On Input-to-State Stability of Impulsive Stochastic Systems with Time Delays

This paper is concerned with pth moment input-to-state stability (p-ISS) and stochastic input-to-state stability (SISS) of impulsive stochastic systems with time delays. Razumikhin-type theorems ensuring p-ISS/SISS are established for the mentioned systems with external input affecting both the continuous and the discrete dynamics. It is shown that when the impulse-free delayed stochastic dynamics are p-ISS/SISS but the impulses are destabilizing, the p-ISS/SISS property of the impulsive stochastic systems can be preserved if the length of the impulsive interval is large enough. In particular, if the impulse-free delayed stochastic dynamics are p-ISS/SISS and the discrete dynamics are marginally stable for the zero input, the impulsive stochastic system is p-ISS/SISS regardless of how often or how seldom the impulses occur. To overcome the difficulties caused by the coexistence of time delays, impulses, and stochastic effects, Razumikhin techniques and piecewise continuous Lyapunov functions as well as stochastic analysis techniques are involved together. An example is provided to illustrate the effectiveness and advantages of our results

Atomic-scale visualization of quasiparticle interference on a type-II Weyl semimetal surface

We combine quasiparticle interference simulation (theory) and atomic resolution scanning tunneling spectro-microscopy (experiment) to visualize the interference patterns on a type-II Weyl semimetal Mo$_{x}$W$_{1-x}$Te$_2$ for the first time. Our simulation based on first-principles band topology theoretically reveals the surface electron scattering behavior. We identify the topological Fermi arc states and reveal the scattering properties of the surface states in Mo$_{0.66}$W$_{0.34}$Te$_2$. In addition, our result reveals an experimental signature of the topology via the interconnectivity of bulk and surface states, which is essential for understanding the unusual nature of this material.Comment: To appear in Phys. Rev. Let

Ginseng Pectin WGPA Alleviates Exercise-Induced Fatigue by Enhancing Gluconeogenesis

With the development of medicine and sport science, growing attention has been paid to the recovery of exercise-induced fatigue. Ginseng pectin has been shown to be important for a variety of biological functions. Although many studies suggest that ginseng pectin plays an important role in the alleviation of exercise-induced fatigue, the underlying mechanism still remains unclear. In this study, C57BL/6J mice were subjected to a wheel apparatus for exhaustive exercise and fed with ginseng pectin WGPA (acidic fraction of water-soluble ginseng polysaccharides) afterwards. Subsequently, a series of physiological and biochemical indexes, such as blood lactic acid, blood glucose, muscle glycogen, insulin, and glucagon, is evaluated. Meanwhile, enzymatic activity and mRNA level of key enzymes involved in hepatic gluconeogenesis are analyzed. Our results demonstrate that the treatment of ginseng pectin WGPA can result in enhanced gluconeogenesis and decreased insulin and in turn facilitate the recovery of exercise-induced fatigue. In response to WGPA treatment, both phosphoenolpyruvate carboxykinase (PEPCK) and glucose 6 phosphatase (G6Pase) activity were upregulated, indicating that these two enzymes play a critical role in WGPA-induced upregulation in gluconeogenesis. Moreover, mRNA level of G6Pase, but not PEPCK, was increased upon WGPA treatment, suggesting that G6Pase expression is regulated by WGPA. Importantly, the presence of WGPA downregulated insulin both in vivo and in vitro, suggesting the upregulation in gluconeogenesis may be due to alterations in insulin. Together, we provide evidence that ginseng pectin WGPA is able to alleviate exercise-induced fatigue by reducing insulin and enhancing gluconeogenesis

Field Complete Coverage Path Planning Based on Improved Genetic Algorithm for Transplanting Robot

The Complete Coverage Path Planning (CCPP) is a key technology in the field of agricultural robots, and has great significance for improving the efficiency and quality of tillage, fertilization, harvesting, and other agricultural robot operations, as well as reducing the operation energy consumption. The traditional boustrophedon- or heuristic-search-algorithm-based CCPP methods, when coping with the field with irregular boundaries, obstacles, and other complex environments, still face many problems and challenges, such as large repeated work areas, multiple turns or U-turns, low operation efficiency, and prone to local optimum. In order to solve the above problems, an improved-genetic-algorithm-based CCPP method was proposed in this paper, the proposed method innovatively extends the traditional genetic algorithm’s chromosomes and single-point mutation into chromosome pairs and multi-point mutation, and proposed a multi-objective equilibrium fitness function. The simulation and experimental results on simple regular fields showed that the proposed improved-genetic-algorithm-based CCPP method achieved the comparable performance with the traditional boustrophedon-based CCPP method. However, on the complex irregular fields, the proposed CCPP method reduces 38.54% of repeated operation area and 35.00% of number of U-turns, and can save 7.82% of energy consumption on average. This proved that the proposed CCPP method has a strong adaptive capacity to the environment, and has practical application value in improving the efficiency and quality of agricultural machinery operations, and reducing the energy consumption

3D Physical Modelling Study of Shield-Strata Interaction under Roof Dynamic Loading Condition

The dynamic hazards in the open face area caused by the impact load of the massive strong roof become increasingly severe with the increase in the cutting height of the longwall face and its depth of cover. Understanding the strata-shield interaction under the dynamic impact loading condition may relieve the dynamic hazards. In this paper, a 3D physical modelling platform is developed to study the interaction between the roof strata and the longwall shield under the dynamic impact load conditions. A steel plate is dropped to the coal face wall at a certain height above the immediate roof to simulate the free fall of the main roof and the dynamic impact loading environment. The occurrence of major roof falls is modelled at different height above the model and at different positions relative to the longwall faceline. The large-cutting-height and top-coal-caving mining methods are modelled in the study to include the nature of the immediate roof. The results show that the level of face and roof failures depends on the magnitude of the dynamic impact load. The position and height of the roof fall have an important influence to the stability of the roof and face. The pressures on the shield and the solid coal face are relieved for the top-coal-caving face as compared to the large-cutting-height face

Detection of Miss-Seeding of Sweet Corn in a Plug Tray Using a Residual Attention Network

With the promotion of artificial intelligence in agriculture and the popularization of plug tray seedling-raising technology, seedling raising and transplanting have become the most popular planting modes. Miss-seeding is one of the most serious problems affecting seedling raising and transplanting. It not only affects the germination rate of seeds but also reduces the utilization rate of the plug tray. The experimental analysis of traditional machine vision-based miss-seeding showed that because of uneven lighting, the plug tray was wrongly identified as a seed under bright light, but the seeds in the dark were not easy to identify. When using the seeding area to identify seeds and noise, sweet corn seeds in a small area can be easily screened out. This paper proposes a method using the ResNet network with an attention mechanism to solve the above-mentioned problems. In this paper, the captured image was segmented into the images of a single plug tray, and a residual attention network was built; the detection scheme of miss-seeding was also converted into a dichotomous picture recognition task. This paper demonstrates that the residual attention network can effectively recognize and detect the seed images of sweet corn with very high accuracy. The results of the experiment showed that the average accuracy of this recognition model was 98%. The feature visualization method was used to analyze the features, further proving the effectiveness of the classification method of plug tray seedlings

Types of abnormal high-pressure gas reservoir in foreland basins of China

Abnormal pressure commonly exists in foreland basins that are closely related to the oil and gas reservoir. A deep understanding of the distribution of the said kind of pressure in various reservoirs and characteristic types of abnormally high-pressure gas reservoirs are supplementary to the theory of the fluid storage tank. This paper analyzed in detail the characteristics of this atypical distribution of high-pressure in typical local and foreign foreland basins, as well as their relationship with gas reservoirs. The three types of abnormal high-pressure gas reservoirs are identified as follows: box type, top seal type, and pressure transfer type. The box type is mainly characterized by the reservoir of a source reservoir superposition with a stable structure. The unusual high pressure occurs in the reservoir and the cap rock, which are mainly results of hydrocarbon supercharging and under-compacting deposition. The top seal type is predominated by the high pressure in the cap rock, while the pressure in the reservoir may be normal or not. The abnormally high pressure may be caused by under-compacted deposition, strong oil and gas filling, as well as tectonic compression. It is also worth noting that the pressure transfer type is dominated by secondary gas reservoirs with active tectonics. The pressure has a decreasing trend from the deep to the shallow area due to the formation of pressure conduction caused by pressure relief or deterioration of storage conditions, as well as lithological changes brought by fractures. Determining the types of abnormal high-pressure gas reservoirs have a great significance to the exploration of the said gas reservoirs themselves. Moreover, their classification can avoid accidental drillings in the foreland basin. Keywords: Foreland basin, Abnormal pressure, Abnormal high-pressure, Pressure coefficient, Types of gas reservoir

Detection of Miss-Seeding of Sweet Corn in a Plug Tray Using a Residual Attention Network

With the promotion of artificial intelligence in agriculture and the popularization of plug tray seedling-raising technology, seedling raising and transplanting have become the most popular planting modes. Miss-seeding is one of the most serious problems affecting seedling raising and transplanting. It not only affects the germination rate of seeds but also reduces the utilization rate of the plug tray. The experimental analysis of traditional machine vision-based miss-seeding showed that because of uneven lighting, the plug tray was wrongly identified as a seed under bright light, but the seeds in the dark were not easy to identify. When using the seeding area to identify seeds and noise, sweet corn seeds in a small area can be easily screened out. This paper proposes a method using the ResNet network with an attention mechanism to solve the above-mentioned problems. In this paper, the captured image was segmented into the images of a single plug tray, and a residual attention network was built; the detection scheme of miss-seeding was also converted into a dichotomous picture recognition task. This paper demonstrates that the residual attention network can effectively recognize and detect the seed images of sweet corn with very high accuracy. The results of the experiment showed that the average accuracy of this recognition model was 98%. The feature visualization method was used to analyze the features, further proving the effectiveness of the classification method of plug tray seedlings