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Gillespie Baird posted an update 5 months, 4 weeks ago
The internal friction angle decreased with increasing soil water content, and the soil cohesion first increased and then decreased with increasing soil water content. Considering that paddy fields were flooded for a long time, the soil strength properties had certain water sensitivity. Effective measures must be taken to reduce the change in soil water content, so as to ensure the stability of the embankment foundation, roadside ditch foundation, and cutting slope. In addition, the influence of changing soil water content on the strength properties of paddy soils should be fully considered in engineering design and construction, and the soil bulk density at the plough pan should reach at least 1.5 g cm-3 or more to ensure better water retention and the anti-seepage function of paddy field. RG7388 research buy The study can provide construction technology for engineering new paddy field construction in a hilly mountainous region of southwestern China.(1) Background Self-determination theory (SDT) claims that need supportive behavior is related to the satisfaction of the basic psychological needs autonomy, relatedness and competence. The student-teacher relationship is of special interest to understand mechanisms of physical activity behavior change in physical education (PE). (2) Methods In this cross-sectional study, 481 girls answered a German version of the Basic Psychological Need Satisfaction (BPNS) in PE Scale. Contrary to previous studies, the psychometric properties of this scale were examined by multilevel confirmatory factor analysis. (3) Results A model with three latent factors on both levels showed acceptable fit and all items showed significant factor loadings. Although one item was excluded due to psychometric reasons, the scale showed good internal consistencies; α = 0.85 at the individual level and α = 0.84 at the class level. Subscales’ internal consistency at the individual levels was good, while at class level, the scores differed from poor to good. Small significant correlations of BPNS with moderate to vigorous physical activity support criterion validity. (4) Conclusion The 11-item scale is a valid measurement tool to assess BPNS in PE and further application in the school setting would broaden the insights into the psychological impacts of SDT in PE.Lyme disease, recognized as one of the most important vector-borne diseases worldwide, has been increasing in incidence and spatial extend in United States. In the Northeast and Upper Midwest, Lyme disease is transmitted by Ixodes scapularis. Currently, many studies have been conducted to identify factors influencing Lyme disease risk in the Northeast, however, relatively few studies focused on the Upper Midwest. In this study, we explored and compared the climatic and landscape factors that shape the spatial patterns of human Lyme cases in these two regions, using the generalized linear mixed models. Our results showed that climatic variables generally had opposite correlations with Lyme disease risk, while landscape factors usually had similar effects in these two regions. High precipitation and low temperature were correlated with high Lyme disease risk in the Upper Midwest, while with low Lyme disease risk in the Northeast. In both regions, size and fragmentation related factors of residential area showed positive correlations with Lyme disease risk. Deciduous forests and evergreen forests had opposite effects on Lyme disease risk, but the effects were consistent between two regions. In general, this study provides new insight into understanding the differences of risk factors of human Lyme disease risk in these two regions.The laser processing of the titania nanotubes has been investigated in terms of morphology, structure, and optical properties of the obtained material. The length of the nanotubes and crystallinity, as well as the atmosphere of the laser treatment, were taken into account. The degree of changes of the initial geometry of nanotubes were checked by means of scanning electron microscopy, which visualizes both the surface and the cross-section. The phase conversion from the amorphous to anatase has been achieved for laser-treated amorphous material, whereas modification of calcined one led to distortion within the crystal structure. This result is confirmed both by Raman and grazing incident XRD measurements. The latter studies provided an in-depth analysis of the crystalline arrangement and allowed also for determining the propagation of laser modification. The narrowing of the optical bandgap for laser-treated samples has been observed. Laser treatment of TiO2 nanotubes can lead to the preparation of the material of desired structural and optical parameters. The usage of the motorized table during processing enables induction of changes in the precisely selected area of the sample within a very short time.This research is concerned with malignant pulmonary nodule detection (PND) in low-dose CT scans. Due to its crucial role in the early diagnosis of lung cancer, PND has considerable potential in improving the survival rate of patients. We propose a two-stage framework that exploits the ever-growing advances in deep neural network models, and that is comprised of a semantic segmentation stage followed by localization and classification. We employ the recently published DeepLab model for semantic segmentation, and we show that it significantly improves the accuracy of nodule detection compared to the classical U-Net model and its most recent variants. Using the widely adopted Lung Nodule Analysis dataset (LUNA16), we evaluate the performance of the semantic segmentation stage by adopting two network backbones, namely, MobileNet-V2 and Xception. We present the impact of various model training parameters and the computational time on the detection accuracy, featuring a 79.1% mean intersection-over-union (mIoU) and an 88.34% dice coefficient. This represents a mIoU increase of 60% and a dice coefficient increase of 30% compared to U-Net. The second stage involves feeding the output of the DeepLab-based semantic segmentation to a localization-then-classification stage. The second stage is realized using Faster RCNN and SSD, with an Inception-V2 as a backbone. On LUNA16, the two-stage framework attained a sensitivity of 96.4%, outperforming other recent models in the literature, including deep models. Finally, we show that adopting a transfer learning approach, particularly, the DeepLab model weights of the first stage of the framework, to infer binary (malignant-benign) labels on the Kaggle dataset for pulmonary nodules achieves a classification accuracy of 95.66%, which represents approximately 4% improvement over the recent literature.
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