Experimental and numerical studies on progressive debonding of grouted rock bolts

Understanding the mechanism of progressive debonding of bolts is of great significance for underground safety. In this paper, both laboratory experiment and numerical simulation of the pull-out tests were performed. The experimental pull-out test specimens were prepared using cement mortar material, and a relationship between the pull-out strength of the bolt and the uniaxial compressive strength (UCS) of cement mortar material specimen was established. The locations of crack developed in the pull-out process were identified using the acoustic emission (AE) technique. The pull-out test was reproduced using 2D Particle Flow Code (PFC2D) with calibrated parameters. The experimental results show that the axial displacement of the cement mortar material at the peak load during the test was approximately 5 mm for cement-based grout of all strength. In contrast, the peak load of the bolt increased with the UCS of the confining medium. Under peak load, cracks propagated to less than one half of the anchorage length, indicating a lag between crack propagation and axial bolt load transmission. The simulation results show that the dilatation between the bolt and the rock induced cracks and extended the force field along the anchorage direction; and, it was identified as the major contributing factor for the pull-out failure of rock bolt

Multi-Omics Study on the Molecular Mechanisms of Tetraodon Nigroviridis Resistance to Exogenous Vibrio Parahaemolyticus Infection

Vibrio parahaemolyticus is an important marine pathogen that causes inflammation and even death in teleost fishes. It has brought significant economic losses to the aquaculture industry as well as high risks to the sustainable development of marine fisheries. In the present study, the fish Tetraodon nigroviridis and the bacterial pathogen Vibrio parahaemolyticus were used to explore the molecular mechanisms underlying the immune response of T. nigroviridis to V. parahaemolyticus exogenous infection. The microRNA (miRNA)–mRNA–protein omics and corresponding experimental validation, followed by comparative analysis, revealed several differentially expressed genes involved in various components of the immune system, including the following: complement system, chemokines, lysosomes, phagocytes, B-cell receptor signaling pathway, T-cell receptor signaling pathway, Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway, and phospholipid metabolism, among others. Especially, the complements component 3 (C3) gene and protein expression levels were significantly higher after V. parahaemolyticus infection, and miRNAs targeting C3, including mir-6089-y, mir-460-y, and mir-1584-x, were significantly down-regulated. The gene and protein expression levels of complement 1 subunit qA (C1qA) were significantly down-regulated, while mir-203 targeting C1qA was significantly up-regulated. Overall, four complement genes (C1qA, IG, C3, and C5), which are key genes in the classical pathway of complement system activation for inflammatory response, were identified. Evolutionary analysis suggested that T. nigroviridis, acquired an increased ability to recognize pathogens by evolving a more complex complement system than terrestrial vertebrates. In addition, quantitative real-time polymerase chain reaction showed high consistency with the obtained multi-omics results, indicating the reliability of the sequencing data generated in the present study. In summary, our findings can serve as a fundamental basis for further in-depth multi-omics studies on the inflammatory processes of aquatic pathogens hindering fish sustainable production

Frontal Vehicular Crash Energy Management Using Analytical Model in Multiple Conditions

When it comes to frontal vehicular crash development, matching the stiffness of the front-end structures reasonably, i.e., impact energy management, can effectively improve the safety of the vehicle. A multi-condition analytical model for a frontal vehicular crash is constructed by a three-dimensional decomposition theory. In the analytical model, the spring is used to express the equivalent stiffness of the local energy absorption space at the front-end structure. Then based on the analytical model, the dynamic responses and evaluation indexes of the vehicle in MPDB and SOB conditions are derived with the input of the crash pulse decomposition scheme. Comparing the actual vehicle crash data and the calculation results of the proposed solution method, the error is less than 15%, which verifies validity of the modeling and the accuracy of the solution. Finally, based on the solution method in the MPDB and the SOB conditions, the sensitivities of the crash pulse decomposition scheme to evaluation indexes are analyzed to obtain qualitative rules which guide crash energy management. This research reveals the energy absorption principle of the front-end structure during the frontal impact process, and provides an effective optimization method to manage the multiple conditions of the vehicle crash energy such as the FRB (frontal rigid barrier), the MPDB (mobile progressive deformable barrier), and the SOB (small overlap barrier)

Frontal Vehicular Crash Energy Management Using Analytical Model in Multiple Conditions

When it comes to frontal vehicular crash development, matching the stiffness of the front-end structures reasonably, i.e., impact energy management, can effectively improve the safety of the vehicle. A multi-condition analytical model for a frontal vehicular crash is constructed by a three-dimensional decomposition theory. In the analytical model, the spring is used to express the equivalent stiffness of the local energy absorption space at the front-end structure. Then based on the analytical model, the dynamic responses and evaluation indexes of the vehicle in MPDB and SOB conditions are derived with the input of the crash pulse decomposition scheme. Comparing the actual vehicle crash data and the calculation results of the proposed solution method, the error is less than 15%, which verifies validity of the modeling and the accuracy of the solution. Finally, based on the solution method in the MPDB and the SOB conditions, the sensitivities of the crash pulse decomposition scheme to evaluation indexes are analyzed to obtain qualitative rules which guide crash energy management. This research reveals the energy absorption principle of the front-end structure during the frontal impact process, and provides an effective optimization method to manage the multiple conditions of the vehicle crash energy such as the FRB (frontal rigid barrier), the MPDB (mobile progressive deformable barrier), and the SOB (small overlap barrier)

The Impact of Short Selling on Bank Holding Companies’ Loss Recognition and Risk Taking

In this study we examine whether the Regulation SHO (Reg-SHO) affects bank loan loss provision practices and risk taking. We argue that when facing the heightened downward price pressure caused by the removal of short-selling constraints, pilot bank holding companies (BHCs) chosen for the SEC randomized experiment in Reg-SHO may delay loan loss recognition relative to both publicly traded and privately held control BHCs. In addition, to potentially offset the unrecognized expected loan loss that will be materialized in the future, pilot BHCs may take on additional risk. We find that pilot BHCs become more delayed in loan loss provisioning and engage in more risk taking in the pilot period compared to both control groups. We also find the increase in risk taking is most significant for pilot BHCs that delay loan loss provisioning the most in the pilot period. Finally, we find these pilot BHCs have the highest crash and tail risks in the recessionary periods. Our study provides insights to the current debate on the role of short selling in the banking industry

Green material selection for sustainability: A hybrid MCDM approach.

Green material selection is a crucial step for the material industry to comprehensively improve material properties and promote sustainable development. However, because of the subjectivity and conflicting evaluation criteria in its process, green material selection, as a multi-criteria decision making (MCDM) problem, has been a widespread concern to the relevant experts. Thus, this study proposes a hybrid MCDM approach that combines decision making and evaluation laboratory (DEMATEL), analytical network process (ANP), grey relational analysis (GRA) and technique for order performance by similarity to ideal solution (TOPSIS) to select the optimal green material for sustainability based on the product's needs. A nonlinear programming model with constraints was proposed to obtain the integrated closeness index. Subsequently, an empirical application of rubbish bins was used to illustrate the proposed method. In addition, a sensitivity analysis and a comparison with existing methods were employed to validate the accuracy and stability of the obtained final results. We found that this method provides a more accurate and effective decision support tool for alternative evaluation or strategy selection

Self-Supervised Voltage Sag Source Identification Method Based on CNN

A self-supervised voltage sag source identification method based on a convolution neural network is proposed in this study. In addition, a self-supervised CNN (Convolutional Neural Networks) voltage sag source identification model is constructed on the basis of the convolution neural network and AutoEncoder. The convolution layer and pool layer in CNN are used to extract the voltage sag characteristics, and the self-supervised network training process is realized based on the principle of AE. In the constructed mode, features which reflect the data characteristics are used rather than artificial features, thus improving the accuracy of practical application. It is unnecessary to input a lot of correct labels before the self-supervised training process. The model can meet the requirements of sag source identification on timeliness, practicability, diversity, and versatility in the context of modern big data. In this study, three-phase asymmetric sag sources in sag sources are classified into more detailed categories according to different fault phases. Therefore, the proposed method can not only identify the voltage sag source, but also accurately determine the specific fault phase. Finally, the optimal parameters of the model are recognized through a case study, and a self-supervised CNN model is established based on the data type of voltage sag. This model extracts features and identifies sag sources through the measured sag data. The superiority of the proposed method is verified by a comparison

Robust Sparse Multichannel Active Noise Control

Multichannel active noise control (MC-ANC) aims to cancel low-frequency noise in an enclosure. If noise sources are distributed sparsely in space, adding an `1-norm constraint to the standard MC-ANC helps to reduce the complexity of the system and accelerate the convergence rate. However, the convergence performance of `1-norm constrained MCANC (c`1-MC-ANC) degrades significantly in reverberant environments. In this paper, we analyze the necessity of using sparsity-inducing algorithms with distinct zero-attracting strengths over loudspeakers, and then derive three algorithms of this kind in the complex domain. Simulation results show that, compared to c`1-MC-ANC, the proposed algorithms exhibit faster convergence or higher noise reduction at steady state in both free field and reverberant environmentsThis work was supported in part by the National Natural Science Foundation of China (NSFC) funding scheme under Project No. 61671380, No. 61671381 and No. 61671382