The tandem “conjugate addition/β-C cleavage/protonation” process (CCP) is reported.

Subsidence data acquisition methods are crucial to mining subsidence research and an essential component of achieving the goal of environmentally friendly coal mining. The origin and history of the existing methods of field monitoring, calculation, and simulation were introduced. It summarized and analyzed the main applications, flaws and solutions, and improvements of these methods. Based on this analysis, the future developing directions of subsidence data acquisition methods were prospected and suggested. The subsidence monitoring methods have evolved from conventional ground monitoring to combined methods involving ground-based, space-based, and air-based measurements. While the conventional methods are mature in technology and reliable in accuracy, emerging remote sensing technologies have obvious advantages in terms of reducing field workload and increasing data coverage. However, these remote sensing methods require further technological development to be more suitable for monitoring mining subsidence. The existing subsidence calculation methods have been applied to various geological and mining conditions, and many improvements have already been made. In the future, more attention should be paid to unifying the studies of calculation methods and mechanical principles. The simulation methods are quite dependent on the similarity of the model to the site conditions and are generally used as an auxiliary data source for subsidence studies. The cross-disciplinary studies between subsidence data acquisition methods and other technologies should be given serious consideration, as they can be expected to lead to breakthroughs in areas such as theories, devices, software, and other aspects.

Intelligent agricultural vehicles have been widely used in the process of farming and harvesting in the field, which has brought great convenience to agricultural production. However, there are also safety issues such as accidental collision of agricultural vehicles or other agricultural machinery during operation. The use of sensing technology for the timely and accurate detection and pre-warning of obstacles during the operation of agricultural machinery is critically important for ensuring safety. In this paper, a two-dimensional lidar is used to detect obstacles in front of tractors with the Density-Based Spatial Clustering of Applications with Noise(DBSCAN) algorithm and the Minimum Cost Maximum Flow algorithm(MCMF). A method to judge whether the obstacle is static or dynamic and a classification model of different security warning levels for obstacles in different states is proposed. Actual vehicle tests were conducted, with static obstacles tested repeatedly, and dynamic obstacles tested at different directions and speeds. The results showed that the overall average warning accuracy rate is 89.95%. Prediction results were robust for obstacles in different states, indicating that this system is able to ensure the safety of agricultural vehicles during their operation and promoted the development of agricultural mechanization.

In order to improve the safety of agricultural vehicle in the field, we established a vehicle kinematics model for hanging agricultural tools, and comprehensively considered driving speed, the agricultural tool rotation radius, and vehicle movement trend to propose an agricultural vehicle field operation cross- boundary warning method based on a Robot Operating System (ROS). Furthermore, we designed a set of agricultural vehicle safety warning systems and employed Qt Creator to develop the agricultural vehicle warning system operation interface. Following this, a test platform was built based on the Oubao 4040 tractor and unilateral cross-boundary warning tests were conducted. Test results demonstrate the ability of the proposed cross-boundary warning system to: i) correctly determine the warning area at different speeds (low (3.6km/h±0.5km/h), medium (10.8km/h±1.0km/h) and high (18.0km/h±1.5km/h)) and driving paths ("V" and "U"-shaped routes); ii) and to prompt the operator in a timely manner. The proposed framework exhibits strong applicability and improves the safety of agricultural vehicle hanging agricultural tools