RESUMO

OBJECTIVES: This study attempts to compare the predictive effects of several prediction models on obstructive coronary artery disease (OCAD) in young patients (30-50 years old), with a view to providing a new evaluation tool for the prediction of premature coronary artery disease (PCAD). METHODS: A total of 532 hospitalized patients aged 30-50 were included in the study.All of them underwent coronary computed tomography angiography (CCTA) for suspected symptoms of coronary heart disease.Coronary artery calcium score (CACS) combined with traditional risk factors and pre-test probability models are the prediction models to be compared in this study.The PTP model was selected from the upgraded Diamond-Forrester model (UDFM) and the Duke clinical score (DCS). RESULTS: All patients included in the study were aged 30-50 years. Among them, women accounted for 24.4%, and 355 patients (66.7%) had a CACS of 0. OCAD was diagnosed in 43 patients (8.1%). The CACS combined with traditional risk factors to predict the OCAD area under the curve of receiver operating characteristic (ROC) (AUC = 0.794,p < 0.001) was greater than the PTP models (AUCUDFM=0.6977,p < 0.001;AUCDCS=0.6214,p < 0.001). By calculating the net reclassification index (NRI) and the integrated discrimination index (IDI), the ability to predict the risk of OCAD using the CACS combined with traditional risk factors was improved compared with the PTP models (NRI&IDI > 0,p < 0.05). CONCLUSION: The predictive value of CACS combined with traditional risk factors for OCAD in young patients is better than the PTP models.

Assuntos

Angiografia por Tomografia Computadorizada , Angiografia Coronária , Doença da Artéria Coronariana , Fatores de Risco de Doenças Cardíacas , Valor Preditivo dos Testes , Calcificação Vascular , Humanos , Feminino , Masculino , Calcificação Vascular/diagnóstico por imagem , Calcificação Vascular/epidemiologia , Pessoa de Meia-Idade , Medição de Risco , Adulto , Doença da Artéria Coronariana/diagnóstico por imagem , Doença da Artéria Coronariana/epidemiologia , Doença da Artéria Coronariana/diagnóstico , Fatores Etários , Prognóstico , Técnicas de Apoio para a Decisão , Fatores de Risco

RESUMO

The offshore oil extraction process generates copious amounts of high-salinity oil-bearing wastewater; at present, treating such wastewater in an efficient and low-consumption manner is a major challenge. In this study, a flat ceramic membrane bioreactor (C-MBR) process combining aerobic microbial treatment technology and ceramic membrane filtration technology was used to treat oil-bearing wastewater. The pilot test results demonstrated the remarkable performance of the combined sequential batch reactor (SBR) and C-MBR process, wherein the chemical oxygen demand (COD) and ammonia nitrogen (NH4+-N) removal rates reached 93% and 98.9%, respectively. Microbial analysis indicated that the symbiosis between Marinobacterium, Marinobacter, and Nitrosomonas might have contributed to simultaneously removing NH4+-N and reducing COD, and the increased enrichment of Nitrosomonas significantly improved the nitrogen removal efficiency. Cleaning ceramic membranes with NaClO solution reduces membrane contamination and membrane cleaning frequency. The combined SBR and C-MBR process is an economical and feasible solution for treating high-salinity oil-bearing wastewater. Based on the pilot application study, the capital expenditure for operating the full-scale combined SBR and C-MBR process was estimated to be 251,717 USD/year, and the unit wastewater treatment cost was 0.21 USD/m3, which saved 62.5% of the energy cost compared to the conventional MBR process.

RESUMO

Reducing energy consumption or running cost associated with the membrane bioreactor (MBR) process is a serious challenge that needs to be addressed in treating sewage. The addition of anaerobic ammonium oxidation bacteria (AnAOB) to a running MBR has the potential to lower the aeration rate, thus decreasing the running cost in treating sewage. The results obtained showed that owing to addition of AnAOB, TN and NH4+-N removal rates increased by 9.8% and 1.13%, respectively, while the aeration rate decreased by 50%. Additionally, high throughput sequencing and isotope experiments showed that both AnAOB and heterotrophic denitrification bacteria could survive simultaneously and play an important role in nitrogen removal, with AnAOB having a significantly greater contribution. It can be concluded that the addition of AnAOB reduced the running cost of MBR in treating sewage.

Assuntos

Compostos de Amônio , Esgotos , Anaerobiose , Bactérias , Reatores Biológicos

RESUMO

Landslide displacement time series can directly reflects landslide deformation and stability characteristics. Hence, forecasting of the non-linear and non-stationary displacement time series is necessary and significant for early warning of landslide failure. Traditionally, conventional machine learning methods are adopted as forecasting models, these forecasting models mainly determine the input and output variables experientially and does not address the non-stationary characteristics of displacement time series. However, it is difficult for these conventional machine learning methods to obtain appropriate input-output variables, to determine appropriate model parameters and to acquire satisfied prediction performance. To deal with these drawbacks, this study proposes the wavelet analysis (WA) to decompose the displacement time series into low- and high-frequency components to address the non-stationary characteristics; then proposes thee chaos theory to obtain appropriate input-output variables of forecasting models, and finally proposes Volterra filter model to construct the forecasting model. The GPS monitoring cumulative displacement time series, recorded on the Shuping and Baijiabao landslides, distance measuring equipment monitoring displacements on the Xintan landslide in Three Gorges Reservoir area of China, are used as test data of the proposed chaotic WA-Volterra model. The chaotic WA-support vector machine (SVM) model and single chaotic Volterra model without WA method, are used as comparisons. The results show that there are chaos characteristics in the GPS monitoring displacement time series, the non-stationary characteristics of landslide displacements are captured well by the WA method, and the model input-output variables are selected suitably using chaos theory. Furthermore, the chaotic WA-Volterra model has higher prediction accuracy than the chaotic WA-SVM and single chaotic Volterra models.