Reduction of Coil-Crack Angle Sensitivity Effect Using a Novel Flux Feature of ACFM Technique

Alternating current field measurement (ACFM) testing is one of the promising techniques in the field of non-destructive testing with advantages of the non-contact capability and the reduction of lift-off effects. In this paper, a novel crack detection approach was proposed to reduce the effect of the angled crack (cack orientation) by using rotated ACFM techniques. The sensor probe is composed of an excitation coil and two receiving coils. Two receiving coils are orthogonally placed in the center of the excitation coil where the magnetic field is measured. It was found that the change of the x component and the peak value of the z component of the magnetic field when the sensor probe rotates around a crack followed a sine wave shape. A customized accelerated finite element method solver programmed in MATLAB was adopted to simulate the performance of the designed sensor probe which could significantly improve the computation efficiency due to the small crack perturbation. The experiments were also carried out to validate the simulations. It was found that the ratio between the z and x components of the magnetic field remained stable under various rotation angles. It showed the potential to estimate the depth of the crack from the ratio detected by combining the magnetic fields from both receiving coils (i.e., the x and z components of the magnetic field) using the rotated ACFM technique

DeepE: a deep neural network for knowledge graph embedding

Recently, neural network based methods have shown their power in learning more expressive features on the task of knowledge graph embedding (KGE). However, the performance of deep methods often falls behind the shallow ones on simple graphs. One possible reason is that deep models are difficult to train, while shallow models might suffice for accurately representing the structure of the simple KGs. In this paper, we propose a neural network based model, named DeepE, to address the problem, which stacks multiple building blocks to predict the tail entity based on the head entity and the relation. Each building block is an addition of a linear and a non-linear function. The stacked building blocks are equivalent to a group of learning functions with different non-linear depth. Hence, DeepE allows deep functions to learn deep features, and shallow functions to learn shallow features. Through extensive experiments, we find DeepE outperforms other state-of-the-art baseline methods. A major advantage of DeepE is the robustness. DeepE achieves a Mean Rank (MR) score that is 6%, 30%, 65% lower than the best baseline methods on FB15k-237, WN18RR and YAGO3-10. Our design makes it possible to train much deeper networks on KGE, e.g. 40 layers on FB15k-237, and without scarifying precision on simple relations.Comment: 10 pages, 5 figures, 7 table

Application of upscaling methods for fluid flow and mass transport in multi-scale heterogeneous media : A critical review

Physical and biogeochemical heterogeneity dramatically impacts fluid flow and reactive solute transport behaviors in geological formations across scales. From micro pores to regional reservoirs, upscaling has been proven to be a valid approach to estimate large-scale parameters by using data measured at small scales. Upscaling has considerable practical importance in oil and gas production, energy storage, carbon geologic sequestration, contamination remediation, and nuclear waste disposal. This review covers, in a comprehensive manner, the upscaling approaches available in the literature and their applications on various processes, such as advection, dispersion, matrix diffusion, sorption, and chemical reactions. We enclose newly developed approaches and distinguish two main categories of upscaling methodologies, deterministic and stochastic. Volume averaging, one of the deterministic methods, has the advantage of upscaling different kinds of parameters and wide applications by requiring only a few assumptions with improved formulations. Stochastic analytical methods have been extensively developed but have limited impacts in practice due to their requirement for global statistical assumptions. With rapid improvements in computing power, numerical solutions have become more popular for upscaling. In order to tackle complex fluid flow and transport problems, the working principles and limitations of these methods are emphasized. Still, a large gap exists between the approach algorithms and real-world applications. To bridge the gap, an integrated upscaling framework is needed to incorporate in the current upscaling algorithms, uncertainty quantification techniques, data sciences, and artificial intelligence to acquire laboratory and field-scale measurements and validate the upscaled models and parameters with multi-scale observations in future geo-energy research.© 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)This work was jointly supported by the National Key Research and Development Program of China (No. 2018YFC1800900 ), National Natural Science Foundation of China (No: 41972249 , 41772253 , 51774136 ), the Program for Jilin University (JLU) Science and Technology Innovative Research Team (No. 2019TD-35 ), Graduate Innovation Fund of Jilin University (No: 101832020CX240 ), Natural Science Foundation of Hebei Province of China ( D2017508099 ), and the Program of Education Department of Hebei Province ( QN219320 ). Additional funding was provided by the Engineering Research Center of Geothermal Resources Development Technology and Equipment , Ministry of Education, China.fi=vertaisarvioitu|en=peerReviewed

Discovery of a novel, liver-targeted thyroid hormone receptor-β agonist, CS271011, in the treatment of lipid metabolism disorders

IntroductionThyroid hormone receptor β (THR-β) plays a critical role in metabolism regulation and has become an attractive target for treating lipid metabolism disorders in recent years. Thus, in this study, we discovered CS271011, a novel THR-β agonist, and assessed the safety and efficiency of CS271011 compared to MGL-3196 in vitro and in vivo. MethodsWe conducted luciferase reporter gene assays to assess the activation of THR-β and α in vitro. C57BL/6J mice were fed a high-fat diet for 12 weeks, CS271011 was administered by gavage at the dose of 1 mg/kg and 3 mg/kg, and MGL-3196 was administered at the dose of 3 mg/kg for 10 weeks. Body weight, food intake, serum and hepatic parameters, histological analysis, pharmacokinetic studies, RNA sequencing of the liver and heart, and expression of hepatic lipid-metabolic genes were determined to evaluate the safety and efficiency of CS271011. ResultsCompared with MGL-3196, CS271011 showed higher THR-β activation in vitro. In the diet-induced obesity mice model, CS271011 demonstrated favourable pharmacokinetic properties in mice and was enriched in the liver. Finally, CS271011 improved dyslipidaemia and reduced liver steatosis in the diet-induced obesity murine model. Mechanistically, CS271011 and MGL-3196 showed potent regulation of lipid metabolism-related genes. ConclusionsCS271011 is a potent and liver-targeted THR-β agonist for treating lipid metabolism disorders

Functional Connectivity Changes Across the Spectrum of Subjective Cognitive Decline, Amnestic Mild Cognitive Impairment and Alzheimer’s Disease

The abnormality occurs at molecular, cellular as well as network levels in patients with Alzheimer’s disease (AD) prior to diagnosis. Most previous connectivity studies were conducted at 1 out of 3 (local, meso and global) scales in subjects covering only part of the entire AD spectrum (subjective cognitive decline, SCD; amnestic mild cognitive impairment, aMCI; and then fully manifest AD). Data interpretation within the framework of disease progression is therefore difficult. The current study included 3 age- and sex-matched cohorts: SCD (n = 32), aMCI (n = 37) and fully-established AD (n = 30). A group of healthy elderly subjects (n = 40) were included as a normal control (NC). Network connectivity was examined at the local (degree centrality), meso [subgraph centrality (SC)], and global (eigenvector and page-rank centralities) levels. As compared to NC, SCD subjects had isolated decrease of SC in primary (somatomotor and visual) networks. aMCI subjects had decreased centralities at all three scales in associative (frontoparietal control, dorsal attention, limbic and default) networks. AD subjects had increased centrality at the global scale in all seven networks. There was a positive association between Montreal Cognitive Assessment (MoCA) scores and DC in the frontoparietal control network in SCD, a negative relationship between Mini-Mental State Examination (MMSE) scores and EC in the somatomotor network in AD. These findings suggest that the primary network is impaired as early as in SCD. Impairment in the associative network also starts at the local level at this stage and may contribute to the cognitive decline. As associative network impairment extends from local to meso and global scales in aMCI, compensatory mechanisms in the primary network are activated