60 research outputs found
Developing a data-driven model for dynamic reservoir operation using a combined hidden Markov-decision tree and classification tree algorithms
No full textReservoir operations are faced with greater challenges than before due to growing water demands and climate change, and thus understanding and improvement of reservoir operations are critical. This study extends the hidden-Markov-decision tree (HM-DT) model developed by Zhao and Cai (2020) and proposes a data-driven reservoir operation model (DROM). The HM-DT model is first applied to individual reservoirs to derive sets of representative operation modules. Then a module classification model based on the Classification and Regression-tree algorithm is developed to determine which module to use for a day. DROM combines the derived operation modules and the module classification model to realize daily release prediction. DROM is tested with 25 reservoirs operated by USBR in north Great Plains regions, and it is shown that DROM can achieve acceptable accuracy in simulating historical releases (NSE > 0.4) and predicting future releases (NSE > 0.2) for 23 reservoirs. Compared with existing data-driven models, DROM shows several advantages including easily satisfied data requirements, transparent model structure, and broad applicability to various reservoirs. Especially, DROM can simulate the dynamic operation patterns through choosing the modules, while other previous models can only derive static operation rules. DROM can be used to better understand real-world reservoir operation behaviors and to explore the improvement of operation via combining with optimization models.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-08-01The student, Yanan Chen, accepted the attached license on 2021-07-08 at 14:07.The student, Yanan Chen, submitted this Thesis for approval on 2021-07-08 at 14:18.This Thesis was approved for publication on 2021-07-13 at 11:29.DSpace SAF Submission Ingestion Package generated from Vireo submission #16805 on 2022-01-12 at 12:54:01Made available in DSpace on 2022-01-12T22:35:06Z (GMT). No. of bitstreams: 2
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Previous issue date: 2021-07-13Embargo set by: Seth Robbins for item 121085
Lift date: 2024-01-12T22:35:30Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Onl
Regulation of Oxidative Stress Pathway by LNCRNA Malat1 as a Linchpin Connecting Malat1 Pleiotropic Functions with Multiple Diseases
No full textThe metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is a long noncoding RNA and its overexpression is associated with poor prognosis of many types of cancers and its silencing has been reported to reverse the course of carcinogenesis in vitro and in vivo in laboratory animals. The MALAT1 is an evolutionarily conserved lncRNA with high level of expression in various tissues and cell types due to its unique stabilizing triple helix structure. Its genome-wide involvement in RNA splicing and transcriptional regulation suggests that it has of functionality in transcriptional regulation. However, its physiological functions are somewhat enigmatic as the MALALT1 null mice show no overt phenotype under normal laboratory conditions. Consistent with its pervasive involvement in gene regulation, results from recent studies indicate that MALAT1 has pleiotropic role in regulating physiological and pathophysiological functions. In this study, we revealed a salient feature of MALAT1 is its novel function in regulating oxidative stress and immune/inflammatory responses which are important etiological factors for many diseases. In transcriptome analysis of MALAT1 null mice we found significant upregulation of nuclear factor-erythroid 2 p45-related factor 2 (Nrf2) regulated antioxidant genes with significant reduction in reactive oxygen species (ROS). MALAT1 null mice exhibited sensitized insulin-signaling response and alleviated lipopolysaccharide-induced innate immune response.
Furthermore, we established a mouse T2DM model, ob/ob model and investigated the role of MALAT1 in regulating the insulin signaling pathway and obesity. Consistent with our previous results, MALAT1 ablation alleviates the hyperglycemia, hyperinsulinemia, and insulin resistance in the obesity-induced T2DM in ob/ob mice, with significantly decreased fat deposition in liver and visceral adipose tissue. In summary, we demonstrate that MALAT1 plays an important role in regulating oxidative stress-mediated diseases and has the potential as a therapeutic target for the treatment of diseases caused by excessive exposure to ROS
Influence of PTFE on water transport in gas diffusion layer of polymer electrolyte membrane fuel cell
No full textThe research of carbon dioxide gas monitoring platform based on the wireless sensor networks
No full textThe analysis of land subsidence in Tianjin basing on interferometric synthetic aperture radar (InSAR) technique
No full textOn Generating Monte Carlo Samples of Continuous Diffusion Bridges
No full textDiffusion processes are widely used in engineering, fiance, physics and other fields. Usually continuous time diffusion processes are only observable at dis-crete time points. For many applications, it is often useful to impute continuous time bridge samples that follow the diffusion dynamics and connect each pair of the consecutive observations. The Sequential Monte Carlo (SMC) method is a useful tool to generate the intermediate paths of the bridge. Often the paths are generated forward from the starting observation and forced in some ways to connect with the end observation. In this paper we propose a constrained SMC algorithm with an effective resampling scheme that is guided by back-ward pilots carrying the information of the end observation. This resamplin
Impact of service recovery quality on consumers' repurchase intention: The moderating effect of customer relationship quality
No full textDesign and implementation of highly efficient resistance measurement system for DSL splitter
No full textSimulation of the optical generation monocycle and doublet UWB signals based on the polarization modulation
No full textAbnormal Power Fluctuation Detection of Wind Turbines Based on Subband Processing and Correlation Coefficient
No full textPeriodic abnormal power fluctuations of wind turbines can easily cause low-frequency oscillations of the power grid, threatening the safety of the power grid. Wind turbine power signals are nonlinear and non-stationary signals, and their abnormal fluctuation Frequencies are uncertain, which makes it difficult to extract and detect abnormal power fluctuation features. This paper proposes a method for detecting abnormal power fluctuations of wind turbine based on subband processing and correlation coefficient. For the application of this method, the author first removed trend items of the original power signal by high-pass filter, and decomposed the signal by the subband processing filter bank to obtain a series of subband signals; calculated the cross-correlation coefficient between the power signal with trend items removed and each subband signal respectively, located the subband where periodic abnormal fluctuation occurred and filtered useful subband according to cross-correlation coefficients; obtained high signal-to-noise ratio signal reconstructed based on the filtered subband signal; and by combining the peak detection method and the autocorrelation analysis method, identified the time period where periodic abnormal fluctuations of specific amplitude of the reconstructed signal, thus realizing the detection of periodic abnormal power fluctuations of wind turbine. Field data tests show that for periodic abnormal power fluctuations of different frequencies such as 0.06Hz and 0.32Hz, the proposed method can effectively identify the subband where periodic abnormal fluctuations occur and the periodic abnormal fluctuation characteristics
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