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In recent years, with the rapid development of the Internet industry, the publicity effect of paperbark walnuts has been substantially increased, and the consumption and demand of paperbark walnuts have been rising, and the output of paperbark walnuts in China has also increased year by year. This paper takes the panel data of the top three prefecture-level cities in each GDP ranking of five provinces in northwest China from 2012 to 2021 as the sample and combines the DEA model with Malmquist to evaluate and analyze the logistics efficiency of paper walnuts in northwest China. It is found that the overall logistics efficiency of paperback walnuts in Northwest China has been improved in recent years, but there are still areas that need to be improved, such as the factor distribution is not reasonable enough; the logistics efficiency of paperback walnuts in Northwest China from 2012 to 2021 is in a fluctuating state; the logistics efficiency of paperback walnuts in Shaanxi Province is better than the other provinces and is in the leading position; the improvement of technological progress has a key role in the total factor productivity improvement. As a result, this paper puts forward five specific suggestions based on the research results to help accelerate the improvement of paper walnut logistics efficiency in Northwest China.

Today, computer graphics and graphic image processing techniques have been widely used in daily life and industrial production. Due to the development of computers, computer graphics has brought more convenience to our daily life. In order to give full play to the value of computers, this paper takes the Hakka paper-cut art with local characteristics as the starting point, first of all its development history, artistic characteristics, compositional forms, expression techniques, cultural connotations, Hakka paper-cut patterns, and the symbolic meaning of folk customs, and then we design a visualization system for the paper-cut works of Gannan Hakka based on computer graphics. In addition, the system provides a solution for the integration of Gannan Hakka paper-cut art and Jiangxi native product packaging design and provides a reference for the theory and practice of modern native product packaging design.

Rewinding machines are used in the paper converting industrial sector to unroll paper veils from large source reels and wind them around smaller rolls, for the production of paper logs to be used in household activities. Many different types of rewinding machine exist, and they are mostly based on stapling devices which automatically unroll a large coil of paper, roll it up around a new log, stop the veil allowing it to be torn off, unload the completed paper log, and reload a new paper core to be rolled up. Winding units generally include a primary and a secondary roller, supplying the desired rotational speed for winding, and a pressure unit consisting of a pressure roller in contact with the paper log being rolled up, which assures adequate containment and avoids the paper log to escape. All known systems have some mechanical limitations, mainly due to the contact of the pressure roller with the paper log, causing instabilities and vibrations, particularly when working with soft paper veils. This limits the maximum possible winding speed. The aim of this paper is it to present a detailed kinematic analysis of a novel pressure unit which overcomes the aforementioned limitation.

Splicing the shredded paper means the technology that, according the paper, which has been shredded to design a particular algorithm to splice and recover the original paper. This paper introduced the algorithm of splicing the shredded paper based on the matching to texture feature. By means of this algorithm, we modeled for the problem of splicing the shredded paper and solved it. And, we used the algorithm to splice both pieces of English shredded paper and Chinese shredded paper. The recovered paper had proved the accuracy of the algorithm.

This paper presents a new method for calculating the insulation health index (HI) of oil-paper transformers rated under 110 kV to provide a snapshot of health condition using binary logistic regression. Oil breakdown voltage (BDV), total acidity of oil, 2-Furfuraldehyde content, and dissolved gas analysis (DGA) are singled out in this method as the input data for determining HI. A sample of transformers is used to test the proposed method. The results are compared with the results calculated for the same set of transformers using fuzzy logic. The comparison results show that the proposed method is reliable and effective in evaluating transformer health condition.

We consider a one-dimensional cutting stock problem (CSP) in which the stock widths are not used to fulfill the order but kept for use in the future for the industrial-use paper production. We present a new model based on the flexible stock allocation and trim loss control to determine the production quantity. We evaluate our approach using a real data and show that we are able to solve industrial-size problems, while also addressing common cutting considerations such as aggregation of orders, multiple stock widths, and cutting different patterns on the same machine. In addition, we compare our model with others, including trim loss minimization problem (TLMP) and cutting stock problem (CSP). The results show that the proposed model outperforms the other two models regarding total flexibility and trim loss ratio.

We give a counter example to the comparison principle for the multipoint BVPs (by Xuxin Yang, Zhimin He, and Jianhua Shen, in Mathematical Problems in Engineering, Volume 2009, Article ID 258090, doi:10.1155/2009/258090). Then we suggest and prove a corrected version of the comparison principle.

This paper analyzed the drying process of the household gas clothes dryer (GCD) based on the principle of heat and mass transfer. The drying models in three scenarios were established: natural gas burned in combustion chamber, ambient air mixed with exhausted gas in hot gas duct, and clothes were dried in the drying drum. The simulation of the drying process was performed on MATLAB software. And the effectiveness of the drying model was verified by comparison with experiment results. The effects of gas flow rate, dry mass of clothes, ambient temperature, ambient humidity, textile type, and moisture extraction rate (MER) on the performance of dryers were studied. This paper provided a theoretical basis for the design and optimization of GCDs. It was found that the drying time of the gas clothes dryer was mostly affected by the gas flow and dry mass of clothes, and the specific moisture extraction rate (SMER) was mostly affected by the ambient temperature and relative humidity. The nylon clothes have the fastest MER and the smallest SMER. Moreover, reducing operating time in falling-rate drying period can improve energy efficiency.

In recent years, modeling and recognizing driver behavior have become crucial to understanding intelligence transport systems, human-vehicle systems, and intelligent vehicle systems. A wide range of both mathematical identification methods and modeling methods of driver behavior are presented from the control point of view in this paper based on the driving data, such as the brake/throttle pedal position and the steering wheel angle, among others. Subsequently, the driver’s characteristics derived from the driver model are embedded into the advanced driver assistance systems, and the evaluation and verification of vehicle systems based on the driver model are described.

On the basis of basic law in AASHTO2007 model, the forecasting mathematical model of drying shrinkage of recycled aggregate concrete (RAC) is established by regression analysis and experimental study. The research results show that (1) with the replacement rate of RCA increases, the drying shrinkage value of RAC increases; this trend is even more obvious in the early drying time. (2) The addition of fly ash can inhibit the drying shrinkage of RAC, but the effect is not very obvious. Specifically, the addition of fly ash will increase the shrinkage to some extent when the mixing amount is 20%. (3) The addition of expansive agent can obviously inhibit the shrinkage of RAC; the inhibition affection is better than that of fly ash. (4) The forecasting mathematical models of drying shrinkage of RAC established in this paper have high accuracy and rationality according to experiment validation and error analysis.

A sludge composite modifier (SCM) which comprises a mixture of three cementitious components was proposed for sludge drying and stabilization. Effect of SCM components on sludge moisture content was analyzed using uniform design and the optimum composition of SCM was determined by computer-aided modeling and optimization. To compare the drying effect of SCM, quicklime, and Portland cement, the effects of material content and curing time on moisture content of sludge were also studied. The results showed that the optimum ratio of modifier component was slag/cement clinker/dihydrate gypsum = 0.64/0.292/0.068 and the moisture content of SCM-stabilized sludge decreased with the increasing material content and extending curing time. Besides, the experimental results showed that optimized SCM behaved better than quicklime and Portland cement in sludge semi-drying and XRD analysis revealed that the main hydrated product of stabilization was ettringite, which played an important role in the effective drying process. Sewage sludge stabilized using SCM could be used as an effective landfill cover.

This paper aims to study the law of pressure behavior and the characteristics of mutual influence in the process of steeply inclined coal seam mining. Based on the geological and engineering conditions of the B8 coal seam in Nanshan coal mine, this paper explores the law of mine pressure behavior in the process of steeply inclined coal seam mining by means of theoretical analysis and FLAC3D numerical simulation. Based on this, this paper also examines the influence of the interaction range between coal seams, the reasonable setting size of the section coal pillar, and the mining distance of the working face on the return air roadway. The results suggest as follows: (1) With an increase in the coal seam dip angle, the difference in the abutment pressure plastic zone between upper and lower coal pillars increases, and when mining in steeply inclined coal seams, the stress concentration in the lower and upper part of the working face deviates to the roof and floor respectively, which is obviously different from that of gently inclined coal seams. (2) The vertical stress above the section coal pillar changes in three stages with an increase in the width of the coal pillar, and the width of the coal pillar in the second stage can be taken as the reasonable size of the width of the coal pillar. The numerical simulation results are consistent with the theoretical calculation results, which indicate the high reliability of this method in determining the reasonable coal pillar size. (3) With an increase in the mining distance in steeply inclined coal seams, the maximum pressure around the working face and above the return air roadway has gone through three stages. When the distance between the working face and the adjacent return air roadway is close, the pressure above the return air roadway increases sharply, and disturbance is stronger.

With the development of smart classrooms, analyzing students’ emotions for classroom learning is an effective means of accurately capturing their learning process. Although facial expression-based emotion analysis methods are effective in analyzing classroom learning emotions, current research focuses on facial expressions and does not consider the fact that expressions in different postures do not represent the same emotions. To provide a continuous and deeper understanding of students’ learning emotions, this study proposes an algorithm to characterize learning emotions based on classroom time-series image data. First, face expression data for classroom scenarios are established to address the lack of expression databases in real teaching environments. Second, to improve the accuracy of facial expression recognition, a residual channel cross transformer masking net expression recognition model is proposed in this paper. Finally, to address the problem that the existing research dimension of learning emotion is too single, this paper uses the facial expression and head posture data obtained from deep learning models for fusion analysis and innovatively proposes a Dempster–Shafer evidence-theoretic fusion model to characterize the learning emotion within the lecture duration of knowledge points. The experiments show that both the proposed expression recognition model and the learning sentiment analysis algorithm have good performance, with the expression recognition model achieving an accuracy of 73.58% on the FER2013 dataset. The proposed learning emotion analysis method provides technical support for holistic analysis of student learning effects and evaluation of students’ level of understanding of the knowledge points.

Convolutional neural networks (CNNs) are often used in tasks involving vision processing, and unclear images can hinder the performance of convolutional neural networks and increase its computational time. Furthermore, artificial intelligence (AI) and machine learning (ML) are related technologies, which are considered a branch of computer science, which are used to simulate and enhance human intelligence. In e-healthcare, AI and ML can be used to optimize the workflow, automatically process large amounts of medical data, and provide effective medical decision support. In this paper, the authors take several mainstream artificial intelligence models currently open on the market for reference. In this paper, the optimized model (AL-CNN) is tested for noise image recognition, and the AL-CNN model is established by using activation functions, matrix operations, and feature recognition methods, and the noisy images are processed after custom configuration. Not only does this model require no prior preparation when processing images, but it also improves the accuracy of dealing with noise in convolutional neural networks. In the AL-CNN in this paper, the architecture of the convolutional neural network includes a noise layer and a layer that can be automatically resized. After the comparison of the recognition experiments, the accuracy rate of AL-CNN is 20% higher than that of MatConvNet-moderate, and the accuracy rate is 40% higher than that of MatConvNet-chronic. In the second set of experiments, the accuracy exceeds MXNet and TensorFlow by 50% and 70%, respectively. In addition, the authors optimized the convolutional layer, pooling layer, and loss function of AL-CNN in different parameters, which improved the stability of noise processing, respectively. After customizing the two configuration optimizations, the authors found that the second optimized AL-CNN has higher recognition accuracy, and after the optimization test, the error rate can be continuously decreased as the number of recognition increases in a very short number of times.

The choice and the dimensioning of the controller for the maximum power point tracking (MPPT) are determined for the ideal energy efficiency of the photovoltaic (PV) systems. Many works have been developed in the field of MPPT methods, especially fuzzy logic controllers. However, these are robust if the parameters of the membership functions have been well designed. In this paper, the performances of an intelligent fuzzy logic controller (FLC)-based MPPT method have been optimized by an evolutionary genetic algorithm (GA). The works presented in the literature have shown the efficiency of the proposed method compared to classical methods. In our paper, the validation of the experimental results obtained is given with respect to a reference signal. The control of the simulated PV source and the proposed method are built using the Simulink/Matlab environment and implemented on the dSPACE DS1104 controller to validate the practical execution of the suggested method. The standalone PV system has been tested in an emulated test bench experimentation. Experimental results confirm the efficiency of the proposed method and its high accuracy in handling fast varying load conditions.

Targeting the problem of autonomous navigation of indoor robots in large-scale, complicated, and unknown environments, an autonomous online decision-making algorithm based on deep reinforcement learning is put forward in this paper. Traditional path planning methods rely on the environment modeling, which can cause more workload of calculating. In this paper, the sensors to detect surrounding obstacles are combined with the DDPG (deep deterministic policy gradient) algorithm to input environmental perception and control the action direct output, which enables robots to complete the tasks of autonomous navigation and distribution without relying on environment modeling. In addition, the algorithm preprocesses the relevant data in the learning sample with Gaussian noise, facilitating the agent to adapt to noisy training environment and improve its robustness. The simulation results show that the optimized DL-DDPG algorithm is more efficient on online decision-making for the indoor robot navigation system, which enables the robot to complete autonomous navigation and intelligent control independently.

Image semantic segmentation is one of the core tasks for computer vision. It is widely used in fields such as unmanned driving, medical image processing, geographic information systems, and intelligent robots. Aiming at the problem that the existing semantic segmentation algorithm ignores the different channel and location features of the feature map and the simple method when the feature map is fused, this paper designs a semantic segmentation algorithm that combines the attention mechanism. First, dilated convolution is used, and a smaller downsampling factor is used to maintain the resolution of the image and to obtain its detailed information. Secondly, the attention mechanism module is introduced to assign weights to different parts of the feature map, which reduces the accuracy loss. The design feature fusion module assigns weights to the feature maps of different receptive fields obtained by the two paths and merges them together to obtain the final segmentation result. Finally, through experiments, it was verified on the Camvid, Cityscapes, and PASCAL VOC2012 data sets. Mean intersection over union (MIoU) and mean pixel accuracy (MPA) are used as metrics. The method in this paper can make up for the loss of accuracy caused by downsampling while ensuring the receptive field and improving the resolution, which can better guide the model learning. And the proposed feature fusion module can better integrate the features of different receptive fields. Therefore, the proposed method can significantly improve the segmentation performance compared to the traditional method.

For over a hundred years, the model of prey–predator has been handled extensively. It has been proved that Lotka–Volterra (LV) models are not stable from all previous studies. In these models, there is divergent extinction of one species or cyclic oscillation. There is no solution for asymptotic stability for the prey–predator models. In this paper, a Caputo fractional-order one prey-two predators’ model with an immigration effect is investigated. Then, the effect of adding a small immigration term to the prey or predators’ populations is studied. The extension is both the inclusion of a second predator and the use of Caputo derivatives. Although LV systems have been extensively investigated, there are no previous studies on our proposed nonlinear modifications. The Grünwald–Letnikov method was applied to find numerical solutions for the proposed model to validate our findings. Several different cases were studied to reach confirmed conclusions. The effect of the variation of fractional-order derivative is handled in this study. From all the results, the effect of adding the small immigration terms is positive for stability. Hence, it can be concluded that the proposed small immigration terms can stabilize the natural populations of the one prey-two predators’ model. An optimal control strategy for the proposed model is shown at the end of the paper.

This paper is intended to introduce the subtractive derivations and study some of their algebraic properties on R l -monoids. Also, we give some characterizations of subtractive derivations on the Gödel center. Moreover, Gödel algebras are characterized by a fixed set of subtractive derivations. Finally, we discuss the relationship between subtractive derivations and other derivations for R l -monoids. These results of the paper can provide the common properties of subtractive derivations in the t-norm-based fuzzy logical algebras.

The convolution neural network (CNN) not only has high fault tolerance but also has high computing capacity. The image classification performance of CNN has an important relationship with its network depth. The network depth is deeper, and the fitting ability of CNN is stronger. However, a further increase in the depth of CNN will not improve the accuracy of the network but will produce higher training errors, which will reduce the image classification performance of CNN. In order to solve the above problems, this paper proposes a feature extraction network, AA-ResNet with an adaptive attention mechanism. The residual module of the adaptive attention mechanism is embedded for image classification. It consists of a feature extraction network guided by the pattern, a generator trained in advance, and a complementary network. The feature extraction network guided by the pattern is used to extract different levels of features to describe different aspects of an image. The design of the model effectively uses the image information of the whole level and the local level, and the feature representation ability is enhanced. The whole model is trained as a loss function, which is about a multitask problem and has a specially designed classification, which helps to reduce overfitting and make the model focus on easily confused categories. The experimental results show that the method in this paper performs well in image classification for the relatively simple Cifar-10 dataset, the moderately difficult Caltech-101 dataset, and the Caltech-256 dataset with large differences in object size and location. The fitting speed and accuracy are high.

The electromagnet pneumatic proportional servo valve is widely used in industrial automation, medical equipment, scientific composition detectors, and other fields that require precise adjustment of high-pressure air flow due to its simple working structure, large output power, large working frequency response, and good control performance. However, the output pressure of the valve is small, making it vulnerable to the impact of the external environment. Based on the practical electromagnet-type pneumatic proportional servo valve, this paper establishes a finite element dynamic mathematical model, conducts finite element analysis on the electromagnet magnetic field in the valve, then improves the structural parameters of the original proportional valve through the analysis of the valve performance parameters and structural optimization, and finally, the physical experiment proves that the optimized electric proportional valve can effectively reduce the hysteresis within the working pressure range and has good response characteristics to step signals. This paper can provide support for performance analysis and structural parameter improvement of the electric proportional valve.

This paper investigates operational decisions and online channel mode selection for green products with an e-commerce supply chain in which the manufacturer is subject to capital constraint. Starting from a Stackelberg game framework where the platform acts as a leader, two different online channel modes applied with an e-commerce supply chain are explored for equilibria, respectively. This paper then extends to a different supply chain with a reversed channel leadership, so as to reveal how the supply chain leaderships affect the equilibriums and supply chain performance. With the analytical studies, several important and interesting insights are derived. For example, but not limited to, the following is found: (a) the equilibrium decisions of the capital-constrained supply chain highly rely on the associated interest rate but are independent of the initial capital level of the financially-constrained manufacturer; (b) the platform and the capital-constrained manufacturer act largely differently when choosing the optimal online channel mode for themselves; (c) a win-win situation benefiting two parties holds when and only when the two members choose the agency mode under certain conditions; (d) the impacts of the supply chain leaderships and online channel modes on the consumer surplus and social welfare are clearly revealed. These results derived in our paper shed lights on industrial practicer on how to effectively make such decision-making in platform economy. The members in an e-commerce supply chain can appropriately determine their decisions such as commission and slotting fee to reach a win-win situation for two parties.

Artificial intelligence and deep learning have attracted much attention from researchers in industry and academia. The volleyball movement standardization and recognition model involve the application of artificial intelligence and deep learning. In order to solve the problem that human action in volleyball video is continuous and effective spatial and temporal features need to be extracted from the video stream, the Inception module is decoupled and heterogeneous, replacing the original 5 × 5 convolutional structures with two 3 × 3 convolutional structures, as well as replacing the 3 × 3 convolutional structures with 1 × 3 and a 3 × 1 convolutional structure with internal parameter optimization to ensure the accuracy of recognition. The model uses the input motion video RGB map as the spatial input and the optical flow map as the temporal input, and the two are weighted 1 : 1 for feature fusion. Experiments are conducted on the volleyball action video and homemade dataset in UCF101, and the experimental data show that the accuracy of the DNet volleyball action standardization recognition model proposed in this paper is 94.12%, which proves that the method improves the recognition ability of the model while speeding up the training speed. The research presented in this paper provides important theoretical guidance for artificial intelligence and deep learning.

Breast cancer is the most common and deadly type of cancer in the world. Based on machine learning algorithms such as XGBoost, random forest, logistic regression, and K-nearest neighbor, this paper establishes different models to classify and predict breast cancer, so as to provide a reference for the early diagnosis of breast cancer. Recall indicates the probability of detecting malignant cancer cells in medical diagnosis, which is of great significance for the classification of breast cancer, so this article takes recall as the primary evaluation index and considers the precision, accuracy, and F1-score evaluation indicators to evaluate and compare the prediction effect of each model. In order to eliminate the influence of different dimensional concepts on the effect of the model, the data are standardized. In order to find the optimal subset and improve the accuracy of the model, 15 features were screened out as input to the model through the Pearson correlation test. The K-nearest neighbor model uses the cross-validation method to select the optimal k value by using recall as an evaluation index. For the problem of positive and negative sample imbalance, the hierarchical sampling method is used to extract the training set and test set proportionally according to different categories. The experimental results show that under different dataset division (8 : 2 and 7 : 3), the prediction effect of the same model will have different changes. Comparative analysis shows that the XGBoost model established in this paper (which divides the training set and test set by 8 : 2) has better effects, and its recall, precision, accuracy, and F1-score are 1.00, 0.960, 0.974, and 0.980, respectively.

In this paper, we present a newly emerging approach for the use of the oedometer test for the determination of some important mechanical properties of an elastic-plastic soil using the Mohr–Coulomb yield criterion and an associated flow rule. Analysis of the oedometer test is performed in low strains. The identification of constitutive parameters is carried out by means of the Newton optimization procedure used to solve inverse problems. The consolidation test problem is solved using new equations proposed in this paper. The solution of the problem according to the equations developed in this paper was validated by the numerical modeling of an oedometric test by following the requirements and the experimental protocol from the Plaxis Finite Element code. The method has been validated through Plaxis’ software, making it reliable. The sensitivity study of the model with respect to its parameters was carried out. Laboratory results analysis over nine samples of the southern Cameroon soils are compared to those computed analytically. The major parameters are Young’s modulus, Poisson’s ratio, the internal friction angle, the cohesion, and the dilatancy angle. The mechanical properties of those soils obtained from the proposed model are very close to, or almost identical in some cases with values obtained in the laboratory. The practical validation and the application of our method have been successfully applied on the lateritic soils of Cameroon. The compressibility curves reconstituted from the proposed theoretical solution are superimposed on those resulting from laboratory tests with low dispersions.

The identification of disruptive technologies is intended to focus on training and incubation in advance and is an important means to accelerate the upgrading of industrial structure and the transformation of developmental mode and seize the commanding heights of future development. Based on summarizing the existing major identification methods of disruptive technologies, this paper concludes the rule that “disruptive technologies are always at the root node of a certain classification in the deeply classified technology development network”. It also proposes a new algorithm to use term frequency-inverse document frequency technology and a patented Subject-Action-Object structure to extract technical features, develop networks based on similarity matrix generation technology, and identify subversive technologies based on the depth classification model. Using patent data, it is found that the technology development network generated by the algorithm proposed in this paper can effectively show the trajectory of technology development by fitting the patent citation relationship. Through this algorithm, we have successfully identified technologies that have had disruptive effects in the field and verified the effectiveness of this algorithm.

With the popularization of the Internet and the widespread application of mobile terminal, travelers are increasingly dependent on traffic information. It is particularly important to construct and develop more accurate discrete traffic state prediction models. Considering that most of the previous studies about traffic state prediction are essentially a prediction of specific parameters, this paper proposes a generalized fractional difference (GFD)-auto regressive moving average (ARMA)-Fisher combined model that can directly predict the discrete traffic state. First, using the original historical traffic flow data as training samples for cluster analysis, we obtain different traffic state classification samples. Then, using the Fisher discriminant method, we develop a discrete traffic state recognition model based on the traffic state classification samples. The model can help us identify the discrete state of the future traffic flow when we input a set of predicted traffic parameters to it. In order to improve the accuracy of the model prediction, this paper creatively proposes to apply the GFD method to the stabilization of original traffic flow data and then develop an ARMA model to predict the traffic parameters processed by GFD (GFD-ARMA). Last, we obtain the predicted discrete traffic state using the calibrated Fisher discrimination model whose inputs are the parameters predicted by the GFD-ARMA. The proposed method is tested using field data from CHangzhou, China. The results suggest that the developed GFD-ARMA-FISHER method shows a higher accuracy for traffic state prediction and is better than other similar methods.

This paper designs and develops a virtual golf simulation putting simulator based on the existing computer technology and conducts in-depth research and analysis on the relationship between its motion performance and user experience. The network architecture of the distributed virtual golf simulation system and the scene data management model are established, based on which the server-side system design and the client-side network communication module design of the distributed virtual golf simulation system are carried out. In the requirement analysis, the functional requirements such as building VR scenes, data communication and recognition models, and the non-functional requirements such as system security and ease of use are analyzed; in the outline design, the hardware equipment and logical architecture of the automatic user experience optimization system are described; in the detailed design, the functional modules of the system are designed in detail, including VR induction experience, physiological signal dataset user experience identification, data communication, optimization strategy, and so on, and important class diagrams and flowcharts are given. The intervention effects of positive thinking training on sports performance and improving athletes’ attention and receptivity have been verified and recognized by coaches and athletes. The putting simulator in the experimental class had higher hole-in-hole parameters than the control class, a highly significant difference; the putting simulator in the experimental class had higher hole-in-hole parameters than the control class, with a highly significant difference. These 3D models may contain more detailed information. In a virtual scene, the more detailed information a model contains, the more polygons the model needs, so that the computer needs to draw many polygons per frame, which has a great impact on the real-time performance of scene drawing. The parameters of the 5-yard chip-and-shoot in the experimental class were higher than those in the control class, and there was a very significant difference between the parameters of the 15-yard chip-and-shoot in the experimental class and those in the control class. The experimental results show that the model optimization processing method and rendering acceleration technology proposed in this paper can largely improve the rendering efficiency of 3D virtual scenes.

This paper made an in-depth analysis of the articulation between a substation’s design parameters and water supply and drainage structure quantities and costs, innovatively built a hierarchical structure model of influencing factors of quantities, and eventually extracted the key parameters affecting quantities. Meanwhile, the influence path of the price level change on the water supply and drainage structure costs was also analyzed in this paper. The BP neural network model and linear regression model were employed to estimate the ontology engineering cost and the price difference due to preparation time, so as to realize the purpose of quickly estimating the cost of substation water supply and drainage structure by knowing a few key parameters. Through calculation and stimulated analysis, the error of the model is controlled within ±3%, which provides an effective tool for quickly estimating the cost of substation water supply and drainage structure. Therefore, this model has practical significance and application value.

Under the background of the gradual development and popularization of mobile Internet information technology, this paper realizes network public opinion monitoring and emotion analysis based on the deep learning method, aiming at the research needs of people’s ideological changes and emotional trends. Aiming at the shortcomings of sentiment dictionaries or machine learning methods in sentiment analysis tasks, this paper builds a sentiment classification model based on deep learning methods. First, the current main text preprocessing methods are introduced, and then a sentiment classification model, BCBL, is proposed, combining BERT, CNN, and Bi LSTM. Compared with traditional models, BCBL can better complete text sentiment classification tasks on standard datasets. Next, in view of the problem that BCBL does not consider the distribution of vocabulary weights, an attention mechanism is introduced to improve BCBL, and then the BCBL-Att model is proposed. Set up multiple sets of comparative experiments again and find that the classification effect and overall performance of BCBL-Att on standard datasets are better than BCBL, indicating that BCBL-Att has more advantages in text sentiment classification tasks.

The hepatitis B infection is a global epidemic disease which is a huge risk to the public health. In this paper, the transmission dynamics of hepatitis B deterministic model are presented and studied. The basic reproduction number is attained and by applying it, the local as well as global stability of disease-free and endemic equilibria of continuous hepatitis B deterministic model are discussed. To better understand the dynamics of the disease, the discrete nonstandard finite difference (NSFD) scheme is produced for the continuous model. Different criteria are employed to check the local and global stability of disease-free and endemic equilibria for the NSFD scheme. Our findings demonstrate that the NSFD scheme is convergent for all step sizes and consequently reasonable in all respect for the continuous deterministic epidemic model. All the aforementioned properties and their effects are also proved numerically at each stage to show their mathematical as well as biological feasibility. The theoretical and numerical findings used in this paper can be employed as a helpful tool for predicting the transmission of other infectious diseases.

In this paper, we shall give some properties of complex differential-difference equation of the form ∑ μ = 1 n α μ z ∏ i = 1 t f z + c i a i 0 μ f ′ z + c i a i 1 μ ⋯ f k i z + c i a i k i μ / ∑ ν = 1 m β ν z ∏ i = 1 t f z + c i b i 0 ν f ′ z + c i b i 1 ν ⋯ f k i z + c i b i k i ν = R z , f z , where a i j μ μ = 1,2 , … , n and b i j ν ν = 1,2 , … , m i = 1,2 , … , t ; j = 0,1 , … , k i are non-negative integers, c i i = 1,2 , … , t are distinct, nonzero complex numbers, α μ z μ = 1,2 , … , n and β ν z ν = 1,2 , … , m are small functions relative to f z , and R z , f z is a rational function in f z with coefficients, which are small functions of f z . We also consider related complex functional equations in the paper. These results are improvements of some several previous results.

In the investigations of fish swimming, the relationship between fish swimming and the corresponding characteristics of flow field attracts lots of attention. In this paper, the influence of different Strouhal numbers on the flow field characteristics and mechanical characteristics of fish-like body is investigated. It is found that the strength of the anti-Karman vortex and the thrust on the fish-like body are affected by the Strouhal number. When the Strouhal number is small, the thrust coefficient is small but the propulsion efficiency is high. Similarly, when the Strouhal number is large, the thrust coefficient is large and the propelling efficiency is low. In order to better reveal the swimming status of the flexible body of the fish imitation, the effect of the Strouhal number is analyzed in detail in this paper. Besides, the influence of the three parameters in the Strouhal number formula on the flow field characteristics and thrust coefficient of the fish-like body is further investigated by the Taguchi method. The thrust coefficient is positively correlated with the swing amplitude and frequency, which is found to be negatively correlated with the inflow velocity. Swing amplitude and swing frequency are the main factors affecting the thrust coefficient; there is little effect of the inflow velocity on the thrust coefficient.

In recent years, anomaly detection techniques in time-series data have been widely used in manufacturing, cybersecurity, and other fields. Meanwhile, various anomaly detection models based on generative adversarial networks (GAN) are gradually used in time-series anomaly detection tasks. However, there are problems of unstable generator training, missed detection of anomalous data, and inconsistency between the discriminator’s discriminant and the anomaly detection target in GAN networks. Aiming at the above problems, the paper proposes a DUAL-ADGAN (Dual Anomaly Detection Generative Adversarial Networks) model for the detection of anomalous data in time series. First, the Wasserstein distance satisfying the Lipschitz constraint is used as the loss function of the data reconstruction module, which improves the stability of the traditional GAN network training. Second, by adding a data prediction module to the DUAL-ADGAN model, the distinction between abnormal and normal samples is increased, and the rate of missing abnormal data in the model is reduced. Third, by introducing the Fence-GAN loss function, the discriminator is aligned with the anomaly detection target, which effectively reduces the anomaly data false detection rate of the DUAL-ADGAN model. Finally, anomaly scores derived from the DUAL-ADGAN model are compared with dynamic thresholds to detect anomalies. The experimental results show that the average F1 of the DUAL-ADGAN model is 0.881, which is better than the other nine baseline models. The conclusions demonstrate that the DUAL-ADGAN model proposed in the paper is more stable in training while effectively solving the problems of anomaly miss detection and discriminator inconsistency with the anomaly detection target in the anomaly detection task.

Numerous mathematical models simulating the phenomenon in science and engineering use delay differential equations. In this paper, we focus on the semilinear delay differential equations, which include a wide range of mathematical models with time lags, such as reaction-diffusion equation with delay, model of bacteriophage predation on bacteria in a chemostat, and so on. This paper is concerned with the stability and convergence properties of exponential Runge–Kutta methods for semilinear delay differential equations. GDN-stability and D-convergence of exponential Runge–Kutta methods are investigated. These two concepts are generalizations of the classical AN-stability and B-convergence for ordinary differential equations to delay differential equations. Sufficient conditions for GDN-stability are given by a newly introduced concept of strong exponential algebraic stability. Further, with the aid of diagonal stability, we show that exponential Runge–Kutta methods are D-convergent. The D-convergent orders are also examined. Numerical experiments are presented to illustrate the theoretical results.

This paper aims at exploring the fracture evolution as well as capturing the fracture network formation of jointed shale during hydraulic fracturing process by numerical simulation using the Rock Failure Process Analysis System of Flow (RFPA—Flow). The simulations were successfully completed following two stages. In the first stage, a series of simulations are performed to investigate the influence of different angle-oriented perforations on fracture initiation and propagation. The simulation results agreed well with the experimental results confirming the validity of the RFPA-Flow code in the application. In the second stage, extensive hydraulic fracturing simulations on jointed shale with different angled-joint planes and different angle-oriented perforations were performed. The simulation results demonstrated that the fracture evolution changes significantly as the magnitude of α (the angle between perforation and the joint plane) increased. It was found that a large α value could lead to a larger the tensile stress region around the fracture tip resulting in more joint planes pulled apart. It could also induce more secondary fractures and hence a more complex fracture could be formed. As a result, the α value of 60° to 90° were found to aid in the fracture network generation. On the other hand, the angle β being the angle between the parallel joints and the maximum stress direction was another important factor in fracture evolution. Its influence on fracturing performance was similar to angle α. It was found that the initiation and breakdown pressure of jointed shale were in proportional to the magnitude of angle β. Therefore, this paper introduced an efficient approach to promote the formation of a complex fracture network in jointed shale with the presence of oriented perforation, which could offer valuable guidance for the design of unconventional reservoir reconstruction.

The purpose of this paper is to solve the problem of big data and small samples caused by the high manual annotation cost of a military corpus. The deep learning algorithm of entity extraction in the military field was organically combined with the method of bootstrapping loop iteration to complete a study on the application of intelligent corpus annotation of military field entities. With the experimental research showing that using a small number of military field entity corpus annotations for RoBERTa pretraining word vectors and BiLSTM-CRF models and based on the bootstrapping algorithm idea to complete 3 rounds of loop iterations and 10 rounds of cross-validation joint-voting model iterations, the best entity extraction model evaluation F value reached up to 91.5%. Finally, the 60M intelligent corpus annotation application testing was completed using the best model of iteration of this round, with a total of 178,177 sentences of military field corpus intelligently labeled, the number of entities that should be labeled reaching 417,734. Therefore, this is an efficient way of construction and evaluation of intelligent corpus annotation model in the military entity extraction field. The findings of this paper provide an effective way of how to complete the labeled corpus. The research serves as a first step for future research, for example, the construction of knowledge graphs and military intelligent Q&A.

In the context of the continuous promotion of China’s big data development strategy, this paper quantitively analyses China’s existing national-level big data policies from the perspective of policy instruments and coword networks, discusses the rationality of existing policies, explores ways to improve policies, and provides a reference for the innovation of China’s big data policies. This paper carries out a quantitative textual analysis of China’s national big data policy from the perspective of policy instruments using word frequency analysis to obtain a keyword coword matrix and visualization analysis tools to obtain a coword network. This paper further studies the network node characteristics and structure using social network analysis methods, including degree centrality, clustering analysis, and multidimensional scale analysis, to identify the policy structure and characteristics. Improving big data policy requires improvements in policy instruments on the supply side, resolving existing policy gaps, and strengthening coordination with other policies.

Aiming at the problem of mismatch between talent cultivation and social demand in the process of undergraduate education, this paper proposes a big data-driven method of adaptability analysis and collaborative path mining between vocational undergraduate talent cultivation and social demand. Starting from the big data-driven vocational undergraduate talent training and social needs, this paper points out the problems existing in the current social needs and puts forward the basic framework of vocational undergraduate talent training mode. Secondly, the clustering model of talent training and social demand is analyzed, and the clustering mining method is proposed. Finally, the big data-driven personnel training and social adaptation mining analysis, in their own ability and social needs adaptability analysis, the basic adaptation accounted for a higher proportion. Professional competence has a higher trust value in cluster analysis. Today’s social employment situation is becoming more and more severe, and how to enhance the quality of profession undergraduate students has become one of the theoretical and practical issues worthy of attention in China’s colleges at this stage. The talent training model of colleges and universities is closely in route with the demands of society, and the problem of student hire is prominent. Therefore, this paper proposes a student employability training program that combines the elements of student employability through social needs research.

In this paper, a neural network algorithm is used to conduct in-depth research and analysis on the sales dynamics prediction of virtual community knowledge sharing in cross-border e-commerce. Both the expected returns and the social network structure are analyzed, and both have positive effects on knowledge sharing in the actual development process, but the degree of them also possesses certain variability. A model of the factors influencing the quality of knowledge shared by users is constructed to explore the relationship between the dimensions in social capital and how they affect the community users’ perceptions of knowledge quality. Exploring the strong influencing factors of product repurchase rate has key implications for promoting product sales and sales forecasting. The scale of this paper has undergone several minor revisions, and the content validity is very good. Criterion validity is generally reflected by Person correlation coefficient, and construct validity includes exploratory factor analysis and confirmatory factor analysis. First, the values were clustered, and the optimal variables were selected using stepwise regression and fitted with Poisson regression models to explore the relationship between the repurchase rate of different products and the factors that strongly influence the repurchase rate in the case. To predict the sales of goods, the advantages of the BP neural network, LSTM neural network, and Verhulst gray model are combined: BP neural network can predict sales by combining the current data corresponding to independent variables, LSTM neural network can explore the influence of historical data on sales, and Verhulst model can predict sales based on the growth trend of variables. A BP-LSTM-Verhulst nested neural network based on the AP algorithm is constructed to predict sales volume, and the accuracy of this method is proved by example. Finally, it is found that the proposed sales prediction method has higher accuracy than exponential regression and shallow neural networks. The deep learning prediction method combining unstructured data such as images proposed in the paper not only provides a more accurate sales prediction method for short life cycle products in e-commerce but also provides an effective deep learning method for management practices. The KMO value obtained after the test is less than 0.5, which is not suitable for factor analysis. Using SPSS22.0 to expand the KMO value and Bartlett’s sphericity test, the KMO value is significantly greater than the minimum requirement of 0.5, the KMO value of self-efficacy is 0.826, and the KMO value of expected return is 0.870.

The nature of construction and formulation of the multi-objective functions in the flexible job shop (FJS) provides great insights about the study of the technique and the mechanism in scheduling helping to prioritize efforts for optimizing the manufacturing execution performance, which draw the focus of the researchers at home and abroad. From the perspective of the model, the solution method and the objective function, this paper reviews and analyzes the related academic papers and conferences articles during past few years. As a result, the construction forms of the multi-objective function and the scheduling scenarios show strongly positive correlation with the Makespan, the delivery tardiness, the energy consumption or the cost. Consequently, facing with the characteristic in a general way, the detailed construction analysis on the function for the flexible job shop scheduling problem (FJSP) is critical reviewed, and a total 6 types with 49 kinds classification are discussed. Obviously, the main goals are to summarize, analyze, discuss, and synthesize the existing achievements, the current research status, and the ongoing studies on the FJSP functions construction, and additionally to give useful insight into future research. Combined with the development trend of the intelligent manufacturing and the operations research, arguing for the formulation of the multi-obj. function, this review is expected to contribute to the future research of FJSP.

Macroeconomic situation is the overall performance of the economic situation of a country and region. Making accurate forecasts of macroeconomic trends is of great significance for analyzing the success or failure of macroeconomic control policies, evaluating the quality of economic system operation, and correctly formulating future development planning strategies. The macroeconomic system is a nonlinear system, the environment is constantly changing, and additional disturbing factors directly affect the operation of the macroeconomic system, which has a great impact on the forecast results. The historical information required for macroeconomic modeling is unstable, unclear, and incomplete, which makes it very difficult to solve such problems with traditional forecasting methods. In response to the multivariate and nonlinear characteristics of macroeconomic forecasting, this paper proposes the application of artificial neural networks for forecasting. This paper introduces the recurrent neural network into the field of economic forecasting to solve the problems of the traditional BP (back propagation) neural network method. The experimental data are verified and the experimental results prove that the studied scheme based PSO-GRU improve the performance of economic forecasting.

The development of big data technology makes the feature selection technology gradually perfect. The advantages of different feature selection technologies are different. Among them, random forest algorithm belongs to integrated feature selection algorithm. Integrated processing of classification results can screen out the most representative feature impact. Based on this background and random forest algorithm, this paper analyzes the evaluation of motion effect. After the measurement, this paper obtains the body data before and after the training. After the calculation, the change data of the body index are determined. The random forest feature selection method is used as the carrier to determine the corresponding index attribute set. In the process of data set input, the corresponding whole input data set is formed through data classification. The completion of training, through the comparative experiment, is conducive to clear the degree of influence of physical indicators and then complete the exercise effect evaluation. The research shows that the random forest algorithm has significant advantages in the evaluation of sports effect and can effectively improve the accuracy of classification.

The paper investigates DOA estimation of coherent Signals with the limited aperture sparse array. Mutual coupling between the sensors of the array cannot be ignored in practical radar with a limited aperture of array sensors, which will result in a degradation in the performance of Direction of Arrival (DOA) estimation. This paper proposes a Mutual-coupling-optimized array (MCOA) with a limited aperture in this scenario to reduce the mutual coupling effect. Firstly, we prove the sparse uniform linear array (SULA) has the smallest mutual coupling leakage when the array aperture and the number of sensors is determined. Secondly, we modify the spacing of the array sensors in SULA to make sure that the spacing between all array sensors and the reference sensor are coprime aiming to estimate DOA without spatial aliasing. Thirdly, we give an expression for the array element spacing arrangement with reduced mutual coupling leakage. Finally, the coherent signals are well resolved by the Sparse Bayesian Learning (SBL) algorithm. Numerous simulations are conducted to validate the advantages of the proposed array compared to several sparse arrays for estimating coherent signals in the presence of mutual coupling.

Because most of the production and transmission components of the power system are exposed to the natural environment, they are vulnerable to the threat of natural disasters for a long time and the resulting circuit damage is also large. With the improvement of people’s living standards and the improvement of electricity demand, more and more attention has been paid to the normal operation of the power system. Improving the disaster resistance ability of the power system is an important prerequisite to ensure the national economy and life. In view of the above problems, in order to solve and improve the power supply recovery ability of the distribution network in the face of various disasters, this paper focuses on the analysis of the disaster response stage of the intelligent distribution network under natural disasters and the comprehensive data and related indicators of the power grid system, so as to establish the evaluation index system of the power grid's predisaster tolerance and postdisaster resilience and, through the introduction of the fuzzy comprehensive evaluation theory, quantifying the disaster response capability of the distribution network. Finally, through the introduction of case analysis, the case results show that the intelligent distribution network disaster response ability evaluation algorithm based on fuzzy comprehensive evaluation constructed in this paper can accurately calculate the disaster response ability of the distribution network and has an important guiding role in the disaster prevention and reduction of the distribution network.

Breast cancer is a cancer disease that seriously threatens women’s health and occupies the first place in female cancer mortality. At present, the incidence rate of breast cancer in China is the first in the world and is on the rise. In view of the serious harm of breast cancer to life and health, researchers and institutions are making unremitting efforts to find a perfect diagnosis and treatment plan. With the improvement of computer performance and machine learning levels, intelligent algorithms have been able to replace human behavior and judgment in some fields. The traditional breast cancer diagnosis process requires medical experts to observe patient data repeatedly. In this case, the algorithm technology is used to quickly feedback a high probability reference result to doctors, which is particularly important to increase the diagnosis efficiency and reduce the burden of doctors. In order to improve the accuracy of existing breast cancer recognition methods, this paper proposes and implements a scheme based on a whale optimization algorithm to iteratively adjust the key parameters of the support vector machine to improve the accuracy of breast cancer recognition. In order to verify the performance of the WOA-SVM algorithm, this paper uses the Wisconsin breast cancer data in the UCI database for performance verification experiments. Experiments show that the WOA-SVM model has higher recognition accuracy than the traditional breast cancer recognition model.

The Internet of Things needs to connect different types of sensors in accordance with the agreement, and different agreements can be used for information and information exchange systems, which can facilitate the identification and control of intelligent systems. In many scenarios, location service application data are very important for obtaining accurate location information about nodes. In this paper, a wrist motion sensing sensor based on the PVDF piezoelectric film is developed. To realize the monitoring of wrist motion signals, this paper designs and manufactures a series of PVDF noninvasive sensors for motion perception. The characteristics of sports dance movements are reflected in the coordination between men and women, and the dance posture and movements must be synchronized with the music according to the level of music rhythm structure. The transfer of center of gravity is the main driving force of sports, and the gait of athletes is related to the transfer of center of gravity. Posture stability is the basic element of sports dance training. Only on this basis can it be possible to develop specific motion function trajectories and technical action forms.