Research on vacuum plume and its effects

AbstractIn vacuum environment, the exhaust flow of attitude control thrusters would expand freely and produce the plume, which possibly causes undesirable contamination, aerodynamic force and heating effects to the spacecraft. Plume work station (PWS) is developed by Beihang University (BUAA) for numerically simulating the vacuum plume and its effects. An approach which combines the direct simulation Monte Carlo (DSMC) method and difference solution of Navier–Stokes (N–S) equations is applied. The internal flows in nozzles are simulated by solving the NS equations. The flow parameters at nozzle exit are used as the inlet boundary condition for the DSMC calculation. Experimental studies are carried out in a supersonic low density wind tunnel which could simulate the 60–80km altitude environment to investigate the plume and its effects. To demonstrate the capability of PWS, numerical simulations are performed for the vacuum plume of several typical attitude control thrusters. The research results are of great help for the engineering design

A NEW METHOD TO CONTROL THE REGIONAL STRATA MOVEMENT OF SUPER-THICK WEAK CEMENTATION OVERBURDEN IN DEEP MINING

In the western of china, the deep mining area with super-thick and weak cementation overburden is vast, sparsely populated and the ecological environment is extremely fragile. With the large-scale exploitation of deep coal resources, it is inevitable to face green mining problem, whose essence is the surface subsidence control. Therefore, it is necessary to study the control technology for the regional mining based on the evolution law of subsidence movement and energy-polling of super-thick and weak cementation overburden, and put forward the economically design scheme that can control strata movement and surface subsidence in a certain degree. Based on the key strata control theory, this paper puts forward the subsidence control scheme of partial filling -partial caving in multi-working face coordinated mining, and further studies its control mechanism through the numerical simulation and then analyzes the control effect of the strata movement and energy-polling in the fully caving mining, backfill mining, wide strip skip-mining and mixed filling mining method etc., the following conclusions are detailed as follows: (1) The maximum value of energy-polling occurs on the coal pillars or on both sides of goaf. With the width of goaf, the maximum value of energy-polling increases in a parabola. (2) In the partial filling-partial caving multiple working faces coordinated mining based on the main key stratum, the stress distribution of the composite backfill in the filling working face is parabolic, and it is high on both sides and low in the middle. Moreover, in the composite backfill, the stress concentration degree of a outside coal pillar is greater than that of the inside coal pillar. (3)The control mechanism of partial filling-partial caving harmonious mining based on main key layer structure is the double-control cooperative deformation system, formed by the composite backfill and the main and sub-key layers structure. They jointly control the movement and energy accumulation of overlying strata by greatly reducing the effective space to transmit upward, and absorb the wave subsidence trend of the overburden until it develops into a single flat subsidence basin. (4) Considering the recovery rate, pillar rate, area filling rate, technical difficulty and subsidence coefficient etc., the partial filling-partial caving multiple working faces coordinated mining based on the main key stratum is the most cost-effective mining method to control surface subsidence. This paper takes a guiding role in controlling the regional strata movement and surface subsidence of deep mining with super-thick and weak cementation overburden

Towards Deep Network Steganography: From Networks to Networks

With the widespread applications of the deep neural network (DNN), how to covertly transmit the DNN models in public channels brings us the attention, especially for those trained for secret-learning tasks. In this paper, we propose deep network steganography for the covert communication of DNN models. Unlike the existing steganography schemes which focus on the subtle modification of the cover data to accommodate the secrets, our scheme is learning task oriented, where the learning task of the secret DNN model (termed as secret-learning task) is disguised into another ordinary learning task conducted in a stego DNN model (termed as stego-learning task). To this end, we propose a gradient-based filter insertion scheme to insert interference filters into the important positions in the secret DNN model to form a stego DNN model. These positions are then embedded into the stego DNN model using a key by side information hiding. Finally, we activate the interference filters by a partial optimization strategy, such that the generated stego DNN model works on the stego-learning task. We conduct the experiments on both the intra-task steganography and inter-task steganography (i.e., the secret and stego-learning tasks belong to the same and different categories), both of which demonstrate the effectiveness of our proposed method for covert communication of DNN models.Comment: 8 pages. arXiv admin note: text overlap with arXiv:2302.1452

Transformer-based models for multimodal irony detection

Irony is nowadays a pervasive phenomenon in social networks. The multimodal functionalities of these platforms (i.e., the possibility to attach audio, video, and images to textual information) are increasingly leading their users to employ combinations of information in different formats to express their ironic thoughts. The present work focuses on the study of irony detection in social media posts involving image and text. To this end, a transformer architecture for the fusion of textual and image information is proposed. The model leverages disentangled text attention with visual transformers, improving F1-score up to 9% over previous existing works in the field and current state-of-the-art visio-linguistic transformers. The proposed architecture was evaluated in three different multimodal datasets gathered from Twitter and Tumblr. The results revealed that, in many situations, the text-only version of the architecture was able to capture the ironic nature of the message without using visual information. This phenomenon was further analysed, leading to the identification of linguistic patterns that could provide the context necessary for irony detection without the need for additional visual information.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was partially supported by the Spanish Ministry of Science and Innovation and Fondo Europeo de Desarrollo Regional (FEDER) in the framework of project “Technological Resources for Intelligent VIral AnaLysis through NLP (TRIVIAL)” (PID2021-122263OB-C22)

Chitosan-salvianolic acid B coating on the surface of nickel-titanium alloy inhibits proliferation of smooth muscle cells and promote endothelialization

Introduction: Intracranial stents are of paramount importance in managing cerebrovascular disorders. Nevertheless, the currently employed drug-eluting stents, although effective in decreasing in-stent restenosis, might impede the re-endothelialization process within blood vessels, potentially leading to prolonged thrombosis development and restenosis over time.Methods: This study aims to construct a multifunctional bioactive coating to enhance the biocompatibility of the stents. Salvianolic acid B (SALB), a bioactive compound extracted from Salvia miltiorrhiza, exhibits potential for improving cardiovascular health. We utilized dopamine as the base and adhered chitosan-coated SALB microspheres onto nickel-titanium alloy flat plates, resulting in a multifunctional drug coating.Results: By encapsulating SALB within chitosan, the release period of SALB was effectively prolonged, as evidenced by the in vitro drug release curve showing sustained release over 28 days. The interaction between the drug coating and blood was examined through experiments on water contact angle, clotting time, and protein adsorption. Cellular experiments showed that the drug coating stimulates the proliferation, adhesion, and migration of human umbilical vein endothelial cells.Discussion: These findings indicate its potential to promote re-endothelialization. In addition, the bioactive coating effectively suppressed smooth muscle cells proliferation, adhesion, and migration, potentially reducing the occurrence of neointimal hyperplasia and restenosis. These findings emphasize the exceptional biocompatibility of the newly developed bioactive coating and demonstrate its potential clinical application as an innovative strategy to improve stent therapy efficacy. Thus, this coating holds great promise for the treatment of cerebrovascular disease

Dynamic modeling and simulation of an integral bipropellant propulsion double-valve combined test system

This work was financially supported by the National Natural Science Foundation of China (No. 11101023) and the China Scholarship Council (No. 201203070237)

Biomedical compound figure detection using deep convolutional neural network

Scientific figures contain significant amounts of information but present different challenges relative to image retrieval. One such challenge is compound figures or images made up of two or more subfigures. A deep convolutional neural network model is proposed for compound figure detection (CFD) in the biomedical article domain. Our architecture is inspired by the success of VGG16 and uses large-size convolution kernel in first layer. The proposed model obtained a best test accuracy of 97.08% outperforming traditional hand-crafted and other deep learning representations on the ImageCLEF2016 CFD subtask datasets

Biomedical compound figure detection using deep convolutional neural network

Scientific figures contain significant amounts of information but present different challenges relative to image retrieval. One such challenge is compound figures or images made up of two or more subfigures. A deep convolutional neural network model is proposed for compound figure detection (CFD) in the biomedical article domain. Our architecture is inspired by the success of VGG16 and uses large-size convolution kernel in first layer. The proposed model obtained a best test accuracy of 97.08% outperforming traditional hand-crafted and other deep learning representations on the ImageCLEF2016 CFD subtask datasets