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In order to improve the effect of ballet training, this paper combines digital technology to improve the motion recognition process, analyzes the imaging process, analyzes the system process combined with the human node model, and combines digital feature recognition technology to construct a ballet training motion correction system. Moreover, this paper inputs the ballet training action recognition results into the system and compares the standard actions to judge the rationality of the actions and builds a ballet training action correction system based on digital feature recognition. In addition, this paper designs experiments to conduct ballet training movements in the system effect evaluation. The experimental research verifies that the digital feature recognition technology proposed in this paper can play an important role in ballet movement recognition and has a good action correction effect. [ABSTRACT FROM AUTHOR]

Face-centered composite design (FCC) was used to study the effect of pulping variables: soda concentration (4-5 wt%), temperature (90–130°C), and pulping time (20–60 min) on the properties of pineapple leaf pulp and paper employing soda pulping. Studied pulp responses were screened yield and lignin content (kappa number). Paper properties, which include tensile index, burst index, and tear index, were also investigated. Effects of the pulping variables on the properties were statistically analyzed using Minitab 16. The optimum conditions to obtain the maximum tensile index were soda concentration of 4 wt%, pulping temperature of 105°C, and pulping time of 20 min. The predicted optimum conditions provided tensile index, burst index, tear index, screened yield, and kappa number of 44.13 kN·m/kg, 1.76 kPa·m2, 1.68 N·m2/kg, 21.29 wt%, and 28.12, respectively, and were experimentally confirmed. [ABSTRACT FROM AUTHOR]

Background. The deterministic fiber tracking method has the advantage of high computational efficiency and good repeatability, making it suitable for the noninvasive estimation of brain structural connectivity in clinical fields. To address the issue of the current classical deterministic method tending to deviate in the tracking direction in the region of crossing fiber region, in this paper, we propose an adaptive correction-based deterministic white matter fiber tracking method, named FTACTD. Methods. The proposed FTACTD method can accurately track white matter fibers by adaptively adjusting the deflection direction strategy based on the tensor matrix and the input fiber direction of adjacent voxels. The degree of correction direction changes adaptively according to the shape of the diffusion tensor, mimicking the actual tracking deflection angle and direction. Furthermore, both forward and reverse tracking techniques are employed to track the entire fiber. The effectiveness of the proposed method is validated and quantified using both simulated and real brain datasets. Various indicators such as invalid bundles (IB), valid bundles (VB), invalid connections (IC), no connections (NC), and valid connections (VC) are utilized to assess the performance of the proposed method on simulated data and real diffusion-weighted imaging (DWI) data. Results. The experimental results of the simulated data show that the FTACTD method tracks outperform existing methods, achieving the highest number of VB with a total of 13 bundles. Additionally, it identifies the least number of incorrect fiber bundles, with only 32 bundles identified as wrong. Compared to the FACT method, the FTACTD method reduces the number of NC by 36.38%. In terms of VC, the FTACTD method surpasses even the best performing SD_Stream method among deterministic methods by 1.64%. Extensive in vivo experiments demonstrate the superiority of the proposed method in terms of tracking more accurate and complete fiber paths, resulting in improved continuity. Conclusion. The FTACTD method proposed in this study indicates superior tracking results and provides a methodological basis for the investigating, diagnosis, and treatment of brain disorders associated with white matter fiber deficits and abnormalities. [ABSTRACT FROM AUTHOR]

For Internet information services, it is very important to closely monitor a large number of key time series data generated by core business for anomaly detection. Although there have been many anomaly detection models in recent years, its practical application is still a big challenge. The model usually needs repeated iteration and parameter adjustment; and for different types of time series data, we need to select different models. Therefore, this paper proposes an anomaly detection model based on time series. The model first designs the statistical features, fitting features, and time-frequency domain features for the time series, and then uses the random forest integration model to automatically select the appropriate features for anomaly classification. In addition, this paper presents an anomaly evaluation index ADC score with timeliness window, which adds the time delay factor of anomaly detection on the basis of F1-score. We use the KPI time series, a representative key performance index in the industry, as the experimental data. It is found that the ADC score of the anomaly detection model in this paper reaches the level of 0.7–0.8, which can meet the needs of practical application. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth study and analysis of the relevance of early childhood education to occupational psychotherapy using a virtual-assisted affective interaction approach. Starting from the educational theory of interactive cognitive psychology, the theoretical basis for parent-child picture book education for interactive learning is explored, as well as the theoretical development after the introduction of AR technology. Firstly, the analysis of young children's emotions involves massive image processing, and the use of cloud computing architecture leads to high latency, while young children's safety is a latency-sensitive service. Secondly, face recognition accuracy based on static images is not high due to problems such as inconspicuous facial features of toddlers and low-quality kindergarten surveillance videos. In this paper, a face identity correction model based on location features is proposed and the superiority of the model is demonstrated through experiments. Finally, this paper analyzes and mines the emotional data of young children. The level of kindergarten teachers' awareness of early childhood mental health education generally showed an upward trend as their titles rose, and there were significant differences in the seven dimensions of early childhood mental health, the purpose and meaning of early childhood mental health education, implementers, targets, content, pathways, and effects; significant differences existed between teachers of different kindergarten natures, and there were significant differences in the purpose and meaning of early childhood mental health education, implementers, targets, content, pathways, effects, and mental health education for young children. Therefore, this paper proposes a face identity correction model based on position information, which considers both the correlation between pixel values in the spatial domain and the correlation between frames in the temporal domain. This paper has developed an emotion analysis system for kindergartens and put it into use in kindergartens to meet the needs of monitoring the safety of young children and evaluating early childhood education and has received good feedback from users, demonstrating the effectiveness of the system. [ABSTRACT FROM AUTHOR]

In recent years, with the continuous advancement of multimedia technology and network technology, network-based multimedia applications are increasing day by day, both in scale and in the number of users. However, the randomness, time-varying, and complexity of user behavior and network make it impossible to guarantee the service quality (bandwidth, delay, etc.) of multimedia services, thus making it difficult to meet the demands of growing users for multimedia services. While meeting the application requirements, how to design and develop a scalable multimedia service system has become one of the key issues to support good multimedia services and better develop multimedia service services. In addition, the surge in user demand comes with various challenges. High-definition and smooth multimedia services require stable bandwidth. However, the randomness, time-varying, and complexity of user behaviors and network states usually make it difficult to guarantee the quality of services. It is of practical significance to study how to overcome these problems, and it is also a hotspot of current research. This paper mainly discusses the extensibility elements of the multimedia service system and puts forward the relationship between the extensibility of various elements and some design principles of the extensibility. In order to overcome the influence of random variation of the wireless channel on video playback, this paper adopts scalable coding technology. That is, through the analysis of the playback process, the underflow probability is proposed and defined, thereby establishing a constrained optimization problem. Finally, this paper also proposes the multimedia adaptive transmission in the network to do the optimization. [ABSTRACT FROM AUTHOR]

In this paper, we construct a model for intelligent detection of lower limb posture fall prevention sensors, conduct an in-depth study of bright clothing for the elderly with smart detection of fall prevention, and design an article of colorful clothing for the elderly based on intelligent detection of lower limb posture fall prevention sensors. In this paper, the overall system scheme is determined according to the application, including the characteristic position of the device to be worn, the establishment of the reference coordinate system, and the setting of the overall system architecture. The data acquisition, transmission, and saving functions are realized, and the two-step extended Kalman filter, complementary filter, and DMP-Kalman filter are used to solve the posture angle of the measured object. The feasibility of the prototype was verified by testing the daily behavioral activity data and fall behavior data of the elderly, and the collected data were processed and analyzed by three data fusion algorithms, which proved the effectiveness of the algorithms, among which the DMP-Kalman filter algorithm has better performance. From the test results, the design of this paper can detect the posture angle of the human body more accurately, which provides a practical reference for fall detection equipment. Using clothing as a carrier, the fall detection and warning module is integrated with clothing to study an article of intelligent clothing for fall detection and warning for the elderly. A core issue in the integration process is the influence of the clothing on the detection module when the human body is moving. The effect of the material and loft of the clothing on the model is studied through experiments. The results of the two indexes, i.e., sensitivity and specificity, of the integrated model and the model not integrated with the clothing are around 98%. The experimental results of the material and loft of the clothing within a specific range have little influence on the model. The final intelligent garment design scheme was determined on this basis. [ABSTRACT FROM AUTHOR]

This article describes an efficient and effective way to apply the PDCA (Plan-Do-Check-Act) method in the design process to meet quality and stakeholders' expectations. Through the case study of developing a smart workstation to train workers in the assembly process with a target to reduce the defects and improve the management task, the paper explores the main barriers and success factors for the PDCA cycle implemented in complex quality improvement projects. A prototype of the new workstation design is tested and shows significant benefits not only in defect reduction and management efficiency but also in newcomers' learning process. This research can be used as a benchmark application of PDCA in quality improvement and engineering design processes with systematic and comprehensible guidance of the cycle. [ABSTRACT FROM AUTHOR]

Purpose. Single-period segmentation is one of the important steps in time-domain analysis of pulse signals, which is the basis of time-domain feature extraction. The existing single-period segmentation methods have the disadvantages of generalization, reliability, and robustness. Method. This paper proposed a period segmentation method of pulse signals based on long short-term memory (LSTM) network. The preprocessing was performed to remove noises and baseline drift of pulse signals. Thus, LabelMe was used to label each period of the pulse signals into two parts according to the location of the starting point of main wave and the dicrotic notch, and the dataset of the pulse signal period segmentation was established. Consequently, the labeled dataset was input into the LSTM for training and testing, and the results were compared with sum slope function method. Result. The remarkable result with the whole period segmentation accuracy of 92.8% was achieved for the segmentation of seven types of pulse signals. And the segmentation accuracies of the systolic phase, diastolic phase, and whole period using this method were higher than those of the sum slope function method. Conclusion. LSTM-based pulse signal segmentation method can achieve outstanding, robust, and reliable segmentation effects of the systolic phase, diastolic phase, and whole period of pulse signals. The research provides a new idea and method for the segmentation of pulse signals. [ABSTRACT FROM AUTHOR]

At present, the teaching, training, and assessment programs of English speaking courses in schools are basically carried out by the teaching method of face-to-face teaching by teachers, especially in the English speaking assessment; students and teachers communicate with each other to answer questions, while the students' performance is subjectively judged by the school English teachers. This model not only consumes the teaching resources of school English teachers but also cannot ensure the uniformity of grading standards. To this end, this paper designs and proposes an English speaking test system with the aim of building an intelligent speaking test system by designing a speech recognition deepening frequency processing function through SSM framework combined with JSP technology and deep learning theory. The English speaking test system designed in this paper integrates several open source frameworks, adopts modular design, and divides it into two subsystems, student-side module system and teacher-side module system for system framework design, and finally, this system is tested for functionality. The system was designed and implemented through the framework description, functional description, and functional testing of this system, and finally, the English speaking test system was designed and implemented. The system is designed and implemented to change the traditional one-to-one mode of school teaching, save teachers' manpower, strengthen the content of intensive training for students' English proficiency and improve the frequency of exams, facilitate school administrators and English teachers to evaluate the effectiveness of their teaching implementation, and develop corresponding effective individualized teaching and guidance strategies for schools to provide convenience, which can greatly reduce the recognized labor intensity of English teachers and provide a correct evaluation of students, a correct evaluation of the level, and other roles. [ABSTRACT FROM AUTHOR]

Nowadays, TLS (terrestrial laser scanning) has been a relatively mature measuring equipment categorized to indoor measuring robots, but it is not widely adopted in indoor construction measurement at present. What accounts for its limited application are as follows: (1) the high cost of high-accuracy laser LIDAR and (2) existing TLS equipment does not possess self-adaptation scanning planning and takes no account of efficiency of point cloud processing and consumption of computing power. This paper proposes a novel TLS equipment and a high-efficiency point cloud processing method customized for the novel equipment, with purpose to achieve self-adaption measurement on the basis of indoor characteristics of construction during civil engineering at low cost. This paper mainly presents two parts of innovations: (1) for planning of scanning, the novel TLS features planning sampling density of scanning according to room size and converting scanning data from poses to point clouds, and (2) for processing of point clouds, this paper proposes two novel segmentation algorithms, namely, "on-boundary segmentation algorithm" and "on-angular-distance segmentation algorithm," based on indoor spatial structure features and characteristics of TLS. Besides, this paper presents modified RANSAC-TLS (random sample consensus-total least squares) plane fitting algorithm, on basis of TLS point cloud distribution characteristics and spatial transformation. Through actual measurement test, it is concluded that the "on-boundary segmentation algorithm" and "on-angular-distance segmentation algorithm" are suitable for point cloud segmentation in different types of scenes. The modified RANSAC-TLS have made a great improvement on accuracy of fitting versus LS (least squares), TLS (total least squares), and RANSAC-LS. Finally, this paper conducts an experiment by executing an actual measurement and then preliminarily testifies the potential and future application of the proposed novel TLS (terrestrial laser scanning) equipment, with measurement parameters from it being changed in the experiment, by comparing with one existing TLS equipment—FARO. The test thus proves the relatively high feasibility and potential of the novel TLS presented in the paper (terrestrial laser scanning) in actual indoor measurement. [ABSTRACT FROM AUTHOR]

In order to explore the teaching efficiency of online intelligent courses and improve the quality of online teaching, this paper builds a classroom intelligent auxiliary management model based on the grid simplification method. Moreover, this paper formulates corresponding teaching strategies through the recognition of student state features, uses a target detector to detect all detection targets from the scene, and then counts the number of detection targets, identifies specific individuals, and judges the individual state. Simultaneously, this paper intercepts candidate subregions from the scene image and then inputs the subregion image to the detector to determine whether the candidate region image is a detection target and formulate corresponding countermeasures. In addition, on the basis of the existing 3D mesh model stitching and editing method, this paper proposes a grid splicing and fusion method based on the idea of partial reuse of the model to calculate the result of the target. Finally, this paper designs experiments to verify the performance of the model. The research results show that the model constructed in this paper is effective. [ABSTRACT FROM AUTHOR]

As a form of animation that is closest to the essence of movies among all kinds of animation, stop-motion animation is deeply loved by people and has a lot of room for development in the new era. The steady progress of science and technology has raised people's requirements for the presentation form of stop-motion animation. People are no longer satisfied with the design of stop-motion animation, which only unilaterally transmits information to the audience and does not interact with the audience. To meet people's needs, this paper applied the augmented reality technology of digital media to the design and production of live-action stop-motion animation. It used 3D tracking and registration technology to integrate virtual characters into real-life stop-motion animation, and presented it to the audience through augmented reality equipment to improve the interaction between real-life stop-motion animation and the audience. Finally, this paper conducted an experimental design on the fluency, accuracy, animation playback, and user satisfaction of the real-life stop-motion animation of augmented reality. The results showed that the smoothness and accuracy of the live-action stop-motion animation using the augmented reality technology of digital media have been correspondingly improved, and the user's satisfaction with the augmented reality live-action stop-motion animation has increased by 3.1%. The augmented reality technology of digital media has greatly improved the interaction between live-action stop-motion animation and the audience and promoted the development of live-action stop-motion animation. [ABSTRACT FROM AUTHOR]

In order to improve the monitoring effect of building indoor environment, this paper carries out indoor environment simulation through 3D modeling, uses image processing technology to collect environmental monitoring data, and proposes a routing algorithm applied in this environment. Moreover, this paper introduces the energy consumption model of sensor nodes and the multi-attribute decision-making method and uses the multi-attribute decision-making to propose an adaptive energy balance routing algorithm based on the indoor environment of the building. In addition, this paper establishes a simulation analysis, compares it with several other routing algorithms, and verifies that the proposed algorithm has better balance in network energy consumption and longer network life cycle. Finally, this paper combines three-dimensional modeling and image processing technology to construct the building indoor environment monitoring system. It can be seen from the simulation experiments that the proposed building indoor environment monitoring system based on 3D modeling and image processing can meet the actual needs of intelligent building indoor monitoring. [ABSTRACT FROM AUTHOR]

Aiming at the problems of long life cycle complex roles of participants, diverse risk factors, poor supervision coverage and difficult information traceability of TCM (Traditional Chinese Medicine) supply chain, this paper constructs a TCM quality safety traceability system based on blockchain technology by analyzing the business process and supervision characteristics of TCM supply chain. Blockchain technology is a new application mode of computer technology such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, and other computer technologies. It has the characteristics of "decentralization, nontampering, transparent and open, and data traceability." The function of nontampering, hash function, and time stamp can effectively solve the traceability problem of TCM supply chain. Through the analysis of the blockchain TCM traceability principle and network structure, combined with the actual business process of TCM traceability, this paper designs the blockchain TCM quality safety traceability system, analyzes its overall structure, data storage mode, digital signature, consensus algorithm, and main functions, and preliminarily constructs a new model of TCM quality safety traceability management based on blockchain technology. Using blockchain technology to realize the quality safety traceability of TCM, we can complete the omnidirectional, multiangle, and wide coverage of the data and information in whole supply chain of TCM breeding and seedling raising, planting, innovation and research and development, processing of TCM pieces, circulation of TCM, and consumption, so as to realize that the main body of responsibility of TCM can be put on record, the production records can be queried, product flow can be traced, quality safety can be forewarned, main responsibility can be identified, regulatory information can be shared, and product source can be traced. [ABSTRACT FROM AUTHOR]

It is essential for realizing the most suitable product buffer packaging design to quantify the vibration transmission characteristics of the product packaging system. The experiment system for the vibration transmission path of protective packaging is designed in this paper. The practical system is used to analyze the vibration transfer path of the product packaging system and identify the critical transfer path. The concepts of the cushions' contribution rate and the cushions' weighted contribution rate are introduced. The product cushioning based on the weighted equal contribution rate of the cushions is proposed. It has been verified by experiments that the system can accurately identify the transfer path with the weighted contribution rate of the cushions as a reference for the design of product buffer packaging, which improves the utilization rate of buffer packaging materials and reduces the cost of packaging materials. The weighted equal contribution rates of buffer pads 1, 2, 3, and 4 are 40%, 27%, 22%, and 11%, respectively. For the needs of experiment teaching, the teaching content based on the protective packaging transfer path testing system is designed, which provides a reference for the practical education of the packaging specialty. [ABSTRACT FROM AUTHOR]

Existing research combines acupuncture theory with network science and proposes a new paradigm for the study of acupoint selection patterns—a key acupoint mining algorithm based on acupoint networks. However, the basic idea of this study for finding key acupoints is based on binary acupoint synergy relationships, which ignores the higher-order synergy among multiple acupoints and does not truly reflect the implicit patterns of each acupoint among meridian systems. Moreover, the mining results assessment method, which this new paradigm involves, does not have wide applicability and universality. In this paper, with the introduction of higher-order interactions between multiple acupoints, a high-specificity key acupoint mining algorithm based on 3-node motif is proposed in the acupoint-disease network (ADN). In response to the narrow applicability of the new research paradigm involving the evaluation of algorithms' measures, new and widely applicable and universal evaluation criteria are introduced in terms of resolution, network loss, and accuracy, respectively. Based on the principles of acupoint selection involved in acupuncture clinics in Chinese medicine, the acupoints involved in the data were divided into a total of 19 regions according to their distribution characteristics. From these 19 regions, we selected the key acupoints that have a large impact on the global network. Finally, we compared this algorithm with five other acupoint importance assessment algorithms in terms of resolution, network loss, and accuracy, respectively. The comprehensive results show that the algorithm identifies key acupoints with an accuracy of 63%, which is 14% to 21% higher than other existing methods. The key acupoints identified by the algorithm have a significant disruptive effect on the connectivity of the network, indicating that the key acupoints are at the core of the acupoint-disease network topology. They have a significant propagation influence on other acupoints, which means that the key acupoints have high-synergistic cooperation with other acupoints. Meanwhile, the stability and specificity of the algorithm ensure the reliability of the key acupoints. We believe that the key acupoints identified by the algorithm can be used as core acupoints from the perspective of network topology and high synergy of other acupoints, respectively, and help researchers explore targeted and high-impact combinations of acupoints to optimize existing acupuncture prescriptions under condition constraints. [ABSTRACT FROM AUTHOR]

At present, artificial intelligence technology has developed rapidly and has been gradually applied to various fields of society. With the popularization and development of computers, under the influence of artificial intelligence technology, digital media art and artificial intelligence technology merge with each other, which makes modern culture bloom with greater charm. Based on the era of artificial intelligence, this paper discusses the development core of the integration of artificial intelligence technology and digital media art, analyzes the current development status of digital media art and technology, and finally puts forward the innovative development direction and future trend. At present, China's artificial intelligence technology has gradually entered a prosperous stage in the continuous development and has become one of the high and new technologies in the new era, bringing many changes to people's work and life. With the development of society, modern people are pursuing the two-way needs of material and spirit, and art, as a special field, has attracted much attention. By involving artificial intelligence in the art field, it has become a new challenge to be faced at present, which will promote art and technology to present different forms of expression. Of course, the artistic content of digital media art will be richer, and the attention will be higher. Under the background of artificial intelligence era, the integration of artificial intelligence and digital media is the core industry at present, which promotes the development of China's economy in a better direction. [ABSTRACT FROM AUTHOR]

In order to meet people's diverse, complex, and changeable living requirements and aesthetic requirements, it is necessary to rationalize the design of indoor three-dimensional space. The optimization of the indoor environmental space layout is affected by the energy consumption and rationality of functional areas, resulting in indoor environmental space The performance of the layout optimization system deteriorates. In order to improve the performance of the indoor environment spatial layout and meet the living needs of more people, this paper proposes an optimal design of a spatial layout based on multi-intelligence decision-making. The research results of the article show that (1) the average confidence and the average recognition time of logo set B are both smaller than those of logo set A , indicating that logo set B is easier to be recognized by users in the same commercial building environment and can be more quickly recognized. In terms of discrimination, the time used to correct the misrecognition of identification set B is shorter than that of identification set A , which means that the different types of identifications in identification set B are easier to distinguish. (2) The spatial positioning algorithm proposed in this paper has good reliability and practicability, high positioning accuracy, and small error for indoor spatial coordinate positioning. Compared with the traditional method, the projection method of this paper has a significantly smaller error, and the maximum error is only 0.272, which makes the indoor space design more rational. (3) Comparing the energy consumption coefficients of the three spatial layout optimization systems, it can be seen that the indoor environmental spatial layout optimization system based on multi-intelligence decision-making has the lowest energy consumption when optimizing the indoor environmental spatial layout, because the system is in the design process. The indoor environment space layout model is designed, which effectively reduces the energy consumption of the indoor environment space layout optimization. When the indoor environment space layout optimization system based on multi-intelligence decision-making is used to optimize the indoor environment space layout, the average optimization accuracy is 98.32%, while the average indoor environment space layout optimization accuracy of the space layout optimization system with curved shading space layout optimization is 42.2% %, the layout optimization of binocular stereo vision space. When optimizing the indoor space layout, the average optimization accuracy is 70.87%. [ABSTRACT FROM AUTHOR]

In the machine learning model, intelligent recommendation system can select valuable information from a lot of data to help users find the products or services they need, which has been more and more widely used in recent years. However, there are still many problems in machine learning recommender systems, such as data sparsity, natural noise, and cold start, which leads to the fact that machine learning recommender systems cannot obtain accurate user preferences. When a project is rated, the quality of the recommendation is greatly affected. In order to solve the problem that the existing recommendation algorithms have poor recommendation results in sparse data sets, this paper proposes a machine learning method for recommendation rating prediction based on user interest concept lattice. Firstly, the nearest neighbors are divided into direct nearest neighbors and indirect nearest neighbors by user interest concept lattice. Then, different methods are used to calculate the similarity between the direct "nearest neighbor" and the target user, and the similarity between the indirect "nearest neighbor" and the target user. Finally, the invisible item score of the target user is calculated by the similarity value. Experiments are carried out on real data sets, and the experimental results show that the CFCNN-CL algorithm and RRP-UI CL algorithm proposed in this paper have high recommendation accuracy and still have good performance in the case of sparse data. [ABSTRACT FROM AUTHOR]

The current social situation is that the rapid development of science and technology has brought about rapid economic growth, but the wanton development of ecological environmental resources, resulted in environmental deterioration, so that human survival faces major challenges. This is not only the current state of life but also an urgent problem that people will work together to solve. Therefore, in order to cultivate environmental awareness, make students intuitively and clearly understand the benign operation of ecological environment and the close relationship between people and the environment, this paper designs and studies the existing information technology to design biology teaching on the basis of the sustainable development of ecological environment and encourages students to strengthen the protection of the environment. This paper studied and designed the practical inquiry, analysis, and questionnaire survey. The experimental results show that the students in information technology teaching perform better than the traditional students, which can stimulate students' enthusiasm, arouse their interest in learning, and deepen students' understanding. The number of outstanding students increased by 5, and the number of failed students decreased by 8. The research significance of this paper is to provide new teaching design ideas for the existing biology teaching mode so that the development of information technology can benefit biology teaching. [ABSTRACT FROM AUTHOR]

In this paper, a special design system is developed based on the design of the packaging bottle to achieve the effective acquisition of the image of the cross-section of the packaging bottle to be measured under the condition of limited space size, avoiding the distortion of the object to be measured. At the same time, the image of the area where the target packaging bottle is located is segmented, and the curve features are quickly determined and effectively matched with the template library to realize the recognition of the shape features of the bottle. In this paper, the design of the packaging bottle is first designed by mechanism design and 3D modeling, followed by rapid prototyping methods such as 3D printing, and the prototype is made for functional verification. Finally, the transmission speed and stability of the design system for packaging bottle recognition are improved through structural analysis and optimization methods. To realize the intelligent control of the packaging bottle transmission and identification system, the hardware control circuit is designed and the relevant intelligent control program is prepared based on the embedded system so that the packaging bottles in the transmission process can be quickly and accurately positioned and identified. An improved AdaBoost algorithm is proposed for packaging bottle detection. In the process of algorithm learning, the Haar features are too large and time-consuming, and the training sample is cropped to remove the sample edge pixels, which effectively reduces the number of features, thus reducing the computation. The proposed optical flow method is used to obtain the motion region in the video image as the region of interest, and the canny operator is used in the region of interest for edge detection, and the region of interest is filtered by the edge energy to exclude the noninterest region. Finally, the AdaBoost algorithm is used to detect the region of interest, which reduces the detection area and decreases the detection time. The improved AdaBoost algorithm has a high accuracy improvement over the traditional AdaBoost algorithm for the recognition of various packaging bottles with relatively suitable training set samples, and the system recognition time has reached the requirements of industrial recognition. [ABSTRACT FROM AUTHOR]

Space has become a driver not only for knowledge, inspiration, cooperation, and policymaking, but also for high-end technology integration into the manufacturing domain. As the high-density city quarters, the Macau Peninsula carries multiple functions via its public outdoor spaces, and they are of vital significance. Closely intertwined with Industry 4.0 is therefore the next industrial revolution that is already showcasing the benefits of increased connectivity, intelligence, and flexible automation. The rapid urbanization of Macao was not guided by sound land use planning, induced the scattering of many negative spaces on narrow streets. Because of their irregular shapes and utilization difficulties, these spaces gradually become leftover spaces. A number of measurement-based street leftover spaces models have been developed over the years by researchers as well as standards bodies to model path in typical wireless environments. In this paper, the streets of Macao are analyzed based on the city characteristics. The pedestrians and spatial relationships are investigated via space syntax and SPSS regression fitting. Finally, activation design of street leftover spaces is conducted, for making better use of leftover spaces, compensate for the functional deficiencies of streets, and endow them with more vigor and vitality. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence and big data, the concept of "Internet plus education" has gradually become popular, including automatic scoring system based on machine learning. Countries all over the world vigorously promote the deep integration of information technology and discipline teaching in various fields. English is a medium of communication in the current era of education information development trend. English composition automatic scoring mode is gradually accepted by the majority of educators and applied in the actual classroom teaching. However, the research of English composition automatic grading in teaching space is not perfect. Most systems have used traditional algorithms. Therefore, this paper constructs the automatic scoring algorithm and sentence elegance feature scoring algorithm of English composition based on machine learning, explores the influence of the algorithm on English writing teaching, and proves the correctness of the design idea and algorithm of this paper through a lot of experiments. [ABSTRACT FROM AUTHOR]

In order to explore the correlation between stocks and the PMI index, based on the generalized logistic loss and margin distribution, this paper designs a margin distribution logistic regression model that is easy to optimize, has robustness, and generalization ability, and gives a multiclass margin distribution logistic regression framework. This framework can be used to perform two-classification, multiclassification, and feature selection tasks. Moreover, this paper gives a training algorithm for margin distribution logistic regression on large-scale data sets through the pairwise stochastic gradient descent method. In addition, this paper combines the logistic regression model to construct a correlation analysis model between stocks and PMI index and uses the PMI data of the National Bureau of Statistics as a sample to design experiments to verify the performance of the system model constructed in this paper. From the experimental analysis, it can be seen that the algorithm constructed in this paper has a certain effect, and the strong correlation between PMI and stocks has been further verified. [ABSTRACT FROM AUTHOR]

Although individual factors play a vital role in determining professional autonomy, their specific impact during the coronavirus disease 2019 (COVID-19) pandemic has not been studied. This study aimed to compare nurses' professional autonomy when caring for patients with COVID-19 and for other patients and explore factors related to autonomy when caring for these patients. A paper-based questionnaire survey was conducted from June to August 2022 among nurses working at a university hospital in Japan. The survey included demographic factors (10 items, including, age, section, years of nursing experience, position, educational background, ladder level, and having certified nurse specialists or certified nurse qualifications) and individual experience factors (4 items: number of COVID-19 patients experienced, frequency and contents of searching for the latest information about COVID-19, frequency of using scientific sources, and frequency of training/study sessions on COVID-19 attended at the hospital). Additionally, basic knowledge of COVID-19 was evaluated. The scale for nurses' professional autonomy was developed based on a previous study. A paired t-test and stepwise multiple linear regression were used for the analyses. Overall, 241 nurses participated in the survey. The average length of nursing experience was 10.3 ± 9.2 years. The total scores for nurses' professional autonomy in all 5 factors 27 items were significantly lower (t = −12.1, p < 0.001) when caring for COVID-19 patients than when caring for other patients. Specifically, Factor 1 (Cognition) exhibited the most decreased scores when caring for COVID-19 patients than when caring for other patients. Factor 4 (Abstract judgment) differed the least between caring for COVID-19 and for other patients, but the average score was the lowest. More years of nursing experience (β = 0.208, p = 0.001) and a higher number of patients with COVID-19 cared for (β = 0.140, p = 0.026) were associated with higher autonomy scores. In conclusion, to enhance professional autonomy during an unprecedented pandemic, nurses must enhance cognition and abstract judgment. In the event of a future pandemic, nurses need to create an environment in which they routinely access and utilize the latest information and scientific evidence to provide high-quality nursing care based on their professional judgment and competence. [ABSTRACT FROM AUTHOR]

Steam-assisted gravity drainage (SAGD) has been used to develop the "super heavy" oil reservoirs in Canada. The viscosity can reach more than 30,000 cp at 50°C. Moreover, owing to their continental deposit origin, the reservoirs have a low porosity and permeability. Because of these challenges, the conventional steam circulation start-up process takes 6 to 12 months before the well pair can be switched to production. Solvent has been used to start-up SAGD with success. But now, low price of oil and high cost of solvent make solvent-assisted start-up process limited. This paper applies experimental schemes, such as viscosity reduction rate evaluation, core flooding, and 3D physical simulation, tests solvent performance, optimizes process parameters, and designs process solutions. Apply numerical simulation to test solvent-assisted SAGD start-up effect and calculate the cost. This paper researches a unique low-cost solvent compare with xylene. The basic properties and core flood experiment show that the two solvents are similar with viscosity reduction rate, asphalt dissolution rate, and injection pressure, and the price of solvent is 18% lower. The 3D model experiment shows that the average start-up time is reduced by 15%, and steam injection volume is reduced by 21.4%. The numerical simulation results show that without solvent, it will take 180 d for start-up process, and with solvent, the time has reduced by 50% and takes 90 days. Cost calculation results show that the cost will reduce 18% by solvent compared to xylene. Moreover, the production rate has been improved in production stage. This paper applies a 3D physical model to simulate the solvent-assisted SAGD start-up process. Research conclusions show the start-up mechanism of solvent and the process of temperature change of steam chamber. [ABSTRACT FROM AUTHOR]

Big data is a massive and diverse form of unstructured data, which needs proper analysis and management. It is another great technological revolution after the Internet, the Internet of Things, and cloud computing. This paper firstly studies the related concepts and basic theories as the origin of research. Secondly, it analyzes in depth the problems and challenges faced by Chinese government management under the impact of big data. Again, we explore the opportunities that big data brings to government management in terms of management efficiency, administrative capacity, and public services and believe that governments should seize opportunities to make changes. Brainlike computing attempts to simulate the structure and information processing process of biological neural network. This paper firstly analyzes the development status of e-government at home and abroad, studies the service-oriented architecture (SOA) and web services technology, deeply studies the e-government and SOA theory, and discusses this based on the development status of e-government in a certain region. Then, the deep learning algorithm is used to construct the monitoring platform to monitor the government behavior in real time, and the deep learning algorithm is used to conduct in-depth mining to analyze the government's intention behavior. [ABSTRACT FROM AUTHOR]

The detection of moving targets is to detect the change area in a sequence of images and extract the moving targets from the background image. It is the basis. Whether the moving targets can be correctly detected and segmented has a huge impact on the subsequent work. Aiming at the problem of high failure rate in the detection of sports targets under complex backgrounds, this paper proposes a research on the design of an intelligent background differential model for training target monitoring. This paper proposes a background difference method based on RGB colour separation. The colour image is separated into independent RGB three-channel images, and the corresponding channels are subjected to the background difference operation to obtain the foreground image of each channel. In order to retain the difference of each channel, the information of the foreground images of the three channels is fused to obtain a complete foreground image. The feature of the edge detection is not affected by light; the foreground image is corrected. From the experimental results, the ordinary background difference method uses grey value processing, and some parts of the target with different colours but similar grey levels to the background cannot be extracted. However, the method in this paper can better solve the defect of misdetection. At the same time, compared with traditional methods, it also has a higher detection efficiency. [ABSTRACT FROM AUTHOR]

The value of pathological linguistics in children's cognitive development has attracted more and more experts' attention. Based on pathological linguistics, this paper establishes an intervention system for children's cognition-language assessment and cognitive education, guided by a set of fixed procedures, which can quickly collect children's speech data in a short period of time. Based on this evaluation paradigm, the lab collected a large number of speech data of children aged 2-14 in the process of verbal communication. Based on the six linguistic dimensions of phonology, productivity, fluency, grammar, semantics, and logic, the corpus was subdivided into 16 indicators for manual annotation and machine recognition. A cognitive-language assessment database is established for language ability assessment and language barrier screening, and on this basis, the children's language assessment and cognitive education intervention system is completed based on six modules: user, assessment, scale, resources, teaching, and data. Through experimental research, this paper proves that there are differences in the contribution rates of six language dimensions to the screening of children's language disorders, and the top three are fluency (29.6%), pronunciation (25.7%), and productivity (19.3%). By analyzing the contribution rates, the development of children's comprehensive speech ability can be evaluated more accurately. The early intervention education of children's cognition has a great relationship with their comprehensive language ability. The intervention education before the age of 3 is beneficial to the language development of children with disabilities. Children's cognitive education has improved in cognitive performance, cognitive generalization level, emotional cognition, and language expression, indicating that the use of cognitive education system has a good effect, improving the level of children's cognitive education. [ABSTRACT FROM AUTHOR]

Physical education pedagogy serves as a carrier of physical education theory and practical activities and plays an important role in physical education. The reform and development of education are always related closely to the growth of scientific and technological development, and people's physical education training methods are also undergoing subtle changes. The traditional single and formulaic training methods in the past have gradually turned to modern training methods with better interaction and visibility. All of this prompts people to have new requirements on sports training concepts and training modes. On this basis, teachers can conduct virtual teaching in a more interactive, more involved, and orderly manner in the virtual classroom. This paper constructs the implementation framework of VR basketball teaching auxiliary training, divides students into an experimental group and a control group and tests the physical fitness of students before and after the experiment. The students' basic basketball skills were tested by using embedded intelligent sensors to recognize the students' movements. The experimental results showed that after the test, the four basic basketball skills scores of the students in the experimental group were 68.78, 74.02, 73.13, and 72.34, respectively. The virtual reality basketball teaching system designed in this paper can effectively improve the basketball level of students. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to provide a group music therapy intervention for elementary and middle school students with mood disorders through music therapy. To explore the improvement of group music therapy on the dysphoria and state of adolescent patients with mood disorders and to explore the relationship with self-identity. A music activity-based mental health education curriculum was designed based on the results of the current survey, while two homogenous classes were selected as the experimental and control classes; subsequently, the experimental teaching of the music activity-based mental health education curriculum was conducted in the experimental class, and the regular school mental health education curriculum was conducted in the control class, and the pre- and postexperimental scale tests were used to analyze that the curriculum of this study had a positive effect of this study's curriculum on elementary school students' positive emotions analyzed through pre- and postexperimental scale tests. The music activity-based mental health education program had a significant positive effect on the overall level of positive emotions of elementary school students, especially in the positive emotion index, happiness index, mental toughness, goal focus, and interpersonal assistance factor. The positive psychology-oriented music activity-based mental health education curriculum is well suited for elementary and middle school students, especially for upper elementary and middle school students. The music activity-based mental health education curriculum realizes the value of applying positive psychology to mental health education efforts at the elementary school level and flexibly uses a variety of related theories to promote positive emotional experiences for elementary and middle school students. [ABSTRACT FROM AUTHOR]

The construction of the economic management information system ensures the flow and sharing of information, business coordination, and scientific decision-making between economic functions. Therefore, project construction is absolutely necessary and urgent. Project management has achieved significant results in terms of shortening project time and reducing management costs. Information system is an important model information structure in artificial intelligence. This paper focuses on the design of a fusion method for economic management problems based on decision information systems. This paper summarizes the value of the whole project through the research on the project management of the economic management information system. The results of this study show that when the threshold is fixed, the information granularity Gtheta and rough entropy (Er) theta decrease monotonically with increasing attribute subset cardinality. Meanwhile, θ-information granularity Etheta and θ-information entropy Htheta monotonically increase with the increase of attribute subset cardinality, which indicates that when attribute subset cardinality increases, the uncertainty of the information system decreases. [ABSTRACT FROM AUTHOR]

To explore the application ways and effects of in-depth learning in English Teaching for Non-English Majors in Colleges and universities, 419 students are selected for the experiment, 205 students from grade 2018 were used as the reference group and 214 students from grade 2019 were used as the observation group to observe the effect of examination-oriented education and the impact of quality through in-depth learning. The performance of students of grade 2019 in examination-oriented education performance data and quality education performance data was significantly better than that of students of grade 2018, T < 10.000, P < 0.01 , which was statistically significant, Finally, it is found that the intelligent deep learning algorithm is applied to the analysis of the knowledge structure of non-English students' English papers, and the application scenarios of the analysis results are widely developed, which is helpful to improve the English teaching effect of non-English students, improve students' learning efficiency and reduce students' pressure. [ABSTRACT FROM AUTHOR]

The emotional stress of English translation practitioners is an important research content of psychological organization counseling behavior. Based on the theory of role cognition, this paper adopts the perspective of emotional stress experience, integrates the construction theory and the self-determination theory, and builds an emotional stress relief mechanism for English translation practitioners. On the basis of sorting out psychological capital, emotional commitment, and behavioral variables of organizational practitioners, the article summarizes the concept, dimension, measurement, and causes and effects of variables and solves the problem of quantitative analysis of emotional stress. In the simulation process, the role cognitive variables and local cognitive variables are extracted separately using the dual-branch mitigation mechanism structure to enhance the diversity of the extracted cognitive variables; in the local branch, a loss function of emotional stress overlapping partial penalty mechanism is constructed. This mechanism is used to select a suitable destination sink node for the sensor nodes around the sink node with heavy load to transmit data and effectively relieve the data collection pressure of the original sink node. The experimental results show that by using role cognitive variables and local cognitive variables in the training of classification loss and triple loss, respectively, the mitigation mechanism can mine subtle and significant cognitive variables from the local area and finally integrate role cognitive cognition. The accuracy rate of obtaining 297 questionnaires reached 91.4%, which effectively promoted the mechanism research of emotional stress on the behavior of English translation practitioners. [ABSTRACT FROM AUTHOR]

The suspension bridges in mountainous areas are commonly designed with asymmetrical overall layouts to match the terrain and construction under limited space. However, the effects and influences of the asymmetry parameters on the bridge performance have yet to be thoroughly investigated. To address this gap, based on a real-world suspension bridge with a main span length of 700 m, this paper first presents an improved shape-finding method that can fully consider the components of hanger forces in each construction step. In the iterative process, the shape of the cable is determined based on the equilibrium equations, and the hanger forces are calculated through the nonlinear finite element analysis. After deciding the bridge's initial state, the asymmetry parameters' effects are carefully investigated through finite element analysis under the static and seismic conditions. Results show that the side-to-span ratio of the main cable and the tower's stiffness can affect the horizontally constrained stiffness, resulting in distinct bridge behavior. Moreover, reasonably designing the dampers between the tower and the girder can be beneficial in minimizing the longitudinal displacement and controlling the tower's moment under the seismic situations. [ABSTRACT FROM AUTHOR]

A flexible pneumatic wrist with spatial position maintenance function is developed in this paper to settle the matter of insufficient flexibility of the robot wrist and self-braking technology. The wrist is made up of four artificial muscles and a pneumatic spherical brake in parallel, with 2 degrees of freedom. The wrist can realize multidirectional bending and adjust the wrist damping and braking in real time as required to achieve position maintenance. The theoretical model of the wrist bending angle is built based on torque balance. The variation of wrist bending angle and motion trajectory with air pressure is acquired through experiments. Simultaneously, the validity of the bending angle theoretical model is verified experimentally. A normally open pneumatic spherical brake is developed, and the mathematical model of braking torque is built and experimentally validated. Using a 3D dynamic capture system to compare the dynamic characteristics of the wrist under different excitation signals and different damping conditions. The experimental results reveal that the wrist has good flexibility and can achieve the functions of human hand pitch and yaw. The bending angle is 20.72° at 0.34 MPa. The pneumatic spherical brake has the function of spatial multidirection braking, and the braking force is adjustable. The maximum braking torque can reach 1.4 N m at 0.35 MPa. [ABSTRACT FROM AUTHOR]

This paper constructs an algorithm for youth school violence recognition and an occupational therapy education model for victims through the extraction of action speech features. For the characteristics of violent actions and daily actions, action features in time and frequency domains are extracted and action categories are recognized by BP neural network; for complex actions, it is proposed to decompose complex actions into basic actions to improve the recognition rate; then, LDA dimensionality reduction algorithm is introduced for the problem of the high complexity of algorithm due to high dimensionality of features, and the feature dimensionality is reduced to 8 dimensions by LDA dimensionality reduction algorithm, which reduces the system running time by about 51% and improves the accuracy of violent action recognition by 3.3% while ensuring the overall performance of the system. The LDA dimensionality reduction algorithm reduces the number of features to 8 dimensions, which reduces the running time of the system by 51%, increases the accuracy rate of violent action recognition by 3.3%, and increases the recall rate of violent action recognition by 8.86% while ensuring the overall performance of the system. Based on the classical D-S theory, we proposed an improved D-S evidence fusion algorithm by modifying the original evidence model with a new probability distribution function and constructing new fusion rules, which can solve the fusion conflict problem well. The recall rate for violent actions is increased to 90.0%, thus reducing the missed alarm rate of the system. [ABSTRACT FROM AUTHOR]

This paper constructs a platform framework for extensive data analysis of college students' psychological quality with the help of the thinking mode of big data and related technologies and proposes the construction principles, data sources, data processing methods, data platform construction, and platform application of big data analysis platform for college students' psychological quality assessment. This paper combines the application methods of big data technology, collects the management data related to the psychological quality assessment of college students, saves them into the system database with certain storage logic, and realizes the function of psychological quality assessment through the design of selected psychological quality assessment data, data management and data resource management and other parts based on the data results of extensive data analysis. This study provides some insights into the psychological quality assessment of college students. The strength of association between the variables of psychological quality assessment of college students changes over time, but the overall psychological structure is more stable. This stable psychological structure characteristic is conducive to constructing the policy of constant psychological education in large universities. [ABSTRACT FROM AUTHOR]

Simulating the flight of dragonflies has always attracted scientists' interest. This paper studies the theory and method of the transmission mechanism of dragonfly robot, establishes the relationship function model between wing swing angle and geometric parameters, and designs a kind of gear and rod mechanism transmission chain, including a pair of wing angle turnover mechanism, so that the wings swing smoothly, no jam phenomenon occurs, and improves the lift ratio. In this paper, the dynamic principle of Bionic Flapping Wing robot was analyzed, and the design scheme of driving mechanism is put forward; the project realizes the three-dimensional design of robot parts, the virtual digital prototype and simulation, and 3D printing of the prototype robot. During the design and manufacture, the lightweight design and processing of the fuselage and wings are carried out to reduce its weight. The drive circuit and algorithm are designed with 8051 chip as the CPU; the balance and attitude control methods are proposed. [ABSTRACT FROM AUTHOR]

In order to study the suit style design based on computer interactive genetic algorithm. Firstly, this paper summarizes the development status of garment customization and interactive evolutionary computing and studies the steps and system flow of customized garment design based on interactive evolutionary computing and the automatic generation method of garment style. Then, taking the suit as an example, the principal component analysis method is used to extract the elements of clothing design and code it. Combined with the interactive genetic algorithm, the user-satisfied clothing style binary string is generated, and the style binary string is decoded into a visual style map through the decoding algorithm. Finally, based on MATLAB platform, a customized clothing system prototype based on interactive evolutionary computing is realized. Users can carry out innovative clothing design through interactive graphical interface, automatically draw the clothing style designed by users, and provide method and technical support for clothing customization. The experimental results show that in the process of interaction design, try to solve the initial population required by the design, evolve and derive offspring on a large scale through genetic operation, retrieve the whole design feasible region, and obtain the optimal solution in line with user preferences. [ABSTRACT FROM AUTHOR]

Through in-depth analysis of the typical international wireless sensor network experimental system, the current design generally lacks the real-time diagnosis of the health state of the fault node and the experimental platform, the management and control mechanism to minimize the interference of the experimental task, and the highly integrated and friendly experimental entrance, which will have an obvious distortion impact on the experimental results. In view of the above shortcomings, on the basis of fully absorbing the design advantages in this research field, this paper studies and designs a high fidelity WSN experimental system, which covers the elements of open source, heterogeneity, analysis visualization, and experimental repeatability and innovates the mechanisms of real-time diagnosis, hierarchical parallel control, and TDMA to collect a large amount of experimental data. In order to support a variety of applications based on IEEE802.15.4 band, a high-fidelity and user-friendly experimental platform software and hardware system with 15 clusters and 130 heterogeneous nodes is studied and designed, which can simulate outdoor mesh networks with more than 6 hops. This paper is designed and implemented on Linux open-source platform. The whole system has low cost and can be replicated. The design results have good reference and application value for the research and design of wireless sensor network experimental system. [ABSTRACT FROM AUTHOR]

Protein-protein interactions (PPIs) are important for almost all cellular processes, including metabolic cycles, DNA transcription and replication, and signaling cascades. The experimental methods for identifying PPIs are always time-consuming and expensive. Therefore, it is important to develop computational approaches for predicting PPIs. In this paper, an improved model is proposed to use a machine learning method in the study of protein-protein interactions. With the consideration of the factors affecting the prediction of the PPIs, a method of feature extraction and fusion is proposed to improve the variety of the features to be considered in the prediction. Besides, with the consideration of the effect affected by the different input order of the two proteins, we propose a "Y-type" Bi-RNN model and train the network by using a method which both needs backward and forward training. In order to insure the training time caused on the extra training either a backward one or a forward one, this paper proposes a weight-sharing policy to minimize the parameters in the training. The experimental results show that the proposed method can achieve an accuracy of 99.57%, recall of 99.36%, sensitivity of 99.76%, precision of 99.74%, MCC of 99.14%, and AUC of 99.56% under the benchmark dataset. [ABSTRACT FROM AUTHOR]

Moxibustion is a thermal therapy in traditional Chinese medicine that relies on the heat from burning moxa to be transferred beneath the skin surface. Although moxibustion has long been in widespread practice, the mechanism of heat transfer modality and temperature distribution during this treatment is not yet well understood. The current paper presents the first examination by magnetic resonance imaging (MRI) of the three-dimensional temperature elevation during moxibustion treatment. A mathematical model for the prediction of temperature elevation during moxibustion therapy has been constructed and compared with the experimental data. Good agreement between the measured temperature and the results of numerical calculations has been found. Tissue up to 3 cm deep can be heated during the treatment. It was revealed that both heat conduction and radiation heat transfer play important roles during the treatment. The results presented in the current paper can be used for understanding the mechanisms of Chinese medicine and developing useful guidelines for Chinese medicine doctors. [ABSTRACT FROM AUTHOR]

Although DEM occupies an important basic position in spatial analysis, so far, the quality of DEM modeling has still not reached a satisfactory accuracy. This research mainly discusses the influence of interpolation parameters in the inverse distance-weighted interpolation algorithm on the DEM interpolation error. The interpolation parameters to be studied in this paper are the number of search points, the search direction, and the smoothness factor. In order to study the optimization of IDW parameters, the parameters that have uncertain effects on DEM interpolation are found through analysis, such as the number of search points and smoothing factor. This paper designs an experiment for the optimization of the interpolation parameters of the polyhedral function and finds the optimal interpolation parameters through experimental analysis. Of course, the "optimum" here is not the only one, but refers to different terrain areas, which makes the interpolation results relatively good. The selection of search points will be one of the research focuses of this article. After determining the interpolation algorithm, the kernel function is also one of the important factors that affect the accuracy of DEM. The value of the smoothing factor in the kernel function has always been the focus of DEM interpolation research. Different terrains, different interpolations, and functions will have different optimal smoothing factors. The search direction is to ensure that the sampling points are distributed in all directions when the sampling points are sparse and to improve the contribution rate of the sampling points to the interpolation points. The selection of search shape is to improve computing efficiency and has no effect on DEM accuracy; the search radius is mainly controlled by the number of search points, and there are two methods: adaptive search radius and variable length search radius. When the weight coefficient k = 1 , 2 , 3 , 4 , the number of sampling points involved in the interpolation calculation is different, and the error in the residual varies greatly, and both increase with the increase of the number of sampling points in the parameter interpolation calculation. This research will help improve the quality evaluation of DEM. [ABSTRACT FROM AUTHOR]

This paper presents the design of an adaptive controller that solves the synchronization control problem of two identical Nwachioma chaotic systems in a master-slave configuration. The closed-loop stability is guaranteed by means of a Lyapunov-like analysis. With the aim of verifying the feasibility and performance of the proposed approach, a comparison with an active control algorithm is developed at the numerical simulation level. Based on such results, the master-slave Nwachioma chaotic system in closed-loop with adaptive control is now being experimentally tested by using two personal computers and two low-cost Arduino UNO boards. The experimental results not only show the good performance of the adaptive control but also that Arduino UNO boards are an excellent option for the experimental setup. [ABSTRACT FROM AUTHOR]

Cervical cancer is a critical imperilment to a female's health due to its malignancy and fatality rate. The disease can be thoroughly cured by locating and treating the infected tissues in the preliminary phase. The traditional practice for screening cervical cancer is the examination of cervix tissues using the Papanicolaou (Pap) test. Manual inspection of pap smears involves false-negative outcomes due to human error even in the presence of the infected sample. Automated computer vision diagnosis revamps this obstacle and plays a substantial role in screening abnormal tissues affected due to cervical cancer. Here, in this paper, we propose a hybrid deep feature concatenated network (HDFCN) following two-step data augmentation to detect cervical cancer for binary and multiclass classification on the Pap smear images. This network carries out the classification of malignant samples for whole slide images (WSI) of the openly accessible SIPaKMeD database by utilizing the concatenation of features extracted from the fine-tuning of the deep learning (DL) models, namely, VGG-16, ResNet-152, and DenseNet-169, pretrained on the ImageNet dataset. The performance outcomes of the proposed model are compared with the individual performances of the aforementioned DL networks using transfer learning (TL). Our proposed model achieved an accuracy of 97.45% and 99.29% for 5-class and 2-class classifications, respectively. Additionally, the experiment is performed to classify liquid-based cytology (LBC) WSI data containing pap smear images. [ABSTRACT FROM AUTHOR]

Breast cancer is a highly harmful malignant tumor, which poses a great threat to women's body and mind, and the mortality rate ranks second among all women's diseases. The incidence rate accounts for 7-10% of various malignant tumors in the whole body, second only to uterine cancer in women, and has become the main cause of threatening women's health. Advanced breast cancer is often considered an incurable disease. The family of heterogeneous nuclear ribonucleoprotein complexes is composed of about 20 hnRNP proteins with molecular weights ranging from 32 to 120 kDa, and they are named according to their molecular weights. Among them, hnRNPA2 and hnRNPB1 are the two most important members of the hnRNP family, both derived from the same gene on chromosome 7p15. Therefore, research to understand the molecular mechanism and process of breast cancer progression has an important role in promoting the current medical research on breast cancer treatment methods. Therefore, studying the mechanism of tumorigenesis is the key to tumor prevention and treatment. Therefore, this paper proposes that A2/B1 promotes the stability of NRF2 mRNA and inhibits ferroptosis and cell proliferation in breast cancer cells. The article mainly introduces the disease diagnosis method based on artificial neural network and its neural network algorithm. In the experimental part, the activity of hnRNP A2/B1 on cancer cells is deeply studied. The results show that the absorbance of the MTT method increases continuously with the extension of the culture time, and the maximum reaches 1.2. This fully shows that its absorption capacity is very strong, especially after 24 hours, the absorption rate rises from 0.6 to 0.9, which shows that 24 hours is the best absorption time. And it can also be found that hnRNPA2/B1 has a significant inhibitory effect on breast cancer cells; it can reduce the effect on breast cancer cell cycle and apoptosis. [ABSTRACT FROM AUTHOR]

In order to solve the problems of high labor cost, long detection period, and low degree of information in current water environment monitoring, this paper proposes a lake water environment monitoring system based on LoRa and Internet of Things technology. The system realizes remote collection, data storage, dynamic monitoring, and pollution alarm for the distributed deployment of multisensor node information (water temperature, pH, turbidity, conductivity, and other water quality parameters). Moreover, the system uses STM32L151C8T6 microprocessor and multiple types of water quality sensors to collect water quality parameters in real time, and the data is packaged and sent to the LoRa gateway remotely by LoRa technology. Then, the gateway completes the bridging of LoRa link to IP link and forwards the water quality information to the Alibaba Cloud server. Finally, end users can realize the water quality control of monitored water area by monitoring management platform. The experimental results show that the system has a good performance in terms of real-time data acquisition accuracy, data transmission reliability, and pollution alarm success rate. The average relative errors of water temperature, pH, turbidity, and conductivity are 0.31%, 0.28%, 3.96%, and 0.71%, respectively. In addition, the signal reception strength of the system within 2 km is better than -81 dBm, and the average packet loss rate is only 94%. In short, the system's high accuracy, high reliability, and long distance characteristics meet the needs of large area water quality monitoring. [ABSTRACT FROM AUTHOR]

The operation and maintenance management of telecom platforms puts more emphasis on operation and alarm monitoring and less on business flow, and it is difficult to have a seamless connection between operation and maintenance and sales. To improve the cooperative efficiency and the informatization level of telecom platform operation and maintenance, a new operation and maintenance system integrating Apriori association rules for a telecom platform is proposed. In this paper, the Apriori algorithm is firstly used to analyse the fault correlation of the operation and maintenance system of the telecommunications platform to get the alarm message. Then, the fault risk of the operation and maintenance platform is evaluated intelligently by the system-business alarm causality model. Finally, based on the fixed end and the mobile end, the dynamic presentation of the system running state and alarm monitoring, device query and positioning, and online and offline inspection are implemented and designed, respectively. Experimental results show that the proposed operation and maintenance systems integrating Apriori association rules for the telecom platform innovate the cooperative mode between the operations and sales team, and the average time reduces to 3.9 minutes from 4.7 minutes and the risk forecasting accuracy increases from 8% to 26%. The assumed method can significantly improve the operational efficiency and intelligent level of the telecom platform's system and business. [ABSTRACT FROM AUTHOR]