Pose Adjusting Simulation of Hydraulic Support Based on Mechanical-Electrical-Hydraulic Coordination

The pose variations of the hydraulic support have significant influence on its performance. And the pose adjusting operations are required to be fast and precise, while they have typical mechanical-electric-hydraulic coordination characteristics which are challenging to simulate. In view of these problems, a method has been proposed in this work for accurately monitoring and controlling the pose of the hydraulic support, and using a suitable multi-software co-simulation model to simulate it. A mathematical model for pose monitoring and control was initially established.A teaching-learning-based optimization algorithm (TLBO) was then introduced to obtain the numerical solution of the nonlinear equations.Finally, a numerical model based on mechanical-electrical-hydraulic co-simulation was established. The model was tested and validated by using different pose signals. The results indicate that the pose controller developed in this paper can control the support pose well (iteration time less than 1 s and cumulative error less than 1.5 mm). Moreover, the multi-software co-simulation approach was effective for describing the complex system. The co-simulation platform proposed in this study can benefit the virtual monitoring technology for hydraulic supports.The research provides theoretical basis and technical guidance for the automation and unmanned control of underground mining

A New Parallel Framework of SPH-SWE for Dam Break Simulation Based on OpenMP

Due to its Lagrangian nature, Smoothed Particle Hydrodynamics (SPH) has been used to solve a variety of fluid-dynamic processes with highly nonlinear deformation such as debris flows, wave breaking and impact, multi-phase mixing processes, jet impact, flooding and tsunami inundation, and fluid–structure interactions. In this study, the SPH method is applied to solve the two-dimensional Shallow Water Equations (SWEs), and the solution proposed was validated against two open-source case studies of a 2-D dry-bed dam break with particle splitting and a 2-D dam break with a rectangular obstacle downstream. In addition to the improvement and optimization of the existing algorithm, the CPU-OpenMP parallel computing was also implemented, and it was proven that the CPU-OpenMP parallel computing enhanced the performance for solving the SPH-SWE model, after testing it against three large sets of particles involved in the computational process. The free surface and velocities of the experimental flows were simulated accurately by the numerical model proposed, showing the ability of the SPH model to predict the behavior of debris flows induced by dam-breaks. This validation of the model is crucial to confirm its use in predicting landslides’ behavior in field case studies so that it will be possible to reduce the damage that they cause. All the changes made in the SPH-SWEs method are made open-source in this paper so that more researchers can benefit from the results of this research and understand the characteristics and advantages of the solution proposed

UAV first view landmark localization with active reinforcement learning

We present an active reinforcement learning framework for unmanned aerial vehicle (UAV) first view landmark localization. We formulate the problem of landmark localization as that of a Markov decision process and introduce an active landmark-localization network (ALLNet) to address it. The aim of the ALLNet is to locate a bounding box that surrounds the landmark in a first view image sequence. To this end, it is trained in a reinforcement learning fashion. Specifically, it employs support vector machine (SVM) scores on the bounding box patches as rewards and learns the bounding box transformations as actions. Furthermore, each SVM score indicates whether or not the landmark is detected by the bounding box such that it enables the ALLNet to have the capability of judging whether the landmark leaves or re-enters a first view image. Therefore, the operation of the ALLNet is not only dominated by the reinforcement learning process but also supplemented by an active learning motivated manner. Once the landmark is considered to leave the first view image, the ALLNet stops operating until the SVM detects its re-entry to the view. The active reinforcement learning model enables training a robust ALLNet for landmark localization. The experimental results validate the effectiveness of the proposed model for UAV first view landmark localization

Analysis on the Effect of Slideway Friction to the Slider-Type Hydraulic Powered Support

This paper presents a design concept of the slider-type hydraulic powered support. The equivalent mechanical model is established when the hydraulic powered support supporting the stable roof pressure and deriving the numerical calculation formula of the supporting efficiency is based on the slideway frictional coefficient. Meanwhile, theoretical solutions of supporting efficiency at different working heights are obtained. On this basis, the rigid-flexible coupling simulation model of the support was established by using Hypermesh and Adams and the dynamic simulation was carried out under the condition that the roof is bearing the stable pressure, and finally, obtaining the force response curves and the simulation solutions of the supporting efficiency. The final analysis shows the following: The slider-type powered support is suitable for working at high position; with the increase of the friction coefficient between the slider and the slideway, the supporting efficiency increases gradually, the working safety and reliability are enhanced, furthermore, its shock resistance and stability are enhanced

PRL-3 promotes the motility, invasion, and metastasis of LoVo colon cancer cells through PRL-3-integrin β1-ERK1/2 and-MMP2 signaling

<p>Abstract</p> <p>Background</p> <p>Phosphatase of regenerating liver-3 (PRL-3) plays a causative role in tumor metastasis, but the underlying mechanisms are not well understood. In our previous study, we observed that PRL-3 could decrease tyrosine phosphorylation of integrin β1 and enhance activation of ERK1/2 in HEK293 cells. Herein we aim to explore the association of PRL-3 with integrin β1 signaling and its functional implications in motility, invasion, and metastasis of colon cancer cell LoVo.</p> <p>Methods</p> <p>Transwell chamber assay and nude mouse model were used to study motility and invasion, and metastsis of LoVo colon cancer cells, respectively. Knockdown of integrin β1 by siRNA or lentivirus were detected with Western blot and RT-PCR. The effect of PRL-3 on integrin β1, ERK1/2, and MMPs that mediate motility, invasion, and metastasis were measured by Western blot, immunofluorencence, co-immunoprecipitation and zymographic assays.</p> <p>Results</p> <p>We demonstrated that PRL-3 associated with integrin β1 and its expression was positively correlated with ERK1/2 phosphorylation in colon cancer tissues. Depletion of integrin β1 with siRNA, not only abrogated the activation of ERK1/2 stimulated by PRL-3, but also abolished PRL-3-induced motility and invasion of LoVo cells in vitro. Similarly, inhibition of ERK1/2 phosphorylation with U0126 or MMP activity with GM6001 also impaired PRL-3-induced invasion. In addition, PRL-3 promoted gelatinolytic activity of MMP2, and this stimulation correlated with decreased TIMP2 expression. Moreover, PRL-3-stimulated lung metastasis of LoVo cells in a nude mouse model was inhibited when integrin β1 expression was interfered with shRNA.</p> <p>Conclusion</p> <p>Our results suggest that PRL-3's roles in motility, invasion, and metastasis in colon cancer are critically controlled by the integrin β1-ERK1/2-MMP2 signaling.</p

Post-traumatic stress disorder and depressive symptoms among firefighters: a network analysis

BackgroundFirefighters, as first responders with a high risk of occupational exposure to traumatic events and heavy working stress, have a high prevalence of PTSD symptoms and depressive symptoms. But no previous studies analyzed the relationships and hierarchies of PTSD and depressive symptoms among firefighters. Network analysis is a novel and effective method for investigating the complex interactions of mental disorders at the symptom level and providing a new understanding of psychopathology. The current study was designed to characterize the PTSD and depressive symptoms network structure in the Chinese firefighters.MethodThe Primary Care PTSD Screen for DSM-5 (PC-PTSD-5) and the Self-Rating Depression Scale (SDS) were applied to assess PTSD and depressive symptoms, respectively. The network structure of PTSD and depressive symptoms was characterized using “expected influence (EI)” and “bridge EI” as centrality indices. The Walktrap algorithm was conducted to identify communities in the PTSD and depressive symptoms network. Finally, Network accuracy and stability were examined using the Bootstrapped test and the case-dropping procedure.ResultsA total of 1,768 firefighters were enrolled in our research. Network analysis revealed that the relationship between PTSD symptoms, “Flashback” and “Avoidance,” was the strongest. “Life emptiness” was the most central symptom with the highest EI in the PTSD and depression network model. Followed by “Fatigue” and “Interest loss.” Bridge symptoms connecting PTSD and depressive symptoms in our study were “Numb,” “High alertness,” “Sad mood,” and “Compunction and blame,” successively. The data-driven community detection suggested the differences in PTSD symptoms in the clustering process. The reliability of the network was approved by both stability and accuracy tests.ConclusionTo the best of our knowledge, the current study first demonstrated the network structure of PTSD and depressive symptoms among Chinese firefighters, identifying the central and bridge symptoms. Targeting interventions to the symptoms mentioned above may effectively treat firefighters suffering from PTSD and depressive symptoms

Factors related to the length of stay for major depressive disorder patients in China: A real-world retrospective study

BackgroundAs numerous patients with depression have to be hospitalized because of various reasons, the demand far exceeds the limited bed count in the psychiatry department. Controlling the length of stay (LOS) of the patient is gradually being considered an effective method to alleviate this problem. Given the lack of statistical evidence of the LOS of patients with major depressive disorder (MDD) in China and the strain on the limited psychiatric resources, the purpose of our study was to investigate the LOS of patients with MDD among in-patient samples and to analyze related factors of the LOS in China by building a regression model.MethodThe data were exported from the electronic medical record system. A total of three categories of independent variables were enrolled in our study, namely, demographic, clinical, and biochemical. Univariate analysis and binominal regression analysis were applied comprehensively to find the factors related to the LOS among MDD samples. The discrimination accuracy of the model was evaluated by the receiver operating characteristic (ROC) analysis. ROC analysis indicated that the discrimination accuracy of our model was acceptable (AUC = 0.790, 95% CI = 0.714–0.865, P &lt; 0.001).ResultA total of 254 patients were finally brought into analysis after filtering. Regression analysis indicated that abnormal LDL was the only risk factor of long LOS (OR = 3.352, 95% CI = 1.087–10.337, P = 0.035) among all the kinds of variables. Notably, in the statistically irrelevant factors of the LOS, the category of anti-depressant drugs [serotonin–norepinephrine reuptake inhibitor (SNRI) or selective serotonin reuptake inhibitor (SSRI)] prescribed to patients with MDD was not associated statistically with the LOS, which was against our initial hypothesis that the LOS of patients with MDD treated with SNRI would vary from that of the patients treated with SSRI.ConclusionUp to our knowledge, our research is the first study to show the potential factors related to the LOS from various domains, especially biochemical indexes, and the effect of drugs, among clinical patients with MDD in China. Our results could provide a theoretical reference for efficient psychiatry hospitalization management and prioritization of allocating medical resources. Future studies are required for updating independent variables which are potentially related to the LOS and verifying existing results in a larger sample