Survey and Research on Health Information Assistance Needs of Junior Middle School Students in Different Regions of Guangdong Province

Objective: To study the health information assistance needs of junior high school students in 8 different regions of Guangdong Province in a cluster, to understand the current situation of junior high school students' health information assistance needs, and to collect feasibility data for hospitals and schools to jointly promote the healthy development of students. Methods: In June 2019, a group of junior high school students from 8 different regions in Guangdong Province [678 students (in 2 towns), 352 students (in 2 counties and prefecture-level cities), and 1098 students (in 4 provincial-level cities)] were selected in a group. A questionnaire survey was conducted by 2128 people, the results of the questionnaire survey were collected, and statistical analysis was performed. Results: Of the 2128 junior high school students in 8 different regions, only 52.07% had confidence in their health, and there were no regional differences. Health information for students seeking professional medical assistance includes: 1578 person-times (74.15%) of nutritional diets, 1084 person-times (50.94%) to eliminate tiredness, 1190 person-times (55.92%) to improve sleep quality, 1002 person-times (47.09%) to reduce anxiety, making him happier and stronger 1164 person-times ( 54.70%). Students in different regions asked for help on how to make their hearts happier and stronger. The results suggest that provincial and county-level students have greater needs than urban students. Conclusion: The results of this research show that junior high school students in different regions of Guangdong Province have insufficient awareness of health, and there is a large demand for various health help information, and the focus is on prevention. It is of practical significance to strengthen and meet the health information needs of junior high school students

Fabric-Based Triboelectric Nanogenerators

In the past decades, the progress of wearable and portable electronics is quite rapid, but the power supply has been a great challenge for their practical applications. Wearable power sources, especially wearable energy-harvesting devices, provide some possible solutions for this challenge. Among various wearable energy harvesters, the high-performance fabric-based triboelectric nanogenerators (TENGs) are particularly significant. In this review paper, we first introduce the fundamentals of TENGs and their four basic working modes. Then, we will discuss the material synthesis, device design, and fabrication of fabric-based TENGs. Finally, we try to give some problems that need to be solved for the further development of TENGs

Predicting Leaf Nitrogen Content in Cotton with UAV RGB Images

Rapid and accurate prediction of crop nitrogen content is of great significance for guiding precise fertilization. In this study, an unmanned aerial vehicle (UAV) digital camera was used to collect cotton canopy RGB images at 20 m height, and two cotton varieties and six nitrogen gradients were used to predict nitrogen content in the cotton canopy. After image-preprocessing, 46 hand features were extracted, and deep features were extracted by convolutional neural network (CNN). Partial least squares and Pearson were used for feature dimensionality reduction, respectively. Linear regression, support vector machine, and one-dimensional CNN regression models were constructed with manual features as input, and the deep features were used as inputs to construct a two-dimensional CNN regression model to achieve accurate prediction of cotton canopy nitrogen. It was verified that the manual feature and deep feature models constructed from UAV RGB images had good prediction effects. R2 = 0.80 and RMSE = 1.67 g kg−1 of the Xinluzao 45 optimal model, and R2 = 0.42 and RMSE = 3.13 g kg−1 of the Xinluzao 53 optimal model. The results show that the UAV RGB image and machine learning technology can be used to predict the nitrogen content of large-scale cotton, but due to insufficient data samples, the accuracy and stability of the prediction model still need to be improved

Predicting Leaf Nitrogen Content in Cotton with UAV RGB Images

Rapid and accurate prediction of crop nitrogen content is of great significance for guiding precise fertilization. In this study, an unmanned aerial vehicle (UAV) digital camera was used to collect cotton canopy RGB images at 20 m height, and two cotton varieties and six nitrogen gradients were used to predict nitrogen content in the cotton canopy. After image-preprocessing, 46 hand features were extracted, and deep features were extracted by convolutional neural network (CNN). Partial least squares and Pearson were used for feature dimensionality reduction, respectively. Linear regression, support vector machine, and one-dimensional CNN regression models were constructed with manual features as input, and the deep features were used as inputs to construct a two-dimensional CNN regression model to achieve accurate prediction of cotton canopy nitrogen. It was verified that the manual feature and deep feature models constructed from UAV RGB images had good prediction effects. R2 = 0.80 and RMSE = 1.67 g kg−1 of the Xinluzao 45 optimal model, and R2 = 0.42 and RMSE = 3.13 g kg−1 of the Xinluzao 53 optimal model. The results show that the UAV RGB image and machine learning technology can be used to predict the nitrogen content of large-scale cotton, but due to insufficient data samples, the accuracy and stability of the prediction model still need to be improved

Reducing the Cold Bias of the WRF Model Over the Tibetan Plateau by Implementing a Snow Coverage‐Topography Relationship and a Fresh Snow Albedo Scheme

Abstract Most climate models show systematic cold biases during snow‐covered period over the Tibetan Plateau (TP), which is associated with snow and surface albedo overestimations. In this work, a snow cover fraction (SCF) scheme and a recently developed albedo scheme for shallow snow are implemented in the Noah‐MP land surface model coupled with the Weather Research and Forecasting (WRF) model. The SCF scheme introduces subgrid orographic variability to reduce the SCF, and the shallow‐snow albedo scheme parameterizes the fresh‐snow albedo as a function of the snow depth (SD). Evaluations by remote sensing data show that both schemes can effectively alleviate the overestimation of the simulated surface albedo, SCF, snow water equivalent, and SD over the TP. The reductions in the modeled SCF and snow albedo directly lead to lower surface albedo values and thus more surface solar radiation absorption, which accelerates snow melting and causes surface warming effects. Further comparisons with Moderate Resolution Imaging Spectroradiometer data and station observations show that both schemes can significantly reduce the cold biases in the surface skin temperature (from −4.39°C to 0.19°C for the TP mean) and 2‐m air temperature (from −4.48°C to −1.05°C for the station mean) during the cold season (October to May of next year) in the study region. This work provides guidance for advancing the snow‐related physics in climate models and the improved WRF model could facilitate weather forecasting and climate prediction for the plateau region

Effects of element complexes containing Fe, Zn and Mn on artificial morel’s biological characteristics and soil bacterial community structures

<div><p>This study described the effects of elements (including Fe, Zn, Mn and their complexes) on the following factors in artificial morel cultivation: the characteristics of mycelia and sclerotia, soil bacterial community structures, yields and contents of microelements. The results indicated that the groups containing Mn significantly promoted mycelia growth rates, and all the experimental groups resulted in higher yields than the control (P<0.01), although their mycelia and sclerotia did not show obvious differences. It was also found that <i>Proteobacteria</i>, <i>Chloroflexi</i>, <i>Bacteroides</i>, <i>Firmicutes</i>, <i>Actinobacteria</i>, <i>Acidobacteria</i> and <i>Nitrospirae</i> were the dominated bacterial phyla. The Zn·Fe group had an unexpectedly high proportion (75.49%) of <i>Proteobacteria</i> during the primordial differentiation stage, while <i>Pseudomonas</i> also occupied a high proportion (5.52%) in this group. These results suggested that different trace elements clearly affected morel yields and soil bacterial community structures, particularly due to the high proportions of <i>Pseudomonas</i> during the primordial differentiation stage.</p></div

A Fully Self-Healing Piezoelectric Nanogenerator for Self-Powered Pressure Sensing Electronic Skin

As an important way of converting mechanical energy into electric energy, a piezoelectric nanogenerator (PENG) has been widely applied in energy harvesting as well as self-powered sensors in recent years. However, its robustness and durability are still severely challenged by frequent and inevitable mechanical impacts in real application environments. Herein, a fully self-healing PENG (FS-PENG) as a self-powered pressure sensing electronic skin is reported. The self-healing piezoelectric composite and self-healing Ag NW electrode fabricated through mixing piezoelectric PZT particles and conductive Ag NWs into self-healing polydimethylsiloxane (H-PDMS) are assembled into the sandwich structure FS-PENG. The FS-PENG could not only effectively convert external stimulation into electrical signals with a linear response to the pressure but also retain the excellent self-healing and stable sensing property after multiple cycles of cutting and self-healing process. Moreover, a self-healing pressure sensor array composed of 9 FS-PENGs was attached on the back of the human hand to mimic the human skin, and accurate monitoring of the spatial position distribution and magnitude of the pressure was successfully realized

Effects of trace elements on mycelia growth and sclerotial formation.

<p>Effects of trace elements on mycelia growth and sclerotial formation.</p

Wearable Triboelectric Generator for Powering the Portable Electronic Devices

A cloth-base wearable triboelectric nanogenerator made of nylon and Dacron fabric was fabricated for harvesting body motion energy. Through the friction between forearm and human body, the generator can turn the mechanical energy of an arm swing into electric energy and power an electroluminescent tubelike lamp easily. The maximum output current and voltage of the generator reach up to 0.2 mA and 2 kV. Furthermore, this generator can be easily folded, kneaded, and cleaned like a common garment