Effect of Parents’ Encouragement on Reading Motivation: The Mediating Effect of Reading Self-Concept and the Moderating Effect of Gender

Previous research has found that parental encouragement is associated with children’s motivation to read. However, little is known about the underlying mechanisms of this association or factors that might strengthen or weaken these processes. The current research scrutinized a moderated mediation model that comprised of parental encouragement (predictor variable), reading self-concept (mediator), gender (moderator), and reading motivation (outcome variable) simultaneously. A total of 254 Chinese students (Mage = 11.35 years, SDage = 0.87) completed the Parents’ Encouragement of Extracurricular Reading Questionnaire, Reading Self-Concept Scale, and Pupil Reading Motivation Scale. Path analysis revealed that parents’ encouragement was associated with children’s reading motivation both directly and indirectly via reading self-concept, and the effect of parents’ encouragement on children’s motivation was more positive for boys than girls, while the effect of reading self-concept on children’s motivation was more positive for girls than boys. Our results highlight the importance of parental encouragement in improving children’s reading motivation, and the findings and their implications are discussed

Deep Learning-Based Weld Contour and Defect Detection from Micrographs of Laser Beam Welded Semi-Finished Products

Laser beam welding is used in many areas of industry and research. There are many strategies and approaches to further improve the weld seam properties in laser beam welding. Metallography is often needed to evaluate welded seams. Typically, the images are examined and evaluated by experts. The evaluation process qualitatively provides the properties of the welds. Particularly in times when artificial intelligence is being used more and more in processes, the quantization of properties that could previously only be determined qualitatively is gaining importance. In this contribution, we propose to use deep learning to perform semantic segmentation of micrographs of complex weld areas to achieve the automatic detection and quantization of weld seam properties. A semantic segmentation dataset is created containing 282 labeled images. The training process is performed with DeepLabv3+. The trained model achieves a value of around 95% for weld contour detection and 76.88% of mean intersection over union (mIoU). © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Activation of Egr-1 in human lung epithelial cells exposed to silica through MAPKs signaling pathways.

The alveolar type II epithelial cell, regarded historically as a key target cell in initial injury by silica, now appears to be important in both defense from lung damage as well as elaboration of chemokines and cytokines. The molecular basis for silica-induced epithelial cell injury is poorly understood. In this study we explored the activation of nuclear factor Egr-1 and related signal pathway. Human II alveolar epithelial line A549 cells were exposed to silica for indicated time to assay the expression and activation of Egr-1 and upstream MAPKs. Immunofluorescence, western-blot techniques, RT-PCR, Electrophoretic mobility shift assay (EMSA), transient transfection assay, kinase inhibitor experiments were performed. It was found that the expression of Egr-1 at mRNA and protein level was significantly increased in A549 cells after administration with silica and the activity of Egr-1 peaked by silica treatment for 60 minutes. Furthermore, phosphorylated-ERK1/2, P38 MAPKs (the upstream kinase of Egr-1) ballooned during 15-30minutes, 30-60minutes respectively after silica exposure in A549 cells. By administration of ERK1/2, P38 inhibitor, the expression and transcription of Egr-1 were both markedly decreased. But PKC inhibitor did not prevent the increase of Egr-1. These results indicated Egr-1 played a critical role in silica-induced pulmonary fibrosis in an ERK1/2, P38 MAPKs-dependent manner, which suggests Egr-1 is an essential regulator in silicosis, and underlines a new molecular mechanism for fibrosis induced by silica

The over-step coalescence of carbon atoms on copper surface in the CVD growth of graphene: density functional calculations

The ways in which carbon atoms coalesce over the steps on copper (111) surface are ascertained by density functional theory (DFT) calculations in the context of chemical vapor deposition (CVD) growth of graphene. Two strategies, (1) by putting carbon atoms on and under the steps separately and (2) by importing additional carbon atoms between the ones separated by the steps, have been attempted to investigate if an over-step coalescence of carbon atoms could take place. Based on analyses about the optimized configurations and adsorption energies of carbon atoms nearby the steps, as well as the energy evolution curve of the system throughout the geometry optimizations process, we determined the main way in which graphene grows over the steps continuously: the carbon atoms, adsorbed additionally on the locations between the already existing ones which are separated by the steps, link them (these carbon atoms separated by the steps) together. The direct over-step coalescence of the carbon atoms separated by the steps is very difficult, although the energy barrier preventing their coalescence can be weakened by importing carbon atoms on and under the steps gradually. Our results imply potential applications in directing the fabrication of graphene with particular structure by controlling the surface topography of copper substrate

Deep Learning-Based Weld Contour and Defect Detection from Micrographs of Laser Beam Welded Semi-Finished Products

Laser beam welding is used in many areas of industry and research. There are many strategies and approaches to further improve the weld seam properties in laser beam welding. Metallography is often needed to evaluate welded seams. Typically, the images are examined and evaluated by experts. The evaluation process qualitatively provides the properties of the welds. Particularly in times when artificial intelligence is being used more and more in processes, the quantization of properties that could previously only be determined qualitatively is gaining importance. In this contribution, we propose to use deep learning to perform semantic segmentation of micrographs of complex weld areas to achieve the automatic detection and quantization of weld seam properties. A semantic segmentation dataset is created containing 282 labeled images. The training process is performed with DeepLabv3+. The trained model achieves a value of around 95% for weld contour detection and 76.88% of mean intersection over union (mIoU)

Silicon nanowire arrays coated with electroless Ag for increased surface-enhanced Raman scattering

The ordered Ag nanorod (AgNR) arrays are fabricated through a simple electroless deposition technique using the isolated Si nanowire (SiNW) arrays as the Ag-grown scaffold. The AgNR arrays have the single-crystallized structure and the plasmonic crystal feature. It is found that the formation of the AgNR arrays is strongly dependent on the filling ratio of SiNWs. A mechanism is proposed based on the selective nucleation and the synergistic growth of Ag nanoparticles on the top of the SiNWs. Moreover, the special AgNR arrays grown on the substrate of SiNWs exhibit a detection sensitivity of 10−15M for rhodamine 6G molecules, which have the potential application to the highly sensitive surface-enhanced Raman scattering sensors

Activation of ERK1/2, P38 MAPKs in A549 cells induced by silica.

<p>(A) The level and localization of phosphorylated ERK1/2 and P38 were detected by immunochemistry. magnification: ×400. The expression of phosphorylated ERK1/2 (4A1) and P38 (picture not shown) was mainly in cytoplasm of untreated cells, and increased and located in nuclears (4A2-3) by silica treatment for 30 minutes. (B) The semi-quantitative expression of phosphorylated ERK1/2 (4B1) and P38 (4B2) in A549 cells was determined by western-blot, intensity of the bands on western blot were analyzed by Scion image and the relative expression of phosphorylated ERK1/2 and P38 to total ERK1/2 and P38 was calculated. ** p<0.01 compared to untreated cells.</p

Egr-1 activation by silica was mainly dependent upon activation of the MAPKs pathway.

<p>(a) The effects of Egr-1 nuclear protein expression after administration of kinase inhibitor was determined by western-blot (5B、5C). The Egr-1 expression at nuclear protein and mRNA level was inhibited by U0126 and SB230580 respectively, but not completely disappeared by combination of both; The expression of Egr-1 was not changed by pretreatment with H7, a PKC inhibitor. The significance of Egr-1 expression is noted as P<0.05 (*), P<0.01(**).</p

Silica increased the Egr-1 DNA binding activity in A549 cells.

<p>The binding activity of Egr-1 in A549 cells after exposure to silica was determined by EMSA experiments, and the binding activity of Egr-1 peaked after 60-minute exposure. The binding activity of Egr-1 to specific oligonucleotides probe increased from 30-minute treatment and peaked for 60-minute exposure, then decreased. And as shown in 3B: the promoter activity increased from 30-min incubation with silica and peaked at 60-min, recovered to the level of resting control till 480-min incubation. Significant differences in binding activity and luciferase activity are noted at P<0.01(**).</p