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The paper describes the stages of paper production, considers the structure of a paper-making machine. Questions related to the proof and use of the Pontryagin’s maximum principle in the theory of optimal control are considered. Optimal paper web weight control system based on the Pontryagin’s maximum principle is presented. Adaptive learning methods for modeling nonlinear systems represent some of the latest advances in adaptive algorithms and machine learning techniques designed to model and identify nonlinear systems. Real-world problems always involve a certain degree of non-linearity, which makes linear models a suboptimal choice. This article may be of interest to research engineers and practitioners in the study and application of control systems using adaptive regulators. This book serves as an essential resource for researchers, graduate students and doctoral students working in the field of machine learning, signal processing, adaptive filtering, nonlinear control, system identification, cooperative systems, and computational intelligence. This book may also be of interest to the industry market and practitioners working with a wide range of nonlinear systems.

The paper based on the analysis of the working conditions of railway transport shows that, ceteris paribus, an increase in actual durability and mean time before failure of rolling stock units is obtained through maintenance of wheelsets, especially during logistic processes on nonmechanized humps. One of the main causes for the service life decrease of wheelsets is brake damage. To eliminate such brake damage on highways, on-board braking dis-tance regulators mounted on the locomotive are used. In the hump yards, using such systems is not possible, since under marshalling, cars are detached from the locomotive. In this regard, an industrial challenge turns up to design a similar in purpose braking distance regulator for humps through skidding. To solve this problem, we developed a method of controlling the speed of cars to be sorted, which complements the widely used technology of the shoe braking and avoids the formation of one-way sliders. The paper presents a skid adjuster, which excludes brake damage to the rolling stock wheels by the slides on the humps using the shoe braking techniques. The motion equation of the rolling stock equipped with a skid adjuster on the hump tracks is developed. In the result of this study, an equation of car motion in the system of “rolling stock Hughes regulator way” allowing to enter the working elements of Hughes controller to an existing and debugged algorithm of any slides with minimal change it.

The parameters of electrical network modes often do not meet the requirements of Russian GOST 32144-2013 and the guidelines of Vietnam. In the actual operating conditions while there is the non-sinusoidal mode in electrical networks voltage and current harmonics are present. Harmonics result in overheating and damage of power transformers since they cause additional active power losses. Additional losses lead to the additional heat release, accelerating the process of insulating paper, transformer oil and magnetic structure deterioration consequently shortening the service life of a power transformer. In this regard there arises a need to develop certain scientific methods that would help demonstrate that low power quality, for instance could lead to a decrease in the electrical equipment service life. Currently we see a development of automated systems for continuous monitoring of power quality indices and mode parameters of electrical networks. These systems could be supplemented by characteristics calculating programs that give out a warning upon detection of the adverse influence of voltage and current harmonics on various electrical equipment of both electric power providers and electric power consumers. A software program presented in the article may be used to predict the influence of voltage and current harmonics on power transformers.

Sustainable construction contributed to the usage of recycled and waste materials to substitute conventional concrete. This research focuses on prediction of normalized bond strength of cement concrete substituted by large amounts of waste materials and products with strong mechanical properties and sustainability. It also emphases on using analytical model for the prediction of bond strength of the green concrete, so that there is a reduction in the cost of construction, con-serve energy, and it will lead to a reduction of CO2 production from cement industries within reliable limits. In this paper machine learning approach has been used to predict the normalized bond strength of green and sustainable concrete. Machine learning empowers machines to learn from their experiences and data provided. The system analyses the datasets and finds different patterns formed in the given data. Then, based on its learnings the machine can make certain predictions. In civil engineering application, a special computing technique called the Machine learning (ML) is in huge demand. ANN is a soft computing technique that learns from previous situations and adapts without constraints to a new environment. In this work, a ML network model for prediction of normalized bond strength of concrete has been illustrated. Different sets of data based upon several concrete design mixes were taken from technical literature and were fed to the model. The model is then trained for prediction, which are being influenced by several input attributes and were jotted down a linear regression analysis.

In the light of more and more urgent hidden fire abnormal detection problem in complex conditions of mine, a method which is used directional drilling technology is put forward. The method can avoid the obstacles in mine, and complete the fire abnormal detection. This paper based on analyzing the trajectory control of directional drilling, measurement while drilling and the characteristic of open branch process, the project of the directional drilling is formulated combination with a complex condition mine, and the detection of fire abnormal is implemented. This method can provide technical support for fire prevention, which also can provide a new way for fire anomaly detection in the similar mine.

Food safety is a matter of national importance, and it is important to establish and improve a whole process food safety supervision system with high standard. Along with the advent of the era of big data, to achieve this goal requires not only the reform and optimization of the old regulatory approach needs to establish the idea of information-based supervision, and actively apply big data technology to food safety testing. In this paper, we introduce the current situation of food safety in society, analyse the shortcomings of current food safety supervision and the challenges faced in the era of big data, discuss the feasibility of using big data for food safety testing with big data technology as the core, and give the design idea of establishing a corresponding food safety subject database. This paper takes food safety testing as the foothold and innovatively combines big data with food safety testing, with a view to providing reference for food safety regulatory authorities and promoting the healthy development of the food industry.

This paper introduces the development course of cryosurgery, mechanism, advantages and disadvantages, application scope, the analysis of the current world advanced the development of cryogenic surgical instruments and its principle and performance, found that at present, the development of the cryoablation equipment mainly for the treatment of diseases such as cancer such heavy research, thus ignore the crowd larger quantity of the treatment of skin disease research. cryopen are introduced in this paper to solve small pathological tissues such as skin disease treatment. we study a new type of freezing and melting equipment, named cryopen. at the same time, the simulation of treating abnormal skin is completed effectively. The results show that cryopen is very good for the treatment of abnormal skin. The future development of small cryoablation equipment is also put forward.

The paper considers possible aspects of unmanned aerial vehicles (UAVs) to predict and prevent dangerous situations associated with floods, waterlogging, and stream rise in the territories of settlements in modern conditions. Besides, a historical analysis of such cataclysms was carried out for the period from 2019 to 2020, which deserves the special attention of scientists. The relevance of the paper is undoubted, since the symbiosis of nature and man gives rise to destructive phenomena against the background of an incorrect interaction of these two units. The paper is divided into 5 main sections, which include: introduction, history of floods, possible use of UAVs in water accidents, and further actions after the work of UAVs is done and the conclusions.

Sentiment classification is one of the hottest research areas among the Natural Language Processing (NLP) topics. While it aims to detect sentiment polarity and classification of the given opinion, requires a large number of aspect extractions. However, extracting aspect takes human effort and long time. To reduce this, Latent Dirichlet Allocation (LDA) method have come out recently to deal with this issue.In this paper, an efficient preprocessing method for sentiment classification is presented and will be used for analyzing user’s comments on Twitter social network. For this purpose, different text preprocessing techniques have been used on the dataset to achieve an acceptable standard text. Latent Dirichlet Allocation has been applied on the obtained data after this fast and accurate preprocessing phase. The implementation of different sentiment analysis methods and the results of these implementations have been compared and evaluated. The experimental results show that the combined uses of the preprocessing method of this paper and Latent Dirichlet Allocation have an acceptable results compared to other basic methods.

Microclimate for cultural heritage plays a fundamental role in the degradation process of artefacts exhibited in museums. Generally, the Indoor Air Quality (IAQ) monitoring inside museums is based on conventional measurements of thermo-hygrometry parameters and longtime averaged measurements of atmospheric pollutants’ concentration. Unfortunately, all these approaches require electric supply and are usually characterized by a noticeable visual impact on the exhibition space, which clearly negatively affects the visitors’ experience. This paper intends to revisit and discuss a methodology (and its test application on field, to estimate the risk of damage of three artefacts displayed in an old museum of Palermo – Italy) perhaps prematurely underestimated that could usefully be re-proposed in particular contexts in which it is necessary to be careful to be as less invasive as possible, as in the case of museums housed in buildings of particular historical-cultural prestige. The discussed approach uses a non-destructive (or minimally invasive, at least) technique consisting in using two types of coupons, bi-metallic and marble ones, that allows an off-line monitoring of the indoor air aggressiveness inside museums, while avoiding consuming electricity and undesirable visual impacts on the site. Specifically, the estimated coupon damage is regarded as gauge to air aggressiveness, in order to provide an early warning of the risk of damage for the artefacts made of materials similar to those which the coupons are made of. Based on the considerations reported in the paper, the indirect evaluation of the air chemical aggressiveness through the assessment of the bi-metallic and marble coupons’ erosion seems to still candidates itself as a feasible method to be applied in complex exhibition scenes.

The application of artificial intelligence in environmental design has entered a stage of rapid development, artificial intelligence in environmental design tools, methods, expression, as well as the expansion of design content, the richness of design language and other aspects have produced a wide range of changes. From the perspective of artificial intelligence in environmental design, this paper discusses the future trend and possible revolutionary changes of artificial intelligence in environmental design. This paper combs the application of artificial intelligence in environmental design, analyzes the design works with wide influence, and discusses the value and significance of artificial intelligence in applied environmental design, as well as its possible future development trend. The application of artificial intelligence in environmental design is a great change from design means to design content. From the interactive ability, virtual ability, to the integration of big data, all are new opportunities and challenges brought by the ability of artificial intelligence to environmental design. In response to such changes, environmental art design practitioners should establish a more systematic, three-dimensional and data-based design activity system and a broader design theory system.

Recent achievements in high-performance computing significantly narrow the performance gap between single and multi-node computing, and open up opportunities for systems with remote shared memory. The combination of in-memory storage, remote direct memory access and remote calls requires rethinking how data organized, protected and queried in distributed systems. Reviewed models let us implement new interpretations of distributed algorithms allowing us to validate different approaches to avoid race conditions, decrease resource acquisition or synchronization time. In this paper, we describe the data model for mixed memory access with analysis of optimized data structures. We also provide the result of experiments, which contain a performance comparison of data structures, operating with different approaches, evaluate the limitations of these models, and show that the model does not always meet expectations. The purpose of this paper to assist developers in designing data structures that will help to achieve architectural benefits or improve the design of existing distributed system.

Under the guidance of the new strategic objectives of the power grid company, it is necessary to inject innovative vitality into personnel training, capital investment, and technological progress in order to achieve the goal of increasing the value of power grid companies. This paper analyzes the coupling relationship of “innovation chain, talent chain, technology chain, capital chain and value chain”, and establishes the evaluation index system of “five chain” integration effectiveness. Then, the traditional Analytic Hierarchy Process (AHP) is improved, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model is constructed. Finally, three provincial power grid companies are selected for example analysis, and the innovation effectiveness ranking of each company is obtained, which verifies the effectiveness of the model proposed in this paper.

In nowadays the pneumatic controlled systems are widely used in industrial applications where valves must be operated, where there is a fire ignition risk, or in different automation systems where a positioning action is desired. The paper presents the experimental results of a pneumatic actuator controlled by a proportional control valve. The goal of the paper is to compare the experimental results with the numerical simulation results and to improve the mathematical model associated with the experiment.

Greenhouse is an important part of facility agriculture and a typical application scenario of modern agricultural technology. The greenhouse environment has the characteristics of nonlinearity, strong coupling, large inertia, and multiple disturbances. There are many environmental factors and it is a typical complex system [7]. In smart greenhouses, control commands are mostly triggered by complex events with multi-dimensional information. In this paper, by building the aggregation structure of complex events in the greenhouse, the technology is applied in the greenhouse as a whole. The core innovations of this paper are as follows: through the analysis of the information transmission process in the greenhouse, combined with the characteristics of the scene, a CEP information structure with predictive modules is formed, which is conducive to the popularization and application of CEP technology in the agricultural field. Pointed out the importance of extreme conditions in the prediction of the greenhouse environment for model evaluation. By improving the loss function in the machine learning algorithm, the prediction performance of a variety of algorithms under this condition has been improved. Applying CEP technology to intelligent greenhouse control scenarios, a set of practical complex event processing systems for greenhouse control has been formed.

This paper takes the teaching area unit space of primary and secondary schools as the main research object, analyzes, discusses and studies the reasonable use size of teaching unit space, commonly used space combination mode and internal detail design of education space, analyzes the actual use of various space units, and discusses the advantages and disadvantages of different design methods. Finally, from the perspective of teachers and students, combined with the analysis of specific project cases, this paper puts forward the optimization design strategy of school teaching unit space in line with the use requirements and architectural aesthetics.

Crowdsourcing logistics mode is the product of sharing economy by integrating idle resources to reduce the operating cost and improve the efficiency of enterprises. Taking the service quality of crowdsourcing logistics as the research object, this paper analyzes the key influencing factors of the service quality of crowdsourcing logistics, and constructs a crowdsourcing logistics service quality evaluation index system with 4 first level indicators and 15 second level indicators. At the same time, by using AHP and entropy weight method, The paper select dada and Renren express to evaluate their service quality. Through the case analysis of the two express companies’ service quality, the paper draw the conclusion that safety is still the most concerned factor in the evaluation of crowdsourcing logistics service quality at this stage.

Nowadays, the international society began to see clearly the impact of neglecting the environmental aspect in our lives, especially the issue of global warming. This paper aims in the first degree to minimize the carbon emission by vehicles and in doing so help to reduce the pollution ratio that is increasing day by day. In the home care sector, a patient requires from a health structure offering home care services one or many services to be done at home. For this purpose a health staff is mobilized to provide these services and must be done optimally. Many papers dealing with this kind of problems are suggesting that the way to optimize the problem encountered efficiently is to use the resources put in place to minimize the total travelled distance, but in this perspective, the study prioritizes the reduction and minimizing the fuel consumption. To answer all this questions, an exploration and an investigation of the problems addressed in the literature is performed, after that a mathematical formulation of the problem and two metaheuristics methods are proposed to resolve this problem. In the end, a comparison of the results of the two methods is presented.

There are a large number of distribution network construction projects with different types, and a large number of complex data relationships are generated in the construction process. This paper combs the key management and control indicators of distribution network construction projects, and constructs the management and control index system of each stage of the project. Based on Apriori algorithm, this paper studies the relationship between the key management and control indicators, and provides reference for the management and control of distribution network construction.

Prefabricated construction becomes an increasingly important construction mode especially for buildings that require a short construction time. The construction of Huoshenshan Hospital shows how efficient prefabricated construction could be. In order to provide a clearer blueprint for how to improve time performance in prefabricated construction, this paper identifies 13 important critical success factors associated with time performance and groups them into five categories—project planning and design, supply chain, techniques, teamwork, and external influences. The findings of this paper provide a better understanding of time management in prefabricated construction, which can help to develop appropriate strategies to improve productivity.

This paper presents the concept of building full-fledged sea convoys, which include unmanned ships. Advantages of this approach to combining unmanned ships over those where the ship is considered as a separate unit are considered. The scheme of distribution (combining) of computing power inside the convoy is given. In this paper, a scheme is considered when the crew is present only on the lead ship. However, this does not mean that all other ships in the convoy should be only unmanned. It doesn’t matter when considering this concept. The paper also proposes an algorithm for determining obstacles using optical sensors. The aspects of information security in such convoys are also considered. Particular attention is paid to the investigation of computer incidents on unmanned ships.

This paper deals with study of wind energy conversion system when it is interconnected with the grid. Main objective of the paper is to improve the quality of the power when wind energy system is linked with the non-linear loads. In this work DSTATCOM (FACTS Device) is used as a shunt active controller which used to reduce harmonics, when grid is interconnected with the non-linear load. In this work a new methodology is proposed i.e., Fuzzy Logic technique is integrated in the control circuit of DSTATCOM that helps to compare the reference voltage and output voltage of grid in order to minimize the error and improve power quality

Cancer has been described as a diverse illness with several distinct subtypes that may occur simultaneously. As a result, early detection and forecast of cancer types have graced essentially in cancer fact-finding methods since they may help to improve the clinical treatment of cancer survivors. The significance of categorizing cancer suffers into higher or lower-threat categories has prompted numerous fact-finding associates from the bioscience and genomics field to investigate the utilization of machine learning (ML) algorithms in cancer diagnosis and treatment. Because of this, these methods have been used with the goal of simulating the development and treatment of malignant diseases in humans. Furthermore, the capacity of machine learning techniques to identify important characteristics from complicated datasets demonstrates the significance of these technologies. These technologies include Bayesian networks and artificial neural networks, along with a number of other approaches. Decision Trees and Support Vector Machines which have already been extensively used in cancer research for the creation of predictive models, also lead to accurate decision making. The application of machine learning techniques may undoubtedly enhance our knowledge of cancer development; nevertheless, a sufficient degree of validation is required before these approaches can be considered for use in daily clinical practice. An overview of current machine learning approaches utilized in the simulation of cancer development is presented in this paper. All of the supervised machine learning approaches described here, along with a variety of input characteristics and data samples, are used to build the prediction models. In light of the increasing trend towards the use of machine learning methods in biomedical research, we offer the most current papers that have used these approaches to predict risk of cancer or patient outcomes in order to better understand cancer.

Low- and medium-frequency noise from ship cabins is difficult to control effectively. Excessive noise can seriously affect the acoustic stealth performance of ships. A novel membrane-type acoustic metamaterial is proposed in this paper with light weight and good sound insulation performance at low frequencies. The sound insulation performance of the metamaterial structure is analysed by using the acoustic-solid coupling module in COMSOL software. Then, the ability to change the sound insulation performance of membrane-type acoustic metamaterials with cell structure and material parameters is obtained. The research results in this paper provide powerful technical support for noise control in ship cabins.

In this paper, we extract spectral image features from a hyperspectral image database, and use big data technology to classify spectra hierarchically, to achieve the purpose of efficient database matching. In this paper, the LDMGI (local discriminant models and global integration) algorithm and big data branch definition algorithm are used to classify the features of the hyperspectral image and save the extracted feature data. Hierarchical color similarity is used to match the spectrum. By clustering colors, spectral information can be stored as chain nodes in the database, which can improve the efficiency of hyperspectral image database queries. The experimental results show that the hyperspectral images of color hyperspectral images are highly consistent and indistinguishable, and need to be processed by the machine learning algorithm. Different pretreatment methods have little influence on the identification accuracy of the LDMGI model, and the combined pretreatment has better identification accuracy. The average classification accuracy of the LDMGI model training set is 95.62%, the average classification accuracy of cross-validation is 94.36%, and the average classification accuracy of the test set is 89.62%. Therefore, using big data analysis technology to process spectral features in hyperspectral image databases can improve query efficiency and more accurate query results.

Increasing the production capacity of ports is seen as one of the most significant goals for the development of maritime transport infrastructure, which requires the use of adequate forecasting methods. The problem of choosing methods of management in transport in conditions of instability of cargo flows, non-stationary functioning, the need for digitalization and transformation of processes and technologies is of particular importance. Taking advantage of the process approach in the management of seaport development, such as flexibility and transparency, is becoming an effective way to increase the productivity and efficiency of maritime transport in general. The paper presents the author’s presentation of the peculiarities of the process management of the seaport development. The use of the concept of modularity in managing the development of a seaport through a system of business processes is proposed. A method for assessing the significance of the seaport business processes for achieving its goals from the standpoint of their further optimization is presented. The research results presented in the paper were obtained on the basis of the description and economic and mathematical modeling of the basic business processes that form the process outputs. Using the seaport of Baku as the example, predictive models of cargo flows are described.

This paper describes the development and experimental study of a software and hardware complex designed for studying the conductive tracks of printed circuit boards. In the course of this development, a subminiature eddy-current transducer and a measuring system capable of performing measurements using the newly developed transducer were created. The paper contains descriptions of the main parameters of the developed measuring system, and also presents the results of experiments conducted with various printed circuit boards, both with defect-free and those with artificial model defects. These experiments made it possible to confirm the possibility of using the developed measuring system and eddy-current transducer for controlling the conductive tracks on printed circuit boards and determining their quality.

The relevance of the study of the movements of base reference stations is determined by the task of improving the accuracy and reliability of the results of geodetic monitoring carried out using GNSS technologies. The paper presents the results of an analysis of the position of five permanent GNSS stations located in St. Petersburg for 2013-2020. Data processing by the method of precise positioning was performed using the online service CSRS-PPP. The time series were processed using the “Caterpillar”-SSA algorithm implemented in the MathCAD software package. The main attention is paid to the identification of periodic components in the movement of stations. The analysis of the series showed consistently identified periods of 3 and 12 months in the plane coordinates and 70 days and 12 months in altitude. The amplitudes of the periodicals were in the range from 1 to 10 millimeters. A trend was also identified, demonstrating a tendency for all stations to move to the northeast. The paper presents comparisons of the results obtained with previous studies.

Remanufacturing is one of the most effective forms of the recycling economy, and the “Trade-in for Remanufacturing” (TIFR) program in China plays an important role in the development of remanufacturing. To understand this new business model of remanufacturing, the price and demand relationship of the NP and RP are examined in two periods, considering TIFR subsidy which is in practice in China. Comparing integrated optimization scheme and tiered optimization scheme, this paper then researches the pricing strategy with TIFR subsidy respectively, as multi-object optimization is employed in the latter scheme. Tiered optimization pricing is found to improve the old customer’s TIFR enthusiasm, balance the remanufacturing system and work better with TIFR subsidy through numerical experiments. This paper studies the impacts of subsidy rate, receptiveness to RP, and the mixture of them, then verifies the proposed model and gives an inspiration to the management of the new remanufacturing business model about TIFR subsidy.

Food safety testing is of great significance for building a harmonious society. With the advent of the Internet era, the number of data indicators to be considered in food safety testing has gradually increased, and traditional food testing cannot adapt to the fast pace of today’s times. In order to meet the current challenges of food testing, this paper analyses the advantages of “Internet +” in building a food testing laboratory based on the challenges of food safety testing in the Internet era, and discusses the advantages of “Internet +” in the era of food testing. The paper discusses the intelligent working mode of food testing laboratories in the “Internet +” era, and combines big data technology to build an intelligent and comprehensive laboratory management platform, so as to build a new era of food testing intelligent laboratory, which improves the efficiency of food testing managers, reduces the workload, improves the accuracy of food safety testing, and helps the healthy development of the food industry.

This paper uses the qualitative comparative analysis method to define the conditional variables and the outcome variables of the impact path of the sustainable development of green buildings in Tianjin. Secondly, the paper analyzes the relationship between the conditional variables and the outcome variables to pave the way for the subsequent suggestions on the sustainable development of green buildings. Finally, by assigning values to the cases, the truth table of the impact path of sustainable development of green buildings is generated, the impact path is established, and the impact path is analyzed through the three aspects of the government, technology and enterprises.

This paper presents a detailed review of the use of digital image correlation on structural health monitoring. The durability of structures like bridges and buildings etc., need to monitor continuously. Structural health monitoring plays a vital role in assets and planning. Detailed knowledge on digital image correlation concerning structural health monitoring and civil engineering applications is presented in this paper.

In order to rationally design the capacity of each energy coupling unit of the integrated energy system, effectively coordinate and optimize the control of the integrated energy system equipment. This paper proposes an improved cloud adaptive particle swarm algorithm design control method. First, three busbars and multi-energy coupling equipment models based on electric, thermal, and gas loads are established, and then the model has better global optimization capabilities and defenses. Then, an improved cloud adaptive particle swarm algorithm with better global optimization capabilities and anti-premature convergence characteristics is used to optimize the annual economic optimization model established to meet the power balance constraints of each bus and energy coupling equipment. Finally, under the conditions of output constraints and system energy purchase constraints, taking a typical park as an example, the simulation verifies the effectiveness of the method proposed in this paper in the optimization design and control operation of the integrated energy system.

One of the core tasks of computer vision is target detection. With the rapid development of deep learning, target detection technology based on deep learning has become the mainstream algorithm in this field. As one of the main application fields, damage identification has achieved important development in the past decade. This paper systematically summarizes the research progress of damage identification algorithm based on deep learning, analyzes the advantages and disadvantages of each algorithm and its detection results on voc2007, voc2012 and coco data sets. Finally, the main contents of this paper are summarized, and the research prospect of deep learning based damage identification algorithm is prospect.

As a new decentralized infrastructure and distributed computing paradigm, blockchain is of great significance to break through the “mutual trust problem” between statistical specialty and basic data providing departments, and between the upper and lower levels of statistical data reporting units, and further improve the quality of statistical work. This paper first briefly analyzes the problems existing in the current investment statistics business, and introduces the basic principles and characteristics of blockchain technology. This paper studies the idea and process optimization of the application of blockchain technology in investment statistics business, and expounds the specific application schemes of blockchain technology in different links, such as automatic data collection, automatic data verification, automatic report generation, and automatic index release. Finally, the expected effect of the application of blockchain technology in investment statistics business is analyzed.

Big data technology is a new stage of information development. In recent years, it has been widely used in many fields, especially in social science research. This paper analyzes the development status and significance of the combination of big data technology and social science research, on the basis of summarizing and combing the concept of big data and its important role. Taking the application of big data method in the research of innovation education as an example, this paper makes a series of visualization analysis with Citespace software on the related literature with the theme of “big data and innovation education” collected by CNKI, such as annual analysis, literature source analysis, co-occurrence analysis of authors, organization analysis, keyword clustering analysis and keyword timing analysis. This paper also draws the corresponding knowledge mapping, clarifies its research status, hot spots and development trend, and provides scientific basis for the research of innovation education. Thus the paper believes that the research on big data and innovation education needs to strengthen interdisciplinary communication and cooperation, refine and deepen the research theme and content.

Valve positioner is the core component of the pneumatic control valve. A new software and hardware design scheme of intelligent valve positioner is presented in this paper. The circuit composition of each part of the intelligent valve positioner is introduced in hardware part. Based on it, a hardware solution to realize HART ‘multi-point’ communication is proposed in this research. In the software design, a novel combined PID control algorithm is proposed to solve the nonlinear problem caused by the friction between the valve stem and the packing during the control process. Simulation results show that the method proposed in this paper is better than traditional PID method and fuzzy PID method. The software and hardware design scheme of the valve positioner proposed in this paper has certain guiding significance for the development of related products.

In the present market, there are various kinds of financial products and derivatives designed for customers, the most basic and universal of which are stocks. However, while understanding how much profit and risk that one stock can bring is relatively simple, it is more difficult and trickier to find out a suitable investment portfolio for customers, especially under particular circumstances. Therefore, this paper try to build a model to figure out the portfolio with the highest utility in the crown virus background. The utility is defined based on several theories on customers’ behavior and takes many other factors that may play a crucial role on the extent of customer satisfaction into consideration. To simplify the model and focus on significant financial products, this article comes up with several necessary implications. After numerical computation, the result, a stock portfolio with the highest utility function for a given condition, is presented as an example to show how our model works. Finally, the paper analyzes the result obtained, provides a brief discussion on the pros and cons of our model, and eventually lists our possible future research fields.

The widespread application of distributed generation and electric vehicles are two important ways to save energy and reduce emissions. Therefore, the location and capacity of distributed generation and electric vehicle charging stations are particularly important. From the perspective of environmental protection, distributed power generation has obvious advantages over traditional power generation methods. Based on this, this paper establishes a model of location and capacity of electric vehicle charging stations with distributed generation with the lowest sum of investment, operation and maintenance costs, network losses and environmental costs. And this paper uses the Grasshopper Optimization Algorithm to solve the model. Finally, the IEEE 33-node distribution system is used as an example to perform calculations to verify the effectiveness and feasibility of the proposed model and algorithm.

This paper takes the project management of real estate development enterprises as the research object. The five elements of progress, quality, cost, safety and risk are selected to establish a maturity evaluation index system. Aiming at the target layer, element layer and index layer based on the application of BIM technology, this paper constructs the corresponding maturity model, and conducts case analysis on the enterprise project management level before and after the application of BIM technology to verify the application effect of BIM technology. The research results provide important management theories, standards and tools for improving the application effect of BIM technology.

With battery energy storage technology development, the centralized battery energy storage system (CBESS) has a broad prospect in developing electricity. In the meantime, the retired lithium-ion batteries from electric vehicles (EV) offer a new option for battery energy storage systems (BESS). This paper studies the centralized reused battery energy storage system (CRBESS) in South Australia by replacing the new lithium-ion batteries with lithium-ion second-life batteries (SLB) and evaluating the economic benefits with economic indicators as net present value (NPV), discounted payback period (DPBP), Internal rate of return (IRR) to depict a comprehensive understanding of the development potential of the CRBESS with the lithium-ion SLB as the energy storage system. This paper proposes a calculation method of frequency control ancillary services (FCAS) revenue referring to market share rate (MSR) when building the economic model. Moreover, the residual value of lithium-ion batteries is considered. This paper uses the economic model to calculate the profitability and development potential of CRBESS. From an economic perspective, the superiority and feasibility of CRBESS compared with CBESS were analyzed.

Because of the COVID-19 epidemic situation, the advantages of intelligent express cabinet stand out, compared with the traditional terminal distribution. It can not only solve the problem of terminal delivery difficulty but also that of the user privacy assurance, in which the contactless delivery becomes deeply popular because cross-infection is avoided. This paper studies the domestic present situation of the application of intelligent express cabinet and the existing problems, according to the data of 207 copies of questionnaires, analyses the consumer cognition and attitude towards intelligent express cabinet. In order to expand the application market of intelligent express cabinets, the paper puts forward measures such as integrating resources, improving the utilization rate, investigating the needs, reasonable layout, integrating community services, and enriching the profit model. © 2021 The Authors, published by EDP Sciences.

With the increase of the penetration rate of distributed generation on the distribution network side, the access of a large number of prosumers makes the trading information massive, and the demand of prosumers for more flexible power trading mechanism is also strengthened. Therefore, a weak-centralized power trading mode based on blockchain is proposed in this paper. Trading information is automatically stored in the blockchain in the form of smart contracts. The centralized organization only manages congestion and does not participate in the process of trading matching and settlement. In the distributed security verification, the successive over relaxation (SOR) iterative method is improved in this paper, which improves the iterative efficiency and convergence stability of the distributed algorithm. Finally, a case consisting of six nodes is presented to verify the feasibility of the method.

There are some problems in China's coal mining, such as limited mining technology, low degree of mechanization, lack of effective safety facilities, imperfect accident early warning mechanism and untimely response, which make the production safety problems perplex coal enterprises for a long time. This paper uses AMOS and SPSS 19.0 to analyze the coal risk data. The first is to establish the research hypotheses and conceptual model, set up structural equation model and path map. Secondly, the structural equation model and path coefficient are obtained by AMOS software, and the model is identified, evaluated and modified. Finally, the paper analyzes the direct effect and indirect effect between latent variables, latent variables and observed variables, and clarifies the risk factors of coal mining enterprises and the path of risk formation.

Through the sub-network construction and the application of critical path method, this paper analyzes the impact of work schedule delay on the schedule of contracted project itself, the success or contracted projects, other follow-up contracted projects, and the program on the basis of double-code network and related lemmas. Based on this, a penalty model of work schedule delay is constructed and a case study is carried out. The research results show that the penalty model of work schedule delay can effectively deal with the impact of work schedule delay on the whole program under the condition of multiple stakeholders. The research results of this paper are conducive to the real-time control of the schedule of various kinds of work, contracted projects, and the program, and lay a foundation for the improvement of FIDIC penalty conditions (8.7) of contract, the definition of responsibility division within and among contractors, and the analytic processing of claims for contracted projects.

The present paper considers the presentation of practical solutions for the realization of smart location, materialized through research on solutions for monitoring parameters and solutions for these locations. In this paper we present: description of the concept of smart location, structures and architectures, realization of a pilot installation of smart location and data aquisition. For these researches was used to monitor electro-mechanical radiation of its own design. We obtained the following results: realization and design of the elements of the housing components with the 123D Design program, processing with the Cura program, obtaining g-code files for transfer to 3D printing, monitors in real time and detects errors or deficiencies in location. The main objective of this paper is to propose a model of technical solution for an smart location, which can be applicable in the maritime field, respectively to ships in the engine room or other location onboard.

Decision support water. Management (DSWM) is an approach to water development and water management, for the best water management framework. In the field of water management for watter storage, and preservation of land is very important criteria which is linked with various communities involvement, economical issues and environmental issues. DSWM. water resources needed extensive advance technology with reliable activity. For this purpose in this paper it need to analyse the various volume, quality and quantity parameters and other enhance model usage are adopted to maintain the effective water resource management.. Parameters collect the management tools for efficient management of water resources under varied local situations. In This paper discussed some of the analytical management techniques and development decision support water management system as a framework for decision makers to have reliable decisions for water management practises. This paper includes overall DSWM functions and their highlights to achieve the effective water management.

According to the scientific papers of the Russian scientists, the parameters for assessing the quality of economic space are population density, spatial connectivity and location of production. The parameter of the spatial connectivity is considered. In most of the Russian scientific papers, the term “the spatial connectivity” is determined by the level of transport networks development. In most Russian scientific papers when forming models for assessing the spatial connectivity of regions, the issue of territorial connectivity by information and telecommunication networks is not investigated. The papers analyses the main characteristics of the concept of regional space connectivity and its properties. An econometric analysis of statistical data on indicators characterizing information and telecommunication networks for each of the regions of the Siberian Federal District has been carried out. The mathematical model and an algorithm for automating the procedure for calculating statistical indicators in the Python language in the Spyder programming environment were proposed for calculating the indicators. The most and least developed regions within developing the information and telecommunication networks have been identified. The resulting mechanism for determining the level of development of information and telecommunication networks will be useful for monitoring the territory and drawing up a passport of an each region.

In recent years, convolution neural network has achieved great success in single image super-resolution detection. Compared with the traditional method, this method achieves better reconstruction detection effect. However, the network structure of the existing reconstruction model is shallow, and the convolution kernel has a small acceptance, so it is difficult to learn a wide range of motion image features, which affects the quality of motion image information detection. Aiming at the problems and shortcomings of the existing sports image information detection based on convolution neural network, this paper proposes the application of convolution network model based on deep learning in sports image information detection. In this paper, we get the average SSIM value from the data of set5, set14, bsd100 and urban100 by using the X4 model of different algorithms. The average SSIM value of set5 is 0.865, which shows that the quality of sports image reconstruction and the reconstruction efficiency of the model can be improved by using the local image features of different scales, which provides technical support for sports image information detection. The research in this paper has important practical significance for the further development of the two and the reform of the convolution network model in sports image information detection.

With the development of smart distribution technology in the future, electric vehicle users can not only charge reasonably based on peak-valley price, they can also discharge electricity into the power grid to realize their economic benefit when it’s necessary and thus promote peak load shifting. According to the characteristic that future electric vehicles can discharge, this paper studies the interactive characteristics between electric vehicles and smart grid. In this paper, the example shows that the charging and discharging behaviour of EV users will bring significant fluctuation effect to the power grid load, and the reasonable TOU strategy can stimulate EV users to conduct reasonable charging and discharging, so as to smooth the peak and valley difference of some power grid.