The Gradational Route Planning for Aircraft Stealth Penetration Based on Genetic Algorithm and Sparse A-Star Algorithm

It is established for a gradational route planning algorithm which includes two layers. The first layer makes use of genetic algorithm to obtain the global optimal path by its global optimal characteristics. The second layer makes use of A* algorithm to obtain the local optimal path by its dynamic characteristic. When flying along the global optimal path, locating the new threat and confirming its performance, the aircraft can plan the local optimal path timely by A* algorithm. It is constructed for the cost function with two goals of the range and the average detection probability, which is used as the goal function for optimal path planning. Two paths that obtained from two optimal methods are merged to construct the optimal route comprehensively considering the threats and range. The simulation result shows that the cost of new optimal route is lower than the original optimal path obtained only by the genetic algorithm.It revealed that our algorithm could obtain an optimal path when a new radar threas occured

MEASUREMENT MEASUREMENT MEASUREMENT MEASUREMENT OF OF OF OF INHAL INHAL INHAL INHALED ED ED ED URANIUM URANIUM URANIUM URANIUM USING USING USING USING LUNG LUNG LUNG LUNG COUNTING COUNTING COUNTING COUNTING SYSTEM SYSTEM SYSTEM SYSTEM

ABSTRACT ABSTRACT ABSTRACT ABSTRACT The coefficient of detector efficiency is an essential while we use lung counting system to assess the internal exposure dose. This paper used an anthropomorphic phantom which was made accordance with Chinese Reference Man to calibrate our system. We cooperated with China Institute for Radiation Protection to manufacture the phantom which was made of tissue equivalent materials. In the lung there are 246 club-shaped uranium sources. Each source includes is obtained. In our application, about 22 workers have been measured finally. We find in our research that the Minimum Detectable Activity (MDA) of natural uranium changes from 2.5mg to 5.9mg as the CWT varies from 16.2mm to 40.5mm for our lung counting system in 4000s detecting time by analyzing 63.3keV photo-electricity peak of gamma ray

dUev1a modulates TNF-JNK mediated tumor progression and cell death in Drosophila

AbstractLoss of cell polarity cooperates with oncogenic Ras to induce JNK-dependent tumor growth and invasion. To identify additional genes that modulate tumor progression, we have performed a genetic screen in Drosophila and found that loss of dUev1a, the ortholog of mammalian Uev1, suppressed lgl−/−/RasV12 induced JNK-mediated tumor growth and invasion. Furthermore, loss of dUev1a suppressed TNF ortholog Eiger-induced JNK-mediated cell invasion and cell death. Finally, dUev1a cooperated with Bendless to activate JNK signaling through dTRAF2. Together, our data indicate that dUev1a encodes an essential component of the evolutionary conserved TNF–JNK signaling pathway that modulates tumor progression and cell death in metazoan

A Microscopic Traffic Flow Data Generation Method Based on an Improved DCGAN

Microscopic traffic flow data, an important input to virtual test scenarios for autonomous driving, are often difficult to obtain in large quantities to allow for batch testing. In this paper, a neural network for generating microscopic traffic flow scene fragments is proposed, which is improved by adding Gate Recurrent Units (GRU) to the discriminator of the Deep Convolutional Generative Adversarial Network (DCGAN) to enable it to better discriminate continuous data. Subsequently, this paper compares individual sample motion trajectories of the generated data using Grey Relational Analysis (GRA) and Dynamic Time Warping algorithm (DTW) at the microscopic scale, and evaluates the overall scenes generated using averaged statistics at the macroscopic scale. The results show that the method proposed in this paper can generate realistic microscopic traffic flow data very well and that the neural network proposed in this paper can generate better near-realistic microscopic traffic flow data than the original DCGAN under the evaluation metrics used in this paper