Optimal real-time power dispatch of power grid with wind energy forecasting under extreme weather

With breakthroughs in the power electronics industry, the stability and rapid power regulation of wind power generation have been improved. Its power generation technology is becoming more and more mature. However, there are still weaknesses in the operation and control of power systems under the influence of extreme weather events, especially in real-time power dispatch. To optimally distribute the power of the regulation resources in a more stable manner, a wind energy forecasting-based power dispatch model with time-control intervals optimization is proposed. In this model, the outage of the wind energy under extreme weather is analyzed by an autoregressive integrated moving average model (ARIMA). Additionally, the other regulation resources are used to balance the corresponding wind power drop and power mismatch. Meanwhile, an algorithm names weighted mean of vectors (INFO) is employed to solve the real-time power dispatch and minimize the power deviation between the power command and real output. Lastly, the performance of the proposed optimal real-time power dispatch is executed in a simulation model with ten regulation resources. The simulation tests show that the combination of ARIMA and INFO can effectively improve the power control performance of the PD-WEF system

A predictive model for the risk of sepsis within 30 days of admission in patients with traumatic brain injury in the intensive care unit: a retrospective analysis based on MIMIC-IV database

Abstract Purpose Traumatic brain injury (TBI) patients admitted to the intensive care unit (ICU) are at a high risk of infection and sepsis. However, there are few studies on predicting secondary sepsis in TBI patients in the ICU. This study aimed to build a prediction model for the risk of secondary sepsis in TBI patients in the ICU, and provide effective information for clinical diagnosis and treatment. Methods Using the MIMIC IV database version 2.0 (Medical Information Mart for Intensive Care IV), we searched data on TBI patients admitted to ICU and considered them as a study cohort. The extracted data included patient demographic information, laboratory indicators, complications, and other clinical data. The study cohort was divided into a training cohort and a validation cohort. In the training cohort, variables were screened by LASSO (Least absolute shrinkage and selection operator) regression and stepwise Logistic regression to assess the predictive ability of each feature on the incidence of patients. The screened variables were included in the final Logistic regression model. Finally, the decision curve, calibration curve, and receiver operating character (ROC) were used to test the performance of the model. Results Finally, a total of 1167 patients were included in the study, and these patients were randomly divided into the training (N = 817) and validation (N = 350) cohorts at a ratio of 7:3. In the training cohort, seven features were identified as key predictors of secondary sepsis in TBI patients in the ICU, including acute kidney injury (AKI), anemia, invasive ventilation, GCS (Glasgow Coma Scale) score, lactic acid, and blood calcium level, which were included in the final model. The areas under the ROC curve in the training cohort and the validation cohort were 0.756 and 0.711, respectively. The calibration curve and ROC curve show that the model has favorable predictive accuracy, while the decision curve shows that the model has favorable clinical benefits with good and robust predictive efficiency. Conclusion We have developed a nomogram model for predicting secondary sepsis in TBI patients admitted to the ICU, which can provide useful predictive information for clinical decision-making

Image1_Exploring the oncogenic roles of LINC00857 in pan-cancer.pdf

Although aberrant LINC00857 expression may play a key role in oncogenesis, no research has analyzed the pan-cancer oncogenic roles of LINC00857, particularly in tumor immunology. Here, we integrated data from several databases to analyze the characteristics of LINC00857 in pan-cancer. We found that LINC00857 was overexpressed and correlated with a poor prognosis in a variety of cancers. Furthermore, high-expression of LINC00857 was negatively associated with immune cell infiltration and immune checkpoint gene expression. Notably, LINC00857 expression was negatively related to microsatellite instability and tumor mutation burden in colorectal cancer, implying poor reaction to immunotherapy when LINC00857 was highly expressed. Targeting LINC00857 could dramatically impair the proliferative ability of colorectal cancer cells. After RNA-sequencing in HCT116 cells, gene set enrichment analysis showed that LINC00857 may accelerate cancer progression by inhibiting the ferroptosis pathway and promoting glycolipid metabolism in colorectal cancer. Screening by weighted gene co-expression network analysis determined PIWIL4 as a target of LINC00857, which also performed an immunosuppressive role in colorectal cancer. Based on the structure of PIWIL4, a number of small molecule drugs were screened out by virtual screening and sensitivity analysis. In summary, LINC00857 expression was closely correlated with an immunosuppressive microenvironment and may be a novel diagnostic and prognostic biomarker for diverse cancers. The LINC00857/PIWIL4 axis may be predictive biomarkers for immunotherapy and valuable molecular targets for malignant tumors.</p

Smoking and smoking cessation in relation to risk of diabetes in Chinese men and women: a 9-year prospective study of 0·5 million people

Summary: Background: In developed countries, smoking is associated with increased risk of diabetes. Little is known about the association in China, where cigarette consumption has increased (first in urban, then in rural areas) relatively recently. Moreover, uncertainty remains about the effect of smoking cessation on diabetes in China and elsewhere. We aimed to assess the associations of smoking and smoking cessation with risk of incident diabetes among Chinese adults. Methods: The prospective China Kadoorie Biobank enrolled 512 891 adults (59% women) aged 30–79 years during 2004–08 from ten diverse areas (five urban and five rural) across China. Participants were interviewed at study assessment clinics, underwent physical measurements, and had a non-fasting blood sample taken. Participants were separated into four categories according to smoking history: never-smokers, ever-regular smokers, ex-smokers, and occasional smokers. Incident diabetes cases were identified through linkage with diabetes surveillance systems, the national health insurance system, and death registries. All analyses were done separately in men and women and Cox regression was used to yield adjusted hazards ratios (HRs) for diabetes associated with smoking. Findings: 68% (n=134 975) of men ever smoked regularly compared with 3% (n=7811) of women. During 9 years' follow-up, 13 652 new-onset diabetes cases were recorded among 482 589 participants without previous diabetes. Among urban men, smokers had an adjusted HR of 1·18 (95% CI 1·12–1·25) for diabetes. HRs increased with younger age at first smoking regularly (1·12, 1·20, and 1·27 at ≥25 years, 20–24 years, and <20 years, respectively; p for trend=0·00073) and with greater amount smoked (1·11, 1·15, 1·42, and 1·63 for <20, 20–29, 30–39 and ≥40 cigarettes per day; p for trend<0·0001). Among rural men, similar, albeit more modest, associations were seen. Overall, HRs were more extreme at higher levels of adiposity. Among men who stopped by choice, there was no excess risk within 5 years of cessation, contrasting with those who stopped because of illness (0·92 [0·75–1·12] vs 1·42 [1·23–1·63]). Among the few women who ever smoked regularly, the excess risk of diabetes was significant (1·33 [1·20–1·47]). Interpretation: Among Chinese adults, smoking was associated with increased risk of diabetes, with no significant excess risk following voluntary smoking cessation. Funding: Wellcome Trust, Medical Research Council, British Heart Foundation, Cancer Research UK, Kadoorie Charitable Foundation, Ministry of Science and Technology, National Natural Science Foundation of China, and China Scholarship Council