Performance improvement of a direct carbon solid oxide fuel cell through integrating an Otto heat engine

This research is supported by the Natural Science Foundation of Zhejiang Province (Grant No. LQ14E060001), National Natural Science Foundation of China (Grant No. 51406091), a grant (PolyU 152127/14E) from Research Grant Council, University Grants Committee, Hong Kong SAR, a grant from Environment and Conservation Fund (ECF 54/2015), Hong Kong SAR, and the K. C. Wong Magna Fund in Ningbo University.A novel system consisting of an external heat source, a direct carbon solid oxide fuel cell (DC-SOFC), a regenerator and an air standard Otto cycle engine is proposed to improve the performance of the DC-SOFC. Considering the electrochemical/chemical reactions, ionic/electronic charge transport, mass/momentum transport and heat transfer, a 2D tubular DC-SOFC model shows that the overall heat released in the cell can be smaller than, equal to or larger than the heat required by the internal Boudouard reaction. Three different operating modes of the proposed system are identified, and accordingly, analytical expressions for the equivalent power output and efficiency of the proposed system are derived under different operating conditions. The modeling results show that the Otto heat engine can effectively recover the waste heat from the DC-SOFC for additional power production especially at large operating current density. Comprehensive parametric studies are conducted to investigate the effects of the different operating conditions of DC-SOFC on its performance and heat generation. The effects of compression ratio, internal irreversibility factor and power dissipation of the Otto heat engine on the system performance improvement are also studied.PostprintPeer reviewe

Manometric Measurement of the Sphincter of Oddi in Patients with Common Bile Duct Stones: A Consecutive Study of the Han Population of China

Objective. Role of dysfunction of the sphincter of Oddi (SO) in choledocholithiasis is controversial. This study was to evaluate SO motor activity in patients with common bile duct (CBD) stones in the Han population of China. Patients and Methods. In this study, 76 patients with CBD stones were enrolled in a single tertiary endoscopy center. Data of SO motor activities was prospectively evaluated by endoscopic manometry. Mean basal SO pressure, amplitude, and frequency were collected and analyzed. Results. The mean basal SO pressure, amplitude, and frequency were 52.7±40.0 (1.60–171.1) mmHg, 39.9±19.7 (14.9–115.5) mmHg, and 5.7±3.2 (1.3–13.8)/min, respectively. The basal SO pressure was higher in patients with CBD stones < 10 mm in diameter than that in those with CBD stones larger than 10 mm in diameter (60.7±41.0 mmHg versus 36.8±29.4 mmHg, P=0.043). There was no significant difference in the basal SO pressure, amplitude, and frequency when compared with the CBD diameter, CBD stone number, prior cholecystectomy, periampullary diverticula, and symptoms. Levels of alanine aminotransferase, aspartate transaminase, γ-glutamyl transpeptidase, and alkaline phosphatase showed no significant difference in patients with normal or elevated basal SO pressure. Conclusion. These results identify that, in Chinese Han population, abnormalities of SO motor activity are associated with CBD stones

National Forest Aboveground Biomass Mapping from ICESat/GLAS Data and MODIS Imagery in China

Forest aboveground biomass (AGB) was mapped throughout China using large footprint LiDAR waveform data from the Geoscience Laser Altimeter System (GLAS) onboard NASA’s Ice, Cloud, and land Elevation Satellite (ICESat), Moderate Resolution Imaging Spectro-radiometer (MODIS) imagery and forest inventory data. The entire land of China was divided into seven zones according to the geographic characteristics of the forests. The forest AGB prediction models were separately developed for different forest types in each of the seven forest zones at GLAS footprint level from GLAS waveform parameters and biomass derived from height and diameter at breast height (DBH) field observation. Some waveform parameters used in the prediction models were able to reduce the effects of slope on biomass estimation. The models of GLAS-based biomass estimates were developed by using GLAS footprints with slopes less than 20° and slopes ≥ 20°, respectively. Then, all GLAS footprint biomass and MODIS data were used to establish Random Forest regression models for extrapolating footprint AGB to a nationwide scale. The total amount of estimated AGB in Chinese forests around 2006 was about 12,622 Mt vs. 12,617 Mt derived from the seventh national forest resource inventory data. Nearly half of all provinces showed a relative error (%) of less than 20%, and 80% of total provinces had relative errors less than 50%

Estimation of Forest Aboveground Biomass in Changbai Mountain Region Using ICESat/GLAS and Landsat/TM Data

Mapping the magnitude and spatial distribution of forest aboveground biomass (AGB, in Mg·ha−1) is crucial to improve our understanding of the terrestrial carbon cycle. Landsat/TM (Thematic Mapper) and ICESat/GLAS (Ice, Cloud, and land Elevation Satellite, Geoscience Laser Altimeter System) data were integrated to estimate the AGB in the Changbai Mountain area. Firstly, four forest types were delineated according to TM data classification. Secondly, different models for prediction of the AGB at the GLAS footprint level were developed from GLAS waveform metrics and the AGB was derived from field observations using multiple stepwise regression. Lastly, GLAS-derived AGB, in combination with vegetation indices, leaf area index (LAI), canopy closure, and digital elevation model (DEM), were used to drive a data fusion model based on the random forest approach for extrapolating the GLAS footprint AGB to a continuous AGB map. The classification result showed that the Changbai Mountain region was characterized as forest-rich in altitudinal vegetation zones. The contribution of remote sensing variables in modeling the AGB was evaluated. Vegetation index metrics account for large amount of contribution in AGB ranges &lt;150 Mg·ha−1, while canopy closure has the largest contribution in AGB ranges ≥150 Mg·ha−1. Our study revealed that spatial information from two sensors and DEM could be combined to estimate the AGB with an R2 of 0.72 and an RMSE of 25.24 Mg·ha−1 in validation at stand level (size varied from ~0.3 ha to ~3 ha)

Lightning Identification Method Based on Deep Learning

In this study, a deep learning method called Lightning-SN was developed and used for cloud-to-ground (CG) lightning identification. Based on artificial scenarios, this network model selects radar products that exhibit characteristic factors closely related to lightning. Advanced time of arrival and direction lightning positioning data were used as the labeling factors. The Lightning-SN model was constructed based on an encoder–decoder structure with 25 convolutional layers, five pooling layers, five upsampling layers, and a sigmoid activation function layer. Additionally, the maximum pooling index method was adopted in Lightning-SN to avoid characteristic boundary information loss in the pooling process. The gradient harmonizing mechanism was used as the loss function to improve the model performance. The evaluation results showed that the Lightning-SN improved the segmentation accuracy of the CG lightning location compared with the traditional threshold method, according to the 6-minute operating period of the current S-band Doppler radar, exhibiting a better performance in terms of lightning location identification based on high-resolution radar data. The model was applied to the Ningbo area of Zhejiang Province, China. It was applied to the lightning hazard prevention in the hazardous chemical park in Ningbo. The composite reflectivity and radial velocity were the two dominant factors, with a greater influence on the model performance than other factors

LncRNA SNHG3 enhances BMI1 mRNA stability by binding and regulating c‐MYC: Implications for the carcinogenic role of SNHG3 in bladder cancer

Abstract The transformation of nonmuscle‐invasive bladder cancer (BLCa) to muscle‐invasive type and distant metastasis are the two major threats to patients after surgery. Thus, it is important to identify the key genes of BLCa cell invasion and metastasis. Long noncoding RNA (lncRNA) is a potential clinical tool for cancer diagnosis and treatment. Herein, we verified that lncRNA SNHG3 is upregulated in human BLCa specimens and is proportional to poor clinical prognosis via a combination of bioinformatic analyses and wet bench experiments. Then, we constructed SNHG3 knockdown and overexpression cell models via lentiviral packaging and CRISPR‐Cas9 technique. Fluorescence in situ hybridization assay showed that SNHG3 is distributed in both the nucleus and cytoplasm of BLCa cell lines. In vitro assays including CCK‐8, EdU, colony formation, wound healing, transwell, and tube formation demonstrated that SNHG3 knockdown and overexpression potently inhibited and enhanced BLCa cell proliferation, migration, invasion, and angiogenesis. In addition, IVIS imaging revealed that SNHG3 knockdown could significantly inhibit M‐NSG mice xenograft tumor growth. Next, RNA sequencing, bioinformatics analyses and western blots indicated that SNHG3 could promote c‐MYC expression. RNA immunoprecipitation, actinomycin D assay and western blot assays suggested that SNHG3 could also bind c‐MYC protein which subsequently facilitate the stabilization of BMI1 mRNA, thus enhancing BMI1 protein level. However, SNHG3 knockdown had a slightly weaker inhibitory effect on BMI1 expression than c‐MYC knockdown. Further, in vitro assays demonstrated that BMI1 knockdown could suppress the SNHG3 activation‐induced tumor promoting effect in BLCa cells. Overall, this study has provided new insights into the potential implication of lncRNA SNHG3 in the pathogenesis of BLCa. Importantly, SNHG3/c‐MYC/BMI1 axis may be a novel target for regulating tumor growth and metastasis in BLCa patients

Lightning Identification Method Based on Deep Learning

In this study, a deep learning method called Lightning-SN was developed and used for cloud-to-ground (CG) lightning identification. Based on artificial scenarios, this network model selects radar products that exhibit characteristic factors closely related to lightning. Advanced time of arrival and direction lightning positioning data were used as the labeling factors. The Lightning-SN model was constructed based on an encoder&ndash;decoder structure with 25 convolutional layers, five pooling layers, five upsampling layers, and a sigmoid activation function layer. Additionally, the maximum pooling index method was adopted in Lightning-SN to avoid characteristic boundary information loss in the pooling process. The gradient harmonizing mechanism was used as the loss function to improve the model performance. The evaluation results showed that the Lightning-SN improved the segmentation accuracy of the CG lightning location compared with the traditional threshold method, according to the 6-minute operating period of the current S-band Doppler radar, exhibiting a better performance in terms of lightning location identification based on high-resolution radar data. The model was applied to the Ningbo area of Zhejiang Province, China. It was applied to the lightning hazard prevention in the hazardous chemical park in Ningbo. The composite reflectivity and radial velocity were the two dominant factors, with a greater influence on the model performance than other factors