A Large-field J=1-0 Survey of CO and Its Isotopologues Toward the Cassiopeia A Supernova Remnant

We have conducted a large-field simultaneous survey of $^{12}$CO, $^{13}$CO, and C$^{18}$O $J=1-0$ emission toward the Cassiopeia A (Cas A) supernova remnant (SNR), which covers a sky area of $3.5^{\circ}\times3.1^{\circ}$. The Cas giant molecular cloud (GMC) mainly consists of three individual clouds with masses on the order of $10^4-10^5\ M_{\odot}$. The total mass derived from the $\rm{^{13}CO}$ emission of the GMC is 2.1$\times10^{5}\ M_{\odot}$ and is 9.5$\times10^5\ M_{\odot}$ from the $\rm{^{12}CO}$ emission. Two regions with broadened (6$-$7 km s$^{-1}$) or asymmetric $^{12}$CO line profiles are found in the vicinity (within a 10$'\times10'$ region) of the Cas A SNR, indicating possible interactions between the SNR and the GMC. Using the GAUSSCLUMPS algorithm, 547 $^{13}$CO clumps are identified in the GMC, 54$\%$ of which are supercritical (i.e. $\alpha_{\rm{vir}}<2$). The mass spectrum of the molecular clumps follows a power-law distribution with an exponent of $-2.20$. The pixel-by-pixel column density of the GMC can be fitted with a log-normal probability distribution function (N-PDF). The median column density of molecular hydrogen in the GMC is $1.6\times10^{21}$ cm$^{-2}$ and half the mass of the GMC is contained in regions with H$_2$ column density lower than $3\times10^{21}$ cm$^{-2}$, which is well below the threshold of star formation. The distribution of the YSO candidates in the region shows no agglomeration.Comment: 24 pages, 18 figure

LSTM Deep Neural Network Based Power Data Credit Tagging Technology

The value of power data credit reporting in the social credit system continues to increase, and the government, users and the whole society have deep expectations and support for power data credit reporting. This paper will combine the data labeling theory as the support, define the power data label and explain its labeling implementation. Based on the construction of knowledge graph, the method of labeling power data is introduced in detail: demand analysis method, index selection method, data cleaning method and data desensitization method. Use the sorted data labels to establish a label system for power data, and through its system, visualize the comprehensive situation of enterprise power data credit information to meet the development of power data credit business. This paper takes shell enterprises as the main representatives of credit risk enterprises, analyzes the power data in the three stages before and after loans, and builds a value mining model for power credit data. In the future, the data labeling technology and value mining model of the power data credit business will be comprehensively applied, and the power data label library and credit model library will be established and continuously improved, so as to facilitate the evaluation of the operation of the enterprise at different stages

Changes in Storage Quality, Gelatinization Characteristics and Edible Quality of Selenium-Rich Rice at 35 ℃

In order to study the pattern of quality changes in selenium-rich rice during storage, selenium-rich rice and non-selenium-rich rice produced in Enshi and Jingzhou of Hubei province were subjected to accelerated aging for 180 days under high temperature storage conditions (35 ℃, and 50% relative humidity) in an artificial climate incubator. The storage characteristics, gelatinization characteristics and edible quality were measured and analyzed every 30 days. The results showed that with increasing storage time, the trends of changes in all tested indicators were consistent between selenium-rich rice and non-selenium-rich rice, but the degree of change was different. On day 180, the germination rate of non-selenium-rich rice was 69.7% and 65.7% for the cultivars Ezhong 6 and Daojingliangyou, the content of fatty acids increased by 40.43% and 59.74% compared with those on day 0, and α-amylase activity was 0.26 and 0.22 U/g, respectively. For these two cultivars, the germination rate of selenium-rich rice was 66.7% and 64.0%, and the content of fatty acids increased by 53.99% and 78.47% compared with those on day 0, and α-amylase activity was 0.24 and 0.19 U/g, respectively. Compared with the control group, selenium-rich rice had lower germination rate, lower α-amylase activity and higher fatty acid value, indicating no obvious advantages in storage quality. On day 180, the content of malondialdehyde (MDA) was 0.36 and 0.28 μmol/g in ordinary Ezhong 6 and Daojingliangyou rice, the content of free phenols was 341.78 and 371.59 μg/g, and free sulfhydryl group content was 0.67 and 0.64 μmol/g, respectively; for selenium-enriched Ezhong 6 and Daojingliangyou rice, MDA content was 0.31 and 0.24 μmol/g, free phenol content was 368.33 and 399.22 μg/g, and free sulfhydryl group content was 0.89 and 0.74 μmol/g, respectively. Compared with the control group, selenium-rich rice had lower MDA content, and higher contents of free phenols and free sulfhydryl groups, indicating better antioxidant capacity during storage. In terms of gelatinization characteristics, texture characteristics and edible quality, compared with the control group, selenium-rich rice had higher peak viscosity, lower gelatinization temperature, lower hardness, higher viscosity, higher elasticity and higher taste score during the same storage period. In conclusion, selenium-rich rice had better antioxidant capacity and higher taste score during storage at 35 ℃, but did not show anti-aging advantages