Determination of impact parameter in high-energy heavy-ion collisions via deep learning

In this study, Au+Au collisions with the impact parameter of $0 \leq b \leq 12.5$ fm at $\sqrt{s_{NN}} = 200$ GeV are simulated by the AMPT model to provide the preliminary final-state information. After transforming these information into appropriate input data (the energy spectra of final-state charged hadrons), we construct a deep neural network (DNN) and a convolutional neural network (CNN) to connect final-state observables with impact parameters. The results show that both the DNN and CNN can reconstruct the impact parameters with a mean absolute error about $0.4$ fm with CNN behaving slightly better. Then, we test the neural networks for different beam energies and pseudorapidity ranges in this task. It turns out that these two models work well for both low and high energies. But when making test for a larger pseudorapidity window, we observe that the CNN shows higher prediction accuracy than the DNN. With the method of Grad-CAM, we shed light on the `attention' mechanism of the CNN model

Detecting Chiral Magnetic Effect via Deep Learning

The search of chiral magnetic effect (CME) in heavy-ion collisions has attracted long-term attentions. Multiple observables have been proposed but all suffer from obstacles due to large background contaminations. In this Letter, we construct an observable-independent CME-meter based on a deep convolutional neural network. After trained over data set generated by a multiphase transport model, the CME-meter shows high accuracy in recognizing the CME-featured charge separation from the final-state pion spectra. It also exhibits remarkable robustness to diverse conditions including different collision energies, centralities, and elliptic flow backgrounds. In a transfer learning manner, the CME-meter is validated in isobaric collision systems, showing good transferability among different colliding systems. Based on variational approaches, we utilize the DeepDream method to derive the most responsive CME-spectra that demonstrates the physical contents the machine learns.Comment: 7 pages, 10 figure

4-Benzyl-7-chloro-2H-1,4-benz­oxazin-3(4H)-one

In the title compound, C15H12ClNO2, the two benzene rings are nearly perpendicular to each other [dihedral angle = 89.99 (13)°]. The O atom of the six-membered heterocyclic ring is disordered over two sites in a ratio of 0.46 (4):0.54 (4) and is displaced from the mean plane formed by other five atoms, resulting an envelope conformation of the six-membered hetercycle ring

Epimedium brevicornu Maxim extract shows protective activity against Alzheimer's disease in mice

Purpose: To investigate the protective effect of Epimedium brevicornu Maxim extract (EBME) against Alzheimer's disease in 3xTg-AD mice. Methods: The cognitive function of 3xTg-AD mice was assessed using Morris water maze test. The levels of amyloid beta deposits and NeuN in the mouse hippocampus were evaluated by immunohistochemistry. Brain neurotrophic-derived factor (BDNF) and tyrosine kinase B (TrkB) expressions were examined by western blot analysis. Results: EBME treatment significantly ameliorated learning and memory deficits in AD mice, as shown by the increased time spent in the target zone during probe tests. Compared with the 3xTg-AD mice (8.4 ± 1.1 s), the escape latency in animals treated with 600 mg/kg EBME (21.5 ± 1.1 s) was significantly increased (p < 0.01). In addition, EBME significantly decreased Aβ deposits, increased NeuN-positive cells, and upregulated the expressions of BDNF (1.5 ± 0.2, p < 0.05) and TrkB (1.6 ± 0.2, p < 0.05) in the 3xTg AD mice. Conclusion: EBME treatment may be a useful therapeutic strategy for managing memory impairment

Experimental research on evolving rules of segregation ice in artificial frozen soil

AbstractThe foundation of frost heave controlling is the research on evolving rules of segregation ice. The evolving rules of segregation ice have been researched systematically by one-dimension freezing experiments. The technique of dynamic photograph has been applied in research for the first time. The research on segregation ice indicated that three phases can be divided according to the change of temperature field: few segregation ices appeared in the first phases, several thin and discontinuous segregation ices appeared in the second phases, segregation ice evolvement was mainly the growth of final ice lens in the third phase when the freezing front tended to be stable

Identification of Black Rot Resistance in a Wild Brassica Species and Its Potential Transferability to Cauliflower

Black rot is a destructive disease that affects B. oleracea crops, causing significant losses to growers throughout the world. The purpose of this study was to screen out new sources resistant to Xanthomonas campestris pv. campestris race 4 (Xcc4) in 26 cauliflower and six related wild species, and to assess the inheritance of resistance. The results indicate that most of the tested accessions were susceptible or had intermediate resistance, except the Boc4601 (a cauliflower stable inbred line) and PI435896, UNICT5168, and UNICT5169 (wild accessions). Among them, UNICT5169 (Brassica montana) and PI435896 (Brassica balearica) showed the strongest resistance to Xcc4, with significantly lower disease index (DI), area of the infected part (AIP) and proportion of the infected part to the total leaf area (PTL) values. UNICT 5169 was selected as an Xcc4-resistant parent because of its relatively good cross seed-setting rate with cauliflower cultivars. F1 hybrids were successfully produced between this wild resistant accession (UNICT 5169) and one susceptible cauliflower breeding line (Boc3202-4), indicating the potential transferability of this resistance to cauliflower. The results of the symptoms severity evaluation of the F2 population indicate that Xcc4 resistance in UNICT5169 is a quantitative trait, which guides future resistance gene location and black rot resistance breeding

Scaling Behavior and Variable Hopping Conductivity in the Quantum Hall Plateau Transition

We have measured the temperature dependence of the longitudinal resistivity $$ of a two-dimensional electron system in the regime of the quantum Hall plateau transition. We extracted the quantitative form of scaling function for $\rho_{xx}$ and compared it with the results of ordinary scaling theory and variable range hopping based theory. We find that the two alternative theoretically proposed scaling functions are valid in different regions.Comment: 4 pages, 4 figure

Visual Reconstruction and Feature Analysis of the Three-Dimensional Surface of Earthworm

This paper demonstrates a method for visual reconstruction and feature analysis of the three-dimensional surface of earthworm in CATIA (Computer Aided Three Dimensional Interactive Application) and IDL (Interactive Data Language). The earthworm, with a relatively simple surface morphology and good capability in reducing soil adhesion and resistance, was selected to study the feasible methods in the visual reconstruction and feature analysis of the three-dimensional surface of living things. The digital measurements of surfaces of the earthworm were carried out using a three-dimensional laser scanner. Point clouds, the scanning digital data of the surface of the earthworm, were processed by screening unwanted data, reconstructing surface and analysing feature in CATIA. In order to get more detail information about the point clouds, IDL, which integrates a powerful, array-oriented language with numerous mathematical analysis and graphical display techniques, was adopted for the visual reconstruction and feature analysis of three- dimensional surface of the earthworm. Importing of point clouds and reconstruction of the surface of earthworm were conducted in CATIA. Analysis feature of the scanning data and reconstructing surface were carried out in IDL, which provides a high level of flexibility to access, analyse and visualize the data using different methods. Polynomial regression equation of the surface of earthworm in the longitudinal plane was derived. In addition, point clouds were more easily displayed and analysed by resizing, rotating and zooming in IDL. Methods and results presented in this paper prove to be potentially useful for analyzing the feature of biological prototype, optimizing the mathematical model and affording deformable physical model to bionic engineering, those works would have great implications to the research of biological coupling theory and technological creation in bionic engineering. Keywords: Visual Reconstruction; Feature Analysis; Three-Dimensional Surface; Earthworm; CATIA; ID

Comparison of Microbial Community Compositions of Injection and Production Well Samples in a Long-Term Water-Flooded Petroleum Reservoir

Water flooding plays an important role in recovering oil from depleted petroleum reservoirs. Exactly how the microbial communities of production wells are affected by microorganisms introduced with injected water has previously not been adequately studied. Using denaturing gradient gel electrophoresis (DGGE) approach and 16S rRNA gene clone library analysis, the comparison of microbial communities is carried out between one injection water and two production waters collected from a working block of the water-flooded Gudao petroleum reservoir located in the Yellow River Delta. DGGE fingerprints showed that the similarities of the bacterial communities between the injection water and production waters were lower than between the two production waters. It was also observed that the archaeal composition among these three samples showed no significant difference. Analysis of the 16S rRNA gene clone libraries showed that the dominant groups within the injection water were Betaproteobacteria, Gammaproteobacteria and Methanomicrobia, while the dominant groups in the production waters were Gammaproteobacteria and Methanobacteria. Only 2 out of 54 bacterial operational taxonomic units (OTUs) and 5 out of 17 archaeal OTUs in the injection water were detected in the production waters, indicating that most of the microorganisms introduced by the injection water may not survive to be detected in the production waters. Additionally, there were 55.6% and 82.6% unique OTUs in the two production waters respectively, suggesting that each production well has its specific microbial composition, despite both wells being flooded with the same injection water