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The application of big data in enterprise management is more and more common. With the data of A-share listed companies in Shenzhen Stock Exchange from 2014 to 2018, this paper examines the influence of controlling shareholders equity pledge on the quality of information disclosure and the moderating effect of financing constraints in the age of big data. There are two key findings. First, the controlling shareholders will take opportunistic action to reduce the quality of information disclosure so as to maintain the stock price and avoid the loss of control. And the higher the proportion of equity pledged, the stronger the incentive. Second, this phenomenon is influenced by the financing constraints, which means the negative relationship between equity pledge and the quality of information disclosure is more significant in high financing constraint enterprise than in low financing constraint enterprise. The above conclusions provide enlightenment for optimizing enterprise information disclosure in the era of big data.

Fast and accurate identification of source locations and release rates is particularly important for improving indoor air quality and ensuring the safety and health of people. Existing methods based on adjoint probability are difficult to distinguish the release rate of dynamic sources, and optimization algorithms based on regularization are limited to analysing only a small amount of potential pollutant source information. Therefore, this study proposed an algorithm combining adjoint equations and regularization models to identify the location and release intensity of pollutant sources in the entire computational domain of a room. Based on the validated indoor CFD computational model, we first obtained a series of response matrices corresponding to the sensor position by solving the adjoint equation, and then used the regularization method and Bayesian inference to extrapolate the release rate and location of dynamic pollutant source in the room. The results shown that the proposed algorithm is convenient and feasible to identify the location and intensity of the indoor pollutant source. Compared with the real source intensity, the identification of constant source intensity is lower than the error threshold (10%) in 97.4% of the time nodes, and the identification of periodic source is lower than the error threshold (10%) in 95.4% of the time nodes. This research provides a new method and perspective for the estimation of indoor pollutant source information.

The congestion control problem for TCP network systems with UDP flows is considered. A nonlinear TCP network model with strict-feedback structure is established. The unknown UDP flow is considered as the disturbance to the system, and the maximum UDP flow is calculated by using the minimax approach. And then, a congestion control algorithm is proposed by using backstepping approach. Further, a state-feedback congestion controller is presented to make the TCP networks asymptotically stable. The simulation results show the superiority and feasibility of the proposed method.