The most powerful flare ever registered in the Galactic water-maser source W49N has been detected in long-term monitoring data in the 616–523 transition with line frequency f = 22.235 GHz carried out on the 22-m Simeiz, 32-m Toruń, 100-m Effelsberg, and 32-m Medicina radio telescopes, beginning in September 2017 and continuing in 2018. Some stages of the flare were monitored daily. Detailed variations of the source spectral flux density with time have been obtained. At the flare maximum, the flux exceeded P ≈ 8 × 104 Jy, and this was record highest flux registered over the entire history of observations of this source. Important conclusions related to details of the mechanism for the H2O line emission have been drawn. An exponential increase in the flare flux density was detected during both the rise and decline of the flare. The data obtained indicate that the maser is unsaturated, and remained in this state up to the maximum observed flux densities. Additional support for the idea that the maser is unsaturated is the shape of the dependence of the line width on the flux. The characteristics of the variations of the spectral flux density are probably associated with a sharp increase in the density of the medium and the photon flux that led to an increase in the temperature from an initial level of 10–40 K to hundreds of Kelvins. Interferometric maps of the object during the increase in the spectral flux density of the flare have been obtained. A possible mechanism for the primary energy release in W49N is considered.
Most conventional wastewater treatment plants remove very small amounts of micropollutants, such as pharmaceuticals. Here, the ability of two different types of submerged nanofiltration flat sheet modules to remove pharmaceuticals from wastewater is analyzed. The two nanofiltration membranes were used at relatively low pressures of only 0.3 and 0.7 bar. At such low pressures, the membranes did not retain salts to a great extent. This is advantageous in wastewater treatment because no salt concentrate is produced. Carbamazepine was retained only slightly by the nanofiltration membranes, whereas approximately 60% of diclofenac and naproxen were retained by both membranes. This level of effectiveness might not be enough to justify the use of such a system as an additional treatment step in wastewater treatment plants. 相似文献
Summary It is shown that there is a close mathematical similarity between the cell and parcel formulations of the convection problem, and analogies are drawn between some of the more significant properties of the solutions in the two cases. 相似文献
Adult Palaemonetes pugio were collected from two tidal creek systems, Piles Creek (PC), a mercury polluted estuary, and Big Sheepshead Creek (BSC), a relatively pristine creek. Adult killifish (Fundulus heteroclitus), a natural predator of P. pugio, were obtained from BSC. For each test, ten treated (0·01 mg/liter mercuric chloride (HgCl), or 0·01 mg/liter methylmercuric chloride (MeHg)), or control shrimp here introduced into a tank containing three fish. The time between capture of the first and second BSC HgCl treated shrimp was significantly faster (P < 0·05), as was the time between the first and second capture (P < 0·05) of MeHg treated BSC shrimp when compared with their respective controls. In addition, significantly more (P < 0·025) BSC HgCl treated shrimp were captured after 120 min. No significant difference existed between control and HgCl treated PC shrimp; however, significantly more PC MeHg treated shrimp were captured after 60 (P < 0·05) min when compared with their respective controls. These data suggest that PC shrimp, subjected to mercury in their natural environment, are more tolerant to the sublethal effects of both HgCl and MeHg. These data also suggest that behavioral studies can be very sensitive assays for determining the effects of sublethal concentrations of toxicants on populations of organisms. 相似文献
This paper presents the development of a Regional Neural Network for Water Level (RNN_WL) predictions, with an application to the coastal inlets along the South Shore of Long Island, New York. Long-term water level data at coastal inlets are important for studying coastal hydrodynamics sediment transport. However, it is quite common that long-term water level observations may be not available, due to the high cost of field data monitoring. Fortunately, the US National Oceanographic and Atmospheric Administration (NOAA) has a national network of water level monitoring stations distributed in regional scale that has been operating for several decades. Therefore, it is valuable and cost effective for a coastal engineering study to establish the relationship between water levels at a local station and a NOAA station in the region. Due to the changes of phase and amplitude of water levels over the regional coastal line, it is often difficult to obtain good linear regression relationship between water levels from two different stations. Using neural network offers an effective approach to correlate the non-linear input and output of water levels by recognizing the historic patterns between them. In this study, the RNN_WL model was developed to enable coastal engineers to predict long-term water levels in a coastal inlet, based on the input of data in a remote NOAA station in the region. The RNN_WL model was developed using a feed-forwards, back-propagation neural network structure with an optimized training algorithm. The RNN_WL model can be trained and verified using two independent data sets of hourly water levels.The RNN_WL model was tested in an application to Long Island South Shore. Located about 60–100 km away from the inlets there are two permanent long-term water level stations, which have been operated by NOAA since the1940s. The neural network model was trained using hourly data over a one-month period and validated for another one-month period. The model was then tested over year-long periods. Results indicate that, despite significant changes in the amplitudes and phases of the water levels over the regional study area, the RNN_WL model provides very good long-term predictions of both tidal and non-tidal water levels at the regional coastal inlets. In order to examine the effects of distance on the RNN_WL model performance, the model was also tested using water levels from other remote NOAA stations located at longer distances, which range from 234 km to 591 km away from the local station at the inlets. The satisfactory results indicate that the RNN_WL model is able to supplement long-term historical water level data at the coastal inlets based on the available data at remote NOAA stations in the coastal region. 相似文献