Effect of dithiocyano-methane on hexose monophosphate pathway in the respiratory metabolism of Escherichia coli.

This paper studied the inhibitory effects of dithiocyano-methane (DM) on the glucose decomposition pathway in the respiratory metabolism of Escherichia coli. We investigated the effects of DM on the activities of key enzymes (ATPase and glucose-6-phosphate dehydrogenase, G6PDH), the levels of key product (nicotinamide adenosine denucleotide hydro-phosphoric acid, NADPH), and gene expression in the hexose monophosphate pathway (HMP). The results showed that the minimum inhibitory concentration (MIC) and the minimum bactericide concentration (MBC) of DM against the tested strains were 5.86 mg/L and 11.72 mg/L, respectively. Bacteria exposed to DM at MIC demonstrated an increase in bacterial ATPase and G6PDH activities, NADPH levels, and gene expression in the HMP pathway compared to bacteria in the control group, which could be interpreted as a behavioral response to stress introduced by DM. However, DM at a lethal concentration of 10 × MIC affected glucose decomposition by inhibiting mainly the HMP pathway in E. coli.

Mechanical Fault Diagnosis of a DC Motor Utilizing United Variational Mode Decomposition, SampEn, and Random Forest-SPRINT Algorithm Classifiers.

Traditional fault diagnosis methods of DC (direct current) motors require establishing accurate mathematical models, effective state and parameter estimations, and appropriate statistical decision-making methods. However, these preconditions considerably limit traditional motor fault diagnosis methods. To address this issue, a new mechanical fault diagnosis method was proposed. Firstly, the vibration signals of motors were collected by the designed acquisition system. Subsequently, variational mode decomposition (VMD) was adopted to decompose the signal into a series of intrinsic mode functions and extract the characteristics of the vibration signals based on sample entropy. Finally, a united random forest improvement based on a SPRINT algorithm was employed to identify vibration signals of rotating machinery, and each branch tree was trained by applying different bootstrap sample sets. As the results reveal, the proposed fault diagnosis method is featured with good generalization performance, as the recognition rate of samples is more than 90%. Compared with the traditional neural network, data-heavy parameter optimization processes are avoided in this method. Therefore, the VMD-SampEn-RF-based method proposed in this paper performs well in fault diagnosis of DC motors, providing new ideas for future fault diagnoses of rotating machinery.