Operation Efficiency Evaluation of the China-Europe Freight Train Based on Grey Cross-Efficiency DEA

The China-Europe Freight Train (CEFT) serves as an important carrier and platform for international economic cooperation and international trade circulation between China and Europe. Since it worked, its actual operation and development have been affected by many factors, but the level of its actual operating efficiency and the main affecting factors of CEFT have been difficult to find, which has severely limited its sustainable development. Therefore, this paper scientifically selected the operation efficiency evaluation indicator system of CEFT and combined grey system theory, cross-efficiency method, and DEA to construct a new DEA evaluation model based on grey cross-efficiency, which can not only overcome the problem of ignoring the relative importance ratings of the evaluation indicator in the general DEA evaluation model and the traditional cross-efficiency DEA evaluation model but also more accurately evaluate the actual operation efficiency of CEFT. At the same time, based on the actual operating data of CEFT from 2011 to 2018 and the above new evaluation models, the CEFT’s operation efficiency was evaluated and tested by examples, showing that on the one hand, the grey cross-efficiency DEA evaluation model can more accurately evaluate the actual operation efficiency of CEFT than other traditional evaluation models; on the other hand, it is found that the “overseas cities,” “operating lines,” and “entry-exit nodes” are currently the main factors that limit the actual operation efficiency of CEFT and indicating improvement direction for the future efficient and sustainable development of CEFT

Forest Canopy Height Mapping by Synergizing ICESat-2, Sentinel-1, Sentinel-2 and Topographic Information Based on Machine Learning Methods

Spaceborne LiDAR has been widely used to obtain forest canopy heights over large areas, but it is still a challenge to obtain spatio-continuous forest canopy heights with this technology. In order to make up for this deficiency and take advantage of the complementary for multi-source remote sensing data in forest canopy height mapping, a new method to estimate forest canopy height was proposed by synergizing the spaceborne LiDAR (ICESat-2) data, Synthetic Aperture Radar (SAR) data, multi-spectral images, and topographic data considering forest types. In this study, National Geographical Condition Monitoring (NGCM) data was used to extract the distributions of coniferous forest (CF), broadleaf forest (BF), and mixed forest (MF) in Hua’ nan forest area in Heilongjiang Province, China. Accordingly, the forest canopy height estimation models for whole forest (all forests together without distinguishing types, WF), CF, BF, and MF were established, respectively, by Radom Forest (RF) and Gradient Boosting Decision Tree (GBDT). The accuracy for established models and the forest canopy height obtained based on estimation models were validated consequently. The results showed that the forest canopy height estimation models considering forest types had better performance than the model grouping all types of forest together. Compared with GBDT, RF with optimal variables had better performance in forest canopy height estimation with Pearson’s correlation coefficient (R) and the root-mean-squared error (RMSE) values for CF, BF, and MF of 0.72, 0.59, 0.62, and 3.15, 3.37, 3.26 m, respectively. It has been validated that a synergy of ICESat-2 with other remote sensing data can make a crucial contribution to spatio-continuous forest canopy height mapping, especially for areas covered by different types of forest

Data-driven Fault Diagnosis for PEM Fuel Cell System Using Sensor Pre-Selection Method and Artificial Neural Network Model

International audienceFault diagnosis is a critical process for the reliability anddurability of proton exchange membrane fuel cells (PEMFCs). Due tothe complexity of internal transport processes inside the PEMFCs,developing an accurate model considering various failure mechanismsis extremely difficult. In this paper, a novel data-driven approachbased on sensor pre-selection and artificial neural network (ANN)are proposed. Firstly, the features of sensor data in time-domainand frequency-domain are extracted for sensitivity analysis. Thesensors with poor response to the changes of system states arefiltered out. Then experimental data monitored by the remainingsensors are utilized to establish the fault diagnosis model byusing the ANN model. Levenberg-Marquardt (LM) algorithm, resilientpropagation (RP) algorithm, and scaled conjugate gradient (SCG)algorithm are utilized in the training process, respectively. Theresults demonstrate that the diagnostic accuracy reaches 99.2% andthe recall reaches 98.3%. The effectiveness of the proposed methodis verified by comparing the diagnostic results in this work andthat by support vector machine (SVM) and logistic regression (LR).Besides, the high computational efficiency of the proposed methodsupports the possibility of online diagnosis. Meanwhile, timelyfault diagnosis can provide guidance for fault tolerant control ofthe PEMFCs system

IgY antibodies against Ebola virus possess post-exposure protection in a murine pseudovirus challenge model and excellent thermostability.

Ebola virus (EBOV) is one of the most virulent pathogens that causes hemorrhagic fever and displays high mortality rates and low prognosis rates in both humans and nonhuman primates. The post-exposure antibody therapies to prevent EBOV infection are considered effective as of yet. However, owing to the poor thermal stability of mammalian antibodies, their application in the tropics has remained limited. Therefore, a thermostable therapeutic antibody against EBOV was developed modelled on the poultry(chicken) immunoglobulin Y (IgY). The IgY antibodies retaining their neutralising activity at 25°C for one year, displayed excellent thermal stability, opposed to conventional polyclonal antibodies (pAbs) or monoclonal antibodies (mAbs). Laying hens were immunised with a variety of EBOV vaccine candidates and it was confirmed that VSVΔG/EBOVGP encoding the EBOV glycoprotein could induce high titer neutralising antibodies against EBOV. The therapeutic efficacy of immune IgY antibodies in vivo was evaluated in the newborn Balb/c mice who have been challenged with the VSVΔG/EBOVGP model. Mice that have been challenged with a lethal dose of the pseudovirus were treated 2 or 24 h post-infection with different doses of anti-EBOV IgY. The group receiving a high dose of 106 NAU/kg (neutralising antibody units/kilogram) showed complete protection with no symptoms of a disease, while the low-dose group was only partially protected. Conversely, all mice receiving naive IgY died within 10 days. In conclusion, the anti-EBOV IgY exhibits excellent thermostability and protective efficacy. Anti-EBOV IgY shows a lot of promise in entering the realm of efficient Ebola virus treatment regimens

LncRNA LINC01094 Promotes Cells Proliferation and Metastasis through the PTEN/AKT Pathway by Targeting AZGP1 in Gastric Cancer

Long noncoding RNAs (lncRNAs) were recently reported to play an essential role in multiple cancer types. Herein, through next-generation sequencing, we screened metastasis-driving molecules by using tissues from early-stage gastric cancer (GC) patients with lymph node metastasis, and we identified a lncRNA LINC01094, which was associated with the metastasis of GC. According to the clinical data from the TCGA, GSE15459, and GSE62254 cohorts, the high expression of LINC01094 was associated with an unfavorable prognosis. Moreover, 106 clinical GC and paired normal samples were collected, and the qRT-PCR results showed that the high expression of LINC01094 was associated with high T and N stages and a poor prognosis. We found that LINC01094 promotes the proliferation and metastasis of GC in vitro and in vivo. AZGP1 was found as the protein-binding partner of LINC01094 by using RNA pulldown and RNA-binding protein immunoprecipitation (RIP) assays. LINC01094 antagonizes the function of AZGP1, downregulates the expression of PTEN, and further upregulates the AKT pathway. Collectively, our results suggested that LINC01094 might predict the prognosis of GC patients and become the therapy target for GC

High-precision and efficiency diagnosis for polymer electrolyte membrane fuel cell based on physical mechanism and deep learning

International audienceAs a nonlinear and dynamic system, the polymer electrolyte membranefuel cell (PEMFC) system requires a comprehensive failureprediction and health management system to ensure its safety andreliability. In this study, a data-driven PEMFC health diagnosisframework is proposed, coupling the fault embedding model, sensorpre-selection method and <a href="https://www.sciencedirect.com/topics/engineering/deep-learning"title="Learn more about deep learning from ScienceDirect's AI-generated Topic Pages"class="topic-link"&gtdeep learning</a&gt diagnosis model. Firstly, aphysical-based mechanism fault embedding model of PEMFC isdeveloped to collect the data on various health states. This modelcan be utilized to determine the effects of different faults oncell performance and assist in the pre-selection of sensors. Then,considering the effect of fault pattern on decline, a sensorpre-selection method based on the analytical model is proposed tofilter the insensitive variable from the sensor set. The diagnosisaccuracy and computational time could be improved 3.7% and 40% withthe help of pre-selection approach, respectively. Finally, the datacollected by the optimal sensor set is utilized to develop thefault diagnosis model based on 1D-convolutional neural network(CNN). The results show that the proposed health diagnosisframework has better diagnosis performance compared with otherpopular diagnosis models and is conducive to online diagnosis, with99.2% accuracy, higher computational efficiency, faster convergencespeed and smaller training error. It is demonstrated that fasterconvergence speed and smaller training error are reflected in theproposed health diagnosis framework, which can significantly reducecomputational costs