Spatial location differentiation and development decision optimization of characteristic villages and towns in China

Due to rapid urbanization and industrialization,the gap between urban and rural development has gradually increased.Rural development problems have been a significant topic of discussion,and are related to people’s livelihoods.This article built a point-axis-region location driving system to analyze the spatial location differentiation of characteristic villages and towns(CVTS)using the kernel density model,and explored the mechanism of location driving factors with a geographical detector model.The results show that vegetables and fruits are the main types of products in CVTS.They account for 27.60%and 34.68%of all types of products,and occur mainly in the east and central regions of China.Moreover,all point-axis-region driving factors have a significant influence on grain crops.The mean values of driving forces of vegetables and fruits are larger than other types of CVTS,and their values are 0.12 and 0.11.The average driving forces on all CVTS in the northeast are higher than those in other regions,especially the driving forces of vegetables and medicinal crops(0.24 and 0.18,respectively).Finally,we proposed that the Chinese government should employ engineering technology,invest on road networks,e-commerce and blockchain technology to optimize the point-axis-region location advantages,to promote the sustainable development of CVTS.The detection of driving mechanisms on spatial location differentiation of CVTS has important research value for location theory and rural region systems research.

Distributed optimization of electricity-Gas-Heat integrated energy system with multi-agent deep reinforcement learning

The coordinated optimization problem of the electricity-gas-heat integrated energy system(IES)has the characteristics of strong coupling,non-convexity,and nonlinearity.The centralized optimization method has a high cost of communication and complex modeling.Meanwhile,the traditional numerical iterative solution cannot deal with uncertainty and solution efficiency,which is difficult to apply online.For the coordinated optimization problem of the electricity-gas-heat IES in this study,we constructed a model for the distributed IES with a dynamic distribution factor and transformed the centralized optimization problem into a distributed optimization problem in the multi-agent reinforcement learning environment using multi-agent deep deterministic policy gradient.Introducing the dynamic distribution factor allows the system to consider the impact of changes in real-time supply and demand on system optimization,dynamically coordinating different energy sources for complementary utilization and effectively improving the system economy.Compared with centralized optimization,the distributed model with multiple decision centers can achieve similar results while easing the pressure on system communication.The proposed method considers the dual uncertainty of renewable energy and load in the training.Compared with the traditional iterative solution method,it can better cope with uncertainty and realize real-time decision making of the system,which is conducive to the online application.Finally,we verify the effectiveness of the proposed method using an example of an IES coupled with three energy hub agents.

Adaptive Application Offloading Decision and Transmission Scheduling for Mobile Cloud Computing

Offloading application to cloud can augment mobile devices' computation capabilities for the emerging resource-hungry mobile application, however it can also consume both much time and energy for mobile device offloading application remotely to cloud. In this paper, we develop a newly adaptive application offloading decision-transmission scheduling scheme which can solve above problem efficiently. Specifically, we first propose an adaptive application offloading model which allows multiple target clouds coexisting. Second, based on Lyapunov optimization theory, a low complexity adaptive offloading decision-transmission scheduling scheme has been proposed. And the performance analysis is also given. Finally, simulation results show that,compared with that all applications are executed locally, mobile device can save 68.557% average execution time and 67.095% average energy consumption under situations.

Demand Responsive Market Decision-Makings and Electricity Pricing Scheme Design in Low-Carbon Energy System Environment

The two-way interaction between smart grid and customers will continuously play an important role in enhan-cing the overall efficiency of the green and low-carbon electric power industry and properly accommodating intermittent renewable energy resources.Thus far,the existing electricity pricing mechanisms hardly match the technical properties of smart grid;neither can they facilitate increasing end users participating in the electri-city market.In this paper,several relevant models and novel methods are proposed for pricing scheme design as well as to achieve optimal decision-makings for market participants,in which the mechanisms behind are com-patible with demand response operation of end users in the smart grid.The electric vehicles and prosumers are jointly considered by complying with the technical constraints and intrinsic economic interests.Based on the demand response of controllable loads,the real-time pricing,rewarding pricing and insurance pricing methods are proposed for the retailers and their bidding decisions for the wholesale market are also presented to increase the penetration level of renewable energy.The proposed demand response oriented electricity pricing scheme can provide some useful operational references on the cooperative operation of controllable loads and renewable energy through the feasible retail and wholesale market pricing methods,and thereby enhancing the development of the low-carbon energy system.

Optimal pricing decision model based on activity-based costing

In order to find out the applicability of the optimal pricing decision model based on conventional cost behavior model after activity based costing has given strong shock to the conventional cost behavior model and its assumptions, detailed analyses have been made using the activity based cost behavior and cost volume profit analysis model, and it is concluded from these analyses that the theory behind the construction of optimal pricing decision model is still tenable under activity based costing, but the conventional optimal pricing decision model must be modified as appropriate to the activity based costing based cost behavior model and cost volume profit analysis model, and an optimal pricing decision model is really a product pricing decision model constructed by following the economic principle of maximizing profit.

Private banking development in China under two organizational structures:Economic analysis from an organizational innovation perspective

This paper investigates optimal decisions for private banking development in China under two innovative organizational structures—the big retail mode(BRM)and the independent development mode(IDM).Under the BRM,the retail and private banking divisions form a cooperative relationship wherein the former transfers highnet-worth customers to the latter.In addition,retail banking receives a share of private banking revenues.We investigate the optimal revenue-sharing ratio between the two divisions and the optimal effort by private banking to serve transferred customers within the cooperative relationship.The analytical results show that as the private banking division becomes more developed,the optimal revenue-sharing ratio decreases,and the private banking division’s optimal effort to serve transferred customers decreases because it puts more effort into acquiring new customers.Under the IDM,the two divisions form a competitive relationship since they compete to acquire customers independently.We investigate customer acquisition efforts in this interdivisional competition.Optimal customer acquisition efforts by both divisions increase in potential assets and rates of return.This paper contributes to the literature by(1)analyzing financial innovation by private banks from an organizational perspective;(2)providing an economic analysis for private banking development in China.

Sensor scheduling for ground maneuvering target tracking in presence of detection blind zone

Continuous and stable tracking of the ground maneuvering target is a challenging problem due to the complex terrain and high clutter. A collaborative tracking method of the multisensor network is presented for the ground maneuvering target in the presence of the detection blind zone(DBZ). First, the sensor scheduling process is modeled within the partially observable Markov decision process(POMDP) framework. To evaluate the target tracking accuracy of the sensor, the Fisher information is applied to constructing the reward function. The key of the proposed scheduling method is forecasting and early decisionmaking. Thus, an approximate method based on unscented sampling is presented to estimate the target state and the multi-step scheduling reward over the prediction time horizon. Moreover, the problem is converted into a nonlinear optimization problem, and a fast search algorithm is given to solve the sensor scheduling scheme quickly. Simulation results demonstrate the proposed nonmyopic scheduling method(Non-MSM) has a better target tracking accuracy compared with traditional methods.

Optimal spectrum allocation of primary users in light-handed cognitive radio networks

In traditional cognitive radio(CR) network,secondary users(SUs) are always assumed to obey the rule of"introducing no interference to the primary users(PUs) ".However,this assumption may be not realistic as the CR devices becoming more and more intelligent nowadays.In this paper,with the concept of lighthanded CR,which is proposed to deal with the above mentioned problem by enforcing"punishment"to illegal CR transmissions,the action decisions of primary users(PUs) are modeled as a partially observable Markov decision process(POMDP),and the optimal spectrum allocation scheme with the objective of maximizing their reward is proposed,which is defined by the utility function.Furthermore,a reduced scheme with much smaller state space has been proposed in this paper for lower computational complexity.Extensive simulation results show that the proposed schemes improve the reward significantly compared to the existing scheme.

Differences and relations of objectives, constraints, and decision parameters in the optimization of individual heat exchangers and thermal systems

Performance improvement of heat exchangers and the corresponding thermal systems benefits energy conservation, which is a multi-parameters, multi-objectives and multi-levels optimization problem. However, the optimized results of heat exchangers with improper decision parameters or objectives do not contribute and even against thermal system performance improvement. After deducing the inherent overall relations between the decision parameters and designing requirements for a typical heat exchanger network and by applying the Lagrange multiplier method, several different optimization equation sets are derived, the solutions of which offer the optimal decision parameters corresponding to different specific optimization objectives, respectively. Comparison of the optimized results clarifies that it should take the whole system, rather than individual heat exchangers, into account to optimize the fluid heat capacity rates and the heat transfer areas to minimize the total heat transfer area, the total heat capacity rate or the total entropy generation rate, while increasing the heat transfer coefficients of individual heat exchangers with different given heat capacity rates benefits the system performance. Besides, different objectives result in different optimization results due to their different intentions, and thus the optimization objectives should be chosen reasonably based on practical applications, where the inherent overall physical constraints of decision parameters are necessary and essential to be built in advance.

A drilling technology guided by well-seismic information integration

The predictions by drilling-related mechanical and geological models are in some degree inaccurate due to non-unique solution of seismic velocity model.To address this problem,a new drilling technology guided by well-seismic information integration is proposed which consists of seismic velocity update of drilled formations,seismic velocity prediction of the formation ahead of drilling bit,and the prediction of geological feature and drilling geological environmental factors ahead of bit.In this technology,real information(velocity,formation and depth)behind the drilling bit and local pre-stack seismic data around the wellbore being drilled are used to correct the primitive seismic velocity field for a re-migration of seismic data and to update geological features and drilling geological environmental factors ahead of the drilling bit.Field application shows that this technology can describe and predict the geological features,drilling geological environmental factors and complex drilling problems ahead of the bit timely and improve the prediction efficiency and accuracy greatly.These new updated results are able to provide scientific basis for optimizing drilling decisions.

Digital Twin-Driven Intelligent Construction:Features and Trends

Digital twin(DT)can achieve real-time information fusion and interactive feedback between virtual space and physical space.This technology involves a digital model,real-time information management,comprehensive intelligent perception networks,etc.,and it can drive the rapid conceptual development of intelligent construction(IC)such as smart factories,smart cities,and smart medical care.Nevertheless,the actual use of DT in IC is partially pending,with numerous scientific factors still not clarified.An overall survey on pending issues and unsolved scientific factors is needed for the development of DT-driven IC.To this end,this study aims to provide a comprehensive review of the state of the art and state of the use of DT-driven IC.The use of DT in planning,design,manufacturing,operation,and maintenance management of IC is demonstrated and analyzed,following which the driving functions of DT in IC are detailed from four aspects:information perception and analysis,data mining and modeling,state assessment and prediction,intelligent optimization and decision-making.Furthermore,the future direction of research,using DT in IC,is presented with some comments and suggestions.This work will help researchers gain in-depth and systematic understanding of the use of DT,and help practitioners to better promote its implementation in IC.

Repair and Strengthening Assessment of Existing Bridges Based on Time-Dependent Reliability Analysis

The strategy on repair and strengthening of existing bridges based on time-dependent reliability was analyzed with the maximum expected benefit as the objective function. A sample of risk-ranking decision was illustrated based on updated inspection information with 35 survival age. The effect of improvement of live loads and difference of repair methods on time-dependent reliability of existing bridges are considered. The results show that the decision method can be used in real project, with the cost of failure consequence and the risk of failure considered.

Artificial Logging or Natural Growth

Global climate change makes forestry carbon sequestration a hot issue. In order to improve the comprehensive benefits of forest management, this paper studies the carbon accounting problem, and uses the forest stock conversion factor method to create a carbon sequestration accounting model based on the reserve transformation method. Then, the HWP carbon sequestration accounting algorithm is obtained after the improvement of the reserve change method and the atmospheric flow method with the HWP half-life as a bridge. Based on the ecological and economic benefits, a multi-objective and multi-attribute decision-making model for forest management plan is constructed, and the optimal strategy of stand structure based on selective cutting is proposed. Finally, the entropy weight TOPSIS method is used to quantitatively analyze the comprehensive benefit value and provide suggestions for forestry departments. To verify the model, we chose the Greater Khingan Mountains forest region as the research site. Through successive iterations of CSAM, we calculate that the forest will absorb 534 million tons of live forest and forest products in 100 years. From the stand structure of the forest area, when the selected cutting intensity is 20% and the selected cutting cycle is 10.7 years, the comprehensive benefit value of the Greater Khingan Mountains is the highest.

Supply chain decisions and coordination in the presence of an imperfect spot market

This study considers a supply chain consisting of a commodity supplier and a final product manufacturer with uncertain demand.In addition to purchasing from the supplier through a forward contract,the manufacturer can adjust their inventory by trading the commodity in an online spot market after observing the actual demand.However,the spot market is imperfect in that transactions cannot be certainly realized and come with additional transaction costs.Furthermore,the spot price is volatile such that overly relying on the spot market is unwise.To investigate how the spot market affects the decisions and coordination in a supply chain,we develop a game-theoretical model incorporating spot trading.We derive the optimal ordering decision in a centralized supply chain,as well as the supplier's and manufacturer's equilibrium pricing and ordering decisions in a decentralized supply chain.The impact of the imperfect spot market on the optimal decisions and profits is analyzed.This study also demonstrates how the supply chain can be coordinated in the presence of an imperfect spot market.Finally,a numerical analysis is performed to examine the analytical results.Our results indicate that the spot market can generally improve the performance of the centralized supply chain and benefit the manufacturer in the decentralized one.However,it can be detrimental to the supplier.The supply chain can be coordinated by a revenue-sharing contract,and both parties'profits can be improved.Our findings suggest that the manufacturer could take advantage of the spot market,and the supplier should attempt to integrate or coordinate the supply chain to share the benefits of spot trading.

FUZZY OPTIMAL DECISIONS FOR MULTIOBJECTIVE FLOOD CONTROL SYSTEM OF CASCADE RESERVOIRS

Based on the practice of reservoir operation, a decision making model of multi objective flood control of cascade reservoir system is presented, which is realistic and effective, combining traditional optimal techniques with fuzzy set theory. In the present paper, the operation alternative is chosen as the decision variable, the outflow process as the state variable, and optimal operation decisions are determined by the fuzzy optimal technique. Finally, the model is delineated by taking a case study of comprehensive flood operation for a cascade reservoir system.

STRONG N-DISCOUNT AND FINITE-HORIZON OPTIMALITY FOR CONTINUOUS-TIME MARKOV DECISION PROCESSES

This paper studies the strong n(n =—1,0)-discount and finite horizon criteria for continuoustime Markov decision processes in Polish spaces.The corresponding transition rates are allowed to be unbounded,and the reward rates may have neither upper nor lower bounds.Under mild conditions,the authors prove the existence of strong n(n =—1,0)-discount optimal stationary policies by developing two equivalence relations:One is between the standard expected average reward and strong—1-discount optimality,and the other is between the bias and strong 0-discount optimality.The authors also prove the existence of an optimal policy for a finite horizon control problem by developing an interesting characterization of a canonical triplet.

EXISTENCE OF OPTIMAL POLICY FOR TIME NON-HOMOGENEOUS DISCOUNTED MARKOVIAN DECISION PR0GRAMMING

In this paper we discuss the discrete, time non--homogeneous discounted Markovian decisionprogramming, where the state space and all action sets are countable. Suppose that the optimumvalue function is finite. We give the necessary and sufficient conditions for the existence of anoptimal policy. Suppose that the absolute mean of rewards is relatively bounded. We also give thenecessary and sufficient conditions for the existence of an optimal policy.

Optimal advertising duration for profit maximization

The current research elucidates the advertising scheme of automotive innovation by incorporating the various stages of the product life cycle.The study proposes an empirical model for the automotive industry to evaluate a time-point known as a switch-point or a take-off point at which firms should modify the advertising and sales promotion strategies to boost sales volume.The problem applies a time-series innovation diffusion model wherein adoption rate changes when a product enters a growth stage and then again when the company stops the advertising campaign in the maturity stage.The present paper develops a profit maximization problem,which optimizes the overall advertising duration and advertising take-off point.A numerical illustration is provided using the actual sales data of automobile industries,and sensitivity analysis is further performed to validate the effect of critical parameters on the optimization problem.

Analysis of Green Technology Upgrading Strategy Based on Collaborative Incentive of Environmental Policy and Green Finance

With the continuous development of economy, the bottleneck problem of environmental resources has become increasingly prominent. Enterprise environmental governance technology innovation incentive has become an important issue for the development of government, society and enterprises. Under the control of enterprise's expected economic target, this paper discusses the synergistic incentive effect of environmental policy and green finance on enterprise's environmental governance technology innovation decision by using nonlinear programming model. The results show that when the funds for environmental governance technological innovation are insufficient, there is an optimal decision space to use green financial loans to implement technological innovation and upgrade, and then achieve the expected economic goals;Under a given level of environmental governance technology, environmental policies affect whether enterprises can make decisions on technological innovation and upgrading of environmental governance;Green financial mechanism will not. However, when the enterprise makes the decision of environmental governance technology upgrading, it will affect the enterprise's decision on green financial loan amount. The results of the study have guiding significance for the formulation of environmental policy and green financial policy, as well as the decision-making of enterprise environmental governance technology innovation and upgrading.

A Survey on Noncooperative Games and Distributed Nash Equilibrium Seeking over Multi-Agent Networks

The work gives a review on the distributed Nash equilibrium seeking of noncooperative games in multi-agent networks,which emerges as one of the frontier research topics in the area of systems and control community.Firstly,we give the basic formulation and analysis of noncooperative games with continuous action spaces,and provide the motivation and basic setting for distributed Nash equilibrium seeking.Then we introduce both the gradient-based algorithms and best-response based algorithms for various type of games,including zero-sum games,aggregative games,potential games,monotone games,and multi-cluster games.In addition,we provide some applications of noncooperative games.