Experiments and transient simulation on spring-loaded pressure relief valve under high temperature and high pressure steam conditions

Reliable performances of high temperature and high pressure operating steam pressure relief valves (HTHP PRVs) are extremely important for the safety of nuclear power plants. It is still a challenge to accurately describe the dynamic performance of HTHP PRVs. In this study, the accuracy of computational fluid dynamics (CFD) based modelling of the transient processes is examined. For one of the HTHP PRVs named DWPRV, the effects of different parameters on the dynamic performance were investigated by combining CFD simulation and experiments. In the simulation, the domain decomposition method (DDM) and the Grid Pre-deformation Method (GPM) were adopted to handle the moving disk geometry and the large mesh deformation. The effect of damping was also studied. It is confirmed that the use of CFD simulation can improve the design and settings of a HTHP PRV in a highly energetic service that is difficult to test due to safety reasons. For the DWPRV, it was found that the maximum flow rate occurs when the curtain area is 1.18 times the throat area. The degree of superheat ranging from 0 C to 100 C has a negligible effect on the performance of DWPRV regardless of the changes in the material mechanical properties with operating temperatures. The reseating pressure increases linearly with the rise in the distance between the upper adjusting ring and the sealing face. The lower adjusting ring exhibits a weak effect on the reseating pressure. For the ratios of rated lift to throat diameter equalling to 0.3 and 0.35, the DWPRV exhibits the higher blowdown for the ratio of 0.3

Paeonol Protects Memory after Ischemic Stroke via Inhibiting β-Secretase and Apoptosis

Poststroke dementia commonly occurs following stroke, with its pathogenesis related to β-amyloid production and apoptosis. The present study evaluate the effects of paeonol, one of the phenolic phytochemicals isolated from the Chinese herb Paeonia suffruticosa Andrews (MC), on protection from memory loss after ischemic stroke in the subacute stage. Rats were subjected to transient middle cerebral artery occlusion (tMCAo) with 10 min of ischemia. The data revealed that paeonol recovered the step-through latency in the retrieval test seven days after tMCAo, but did not improve the neurological deficit induced by tMCAo. Levels of Amyloid precursor protein (APP)- and beta-site APP cleaving enzyme (BACE; β-secretase)-immunoreactive cells, and terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling (TUNEL)-positive cells decreased in the paeonol-administered group. Western blotting revealed decreased levels of Bax protein in mitochondria and apoptosis-inducing factor (AIF) in cytosol following paeonol treatment. In conclusion, we speculate that paeonol protected memory after ischemic stroke via reducing APP, BACE, and apoptosis. Supression the level of Bax and blocking the release of AIF into cytosol might participate in the anti-apoptosis provided by paeonol

Prediction of blowdown of a pressure relief valve using response surface methodology and CFD techniques

In this study, parametric assessment of the main geometric design features of a pressure relief valve (PRV) with a backpressure chamber and two adjusting rings was conducted using response surface methodology. This design approach was established by using computational fluid dynamics (CFD) to model the dynamic performance of the opening and closing of a nuclear power main steam pressure relief valve (NPMS PRV). An experimental facility was established to test the NPMS PRV in accordance with the standard ASME PTC 25, and to validate the CFD model. It was found that the model can accurately simulate the dynamic performance of the NPMS PRV; the difference in blowdown between the simulation and experiment results is found to be below 0.6%. Thus, themodel can be used as part of a design analysis tool. The backpressure chamber assisted in the reseating and decreased the blowdown of the NPMS PRV from 18.13% to 5.50%. The sensitivity to valve geometry was investigated, and an explicit relationship between blowdown and valve geometry was established (with a relative error less than 1%) using the response surface methodology; this will allow designers to assess the valve settings without the need for a CFD model

Computational modeling with forward and reverse engineering links signaling network and genomic regulatory responses: NF-κB signaling-induced gene expression responses in inflammation

<p>Abstract</p> <p>Background</p> <p>Signal transduction is the major mechanism through which cells transmit external stimuli to evoke intracellular biochemical responses. Diverse cellular stimuli create a wide variety of transcription factor activities through signal transduction pathways, resulting in different gene expression patterns. Understanding the relationship between external stimuli and the corresponding cellular responses, as well as the subsequent effects on downstream genes, is a major challenge in systems biology. Thus, a systematic approach is needed to integrate experimental data and theoretical hypotheses to identify the physiological consequences of environmental stimuli.</p> <p>Results</p> <p>We proposed a systematic approach that combines forward and reverse engineering to link the signal transduction cascade with the gene responses. To demonstrate the feasibility of our strategy, we focused on linking the NF-κB signaling pathway with the inflammatory gene regulatory responses because NF-κB has long been recognized to play a crucial role in inflammation. We first utilized forward engineering (Hybrid Functional Petri Nets) to construct the NF-κB signaling pathway and reverse engineering (Network Components Analysis) to build a gene regulatory network (GRN). Then, we demonstrated that the corresponding IKK profiles can be identified in the GRN and are consistent with the experimental validation of the IKK kinase assay. We found that the time-lapse gene expression of several cytokines and chemokines (TNF-α, IL-1, IL-6, CXCL1, CXCL2 and CCL3) is concordant with the NF-κB activity profile, and these genes have stronger influence strength within the GRN. Such regulatory effects have highlighted the crucial roles of NF-κB signaling in the acute inflammatory response and enhance our understanding of the systemic inflammatory response syndrome.</p> <p>Conclusion</p> <p>We successfully identified and distinguished the corresponding signaling profiles among three microarray datasets with different stimuli strengths. In our model, the crucial genes of the NF-κB regulatory network were also identified to reflect the biological consequences of inflammation. With the experimental validation, our strategy is thus an effective solution to decipher cross-talk effects when attempting to integrate new kinetic parameters from other signal transduction pathways. The strategy also provides new insight for systems biology modeling to link any signal transduction pathways with the responses of downstream genes of interest.</p

Rainfall Variation under Different Climate Change Scenarios Based on Cmip3 and Cmip5 Projections: a Case Study in Taiwan

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive

A model for predicting physical function upon discharge of hospitalized older adults in Taiwan—a machine learning approach based on both electronic health records and comprehensive geriatric assessment

BackgroundPredicting physical function upon discharge among hospitalized older adults is important. This study has aimed to develop a prediction model of physical function upon discharge through use of a machine learning algorithm using electronic health records (EHRs) and comprehensive geriatrics assessments (CGAs) among hospitalized older adults in Taiwan.MethodsData was retrieved from the clinical database of a tertiary medical center in central Taiwan. Older adults admitted to the acute geriatric unit during the period from January 2012 to December 2018 were included for analysis, while those with missing data were excluded. From data of the EHRs and CGAs, a total of 52 clinical features were input for model building. We used 3 different machine learning algorithms, XGBoost, random forest and logistic regression.ResultsIn total, 1,755 older adults were included in final analysis, with a mean age of 80.68 years. For linear models on physical function upon discharge, the accuracy of prediction was 87% for XGBoost, 85% for random forest, and 32% for logistic regression. For classification models on physical function upon discharge, the accuracy for random forest, logistic regression and XGBoost were 94, 92 and 92%, respectively. The auROC reached 98% for XGBoost and random forest, while logistic regression had an auROC of 97%. The top 3 features of importance were activity of daily living (ADL) at baseline, ADL during admission, and mini nutritional status (MNA) during admission.ConclusionThe results showed that physical function upon discharge among hospitalized older adults can be predicted accurately during admission through use of a machine learning model with data taken from EHRs and CGAs

Facile Synthesis of Monodisperse CdS Nanocrystals via Microreaction

CdS-based nanocrystals (NCs) have attracted extensive interest due to their potential application as key luminescent materials for blue and white LEDs. In this research, the continuous synthesis of monodisperse CdS NCs was demonstrated utilizing a capillary microreactor. The enhanced heat and mass transfer in the microreactor was useful to reduce the reaction temperature and residence time to synthesize monodisperse CdS NCs. The superior stability of the microreactor and its continuous operation allowed the investigation of synthesis parameters with high efficiency. Reaction temperature was found to be a key parameter for balancing the reactivity of CdS precursors, while residence time was shown to be an important factor that governs the size and size distribution of the CdS NCs. Furthermore, variation of OA concentration was demonstrated to be a facile tuning mechanism for controlling the size of the CdS NCs. The variation of the volume percentage of OA from 10.5 to 51.2% and the variation of the residence time from 17 to 136 s facilitated the synthesis of monodisperse CdS NCs in the size range of 3.0–5.4 nm, and the NCs produced photoluminescent emissions in the range of 391–463 nm