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Background Maternal and child health (MCH)–related mobile apps are becoming increasingly popular among pregnant women; however, few apps have demonstrated that they lead to improvements in pregnancy outcomes. Objective This study aims to investigate the use of MCH apps among pregnant women in China and explore associations with pregnancy outcomes. Methods A retrospective study was conducted at 6 MCH hospitals in northern China. Women who delivered a singleton baby at >28 weeks’ gestation at the study hospitals were sequentially recruited from postnatal wards from October 2017 to January 2018. Information was collected on the women’s self-reported MCH app use during their pregnancy, along with clinical outcomes. Women were categorized as nonusers of MCH apps and users (further divided into intermittent users and continuous users). The primary outcome was a composite adverse pregnancy outcome (CAPO) comprising preterm birth, birth weight Results The 1850 participants reported using 127 different MCH apps during pregnancy. App use frequency was reported as never, 24.7% (457/1850); intermittent, 47.4% (876/1850); and continuous, 27.9% (517/1850). Among app users, the most common reasons for app use were health education (1393/1393, 100%), self-monitoring (755/1393, 54.2%), and antenatal appointment reminders (602/1393, 43.2%). Nonusers were older, with fewer years of education, lower incomes, and higher parity (P Conclusions Women in China access a large number of different MCH apps, with social disparities in access and frequency of use. Any app use was not found to be associated with improved pregnancy outcomes, highlighting the need for rigorous development and testing of apps before recommendation for use in clinical settings.

Micro pillar arrays were fabricated on silicon surfaces based on the reactive ion etching technology. Low surface energy coatings were deposited on textured surfaces in order to prepare hydrophobic/superhydrophobic surfaces. After measuring the static contact angles and roll angles, the dynamic characteristics of droplets impacting on these hydrophobic/superhydrophobic surfaces were investigated in detail. Both the static contact angle and the roll angle increased with the increase of micro pillars' height. With the increase of micro pillars' spacing, the roll angle decreased, but the change of static contact angle was irregular. On superhydrophobic surfaces, the spreading coefficient of droplet was affected by both the static contact angle and the roll angle, and the rebounding coefficient of droplet was highly relevant with the roll angle. In addition, the bigger the incline angle of surface, the smaller the spreading coefficient and the smaller the rebounding coefficient of droplet.

Person re-identification is a challenging task due to the viewpoint, illumination and pose variations. Recent works focus on extracting part-level features to offer beneficial fine-grained information. However, the part misalignment as well as the multi-stage training process limits their performance. Inspired by the human visual attention mechanism, this paper builds a cascade attention network(CAN) to learn the discriminative person features in a coarse-to-fine manner. Firstly, we employ the human semantic parsing module to generate coarse-grained part-level attention, which corresponds to the division of human body parts and can effectively filter the background noise. Then, to extract the local detailed features within each part, we introduce spatial-channel attention module to generate fine-grained pixel-level attention, which can further highlight the distinctive characteristics and repress the irrelevant ones. Finally, we can obtain an efficient person feature descriptor by combining both the global and local features. The whole learning process is conducted end-to-end. Experimental results show that the proposed method not only considerably outperforms its counter part but also achieves competitive performance on Market-1501 and DukeMTMC.

An effective vehicle-license plate detection and recognition method based on the deep learning was presented that aim to address the limitations of traditional techniques failed in dealing with images from vehicle monitoring system under complex scene. Firstly, the coordinate information of license plate is located by the YOLOv2 algorithm with color clues. Then, the Radon transformation is used to rectify the license plate region which maybe tilted. Finally, the license plate recognition results are obtained by virtue of CRNN based on license plate coding rule. Experiments show the effectiveness and efficiency of our proposed license plate detection and recognition method.

Characteristics of bubble nucleation and growth are critical for its application. It is affected by several factors including viscosity, surface tension and temperature. However, the effect of pressure on bubble nucleation and growth has been underreported, although it processes significant effect on above characteristics. In this work, a micro copper electrode is etched on a slab covered with copper to produce bubble on the surface by current input. The nucleation time of bubble is measured under different heat flux and system pressures. The nucleation and growth processes are recorded with a high speed camera in order to discuss the effects of heat flux and system pressure on bubble characteristics. The experiment results indicate that the micro electrode with higher heat flux produces more thermal energy, which makes the time of bubble nucleation shorter and the speed of bubble growth faster. Higher system pressure causes the increase of the critical nucleation temperature and also baffles the bubble nucleation and growth. Bubble growth includes the stages of rapid growth and dynamic equilibrium, with the speed being from fast to slow. In the former part of rapid growth, heat flux plays a dominant role in bubble growth. While the effect of system pressure on bubble growth becomes significant in the latter part of rapid growth. Both the nucleation time and bubble growth agree well with the theoretical analysis. The obtained results help to accurately control bubble nucleation and growth required in different application.

In this paper, meshing impact on numerical simulation of marine systems is investigated. Different mesh topologies, meshing spacing, meshing refining area are discussed. Different shaped bodies such as ball, AUV and complicated motion such as propeller rotating are simulated with compared meshes in the paper. And its converging time, solution accuracy, and local domain flow characters are compared in detail. The research results of mesh refining area showed that a mesh refining area near the stern is more important than that near the bow, although there are large pressure gradients at both areas. In regards to the necessary of boundary layer mesh, we find out that for a translational motion of a marine system such as surge, sway and heave, the near wall prismatic layer mesh is necessary. And the sensitivity of numerical accuracy to the spacing of the first grid point away from the wall and numbers of layers of the near wall are discussed. While for a rotational motion, such as propeller motion, numerical calculation showed that there is not necessary to construct a boundary layer mesh in the near wall.

In this paper the performance test results of a liquid desiccant air-conditioning (LDAC) system which uses waste hot water from combined cooling, heat and power system (CCHP) as driven heat resource have been presented. The supply air volume of the air-conditioning system is around 7000m 3 /h ~ 7500m 3 /h and its cooling capacity is about 60.43 ~ 95.74KW. It can maintain the supply air temperature under 22°C, while the ambient temperature is 40°C. By the application of LDAC, cooling capacity made from waste heat rises up to 537kW and the energy utilization efficiency of the whole system has reached 94.81% and rose by 8%. Because the application of this technology, the temperature of equipment room falls 4°C and the working condition is greatly improved.