Calcium–magnesium–alumina–silicate (CMAS) resistance of LaPO4 thermal barrier coatings

Nanostructured LaPO4 thermal barrier coatings (TBCs) were prepared by air plasma spraying, and their resistance to calcium–magnesium–alumina–silicate (CMAS) attack at 1250 °C, 1300 °C and 1350 °C was investigated. The reaction products were characterized by X-ray diffraction, scanning electron microscopy, energy dispersive spectroscopy and transmission electron microscopy. Exposed to CMAS attack for 0.5 h, a continuous dense reaction layer formed, which was mainly composed of P–Si apatite based on Ca2+xLa8-x(PO4)x(SiO4)6-xO2, anorthite and spinel phases. Beneath the reaction layer, little evidence of CMAS trace could be found. With the increase in temperature and heat treatment duration, the reaction layer became thick, while penetration depth of the molten CMAS changed slightly. Due to the formation of a reaction layer suppressing CMAS further infiltration, LaPO4 TBCs are highly resistant to CMAS attack

Advances and Challenges of Multi-task Learning Method in Recommender System: A Survey

Multi-task learning has been widely applied in computational vision, natural language processing and other fields, which has achieved well performance. In recent years, a lot of work about multi-task learning recommender system has been yielded, but there is no previous literature to summarize these works. To bridge this gap, we provide a systematic literature survey about multi-task recommender systems, aiming to help researchers and practitioners quickly understand the current progress in this direction. In this survey, we first introduce the background and the motivation of the multi-task learning-based recommender systems. Then we provide a taxonomy of multi-task learning-based recommendation methods according to the different stages of multi-task learning techniques, which including task relationship discovery, model architecture and optimization strategy. Finally, we raise discussions on the application and promising future directions in this area

Accelerated partial separable model using dimension-reduced optimization technique for ultra-fast cardiac MRI

Objective. Imaging dynamic object with high temporal resolution is challenging in magnetic resonance imaging (MRI). Partial separable (PS) model was proposed to improve the imaging quality by reducing the degrees of freedom of the inverse problem. However, PS model still suffers from long acquisition time and even longer reconstruction time. The main objective of this study is to accelerate the PS model, shorten the time required for acquisition and reconstruction, and maintain good image quality simultaneously. Approach. We proposed to fully exploit the dimension reduction property of the PS model, which means implementing the optimization algorithm in subspace. We optimized the data consistency term, and used a Tikhonov regularization term based on the Frobenius norm of temporal difference. The proposed dimension-reduced optimization technique was validated in free-running cardiac MRI. We have performed both retrospective experiments on public dataset and prospective experiments on in-vivo data. The proposed method was compared with four competing algorithms based on PS model, and two non-PS model methods. Main results. The proposed method has robust performance against shortened acquisition time or suboptimal hyper-parameter settings, and achieves superior image quality over all other competing algorithms. The proposed method is 20-fold faster than the widely accepted PS+Sparse method, enabling image reconstruction to be finished in just a few seconds. Significance. Accelerated PS model has the potential to save much time for clinical dynamic MRI examination, and is promising for real-time MRI applications.Comment: 23 pages, 11 figures. Accepted as manuscript on Physics in Medicine & Biolog

Dietary Probiotic Bacillus licheniformis TC22 Increases Growth, Immunity, and Disease Resistance, against Vibrio splendidus Infection in Juvenile Sea Cucumbers Apostichopus japonicus

In this study we examined the effects of probiotic Bacillus licheniformis TC22 on growth, immunity, and disease resistance against Vibrio splendidus in juvenile sea cucumbers Apostichopus japonicus. For 30 days, sea cucumbers were fed diets with TC22 at 0 (control), 105, 107, and 109 CFU/g respectively. Results showed that dietary TC22 at 109 CFU/g significantly improved (P0.05). Dietary TC22 at 109 CFU/g significantly improved phagocytosis, and total nitric oxide synthase activity in sea cucumbers (P0.05). Respiratory burst in sea cucumbers fed dietary TC22 at 109 CFU/g was significantly higher than those fed dietary TC22 at 107 CFU/g (P<0.05). Cumulative mortality after V. splendidus challenge decreased significantly in the sea cucumbers fed with TC22 at 109 CFU/g (P<0.05). The present study confirmed dietary B. licheniformis TC22 at 109 CFU/g could significantly improve immunity and disease resistance in juvenile A. japonicus

CylinderTag: An Accurate and Flexible Marker for Cylinder-Shape Objects Pose Estimation Based on Projective Invariants

High-precision pose estimation based on visual markers has been a thriving research topic in the field of computer vision. However, the suitability of traditional flat markers on curved objects is limited due to the diverse shapes of curved surfaces, which hinders the development of high-precision pose estimation for curved objects. Therefore, this paper proposes a novel visual marker called CylinderTag, which is designed for developable curved surfaces such as cylindrical surfaces. CylinderTag is a cyclic marker that can be firmly attached to objects with a cylindrical shape. Leveraging the manifold assumption, the cross-ratio in projective invariance is utilized for encoding in the direction of zero curvature on the surface. Additionally, to facilitate the usage of CylinderTag, we propose a heuristic search-based marker generator and a high-performance recognizer as well. Moreover, an all-encompassing evaluation of CylinderTag properties is conducted by means of extensive experimentation, covering detection rate, detection speed, dictionary size, localization jitter, and pose estimation accuracy. CylinderTag showcases superior detection performance from varying view angles in comparison to traditional visual markers, accompanied by higher localization accuracy. Furthermore, CylinderTag boasts real-time detection capability and an extensive marker dictionary, offering enhanced versatility and practicality in a wide range of applications. Experimental results demonstrate that the CylinderTag is a highly promising visual marker for use on cylindrical-like surfaces, thus offering important guidance for future research on high-precision visual localization of cylinder-shaped objects. The code is available at: https://github.com/wsakobe/CylinderTag.Comment: 15 pages, 22 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs

Graphs are widely used to encapsulate a variety of data formats, but real-world networks often involve complex node relations beyond only being pairwise. While hypergraphs and hierarchical graphs have been developed and employed to account for the complex node relations, they cannot fully represent these complexities in practice. Additionally, though many Graph Neural Networks (GNNs) have been proposed for representation learning on higher-order graphs, they are usually only evaluated on simple graph datasets. Therefore, there is a need for a unified modelling of higher-order graphs, and a collection of comprehensive datasets with an accessible evaluation framework to fully understand the performance of these algorithms on complex graphs. In this paper, we introduce the concept of hybrid graphs, a unified definition for higher-order graphs, and present the Hybrid Graph Benchmark (HGB). HGB contains 23 real-world hybrid graph datasets across various domains such as biology, social media, and e-commerce. Furthermore, we provide an extensible evaluation framework and a supporting codebase to facilitate the training and evaluation of GNNs on HGB. Our empirical study of existing GNNs on HGB reveals various research opportunities and gaps, including (1) evaluating the actual performance improvement of hypergraph GNNs over simple graph GNNs; (2) comparing the impact of different sampling strategies on hybrid graph learning methods; and (3) exploring ways to integrate simple graph and hypergraph information. We make our source code and full datasets publicly available at https://zehui127.github.io/hybrid-graph-benchmark/.Comment: Preprint. Under review. 16 pages, 5 figures, 11 table

Recombinant amelogenin peptide TRAP promoting remineralization of early enamel caries: An in vitro study

Objective: To explore the regulatory effect of recombinant amelogenin peptide TRAP on the remineralization of early enamel carious lesions.Methods: Forty-eight bovine enamel blocks that prepared initial lesions in vitro were split at random into four groups for immersion treatment for 12 days: 1) remineralizing medium; 2) studied peptide 1 (consisting of the N- and C-termini of porcine amelogenin) + remineralizing medium; 3) studied peptide 2 (TRAP) + remineralizing medium; 4) fluoride + remineralizing medium. After demineralization and remineralization immersion, each specimen’s mean mineral loss and lesion depth were measured using micro-computed tomography (micro-CT). The changes in lesion depth (∆LD) and mineral gain (∆Z) were computed following remineralization. The enamel samples were then cut into sections and examined with polarized light microscopy (PLM). The cross-section morphology was observed by scanning electron microscopy (SEM). The crystal phase was analyzed by an X-ray micro-diffractometer (XRD). The calcium-binding properties were determined using isothermal titration calorimetry (ITC).Results: Micro-CT analysis revealed a significant reduction in mineral loss in the four groups following the remineralization treatment (p &lt; 0.05). The treatment with fluoride resulted in the greatest ∆Z and ∆LD, whereas the treatment with a remineralizing medium showed the least ∆Z and ∆LD among all groups. The ∆Z and ∆LD of the studied peptide 1 and studied peptide 2 groups were greater than those of the remineralizing medium group. However, there was no significant difference between the studied peptide 1 and studied peptide 2 groups (p &gt; 0.05). All of the samples that the PLM analyzed had a thickening of the surface layer. A negative birefringent band changed in the lesion’s body. The SEM displayed that minerals were formed in all four groups of samples. The XRD results indicated that the products of remineralization of the studied peptide were hydroxyapatite crystals (HA). ITC showed that there were two binding modes between the calcium and peptide TRAP.Conclusion: This study confirmed the potential of the recombinant amelogenin peptide TRAP as a key functional motif of amelogenin protein for enamel remineralization and provided a promising biomaterial for remineralization in initial enamel carious lesion treatment