Contract Mechanism of Water Environment Regulation for Small and Medium Sized Enterprises Based on Optimal Control Theory

The small and scattered enterprise pattern in the county economy has formed numerous sporadic pollution sources, hindering the centralized treatment of the water environment, increasing the cost and difficulty of treatment. How enterprises can make reasonable decisions on their water environment behavior based on the external environment and their own factors is of great significance for scientifically and effectively designing water environment regulation mechanisms. Based on optimal control theory, this study investigates the design of contractual mechanisms for water environmental regulation for small and medium-sized enterprises. The enterprise is regarded as an independent economic entity that can adopt optimal control strategies to maximize its own interests. Based on the participation of multiple subjects including the government, enterprises, and the public, an optimal control strategy model for enterprises under contractual water environmental regulation is constructed using optimal control theory, and a method for calculating the amount of unit pollutant penalties is derived. The water pollutant treatment cost data of a paper company is selected to conduct empirical numerical analysis on the model. The results show that the increase in the probability of government regulation and public participation, as well as the decrease in local government protection for enterprises, can achieve the same regulatory effect while reducing the number of administrative penalties per unit. Finally, the implementation process of contractual water environmental regulation for small and medium-sized enterprises is designed.

Feasibility of Scaling up the Cost-Competitive and Clean Electrolytic Hydrogen Supply in China

Scaling up clean hydrogen supply in the near future is critical to achieving China’s hydrogen development target.This study established an electrolytic hydrogen development mechanism considering the generation mix and operation optimization of power systems with access to hydrogen.Based on the incremental cost principle,we quantified the provincial and national clean hydrogen production cost performance levels in 2030.The results indicated that this mechanism could effectively reduce the production cost of clean hydrogen in most provinces,with a national average value of less than 2 USD·kg^(-1) at the 40-megaton hydrogen supply scale.Provincial cooperation via power transmission lines could further reduce the production cost to 1.72 USD·kg^(-1).However,performance is affected by the potential distribution of hydrogen demand.From the supply side,competitiveness of the mechanism is limited to clean hydrogen production,while from the demand side,it could help electrolytic hydrogen fulfil a more significant role.This study could provide a solution for the ambitious development of renewables and the hydrogen economy in China.

The Role of Teachers’Apperception in Herbart’s Theory for Chinese Teaching in Shenzhen Primary Schools of China

This paper deeply analyzes the practical application of Herbart’s educational concept in contemporary education and teaching,especially its guiding significance to the primary school Chinese classroom in Shenzhen area.Herbart proposed that teachers should play a key role in students’cognitive process,that is,help students better understand and master new knowledge by combining it with existing knowledge.The survey results show that although teachers have some understanding of Herbart’s educational philosophy,it is not widely used in the concrete teaching process.However,the study also shows that there is a positive relationship between Herbart’s theory and students’academic performance,indicating that it plays an important role in improving students’interest and engagement in learning.The research also reveals the differences between educational concepts and practices,as well as the uneven distribution of educational resources,and puts forward measures to alleviate these contradictions,such as strengthening teacher training,improving teaching methods,and emphasizing moral education.At the same time,the study also highlights the critical role of teachers in promoting knowledge integration,arousing learning enthusiasm,shaping students’moral character,and promoting personal and professional growth.

Optimal Portfolio Selection with Delay under the Framework of Uncertainty Theory

This study focuses on investigating the optimal investment strategy for an optimization problem with delay using the uncertainty theory. The financial market is composed of a risk-free asset and a risk asset with an uncertain price process described by an uncertain differential equation. An optimization problem is assumed that its objective is a nonlinear function of decision variable. By deriving the equation of optimality, an analytical solution is obtained for the optimal delay investment strategy, and the optimal delay value function. Finally, an economic analysis and numerical sensitivity analysis are conducted to evaluate the research results.

A game-theoretic approach for federated learning:A trade-off among privacy,accuracy and energy

Benefiting from the development of Federated Learning(FL)and distributed communication systems,large-scale intelligent applications become possible.Distributed devices not only provide adequate training data,but also cause privacy leakage and energy consumption.How to optimize the energy consumption in distributed communication systems,while ensuring the privacy of users and model accuracy,has become an urgent challenge.In this paper,we define the FL as a 3-layer architecture including users,agents and server.In order to find a balance among model training accuracy,privacy-preserving effect,and energy consumption,we design the training process of FL as game models.We use an extensive game tree to analyze the key elements that influence the players’decisions in the single game,and then find the incentive mechanism that meet the social norms through the repeated game.The experimental results show that the Nash equilibrium we obtained satisfies the laws of reality,and the proposed incentive mechanism can also promote users to submit high-quality data in FL.Following the multiple rounds of play,the incentive mechanism can help all players find the optimal strategies for energy,privacy,and accuracy of FL in distributed communication systems.

Exploring the impact of high density planting system and deficit irrigation in cotton(Gossypium hirsutum L.):a comprehensive review

Lessons learned from past experiences push for an alternate way of crop production.In India,adopting high density planting system(HDPS)to boost cotton yield is becoming a growing trend.HDPS has recently been considered a replacement for the current Indian production system.It is also suitable for mechanical harvesting,which reducing labour costs,increasing input use efficiency,timely harvesting timely,maintaining cotton quality,and offering the potential to increase productivity and profitability.This technology has become widespread in globally cotton growing regions.Water management is critical for the success of high density cotton planting.Due to the problem of freshwater availability,more crops should be produced per drop of water.In the high-density planting system,optimum water application is essential to control excessive vegetative growth and improve the translocation of photoassimilates to reproductive organs.Deficit irrigation is a tool to save water without compromising yield.At the same time,it consumes less water than the normal evapotranspiration of crops.This review comprehensively documents the importance of growing cotton under a high-density planting system with deficit irrigation.Based on the current research and combined with cotton production reality,this review discusses the application and future development of deficit irrigation,which may provide theoretical guidance for the sustainable advancement of cotton planting systems.

Spatiotemporal Evolution of Urban Heat Islands and Optimization of Spatial Network Construction in the Central Urban Area of Fuzhou City, China

Although accelerated urbanization has led to economic prosperity,it has also resulted in urban heat island effects.Therefore,identifying methods of using limited urban spaces to alleviate heat islands has become an urgent issue.In this study,we assessed the spatiotemporal evolution of urban heat islands within the central urban area of Fuzhou City,China from 2010 to 2019.This assessment was based on a morphological spatial pattern analysis(MSPA)model and an urban thermal environment spatial network constructed us-ing the minimum cumulative resistance(MCR)model.Optimization measures for the spatial network were proposed to provide a theor-etical basis for alleviating urban heat islands.The results show that the heat island area within the study area gradually increased while that of urban cold island area gradually decreased.The core area was the largest of the urban heat island patch landscape elements with a significant impact on other landscape elements,and represented an important factor underlying urban heat island network stability.The thermal environment network revealed a total of 197 thermal environment corridors and 93 heat island sources.These locations were then optimized according to the current land use,which maximized the potential of 1599.83 ha.Optimization based on current land use led to an increase in climate resilience,with effective measures showing reduction in thermal environment spatial network structure and function,contributing to the mitigation of urban heat island.These findings support the use of current land use patterns during urban heat island mitigation measure planning,thus providing an important reference basis for alleviating urban heat island effects.

Game Theory Optimization via Diverse Genetic Crossover Intelligence

Game theory is explored via a maze application where combinatorial optimization occurs with the objective of traversing through a defined maze with an aim to enhance decision support and locate the optimal travel sequence while minimizing computation time. This combinatorial optimization approach is initially demonstrated by utilizing a traditional genetic algorithm (GA), followed by the incorporation of artificial intelligence utilizing embedded rules based on domain-specific knowledge. The aim of this initiative is to compare the results of the traditional and rule-based optimization approaches with results acquired through an intelligent crossover methodology. The intelligent crossover approach encompasses a two-dimensional GA encoding where a second chromosome string is introduced within the GA, offering a sophisticated means for chromosome crossover amongst selected parents. Additionally, parent selection intelligence is incorporated where the best-traversed paths or population members are retained and utilized as potential parents to mate with parents selected within a traditional GA methodology. A further enhancement regarding the utilization of saved optimal population members as potential parents is mathematically explored within this literature.

Original optimal method to solve the all-pairs shortest path problem: Dhouib-matrix-ALL-SPP

The All-pairs shortest path problem(ALL-SPP)aims to find the shortest path joining all the vertices in a given graph.This study proposed a new optimal method,Dhouib-matrix-ALL-SPP(DM-ALL-SPP)to solve the ALL-SPP based on column-row navigation through the adjacency matrix.DM-ALL-SPP is designed to generate in a single execution the shortest path with details among all-pairs of vertices for a graph with positive and negative weighted edges.Even for graphs with a negative cycle,DM-ALL-SPP reported a negative cycle.In addition,DM-ALL-SPP continues to work for directed,undirected and mixed graphs.Furthermore,it is characterized by two phases:the first phase consists of adding by column repeated(n)iterations(where n is the number of vertices),and the second phase resides in adding by row executed in the worst case(n∗log(n))iterations.The first phase,focused on improving the elements of each column by adding their values to each row and modifying them with the smallest value.The second phase is emphasized by rows only for the elements modified in the first phase.Different instances from the literature were used to test the performance of the proposed DM-ALL-SPP method,which was developed using the Python programming language and the results were compared to those obtained by the Floyd-Warshall algorithm.

Evaluation method and optimization strategies of resilience for air&space defense system of systems based on kill network theory and improved self-information quantity

Resilience of air&space defense system of systems(SoSs)is critical to national air defense security.However,the research on it is still scarce.In this study,the resilience of air&space defense SoSs is firstly defined and the kill network theory is established by combining super network and kill chain theory.Two cases of the SoSs are considered:(a)The kill chains are relatively homogenous;(b)The kill chains are relatively heterogenous.Meanwhile,two capability assessment methods,which are based on the number of kill chains and improved self-information quantity,respectively,are proposed.The improved self-information quantity modeled based on nodes and edges can achieve qualitative and quantitative assessment of the combat capability by using linguistic Pythagorean fuzzy sets.Then,a resilient evaluation index consisting of risk response,survivability,and quick recovery is proposed accordingly.Finally,network models for regional air defense and anti-missile SoSs are established respectively,and the resilience measurement results are verified and analyzed under different attack and recovery strategies,and the optimization strategies are also proposed.The proposed theory and method can meet different demands to evaluate combat capability and optimize resilience of various types of air&space defense and similar SoSs.

A survey of multi-modal learning theory

Deep multi-modal learning,a rapidly growing field with a wide range of practical applications,aims to effectively utilize and integrate information from multiple sources,known as modalities.Despite its impressive empirical performance,the theoretical foundations of deep multi-modal learning have yet to be fully explored.In this paper,we will undertake a comprehensive survey of recent developments in multi-modal learning theories,focusing on the fundamental properties that govern this field.Our goal is to provide a thorough collection of current theoretical tools for analyzing multi-modal learning,to clarify their implications for practitioners,and to suggest future directions for the establishment of a solid theoretical foundation for deep multi-modal learning.

Developments and Applications of Neutrosophic Theory in Civil Engineering Fields:A Review

Neutrosophic theory can effectively and reasonably express indeterminate,inconsistent,and incomplete information.Since Smarandache proposed the neutrosophic theory in 1998,neutrosophic theory and related research have been developed and applied to many important fields.Indeterminacy and fuzziness are one of the main research issues in the field of civil engineering.Therefore,the neutrosophic theory is very suitable for modeling and applications of civil engineering fields.This review paper mainly describes the recent developments and applications of neutrosophic theory in four important research areas of civil engineering:the neutrosophic decision-making theory and applied methods,the neutrosophic evaluation methods and applications of slope stability,the neutrosophic expressions and analyses of rock joint roughness coefficient,and the neutrosophic structural optimization methods and applications.In terms of these research achievements in the four areas of civil engineering,the neutrosophic theory demonstrates its advantages in dealing with the indeterminate and inconsistent issues in civil engineering and the effectiveness and practicability of existing applied methods.In the future work,the existing research results will be further improved and extended in civil engineering problems.In addition,the neutrosophic theory will also have better application prospects in other fields of civil engineering.

Optimal Configuration Method for the Installed Capacity of the Solar-Thermal Power Stations

Because of the randomness of wind power and photovoltaic(PV)output of new energy bases,the problem of peak regulation capability and voltage stability of ultra-high voltage direct current(UHVDC)transmission lines,we proposed an optimum allocation method of installed capacity of the solar-thermal power station based on chance constrained programming in this work.Firstly,we established the uncertainty model of wind power and PV based on the chance constrained planning theory.Then we used the K-medoids clusteringmethod to cluster the scenarios considering the actual operation scenarios throughout the year.Secondly,we established the optimal configuration model based on the objective function of the strongest transient voltage stability and the lowest overall cost of operation.Finally,by quantitative analysis of actual wind power and photovoltaic new energy base,this work verified the feasibility of the proposed method.As a result of the simulations,we found that using the optimal configuration method of solar-thermal power stations could ensure an accurate allocation of installed capacity.When the installed capacity of the solar-thermal power station is 1×106 kW,the transient voltage recovery index(TVRI)is 0.359,which has a strong voltage support capacity for the system.Based on the results of this work,the optimal configuration of the installed capacity of the solar-thermal power plant can improve peak shaving performance,transient voltage support capability,and new energy consumption while satisfying the Direct Current(DC)outgoing transmission premise.

Game theory approach to optimal capital cost allocation in pollution control

This paper tries to integrate game theory, a very useful tool to resolve conflict phenomena, with optimal capital cost allocation issue in total emission control. First the necessity of allocating optimal capital costs fairly and reasonably among polluters in total emission control was analyzed. Then the possibility of applying game theory to the issue of the optimal capital cost allocation was expounded. Next the cooperative N person game model of the optimal capital cost allocation and its solution ways including method based on Shapley value, least core method, weak least core methods, proportional least core method, CGA method, MCRS method and so on were delineated. Finally through application of these methods it was concluded that to apply game theory in the optimal capital cost allocation issue is helpful to implement the total emission control planning schemes successfully, to control pollution effectively, and to ensure sustainable development.

Integrating Activity-Based Costing （ABC） and Theory of Constraint （TOC） for Improved and Sustained Cost Management

Implementing effective cost management approaches has recently gained momentum due to intense competition and increasing customer demands. Moreover, effective cost management approaches have contributed to firms＇ competitive advantage in relation to cost leadership strategy. Consequently, firms have implemented contemporary cost management systems, such as activity-based management, business process re-engineering, life-cycle costing, target costing, and theory of constraint （TOC）, to enable them to become low-cost producers and compete effectively and sustain their performance. Furthermore, focusing on cost management to improve profitability has led to the integration of activity-based costing （ABC） and TOC. Therefore, the aim of this study is to review literature and discuss how integration of ABC and TOC can result in improved and sustained cost management. While these methods have different approaches in addressing cost management, treating them as complementary cost management approaches can result in improved cost management due to improved product costing, improved cost reporting, improved product-mix decisions, and improved cycle-time management. Improvement in cost management will then result in sustained cost management. Sustained cost management is further enhanced with the investment in information customer and shareholder value technology that supports cross-functional decision making to continue creating to remain competitive in the market.

Application of Grey System Theory in Design For Cost (DFC)

Design For Cost (DFC) is a branch of Design For X (DFX). In this paper, wedefined DFC as a design method that analyzed and evaluated the product's life cycle cost (LCC), thenmodified the design to reduce the LCC. Nowadays it is a very difficult thing to obtain LCC data inChina or in developing countries. Statistical methods can not be used because available LCC data arefew. In order to solve this problem, we used grey system theory. Then relations of cost factorswere analyzed in LCC using grey relevant methods, and a GM(1,1) model between design parameters andLCC was established. Using this model, we can estimate and control LCC by changing design parametersat the beginning of the design stage.

The Optimal Solution of the Model with Physical and Human Capital Adjustment Costs

We prove that the model with physical and human capital adjustment costs has optimal solution when the production function is increasing return and the structure of vetor fields of the model changes substantially when the prodution function from decreasing return turns to increasing return. And it is shown that the economy is improved when the coefficients of adjustment costs become small. Key words optimal solution - nonzero equilibrium - adjustment costs CLC number O 29 Foundation item: Supported by the National Natural Science Foundation of China (79970104)Biography: RAO Lan-lan (1978-), female, Master candidate, research direction: mathematical economy.

Optimal Adaptive Control of a Class of Stochastic Systems Using Game Theory

This paper attempts to study a optimal adaptive con tr ol problem using game theory, and proposes an important practical result that an adaptive processes is a set of sufficient conditions under which pure strategy is essentially complete, and thus the fact that yield a very useful desirable pu re optimal control rule.

A Novel PSO Algorithm for Optimal Production Cost of the Power Producers with Transient Stability Constraints

The application of a novel Particle Swarm Optimization (PSO) method called Fitness Distance Ratio PSO (FDR PSO) algorithm is described in this paper to determine the optimal power dispatch of the Independent Power Producers (IPP) with linear ramp model and transient stability constraints of the power producers. Generally the power producers must respond quickly to the changes in load and wheeling transactions. Moreover, it becomes necessary for the power producers to reschedule their power generation beyond their power limits to meet vulnerable situations like credible contingency and increase in load conditions. During this process, the ramping cost is incurred if they violate their permissible elastic limits. In this paper, optimal production costs of the power producers are computed with stepwise and piecewise linear ramp rate limits. Transient stability limits of the power producers are also considered as addi-tional rotor angle inequality constraints while solving the Optimal Power Flow (OPF) problem. The proposed algo-rithm is demonstrated on practical 10 bus and 26 bus systems and the results are compared with other optimization methods.

New judging model of fuzzy cluster optimal dividing based on rough sets theory

To investigate the judging problem of optimal dividing matrix among several fuzzy dividing matrices in fuzzy dividing space, correspondingly, which is determined by the various choices of cluster samples in the totality sample space, two algorithms are proposed on the basis of the data analysis method in rough sets theory： information system discrete algorithm （algorithm 1） and samples representatives judging algorithm （algorithm 2）. On the principle of the farthest distance, algorithm 1 transforms continuous data into discrete form which could be transacted by rough sets theory. Taking the approximate precision as a criterion, algorithm 2 chooses the sample space with a good representative. Hence, the clustering sample set in inducing and computing optimal dividing matrix can be achieved. Several theorems are proposed to provide strict theoretic foundations for the execution of the algorithm model. An applied example based on the new algorithm model is given, whose result verifies the feasibility of this new algorithm model.