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Plane segmentation is a basic task in the automatic reconstruction of indoor and urban environments from unorganized point clouds acquired by laser scanners. As one of the most common plane-segmentation methods, standard Random Sample Consensus (RANSAC) is often used to continually detect planes one after another. However, it suffers from the spurious-plane problem when noise and outliers exist due to the uncertainty of randomly sampling the minimum subset with 3 points. An improved RANSAC method based on Normal Distribution Transformation (NDT) cells is proposed in this study to avoid spurious planes for 3D point-cloud plane segmentation. A planar NDT cell is selected as a minimal sample in each iteration to ensure the correctness of sampling on the same plane surface. The 3D NDT represents the point cloud with a set of NDT cells and models the observed points with a normal distribution within each cell. The geometric appearances of NDT cells are used to classify the NDT cells into planar and non-planar cells. The proposed method is verified on three indoor scenes. The experimental results show that the correctness exceeds 88.5% and the completeness exceeds 85.0%, which indicates that the proposed method identifies more reliable and accurate planes than standard RANSAC. It also executes faster. These results validate the suitability of the method.

Maintaining a healthy balance of gut bacteria can promote good health. Leonardi et al. show that fungi can also interact with gut immune cells to maintain intestinal well-being. CX3CR1 + mononuclear phagocytes (MNPs) patrol the intestine and promote antifungal immunity. Genetic deletion of CX3CR1 in MNPs caused colitis-like symptoms in mice. CX3CR1 polymorphisms were detected in Crohn's disease patients that were unable to produce antibodies against multiple fungal species. Thus, commensal fungi may be as important as bacteria in maintaining gut health, and antifungal therapy could hold promise for treating intestinal inflammation. Science , this issue p. 232 Phagocytes police the fungal microbiome. Intestinal fungi are an important component of the microbiota, and recent studies have unveiled their potential in modulating host immune homeostasis and inflammatory disease. Nonetheless, the mechanisms governing immunity to gut fungal communities (mycobiota) remain unknown. We identified CX3CR1 + mononuclear phagocytes (MNPs) as being essential for the initiation of innate and adaptive immune responses to intestinal fungi. CX3CR1 + MNPs express antifungal receptors and activate antifungal responses in a Syk-dependent manner. Genetic ablation of CX3CR1 + MNPs in mice led to changes in gut fungal communities and to severe colitis that was rescued by antifungal treatment. In Crohn’s disease patients, a missense mutation in the gene encoding CX3CR1 was identified and found to be associated with impaired antifungal responses. These results unravel a role of CX3CR1 + MNPs in mediating interactions between intestinal mycobiota and host immunity at steady state and during inflammatory disease.

Domain adaptation can handle data distribution in different domains and has been successfully applied to bearing fault diagnosis under variable working conditions. However, most of these methods ignore the influences of noise and data distribution discrepancy on marking pseudo labels. Additionally, most domain adaptive methods require a large amount of data and training time. To overcome the aforementioned challenges, firstly, sample rejection and pseudo label correction using K-means (SRPLC-K-means) were developed and explored to filter the noisy samples and correct the pseudo labels to obtain pseudo labels with higher confidence. Furthermore, a bearing fault diagnosis method based on the improved transfer component analysis and deep belief network is proposed, which can achieve subdomain adaptation and improve the compactness of the samples, leading to a complete bearing fault diagnosis under variable working conditions that is faster and more accurate. Finally, the results of the comparative tests confirmed that the proposed method could boost the average accuracy of 0.73%, 0.99%, and 5.55% in the three tests than the state-of-the-art methods, respectively. Moreover, the comparison of the time required for a fault diagnosis using different methods shows that compared to the end-to-end models, the proposed method reduces the time required by 594.9 s and 1431.6 s, respectively.

The ramjet engine has undergone rapid development in recent decades, and the dual combustion chamber ramjet (DCR) engine was proposed in the last century. Boron, with its high volumetric and gravimetric heating values, is one of the most attractive fuel additives for ramjet engines. However, due to the problem of low combustion efficiency, boron is difficult to achieve high efficiency combustion in practical applications. In order to investigate the combustion characteristics of boron-containing gas solid phase components in DCR engines, the Realizable k-ε model, finite rate/vortex dissipation model and boron particle King model ignition combustion calculation program were written. A three-dimensional full-scale two-phase flow numerical simulation was carried out in the combustion chamber of the DCR engine to calculate the effects of different particle sizes on the combustion characteristics. Through the analysis of the simulation results, the particle size affects the ignition time of boron particles and is positively correlated with the combustion efficiency, but with a non-linear growth. A smaller particle size can promote the ignition of boron particles, improve the solid phase combustion efficiency and achieve efficient combustion.

The segmentation of urban scene mobile laser scanning (MLS) data into meaningful street objects is a great challenge due to the scene complexity of street environments, especially in the vicinity of street objects such as poles and trees. This paper proposes a three-stage method for the segmentation of urban MLS data at the object level. The original unorganized point cloud is first voxelized, and all information needed is stored in the voxels. These voxels are then classified as ground and non-ground voxels. In the second stage, the whole scene is segmented into clusters by applying a density-based clustering method based on two key parameters: local density and minimum distance. In the third stage, a merging step and a re-assignment processing step are applied to address the over-segmentation problem and noise points, respectively. We tested the effectiveness of the proposed methods on two urban MLS datasets. The overall accuracies of the segmentation results for the two test sites are 98.3% and 97%, thereby validating the effectiveness of the proposed method.

New artificial intelligence scenarios, such as high-precision online industrial detection, unmanned driving, etc., are constantly emerging and have resulted in an increasing demand for real-time image processing with high frame rates and low power consumption. Histogram equalization (HE) is a very effective and commonly used image preprocessing algorithm designed to improve the quality of image processing results. However, most existing HE acceleration methods, whether run on general-purpose CPUs or dedicated embedded systems, require further improvement in their frame rate to meet the needs of more complex scenarios. In this paper, we propose an HE acceleration method for FPGAs based on a two-dimensional configurable pipeline architecture. We first optimize the parallelizability of HE with a fully configurable two-dimensional pipeline architecture according to the principle of adapting the algorithm to the hardware, where one dimension can compute the cumulative histogram in parallel and the other dimension can process multiple inputs simultaneously. This optimization also helps in the construction of a simple architecture that achieves a higher frequency when implementing HE on FPGAs, which consist of configurable input units, calculation units, and output units. Finally, we optimize the pipeline and critical path of the calculation units. In the experiments, we deploy the optimized HE on a VCU118 test board and achieve a maximum frequency of 891 MHz (which is up to 22.6 times more acceleration than CPU implementations), as well as a frame rate of 1899 frames per second for 1080p images.

The use of extension nozzles in rocket propulsion systems is one of the effective methods to increase the geometric expansion ratio and thrust of the nozzle. As a new type of extension nozzle, the flexible extension nozzle can achieve continuous changes in nozzle expansion ratio. To explore the characteristics of the flow field structure at the “bulge” structure and the step structure in the actual configuration of the flexible extension nozzle, and the impact on the performance of the nozzle, a three-dimensional numerical simulation model of the flexible extension nozzle was first established to study the flow field at the “bulge” structure, and then a two-dimensional unsteady numerical simulation model was established by using the coupling technology of dynamic grid and overlapping grid to study the flow field at the step structure. The results show that during the unfolding of the nozzle surface, a complex flow structure of “expansion fan-mixing layer-shock-vortex” were formed in the step area at the junction of the fixed section and the moving extension section. The specific impulse loss of the 3D model is larger than that of the 2D model due to the consideration of the “bulge” structure. The flow loss of the “bulge” structure and the step structure to the nozzle is 0.73% and 0.65% respectively.

Imaging has been an important strategy for exploring space weather. The Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) is a joint Chinese Academy of Sciences (CAS) and European Space Agency (ESA) mission, aiming at studying the interaction between Earth’s magnetosphere and solar wind near the subsolar point via soft X-ray imaging. As the boundary of Earth’s magnetosphere, magnetopause is a significant detection target to mirror solar wind’s change for the SMILE mission. In preparation for inverting three-dimensional magnetopause, we proposed an OESA-UNet model to detect the magnetopause position. The model obtains magnetopause with a U-shaped structure, in an end-to-end manner. Inspired by attention mechanisms, these blocks are integrated into ours. OESA-UNet captures low and high-level feature maps by adjusting the receptive field for precise localization. Adaptively pre-processing the image provides a prior for the network. Availability metrics are designed to determine whether it can serve three-dimensional inversion. Lastly, we provided ablation and comparison experiments by qualitative and quantitative analysis. Our recall, precision, and f1 score are 93.8%, 92.1%, and 92.9%, respectively, with an average angle deviation of 0.005 under the availability metrics. Results indicate that OESA-UNet outperforms other methods. It can better serve the purpose of magnetopause tracing from an X-ray image.

Studies show that awe can predict prosocial behaviors. However, the effects of awe on green consumption or its underlying mechanisms remain unclear. Awe promotes the small self that can promote connectedness to nature (CNS). Emotion regulation strategies can modify the effects of emotions on behavior. Therefore, this study investigated the moderating effects of emotion regulation strategies (emotion reappraisal and suppression) and the multi-step mediating effects of the small self and CNS on awe’s impact on consumers’ green purchasing intentions. Through four experiments, the study showed that (a) awe positively impacts consumers’ green purchasing intentions; (b) the small self and CNS play a multi-step mediating role in awe’s positive effect on green purchasing intention; (c) emotion reappraisal moderates awe’s favorable influence on green purchase intentions; and (d) apart from positive awe, negative and unnatural awe also influence green purchasing intention positively. This research has practical implications for companies’ green marketing strategies. Plain language summary Human production and consumption destroy the natural environment. The continued destruction of the natural environment will eventually threaten the survival of human beings. In order to reverse the trend of ecological deterioration, we must change our consumption patterns. Many companies have introduced green products in the market. However, because green products are of poorer quality or more expensive compared to ordinary products, they are not always popular in the market. Green consumption is clearly a moral behavior, so priming moral emotions can promote green consumption. Awe is a typical moral emotion. Awe occurs when we are confronted with a magnificent landscape in nature or a natural disaster. This paper verifies that priming awe can promote green consumption through questionnaires and experiments. On this basis, we found the psychological mechanism of awe promoting green consumption. The awe that arises when people are confronted with a huge natural landscape or a natural disaster makes them realize their insignificance and enhances their connection with nature. This ultimately leads to an increased willingness to consume green. In order to avoid embarrassment, we consciously regulate our emotional responses in our daily lives in order to experience more authentic emotions or to make socially acceptable emotional responses. Emotional regulation strategies include reappraisal and suppression strategies. In this paper, we propose and verify that emotion reappraisal strategies can reinforce the positive effects of awe on green purchasing intentions. The findings of this paper have strong implications for our better use of awe to promote green consumption.

Hybrid recommendation algorithms perform well in improving the accuracy of recommendation systems. However, in specific applications, they still cannot reach the requirements of the recommendation target due to the gap between the design of the algorithms and data characteristics. In this paper, in order to learn higher-order feature interactions more efficiently and to distinguish the importance of different feature interactions better on the prediction results of recommendation algorithms, we propose a light and FM deep neural network (LFDNN), a hybrid recommendation model including four modules. The LightGBM module applies gradient boosting decision trees for feature processing, which improves LFDNN’s ability to handle dense numerical features; the shallow model introduces the FM model for explicitly modeling the finite-order feature crosses, which strengthens the expressive ability of the model; the deep neural network module uses a fully connected feedforward neural network to allow the model to obtain more high-order feature crosses information and mine more data patterns in the features; finally, the Fusion module allows the shallow model and the deep model to obtain a better fusion effect. The results of comparison, parameter influence and ablation experiments on two real advertisement datasets shows that the LFDNN reaches better performance than the representative recommendation models.