The Convergence Rate of Multidimensional Density Kernel Estimation with Bootstrap

AbstractThere have important applications of density kernel estimation in statistics. In certain conditions, we obtain the convergence rate of multidimensional density kernel estimation by exploiting Bootstrap

Huizhou resident population, Guangdong resident population and elderly population forecast based on the NAR neural network Markov model

We propose a nonlinear auto regressive neural network Markov model (NARMKM) to predict the annual Huizhou resident population, Guangdong resident population and elderly population in China, and improve the accuracy of population forecasting. The new model is built upon the traditional neural network model and utilized matrix perturbation theory to study the natural and response characteristics of a system when the structural parameters change slightly. The delay order and hidden layer number of neurons has a greater effect the prediction result of NAR neural network model. Therefore, we make full use of prior information to constrain and test when making predictions. We choose reasonable parameter settings to obtain more reliable prediction results. Three experiments are conducted to validate the high prediction accuracy of the NARMKM model, with mean absolute percentage error (MAPE), root mean square error (RMSE), STD and R2. These results demonstrate the superior fitting performance of the NARMKM model when compared to other six competitive models, including GM (1, 1), ARIMA, Multiple regression, FGM (1, 1), FANGBM and NAR. Our study provides a scientific basis and technical references for further research in the finance as well as population fields

Identifying Users' Gender via Social Representations

Gender prediction has evoked great research interests due to its potential applications like targeted advertisement and personalized search. Most of existing studies rely on the content texts. However, the text information is hard to access. This makes it difficult to extract text features. In this paper, we propose a novel framework which only involves the users' ids for gender prediction. The key idea is to represent users in the embedding connection space. We present two strategies to modify the word embedding technique for user embedding. The first is to sequentialize users' ids to get the order of social context. The second is to embed users into a large-sized sliding window of contexts. We conduct extensive experiments on two real data sets from Sina Weibo. Results show that our method is significantly better than the state-of-the-art graph embedding baselines. Its accuracy also outperforms that of the content based approaches

Developing Train Station Parking Algorithms: New Frameworks Based on Fuzzy Reinforcement Learning

Train station parking (TSP) accuracy is important to enhance the efficiency of train operation and the safety of passengers for urban rail transit. However, TSP is always subject to a series of uncertain factors such as extreme weather and uncertain conditions of rail track resistances. To increase the parking accuracy, robustness, and self-learning ability, we propose new train station parking frameworks by using the reinforcement learning (RL) theory combined with the information of balises. Three algorithms were developed, involving a stochastic optimal selection algorithm (SOSA), a Q-learning algorithm (QLA), and a fuzzy function based Q-learning algorithm (FQLA) in order to reduce the parking error in urban rail transit. Meanwhile, five braking rates are adopted as the action vector of the three algorithms and some statistical indices are developed to evaluate parking errors. Simulation results based on real-world data show that the parking errors of the three algorithms are all within the "mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"""mml:mrow""mml:mo"±"/mml:mo""/mml:mrow""/mml:math"30cm, which meet the requirement of urban rail transit. Document type: Articl

Towards a Graph-based Data Model for Semantics Evolution

Semantic information comes from the things being recognized and understood gradually, and thus it is often in evolution during the modeling process. Existing semantic models usually describe the objects and the relationships in an application-oriented way, which is unsuitable to reuse the schemas during the semantics evolution. In this paper, we propose a new graph-based semantic data model to overcome the limitation. SemGraph adopts a meaning-oriented approach to specifying the subjective view of the things and uses the certain meta-meaning relationships to build a graph-based semantic model. The model is simple but expressive, and is especially fit for the semantics evolution. We introduce the basic concepts and the essential mechanisms of the model, demonstrate its features with examples and typical cases of semantics evolution

3D Macroscopic Architectures from Self‐Assembled MXene Hydrogels

Assembly of 2D MXene sheets into a 3D macroscopic architecture is highly desirable to overcome the severe restacking problem of 2D MXene sheets and develop MXene‐based functional materials. However, unlike graphene, 3D MXene macroassembly directly from the individual 2D sheets is hard to achieve for the intrinsic property of MXene. Here a new gelation method is reported to prepare a 3D structured hydrogel from 2D MXene sheets that is assisted by graphene oxide and a suitable reductant. As a supercapacitor electrode, the hydrogel delivers a superb capacitance up to 370 F g−1 at 5 A g−1, and more promisingly, demonstrates an exceptionally high rate performance with the capacitance of 165 F g−1 even at 1000 A g−1. Moreover, using controllable drying processes, MXene hydrogels are transformed into different monoliths with structures ranging from a loosely organized porous aerogel to a dense solid. As a result, a 3D porous MXene aerogel shows excellent adsorption capacity to simultaneously remove various classes of organic liquids and heavy metal ions while the dense solid has excellent mechanical performance with a high Young's modulus and hardness

An optimal fractional-order accumulative Grey Markov model with variable parameters and its application in total energy consumption

In this paper, we propose an optimal fractional-order accumulative Grey Markov model with variable parameters (FOGMKM (1, 1)) to predict the annual total energy consumption in China and improve the accuracy of energy consumption forecasting. The new model is built upon the traditional Grey model and utilized matrix perturbation theory to study the natural and response characteristics of a system when the structural parameters change slightly. The particle swarm optimization algorithm (PSO) is used to determine the number of optimal fractional order and nonlinear parameters. An experiment is conducted to validate the high prediction accuracy of the FOGMKM (1, 1) model, with mean absolute percentage error (MAPE) and root mean square error (RMSE) values of 0.51% and 1886.6, respectively, and corresponding fitting values of 0.92% and 6108.8. These results demonstrate the superior fitting performance of the FOGMKM (1, 1) model when compared to other six competitive models, including GM (1, 1), ARIMA, Linear, FAONGBM (1, 1), FGM (1, 1) and FOGM (1, 1). Our study provides a scientific basis and technical references for further research in the finance as well as energy fields and can serve well for energy market benchmark research

Observational studies of the effects of wind mixing and biological process on the vertical distribution of dissolved oxygen off the Changjiang Estuary

Wind mixing is important in regulating dissolved oxygen (DO) variability; however, the transect response of DO dynamics to wind disturbance has seldom been documented with field data. In the summer of 2017, repeat transect observations off the Changjiang Estuary were conducted throughout a fresh wind (the maximum wind speed was 9.8 m s–1) event to reveal the role of physical mixing and biological activity in DO variations. After the wind event, hypoxia was alleviated presenting as the hypoxia thickness decreased from 30 m to 20 m. However, poorly ventilated near-bottom hypoxia was aggravated with a further decrease in DO. Generally, the saturation of dissolved oxygen (DOs) in depth-integrated water column increased by 9%–49% through physical diffusion with a weakened stratification and enhanced phytoplankton bloom. However, in this case, the wind-induced physical water mass mixing by transporting DO downward had a limited contribution to the water-column DO budget, while upwards nutrients induced by mixing fueled the larger vertical area of algae bloom and subsequent substantial oxygen consumption. As the wind speed increased, the air-sea exchange would be important in supplying DO, especially in nearshore areas, which could effectively offset the DO deficiency. In summary, frequently occurring fresh wind-mixing events off the Changjiang Estuary would alleviate hypoxia in the water column but probably exacerbate hypoxia at the bottom, as determined by competing ventilation and respiration roles. Such complex interactions likely occur and perform differently as wind stress varies. Thus, high-spatial and long-term process observations are required to better understand the net effects of bottom hypoxia evolution

Massive nutrients offshore transport off the Changjiang Estuary in flooding summer of 2020

Flood events significantly increase water discharges and terrigenous material inputs to coastal waters. Riverine nutrients in the Changjiang Estuary are transported by the dispersion of Changjiang Diluted Water (CDW) plumes and detached low-salinity water patches. However, the effects of flooding on nutrient offshore transports have not been well explored. Here, we present the nutrient conditions in the Changjiang Estuary and adjacent East China Sea in the historical flooding year 2020. Comparisons of nutrient distributions between flooding years, drought year and non-flooding years were also made. Our results showed that nitrate flux from the Changjiang River in August 2020 was 1.5 times that of the multi-year averaged flux in non-flooding years. Enormous riverine nutrient input resulted in much higher nutrient concentrations in the outer estuary than those in non-flooding years. In addition, a detached low-salinity water patch was observed, which made the salinity of the northern estuary even lower than that in the historical flooding year 1998. Surface dissolved inorganic nitrate (DIN) level in the low-salinity water patch was even ~16 times of that at nearby station in the drought year 2006. While phosphate (PO43−) concentrations were less than 0.1 μmol L−1 east of 123°E, which was probably caused by intensive biological uptake, as indicated by a high Chlorophyll a (Chl a) concentration (29.08 μg L−1). The depleted PO43− and high N/P of the low-salinity water patch suggested PO43− limitation even under flood conditions. A three end-member mixing model was adopted to identify the contributions of the CDW end-member (CDWend-member) and biological process to nutrient distributions. Our model results showed that the nutrient contribution of the CDWend-member to the estuary (122–124°E, 31–32.5°N) in flooding year 2020 was over double that in drought year 2006. Model-derived biological DIN uptake was as high as 24.65 μmol L−1 at the low-salinity water patch. Accordingly, the estimated net community production was 566–1131 mg C m−2 d−1 within the euphotic zone. The offshore transport of a low-salinity, high-DIN water patch during flooding could probably have a significant influence on biogeochemical cycles in the broad shelf, and even the adjacent Japan Sea