The metric on Riemann surface with singularities is one of important objects in complex geometry. We study conformal metrics on Riemann surfaces with only cusp singularities,whose area and Calabi energy are both finite, and obtain the exact expression of HCMU metrics near cusp singularities.
In this work, by introducing two parameters λ1 and λ2 and using the method of weight function and the technique of functional analysis, a two-parameter Hilbert-type integral operator is defined and the norm of the operator is given. As applications, a few improved results and some new Hilbert-type integral inequalities with the particular kernels are obtained.
Based on LAMOST data,we use F/G main-sequence star sample to study the orbital eccentricity distri bution of the thick-disk stars in the Galaxy. The thick-disk stars are identified by spatial position and metallicity, and the contamination from the thin-disk stars is considered. We find that the observed thick-disk stellar orbital eccentricity distribution has a peak at low eccentricity (~0.2) and it extends to high eccentricity (e~0.8). We compare our results with the four thick-disk formation models, and our results are in the best agreement with the gas-rich merger model.
Aldol condensation of methyl propionate (MP) and formaldehyde (FA) is a green and sustainable route to synthesize methyl methacrylate (MMA). The key to the route is to develop effective catalysts, and the supports of catalysts play an important role in the catalytic performance. SBA-15 with different crystallization time was synthesized by the hydrothermal method and the Cs-La/SBA-15 catalysts were prepared by wetness impregnation method. The catalysts were characterized by XRD, SEM, IR, BET, TPD, etc. The effect of crystallization time of SBA-15 on the catalytic performance of Cs-La/SBA-15 for aldol condensation was studied. The results showed that regular mesoporous materials with 2D-hexogonal rod-like structure were obtained when the crystallization time was more than 24 h. The Cs-La/24SBA-15 (with SBA-15 crystallized 24 h) exhibited higher catalytic activity than the other catalysts because of the higher density of medium base sites as well as the great surface area of SBA-15 (around 1 000 m2/g) and uniform pore size (6 nm). When the MP/FA=1/1, the conversion of MP is 30.9% and the selectivity of MMA reachs 90.3%.
In this study, we find that one standard natural organic matter (NOM) called Suwannee River humic acid (SRHA) reduces Au3+ to Au nanoparticles under short-time simulated sunlight irradiation or in full-course darkness. The results suggest that the reduction of metal ions by the active NOM occurs widely, even in special aquatic environments that lack sunlight exposure, such as underground and cavern water. Moreover, analogous experiments covering other two kinds of NOMs, Pony Lake fulvic acid (PLFA) and Aldrich humic acid (AHA), demonstrate that NOMs behave differently in reduction of Au3+ with limited irradiation.
Two strucutres of the permanent magnetic field have been designed for paraffin control in oilfield. Generally, the favorable design for such magnetic field meets the following two structural requirements. The magnetic flux densities in paraffin deposit zone in the cross-sectional direction are as strong as possible, and the gradients of magnetic flux density in the axial oil passage are as big as possible. Based on the concept of Halbach magnetic field, two structures have been designed and numerically analyzed to meet the above requirements for the purpose of the paraffin control. The results confirmed our conceivement and may serve as the application of the magnetic field construction for paraffin control in oilfield.
This study investigated the variation in the soil temperature in the top 5 cm of the soil on multiple temporal scales and its driving factors for an urban riparian zone in summer. Wavelet analysis showed that the top soil temperature varied within multiple domains of temporal scale. The variation in the top soil temperature on small temporal scales (daily, ten-day, and twenty-day) was mainly due to the variation in total solar radiation, while on large temporal scales (monthly and seasonal) it was mainly influenced by vegetation composition and configuration. Firstly, plant growth had significant effects on top soil temperature. Vegetation coverage and height and tree leaf biomass played different roles in variation in the top soil temperature when vegetation types were different. Secondly, the top soil temperature was also influenced by vegetation type and canopy structure as vegetation coverages were similar. In addition, compared with the broad-leaved trees, the coniferous trees had more noticeable cooling effect.
Net surface shortwave radiation (NSSR) with high temporal and spatial resolutions is of great significance to studies of the Earth surface radiation budget, climate change, and some other relevant fields. However, most of the present investigations on the estimation of NSSR focus on the polar-orbit satellite observations which have only one or two images available for a given study area. This study primarily develops a method for obtaining NSSR by directly using the geostationary satellite data, and we provide multi-temporal NSSR estimates in regional scale using the Chinese operational geostationary meteorological satellite FengYun-2E (FY-2E) data. Results indicate a very significant correlation between our estimated NSSR and the tower-based NSSR measurements with the coefficient of determination (R2) of 0.99 and a root mean square error (RMSE) of 27.51 W/m2. The R2 value is 0.99 and RMSE is 15.36 W/m2, excluding winter. This indicates that NSSR can be estimated by using the proposed method which benefits climate change and some other fields at the regional scale with high temporal resolution.
Spatial connection intensity is an important indicator for the development stage, situation, and function position of urban agglomeration. With the wide use of mobile terminals and networking platform, population mobility among different regions can be traced and expressed more accurately by using big data analysis technique. Based on the database of microblog, a new measurement method for spatial connection intensity is proposed in this study. The new method makes up for the traditional disadvantages resulting from the lack of commuting data. Therefore, it provides guidance for accurate identification of urban agglomeration.
New town or new area construction is an important means to promote new rural industrialization and urbanization strategy, which promotes spatial and function restructuring. The production of space provides an important perspective for new town or new area study in period of social-economic transition. Pudong new district (PND) of Shanghai was chosen as the study area to build a theoretical analysis framework of power, capital, and production of space. On this basis, we analyzed the process and mechanism of production of space in PND. The results show that PND was undergoing the conceived space and industrial space production processes, and now it is turning into consumption space. Growth coalition between local government and enterprises plays a crucial role. The influence of residents on production of space gradually grows. Power promotes production of space via institutional environment, guidance of planning, infrastructure construction, and political elite influence. Domestic and foreign capital promotes production of space rapidly. Faced with the change of space requirement, enterprises and residents reshape conceived space which is mainly controlled by the government. All of these have changed the social and economic structure in PND.
The Chinese DY115-21 cruse acquired valuable OBS seismic data at hydrothermal area A of SWIR and discovered a low-velocity zone which located on the 27th segment of SWIR. Based on velocity section of P wave, we use the finite element method to model the heating process, aiming to discover the possible magmatism and heat effect of the low-velocity zone. The results are given as follows. 1) The current seafloor hydrothermal activity indicates the existence of bottom heating power in the magma chamber. Hotspot causes thermal abnormality in the process of magma moving along the fracture and creates a new magma chamber to provide heat resource for current hydrothermal activities. 2)Temperature-dependent thermal conductivity greatly influenced the results of the thermal modeling. The modeling results show that the moho temperature of the research area is about 910℃ and the heat flux at the hotspot is about 190 mW·m-2.
In response to the explosive increase of data, it is necessary to deploy cache in small cells to relieve the pressure of capacity-constrained backhauls. Considering vast personalized information implied in the user history logs, we utilize a user-based Top N collaborative filtering recommender system to predict user requests and determine cache contents, and propose a user association scheme maximizing the system throughput. Through relaxing the constraints, we find the relationship between user association and ratio of user throughput, and propose a low-complexity algorithm. Simulation results show the obvious gains in hit-ratio and system throughput compared to the existing algorithms.
The accuracies of traditional fingerprint pattern classification algorithms rely heavily on the corresponding feature extraction algorithms. Further more, the within-class variance of fingerprint patterns increases while the between-class variance decreases in large-scale database. So it is difficult for hand-designed features to suit with all fingerprint data. In order to remove the coupling with hand-designed feature extraction algorithms, we propose an approach to directly recognize patterns in raw fingerprint images. It takes advantage of the automatic feature extraction ability of convolutional neural networks to learn patterns from large amount of images. The training data are carefully designed to fit the variety of fingerprints and to improve the robutness. Meanwhile, the accuracy is further improved by averaging multi-scale models. In our experiment, an accuracy of 94.2% for four-class classification has been achieved in the international opening fingerprint dataset NIST DB 4. Our algorithm surpasses many classical algorithms, and it is both practical and meaningful.
This study tried to recognize the Chinese mood variations in different seasons and time periods via the big data on Internet. Participants were 1.95 million active users of Sina Weibo. We downloaded their data of one week selected in each of the four seasons of one year. Then we used TextMind to calculate the ratios of positive affect (PA) and negative affect (NA) words. Results are given as follows. 1) Integrated mood had two peaks at noon and 8 o'clock in the evening. 2) Although PA in weekends was not different from that on the weekdays, NA in weekends was lower than that on weekdays. 3) In summer, both PA and NA were the highest. In autumn, both PA and NA were the lowest. 4) The two genders had similar mood variation trends, but females had more mood expressions than males and they were sentimental and susceptive.
Time series prediction has attracted more and more attention due to its applications in various areas, but scant attention has focused on multivariate time series prediction. By using multi-dimensional time series one can get more information about the system. Considering this problem, we propose a combined prediction model for multi-dimensional time series based on k-NN and BP neural network. We use k-NN and BP neural network to get two prediction results, respectively. Then we reuse BP neural network to get the final prediction results. The experimental results show that the proposed method outperforms k-NN and BP neural network in prediction.
A comprehensive analysis of the variations of air quality and meteorological conditions was conducted for two major events(IAAF World Championships and September 3 Military Parade ceremony) in Beijing during the period from 20 August 2015 to 3 September 2015 when intense control measures were taken. Average concentrations of NO, NO2, and PM2.5 were 2.0, 22.7, and 17.8 μg·m3, respectively, during the two major events, and significantly decreased by 58.3%, 52.1%, and 73.2%, respectively, compared to those during the same period in 2014. The air quality in that period is called as "Parade Blue". After implementation of emission control measures, peak concentrations of NO were reduced by about 43% at traffic stations and by about 45% at urban environmental stations, and NO2 concentrations for both early peak hours and nighttime accumulated slower than usual. Furthermore, the regional high NO2 concentration center and a gradient PM2.5 distribution pattern in Beijing both disappeared during the events. Due to the weak northern wind, the NO2 concentration peak in early morning disappeared, which was different from the situation during the APEC meeting in 2014. However, the NO2 peak in early morning was significantly higher than that at night during the two major events. Reductions in emissions and ambient pollutant concentrations during the two major events were both slightly larger than those during the APEC meeting.
When educators and learners choose wide thick or sophisticated knowledge system, they face judgment problem:teaching core concepts in synchronization with the times and understanding the subject of global knowledge map. This work collects the curriculum outline guides of ACM/IEEE from 1991 to 2014. We start from the smallest component of knowledge unit, and we analyze now the core area and the concept change after the concept of computer science in the past 60 years has been increased from 591 to 5 824. The analysis in this work shows that the growth rate of the subject concept slowed down from 354% to 1.41% in 2013 and 2014; knowledge areas expanded from 9 to 19; and knowledge units expanded from 10 to 18. The main contributions of this work are as follows. We collect the data and establish the dataset. This is the first time to use the knowledge framework of curricula, and in three aspects we reveal the evolution law of computer science; and then we have set up an online platform.