On the pulsational-radial oscillations instability of isothermal magnetized accretion disk with self-gravity

This is the first paper to consider the effects of both magnetic field and self-gravity on the pulsational instability. Our main new results are that the self-gravity enhances the instability of the magneto-acoustic mode in the outer disk strongly, and also affects the instability in the inner disk, but stabilized the viscous mode. The effect of self-gravity is much greater than that of magnetic field in the outer disk, while the effect of magnetic field on the instability is weaker than that in the previous work's (Wuet al., 1995; Yanget al., 1995), in which the self-gravity has not been considered. Finally, we discuss our results.     

Change features and regional distribution of temperature trend and variability joint mode in mainland China

Theoretical and Applied Climatology - Adaption for temperature should be suitable to local conditions for regional differences in temperature change features. This paper proposed to utilize nine...     

Spatial and temporal variability of reference evapotranspiration and influenced meteorological factors in the Jialing River Basin,China

Theoretical and Applied Climatology - Reference evapotranspiration (ETr) is one of the important parameters in the hydrological cycle. The spatio-temporal variation of ETr and other meteorological...     

Construction and Application of a Climate Risk Index for China

In the context of global warming, China is facing with increasing climate risks. It is imperative to develop quantitative indices to reflect the climate risks caused by extreme weather/climate events and adverse climatic conditions in association with different industries. Based on the observations at 2288 meteorological stations in China and the meteorological disasters data, a set of indices are developed to measure climate risks due to water-logging, drought, high temperature, cryogenic freezing, and typhoon. A statistical method is then used to construct an overall climate risk index (CRI) for China from these individual indices. There is a good correspondence between these indices and historical climatic conditions. The CRI, the index of water-logging by rain, and the high temperature index increase at a rate of 0.28, 0.37, and 0.65 per decade, respectively, from 1961 to 2016. The cryogenic freezing index is closely related to changes in the consumer price index for food. The high temperature index is correlated with the consumption of energy and electricity. The correlation between the yearly growth in claims on household property insurance and the sum of the water-logging index and the typhoon index in the same year is as high as 0.70. Both the growth rate of claims on agricultural insurance and the annual growth rate of hospital inpatients are positively correlated with the CRI. The year-on-year growth in the number of domestic tourists is significantly negatively correlated with the CRI in the same year. More efforts are needed to develop regional CRIs.     

Mining transition rules of cellular automata for simulating urban expansion by using the deep learning techniques

Along with the gradually accelerated urbanization process, simulating and predicting the future pattern of the city is of great importance to the prediction and prevention of some environmental, economic and urban issues. Previous studies have generally integrated traditional machine learning with cellular automaton (CA) models to simulate urban development. Nevertheless, difficulties still exist in the process of obtaining more accurate results with CA models; such difficulties are mainly due to the insufficient consideration of neighborhood effects during urban transition rule mining. In this paper, we used an effective deep learning method, named convolution neural network for united mining (UMCNN), to solve the problem. UMCNN has substantial potential to get neighborhood information from its receptive field. Thus, a novel CA model coupled with UMCNN and Markov chain was designed to improve the performance of simulating urban expansion processes. Choosing the Pearl River Delta of China as the study area, we excavate the driving factors and the transformational relations revealed by the urban land-use patterns in 2000, 2005 and 2010 and further simulate the urban expansion status in 2020 and 2030. Additionally, three traditional machine-learning-based CA models (LR, ANN and RFA) are built to attest the practicality of the proposed model. In the comparison, the proposed method reaches the highest simulation accuracy and landscape index similarity. The predicted urban expansion results reveal that the economy will continue to be the primary factor in the study area from 2010 to 2030. The proposed model can serve as guidance in urban planning and government decision-making.     

Where Should the Start Zone Be Located for Xiong’an New Area? A Land Use Perspective

China announced plans on April 1, 2017 to create Xiong’an New Area, a new economic zone about 110 km southwest of Beijing. The new area of national significance covers the three counties of Xiongxian, Rongcheng and Anxin in Hebei Province, and is home to Baiyangdian, a major wetland in northern China. It will cover around 100 km² initially (called “Starting Zone”) and will be expanded to 200 km² in the mid-term and about 2000 km² in the long term. In this context, it is important to assess the land use pattern for the new area’s planning and development. Based on remote sensing interpretation of Landsat OLI images, we examine the current land use features, the potential for built-up land saving and intensive use, and the spatial variation of arable land quality. The results indicate that the arable lands, built-up lands and wetlands account for around 60%, 20% and 10%, respectively. The potential for rural built-up land saving and intensive use is quite large. The arable land quality and use intensity in Rongcheng and Anxin counties are much better than that of Xiongxian. The pros and cons for three potential options for the Starting Zone are listed and compared. The eastern area of Rongcheng county, to the north of Baiyangdian, is believed to be the most suitable Starting Zone. Policy recommendations for built-up land expansion, farmland saving and flood risk mitigation for Xiong’an New Area are also proposed in this paper.     

The sulfur isotopic composition of sulfides from ores and host rocks of the Upper Kolyma region,Magadan Oblast

More than 200 analyses of the sulfur isotopic composition of sulfides from various terrigenous and intrusive host rocks, metasomatically altered wall rocks, and gold lodes of the Upper Kolyma region are presented. In accessory pyrite of the metaterrigenous rocks, δ³⁴S varies from ?23.1 to +5.7‰ δ³⁴S of pyrite and arsenopyrite from gold-quartz mineralization is within the range ?10.6 to ?0.4‰ and is close to the average δ³⁴S of pyrite from the metaterrigenous rocks (?4.4‰). In the intrusive rocks, δ³⁴S of pyrite varies from ?3.8 to +2.6‰ (+0.7‰, on average) and drastically differs from δ³⁴S of arsenopyrite from postmagmatic gold-rare-metal mineralization (?7.9 to ?2.7‰; ?5.2‰, on average). The comparison of the δ³⁴S of accessory sulfides from the host rocks with δ³⁴S of sulfides from the gold deposits suggests that sulfur mobilized from the terrigenous sequences participated in the hydrothermal process. The results obtained are consistent with the metamorphic model of the formation of gold-quartz deposits in the Upper Kolyma region.     

Rapid PPP ambiguity resolution using GPS+GLONASS observations

Integer ambiguity resolution (IAR) in precise point positioning (PPP) using GPS observations has been well studied. The main challenge remaining is that the first ambiguity fixing takes about 30 min. This paper presents improvements made using GPS+GLONASS observations, especially improvements in the initial fixing time and correct fixing rate compared with GPS-only solutions. As a result of the frequency division multiple access strategy of GLONASS, there are two obstacles to GLONASS PPP-IAR: first and most importantly, there is distinct code inter-frequency bias (IFB) between satellites, and second, simultaneously observed satellites have different wavelengths. To overcome the problem resulting from GLONASS code IFB, we used a network of homogeneous receivers for GLONASS wide-lane fractional cycle bias (FCB) estimation and wide-lane ambiguity resolution. The integer satellite clock of the GPS and GLONASS was then estimated with the wide-lane FCB products. The effect of the different wavelengths on FCB estimation and PPP-IAR is discussed in detail. We used a 21-day data set of 67 stations, where data from 26 stations were processed to generate satellite wide-lane FCBs and integer clocks and the other 41 stations were selected as users to perform PPP-IAR. We found that GLONASS FCB estimates are qualitatively similar to GPS FCB estimates. Generally, 98.8% of a posteriori residuals of wide-lane ambiguities are within \(\pm 0.25\) cycles for GPS, and 96.6% for GLONASS. Meanwhile, 94.5 and 94.4% of narrow-lane residuals are within 0.1 cycles for GPS and GLONASS, respectively. For a critical value of 2.0, the correct fixing rate for kinematic PPP is only 75.2% for GPS alone and as large as 98.8% for GPS+GLONASS. The fixing percentage for GPS alone is only 11.70 and 46.80% within 5 and 10 min, respectively, and improves to 73.71 and 95.83% when adding GLONASS. Adding GLONASS thus improves the fixing percentage significantly for a short time span. We also used global ionosphere maps (GIMs) to assist the wide-lane carrier-phase combination to directly fix the wide-lane ambiguity. Employing this method, the effect of the code IFB is eliminated and numerical results show that GLONASS FCB estimation can be performed across heterogeneous receivers. However, because of the relatively low accuracy of GIMs, the fixing percentage of GIM-aided GPS+GLONASS PPP ambiguity resolution is very low. We expect better GIM accuracy to enable rapid GPS+GLONASS PPP-IAR with heterogeneous receivers.     

基于位置轨迹挖掘的城市居民职住地识别方法研究

城市居民居住与就业的空间组织在城市空间结构的研究中非常重要,居民职住地的识别则是城市居民职住空间组织研究的首要任务。本文从位置轨迹入手,在基于时间聚类提取出停留点序列的基础上,重点对停留点序列的时间特征进行分析,对其做进一步的归纳,筛选出职住地出候选类,最终完成居住地、工作地的识别。实验结果表明,该方法能够有效地从位置轨迹数据中识别出城市居民的居住地和工作地。