Search

Based on a multi-source error model, this paper discusses the principle of error element identification and uses the mirror bias method to compensate the geometric errors of a process system. Firstly, a nine-line measurement method to determine the geometric error of the linear feed axes of machine tools is introduced, and the geometric error identification model based on the “nine-line method” is established. Then, using a ballbar mounted in the axial, tangential, and radial directions of the machine, the geometric error elements of the rotation axis are identified by three simple measurements in each direction. Subsequently, for the more common flat vise clamping workpiece in actual production, the workpiece position error is identified by using the traditional process of dimensional chain, and the workpiece attitude error is identified by fitting the angle between the positioning plane and the horizontal plane by the least squares method. Finally, based on the tool position points and tool axis vectors obtained from the multi-source error model, the error compensation value is solved using inverse machine tool kinematics to offset the machining error by mirroring the error value of the same size, and based on the “S-shaped specimen” to compensate the processing experiments, after compensation, the processing error is reduced by 30~45%, verifying the effectiveness of the compensation method.

Since COVID-19 might have a lasting impact on global public health, it is crucial to analyze its effect on drug-resistant bacterial infections in the respiratory system for the prevention and control of hospital infections. This work aimed to investigate the impact of the COVID-19 outbreak on the clinical distribution and antibiotic resistance of bacterial infection among hospitalized patients in the respiratory unit in order to establish strategies to control antibiotic-resistant infections. Electronic clinical data registry records from 2018 to 2022 were retrospectively analyzed. A total of 36,829 clinical specimens, including sputum, bronchoalveolar lavage fluid, blood, and urine, were collected from 16,073 patients admitted to the Guangzhou First People’s Hospital from January 2018 to December 2022. Among them, 2209 samples were culture-positive. The bacterial isolation rates of different types of samples showed a similar trend from 2019 to 2022, with an increase in 2020 and 2022 and a decrease in 2021. Different bacterial species were separated from different types of samples. The most reported pathogens were identified in sputum samples. Gram-positive isolates were prevalent in urine samples, while Gram-negative bacilli were the predominant pathogenic bacteria isolated from respiratory tract and blood samples. Pseudomonas aeruginosa (P. aeruginosa), Acinetobacter baumannii (A. baumannii) complex, and Klebsiella pneumoniae (K. pneumoniae) were the most abundant Gram-negative bacteria in sputum samples, of which A. baumannii complex had the highest resistance to all tested antibiotics except colistin. Notably, there has been a substantial prevalence of carbapenem-resistant P. aeruginosa, A. baumannii, and K. pneumoniae in the past five years. This alarming situation calls for greater attention and precaution with prescribed antibiotics to limit the generation and spread of new multidrug-resistant bacteria and improve therapeutic management.

Water management is a key issue in the design and operation of proton exchange membrane fuel cells (PEMFCs). For an efficient and stable operation, the accumulation of liquid water inside the flow channels has to be prevented. Existing measurement methods for localizing water are limited in terms of the integration and application of measurements in operating PEMFC stacks. In this study, we present a measurement method for the localization of liquid water based on ultrasonic guided waves. Using a sparse sensing array of four piezoelectric wafer active sensors (PWAS), the measurement requires only minor changes in the PEMFC cell design. The measurement method is demonstrated with ex situ measurements for water drop localization on a single bipolar plate. The wave propagation of the guided waves and their interaction with water drops on different positions of the bipolar plate are investigated. The complex geometry of the bipolar plate leads to complex guided wave responses. Thus, physical modeling of the wave propagation and tomographic methods are not suitable for the localization of the water drops. Using machine learning methods, it is demonstrated that the position of a water drop can be obtained from the guided wave responses despite the complex geometry of the bipolar plate. Our results show standard deviations of 4.2 mm and 3.3 mm in the x and y coordinates, respectively. The measurement method shows high potential for in situ measurements in PEMFC stacks as well as for other applications that require deposit localization on geometrically complex waveguides.

Carbapenem-resistant Klebsiella pneumoniae (CRKP) are usually multidrug resistant (MDR) and cause serious therapeutic problems. Colistin is a critical last-resort therapeutic option for MDR bacterial infections. However, increasing colistin use has led to the emergence of extensively drug-resistant (XDR) strains, raising a significant challenge for healthcare. In order to gain insight into the antibiotic resistance mechanisms of CRKP and identify potential drug targets, we compared the molecular characteristics and the proteomes among drug-sensitive (DS), MDR, and XDR K. pneumoniae strains. All drug-resistant isolates belonged to ST11, harboring blaKPC and hypervirulent genes. None of the plasmid-encoded mcr genes were detected in the colistin-resistant XDR strains. Through a tandem mass tag (TMT)-labeled proteomic technique, a total of 3531 proteins were identified in the current study. Compared to the DS strains, there were 247 differentially expressed proteins (DEPs) in the MDR strains and 346 DEPs in the XDR strains, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that a majority of the DEPs were involved in various metabolic pathways, which were beneficial to the evolution of drug resistance in K. pneumoniae. In addition, a total of 67 DEPs were identified between the MDR and XDR strains. KEGG enrichment and protein–protein interaction network analysis showed their participation in cationic antimicrobial peptide resistance and two-component systems. In conclusion, our results highlight the emergence of colistin-resistant and hypervirulent CRKP, which is a noticeable superbug. The DEPs identified in our study are of great significance for the exploration of effective control strategies against infections of CRKP.

This paper proposes a novel 1D-3D approach for the stability characteristics of the hydropower generation system (HGS) in transition processes. First, a 1D-3D coupling model was established for the HGS in the load-reduction process. Second, a sensitivity analysis of the HGS’s parameters to the rotation speed and discharge was conducted. Third, the pressure pulsation characteristics of the HGS with three typical guide vane openings were analyzed during the load-reduction process. The results show that with the closure of the guide vane, the discharge gradually decreases and it is sensitive to the change in hydraulic parameters. The rotation speed fluctuates at the early stage of the transition process and is easily affected by mechanical parameters. In addition, the pressure pulsation inside the Francis turbine is more intense under small openings than large openings, and the primary frequency of pressure pulsation under three opening degrees is the blade frequency. The 1D-3D coupling model successfully integrates the advantages of traditional methods and provides a reference for predicting system stability and exploring the stability mechanism.

Artificial grasslands have been regarded as an effective method to improve grass production and quality, especially on the Qinghai–Tibet Plateau. Soil ecosystem multifunctionality (EMF) plays an important role in sustainable regional development. However, few studies have investigated the impacts of artificial grasslands on soil EMF. Here, we constructed single-species artificial grasslands in a natural temperate steppe and investigated soil microbial communities, abiotic factors (soil moisture and pH), and functions related to biogeochemical cycles to explore (1) how the transformation from temperate steppe to artificial grasslands affected soil EMF and (2) the roles of species and phylogenetic microbial diversities, microbial community composition, and abiotic factors in driving differences in soil EMF. Our results showed that artificial grasslands decreased soil EMF regardless of planting species; that the bacterial and fungal community composition contributed more to soil EMF prediction than species and phylogenetic diversities; and that microbial phylogenetic diversities were negatively associated with soil EMF. Soil pH played an important role in the effects of artificial grasslands on soil EMF—artificial grasslands increased soil pH, which was negatively associated with soil EMF. Overall, the benefits of establishing artificial grasslands, for example, higher grass production and quality, might be at the expense of soil EMF. Further studies should explore mixed-species artificial grasslands.