Hindawi

Discrete Dynamics in Nature and Society

Published by Hindawi
Online ISSN: 1607-887X
Print ISSN: 1026-0226
Discipline: Chaos / Fractal / Dynamical Systems
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Aims and scope

The main objective of Discrete Dynamics in Nature and Society (DDNS) is to foster links between basic and applied research relating to discrete dynamics of complex systems encountered in the natural and social sciences. Discrete dynamics reflects a new emerging tendency towards utilization of iterative mathematical models—systems of difference equations—to describe the behavior of complex systems. It has became clear from the latest development in discrete modeling that such models have a simpler structure and provide many more possibilities for generating and describing complex non-linear phenomena, including chaotic regimes and fractals.

However, further developments in such a discrete mathematical approach are restricted by the absence of general principles that could play the same role as the variational principles in physics. Discrete Dynamics in Nature and Society aims to elaborate such principles, which are expected to lead to a better understanding of the exact meaning of “discrete” time and space, and, to the creation of a new “calculus” for discrete complex dynamics. These general principles should provide direct construction of difference equations for their further use in mathematical modeling of complex, living and thinking systems as it was happened in classical mechanics for the inert matter.

The journal intends to stimulate publications directed to the analyses of computer generated solutions and chaotic in particular, correctness of numerical procedures, chaos synchronization and control, discrete optimization methods among other related topics. The journal will provide a channel of communication between scientists and practitioners working in the field of complex systems analysis and will stimulate the development and use of discrete dynamical approach. Discrete Dynamics in Nature and Society will publish original, high-quality, research papers. In addition there will be regular editorials, invited reviews, a letters section and a news section containing information on future events, and book reviews.

 
Editors
Recent publications
Schematic diagram of regenerative energy utilization process.
Parameter setting and solution steps.
Control structure diagram of the follower train.
:e train operation parameters.
  • Ruxun XuRuxun Xu
  • Jianjun MengJianjun Meng
  • Juhui ZhangJuhui Zhang
  • [...]
  • Decang LiDecang Li
To reduce the traction energy consumption of urban rail trains by regenerating energy, a train traction and braking model was designed based on algebraic graph theory and train dynamics theory and the following consistency model of energy-saving operation of urban rail trains was constructed based on the conditions of the coordination coefficient and the operating condition conversion of trains. Not only to constantly update the consistency controller and save communication resources but also to optimize the energy-saving effect, the consistency algorithm of event triggering was used to search for the optimal operating conditions of trains and the energy-saving operation scheme for multiple trains was established. Taking the train diagram of a subway line in Jinan as an example, the energy-saving control scheme of four trains was solved by MATLAB simulation. The simulation results show that the model can not only ensure parking accuracy and punctuality but also energy savings effectively; that is, the proportion of the total regenerative energy used by the follower train in the actual energy consumption is increased from 3.32% to 10.76%, and the actual total energy consumption of the train is reduced by 9.23%.
 
The main purpose of this paper is to construct the traveling wave solution of the Kaup–Boussinesq system with beta derivative arising from water waves. By using the complete discriminant system method of polynomial, the rational function solution, the trigonometric function solution, the exponential function solution, and the Jacobian function solution of the Kaup–Boussinesq system with beta derivative are obtained. In order to further explain the propagation of the Kaup–Boussinesq system with beta derivative in water waves, we draw its three-dimensional diagram, two-dimensional diagram, density plot, and contour plot by using Maple software.
 
Dengue is an epidemic disease rapidly spreading throughout many parts of the world, which is a serious public health concern. Understanding disease mechanisms through mathematical modeling is one of the most effective tools for this purpose. The aim of this manuscript is to develop and analyze a dynamical system of PDEs that describes the secondary infection caused by DENV, considering (i) the diffusion due to spatial mobility of cells and DENV particles, (ii) the interactions between multiple target cells, DENV, and antibodies of two types (heterologous and homologous). Global existence, positivity, and boundedness are proved for the system with homogeneous Neumann boundary conditions. Three threshold parameters are computed to characterize the existence and stability conditions of the model’s four steady states. Via means of Lyapunov functional, the global stability of all steady states is carried out. Our results show that the uninfected steady state is globally asymptotically stable if the basic reproduction number is less than or equal to unity, which leads to the disappearance of the disease from the body. When the basic reproduction number is greater than unity, the disease persists in the body with an active or inactive immune antibody response. To demonstrate such theoretical results, numerical simulations are presented.
 
Pictorial representation of the emergency procurement.
The influence of the probability of emergency occurrence and the reserve period on the optimal reserve amount of supplier 1.
The influence of the probability of emergency occurrence and reserve period on the optimal government reserve.
The influence of the probability of occurrence of emergencies and risk avoidance coefficient on optimal government reserves.
The uncertainty of emergencies makes the emergency procurement face many risks, so the risk management is particularly important of the emergency procurement. The risk attitude of decision makers will significantly affect the decision-making of risk management. In this paper, the risk management problem with different risk attitudes of emergency procurement consisting of dual-source suppliers and the single government is studied, and a government-led Stackelberg game is used to analyze the risks of each link to establish an emergency procurement model under the option contract, and the optimal decision-making is obtained. The effects of reserve period, risk avoidance coefficient, and probability of emergency on optimal decision-making are analyzed with different risk attitude. Moreover, we investigate the coordination of the government-led supply chain coordination under the risk aversion and risk-neutral conditions of emergency supply chain participants. The results show that the model can control the risk while reducing the cost of government procurement and ensuring the revenue of suppliers. Finally, the influence of each parameter on the optimization decision is verified by a numerical example.
 
The high-speed development of mobile broadband networks and IoT applications has brought about massive data transmission and data processing, and severe traffic congestion has adversely affected the fast-growing networks and industries. To better allocate network resources and ensure the smooth operation of communications, predicting network traffic becomes an important tool. We investigate in detail the impact of variable sampling rate on traffic prediction and propose a high-speed traffic prediction method using machine learning and recurrent neural networks. We first investigate a VSR-NLMS adaptive prediction method to perform time series prediction dataset transformation. Then, we propose a VSR-LSTM algorithm for real-time prediction of network traffic. Finally, compared with the traditional traffic prediction algorithm based on fixed sampling rate (FSR-LSTM), we simulate the prediction accuracy of the VSR-LSTM algorithm based on the variable sampling rate proposed. The experiment shows that VSR-LSTM has higher traffic prediction accuracy because its sampling rate varies with the traffic.
 
Marine ecological aquaculture is considered a robust scientific farming model, but it has not been widely promoted in China. Although some studies have examined stakeholders’ interests in ecological transformation, few studies to date have analyzed the interaction mechanism of the stakeholders in ecological transformation. Therefore, drawing on evolutionary game theory, this study analyzed the different behavioral strategies and evolutionary mechanisms of the government, marine aquaculture farmers, and aquatic enterprises engaged in marine farming processes. Furthermore, a numerical simulation was conducted to evaluate the rationality of the theoretical model. The results show that several factors affected the ecological transformation of mariculture. Government subsidies reduced farmers’ and aquatic enterprises’ costs of adopting ecological farming. The government’s increasing fines for aquaculture pollution slowed down the speed of the system to a stable point. The cost of adopting ecological farming by farmers and aquatic enterprises will affect their decision to adopt or invest in it. The increase in the market price of eco-aquatic products helped accelerate the ecological transformation of mariculture. The brand effect obtained by aquatic enterprises by investing in ecological farming helped increase participation to further improve this practice. In terms of policy implications, we recommend the promotion and guidance of marine aquaculture technology, government support and investment, environmental control of the aquaculture industry, and brand building of eco-aquatic products to transform and upgrade marine aquaculture farming.
 
Edge Intelligence, which blends Artificial Intelligence (AI) with Radio Access Network (RAN) and edge computing, is recommended as a crucial enabling technology for 6G to accommodate intelligent and efficient applications. In this study, we proposed Edge Intelligent Radio Access Network Architecture (EIRA) by introducing new intelligence modules, which include broadband edge platforms that allow policies to interact with virtualized RAN for various applications. We also developed a Markov chain-based RAN Intelligence Control (RIC) scheduling policy for allocating intelligence elements. Experimental results justified that the virtualized RAN delivers on its performance promises in terms of throughput, latency, and resource utilization.
 
We explore the boundedness and persistence, existence of an invariant rectangle, local dynamical properties about the unique positive fixed point, global dynamics by the discrete-time Lyapunov function, and the rate of convergence of some 2,3-type exponential systems of difference equations. Finally, theoretical results are numerically verified
 
Based on the advection-diffusion equation of suspended sediment, a general formula of vertical distribution of suspended sediment concentration was derived by considering the influence of vertical velocity. The new formula was tested against over 3000 sets of field data and obtained a reasonable agreement. Comparing with the Rouse equation of concentration, the accuracy of the new formula increases significantly, and the shortcoming of the underestimation of the Rouse profile in practical application is revised. Through the analysis of the new formula with different vertical time-averaged velocity coefficients m , it was found that vertical velocity does have an impact on the accurate estimation of sediment concentration, and the extent of which is related to the value of sediment concentration. Utilizing the regression analysis, it was found the vertical time-averaged velocity coefficient m increases with the height above the bed.
 
In this article, we use a finite difference scheme to discretize the Cahn-Hilliard equation with the space step size h . We first prove that this semidiscrete system inherits two important properties, called the conservation of mass and the decrease of the total energy, from the original equation. Then, we show that the semidiscrete system has an attractor on a subspace of ℝ N + 1 . Finally, the convergence of attractors is established as the space step size h of the semidiscrete Cahn-Hilliard equation tends to 0.
 
Optimal prices for the dual sales channels in the time remaining (t).
The optimal prices for the dual sales channels in the remaining inventory level of the offline channel.
Effect of the proportion of consumers with a high low-carbon preference on the optimal prices.
Effect of the low-carbon utility on the optimal prices.
This paper investigates the dynamic pricing strategy for perishable products sold through online and offline channels with the consideration of consumers’ low-carbon preferences. The MNL stochastic utility model is used to describe the purchasing decisions of consumers with different low-carbon preferences. On this basis, we establish a dual-channel dynamic pricing model for perishable products to maximize the firm’s expected revenue by using the dynamic programming method. We also study the influence of consumers’ low-carbon preferences on optimal prices. The conclusions show that the low-carbon utility and the proportion of consumers with high low-carbon preference have positive effects on the optimal prices of the dual sales channels. Moreover, consumers are more inclined to purchase products through the online channel in the presence of low-carbon preference, so the optimal price of the online channel product is higher than that of the offline channel product.
 
This paper designs and implements a methodology to model the evolution of the COVID-19 pandemic, produced by the SARS-CoV-2 virus, in what was called the first wave in Chile, which lasted from March 2 to 31 October 2020. The models are based on sigmoidal growth curves and can be used to predict the number of daily infections and deaths in future days, making them a useful tool for sanitary authorities to manage an epidemic. The methodology is applied to the entire country and to each of its most affected regions. In addition, the dynamics of these models allow it to be nurtured with the new information that is being produced and forecast a tentative date on which there would be some control over the pandemic. Moreover, these models allow for predicting the total number of infected and deceased people at the time the pandemic is under control. However, the simplicity of these models, which consider only the accumulated data of those infected and deceased, does not contemplate an intervention analysis such as vaccinations, which, as is known, are being effective in controlling the pandemic.
 
resholds of retailer altruism's innuence on nrst-period ordering quantities and inventories (a � 1, b � 0.5, c � 0.2, c � 1).
With improvements in consumers’ environmental awareness and the promulgation of environmental regulations, an increasing number of companies are beginning to pay attention to green product design, pricing, and purchasing strategies. However, due to demand fluctuations and cost changes brought about by green product design and manufacturing, understanding corporate behavior preferences and constructing non-single-period pricing and procurement strategies can profoundly affect long-term cooperation among green supply chain members. This paper constructs six scenarios in which decision-makers have altruistic preferences simultaneously or separately and whether the retailer adopts strategic inventory. In addition, the impact of altruistic preferences and strategic inventory on the decision-making and profits of the two-period supply chain for marginal cost-intensive green products (MIGPs) are analyzed. The results show that altruistic preferences and purchasing strategies do not affect MIGPs’ greening levels. Besides, the retailer’s strategic inventory is still an effective bargaining tool but is not necessarily beneficial to profits. Noteworthy, when deciders exhibit altruism simultaneously or alone, the effects on certain decisions and strategic inventory range are significantly different. Finally, the retailer’s altruistic preference may not affect the green supply chain’s profits, but the manufacturer’s altruism improves total profits.
 
The solution of Example 1.
The solution of Example 2.
The UH-stability of Example 2 with ε=0.1,0.01.
Fractional Langevin system has great advantages in describing the random motion of Brownian particles in complex viscous fluid. This manuscript deals with a delayed nonlinear fractional Langevin system with nonsingular exponential kernel. Based on the fixed point theory, some sufficient criteria for the existence and uniqueness of solution are established. We also prove that this system is UH- and UHR-stable attributed to the nonlinear analysis and inequality techniques. As applications, we provide some examples and simulations to illustrate the availability of main findings.
 
With the gradual retirement of the first batch of new energy vehicles in recent years, determining the optimal recycling mode has become an urgent concern. Considering the closed-loop supply chain, the government subsidy system, and different market power structures, this paper studies new energy vehicle recycling decisions and supply chain contract coordination. The results show that given an increase in government subsidies, the profit of the dominant agent in the closed-loop supply chain will be higher than that of the follower, and an increase in wholesale and recovery prices may lead to an increase in sales prices. In addition, the effect of government subsidies on battery recovery is better in cases of vehicle manufacturer dominance than in those of battery manufacturer dominance. Finally, when a battery manufacturer is in the dominant position, a revenue sharing contract can incentivize supply chain coordination; when a vehicle manufacturer is in the dominant position, a two-part tariff contract can realize coordination in the supply chain.
 
The grey model, which is abbreviated as GM (1, 1), has been widely applied in the fields of decision and prediction, particularly in the prediction of time series with few observations, referred to as the poor information and small sample in the literature related to grey model. Previous studies focus on improving the accuracy of prediction but pay less attention to the robustness of the grey model to outliers, which often occur in practice due to an incorrect record by chance or an accidental failure in equipment. To fill that void, we develop a robust grey model, whose structural parameters are obtained from the least trim squares, to forecast Chinese electricity demand. Also, we use the last value in the first-order accumulative generating time series as the initial value, according to the new information priority criterion. We name the novel grey model, proposed in this paper, the novel robust grey model integrating the new information priority criterion, which could be abbreviated as NIPC-GM (1, 1). In addition, we introduce a novel approach, that is, the bootstrapping test, to investigate the robustness against outliers for the novel robust grey model and the classical grey model, respectively. Using the data on Chinese electricity demand from 2011 to 2021, we find that not only does the novel robust grey model integrating the new information priority criterion have a better robustness to outliers than the classical grey model, but it also has a higher accuracy of prediction than the classical grey model. Finally, we apply the novel robust grey model integrating the new information priority criterion to forecasting the future values in Chinese electricity demand during the period 2022 to 2025. We see that Chinese electricity demand would continue to rise in the next four years.
 
This study investigates the effects of resale allowance on entry strategies, seller’s expected revenue, and social welfare in a second-price auction with two-dimensional private information on values and participation costs. We characterize the perfect Bayesian equilibrium in cutoff strategies and identify sufficient conditions under which the equilibrium is unique. Our analysis suggests that resale allowance leads the low-value bidder to become more aggressive on entry, while high-value bidder has a lower incentive to enter. Furthermore, the allowance of resale can increase the social welfare under a sufficient condition, and its effect on expected revenue is ambiguous.
 
A working vacation queueing model is described in this work, with three different classes of customers: regular, priority, and disaster. The regular server serves all arriving customers, whereas the optional reservice is only provided to those who request it. The Bernoulli working vacation (BWV) schedule is considered. In WV time, the server serves at a slower rate. The generating functions (GF) technique is used to determine the system capacity of various server states. Different system performances, reliability indices, and cost optimization values are numerically shown. For the current COVID-19 pandemic situation, the motivation for this approach is presented in a telephonic communication system.
 
The Beijing-Tianjin-Hebei (BTH) synergistic development strategy is an important initiative proposed by China to achieve sustainable regional development in order to reduce regional development disparities. Based on the synthetic control method, this article constructs a quasi-natural experiment using Chinese panel data from 2006 to 2020 to empirically test the impact of the event of BTH cooperative development as a national strategy on the financial development of the BTH region in China. It assesses the policy effects of the strategy on three dimensions of financial development as follows: scale, structure, and efficiency. The results of the study show that BTH synergistic development strategy has a positive effect on the scale of finance and effectively controls the financial liquidity risk in the region but has no significant effect on the optimisation of the financial structure. Therefore, it is necessary to take the sustainable development of the region as the premise to continuously promote the BTH regional synergistic development strategy, reduce the difference in financial scale development in the region, construct a financial efficiency evaluation mechanism, optimise the regional financial structure, and finally, achieve the goal of regional financial synergy and regional sustainable development.
 
In this paper, the well-known Hölder’s inequality is proved via Hahn differential and integral operators, which is a helping tool to establish some Opial-type inequalities via Hahn’s calculus. The weight functions involved in these Opial-type inequalities are positive and monotone. In search of applications, some new as well as some existing inequalities in the literature are obtained by applying suitable limits.
 
This paper analyzes the dynamic time-frequency volatility spillovers among the international stock markets during the Russian-Ukraine conflict. We use the VAR-based connectedness framework to calculate the volatility spillovers. Results show that (1) the trend of the total spillover is consistent with the time of the Russian-Ukraine conflict; (2) Russian stock market is the primary source and net exporter of risk; (3) the Russian government has effectively controlled the further spread of risk through policy adjustments; and (4) Russian stock market may generate long-run volatility spillovers among the international stock market. We add research related to the impact of the Russia-Ukraine conflict on international stock markets by analyzing the results of the volatility spillovers.
 
Digital transformation gives enterprises new development momentum and vitality. The existing research studies mainly focused on the economic consequences of the digital transformation of enterprises, and few literatures pay close attention to the driving factors of digital transformation. In particular, they ignored the important role played by the core element of executives. To fill this gap, this study empirically examines the influence mechanism of executive ownership on enterprise digital transformation based on Chinese A-share manufacturing listed companies from 2008 to 2021. The results show that there is an inverted U-shaped relationship between executive ownership and enterprise digital transformation. When the shareholding ratio is low, “interest convergence” is dominant; when the shareholding ratio is high, “trench defense” is more obvious. Heterogeneity analysis shows that the nature of property rights and the degree of industry competition can alleviate the threshold effect of executive ownership on digital transformation, while ownership concentration will aggravate the threshold effect of executive ownership on digital transformation. This study not only enriches the research on the influencing factors of digital transformation but also provides practical guidance for enterprises to improve the design of incentive mechanism and promote the digital transformation of enterprises.
 
Public safety-related problems exist in all countries, causing the public to fear for their personal safety and that of their property. Maintaining public safety, providing citizens with safe living environments, and realizing sustainable social development are issues that concern not only the public but also local and central governments. Accordingly, this study proposed a measurement model that combines the analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) to obtain public safety ratings. First, this study used the AHP to analyze the contributions of public safety-related criteria, and the relative weights of the criteria were calculated. Subsequently, TOPSIS was used to calculate the relative closeness coefficients between public safety performance and positive-ideal solutions to evaluate public safety performance. The measurement model proposed in this study was used to rate the public safety performance of 22 cities and counties in Taiwan. The results showed that the criterion weights matched the perceptions of the public, and Lienchiang County, Taitung County, and Penghu County had the best public safety performance. The applicability of the proposed measurement model has been confirmed using real-world data. Thus, it can be used to help decision-makers make complex public safety-related decisions.
 
The continuous increase in the market capitalization of digital currencies has determined them to be an essential force driving global financial development. Research on digital currency connectedness has implications for the pricing of related financial products and the development of risk hedging strategies. This study aims to analyse the changing relationship among four prominent digital currencies over time. Our research period covers normal periods, outbreaks, and the post-epidemic phase. A refined TVP-VAR method was adopted to conduct this study, which ensures time-varying analysis and avoids errors caused by the rolling-window size and the calculation of the observation loss. It is found that the total connectedness of major digital currencies is in an upward trend in the majority of the time, which, however, dropped dramatically in 2020 as the epidemic spreads internationally. It is also found that ETH is a consistent spillover transmitter and that although BTC is often shown as a transmitter, its spillover initially declines considerably and then remains weak until recently. BNB and XRP are typically spillover recipients, with BNB’s spillover varying more greatly.
 
A research framework.
Proportion of university network public opinion events.
The study aims to address Chinese universities’ image repair strategies after network public opinion events in the field of crisis management; therefore, it takes 43 network public opinion events in Chinese universities as the research object, encodes the official texts issued by universities according to the image restoration strategy, and sums up the image repair strategies commonly used by Chinese universities. Then, natural language processing is used to conduct the sentiment analysis of the online comments obtained. Accordingly, the sentiment index is constructed to evaluate the effect of Chinese universities’ image repair strategies. We find that Chinese universities commonly use the image repair strategy combination of bolstering, provocation, and corrective action; they have not used the apology strategy commonly used in western discourse systems. We also find that the complete information release process has a better image repair effect, particularly in teachers’ lapse and personal safety events. The sentiment index in teachers’ lapse events is the highest and is related to the universities’ corrective actions. The sentiment index in different public opinion hot events is quite different, which may be related to the nature of specific events. In personal safety events, netizens are more satisfied with image repair strategies.
 
University class scheduling problem is one of the most important and complex issues in the academic eld. is problem is recognized as one of the NP-HARD issues due to its various limitations. On the contrary, genetic algorithms are commonly used to solve NP-HARD problems, which is one of the decision-making problems and is basically one of the most fundamental classes of complexity. e university course planning includes severe constraints such as classroom, classroom curriculum, and faculty. At the same time, some soft constraints should be considered, such as student and faculty preferences and favorite class time. In this research, as a novel contribution, an integer model for scheduling university classes is presented. In this model, the preferences of professors and students are in accordance with the satisfaction values obtained through questionnaires. Moreover, a genetic algorithm has been developed to solve the model. e results show that the classroom timeline by this algorithm goes well during each run. Moreover, considering an exploratory search for the genetic algorithm can greatly improve the performance of this algorithm.
 
Currently, policy instruments are classified mainly by means of manual encoding and checking, which is highly subjective and inefficient, which greatly hinders the development of policy research. The research tries to apply the automatic classification algorithm based on BERT (Bidirectional Encoder Representation from Transformer) to the policy instruments to improve the efficiency and accuracy of policy instruments classification. An entrepreneurship policy instrument classification model was established on the basis of the pretraining language model to realize the automatic classification of entrepreneurship policy instruments. The research showed that through optimization and improvement of the model, the F1 value was 0.86 on the test set, indicating a good classification effect; through the comparative experiment, it was further proved that the classification effect of this model was far superior to other three commonly used text classification models. The model greatly improves the efficiency and objectivity of policy instrument classification and provides a new idea for investigating entrepreneurship policies and more generalized policy instruments.
 
In this paper, a mathematical model for the system of prey-predator with immigrant prey has been analyzed to find an approximate solution for immigrant prey population density, local prey population density, and predator population density. Furthermore, we present a novel soft computing technique named LeNN-WOA-NM algorithm for solving the mathematical model of the prey-predator system with immigrant prey. The proposed algorithm uses a function approximating ability of Legendre polynomials based on Legendre neural networks (LeNNs), global search ability of the whale optimization algorithm (WOA), and a local search mechanism of the Nelder–Mead algorithm. The LeNN-WOA-NM algorithm is applied to study the effect of variations on the growth rate, the force of interaction, and the catching rate of local prey and immigrant prey. The statistical data obtained by the proposed technique establish the effectiveness of the proposed algorithm when compared with techniques in the latest literature. The efficiency of solutions obtained by LeNN-WOA-NM is validated through performance measures including absolute errors, MAD, TIC, and ENSE.
 
Research design.
Localization degree of punishment citation.
Platform of people talking in J City.
Statistics of people's messages to government departments in J City.
Evolution process of governance attention in J City.
Analysing the evolution process of attention allocation in city governance is an effective way to understand modern governance. Among the city types, heavy industry cities are special cities. It relies too much on heavy industry and is difficult to achieve ecological and environmental governance. This study takes J City in Northeast China as an example. Based on big data analysis, this study analyses the evolution process of governance attention allocation in J City. It can be found that the realization of ecological and environmental governance requires people’s participation. City development needs multiple synergies and an ecosystem-led governance model. However, this process is not a subjective product but needs to be promoted by history. Fundamentally, people need to change the logic of economic development.
 
Conceptual model of research.
General model of standard research.
General model of research in a meaningful state.
In the modern era, intellectual capital encompasses all resources within an organization that enhance the value and competence of the organization. Consequently, this indicates that managing intellectual capital effectively will enhance the value and performance of an organization. This study aims to investigate the effects of intellectual capital on business performance through the use of customer knowledge management in the Bank Mellat branches of Iran. In this study, all managers and employees working for Bank Mellat in Tehran are included. Based on Morgan’s table, the sample size was 220 people. Sampling was done by the simple random method. We used a descriptive correlation method to conduct this study and a questionnaire was used to collect data. The questionnaires were scored using a Likert scale. It was confirmed by a consensus of experts that the research instrument was valid, and the reliability of the research was 0.894%. Structural equation modeling was used to analyze the data. According to the results, the dimensions of intellectual capital (human, structural, and relational) have a significant impact on business performance. However, relational capital has been more influential on business performance than other factors.
 
The impact of price-based policies on the financial benchmark rate of return of the investment industry in the past five years.
Capital sources of venture capital.
Transmission mechanism of monetary policy.
Structural proportion relationship of indicators in a long-term debt structure.
Structural proportion relationship of various indicators in a short-term debt structure.
The financial benchmark rate of return is gradually declining, and the free trade port policy is not enough to improve the investment financial benchmark rate of return. Therefore, a comprehensive evaluation model for free trade port investment under the support of macroeconomics is proposed. A theoretical model of free trade port investment price fluctuations and monetary policy response is constructed under the support of macroeconomics, and the model is used to observe the impact of changes in free trade port investment prices on the economic effects of monetary control policies; thus, free trade port investment price fluctuations are constructed and as a result, a theoretical model of investment price fluctuations and interest rate policy responses in the free trade port is constructed to observe the impact of changes in investment prices in the free trade port on the economic effects of interest rate regulation policies; a theoretical model of investment and fiscal and taxation policy responses in the free trade port is constructed to observe the impact of changes in investment in the free trade port on the economic effects of fiscal and taxation policies. Explore the path of optimizing the comprehensive investment evaluation environment in the pilot free trade zone, and realize the development of a comprehensive evaluation model for investment in the free trade port. The experimental results show that the model can realize the comprehensive evaluation of free trade port investment under the support of the macroeconomy and has a good evaluation effect.
 
The traditional meat and poultry farms use a fixed quantity of supply, which creates an imbalance between demand and supply. Due to this imbalance, a huge amount is spent on balancing the requirements. There is an inequality among demand and supply since typical meat and poultry farms use a fixed amount of supply. A lot of money is spent trying to balance the requirements because of this mismatch. In addition, when connecting and building the meat and poultry farm system, the procedure ignores the impact on the environment. The owner’s primary goals are to retain massive profits and raise reliability. The classical method neglects the effect on the environment while linking and designing the meat and poultry farm system. The main aim of the owner is to increase the quality and maintain the maximum profit. This paper deals with the meat and poultry farms in two folds. In the first step, the IoT based system is implemented for the traceability and demand-supply monitoring. The second steps include optimization of the supply network to reduce the carbon emission from the transportation. Both steps take data analytics as an input to process the final result for the farm to run and optimize. Effective inventory optimization algorithms have been shown to be able to evaluate a significant portion of previous sales data and anticipate inventory future demand by taking seasonality and lead times into account. Revenue, productivity, and customer satisfaction are just a few of the business variables that these strategies may affect. Finally, the comparison is done with the traditional farm and supply chain on the points of demand-supply balance, cost, carbon emission, and wastage. It is found that the farms using data analytics to optimize the overall system perform better and with 37% more efficient than the traditional systems.
 
One of the most important aspects of supply chain management (SCM) is the recovery network (RN), which covers all activities associated with return products (such as collection, recovery, repair, recycling, and waste disposal). Our goal in this paper is to provide a new mathematical model of sustainable end-of-life management (SEOLM) during the COVID-19 pandemic for readers. The suggested recovery network model (RNM) can explain the trade-offs between economic (minimizing total costs), environmental (minimizing bad environmental impacts), and social (minimizing bad social impacts) aspects during the pandemic and the great lockdown. A new RN can be designed with a sustainable and hygienic design when taking environmental, economic, and social considerations into account. It proposes guidelines for managers and scholars on how to address recovery network design (RND) challenges during the pandemic through a mathematical article with a sustainable approach. The scalarization approach of a multi-objective mixed-integer programming (MOMIP) problem in this paper is the weighted sum method. The validation of the presented model and the related Pareto frontier has been illustrated by a case study and numerical example. To perform the optimization process, Lingo software is used.
 
In view of the poor initialization performance of ecological environment monitoring network node location, a method of ecological environment monitoring network node location based on Zigbee is proposed. The node data collection model of ecological environment monitoring network is built based on Zigbee, and the performance is stable, which is more suitable for the node location of the ecological environment monitoring network; it is hoped that this study can provide reliable value reference and help for the future ecological studies. Through the installation of different types of sensors, the data of the ecological environment monitoring network nodes are automatically collected and sent to the server. The static weight coefficient of the collected data of the ecological environment monitoring network nodes is modified. According to the modified results, the ecological environment monitoring network is modified by DV-HOP positioning algorithm. The nodes of the ecological environment monitoring network are located by the three-way positioning method. The experimental results show that the initialization performance of this method is better, the accuracy is about 98%, and it is stable. It is more suitable for the node location of ecological environment monitoring network, which mainly includes ZigBee wireless sensor network module, embedded ARM, and Linux.
 
Revenue and expenditure flows.
The phase diagram.
A previous study investigates the advertising strategies of the platform and App by assuming that the platform’s advertisement will increase the number of the App’s users, but the App’s advertisement will not increase the number of the platform’s users; and the platform overcharges the App and takes the advertising fee as a source of its revenue. However, the existing users of the App may recommend the App to the users who have not used it. As a result, the App’s advertisement may increase the number of the platform’s users. Additionally, it is not reasonable that the platform takes the advertising fee of the App as a source of its revenue. These motivate us to reanalyze the previous work. This paper reanalyzes the previous work by additionally assuming that the App’s advertisement will increase the number of the platform’s users and assuming that the platform receives its revenue from its users and shares a proportion of the App’s sales revenue and the App receives its revenues from its users and a proportion of its advertising cost subsidized by the platform. We find that these new assumptions have some significant effects on the previous results. We use dynamical optimization approaches to analyze a decentralized system and find the two parties’ optimal advertisement efforts and proportions. To achieve the efficiency of the integrated system that is proved to be more efficient than the decentralized system, we design a bilateral advertising contact for the decentralized system and show that there exists a unique contact that can coordinate the decentralized system. We find that both parties are better off under some mild conditions and the proportion that the platform bears the App’s advertising cost becomes greater with the contract than without the contract. We have gained some managerial insights.
 
In this paper, the operating mechanism of proposed bandpass filters with a single multimode resonator loaded with branches is introduced. Based on the design procedure, the center frequencies of the proposed bandpass filters can be controlled due to the design freedom. Meanwhile, the proposed bandpass filters (BPFs) feature compact sizes and small insertion loss. To validate the design and analysis, a prototype was fabricated and measured with six passbands centered at 1.23/1.76/2.38/4.24/5.23/6.75 GHz. The measured result of the fabricated filter agrees well with the simulation, which indicates that the proposed structure can serve as a potential candidate for multiband BPF designs.
 
Rating reviews in the game industry aim to enhance the protection of young users, promote game ethics, and prevent negative use of it as a gambling but recent studies and public opinions have reported that the current rating system in South Korea is excessively regulative. To address this issue, this study was conducted to redesign the public game rating system on the Game Ratings and Administration Committee (GRAC) for the public data usage based on the comparison with other better structured media rating systems. The redesigned system utilized a parsing technique to easily access specific data or items, and a Jsoup library was utilized in the Java environment. The system consists of a URL collection module, connection module, detailed collection module, and storage module. If a user requests information from a game rating database through the proposed system, the requested information is arranged sequentially and provided to the user in XML and JSON forms. The designed and implemented collection data were comparatively inclusive and structural to satisfy the public for the better and easier public data usage. This study is expected to help build an environment where game users can obtain information both easily and correctly, and it will eventually lead to a better understanding of the current game industry in South Korea and its clear way to go.
 
The variation trend of waves.
The main purpose of this paper is to study the formation of singularity for a coupled system of wave equations with damping terms, negative mass terms, and divergence form nonlinearities. Upper bound lifespan estimates of solutions to the system are obtained by using the iteration method. The results are the same as the corresponding coupled system of the wave equation with power nonlinearities vpand uq. To the best of our knowledge, the results in Theorems 1–5 are new. In addition, the variation trend of the wave is analyzed by using numerical simulation.
 
Driven by the pain points of the organic food supply chain, which has been plagued by counterfeiting and difficulties in pursuing accountability, this paper investigates a secondary organic food supply chain consisting of suppliers and retailers and establishes two supply chain models under the traditional model and in the blockchain traceability context. In order to effectively solve the problem of unrealized Pareto improvement in organic food supply chain after applying blockchain, a new hybrid contract based on benefit-sharing and cost-sharing is designed to coordinate the supply chain and realize Pareto improvement, and this solution is gradually applied to organic food enterprises. Based on the fact that blockchain can improve trust in the supply chain and eliminate counterfeiting of organic food, the relationship between the rate of genuine products and market demand and the cost of blockchain is established, and then the analysis is developed using the Stackelberg game. We compare the traditional model with the model in the blockchain context and analyze the optimal profit of each supply chain entity, comparing the change in optimal profit before and after the blockchain implementation, and clarifying the cost threshold of the blockchain technology input application. We find that: (i) The adoption of blockchain can not only improve the authenticity of products and combat counterfeit and shoddy organic food, but at the same time, the improvement of organic level in the context of blockchain will also attract some consumers to buy organic food, which will increase the main body of the supply chain and the overall profit. (ii) Blockchain-adopted supply chains are consistently more profitable for all parties and overall than traditional supply chains. The main contribution of this study is that in the organic food supply chain under the application of blockchain technology model, by introducing revenue-sharing and cost-sharing contracts, the profit between each member of the organic food supply chain is further improved than the traditional model, and also, all of them are optimized, which further improves the stability of the supply chain and brings the supply chain to a coordinated state. Finally, in this context, the obtained results show the effectiveness and realistic operational efficiency of the proposed approach for companies compared to traditional single revenue-sharing covenants. A combination of revenue-sharing and cost-sharing covenants is the best approach to solve such problems. In conclusion, it should be noted that the analysis presented in this study will help decision makers choose the most appropriate option among the possible solutions according to their criteria. This proposed framework can also be extended in various cases where profits are out of balance in the organic food supply chain, such as safety and value gain.
 
This study evaluates the profitability and marketability efficiencies of digital firms ranked in Forbes’ list of top companies by using a two-stage network data envelopment analysis (DEA) model with multiplicative efficiency aggregation under the second-order cone programming (SOCP) and examines the respective impacts of the 1995–2001 dot-com bubble and the 2007–2009 global financial crisis on the companies’ efficiencies by applying impulse response function (IRF) analysis. The data of our 49 sampled companies are derived from the Compustat database. The covered period is 1999–2018. These results present the stable and increasing improvement of profitability and marketability efficiencies; in addition, two crisis events have no significant impact on the performance of digital firms. This research is supposed to offer a reasonable and objective evaluation model to measure the performance of digital firms, providing the managers and investors a reference for making their decision.
 
The novel coronavirus disease (COVID-19) pandemic has had devastating effects on healthcare systems and the global economy. Moreover, coronavirus has been found in human feces, sewage, and in wastewater treatment plants. In this paper, we highlight the transmission behavior, occurrence, and persistence of the virus in sewage and wastewater treatment plants. Our approach follows the process of identifying a coronavirus hotspot through existing wastewater plants in major cities of Saudi Arabia. The mathematical distributions, including the log-normal distribution, Gaussian model, and susceptible-exposed-infected-recovered (SEIR) model, are adopted to predict the coronavirus load in wastewater plants. We highlight not only the potential virus removal techniques from wastewater treatment plants but also methods of tracing SARS-CoV-2 in humans through wastewater treatment plants. The results indicate that our modeling approach may facilitate the analysis of SARS-CoV-2 loads in wastewater for early prediction of the epidemic outbreak and provide significant implications to the public health system.
 
One of the main goals of supply chain management is to ensure proper flows of products and information through all nodes to supply them in the right place at the right time. To achieve this objective, it is very important to consider flows of products and finances among supply chain nodes. Traditionally, operational and financial processes have been optimized as separate problems. The developed model addresses the problem of designing a supply chain network and tries to integrate both areas of operations and financial aspects to maximize the value created and measured by the Shareholder Value Analysis (SVA). The results show that with appropriate financial decisions, creating more value for the company and its shareholders is achievable. The developed model with a new financial approach is able to improve the total created shareholder value as much as 0.7% larger than the SVA obtained without financial aspects and 0.93% larger than the value created by the basic model. The main reason for an increase in value creation is due to new operational and financial aspects, which mainly show the possibility of closing facilities and bank debt repayments. To validate and show the applicability of the proposed model, it was solved by GAMS-BARON solver with data provided from the literature. Sensitivity analyses on financial parameters were performed to evaluate the results.
 
Presently, most of the existing rumor detection methods focus on learning and integrating various features for detection, but due to the complexity of the language, these models often rarely consider the relationship between the parts of speech. For the first time, this paper integrated a knowledge graphs and graph attention networks to solve this problem through attention mechanisms. A knowledge graphs can be the most effective and intuitive expression of relationships between entities, providing problem analysis from the perspective of “relationships”. This paper used knowledge graphs to enhance topics and learn the text features by using self-attention. Furthermore, this paper defined a common dependent tree structure, and then the ordinary dependency trees were reshaped to make it generate a motif-dependent tree. A graph attention network was adopted to collect feature representations derived from the corresponding syntax-dependent tree production. The attention mechanism was an allocation mechanism of weight parameters that could help the model capture important information. Rumors were then detected accordingly by using the attention mechanism to combine text representations learned from self-attention and graph representations learned from the graph attention network. Finally, numerous experiments were performed on the standard dataset Twitter, and the proposed model here had achieved a 7.7% improved accuracy rate compared with the benchmark model.
 
The composition of HBase.
Marine fisheries culture scenario.
Relation between the cost bias coefficient and total assets.
Cost dynamic control index distribution.
Descriptive statistical analysis of marine fisheries culture control.
In order to improve the stability of the cost control of marine fishery culture, a method of controlling the cost of marine fishery culture based on big data analysis algorithm was proposed. We establish the cost analysis model of marine fishery, use the big data correlation analysis method to conduct distributed mining on the cost characteristics of marine fishery, deeply grasp the relevance of data characteristics, use the information fusion method to build the constraint parameter analysis model of aquaculture cost operation, limit the amount of calculation, and use the adaptive neural network weighted training method to adaptively optimize the cost control to avoid falling into local optimization. The objective model of aquaculture cost control is established, and the cost constraint is carried out by the parameter optimization method. The statistical feature quantity of marine fishery cost control is obtained, and the cost control of marine fishery is realized by the feature recombination method. The simulation results show that this method is more stable in the cost control of marine fishery culture and improves the adaptability of the cost control of marine fishery culture.
 
Technology forecasting is an important and critical issue that determines the starting point of planning and is considered as a management tool directly related to the future. In the previous research items, the development of renewable energy technologies was of concern. Moreover, due to the increasing need of countries to produce electricity and facing the lack of resources, this research focuses on forecasting photovoltaic technology. Accordingly, in this paper, for technological research in the field of solar energy, the patents extracted from one of the most famous renewable energy databases in the United States (US patent database) between 200 and 2020 were examined. Next, research gaps were analyzed by using the artificial neural network clustering method and also by analyzing covered and uncovered compounds. The results show that in the future, photovoltaic technology research will move towards the third generation of technology (organic materials) as well as focus on environmental parameters and their effects on the performance of photovoltaic systems.
 
How the audience perceive the display of intangible cultural heritages (ICHs) and what are their psychological needs of ICH display are of great value to the display design of ICHs. This article carries out a questionnaire survey on the audience of the “Splendid China” ICH costume show and empirically analyzes the survey results. After constructing the American Customer Satisfaction Index (ACSI), the authors classified the psychological needs of customers for the on-site display of ICHs through analytical hierarchy process (AHP) and factor analysis, conducted the theoretical discussion, and verified the results with actual data. The research explores how should the digital display of ICHs be designed to satisfy the audience and provides a reference for China’s ICH display designers. The research shows that the construction of a public satisfaction evaluation model for the digital display of ICHs is the key to satisfaction evaluation, there is ample room to improve public satisfaction with the digital display of ICHs, the dissemination of ICH can be strengthened through model construction, and personalized service is an effective way to promote the digital development of ICHs.
 
Regression results (H1).
Regression results (H2).
:e endogeneity checks using Panel A and Panel B to show the results after replacing two explanatory variables, re- spectively. .e cross term X1 becomes X3 and X4, respectively.
:e robust checks using fixed effect model to reduce the endogenous issue. Panel A and Panel B show the results for time fixed effects model and industry fixed effect model, respectively.
This paper aims to investigate the impact of managerial ownership on the stock price volatility in China by considering corporate transparency as a mediator. By analyzing data from 558 Chinese listed companies between 2016 and 2020, empirical results from a multiple linear regression model show a positive correlation between managerial ownership and corporate transparency. The results also provide the evidence that the negative correlation between managerial ownership and stock volatility is more (less) pronounced in companies with less (more) transparency. Enterprises should cooperate with financial analysts to increase corporate transparency. Individual investors can analyze the market performance by examining the company’s equity structure, the number of cooperative analysts, and the number of research reports so as to provide more reliable basis for investment.
 
A supply chain is a set of resources, facilities, customers, products, and methods of inventory control, purchasing, and distribution. Every manufacturer or distributor has a subset of the supply chain that they have to manage and implement beneficially and efficiently for their survival and growth. Therefore, the production and delivery of products in the proper amounts and at the right time is as important as minimizing costs as well as customer satisfaction. Considering these reasons, supply chain network optimization includes decisions from many different aspects. In this study, a single-product three-level supply chain with manufacturer-customer loops is considered that customer demand, percentage of returned products from customers, and delivery time from manufacturers to customers are considered as fuzzy variables with uncertainty. Simultaneously considering the issues of selecting suppliers and distributors and determining the effective customers in the system is under uncertainty. This project aims to provide a model that, in addition to integrating the conflicting goals of the loops, also increases the number of uncertain parameters of the model. Therefore, the objectives of the proposed model are to maximize product quality; minimize total cost; minimize delivery time from distributors to customers; and maximize revenue from selling products to customers.
 
Africa’s first COVID-19 case was recorded in Egypt on February 14, 2020. Although it is not as expected by the World Health Organization (WHO) and other international organizations, currently a large number of Africans are getting infected by the virus. In this work, we studied the trend of the COVID-19 outbreak generally in Africa as a continent and in the five African regions separately. The study also investigated the validity of the ARIMA approach to forecast the spread of COVID-19 in Africa. The data of daily confirmed new COVID-19 cases from February 15 to October 16, 2020, were collected from the official website of Our World in Data to construct the autoregressive integrated moving average (ARIMA) model and to predict the trend of the daily confirmed cases through STATA 13 and EViews 9 software. The model used for our ARIMA estimation and prediction was (3, 1, 4) for Africa as a continent, ARIMA (3, 1, 3) for East Africa, ARIMA (2, 1, 3) for West Africa, ARIMA (2, 1, 3) for Central Africa, ARIMA (1, 1, 4) for North Africa, and ARIMA (4, 1, 5) for Southern Africa. Finally, the forecasted values were compared with the actual number of COVID-19 cases in the region. At the African level, the ARIMA model forecasted values and the actual data have similar signs with slightly different sizes, and there were some deviations at the subregional level. However, given the uncertain nature of the current COVID-19 pandemic, it is helpful to forecast the future trend of such pandemics by employing the ARIMA model.
 
Boosting green technology innovation of enterprise is the key to achieving a win-win situation for both environmental performance and economic performance. However, some Chinese enterprises still have hesitations and misgivings as to whether they should adopt green technology. Considering the uncertainty of the innovation and the irrational psychological factors of decision makers, the purpose of this paper is to analyse the driving mechanisms and the long-term behaviour of enterprises green technology innovation, as well as to explore what preconditions are required for enterprises to adopt green technology innovation. The methods are prospect theory and evolutionary games. This paper first calculates the equilibrium stability and evolutionary stability strategies of the enterprise green technology innovation system and then simulates the effect of subjective gains and losses values and other psychological parameters in the prospect editing and evaluation stage. Results show that increase in subjective gain and decrease in reference points and subjective spill benefit will motivate enterprises to adopt green technology innovation in the prospect editing stage; higher risk preference and lower loss aversion will increase enterprises’ motivation for green technology innovation in the prospect evaluation stage. Besides, we find that enterprise decisions are influenced by risk perception and loss aversion rather than just the magnitude of the benefits and cost. Small- and medium-sized enterprises are more likely to turn to green technology innovation than large enterprises under the same level of risk preference and loss aversion. Finally, some suggestions are put forward for the government to encourage enterprises to adopt green technology innovation. This paper can provide a reference for theoretical and practical research on evolutionary game and prospect theory on green technology innovation of enterprises.
 
Journal metrics
$2,300
Article Processing Charges (APC)
42%
Acceptance rate
8 days
Submission to first decision
50 days
Submission to final decision
21 days
Acceptance to publication
1.457 (2021)
Journal Impact Factor™
1.6 (2021)
CiteScore™
Top-cited authors
Manuel De la Sen
  • Universidad del País Vasco / Euskal Herriko Unibertsitatea
Jinde Cao
  • Southeast University (China)
Thabet Abdeljawad
  • Prince Sultan University
Changpin Li
  • Shanghai University
YangQuan Chen
  • University of California, Merced