Evolutionary dynamics under periodic switching of update rules on regular networks

Microscopic strategy update rules play an important role in the evolutionary dynamics of cooperation among interacting agents on complex networks. Many previous related works only consider one \emph{fixed} rule, while in the real world, individuals may switch, sometimes periodically, between rules. It is of particular theoretical interest to investigate under what conditions the periodic switching of strategy update rules facilitates the emergence of cooperation. To answer this question, we study the evolutionary prisoner's dilemma game on regular networks where agents can periodically switch their strategy update rules. We accordingly develop a theoretical framework of this periodically switched system, where the replicator equation corresponding to each specific microscopic update rule is used for describing the subsystem, and all the subsystems are activated in sequence. By utilizing switched system theory, we identify the theoretical condition for the emergence of cooperative behavior. Under this condition, we have proved that the periodically switched system with different switching rules can converge to the full cooperation state. Finally, we consider an example where two strategy update rules, that is, the imitation and pairwise-comparison updating, are periodically switched, and find that our numerical calculations validate our theoretical results

Neutrophil Extracellular Traps Promote Inflammatory Responses in Psoriasis via Activating Epidermal TLR4/IL-36R Crosstalk

Epidermal infiltration of neutrophils is a hallmark of psoriasis, where their activation leads to release of neutrophil extracellular traps (NETs). The contribution of NETs to psoriasis pathogenesis has been unclear, but here we demonstrate that NETs drive inflammatory responses in skin through activation of epidermal TLR4/IL-36R crosstalk. This activation is dependent upon NETs formation and integrity, as targeting NETs with DNase I or CI-amidine in vivo improves disease in the imiquimod (IMQ)-induced psoriasis-like mouse model, decreasing IL-17A, lipocalin2 (LCN2), and IL-36G expression. Proinflammatory activity of NETs, and LCN2 induction, is dependent upon activation of TLR4/IL-36R crosstalk and MyD88/nuclear factor-kappa B (NF-κB) down-stream signaling, but independent of TLR7 or TLR9. Notably, both TLR4 inhibition and LCN2 neutralization alleviate psoriasis-like inflammation and NETs formation in both the IMQ model and K14-VEGF transgenic mice. In summary, these results outline the mechanisms for the proinflammatory activity of NETs in skin and identify NETs/TLR4 as novel therapeutic targets in psoriasis

Adaptive Nonlinear Model Predictive Control of the Combustion Efficiency under the NOx Emissions and Load Constraints

A data-driven modeling method with feature selection capability is proposed for the combustion process of a station boiler under multi-working conditions to derive a nonlinear optimization model for the boiler combustion efficiency under various working conditions. In this approach, the principal component analysis method is employed to reconstruct new variables as the input of the predictive model, reduce the over-fitting of data and improve modeling accuracy. Then, a k-nearest neighbors algorithm is used to classify the samples to distinguish the data by the different operating conditions. Based on the classified data, a least square support vector machine optimized by the differential evolution algorithm is established. Based on the boiler key parameter model, the proposed model attempts to maximize the combustion efficiency under the boiler load constraints, the nitrogen oxide (NOx) emissions constraints and the boundary constraints. The experimental results based on the actual production data, as well as the comparative analysis demonstrate: (1) The predictive model can accurately predict the boiler key parameters and meet the demands of boiler combustion process control and optimization; (2) The model predictive control algorithm can effectively control the boiler combustion efficiency, the average errors of simulation are less than 5%. The proposed model predictive control method can improve the quality of production, reduce energy consumption, and lay the foundation for enterprises to achieve high efficiency and low emission

Controller Design Based on Echo State Network with Delay Output for Nonlinear System

For the nonlinear systems with delay output, the control performance of the system is affected by the previous output of the system, such as crawling robots of photovoltaic panels. In this paper, an improved controller design method based on echo state network with delay output (DO-ESN) is proposed for designing the controller of a class of nonlinear system. According to the internal characteristics of DO-ESN, the DO-ESN can match the system characteristics of nonlinear systems with delay output, such that the proposed controller can quickly meet the control performance of the nonlinear system. In order to ensure the stability of the controller, a sufficient condition is given for the echo state property of DO-ESN. Finally, a simulation example is used to illustrate the effectiveness of the proposed method

A Fully Distributed Approach for Economic Dispatch Problem of Smart Grid

The cooperative, reliable and responsive characteristics make smart grid more popular than traditional power grid. However, with the extensive employment of smart grid concepts, the traditional centralized control methods expose a lot of shortcomings, such as communication congestion, computing complexity in central management systems, and so on. The distributed control method with flexible characteristics can meet the timeliness and effectiveness of information management in smart grid and ensure the information collection timely and the power dispatch economically. This article presents a decentralized approach based on multi agent system (MAS) for solving data collection and economic dispatch problem of smart grid. First, considering the generators and loads are distributed on many nodes in the space, a flooding-based consensus algorithm is proposed to achieve generator and load information for each agent. Then, a suitable distributed algorithm called λ-consensus is used for solving the economic dispatch problem, eventually, all generators can automatically minimize the total cost in a collective sense. Simulation results in standard test cases are presented to demonstrate the effectiveness of the proposed control strategy

Distributed Absorption and Half-Search Approach for Economic Dispatch Problem in Smart Grids

The economic dispatch problem (EDP) is a significant class of optimization issues in the power system, which works on minimizing the total cost when generating a certain amount of power. A novel distributed approach for EDP is proposed in this paper. The presented approach consists of two steps. The first step, named absorption search, is to simplify the network structure through absorption searching. A flooding-based consensus approach is applied in the first step, which can be used to achieve consensus information among nodes. After the first step, only the generation nodes are kept in the network. The data collection can be completed by local computation and communication between neighbors. The first step can be considered as the stage of gathering information. In the second step, a distributed half-search algorithm makes the nodes obtain the final optimal solution in a distributed way. The results on three case studies demonstrate that the proposed approach is highly effective for solving the EDP

Modeling the cleaning cycle dynamics for air cooling condensers of thermal power plants: Optimization and global sensitivity analysis

The air-cooled condenser (ACC) technology drives the decoupling of China’s water consumption and energy production. However, the optimal cleaning frequency of the ACC system has yet to be thoroughly studied. We develop a theoretical model for the total cost of the dust-fouling energy loss and direct cleaning service costs. This extended model is the first to consider energy loss in the cleaning and production phases with field validation. The cleaning period is optimized to minimize the total cost. Numerical solutions are sought to demonstrate the relationship between the normalized optimized cleaning period and the dimensionless inputs. An empirical fitting equation is developed for convenient use in industrial applications. An innovative variance-based global sensitivity analysis (SA) is performed to estimate the sensitivity of the optimization result to the input parameters. We found that heat resistance (Rf), installed capacity, utilization rate, grid electricity price (Enet), and cleaning cost rate have substantial impacts. The present study has the potential to improve the cleaning service plan of the onsite maintenance, to provide a theoretical framework for the life cycle analysis of the power plant, and to inform the decision-makers of the priority of data collection and sensor network deployment.</p