Propagation of Gevrey regularity for solutions of Landau equations

By using the energy-type inequality, we obtain, in this paper, the result on propagation of Gevrey regularity for the solution of the spatially homogeneous Landau equation in the cases of Maxwellian molecules and hard potential

Gevrey Regularity for Solution of the Spatially Homogeneous Landau Equation

In this paper, we study the Gevrey class regularity for solutions of the spatially homogeneous Landau equations in the hard potential case and the Maxwellian molecules case

Research on Non-destructive Testing Technique for Predicting Joint Resistance of Mechanical Lap Joint at Remountable High-Temperature Superconducting Magnet

Tohoku University橋爪秀利課

Smart Manufacturing Capability Maturity Model: Connotation, Feature And Trend

In March 2015, the Chinese government unveiled InternetPlus, an action plan expected to push forward the Chinese economy. The plan aims to integrate mobile Internet, cloud computing, big data, and the Internet of Things (IoT) with traditional industries to promote economic restructuring, improve people’s livelihoods, and even transform government and enterprises functions. However for the enterprises, how to evaluate the capability is still an unsolved issue. In this study, considering capability maturity theory and model existed, we summarized the concepts of smart manufacturing and relative research field, combined with the development trend of smart manufacturing and characteristics of the enterprise\u27s competition, a smart manufacturing capability maturity initial model with five levels and seven dimensions was defined. With this model, the connotation of smart manufacturing capability was unveiled and the model also provides reference for enterprises to assess and improve smart manufacturing capability

Fabrication of imitative cracks by 3D printing for electromagnetic nondestructive testing and evaluations

AbstractThis study demonstrates that 3D printing technology offers a simple, easy, and cost-effective method to fabricate artificial flaws simulating real cracks from the viewpoint of eddy current testing. The method does not attempt to produce a flaw whose morphology mirrors that of a real crack but instead produces a relatively simple artificial flaw. The parameters of this flaw that have dominant effects on eddy current signals can be quantitatively controlled. Three artificial flaws in type 316L austenitic stainless steel plates were fabricated using a powderbed-based laser metal additive manufacturing machine. The three artificial flaws were designed to have the same length, depth, and opening but different branching and electrical contacts between flaw surfaces. The flaws were measured by eddy current testing using an absolute type pancake probe. The signals due to the three flaws clearly differed from each other although the flaws had the same length and depth. These results were supported by subsequent destructive tests and finite element analyses

EDIS: Entity-Driven Image Search over Multimodal Web Content

Making image retrieval methods practical for real-world search applications requires significant progress in dataset scales, entity comprehension, and multimodal information fusion. In this work, we introduce \textbf{E}ntity-\textbf{D}riven \textbf{I}mage \textbf{S}earch (EDIS), a challenging dataset for cross-modal image search in the news domain. EDIS consists of 1 million web images from actual search engine results and curated datasets, with each image paired with a textual description. Unlike datasets that assume a small set of single-modality candidates, EDIS reflects real-world web image search scenarios by including a million multimodal image-text pairs as candidates. EDIS encourages the development of retrieval models that simultaneously address cross-modal information fusion and matching. To achieve accurate ranking results, a model must: 1) understand named entities and events from text queries, 2) ground entities onto images or text descriptions, and 3) effectively fuse textual and visual representations. Our experimental results show that EDIS challenges state-of-the-art methods with dense entities and a large-scale candidate set. The ablation study also proves that fusing textual features with visual features is critical in improving retrieval results

Financing small and innovative firms during Covid-19

Previous research on the financing of smaller innovative firms has established that being small per se creates problems in accessing finance, but being small and innovative adds another layer of difficulty. This new research explicitly questions whether the Covid-19 crisis has added to the debt access problems of an already disadvantaged group of firms. Using a unique Covid-19 period dataset of 9,000 UK SMEs, we find that the most innovative firms had the highest demand for loans during the Covid-19 crisis and evidence that those firms trying to introduce new products and services faced more severe borrowing constraints. As the vast majority of Covid-19 loans in the UK were government guaranteed, we also find that several classes of innovative firms found it more difficult to access government supported loans. It was also not the case that those most impacted by the crisis had the most privileged access to government loan schemes despite a greater need for liquidity. These findings have potential implications for financing innovative firms in the post-Covid-19 world, such as proposing a specific innovation loan guarantee scheme with higher than conventional guarantee rates

Decomposed Human Motion Prior for Video Pose Estimation via Adversarial Training

Estimating human pose from video is a task that receives considerable attention due to its applicability in numerous 3D fields. The complexity of prior knowledge of human body movements poses a challenge to neural network models in the task of regressing keypoints. In this paper, we address this problem by incorporating motion prior in an adversarial way. Different from previous methods, we propose to decompose holistic motion prior to joint motion prior, making it easier for neural networks to learn from prior knowledge thereby boosting the performance on the task. We also utilize a novel regularization loss to balance accuracy and smoothness introduced by motion prior. Our method achieves 9\% lower PA-MPJPE and 29\% lower acceleration error than previous methods tested on 3DPW. The estimator proves its robustness by achieving impressive performance on in-the-wild dataset

Divergent Effects of Factors on Crash Severity under Autonomous and Conventional Driving Modes Using a Hierarchical Bayesian Approach

Influencing factors on crash severity involved with autonomous vehicles (AVs) have been paid increasing attention. However, there is a lack of comparative analyses of those factors between AVs and human-driven vehicles. To fill this research gap, the study aims to explore the divergent effects of factors on crash severity under autonomous and conventional (i.e., human-driven) driving modes. This study obtained 180 publicly available autonomous vehicle crash data, and 39 explanatory variables were extracted from three categories, including environment, roads, and vehicles. Then, a hierarchical Bayesian approach was applied to analyze the impacting factors on crash severity (i.e., injury or no injury) under both driving modes with considering unobserved heterogeneities. The results showed that some influencing factors affected both driving modes, but their degrees were different. For example, daily visitors\u27 flowrate had a greater impact on the crash severity under the conventional driving mode. More influencing factors only had significant impacts on one of the driving modes. For example, in the autonomous driving mode, mixed land use increased the severity of crashes, while daytime had the opposite effects. This study could contribute to specifying more appropriate policies to reduce the crash severity of both autonomous and human-driven vehicles especially in mixed traffic conditions