Hindawi

Scientific Programming

Published by Hindawi
Online ISSN: 1875-919X
Discipline: Programming & Software Development
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Aims and scope

Scientific Programming is a peer-reviewed, Open Access journal that provides a meeting ground for research results in, and practical experience with, software engineering environments, tools, languages, and models of computation aimed specifically at supporting scientific and engineering computing.

The journal publishes papers on language, compiler, and programming environment issues for scientific computing. Of particular interest are contributions to programming and software engineering for grid computing, high performance computing, processing very large data sets, supercomputing, visualization, and parallel computing. All languages used in scientific programming as well as scientific programming libraries are within the scope of the journal.

 

Editors

Recent publications
  • Yunxia CaoYunxia Cao
Modern landscape greening plays an important role in the construction of modern cities and plays a positive role in improving the natural environment of cities and building a good image of cities. With the continuous progress of society and technology, human’s understanding of artificial intelligence is deepening, and intelligent technology is gradually integrated into all aspects of life. Because media technology has rich design elements and can carry out rich design structures, it will be more intuitive to use multimedia means for garden landscape design. Therefore, for meeting people’s requirements for the diversification of modern urban gardening construction, this study makes a deep analysis of the current status and problems of landscape design and tries to study the effective application methods of artificial intelligence technology in landscape design, to promote the combination of landscape design and artificial intelligence design. At the same time, the combination of AI lighting planning, AI water landscape planning, AI sprinkler planning, and AI paving planning is used to illustrate the application of AI in the specific project design of landscape design. The use of artificial intelligence not only promotes the innovation and optimization of landscape design, but also ensures the quality of modern landscape design and effectively improves the efficiency of modern landscape design.
 
  • Kan WangKan Wang
  • Kaichen CaoKaichen Cao
  • Mingren ChenMingren Chen
  • [...]
  • Shimin CaiShimin Cai
The front-page news in authoritative newspapers usually represents extremely significant national policies. Accurately classifying the front-page news from an amount of news helps us to quickly acquire and deeply understand the changing political and economic situations. In this paper, we propose a front-page news classification model StackText based on stacking the textual context and attribute information of news. In the proposed model, we first balance the classification size in the training set through the weighted random sampling algorithm, then construct the context and attribute feature vectors through the textual embedding and statistical analysis of news, and finally pretrain a classifier StackNet based on a neural network to realize the front-page news classification in the testing set. Taking People’s Daily as the experimental example, we compare the proposed model with benchmark methods based on statistical learning and deep learning. Based on the news sets in four stages of People’s Daily, the experimental results of the front-page news classification show that the proposed model achieves the highest average accuracy and a better balance between precision and recall, which is verified by the corresponding F 1 -score.
 
  • Rahman AliRahman Ali
  • Farkhund IqbalFarkhund Iqbal
  • Muhammad Sadiq Hassan ZadaMuhammad Sadiq Hassan Zada
The fast industrial revolution all over the world has increased emission of carbon dioxide (CO2), which has badly affected the atmosphere. Main sources of CO2 emission include vehicles and factories, which use oil, gas, and coal. Similarly, due to the increased mobility of automobiles, CO2 emission increases day-by-day. Roughly, 40% of the world’s total CO2 emission is due to the use of personal cars on busy and congested roads, which burn more fuel. In addition to this, the unavailability of parking in all parts of the cities and the use of conventional methods for searching parking areas have added more to this problem. To solve the problem of reducing CO2 emission, a novel cloud-based smart parking methodology is proposed. This methodology enables drivers to automatically search for nearest parking(s) and recommend the most preferred ones that have empty lots. For determining preferences, the methodology uses the analytical hierarchy process (AHP) of multicriteria decision-making methods. For aggregating the decisions, the weighted sum model (WSM) is adopted. The methods of sorting, multilevel multifeatures filtering, exploratory data analysis (EDA), and weighted sum model (WSM) are used for ranking parking areas and recommending top-k parking to the drivers for parking their cars. To implement the methodology, a scenario comprising cars, smart parkings are considered. To use EDA, a freely available dataset “2020testcar-2020-03-03” is used for the estimation of CO2 emitted by cars. For evaluation purpose, the results obtained are compared with the results of traditional approach. The comparison results show that the proposed methodology outperforms the traditional approach.
 
  • Chao ShiChao Shi
  • Hongwei SunHongwei Sun
  • Chao LiuChao Liu
  • Zhaojia TangZhaojia Tang
In terms of the problems of five categories of nonweld seam stripes, including inclusion, oil-spot, silk-spot, and water-spot, which interfere with weld seam recognition during robotic welding, a convolutional neural network (CNN) algorithm, combined with a multistage training strategy, is used to construct a digital model for weld seam recognition, on the basis of which the classification accuracy is compared with the standard model of seven categories of representative CNN. The results show that the ResNet model with a multistage training strategy classifies weld seams with an accuracy of 83.8%, which is superior to other standard models. In this study, the physical scenario of weld seam recognition is migrated to a neural network digital model, fulfilling the intelligent recognition of weld seams in complex scenarios based on the CNN digital model.
 
Functional modules of the smart party building platform.
Service objects of the “smart team building” platform system.
Technology roadmap.
Functional framework.
Organizational activity management process.
In the era of “Internet +,” the deep integration of party building in colleges and universities and information technology is the objective need for the modernization of the party’s government in colleges and universities and the realization of science. Starting from the concept of the smart party-building platform in colleges and universities, this study analyzes the current situation of the informatization and intelligence of party-building work in colleges and universities. Combined with the current development needs of the party-building work in colleges and universities, starting from the significance of the construction of the smart party-building platform in colleges and universities, the ideas and main research studies on the construction of the smart party-building platform in colleges and universities are expounded. Contents describe the construction path and implementation method of the university wisdom party-building platform in order to construct a reasonable and efficient university wisdom party-building platform construction plan.
 
Knowledge graph information business relationship.
Feature recognition results.
Digital audit workflow based on knowledge graph.
Construction of knowledge map of power marketing inspection rules.
The traditional topic-based auditing model lacks the ability of multi-object and multi-abnormal correlation auditing, which makes it impossible to solve the multi-scenario and multi-factor correlation-based auditing problem. This paper designs an intelligent power marketing audit model based on a knowledge graph. First, an entity identification and relationship extraction method for power marketing business based on NLP (natural language processing) and sequence annotation technology is proposed, and the description content is imported into the knowledge graph database; then, semantic disambiguation and knowledge are carried out by using bidirectional encoder representation from transformers (BERT). Link to build a knowledge map of business audit rules: Finally, an experimental analysis is carried out by taking the copying and receiving business with a large business volume in the marketing audit work as an example, and it is verified that the proposed model can effectively improve the information analysis ability and the audit accuracy of the audit work.
 
Results of logistic progressive regression model.
Results of the progressive regression model.
Based on the internal and external constraints of serious disease insurance and commercial serious disease insurance, the logistic model of “whether you are willing to connect with commercial serious disease insurance on the basis of serious disease insurance” and “to understand the objective conditions of the connection between serious disease insurance and commerci”a“ serious disease insurance” is designed. Based on the empirical research on the micro survey panel data of 6 municipal districts in W City in April 2020, the following research results are obtained: among the many influencing factors, “health status,” “per capita family income,” “gender,” “age,” “education level,” “number of children,” “operating rules,” “public value recognition,” “settlement linkage” Internal Factors of “Information Mastery” and “Implicit Restrictions.” Therefore, the family should be insured in the optimal allocation of family members in the order of “family pillar-children-the elderly” and the scientific layout of insurance products on the basis of family per capita income. The government should promote the institutional integration by strengthening the mechanism construction, building an information platform, and optimizing the management system, so as to promote the health big data sharing, and provide a superior environment for the connection between serious disease insurance and commercial serious disease insurance.
 
Sitting in a row in the classroom of the learning community: sitting in groups around the front and back.
Sitting in a row in the classroom of the learning community: sitting in groups around the left and right sides.
Sitting in a row in the classroom of the learning community: U-shaped sitting in groups.
Traditional classroom space layout.
Classroom space layout based on the learning community.
With the development of the times, building a learning community in ideological and political teaching has become an urgent need for teaching reform. In ideological and political teaching, the common vision of teachers and students is to carry out cooperative exploration and ultimately realize the progress and growth of common teachers and students, and implement the core of political science. Learning community in ideological and political teaching has the characteristics of group symbiosis, common value, cooperation and sharing, and democratic openness. Therefore, building a learning community in ideological and political teaching is conducive to the formation of a new teaching model of cooperative inquiry and the establishment of equality and democracy. The new teacher-student relationship can cultivate students’ higher-order thinking and promote more meaningful learning. It has been proved by practice that it is valuable to construct a learning community in the ideological and political teaching of universities. Teachers and students should change the traditional teaching concept. University political teachers should carefully prepare lessons, set up teaching design, be considerate to students, be close to reality, be close to knowledge itself, enhance the communication and interaction between teachers and students, and students and students, and actively build a learning community in the process of ideological and political teaching. To enable students to get new knowledge, solve challenging problems, and finally achieve progress and growth.
 
Based on the design structure matrix, the changing dependency analysis method is used to study the law of change propagation between parts in the process of product change, in view of the complexity of the product structure and the strong correlation between parts. First, the product components are divided into different modules according to different requirements, and a design structure matrix with weights is established according to the change dependencies between the components within the modules; then, a part changes propagation network is established to analyze the change dependency degree between parts and the influence of the part propagation mode on change propagation and to analyze the possible change propagation impact of part changes. The feasibility and rationality of the method are verified using the frame module as an example. The experimental results show that the method is effective in predicting the changing risk of a part and analyzing the change propagation impact arising from a part change.
 
With the advent of the era of big data, the TV media industry has begun a new round of survival of the fittest. Some new and original traditional cultural TV programs have successfully attracted the attention of the audience, but some programs have lost the impact of the big data environment and are facing the crisis of revision or suspension. Based on the background of big data and from the macro perspective of society, this article made a specific description and objective analysis of the concept, characteristics, and real development of opera communication and proposed an understanding. On this basis, through the analysis of actual cases and data, combined with the dissemination of opera and the new and original traditional cultural TV programs in recent years, this article analyzed the impact of big data on the dissemination of opera and further explored the survival state of opera dissemination in the context of the era of big data, and the experiment was to crawl the data of 10 traditional Chinese opera categories in the Shipin Opera Network through web crawler. It was sorted according to the amount of play, the top 10 songs of each genre and their play volume were selected, and then the Internet correlation between the genres was quantified. And a big data analysis of the traditional opera program “Liyuanchun” was carried out. The experimental results showed that the show was broadcast 65,984 times a day, the highest broadcast volume in a week. At the same time, according to the big data analysis of users of self-media opera dissemination, the majority of the audiences had a high school or technical secondary school education, which accounted for 27%, followed by junior high school, which accounted for 18.7%. It can be seen that the current Chinese traditional culture TV programs have a good development trend and a lot of room for development.
 
With the core and small-scale family structure, parents and children have less time and opportunities to interact, which leads to the lack of care and emotional companionship for preschool children, which is easy to cause physical and mental disorders. In response to this phenomenon, a series of child care robot products have emerged on the market and have continued to be the focus of attention in the past two years. However, such products are still generally deficient in terms of interaction and content. It is difficult for users to choose and use. This paper takes the design and application of remote parent-child interaction of preschool children’s escort machine as the research object. First, it examines the physical, psychological, and behavioral characteristics of escort robots, namely, preschool children. To sum up, the remote parent-child interaction design strategy of children’s escort robot products is proposed for the design practice and industry reference of this paper. The experimental results show that the children in the experimental group pay more attention to the main content and off-topic content than the control group, and the attention rate is more than 95%. To a certain extent, it can be said that the games in the system have the ability of parent-child interaction. This paper abandons the traditional research direction of pursuing human-computer interaction between childcare products and high technology. Instead, it studies parent-child emotional interaction, so that the emotional interaction between parents and children is not replaced by technology products but helps parents deepen the relationship with their children. Emotional interaction allows parents to truly accompany their children to grow up.
 
With the rapid development of agricultural economy, people are paying more and more attention to how to apply high-efficiency technologies that save resources to improve agricultural production efficiency, so water-saving irrigation technology has gradually developed. China’s agricultural irrigation technology is relatively backward. In addition to the inappropriate irrigation methods and irrigation systems, problems such as siltation, seepage, and frost heave damage in irrigation canals have seriously affected the durability and service life of the canals. The backward irrigation technology seriously restricts the development of agricultural water-saving irrigation. Compared with large irrigation channels, the fabricated reinforced concrete small irrigation channels studied in this paper are less prone to frost heave damage and infiltration problems, and have the advantages of standardized production, simple transportation and installation, and convenient maintenance. In order to study the durability issues such as the basic characteristics, frost heave damage, and service life of fabricated irrigation channels, this paper takes the channel concrete and the formed channel as the research objects, and discusses the research on the properties of the channel concrete through theoretical research, numerical analysis, experiments, and other methods. Strength properties, water penetration resistance, cyanide ion penetration resistance and frost resistance; simulate the seasonal frost heave failure process of the channel; finally, on the basis of the test data, the service life aims to explore the safety and applicability of the fabricated reinforced concrete irrigation channel during the design use period.
 
With the rapid development of deep learning, its application in the field of education has gradually attracted attention. This study introduces a deep learning-based moral education evaluation system for college students. The evaluation of the ideological and political education of college students is an important driving force to strengthen and improve the ideological and political education of college students, and its connotation is very rich. However, at present, there are many difficulties in the evaluation of ideological and political education in colleges and universities, such as narrow evaluation objectives, monotonous evaluation structures, lack of pertinence in the evaluation process, and subjective evaluation standards. The internal mechanism and external mechanism of the evaluation mechanism, the qualitative analysis and quantitative analysis of the evaluation method, the absoluteness and relativity of the evaluation standard, the dynamic and static evaluation process, and the systematic and specialized evaluation are combined to ensure the college students’ thinking the objectivity and effectiveness of political education evaluation.
 
Classroom teaching activities have always been the focus of research in the field of pedagogy. The main body of classroom teaching activities is students, and students’ classroom behavior status can reflect classroom efficiency to a certain extent, making it an important reference index for classroom quality assessment. With the rapid development of artificial intelligence, school education is gradually becoming more intelligent. At present, most of the classrooms are equipped with video equipment. These videos record the real behavior status of the students in the classroom. For example, by analyzing the data, combining artificial intelligence, deep learning, and other related technologies with education to develop behavioral intelligence, the analysis system has a certain positive effect on helping the reform of classroom education. This study proposes an improved SSD behavior recognition model. The network model is optimized and the model convergence speed is accelerated based on the RMSProp optimization algorithm Through a database of 2,500 images of five behaviors, including raising hands, sitting up, writing, sleeping, and playing with mobile phones, and using them as object detection datasets, we use the OpenCV library to extract frames from classroom screen recording videos as image data sources for student behavior recognition and face recognition. Finally, an improved method is proposed to change the virtual network to MobileNet and complete the fusion function. The results show that compared with the traditional SSD method, the improved model has a significantly improved effect in recognizing small objects and the recognition speed is not significantly reduced.
 
The flow rate of a piston cooling nozzle is usually adjusted by changing the parameters of convergent length and inner diameter of the nozzle in engineering. However, the influence law and quantitative relationship between the flow rate and them are not clear. In this paper, the structural model and three-dimensional model of internal flow field of piston cooling nozzle are established by analyzing the structural characteristics and actual working conditions of piston cooling nozzles. Based on Fluent software, the flow field of piston cooling nozzles is simulated and analyzed. The distribution of velocity and pressure inside the piston cooling nozzle are obtained. The flow rate of fluid field is also obtained inside a piston cooling nozzle. In addition, the variation law of flow rate of the piston cooling nozzle is studied with the increasing of nozzle convergent length and diameter through several simulation experiments, respectively. The results show that the flow rate of piston cooling nozzle decreases linearly with the increase of the nozzle closing length. The flow rate of nozzle increases nonlinearly with the increase of the convergent diameter. Compared with the convergent length, the change of convergent diameter has a greater influence on the flow rate of piston cooling nozzles. Finally, the analytical expression of the flow rate of piston cooling nozzle about the convergent diameter is obtained, which is of great value and guiding significance to the nozzle engineering design.
 
Aiming at the problems of uneven distribution of initialized populations and unbalanced exploration and exploitation leading to slow convergence, low convergence accuracy, and easy to fall into local optimality of marine predators algorithm (MPA), a marine predators algorithm based on adaptive weight and chaos factor is proposed (ACMPA), the algorithm is applied to the traveling salesman problem (TSP), and the shortest path planning and research are carried out for the traveling salesman problem. Firstly, the improved adaptive weight strategy is used to balance the exploration and exploitation stage of the algorithm and improve the convergence accuracy of the algorithm. Secondly, the chaos factor is used to replace the random factor, and the ergodicity of the chaos factor is used to make it easier for predators to jump out of local optimization and enhance the optimization ability of the algorithm. Finally, 10 benchmark test functions, the CEC2015 test set, and the CEC2017 test set are used to evaluate the effectiveness of the ACMPA. The results show that, compared with the other four intelligent optimization algorithms, the improved ACMPA achieves better results in both mean and standard deviation, and the algorithm has a better effect on the shortest path problem.
 
Six areas where big data and education intersect.
The flow of the diversified blended teaching model.
The overall structure of the diversified blended teaching model.
Mathematical model of support vector machine regression algorithm.
The system function diagram of this platform.
Language is a symbol reflecting ideas and is an important medium for people to realize information transfer and communication. Culture is all the material and spiritual wealth created during the historical development of human society. Language and culture are inseparable. When we learn a language, we must also understand the cultural content it contains. Learning English requires an understanding of the culture of British and American countries, and thus teachers must necessarily teach culture in the English classroom. At present, the software and hardware of Internet technology in our country have been developed in-depth, and the total amount of big data has been in the trend of growth, and China has officially entered the era of big data. This paper takes the reform of teaching mode of British and American culture courses as the main research object and explores how to build a diversified and blended teaching mode based on the existing teaching mode and integrating various online teaching resources under the premise of various technologies in the background of “big data.” In order to accelerate the integration and optimization of such course resources, promote the development of theoretical teaching and practical teaching of such courses and improve students’ professional ability.
 
Source code comments can improve the efficiency of software development and maintenance. However, due to the heterogeneity of natural language and program language, the quality of code comments is not so high. So, this paper proposes a novel method Code2tree, which is based on the encoder-decoder model to automatically generate Java code comments. Code2tree firstly converts Java source code into abstract syntax tree (AST) sequences, and then the AST sequences are encoded by GRU encoder to solve the long sequence learning dependency problem. Finally, the attention mechanism is introduced in the decoding stage, and the quality of the code comment is improved by increasing the weight of the key information. We use the open dataset java-small to train the model and verify the effectiveness of Code2tree based on common-used indicators BLEU and F1-Score.
 
Among many tunnel construction projects, small clear tunnels have been the focus of urban rail transit construction in recent years. The purpose of this research work is to study the dynamic response characteristics of the surrounding rock structure of a heavy-duty railway with a small clearance crossing tunnel. It is proposed to analyze the dynamic response characteristics of the surrounding rock structure through the frequency response function. The vertical acceleration, tensile force, stress, and internal strength of the pipe section are specifically analyzed. The influence of the pipe joint and the assembly method on the dynamic response is also analyzed. The influencing factors of stability are analyzed from buried depth, clear distance, and surrounding rock grade. Studies have shown that the minimum clear distance of crossing tunnels increases nonlinearly with the increase of tunnel depth and gravity, and decreases nonlinearly with the increase of cohesion, internal friction angle, calculated internal friction angle, and lateral pressure coefficient. When other parameters are the same and when the side pressure coefficient is less than 1, the minimum clear distance is larger than that of the side pressure coefficient when it is equal to 1. When the vibration frequency exceeds 100 Hz, the coherence coefficient is basically close to 1, indicating that the frequency response function response result of this section is the most reliable. It is hoped that it can provide a reference for the dynamic stability analysis of the surrounding rock structure of the heavy-duty railway surrounding rock structure and the surrounding stratum and the research of structural vibration reduction technology for the small clearance crossing tunnel in the future.
 
The comprehensive reasons for the cross-regional flow of enterprises in their own economy are studied in this paper. Most traditional enterprise migration research focuses on the impact of a single factor. Even the research on the influence of multiple factors often ignores the interaction of various factors. Therefore, this paper proposes and logicalises the “push and pull” mechanism of enterprise migration to address the limitations of previous research. Through bibliometric analysis and investigation, we systematically think about the factors that affect the corporate migration mechanism and improve the Push-Pull Theory. Besides, by deploying a conceptual system dynamics model, this paper explains how various factors affect enterprise migration. In addition, we discussed how to adopt systems thinking to promote the development of emerging economies. We selected and tested 6 companies that migrated in China, and the results are in line with reality. The conclusion supports the theory that systematic thinking and decision-making influence corporate behaviour.
 
With the development of wireless network and various communication technologies, the information on the Internet is expanding rapidly. The development of wireless network and various communication technologies has promoted the development of e-commerce, and people can understand a large amount of commodity information without leaving home. However, due to the complex information on the network, users need to pass a lot of screening to obtain the information they want, and a large amount of irrelevant information will cause users to consume a large amount of irrelevant information. To solve these problems, the personalized recommendation system is created, but the recommendation system is recommended according to the characteristics of users’ interest and shopping behavior. However, users’ interests will change, so they need to use other technologies to screen for relevant commodity information. Evidence theory has a strong ability to distinguish between true and false information and to deal with uncertain information. To this end, this article studies the application of the evidence theory in the recommendation system and finds that the evidence theory algorithm can infer the information needed by users based on the uncertain information. Moreover, the experiment in this article proves that the algorithm application of the evidence theory in the recommendation system can well grasp the interests of users and recommend the information needed by users. This improves the efficiency of users to obtain the required information and achieves 80 points for content recommendations.
 
The Internet of Things (IoT) is developing rapidly and is integrated into all aspects of life. Clothing is an indispensable part of meeting the basic needs of the human body, and its traditional functions include warmth, health care, decoration, and beauty. While as a special type of clothing that integrates multidisciplinary technology, smart clothing has a wider scope of action. With the update and iteration of science and technology, many technologies that originally belonged to nonclothing disciplines have also been applied to the clothing field. Accordingly, for the special clothing category of smart clothing based on embedded system, a set of design process that can be used for this category of smart clothing was proposed. Using this design process, an intelligent fire suit that can be used to protect the personal safety of firefighters and assist firefighters to cooperate was designed and implemented. Starting from the daily working environment and working characteristics of firefighters, this firefighting obedience analyzed the design points from the perspectives of clothing comfort, warning, toxic and harmful gas monitoring, and firefighters’ cooperation. After testing, the test results of the outer layer fabric, waterproof and moisture-permeable layer fabric, and thermal insulation and comfort layer fabric of the fire suit all met the corresponding national standards; the monitoring sensitivity of harmful gases was high; it could achieve a “good” warning effect in a dark environment. Compared with ordinary firefighting suits, it was more comfortable under the subjective and objective test and scored 0.669 higher under the seven-point scale; its interactive performance met the actual needs. The clothing has complete functions and a complete feedback mechanism, which has a positive effect on ensuring the personal safety of firefighters.
 
Research technology roadmap.
Histogram of indoor area of children’s room.
Framework diagram of children’s furniture design system.
Maslow’s hierarchy of needs diagram.
Complete human-machine system.
Furniture is one of the most enduring items that children grow up with. However, there is a big gap between children’s rapidly growing physical and mental needs and existing children’s furniture. It is necessary to design children’s furniture that is both interesting and multifunctional from the perspective of children’s physiology and psychology. In the context of artificial intelligence, this paper applies the artificial intelligence design concept to the design of children’s furniture and discusses the necessity and design principles of its application in the design of children’s furniture from the aspects of society, children, and development trends.
 
Software defect prediction (SDP) is an important technology which is widely applied to improve software quality and reduce development costs. It is difficult to train the SDP model when software to be test only has limited historical data. Cross-project defect prediction (CPDP) has been proposed to solve this problem by using source project data to train the defect prediction model. Most of CPDP methods build defect prediction models based on the similarity of feature space or data distance between different projects. However, when the target project has a small amount of label data, these methods usually do not consider this part of data information. Therefore, when the distribution between source project and target project is quite different, these methods are difficult to achieve good prediction performance. To solve this problem, this paper proposes a CPDP method based on a semisupervised clustering (namely, Tsbagging). Tsbagging has two stages; in the first stage, we cluster to the source project data based on the limited labeled data in the target project and assign different weights to these source project data according to the clustering results. In the second stage, we use bagging method to train the prediction model based on the weight assigned in the first stage. The experimental results show that the performance achieved by Tsbagging is better than other existing SDP methods.
 
The financial status of an enterprise is related to its healthy and long-term development, and whether the interests of investors and bank loans can be guaranteed. To improve the prediction accuracy of corporate financial risk, this paper proposes a prediction model for corporate financial risk that integrates GRA-TOPSIS and SMOTE-CNN. First, using GRA-TOPSIS to make a comprehensive evaluation of the financial situation of listed companies. Second, the evaluation results are clustered to obtain the scientific level and interval of financial risk, which lays the foundation for the supervised learning of the convolutional neural network. Then, the SMOTE algorithm is introduced to solve the problem of data imbalance of enterprises at all levels, and the focal loss function is used instead of the cross-entropy loss function to further balance the data. Finally, the listed companies in A shares are randomly selected, and experiments were designed to verify the performance of the model built in this paper. The results show that the prediction accuracy of the financial risk prediction model based on GRA-TOPSIS and SMOTE-CNN is 98.57%, which indicates that the model is feasible and has certain reference value.
 
Traditional Chinese medicine (TCM) has been shown to be effective in the treatment of diseases such as malaria, being better understood and accepted by the world. TCM physical health management is based on the policy of “preventive disease,” comprehensively collects patients’ information, and provides timely and appropriate rehabilitation guidance to achieve the best nursing effect. However, the current TCM physical health management has not been understood by the public, and the effect of its nursing evaluation has not been concluded yet. Therefore, this paper aims to design a TCM physical health management training and learning system based on digital twin technology and to evaluate and analyze the nursing effect. For TCM physical health management training, this paper designed a training system based on the VIKOR algorithm. Based on digital twin technology, the training can be carried out at different times and places, and the teaching content can also be displayed in real time through the cloud platform, which intuitively and comprehensively reflects the teaching content. For the evaluation of nursing effect, this paper selected 100 patients and divided them into two groups to compare the nursing effect of TCM physical health management and general Western medicine nursing. The experimental results of this paper found that the nursing effect of TCM physical health management is 20%–60% better than that of Western medicine nursing in terms of blood pressure, TCM syndromes, exercise tolerance, and quality of life.
 
A multiconvolution kernel.
Classification model.
Convolutional neural network classification model.
GoogLeNet network.
ResNet workflow model.
A convolutional neural network (CNN) is a machine learning method under supervised learning. It not only has the advantages of high fault tolerance and self-learning ability of other traditional neural networks but also has the advantages of weight sharing, automatic feature extraction, and the combination of the input image and network. It avoids the process of data reconstruction and feature extraction in traditional recognition algorithms. For example, as an unsupervised generation model, the convolutional confidence network (CCN) generated by the combination of convolutional neural network and confidence network has been successfully applied to face feature extraction.
 
Purpose. The AI era has brought rapid progress and many changes in life, which challenge the survival of cultural heritage. One of the topics that has attracted much attention at the moment is how to better inherit and develop the intangible cultural heritage of national sports. In order to improve the development level of national sports intangible cultural heritage, this article explores a new path for its development. Methodology. Through the analysis and research of scene theory and computer nonlinear three-dimensional (3D) model modeling technology, it can be applied to the development of national sports intangible cultural heritage. This article analyzes the scene theory, three-dimensional modeling, and intangible cultural heritage of national sports, conducts experimental analysis on its functions, and uses relevant theoretical formulas to explain. Research Findings. The results show that this development path is more effective than the traditional development path. It has a lower error rate than the traditional model, which differs by 0.124. The public satisfaction increased by 56.7% before and after. Research Implications: The new method proposed in this article provides a new path reference for the development of national sports intangible cultural heritage in the future. Practical Implications. This method can meet the needs of the development of intangible cultural heritage of national sports culture, and the satisfaction and development level of the masses have been greatly improved.
 
This paper has explored the relationship between CO2 emissions, eco-efficiency, and economic growth, which has important theoretical and practical significance for realizing high-quality development of the Yellow River Basin (YRB). Based on the data of 99 prefecture-level cities in the YRB from 2003 to 2017, this paper analyses the temporal and spatial variation and agglomeration characteristics of CO2 emissions and eco-efficiency in the YRB. The spatial Durbin model (SDM) with fixed effects is used to test the relationship between CO2 emissions, eco-efficiency, and economic growth in the YRB by using new structural economics theory and the EKC hypothesis. The results show that (1) the YRB is in a stage of extensive economic development with the coexistence of high CO2 emissions and low eco-efficiency and shows a trend of transformation and upgrading to a stage of high-quality economic development; (2) CO2 emissions and eco-efficiency are spatially heterogeneous; CO2 emissions and eco-efficiency in the upper reaches are low, and the middle maintains relatively high eco-efficiency and high CO2 emissions while the lower reaches have both high CO2 emissions and low eco-efficiency; (3) the relationship between CO2 emissions and economic growth is N-shaped, and there is an inflection point of accelerated increase (per capita GDP is 45,558 yuan). The YRB has crossed this inflection point, and CO2 emissions are accelerating with economic development at this stage. The relationship between eco-efficiency and economic growth is U-shaped, with the inflection point of per capita GDP at 46,483 yuan. The YRB has just crossed the point, and eco-efficiency will subsequently steadily rise with economic development.
 
In order to improve the problems of poor accuracy and low efficiency in tobacco leaves disease recognition and diagnosis and avoid the misjudgment in tobacco disease recognition, a disease recognition and spot segmentation method based on the improved ORB algorithm was proposed. The improved FAST14-24 algorithm was used to preliminarily extract corners. It overcame the deficiency of the sensitivity of the traditional ORB corner detection algorithm to image edges. During the experiment, 28 parameters were obtained through the extraction of color features, morphological features, texture, and other features of tobacco disease spots. Through the experimental comparisons, it was found that the fitness of the improved ORB algorithm was 96.68 and the cross-checking rate was 93.21%. The validation and recognition rate for samples was 96%. The identification rate of tobacco brown spot disease and frog eye disease was 92%, and the identification rate of 6 categories in different periods was over 96%. The experimental results verified the effects of the disease identification fully.
 
This study proposes an optimal approach to reduce noise in mammographic images and to identify salt-and-pepper, Gaussian, Poisson, and impact noises to determine the exact mass detection operation after these noise reductions. It therefore offers a method for noise reduction operations called quantum wavelet transform filtering and a method for precision mass segmentation called the image morphological operations in mammographic images based on the classification with an atrous pyramid convolutional neural network (APCNN) as a deep learning model. The hybrid approach called a QWT-APCNN is evaluated in terms of criteria compared with previous methods such as peak signal-to-noise ratio (PSNR) and mean-squared error (MSE) in noise reduction and accuracy of detection for mass area recognition. The proposed method presents more performance of noise reduction and segmentation in comparison with state-of-the-art methods. In this paper, we used the APCNN based on the convolutional neural network (CNN) as a new deep learning method, which is able to extract features and perform classification simultaneously, but it is intended as far as possible, empirically for the purpose of this research to be able to determine breast cancer and then identify the exact area of the masses and then classify them according to benign, malignant, and suspicious classes. The obtained results presented that the proposed approach has better performance than others based on some evaluation criteria such as accuracy with 98.57%, sensitivity with 90%, specificity with 85%, and also ROC and AUC with a rate of 86.77.
 
Theoretical model of how music stimulates the brain.
Tempo × genre × time on SCL. (a) Rock genre. (b) Classical genre. (c) Swing genre.
Tempo × genre × time on SCL. (a) Rock genre. (b) Classical genre. (c) Swing genre.
Tempo × genre × time on SCL. (a) Rock genre. (b) Classical genre. (c) Swing genre.
We can hear sweet and touching music in our daily life. We like listening to music because music can affect our emotions. Dynamic music makes us very excited. When we are sad, hearing beautiful music can make us happy. In physiology, music affects many physiological processes. It can inhibit fatigue and affect pulse, respiratory rate, and blood pressure level. “Listening music helps improve mood.” Although the pursuit of personal happiness is likely to be considered a self-centered adventure, research shows that happiness is positively correlated with socially beneficial behavior, better health, higher income, and better interpersonal relationships. Another reason why we like music and music can be used very effectively for various therapeutic goals is that music is used in many ways in our society. When a group of people come together to sing a chorus or engage in musical activities, concerts establish new ties between people and make them closer. People grow up listening to lullabies from birth. When they die, they end their lives with funeral music (songs). It may be said that one’s life begins with music and ends with music. Through music, we sing about social phenomena, express ourselves, and communicate with others. The themes and hidden contents that the music production society is unwilling to express publicly are not limited by any judgment. It should be noted that the functions of the above music are flexibly applied according to personal conditions, rather than being classified and limited by functions.
 
Now, the use of deep learning technology to solve the problems of the low multiclassification task detection accuracy and complex feature engineering existing in traditional intrusion detection technology has become a research hotspot. In all kinds of deep learning, recurrent neural networks (RNN) are very important. The RNN processes 41 feature attributes and maps them to a 122-dimensional high-dimensional feature space. To detect multiclassification tasks, this study proposes an intrusion detection method based on fully connected recurrent neural networks and compares its performance with previous machine learning methods on benchmark datasets. The research results show that the intrusion detection system (IDS) model based on fully connected recurrent neural network is very suitable for classification of intrusion detection. Classification methods, especially in multiclassification tasks, have high detection accuracy, significantly improve the detection performance of detection attacks and DoS attacks, and it provides a new research direction for the future attempts of intrusion detection methods for industrial control systems.
 
To prevent the occurrence of elevator safety accidents, in this study, an Internet of things-based elevator failure monitoring system is investigated. First, it introduces the Internet of things technology, preprocesses the relevant data, and extracts the features of the elevator operation data continuously collected by the elevator sensors. The objective of this paper is to study the application of IoT in elevators. Method. The Relief-F algorithm is used to evaluate the potential influencing factors. The results show that, as the batch size increases, the accuracy rate will gradually increase, but after more than 20, the accuracy rate will decrease. When the batch size is 20, the training result is the best. It can be seen that, as the time step increases, the accuracy of the prediction will be significantly improved. When the time step is 24, the prediction accuracy is the highest; after 24, the prediction rate will decline. In the diagram of the influence of learning rate on the model, the blue line indicates that the learning rate is 1.0, the red line is 0.1, and the black line is 0.01. With the increase of the iteration times, the effect is the best when the learning rate is 0.1. This system has high research value and broad application prospects and can make up for the current lack of monitoring in the elevator industry.
 
Washing machines (WMs) are common household appliances that help to save time and effort used in brushing and washing clothes. It is a common practice to use manually operated WMs, and based on their uses, WMs are classified into top and front open cloth washing machines, which operate based on an automatic control mechanism. In this paper, we present the design and simulation of an Arduino fuzzy logic-based WM control system, with an emphasis on improvement in its operating algorithm. Simulations were performed to determine the optimal time the WM takes to wash clothes, the maximum number of clothes the WM can wash per time, the acceptable dirtiness level of cloths, and the type of clothes that the machine can wash. The number of clothes to be washed, the degree of dirtiness, and the type of clothes govern the fuzzy logic control process adopted by the WM. The output voltage of the WM varies as the degree of dirtiness of the water varies from 0 to 1.95 V for very dirty water output from washed clothes and varies from 4 to 4.89 V for low-contaminated water. Another constraint considered was the load current, which increased as the number of clothes increased. The WM’s operation time is determined by the amount of voltage and load current used during its operation. As a result, the control of the WM is dependent on the dirtiness level of the clothes and the amount of load.
 
With the rapid development of modern technology, due to the light-weight, small size, and good concealment, unmanned aerial vehicle (UAV) has received much attention from the society and has been vigorously promoted. Photoelectric tracking detection system is an important means in the field of modern detection. The combination of a UAV and photoelectric detection system can effectively play an important role in reconnaissance and exploration, target positioning, communication, and navigation. At present, the relevant personnel have higher and higher requirements for the accuracy of the photoelectric detection function of UAV, and the original POS data relied on by the existing photoelectric detection devices of UAV has systematic errors, which leads to the low accuracy of photoelectric detection control. Therefore, in order to achieve the purpose of improving the high-precision control of the photoelectric detection device of UAV, this paper designed an optimization method for the high-precision control device of photoelectric detection of UAV based on POS data. Firstly, the improved PID control algorithm is applied to the optimization of the UAV control device, and secondly the error correction model is established by analyzing the error source, and the original POS data is corrected by the model. This paper used the designed high-precision control device optimization algorithm and the traditional algorithm to compare the stability control experiments of the UAV platform, respectively. The experimental results showed that the application of the improved UAV photoelectric detection control device optimization method could effectively improve the control device optimization accuracy of UAV photoelectric detection by 8.23%, which was conducive to the efficient implementation of the project.
 
Tea is the national drink of the Chinese nation. It combines the thoughts of various Chinese schools and has experienced the changes and baptism of successive dynasties and has been spread to this day. It is difficult to achieve the inheritance of tea culture by relying on ordinary display methods. In order to better display the digital effect of the nonlinear system model of the tea space in the Song Dynasty, using digital display, we can design the layout of the tea space in the Song Dynasty through video, audio, and dynamic pictures, which plays an important role in the inheritance of tea culture. This study analyzes the tea drinking space and tea culture and spreads the historical and cultural value of digital display; by comparing the comprehensive performance analysis and cultural extension results under different displays, as well as the desire for knowledge of history and culture, we can make an in-depth exploration. Through the statistical data information, we can conclude that under the function of digital technology, advanced science and technology and digital technology have a good effect in cultural inheritance, they can better promote the spirit of history and culture, and make the torch of history and culture live and pass on from generation to generation.
 
The arms of the Internet octopus have reached the ends of the planet. As it has become indispensable in our daily lives, huge amounts of information are transmitted through this network, and it is growing momentarily, which has led to an increase in the number of attacks on this information. Keeping the security of this information has become a necessity today. Therefore, the scientists of cryptography and steganography have seen a great and rapid development in the previous years to the present day, where various security and protection techniques have been used in these two technologies. In this research, it was emphasized to secure the confidentiality and security of the transmitted data between the sending and receiving parties by using both techniques of encryption and steganography. In contrast, where genetic algorithms and logic gates are exploited in an encryption process, in an unprecedented approach, protein motifs are used to mask the encoded message, gaining more dispersion because there are 20 bases used to represent the protein. The real payload gained ranges between 0.8 and 2.666, which outperforms the algorithms that depend on DNA sequences.
 
This research topic investigates the inquiry on how national cultures shape the organisational management cultures. Similarities and differences between the national cultures of China and the USA are being scrutinised for the purpose to examine the impacts of such features on the management cultures and strategies of organizations located in these two main world financial centres so as to achieve a majority of data to confirm how national culture relates and assists to shape the organisational management. This research uses the data collection methods of non-governmental organizations, including the invitation of participants or volunteers via social media, working emails, and invitation letters, involving the issues such as designing human rights and privacy. The result has established that high mobilization of culture differences in the USA had a notable positive consequence on companies’ organisational management culture. Alternatively, the Chinese cultures may bring some positive effect to the companies’ culture, but it was only significant to shape management culture influence in their domestic companies, excluding most of the multinational companies. Moreover, the differences in national cultural characteristics will greatly affect each organisation to choose their own management strategies. Raising up for cross-cultural and transnational management will be a huge challenge for organizations to take, especially if countries wish to establish bilateral or trilateral business relations and partnerships.
 
Visualization of two special effects’ technologies in artistic creation (%).
Impact visualization of two special effects’ technologies (%).
Comparison of visual perception of two special effects’ technologies (%).
Natural interactive emotional visual analysis of two special effects’ technologies (%).
Art special effects, as a kind of new media art form, bring different visual impacts to viewers in film and television animation. This study discusses the application of digital media art film and television special effects’ technology through virtual and realistic algorithms. The wide application of digital media art film and television special effects’ technology has achieved the purpose of saving production time and cost for the creation of film and television dramas. Through the comprehensive analysis and research on art creation, image impact, film viewing perception, and natural interactive emotion under different technological environments, and the analysis of the comparison results, it can be seen that digital media art film and television special effects’ technology has a far-reaching impact on film and television animation, can better carry out the sustainable development of film and television field, also promote the sustainable development of science and technology to a certain extent, promote the modern development of digital media art design, support society's continuous development and progress, and better promote the all-round integration of digital media art and film and television creation.
 
In order to further improve the identification efficiency of tobacco mildew, a rapid identification model of tobacco mildew based on random forest algorithm was proposed in this study. In order to ensure the feasibility and pertinence of the model study, this study takes redried leaf tobacco as the research object, selects high-temperature and high-humidity environment as the experimental conditions, and obtains the sample data of the degree of tobacco mildew under different experimental conditions. At the same time, this paper constructs a rapid identification model of tobacco mildew with the help of random forest algorithm. Through the model experimental results, it is found that the accuracy of the model for the rapid identification of training samples can reach 93.82%, while the accuracy of independent testing is 94.84%. The experimental results fully reflect the availability and efficiency of the random forest algorithm model in the rapid identification of tobacco mildew.
 
Fiber reinforced composites can meet the needs of lightweight, heavy load, long-span, high strength, and modern structures and work under strict conditions. Therefore, it is widely used in various fields. Sports equipment generally cannot meet the requirements of high-strength use, so there is an urgent need for new fiber materials to make these instruments. Aiming at improving the efficiency of measurement and the reliability of measurement results, this paper studies the quantitative characterization method of the microstructure of short fiber reinforced composites. The swept-frequency OCT system is a newly developed high-resolution biomedical imaging system. The method of this paper is to study the performance parameters of swept frequency OCT system, deduce the application of image processing technology in fiber material detection, and then study the detection of residual strength and residual stiffness of composites, so as to obtain a composite material detection method that can be popularized. On this basis, image processing and performance analysis of the composites were carried out, and the composites were tested. And the prospect of application in this field has been analyzed. The experimental results show that better fiber length can be obtained by this method, and the maximum relative error is only 4.5%, thus ensuring the accurate determination of fiber length. Calculations were carried out using the experimental data, and the results showed that the performance of the sports product increased from 15.4% to 48.6% with the graphite-rubber composite. Using this material can improve the comprehensive performance of sports equipment by 2.1%∼4.5%.
 
Analysis of the transformation and upgrading stages of traditional industries.
Research framework model.
Innovation-driven transformation and the upgrading of traditional industries is an important task in the present. This paper attempts to further study the existing basis. Based on the perspective of continuous innovation driving, this paper divides the transformation and upgrading of traditional industries into two stages, namely, industrial transformation and industrial upgrading, using the interprovincial data from 2008 to 2019, OLS, HAUSMAN, and SYS-GMM. This paper analyzes the macro- and micromechanism driven by continuous innovation in the transformation and upgrading of traditional industries. The results show that in the macromechanism, the innovation drive has a significant positive effect on the transformation of traditional industries but not on the upgrading of industries, entrepreneurship, network capability, and organizational learning, which significantly affect the transformation and upgrading of traditional industries.
 
With the rapid development of China’s economy, the protection of buildings has attracted the attention of many researchers. Although there is no such massive demolition in the past, natural damage still exists. Identify the collected historical building protection data through multifeature deep learning, and provide protection plans through the information in the database. In order to solve the problem of restoration of natural damage more professionally and efficiently, this paper collects the architectural features and restoration methods of each building in different processes through multifeature deep learning based on the current state of building information in China. Based on the collected information, this paper establishes the building information model, and stores and manages the building information. According to the Newton deep learning optimization algorithm, this paper enhances the algorithm to accurately collect building information and uses the collaborative filtering algorithm to provide users with a repair plan. This paper uses the GRU-based recommendation model to pass the threshold cycle unit algorithm for the probability of each building being selected in the list of similar buildings at a time point. Under the two conditions of 10 and 20 recommended numbers, the user coverage rate of the recommended case of deatomized building photos can reach 100%. And this paper recommends high-probability solutions for users to achieve automation, diversification, and intelligence.
 
Number of movies in each region.
Number of movies for each score.
Number of movies for each score.
Effect diagram of emotion analysis.
Scale diagram of star evaluation.
With the growing development of the era of big data, data acquisition and analysis have become hot spots, and Python-based crawler technology is one of the most widely used tools in data analysis work at present. In this paper, we apply Python crawler key technology to acquire data of movie list and hot movies on Cat’s Eye movie network, analyze data based on Python development environment Spyder, use the Numpy system to store and process large data, Chinese Jieba word separation tool to crawl data for word separation text processing, Snownlp library to process text sentiment, and finally by the word cloud map and web dynamic map display information such as viewers’ emotional tendency and movie rating statistics, and provide decision support for users’ movie viewing.
 
The rapid development of computer software and hardware, network technology, and various Internet platforms has brought mankind into a new era. In recent years, “virtual reality” can be regarded as a huge hot spot, whether in the field of industry, education, or research. At present, although the heat has subsided a little, the technical teams involved in various fields are also working collectively to continuously innovate. Based on the mixed teaching mode of English education, this article conducts in-depth research on deep learning and virtual reality technology, integrates deep learning and virtual reality learning environment, and builds a learning model of English education learning environment based on virtual reality. The teaching design of the course aims to fully combine the main content of deep learning with the virtual reality environment. Through experimental research, it is explored whether the learning environment based on virtual reality can promote deep learning. The relevant data of the experimental class and the control class are collected through questionnaires, starting from the four dimensions of motivation dimension, investment dimension, strategy dimension, and result dimension, and conduct a comparative analysis, and use the auxiliary interview method to understand the experience of students and teachers on virtual reality equipment and put forward relevant suggestions.
 
Machine translation is different from written translation. How to improve the performance of machine translation has been a research hotspot in current research on machine translation. In this paper, based on the semantic analysis and research of Japanese passive, a joint optimization algorithm of scheduling has been proposed, and the machine translation of Japanese passive has been studied. At present, machine translation is more and more widely used. Machine translation has solved many vocabulary problems, and it can complete a large amount of translation work and save a lot of manual translation time. While improving the translation speed, in the process of Japanese passive translation, it is also found that direct machine translation shows many shortcomings, and the quality of passive translation is not particularly ideal, exposing the basic problems of machine translation, such as semantic errors, syntactic errors, unclear and rigid expressions, and messy structures. In response to the problems above, this paper has improved the machine translation model for scheduling joint optimization algorithms. The paper has proposed several optimization algorithms and used resource awareness and computing power scheduling algorithms to conduct experimental analysis of translation performance. Finally, it is found that, among the two scheduling optimization algorithms, the resource-aware scheduling algorithm has better performance. With the same data, the resource-aware scheduling algorithm has saved 15.5% of the time compared with the computing power scheduling algorithm, and the accuracy of Japanese passive translation was 6%, 5%, and 21% higher than the computing power scheduling algorithm under different data volumes. Not only has the time taken been shortened, but the translation accuracy has also been improved.
 
The number of hearing-impaired people is increasing year by year; robotic cochlear drilling surgery is one of the safest methods to treat deafness. Looking at the issue of low efficiency of temporal bone posture positioning in cochlear implantation robotic drilling, a novel auxiliary ring marker temporal bone positioning method was proposed to improve temporal bone posture positioning efficiency, optimize the operation time, and reduce auxiliary injuries caused by the surgery. First, the temporal bone visual positioning assistant ring was designed based on the requirements for cochlear robotic drilling surgery. The target detection was conducted on the auxiliary ring and image processing and feature point extraction methods were designed. Then, the three-dimensional coordinates of the measured feature points were obtained by binocular vision, and the auxiliary ring and temporal bone postures were estimated. Finally, the auxiliary ring and temporal bone localization methods were validated. The experiment results indicated that the temporal bone was located quickly and effectively in a total time of about 33 ms, which was faster and more accurate than traditional visual localization methods and could satisfy real-time temporal bone localization during surgery. This study can reduce the time of temporal bone visual positioning in cochlear implant drilling operations, greatly improving the robot’s capabilities to extract visual information during the operation, which has a better auxiliary role for future research and applications of the cochlear implant drilling operation.
 
Stock markets are becoming the center of attention for many investors and hedge funds, providing them with a wide range of tools and investment opportunities to grow their wealth and participate in the economy. However, investing in the stock market is not trivial. Stock traders and financial advisors are required to frequently monitor market actions, search for profitable companies, and analyze stock price movements to generate various trading ideas (e.g., selecting a stock symbol and making the decision when to enter or exit a trade), potentially leading to investment returns. Therefore, this study aims to address this challenge through exploring the adaptation of machine learning methods combined with risk management techniques to develop a framework for automating the task of stock trading. We evaluated our framework by creating a diverse portfolio containing several companies listed on the Saudi Stock Exchange (Tadawul) and using the simulated trading actions (executed by the framework) to estimate the portfolio’s returns for 3.7 years. The findings show that in terms of investment returns, the proposed framework is very promising; it has generated over 86% returns and outperformed almost all hedge funds by the top investment banks in Saudi Arabia.
 
Environmental factors have a direct impact on the development of agriculture, so it is particularly important to detect the environment of the agroecological cycle index system. Although there are some plans for environmental monitoring, most of them are environmental monitoring for a larger concept, but this study is specific to the actual object. This article takes farmland as the research object and comprehensively uses embedded technology, information monitoring technology, and network technology based on the analysis of the research status and system application of the farmland environmental monitoring system. This article studies and designs a real-time online remote monitoring system for farmland ecological environment based on embedded architecture and GPRS technology. By using sensors to obtain farmland information, it is displayed on the monitoring center and mobile client, and data information is obtained in real time. It manages and protects the farmland environment in advance. This article measures and analyzes the data from the final experiment. The experimental results show that the monitoring system can accurately collect farmland data in real time. And through the embedded server and the Internet, the data can be remotely transmitted in real time and displayed on the monitoring center and mobile client software. The results showed that PM2.5 was 30 μg/m3 at 20:00. The experimental data have the characteristics of real time and stability, which can meet the requirements of real-time network remote monitoring and transmission of data.
 
Journal metrics
$1,975
Article Processing Charges (APC)
48%
Acceptance rate
7 days
Submission to first decision
40 days
Submission to final decision
23 days
Acceptance to publication
1.672 (2021)
Journal Impact Factor™
1.1 (2021)
CiteScore™
Top-cited authors
Shah Nazir
  • University of Swabi
Shabana Ramzan
  • The Government Sadiq College for Women University
Rami S. Alkhawaldeh
  • University of Jordan
Zhou Zhou
  • Changsha University and Hunan University
Zhigang Hu
  • China Three Gorges University