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Pseudomonas aeruginosa is an opportunistic pathogen that causes chronic airway infection in bronchiectasis patients and is closely associated with poor prognosis. Strains isolated from chronically infected patients typically have a mucoid phenotype due to the overproduction of alginate. In this study, we isolate a P. aeruginosa strain from the sputum of a patient with bronchiectasis and find that a truncated mutation occurred in mucA, which is named mucA117. mucA117 causes the strain to transform into a mucoid phenotype, downregulates the expression of T3SS and inflammasome ligands such as fliC and allows it to avoid inflammasome activation. The truncated mutation of the MucA protein may help P. aeruginosa escape clearance by the immune system, enabling long-term colonization.

Based on the high-resolution satellite image data, the information mining technology of the available surface elements of PV is studied, and the investigation of the available surface elements of PV in 98 counties and cities of South Hebei grid is realized. Based on the large-scale and high-resolution remote sensing data obtained by multi-source remote sensing data fusion technology, the depth-learning-based surface feature recognition technology for photovoltaic development is studied. Based on the method of automatic identification and artificial combination of depth-learning, it can identify the available ground elements (roof, water surface, road surface, dry beach, etc.), the available surface elements of PV in 98 counties and cities of Hebei South Grid were obtained. From the overall point of view, the photovoltaic land, the building occupies the main position, in the four cities are relatively high, are in the 6% ~ 15%

In order to solve the problems of high consumption and cost of zinc powder in the conventional impurity removal method, the purification of copper, cadmium, nickel and cobalt from zinc sulfate solution(containing 30 -- 60 mg/L cadmium, 300 -- 400 mg/L cobalt, and 60 -- 100 mg/L nickel) was achieved by using sodium dimethyl dithiocarbamate. The optimized impurity removal conditions are shown as follows: the dosage of sodium dimethyl dithiocarbamate was 1.5 times the theoretical amount, the dosage of sodium nitrite was 1 times the theoretical amount, the reaction time was 1 h, and the reaction temperature was 75 -- 85 °C. The slag content was 30 kg/m³-liquid. The zinc and cobalt content in slag was about 8% and the cadmium and nickel content was about 1.5%, the loss percent of zinc was 0.02%. The removal efficiency was 99.11% for copper, 99.8% for cadmium, 99.99% for cobalt and 98.94% for nickel. The synchronous removal of copper, cobalt, nickel and cobalt can be achieved. [ABSTRACT FROM AUTHOR]

Reservoirs and dams are vital human-built infrastructures that play essential roles in flood control, hydroelectric power generation, water supply, navigation, and other functions. The realization of those functions requires efficient reservoir operation, and the effective controls on the outflow from a reservoir or dam. Over the last decade, artificial intelligence (AI) techniques have become increasingly popular in the field of streamflow forecasts, reservoir operation planning and scheduling approaches. In this study, three AI models, namely, the backpropagation (BP) neural network, support vector regression (SVR) technique, and long short-term memory (LSTM) model, are employed to simulate reservoir operation at monthly, daily, and hourly time scales, using approximately 30 years of historical reservoir operation records. This study aims to summarize the influence of the parameter settings on model performance and to explore the applicability of the LSTM model to reservoir operation simulation. The results show the following: (1) for the BP neural network and LSTM model, the effects of the number of maximum iterations on model performance should be prioritized; for the SVR model, the simulation performance is directly related to the selection of the kernel function, and sigmoid and RBF kernel functions should be prioritized; (2) the BP neural network and SVR are suitable for the model to learn the operation rules of a reservoir from a small amount of data; and (3) the LSTM model is able to effectively reduce the time consumption and memory storage required by other AI models, and demonstrate good capability in simulating low-flow conditions and the outflow curve for the peak operation period.

In order to improve the reliability of the acquisition/output link in the PLC control system, the design principle of the redundant system is analyzed, combined with the field application environment and actual needs, a distributed I/O redundancy method based on domestic PLC is developed. The article introduces the digital input/output and analog input/output redundancy schemes respectively for the type of data collected. This method takes the self-diagnosis results of the self-diagnosis periodically during operation as the basis for redundant diagnosis, combined with the user-configured redundancy scheme to improve accuracy of collecting/outputting data. It can be known from experiments and on-site base application that this method improves the reliability of the PLC control system and can ensure that the PLC system operates stably, efficiently and safely for a long time.