Microbial analysis and enrichment of anaerobic phenol and p-cresol degrading consortia with addition of AQDS.

Quinones and humus are ubiquitous in the biosphere and play an important role in the anaerobic biodegradation and biotransformation of organic acids, poisonous compounds as well as inorganic compounds. The impact of humic model compound, anthraquinone-2, 6-disulfonate (AQDS) on anaerobic phenol and p-cresol degradation were studied. Four methanogenic AQDS-free phenol and p-cresol enrichments and two phenol-AQDS enrichments were obtained using two sludges with potential biodegradability of phenol and cresol isomers as inoculum. 16S rRNA gene-cloning analysis combined with fluorescence in situ hybridization revealed that syntrophic aromatic compound degrading bacterium Syntrophorhabdus aromaticivorans was dominant in four AQDS-free enrichments, whereas phenol degrading Cryptanaerobacter phenolicus was dominant in two phenol-AQDS enrichments. Neither co-culture of S. aromaticivorans with Methanospirillum hungatei nor two phenol-AQDS enrichments could metabolize phenol using AQDS as the terminal electron acceptor. Further degradation experiments suggested that C. phenolicus related microbes in two phenol-AQDS enrichments were responsible for the conversion of phenol to benzoate, and benzoate was further degraded by benzoate degraders of Syntrophus aciditrophicus or Sporotomaculum syntrophicum to acetate.

[The investigation of exercise-induced fatigue on pesticide spraying operation].

OBJECTIVE: To evaluate the exercise-induced fatigue in operators during pesticide spraying based on surface electromyography (sEMC) signals and heart rate. METHODS: The sEMG signals were recorded from the right trapezius, biceps brachii, extensor digitorum, and flexor digitorum of 19 operators by portable electromyography units during pesticide spraying using 3WS-16 manual sprayers. Meanwhile, the heart rate of operators was measured by the ambulatory blood pressure monitoring system. The changes in median frequency and heart rate were analyzed. RESULTS: Pesticide spraying was defined as low-intensity labor. The median frequency of the right trapezius, biceps brachii, extensor digitorum, and flexor digitorum decreased significantly as pesticide spraying went on (P < 0.05). The body remained in a state of slight fatigue from 8 min after pesticide spraying began to the end of the operation. CONCLUSION: The degree of exercise-induced fatigue of operators can be evaluated based on sEMG signals and heart rate during pesticide spraying.

A PDCA Model for Disinfection Supply Rooms in the Context of Artificial Intelligence to Reduce the Incidence of Adverse Events and Improve the Disinfection Compliance Rate.

As an important section for controlling hospital infection, the main responsibility of the sterilization supply room is to clean, disinfect, sterilize, store, and distribute all medical devices that need to be reused in the hospital, and the quality of its work is closely related to the normal work of the hospital. Disinfection and supply department is the premise and foundation of the hospital department, mainly responsible for the recovery, cleaning, disinfection, sterilization, storage, and distribution of medical devices. The cleaning and disinfection work is characterized by strong technicality and high requirements, and the work effect is directly related to the safety of patients' lives and the occurrence of hospital infections. Therefore, there is an urgent need for a scientific and efficient management mode to be applied to the work of the supply room. The traditional management mode has some drawbacks, which affects the actual work of the hospital. Disinfection and supply rooms are an important part of hospital infection control and an important department to ensure the quality of health care. An effective management mode can not only improve the efficiency but also the overall quality of work, and PDCA (plan-do-check action cycle) as an advanced management mode can effectively improve the quality of management. This study investigates the effect of PDCA cycle management based on artificial intelligence algorithms in the nursing management of sterile supply rooms, and the experimental results show that the algorithm model can effectively reduce the incidence of adverse events and improve the rate of sterilization standards, which has certain practical significance.

Extraordinary optical transmission through periodic Drude-like graphene sheets using FDTD algorithms and its unconditionally stable approximate Crank-Nicolson implementation.

Based upon the approximate Crank-Nicolson (CN) finite-difference time-domain method implementation, the unconditionally stable algorithm is proposed to investigate the wave propagation and transmission through extremely thin graphene layers. More precisely, by incorporating the CN Douglas-Gunn algorithm, the piecewise linear recursive convolution method and the auxiliary differential equation method, the analytical model is proposed for Drude-like graphene model. To obtain the solution of the governing equations, the perfectly matched layer and the periodic boundary condition are applied to the graphene structure with two dimensional nano-materials. Numerical examples are carried out for further investigation. During the simulation, the influences of the parameters such as the grating slit and its thickness on the wave transmission are investigated and discussed. The result shows that not only the graphene grating has high transmission performance but also the proposed methods have considerable performance and accuracy.