Prediction and sensitivity analysis of chlorophyll a based on a support vector machine regression algorithm.

Outbreaks of planktonic algae seriously affect the water quality of rivers and are difficult to control. Based on the analysis of the temporal and spatial variation characteristics of environmental factors, this study uses a support vector machine regression (SVR) algorithm to establish a chlorophyll a (Chl-a) prediction model and conduct Chl-a sensitivity analysis. In 2018, the average Chl-a content was 126.25 ug/L. The maximum total nitrogen (TN) content was 16.68 mg/L and high year-round. The average NH4+-N and total phosphorous (TP) contents were only 0.78 and 0.18 mg/L. The content of NH4+-N was higher in spring and increased significantly along the water flow, while TP decreased slightly along the water flow. We used a radial basis function kernel SVR model and tenfold cross-validation method to optimize parameters. The penalty parameter c was 1.4142, the kernel function parameter g was 1, and the training and verification errors were only 0.032 and 0.067, respectively, indicating a good model fit. Based on a sensitivity analysis of the SVR prediction model, the maximum sensitivity coefficients of Chl-a to TP and WT were 0.571 and 0.394, respectively, and the contributions were 33% and 22%, respectively. The next highest sensitivity coefficients were those of DO (0.28, 16%) and pH (0.243, 14%). The sensitivity coefficients of TN and NH4+-N were the lowest. According to the current water environment pollution conditions, TP is the limiting factor of Chl-a in the Qingshui River, and it is also the main prevention and control factor of phytoplankton outbreak.

Application of the PDCA cycle for standardized nursing management in sepsis bundles.

BACKGROUND: To explore the application effect of plan, do, check and action circulation management mode in improving the compliance of sepsis bundle treatment. METHODS: 113 patients with sepsis admitted from January 1 to December 31, 2018 were selected as the control group, and the bundle treatment measures of sepsis were routinely implemented. The above treatment measures were completed within 6 h. 113 patients with sepsis admitted from January 1 to December 31, 2019 were selected as the study group. All clinical staff took the same measures as the control group, supplemented by PDCA cycle management. Objective to compare the changes of compliance of clinical staff to sepsis bundle treatment before and after the implementation of PDCA cycle management. RESULTS: Compared with the control group, the study group achieved the completion rate of sepsis bundle treatment in 1 h from 66.4 to 81.4%, the completion rate in 3 h from 77.0 to 89.4%, and the completion rate in 6 h from 82.3 to 95.6%. The difference was statistically significant (P < 0.05 for all). CONCLUSIONS: The implementation of PDCA cycle management mode can effectively improve the compliance of clinical staff to the bundle treatment of sepsis, improve the treatment efficiency of sepsis, and improve the quality of medical care.

Study on clinical nursing pathway to promote the effective implementation of sepsis bundle in septic shock.

BACKGROUND: There is still a certain gap between the effective implementation and requirements of sepsis bundle. Our aim is to establish the clinical nursing pathway of the cluster treatment of septic shock in the Intensive Care Unit and promote effective implementation of the cluster treatment of septic shock. METHODS: By means of evidence-based method, quality control index requirements and on-site investigation, the implementation process of clinical nursing pathway of the cluster treatment within 6 h of diagnosis of septic shock was established. RESULTS: After the implementation of clinical nursing pathway, the completion rate of septic shock cluster treatment was 81.4% (66.4%) in 1 h, 89.4% (77.0%) in 3 h, 95.5% (82.3%) in 6 h (P < 0.05), which was significantly improved in the experimental group compared with the control group. CONCLUSIONS: The clinical nursing pathway of septic shock cluster treatment is guided by evidence-based nursing, which emphasizes standardization and standardization of septic shock cluster treatment nursing under the guidance of the guideline, and can promote the effective implementation of septic shock cluster treatment, significantly improve efficiency of septic shock treatment and the quality of medical care.