Stress-strain response analysis and protective device design for buried pipeline impacted by perilous rock

Collapse of perilous rocks is one of the most severe geological disasters for pipeline security. Stress-strain response of a buried pressure pipeline impacted by a perilous rock was simulated. Effects of impact velocity, rock’s radius, pipeline’s wall thickness, surrounding soil’s elastic modulus and Poisson’s ratio on stress, strain and deformation of the buried pipeline were investigated. The results show that the buried pipeline’s upper part is prone to instability under the rock impact. Plastic area of the buried pipeline becomes from oval to bat type with the impact load increases. Strain of the impact dent center is a compressive strain, while it is a tensile strain on the two sides of the dent. High stress area, axial strain and plastic strain of the buried pipeline increase with the increasing of impact velocity and rock’s radius, but they decrease with the increasing of wall thickness and soil’s elasticity modulus. Surrounding soil’s Poisson’s ratio has a small effect on the stress and strain of the pipeline. Impact dent’s size increases with the increasing of impact velocity and rock’s radius. Dent depth decreases with the surrounding soil’s elasticity modulus increases. With the wall thickness increases, impact dent depth first increases and then decreases. Finally, a protective device of buried pipeline is designed for preventing perilous rock impact. It can reduce the failure probability and improve the service life of buried pipeline for its simple structure and convenient installation

A Highly Specific Probe for Sensing Hydrogen Sulfide in Live Cells Based on Copper-Initiated Fluorogen with Aggregation-Induced Emission Characteristics

Here we reported the first fluorescent probe with aggregation-induced emission characteristics, namely AIE-S, for the detection of hydrogen sulfide (H2S) in live cells. The detection system is selective for complicated biological application and the response is fast enough to complete within seconds. Moreover, the probe exhibits the unique advantage of being immune to aggregation-caused quenching which is a detrimental phenomenon limiting the application of most current available H2S fluorescent probes. The detection mechanism was investigated and postulated to be S2- initiated de-coordination and thereafter aggregation of the AIE-S complex

Multi-head attention-based long short-term memory for depression detection from speech.

Depression is a mental disorder that threatens the health and normal life of people. Hence, it is essential to provide an effective way to detect depression. However, research on depression detection mainly focuses on utilizing different parallel features from audio, video, and text for performance enhancement regardless of making full usage of the inherent information from speech. To focus on more emotionally salient regions of depression speech, in this research, we propose a multi-head time-dimension attention-based long short-term memory (LSTM) model. We first extract frame-level features to store the original temporal relationship of a speech sequence and then analyze their difference between speeches of depression and those of health status. Then, we study the performance of various features and use a modified feature set as the input of the LSTM layer. Instead of using the output of the traditional LSTM, multi-head time-dimension attention is employed to obtain more key time information related to depression detection by projecting the output into different subspaces. The experimental results show the proposed model leads to improvements of 2.3 and 10.3% over the LSTM model on the Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) and the Multi-modal Open Dataset for Mental-disorder Analysis (MODMA) corpus, respectively

An Online Full-Body Motion Recognition Method Using Sparse and Deficient Signal Sequences

This paper presents a method to recognize continuous full-body human motion online by using sparse, low-cost sensors. The only input signals needed are linear accelerations without any rotation information, which are provided by four Wiimote sensors attached to the four human limbs. Based on the fused hidden Markov model (FHMM) and autoregressive process, a predictive fusion model (PFM) is put forward, which considers the different influences of the upper and lower limbs, establishes HMM for each part, and fuses them using a probabilistic fusion model. Then an autoregressive process is introduced in HMM to predict the gesture, which enables the model to deal with incomplete signal data. In order to reduce the number of alternatives in the online recognition process, a graph model is built that rejects parts of motion types based on the graph structure and previous recognition results. Finally, an online signal segmentation method based on semantics information and PFM is presented to finish the efficient recognition task. The results indicate that the method is robust with a high recognition rate of sparse and deficient signals and can be used in various interactive applications

Detecting Depression from Speech through an Attentive LSTM Network

Depression endangers people's health conditions and affects the social order as a mental disorder. As an efficient diagnosis of depression, automatic depression detection has attracted lots of researcher's interest. This study presents an attention-based Long Short-Term Memory (LSTM) model for depression detection to make full use of the difference between depression and non-depression between timeframes. The proposed model uses frame-level features, which capture the temporal information of depressive speech, to replace traditional statistical features as an input of the LSTM layers. To achieve more multi-dimensional deep feature representations, the LSTM output is then passed on attention layers on both time and feature dimensions. Then, we concat the output of the attention layers and put the fused feature representation into the fully connected layer. At last, the fully connected layer's output is passed on to softmax layer. Experiments conducted on the DAIC-WOZ database demonstrate that the proposed attentive LSTM model achieves an average accuracy rate of 90.2% and outperforms the traditional LSTM network and LSTM with local attention by 0.7% and 2.3%, respectively, which indicates its feasibility

Structural and Biochemical Bases for the Inhibition of Autophagy and Apoptosis by Viral BCL-2 of Murine γ-Herpesvirus 68

All gammaherpesviruses express homologues of antiapoptotic B-cell lymphoma-2 (BCL-2) to counter the clearance of infected cells by host antiviral defense machineries. To gain insights into the action mechanisms of these viral BCL-2 proteins, we carried out structural and biochemical analyses on the interactions of M11, a viral BCL-2 of murine γ-herpesvirus 68, with a fragment of proautophagic Beclin1 and BCL-2 homology 3 (BH3) domain-containing peptides derived from an array of proapoptotic BCL-2 family proteins. Mainly through hydrophobic interactions, M11 bound the BH3-like domain of Beclin1 with a dissociation constant of 40 nanomole, a markedly tighter affinity compared to the 1.7 micromolar binding affinity between cellular BCL-2 and Beclin1. Consistently, M11 inhibited autophagy more efficiently than BCL-2 in NIH3T3 cells. M11 also interacted tightly with a BH3 domain peptide of BAK and those of the upstream BH3-only proteins BIM, BID, BMF, PUMA, and Noxa, but weakly with that of BAX. These results collectively suggest that M11 potently inhibits Beclin1 in addition to broadly neutralizing the proapoptotic BCL-2 family in a similar but distinctive way from cellular BCL-2, and that the Beclin1-mediated autophagy may be a main target of the virus

A Novel Inhibitory Mechanism of Mitochondrion-Dependent Apoptosis by a Herpesviral Protein

Upon viral infection, cells undergo apoptosis as a defense against viral replication. Viruses, in turn, have evolved elaborate mechanisms to subvert apoptotic processes. Here, we report that a novel viral mitochondrial anti-apoptotic protein (vMAP) of murine γ-herpesvirus 68 (γHV-68) interacts with Bcl-2 and voltage-dependent anion channel 1 (VDAC1) in a genetically separable manner. The N-terminal region of vMAP interacted with Bcl-2, and this interaction markedly increased not only Bcl-2 recruitment to mitochondria but also its avidity for BH3-only pro-apoptotic proteins, thereby suppressing Bax mitochondrial translocation and activation. In addition, the central and C-terminal hydrophobic regions of vMAP interacted with VDAC1. Consequently, these interactions resulted in the effective inhibition of cytochrome c release, leading to the comprehensive inhibition of mitochondrion-mediated apoptosis. Finally, vMAP gene was required for efficient γHV-68 lytic replication in normal cells, but not in mitochondrial apoptosis-deficient cells. These results demonstrate that γHV-68 vMAP independently targets two important regulators of mitochondrial apoptosis-mediated intracellular innate immunity, allowing efficient viral lytic replication

Signal Processing of MEMS Gyroscope Arrays to Improve Accuracy Using a 1st Order Markov for Rate Signal Modeling

This paper presents a signal processing technique to improve angular rate accuracy of the gyroscope by combining the outputs of an array of MEMS gyroscope. A mathematical model for the accuracy improvement was described and a Kalman filter (KF) was designed to obtain optimal rate estimates. Especially, the rate signal was modeled by a first-order Markov process instead of a random walk to improve overall performance. The accuracy of the combined rate signal and affecting factors were analyzed using a steady-state covariance. A system comprising a six-gyroscope array was developed to test the presented KF. Experimental tests proved that the presented model was effective at improving the gyroscope accuracy. The experimental results indicated that six identical gyroscopes with an ARW noise of 6.2 °/√h and a bias drift of 54.14 °/h could be combined into a rate signal with an ARW noise of 1.8 °/√h and a bias drift of 16.3 °/h, while the estimated rate signal by the random walk model has an ARW noise of 2.4 °/√h and a bias drift of 20.6 °/h. It revealed that both models could improve the angular rate accuracy and have a similar performance in static condition. In dynamic condition, the test results showed that the first-order Markov process model could reduce the dynamic errors 20% more than the random walk model