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The present study investigates the transport of dilute bubbles by transitional flow in a joining, cross-flow-type T-junction channel geometry with Reynolds numbers at the outlet branch from Re3 = 600 to 1800 and an inlet volume flow rate ratio of 1. Bubbles with diameters between db = 400 and 600 µm are considered. The schematic pattern of the single-phase flow is introduced based on streakline dye visualizations. Complex 3D flow due to the narrow channel design dominates the recirculation area and flow instabilities become important with increasing Reynolds number, which can be observed by the fading of dye intensity. A numerical method is presented with unsteady boundary conditions based on laser Doppler velocimetry measurements. Bubble trajectories are obtained by an Euler-Lagrange approach. Using high-speed shadowgraphy method combined with image processing, bubble sizes were measured, and bubble trajectories were evaluated. Experimental bubble trajectories and numerically predicted bubble positions show good agreement for Re3 = 600, which is also the case with the dye visualization image. For higher Reynolds numbers, measurements of the bubble trajectories are reported and compared to dye visualization images. The increasing flow instabilities influence the bubble transport, resulting in large variations of bubble locations.

The Ms 5.1 Tangshan earthquake that occurred on 11 July 2020 was recorded by the infrasound array DQS at a source-to-receiver distance of 196 km and an azimuth of 278°. Relatively high-apparent velocity, celerity of epicenter infrasound, and propagation modeling indicate a stratospheric arrival. The backprojection of the epicentral infrasound detection delineates a northeast direction-extended infrasound radiation region with a long axis of about 57 km and a short axis of about 28 km, which is in good agreement with the fault rupture direction. The robustness of the infrasound radiation source location is also tested. The consistency between the spatial distribution of the radiation source and the epicenter confirms that the extended radiating zone around the epicenter excited infrasound signals lasting 2.5 min at a distance of 196 km. A comparison between the backprojected sound pressure level and peak surface pressure derived from measured seismic ground motions shows some disagreement, which may involve propagation path and/or topography around the epicenter. Therefore, these factors should be taken into account when locating the sources of infrasound and estimating the distribution of ground motion. This study shows the ability of infrasound arrays to detect, locate, and research moderate earthquakes and help reconstruct the seismoacoustic coupling in the source region and fault rupture. Moreover, the complexity of the infrasound radiation source and seismoacoustic coupling mechanism is recognized. It is expected that infrasonic observations of moderate earthquakes will increase with the number of infrasound arrays deployed worldwide. Our study is also helpful to the scientific design and optimal layout of infrasound monitoring systems, which will allow for the detection of earthquakes in addition to explosive sources.

A critical challenge in machine learning is the vulnerability of learning models in defending attacks from malicious adversaries. In this research, we propose game theoretical learning between a variational adversary and a Convolutional Neural Network (CNN), participating in a variable-sum two-player sequential Stackelberg game. Our adversary manipulates the input data distribution to make the CNN misclassify the manipulated data. Our ideal adversarial manipulation is a minimum change to the data which yet is large enough to mislead the CNNs. We propose an optimization procedure to find optimal adversarial manipulations by solving for the Nash equilibrium of the Stackelberg game. Specifically, the adversary's payoff function depends on the data manipulation which is determined by a Variational Autoencoder, while the CNN classifier's payoff functions are evaluated by misclassification errors. The optimization of our adversarial manipulations is defined by Alternating Least Squares and Simulated Annealing. Experimental results demonstrate that our game-theoretic manipulations are able to mislead CNNs that are well trained on the original data as well as on data generated by other models. We then let the CNNs to incorporate our manipulated data which leads to secure classifiers that are empirically the most robust in defending various types of adversarial attacks.

Real-world traffic sign recognition is an important step toward building autonomous vehicles, most of which highly dependent on deep neural networks (DNNs). Recent studies demonstrated that DNNs are surprisingly susceptible to adversarial examples. Many attack methods have been proposed to understand and generate adversarial examples, such as gradient-based attack, score-based attack, decision-based attack, and transfer-based attacks. However, most of these algorithms are ineffective in real-world road sign attack, because 1) iteratively learning perturbations for each frame is not realistic for a fast moving car and 2) most optimization algorithms traverse all pixels equally without considering their diverse contribution. To alleviate these problems, this article proposes the targeted attention attack (TAA) method for real-world road sign attack. Specifically, we have made the following contributions: 1) we leverage the soft attention map to highlight those important pixels and skip those zero-contributed areas—this also helps to generate natural perturbations; 2) we design an efficient universal attack that optimizes a single perturbation/noise based on a set of training images under the guidance of the pretrained attention map; 3) we design a simple objective function that can be easily optimized; and 4) we evaluate the effectiveness of TAA on real-world data sets. Experimental results validate that the TAA method improves the attack successful rate (nearly 10%) and reduces the perturbation loss (about a quarter) compared with the popular RP2 method. Additionally, our TAA also provides good properties, e.g., transferability and generalization capability. We provide code and data to ensure the reproducibility: https://github.com/AdvAttack/RoadSignAttack .

In this work, an efficient model for simulating bubble dispersion and coalescence due to turbulence is developed in the Euler-Lagrange framework. The primary liquid phase is solved on the Euler grid with the RANS turbulence model. Bubble motion is computed with the force balance equations. One-way coupling between two phases is assumed and the framework is designed for the computation of disperse bubbly flows at low Eötvös number. The turbulent dispersion of the dispersed phase is reconstructed with the continuous random walk (CRW) model. Bubble-bubble collisions and coalescence are accounted for deterministically. To accelerate the time-consuming search for potential collision partners in dense bubbly flows, the sweep and prune algorithm is employed, which can be utilized in arbitrary mesh types and sizes. Validation against experiments of turbulent pipe flows demonstrates that the one-way coupled EL-CRW dispersion model can well reproduce the bubble distribution in a typical dense bubbly pipe flow. Good agreement of the bubble size distribution at the pipe outlet between the simulation and the experiment is obtained.

Modern Natural Language Processing (NLP) models are known immensely brittle towards text adversarial examples. Recent attack algorithms usually adopt word-level substitution strategies following a pre-computed word replacement mechanism. However, their resultant adversarial examples are still imperfect in achieving grammar correctness and semantic similarities, which is largely because of their unsuitable candidate word selections and static optimization methods. In this research, we propose BESA, a BERT-based Simulated Annealing algorithm, to address these two problems. Firstly, we leverage the BERT Masked Language Model (MLM) to generate contextual-aware candidate words to produce fluent adversarial text and avoid grammar errors. Secondly, we employ Simulated Annealing (SA) to adaptively determine the word substitution order. The SA provides sufficient word replacement options via internal simulations, with an objective to obtain both a high attack success rate and a low word substitution rate. Besides, our algorithm is able to jump out of local optima with a controlled probability, making it closer to achieve the best possible attack (i.e., the global optima). Experiments on five popular datasets manifest the superiority of BESA compared with existing methods, including TextFooler, BAE, BERT-Attack, PWWS, and PSO.

On-orbit proximity operations such as rendezvous need to obtain high-precision pose information. However, the pose estimation of space target is difficult, because the space target is greatly affected by the illumination and the earth background. In order to solve the above problems, a Convolutional Neural Networks (CNN) based pose estimation method for known non-cooperative spacecraft is proposed. Three branches CNNs are designed to estimate orientation, position and spacecraft category respectively, and the loss function of three tasks is balanced by the method of automatic learning coefficient. Because CNN needs a lot of training data, the manual annotation method will bring huge workload. In this paper, we use 3D Max to generate spacecraft rendering data quickly without manual annotation. The experimental results show that the method can accurately predict the position and orientation of spacecraft, and classify spacecraft types at the same time.

Our study firstly elaborated the underlying mechanism of endogenous CH4-induced abiotic tolerance, along with an alteration of ABA sensitivity by mimicking the endogenous CH4 production in MtMCR transgenic Arabidopsis. Endogenous methane (CH4) production and/or emission have been ubiquitously observed in stressed plants. However, their physiological roles remain unclear. Here, the methyl-coenzyme M reductase gene from Methanobacterium thermoautotrophicum (MtMCR), encoding the enzyme of methanogenesis, was expressed in Arabidopsis thaliana, to mimic the production of endogenous CH4. In response to salinity and osmotic stress, MtMCR expression was up-regulated in transgenic plants, resulting in significant increase of endogenous CH4 levels. Similar results were observed in abscisic acid (ABA) treatment. The functions of endogenous CH4 were characterized by the changes in plant phenotypes related to stress and ABA sensitivity during the germination and post-germination periods. When challenged with osmotic stress, a reduction in water loss and stomatal closure, were observed. Redox homeostasis was reestablished during osmotic and salinity stress, and ion imbalance was also restored in salinity conditions. The expression of several stress/ABA-responsive genes was up-regulated, and ABA sensitivity, in particularly, was significantly altered in the MtMCR transgenic plants. Together, our genetic study for the first time elaborated the possible mechanism of endogenous CH4-enhanced salinity and osmotic tolerance, along with an alteration of ABA sensitivity. These findings thus provided novel cues for understanding the possible roles of endogenous CH4 in plants.

Ethnopharmacological relevance Banxia Xiexin Decoction (BXD) is a representative prescription to regulate spleen and stomach in “Treatise on Febrile Diseases”, which has been proven effective for the clinical treatment of irritable bowel syndrome (IBS) in the past decades. However, the active principles and molecular mechanisms involved in BXD against IBS are vague yet. Aim of the study To unfold multicomponent synergy mechanism of BXD on irritable bowel syndrome, this work explored active principles, drug targets and crucial pathways using a systems pharmacology strategy. Materials and methods In this study, a systems pharmacology based strategy was applied by the procedures integrating compound database construction, ADME evaluation, target identification, functional annotation, pathway enrichment analysis, network analysis, and molecular docking verification. The 158 compounds from BXD were selected for the screening. The Compound-Target network (C-T) and the Target-Pathway network (T-P) were constructed. The bioinformatics and network topology were employed to systematically reveal multicomponent-target interactions of BXD. The affinity between important ingredients and the kernel targets was validated using molecular mechanics simulation. Results The 35 potential important ingredients and the 16 associated kernel targets were identified. 27 crucial pathways, in which the kernel targets participated, could regulate the biological processes, such as synthesis of inflammatory mediators, smooth muscle relaxation and synaptic plasticity, closely related to pathological mechanism of IBS. The cross-talk interactions were revealed between TNF signaling pathway, Dopaminergic synapse and cGMP-PKG signaling pathway, which would exert the synergistic influences on the occurrence and treatment of the IBS. PTGS2, CALM, NOS2, SCN5A, and PRSS1 might become novel drug targets for IBS. Conclusions The study demonstrated that the synergy molecular mechanisms of BXD mainly involved three therapeutic modules including inhibiting inflammatory reaction, maintaining intestinal function and improving psychological regulation via the multicomponent-target interaction networks. It may also provide the promising drug targets and therapeutic agents for the development of new medicines.

An efficient data-driven public transportation system can improve urban potency. In this research, we propose IntRoute, an Integer Programming (IP) based approach to optimize bus route planning. Specifically, IntRoute first contracts bus stops via clustering and then derives a new bus route via a mixed integer linear program (ILP). This two-phase strategy brings three major merits, i.e., a single bus route without any transfer, the minimal total time consuming, and an efficient optimization algorithm for large-scale problems. Experimental results show that our IntRoute significantly reduces the traditional commuting time in Sydney from 31.53 min down to 18.06 min on average.

Hydrogen sulfide (H2S) is well known as a gaseous signal in response to heavy metal stress, while methane (CH4), the most prevalent greenhouse gas, confers cadmium (Cd) tolerance. In this report, the causal link between CH4 and H2S controlling Cd tolerance in alfalfa (Medicago sativa) plants was assessed. Our results observed that the administration of CH4 not only intensifies H2S metabolism, but also attenuates Cd-triggered growth inhibition in alfalfa seedlings, which were parallel to the alleviated roles in the redox imbalance and cell death in root tissues. Above results were not observed in roots after the removal of endogenous H2S, either in the presence of either hypotaurine (HT; a H2S scavenger) or DL-propargylglycine (PAG; a H2S biosynthesis inhibitor). Using in situ noninvasive microtest technology (NMT) and inductively coupled plasma mass spectroscopy (ICP-MS), subsequent results confirmed the participation of H2S in CH4-inhibited Cd influx and accumulation in roots, which could be explained by reestablishing glutathione (GSH) pool (reduced/oxidized GSH and homoglutathione) homeostasis and promoting antioxidant defence. Overall, our results clearly revealed that H2S operates downstream of CH4 enhancing tolerance against Cd stress, which are significant for both fundamental and applied plant biology.

Melatonin (MT) plays positive roles in salinity stress tolerance. However, the upstream signalling components that regulate MT are poorly understood. Here, we report that endogenous MT acts downstream of molecular hydrogen (H2 ) in the salinity response in Arabidopsis. The addition of hydrogen-rich water and expression of the hydrogenase1 gene (CrHYD1) from Chlamydomonas reinhardtii increased endogenous H2 and MT levels and enhanced salinity tolerance. These results were not observed in the absence of serotonin N-acetyltransferase gene (SNAT). H2 increased the levels of SNAT transcripts in the wild-type and CrHYD1 lines, which had lower Na+ /K+ ratios and higher levels of ion transport-related gene transcripts. These changes were not observed in atsnat/CrHYD1-4 hybrids. The increased MT-dependent Na+ extrusion observed in the CrHYD1 plants resulted, at least in part, from enhanced Na+ /H+ antiport across the plasma membrane. The endogenous H2 -induced MT-dependent regulation of ion and redox homeostasis was impaired in the atsnat/CrHYD1-4 hybrids. Taken together, these results demonstrate that MT-induced salinity tolerance is induced by a H2 signalling cascade that regulates ion and redox homeostasis in response to salinity.

Estimations on people movement behaviour within a country can provide valuable information to government strategic resource plannings. In this paper, we propose to utilize multi-domain statistical data to estimate people movements under the assumption that most population tend to move to areas with similar or better living conditions. We design a Multi-domain Matrix Factorization (MdMF) model to discover the underlying consistency patterns from these cross-domain data and estimate the movement trends using the proposed model. This research can provide important theoretical support to government and agencies in strategic resource planning and investments.

Convolutional neural network (CNN) has been proven an effective way to extract high-level features from remote sensing (RS) images automatically. Many variants of the CNN model have been proposed, including principal component analysis network (PCANet), canonical correlation analysis network (CCANet), multiple-scale CCANet (MS-CCANet) and multiview CCANet (MCCANet). The PCANet is specialized for single view feature abstraction, while in many real-world practices, the RS data are frequently observed from many more views. Although CCANet, MS-CCANet and MCCANet can be applied to two or more view data, they consider only the pair-wise correlation by calculating a series of two-order covariance matrices. Moreover, it remains undiscovered how to handle high-order consistencies which can only be explored by simultaneously examining all views. In this paper, we propose the tensor canonical correlation analysis network (TCCANet) to tackle this problem. Particularly, TCCANet learns filter banks by simultaneously maximizing arbitrary number of views with high-order-correlation and solves the optimization problem by decomposing a covariance tensor. After the convolutional stage, we utilize binarization and block-wise histogram strategies to generate the final feature. Numerical experiment results on RSSCN7 and SAT-6 datasets demonstrate the advantages of TCCANet and MS-TCCANet for RS scene recognition.

Canonical correlation analysis (CCA) is a main technique of linear subspace approach for two-view dimension reduction by finding basis vectors with maximum correlation between the pair of variables. The shortcoming of the traditional CCA lies that it only handles data represented by two-view features and cannot reveal the nonlinear correlation relationship. In recent years, many variant algorithms have been developed to extend the capability of CCA such as discriminative CCA, sparse CCA, kernel CCA, locality preserving CCA and multiset canonical correlation analysis (MCCA). One representative work is Laplacian multiset canonical correlations (LapMCC) that employs graph Laplacian to exploit the nonlinear correlation information for multiview high-dimensional data. However, it possibly leads to poor extrapolating power because Laplacian regularization biases the solution towards a constant function. In this paper, we present Hessian multiset canonical correlations (HesMCC) for multiview dimension reduction. Hessian can properly exploit the intrinsic local geometry of the data manifold in contrast to Laplacian. HesMCC takes the advantage of Hessian and provides superior extrapolating capability and finally leverage the performance. Extensive experiments on several popular datasets for handwritten digits classification, face classification and object classification validate the effectiveness of the proposed HesMCC algorithm by comparing it with baseline algorithms including TCCA, KMUDA, MCCA and LapMCC.

Dispersed multiphase flow can be simulated by the Euler-Lagrange approach where the continuous phase is described on the Euler grid and the dispersed phase is treated as Lagrange points. The point-mass Euler-Lagrange method requires the bubble or particle size to be much smaller than the cell size when the interphase coupling is considered. Otherwise, numerical instabilities may arise. However, the point-mass prerequisite is hard to achieve, for example, if very fine grids are used to resolve turbulence in LES. Therefore, interpolation methods between the Euler and the Lagrange phase for finite-size bubbles which are bigger than or of the same size as numerical cells are discussed in this work. The spatially distributed coupling method is used for the Lagrange-to-Euler coupling. For the Euler-to-Lagrange coupling, a new approach is proposed and validated in different cases. Finally, a combination of coupling approaches is employed in a square bubble column reactor.

Background SiNiSan (SNS) is an ancient Chinese herbal prescription, and the current clinical treatment of irritable bowel syndrome (IBS) is effective. In the previous study of the research team, the multi-functional co-synergism of SNS against IBS was presented. Some potential drug targets and candidate ligands were predicted. Purpose This study attempts to explore the crucial ingredient combinations from SNS formula and reveal their synergistic mechanism for IBS therapy. Materials and methods In present study, a comprehensive strategy was performed to reveal IBS related pathways and biological modules, and explore synergistic effects of the ingredients, including ADME (absorption, distribution, metabolism, excretion) screening, Text mining, Venn analysis, Gene ontology (GO) analysis, Pathway cluster analysis, Molecular docking, Network construction and Experimental verification in visceral hypersensitivity (VHS) rats. Results Three compressed IBS signal pathways were derived from ClueGO KEGG analysis of 63 IBS genes, including Neuroactive ligand-receptor interaction, Inflammatory mediator regulation of TRP (transient receptor potential) channels and Serotonergic synapse. A multi-module network, composed of four IBS therapeutic modules (psychological, inflammation, neuroendocrine and cross-talk modules), was revealed by Target-Pathway network. Nine kernel targets were considered closely associated with the IBS pathways, including ADRA2A, HTR2A, F2RL1, F2RL3, TRPV1, PKC, PKA, IL-1Β and NGF. In silico analysis revealed that three crucial ingredients (synephrine, paeoniflorin and naringin) were assumed to coordinate the network of those IBS therapeutic modules by acting on these kernel targets in the important pathways. In vivo experimental results showed that the crucial ingredient combinations synergistically affected the expressions of the kernel biological molecules, and improved the minimum capacity threshold of AWR in VHS rats. Conclusion The study proposes the important IBS associated pathways and the network regulation mechanisms of the crucial ingredients. It reveals the multi-target synergistic effect of the crucial ingredient combinations for the novel therapy on IBS.

In the past decade, deep learning (DL) algorithms have been widely used for remote sensing (RS) image recognition tasks. As the most typical DL model, convolutional neural networks (CNNs) achieves outstand performance for big RS data classification. Recently, a variant of CNN, dubbed canonical correlation analysis network (CCANet), was proposed to abstract the two-view image features. Extensive experiments conducted on several benchmark databases validate the effectiveness of CCANet. However, the CCANet structure is powerless when the observations arrive from more than two sources. To serve the multiview purpose, in this letter, we propose multiview CCANets (MCCANets). Particularly, the MCCANet model learns the stacked multiperspective filter banks by the MCCA method and builds a deep convolutional structure. In the output stage, the binarization and the blockwise histogram are employed as nonlinear processing and feature pooling, respectively. To access the effectiveness of the MCCANet, we conduct a host of experiments on the RSSCN7 RS database. Extensive experimental results demonstrate that the MCCANet outperforms the two-view CCANet.

In recent years, deep learning has attracted an increasing amount of attention in machine learning and artificial intelligence areas. Currently, many deep learning network-related architectures such as deep neural networks (DNNs), convolutional neural network (CNN), wavelet scattering network (ScatNet) and principal component analysis network (PCANet) have been proposed. The most effective network is PCANet, which has achieved promising performance in image classification, such as for face, object and handwritten digit recognition. PCANet can only handle data that are represented by single-view features. In this paper, we present a canonical correlation analysis network (CCANet) to address image classification, in which images are represented by two-view features. The CCANet learns two-view multistage filter banks by a canonical correlation analysis (CCA) method and constructs a cascaded convolutional deep network. Then, we incorporate filters with binaryzation and block-wise histogram processes to form the final depth structure. In addition, we introduce a variation of CCANetdubbed RandNet-2in which the filter banks are randomly generated. Extensive experiments are conducted using the ETH-80, Yale-B, and USPS databases for object classification, face classification and handwritten digits classification, respectively. The experimental results demonstrate that the CCANet algorithm is more effective than PCANet, RandNet-1 and RandNet-2.