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- Dong, Yi-Min, et al.
(författare)
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Reply to Collins et al
- 2021
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Ingår i: Clinical Infectious Diseases. - : Oxford University Press. - 1058-4838 .- 1537-6591. ; 73:3, s. 558-559
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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- An, Dong, et al.
(författare)
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APPLICATION OF DIFFERENT HYDROLOGIC MODELS IN FLASH FLOODS SIMULATION
- 2016
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- In recent years, the flash flood occurs frequently and intensively. It has become a world-wide focus in the field of disaster prevention and mitigation. The flash flood critical rainfall determination method, flash floods forecasting model and experience forecasting method are the most commonly used methods. This study focused on the application of hydrological models for flash floods simulation.The research has adopted 7 flood forecasting models with different types and structures: topography-based API model, Xinanjiang Model, Xinanjiang Model with excess infiltration, Sacramento Model, IHACRES, BP-KNN Model and TOPKAPI. Every model will be applied in semi-humid and semi-arid watersheds Banqiao, Maduwang, Zhidan in China, which have high risk of flash floods. According to the characteristic of flash floods, a set of evaluation for the simulation results isput forward. The purpose is to find out one or several hydrological models fit for research area and to provide reference for future related research.Combining with topography and runoff characteristics of each watershed, the paper collected and compared various simulation results of different models. Results indicated that models performed varies in semi-humid and semi-arid basins because of the complicated runoff mechanism in these areas. Among the conceptual hydrological models, Xinanjiang Model with excess infiltration performed better than the models with single runoff mechanism. TOPKAPI has a better simulation results than the lumped models topography-based API model and IHACRES. However, it has higher data requirements. BP-KNN model contains no physical mechanism and performed best in calibration period, but the accuracy falls greatly in validation period.
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- Chen, Dorothy M, et al.
(författare)
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Transcriptome-Wide Association Analysis Identifies Novel Candidate Susceptibility Genes for Prostate-Specific Antigen Levels in Men Without Prostate Cancer
- 2023
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Deciphering the genetic basis of prostate-specific antigen (PSA) levels may improve their utility to screen for prostate cancer (PCa). We thus conducted a transcriptome-wide association study (TWAS) of PSA levels using genome-wide summary statistics from 95,768 PCa-free men, the MetaXcan framework, and gene prediction models trained in Genotype-Tissue Expression (GTEx) project data. Tissue-specific analyses identified 41 statistically significant (p < 0.05/12,192 = 4.10e-6) associations in whole blood and 39 statistically significant (p < 0.05/13,844 = 3.61e-6) associations in prostate tissue, with 18 genes associated in both tissues. Cross-tissue analyses that combined associations across 45 tissues identified 155 genes that were statistically significantly (p < 0.05/22,249 = 2.25e-6) associated with PSA levels. Based on conditional analyses that assessed whether TWAS associations were attributable to a lead GWAS variant, we found 20 novel genes (11 single-tissue, 9 cross-tissue) that were associated with PSA levels in the TWAS. Of these novel genes, five showed evidence of colocalization (colocalization probability > 0.5): EXOSC9, CCNA2, HIST1H2BN, RP11-182L21.6 , and RP11-327J17.2 . Six of the 20 novel genes are not known to impact PCa risk. These findings yield new hypotheses for genetic factors underlying PSA levels that should be further explored toward improving our understanding of PSA biology.
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- Dong, Beibei
(författare)
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Dynamic modeling of MEA-based CO2 capture in biomass-fired CHP plants
- 2024
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Global warming is a significant threat to our planet. Adopting the Paris Agreement is a global action that aims to reduce greenhouse gas emissions. An extensive deployment of negative emission technologies (NETs) is required to achieve the targets set by the Paris Agreement. Bioenergy with carbon capture and storage (BECCS) is emerging as one of the most promising NETs. Among different biomass utilization processes, integrating BECCS with biomass-fired and waste-fired combined heat and power (bio-CHP and w-CHP) plants has been considered the most feasible solution. Bio/w-CHP plants are characterized by high fluctuations in operation, which can result in more dynamic variations of flue gas (FG) flowrates and compositions and available heat for CO2 capture. Such changes can clearly affect the performance of CO2 capture; therefore, doing dynamic simulations becomes crucial.This thesis aims to investigate the performance of different dynamic physical model-based approaches and provide suggestions for approach selection. In addition, the data-driven modeling approach, which is an emerging technology, has also been tested.Three physical model-based approaches include the ideal static model (IST), the dynamic approach without control (Dw/oC), and the dynamic approach with control (DwC). To compare their performance, the operating data from an actual waste CHP plant is employed. Various cases have been defined considering different critical operating parameters, including the FG flowrate, the CO2 concentration (CO2vol%), and the available heat for CO2 capture. Apparent differences can be observed in the results from different approaches. For example, when the CO2vol% drops from 15.7 % to 9.7 % (about 38 %) within 4 hours, the difference in the captured CO2 can be up to 22% between DwC and Dw/oC. It is worth noting that when there are both increases and decreases in the variations of parameters, the differences become smaller. Based on the comparison, the recommendations on approaches have been summarized. Dw/oC is recommended for checking the boundary of safety operation by the response analysis. DwC is recommended for designing the control system, observing the flexible dynamic operation, estimating the short-term CO2 capture potential, and optimizing the hourly dynamic operation. IST is recommended for estimating the long-term CO2 capture potential, and optimizing the long-term dynamic operation when the input parameters vary not as often as hourly.A data-driven model, Informer, is developed to model CO2 capture dynamically. The dataset is generated by using a physical model. The FG flowrate, the CO2vol%, the lean solvent flowrate, and the available heat for CO2 capture are employed as input parameters, and the CO2 capture rate and the energy penalty are chosen as outputs. The results show that Informer can accurately predict dynamic CO2 capture. The mean absolute percentage error (MAPE) was found to be 6.2% and 2.7% for predicting the CO2 capture rate and energy penalty, respectively.
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