Extraction of Music Main Melody and Multi-Pitch Estimation Method Based on Support Vector Machine in Big Data Environment

Main melody extraction and multi-pitch estimation are two important research topics in the MIR field. In this article, the SVM algorithm is used to analyze and discuss music melody extraction and multi-pitch estimation. In the part of multi-fundamental frequency extraction, this article first filters the song signal with equal loudness and weakens the energy of the high-frequency and low-frequency parts of the song signal. Thereafter, the multi-resolution short-time Fourier transform suitable for processing song signals is introduced. In addition, in order to avoid the sharp jump of the estimated melody pitch in the same note duration range, this article proposes a main melody extraction method combining the SVM algorithm with dynamic programming. In this article, more features are used to distinguish the pitch contour of vocal fundamental frequency from that of the nonvocal fundamental frequency, which does not only depend on energy or a certain feature. The experimental results show that the lowest octave error of this method is 1.46. Meanwhile, the recall rate of the algorithm can reach about 95%. This method not only improves the recall rate of the fundamental frequency of the human voice but also improves the recall rate and pitch accuracy rate of the whole main melody extraction system.

An optimal elliptic curve cryptography based encryption algorithm for blockchain-enabled medical image transmission

In recent times, a number of Internet of Things (IoT) related healthcare applications have been deployed for automating healthcare services and offering easy accessibility to patients. Several issues like security, fault-tolerant, and reliability have restricted the utilization of IoT services in real-time healthcare environments. To achieve security, blockchain technology can be utilized which offers effective interoperability of healthcare databases, ease of medical data access, device tracking, prescription database, hospital assets, etc. Therefore, this paper presents an optimal Elliptic curve cryptography-based encryption algorithm for a blockchain-enabled medical image transmission model, named OECC-BMIT. The presented OECC-BMIT model involves different stages of operations such as encryption, optimal key generation, blockchain-enabled data transmission, and decryption. Firstly, the OECC-BMIT model performs Elliptic curve cryptography (ECC) based encryption technique to securely transmit the medical images. In order to generate the optimal set of keys for the ECC technique, modified bat optimization (MBO) algorithm is applied. Then, the encrypted images undergo secure transmission via blockchain technology. The encrypted images are decrypted on the recipient side and the original medical image is reconstructed effectively. Extensive sets of experimentations were performed to highlight the goodness of the OECC-BMIT algorithm and the obtained results pointed out the improved outcome over the state of art methods in terms of different measures.

Exploring the Path of Enhancing Ideological and Political Education in Universities in the Era of Big Data

The emergence of the big data era has drastically altered people's lives and perceptions. One needs a thorough understanding of the topic to effectively apply big data's benefits to the ideological and political education work that colleges and universities carry out. By doing so, the advantages of big data can be better exploited and integrated into the educational process, enhancing the work's overall quality. To enhance the path of ideological and political education in colleges and universities, it is necessary to change according to the matter, advance according to the time, and make new changes according to the situation, and therefore, it is important to actively explore the path of ideological and political education in colleges and universities under the times. In this study, we research and analyze the opportunities and challenges facing the ideological and political education of universities in the era of big data, reexamine the subjective and objective environment in which the ideological and political education of universities is located, and explore the innovative development path of the ideological and political education of universities in the new environment. We will also encourage the innovative growth of ideological and political work in four areas, such as cultivating big data thinking innovation, working method innovation, working carrier innovation, and ideological work team construction, and conduct a ranking analysis on the significance of the exploration variables to improve the path of ideological work. The importance score measures the value of features in the construction of the ascending decision in the model, so the XGBoost algorithm is used to sort and analyze the significance of exploring variables to enhance the political and ideological work trajectory. The analysis of the experimental results shows that the innovation of working methods has greatly enhanced the conditions for carrying out ideological and political education in the new environment and has far-reaching implications and important significance for the innovation of ideological and political education in universities.

Classification of Artistic Styles of Chinese Art Paintings Based on the CNN Model

People’s appreciation needs of Chinese paintings have gradually increased. The research on automatic classification and recognition of Chinese painting artistic style and its authors have great practical value. This study presents a Chinese painting classification algorithm with higher classification accuracy and better robustness. Using a convolutional neural network (CNN) to extract the features of Chinese painting, the image features of Chinese painting are extracted by fine-tuning the pretrained VGG-F model. The mutual information theory is introduced into embedded machine learning, so that the embedded principle is affected by feature selection and feature importance. An embedded classification algorithm based on mutual information is proposed, and Chinese painting is classified.

Exploring the Path of Innovative Development of Traditional Culture under Big Data

Chinese traditional culture is the treasure of our cultural field. In the new era, it is of great significance to give traditional culture a new life and vitality. The term “big data” is hotly debated all over the world, while the development of big data is gradually occupying all aspects of the society that people are compatible with society. It is an imperative initiative to build a cultural data system by making use of big data technology, and cultural big data can make Chinese traditional culture release more vitality. This paper analyzes the new characteristics of traditional culture development from big data in helping traditional culture inheritance and innovation and proposes new ideas and creates more possibilities for the development of traditional culture. Combining with big data technology, this paper proposes an improvement to the data sparsity problem and cold-start problem of collaborative filtering recommendation algorithm and also improves the recommendation algorithm based on association rules. The association rule technique is used to compensate for the cold-start and data sparsity problems of new users often encountered by collaborative filtering techniques; the aim is to obtain recommendation results with high user satisfaction. Experiments on traditional cultural resource datasets show that the method in this paper effectively solves the data sparsity and cold-start problems that exist in traditional collaborative filtering techniques, and the recommendation accuracy surpasses that of other methods.

Skin Diseases Classification Using Hybrid AI Based Localization Approach

One of the most prevalent diseases that can be initially identified by visual inspection and further identified with the use of dermoscopic examination and other testing is skin cancer. Since eye observation provides the earliest opportunity for artificial intelligence to intercept various skin images, some skin lesion classification algorithms based on deep learning and annotated skin photos display improved outcomes. The researcher used a variety of strategies and methods to identify and stop diseases earlier. All of them yield positive results for identifying and categorizing diseases, but proper disease categorization is still lacking. Computer-aided diagnosis is one of the most crucial methods for more accurate disease detection, although it is rarely used in dermatology. For Feature Extraction, we introduced Spectral Centroid Magnitude (SCM). The given dataset is classified using an enhanced convolutional neural network; the first stage of preprocessing uses a median filter, and the final stage compares the accuracy results to the current method.

Mayfly Taylor Optimisation-Based Scheduling Algorithm with Deep Reinforcement Learning for Dynamic Scheduling in Fog-Cloud Computing

Fog computing domain plays a prominent role in supporting time-delicate applications, which are associated with smart Internet of Things (IoT) services, like smart healthcare and smart city. However, cloud computing is a capable standard for IoT in data processing owing to the high latency restriction of the cloud, and it is incapable of satisfying needs for time-sensitive applications. The resource provisioning and allocation process in fog-cloud structure considers dynamic alternations in user necessities, and also restricted access resources in fog devices are more challenging. The global adoption of IoT-driven applications has led to the rise of fog computing structure, which permits perfect connection for mobile edge and cloud resources. The effectual scheduling of application tasks in fog environments is a challenging task because of resource heterogeneity, stochastic behaviours, network hierarchy, controlled resource abilities, and mobility elements in IoT. The deadline is the most significant challenge in the fog computing structure due to the dynamic variations in user requirement parameters. In this paper, Mayfly Taylor Optimisation Algorithm (MTOA) is developed for dynamic scheduling in the fog-cloud computing model. The developed MTOA-based Deep Q-Network (DQN) showed better performance with energy consumption, service level agreement (SLA), and computation cost of 0.0162, 0.0114, and 0.0855, respectively.

Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model

Rice is considered one the most important plants globally because it is a source of food for over half the world’s population. Like other plants, rice is susceptible to diseases that may affect the quantity and quality of produce. It sometimes results in anywhere between 20–40% crop loss production. Early detection of these diseases can positively affect the harvest, and thus farmers would have to be knowledgeable about the various disease and how to identify them visually. Even then, it is an impossible task for farmers to survey the vast farmlands on a daily basis. Even if this is possible, it becomes a costly task that will, in turn, increases the price of rice for consumers. Machine learning algorithms fitted to drone technology combined with the Internet of Things (IoT) can offer a solution to this problem. In this paper, we propose a Deep Convolutional Neural Network (DCNN) transfer learning-based approach for the accurate detection and classification of rice leaf disease. The modified proposed approach includes a modified VGG19-based transfer learning method. The proposed modified system can accurately detect and diagnose six distinct classes: healthy, narrow brown spot, leaf scald, leaf blast, brown spot, and bacterial leaf blight. The highest average accuracy is 96.08% using the non-normalized augmented dataset. The corresponding precision, recall, specificity, and F1-score were 0.9620, 0.9617, 0.9921, and 0.9616, respectively. The proposed modified approach achieved significantly better results compared with similar approaches using the same dataset or similar-size datasets reported in the extant literature.

Neural Decoders Using Reinforcement Learning in Brain Machine Interfaces: A Technical Review

Creating flexible and robust brain machine interfaces (BMIs) is currently a popular topic of research that has been explored for decades in medicine, engineering, commercial, and machine-learning communities. In particular, the use of techniques using reinforcement learning (RL) has demonstrated impressive results but is under-represented in the BMI community. To shine more light on this promising relationship, this article aims to provide an exhaustive review of RL's applications to BMIs. Our primary focus in this review is to provide a technical summary of various algorithms used in RL-based BMIs to decode neural intention, without emphasizing preprocessing techniques on the neural signals and reward modeling for RL. We first organize the literature based on the type of RL methods used for neural decoding, and then each algorithm's learning strategy is explained along with its application in BMIs. A comparative analysis highlighting the similarities and uniqueness among neural decoders is provided. Finally, we end this review with a discussion about the current stage of RLBMIs including their limitations and promising directions for future research.

Real-World Testing of a Machine Learning-Derived Visual Scale for Tc99m TRODAT-1 for Diagnosing Lewy Body Disease: Comparison with a Traditional Approach Using Semiquantification

Objectives: Abnormal dopamine transporter (DAT) uptake is an important biomarker for diagnosing Lewy body disease (LBD), including Parkinson’s disease (PD) and dementia with Lewy bodies (DLB). We evaluated a machine learning-derived visual scale (ML-VS) for Tc99m TRODAT-1 from one center and compared it with the striatal/background ratio (SBR) using semiquantification for diagnosing LBD in two other centers. Patients and Methods: This was a retrospective analysis of data from a history-based computerized dementia diagnostic system. MT-VS and SBR among normal controls (NCs) and patients with PD, PD with dementia (PDD), DLB, or Alzheimer’s disease (AD) were compared. Results: We included 715 individuals, including 122 NCs, 286 patients with PD, 40 with AD, 179 with DLB, and 88 with PDD. Compared with NCs, patients with PD exhibited a significantly higher prevalence of abnormal DAT uptake using all methods. Compared with the AD group, PDD and DLB groups exhibited a significantly higher prevalence of abnormal DAT uptake using all methods. The distribution of ML-VS was significantly different between PD and NC, DLB and AD, and PDD and AD groups (all p < 0.001). The correlation coefficient of ML-VS/SBR in all participants was 0.679. Conclusions: The ML-VS designed in one center is useful for differentiating PD from NC, DLB from AD, and PDD from AD in other centers. Its correlation with traditional approaches using different scanning machines is also acceptable. Future studies should develop models using data pools from multiple centers for increasing diagnostic accuracy.

Characteristics, Experience, and Enlightenment of Leisure and Sports Policy and Public Health Development in Developed Countries

Public health highlights "public utilities," emphasizes the attributes of public goods and services, and aims to clarify the responsibility of government leadership; highlighting "maintaining health equity" aims to make it clear that public health not only guarantees health but also focuses on fairness; the purpose extends to "maintaining social stability and development," emphasizing that the function of public health is not only to protect health; emphasizing that public health is "one science" and aims to call for respect for the laws of science and the cross-integration of medicine with other natural and social sciences; and emphasizing that public health "focuses on practice" makes it clear that strengthening the implementation of various policies and measures is the key to achieving purpose and values. The development of the entertainment and sports industry is directly related to the development, stability, and people's welfare of the country. Under the common influence of various factors, such as the increasing level of social economy and technology, the growth of people's leisure time, and the change in social consumption concept, the development of China's entertainment and sports industry has shown a certain scale effect, social correlation effect, and employment effect. However, due to the continuous advancement of the development process of China's entertainment and sports industry, some problems have emerged, such as uneven development speed, small industrial scale, low industrial technology level, lack of service commodity types, unreasonable market resource allocation, and serious shortage of professionals in relevant fields. Thus, it limits the development process of China's entertainment and sports industry and is not conducive to the ecological development of China's entertainment and sports industry. On this basis, the state has formulated the guiding opinions on improving China's leisure sports policies and regulations: adjusting the method, intensity, and direction of the state's direct financial investment; strengthening the support for social funds and sports clubs; reducing the price and reducing the cost of entertainment; and establishing a reasonable industrial integrity system and standards; a government supervision and performance evaluation system has been initially formed. It aims to highlight that all aspects of society should also serve health while enjoying health; comprehensive policies are the guarantee for promoting the benign operation of the system.

Analysis of College Course Scheduling Problem Based on Ant Colony Algorithm

Ant colony algorithm is a new evolutionary algorithm, which is gradually applied due to its easy robustness with other methods and excellent distributed computing mechanism. Currently, the application field of ant colony algorithm has been infiltrated by a single TSP problem. Ant colony algorithm moves the algorithm toward the optimal solution through the combination of positive feedback and negative feedback. This paper briefly analyzes the basic characteristics of the basic idea and principle of the ant colony algorithm to apply it to the ant colony problem. Abstract the course scheduling problem, transform the course arrangement problem into the maximum matching problem of solving the bipartite diagram, and discharge the high-quality curriculum that basically meet the needs.

A Simultaneous Gesture Classification and Force Estimation Strategy Based on Wearable A-Mode Ultrasound and Cascade Model

The existing Human-Machine Interfaces (HMI) based on gesture recognition using surface electromyography (sEMG) have made significant progress. However, the sEMG has inherent limitations as well as the gesture classification and force estimation have not been effectively combined. There are limitations in applications such as prosthetic control and clinical rehabilitation, etc. In this paper, a grasping gesture and force recognition strategy based on wearable A-mode ultrasound and two-stage cascade model is proposed, which can simultaneously estimate the force while classifying the grasping gesture. This paper experiments five grasping gestures and four force levels (5-50%MVC). The results demonstrate that the performance of the proposed model is significantly better than that of the traditional model both in classification and regression (p < 0.001). Additionally, the two-stage cascade regression model (TSCRM) used the Gaussian Process regression model (GPR) with the mean and standard deviation (MSD) feature obtains excellent results, with normalized root-mean-square error (nRMSE) and correlation coefficient (CC) of 0.10490.0374 and 0.94610.0354, respectively. Besides, the latency of the model meets the requirement of real-time recognition (T < 15ms). Therefore, the research outcomes prove the feasibility of the proposed recognition strategy and provide a reference for the field of prosthetic control, etc.

Aerial Separation and Receiver Arrangements on Identifying Lung Syndromes Using the Artificial Neural Network

Lung disease is one of the most harmful diseases in traditional days and is the same nowadays. Early detection is one of the most crucial ways to prevent a human from developing these types of diseases. Many researchers are involved in finding various techniques for predicting the accuracy of the diseases. On the basis of the machine learning algorithm, it was not possible to predict the better accuracy when compared to the deep learning technique; this work has proposed enhanced artificial neural network approaches for the accuracy of lung diseases. Here, the discrete Fourier transform and the Burg auto-regression techniques are used for extracting the computed tomography (CT) scan images, and feature reduction takes place by using principle component analysis (PCA). This proposed work has used the 120 subjective datasets from public landmarks with and without lung diseases. The given dataset is trained by using an enhanced artificial neural network (ANN). The preprocessing techniques are handled by using a Gaussian filter; thus, our proposed approach provides enhanced classification accuracy. Finally, our proposed method is compared with the existing machine learning approach based on its accuracy.

Situation Element Extraction Based on Fuzzy Rough Set and Combination Classifier

Generalized network security situation awareness technology is divided into three processes: situation element extraction, situation understanding, and situation prediction. Situation element extraction is the most critical step in the whole process, and its extraction quality will directly affect the accuracy of situation understanding and prediction. In view of the shortcomings of current situation element extraction methods, this study makes an in-depth study on the network security situation element extraction algorithm and proposes a situation element extraction model based on the fuzzy rough set and combined classifier, which is used to improve the accuracy of situation elements acquisition, so as to provide a better data basis for situation understanding and prediction. In this study, the theory of fuzzy rough set is used to reduce the attributes of data without reducing the ability of data classification, which reduces the complexity of data; using the combination classifier theory and particle swarm optimization algorithm, a framework of situation element extraction is built, which can extract situation elements more accurately. The experimental results show that the network security situation element extraction framework proposed in this study can effectively shorten the extraction time of situation elements and improve the accuracy of situation element acquisition under the premise of ensuring the ability of data classification, thus proving the effectiveness and feasibility of the situation element extraction framework proposed in this study.

Application of Graph Neural Network in Driving Fatigue Detection Based on EEG Signals

The objective of this article is to solve the current social phenomenon of a large number of fatigue driving, so that social safety becomes more stable in the future, and the detection and application of driving fatigue are more meaningful. This article aims to study the application of graph neural network (GNN) in driving fatigue detection (this article is abbreviated as DFD) based on EEG signals. This article uses a pattern classification method based on a multilayer perceptual overlimit learning machine to find the hidden information of the signal through an unsupervised learning self-encoding structure, which achieves the optimization purpose and has a better classification effect than traditional classifiers. An improved soft threshold (the soft threshold can be used to solve the optimization problem, and the optimization problem solved is similar to the base pursuit noise reduction problem, but it is not the same, and it should be noted that the soft threshold cannot solve the base pursuit noise reduction problem) denoising algorithm is selected, and the collected EEG (a technique for capturing brain activity using electrophysiological markers is the electroencephalogram). The sum of the postsynaptic potentials produced simultaneously by a large number of neurons occurs when the brain is active. It records the process of brain activity in the cerebral cortex or scalp surface) signals are preprocessed, so that the feature extraction efficiency of extracting EEG signals is improved. The final experimental data show that the traditional support vector machine, SVM algorithm, and the KNN convolutional neural (the K-nearest neighbor method, often known as KNN, was first put forth by Cover and Hart in 1968. It is one of the most straightforward machine learning algorithms and a theoretically sound approach) algorithms has a recognition rate of 79% and 81% for fatigue. The improved algorithm in this article has an average recognition rate of 87.5% for driver fatigue, which is greatly improved.

Deep Convolutional Neural Network Mechanism Assessment of COVID-19 Severity

As an epidemic, COVID-19's core test instrument still has serious flaws. To improve the present condition, all capabilities and tools available in this field are being used to combat the pandemic. Because of the contagious characteristics of the unique coronavirus (COVID-19) infection, an overwhelming comparison with patients queues up for pulmonary X-rays, overloading physicians and radiology and significantly impacting the quality of care, diagnosis, and outbreak prevention. Given the scarcity of clinical services such as intensive care and motorized ventilation systems in the aspect of this vastly transmissible ailment, it is critical to categorize patients as per their risk categories. This research describes a novel use of the deep convolutional neural network (CNN) technique to COVID-19 illness assessment seriousness. Utilizing chest X-ray images as contribution, an unsupervised DCNN model is constructed and suggested to split COVID-19 individuals into four seriousness classrooms: low, medium, serious, and crucial with an accuracy level of 96 percent. The efficiency of the DCNN model developed with the proposed methodology is demonstrated by empirical findings on a suitably huge sum of chest X-ray scans. To the evidence relating, it is the first COVID-19 disease incidence evaluation research with four different phases, to use a reasonably high number of X-ray images dataset and a DCNN with nearly all hyperparameters dynamically adjusted by the variable selection optimization task.

The Acceptability of Traditional Culture under the Background of Deep Learning

The cultural values of a country impact its national psychology and identity. Citizens' values and public opinions are conveyed to state leaders over the media and other information channels, both directly and indirectly influencing decisions on foreign policy. The traditional cultural values that affect the psyche of the Chinese people are harmony, generosity, morality, courtesy, wisdom, honesty, loyalty, and filial piety. This study aims to analyze the attitudes of Chinese college students toward traditional culture. The reliability and effect factors of the traditional culture acceptability questionnaire are set in the context of deep learning. A questionnaire on traditional culture education of college students is compiled using the indicator evaluation methods, and the current situation of traditional culture education is investigated for college students. A total of 300 valid respondents are returned from five universities including Shanghai Jiaotong University, Fudan University, Yunnan University, Kunming University of Science and Technology, and Yunnan Normal University. Results show that 28% of college students believe that practical activities including visiting and learning and traditional festival commemoration are the most effective ways to educate traditional culture for them, which accounts for the largest percentage. Similarly, 19% of students suggest online publicity, while 16% believe that lecture reports are particularly important, and 12% of students advocate the teaching courses. In addition, about 23% of the students choose other methods, such as seminars, setting up Chinese culture festivals, and building cultural associations. The outcomes of this study provide data support for identifying the shortcomings in traditional cultural education and formulating strategies.

Artificial intelligence assisted compositional analyses of human abdominal aortic aneurysms ex vivo

Quantification of histological information from excised human abdominal aortic aneurysm (AAA) specimens may provide essential information on the degree of infiltration of inflammatory cells in different regions of the AAA. Such information will support mechanistic insight in AAA pathology and can be linked to clinical measures for further development of AAA treatment regimens. We hypothesize that artificial intelligence can support high throughput analyses of histological sections of excised human AAA. We present an analysis framework based on supervised machine learning. We used TensorFlow and QuPath to determine the overall architecture of the AAA: thrombus, arterial wall, and adventitial loose connective tissue. Within the wall and adventitial zones, the content of collagen, elastin, and specific inflammatory cells was quantified. A deep neural network (DNN) was trained on manually annotated, Weigert stained, tissue sections (14 patients) and validated on images from two other patients. Finally, we applied the method on 95 new patient samples. The DNN was able to segment the sections according to the overall wall architecture with Jaccard coefficients after 65 epocs of 92% for the training and 88% for the validation data set, respectively. Precision and recall both reached 92%. The zone areas were highly variable between patients, as were the outputs on total cell count and elastin/collagen fiber content. The number of specific cells or stained area per zone was deterministically determined. However, combining the masks based on the Weigert stainings, with images of immunostained serial sections requires addition of landmark recognition to the analysis path. The combination of digital pathology, the DNN we developed, and landmark registration will provide a strong tool for future analyses of the histology of excised human AAA. In combination with biomechanical testing and microstructurally motivated mathematical models of AAA remodeling, the method has the potential to be a strong tool to provide mechanistic insight in the disease. In combination with each patients’ demographic and clinical profile, the method can be an interesting tool to in supportof a better treatment regime for the patients.

Study on the Cultivation of College Students' Internet Literacy in Ideological and Political Teaching under the Application of Virtual Reality Technology

People are living in an age of artificial intelligence. Artificially intelligent technology, such as virtual reality technology, is creating new horizons in every field. In this article, people are learning about the cultivation of Internet literacy among college in ideological and also in the political teaching under the application of virtual reality technology. Internet literacy is defined as the ability to search and utilise information from the Internet. It includes the person's ability to communicate with people, the ability to protect their own privacy, and stay away from harmful and malicious content on the Internet. The basic computer skills, along with the ability to assess social media and search engines, the knowledge to handle Microsoft Office tools, send and receive emails, search for answers online, ask questions in forums, enrol in educational courses, etc., form the basis of Internet literacy. Ideological and political education is vital educational courses that need to be upgraded with the latest technological growth. The main idea of this proposed system is to cultivate Internet literacy among ideological and political education students by using VI technology. The model is found to deliver great results under the application of virtual reality technology. The proposed model implements a Back-Propagation Network Algorithm for the cultivation of Internet literacy among college students.

Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis

Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice. A systematic, detailed search of the main electronic medical databases was undertaken in concordance with the PRISMA guidelines. A total of 30 articles were retrieved from the database and reviewed. Key findings of current AI applications were compiled and summarised. The main clinical applications of AI techniques include image processing, diagnosis, decision support, treatment assistance and prognostic outcomes. In the realm of spinal oncology, artificial intelligence technologies have achieved relatively good performance and hold immense potential to aid clinicians, including enhancing work efficiency and reducing adverse events. Further research is required to validate the clinical performance of the AI tools and facilitate their integration into routine clinical practice.

Tooth CT Image Segmentation Method Based on the U-Net Network and Attention Module

Traditional image segmentation methods often encounter problems of low segmentation accuracy and being time-consuming when processing complex tooth Computed Tomography (CT) images. This paper proposes an improved segmentation method for tooth CT images. Firstly, the U-Net network is used to construct a tooth image segmentation model. A large number of feature maps in downsampling are supplemented to downsampling to reduce information loss. At the same time, the problem of inaccurate image segmentation and positioning is solved. Then, the attention module is introduced into the U-Net network to increase the weight of important information and improve the accuracy of network segmentation. Among them, subregion average pooling is used instead of global average pooling to obtain spatial features. Finally, the U-Net network combined with the improved attention module is used to realize the segmentation of tooth CT images. And based on the image collection provided by West China Hospital for experimental demonstration, compared with other algorithms, our method has better segmentation performance and efficiency. The contours of the teeth obtained are clearer, which is helpful to assist the doctor in the diagnosis.

Research on Organization Design of College Chinese Teaching under Big Data Environment

The pivotal and most important technology point of the application of big data in modern network teaching is the establishment of teaching platform. Based on Hadoop big data technology, this paper establishes a big data platform designed by Chinese teaching institutions in colleges and universities. The teaching system not only realizes the diversified development of network teaching but also realizes the discovery of teaching platform users' needs and the push of resources. In addition, it is found that a small number of people do not have a high understanding of the teaching mode of big data through extensive studies, which also shows that traditional teaching is still lagging behind in big data education. However, on the whole, the Chinese teaching model in universities based on the big data environment deserves further promotion and application in universities.

Analysis of the Current Situation of Teaching and Learning of Ideological and Political Theory Courses by Deep Learning

The objectives are to solve the problems existing in the current ideological and political theory courses, such as the difficulty of classroom teaching quality assessment, the confusion of teachers' classroom process management, and the lack of objective assessment basis in teaching quality monitoring. Based on Artificial Intelligence (AI) technology, a designed evaluation method is proposed for teachers' classroom teaching and solves some problems such as high system cost, low evaluation accuracy, and imperfect evaluation methods. Firstly, the boundary algorithm system is introduced in the research, and the Field Programmable Gate Array (FPGA) by deep learning (DL) is used to accelerate the server hardware network platform and equipped with pan tilt zoom (PTZ) and manage multiple AI + embedded visual boundary algorithm devices. Secondly, the network platform can manage the PTZ and focal length of Internet protocol (IP) cameras, measure, and capture face images, transmit data, and recognize students' face, head, and body postures. Finally, classroom teaching is evaluated, and students' behavioral data and functions are designed, debugged, and tested. The research results demonstrate that the method overcomes the problem of high system cost through edge computing and hardware structure, and DL technology is used to overcome the problem of low accuracy of classroom teaching evaluation. Various indicators such as attendance rate, concentration, activity, and richness of teaching links in classroom teaching are obtained. The method involved can make an objective evaluation of classroom teaching and overcome the problem of incomplete classroom teaching evaluation.

Hot topics and frontier evolution in college flipped classrooms based on mapping knowledge domains

With the outbreak of COVID-19 and the development of online teaching, the online flipping teaching mode has attracted increasing attention. Systematic analysis of the research status and development trend of the flipped classrooms is significant for guiding the improvement of the quality of online flipped teaching. This study used the metrology software CiteSpace to draw a scientific knowledge map of relevant research in the web of science database from 2013 to 2021. It performed visual analysis of research authors, research institutions and countries, keyword clustering, keywords co-occurrence, and keyword time zone distribution. The results showed that: (1) The flipped classrooms research has attracted increasing attention from the social and educational circles, however, the relationship between relevant research authors, institutions, and countries is not close enough, and there is little cooperation. We need to strengthen cooperation further and realize the sharing of high-quality resources; (2) Based on keyword co-occurrence cluster analysis, this study identified three hot topics, namely, preparation before class, classroom activities and consolidation after class; (3) According to the keyword time zone map, this study divided three frontier evolution trends: exploration period, adaptation period, and growth period; (4) Finally, with the spread of novel coronavirus, it is suggested to promote the online flipped classroom teaching mode, and put forward reasonable suggestions from the perspective of teachers, students and researchers, and look forward to the future digital development direction of the flipped classroom.

Sparrow Search Algorithm-Optimized Long Short-Term Memory Model for Stock Trend Prediction

The long short-term memory (LSTM) network is especially suitable for dealing with time series-related problems, which has led to a wide range of applications in analyzing stock market quotations and predicting future price trends. However, the selection of hyperparameters in LSTM networks was often based on subjective experience and existing research. The inability to determine the optimal values of the parameters results in a reduced generalization capability of the model. Therefore, we proposed a sparrow search algorithm-optimized LSTM (SSA-LSTM) model for stock trend prediction. The SSA was used to find the optimal hyperparameters of the LSTM model to adapt the features of the data to the structure of the model, so as to construct a highly accurate stock trend prediction model. With the Shanghai Composite Index stock data in the last decade, the mean absolute percentage error, root mean square error, mean absolute error, and coefficient of determination between stock prices predicted by the SSA-LSTM method and actual prices are 0.0093, 41.9505, 30.5300, and 0.9754. The result indicates that the proposed model possesses higher forecasting precision than other traditional stock forecasting methods and enhances the interpretability of the network model structure and parameters.

Improved disease diagnosis system for COVID-19 with data refactoring and handling methods

The novel coronavirus illness (COVID-19) outbreak, which began in a seafood market in Wuhan, Hubei Province, China, in mid-December 2019, has spread to almost all countries, territories, and places throughout the world. And since the fault in diagnosis of a disease causes a psychological impact, this was very much visible in the spread of COVID-19. This research aims to address this issue by providing a better solution for diagnosis of the COVID-19 disease. The paper also addresses a very important issue of having less data for disease prediction models by elaborating on data handling techniques. Thus, special focus has been given on data processing and handling, with an aim to develop an improved machine learning model for diagnosis of COVID-19. Random Forest (RF), Decision tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Support vector machine, and Deep Neural network (DNN) models are developed using the Hospital Israelita Albert Einstein (in São Paulo, Brazil) dataset to diagnose COVID-19. The dataset is pre-processed and distributed DT is applied to rank the features. Data augmentation has been applied to generate datasets for improving classification accuracy. The DNN model dominates overall techniques giving the highest accuracy of 96.99%, recall of 96.98%, and precision of 96.94%, which is better than or comparable to other research work. All the algorithms are implemented in a distributed environment on the Spark platform.

Arm Movement Analysis Technology of Wushu Competition Image Based on Deep Learning

In order to improve the recognition accuracy of action poses for athletes in martial arts competitions, it is considered that a single frame pose does not have the temporal features required for sequential actions. Based on deep learning, this paper proposes an image arm movement analysis technology in martial arts competitions. The motion features of the arm are extracted from the bone sequence. Taking human bone motion information as temporal dynamic information, combined with RGB spatial features and depth map, the spatiotemporal features of arm motion data are formed. In this paper, we set up a slow frame rate channel and a fast frame rate channel to detect sequential motion of images. The deep learning model takes 16 frames from each video as samples. The softmax classifier is used to get the classification result of which action category the human action in the video belongs to. The test results show that the accuracy and recall rate of the arm motion analysis technology based on deep learning in martial arts competitions are 95.477% and 92.948%, respectively, with good motion analysis performance.

Precise Marketing of E-Commerce Products Based on KNN Algorithm

In order to better understand the purchase decision-making process of consumers, this paper makes an in-depth study on the precision marketing of e-commerce products on the basis of KNN algorithm. Through data mining, classic KNN algorithm, BPNN algorithm, and other methods, this paper takes the price and purchase intention of e-commerce agricultural products as an example. Based on the classic nearest neighbor algorithm, binomial function is combined with Euclidean distance formula when calculating the nearest neighbor through similarity. The particle swarm optimization algorithm is used to optimize the binomial function coefficient and the K value of the nearest neighbor algorithm, and the results of the best prediction model for the prediction application of e-commerce agricultural product price and purchase intention are established. Both pricing strategies and promotion strategies will weaken the compromise effect of consumers when they choose e-commerce agricultural products. After studying the calculation method of the KNN algorithm, it not only correctly predicts the price of e-commerce agricultural products but also makes a corresponding prediction and analysis of consumers' purchase intention of e-commerce agricultural products, with the highest accuracy of 94.2%. At the same time, in the future precision marketing process, e-commerce agricultural products enterprises use data technology to achieve precision marketing, which effectively changes the shortcomings of traditional marketing and improves the product marketing effect and economic benefits.

Machine learning based personalized promotion strategy of piglets weaned per sow per year in large-scale pig farms

Background

The purpose of this study was to analyze the relationship between different productive factors and piglets weaned per sow per year (PSY) in 291 large-scale pig farms and analyze the impact of the changes in different factors on PSY. We chose nine different algorithm models based on machine learning to calculate the influence of each variable on every farm according to its current situation, leading to personalize the improvement of the impact in the specific circumstances of each farm, proposing a production guidance plan of PSY improvement for every farm. According to the comparison of mean absolute error (MAE), 95% confidence interval (CI) and R², the optimal solution was conducted to calculate the influence of 17 production factors of each pig farm on PSY improvement, finding out the bottleneck corresponding to each pig farm. The level of PSY was further analyzed when the bottleneck factor of each pig farm changed by 0.5 standard deviation (SD).

Results

17 production factors were non-linearly related to PSY. The top five production factors with the highest correlation with PSY were the number of weaned piglets per litter (WPL) (0.6694), mating rate within 7 days after weaning (MR7DW) (0.6606), number of piglets born alive per litter (PBAL) (0.6517), the total number of piglets per litter (TPL) (0.5706) and non-productive days (NPD) (− 0.5308). Among nine algorithm models, the gradient boosting regressor model had the highest R², smallest MAE and 95% CI, applied for personalized analysis. When one of 17 production factors of 291 large-scale pig farms changed by 0.5 SD, 101 pig farms (34.7%) can increase 1.41 PSY (compared to its original value) on average by adding the production days, and 60 pig farms (20.6%) can increase 1.14 PSY on average by improving WPL, 45 pig farms (15.5%) can increase 1.63 PSY by lifting MR7DW.

Conclusions

The main productive factors related to PSY included WPL, MR7DW, PBAL, TPL and NPD. The gradient boosting regressor model was the optimal method to individually analyze productive factors that are non-linearly related to PSY.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40813-022-00280-z.

Intelligent yield estimation for tomato crop using SegNet with VGG19 architecture

Yield estimation (YE) of the crop is one of the main tasks in fruit management and marketing. Based on the results of YE, the farmers can make a better decision on the harvesting period, prevention strategies for crop disease, subsequent follow-up for cultivation practice, etc. In the current scenario, crop YE is performed manually, which has many limitations such as the requirement of experts for the bigger fields, subjective decisions and a more time-consuming process. To overcome these issues, an intelligent YE system was proposed which detects, localizes and counts the number of tomatoes in the field using SegNet with VGG19 (a deep learning-based semantic segmentation architecture). The dataset of 672 images was given as an input to the SegNet with VGG19 architecture for training. It extracts features corresponding to the tomato in each layer and detection was performed based on the feature score. The results were compared against the other semantic segmentation architectures such as U-Net and SegNet with VGG16. The proposed method performed better and unveiled reasonable results. For testing the trained model, a case study was conducted in the real tomato field at Manapparai village, Trichy, India. The proposed method portrayed the test precision, recall and F1-score values of 89.7%, 72.55% and 80.22%, respectively along with reasonable localization capability for tomatoes.

Evaluation of Ideological and Political Education under Deep Learning Neural Network

Under the background of the rapid development of the new generation of information technology, artificial neural networks (ANNs) have made some progress in the evaluation research of various courses in colleges and universities. However, there is little research on the application of ANNs in Ideological and Political Education (IPE) courses. Based on this, this work attempts to introduce the Backpropagation Neural Network (BPNN) into the evaluation system of IPE courses. Firstly, the structure and characteristics of the BPNN are given, and it is optimized by the genetic algorithm based on its characteristics. Secondly, the theoretical framework of IPE course evaluation is established, and the corresponding evaluation model is constructed using the optimized BPNN. Finally, a questionnaire survey is designed to analyze the current situation of IPE evaluation in colleges and universities, and a simulation experiment is set up to test the BPNN evaluation model before and after optimization. The results are as follows. First, there are mainly five evaluation methods for IPE courses in Chinese colleges and universities: 6/4, 3/7, 2/2/6, 5/5, and 3/3/4. Second, the training error value of the BPNN model is in the interval (-2.3, 2.2), and when the number of cycles is 552, the error is infinitely close to zero. Thirdly, the training error value of the optimized BPNN model is in the interval (-0.22, 1.2), and when the number of cycles is 775, the error is infinitely close to zero. Fourthly, the error between the output value of the optimized BPNN model and the expert score value is generally smaller than the error value before optimization. This work aims to provide a theoretical reference for the further application of neural network technology in the evaluation of IPE.

SOFA Score in relation to Sepsis: Clinical Implications in Diagnosis, Treatment, and Prognostic Assessment

Purpose

To analyze the clinical significance of the sequential organ failure assessment (SOFA) score in the diagnosis, treatment, and prognostic assessment of sepsis.

Methods

140 patients with sepsis from January 2020 to January 2021 were selected as the observation group, and 40 healthy people were selected as the control group. The observation group was divided into mild group, severe group, and septic shock group by single blind grouping according to the condition of the disease, and they were also divided into survival group and death group according to the prognosis. Collect the fasting venous blood of the subjects in each group in the morning, compare the levels of total bilirubin (TBIL), blood creatinine (CR), and platelet count (PLT) in each group, and record and compare the patients' respiratory system oxygen partial pressure/inhaled oxygen concentration (po2/fio2), acute physiology and chronic health scoring system II (APACHE II), sequential organ failure assessment (sofa) score, q-SOFA score, and △SOFA score; Pearson analysis was used to analyze the correlation between SOFA score and other indicators; multivariate logistic regression was used to analyze the prognostic risk factors of patients with sepsis; receiver-operating characteristic curve (ROC) was used to analyze the value of SOFA score alone and in combination in the diagnosis, condition, and prognosis of sepsis.

Results

There were significant differences in Apache II score, SOFA score, q-SOFA score map, po2/fio2, PLT, GCS, TBIL, and serum creatinine (SCR) between the control group and the observation group (P < 0.05). There were significant differences in Apache II score, SOFA score, q-SOFA score, mean arterial pressure (map) po2/fio2, PLT, Glasgow Coma Score (GCS), TBIL, SCR, and △SOFA score among patients in mild, severe, and septic shock groups (P < 0.05). There were significant differences in age, Apache II score, SOFA score, q-SOFA score, map, po2/fio2, PLT, GCS, TBIL, SCR, and △SOFA score between survival group and death group (P < 0.05). SOFA score and q-SOFA score were significantly positively correlated with TBIL and SCR and significantly negatively correlated with po2/fio2 and PLT; △SOFA score was significantly negatively correlated with TBIL and SCR and significantly positively correlated with map, po2/fio2, PLT, and GCS. Apache II score, SOFA score, and q-SOFA score were independent risk factors for sepsis patients, and △SOFA score, po2/fio2, and GCS score were protective factors (P < 0.05). ROC curve analysis showed that the AUC of sepsis combined with SOFA score and q-SOFA score was 0.880; the AUC of sepsis assessed by SOFA score, q-SOFA score, and △SOFA score was 0.929; the AUC of sepsis prognosis assessed by SOFA score, q-SOFA score, and △SOFA score was 0.900.

Conclusion

SOFA score, q-SOFA score, and △SOFA score were abnormally expressed in patients with sepsis and were risk factors for the severity of the patient's condition and prognosis. The SOFA score, q-SOFA score, and △SOFA score were risk factors for the severity and prognosis of patients with sepsis and had some value in diagnosing sepsis and assessing the condition and prognosis, of which the combined value of the three was higher.

Construction of Curriculum Ideological and Political Collaborative Education Mechanism Based on Edge Computing and Neural Network Algorithm

The ideological and political collaborative education mechanism is an important course teaching method that uses all courses as a carrier to cultivate students' all-round development in morality, intelligence, physique, and beauty. The purpose of this paper is to conduct a better research on the construction of the ideological and political collaborative education mechanism by building models based on edge computing and neural network algorithm. This paper first gave a general introduction to edge computing and neural network algorithm and then analyzed the current situation of ideological and political courses in a certain school. Then, edge computing and neural network algorithm were introduced into the analysis of an important course teaching method that used all courses as a carrier to cultivate students' comprehensive development in morality, intelligence, physique, and beauty. The BP neural network model was established. Through analysis and comparison, the experimental results showed that 56.47% of the students believed that the impact of personal morality on the future development of college students was the first in the relationship between "virtue" and "talent." More than half of the students believed that the "virtue" of building morality and cultivating people was mainly civic morality, and about 30% of the students thought that the main value was loving the party and patriotism, which meant that most students believed that the main value of building morality and cultivating people was to establish morality.

The Urban Development in Relation to the Occurrence of Diseases in the Casa-Settat Region of Morocco during the Emergence of SARS-CoV-2

The Casa-Settat region is experiencing very worrying environmental and epidemiological problems and challenges, namely, population growth, the significant development of unsupervised industrial activities, road traffic, the significant weight of the spread of diseases with high epidemiological potential such as SARS-CoV-2, the increase in hospital activities, and the significant discharge of hospital effluents highly contaminated and untreated. To understand and analyze the factors influencing the high prevalence of deaths and the occurrence of diseases under surveillance, among others SARS-CoV-2, on the quantitative data recorded relating to ten regions of Morocco, and informing, on the one hand, on intrinsic data linked to the urban development, and on the other hand, on the evolution of diseases under epidemiological surveillance, a multidimensional analysis was made. The results reveal the typological framework highlighted by the factorial map F1 × F2 which showed the individualization of the region of Casablanca explained by a large number of variables and diseases that affect it. Finally, these results call for a diagnosis that will make it possible to model new approaches and implement new actions promoting the dynamics of environmental and epidemiological change in one of the most polluted and infected regions of Morocco.

Pigeon Inspired Optimization with Encryption Based Secure Medical Image Management System

Presently, technological advancements in the healthcare sector pose a challenging problem relevant to the security and privacy of health-related applications. Medical images can be considered significant and sensitive data in the medical informatics system. In order to transmit medical images in an open medium, the design of secure encryption algorithms becomes essential. Encryption can be considered one of the effective solutions for accomplishing security. Although numerous models have existed in the literature, they could not adaptable to the rising number of medicinal images in the health sector. At the same time, the optimal key generation process acts as a vital part in defining the performance of the encryption techniques. Therefore, this article presents a Pigeon Inspired Optimization with Encryption-based Secure Medical Image Management (PIOE-SMIM) technique. The proposed PIOE-SMIM approach majorly concentrates on the development of secret share creation (SSC) and the encryption process. At the initial stage, the medical images are converted into a collection of 12 shares using the SSC approach. In addition, an elliptic curve cryptography (ECC) scheme is employed for the encryption process. In order to optimum key creation procedure in the ECC model, the PIO technique is exploited with the aim of maximizing PSNR. Finally, on the receiver side, the decryption and share reconstruction processes are performed to construct the original images. The PIOE-SMIM model displayed an enhanced PSNR of 59.37 dB in image 1. Improved PSNR of 59.53 dB is given for image 5 using the PIOE-SMIM model. For demonstrating an enhanced performance of the PIOE-SMIM method, a widespread experimental study is made and the results highlighted the supremacy of the PIOE-SMIM model over other techniques.

Prediction of College Students' Sports Performance Based on Improved BP Neural Network

Sports performance prediction has gradually become a research hotspot in various colleges and universities, and colleges and universities pay more and more attention to the development of college students' comprehensive quality. Aiming at the problems of low accuracy and slow convergence of the existing college students' sports performance prediction models, a method of college students' sports performance prediction based on improved BP neural network is proposed. First, preprocess the student's sports performance data, then use the BP neural network to train the data samples, optimize the selection of weights and thresholds in the neural network through the DE algorithm, and establish an optimal college student's sports performance prediction model, and then based on cloud computing, the platform implements and runs the sports performance prediction model, which speeds up the prediction of sports performance. The results show that the model can improve the accuracy of college students' sports performance prediction, provide more reliable prediction results, and provide valuable information for sports training.

Feasibility Study of Mass Sports Fitness Program Based on Neural Network Algorithm

Mass sports has become a world trend, setting off a new health revolution in the world. Mass fitness programs not only enrich people's lives. It not only relieves the psychological pressure of modern people but also promotes people's health and improves people's quality of life. According to the time-consuming stability of neural network algorithm, this paper proposes a sports video recognition algorithm based on BP neural network. The static and dynamic features are classified by BP neural network, and the basic probability assignment is constructed according to the preliminary recognition results. At the same time, we use evidence theory to fuse the preliminary results and get the results of motion video recognition. It can be applied to the generation model of the feasible scheme of mass sports fitness. Relevant experiments show that the whole model that generates the feasible mass sports fitness scheme can accurately generate the sports fitness scheme of multiple patient users and ensure the rationality and safety of the sports fitness scheme.

Application of Image Processing Technology in Aerobics Injury Diagnosis

Aerobics sports injury diagnosis is a rapid diagnosis of sports injuries caused by athletes in the process of aerobics training or competition. The purpose of this paper is to use image processing technology to study and analyze the diagnosis of aerobics sports injury, so that the diagnosis results can be obtained more quickly and effectively. This paper first introduces the image processing technology and then analyzes the sports injury in colleges and universities. Firstly, the algorithm formula of image processing technology is given, and then the algorithm is introduced into the dynamic analysis of aerobics injury diagnosis. The two are compared through example analysis. The experimental results show that joint strain, sprains, and muscle strain are the main types of sports injury of College Aerobics students, reaching 59, 50, and 31 times, respectively. When using image processing technology in the diagnosis of sports injury, the diagnosis results can be obtained quickly and effectively.

Prediction of conversion to dementia using interpretable machine learning in patients with amnestic mild cognitive impairment

Purpose

Amnestic mild cognitive impairment (aMCI) is a transitional state between normal aging and Alzheimer’s disease (AD). However, not all aMCI patients are observed to convert to AD dementia. Therefore, developing a predictive algorithm for the conversion of aMCI to AD dementia is important. Parametric methods, such as logistic regression, have been developed; however, it is difficult to reflect complex patterns, such as non-linear relationships and interactions between variables. Therefore, this study aimed to improve the predictive power of aMCI patients’ conversion to dementia by using an interpretable machine learning (IML) algorithm and to identify the factors that increase the risk of individual conversion to dementia in each patient.

Methods

We prospectively recruited 705 patients with aMCI who had been followed-up for at least 3 years after undergoing baseline neuropsychological tests at the Samsung Medical Center between 2007 and 2019. We used neuropsychological tests and apolipoprotein E (APOE) genotype data to develop a predictive algorithm. The model-building and validation datasets were composed of data of 565 and 140 patients, respectively. For global interpretation, four algorithms (logistic regression, random forest, support vector machine, and extreme gradient boosting) were compared. For local interpretation, individual conditional expectations (ICE) and SHapley Additive exPlanations (SHAP) were used to analyze individual patients.

Results

Among the four algorithms, the extreme gradient boost model showed the best performance, with an area under the receiver operating characteristic curve of 0.852 and an accuracy of 0.807. Variables, such as age, education, the scores of visuospatial and memory domains, the sum of boxes of the Clinical Dementia Rating scale, Mini-Mental State Examination, and APOE genotype were important features for creating the algorithm. Through ICE and SHAP analyses, it was also possible to interpret which variables acted as strong factors for each patient.

Conclusion

We were able to propose a predictive algorithm for each aMCI individual’s conversion to dementia using the IML technique. This algorithm is expected to be useful in clinical practice and the research field, as it can suggest conversion with high accuracy and identify the degree of influence of risk factors for each patient.

Biomechanical Analysis of Volleyball Players’ Spike Swing Based on Deep Learning

Deep learning is to learn the inherent laws and representation levels of sample data. The information obtained during these learning processes is of great help in the interpretation of data such as text, images, and sounds. Through the deep learning method, the image features are learned independently, and feature extraction is realized, which greatly simplifies the feature extraction process. It uses deep learning technology to capture the motion of volleyball players and realizes the recognition and classification of motion types in the data. It finds the characteristics and deficiencies of the current volleyball players' spiking skills by comparing the test data of 8 volleyball players' spiking skills and biological analysis. The results show that the front and rear spiking balls with double-arm preswing technology have very obvious technical differences. In the take-off stage, there was no significant difference in the buffering time, the kick-off time, and the take-off time in the front and rear row spikes of the A-type. The buffer time of the B-type spike is 0.26 s in the front row and 0.44 s in the rear row. The range of motion of the front row spike is greater than the range of motion of the back row spike. In the air hitting stage, the range of action of the back row spiking is larger than that of the front row spiking, but the range of action of the back row is greater than that of the front row spiking.

Coupling Coordination Analysis of the Ecology and Economy in the Yellow River Basin under the Background of High-Quality Development

The concept of high-quality development has become the current theme of China’s economic construction. High-quality development requires maintaining a healthy and cyclical approach to economic development, which is a challenge in the original development approach. Yet, a great deal of evidence suggests that there is a strong interrelationship between economic development and the ecological environment, and developing a method to quantify this interrelationship is important for studying the extent of high-quality development. Here, we propose a new indicator system using the coupling degree model and the coupling coordination degree model to assess the coupled coordination of economic development and the ecological environment in the Yellow River basin as a whole and in each province. We found that: (1) the economic development and ecological health of the Yellow River basin exhibit a slowly increasing trend; (2) the coupling degree of the economic development and ecological environment is high, indicating that the interaction between the economy and ecology is very strong; and (3) the increasing degree of coupling and coordination reflects the trend of continuous improvement and coordination in the relationship between the economy and ecological environment, and the level of high-quality development in the basin has continuously increased. The results of this study indicate that to continue to strengthen the high-quality development in the Yellow River basin, the contradiction between the economy and ecology should be alleviated, and coordinated development of both should be achieved.

5G Digital Twin: A Study of Enabling Technologies

5G networks require dynamic network monitoring and advanced security solutions. This work performs the essential steps to implement a basic 5G digital twin (DT) in a warehouse scenario. This study provides a paradigm of end-to-end connection and encryption to internet of things (IoT) devices. Network function virtualization (NFV) technologies are crucial to connecting and encrypting IoT devices. Innovative logistical scenarios are undergoing constant changes in logistics, and higher deployment of IoT devices in logistic scenarios, such as warehouses, demands better communication capabilities. The simulation tools enable digital twin network implementation in planning. Altair Feko (WinProp) simulates the radio behavior of a typical warehouse framework. The radio behavior can be exported as a radio simulation dataset file. This dataset file represents the virtual network’s payload. GNS3, an open-source network simulator, performs data payload transmission among clients to servers using custom NFV components. By transmitting data from client to server, we achieved end-to-end communication. Additionally, custom NFV components enable advanced encryption standard (AES) adoption. In summary, this work analyzes the round-trip time (RTT) and throughput of the payload data packages, in which two data packages, encrypted and non-encrypted, are observed.

Effect of Nutritional Protein Food on Metabolism and Physical Fitness of Wushu Athletes

In order to greatly improve the physical function of martial arts athletes, this topic studies the effect of high-protein food on the physical function of martial arts athletes. Forty-five athletes in martial arts events took 5 g of high-protein food every day, 6 times a week for 4weeks, and the left and right forearm, calcaneus bone mineral density, and venous blood was drawn to detect bone metabolism and biochemical indicators related to physical function. The experimental results showed that the bone mineral density of the right calcaneus of male martial arts athletes increased significantly after taking high-protein food, and the bone mineral density of left and right forearms and calcaneus of female martial arts athletes increased significantly. After taking high-protein food, the serum calcium and phosphorus of female athletes and the serum calcium of male athletes were significantly increased. Sex decreased, female athletes significantly decreased serum creatine kinase, and male athletes significantly increased IgM. It can be seen that taking high-protein food for 4 weeks has a certain improvement effect on the bone mineral density of female athletes’ forearm and calcaneus, but has little effect on the bone mineral density of male athletes’ forearm and calcaneus. It can be concluded that high-protein food has no adverse effect on athletes’ bone metabolism, blood biochemical indexes, and immune globulin, and can better maintain the physical function level of martial arts athletes.

Micro Learning Support Vector Machine for Pattern Classification: A High-Speed Algorithm

The support vector machine theory has been developed into a very mature system at present. The original support vector machine to solve the optimization problem is transformed into a direct calculation formula of line in this paper and the model is o(n²) time complexity. In the model of this article, weited theory, multiclassification problem and online learning have all become the direct inference, and we have applied the new model to the UCI data set. We hope that in the future, this model will be useful in real-world problems such as stock forecasting, which require nonlinear hi-speed algorithms.

Traceability Information Model for Sustainability of Black Soybean Supply Chain: A Systematic Literature Review

Traceability information as a solution option becomes an important task for the industry in providing products, preparing sustainable raw materials, and ensuring adequate safety quality. The emergence of these demands makes the industry perform tracking in order to prepare product inventories ranging from raw materials to products that have been produced. Based on these reasons, the scope of this paper is to provide a systematic review of the literature on various aspects of implementing information traceability models and sustainability of supply chain on economic, social, environmental, technological, institutional, and infrastructural dimensions. For this purpose, we use the Scopus, Science Direct, EBSCO Host, and ProQuest databases. We used the PRISMA model to identify, filter, and test for the eligibility of articles to be included. We selected 52 articles contributed by this search engine. We found was that between 2018 to 2021 there was increasing interest in this research. The dominant traceability information model in the article uses blockchain, the rest use operations research (OR), Google Earth Engine (GEE), website-based, Unified Modeling Language (UML), Extensible Markup Language (XML), physical markup language (PML), logit, enterprise resource planning (ERP), soft independent modelling of class analogies (SIMCA), and Spatially Explicit Information on Production to Consumption Systems (SEI-PCS).