A new mechanism is suggested to explain the physical phenomenon of the appearance of additional new emission components of hydrogen lines in the spectra of active galactic nuclei (AGNs). The mechanism is based on the assumption that a dense clump of hydrogen is ejected from an AGN and expands rapidly due to a presumed explosion. Two main features of this phenomenon are explained fairly simply: a) the pronounced shift of the additional components from the main components (up to several thousand kilometers per second); b) the large width of the additional components, reaching 100–200 Å. The large share of emission by the additional components in comparison with the main lines is also explained well. Estimates obtained for the physical parameters of the new formations in AGNs fit well into modern concepts of AGNs and the forms of their activity. 相似文献
The estimation of snow hazard and load faces the small sample size effect because of the short snow depth record at a station. To reduce such an effect, we propose to estimate the return period value of the annual maximum ground snow depth S, sT, for Canada sites by applying the regional frequency analysis (RFA) and the region of influence approach (ROIA). The use of RFA and ROIA to map Canadian snow hazard is new. The comparison of their performance for snow hazard mapping has not been explored in the literature. We also consider the at-site analysis approach (ASA) for estimating sT by using three often used probability distributions for S. A comparison of the estimated sT by using the three approaches (ASA, RFA, ROIA) indicates that there is considerable scatter between the estimated sT value although the identified overall spatial trends of sT are similar. It is shown that the two-parameter lognormal distribution for S at most Canadian sites, based on the at-site analysis, is preferred; this differs from the Gumbel distribution used to develop the design snow load in Canadian structural design code. The new findings indicate that it is valuable to consider the lognormal distribution for developing design snow load for Canadian sites.
The Baishan molybdenum deposit is located in the central part of the Eastern Tianshan-Beishan tectonic belt, NW China. The deposit is hosted in early Carboniferous Gandun Formation biotite-rich hornfels and is genetically related to unexposed granodiorite porphyry beneath the orebodies. The molybdenite occurs in three different types from early to late stage: Molybdenite - Fe-Cu-sulfides - K-feldspar - quartz veins (Group 1); Molybdenite - Fe-Cu-sulfides - quartz veins (Group 2); and disseminated molybdenite in the wall rock (Group 3). Rhenium concentrations in the molybdenite grains range from 108 to 277 ppm in Group 1, 69–121 ppm in Group 2 and 46–135 ppm in Group 3. The Re concentrations of molybdenite in the Baishan Mo deposit decrease from early to late and from the center to periphery, and molybdenite types vary from the 2H1 poly-type in Groups 1 and 2 to the 2H1 + 3R2H1 poly-type in Group 3, based on X-ray diffraction results. The Re-enriched molybdenite probably formed from an oxidized magmatic fluid that separated from a highly oxidized and H2O- and volatile-enriched adakitic intrusion generated in the lower crust. 相似文献
The occurrence of landslides is affected by various environmental factors. When predicting landslides, conventional neural networks optimize parameters using global connectivity, which limits their efficiency in extracting features of contributing factors. In this study, we developed an attention-constrained neural network with overall cognition (OC-ACNN) to focus on important features from the complex data. The method has four steps: (1) extract the overall cognition as the prior input based on historical landslide distribution and contributing factors, (2) embed an attention mechanism in hidden layers to allocate more weight to noteworthy features, (3) update weights and fit the nonlinear relationship by the back-propagation neural network (BPNN), and (4) generate prediction results using a classifier. This model was applied to the Sichuan-Tibet Highway, considering 10 predisposing factors and 1449 historical landslides. The evaluation results indicate that OC-ACNN (0.822) had a higher predictive capability than multiple linear regression (MLR, 0.734) and BPNN (0.789) in terms of the area under the receiver operating characteristic curve (AUC). Further, we compared different attention patterns and score functions for use with the proposed model. The results show that OC-ACNN offered greater predictive performance than Self-ACNN (without OC, 0.803) and that the improved cosine (0.822) score function had better results and stability than others (0.819 highest).
Assessment of the radiative forcing of aerosols in models still lacks sufficient input data for aerosol hygroscopicity. The light scattering enhancement factor [ f(RH, λ)] is a crucial parameter for describing aerosol hygroscopic growth properties.In this paper, we provide a survey of f(RH, λ) studies in China for the past seven years, including instrument developments of humidified nephelometers, ambient f(RH, λ) measurements in China, f(RH, λ) parameterization schemes, and f(RH, λ)applications in aerosol measurements. Comparisons of different f(RH, λ) parameterizations are carried out to check their performance in China using field measurement datasets. We also summary the parameterization schemes for predicting f(RH, λ)with aerosol chemical compositions. The recently developed methods to observe other aerosol properties using f(RH, λ)measurements, such as calculating the aerosol hygroscopicity parameter, cloud condensation nuclei number concentration,aerosol liquid water content, and aerosol asymmetry factor, are introduced. Suggestions for further research on f(RH, λ) in China are given. 相似文献
