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As the scale of units within virtual power plants (VPPs) continues to expand, establishing an effective operational game model for these internal units has become a pressing issue for enhancing management and operations. This paper integrates photovoltaic generation, wind power, energy storage, and constant-temperature responsive loads, and it also considers micro gas turbines as auxiliary units, collectively forming a typical VPP case study. An operational optimization model was developed for the VPP control center and the micro gas turbines, and the game relationship between them was analyzed. A Stackelberg game model between the VPP control center and the micro gas turbines was proposed. Lastly, an improved D3QN (Dueling Double Deep Q-network) algorithm was employed to compute the VPP's optimal operational strategy based on Stackelberg game theory. The results demonstrate that the proposed model can balance the energy complementarity between the VPP control center and the micro gas turbines, thereby enhancing the overall economic efficiency of operations. [ABSTRACT FROM AUTHOR]

Decoupling economic growth from CO2 emissions is imperative for China. Meanwhile, establishing a consistent and comprehensive decoupling inventory that includes national (N), regional and provincial (RP), and city and county (CC) levels is essential for further policy formulation. This research aims to investigate the decoupling status using the "N-RP-CC" approach while considering changes in decoupling trends at the different levels. A combination of the Tapio decoupling model and cluster analysis is employed to study the decoupling's spatiotemporal characteristics and trends. The study first calculates the decoupling value for "national, 7; regions, 30; provinces, 1501 CCs" in China, 2006–2017. The results show that there continues to be an improvement in the decoupling trend at the national level. Conversely, the regional scale exhibits a more vulnerable decoupling trend compared to the national level, with weak and extended negative decoupling observed in northeastern and northern China. Moreover, provincial heterogeneities are increasingly evident, with poor decoupling statuses appearing in Jilin, Heilongjiang, Liaoning, and Xinjiang, as well as many central provinces. Additionally, although more than half of CCs exhibit weak decoupling during most years, seven different states of decoupling were also identified during the time frame. These findings further indicate that spatiotemporal heterogeneities extend beyond RP scales within CCs. Taking the Yangtze River as a boundary line reveals a severe situation in northern areas along with rapid development trends observed in southern regions. Finally, we clustered 1414 CCs based on their industrial proportions for 2017 which further highlights increasingly prominent heterogeneities that should be carefully considered. Based on these findings, policy recommendations such as spatial organization and optimization and technique investment are proposed to achieve CO2 emission decoupling under the N-RP-CC levels. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to analyze a mathematical model for malware dissemination on wireless sensor networks with time delay. Local stability and exhibition of the Hopf bifurcation are explored by means of analysis of the distribution of roots of the consequential characteristic equation. Moreover, global exponential stability is established with the help of linear matrix inequality techniques. Furthermore, properties of the Hopf bifurcation such as the direction and stability are studied by utilizing the normal form theory and the center manifold theorem. Finally, a computer numerical simulation example is presented to certify the rationality of our obtained results. [ABSTRACT FROM AUTHOR]

Eco-urbanism encapsulates a range of approaches to the management of urbanisation processes and environmental imperatives in contexts of rapid industrial and economic change. The paper focuses on two eco-urban initiatives in China and the UK over a temporal lens stretching from the mid-2000s to the early 2020s, in order to critically engage with the question of how eco-urban projects develop and often undergo radical changes over time, as well as being changed through significant ruptures, periods of stasis and fluidity in the actor-networks involved in project visions, financing and development. Our comparative approach identifies cross-cutting processes operative across two projects at different scales and in different techno-political contexts. The paper argues that longitudinal analysis is key to enabling a view of transitional trajectories as unfolding, and as a way of moving past oft-repeated assessments of specific projects according to a 'failure/success' binary. In order to do so, the paper considers eco-urban projects through a theoretical lens that views projects as unstable assemblages exhibiting change over time on the one hand, but that also recognises the dynamic resilience of geographical imaginations around eco-urban projects, that means that these projects endure (albeit often in different guises) over time. [ABSTRACT FROM AUTHOR]

For the increasing travel demands and public transport problems, dynamically adjusting timetable or bus scheduling is necessary based on accurate real-time passenger flow forecasting. In order to get more accurate passenger flow in future, this paper proposes a novel hierarchical hybrid model based on time series model, deep belief networks (DBNs), and improved incremental extreme learning machine (Im-ELM) to forecast short-term passenger flow. The proposed model is named HTSDBNE with two modelling steps. First, referring the idea of parallelization, the hybrid model, constructed by time series model, DBN, and Im-ELM, is used to forecast short-term passenger flow in different time scales hierarchically and parallel. Second, Im-ELM is utilized to analyse the relationship of forecasting results from the first step, and the weighted outputs of Im-ELM are as the final forecasting results. Comparing with single forecasting models and typical hybrid forecasting models, the testing results indicate that HTSDBNE has better performances. The mean absolute percent error of prediction results is around 10% and fully meets the application requirements of bus operation enterprise., 1. Introduction For current urban bus transport system, it faces more and more problems, such as improper arriving of buses, overcrowded or empty carriages, and so on, which cause passengers [...]