Microservice architecture is widely adopted in cloud computing for building large-scale distributed systems. However, due to frequent calls between microservices, such systems become increasingly complex and fragile, leading to reduced reliability of the system behavior. Therefore, implementing process discovery and constructing process models for the historical logs of microservice systems becomes crucial to analyzing system behavior. However, existing process discovery methods do not effectively capture the synchronous or asynchronous call relations between microservices at the endpoint level, limiting the ability to analyze the impact of microservice interactions on system performance. This paper proposes a novel process discovery capable of capturing call relations in microservice systems at the endpoint level. First, the call events related to the microservice calls are extracted from the interleaved fragmented raw logs by event serialization to form event sequences. Then, an invocation tree is defined and constructed, and pruning is applied to obtain the subsequence that can be used to derive call relations correctly. Based on the defined event relations, the call relations between endpoints are discovered. Finally, the$\alpha$-based miner is applied to perform the mining and generation of the process model to obtain the defined microservice call net. Experimental results show that, compared to state-of-the-art methods, the proposed approach can accurately and effectively build the microservice system process model.

China’s new generation of polar-orbiting meteorological satellite, FengYun-3D (FY-3D), is equipped with the Medium Resolution Spectral Imager II (MERSI-II) sensor, which greatly enhances Chinese capacity for comprehensive space environment detection and meteorological remote sensing. MERSI-II has two mid-infrared (MIR) channels with the potential to apply split-window (SW) algorithms. We have developed an improved nonlinear SW (NSW) algorithm to retrieve the ground brightness temperature (GBT)$T_{\text {gb}}^{0}$without the contribution of the solar direct beam from MERSI-II MIR data, enabling the accurate retrieval of surface bidirectional reflectivity$\rho _{b}$based on the radiative transfer equation (RTE). Compared with the traditional estimation method, this article assumed a linear relationship between$\rho _{b}$of MIR channels 20 and 21, and believed that under dry-cool atmospheric conditions,$T_{\text {gb}}^{0}$is not equal. Considering different solar zenith angles (SZAs), by iterating the combination of atmospheric and surface parameters under reasonable variations, the MODerate spectral resolution atmospheric TRANsmittance (MODTRAN) model version 5.2 was used to obtain the simulation datasets for determining the NSW coefficients. In the practical retrieval, the precise algorithm coefficients are only relevant to SZA. The$R^{2}$fit by the NSW algorithm is 0.99, and the root-mean-square error (RMSE) is less than 0.58 K for different SZAs. Under different SZAs, comparing the actual value of$\rho _{b}$with the proposed method, the bias is less than$1.57\times 10^{-3}$, and the RMSE is less than$2.21\times 10^{-3}$. A detailed sensitivity analysis found that the errors in atmospheric water vapor content (WVC), CO2 concentrations, O3 concentrations, horizontal visibilities (VISs), and instrumental noise were below the order of$10^{-4}$, and their effects on the retrieval of$\rho _{b}$were negligible. This study employed two methods to validate$T_{\text {gb}}^{0}$, thereby indirectly validating$\rho _{b}$. First, the moderate resolution imaging spectrometer (MODIS) land surface emissivity (LSE) product MYD11C1 and the ERA5-Land land surface temperature (LST) product were used to validate$T_{\text {gb}}^{0}$, yielding a bias of 0.9 K and an RMSE of 1.13 K. Second, in situ measurements from the surface radiation budget network (SURFRAD) sites were used to validate$T_{\text {gb}}^{0}$, resulting in a bias of 0.74 K and an RMSE of 1.83 K. Overall, results showed that the proposed algorithm has good accuracy and can improve its applications in relevant fields.

With the rapid development of mobile crowdsourcing, incentive mechanism has become one of the research hotspots. As participants in the crowdsourcing system, requesters, crowdsourcing platforms, and workers will inevitably have conflicts of interest. The game theory provides a good solution to conflicts, but some existing studies dichotomize the strategies of worker, crowdsourcing platform, and requester. However, some studies only consider the conflict between worker and requester, but ignore that the platform is also a selfish and rational individual. Therefore, this paper proposes a three-party multi-strategy evolutionary game model and analyzes the causes of crowdsourcing dilemma. To solve the crowdsourcing dilemma, a game-based incentive mechanism is designed according to the principle of fair norms. Finally, the experimental results show that the model can reflect the existence of crowdsourcing dilemma in crowdsourcing systems. After adding incentive measures, all participants can evolve in the direction conducive to the stability of crowdsourcing system.

As a new paradigm to solve problems by gathering the intelligence of crowds, mobile crowdsourcing has become one of the hot spots in academic and industrial fields. Task requester, platform, and crowd workers are stakeholders in mobile crowdsourcing, which inevitably leads to conflicts of interest. In order to solve this problem, this article constructs a three-party evolutionary game model among task requester, platform, and crowd workers. This model also considers the collusion between crowd workers and the platform to make it more realistic. Then, the replication dynamics method is utilized to analyze the evolutionary stability strategy. The strategies of rewards and penalties are given to avoid free-riding and false-reporting problems. Finally, the stability of the equilibrium point in the three-party game system is verified through simulation experiments, and the effective methods to motivate each player to choose a trusted strategy are given.