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The construction of new towns is one of the main measures to evacuate urban populations and promote regional coordination and urban–rural integration in China. Mining the spatio-temporal pattern of new town hot spots based on multivariate data and analyzing the influencing factors of new town construction hot spots can provide a strategic basis for new town construction, but few researchers have extracted and analyzed the influencing factors of new town internal hot spots and their classification. In order to define the key points of Wuhan's new town construction and promote the construction of new cities in an orderly and efficient manner, this paper first constructs a space-time cube based on the luminous remote sensing data from 2010 to 2019, extracts hot spots and emerging hot spots in Wuhan New City, selects 14 influencing factor indicators such as population density, and uses bivariate Moran's index to analyze the influencing factors of hot spots, indicating that the number of bus stops and vegetation coverage rate are the most significant. Secondly, the disorderly multivariate logistic regression model is used to analyze the influencing factors of emerging hot spots. The results show that population density, vegetation coverage, road density, distance to water bodies, and distance to train stations are the most significant factors. Finally, based on the analysis results, some relevant suggestions for the construction of Wuhan New City are proposed, providing theoretical support for the planning and policy guidance of new cities, and offering reference for the construction of new towns in other cities, promoting the construction of high-quality cities. [ABSTRACT FROM AUTHOR]

Governmental attention towards the high-quality development of the Yellow River basin has brought new development opportunities for the hotel industry. This study aims to reveal the spatial-temporal evolution patterns and influencing factors of hotels in the Yellow River Basin from 2012 to 2022, based on economic, social, and physical geographic data of 190,000 hotels in the Yellow River flowing. With the help of a GIS technology system, the spatial-temporal evolution patterns of all hotels, star hotels, and ordinary hotels were explored, respectively. Then, the significant influencing factors of these patterns were revealed by using geographic detector and Person correlation analysis. The following conclusions were drawn: (1) the overall scale of the hotel industry in the Yellow River Basin expanded year by year, achieving rapid growth from 2016, and fluctuating around 2020 due to the impact of the novel coronavirus epidemic; the overall spatial distribution had significant regional differences, showing the structural characteristics of "southeast more, northwest less"; (2) there was a great difference in the degree of spatial autocorrelation agglomeration among prefecture-level cities, and the degree of agglomeration of both the hotel industry as a whole and general hotels decreased year by year, showing a random distribution in 2022; star hotels were always distributed randomly. Additionally, a strong synergistic correlation was shown between the number of ordinary hotels and the number of star hotels in local space; (3) overall, the development of the hotel industry was significantly affected by seven factors: structural force, macro force, ecological force, internal power, consumption power, intermediary power, and external power. There were differences in the forces acting on different types of hotels, which gives a pattern recognition in-depth. [ABSTRACT FROM AUTHOR]

Summary: This article focuses on the sliding mode control of a class of interval type‐2 fuzzy systems with immeasurable states. During the data transmission from the sensor to the controller, the intermittent denial‐of‐service (DoS) attacks may be launched by vicious opponents. A key issue is to evaluate the tolerable region of the attacks for keeping the desired dynamic performance of the controlled interval type‐2 fuzzy systems. To this end, two weighting coefficients are first introduced to construct new membership functions. Based on this, a switched interval type‐2 fuzzy estimator is proposed to estimate the immeasurable state and compensate for the lost measurements caused by DoS attacks. And then, considering different DoS statues, a switched sliding mode controller transiting between the attack free and active cases is synthesized such that the specified sliding surface can be reached. In addition, the acceptable DoS region is quantitatively analyzed via a switched Lyapunov function. It is proven that the input‐to‐state stability of the sliding mode dynamics and the error system can be ensured within this tolerable DoS region. Finally, a simulation example is provided to illustrate the proposed control scheme. [ABSTRACT FROM AUTHOR]

Summary: This work considers the sliding mode control problem of a class of discrete‐time uncertain switched systems subject to detecting‐delay on mode signals, which may result in the asynchronous phenomenon between the controller and the switched system. Since the mode information of the controlled system is not available for the controller in time, a mode‐independent sliding surface will be introduced, by which an asynchronous sliding mode controller is designed, whose control gain and robust parameter will be changing according to the controller mode. In the analysis on the stability of the closed‐loop control system and the reachability of the specified sliding surface, the asynchronous characteristics are detailedly investigated. It is shown that the Lyapunov function may be not always decreasing along the state trajectories during the unmatched interval of controller modes and system modes. Nevertheless, it is proven that the state trajectories can be driven into a sliding region around the specified sliding surface in finite time. Finally, some numerical simulation results are provided. [ABSTRACT FROM AUTHOR]