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Two transportation modes have been used to solve the "last kilometer" delivery problem of perishable products in China. One mode involves the use of refrigerated vehicles, and the other involves transportation with nonrefrigerated vehicles but with thermal insulation packaging and phase-change refrigerants. In this paper, we studied the distribution of fresh products using nonrefrigerated vehicles under the community group purchase model. A new integrated model that can simultaneously select insulation packaging methods and vehicle paths was developed. We designed a heuristic algorithm based on genetic algorithm to solve larger-scale problems. We found that nonrefrigerated vehicle delivery was better than refrigerated vehicle delivery when neither the cost of the phase-change refrigerant nor the cost of packaging accounts for more than 50% of the total cost. There was an optimal balance between insulation packaging cost and shipping cost. The combination of thin and light packaging materials and high-efficiency cold storage materials could achieve the goals of ensuring the quality of fresh products and reducing transportation costs simultaneously. [ABSTRACT FROM AUTHOR]

Linguistic q-rung orthopair fuzzy numbers (Lq-ROFNs) are composed of a set of q-rung orthopair fuzzy numbers (q-ROFNs) with membership and nonmembership degrees of linguistic variables, which can be regarded as an extension of linguistic intuitionistic fuzzy numbers (LIFNs) and linguistic Pythagorean fuzzy numbers (LPFNs). Compared with LIFNs and LPFNs, Lq-ROFNs have a wider range of applicability because the value of q can have a wider range, thus allowing to handle more multiattribute group decision making (MAGDM) problems. Based on Lq-ROFS, this paper first proposes the Chebyshev distance metric, then develops the Chebyshev distance entropy for deriving objective solution of decision makers' (DMs') weight vector and attribute weight vector by combining the metric and entropy measure. The TODIM method has been widely valued by the society and has achieved good results in many MAGDM problems. Subsequently, the TODIM decision method in Lq-ROFS is presented and combined with the Chebyshev distance entropy model as a new decision method (CDE-TODIM) to solve the MAGDM problems. This method process is objective and direct and takes into account the decision maker's preferences, and the decision results are more persuasive. Finally, the effectiveness and rationality of this MAGDM method are illustrated by a case as well as comparisons. [ABSTRACT FROM AUTHOR]

Topping is an important part in cotton field management, the spraying time has a great impact on cotton quality. In agricultural production, the strategy of timing the cotton topping mainly relies on manual inspections and experience, which is lack of efficiency and science. To solve the problem, this paper uses a drone equipped with a multispectral camera to collect the multispectral information of the cotton canopy of 12 days which includes before and after the topping operation in Shihezi. At the same time, the information of cotton plant height, the number of fruiting branches, and flower buds are collected. Compare multiple band combinations and vegetation index; the combined data of 550 + 730 + 790 nm band is selected as the model input. AdaBoost + decision tree method is proposed as a fitting model, the fitting results show that the coefficient of determination (R2) between multispectrum and cotton plant height is 0.96, and the average prediction error (RMSEP) is 0.40 cm, the coefficient of determination (R2) between multispectrum information and the fruiting branches is 0.97, the prediction mean error (RMSEP) is 0.54, and the correlation determination (R2) with the flower buds is 0.84, and the prediction mean error is (RMSEP) 0.49. The output data of the fitting model is used as the input of the topping time discriminant model, and the discriminant model can obtain an accuracy of 94.03%. The method in this paper can effectively monitor the growth status of cotton in the topping time and provide a technical path to scientifically determine the cotton topping time. [ABSTRACT FROM AUTHOR]

Technical movement analysis requires specialized domain knowledge and processing a large amount of data, and the advantages of AI in processing data can improve the efficiency of data analysis. In this paper, we propose a feature pyramid network-based temporal action detection (FPN-TAD) algorithm, which is used to solve the problem that the action proposal module has a low recall rate for small-scale temporal target action regions in the current video temporal action detection algorithm research. This paper is divided into three parts. The first part is an overview of the algorithm; the second part elaborates the network structure and the working principle of the FPN-TAD algorithm; and the third part gives the experimental results and analysis of the algorithm. [ABSTRACT FROM AUTHOR]

The biggest view of the whole world on science and technology and sports is that science and technology and sports both represent national strength. At present, the integration of sports and science and technology has not reached a certain height, especially in the prediction of sports behavior and injury assessment, and the investment in science and technology is still lacking. This leads to a high number of injuries caused by sports every year. However, swarm intelligence algorithm has made few breakthrough achievements in the past few years, and the combination of sports behavior and swarm intelligence algorithm can just solve this problem. It is very important to choose the algorithm for predicting and assessing sports behavior. We should choose an efficient algorithm with high stability, high convergence speed, and optimization ability. In this paper, the IPSGWO algorithm is proposed to realize this application. IPSGWO algorithm is based on the GWO algorithm, with appropriate strategies and ideas, to maximize the improvement. In this paper, the convergence curve of PSO, GWO, and IPSGWO is tested to determine whether the IPSGWO algorithm has more stable and higher performance, and the simulation experiment is used to determine whether the IPSGWO algorithm is suitable for prediction and injury assessment compared with the other two. From the experimental results, the IPSGWO algorithm does have higher performance; because of this, it is more accurate for prediction and injury assessment. [ABSTRACT FROM AUTHOR]

This paper takes the southeast coastal real estate listed enterprises that have been at the forefront of China's reform for many years as the research object. Through theoretical analysis and descriptive statistical analysis, it studies the impact of the reduction of VAT (value-added tax) rate in 2019 on the southeast coastal listed real estate enterprises from the perspectives of sales revenue, costs, tax burden, profit, and cash flow. The overall operating tax rate has shown a volatile downward trend; the average operating income level has shown a volatile upward trend; the purchase cost has decreased slightly and the change was not significant; the total profits of enterprises have shown a cyclical upward trend. Due to the impact of the real estate purchase restriction policy and financing environment in recent years, the reduction of tax rate has an effective but not significant impact on the enterprise cash flow. Overall, the tax rate reduction still has a positive impact on enterprises. Combined with the problems in the process of policy implementation, this paper puts forward that those enterprises should make scientific and reasonable tax planning, pay attention to the management of purchase and sales contracts and the mode of cooperation with upstream enterprises, regulate taxation to reduce tax-related risks, improve the management level of enterprises, and establish an industry finance integration system. At the national policy level, there is limited room and effect for the reduction of the VAT rate, and more preferential tax relief policies should be given directly, not limited to VAT, but can be extended to corporate income tax. In this way, that could improve the effectiveness of the tax reduction policy in the southeast coastal real estate industry and provide a reference for relevant industries in other regions to solve similar problems. [ABSTRACT FROM AUTHOR]

With the increase in renewable energy, improving the utilization rate of renewable energy is of great practical significance. The microgrid has been proved effective in addressing this issue. As a flexible load, electric vehicles are connected to the grid on a large scale, which will have an impact on the grid. In order to solve this problem, this paper proposes a microgrid energy management model for electric vehicle charging stations, which takes into account the economics of microgrid operation and the stability of grid operation. Subsequently, this paper proposes an evolutionary multiobjective optimization algorithm to deal with constraints. Finally, this paper verifies the effectiveness of the proposed model and algorithm through experiments. [ABSTRACT FROM AUTHOR]

The purpose of social basic medical insurance is to solve the problem that "medical treatment is difficult and expensive." With the implementation of new technologies and new diagnosis and treatment methods, the price of medicine has risen sharply. Under this background, can basic medical insurance still relieve the economic pressure of patients? Based on the tracking data of China Health and Nutrition Survey (CHNS) from 2006 to 2015, by constructing Heckman sample selection model, the paper finds that the basic medical insurance for urban employees (UEBMI) leads to an increase in medical expenses, among which only 0.61% can be attributed to the release of normal medical demand and 114.47% can be attributed to moral hazard; for flexible employees, 249.52% increase can be attributed to adverse selection. Based on this, the paper puts forward two suggestions: first, promoting Hierarchical Treatment Model to control the growth of medical expenses and second, guiding flexible employees to participate in the UEBMI. [ABSTRACT FROM AUTHOR]

In practice, the assumption of failure independence between components is seldom valid, especially for those complex systems with complicated failure mechanism. Users can decide whether to purchase extended warranty (EW) at the end of basic warranty, and there are many factors that influence this decision, such as product reliability and EW price. In order to solve the problem of EW pricing for multi-component systems with failure interaction reasonably, considering the failure interaction characteristics between components of the multi-component systems, under the condition of type II failure interaction, this paper constructed a dependent failure rate model and developed a EW cost model of two-component systems. Thus, after optimizing the preventive maintenance (PM) strategy, this paper obtained the optimal PM interval when EW cost is the lowest, which is a win-win strategy to reduce the EW price for manufacturers and users under the premise of ensuring the manufacturer's profit demand. Finally, the validity of the model was verified by a numerical example and sensitivity analysis for important parameters was presented. [ABSTRACT FROM AUTHOR]

There are several problems with the performance analysis of corporate human resource management (HRM), namely, the incomplete index system and the difficulty in decision-making under multiple fuzzy factors. To solve these problems, this paper improves the method for corporate HRM performance analysis (HRMPA). First, a novel corporate HRMPA index system was established under the principle of comprehensiveness. Next, the fuzzy closeness of corporate HRMPA was calculated based on fuzzy theory, and the extenics correlation coefficient of corporate HRMPA was computed with the aid of the extenics theory. Then, fuzzy extenics factors were introduced to create an integrated corporate HRMPA model. The proposed model was proved valid and feasible through case analysis. The research results shed new light on the theories of HRMPA and enjoy great applicable value in engineering. [ABSTRACT FROM AUTHOR]

In the past few decades, metaheuristic algorithms (MA) have been developed tremendously and have been successfully applied in many fields. In recent years, a large number of new MA have been proposed. Slime mould algorithm (SMA) is a novel swarm-based intelligence optimization algorithm. SMA solves the optimization problem by imitating the foraging and movement behavior of slime mould. It can effectively obtain a promising global optimal solution. However, it still suffers some shortcomings such as the unstable convergence speed, the imprecise search accuracy, and incapability of identifying a local optimal solution when faced with complicated optimization problems. With the purpose of overcoming the shortcomings of SMA, this paper proposed a multistrategy enhanced version of SMA called ESMA. The three enhanced strategies are chaotic initialization strategy (CIS), orthogonal learning strategy (OLS), and boundary reset strategy (BRS). The CIS is used to generate an initial population with diversity in the early stage of ESMA, which can increase the convergence speed of the algorithm and the quality of the final solution. Then, the OLS is used to discover the useful information of the best solutions and offer a potential search direction, which enhances the local search ability and raises the convergence rate. Finally, the BRS is used to correct individual positions, which ensures the population diversity and enhances the overall search capabilities of ESMA. The performance of ESMA was validated on the 30 IEEE CEC2014 functions and three IIR model identification problems, compared with other nine well-regarded and state-of-the-art algorithms. Simulation results and analysis prove that the ESMA has a superior performance. The three strategies involved in ESMA have significantly improved the performance of the basic SMA. [ABSTRACT FROM AUTHOR]

The driving force of high-quality development of regional economy is inseparable from the support of technology. With the support of big data, we need to solve this problem in order to solve the difficulty of large-scale experimental testing and accurately reflect the feasibility growth of data sample changes. This paper proposes a discrete dynamic modeling technology based on big data background to analyze the development and change of regional economy. The reliability AMSAA model is usually used for dynamic discrete modeling. It can be combined with the change data provided by big data to form a dynamic modeling method for reliability growth evaluation. Then, the Bayesian regression method is used to predict the change parameters of the model, and the spatial econometric method is used to analyze the regional economic change. The results show that compared with the traditional methods, the discrete dynamic modeling method is more accurate and can effectively solve the problem of reliable growth under the condition of big data. After introducing the spatial effect measurement model, it can also reflect the main factors of the growth and change of regional economic real output value. In addition to the development of high and new technology, terrain factors, investment, and government support have also had different effects. Therefore, according to the above results, it is proved that the discrete dynamic modeling technology can accurately obtain the experimental data and provide reliable technical support for dynamic data processing. [ABSTRACT FROM AUTHOR]

This paper aims to solve the problem of food safety in catering O2O distribution link. We applied the system dynamics method to model the formation mechanism of food safety risk in the distribution link. The results of our experiment include identifying the risk factors that may be faced by food safety in the distribution link from five perspectives: O2O catering enterprise's own risk, logistics distribution team's distribution risk, O2O catering platform supervision risk, user-supervision risk, and government department supervision risk, and establishing a risk index evaluation system based on the Analytic Hierarchy Process. With the help of the system dynamics model, the corresponding risk formation mechanism system model flow diagram is established, and the model simulation analysis is carried out. Through this research, we concluded that we can use the risk model to understand the risks faced by different subjects so as to make targeted countermeasures. [ABSTRACT FROM AUTHOR]

For multicomponent products, the maintenance of every component separately will increase the downtime and reduce the availability of products during the warranty period. To solve this problem, the economic dependence between the components is considered in this paper. Firstly, a single-component two-dimensional (2D) preventive maintenance (PM) availability model is established, and the simulated annealing algorithm is adopted to calculate the optimal 2D PM interval to achieve the maximal availability of any single component. Then, to ensure that the warranty cost of each component does not exceed the budget, the PM benchmark interval is introduced, and the PM work is optimized following the method of grouping maintenance. Based on this, the 2D preventive grouping maintenance availability model of multicomponent products is established. Finally, an example is given to verify the proposed method, and the results indicate that the proposed method increases the availability of multicomponent products during the 2D warranty period. [ABSTRACT FROM AUTHOR]

Transnational investment is featured by its large scale and high risks. During transnational investment, the frequent risks often bring a huge loss to the investors. Therefore, the risk factors should be included in the economic evaluation of transnational investment projects. However, the existing evaluation models and systems are rather complex, lacking a unified framework. Besides, there are few practical applications of these models or systems. To solve the problem, this paper explores the factors and economic evaluation of transnational investment risks. Firstly, an economic evaluation system was constructed for transnational investment projects, and the economic evaluation result was depicted in two aspects: economic income factors and investment risk factors. Then, the applicability and economic meanings of common indices were clarified one after another. After that, the economic evaluation flow was designed for risk-based transnational investment projects. Finally, an economic evaluation was performed on actual projects, which verifies the feasibility of the proposed evaluation method. [ABSTRACT FROM AUTHOR]

Influence maximization (IM) is fundamental to social network applications. It aims to find multiple seed nodes with an enormous impact cascade to maximize these nodes' spread of influence in social networks. Traditional methods for solving influence maximization of the social network, such as the distance method, greedy method, and PageRank method, may suffer from issues of low calculation accuracy and high computational cost. In this paper, we propose a new bacterial foraging optimization algorithm to solve the IM problem based on the complete-three-layer-influence (CTLI) evaluation model. In this algorithm, a novel grid-based reproduction strategy and a direction-adjustment-based chemotaxis strategy are devised to enhance the algorithm's searchability. Finally, we conduct comprehensive experiments on four social network cases to verify the effectiveness of the proposed algorithm. The experimental results show that our proposed algorithm effectively solves the social network's influence maximization. [ABSTRACT FROM AUTHOR]

Cooperative coevolution (CC) is an effective framework for solving large-scale global optimization (LSGO) problems. However, CC with static decomposition method is ineffective for fully nonseparable problems, and CC with dynamic decomposition method to decompose problems is computationally costly. Therefore, a two-stage decomposition (TSD) method is proposed in this paper to decompose LSGO problems using as few computational resources as possible. In the first stage, to decompose problems using low computational resources, a hybrid-pool differential grouping (HPDG) method is proposed, which contains a hybrid-pool-based detection structure (HPDS) and a unit vector-based perturbation (UVP) strategy. In the second stage, to decompose the fully nonseparable problems, a known information-based dynamic decomposition (KIDD) method is proposed. Analytical methods are used to demonstrate that HPDG has lower decomposition complexity compared to state-of-the-art static decomposition methods. Experiments show that CC with TSD is a competitive algorithm for solving LSGO problems. [ABSTRACT FROM AUTHOR]

Influence maximization (IM) is fundamental to social network applications. It aims to find multiple seed nodes with an enormous impact cascade to maximize these nodes' spread of influence in social networks. Traditional methods for solving influence maximization of the social network, such as the distance method, greedy method, and PageRank method, may suffer from issues of low calculation accuracy and high computational cost. In this paper, we propose a new bacterial foraging optimization algorithm to solve the IM problem based on the complete-three-layer-influence (CTLI) evaluation model. In this algorithm, a novel grid-based reproduction strategy and a direction-adjustment-based chemotaxis strategy are devised to enhance the algorithm's searchability. Finally, we conduct comprehensive experiments on four social network cases to verify the effectiveness of the proposed algorithm. The experimental results show that our proposed algorithm effectively solves the social network's influence maximization. [ABSTRACT FROM AUTHOR]

The development level of higher education (HE) is an important indicator of the development level and development potential of a country. The HE-related document is the mirror to reflect the develop process of the HE. The research of high education (HE) has been developing rapidly in China, resulting in a huge number of texts, such as relevant policies, speech drafts, and yearbooks. The traditional manual classification of HE texts is inefficient and unable to deal with the huge number of HE texts. Besides, the effect of direct classification is rather poor because HE texts tend to be long and exist as an imbalanced dataset. To solve these problems, this paper improves the convolutional neural network (CNN) into the HE-CNN classification model for HE texts. Firstly, Chinese HE policies, speech drafts, and yearbooks (1979–2020) were downloaded from the official website of Chinese Ministry of Education. In total, 463 files were collected and divided into four classes, namely, definition, task, method, and effect evaluation. To handle the huge number of HE texts, the Twitter-latent Dirichlet allocation (LDA) topic model was employed to extract word frequency and critical information, such as age and author, enhancing the training effect of CNN. To address the dataset imbalance problem, CNN parameters were optimized repeatedly through comparative experiments, which further improve the training effect. Finally, the proposed HE-CNN model was found more effective and accurate than other classification models. [ABSTRACT FROM AUTHOR]

The research on logistics heat facilitates the understanding of the drivers of regional logistics development. However, many scholars ignore the difference between prediction methods in terms of attributes and focal points of data analysis during the selection of regional logistics heat prediction model. Regional logistics interacts with regional economy. However, the studies on the coupling development between the two systems fail to make a detailed analysis in the light of their actual situation. Therefore, the evaluation of the coordination degree is often biased. To solve the problem, this paper probes into the prediction of regional logistics heat and the coupling development between regional logistics and economic systems. Firstly, an index system was established to measure the level of coupling development between the two systems, and a grey relational analysis was performed on the indices, leading to the evaluation results on coordination degree. Next, a composite model of GM (1, 1) and backpropagation (BP) neural network was proposed, and the deviation interval of the composite predictions was predicted based on Markov chain prediction model. The proposed algorithm proved effective through experiments. [ABSTRACT FROM AUTHOR]

Scientific expenditure and distribution of higher education drive the sustainable and stable development of regional economy. Currently, China faces an imbalance in the allocation of higher education resources. To solve the problem, it is necessary to study the spatiotemporal difference of higher education development. Therefore, this paper explores how higher education varies in time and space and measures how the spatiotemporal difference and input of higher education affects regional economy. Firstly, the authors divided the influence of spatiotemporal difference and input of higher education on regional economy into four aspects. Next, the spatiotemporal differences of higher education were examined in detail. Based on the four aspects, an equilibrium model was established for regional economic development, and the law of evolution to equilibrium was analyzed. Experimental results verify the effectiveness of the established model. [ABSTRACT FROM AUTHOR]

A well-established distribution network is fundamental to the sound management of the green supply chain. To adapt to the market demand and policies for green products, it is urgent to build an efficient and rational logistic distribution network for the green supply chain. Many researchers have tried to design distribution networks through the coordinated optimization of the green supply chain, in the light of realistic situation. However, there are very few optimization models that consider all kinds of influencing factors. To solve the problem, this paper attempts to establish a coordinated optimization model of the complex system of the green supply chain distribution network (GSCDN). Firstly, the authors plotted the structure and game logic of the GSCDN and defined the upper limit of sales induced by the limited production capacity of producers. Secondly, the coordinated optimization conditions were configured for the distributor layer, producer layer, and market demand layer, and a coordinated optimization model was set up for the complex system. Finally, the contractual coordinated optimization mechanism was detailed for the complex system under the profit-sharing contract. The proposed model and solving algorithm were proved valid through experiments. [ABSTRACT FROM AUTHOR]

In this paper, the traffic area of subzone division in urban road network is studied and a subzone division method based on the combination of static partition and dynamic partition is proposed. The static partition is carried out for the road network when the traffic flow is in a noncongested state, so as to provide the decision-making basis for the traffic green wave signal control strategy. At the same time, aiming at the road network when the traffic flow is congested, the dynamic partition is carried out on the basis of static partition to provide the decision-making basis for the traffic maximum flow signal control strategy. In view of the fact that it is difficult to determine the clustering center point during the initial division, this method proposes to determine the clustering center point according to the value of nodes of betweenness centrality. In order to solve the problem that it is difficult to collect traffic data, a method for estimating traffic flow density is proposed. In order to solve the problem of normalization of different probability distribution among various parameters, Mahalanobis distance is used as the fusion index of subzone division. Model verification shows that the method is feasible and effective. [ABSTRACT FROM AUTHOR]

Getting medical services has become more difficult and expensive in China, which led to a problem of illness not being treated and a large number of zeros in the statistics of being hospitalized for the elderly. Traditional classic models such as the Poisson model and the negative binomial model cannot fit this kind of data well. One aim of this study was to use zero-inflated and hurdle models to better solve the problem of excess zeros. Another aim was to discover the factors affecting the decision-making behavior of the elderly being hospitalized and hospitalization service utilization. Therefore, the XGBoost model was firstly introduced to rank the importance of influencing factors in this paper. It was found that the zero-inflated negative binomial model performed best. The results showed that the elderly who had enjoyed NRCM or ERBMI/URBMI were more likely to have a higher number of hospitalizations. This indicated that the high cost of hospitalization had prevented the willingness of the elderly being hospitalized, but the basic medical insurance had increased the times of their repeated hospital readmissions. Policy efforts should be made to improve the level of basic medical insurance. [ABSTRACT FROM AUTHOR]

When using tubular MBR to treat sewage, the water production is an important parameter to measure the efficiency of the tubular MBR system. The problem to be solved in this paper is to calculate the water yield of the tubular MBR system, so as to evaluate the sewage treatment efficiency of the MBR system. This research uses the CFD simulation software ANSYS 16.0 to study the water yield of the tubular MBR system. The MBR model of a single membrane filament tube was established using the ICEM CFD preprocessor in ANSYS 16.0, and the structured grid was divided to obtain a grid file. Then, the fluid solver was used to solve the mesh file and through the flow monitoring window to obtain the water output of the tubular MBR system. Finally, the CFD postprocessor in ANSYS 16.0 was used to visualize the calculation results and compare them with the waste-water treatment results of some actual MBR systems. The results show that the water yield calculated by the fluent solver is basically the same as that of the actual MBR system. This research realizes the purpose of calculating the water yield of the tubular MBR system with CFD technology, solves the problem of evaluating the working efficiency of the tubular MBR system with water consumption, and realizes the MBR before deployment The evaluation of the working efficiency of the system has certain reference value for the planning, design, and deployment of MBR. [ABSTRACT FROM AUTHOR]

This paper proposes a new fractional-order chaotic system with five terms. Firstly, basic dynamical properties of the fractional-order system are investigated in terms of the stability of equilibrium points, Jacobian matrices theoretically. Furthermore, rich dynamics with interesting characteristics are demonstrated by phase portraits, bifurcation diagrams numerically. Besides, the control problem of the new fractional-order system is discussed via numerical simulations. Our results demonstrate that the new fractional-order system has compound structure. [ABSTRACT FROM AUTHOR]

This paper focuses on the impacts of renewable energy policy on a large-scale power generation system, including thermal power, hydropower, and wind power generation. As one of the most important clean energy, wind energy has been rapidly developed in the world. But in recent years there is a serious waste of wind power equipment and investment in China leading to many problems in the industry from wind power planning to its integration. One way overcoming the difficulty is to analyze the influence of wind power policy on a generation system. This paper builds a system dynamics (SD) model of energy generation to simulate the results of wind energy generation policies based on a complex system. And scenario analysis method is used to compare the effectiveness and efficiency of these policies. The case study shows that the combinations of lower portfolio goal and higher benchmark price and those of higher portfolio goal and lower benchmark price have large differences in both effectiveness and efficiency. On the other hand, the combinations of uniformly lower or higher portfolio goal and benchmark price have similar efficiency, but different effectiveness. Finally, an optimal policy combination can be chosen on the basis of policy analysis in the large-scale power system. [ABSTRACT FROM AUTHOR]

To satisfy the demand of congestion problem solving in intersections, this paper studies the method of automation countermeasure system for intersection optimization (ACSIO). Taking into account the extensive contents and objectives of intersection optimization, this paper puts forward the functions and architecture of ACSIO based on intersection optimization problem statement. Seeking optimal design of intersection channelization and signal control, the main goal of ACSIO is to achieve dynamic and coordination management of intersection. The problem is formulated as a multiobjective program, with each objective corresponding to a different player in the system. Moreover, it presents system design of ACSIO. A case study based on a real-world intersection is implemented to test the efficiency and applicability of the proposed modeling and computing methods. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of two kinds of function projective synchronization of financial chaotic system with definite integration scaling function, which are not fully considered in the existing research. Different from the previous methods, in this paper, the following two questions are investigated: (1) two kinds of the definite integration scaling function projective synchronization are given; (2) the upper and lower limit of the definite integral scaling function are the bound dynamical systems. Finally, illustrative example is provided to show the effectiveness of this method. [ABSTRACT FROM AUTHOR]

Animal migration optimization (AMO) is one of the most recently introduced algorithms based on the behavior of animal swarm migration. This paper presents an improved AMO algorithm (IAMO), which significantly improves the original AMO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique and it is used in many fields. The well-known method in solving clustering problems is k-means clustering algorithm; however, it highly depends on the initial solution and is easy to fall into local optimum. To improve the defects of the k-means method, this paper used IAMO for the clustering problem and experiment on synthetic and real life data sets. The simulation results show that the algorithm has a better performance than that of the k-means, PSO, CPSO, ABC, CABC, and AMO algorithm for solving the clustering problem. [ABSTRACT FROM AUTHOR]

Rumors have rapidly increasing influence on the society as well as individual life in the information age. How to control the spread of such rumors effectively has become an urgent problem to be solved. In this paper, we consider an optimal control of rumor spreading model with psychological factors and time delay. Firstly, we introduce a realistic optimal control of rumor spreading model with consideration of Holling-type II functional response and time delay. Secondly, by introducing two control strategies of both promoting scientific knowledge and releasing official information, we formulate an optimal control problem to minimize both the number of ignorant individuals and spreaders and the control cost. Thirdly, we prove the existence and the necessary conditions of optimal control strategies theoretically based on Pontryagin’s maximum principle. Our results indicate that the proposed control strategies are effective in reducing the number of spreaders and ignorant individuals and minimizing control cost. [ABSTRACT FROM AUTHOR]

Various problems of pure and applied sciences can be studied in the unified framework of nonlinear equations. In this paper, a new family of iterative methods for solving nonlinear equations is developed by using a new decomposition technique. The convergence of the new methods is proven. For the implementation and performance of the new methods, some examples are solved and the results are compared with some existing methods. [ABSTRACT FROM AUTHOR]

The research on the triangle packing problem has important theoretic significance, which has broad application prospects in material processing, network resource optimization, and so forth. Generally speaking, the orientation of the triangle should be limited in advance, since the triangle packing problem is NP-hard and has continuous properties. For example, the polygon is not allowed to rotate; then, the approximate solution can be obtained by optimization method. This paper studies the triangle packing problem by a new kind of method. Such concepts as angle region, corner-occupying action, corner-occupying strategy, and edge-conjoining strategy are presented in this paper. In addition, an edge-conjoining and corner-occupying algorithm is designed, which is to obtain an approximate solution. It is demonstrated that the proposed algorithm is highly efficient, and by the time complexity analysis and the analogue experiment result is found. [ABSTRACT FROM AUTHOR]

The paper presents a new urban public traffic supernetwork model by using the existing bus network modeling method, consisting of the conventional bus traffic network and the urban rail traffic network. We investigate the synchronization problem of urban public traffic supernetwork model by using the coupled complex network’s outer synchronization theory. Analytical and numerical simulations are given to illustrate the impact of traffic dispatching frequency and traffic lines optimization to the urban public traffic supernetwork balance. [ABSTRACT FROM AUTHOR]

Construction industry development transformation is one of the most important issues in China. Along with the improving construction industry development transformation, problems have increased and need to be solved. For effectively improving the construction industry development transformation, this paper studied the factors influencing mechanism of construction industry development transformation. Construction industry development transformation is influenced by many factors. Firstly, 10 significant influential factors were extracted and 25 observable variables were used. Secondly, Structural Equation Modeling (SEM) was used to analyze the relationship between construction industry development transformation and its influential factors. Then, SEM hypothesis based on the research on the factors influencing construction development transformation was constructed. Through empirical results and its analysis, the role of the basic production factors for construction cannot be ignored. The internal industry directly influenced the transformation in the construction. The external industry indirectly influenced construction transformation. And industrial demands stimulated the basic influencing factors. [ABSTRACT FROM AUTHOR]

This paper presents a model for solving a multiobjective vehicle routing problem with soft time-window constraints that specify the earliest and latest arrival times of customers. If a customer is serviced before the earliest specified arrival time, extra inventory costs are incurred. If the customer is serviced after the latest arrival time, penalty costs must be paid. Both the total transportation cost and the required fleet size are minimized in this model, which also accounts for the given capacity limitations of each vehicle. The total transportation cost consists of direct transportation costs, extra inventory costs, and penalty costs. This multiobjective optimization is solved by using a modified genetic algorithm approach. The output of the algorithm is a set of optimal solutions that represent the trade-off between total transportation cost and the fleet size required to service customers. The influential impact of these two factors is analyzed through the use of a case study. [ABSTRACT FROM AUTHOR]

Both reliability and security are two important subjects in modern digital communications, each with a variety of subdisciplines. In this paper we introduce a new proposed secure turbo coding system which combines chaotic dynamics and turbo coding reliability together. As we utilize the chaotic maps as a tool for hiding and securing the coding design in turbo coding system, this proposed system model can provide both data secrecy and data reliability in one process to combat problems in an insecure and unreliable data channel link. To support our research, we provide different schemes to design a chaotic secure reliable turbo coding system which we call chaotic-switched turbo coding schemes. In these schemes the design of turbo codes chaotically changed depending on one or more chaotic maps. Extensions of these chaotic-switched turbo coding schemes to half-duplex relay systems are also described. Results of simulations of these new secure turbo coding schemes are compared to classical turbo codes with the same coding parameters and the proposed system is able to achieve secured reasonable bit error rate performance when it is made to switch between different puncturing and design configuration parameters especially with low switching rates. [ABSTRACT FROM AUTHOR]

Ship block construction space is an important bottleneck resource in the process of shipbuilding, so the production scheduling optimization is a key technology to improve the efficiency of shipbuilding. With respect to ship block construction space scheduling problem, a hybrid heuristic algorithm is proposed in this paper. Firstly, Bottom-Left-Fill (BLF) process is introduced. Next, an initial solution is obtained by guiding the sorting process with corners. Then on the basis of the initial solution, the simulated annealing arithmetic (SA) is used to improve the solution by offering a possibility to accept worse neighbor solutions in order to escape from local optimum. Finally, the simulation experiments are conducted to verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Artificial bee colony (ABC) is one of the newest additions to the class of swarm intelligence. ABC algorithm has been shown to be competitive with some other population-based algorithms. However, there is still an insufficiency that ABC is good at exploration but poor at exploitation. To make a proper balance between these two conflictive factors, this paper proposed a novel ABC variant with a time-varying strategy where the ratio between the number of employed bees and the number of onlooker bees varies with time. The linear and nonlinear time-varying strategies can be incorporated into the basic ABC algorithm, yielding ABC-LTVS and ABC-NTVS algorithms, respectively. The effects of the added parameters in the two new ABC algorithms are also studied through solving some representative benchmark functions. The proposed ABC algorithm is a simple and easy modification to the structure of the basic ABC algorithm. Moreover, the proposed approach is general and can be incorporated in other ABC variants. A set of 21 benchmark functions in 30 and 50 dimensions are utilized in the experimental studies. The experimental results show the effectiveness of the proposed time-varying strategy. [ABSTRACT FROM AUTHOR]

Let T3(N0) denote the semigroup of 3×3 upper triangular matrices with nonnegative integral-valued entries. In this paper, we investigate factorizations of upper triangular nonnegative matrices of order three. Firstly, we characterize the atoms of the subsemigroup S of the matrices in T3(N0) with nonzero determinant and give some formulas. As a consequence, problems 4a and 4c presented by Baeth et al. (2011) are each half-answered for the case n=3. And then, we consider some factorization cases of matrix A in S with ρ(A)=1 and give formulas for the minimum factorization length of some special matrices in S. [ABSTRACT FROM AUTHOR]

This paper discusses the flow shop scheduling problem to minimize the makespan with release dates. By resequencing the jobs, a modified heuristic algorithm is obtained for handling large-sized problems. Moreover, based on some properties, a local search scheme is provided to improve the heuristic to gain high-quality solution for moderate-sized problems. A sequence-independent lower bound is presented to evaluate the performance of the algorithms. A series of simulation results demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper addresses a special truck scheduling problem in the open-pit mine with different transport revenue consideration. A mixed integer programming model is formulated to define the problem clearly and a few valid inequalities are deduced to strengthen the model. Some properties and two upper bounds of the problem are proposed. Based on these inequalities, properties, and upper bounds, a heuristic solution approach with two improvement strategies is proposed to resolve the problem and the numerical experiment demonstrates that the proposed solution approach is effective and efficient. [ABSTRACT FROM AUTHOR]

This paper deals with the permutation flowshop scheduling problem without intermediate buffers and presents a hybrid algorithm based on the scatter search and the variable neighborhood search. In the hybrid algorithm, the solutions with good quality and diversity are maintained by a reference set of scatter search, and the search at each generation starts from a solution generated from the reference set so as to improve the search diversity while guaranteeing the quality of the initial solution. In addition, a variable neighbourhood based on the notion of job-block is developed, and the neighbourhood size can adaptively change according to the construction of the job-block. Such a dynamic strategy can help to obtain a balance between search depth and diversity. Extensive experiments on benchmark problems are carried out and the results show that the proposed hybrid algorithm is powerful and competitive with the other powerful algorithms in the literature. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid scatter search (SS) algorithm for continuous global optimization problems by incorporating the evolution mechanism of differential evolution (DE) into the reference set updated procedure of SS to act as the new solution generation method. This hybrid algorithm is called a DE-based SS (SSDE) algorithm. Since different kinds of mutation operators of DE have been proposed in the literature and they have shown different search abilities for different kinds of problems, four traditional mutation operators are adopted in the hybrid SSDE algorithm. To adaptively select the mutation operator that is most appropriate to the current problem, an adaptive mechanism for the candidate mutation operators is developed. In addition, to enhance the exploration ability of SSDE, a reinitialization method is adopted to create a new population and subsequently construct a new reference set whenever the search process of SSDE is trapped in local optimum. Computational experiments on benchmark problems show that the proposed SSDE is competitive or superior to some state-of-the-art algorithms in the literature. [ABSTRACT FROM AUTHOR]

The present paper is devoted to the improvement of the R-order convergence of with memory derivative free methods presented by Lotfi et al. (2014) without doing any new evaluation. To achieve this aim one more self-accelerating parameter is inserted, which is calculated with the help of Newton’s interpolatory polynomial. First theoretically it is proved that the R-order of convergence of the proposed schemes is increased from 6 to 7 and 12 to 14, respectively, without adding any extra evaluation. Smooth as well as nonsmooth examples are discussed to confirm theoretical result and superiority of the proposed schemes. [ABSTRACT FROM AUTHOR]

This paper proposes power series method (PSM) in order to find solutions for singular partial differential-algebraic equations (SPDAEs). We will solve three examples to show that PSM method can be used to search for analytical solutions of SPDAEs. What is more, we will see that, in some cases, Padé posttreatment, besides enlarging the domain of convergence, may be employed in order to get the exact solution from the truncated series solutions of PSM. [ABSTRACT FROM AUTHOR]

Remanufacturing is a practice of growing importance due to increasing environmental awareness and regulations. Facility layout design, as the cornerstone of effective facility planning, is concerned about resource localization for a well-coordinated workflow that leads to lower material handling costs and reduced lead times. However, due to stochastic returns of used products/components and their uncontrollable quality conditions, the remanufacturing process exhibits a high level of uncertainty challenging the facility layout design for remanufacturing. This paper undertakes this problem and presents an optimization method for remanufacturing dynamic facility layout with variable process capacities, unequal processing cells, and intercell material handling. A dynamic multirow layout model is presented for layout optimization and a modified simulated annealing heuristic is proposed toward the determination of optimal layout schemes. The approach is demonstrated through a machine tool remanufacturing system. [ABSTRACT FROM AUTHOR]

Evolutionary spatial game is a promising way to unravel the mystery of cooperation, and it has been well recognized that spatial structure could enable cooperation to persist. Schweitzer et al.’s lattice model provides an innovative method to solve the problem. In this paper, we conduct simulations using the same von Neumann neighborhood as in Schweitzer et al.’s study (2002) and observe the effect of initial population and lattice size on the evolution of cooperation. Then, we extend the model with a more complicated Moore neighborhood and self-playing rule for each central player. Simulation results not only provide new evidence for the persistence of cooperation in the evolution with spatial structures, but also investigate critical conditions for the spatial coexistence or the invasion of cooperators and defectors with the more complicated neighborhood. [ABSTRACT FROM AUTHOR]

With the great risk exposed in IT outsourcing, how to assess IT outsourcing risk becomes a critical issue. However, most of approaches to date need to further adapt to the particular complexity of IT outsourcing risk for either falling short in subjective bias, inaccuracy, or efficiency. This paper proposes a dynamic algorithm of risk assessment. It initially forwards extended three layers (risk factors, risks, and risk consequences) of transferring mechanism based on transaction cost theory (TCT) as the framework of risk analysis, which bridges the interconnection of components in three layers with preset transferring probability and impact. Then, it establishes an equation group between risk factors and risk consequences, which assures the “attribution” more precisely to track the specific sources that lead to certain loss. Namely, in each phase of the outsourcing lifecycle, both the likelihood and the loss of each risk factor and those of each risk are acquired through solving equation group with real data of risk consequences collected. In this “reverse” way, risk assessment becomes a responsive and interactive process with real data instead of subjective estimation, which improves the accuracy and alleviates bias in risk assessment. The numerical case proves the effectiveness of the algorithm compared with the approach forwarded by other references. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is inspired by sociological behavior. In this paper, we interpret PSO as a finite difference scheme for solving a system of stochastic ordinary differential equations (SODE). In this framework, the position points of the swarm converge to an equilibrium point of the SODE and the local attractors, which are easily defined by the present position points, also converge to the global attractor. Inspired by this observation, we propose a class of modified PSO iteration methods (MPSO) based on local attractors of the SODE. The idea of MPSO is to choose the next update state near the present local attractor, rather than the present position point as in the original PSO, according to a given probability density function. In particular, the quantum-behaved particle swarm optimization method turns out to be a special case of MPSO by taking a special probability density function. The MPSO methods with six different probability density functions are tested on a few benchmark problems. These MPSO methods behave differently for different problems. Thus, our framework not only gives an interpretation for the ordinary PSO but also, more importantly, provides a warehouse of PSO-like methods to choose from for solving different practical problems. [ABSTRACT FROM AUTHOR]