USE OF EXPERT SOFTWARE AND PHEROMONES TRAPS FOR BIOCENOTIC STRESS MONITORING AND EARLY WARNING OF TREATMENTS AGAINST INSECTS IN ORCHARDS

This paper presents our results in use of the specialized software and specific modules for microclimate monitoring and pest biological cycle assessment, to evaluate and quantify the attack risk for microclimate monitoring, combined with 6 type specific pheromones produced in Romania, in order to determine their efficacy in detecting the targeted micro Lepidoptera, assess their population flight pattern, as well and the biocenotic stress, both tools categories aiming to the precise positioning of the treatments to achieve integrated pests management and reduce the overall impact of the treatments with insecticides on the environment. According the fruit species, several strategies have been defined and followed by several insecticide applications into the bearing orchards, to achieve a better control of damaging micro Lepidoptera. Use of the mixed monitoring systems in tandem with specific pheromones contributed to a more efficient use of the insecticides and increased performances, both for pome and stone fruit species as well.

An Empirical Study on the Employment Monitoring and Early Warning Mechanism of Medical Graduates in Universities with Big Data and Complex Computing System

Based on the management of big data, the analysis and forecast of the employment demand cycle business situation studied in this article is based on the employment cycle theory and a complete set of employment monitoring, employment evaluation, employment forecasting, and policy selection theories and strategies developed around the employment cycle fluctuations, a specific employment phenomenon. First, systematically evaluate the current state of the employment demand boom, appropriately reflect the hot and cold degree of the employment demand boom, and provide necessary information for the government’s regulatory measures, content, and timing. Secondly, it reflects the regulatory effects of graduate employment monitoring, judging whether graduate employment monitoring measures are properly applied, whether they have the effect of smoothing out employment fluctuations, and promoting the country’s employment demand; in addition, business decision makers can take advantage of the employment demand boom, by monitoring the information provided by the early warning system and timely foreseeing the upcoming macrocontrol measures, so that enterprises’ labor adjustments can adapt to the government’s regulatory measures. At the same time, the model proposes a prosperity index method for monitoring and early warning of the employment demand cycle. After selecting and dividing three types of prosperity indicators, the DI index reflecting the trend of the prosperity change and the CI index reflecting the strength of the prosperity change are calculated and constructed. The national employment demand boom monitoring and early warning signal system predicts the trend of the employment boom cycle outside the sample period. The experimental results show that the cyclic prosperity forecast results are consistent not only with the national employment demand prosperity in recent months, but also with the use of the structural measurement ARIMA (p, d, q) model. The alertness value is close, indicating that this indicator system has a good effect on the national employment demand boom monitoring and early warning.

Characteristics of the disastrous debris flow of Chediguan gully in Yinxing town, Sichuan Province, on August 20, 2019

On August 20, 2019, at 2 a.m., a disastrous debris flow occurred in Chediguan gully in Yinxing town, China. The debris flow destroyed the drainage groove and the bridge at the exit of the gully. In addition, the debris flow temporarily blocked the Minjiang River during the flood peak, flooding the Taipingyi hydropower station 200 m upstream and leaving two plant workers missing. To further understand the activity of the debris flow after the Wenchuan earthquake, the characteristics of this debris flow event were studied. Eleven years after the Wenchuan earthquake, a disastrous debris flow still occurred in the Chediguan catchment, causing more severe losses than those of earlier debris flows. In this paper, the formation mechanism and dynamic characteristics of this debris flow event are analysed based on a drone survey, high-definition remote sensing interpretations and other means. The catastrophic debris flow event indicates that debris flows in the Wenchuan earthquake area are still active. A large amount of dredging work in the main gully could effectively reduce the debris flow risk in the gully. In addition, it is also important to repair or rebuild damaged mitigation measures and to establish a real-time monitoring and early warning system for the high-risk gully.

Deep Transfer Learning Approach for Identifying Slope Surface Cracks

Geohazards such as landslides, which are often accompanied by surface cracks, have caused great harm to public safety and property. If these surface cracks could be identified in time, this would be of great significance for the monitoring and early warning of geohazards. Currently, the most common method for crack identification is manual detection, which has low efficiency and accuracy. In this paper, a deep transfer learning approach is proposed to effectively and efficiently identify slope surface cracks for the sake of fast monitoring and early warning of geohazards, such as landslides. The essential idea is to employ transfer learning by training (a) a large sample dataset of concrete cracks and (b) a small sample dataset of soil and rock masses’ cracks. In the proposed approach, (1) pretrained crack identification models are constructed based on a large sample dataset of concrete cracks; (2) refined crack identification models are further constructed based on a small sample dataset of soil and rock masses’ cracks. The proposed approach could be applied to conduct UAV surveys on high and steep slopes to provide monitoring and early warning of landslides to ensure the safety of people and property.

Early Warning Indicators of Landslides Based on Deep Displacements: Applications on Jinping Landslide and Wendong Landslide, China

Real-time monitoring and early warning have great significance in reducing/avoiding the consequences caused by landslides. The deep displacement-based monitoring method has been proven to be a suitable solution for landslide risk management. However, the early warning indicators based on the deep displacement method need to be fully understood. This paper reports on an investigation into early warning indicators and deformation monitoring of several natural landslides. A series of indicators using the profiles of the accumulative displacement, kinetic energy, and their rates against time for early warning are developed and calibrated by monitoring and analyzing a natural landslide. The early warning indicators are then applied to monitor and identify the different deformation stages of the Jinping County North Landslide and the Wendong Town Landslide.

Study on debris flow risk monitoring and Early Warning in Nujiang Prefecture, Yunnan Province, China

Debris flow is the mainly the geological disasters in Nujiang Prefecture, while precipitation is the trigger of it, how to implement debris flow forecast based on precipitation monitoring data or forecast data is a hot issue in current debris flow disaster research field. Because of the special geomorphology in Nujiang Prefecture, due to the influence of human activities, geological disasters occur frequently, severely affect the local economic development. As a demonstration area of geological disaster monitoring and early warning in Yunnan Province, to build a well-developed geological disaster warning system, it is very important to spread it to other parts of Yunnan province. Based on the analysis of the current situation of geological disasters in Nujiang Prefecture, adopt appropriate monitoring method and calculation method to select the primary sites for debris flow monitoring and early warning in the Nu River basin for research.

Monitoring and Early Warning of Transmission Line External Breakage Based on Satellite-Ground Coordination

This article first proposes a high-precision spatio-temporal registration method between satellite remote sensing images and ground sensors. Then, using satellite remote sensing images, an intelligent identification model for typical external damage hidden dangers of transmission lines based on satellite remote sensing is established to realize intelligent identification of transmission line construction work areas and mining affected areas. Aiming at the results of intelligent identification of construction work areas and mining-affected areas, the proposed YOLOv4-based external damage identification algorithm for transmission lines is used to detect external damage hidden dangers. Through the method in this paper, it is possible to realize a regular general survey of hidden dangers of external damage (construction work area, mining affected area) with full coverage of transmission channels, and carry out targeted 24-hour monitoring on the ground. The test results show that the satellite-ground coordinated transmission line external damage monitoring and early warning in this paper. The method timely and accurately realizes the monitoring and early warning of the external breakage of the transmission line.