ALBERT

Description: In general, the stock trend is mainly driven by the big order transactions. Believing that the stock rise with a large volume is closely associated with the big order net inflow, we propose an efficient stock recommendation model based on big order net inflow in the paper. In order to compute the big order net inflow of stock, we use the M/G/1 queue system to measure all tick-by-tick transaction data. Based on an indicator of the big order net inflow of stock, we select some stocks with the higher value of the net inflow to constitute the prerecommended stock set for the target investor user. In order to recommend some stocks with which this style is familiar them to the target users, we divide lots of investors into several categories using fuzzy clustering method and we should do our best to choose stocks from the stock set once operated by those investors who are in the same category with the target user. The experiment results show that the recommended stocks have better gains during the several days after the recommended stock day and the proposed model can provide reliable investment guidance for the target investors and let them get more stock returns.

Description: Geothermal energy is one of the cleanest sources of energy which is gaining importance as an alternative to hydrocarbons. Geothermal energy reserves in China are enormous and it has a huge potential for exploitation and utilization. However, the development of the geothermal industry in China lags far behind other renewable energy sources because of the lack of fiscal and taxation policy support. In this paper, we adopt the system dynamics method and use the causal loop diagram to explore the development mechanism of fiscal and taxation policies in the geothermal industry. The effect of the fiscal and taxation policy on the development of the geothermal industry is analyzed. In order to promote sustainable development of the geothermal industry in China, the government should pay more attention to subsidies for the geothermal industry in the life-cycle stage of the geothermal industry. Furthermore, a plan is necessary to provide a reasonable system of fiscal and taxation policies.

Description: We propose a compact 1-mm-radius microring resonator sensor based on a hybrid plasmonic waveguide on a silicon-on-insulator substrate. The hybrid waveguide is composed of a metal-gap-silicon structure, where the optical energy is greatly enhanced in the narrow gap. We use the finite element method to numerically analyze the device optical characteristics as a biochemical sensor. As the optical field in the hybrid micoring resonator has a large overlap with the upper-cladding sensing medium, the sensitivity is very high compared to other dielectric microring resonator sensors. The compactness of the hybrid microring resonator is resulted from the balance between bending radiation loss and metal absorption loss. The proposed hybrid microring resonator sensors have the main advantages of small footprint and high sensitivity and can be potentially integrated in an array form on a chip for highly-efficient lab-on-chip biochemical sensing applications.

Description: The objective of this work is to analyze the temporal and spatial variability of the total ozone column (TOC) trends over the Yangtze River Delta, the most populated region in China, during the last 35 years (1978–2013) using remote sensing-derived TOC data. Due to the lack of continuous and well-covered ground-based TOC measurements, little is known about the Yangtze River Delta. TOC data derived from the Total Ozone Mapping Spectrometer (TOMS) for the period 1978–2005 and Ozone Monitoring Instrument (OMI) for the period 2004–2013 were used in this study. The spatial, long-term, seasonal, and short-term variations of TOC in this region were analyzed. For the spatial variability, the latitudinal variability has a large range between 3% and 13%, and also represents an annual cycle with maximum in February and minimum in August. In contrast, the longitudinal variability is not significant and just varies between 2% and 4%. The long-term variability represented a notable decline for the period 1978–2013. The ozone depletion was observed significantly during 1978–1999, with linear trend from (−3.2 ± 0.7) DU/decade to (−10.5 ± 0.9) DU/decade. As for seasonal variability, the trend of TOC shows a distinct seasonal pattern, with maximum in April or May and minimum in October or November. The short-term analysis demonstrates the day-to-day changes as well as the six-week system persistence of the TOC. The results can provide comprehensive descriptions of the TOC variations in the Yangtze River Delta and benefit climate change research in this region.

Description: Sensors, Vol. 18, Pages 1950: An Optimization Routing Algorithm for Green Communication in Underground Mines Sensors doi: 10.3390/s18061950 Authors: Heng Xu Qiyue Li Jianping Wang Guojun Luo Chenghui Zhu Wei Sun With the long-term dependence of humans on ore-based energy, underground mines are utilized around the world, and underground mining is often dangerous. Therefore, many underground mines have established networks that manage and acquire information from sensor nodes deployed on miners and in other places. Since the power supplies of many mobile sensor nodes are batteries, green communication is an effective approach of reducing the energy consumption of a network and extending its longevity. To reduce the energy consumption of networks, all factors that negatively influence the lifetime should be considered. The degree constraint minimum spanning tree (DCMST) is introduced in this study to consider all the heterogeneous factors and assign weights for the next step of the evaluation. Then, a genetic algorithm (GA) is introduced to cluster sensor nodes in the network and balance energy consumption according to several heterogeneous factors and routing paths from DCMST. Based on a comparison of the simulation results, the optimization routing algorithm proposed in this study for use in green communication in underground mines can effectively reduce the network energy consumption and extend the lifetimes of networks.

Description: Assessing the failure potential of slopes is of great significance for land use and management. The objective of this paper is to develop a novel model for evaluating the failure probability of slopes based on a relevance vector machine (RVM), with a special attention to the characteristics of failed slopes along the lower reaches of the Jinsha River, close to the Wudongde dam site. Seven parameters that influence the occurrence of landslides were selected as environmental factors; namely lithology, slope angle, slope height, slope aspect, slope structure, distance from faults, and land use. A total of 55 landslides mapped in the study area were used to train and test the RVM model. The results suggest that the accuracy of the model in predicting the failure probability of slopes, using both training and testing data sets, is very high and deemed satisfactory. To validate the model performance, it was applied to 28 landslide cases identified in the upper reaches of the Jinsha River, where environmental and geological conditions are similar to those of the study area. An accuracy of approximately 92.9% was obtained, which demonstrates that the RVM model has a good generalization performance.

Description: IJERPH, Vol. 15, Pages 114: Spatiotemporal Patterns of Ground Monitored PM2.5 Concentrations in China in Recent Years International Journal of Environmental Research and Public Health doi: 10.3390/ijerph15010114 Authors: Junming Li Xiulan Han Xiao Li Jianping Yang Xuejiao Li This paper firstly explores the space-time evolution of city-level PM 2.5 concentrations showed a very significant seasonal cycle type fluctuation during the period between 13 May 2014 and 30 May 2017. The period from October to April following each year was a heavy pollution period, whereas the phase from April to October of the current year was part of a light pollution period. The average monthly PM 2.5 concentrations in mainland China based on ground monitoring, employing a descriptive statistics method and a Bayesian spatiotemporal hierarchy model. Daily and weekly average PM 2.5 concentrations in 338 cities in mainland China presented no significant spatial difference during the severe pollution period but a large spatial difference during light pollution periods. The severe PM 2.5 pollution areas were mainly distributed in the Beijing-Tianjin-Hebei urban agglomeration in the North China Plain during the beginning of each autumn-winter season (September), spreading to the Northeast Plains after October, then later continuing to spread to other cities in mainland China, eventually covering most cities. PM 2.5 pollution in China appeared to be a cyclic characteristic of first spreading and then centralizing in the space in two spring-summer seasons, and showed an obvious process of first diffusing then transferring to shrinkage alternation during the spring-summer season of 2015, but showed no obvious diffusion during the spring-summer season of 2016, maintaining a stable spatial structure after the shrinkage in June, as well as being more concentrated. The heavily polluted areas are continuously and steadily concentrated in East China, Central China and Xinjiang Province.

Description: The Qinghai-Tibet Plateau (QTP) holds massive freshwater resources and is one of the most active regions in the world with respect to the hydrological cycle. Soil moisture (SM) plays a critical role in hydrological processes and is important for plant growth and ecosystem stability. To investigate the relationship between climatic factors (air temperature and precipitation) and SM during the growing season in various climate zones on the QTP, data from three observational stations were analyzed. The results showed that the daily average (Tave) and minimum air temperatures (Tmin) significantly influenced SM levels at all depths analyzed (i.e., 10, 20, 30, 40, and 50 cm deep) at the three stations, and Tmin had a stronger effect on SM than did Tave. However, the daily maximum air temperature (Tmax) generally had little effect on SM, although it had showed some effects on SM in the middle and deeper layers at the Jiali station. Precipitation was an important factor that significantly influenced the SM at all depths at the three stations, but the influence on SM in the middle and deep layers lagged the direct effect on near-surface SM by 5–7 days. These results suggest that environment characterized by lower temperatures and higher precipitation may promote SM conservation during the growing season and in turn support ecosystem stability on the QTP.

Description: The heart of the current wireless communication systems (including 5G) is the Fourier transform-based orthogonal frequency division multiplex (OFDM). Over time, a lot of research has proposed the wavelet transform-based OFDM as a better replacement of Fourier in the physical layer solutions because of its performance and ability to support network-intensive applications such as the Internet of Things (IoT). In this paper, we weigh the wavelet transform performances against the future wireless application system requirements and propose guidelines and approaches for wavelet applications in 5G waveform design. This is followed by a detailed impact on healthcare. Using an image as the test data, a comprehensive performance comparison between Fourier transform and various wavelet transforms has been done considering the following 5G key performance indicators (KPIs): energy efficiency, modulation and demodulation complexity, reliability, latency, spectral efficiency, effect of transmission/reception under asynchronous transmission, and robustness to time-/frequency-selective channels. Finally, the guidelines for wavelet transform use are presented. The guidelines are sufficient to serve as approaches for tradeoffs and also as the guide for further developments.

Description: MOOCs (massive open online courses) are developing rapidly, but they also face many problems. As the MOOC’s most important resource, the course videos have a very important influence on the learning. This article defines the ratio R (R=Average  viewing  duration/Video  length), which reflects the popularity of the video. By analyzing the relationship between the video length, release time, and R, we found a significant negative linear correlation between video length and R and video release time and R. However, when the number of videos is less than the threshold, the release time has less influence on R. This paper presents a video viewing behavior analysis algorithm based on multiple linear regression. The residual independence test proved that the algorithm has a good approximation to the data. It can predict the popularity of similar course videos to help producers optimize video design.