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Paper-cut elements in the design of boat space can effectively improve the homogenisation tendency of existing boat space design, effectively enhance the characteristics of traditional Chinese culture and enrich the development ideas of boat space design. Therefore, based on the Chinese folk art of paper cutting, this paper combines theoretical analysis and empirical research to deeply analyse the artistic expression of paper-cut elements in the spatial design of boats. This paper mainly studies the artistic integration of paper-cut elements in the decorative design of boats and the artistic integration of paper-cut elements in the practical design of boats. Through the calculation and analysis of the sparse autoencoder algorithm model, it can be seen that the decorative integration of paper-cut elements in wallpaper is 82%, which is higher than that of traditional materials (24%). The integration of paper-cut elements in the space design of cabins and guest rooms is as high as 97% and 94%, respectively, which is far better than the integration of traditional materials in the space design of cabins and guest rooms. This shows that the application of folk paper-cut elements to the space design of boats not only highlights the theme of boats but also enhances the cultural value of boats.

English is a universal language and is widely used. In order to deepen the cognition of language, this paper proposes to analyse the variation of metadiscourse verb patterns in English academic papers from within and between disciplines. By comparing the metadiscourse chunks used in various disciplines, it is concluded that there are obvious differences in the verb types of the subject papers. Analysing various identification algorithms of verb types, it is obtained that the combination of the word movement distance (WMD) model and the R&L density peak clustering algorithm is the best. Using the R&L density peak clustering, identification parameters are easy to determine, and when combined with the WMD model to calculate the similarity of words, it improve the accuracy of verb pattern clustering. By comparison, it is proved that the accuracy of the algorithm combining the WMD model and R&L density peak clustering reaches 30.06%, and the effect of identifying verb pattern variation is the best.

In this paper, through the collection and analysis of data, the art of Guangling paper-cutting is theoretically researched, and the general features and shapes of Guangling paper-cutting are summarized. In order to overcome the limitations of traditional algorithms in the extraction of features of Guangling paper-cutting, the combination of spatial pyramid matching and context-dependent histogram methods are used to extract the shape features of paper-cutting images with stronger differentiation. The support vector machine is used to solve the problem of recognizing and classifying the image patterns in the digitized display. The design of the digital promotion of the Shanxi Guangling paper-cutting brand is carried out. Research variables are designed to analyze the effect of digital transformation and promotion of paper-cutting brands. The results show that the discrepancy of Guangling paper-cutting features extracted by the algorithm in this paper is very low, the e1 difference is maintained within 0.1, and the total recognition rate of SVM on Guangling paper-cutting features can reach 88.06%, and the overall effect is more satisfactory, and the regression equation of the influence of paper-cutting = −0.807+0.111*history+0.122* style+0.234*paper-cutting+0.216*display+0.136 * linkage, and propose strategies to enhance the influence of paper-cutting brand. The technical methods used in this study guarantee the quality of the digital transformation of Guangdong paper-cutting from the source.

To deeply analyze the performance of paper-cutting elements in interior space design, this paper explores the artistic expression of paper-cutting elements. The BIM model is constructed based on the TVL algorithm, and a noise-reducing style migration method with an additional total variable loss function is used to fuse the texture features of a specific paper-cutting style so that the paper-cutting texture is generated in the target object area more intensively. The algorithm is supplemented with a U-shaped network structure to obtain the mask map of the target object, modify and expand the ScanNet training data set, and input a content map and a style map to generate the stylized paper cutout image automatically. The results show that compared with other elements, the stability of paper-cut elements reaches 96.31%, the image smoothness reaches 89.34%, and the retention of significant regions reaches 93.26%. In modern design, the peak signal-noise of paper-cut elements improved by 13.418d on average, the structural similarity also improved by 0.34807, and the recognition rate of paper-cut elements in the furnishing category reached 95%. Applying paper-cut art in interior space design can give interior furnishing design better visual effects, provide inspiration and ideas for interior space design innovation, and contribute to improving aesthetics.

In the realm of Chinese traditional arts, paper-cutting symbols embody deep-seated aesthetic and cultural values. Within the framework of artificial intelligence, the fusion of these conventional symbols with contemporary pattern design through intelligent reconstruction processes promotes innovation in modern design. This paper introduces a novel pattern feature extraction algorithm specifically designed for identifying and extracting the essence of traditional paper-cutting art. Utilizing Deep Convolutional Generative Adversarial Networks (DCGAN), an automated method for generating woven patterns is developed, integrating conventional motifs with modern design elements. Additionally, a segmentation model based on DeepLabV3+ semantics is constructed to facilitate this integration. The study focuses on the fusion of traditional paper-cutting symbols with fabric patterns, providing a new direction for textile design research. The empirical evaluation involved seven experimenters who predominantly favored patterns that blended abstract and traditional styles, with the average style rating exceeding 3.2. Moreover, the color performance analysis of the reconstructed fabric patterns across five regions showed color difference values greater than 4, indicating superior color fidelity. These findings underscore the potential of combining AI-driven techniques with traditional art forms to enhance and revolutionize modern design practices.

The study of metadiscourse word resources is an important way to reveal academic knowledge constructs and genre features in English papers. This paper selects intra- and interdisciplinary English academic papers as data samples, and the text representation is based on the vector space model. The text similarity is calculated using the word movement distance model to complete the clustering based on the density peak, and then the semantic annotation of metadiscourse verb patterns is performed using the conditional random field. Then, the intra- and interdisciplinary metadiscourse annotations are compared to analyze the variation of verb patterns. The intra-disciplinary variation of metadiscourse verb patterns was mainly found using attitude markers, where the log-likelihood ratios of 48.56, 30.25, and 35.64 were obtained for affirmative, critical, and critical declarative attitudes, respectively. In interdisciplinary studies, basic disciplines preferred to use verb patterns as transitions, while applied disciplines preferred to express the author’s point of view. The study of metadiscourse verb patterns effectively enriches the linguistic resources in academic English teaching and deepens the understanding of the characteristics of disciplinary essay genres.

This investigation delves into the evolving patterns and regional variances in industry-academia collaboration (IAC) within Functional Nucleic Acids (FNAs) research. Analyzing publications and patents from 1990 to 2021, we dissected IAC at individual and collective levels, documenting the count and share of IAC endeavors, key partners, and regional differences. Results indicate a rising trend in IAC publications and collaborating institutions, while patent trends display a mix of increases, decreases, and steady states. The United States leads in IAC activities, correlating with its prowess in scientific research and technological innovation. Notably, regional discrepancies in IAC are mainly due to intra-regional variations, with Asia and Europe experiencing the most pronounced disparities.

Leveraging artificial intelligence (AI), this study revolutionizes examination management and education in universities by developing an intelligent system encompassing comprehensive management, pre-examination activities, scheduling, and preparation. The system also features a quality management component for educational outcomes. An enhanced genetic algorithm introduces an adaptation function to optimize intelligent grouping, facilitating effective exam paper distribution. Applied at Y University, our innovative approach significantly refines exam paper difficulty (ranging between 0.5016 and 0.5581) and differentiation (0.3845 to 0.4596), showcasing the intelligent algorithm’s effectiveness in exam management and contributing valuable insights to educational research.

With the innovation and development of commercial shopping centers in China, the shopping environment is becoming more and more colorful. However, in today’s social environment, the central function of low-level consumption experience can no longer meet the needs of the masses. Therefore, in the design of indoor environment, we should start from a new perspective, focusing on the difference of internal space and the experience of consumption mode, so as to meet the development needs of commercial shopping in the future. Based on the research on the indoor environment of comprehensive commercial shopping center, comprehensive commercial shopping center in a certain area as the research target, the corresponding mathematical model was constructed, and using the measured method validation build the model of reliability and validity, thus obtains using CFD numerical simulation software of thermal and moisture environment of building an in-depth exploration, The effective countermeasures are put forward for the original air distribution mode. Comparing the improved scheme with the original scheme, it is found that the former can provide effective basis for the interior environment design of similar comprehensive commercial shopping malls.

Chaotic behaviour of dynamical systems, their routes to chaos, and the intermittency in particular are interesting and investigated subjects in nonlinear dynamics. The studying of these phenomena in non-smooth dynamical systems is of the special scientists’ interest. In this paper we study the type-III intermittency route to chaos in strongly nonlinear non-smooth discontinuous 2-DOF vibroimpact system. We apply relatively new mathematical tool – continuous wavelet transform CWT – for investigation this phenomenon. We show that CWT applying allows to detect and determine the chaotic motion and the intermittency with great confidence and reliability, gives the possibility to demonstrate intermittency route to chaos, to distinguish and analyze the laminar and turbulent phases.

Researching different solutions of nonlinear models has been interesting in different fields of science and application. In this study, we investigated different solutions of fourth-order nonlinear Ablowitz– Kaup–Newell–Segur wave equation. We have used the sine-Gordon expansion method (SGEM) during this research. We have given the 2D, 3D, and contour graphs acquired from the values of the solutions obtained using strong SGEM.

We extend the Boltzmann’s ideas that describe the evolution to the equilibrium of many body systems to the multifractal decomposition of the unitary interval 𝕀, in terms of sets Jα conformed by points with the same pointwise dimension, and obtain the D(α) singularity spectrum.

The study of everyday phenomena involving friction continues to maintain a high level of difficulty despite its long history. The causes of this problem lie in the different scale of the characteristics of the phenomenon, macroscopic and microscopic. Thus, very different models, valid in a narrow scope which prevents generalization, have been appearing. This survey presents the application of network simulation method to the numerical solution to the study of friction at very different scales. On the one hand, on a microscopic scale an atomic force microscope model has been studied, related to the analysis of soft surfaces at the atomic scale. Furthermore, on a macroscopic scale model related to the analysis of an industrial device, such as a brake mechanism has been studied. After presenting herein is a review of the different formulations of the friction force, the nature of the surfaces involved in the phenomenon, as well as the definition of the problems to be analyzed. The design of network models and the implementation of the initial conditions are explained. The results of the application of network models to selected problems are presented. In order to verify the reliability of the proposed models, their results are compared with the solutions obtained by other numerical methods or experimental results, one from a device developed during the preparation of this report.

We analyzed the first set of complete CCD light curves of the W UMa type eclipsing binary IK Boo in the BVRI bands by using the PHOEBE code and deduced its first photometric parameters with, mass ratio q = 0.648 and orbital inclination i = 63o. We have applied a spotted model due to the light curves asymmetry. The system shows a distinct O’Connell effect. The best solution fit to the light curves suggested the influence of star spot(s) on both components. Such presence of star spot(s) is common among the RS CVn and W UMa chromospheric active late type stars.

This paper is motivated by the paper [2]. The main aim of this paper is to extend the stability result from [16], related to the θ-Euler- Maruyama method (θ ∈ (12{1 \over 2}, 1)) for a class of neutral stochastic differential equations with time-dependent delay. The theta method is defined such that, in general case, it is implicit in both drift coefficient and neutral term. Sufficient conditions of the a.s. exponential stability of the θ-Euler-Maruyama method, including the linear growth condition on the drift coefficient of the equation, are revealed. The stability result is established for larger class of neutral terms than that considered in the second cited paper. An example is provided to support the main results of the paper.

The paper deals with the estimation of sensor faults for dynamic systems as well as the assessment of the uncertainty of the resulting estimates. For that purpose, it is assumed that the external disturbances are bounded within an ellipsoidal domain. This allows considering both stochastic and deterministic process and measurement uncertainties. Under such an assumption, a fault diagnosis scheme is developed with a prescribed convergence rate and accuracy. To achieve fault estimation, a conversion into an equivalent descriptor system is utilized. The paper provides a full stability and convergence analysis of the estimator including observability analysis. As a result, a set of complementary fault uncertainty intervals is obtained, which are minimized in such a way as to obtain a minimum detectable sensor fault. The final part of the paper exhibits a numerical example concerning fault estimation of a multi-tank system. The obtained results clearly confirm the performance of the proposed estimator expressed in the minimum detectable fault intervals.

The paper proposes a non-iterative training algorithm for a power efficient SNN classifier for applications in self-learning systems. The approach uses mechanisms of preprocessing of signals from sensory neurons typical of a thalamus in a diencephalon. The algorithm concept is based on a cusp catastrophe model and on training by routing. The algorithm guarantees a zero dispersion of connection weight values across the entire network, which is particularly important in the case of hardware implementation based on programmable logic devices. Due to non-iterative mechanisms inspired by training methods for associative memories, the approach makes it possible to estimate the capacity of the network and required hardware resources. The trained network shows resistance to the phenomenon of catastrophic forgetting. Low complexity of the algorithm makes in-situ hardware training possible without using power-hungry accelerators. The paper compares the complexities of hardware implementations of the algorithm with the classic STDP and conversion procedures. The basic application of the algorithm is an autonomous agent equipped with a vision system and based on a classic FPGA device.

The paper discusses the inventory management problem with a single product stored in two warehouses, where each has its unique suppliers with certain lead times. Moreover, one of the warehouses may act as a backup supplier for the other. In other words, product exchange between two different warehouses within one company is allowed. The first warehouse operates under an a priori known time-variant contractual demand and a bounded random one. Its secondary goal is to accumulate emergency stock that can be delivered to the second warehouse within one time period. For this warehouse we use a desired trajectory generator to shape the required stock level and then utilize a trajectory following control law. The demand in the second warehouse is unknown but bounded, and its suppliers have limited delivery capacity. The challenge is to fulfill the customers’ needs, although they might exceed the order limit. Therefore, occasional backup supplies from the first warehouse are necessary. For the control of the second warehouse, a simple sliding mode (SM) scheme is applied. The paper proves that, with appropriate compensation of the emergency deliveries in the first warehouse, our proposed control scheme ensures full demand satisfaction in both warehouses despite the second one’s control limit.

In this paper we are focusing on finding the transcendental entire solution of Fermat-type trinomial and binomial equations, by restricting the hyper-order to be less than one. As the hyper-order is a crucial parameter that characterizes the growth of entire functions, it will be interesting to investigate this unexplored domain, as far as practible, with certain restriction on hyper order. Our results are the improvements of previous results reported in recent papers [12], [13]. We have provided a series of examples to demonstrate and validate the effectiveness of our proposed solutions.

In many digital systems, various sequential blocks are used. This paper is devoted to the case where the model of a Mealy finite state machine (FSM) represents the behaviour of a sequential block. The chip area occupied by an FSM circuit is one of the most important characteristics used in logic synthesis. In this paper, a method is proposed which aims at reducing LUT counts for FPGA-based Mealy FSMs with transformation of state codes into FSM outputs. This is done using the combined state codes. Such an approach allows excluding a block of transformation of binary state codes into extended state codes. The proposed method leads to LUT-based Mealy FSM circuits having exactly three levels of logic blocks. Under certain conditions, each function for any logic level is represented by a circuit including a single LUT. The proposed approach is illustrated with an example of synthesis. The results of experiments conducted using standard benchmarks show that the proposed method produces LUT-based FSM circuits with significantly smaller LUT counts than is the case for circuits produced by other investigated methods (Auto and One-hot of Vivado, JEDI, and transformation of binary codes into extended state codes). The LUT count is decreased by an average of 17.96 to 91.8%. Moreover, if some conditions are met, the decrease in the LUT count is accompanied with a slight improvement in the operating frequency compared with circuits based on extended state codes. The advantages of the proposed method multiply with increasing the numbers of FSM inputs and states.

A steady-state sinusoidal and distortion-free excitation source is very important for the accuracy and consistency of the calibration parameters of micro-electro-mechanical systems (MEMS) inertial sensors. To solve the problem that the current MEMS inertial measurement unit (IMU) calibration device is unable to reproduce the spatial motion of linear and angular vibration coupling, research topics on the coupling vibration characteristics and parameter identification for an electromagnetic linear-angular vibration exciter are proposed. This research paper used Ampere’s law and Lorentz force to establish the analytical expressions for the electromagnetic force and electromagnetic torque of the electromagnetic linear-angular vibration exciter. Then, the main purpose of this paper is to establish uniaxial and coupled vibration electromechanical analogy models containing mechanical parameters based on the admittance-type electromechanical analogy principle, and the parameter identification model is also obtained by combining the impedance formula with the additional mass method. Finally, the validity of the coupling vibration characteristics and the parameter identification model are verified by the frequency response simulation and the additional mass method, and the relative error of each parameter identification is within 5% in this paper.

In this paper we generalize a result of R. Urbański from paper [7] which states that for subsets A, B, C of topological vector space X the following implication holds A+B⊂B+C⇒A⊂CA + B \subset B + C \Rightarrow A \subset C provided that B is bounded and C is closed and convex. The generalization is given in Theorem 5 where we prove this result for k- convex subsets of a topological vector space. Also we introduce a notion of some abstract like-closure operation for subsets of linear space and we study its connections to order cancellation law.

This paper explores the concept of normal structure by introducing a generalized form known as P-proximal normal structure. Focusing on the framework of Hausdorff locally convex spaces, the study establishes optimal proximity outcomes for both cyclic and noncyclic relatively P-nonexpansive mappings. Furthermore, the paper presents in the realm of probabilistic normed spaces, considered as instances of Hausdorff locally convex spaces, some theorems that address the existence of best proximity points within this context.

The paper presents a comprehensive design of metrological equipment for torque sensor verification and calibration, detailing the process from conception to construction and highlighting the specifics of the structural design to meet metrological requirements. The measuring device’s functionality and the individual structural components are described, as is the methodology for creating a complete product. The paper addresses the crucial issue of measurement uncertainty and the required accuracy, achieved through the construction of a special measuring arm made of carbon material. FEM analyses of the carbon arm are presented and compared with the required metrological accuracies. In addition, we discuss the different properties of various carbon structures in Pre-preg materials used in the construction of the measuring arm and present the results of measurements on such carbon materials. This paper provides a comprehensive insight into the design and construction of metrological equipment for torque sensors, with a focus on its compliance with metrological requirements. The proposed device aims to establish the foundations for primary metrology of torque in Slovakia and has potential applications in a wide range of industries.

In view of the increasing system management requirements of garden landscapes in the construction of smart cities, this paper combines ordinary differential equations with big data and other information technologies. Design ideas of smart city garden big data information management and display system under differential equation. By analyzing the research literature, combined with space syntax, network text analysis and other methods, the theoretical framework of urban landscape planning method based on ordinary differential equations based on big data is constructed, and the aspect planning of urban garden landscape information management module, three-dimensional display module and recommendation module is determined. content. The experimental results show that the user interaction rate when using the system of this paper for information management of urban gardens shows a significant upward trend compared with the traditional system, and the average user interaction rate is higher than 40%. The application of ordinary differential equations and big data technology guides participatory planning and promotes the improvement of the information management level of smart city garden landscape. The designed information processing system has higher user experience and comfort.

In today’s rapid development of the economic environment, the company’s financial crisis can significantly reflect the enterprise crisis, and it has a significant impact on the development of the enterprise. Therefore, the prediction of the company’s financial crisis and the establishment of an early warning system have important practical significance. Based on the method of pattern recognition and artificial intelligence, this paper establishes a new forecasting model of corporate financial crisis, predicts its financial index data and realises the dynamic financial crisis warning. The model developed in this paper employs pattern recognition and artificial intelligence, and is thus endowed with high classification accuracy, good anti-noise ability and the ability to be sorted according to the importance of its index features.

With the rapid development of Internet technology, the field of hotel management has begun to carry out informatisation construction, through which the management level and operational efficiency of hotels can be improved. The traditional hotel management work with a complex structure and cumbersome process is facing huge challenges. Therefore, this paper aims to design, develop and implement a small and medium-sized hotel management system based on customer relationship management (CRM), according to the characteristics of small and medium-sized hotel management. Management and system setting management of small and medium-sized hotels can reduce the hotel operating costs and increase the profitability by using this system. The department and employee performance appraisal can be divided into four dimensions, finance, customer, internal business process, and learning and growth for job analysis and design, using the key performance indicator method to lock key appraisal indicators, and ignore or omit irrelevant or insignificant indicators. Then the analytic hierarchy process is used to scientifically determine the indicator weights. The Delphi and in-depth interview methods are used to complete construction of the performance appraisal system, and the constructed CRM performance management system is used to achieve the hotel’s strategic goals, thus completing the fundamental goal of using the CRM model. The slacks-based measure model is based on the assumption of variable returns to scale, considering that the input redundancy and output deficiency of ineffective decision-making units are measured based on their slack. This paper defines input inefficiency as the ratio of input slack to actual input, and output inefficiency as the ratio of output slack to actual output. The total input inefficiency rate of the hotel industry in China’s ineffective provinces and regions is 0.458, while the total output inefficiency rate is 0.077, indicating that if the total investment in the hotel industry is reduced by 47.8% on average and the total output is increased by 6.9% on average, the hotels in these ineffective provinces and regions can all be relatively effective. Finally, the overall performance of the system will be evaluated, and a set of safe, convenient and friendly hotel management system will be developed.

As teachers struggle with the usage of high-quality digital educational resources, small coverage and weak pertinence characteristics of traditional evaluation methods are the reasons for this. In this paper, three aspects including the connotation, operation mechanism and evaluation of ‘Artisan Workshop’ are analysed, and the classification factors are explored based on the collected educational data. By using these objective characteristics, the application ability of teachers to use digital educational resources is quantified, and the prediction model of sustainable development in ‘Artisan Workshop’ is constructed by adopting random forest regression, extreme gradient boost regression and light gradient boost regression models. According to the application of educational resources and teachers’ ability, this paper proposes an optimized scheme for the sustainable development of ‘Artisan Workshop’, which provides a reference for improving the quality of talent training in China.

With the increasing importance of music information retrieval, the construction of effective music recognition methods has gradually become one of the research focuses. The vocal map features of the collected music pronunciation signals are extracted. The research in this paper is primarily based on the basic physical characteristics of music notes; once these characteristics are identified, then they are mathematically extracted and analysed (voiceprint feature method), and a recognition model is established. On this basis, the process of adaptive separation and recognition of music signals is completed. Finally, the performance of different recognition types is verified and evaluated through experiments. The results show that the average accuracy rate of the modified algorithm within a certain range reaches 73.6%; additionally, the average accuracy rate is increased by 10.95% compared with the audio recognition based on Internet of Things (IoT) data, and is more accurate than the audio recognition method based on data collection, showing an improvement of 20.75%. This shows that the modified recognition algorithm adopted in the present research has stable and high accuracy for comprehensive music types with different characteristics. Finally, the identification method proposed in this paper shortens the time by 85.71% compared with the identification method of data collection, and shortens the identification time by 83.33% compared with the IoT identification method. This greatly improves the recognition of different musical feature types.

As big data mining technology is known to directly change the development direction and internal management, and the behaviour of college students’ political wisdom network has also changed significantly, hence the application and analysis of data mining technology becomes indispensable. Bidirectional gated cyclic cell network can solve the gradient disappearance or gradient explosion existing in traditional models, but can also make up for the deficiency of extracting the contextual semantic information of long text effectively. Using the combination of big data-based mining technology and college students’ network ideology and politics, this paper designed a set of BiGRU_Attention framework based on college students’ network ideological wisdom framework and adopted big data mining technology to collect data. First, according to the Internet technology and information collection, design a complete set of college students’ ideological wisdom framework. Then, the GRU structure, BiGRU framework and Attention mechanism combined with BiGRU_Attention model are introduced. Meanwhile, Word2Vec is used to train the Chinese word vector. Finally, the BiGRU_Attention proposed in this paper is compared with several models. Experimental results show that the BiGRU_Attention proposed in this paper has a better accuracy of 96.32% over other models. The experimental results also show that the framework of ideological and political intelligence proposed in this paper can be applied to the campus environment of college students.

This paper builds and evaluates a self-learning system for spoken English pronunciation suitable for PC mobile terminal or mobile terminal users, and the core technology of speech recognition is built on Hidden Markov Model (HMM), which is used to decode the speech signal in spoken English learning. This paper studies the related speech recognition theory and signal processing technology, builds a comprehensive English self-learning system in more complex situations and more user types and conducts a comprehensive evaluation of the system. The results show that the overall accuracy of the HMM model in the spoken English recognition and evaluation system built in this paper is good, and the accuracy of the input audio for people of all ages is greater than 90%. In the younger population, the accuracy of male speech signals was the highest in both closed space and open space, reaching 98.12% and 96.53%, respectively. In addition, it is observed that the accuracy gradually decreases as more wrong judgements are made on the speech input signal. When the evaluation is poor, the accuracy of the scoring results decreases to 55%, and when there are fewer voice judgement errors, it is in the excellent range, where the accuracy reached 88%.

In recent years, influenced by the deepening reform of colleges teaching English in China, teaching English for college students has gradually been favoured and promoted. At present, many universities still use traditional English teaching methods. The traditional English teaching method only allows college students to learn basic grammar and vocabulary, without the ability of independent learning. Given these problems, this paper first proposes a framework for cultivating college students’ autonomous learning ability in English teaching based on output-based education (OBE) theory. Then, the OBE theory is introduced in detail. At the same time, the framework is applied in a university. After a semester of study, the results are combined with teachers’ and college students’ questionnaire surveys. Finally, after the questionnaire results and analysis, the framework proposed in this paper is better than other traditional English teaching methods. The experiment also shows that it not only improves the adaptability of college students’ English teaching but also improves their independent learning ability while learning English.

In the process of designing animation characters and background elements, there may be a high degree of visual blur, resulting in a strong sense of picture distortion, and it is easy to expose the incomplete picture. Accordingly, this paper proposes to use directional derivatives to deal with animation-like visual problems. This method is based on the theoretical basis of directional derivatives, combined with the current mathematical methods of animation character and background element design, and defines the important role of directional derivatives in animation and background design, and more effectively enhances the visual results of animation characters. In the research of clarity and distortion, this paper uses directional derivative derivation as the cutting-in method to test the algorithm for visual simulation and restoration of animated characters and background elements so that the algorithm can be used for each animation character and background element. The defect point is calculated, and the clarity and self-healing ability of the video itself are improved by the influence of the mathematical parameters of the surrounding known points on itself and the key variable of the directional derivative in the field. The results show that the directional derivative can play a role in promoting sublimation in the design of animated characters and background elements.

This paper examines the current supply chain network model (SCNM) in order to effectively apply cost accounting information to strengthen supply chain management and solve the problem of low overall efficiency of the model. This paper constructs the SCNM of cost accounting, analyses the characteristics of the model and designs the variables of the model. In order to optimise enterprise cost accounting, the corresponding mathematical model is constructed, and the preconditions and constraints for solving the model are set up. In addition, particle swarm optimisation (PSO) algorithm is used to solve the model, and the moving speed of particle swarm is updated by dynamic relaxation factor. Finally, the validity of the model and the accounting cost are simulated. The results show that the model can effectively solve the optimisation problem of SCN in accounting cost.

Against the social background of the gradual transformation of the social pension mode and the arrival of the ageing age, the tool design of fitness walkers for the elderly was studied. In this paper, the physiological and psychological status of the elderly with mild or moderate stroke or unable to exercise due to the decline of physical function was analysed. In addition, the corresponding elderly fitness walker product market research was studied. Aiming at these problems, this paper proposes a set of elderly fitness walker frames based on the ergonomic semiotic approach of the product architecture design (SAPAD) model. First, the development and structure of artificial engineering is introduced in detail. Then the framework of walking aid for the elderly based on the SAPAD model is proposed, and the SAPAS model and walking aid for the elderly are introduced, respectively, along with the framework of simulation design and application. The experimental results show that the framework of elderly fitness walker based on the ergonomic SAPAD model can be implemented. It can be applied to the elderly’s fitness assistance. A fitness walker can be suitable for the elderly who want to continue to exercise; it also hopes to care for the elderly who need to exercise from the perspective of the product.

A hotel supply chain management system is distributed with considerable computation. In this paper, a multi-colony ant algorithm based on heterogeneous is designed. An optimal scheme based on distributed programming is proposed. A heterogeneous multi-colony ant algorithm is a kind of ant colony with multiple characteristics. Different ant species have other population characteristics and apparent advantages and complementarity. Several heterogeneous ants with optimized performance were studied. In this paper, the collaboration is carried out on several computing nodes. The algorithm of this model can satisfy the characteristics of multiple orders. This algorithm can optimize distributed computing resources in the hotel supply chain to the maximum extent for large and complex suppliers and collaborative time ordering problems. Simulation results show that the proposed method achieves good efficiency, stability and order adaptability.

This paper used multiple linear analyses to analyze the effect of aerobic exercise on the function of 20 aerobics players. This paper divided the athletes into strength and aerobic training groups. Subjects were subjected to a 12-week controlled trial. The results show that different kinds of exercise have different effects on the body structure. Toning exercises strengthen muscles. Aerobic exercise reduces body weight, reduces body fat, and inhibits skeletal muscle growth.

This paper uses the expository structural model (ISM) as the basis for research. Firstly, it briefly analyzes the business structure of our cross-border e-commerce and foreign trade business. The specific organizational structure of the enterprise will encounter a variety of interference. This paper constructs a set of enterprise supply chain performance evaluation indexes based on balanced scoring. The writing improves the information level, logistics ability, customer status maintenance, and other vital links. Simulation results show that this method can improve the overall performance of foreign trade documents.

High-speed assembly line food packaging quality defect detection methods have poor error detection rates, missing rates and accuracy. This paper advances a process of computer-aided online monitoring of food packaging based on a deep neural network model. Firstly, this paper uses the deep convolution method to analyze the defects in food packaging. Then the convolution method of food packaging defects is improved. The correct identification of defects in food packaging can be enhanced by adjusting VGG16. This paper uses a convolutional neural network, transfer learning and adaptive neural network to compare the recognition effect of food packaging defects based on a forward neural network. It is proved that the recognition accuracy of this method is 0.0005. Good identification results can be obtained after 10 times of repeated practices. This method has a good classification effect.

Musical symbols are a good way of expressing human emotions and communication. It is a kind of nonlinear dynamic system with hysteresis. It isn’t easy to analyze its nonlinear characteristics by conventional methods. This paper divides it into several parts from the form of music. Secondly, this paper studies the local attributes of music signals and generalizes and deduces their general features. Then the Lyapunov index of music is numerically simulated, and its energy spectrum is analyzed. It is proved that the method is weak to some extent. Through the music correlation dimension analysis, we find its inherent nonlinear characteristics. The nonlinear music signals corresponding to classical piano works are given in this paper. Finally, the correlation dimension is numerically simulated by differential operation. The results show that the nonlinear characteristics of this method are stable.

Based on the livestreaming of the e-commerce platform Taobao, this paper studied the consumers’ online live streaming purchase decision behavior, taking causal snacks purchase as an example. In the paper, the principal component analysis (PCA) was applied to reduce dimension of the evaluation indicators of consumers purchase decision, and an evaluation index system for consumers’ purchase of casual snacks under livestreaming was established, which consists of 5 first-level indicators and 20 sub-level indicators. In view of the uncertainty of expert evaluation, Using Fuzzy-DEMATEL analysis the evaluation index’s logical relationships and influence relationships, and an Analytic Network Process (ANP) network based on the importance and relevance of consumer purchase influence was constructed. The weighted method of Fuzzy-DEMATEL and ANP combination is used to improve the objectivity of indexing importance. The experimental results showed that the amount of transaction guided by anchors, popularity of anchors, teamwork ability of livestreaming, brand awareness of snack and selection ability of livestreaming team were the key factors for consumers’ purchasing decisions of snack under livestreaming. This research could help livestreaming e-commerce platforms, businesses and livestreaming marketing teams understand the consumers behavior better, thus improve the purchase intention of consumers from the key factors affecting their decision-making, increase the sales volume and the amount of attention of the products, and increase the volume of guided transactions and the number of fans for the livestreaming marketing teams.

In this paper, a typical equivalent fitting linear modeling method is studied by using BIM technology. This paper realizes the quality-efficiency control of construction projects based on BIM technology. The software can realize the cost data statistics and collection of each sample. The system takes the statistical sequence of each department’s project cost as the original data for dimensionality reduction processing. The variance contribution is used as the weight to calculate the weight of each main component. According to the finite element analysis of combined parameters, the project cost and construction benefit are the restrictive factors. A multi-factor comprehensive evaluation method establishes a rapid construction cost evaluation model. The experimental results show that this method agrees with the conventional calculation method and the measured data. The estimated cost of construction differs little from the measured value. The results show that the model constructed according to different stages of the construction projects has good prediction accuracy.

Does work engagement effectively predict subjective well-being? In this paper, we investigated the relationship between work engagement and subjective well-being by synthesizing 176 effects from 59 studies involving 21927 subjects. The results showed that work engagement were positively correlated with subjective well-being, while job burnout were negatively correlated with subjective well-being. Literature sources significantly adjusted the relationship among work engagement, job burnout and subjective well-being. The paper proved that work engagement and subjective well-being are closely related, and literature sources may play a moderating role.

The survival soil on which the traditional martial arts culture depends is becoming thinner and thinner. A large number of superb martial arts skills and martial arts experience disappear with the passing of the older generation of martial artists. In order to realize the information preservation of traditional Wushu, this paper puts forward the establishment and application of traditional Wushu intelligent learning resource database under the background of big data. In the context of big data, data mining technology is used to realize personalized recommended learning services, obtain the feature structure of intelligent learning system through operation, extract the average dynamic features of specific data in the resource database, obtain fuzzy constraints according to the value, determine the membership function, and adjust the membership function parameters through training fuzzy neural network, so as to gradually improve the reasoning accuracy. Finally, the matching rule set is used to filter the resource data packets to achieve the purpose of communication. In order to verify the effect of the model, compared with the traditional model, the results show that when the number of nodes is 500, the average transmission rate is as high as 90%, the average delay is 12 seconds, and the throughput performance is 91%. It can be verified that the model designed in this paper can effectively improve the propagation rate, reduce the average delay and strengthen the throughput performance.

This paper applies a nonlinear differential equation to the information management system of college course selection. A teaching information management system based on an approximate learning strategy is presented by using statistical linearization technology. An imprecise controller is obtained by numerical simulation of Riccati differential equations with statistical linearization. This kind of Riccati differential equation differs significantly from the ordinary one. Then the system proposes a collaborative filtering method based on nonlinear differentiation based on student feature classification. At last, this paper systematically analyzes the differences between course selection systems, business recommendations, and student attributes—the system experiments on college students' choice of a learning platform. The study found that the method was correct 34.6% of the time. This system can provide practical guidance for students to choose courses.

With the rapid development of the Internet, security issues are becoming more and more prominent, and since most information is transmitted through the Internet today, Internet security is particularly important. When the Internet was designed, only mutual compatibility and interoperability between networks were considered, and security issues were not fully considered. As a result, as the Internet continues to grow, security issues are becoming more and more serious. One of the more difficult attacks is the Distributed Denial of Service (DDoS) attack, which has many forms of attacks, is harmful, and is difficult to identify and defend. Therefore, building a global Internet security protection system to achieve effective protection against DDoS attacks is the main work of this research paper. In this paper, we propose an artificial intelligence DDoS attack protection system, which implements a controller and switch auto-detection model by extending the protocol and establishing an optimization model to realize a low-load and low-latency traffic monitoring scheme; for DDoS attacks. We propose the attack inspection algorithm SCVAE based on Variational Encoder (VAE) and Spectral Clustering. in order to mitigate DDoS attack traffic, the protection system uses the QoS traffic control method, builds the application flow hierarchy model, and filters the attack traffic endured by the system by setting the application flow bandwidth limit as well as the traffic priority dual policy. Finally, a Mininet-based simulation test environment is built to evaluate the model, and different test indexes are set for different system modules to evaluate their actual performance. The results of this paper show that in the network traffic monitoring test, the artificial intelligence DDoS attack protection algorithm can respond to the attack more quickly by reducing the average 73ms per sampling compared with other algorithms; in the attack traffic identification test, the comparison accuracy (P) is improved by 15.14%, the accuracy (AC) is improved by 13.26%, the recall (R) is reduced by 9.23%, and the F1 measurement criteria improved by 23%. The test verifies that the artificial intelligence DDoS attack protection system can achieve real-time monitoring of each performance parameter and also illustrates the feasibility and practicality of the research content of this paper, which strengthens the construction of the technical means of Internet security protection and further enhances the Internet security defense capability.

This study conducted a questionnaire survey on the physical condition of high school students based on FIT. The correlation between sedentary behavior, exercise level and physical fitness was studied. In this paper, the fuzzy C-average clustering method of the BP neural network is used to make statistics and classification of students’ physical condition. This paper collates the biological quality data of college students. Then this paper makes a comprehensive classification, classification and quantitative assessment of the data. The study found that college students’ sedentary habits varied from individual and family backgrounds. Family factors influence students’ physical exercise. Physical exercise was positively correlated with sedentary behavior and body mass index in adolescents. The accuracy of clustering combined with the BP neural network can reach 94%.

There are three serious problems in the traditional agricultural supply chain structure in China, namely, the blockage of information channels and the poor transmission of information, the subordination of farmers and the low distribution of network profits, the unstable contract and the loose supply chain. Among them, the first problem is the core problem. This paper attempts to design an innovative ASC structure to solve these problems. Firstly, from the perspective of farmers, this paper analyzes the agricultural sustainable development of network embedding, and draws the following conclusions: with the increase and deepening of farmers’ network embedding relationship, farmers can obtain more effective information about the whole agricultural sustainable development, and finally improve their profit distribution ratio. But how to increase this network embedding relationship? To solve this problem, we introduce the platform theory and consider redesigning ASC by embedding the integrated management information platform (IMIP). Then, we made a mathematical analysis. Through the comparison of absolute value and relative value, we find that the innovative structure can not only increase the amount of effective information that farmers get from ASC, but also increase the total amount of information from all stakeholders and the proportion of farmers’ information in the total amount of ASC information. In view of this, we believe that the innovative structure of ASC can effectively solve the problems existing in the traditional ASC structure.

This paper mainly discusses the formation and development of the core literacy of sports majors. Then, this paper establishes the factor model that affects the professional quality of youth sports employing multiple linear regression analysis. First, this paper uses factor analysis to observe 19-factor patterns. After the maximum variance rotation method, four dimensions were determined from the factor loading matrix, and confirmatory factor analysis was performed. Finally, this paper uses the multiple linear regression method to establish young students’ core literacy education model. This paper analyzes the model and draws the influence of four factors of school, family, society and individual on adolescents.

This paper establishes a cost management model of cross-border e-commerce based on a fractional differential equation. Firstly, this paper constructs a fractional differential equation game model of the platform’s evolution and consumer and government regulation. Then this paper studies each participant’s interests and cost control in the supervision and management of international e-commerce. Finally, this paper makes a dynamic simulation of cross-border e-commerce. It discusses the decision-making process of cross-border e-commerce enterprises in the operation factors of the alliance. Through the numerical simulation of MATLAB, it can be seen that standardized management can reduce the cost sensitivity of enterprises in a sense. The cost control system model of cross-border e-commerce based on fractional differential equation has high accuracy and strong applicability.