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In the early stages of gear faults, the background noise in the signal is pronounced, making it challenging to fully assess the health status of equipment based on a single-channel signal. Processing multi-channel signals proves beneficial for extracting fault information comprehensively. Adaptive multivariate signal decomposition methods, such as multivariate empirical mode decomposition (MEMD) and multivariate local characteristic-scale decomposition (MLCD), employ a fixed multivariate mean curve extraction method for signal decomposition. Consequently, these methods often exhibit suboptimal performance when decomposing different multi-channel signals. This study defines nine multivariate mean curve extraction methods and introduces the multivariate intrinsic wave-characteristic decomposition (MIWD) method based on the principles of mean curve optimization and an adaptive projection method. MIWD dynamically optimizes the multivariate mean curve during the decomposition process, resulting in superior performance in terms of decomposition accuracy, capability, and orthogonality compared to MEMD and MLCD. Furthermore, we apply MIWD to gear fault diagnosis, and simulation and experimental results affirm the superiority of MIWD. [ABSTRACT FROM AUTHOR]

Background: 2-Pyrone-4,6-dicarboxylic acid (PDC), a chemically stable pseudoaromatic dicarboxylic acid, represents a promising building block for the manufacture of biodegradable polyesters. Microbial production of PDC has been extensively investigated, but low titers and yields have limited industrial applications. Results: In this study, a multi-step biosynthesis strategy for the microbial production of PDC was demonstrated using engineered Escherichia coli whole-cell biocatalysts. The PDC biosynthetic pathway was first divided into three synthetic modules, namely the 3-dehydroshikimic acid (DHS) module, the protocatechuic acid (PCA) module and the PDC module. Several effective enzymes, including 3-dehydroshikimate dehydratase for the PCA module as well as protocatechuate 4,5-dioxygenase and 4-carboxy-2-hydroxymuconate-6-semialdehyde dehydrogenase for the PDC module were isolated and characterized. Then, the highly efficient whole-cell bioconversion systems for producing PCA and PDC were constructed and optimized, respectively. Finally, the efficient multi-step biosynthesis of PDC from glucose was achieved by smoothly integrating the above three biosynthetic modules, resulting in a final titer of 49.18 g/L with an overall 27.2% molar yield, which represented the highest titer for PDC production from glucose reported to date. Conclusions: This study lays the foundation for the microbial production of PDC, including one-step de novo biosynthesis from glucose as well as the microbial transformation of monoaromatics. [ABSTRACT FROM AUTHOR]

Abstract Background Clostridium thermocellum is a Gram-positive thermophilic anaerobic bacterium with the unusual capacity to convert cellulosic biomass into ethanol and hydrogen. Identification and characterization of protein complexes in C. thermocellum are important toward understanding its metabolism and physiology. Results A two dimensional blue native/SDS-PAGE procedure was developed to separate membrane protein complexes of C. thermocellum. Proteins spots were identified by MALDI-TOF/TOF Mass spectrometry. 24 proteins were identified representing 13 distinct protein complexes, including several putative intact complexes. Interestingly, subunits of both the F1-F0-ATP synthase and the V1-V0-ATP synthase were detected in the membrane sample, indicating C. thermocellum may use alternative mechanisms for ATP generation. Conclusion Two dimensional blue native/SDS-PAGE was used to detect membrane protein complexes in C. thermocellum. More than a dozen putative protein complexes were identified, revealing the simultaneous expression of two sets of ATP synthase. The protocol developed in this work paves the way for further functional characterization of these protein complexes.

This article proposes a novel data-driven adaptive decomposition method named broadband mode decomposition (BMD) for analyzing complex signals containing broadband components, such as square signals and sawtooth signals. For effective broadband signals with “sharp corners,” an unavoidable error occurs when applying former methods, such as variational mode decomposition (VMD) and ensemble empirical mode decomposition (EEMD), due to the Gibbs phenomenon and the interpolation of extreme points. Therefore, we propose BMD for separating the broadband modes, narrowband modes, and noise in a complex nonstationary signal. First, an associative dictionary library consisting of typical broadband signals and narrowband signals is established. Second, a smoothness function is employed as the optimization objective function, and the amplitude, frequency, and phase of the broadband signals and the filter parameters of the narrowband signals are applied as optimization parameters. Third, the sparsest decomposition results are obtained by searching the dictionary using an artificial chemical reaction optimization algorithm. The simulation and experimental signal analyses indicate that BMD is more accurate than EEMD and VMD in extracting broadband components from a noisy signal and that BMD is suitable for quality evaluations of welding inverter power source signals. [ABSTRACT FROM AUTHOR]