ABSTRACT
Adsorption-desorption experiments of three heavy metal ions (i.e., lead, copper, cadmium) in silty soil were carried out at different temperatures, and the microscopic characteristics of silty soil loaded with the three heavy metal ions were analyzed. A one-dimensional soil column was used to discuss the influences of heavy metal ion types and concentrations on the soil moisture distribution and the migration level of different heavy metal ions, especially during the dynamic change process from an unsaturated state to a saturated state. Studies show that the adsorption of heavy metal ions onto silty soil is closely related to the mineral composition and functional groups in silty soil. In addition to physical adsorption, the adsorption of heavy metal ions is closely related to the hydrolysis reaction of mineral components such as kaolinite, calcite, dolomite, plagioclase and quartz. Under constant temperature, the types and concentrations of heavy metal ions play an important role in the moisture migration of unsaturated soil. In the presence of heavy metal ions, the penetration of lead ions is the greatest, followed by copper ions and then cadmium ions. The greater the ion concentration is, the stronger the penetration of heavy metal ions in silty soils.
ABSTRACT
Racket sports such as tennis are amongst the most popular recreational sports activities. Optimizing tennis teaching methods and improving teaching modes can effectively improve the teaching quality of tennis. In this study, a video and image action recognition system based on image processing techniques and Internet of things is developed to overcome the shortcomings of the traditional tennis teaching methods. To validate its performance, the students of tennis courses are divided into experimental group and control group, respectively. The control group is taught by using the traditional tennis teaching method whereas the experimental group is taught by using the IoT video and image recognition teaching system. Three factors of students including service throwing height, arm elbow angle, and knee bending angles of both groups are measured and compared with those of world elite tennis players. The results show that the students' serving abilities in the experimental group are significantly improved using the video and image recognition system based on IoT, and they are better than those of the students in the control group. The proposed video and image processing technique can be applied in students' physical education and can be employed to provide the basis for the innovation of tennis teaching strategies in physical education.
