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This paper investigates the role of chaotic analysis and deep learning models in combustion instability predictions. To detect the precursors of impending thermoacoustic instability (TAI) in a swirled combustor with various fuel injection strategies, a data-driven framework is proposed in this study. Based on chaotic analysis, a recurrence matrix derived from combustion system is used in deep learning models, which are able to detect precursors of TAI. More specifically, the ResNet-18 network model is trained to predict the proximity of unstable operation conditions when the combustion system is still stable. The proposed framework achieved state-of-the-art 91.06% accuracy in prediction performance. The framework has potential for practical applications to avoid an unstable operation domain in active combustion control systems and, thus, can offer on-line information on the margin of the combustion instability.

Summary: The bZIP transcription factors are well-known transcriptional regulators that are essential for regulating resistance to biotic and abiotic stresses in plants. In this study, a total of 56 putative bZIP members were identified in passion fruit (Passiflora edulis). An integrative analysis was performed using bioinformatics. Transcriptome analysis revealed that most PebZIPs respond to drought, salt, cold and heat stress. By combining the transcriptome results of two different resistant genotypes, four representative members were finally selected for differential expression validation in different tissues and cultivars. Furthermore, transcriptome and metabolome association analysis revealed consistent expression trends of PeZIP20 and PeZIP21, with only one difference at 63aa, with different metabolites including flavonoids, lipids and amino acids. This work will contribute to further studies of the functions of bZIPs and their resistance properties, as well as to the development of novel germplasm.

The accuracy of gridded precipitation products is uncertain in different temporal and spatial dimensions. Analyzing the applicability of precipitation products is a prerequisite before applying them to hydrometeorological and other related research. In this study, we selected three gridded precipitation products, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), Global Satellite Mapping of Precipitation (GSMaP), and the fifth generation of atmospheric reanalysis of the European Centre for Medium-Range Weather Forecasts (ERA5), including their data from 2001 to 2020. Using the data from 699 ground observation stations, we evaluated the applicability of these three precipitation products in China. Based on five statistical and five classification indicators, we first assessed the applicability of the three precipitation products on daily, monthly, and annual time scales, respectively, and then evaluated their applicability in different spatial dimensions, including basins, agriculture, and geomorphology. The results showed that: (1) IMERG data had the best accuracy on annual and monthly time scales, with both correlation coefficient (CC) values greater than 0.95 and Kling–Gupta efficiency (KGE) values greater than 0.90. On a daily time scale, the accuracy of all three precipitation products differed when statistical or categorical indicators were considered alone. However, the applicability of IMERG data was best among the three precipitation products when both types of indicators were considered. (2) The accuracy of the three precipitation products gradually decreased along the southeast–northwest direction. The applicability of ERA5 data was better in northern regions than in other regions in China, especially in arid and semi-arid regions in northern China. The applicability of IMERG data was better in southern regions with more precipitation and in high-altitude regions than in other regions in China. (3) The applicability of the three precipitation products in plain areas was generally better than in mountain areas. Among them, ERA5 data were more accurate in plain areas, while IMERG data were more accurate in mountain areas. This study can provide a reference for the selection of data sources of gridded precipitation products in different time scales and spatial dimensions in China.

Fine particulate matter with diameters less than 2.5 μm (PM2.5) concentration monitoring is closely related to public health, outdoor activities, environmental protection, and other fields. However, the incomplete PM2.5 observation records provided by ground-based PM2.5 concentration monitoring stations pose a challenge to the study of PM2.5 propagation and evolution model. Consequently, PM2.5 concentration data imputation has been widely studied. Based on empirical orthogonal function (EOF), a new spatiotemporal interpolation method, EOF interpolation (EOFI) is introduced in this paper, and then, EOFI is applied to reconstruct the hourly PM2.5 concentration records of two stations in the first half of the year. The main steps of EOFI here are to firstly decompose the spatiotemporal data matrix of the original observation site into mutually orthogonal temporal and spatial modes with EOF method. Secondly, the spatial mode of the missing data station is estimated by inverse distance weighting interpolation of the spatial mode of the observation sites. After that, the records of the missing data station can be reconstructed by multiplying the estimated spatial mode and the corresponding temporal mode. The optimal mode number for EOFI is determined by minimizing the root mean square error (RMSE) between reconstructed records and corresponding valid records. Finally, six evaluation indices (mean absolute error (MAE), RMSE, correlation coefficient (Corr), deviation rate bias, Nash–Sutcliffe efficiency (NSE), and index of agreement (IA)) are calculated. The results show that EOFI performs better than the other three interpolation methods, namely, inverse distance weight interpolation, thin plate spline, and surface spline interpolation. The EOFI has the advantages of less computation, less parameter selection, and ease of implementation, it is an alternative method when the number of observation stations is rare, and the proportion of missing value at some stations is large. Moreover, it can also be applied to other spatiotemporal variables interpolation and imputation.