors. Meanwhile, clustering reveals different reading strategies and provides the ability to assess learning status. [ABSTRACT FROM AUTHOR]

Soft tissue is an important operation object in robot-assisted surgery and its mechanical response is of great significance to the precision of surgical operation. Accurate prediction of soft tissue mechanical properties can effectively avoid the potential damage of biological tissue caused by excessive operating force. In this paper, three typical mechanical responses of soft tissue were obtained by no-slip compression experiments of liver tissue. Second, the prediction model of soft tissue mechanical response based on gray prediction was established, and the influence of key parameters of the model on the precision of mechanical response prediction was analyzed. The results show that the gray prediction model can accurately predict the mechanical response of soft tissue, and the prediction accuracy is the highest when the number of historical data is 7. The prediction method of soft tissue mechanical response proposed in this paper will provide important data reference for accurate operation of surgery. [ABSTRACT FROM AUTHOR]

The emotional well-being of college students is of utmost significance. The psychological states of college students who are on the verge of entering the social work field form the key factor that directly influences the quality of social construction because these students constitute the primary driving force in the field. On the other hand, the overwhelming amount of schoolwork, the intense level of competitiveness, and the undeveloped psychological qualities of college students are the primary contributors to their mental health problems. Currently, an increasing number of college students are struggling with mental health issues, which will have a significant impact on the growth of families and schools and the future construction of the nation. In this paper, deep features and a multiview fuzzy clustering technique are presented, as well as a mental health assessment model (CNN-MV-MEC) that is proposed for college students. The primary purpose of this research is to determine the mental state of the input sample by classifying and identifying an EEG that was acquired through the application of CNN-MV-MEC. If a certain number of samples are found to be in negative emotional states on a regular basis or for an extended period of time, this indicates that the sample most likely contains individuals who struggle with mental health issues. At this point in time, university officials are in a position to implement follow-up mental health management actions based on the outcomes of the model evaluation process. The primary contributions of this study are as follows. First, to extract the deep features from the given dataset, this paper makes use of a traditional convolutional neural network (CNN). In the second step, a classification model is trained using a multiview maximum entropy clustering (MV-MEC) technique. In the final step, the input test data are categorized by employing the trained classification model to determine the emotional state of the sample. The SEED dataset is used as the training data for the mental health assessment model proposed in this paper. Thus, the performance of the model can be evaluated. Model comparison experiments demonstrate that the proposed approach yields more accurate results than competing methods when assessing the mental health of college students. [ABSTRACT FROM AUTHOR]

Objective: This study used CiteSpace to look at the current state of research on epilepsy and synaptic plasticity and to point out the hotspots and frontiers. Method: We searched Web of Science (WoS) for studies related to epilepsy and synaptic plasticity. CiteSpace was used to construct network maps of cooperation across countries, institutions, and authors to identify frontiers and hotspots in epilepsy and synaptic plasticity research. Results: A total of 1700 studies on epilepsy and synaptic plasticity were retrieved from the WoS. The United States and Baylor College of Medicine were the most prolific nation and institution in this field with 680 and 28 publications, respectively. The most prolific author (11 articles) was Xuefeng Wang. The Journal of Neuroscience published the most articles (71, 6.71%) and had the most co-citations (1557, 4.57%). In this paper, the interaction and mechanism between epilepsy and synaptic plasticity, as well as future research hotspots, are highlighted. Conclusion: CiteSpace can reveal the institutions, leaders, journals, cited papers, and research hotspots involved in epilepsy and synaptic plasticity. To the best of our knowledge, this is the first study that visualizes the relationship between epilepsy and synaptic plasticity and provides references for future research directions. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet medical information technology, a large amount of medical data appeared on the Internet, however, how to extract effective information from the massive and complex medical data to provide professional medical services and suggestions to users has become a hot spot for this research. The recommendation system can effectively solve the problem of accurate matching of complex medical data resources; however, the cold start, data sparsity and user interest migration of the system in the complex data environment have a large impact on the recommendation effect; therefore, this paper proposes a weighted neural matrix decomposition improved health management recommendation scheme incorporating deep learning techniques. The scheme first uses an implicit feedback method to improve the prediction scores and improve the linear model performance of the matrix decomposition algorithm to form a weighted neural matrix decomposition health management recommendation algorithm. Second, the improved method and deep neural network are fused to improve the performance of the nonlinear model part of the algorithm by using the structural properties of the neural network. Finally, this paper's method is compared with the mainstream six recommendation algorithms on four publicly available real datasets. The experimental results show that the root mean square error (RMSE) of the WENMF algorithm is smaller than that of the comparison algorithm on all four datasets, and the convergence speed is faster. The hit rate (HR) and normalized discounted cumulative gain (NDCG) of the WENMF algorithm are higher than those of the comparison algorithm on all four datasets, and the maximum difference is 0.04. Therefore, the recommendation accuracy and ranking quality of the WENMF algorithm in the recommendation system are verified, and the cold start and data sparsity problems of the recommendation system are effectively alleviated. [ABSTRACT FROM AUTHOR]

The paper is devoted to the theoretical analysis of one-dimensional (1D) models of blood flow. The non-Newtonian nature of blood is taken into account. The generalized Newtonian models, in which the dynamic viscosity is dependent only on the shear rate, are considered. The models are constructed by averaging the simplified Navier–Stokes system in cylindrical coordinates. The 1D models, corresponding to Newtonian, Power Law, Carreau, Carreau–Yasuda, Cross, Simplified Cross, Yeleswarapu, Modified Yeleswarapu, and Quemada models, are compared in this paper. The comparison is performed in order to estimate the influence of non-Newtonian frictional terms, velocity profiles and hematocrit values on the deviations of the solutions from the solution for the Newtonian case. For this purpose, two problems that can be solved analytically are considered: problems for the infinite interval and finite interval with periodic conditions. For the quantitative comparison of solutions, the non-Newtonian factor, which characterizes the relative deviations of solutions from the solution for the Newtonian model, is introduced. The analytical solutions of nonlinear problems are obtained by the perturbation method. As it is demonstrated for both solved problems, the relative deviations increase with the flattening of the velocity profile. For the models that depended on hematocrit, the non-Newtonian factor values increased with the increase in hematocrit value. The solutions obtained in the paper can be used as a tool for the comparison of different 1D models of blood flow and for the testing of programs that implement numerical algorithms. [ABSTRACT FROM AUTHOR]

Optical coherence tomography (OCT) with high resolution was found lucrative for coronary heart disease diagnosis and treatment such as evaluating the degree of vascular stenosis and guiding stent implantation. Lumen contour is an essential information for assessing the shape and stenosis of blood vessels. The automatic and accurate extraction of lumen contour can significantly reduce the burden of doctors and improve the efficiency of diagnosis and treatment of coronary heart disease. The existing contour segmentation methods have poor performance in extracting the contour of the image with a missing blood vessel wall. Therefore, this paper proposes a two-stage contour extraction method based on deep learning. First, the method uses the basic contour extraction network to complete the feature image extraction and coarse contour segmentation, then inputs the feature image into the attentional class feature (ACF) module, and uses the coarse segmentation results to guide the generation of the new feature map for the refined contour segmentation. Finally, the fully connected conditional random field (CRF) module is used for post-processing yielding a more refined and smooth segmentation boundary. The experiments show that compared with the other methods, the lumen contour segmentation method proposed in this paper can adequately resolve the problem of partial vessel wall absence in OCT images and improve the accuracy of lumen contour extraction. [ABSTRACT FROM AUTHOR]

This preface introduces a journal article titled "Application and Progress of Biomechanics in Medicine - Part II." It discusses the evolution of biomechanics in medicine and invites readers to explore the 27 papers included in the article. These papers cover various topics such as stress monitoring, medical image segmentation, blood flow measurement, and muscle atrophy. The preface emphasizes the potential of biomechanics to revolutionize medical science and patient care. The article collection aims to inspire further research and collaboration in the field. [Extracted from the article]

Reducing the occurrence of diseases has become an important research direction in today’s social medicine. Accurate prediction of heart failure can greatly reduce the complication rate, allowing patients to know the condition in advance and receive treatment. To deal with heart failure, this paper proposes a heart failure prediction algorithm based on a reinforcement learning framework. This paper combines and improves the reinforcement learning algorithm and the swarm intelligence (SI) optimization algorithm, aiming to use the reinforcement learning algorithm to improve the global optimization capability of the SI optimization algorithm. In order to better improve the global search ability of the SI optimization algorithm, the penalty function will be replaced by a combination mode of dynamic and static reward. The improved reinforcement learning algorithm framework is significantly better than the previous algorithm framework and has been used to predict heart failure. The experimental results have proven the effectiveness of the algorithm and its accuracy in prediction of heart failure. [ABSTRACT FROM AUTHOR]

Mechanomyography (MMG) is a low-frequency signal emitted during muscle contraction; it can overcome the inherently unreliable defects of electromyography (EMG) and electroencephalography (EEG). For MMG-based movement pattern recognition, this paper proposes an innovative kernel extreme learning machine (KELM) based on the chaotic particle swarm optimization (CPSO), namely CPSO–KELM. By using CPSO–KELM in MMG-based movement pattern recognition, the classification accuracy of upper-limb movement has been improved, and the results can be better applied to the control of passive rehabilitation training of the upper-limb exoskeleton, which can provide help for the upper extremity rehabilitation of stroke patients. In this paper, MMG which is used for pattern recognition research, is collected by accelerometers when the subjects performed seven types of upper-limb rehabilitation movements. After filtering and segmentation, six time-domain features are extracted for the MMG of each channel, then kernel principal component analysis (KPCA) and principal component analysis (PCA) are used to reduce the feature dimensions. By using different classifiers to build classification models, the average recognition accuracies of movement classification under different processing methods are obtained; it is found that for most classifiers, the recognition rate of MMG after KPCA dimensionality reduction is better than that of PCA, and the overall recognition rate of upper-limb movements using the CPSO–KELM classifier can reach 97.1%, which is better than support vector machine (SVM), back-propagation neural network (BPNN), linear discriminant algorithm (LDA) and other MMG common classifiers in recognition accuracy. Moreover, the experimental analysis shows that compared with genetic algorithm (GA) and particle swarm optimization (PSO), CPSO has faster convergence and smaller training error, and the final recognition accuracy proves that the performance of CPSO–KELM is better than those of GA–KELM and PSO–KELM. [ABSTRACT FROM AUTHOR]

With the popularity of smart phones, Android has a large number of users. Android allows download and installation of other unofficial healthcare system applications, so it has attracted the attention of malware developers for healthcare system. However, most of the papers proposed a variety of different methods to improve accuracy, and the samples tested by each method in the experimental part are different, which may cause errors in comparison. It shows that permission is an important of the development as mobile phone storage. Therefore, this paper proposed Android's Permission and application program interface (API) as two features to perform machine learning to detect malicious programs business intelligence. It also uses machine learning method and other two algorithms to detect malicious programs. The average accuracy of using hybrid detection of unknown malicious programs reaches 89%, and the hit rate near 90%. The results show that under the same sample situation, the accuracy rate of the hybrid method used Random Forest is higher than the other two analyses. Compared with the dynamic analysis with others, it used the hybrid method and dynamic analysis has a high accuracy rate. The contribution of this paper is as follows: the advantage of static detection is that it does not need to execute Android application package (APK) features be extracted, and the speed is the fastest among the three detection modules. The features of dynamic detection for healthcare system are generated through the executed APK, and more powerful features can be extracted. Hybrid detection uses two method features, and extract most advantageous features to generate detection modules to detect APKs. The accuracy of static results about 87% in average, 88% for dynamic, and 89% for hybrid. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of Internet and computer technology, network education has developed rapidly. With the rapid development of learning technology, online education has been widely popularized. Especially in 2020, novel coronavirus pneumonia suddenly came into being. Online education based on Internet technology has played a great role in the crisis control period. It has also enriched teaching forms and teaching methods. The blended teaching under online and offline integration has increased the availability of students' learning data. Therefore, more and more scholars begin to pay attention to the research of learning early warning based on educational data mining or learning analysis. However, most early warning studies use traditional machine learning algorithms, and there are still deficiencies in the granularity of data collection, technical implementation mechanism, early warning state recognition and so on. With the success of deep learning in artificial intelligence and other fields, scholars began to study the application of deep learning to solve the problems in the field of learning early warning. Combining variational self-coding (LVAE) and deep neural network, this paper proposes a scheme (LVAEpre) which can solve the problem of unbalanced distribution of educational data sets. This paper determines the weight value of each dimension and index by adjusting the weight parameters of the model, and obtains the threshold value of the early warning line, and empirically tests its effectiveness. Finally, the paper designs a learning early warning model and builds a learning early warning platform based on process data. The results show that the early warning effect is good. The proposal of the learning early warning model based on process data and the application of the learning early warning platform have greatly improved the teaching quality, reduced the risk of students failing to attend the course, and effectively realized the early warning function. The experimental results show that the framework improves the prediction ability of identifying risk learners as soon as possible, timely intervene and guide risk learners, improves learning efficiency, and provides effective guidance strategies for the development of network education. [ABSTRACT FROM AUTHOR]

Software-defined network (SDN) is a new network structure, which has the characteristics of centralized management and programmable, and is widely used in the field of Internet of things. Distributed denial of service (DDoS) attack is one of the most threatening attacks in SDN network. How to effectively detect DDoS attacks has become a research hotspot in the field of SDN security management. Aiming at the above problems, this paper proposes a DDoS attack detection method based on Deep belief network (DBN) in SDN network architecture. By extracting the characteristics of OpenFlow switch flow table entries, DBN algorithm is trained to detect whether there are DDoS attacks. The experimental results show that the method is better than the other algorithms in accuracy, precision and recall. [ABSTRACT FROM AUTHOR]

Objective: Knee replacement (KR) is a widely accepted procedure for end-stage knee arthritis. Patients' subjective perception of knee joint symptoms is an important factor in their decision to undergo knee replacement. This paper mainly studies the correlation between knee joint symptoms and knee replacement, aimed to establish the corresponding preoperative decision-making model. Methods: 122 knee arthritis patients were included from the knee arthritis database FNIH OAI, of which 61 had knee arthroplasty and 61 without knee arthroplasty. First, WOMAC score was used to quantitatively evaluate the knee joint symptoms of patients; then the correlation between clinical risk factors, single WOMAC score and knee replacement was analyzed. Then K-means cluster analysis was used to divide the multidimensional WOMAC score into different groups of knee arthritis symptoms' severity. Finally, based on the retained clinical risk factors and WOMAC cluster variables, the clinical models, WOMAC scoring model and clinical +WOMAC scoring model were constructed, respectively, and compared. Results: Age and BMI were significant risk factors for knee replacement (p < 0. 0 5), which could be used to construct the clinical model. There was no significant correlation between any single WOMAC score and knee replacement (p > 0. 0 5). The cluster variable of WOMAC score obtained by cluster analysis was significantly correlated with knee replacement (p < 0. 0 5). Based on the above risk factors, we established the Age+BMI, Cluster and Age+BMI+Cluster models, respectively. The comparison results showed that the Age+BMI+Cluster model (AUC = 0. 7 5 0) showed the highest predictive value for knee replacement, and the corresponding nomogram also showed good predictive consistency; the performance of Cluster model (AUC = 0. 6 8 1) was the second. The pure clinical risk factor model Age+BMI showed the worst predictive performance (AUC = 0. 5 7 7). Conclusion: This paper analyzed the correlation between patients' subjective perception of knee arthritis symptoms and the final knee replacement, and constructed a new biomarker based on the WOMAC score. After analysis and modeling, this marker could be used to predict knee replacement. The constructed Age+BMI+Cluster nomogram could be used for personalized assessment of the risk of knee replacement. [ABSTRACT FROM AUTHOR]

RGB-D images can be used to build 3D models with high precision and efficiency. This paper introduces the method and principle of 3D modeling and the 3D reconstruction process of RGB-D images and analyzes KinectFusion and BundleFusion methods. Based on BundleFusion, this paper constructs a set of 3D reconstruction methods for stroke patients' food and carries out 3D reconstruction experiments on watermelon, banana, orange, corn, and dish cold bitter gourd. With the actual measurement data validation, the banana's average error rate of 3.95%; for watermelon, the average error rate is 3.89%; for grapes, the average error rate is 4.28%; for corn, apple, banana mixture composition, the average error rate is 2.61%, the average error rate of caramel treats is 3.48%, and the average error rate of pork floss bread is 2.85%. The average error rate of the hot and dry noodles is 1.85%, and the average error rate of the breakfast mix composition is 2.91%. The error rate in subsequent food volume calculation, quality estimation and calculation application is within the effective range of nutrients. It provides a technical basis and scientific basis for dietary intervention of stroke patients. [ABSTRACT FROM AUTHOR]

Cell deformation and changes in mechanical properties, such as adhesion, elasticity modulus, and height, are some of the most significant signs of cancer. This paper investigates the mechanical parameters of normal and cancerous cells in the breast and lungs. The lung's normal cell studied is MRC-5, and the cancerous ones include A-549, COR-L105, and CALU-6. MCF-10A is the normal breast cell studied, and MDA-MB-468, MDA-MB-231, and ZR-75-1 are cancerous. The mechanical specifications and cellular topography were obtained using nanoindentation by the atomic force microscope (AFM). The elasticity modulus and adhesion amounts for at least 96 indents were computed by mathematical averaging for each cell line in two different modes. The results indicate that cancer decreases the cells' elasticity modulus by one-tenth in the lung and lower than one-third in breast cells. Cancer cells are up to several hundred times more adhesive than normal cells. In addition, a cancerous cell's height is greater than normal cells. This study's elastic modules are compared to the other references. Overall, the results are compatible. [ABSTRACT FROM AUTHOR]

Many medical reports and documents in printed form are available in huge sizes in archive units inside medical centers and hospitals around the world. The printed electrocardiogram paper is one type of archive data and is more powerful for expert cardiologists to study various cardiac diseases. On the other hand, the widespread development in the ability of intelligent computer systems to process digital signals increases the benefit of these data if converted to digital form. In this paper, a new approach for digital recovery of 12-lead electrocardiogram raw data from the printed colored scanned image has been proposed. This approach implements an algorithm with four steps including delineating effective regions, color filtering, contacting detected points, and sampling the resulted digital signals to reconstruct the digital electrocardiogram signal from the printed drawing of the same signal after digital scanning with significant resolution. Also, this algorithm is designed to process various kinds of printed electrocardiogram papers. The performance of the proposed approach is evaluated qualitatively by visual inspection of the recovered and original signals in the same graph. Also, the similarity of these signals is evaluated quantitatively using some standard evaluation metrics. The simulation results show the consistency and robustness of the proposed digital recovery approach to generate electrocardiogram digital data with a high percentage accuracy exceeding 98%. Also, plotting the recovered signal with the original printed signal on the same graph shows a significant percentage of congruence in time and amplitude. Finally, the proposed idea in this study opens the way for an unlimited bank of digital electrocardiogram data with different morphologies. [ABSTRACT FROM AUTHOR]

In this paper, the effects of both geometric parameters and mineral density on the evolution of stresses generated in a cementless hip implant are investigated to enable orthopedic surgeons to make an appropriate selection. A design of experiment was used, followed by the analysis of variance (ANOVA) to identify the effects of geometric and physical parameters in terms of bone density on a femoral implant. Twenty-seven implants of different geometric dimensions were considered, without modification of the topology. Multiple regression analysis was chosen to replace the finite element method to predict the level of stresses generated in the stem, in terms of life expectancy, allowing for optimal geometric parameter selection. Three parameters of the stem were selected, namely, stem length (SL), neck length (NL), and neck shaft angle (NSA), where each parameter is apprehended by three levels. Based on a design (3 3) , 27 femur–implant CAD models were created, to analyze the effect of three limiting densities (low, medium, and high) for the estimation of maximum stresses by finite elements. The results obtained by ANOVA for the three densities, respectively, for the low density the SL had an impact of the order of 58.5% on the level of von Mises constraints followed by the NL and NSA. For the medium density, it was observed that the SL remains the most influential parameter, with a rate of 41.2%, while for the high density the NL becomes the most influential (37.5%) followed by the SL and the NSA. The regression model used showed very good accuracy in terms of predicting the level of von Mises stress, compared to the results obtained by the finite element method. [ABSTRACT FROM AUTHOR]

A form of dementia is Alzheimer's syndrome, also known as a neurodegenerative syndrome, damages the neuron cells in a selective mode. The count of patients that is increased gradually here by the disease is considered a worldwide concern, which may cause death in more cases. The rapid and accurate detection and categorization of Alzheimer's disease have obtained enormous awareness from researchers due to the studies of a deep model. However, the productive detection and categorization of Alzheimer's disease with reliable biomarkers are more challenging tasks. In this paper, Manta crow search optimization (MCSO)-based Actor Critic Neural Network (ACNN) is devised for the Alzheimer classification process. The Region of Interest (ROI) is extracted based on the thresholding process, and the preprocessing is done using the Type 2 Fuzzy and Cuckoo Search (T2FCS) filter. Moreover, the Sparse Fuzzy C-means (Sparse FCM) model has been in employment to segment preprocessed images. The feature extraction is a productive process for classifying Alzheimer's disease. The ACNN classifier is utilized for performing Alzheimer's disease classification process. Furthermore, the ACNN classifier is skilled by a devised optimization algorithm named the MCSO model. The projected MCSO-based ACNN outperformed other existing techniques with a testing accuracy of 0.9295, sensitivity of 0.9378, and specificity of 0.9354. [ABSTRACT FROM AUTHOR]

Many of the used features in sudden cardiac death (SCD) classification algorithms are based on features present in the autonomic system. However, changes in the autonomic system occur in both SCD subjects and patients with congestive heart failure (CHF). Therefore, many overlaps are observed in the features extracted from the cardiac signals of these two groups. To solve this challenge, this paper studies the changes in the multifractal dimension in patients with SCD and compares it with the subjects with CHF using the heart rate variability (HRV) signal processing. For this purpose, HRV signals are initially extracted, and their four sub-signals are determined using the empirical mode decomposition (EMD) method. Afterward, the instant amplitude of each sub-signal obtained in the previous step is calculated using the Teager energy method; thus, new signals are generated through the utilization of these instant amplitudes. Subsequently, modifications in each new signal's fractal dimensions are obtained using the multifractal detrended fluctuation analysis (MF-DFA) method. The appropriate features are selected using the t-test method and are applied to the support vector machine algorithm as input data. The proposed algorithm can differentiate the signal of SCD subjects with an average accuracy of 84.08% in 26 min prior to the event. [ABSTRACT FROM AUTHOR]

In this paper, an attempt has been made to analyze the effect of different physiological conditions i.e., rest, moderate and extremely active on the branch Reynolds number, pressure drop, lost work, alveolar partial O2 pressure, airway resistance and surface tension. From the analysis, it has been observed that local Reynolds number and tracheal pressure drop along with the generations of branch. The alveolar O2 pressure and surface tension decrease with the increased activity level. Work lost due to different major and minor losses in the respiratory tract increases with the increase in activity level. A similar trend was also found for the airway resistance i.e., increases with the increase in activity level. A new parameter, Breathing Work Number (BWN) in respiratory mechanics is introduced. BWN is the ratio of the work of breathing to the sum of work of breathing and work loss, which might find a significant application to denote the respiratory health. From the analysis, it is found that BWN decreases with the increase in activity level. [ABSTRACT FROM AUTHOR]

Dysfunction of hand after stroke can affect patients' ability to perform daily activities and social ability. This paper developed a flexible hand function rehabilitation robot based on sensory input. First, we designed a flexible external finger muscle based on rope drive, then we designed a special two-way output linear actuator to drive fingers for rehabilitation training. At the same time, we performed vibration stimulation on each finger of the patient and low-frequency electrical stimulation on the patient's wrist. Sensory stimulation mobilizes the active participation of the brain in rehabilitation training, thereby effectively promoting the rehabilitation effect of fine functions of fingers after stroke. We have completed the hardware design of the flexible hand robot and developed the control software of the flexible hand, thus completing the development of the hand function robot. Finally, experiments were carried out on the use of equipment by using EEG. Experiments show that when the flexible hand robot developed in this paper is used for training, it mobilizes the active participation of the brain in rehabilitation training. [ABSTRACT FROM AUTHOR]

According to the mechanical conditions of fracture fixation and the oxygen levels in the tissues, a simulation model of fracture healing process was built to describe the relationship among mechanical stability, oxygen levels in tissues and tissue differentiation during the second fracture healing. Different from the previous simulation model, in this paper, we took the three-dimensional model as the research object, solved the mechanical stimulation by finite element method, established the partial differential equation to solve the spatial and temporal variation of the oxygen in tissues. The process of tissue differentiation was described by fuzzy control method. The initial stage of fracture healing, intramembranous ossification, chondrogenesis, cartilage calcification and endochondral ossification during the fracture healing process were simulated, and the properties of tissue materials were continuously updated to complete the iterative process. The simulation program of fracture healing process was independently developed in Eclipse environment, and the simulation results were compared with experimental data and those of other fracture healing simulation models to verify the simulation program in this paper. Finally, the processes of transverse fracture healing in rats with different axial stability under normoxic, hypoxic and hyperoxic conditions was simulated, and the effects of different tissue oxygen levels and interosseous stabilities on fracture healing were analyzed. It is concluded by simulation that the delayed healing or non-union of bone will occur when in state of tissue hypoxia or interosseous instability, normal healing will occur when in state of tissue normoxia, and the healing will be accelerated when in state of tissue hyperoxia. [ABSTRACT FROM AUTHOR]

Image-to-patient space registration is to make the accurate alignment between the actual operating space and the image space. Although the image-to-patient space registration using paired-point is used in some image-guided neurosurgery systems, the current paired-point registration method has some drawbacks and usually cannot achieve the best registration result. Therefore, surface-matching registration is proposed to solve this problem. This paper proposes a surface-matching method that accomplishes image-to-patient space registration automatically. We represent the surface point clouds by the Gaussian Mixture Model (GMM), which can smoothly approximate the probability density distribution of an arbitrary point set. We also use mutual information as the similarity measure between the point clouds and take into account the structure information of the points. To analyze the registration error, we introduce a method for the estimation of Target Registration Error (TRE) by generating simulated data. In the experiments, we used the point sets of the cranium surface and the model of the human head determined by a CT and laser scanner. The TRE was less than 2 mm, and the TRE had better accuracy in the front and the posterior region. Compared to the Iterative Closest Point algorithm, the surface registration based on GMM and the structure information of the points proved superior in registration robustness and accurate implementation of image-to-patient registration. [ABSTRACT FROM AUTHOR]

Cardiac output (CO) refers to the amount of blood ejected from a unilateral ventricle per minute and is an important measure of cardiac function. Thermodilution is the gold standard for CO measurement because of its accuracy. However, the traditional thermodilution method requires calibration of the correction factor before measurement, which makes its practical application difficult. Therefore, conducting CO measurement by using a machine-learning-based thermodilution method is proposed in this paper, and CO is regressed and predicted through the thermodilution curve by a machine learning model. In this paper, we constructed five cardiac vascular models, and three of them were randomly selected to simulate the thermodilution process. Nine features of the thermodilution curve from the time–frequency domains were extracted and fed into the multilayer perceptron model for training. On the basis of a cross-validation method, the accuracy of the final prediction model was 97.99% (± 1. 9 4 %). Simultaneously, a trained neural network was used to predict the CO of the remaining two cardiac vascular models, and the resulting error was within 5%. In this paper, an experimental system consisting of a water pump, a three-way valve and a temperature sensor is also designed, and the thermodilution curves at different quantities of flow are tested and regressed and predicted with the above model, with the error being within 10%, which met the requirement for real-world use, and thus, a method was established for measuring CO by using machine-learning-based thermodilution. [ABSTRACT FROM AUTHOR]

Presently, a significant number of students enrolled in colleges and universities encounter mental health challenges, including academic stress and difficulties in interpersonal relationships. These factors contribute to a decline in mental well-being and necessitate prompt intervention and assistance. The automation of mental health identification in students greatly benefits from the implementation of intelligent emotion recognition systems. Through the analysis of students' emotional states, educational institutions can gain a deeper understanding of students' mental well-being, enabling them to promptly identify and address any issues and offer more complete care and support to students. This paper proposes a method for automated emotion recognition that is driven by data. The method is developed based on the provided background information. The proposed approach incorporates a feature fusion mechanism and an attention mechanism into the ResNet-34 model. This integration enhances the model's capability to analyze intricate details and subsequently improves its classification performance when applied to electroencephalography (EEG) signals. This paper introduces several key innovations. First, an optimization technique is applied to the input component of the model, enabling multifeature fusion. Second, an attention mechanism is incorporated after the residual network module, enabling the model to prioritize parts that contribute to classification and enhance feature extraction. Finally, the network parameters are optimized using both softmax loss and center loss functions. The findings from the analysis of the sentiment EEG public data SJTU Emotion EEG Dataset (SEED) indicate that the proposed sentiment recognition approach not only enhances the classification performance of the model on the sentiment EEG data but also improves the stability of the results. This paper presents a novel approach that enables automatic and efficient recognition of students' emotions on commonly used platforms. The findings of this study hold significant implications for mental health assessment and detection in real-life production settings, offering substantial reference value in this domain. [ABSTRACT FROM AUTHOR]

Thyroid Cancer (TC) is a common malignant tumor, head and neck in the incidence of malignant tumors in the seventh, ranked fourth in the incidence in women. There are several methods to diagnose and recognize TC, such as ultrasonic, computed tomography (CT) and other means. While, it is an important role for CT examination in the diagnosis of TC, because it has the characteristics of objectivity, repeatability, and multi-dimensional imaging, and can clearly understand the scope and spatial characteristics of the lesion. CT has unique advantages in showing lymph nodes and distant metastases, such as coarse-walled or thick-walled ring calcification. The early diagnosis of TC mainly relies on manual labor, which is very inefficient. With the continuous development of information science, the construction of TC diagnosis and recognition models based on artificial intelligence (AI) has gradually become a research hotspot. At present, research on AI-based thyroid screening models mainly focuses on four aspects: first, thyroid region segmentation and image denoising based on image-omics; second, the establishment of a high-precision TC risk prediction model based on multi-omics data; third, screening of biomarkers of TC for clinical diagnosis; fourth, establish the early screening model of TC based on AI. This paper reviews the research status of the AI-based thyroid screening model based on the above four aspects. In addition, this paper also summarizes the main challenges faced by the current AI -based TC recognition and detection model and it proposes a new research idea for the future TC early screening research based on enhanced CT. [ABSTRACT FROM AUTHOR]

This research introduces a novel method for emotion recognition using Electroencephalography (EEG) signals, leveraging advancements in emotion computing and EEG signal processing. The proposed method utilizes a semisupervised deep convolutional generative adversarial network (SSDNet) as the central model. The model fully integrates the feature extraction methods of the generative adversarial network (GAN), deep convolutional GAN (DCGAN), spectrally normalized GAN (SSGAN) and the encoder, maximizing the advantages of each method to construct a more accurate emotion classification model. In our study, we introduce a flow-form-consistent merging pattern, which successfully addresses mismatches between the data by fusing the EEG data with the features. Implementing this merging pattern not only enhances the uniformity of the input but also decreases the computational load on the network, resulting in a more efficient model. By conducting experiments on the DEAP and SEED datasets, we evaluate the SSDNet model proposed in this paper in detail. The experimental results show that the accuracy of the proposed algorithm is improved by 6.4% and 8.3% on the DEAP and SEED datasets, respectively, compared to the traditional GAN. This significant improvement in performance validates the effectiveness and feasibility of the SSDNet model. The research contributions of this paper are threefold. First, we propose and implement an SSDNet model that integrates multiple feature extraction methods, providing a more accurate and comprehensive solution for emotion recognition tasks. Second, by introducing a flow-form-consistent merging pattern, we successfully address the problem of interdata mismatches and improve the generalization performance of the model. Finally, we experimentally demonstrate that the method in this paper achieves a significant improvement in accuracy over the traditional GANs on the DEAP and SEED datasets, providing an innovative solution in the field of EEG-based emotion recognition. [ABSTRACT FROM AUTHOR]

Acupuncture treatment (AT) of depressive insomnia by traditional Chinese medicine has the advantages of fewer side effects, quicker results, and lower prices compared to medication and psychological and cognitive therapy. Clinicians often select multiple acupoints, such as Bai Hui (GV20), San Yin Jiao (SP6), and Shen Men (HT7), for combined treatment in a single AT session to improve sleep quality. Since the ancient literature on AT often only records the general order of acupoints, there needs to be more discussion on the influence of the multiple acupoint sequence on the priority of efficacy for a specific disease. At the same time, determining the ranking of acupoints in-patient treatment in clinical practice is mainly dependent on the treatment experience of practitioners, and there is no transparent quantitative model or evaluation method for generating credible acupoint sequences from a small and limited scale of cases. Therefore, it is essential to explore the optimization of the order of multiple acupoints in treating depressive insomnia by Traditional Chinese Medicine acupuncture both for the symptom relief of depressive insomnia patients and for the efficient use of national health care resources. This paper proposes a reinforcement learning-based method for optimizing the acupoint sequence for depressive insomnia AT to address these issues. This paper provides a post-AT EEG signal prediction model with related interpretable models to construct a reinforcement learning framework to represent the state transfer of the AT environment and a quantitative EEG signal-based AT efficacy model to represent the reward function. Finally, 30 patients with depressive insomnia were recruited to collect EEG signals during AT for depressive insomnia, and the case data were used to quantify the efficacy of AT and to model the post-AT EEG signal prediction. The above two models were applied to optimize the acupoint sequence based on reinforcement learning. Satisfactory results were obtained, verifying the effectiveness and feasibility of the method proposed in this paper. [ABSTRACT FROM AUTHOR]

Epilepsy is a neurological disorder, and its sudden seizures pose a serious threat to the quality of life of patients. Not only does this condition cause patients to potentially lose control and consciousness during seizures, leading to possible injuries or dangerous situations, but it also has a significant impact on their mental health, triggering issues such as anxiety and depression. An intelligent epilepsy diagnosis process based on electroencephalogram (EEG) signals offers notable advantages. First, it provides noninvasive brainwave signals that accurately monitor and record the patterns and characteristics of epileptic seizures, offering objective diagnostic criteria for doctors. Second, with the use of artificial intelligence and machine learning technologies, large amounts of EEG data can be efficiently analyzed, improving the speed and accuracy of diagnosis and providing timely and effective treatment plans for patients. Additionally, intelligent diagnostic systems can achieve real-time monitoring, promptly alert people to potential epileptic seizures, and provide a safer living environment for patients. In this context, this paper proposes an epilepsy diagnosis method based on a transferred AlexNet model and EEG signals. The main contributions of this paper are as follows. (1) A transfer learning mechanism is incorporated into the AlexNet model through the direct transfer of its neural network structure and the modification of some existing neural network structures, followed by collaborative training with the addition of a domain adaptation layer in the network. This introduced transfer mechanism can address small sample size issues. (2) The traditional AlexNet model suffers from redundant feature extraction, leading to slow training. This paper adds batch normalization (BN) layers after each convolutional layer in the AlexNet model to normalize the features extracted from the convolutional layers. This emphasizes the representation of the important features of EEG signals and enables the lower layers of the network to learn the features needed for EEG signal processing. (3) The transferred AlexNet model proposed in this paper is applied to extract the features of epilepsy EEG signals, and the extracted features are input into a support vector machine (SVM) classifier to obtain epilepsy diagnosis results. Comparative experiments show that the diagnostic method used in this paper yields superior results and shorter training times than those of the competing approaches. [ABSTRACT FROM AUTHOR]

A foot posture index (FPI-6) measurement is well presented as a straightforward clinical tool for diagnosing foot posture in patients. However, the FPI-6 diagnosis is mostly performed by a medical practitioner manually utilizing both palpation and rulers. Digital photos of the feet had been considered to digitize the FPI-6 assessment, unfortunately, the two-dimensional image is inadequate to identify two-palpation based on six criteria of FPI-6. This paper proposes a mechanical platform design for generating a three-dimensional (3D) model of the patient's foot. The design is aimed to ease the test process of a large number of samples or participants as statistical data to estimate the foot posture based on the FPI-6 method. The 3D-generated models of the foot belonging to 37 children aged below 12 years old show that the FPI-6 Criteria 1 and 4 can be evaluated more clearly in the 3D models compared with flat 2D photos. [ABSTRACT FROM AUTHOR]

Objective: Most studies in epileptic seizure detection and classification focused on classifying different types of epileptic seizures. However, localization of the epileptogenic zone in epilepsy patient brain's is paramount to assist the doctor in locating a focal region in patients screened for surgery. Therefore, this paper proposed robust models for the localization of epileptogenic areas for the success of epilepsy surgery. Method: Advanced feature extraction techniques were proposed as effective feature extraction techniques based on Electroencephalogram (EEG) rhythms extracted from Fourier Basel Series Expansion Multivariate Empirical Wavelet Transform (FBSE-MEWT). The proposed extracted EEG rhythms of δ , θ , α , β and γ features were used to obtain a joint instantaneous frequency and amplitude components using a sub-band alignment approach. The features are used in Sparse Autoencoder (SAE), Deep Belief Network (DBN), and Support Vector Machine (SVM) with the optimized capability to develop three new models: 1. FMEWT–SVM 2. FMEWT_SAE–SVM, and 3. FMEWT–DBN–SVM. The EEG signal was preprocessed using a proposed Multiscale Principal Component Analysis (mPCA) to denoise the noise embedded in the signal. Main results: The developed models show a significant performance improvement, with the SAE–SVM outperforming other proposed models and some recently reported works in literature with an accuracy of 99.7% using δ -rhythms in channels 1 and 2. Significance: This study validates the EEG rhythm as a means of discriminating the embedded features in epileptic EEG signals to locate the focal and non-focal regions in the epileptic patient's brain to increase the success of the surgery and reduce computational cost. [ABSTRACT FROM AUTHOR]

This paper proposed an improved natural semantic information-based eye-tracking method combing Needleman–Wunsch and SubsMatch technologies in psychological assessments. The natural semantic information of the self-assessment scale in psychological measurement was combined with the participants' eye-tracking data, and a time-space similarity method was proposed to calculate the eye-tracking differences between different participants in the questionnaire response and carry out visual pattern recognition to achieve the screening of the target population in psychological measurement. The proposed method was evaluated by screening a sample at high risk of depression. The comparative results showed that the average screening accuracy of the sample under the stimulation of a single topic was 80.13% and increased to 97.37% after a dimensionality reduction. This paper verified the objectivity and effectiveness of the semantic information-based eye-tracking time-space similarity method and proved it to be a promising objective tool to assist psychological measurement. [ABSTRACT FROM AUTHOR]

QRS complex detection plays an important role in electrocardiogram (ECG) automatic analysis. The accuracy and robustness of the detection algorithm greatly affect its practicability. However, the existing detection algorithms are greatly affected by ECG signal quality, and some detection algorithms cannot even work properly due to the poor signal quality. In this paper, a robust QRS complex detection algorithm is proposed based on Shannon energy envelope and Hilbert transform. The detection algorithm extracts the Shannon energy envelope of the preprocessed ECG signal, performs Hilbert transform on the envelope signal, then detects the suspected R -peaks on the envelope by detecting the position of zero pass and screens the real R -peaks by using a combination of ECG refractory period and backtracking mechanism. The proposed detection algorithm is validated using MIT-BIH Arrhythmia Database, and achieves the average detection accuracy of 99.69%, sensitivity of 99.81% and positive predictivity of 99.88%. Experimental results show that the proposed detection algorithm can still detect QRS complex correctly under complex interference, and the performance of the algorithm is hardly affected. [ABSTRACT FROM AUTHOR]

The inverse halftoning technology refers to restoring a continuous-toned image from a halftoned image with only bi-level pixels. However, recovering the continuous images from their halftoned ones is normally ill-posed, which makes the inverse halftoning algorithm very challenging. In this paper, we propose an optimization model with two alternate projections (TAP) for image inverse halftoning under the weighted nuclear norm minimization (WNNM) framework. The main contributions are twofold. First, the WNNM nonlocal regularization term is established, which offers a powerful mechanism of nonlocal self-similarity to ensure a more reliable estimation. Second, the alternate minimization projections are formulated for solving the image inverse halftoning, which reconstructs the continuous-toned image without destroying the image details and structures. The experimental results showed that the proposed method outperformed the state of the arts in terms of both objective measurements and subjective visual performance. The codes and constructed models are available at: https://github.com/juneryoung2022/IH-WNNM. [ABSTRACT FROM AUTHOR]

In recent years, the number of cardiac disease patients has been increasing. Modern medical research has shown that the complexity of electrocardiogram (ECG) signals is related to cardiovascular diseases. This paper investigates the difference in complexity of ECG data from the people with different cardiovascular diseases, such as atrial fibrillation (AF), ventricular arrhythmia (VA) and congestive heart failure (CHF). The empirical mode decomposition (EMD) and multiscale entropy method are used to analyze the ECG data, and a mathematical model established by a support vector machine is used to identify different diseases. The accuracy recognition rate of the AF recognition is 96.25%, and that of the CHF and VA reach 90.26% and 92.20%, respectively. The experimental results show that the recognition method proposed in this paper is successful. [ABSTRACT FROM AUTHOR]

Currently, diabetic retinopathy is still screened as a three-stage classification, which is a tedious strategy and along these lines of this paper focuses on developing an improved methodology. In this methodology, we taught a convolutional neural network form on a major dataset, which includes around 45 depictions to do mathematical analysis and characterization. In this paper, DR is constructed, which takes the enter parameters as the HRF fundus photo of the eye. Three classes of patients are considered — healthy patients, diabetic's retinopathy patients and glaucoma patients. An informed convolutional neural system without a fully connected model will also separate the highlights of the fundus pixel with the help of the enactment abilities like ReLu and softmax and arrangement. The yield obtained from the convolutional neural network (CNN) model and patient data achieves an institutionalized 97% accuracy. Therefore, the resulting methodology is having a great potential benefiting ophthalmic specialists in clinical medicine in terms of diagnosing earlier the symptoms of DR and mitigating its effects. [ABSTRACT FROM AUTHOR]

Brain tumor segmentation from magnetic resonance (MR) image is vital for both the diagnosis and treatment of brain cancers. To alleviate noise sensitivity and improve stability of segmentation, an effective hybrid clustering algorithm combined with fast guided filter is proposed for brain tumor segmentation in this paper. Preprocessing is performed using adaptive Wiener filtering combined with a fast guided filter. Then simple linear iterative clustering (SLIC) is utilized for pre-segmentation to effectively remove scatter. During the clustering, K-means + + and Gaussian kernel-based fuzzy C-means (K + + GKFCM) clustering algorithm are combined to segment, and the fast-guided filter is introduced into the clustering. The proposed algorithm not only improves the robustness of the algorithm to noise, but also improves the stability of the segmentation. In addition, the proposed algorithm is compared with other current segmentation algorithms. The results show that the proposed algorithm performs better in terms of accuracy, sensitivity, specificity and recall. [ABSTRACT FROM AUTHOR]

The prevention and diagnosis of cardiovascular diseases have become one of the primary problems in the medical community since the mortality of this kind of diseases accounts for 31% of global deaths in 2016. Heart sound, which is an important physiological signal of human body, mainly comes from the pulsing of cardiac structures and blood turbulence. The analysis of heart sounds plays an irreplaceable role in early diagnosis of heart disease since they contain a large amount of pathological information about each part of human heart. Heart sounds can be detected and recorded by Phonocardiogram (PCG). As a noninvasive method to detect and diagnose heart disease, PCG signals have been paid more and more attention by researchers. In this paper, a novel envelope extraction model is proposed and used to estimate the cardiac cycle of each PCG signal. We present a strategy combining empirical mode decomposition (EMD) technique and the proposed envelope model to extract the time-domain features. After applying EMD process to each PCG signal, the second intrinsic mode function is chosen for further analysis. Based on the proposed envelope model, the cardiac cycles of PCG signals can be estimated and then the time-domain features can be extracted. Combining with the frequency-domain features and wavelet-domain features, the feature vectors are obtained. Finally, the support vector machine (SVM) classifier is used to detect the normal and abnormal PCG signals. Two public datasets are used to test our framework in this paper. And classification accuracies of more than 9 6 % on both datasets show the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Three kinds of collision reaction strategies for increasing safety during human and robot interactions without relying on torque sensors are proposed in this paper. In the proposed algorithms, motor torque is estimated by driver current. The generalized momentum observer is used for collision detection, which does not need joints acceleration information and calculates the inverse of the inertia matrix. Three different collision reaction strategies, going away, dragging by hands and mechanical impedance developed in this paper, aim to enhance safety to humans during physical interaction with robots. For verifying the efficiency of the proposed algorithms, experiments are tested between a 1-DOF manipulator system and a human being. At last, the experiments' results show that the proposed collision reaction algorithms are effective. [ABSTRACT FROM AUTHOR]

For severe patients with new coronavirus pneumonia, the use and stability of the ventilator are important. In this paper, we proposed a new type of shock-absorbing structure that can be used to improve the stability of the ventilator's gas pipeline, thereby reducing the risk of the gas pipeline falling due to external interference. The film shape memory alloy (SMA) compared with bulk SMA is more suitable for gas pipeline shock absorption due to its large volume ratio and extremely high energy density. In order to optimize the performance of actuator and enhance the utilization rate of SMA, the nonlinear characteristics of Ti-Ni thin film actuator are researched and the effect of SMA film position on the system are investigated. The van der Pol term is introduced to fit the hysteresis loop of shape memory materials. Thus, the nonlinear dynamic response of SMA thin film actuator under stochastic excitation is studied. The numerical and experiment results show that the position of SMA can change the system's stability, the proposed structure can reduce the system's vibration to the maximum extent. The results indicate that the new proposed polymer/SMA composite film micro-actuator exhibits high energy dissipation ability, and the utilization rate of smart material is enhanced. Therefore, the new damping structure proposed in this paper is of great significance to the damping of gas pipelines. [ABSTRACT FROM AUTHOR]