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Most traditional multivariate statistical monitoring methods require an assumption that the observation values at a certain moment and a past moment are statistically independent. However, in actual chemical and biological processes, the sample at a certain moment is often affected by the previous moment. Therefore, given the problem of more false alarms and poor detection ability based on the traditional principal component analysis, this article proposes a dynamic global–local preserving projections (DGLPP) algorithm. Unlike dynamic local preserving projections (DLPP) and dynamic principal component analysis (DPCA), DGLPP controls the global and local information retained in the dimensionality reduction data by introducing weight coefficients, which makes the algorithm applicable to more types of industrial processes. Moreover, new parameter determination methods are also proposed for improved detection and diagnosis. Through the improved contribution graph method, we can see the influence degree of each variable on the fault, to monitor and isolate the fault. Finally, by verifying the operation of the multivariable process and two practical cases, the results show that compared with DPCA, DLPP, and global local retained projection (GLPP) methods, the performance under this method has been significantly improved. [ABSTRACT FROM AUTHOR]

A large amount of data generated in industrial processes exhibit multi‐modal, nonlinear, time‐domain correlation, and other characteristics. This poses great difficulty for the traditional principal component analysis (PCA) method since it requires that the input data need to conform to the Gaussian distribution. However, the data may have autocorrelation, that is, the data at the current moment will be affected by the past data. To this end, this paper proposes an enhanced dynamic principal component analysis (DPCA) method based on hierarchical clustering analysis. On the basis of the DPCA algorithm, the idea of data classification and enhanced training is used to strengthen the training of the dimensionality reduction matrix. Then, calibration, on‐line monitoring, and fault diagnosis of process data can be conducted. Finally, this paper demonstrates that the performance of the proposed method is greatly improved compared with PCA and DPCA through the Tennessee Eastman process system. [ABSTRACT FROM AUTHOR]

Dunaliella salina is by far the most salt-tolerant organism and contains many active substances, including β-carotene, glycerol, proteins, and vitamins, using in the production of dried biomass or cell extracts for the biofuels, pharmaceutical formulations, food additives, and fine chemicals, especially β-carotene. We report a high-quality genome sequence of D. Salina FACHB435, which has a 472 Mb genome size, with a contig N50 of 458 Kb. A total of 30,752 protein-coding genes were predicted. The annotation results evaluated by BUSCO was shown that completeness was 91.0% and replication was 53.1%. The fragments were 6.3% and the deletions were 2.6%. Phylogenomic and comparative genomic analyses revealed that A. thaliana diverged from Volvocales about 448 million years ago, then Volvocales C. eustigma , D. salina , and other species diverged about 250 million years ago. High light could promote the accumulation of β-carotene in D. salina at a 13 d stage of culture. The enrichment of DEGs in KEGG, it notes that the predicted up-regulated genes of carotenoid metabolic pathway include DsCrtB, DsPDS, DsZ-ISO, DsZDS, DsCRTISO, DsLUT5, DsCrtL-B , and DsCCD8 , while the predicted down-regulated genes include DsCrtF , and DsLUT1 . The four genes that were both up-regulated and down-regulated were DsZEP, DsCrtR-b, DsCruA/P and DsCrtZ 4 . The research results can provide scientific basis for the industrialization practice of D. salina ., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Chen, Li, Shi, Suen, Zheng, Zhang, Chen and Xue.)