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This research paper is about the existence and uniqueness of the coupled system of nonlinear fractional Langevin equations with multipoint and nonlocal integral boundary conditions. The Caputo fractional derivative is used to formulate the fractional differential equations, and the fractional integrals mentioned in the boundary conditions are due to Atangana–Baleanu and Katugampola. The existence of solution has been proven by two main fixed-point theorems: O’Regan’s fixed-point theorem and Krasnoselskii’s fixed-point theorem. By applying Banach’s fixed-point theorem, we proved the uniqueness result for the concerned problem. This research paper highlights the examples related with theorems that have already been proven.

This paper proposed a cubic spline interpolation-based path planning method to maintain the smoothness of moving the robot’s path. Several path nodes were selected as control points for cubic spline interpolation. A full path was formed by interpolating on the path of the starting point, control points, and target point. In this paper, a novel chaotic adaptive particle swarm optimization (CAPSO) algorithm has been proposed to optimize the control points in cubic spline interpolation. In order to improve the global search ability of the algorithm, the position updating equation of the particle swarm optimization (PSO) is modified by the beetle foraging strategy. Then, the trigonometric function is adopted for the adaptive adjustment of the control parameters for CAPSO to weigh global and local search capabilities. At the beginning of the algorithm, particles can explore better regions in the global scope with a larger speed step to improve the searchability of the algorithm. At the later stage of the search, particles do fine search around the extremum points to accelerate the convergence speed of the algorithm. The chaotic map is also used to replace the random parameter of the PSO to improve the diversity of particle swarm and maintain the original random characteristics. Since all chaotic maps are different, the performance of six benchmark functions was tested to choose the most suitable one. The CAPSO algorithm was tested for different number of control points and various obstacles. The simulation results verified the effectiveness of the proposed algorithm compared with other algorithms. And experiments proved the feasibility of the proposed model in different dynamic environments.

Aiming at the problem of taxi low carrying rate, unreasonable route planning, and taxi charges, this paper mainly studies the taxi sharing routes and the sharing expense model which take the maximum carrying rate, the shortest driving distance, and the sharing expense of drivers as the objective function. We consider the problems of taxi capacity limitation, the driving distance limitation, the number of people getting on and off, and the charges. Through using the passenger’s pool to classify passengers in different directions and different starting points, we use the championship selection strategy, station fragment cross design, station-supervised mutation, and pricing algorithm to solve the model. Finally, we analyze the taxi data of Lanzhou City and simulate the new ways of this paper. The results are shown that compared with the daily practice of the nonshared mode, the taxi sharing mode has obvious improvement in terms of carrying rate, driving distance, and driving benefits. Therefore, the sharing mode can be used in taxi sharing route, and the sharing expenses are reasonable and useful for passengers and drivers.

It has been stated that Industry 4.0’s goal is, among others, the sustainable success in a market characterized by exigent and informed consumers demanding personalized products and services, where the level of manufacturing complexity increases with level of product customization. Even though different manufacturing complexity measures have been developed, there seems to be a lack of a comprehensive metric that address both the mass customization variety-induced complexity, and the complexity derived from the adoption of the Industry 4.0 paradigm. The main original contribution of this paper is the development of an entropy-based (entropic) formulation to address this last issue. Its validity and usefulness is put to the test via a discrete-event simulation study of a mass customization production system operating within an Industry 4.0 context. Our findings show that the entropic formulation acts as a fairly good trend indicator of the system’s performance parameter increase/decrease, but not as an estimator of the final values. A discussion of the managerial implications of the obtained results is offered at the end of the paper.

This paper is the summary and enhancement of the previous studies on dynamic output feedback robust model predictive control (MPC) for the linear parameter varying model (described in a polytope) with additive bounded disturbance. When the state is measurable and there is no bounded disturbance, the robust MPC has been developed with several paradigms and seems becoming mature. For the output feedback case for the LPV model with bounded disturbance, we have published a series of works. Anyway, it lacks a unification of these publications. This paper summarizes the existing results and exposes the ideas in a unified framework. Indeed there is a long way to go for the output feedback case for the LPV model with bounded disturbance. This paper can pave the way for further research on output feedback MPC.

To minimize delay cost of flights in multiple airports, this paper studied flights assignment problem under delay conditions. By considering the delay cost, airport capacity, and the slot exchange between airlines, this paper proposed a novel assignment model based on game theory and CDM mechanism. An improved ant colony algorithm was proposed to solve the flight assignment problem. The case studies showed that the proposed model can optimize the minimum delay cost between airlines under multiairport capacity constraints. The performance of proposed method was better than that of traditional non-slot-exchange method.

The paper investigates the dynamical behaviors of a two-species discrete predator-prey system with Crowley–Martin functional response incorporating prey refuge proportional to prey density. The existence of equilibrium points, stability of three fixed points, period-doubling bifurcation, Neimark–Sacker bifurcation, Marottos chaos, and Control Chaos are analyzed for the discrete-time domain. The time graphs, phase portraits, and bifurcation diagrams are obtained for different parameters of the model. Numerical simulations and graphics show that the discrete model exhibits rich dynamics, which also present that the system is a chaotic and complex one. This paper attempts to present a feedback control method which can stabilize chaotic orbits at an unstable equilibrium point.

This paper describes a valuable linear yaw-roll tractor-semitrailer (TST) model with five-degree-of-freedom (DOFs) for control algorithm development when steering and braking. The key parameters, roll stiffness, axle cornering stiffness, and fifth-wheel stiffness, are identified by the genetic algorithm (GA) and multistage genetic algorithm (MGA) based on TruckSim outputs to increase the accuracy of the model. Thus, the key parameters of the simplified model can be modified according to the real-time vehicle states by online lookup table and interpolation. The TruckSim vehicle model is built referring to the real tractor (JAC-HFC4251P1K7E33ZTF6×2) and semitrailer (Luyue LHX9406) used in the field test later. The validation of the linear yaw-roll model of a tractor-semitrailer using field test data is presented in this paper. The field test in the performance testing ground is detailed, and the test data of roll angle, roll rate, and yaw rate are compared with the outputs of the model with maps of the key parameters. The results indicate that the error of the tractor’s roll angle and semitrailer’s roll angle between model data and test data is 1.13% and 1.24%, respectively. The roll rate and yaw rate of the tractor and semitrailer are also in good agreement.

The traditional predictive method cannot fully reflect the complex nonlinear characteristics and regularities of automobile and parts sales data, so the prediction precision is not high. The purpose of this paper is to propose the gray GM(1,1) nonlinear periodic predictive model by introducing the seasonal variation index to improve predictive accuracy of the single GM(1,1) model. Firstly, the paper analyzes concept of GM(1,1) and then proposes the gray GM(1,1) nonlinear periodic predictive model to forecast automobile parts sales. The model algorithm used gray theory and accumulated technology to generate new data and set up unified differential equations to find the fitting curve of automobile parts sales prediction by the seasonal variation index to remove random elements. Lastly, the gray GM(1,1) nonlinear periodic predictive model is used for empirical analysis; the result of example shows that the model proposed in the paper is feasible. The superiority of the proposed predictive model compared with the single gray GM(1,1) model is demonstrated. The reliability of this model is experienced by the accuracy test, which provides a theoretical guidance for the prediction of automobile part sales. And the average relative error is reduced by 8.52% compared with the single GM(1,1) model.

The digging resistance in a normal state is the key to excavator design and automated excavation. It is difficult to accurately predict, simulate, or directly measure the digging resistance in a normal state due to uncertainties in the soil properties and excavation parameters. In this paper, a research idea is proposed that uses the working device as the entry point to indirectly calculate the digging resistance in a normal state by measuring the motion parameters and the cylinder pressure intensity. Based on the rule of combination for spatial force systems, a method for combining and projecting the system of the digging resistance is proposed in which the system is projected as six parts, and the tangential force, normal force, and bending moment in the plane of symmetry of the working device are the objects of the solution to avoid redundant equations. Based on kinematics and dynamics models of the excavator and the force and moment equilibrium conditions of the working device, equations for the active-side calculation of the incomplete digging resistance are derived. Based on these equations, the motion parameters of the working device and data on the cylinder pressure intensity obtained by measurement are used to calculate the incomplete digging resistance. The validation scheme and process proposed use the incomplete digging resistance as the external load to obtain the simulated stress of the working device through transient analysis. The simulated stress and the measured stress corresponding to the position of the measurement point are extracted and compared. The results show that there is a difference in the size of the numerical value between the simulated and measured stress, but the variation law is highly consistent, which validates the calculation method. In this paper, an active-side calculation method is provided for the incomplete digging resistance in a normal state without considering the soil-tool interaction relationships, which lays a theoretical foundation for the study of the digging resistance characteristics in a normal state, as well as excavator design and automated excavation.

This paper examines the stability analysis of discrete-time control systems particularly during the event of actuator saturation and time-varying state delay. With the help of Wirtinger inequality along with Lyapunov-Krasovskii functional gain of state feedback controller is determined for stabilization of above system. The saturation nonlinearity is represented in the terms of convex hull. A new linear matrix inequality (LMI) criterion is settled with reciprocally convex combination based inequality which is dependent on delay. The proposed criterion is less conservative in concern to increase the delay bound and a controller is also simulated for real time problem of missile control system in this paper. It is also attained that projected stability criterion is less conservative compared to other outcomes. Furthermore, an optimization procedure together with LMI constraints has been proposed to maximize the attraction of domain.

Fire emergency response of urban rail transit is a complex multiattribute risk decision-making problem. In emergency response analysis, it is necessary to consider the psychological behaviors of decision-makers such as reference dependence, loss aversion, and judge distortion. Applying different emergency plans can intervene the development and evolution of urban rail emergency fire and may even result in different levels of casualties and property losses. For this situation, this paper proposes an emergency response decision-making method based on prospect theory. First, according to the prospect theory, this paper quantitatively describes the comprehensive psychological perception of decision-makers for casualties and property losses in different situations. Then, the psychological perception of decision-makers for the important levels of different situations is calculated. Third, according to the situation comprehensive values, situation weights, and the cost inputs of emergency plans, the comprehensive prospect values of each emergency plan are calculated, and the fire emergency plans can be ranked based on comprehensive prospect value. At last, the fire emergency disposal of Tianjin rail transportation line 3 is considered as the background in this paper. The feasibility and effectiveness of the purposed method is illustrated through the case study.

Accurate prediction of airborne equipment failure rate can provide correct repair and maintenance decisions and effectively establish a health management mechanism. This plays an important role in ensuring the safe use of the aircraft and flight safety. This paper proposes an optimal combination forecasting model, which mixes five single models (Multiple Linear Regression model (MLR), Gray model GM (1, N), Partial Least Squares model (PLS), Artificial Neural Network model (BP), and Support Vector Machine model (SVM)). The combined model and its single model are compared with the other three algorithms. Seven classic comparison functions are used for predictive performance evaluation indicators. The research results show that the combined model is superior to other models in terms of prediction accuracy. This paper provides a practical and effective method for predicting the airborne equipment failure rate.

This paper investigates active damping of LCL filter resonance in grid-connected inverters with only inverter current feedback control, since it only needs to sample one current to realize both current control and inverter protection. The traditional single-loop inverter current control (SLICC) can damp the LCL filter resonance actively. However, if the control delay is considered in digital control, the system stability will depend on the ratio of the LCL resonance frequency f res to the sampling frequency f s , and the valid damping region is only up to f s / 6 . Considering that the design region of the LCL resonance frequency f res is up to f s / 2 , the system can easily become unstable due to the LCL resonance frequency shifting. Thus, this paper proposes an improved active damping method based on SLICC, including the asymmetric regular sampling method and delay compensation method. The improved sampling method minimizes the control delay without introducing a switching ripple, and the delay compensation method further compensates for the delay effect. With a proper parameter design, the upper limit of the valid damping region is extended up to f s / 2 , which can cover all the possible resonance frequencies, and it has inherent robustness against grid-impedance variation. Finally, a few simulations in MATLAB/SIMULINK and experiments based on a 6 kW prototype are performed to verify the theoretical analysis.

This paper presents a new Weibull family of distributions. The compatibility of the newly developed class is justified through its application in the field of quality control using Weibull-exponential distribution, a special case of the proposed family. In this paper, an attribute control chart using Weibull-exponential distribution is developed. The estimations of the model parameters and the proposed chart parameters are performed through the methods of maximum likelihood and average run-length. The significance of the proposed model is demonstrated using a simulation study and real-life problems. The results of the monitoring process and quick detection are compared with exponential distribution.

This paper focuses on the role of the Internet in empowering social work to intervene in ageing-in-place services for the disabled elderly in terms of time, space, and resources. Through the practical analysis of home care service providers, the advantages and characteristics of the “Internet” for ageing-in-place services for the disabled elderly are identified, as well as some of the problems that exist in the current intelligent home care service platform. This paper proposes to improve and optimize the “Internet+” by fully enhancing the technical empowerment of the Internet, improving the integration and spanning of space, and giving full play to the role of social work on the Internet. This paper proposes to improve and optimize the “Internet+” elderly home care services for the disabled, so as to solve to a certain extent the practical problems faced by the “Internet+” elderly home care services in the early stage of development. The significance of this paper is that, through the study of the “Internet+” home care services for the elderly with disabilities, the process of the services is sorted out and summarized, and suggestions and measures are put forward for its shortcomings, providing practical methods and feasible paths for future generations, which are of great significance for other social work agencies using Internet tools.

Categorizing national football teams by level is challenging because there is no standard of reference. Therefore, the self-organizing feature mapping network is used to solve this problem. In this paper, appropriate sample data were collected and an appropriate self-organizing feature mapping network model was built. After training, we obtained the classification results of 4 grades of 16 major Asian football national teams. As for the classification results, it is different to normalize the input data and not to normalize the input data. The classification results accord with our subjective cognition, which indicates the rationality of self-organizing feature mapping network in solving the classification problem of national football teams. In addition, the paper makes a detailed analysis of the classification results of the Chinese team and compares the gap between the Chinese team and the top Asian teams. It also analyses the impact of the normalization of input data on the classification results, taking Saudi Arabia as an example.

In order to improve the accuracy of sports combination training action recognition, a sports combination training action recognition model based on SMO algorithm optimization model and artificial intelligence is proposed. In this paper, by expanding the standard action data, the standard database of score comparison is established, and the system architecture and the key acquisition module design based on 3D data are given. In this paper, the background subtraction method is used to process the sports video image to obtain the sports action contour and realize the sports action segmentation and feature extraction, and the artificial intelligence neural network is used to train the feature vector to establish the sports action recognition classifier. This paper mainly uses a three-stream CNN artificial intelligence deep learning framework based on convolutional neural network and uses a soft Vlad representation algorithm based on data decoding to learn the action features. Through the data enhancement of the existing action database, it uses support vector machine to achieve high-precision action classification. The test results show that the model improves the recognition rate of sports action and reduces the error recognition rate, which can meet the online recognition requirements of sports action.

To realize real-time detection of the operating environment of bridge cranes, this paper presents a three-dimensional mapping method using a binocular camera and laser ranging sensor. First, the left and right images are obtained by using the binocular camera, and the block matching is used for stereo correspondence to obtain the disparity image. Then, according to the reprojection matrix and the disparity image, the depth value of each pixel is obtained, and the depth image can be transformed into a three-dimensional point cloud of the current frame. Relying on the camera position data obtained by the laser ranging sensor, the three-dimensional point cloud generated from different positions is registered to obtain the three-dimensional point cloud of the craneʼs global operating environment. Finally, an octree map is used to represent the operating environment. To simulate the construction process of the operating environment of the bridge crane, an experimental platform was built, and experiments were carried out using the method proposed in this paper. The experimental results verified the feasibility of the method proposed in this paper.

The main purpose of this paper is to study almost α -cosymplectic pseudo metric manifold satisfying certain η -parallel tensor fields. We first focus on the concept of almost α -cosymplectic pseudo metric manifold and its curvature properties. Then, we obtain some results related to the η -parallelity of h , φ h , and τ . Moreover, the deformation of almost α -Kenmotsu pseudo metric structure is given. We conclude the paper with an illustrative example of almost α -cosymplectic pseudo metric manifold.

After the market-oriented reform of China’s financial industry, there have been some problems in financial risk assessment. In recent years, commercial bank finance has made rapid development, but on the whole, the financial risk assessment of commercial banks is still the weakest link in the Chinese financial system. This experiment selects data from state-owned commercial banks and foreign-funded commercial banks. Through the analysis and deconstruction of the macroenvironment, participants, and business models, this paper systematically combines the factors influencing the financial risk of commercial banks, which can identify the main sources of financial risk in this complex way of financing and clarify the effects of the transfer of financial risk between different participants. Based on this, the paper studies the differences between the assets and liabilities between banks on the risk-taking of banks and the reform of the organizational evolution of fuzzy system. According to the application scenarios and actual needs of commercial banks’ financial risks, the entropy weight analysis method is used to reflect the weight of indicators by the difference degree of observed index values. The information quantity of indicators is measured to ensure that the established indicators can reflect most of the initial information. The experimental results show that, compared with state-owned banks, the proportion of foreign banks’ assets in 2018 is very small. The highest value of public debt assets is 9.2 billion yuan, followed by financial institutions with 2.58 billion yuan, and deposit institutions with 280 million yuan. The central bank has no debt amount.

In this paper, a binary fuzzy cluster analysis algorithm is used for an in-depth study and analysis of high-performance computational early warning in the agricultural economy. The definition of interval type-two fuzzy set and its operation are summarized. Considering the uncertain information that will be encountered in the evaluation process, this paper constructs the evaluation model of rural informatization construction performance based on interval type-two fuzzy numbers, which improves the accuracy of the evaluation results. The fuzzy clustering centres are modified according to the nature of the fuzzy clustering centre matrix, and the optimal fuzzy clustering centres and optimal fuzzy clustering division matrix with consistent order are solved. Using the level eigenvalues to find out in the abundant water period, there are 4 monitoring sections of water quality evaluation results for still clean and 5 monitoring sections of water quality evaluation results for slight pollution. In the flat-water period, there are 2 monitoring sections of the water quality evaluation results that are still clean and 7 monitoring sections of the water quality evaluation results are slightly polluted. In the dry water period, the water quality evaluation results of the 9 monitoring sections are slightly polluted. The results and the use of integrated pollutant index evaluation method results are consistent, indicating that the use of fuzzy clustering model for water quality evaluation is practical and effective. Its functional structure includes management business, innovation business, service business, production business, and operation business functions, in addition to the design of the collection and storage scheme based on Hadoop data warehouse, to achieve accurate matching of information of science and technology agricultural services in the whole agricultural industry chain. Finally, the implementation and maintenance of the cloud-based science and technology agricultural service information system are deployed, and the operational effects of its information system-related functions are demonstrated using prototype design. This paper constructs a comprehensive information system for science and technology agricultural services based on cloud technology that integrates management, innovation, service, production, and operation, which meets not only the needs of traditional science and technology agricultural service information systems, such as expert consultation and training demonstration, but also other needs spawned by the development of agricultural modernization.

The powerful advanced manufacturing industry is the most powerful driving force for economic development and growth, and it is also the main source of environmental pollution. Artificial intelligence and blockchain technology are recognized as a breakthrough technology that can be widely used, changing the way the entire society and economy operate. The main constraints affecting the sustainable development of the manufacturing industry are ecological deterioration and resource shortage, but the development of artificial intelligence and blockchain technology provides new ideas for solving manufacturing problems. Based on this, this paper proposes a research on the sustainable development performance of green manufacturing technology innovation based on artificial intelligence and blockchain technology. This paper deeply grasps the essence and connotation of artificial intelligence and blockchain technology, analyzes its specific application form and research background, searches for the effective effect of manufacturing technology innovation and green manufacturing performance path, and clarifies the mechanism between the two. All measurement items in the questionnaire used in this article use Likert5 scale and use 1–5 options to indicate the degree of conformity with the actual situation of the enterprise. The results show that the average green manufacturing capacity of each measurement item is between 3.18∼3.97, indicating that the company's green manufacturing capacity is relatively high. Among them, the ability of green technology innovation is relatively high, indicating that most companies have noticed that the improvement of the ability of green technology innovation plays a vital role in the future sustainable development of enterprises. Moreover, more and more companies are paying attention to the latest applications of blockchain technology, which can effectively promote the development of enterprises.

Financial supervision plays an important role in the construction of market economy, but financial data has the characteristics of being nonstationary and nonlinear and low signal-to-noise ratio, so an effective financial detection method is needed. In this paper, two machine learning algorithms, decision tree and random forest, are used to detect the company's financial data. Firstly, based on the financial data of 100 sample listed companies, this paper makes an empirical study on the fraud of financial statements of listed companies by using machine learning technology. Through the empirical analysis of logistic regression, gradient lifting decision tree, and random forest model, the preliminary results are obtained, and then the random forest model is used for secondary judgment. This paper constructs an efficient, accurate, and simple comprehensive application model of machine learning. The empirical results show that the comprehensive application model constructed in this paper has an accuracy of 96.58% in judging the abnormal financial data of listed companies. The paper puts forward an accurate and practical method for capital market participants to identify the fraud of financial statements of listed companies and has certain practical significance for investors and securities research institutions to deal with the fraud of financial statements.

When the outbreak of COVID-19 began, people could not go out. It was not allowed to provide agricultural machinery services in different places across regions to reduce the flow and gathering of people. Improvement of utilization efficiency of agricultural machinery resources is required through scientific scheduling of agricultural machinery. With seizing the farming season and stabilizing production as the goal, this paper studied the scientific scheduling of tractors within the scope of town and established agricultural machinery operation scheduling model with the minimization of total scheduling cost as the optimization objective. Factors such as farmland area, agricultural machinery, and farmland location information and operating time window are considered in this model to improve the accuracy of the agricultural machinery operation scheduling model. The characteristics of multiple scheduling algorithms are analyzed comprehensively. The scheduling requirements of agricultural machinery operation to ensure spring ploughing are combined to design the agricultural machinery scheduling algorithm based on the SA algorithm. With Hushu Street, Jiangning District, Nanjing City, as an example, a comparative experiment is conducted on the simulated annealing algorithm (SA) designed in this paper and the empirical algorithm and genetic algorithm (GA). The results suggest that the total cost of the scheduling scheme generated by the SA algorithm is 19,042.07 yuan lower than that by the empirical scheduling algorithm and 779.19 yuan lower than that by the genetic algorithm on average. Compared with the GA algorithm, the transfer distance, waiting cost, and delay cost of the SA algorithm are reduced by 11.6%, 100%, and 98.1% on average, indicating that the transfer distance of agricultural machinery in the scheduling scheme generated by the SA algorithm is shorter, so is the waiting and delay time. Meanwhile, it can effectively obtain the near-optimal solution that meets the time window constraint, with good convergence, stability, and adaptability.

In this paper, by employing the Zhang neural network (ZNN) method, an effective continuous-time LU decomposition (CTLUD) model is firstly proposed, analyzed, and investigated for solving the time-varying LU decomposition problem. Then, for the convenience of digital hardware realization, this paper proposes three discrete-time models by using Euler, 4-instant Zhang et al. discretization (ZeaD), and 8-instant ZeaD formulas to discretize the proposed CTLUD model, respectively. Furthermore, the proposed models are used to perform the LU decomposition of three time-varying matrices with different dimensions. Results indicate that the proposed models are effective for solving the time-varying LU decomposition problem, and the 8-instant ZeaD LU decomposition model has the highest precision among the three discrete-time models.

This paper adopts the intelligent scheduling method to conduct an in-depth study and analysis on the optimization of financial asset liquidity management model, elaborates and analyzes the liquidity risk management theory of commercial banks, and reviews the progress of liquidity risk management research in domestic and foreign academia as the theoretical basis of this paper. After that, we analyze the liquidity risk management of Anhui Tianchang Rural Commercial Bank from both qualitative and quantitative levels and further review and analyze the problems and causes. Finally, the full research is summarized and reviewed, theoretical and practical insights are discussed and analyzed, and future liquidity risk management research priorities and directions are elaborated. Based on the analysis results, the problems of the bank in liquidity risk management are described one by one, and further deep-seated cause discovery is carried out to summarize the problems of liquidity risk management which exist in the bank’s operation process due to the lack of liquidity risk management, unbalanced asset, and liability allocation, as well as weak emergency management capability, insufficient day-to-day liquidity monitoring, and lack of professional talents. For the problems and causes of the study, effective suggestions on how to strengthen the bank’s liquidity risk management in multiple aspects are proposed. It is hoped that, by improving the bank’s liquidity risk management and reducing the chance of liquidity risk occurrence, the bank’s sustainable development can be enhanced, and it is also hoped that it can provide some reference for the liquidity risk management of similar rural small- and medium-sized financial institutions.

The excessive vertical vibration of structures induced by walking pedestrians has attracted considerable attention in the past decades. The bipedal walking models proposed previously, however, merely focus on the effects generated by legs and ignore the effects of the dynamics of body parts on pedestrian-structure interactions. The contribution of this paper is proposing a novel pedestrian-structure interaction system by introducing the concept of the continuum and a different variable stiffness strategy. The dynamic model of pedestrian-structure coupling system is established using the Lagrange method. The classical mode superposition method is utilized to calculate the response of the structure. The state-space method is employed to determine natural frequencies and damping ratio of the coupled system. Based on the proposed model, numerical simulations and parametric analysis are conducted. Numerical simulations have shown that the continuum enables the pedestrian-structure system to achieve the stable state more efficiently than the classic model does, which idealizes the body as a concentrated or lumped mass. The parametric study reveals that the presence of pedestrians is proved to significantly decrease the frequency of human-structure interaction system and improve its damping ratio. Moreover, the parameters of the bipedal model have a noticeable influence on the dynamic properties and response of the pedestrian-structure system. The bipedal walking model proposed in this paper depicts a pattern of pedestrian-structure interactions with different parameter settings and has a great potential for a wide range of practical applications.

The carbon footprint of the cold chain logistics system refers to the greenhouse gas emissions directly or indirectly caused in each link of the cold chain logistics activities. Because cold chain logistics is the main carbon emitter in the field of logistics, research on how to reduce carbon emissions in the field of cold chain logistics plays an important role in energy conservation and emission reduction. Based on the in-depth analysis of the carbon footprint of cold chain logistics, this paper introduces the distance coefficient and freshness parameters into the optimization model innovatively and uses the life cycle assessment method and input-output method to determine the calculation range of the carbon footprint of fresh products of each link in the cold chain logistics. The system calculates the carbon emissions generated by the production and operation activities of each place of origin, distribution center, retailer, and waste disposal during the circulation of fresh products. This paper establishes a carbon footprint optimization model to discuss how to balance carbon constraints and minimized costs. Through the analysis of the simulation results, from the perspective of the government and enterprises, corresponding countermeasures are put forward to more effectively achieve the goal of energy conservation and emission reduction and guide the cold chain logistics industry to sustainable development.

Nowadays, eco-efficiency is one of the most widely used comprehensive indicators in many fields and sectors. Understanding eco-efficiency is of great significance to implement sustainable socioeconomic development for decision makers. To assess the comprehensive performance of industrial investment, the eco-efficiency of industrial investment (EEII) is constructed under the comprehensive perspective of economic benefits, energy consumption, and environmental impact in this paper. Then, a new superefficient undesirable-output slack-based measure DEA (SeUo-SBM-DEA) model is proposed and applied to assess the EEII of 30 provinces in China from 2015 to 2017, and its influencing factors are analyzed using the Tobit regression. The empirical results show the following: (1) the eco-efficiency of China’s industrial investment is generally low (0.613), and there exists a significant regional disparity; namely, the average value of EEII was the highest in the eastern regions (0.838), followed by the central regions (0.6) and western regions (0.397). (2) R&D expenditure, economic development level, and foreign direct investment all had a significant positive effect on the eco-efficiency of industrial investment, while investment in treatment of industrial pollution sources and total education funds all had a significant negative effect. Finally, this paper puts forward some suggestions to promote sustainable development of industrial investment based on our findings.

The maintenance of a production line is becoming more important with the development of demanding higher operational efficiency and safety in industrial system. However, a production line often operates under dynamically operational and environmental conditions and the production scheduling is also a very important factor for the maintenance of a production line. First, this paper proposes an integrated data-driven model that coordinates maintenance planning decisions with production scheduling decisions to solve the problem of scheduling and maintenance planning for a parallel-series production line. The degradation information is considered, and the total cost is to be minimized in the proposed model. Also, the total cost is related with production process and maintenance considering reliability of equipment. Then, in order to better describe the relationship between production and maintenance, the accumulative processing time of equipment is used as the input of its failure function. Also, an ability factor is developed to control its reduced level by adopting preventive maintenance. Finally, a case study is used to demonstrate the implementation and potential applications of the proposed model. The long-term wear test experiments are conducted at a research laboratory facility of Shanghai Pangyuan Machinery Co., Ltd. The result proves that the proposed method is feasible and efficient to solve the joint decision-making problem for a parallel-series production line with multivariety and small batch production. The proposed model in this paper is suitable for semiconductor manufacturing.

Accompaniment production is one of the most important elements in music work, and chord arrangement is the key link of accompaniment production, which usually requires more musical talent and profound music theory knowledge to be competent. In this article, the machine learning model is used to replace manual accompaniment chords’ arrangement, and an automatic computer means is provided to complete and assist accompaniment chords’ arrangement. Also, through music feature extraction, automatic chord label construction, and model construction and training, the whole system finally has the ability of automatic accompaniment chord arrangement for the main melody. Based on the research of automatic chord label construction method and the characteristics of MIDI data format, a chord analysis method based on interval difference is proposed to construct chord labels of the whole track and realize the construction of automatic chord labels. In this study, the hidden Markov model is constructed according to the chord types, in which the input features are the improved theme PCP features proposed in this paper, and the input labels are the label data set constructed by the automated method proposed in this paper. After the training is completed, the PCP features of the theme to be predicted and improved are input to generate the accompaniment chords of the final arrangement. Through PCP features and template-matching model, the system designed in this paper improves the matching accuracy of the generated chords compared with that generated by the traditional method.

When a combine harvester is harvesting in field under complicated conditions, vibration characteristics of the chassis frame under the excitation of multiple sources are complex and changeable. In order to analyze the vibration characteristics of the chassis frame of the combine harvester under complex working conditions and multisource excitation, this paper carried out three-dimensional modeling and static calculation of the chassis frame of the combine harvester. On this basis, the experimental modal analysis was carried out on the designed chassis frame, and finally, the vibration response under multiple excitations and complex working conditions was tested and analyzed in the field. Results showed that when the combine harvester was fully loaded, the maximum bending moment was at the load-bearing beam under the grain box. The maximum bending moment was 2298.08 N/m, and it was located at the cross section of the left end of the bearing beam at 1.61 m. When the components of the whole machine were working, the vibration of the chassis frame at the rear right was the most intense. The horizontal vibration of the threshing drum, the reciprocating motion generated by the vibrating screen, and the vertical vibration generated by the engine were the main vibration sources of the combine harvester in the three directions of X, Y, and Z. With the i\ncrease of engine speed, the excitation capacity of each working component connected to the engine on the whole machine increased. The results of this paper provide experimental basis for the research on the vibration characteristics of the chassis frame of the rice combine harvester under multisource excitation and also provide a reference for the further improvement of the chassis frame of the combine harvester.

Limited by traditional construction project management ideas and systems, the implementation of the PMC model in China still has serious problems such as opposition and frequent conflicts. How to reveal the causes of organizational conflicts and explore the key mechanism of the implementation of the PMC model from the system perspective are urgent problems to be solved. Based on the idea of engineering system view, this paper abstracts the PMC project participants with self-organizing characteristics of the organizational management system, in which the internal structure is closely related, and defines the connotation of synergy and synergistic evolution of the PMC project organizational management system. Using the Cucker–Smale model to describe the group movement, the hierarchical system and the acceleration efficiency function of the project legal person’s free will are constructed, and the structure, movement, and development law of the system itself are emphasized to simulate the ordered evolution trend of PMC project organizational management system and reveal the intrinsic causes of conflicts in PMC project and the key mechanisms of the PMC model application. The results show that first, the intensity of information communication between PMC subjects has a significant positive contribution to the orderliness of the organizational management system; second, too much acceleration of the project legal person’s free will causes group chaos in the system, while too little slows down the group stabilization time, which has a negative impact on cost and schedule; third, the more the organizational structure of PMC contractors tends to the whole-process integrated control, the more it can drive the group to gather in an orderly manner and form a synergistic control mode combining self-organization and other organizations; and fourth, the implementation of the PMC model should focus on eliminating the traditional institutional and conceptual barriers, forming a project management model with integrated control of the whole process of the PMC project contractor and effective macro supervision of the project legal person. The research results of this paper revealed the intrinsic causes of conflicts in PMC projects and the key mechanisms of PMC model application; it can help solve the confrontational situation of PMC project participants, promote the development of the PMC model, and give full play to the investment benefits.

The purpose of this paper is to discuss and present a technical analysis of the recent advancements in autonomous robots equipped with a manipulator. The autonomous robots include unmanned aerial vehicle (UAV), unmanned underwater vehicle (UUV), and unmanned ground vehicle (UGV). A manipulator can make an autonomous robot more adaptable and robust but it can also affect its performance as well. Several issues can arise because of the installation of a manipulator like the robot becoming unstable due to the extra weight, slow convergence, and errors in the path planning. Therefore, this study presents the numerous recent techniques that are in use to counter the aforementioned problems. The methodology and approach used in this paper are to first present the dynamic model of the autonomous robot. Then, the study offers a performance analysis of the specific robot in question. Finally, the paper formulates the limitations of the recently proposed techniques in the form of a table for each vehicle. The key findings of this study are a comprehensive review of the aforesaid techniques and their technical analysis. The unique contribution of this study is to present some of the limitations that these methods have so the researcher can better select the method according to the mission requirement.

This article uses a multiple regression model to evaluate the extent to which financial resources in various regions can be put into the most appropriate direction through the financial system or financial market. This paper uses the Tobit model to conduct empirical analysis on the input and output data of technology finance in multiple provinces and cities and explores the impact of various factors on technology finance efficiency from the perspective of three technology finance entities: high-tech enterprises, governments, and venture capital companies. Based on the DEA model and Malmquist index model in the data envelopment analysis method, this paper uses deap software to calculate the efficiency of agricultural science and technology resource allocation in Gansu Province and the efficiency of agricultural science and technology operation in many provinces in the western region. This paper divides the allocation of scientific and technological innovation resources into the ability to allocate scientific and technological innovation resources and the allocation efficiency of scientific and technological innovation resources. By constructing a performance evaluation system for the allocation of scientific and technological innovation resources, it is found that there are obvious differences in the ability and efficiency of scientific and technological innovation resource allocation in various regions. This paper proposes the selected efficiency evaluation method, the multiple regression model, which uses the data from 2012 to 2018 to evaluate the efficiency of the allocation of scientific and technological financial resources in my country, calculates the efficiency value of each region, and conducts a comparative analysis of provinces and regions. Research shows that, through multiple regression model analysis, the total technical efficiency of technology finance in Northeast China rose from 0.759 fluctuations in 2012 to 0.922 in 2018, gradually reducing the difference with the eastern and western regions. The overall promotion plays a significant role. The innovation of this article lies in the use of multiple regression models to analyze the factors affecting the allocation of regional scientific and technological financial resources, aiming to improve the efficiency of financial resource allocation.

In this paper, an integral sliding mode controller is proposed to realize the synchroniza-tion of a class of finance chaotic systems. First, an integral sliding surface with tanh function is designed. Then, fuzzy logic systems are used to estimate unknown functions, and then based on the Lyapunov stability theorem, the proposed control method can quickly drive the synchroniza-tion error into a small neighborhood of zero, and the range of this neighborhood can be estimated. The simulation results verify that the proposed method in this paper is better than the traditional method.

In this paper, the dependent competing risks’ model is considered in the constant-stress accelerated life test under the adaptive type-II progressive hybrid-censored scheme. The dependency between failure causes is modeled by Marshall–Olkin bivariate Gompertz distribution. The scale and shape parameters in the model both change with the stress levels, and the failure causes of some test units are unknown. Then, the maximum likelihood estimations and approximation confidence intervals of the unknown parameters are considered. And, the necessary and sufficient condition is established for the existence and uniqueness of the maximum likelihood estimations for unknown parameters. The Bayes approach is also employed to estimate the unknown parameters under suitable prior distributions. The Bayes estimations and highest posterior credible intervals of the unknown parameters are obtained. Finally, a simulation experiment has been performed to illustrate the methods proposed in this paper.

Classical choice theory assumes that a decision-maker considers all feasible alternatives. However, a decision-maker in the real world can not consider all alternatives because of limited attention. In this paper, we propose a satisficing choice model to describe the choice procedure based on the incomplete preferences under the limited attention of the decision-maker. Moreover, the existence and rationality properties of the satisficing choice model on the different domains are studied combined with some proposed rationality conditions. Further, the proposed satisficing choice model is applied to a case of quality competition. Results show that the satisficing choice model of this paper is of a certain theoretical guiding significance to a kind of emergency decisions made by decision-makers under the circumstance of time pressure and limited information. It can also be the theoretical foundation for the study on the boundedly rational decision-making.

In recent years, the Internet of Things technology can effectively innovate applications and services. The Internet of Things technology has become more and more popular. It provides an effective and direct bridge between the physical world and virtual objects in cyberspace. With the increase in the intensity of dragon boat training and the increasingly fierce competition, the possibility of injury is increasing. Dragon boat racing is a noncontact team sport based on strength and technology. The purpose of this paper is to solve the problem of people's lack of understanding of the sports injuries and causes of dragon boat athletes. We used the data fusion algorithm and cluster maintenance optimization algorithm to study the application of Internet of Things technology in the cause of dragon boat sports injury. In order to save energy, extend the network life cycle, shorten service interruption time, and increase data packet transmission, the cluster maintenance optimization algorithm in this paper mainly improves and optimizes the startup time of cluster maintenance, which depends on the maintenance cost. The experiment result shows that the etiological detection system proposed in this paper matches the actual sports injury results well. The experiment result shows that the research on the cause of injury in dragon boat sports based on Internet of Things technology can detect the damage law well and can have a more comprehensive understanding for the cause of injury, which helps to prevent injuries better and take effective treatments. In the analysis part, it can be concluded that the detection system is very accurate in detecting the cause, and the accuracy rate is basically 100%.

In order to solve the flood discharge problem of both small- and medium-sized warping dams in the Loess Plateau, a stepped spillway scheme, based on an ecological bag, to achieve full-section water flow and energy dissipation has been proposed in this paper. The hydraulic and energy dissipation characteristics of a stepped spillway layout scheme were studied using 3D numerical simulation. As the height of the dams is low and the spillways are short, the research has shown that the traditional single-step layout scheme leads to a low overall energy dissipation rate due to the small amount of energy dissipated in the initial steps. As a result of this, this paper has put forward two kinds of step layout schemes such as the shunt type and the staggered type for the initial steps. Through analysis of the flow state, the pressure distribution, and the total energy dissipation rate, the results have shown that shunt type and staggered type with front step deformation produced an obvious mixing of the water flow, fewer negative pressure areas, and a higher energy dissipation rate. The optimal energy dissipation rate of the staggered type reached 87.75%, and the maximum energy dissipation rate was increased by 27.97%.

As a special symbolic cultural carrier that reflects people’s material life and spiritual state, jewelry plays an increasingly important role in life. How can we accelerate brand promotion, promote jewelry product sales, and establish a rapid market response mechanism? High efficiency, high quality, and low cost to meet consumers’ increasingly personalized and diversified needs are the problems we currently need to solve. The purpose of this article is to explore the application of VR technology in jewelry displays and provide a brand new idea for jewelry display. In order to realize the virtualization of the jewelry design process, this article uses the Cult 3D VR platform to complete the design and realization of the interactive function of the jewelry virtual model, uses the Photoshop software to design the jewelry virtual display system interface, and finally completes the jewelry virtual display system in the Dreamweaver software integration and release. Through detailed example application, the feasibility of the viewpoint of this subject was effectively verified. In this paper, the two algorithms BRSK and SURF are used in conjunction, and the multiscale expression characteristics of BRISK in space and the rotation-invariant characteristics of SURF are used. Studies have shown that the experimental results of the rotation performance of the method in this paper show that the accuracy is improved by 60%, and the time-consuming is relatively less. Therefore, under the premise of ensuring the rapidity, the method in this paper can guarantee the accuracy and time cost of control matching.

Voltage stability margin is ensured through the reactive power resources. In order to generate the reactive power references and ensure the low-voltage ride-through (LVRT) control of a wind farm system based on squirrel cage induction generator, this paper proposed an optimal control approach based on fractional-order (FO) PI-fuzzy-PI (FOPI-fuzzy-FOPI) controller. The proposed control method ensures, also, the demand for active and reactive power predetermined by the transmission system operator (TSO) and satisfies the grid code recommendations. In order to achieve a faster tracking of state variables of the system, the FO operators are optimized using the particle swarm optimization algorithm (PSOA). Using FO operator and PSOA, the responses of the system can be improved. The proposed controller provides additional parameters for better tracking performance and faster convergence can be achieved. Numerical simulation results are presented to analyze the advantages of the proposed control approach to design a physically, realizable controller. The present results are compared with various control methods to show the superiority of the method proposed in this paper.

This paper examined the three-dimensional steady thin film flow of tangent hyperbolic fluid with nonlinear mixed convection flow and entropy generation past a stretching surface under the influence of magnetic field. For the flow problem, the Cattaneo–Christov heat and mass diffusion model was employed to examine heat and mass transfer characteristics and impacts of the normally directed magnetic field. To transform nonlinear PDEs into ODEs, the variable transformation technique was used. The bvp4c algorithm was implemented to solve these ODEs. The behavior of every leading parameter on the velocities, temperature, concentration profile, entropy generation, and Bejan number was reported with tabular and figurative form. The results show that as the values of Br increase, the entropy generation enhances, but the Bejan number decreases. Moreover, as the values of B increase, the opposite characteristics are observed in entropy generation and Bejan number graphs. Furthermore, the skin friction coefficient number, local Nusselt number, and Sherwood number are graphically discussed for the active involved parameters. The best agreement is recorded when we compare this paper with the previous literature for various values of M .

The constructive English learning approach provides better technical support and learning concepts for English learners. This paper firstly introduces the theoretical basis of constructive English learning and the status of domestic and international research and analyzes the advantages over the traditional English learning approach. This paper also analyzes the RBF neural network model, especially the locally adjusted RBF algorithm, and considers the training model and construction method of the RBF neural network to simulate the learning pattern of good learners of English in grades 4 and 6 to better realize the effect of the English learning platform and allow the English learning platform to play a supportive role in the learning process of students, and through the evaluation of the design, we can better improve the software design, which can be improved by evaluating the design. In the subsequent research, we should add a more user-friendly interface design, use larger model samples to train the model to make it more universal in accuracy and ensure good stability in operation, and integrate multiple algorithms to improve the training algorithm of RBF neural network to obtain a more general algorithm model. The rich teaching methods can cultivate students’ independent inquiry ability, and the construction of this English learning platform can help improve students’ ability to use information technology. By making students become the main subject of teaching and learning, we can stimulate students’ enthusiasm and a good awareness of English learning. Using the radial basis function (RBF) algorithm and the constructive pod language teaching method, this English learning platform is a good and positive aid for students to learn English, and as a better English education tool, it has certain research value and positive significance for the study of English teaching.

With the rapid development of information science today, multifunctional and intelligent applications have gradually become the focus of attention. In the data management system, the first consideration is the reliability of the data source, followed by the intelligent processing after the data are collected. Due to the upgrade of the Internet to the Internet of Things, the way of network information transmission has also become a problem that people need to think about. The transmission mode of network information services will be converted from the passive transmission of information by traditional servers to the form of actively pushing information. The application of intelligent push technology in the field of the Internet of Things is a prominent and important direction in the development of the Internet of Things. This article mainly introduces the research on the intelligent music push and data analysis system based on the 5G Internet of Things, with the intention of providing some ideas and directions for the research of the music intelligent push and play and data analysis system. This paper proposes a research method for music intelligent push playback and data analysis system based on 5G Internet of Things, including current intelligent push related technologies, music evaluation matrix, user dissimilarity matrix, and music feature similarity calculation. The experimental results in this paper show that with the increase in the number of users, the accuracy of the recommended results of the system under the Hadoop framework gradually stabilizes, eventually reaching 91.2%.

The automobile industry serves as the primary industry for national development and also enhances manufacturing development strategies. Its source of power comes from the maturity and operational efficiency of the technology innovation network within the industry. Firstly, this paper takes the automobile industry of the Yangtze River Economic Belt as the research object and depicts the topological structure of the 1985–2015 Industry-University-Research Cooperation Innovation Network (IURCIN) from the perspective of space-time evolution. Then, based on the 2015 industrial network characteristic data, the paper analyses the impact of individual network characteristics such as intermediate centrality, structural hole limitation, and cooperation intensity on the innovation performance of the respective network. At the same time, it adds innovation activity, per capita gross regional product (GRP), and research and development (R&D) fund input intensity as control variables in the quantitative analysis. The results show that the centrality and structural hole limitation significantly and positively affect the subject innovation performance. The cooperation intensity and the subject innovation performance have an inverted U-shaped relationship. The innovation activity has a positive effect on the subject’s innovation performance. Furthermore, per capita GRP and R&D expenditure intensity on the main innovation performance is not significant. Finally, the countermeasures and suggestions are put forward to promote the innovation performance of each subject in the IURCIN.

With the advent of the information age, the way people obtain information has changed profoundly. The wave of informationization in higher education has also come with it, and the teaching mode, teaching content, and teaching form are constantly innovated. How to organically integrate information technology into education teaching in order to care for learners’ learning experience and promote the cultivation of new talents is an issue that current educational technology researchers need to pay great attention to. This paper first builds a complete blended teaching model of public English for higher education, but its application effect needs to be further examined. This paper is an investigation in the background of the current era to build a blended teaching model. Based on the continuous development of the era, the ideology and application technology of this field will keep upgrading, so the teaching model also needs to be changed and updated according to the characteristics of the development of the era. The investigation of mixed teaching modes is not permanent. The investigation of the mixed teaching mode is not permanent. At present, only a few courses apply the blended teaching mode. On the basis of the continuous updating of teaching concepts and the latest technologies, it is foreseen that the focus of subsequent investigations will be on the individualized development of the blended teaching mode.

Self-sustained oscillation has the advantages of harvesting energy from the environment and self-control, and thus, the development of new self-oscillating systems can greatly expand its applications in active machines. In this paper, based on conventional photothermal shrinkable material or photothermal expansive material, a simple pendulum is proposed. The light-powered self-sustained oscillation of the simple pendulum is theoretically studied by establishing a dynamic model of the photothermal-responsive pendulum. The results show that there are two motion modes of the simple pendulum, which are the static mode and the oscillation mode. Based on the photothermal-responsive model, this paper elucidates the mechanism of the self-excited oscillation. The condition for triggering self-excited oscillation is further studied. In addition, the influence of the system parameters on the amplitude and frequency is also obtained. This study may have potential applications in energy harvesting, signal monitoring, and soft machines.

In this paper, we develop three supply chain game models, i.e., the basic model, the single trade credit model, and the trade credit and revenue sharing collaboration model. Conditional value-at-risk (CVaR) criterion is used as the measure of risk assessment in these models. We analyze the optimal decisions in the centralized and decentralized situations, respectively, and verify that single trade credit cannot coordinate the supply chain. However, the collaboration contract can coordinate the supply chain. Furthermore, this paper explores the influence of risk-aversion factor, trade credit period, revenue sharing coefficient, and other parameters on the optimal decisions and studies the feasible range of Pareto improvement in the collaborative model. In numerical experiments, the results show that the decisions and profits of both the manufacturer and the retailer reply on the degree of the risk aversion, the trade credit period, and the revenue sharing coefficient. The collaborative contract effectively improves supply chain performance and achieves a ‘win-win’ situation for the supply chain members. In addition, we also consider two extensions for our research. One extension shows that the collaborative contract of trade credit and buyback can also coordinate the supply chain in a certain range. The other extension considers the optimal decision of a risk-averse manufacturer with CVaR.