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Environmental pollution has become an important obstacle on the path of ecological civilization construction, and it is urgent to control environmental pollution. By establishing an evolutionary game model, this thesis focuses on analyzing how paper-making enterprises choose their own emission reduction strategies under the reward and punishment mechanism. It further analyzes how social welfare changes under the reward and punishment mechanism, and finally through simulation research, this thesis analyzes the evolutionary paths of paper-making enterprises’ pollution emission strategies under the reward and punishment mechanism. The results of the reward and punishment mechanism are as follows: under the static reward and punishment mechanism, the game system will repeatedly oscillate around a point. There is no stable equilibrium point at this time. However, under the dynamic reward and punishment mechanism, the game system will tend to a stable equilibrium point. The results of social welfare analysis show that high-intensity rewards will reduce the amount of pollution discharged by paper-making enterprises, thereby maximizing social welfare. On the contrary, when paper-making enterprises discharge a large amount of pollution, they will be subject to high-intensity penalties. When facing high-intensity punishments, paper-making enterprises will tend to not to discharge. So social welfare is also maximized. The simulation research results show that reasonable punishment strategies are more effective than reward ones. Based on this, the author proposes countermeasures, such as establishing a reasonable reward and punishment mechanism, reasonably determining the reward and punishment intensity for polluting enterprises. The emission reduction strategies of paper-making enterprises will be affected by the government’s reward and punishment mechanism. A deep study of its internal mechanism is not only of great significance for pollution control but also of great significance for the development of a green economy.

In the paper industry, numerous studies have explored means of optimizing order allocation and cutting trim loss. However, enterprises may not adopt the resulting solutions because some widths of the inventory exceed or are less than those required for acceptable scheduling. To ensure that the results better suit the actual requirements, we present a new decision model based on the adjustment of scheduling and limitation of inventory quantity to differentiate trim loss and inventory distribution data. Differential analysis is used to reduce data filtering and the information is valuable for decision making. A numerical example is presented to illustrate the applicability of the proposed method. The results show that our proposed method outperforms the manual method regarding scheduling quantity and trim loss.

We show that the sufficient condition of the above mentioned paper is also necessary for the boundedness of Bergman type projections on a class of regulated domains.

We improve a result in Silverman's paper (1999) and answer a question he posed. We also consider a similar problem and obtain sufficient conditions for starlikeness.

In 1997, J. A. Fridy gave conditions for noninclusion of ordinary and of absolute summability domains. In the present note, these conditions are interpreted in a natural topological context thus giving new proofs and also explaining why one of these conditions is too weak. Also an open question posed in Fridy's paper is answered.

In [1], Craig and Goodman develop the geometrical optics solution of the linearized Korteweg-deVries equation away from caustic, or turning, points. Here we develop an analogous solution valid at caustic points.

We note that a result of Brenner (1962) follows from a theorem of Lerch (1896) which also extends it.

A technique used by S. Haber to prove an elementary inequality is applied here to obtain a more general inequality for convex sequences.

Purpose. As a scientific field, bioinformatics has drawn remarkable attention from various fields, such as information technology, mathematics, and modern biological sciences, in recent years. The topic models originating from the field of natural language processing have become the focus of attention with the rapid accumulation of biological datasets. Thus, this research is aimed at modeling the topic content of the bioinformatics literature presented by Iranian researchers in the Scopus Citation Database. Methodology. This research was a descriptive-exploratory study, and the studied population included 3899 papers indexed in the Scopus database, which had been indexed in this database until March 9, 2022. The topic modeling was then performed on the abstracts and titles of the papers. A combination of LDA and TF-IDF was utilized for topic modeling. Findings. The data analysis with topic modeling resulted in identifying seven main topics "Molecular Modeling," "Gene Expression," "Biomarker," "Coronavirus," "Immunoinformatics," "Cancer Bioinformatics," and "Systems Biology." Moreover, "Systems Biology" and "Coronavirus" had the largest and smallest clusters, respectively. Conclusion. The present investigation demonstrated an acceptable performance for the LDA algorithm in classifying the topics included in this field. The extracted topic clusters indicated excellent consistency and topic connection with each other. [ABSTRACT FROM AUTHOR]

THz time-domain spectroscopy has evolved as a noncontact, safe, and efficient technique for paper characterization. Our previous work adopted peak amplitude and delay time as features to determine paper basis weight using terahertz time-domain spectroscopy. However, peak amplitude and delay time tend to suffer from noises, resulting in degradation of accuracy and robustness. This paper proposes a noise-robust phase-shift based method to enhance measurements of paper basis weight. Based on Fresnel Formulae, the physical relationship between phase shift and paper basis weight is formulated theoretically neglecting multiple reflections in the case of normal incidence. The established formulation indicates that phase shift correlates linearly with paper basis weight intrinsically. Subsequently, paper sheets were stacked to fabricate the samples with different basis weights, and experimental results verified the developed mathematical formulation. Moreover, a comparison was made between phase shift, peak amplitude, and delay time with respect to linearity, accuracy, and noise robustness. The results show that phase shift is superior to the others. [ABSTRACT FROM AUTHOR]

This research aims to improve the rationality and intelligence of AUTOMATICALLY HIGHER MATHEMATICALLY EXAM SYSTEM (AHMES) through some AI algorithms. AHMES is an intelligent and high-quality higher math examination solution for the Department of Computer Engineering at Pai Chai University. This research redesigned the difficulty system of AHMES and used some AI algorithms for initialization and continuous adjustment. This paper describes the multiple linear regression algorithm involved in this research and the AHMES learning (AL) algorithm improved by the Q-learning algorithm. The simulation test results of the upgraded AHMES show the effectiveness of these algorithms. [ABSTRACT FROM AUTHOR]

The objective of this special issue is to address recent research trends and developments in stochastic control systems and their applications to control, filtering, communication, manufacturing, fault detection, and systems [...]

The teaching characteristic of colleges and universities is determined by the teaching quality of teachers, but it is difficult to calculate with a linear mathematical explanation how to judge the teaching quality of teachers. In most colleges and universities, expert evaluations, supervision groups, peers listening to lectures in the classroom, and students' after-class evaluations are still used to determine the teaching ability of professors. It is undeniable that these judgment methods have certain practicality. Especially for new teachers, experts and peers can find problems in time and help new teachers quickly correct them, and they can also know how to better practice the "student-centered" teaching concept from the feedback of students after class. However, these judgment standards also have their limitations. For example, the classroom quality judgment standards for experts and peers are mostly set by some administrative departments according to social requirements, leadership requirements, and subjective cognition. Various institutions should have different weights for the same criterion, and if they all employ the same criteria, it will easily lead to imprecise conclusions. In addition, the expert group and students are also subjective and incomplete in the stage of teacher evaluation. Based on different nonquantitative factors, the evaluation standards for teachers in colleges and universities also need the scientific theoretical basis. Therefore, many scientists are considering whether to use a more intelligent and reasonable way to judge the quality of teaching. Nowadays, with the popularization of artificial intelligence (AI), the use of neural networks (NNs) is becoming more and more comprehensive. According to the characteristics of NNs, this paper uses the GA NNs algorithm to signify that this algorithm can be effectively used in the evaluation of teachers' teaching quality. In this paper, a large number of experimental results show that the use of GA NNs can be used to evaluate the quality of teaching, and the evaluation results can be obtained quantitatively in this way. For the assessment of teaching quality, quantitative results are also more powerful in teaching evaluation. [ABSTRACT FROM AUTHOR]

The development of a country's language and literature is the orientation of the country's spiritual strength, and it is also the embodiment of the national literary quality. Chinese language and literature has a very long history of development, the content of which is broad and deep, and there are countless literary works. It is impossible for one person to fully understand these works after spending his entire life. As a result, these literary resources are optimized and constructed and classified into various disciplinary contents, so that students can systematically learn literary knowledge according to their own interests. Therefore, this paper conducts a disciplinary analysis of the construction of Chinese language and literature resources by integrating the method of fuzzy mathematics. Through the study of fuzzy mathematics, the method of fuzzy cluster analysis is proposed to classify Chinese language and literature resources, and the method of fuzzy comprehensive evaluation is used to evaluate the contents of various subjects. This paper selected the richness of subject content, faculty strength, talent demand, and subject difficulty as evaluation indicators. By comparing the scores of various discipline indicators before and after resource optimization, the results showed that using fuzzy cluster analysis, disciplines 1 and 2 were popular disciplines. Disciplines 4 and 5 were general disciplines, and disciplines 3 and 4 were unpopular disciplines. Using the fuzzy comprehensive average, the comprehensive scores of the six disciplines were 84, 79, 80, 82, 83, and 75, respectively. The quality of resource construction in these disciplines was high. In addition, the scores given by experts were all above 70 points, with an average score of about 83 points. It showed that the method of using fuzzy mathematics to construct Chinese language and literature resources has achieved good results, and the results obtained by using the method of disciplinary analysis were reliable and feasible., Author(s): Yanli Li (corresponding author) [1]; Yun Li [1] 1. Introduction China's education system is continuously optimized, and the construction of Chinese language and literature resources should also be accelerated. [...]

Anemia is a global public health problem, especially common among children in developing countries, which affects their physical and mental health development. However, there is currently a lack of research on the relationship between anemia and academic performance. The objective of this study was to explore the association between anemia and academic performance, and the possible factors mediating this association among Chinese children. The data for this study came from the baseline survey of the Chinese Education Panel Survey Elementary School Cohort. The cohort was conducted from September 2018 to June 2019 in 160 elementary schools, covering 20 provinces and 40 counties/districts throughout China. Paper-based questionnaires were used, completed by 4th grade students, parents, head teachers, main teachers, and principals. The data used included questionnaire responses, physical measurements, and academic performance of 17,695 students. Based on students' hemoglobin levels and school altitude data, we grouped them into anemia and nonanemia categories using WHO criteria. The anemia group had 1,154 individuals, while the nonanemia group had 16,541 individuals. An ordinary least squares regression and mediation effect analysis were conducted. Our findings found the prevalence of anemia was 6.52% among Chinese Grade 4 students. Students without anemia had a higher average test score for three academic subjects than students with anemia (P < 0.001); their test scores for Chinese, Mathematics, and English were also higher (P < 0.05). Multivariate regression analysis showed a negative association between anemia and average test scores as well as individual test scores for the three subjects. Mediation analysis found that anemia affected academic performance directly (P < 0.05), and indirectly by decreasing the cognition score (P < 0.05). The indirect effect was 19.9% of the total effect. Findings highlighted anemia affected academic performance both directly and indirectly. Nutrition-related interventions should be implemented to prevent a decrease in academic performance among students with anemia. [ABSTRACT FROM AUTHOR]

In order to solve the problems of uneven local educational resources, imperfect comprehensive practice systems, and insufficient teachers in rural schools, this paper proposes a research activity based on mobile information system. The research activities proposed in this paper take STEAM (the acronym of Science, Technology, Engineering, Art, and Mathematics) as the educational concept, carry out local research-based learning (research-based learning, hereinafter referred to as "research-based learning"), and create STEAM research activities suitable for local teaching in combination with the requirements of national education reform to improve students' scientific literacy such as innovation, scientific, and technological level, and independent practice and exploration. The results are as follows: 92% of the students in the experimental group think that STEAM research and learning activities are very interesting and recognize the research and learning process; STEAM research activities have a strong role in promoting the cultivation of students' three abilities and the training of core skills, and the scores are increased by about 50%; the excellent, good, qualified, and unqualified students in the experimental group accounted for 15%, 73%, 12%, and 0%, respectively, while the control group accounted for 6%, 55%, 32%, and 7%, respectively. Through the participation of STEAM research activities, the students' learning attitude has been greatly improved. The practice of STEAM research activities proposed in this paper in the research and implementation institute has achieved remarkable results, which provides experience for Western schools in the follow-up stage of compulsory education to carry out STEAM research activities. [ABSTRACT FROM AUTHOR]

Sports culture industry (CI), as a new sunrise industry, has achieved unprecedented development in the world, and many developed countries regard it as a pillar industry. In our country, the sports CI is quickly growing and contributing significantly to national economic growth. The layout mode of the sports CI is crucial in identifying regional growth advantages and resource development, as well as maximizing industrial function allocation and creating sports CI development strategy. This paper takes Dalian's sports CI as the research object and expounds the concept of sports culture, sports CI, and spatial layout of sports CI through analyzing and sorting out previous research results. This study analyses the evolution and spatial distribution of the Dalian sports CI, as well as the current challenges, using theories and methodologies from sports science and economic geography and proposes countermeasures for Dalian to develop its sports CI. Regional development theories, such as central place theory, growth pole theory, point axis theory, and Maslow's hierarchy of needs theory, have important theoretical value and practical guiding significance for the rationalization of spatial distribution of Dalian sports CI. On the basis of analyzing the resources, traffic network, and economic development of Dalian sports CI, the paper constructs the spatial distribution mode of Dalian sports CI, namely, two core points, three development axes, and multiple nodes, and puts forward the measures to optimize the spatial distribution of Dalian sports CI. This paper examines the current condition, resource architecture, and major structure of supply of leisure sports service items in the Zhejiang coastline area using the literature and field inquiry methodologies. On this basis, the optimization countermeasures of combining Zhejiang coastal leisure sports service products with recreational fishery and yacht economy are put forward to perfect the system of coastal leisure sports service products and improve the supply capacity., Author(s): Lu Qi (corresponding author) [1] 1. Introduction In recent years, with the improvement of people's living standard and the increase of leisure time, people's demand for coastal leisure sports [...]

In the field of condition-based maintenance, maintenance costs and system reliability criteria are the primary considerations for traditional maintenance management. These methods lack consideration of the environmental impact caused by equipment degradation, such as excessive emissions and energy consumption. In addition, because equipment degradation has various impacts on the ecological environment, companies with excessive emissions and energy consumption can receive huge fines, making it of great value to study ecoconscious maintenance strategies. In this paper, we propose a condition-based maintenance strategy considering energy consumption and carbon dioxide emissions. The major objective of the research is to extend a model which integrates ecological aspects with maintenance decision-making and optimization. The simulation and sensitivity analyses conducted verify that the model proposed can minimize total costs, as well as the environmental impact., 1. Introduction Maintenance refers to maintaining and repairing equipment and systems to ensure they are in good condition. It is an indispensable activity related to machines, ranging from grinding or [...]

Educational data mining is becoming a more and more popular research field in recent years, mainly with the help of cross research conducted by various disciplines, so as to solve various difficult problems in the teaching and education process. In this paper, we proposed a hybrid approach for student performance prediction. We collected the dataset, including 15 characteristics of students from three categories (individual basic information, individual education information, and individual behavior information). Based on the random forest (RF) and simulated annealing (SA) algorithms, we binary encode the relevant parameters (number of features, tree size, and tree decision weights) as the target variables for algorithm optimization, use the out-of-bag error as the optimization objective function, and then propose the IRFC (improved random forest classifier) algorithm in this paper. Compared with other mainstream improved random forest algorithms, the research results demonstrate that the proposed algorithm in this paper has higher generalization ability and smaller OOB error. This study provides a methodological reference for the prediction of student achievement and also makes a marginal contribution to student management work. [ABSTRACT FROM AUTHOR]

Occupying the same crucial position as land resources, ecological resources originate from the marine system nourish various aspects of human welfare and the world economy. It is significant to apply efficient decision-making approaches, which assist with the achievements of sustainable development and environmental protection. This paper aims to review decision-making approaches in marine management and present a comprehensive bibliometric study. To achieve this goal, bibliometric analysis is used to analyze the current status and emerging trends in this field, including the cluster network, the collaboration network, and burst detection. The results show that the number of publications in this field has increased rapidly, and it has become an increasing interdisciplinary research subject. Besides, marine conservation is the largest clusterand the USA is the most productive country in marine management research and the marine policy and NOAA are the most productive journal and institution, respectively. Furthermore, 'Diversity,' 'Management,' and 'Population' are the most influential keywords, and 'People' and 'Public participation' are the emerging trends in decision-making and marine management. Our work clarifies where we are now in terms of marine management and decision-making and how do they serve each other to achieve sustainable development. More importantly, some deep insights could be given that motivate researchers to learn the connections and collaborations between natural science and social study, which is our effort to deal with the social-ecological obstacle as well., Author(s): Wei Zhou [1]; Yan Chen (corresponding author) [1]; Qiu Shu (corresponding author) [1] 1. Introduction Ecological resources that originate from the marine system nourish various aspects of human welfare [...]

The article discusses various reports published within the issue including one by Li on the fractal time series from the point of view of systems of fractional order, article by Liu on the chaotic time series and an article by Cattani on the fractal shapes and the symmetries of DNA from a view of fractal time series.

With the development of computer technology, computers have become a part of people's lives and the Internet has connected the world's networks as a whole. Computer technology is changing people's study, life, and work. People's traditional education mode, thinking, content, method, and talent training program have a significant impact. The development from traditional to computer technology-based teaching methods has brought new developments and leaps in educational technology. This paper analyzes the research background, significance, and research status of the advanced mathematics auxiliary teaching system, introduces the related technologies and development modes used in the development of the system, and especially discusses the access database technology by ADO and the mathematical expression based on MathML language. Secondly, starting from the actual teaching, we analyze the functional requirements and performance requirements of the system in detail and make detailed planning and design for the system architecture, database selection, functional modules, etc. The design and implementation process of this teaching system are summarized. The teaching strategy inference engine is the key to the personalization and intelligence of the ICAI system. According to the learning models provided by different students, the system designs a corresponding teaching sequence for the learners by controlling the meta-knowledge of the domain knowledge base. The teaching strategy inference engine cuts the domain knowledge tree, selects the knowledge points suitable for the student, and sorts the selected knowledge points reasonably to generate an optimal teaching sequence. According to the students' learning situation, combined with the teaching rules in the teaching rule library, the students' grades are dynamically adjusted, so as to select new learning content for students and provide teaching suggestions in time. The student model is the premise of the ICAI system to achieve individualization and intelligence. The system makes a comprehensive evaluation and diagnosis of students through fuzzy comprehensive evaluation and fuzzy reasoning. On this basis, a cognitive student model is established, which is the teaching strategy that provided the basis for the formulation. [ABSTRACT FROM AUTHOR]

Deep learning is an important concept introduced into modern learning science. It is different from the surface learning of mechanically and passively acquiring knowledge and storing individual information but emphasizes learners' active and critical learning. It wants them to understand the full meaning of what they have learned. By establishing a link between existing knowledge and new knowledge, it transfers existing knowledge to a new environment, makes decisions, and solves problems. Deep learning plays an important role in students' learning. Deep learning ability is the key factor affecting the quality of learning and the development of students' academic ability. The quality of in-depth teaching is difficult to guarantee, which requires a complete, comprehensive, and evaluation system to evaluate it. This paper introduces the analytic hierarchy process to weight the indexes in mathematics deep learning and puts forward some suggestions on creating an environment for deep learning. The experimental results show that teachers' teaching accounts for the highest proportion of primary indicators, reaching 67%. Multimedia resources account for the highest proportion of secondary indicators, reaching 73.01%. This paper then puts forward some suggestions for indicators with large weights. [ABSTRACT FROM AUTHOR]

There is a huge amount of mathematical information in the world, and mathematics is everywhere and nowhere. Bayesian theory is based on a process of statistical inference that requires the calculation of general and prior information to obtain a posteriori information. Its main features are the use of probabilities to represent all forms of uncertainty and the use of probabilistic rules to enable learning and inference, estimating the probability of future occurrences by calculating the probability of a past time. In order to bring mathematics closer to life, this paper explores the teaching of mathematical applications in terms of material selection, teaching arrangement, and professional integration. At the same time, in order to better realize mathematics application teaching, effectively improve the classroom effect of mathematics application teaching, and make students better accept mathematical knowledge and apply it to practical applications, the design of mathematics application teaching in this paper is also based on Naive Bayes. [ABSTRACT FROM AUTHOR]

In feedback control systems, delay as a generic part of many processes, including industrial [1], communication [2], economical [3], traffic [4], automotive technology [5], and biological [6] ones, is a [...]

With the rapid development of artificial intelligence (AI) concept technology, it promotes the innovation of educational concept. Mostly for the education information analysis in the class of mathematics in the university, it should be based on a big data-driven system to promote the quality of teaching in the classroom. In the method of teaching math in university, teachers should take full advantage of the benefits of a big data-driven system powered by AI, grow a good teaching model for students, promote education through big data, progressively teach students according to their aptitude, develop in a tailored direction, increase teaching quality and effectiveness, and finally create more great talents for our country. For the sake of improving the resource sharing and the management level of the curriculum which teaches advanced knowledge about mathematics teaching, based on a particle swarm optimization algorithm, an advanced math teaching system is proposed in this paper. The fusion model that can be used in the teaching process of math in university is constructed, the adaptive scheduling of the curriculum which teaches advanced knowledge about mathematics auxiliary teaching resources is realized by an optimization algorithm used for fusion particle swarm, the autocorrelation feature of the curriculum which teaches advanced knowledge about mathematics auxiliary teaching resources is extracted, and the adaptive optimization of the curriculum which teaches advanced knowledge about mathematics auxiliary teaching resource fusion is realized by fuzzy correlation feature matching and statistical analysis. In the process of particle swarm optimization, the combination of statistical features is studied and managed, the resource scheduling and information fusion are realized, and the management capability of the curriculum which teaches advanced knowledge about mathematics auxiliary teaching is promoted, and the experimental results demonstrate that the designed system has a high integration of teaching information resources and strong information scheduling ability and improved the management level of the curriculum which teaches advanced knowledge about mathematics complementary teaching. [ABSTRACT FROM AUTHOR]

The times are advancing, the Internet is constantly advancing, and there are many examples of the combination of technology and life. The way people buy agricultural products is no longer limited to the physical market, but is being transformed into an agricultural product e-commerce website to achieve digital transformation. So far, a large number of agricultural products have been collected on the agricultural product e-commerce website, they come from all over the country, and different types and different regional agricultural products have appeared. On the other hand, users are faced with a large number of commodities on the Internet. If they simply use search engines, consumers need to spend a lot of time to find the commodities they are interested in by browsing. Searching from the recommended ones greatly reduces the product set and optimizes the performance of the e-commerce platform. Therefore, the generation of the personalized recommendation system meets the needs of the market. This research will improve the related recommendation algorithm by improving the user similarity calculation method based on artificial intelligence technology, improve the execution efficiency and accuracy of the improved algorithm, and use related technologies to develop an agricultural product e-commerce recommendation system. It has important theoretical and practical significance to save users' browsing time, improve users' shopping experience, and speed up the construction of agricultural product e-commerce., Author(s): FengQin ZhuanSun (corresponding author) [1]; JiaoJiao Chen [1]; Wenlong Chen [1]; Yan Sun [1] 1. Introduction With the continuous advancement of the Internet, cloud computing, and big data technologies, [...]

The instability of single-phase grounding faults makes it difficult to track the fault development process accurately. In this paper, a transition conductance tracking method for single-phase grounding fault identification is established to perceive the health state of the distribution network. To solve the situation that the insulation parameters of the neutral ungrounded system are difficult to obtain, a phase-shift method is proposed to measure the insulation parameters accurately. Based on the measurement results of insulation parameters, a transition conductance tracking model is constructed to monitor the changes of transition conductance in real time. Since the transition conductance varies steadily, it can be used as an important parameter to evaluate the development of a single-phase grounding fault. The innovative tracking method is verified by the simulation model, and the error analysis shows that the tracking error of transition conductance is less than 9%. The simulation results demonstrate that the proposed method can track the development of grounding faults effectively and give a quick warning to the operator. The fault identification theory adopted in this paper is not limited by the neutral grounding mode and especially can be applied to situations where the neutral point parameters are not adjusted., Author(s): Zhuochao Wu (corresponding author) [1,2]; Mingyu Fang [1,2]; Jiexuan Cai [1,2]; Weixing Qian [1,2]; Zhenya Ji [1] 1. Introduction The single-phase grounding faults (SPGFs) in the medium voltage distribution [...]

With the rapid development of information technology and the Internet, it is difficult for university readers to find books of real interest or value from a large number of books by relying only on traditional retrieval-based services. This paper applies data mining technology and personalized recommendation algorithm based on semantic classification for new book recommendation service in university libraries. The personalized recommendation algorithm based on semantic classification establishes a book feature model and a reader preference model based on title keywords. The different recommendation strategies in the system framework are detailed. For the borrowing data of different colleges and departments, the improved association rule algorithm is used to mine the book association rules, and the reader's borrowing history is matched with the association rules to generate a book recommendation list; according to the reader's borrowing preference characteristics, the reader preference model is used as the basis. Class subdivision and then combined with the book feature model and reader preference model, the collaborative filtering recommendation algorithm and the content-based recommendation algorithm are applied to generate a book recommendation list. The active service method not only improves the service level of the university library, makes the development of the university library more comprehensive and humanized but also explores the potential information needs of readers, improves the borrowing rate of books in the collection, and maximizes the utilization rate of book resources. In the experiment of this paper, the personalized recommendation algorithm division of semantic classification is adopted. According to the division of its algorithm, the corpus is divided into 9603 training documents and 3299 test documents, with certain accuracy., Author(s): Nan Pang (corresponding author) [1] 1. Introduction The rapid development of the Internet and information technology has gradually freed human beings from the state of information scarcity and stepped [...]

The enterprise financial management cloud platform can conveniently obtain computing resources such as network, storage, and services according to actual needs based on the network. The maintenance of resources can be realized in a general way, and various computers and their storage resources can be effectively integrated and effectively utilized. But internal conflicts within the company, such as asset management issues, can seriously hinder the company's long-term growth. In order to solve the above problems, this paper analyzes the actual needs of the current financial information management personnel of enterprises and the business process of financial management. Combined with the characteristics and requirements of the current enterprise financial management, the integrated fuzzy mathematics cluster analysis was introduced to complete the design and implementation of the financial IMS, and to build a modern, systematic, and scientific enterprise management system. It greatly improves the financial level of the enterprise and the intelligent level of enterprise management, provides objective and strong support for enterprise decision-making through the enterprise's financial management ability, and finally realizes the long-term development of the enterprise. The prediction effect of the samples under the FCM is shown in Table 3. The prediction effect of the training samples of the normal company is 83.33%, and the prediction effect of the training samples of the ST company is 80%. The test sample prediction effect of normal company is 83.33%, the test sample prediction effect of ST company is 80%, and the comprehensive prediction effect under the FCM is 85.28%., Author(s): Xianli Zhang (corresponding author) [1] 1. Introduction Although Chinese enterprises have achieved many achievements in the construction of financial informatization, there are still many deficiencies, especially in the aspect [...]

Fractional variants of distance-based parameters have application in the fields of sensor networking, robot navigation, and integer programming problems. Complex networks are exceptional networks which exhibit significant topological features and have become quintessential research area in the field of computer science, biology, and mathematics. Owing to the possibility that many real-world systems can be intelligently modeled and represented as complex networks to examine, administer and comprehend the useful information from these real-world networks. In this paper, local fractional strong metric dimension of certain complex networks is computed. Building blocks of complex networks are considered as the symmetric networks such as cyclic networks C n , circulant networks C n 1,2 , mobious ladder networks M 2 n , and generalized prism networks G m n . In this regard, it is shown that LSFMD of C n n ≥ 3 and G m n n ≥ 6 is 1 when n is even and n / n − 1 when n is odd, whereas LSFMD of M 2 n is 1 when n is odd and n / n − 1 when n is even. Also, LSFMD of C n 1,2 is n / 2 ⌈ m + 1 / 2 ⌉ where n ≥ 6 and m = ⌈ n − 5 / 4 ⌉. [ABSTRACT FROM AUTHOR]

In order to supplement and improve the landscape evaluation system of traditional villages and play a positive guiding role in the specific protection and development of village landscape, this paper proposes the ecological landscape environment evaluation of traditional villages in Guanzhong from the perspective of rural revitalization. First of all, the relevant national and provincial evaluation standards are deeply studied, and the comprehensive evaluation system of the traditional village landscape in the Guanzhong region is constructed by using expert scoring and an analytic hierarchy process, which includes 6 criteria and 25 indicators. Three typical villages of different types and different development stages, namely, village A, village B, and Village C, were selected for landscape investigation and comprehensive evaluation. The results show that the weight of village location and landscape pattern is the largest, that is, the most important. The second is architectural landscape, with a weight of 0.236, accounting for a high proportion. Then, it is the natural landscape and street landscape, with a weight of 0.1-0.2. Finally, it is the intangible cultural landscape and landscape style protection, with a weight of less than 0.1. The alpha value of the comprehensive evaluation questionnaire of the village A is 0.984. The alpha value of the comprehensive evaluation questionnaire of village B is 0.941. The alpha value of the comprehensive evaluation questionnaire for the landscape of village C is 0.986, which is greater than 0.8. Conclusion. This study makes a comprehensive evaluation of the landscape of typical traditional villages. [ABSTRACT FROM AUTHOR]

This paper describes a novel perspective on the foundations of mathematics: how mathematics may be seen to be largely about "information compression (IC) via the matching and unification of patterns" (ICMUP). That is itself a novel approach to IC, couched in terms of nonmathematical primitives, as is necessary in any investigation of the foundations of mathematics. This new perspective on the foundations of mathematics reflects the facts that mathematics is almost exclusively the product of human brains, and has been developed, as an aid to human thinking, mathematics is likely to be consonant with much evidence for the importance of IC in human learning, perception, and cognition. This perspective on the foundations of mathematics has grown out of a long-term programme of research developing the SP Theory of Intelligence and its realization in the SP Computer Model, a system in which a generalised version of ICMUP—the powerful concept of SP-multiple-alignment—plays a central role. This paper shows with an example how mathematics, without any special provision, may achieve compression of information. Then, it describes examples showing how variants of ICMUP may be seen in widely used structures and operations in mathematics. Examples are also given to show how several aspects of the mathematics-related disciplines of logic and computing may be understood as ICMUP. Also discussed is the intimate relation between IC and concepts of probability, with arguments that there are advantages in approaching AI, cognitive science, and concepts of probability via ICMUP. Also discussed is how the close relation between IC and concepts of probability relates to the established view that some parts of mathematics are intrinsically probabilistic, and how that latter view may be reconciled with the all-or-nothing, "exact," forms of calculation or inference that are familiar in mathematics and logic. There are many potential benefits and applications of the mathematics-as-IC perspective. [ABSTRACT FROM AUTHOR]

Buffer operators can effectively weaken the impact disturbance of the system and restore the original appearance of the system. However, the buffer strength of the existing buffer operator is fixed, and the buffer strength cannot be adjusted. So the buffer effect for the system impact disturbance is not flexible enough, and the accuracy of the prediction model established is not high. In order to solve this problem, this paper proposes weakened buffer operators with time-varying parameters, analyzes the properties of the newly constructed buffer operator, and proves the superiority of the new buffer operators theoretically. Finally, the new buffer operators proposed in this paper are applied to the prediction of the number of employees in China's air transport industry and the unsafe events in China's civil aviation. The results show that the proposed buffer operators are better than the existing buffer operators, which further verify the validity and practicability of the proposed buffer operators., Author(s): Pumei Gao [1]; Jiefang Liu (corresponding author) [2] 1. Introduction In the quantitative prediction of the system, the model often loses its effectiveness due to the disturbance of external [...]

In this paper, we consider the three-dimensional Cauchy problem of the nonisentropic compressible Euler equations with relaxation. Following the method of Wu et al. (2021, Adv. Math. Phys. Art. ID 5512285, pp. 1–13), we show the existence and uniqueness of the global small H k k ⩾ 3 solution only under the condition of smallness of the H 3 norm of the initial data. Moreover, we use a pure energy method with a time-weighted argument to prove the optimal L p – L q 1 ⩽ p ⩽ 2 , 2 ⩽ q ⩽ ∞ -type decay rates of the solution and its higher-order derivatives. [ABSTRACT FROM AUTHOR]

The purpose of this note is (i) to point to a reference missing in the paper (Mayo-Maldonado et al., 2018) and (ii) to show that some of its content is covered in a more general setting by the results in that reference (Goknar, 1972)., 1. Introduction As explained in the sequel, some of the results developed for specific circuits in Sections 5 and 7 of the paper given in [1] can simply be obtained [...]

Earth and Environmental Systems (EES) models play a crucial role in facilitating the research and development in science and engineering disciplines. To better conceptualize the EES by using mathematical equations, [...]

The tremendous development of digital computers in the last decades has provided continuous improvement of various sophisticated methods in computational electromagnetics (CEM). CEM analysis methods provide results within an appreciably [...]

The research presents an analysis and comparison of the Taguchi Design and Response Surface Methodology (RSM) in optimizing the laser cutting machine parameters on dimension accuracy for stainless steel products. The paper studies effects of input factors such as cutting speed, nitrogen pressure, power, and frequency on the quality cutting of stainless steel (304) specimens. In this paper, two objectives are examined: targeting laser-cut edge to perpendicular 90 degrees and maximizing the cutting accuracy. The paper proposes a simple formula to optimize both targets by minimizing one new function definition, i.e., dimensional error. An L9 orthogonal array of Taguchi Methodology is adopted to minimize the number of experiments and shorten analysis time to achieve the optimal parameters. These results are compared with RSM. Box-Behnken Design (BBD) type of RSM requires more experiments than the Taguchi approach. RSM regression models as the quadratic functions of the control factors are developed to minimize the dimensional error of cutting products. Then, Analysis of Variance (ANOVA) and graphs will be analyzed to determine the influences of variables on the responses. Both Taguchi's method and RSM found that the most influential factor on dimension accuracy is cutting speed, followed by laser power. While Taguchi provides good graphic visualization for quickly predicting the optimum condition, it cannot examine the interaction effects as RSM due to the lack of data. Besides, RSM reveals the percentage contribution of factors on dimensional error. Cutting speed has a maximum contribution, i.e., 39% of the total. The interaction of cutting speed and power contributes 16% of the total. In this study, RSM can predict optimum conditions more accurately than Taguchi. There are misleading results from the Taguchi method compared with RSM. However, the difference between these objective values is insignificant. The validation experiments show that the Taguchi method can be a practical approach for optimization problems. It can help reduce cost and time and achieve the desired optimal outputs. With cutting problems requiring high precision, the RSM method is highly recommended for identifying optimal parameter settings and interaction effects. With problems that their experimental runs consume high cost and time, Taguchi can be a suitable method for screening the significant variables. Although Taguchi and RSM are used widely for optimization problems in many fields, choosing the right methodology for various objectives is still a concern with different arguments and needs further research. Therefore, this study could be an adequate reference for parameter optimization problems in various fields., Author(s): Vi Nguyen [1]; Faisal Altarazi [2]; Thanh Tran (corresponding author) [1] 1. Introduction Laser cutting is one of the most widespread methods to shape and separate a metal workpiece [...]

This paper presents a backpropagation neural network algorithm for data compression and data storage. Data compression or establishing model ten coding is the most basic idea of traditional data compression. The traditionally designed ideas are mainly based on reducing the redundancy in the information and focus on the coding design, and its compression ratio has been hovering around dozens of percent. After the traditional coding compression of information, it is difficult to further compress by similar methods. In order to solve the above problems, the information that takes up less signal space can be used to represent the information that takes up more signal space to realize data compression. This new design idea of data compression breaks through the traditional limitation of relying only on coding to reduce data redundancy and achieves a higher compression ratio. At the same time, the information after such compression can be repeatedly compressed, and it has a very good performance. This is the basic idea of the combination of neural network and data compression introduced in this paper. According to the theory of multiobjective function optimization, this paper puts forward the theoretical model of multiobjective optimization neural network and studies a multiobjective data compression method based on neural network. According to the change of data characteristics, this method automatically adjusts the structural parameters (connection weight and bias value) of neural network to obtain the largest amount of data compression at the cost of small information loss. This method has the characteristics of strong adaptability, parallel processing, knowledge distributed storage, and anti-interference. Experimental results show that, compared with other methods, the proposed method has significant advantages in performance index, compression time and compression effect, high efficiency. and high-quality robustness., Author(s): Dahuan Wei [1]; Gang Feng (corresponding author) [1] 1. Introduction In large-scale database, the storage and analysis of its internal information and complex data usually take a lot of [...]

This paper proposed a Bayesian optimized extreme gradient boosting (XGBoost) model to recognize small-scale faults across coalbeds using reduced seismic attributes. Firstly, the seismic attributes of the mining area were preprocessed to remove abnormal samples and high-noise samples. Secondly, chi-square bins were performed for each feature of the processed attributes. The weight of evidence (WOE) was calculated in each container, and each element's information value (IV) was obtained to characterize the importance of each feature. Features with low information values are reduced to remove high-noise attributes. Thirdly, the reduced attributes are decomposed by variational modal decomposition (VMD) to obtain new features. Finally, the optimized XGBoost model and traditional methods were constructed to identify and locate faults across coalbeds. Here, the Bayesian-optimized XGBoost objective function was used to balance the training weights of asymmetric examples. As the Bayesian optimization algorithm quickly falls into local optimums, it does not easily balance the ?exploit? and ?explore? approaches. Therefore, this paper proposed an adaptive balance factor change algorithm to overcome this shortcoming. Comparing the identification outcomes, the optimized XGBoost model has a higher prediction accuracy than the BP neural network, support vector machines (SVMs), K-means clustering, extreme learning machines (ELMs), and random forests (RFs). In summary, the proposed method can improve the identification accuracy of small-scale faults in coal mining areas., Author(s): Xin Wang [1]; Changwei Ding (corresponding author) [1]; Tongjun Chen [2]; Ting Yu [1] 1. Introduction Small-scale faults are prone to geological accidents during coal mining, including roof falls, [...]

This paper presents an efficient energy management scheme for an islanded DC microgrid included with vanadium redox flow battery (VRFB) with supercapacitor based hybrid energy storage system for a 3-phase variable load profile. The analysed islanded DC microgrid system is composed with sources such as lithium-ion batteries, fuel cell, supercapacitors, and vanadium redox flow battery (VRFB), along with involved DC/DC (buck, boost, and bidirectional) and DC/DC converters. In this paper, an adaptive proportional plus integral (PI) control based energy management scheme is implemented. The main theme on which the performance of this system is analysed is the state of charge of energy storage devices (VRFB, supercapacitor, and Li-ion battery) and the overall efficiency of system. The voltage spikes due to sudden change in load have been regulated by using supercapacitor and Li-ion battery with VRFB. To minimize the fuel consumption, an adaptive proportional plus integral control strategy is being used. In this paper the modelling and simulations are carried out by using MATLAB/SIMULINK software to demonstrate minimizing the effect of load fluctuations with the help of supercapacitor and batteries by synchronizing it to the DC microgrid., Author(s): S. Hameed [1]; I. Prabhakar Reddy [2]; V Ganesh [1]; Aruna rai Vadde (corresponding author) [3] 1. Introduction In the modern world, when the conventional sources of energy are [...]

This paper takes the physical fitness test data and the physical health self-assessment data as the research objects. The decision tree algorithm is used to construct a decision tree model for students who fail to meet the physical test. Thus, the classification of students with different physical qualities is realized. The association rule Apriori algorithm is used to mine the association of physical fitness test indexes so as to judge the hidden law between students' physical fitness and behavior habits and get the correlation information of various physical health indexes. The back propagation (BP) neural network algorithm is used to establish the physical fitness test prediction model. By using these data mining models, this paper explores the hidden association information in college students' physical test data, which can provide more scientific and effective guidance for students' physical tests., Author(s): Junwu Suo [1]; Cuixiang Guo (corresponding author) [2]; Guifang Wang [1] 1. Introduction There are many reasons for the decline of young people's physical health, one of which is [...]

Mathematics courses in higher education (advanced mathematics, linear algebra, etc.) are characterized by abstract concepts, many easily confused concepts, many nature theorems, and large amount of calculation of many difficulties. In most cases, due to the knowledge level, knowledge structure, and requirements of the teaching process, the traditional mathematics teaching adopts the straight-to-point approach to the introduction of mathematical concepts, and the explanation of property theorems mostly adopts the method of giving the theorems first and then analyzing and proving them for the calculation problems are mostly manual calculation. Educators are mainly concerned with how to organize teaching so that the trainees can acquire more knowledge, while ignoring the subject status of the trainees, and it is often related to the talents of higher education. The training goals are consistent. The traditional mathematics teaching only reflects a function of knowledge inheritance, but lacks the function of knowledge innovation. The lack of concept generation process in teaching makes the abstraction stronger; the lack of case analysis process makes the applicability weaken; and the serious separation of engineering problem solving and computer operation makes it lack of practicability. The current mathematics knowledge and curriculum make students in a passive position in the process of mastering basic knowledge. From the study about the present condition of the math education and the real requirement, the thesis will talk about content, characteristics, and the relationships for the practical education along with interactive teaching process. What is more, the ways of education and online education is able to be presented. At the same time, this paper also obtains the maximum weight value of each teaching quality evaluation index through analytic hierarchy process, introduces the quadratic fuzzy comprehensive calculation method of fuzzy operator, and determines the maximum component of the evaluation level as the final evaluation level according to the most subordinate degree criterion of fuzzy comprehensive evaluation. Especially in the formulation of teaching quality evaluation model in China, the qualitative description is processed quantitatively by the method of fuzzy mathematics and statistics, so that the qualitative index and the quantitative index are organically combined. [ABSTRACT FROM AUTHOR]

The rapid development of artificial intelligence, "Internet +," big data, 5G, and other technologies in the twenty-first century has not only brought great changes to the field of education but also brought unprecedented opportunities and challenges to the reform and innovation of mathematics teaching. The use of intelligent technology to carry out the reform of college mathematics teaching in depth and effectively improve the quality of teaching has become a hot topic discussed by the majority of mathematics teachers. This paper carefully analyzes and sorts out the problems existing in the current intelligent college mathematics teaching and systematically studies the related theories and design principles of the blended teaching mode. The ideas and approaches of the reform of college mathematics blended teaching supported by intelligent technology are deeply discussed from the aspects of improving the level of teachers, setting up a blended teaching team supported by intelligent technology, optimizing the informatization construction of teaching environment and establishing rich teaching resources, building an online and offline classroom teaching system, personalized learning under the background of microclass and cloud class, cross-university blended learning in the network environment, and mathematics precision teaching evaluation and optimization using big data. [ABSTRACT FROM AUTHOR]

The regression model is generally utilized in several fields of study because of its applications. Regression is an extremely incredible approach; it builds up a connection between dependent and independent variables. We have addressed a powerful computational model by utilizing dengue information joined with fuzzy multiple linear regression. Information is accumulated on dengue fever through the survey. This paper is centered on the comparison of the crisp method with fuzzy multiple linear regression, and then, the utilization of a fuzzy multiple regression method is explained after the comparison. We have used multiple regression and then converted the said technique into three fuzzy cases. The effectiveness of the fuzzy multiple regression model is measured by numerical computation and comparison of both techniques. 2020 Mathematics Subject Classification. Primary 30C45; 30C50; 30C80; Secondary 11B65, 47B38. [ABSTRACT FROM AUTHOR]

This paper mainly deals on the issue of a chaotic synchronization of a master and slave systems. It is generally the requirement of the synchronization that someone needs at least one to one master and slave systems. In the current study, the authors introduce the concept of a synchronization in which there is no need of slave/response system externally. Furthermore, the synchronization has been demonstrated here within a system among the subsystems of different orders. In addition, adaptive control is chosen for the synchronization among various combinations in multiswitching manner. For demonstration purpose, Lorenz Six Dimensional Hyper Chaotic System (L6DHCS) is chosen. There are three different kinds of possible switches presented by the authors formed within the considered system. The numerical simulations are carried out to validate the effectiveness of the analytical technique using Mathematica. [ABSTRACT FROM AUTHOR]

The world economy is affected by fluctuations in the price of crude oil, making precise and effective forecasting of crude oil prices essential. In this study, we propose a combined forecasting scheme, which combines a quadratic decomposition and optimized support vector regression (SVR). In the decomposition part, the original crude oil price series are first decomposed using empirical modal decomposition (CEEMDAN), and then the residuals of the first decomposition (RES) are decomposed using variational modal decomposition (VMD). Additionally, this work proposes to optimize the support vector regression model (SVR) by the seagull optimization algorithm (SOA). Ultimately, the empirical investigation created the feature-variable system and predicted the filtered features. By computing evaluation indices like MAE, MSE, R2, and MAPE and validating using Brent and WTI crude oil spot, the prediction errors of the CEEMDAN -RES.-VMD -SOA-SVR combination prediction model presented in this paper are assessed and compared with those of the other twelve comparative models. The empirical evidence shows that the combination model being proposed in this paper outperforms the other related comparative models and improves the accuracy of the crude oil price forecasting model.