Experimental Investigation on Failure Modes and Mechanical Properties of Rock-Like Specimens with a Grout-Infilled Flaw under Triaxial Compression

Flaws existing in rock mass are one of the main factors resulting in the instability of rock mass. Epoxy resin is often used to reinforce fractured rock mass. However, few researches focused on mechanical properties of the specimens with a resin-infilled flaw under triaxial compression. Therefore, in this research, epoxy resin was selected as the grouting material, and triaxial compression tests were conducted on the rock-like specimens with a grout-infilled flaw having different geometries. This study draws some new conclusions. The high confining pressure suppresses the generation of tensile cracks, and the failure mode changes from tensile-shear failure to shear failure as the confining pressure increases. Grouting with epoxy resin leads to the improvement of peak strengths of the specimens under triaxial compression. The reinforcement effect of epoxy resin is better for the specimens having a large flaw length and those under a relatively low confining pressure. Grouting with epoxy resin reduces the internal friction angle of the samples but improves their cohesion. This research may provide some useful insights for understanding the mechanical behaviors of grouted rock masses.National Natural Science Foundation of China [41672258, 41102162]; Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX18_0622]; Fundamental Research Funds for the Central Universities [2018B695X14]; Chinese Scholarship CouncilOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Quantifying Layerwise Information Discarding of Neural Networks

This paper presents a method to explain how input information is discarded through intermediate layers of a neural network during the forward propagation, in order to quantify and diagnose knowledge representations of pre-trained deep neural networks. We define two types of entropy-based metrics, i.e., the strict information discarding and the reconstruction uncertainty, which measure input information of a specific layer from two perspectives. We develop a method to enable efficient computation of such entropy-based metrics. Our method can be broadly applied to various neural networks and enable comprehensive comparisons between different layers of different networks. Preliminary experiments have shown the effectiveness of our metrics in analyzing benchmark networks and explaining existing deep-learning techniques

A novel adaptive function-dual Kalman filtering strategy for online battery model parameters and state of charge co-estimation.

This paper aims to improve the stability and robustness of the state-of-charge estimation algorithm for lithium-ion batteries. A new internal resistance-polarization circuit model is constructed on the basis of the Thevenin equivalent circuit to characterize the difference in internal resistance between charge and discharge. The extended Kalman filter is improved through adding an adaptive noise tracking algorithm and the Kalman gain in the unscented Kalman filter algorithm is improved by introducing a dynamic equation. In addition, for benignization of outliers of the two above mentioned algorithms, a new dual Kalman algorithm is proposed in this paper by adding a transfer function and through weighted mutation. The model and algorithm accuracy is verified through working condition experiments. The result shows that: the errors of the three algorithms are all maintained within 0.8% during the initial period and middle stages of the discharge; the maximum error of the improved extension of Kalman algorithm is over 1.5%, that of improved unscented Kalman increases to 5%, and the error of the new dual Kalman algorithm is still within 0.4% during the latter period of the discharge. This indicates that the accuracy and robustness of the new dual Kalman algorithm is better than those of traditional algorithm

Online full-parameter identification and SOC estimation of lithium-ion battery pack based on composite electrochemical-dual circuit polarization modeling.

A new composite electrochemistry-dual circuit polarization model (E-DCP) is proposed by combining the advantages of various electrochemical empirical models in this paper. Then, the multi-innovation least squares (MILS) algorithm is used to perform online full parameter identification for the E-DCP model in order to improve data usage efficiency and parameter identification accuracy. In addition, on the basis of the E-DCP model, the MILS and the extended Kalman filter (EKF) are combined to enhance the state estimation accuracy of the battery management system (BMS). Finally, the model and the algorithm are both verified through urban dynamometer driving schedule (UDDS) and the complex charge-discharge loop test. The results indicate that the accuracy of E-DCP is relatively high under different working conditions, and the errors of state of charge (SOC) estimation after the combination of MILS and EKF are all within 2.2%. This lays a concrete foundation for practical use of the BMS in the future

A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries.

As widely used for secondary energy storage, lithium-ion batteries have become the core component of the power supply system and accurate remaining useful life prediction is the key to ensure its reliability. Because of the complex working characteristics of lithium-ion batteries as well as the model parameter changing along with the aging process, the accuracy of the online remaining useful life prediction is difficult but urgent to be improved for the reliable power supply application. The deep learning algorithm improves the accuracy of the remaining useful life prediction, which also reduces the characteristic testing time requirement, providing the possibility to improve the power profitability of predictive energy management. This article analyzes, reviews, classifies, and compares different adaptive mathematical models on deep learning algorithms for the remaining useful life prediction. The features are identified for the modeling ability, according to which the adaptive prediction methods are classified. The specific criteria are defined to evaluate different modeling accuracy in the deep learning calculation procedure. The key features of effective life prediction are used to draw relevant conclusions and suggestions are provided, in which the high-accuracy deep convolutional neural network — extreme learning machine algorithm is chosen to be utilized for the stable remaining useful life prediction of lithium-ion batteries

MXene (Ti3C2Tx) and Carbon Nanotube Hybrid-Supported Platinum Catalysts for the High-Performance Oxygen Reduction Reaction in PEMFC

The metal–support interaction offers electronic, compositional, and geometric effects that could enhance catalytic activity and stability. Herein, a high corrosion resistance and an excellent electrical conductivity MXene (Ti3C2Tx) hybrid with a carbon nanotube (CNT) composite material is developed as a support for Pt. Such a composite catalyst enhances durability and improved oxygen reduction reaction activity compared to the commercial Pt/C catalyst. The mass activity of Pt/CNT-MXene demonstrates a 3.4-fold improvement over that of Pt/C. The electrochemical surface area of Pt/CNT–Ti3C2Tx (1:1) catalysts shows only 6% drop with respect to that in Pt/C of 27% after 2000 cycle potential sweeping. Furthermore, the Pt/CNT–Ti3C2Tx (1:1) is used as a cathode catalyst for single cell and stack, and the maximum power density of the stack reaches 138 W. The structure distortion of the Pt cluster induced by MXene is disadvantageous to the desorption of O atoms. This issue can be solved by adding CNT on MXene to stabilize the Pt cluster. These remarkable catalytic performances could be attributed to the synergistic effect between Pt and CNT–Ti3C2Tx

Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure Prediction

Data-driven predictive methods which can efficiently and accurately transform protein sequences into biologically active structures are highly valuable for scientific research and medical development. Determining accurate folding landscape using co-evolutionary information is fundamental to the success of modern protein structure prediction methods. As the state of the art, AlphaFold2 has dramatically raised the accuracy without performing explicit co-evolutionary analysis. Nevertheless, its performance still shows strong dependence on available sequence homologs. Based on the interrogation on the cause of such dependence, we presented EvoGen, a meta generative model, to remedy the underperformance of AlphaFold2 for poor MSA targets. By prompting the model with calibrated or virtually generated homologue sequences, EvoGen helps AlphaFold2 fold accurately in low-data regime and even achieve encouraging performance with single-sequence predictions. Being able to make accurate predictions with few-shot MSA not only generalizes AlphaFold2 better for orphan sequences, but also democratizes its use for high-throughput applications. Besides, EvoGen combined with AlphaFold2 yields a probabilistic structure generation method which could explore alternative conformations of protein sequences, and the task-aware differentiable algorithm for sequence generation will benefit other related tasks including protein design.Comment: version 2.0; 28 pages, 6 figure

Continuous low-dose cyclophosphamide plus prednisone in the treatment of relapsed and refractory multiple myeloma with severe complications

Background/objectiveWe retrospectively analyzed the effective and safety of continuous low-dose cyclophosphamide combined with prednisone (CP) in relapsed and refractory multiple myeloma (RRMM) patients with severe complications.MethodsA total of 130 RRMM patients with severe complications were enrolled in this study, among which 41 patients were further given bortezomib, lenalidomide, thalidomide or ixazomib on the basis of CP regimen (CP+X group). The response to therapy, adverse events (AEs), overall survival (OS) and progression-free survival (PFS) were recorded.ResultsAmong the 130 patients, 128 patients received therapeutic response assessment, with a complete remission rate (CRR) and objective response rate (ORR) of 4.7% and 58.6%, respectively. The median OS and PFS time were (38.0 ± 3.6) and (22.9±5.2) months, respectively. The most common AEs were hyperglycemia (7.7%), pneumonia (6.2%) and Cushing’s syndrome (5.4%). In addition, we found the pro-BNP/BNP level was obviously decreased while the LVEF (left ventricular ejection fraction) was increased in RRMM patients following CP treatment as compared with those before treatment. Furthermore, CP+X regimen further improved the CRR compared with that before receiving the CP+X regimen (24.4% vs. 2.4%, P=0.007). Also, both the OS and PFS rates were significantly elevated in patients received CP+X regimen following CP regimen as compared with the patients received CP regimen only.ConclusionThis study demonstrates the metronomic chemotherapy regimen of CP is effective to RRMM patients with severe complications