This issue presents proceedings of the "Stars and Interstellar Medium" section of the AllRussian Astronomical Conference VAK-2017. Sixteen papers(selected from about 70 talks) cover different problems related to stars, pulsars, interstellar gas and dust, and star formation. The preface briefly reviews these papers. 相似文献
In radio astronomy, the Ultra-Long Wavelengths (ULW) regime of longer than 10 m (frequencies below 30 MHz), remains the last virtually unexplored window of the celestial electromagnetic spectrum. The strength of the science case for extending radio astronomy into the ULW window is growing. However, the opaqueness of the Earth’s ionosphere makes ULW observations by ground-based facilities practically impossible. Furthermore, the ULW spectrum is full of anthropogenic radio frequency interference (RFI). The only radical solution for both problems is in placing an ULW astronomy facility in space. We present a concept of a key element of a space-borne ULW array facility, an antenna that addresses radio astronomical specifications. A tripole–type antenna and amplifier are analysed as a solution for ULW implementation. A receiver system with a low power dissipation is discussed as well. The active antenna is optimized to operate at the noise level defined by the celestial emission in the frequency band 1 ? 30 MHz. Field experiments with a prototype tripole antenna enabled estimates of the system noise temperature. They indicated that the proposed concept meets the requirements of a space-borne ULW array facility. 相似文献
Atlantic salmon reared in recirculating aquaculture system (RAS) may lead to inappropriately high stocking density, because fish live in a limited space. Finding the suitable stocking density of Atlantic salmon reared in RAS is very important for RAS industry. In this paper, the influence of stocking density on growth and some stress related physiological factors were investigated to evaluate the effects of stocking density. The fish were reared for 220 days at five densities (A: 24 kg/m3; B: 21 kg/m3; C: 15 kg/m3; D: 9 kg/ m3 and E: 6 kg/m3 ). The results show that 30 kg/m3 might be the maximum density which RAS can afford in China. The stocking densities under 30 kg/m3 have no effect on mortality of Atlantic salmon reared in RAS. However, the specific growth rate (SGR), final weight and weight gain in the high density group were significantly lower than the lower density groups and middle density groups. Moreover, feed conversion rate (FCR) had a negative correlation with density. Plasma hormone T3 and GH showed significant decrease with the increase of the stocking density of the experiment. Furthermore, thyroid hormone (T3), GH (growth hormone) activities were decreased with stocking density increase. However, plasma cortisol, GOT (glutamic oxalacetic transaminase) and GPT (glutamic pyruvic transaminase) activities were increase with stocking density increase. And the stocking density has no effects on plasma lysozyme of Atlantic salmon reared in RAS. These investigations would also help devise efficient ways to rear adult Atlantic salmon in China and may, in a way, help spread salmon mariculture in China.
ABSTRACT High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning. 相似文献