scholarly journals Integration Development of Urban Agglomeration in Central Liaoning, China, by Trajectory Gravity Model

2021 ◽  
Vol 10 (10) ◽  
pp. 698
Author(s):  
Ruren Li ◽  
Shoujia Li ◽  
Zhiwei Xie
Keyword(s):  
Gravity Model ◽  
Gravitational Force ◽  
Main Idea ◽  
Correlation Coefficients ◽  
Urban Agglomeration ◽  
Economic Research ◽  
The Core ◽  
Proposed Model ◽  
Two Indices ◽  
Spatial Trajectory

Integration development of urban agglomeration is important for regional economic research and management. In this paper, a method was proposed to study the integration development of urban agglomeration by trajectory gravity model. It can analyze the gravitational strength of the core city to other cities and characterize the spatial trajectory of its gravitational direction, expansion, etc. quantitatively. The main idea is to do the fitting analysis between the urban axes and the gravitational lines. The correlation coefficients retrieved from the fitting analysis can reflect the correlation of two indices. For the different cities in the same year, a higher value means a stronger relationship. There is a clear gravitational force between the cities when the value above 0.75. For the most cities in different years, the gravitational force between the core city with itself is increasing by years. At the same time, the direction of growth of the urban axes tends to increase in the direction of the gravitational force between cities. There is a clear tendency for the trajectories of the cities to move closer together. The proposed model was applied to the integration development of China Liaoning central urban agglomeration from 2008 to 2016. The results show that cities are constantly attracted to each other through urban gravity.

Enhanced context-aware recommendation using topic modeling and particle swarm optimization

2021 ◽  
pp. 1-16
Author(s):  
Ibtissem Gasmi ◽  
Mohamed Walid Azizi ◽  
Hassina Seridi-Bouchelaghem ◽  
Nabiha Azizi ◽  
Samir Brahim Belhaouari
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
State Of The Art ◽  
Weighting Function ◽  
Contextual Factors ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Pso Algorithm ◽  
Context Aware ◽  
Proposed Model

Context-Aware Recommender System (CARS) suggests more relevant services by adapting them to the user’s specific context situation. Nevertheless, the use of many contextual factors can increase data sparsity while few context parameters fail to introduce the contextual effects in recommendations. Moreover, several CARSs are based on similarity algorithms, such as cosine and Pearson correlation coefficients. These methods are not very effective in the sparse datasets. This paper presents a context-aware model to integrate contextual factors into prediction process when there are insufficient co-rated items. The proposed algorithm uses Latent Dirichlet Allocation (LDA) to learn the latent interests of users from the textual descriptions of items. Then, it integrates both the explicit contextual factors and their degree of importance in the prediction process by introducing a weighting function. Indeed, the PSO algorithm is employed to learn and optimize weights of these features. The results on the Movielens 1 M dataset show that the proposed model can achieve an F-measure of 45.51% with precision as 68.64%. Furthermore, the enhancement in MAE and RMSE can respectively reach 41.63% and 39.69% compared with the state-of-the-art techniques.

Randomness in modified general relativity theory: The stochastic f(R) gravity model

2018 ◽  
Vol 96 (11) ◽  
pp. 1173-1177
Author(s):  
Tomer Shushi
Keyword(s):  
Gravity Field ◽  
Gravity Model ◽  
Gravity Models ◽  
Relativity Theory ◽  
Field Equations ◽  
Important Special Case ◽  
Proposed Model ◽  
Stochastic Space ◽  
Wavefunction Collapse ◽  
New Interpretation

We consider a stochastic modification of the f(R) gravity models, and provide its important properties, including the gravity field equations for the model. We show a prediction in which particles are localized by a system of random gravitational potentials. As an important special case, we investigate a gravity model in the presence of a small stochastic space–time perturbation and provide its gravity field equations. Using the proposed model we examine the stochastic quantum mechanics interpretation, and obtain a novel Schrödinger equation with gravitational potential that is based on diffusion in a gravitational field. Furthermore, we provide a new interpretation to the wavefunction collapse. It seems that the stochastic f(R) gravity model causes decoherence of the spatial superposition state of particles.

Aromatic thiol-modulated Ag overgrowth on gold nanoparticles: tracking the thiol's position in the core–shell nanoparticles

Nanoscale ◽  
2019 ◽  
Vol 11 (37) ◽  
pp. 17471-17477 ◽  
Author(s):  
Jiaqi Chen ◽  
Dejing Meng ◽  
Hui Wang ◽  
Haiyun Li ◽  
Yinglu Ji ◽  
...  
Keyword(s):  
Gold Nanoparticles ◽  
Growth Process ◽  
Shell Growth ◽  
Core Shell ◽  
Internal Reference ◽  
Shell Nanoparticles ◽  
Aromatic Thiol ◽  
The Core ◽  
Spatial Trajectory ◽  
Core Shell Nanoparticles

Using DMAB as the Raman internal reference, the spatial trajectory of modulating 4-ATP molecules was tracked during the shell growth process.

scholarly journals Modelling the Characteristics of Ring-Shaped Magnetoelastic Force Sensor in Mohri’s Configuration

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 266 ◽  
Author(s):  
Anna Ostaszewska-Liżewska ◽  
Roman Szewczyk ◽  
Peter Raback ◽  
Mika Malinen
Keyword(s):  
Experimental Data ◽  
High Sensitivity ◽  
Force Sensor ◽  
Inhomogeneous Distribution ◽  
The Finite Element Method ◽  
The Core ◽  
Magnetoelastic Effect ◽  
Proposed Model ◽  
Distribution Of Stresses ◽  
Good Agreement

Magnetoelastic force sensors exhibit high sensitivity and robustness. One commonly used configuration of force sensor with a ring-shaped core was presented by Mohri at al. In this configuration force is applied in the direction of a diameter of the core. However, due to inhomogeneous distribution of stresses, model of such sensor has not been presented yet. This paper is filling the gap presenting a new method of modelling the magnetoelastic effect, which is especially suitable for the finite element method. The presented implementation of proposed model is in good agreement with experimental data and creates new possibilities of modelling other devices utilizing magnetoelastic effect.

Multi-Radius Density Clustering Algorithm Based on Outlier Factor

2014 ◽  
Vol 472 ◽  
pp. 427-431
Author(s):  
Zong Lin Ye ◽  
Hui Cao ◽  
Li Xin Jia ◽  
Yan Bin Zhang ◽  
Gang Quan Si
Keyword(s):  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Similar Process ◽  
The Core ◽  
Dbscan Algorithm ◽  
Proposed Model ◽  
Density Clustering ◽  
Relationship Of ◽  
Core Points ◽  
The Relationship

This paper proposes a novel multi-radius density clustering algorithm based on outlier factor. The algorithm first calculates the density-similar-neighbor-based outlier factor (DSNOF) for each point in the dataset according to the relationship of the density of the point and its neighbors, and then treats the point whose DSNOF is smaller than 1 as a core point. Second, the core points are used for clustering by the similar process of the density based spatial clustering application with noise (DBSCAN) to get some sub-clusters. Third, the proposed algorithm merges the obtained sub-clusters into some clusters. Finally, the points whose DSNOF are larger than 1 are assigned into these clusters. Experiments are performed on some real datasets of the UCI Machine Learning Repository and the experiments results verify that the effectiveness of the proposed model is higher than the DBSCAN algorithm and k-means algorithm and would not be affected by the parameter greatly.

scholarly journals Research on the Measurement and Path of Urban Agglomeration Growth Effect

2019 ◽  
Vol 11 (19) ◽  
pp. 5179 ◽  
Author(s):  
Jing Han ◽  
Ming Gao ◽  
Yawen Sun
Keyword(s):  
Human Capital ◽  
Panel Data ◽  
Influencing Factors ◽  
Dynamic Panel Data ◽  
Sustainable Growth ◽  
Urban Agglomeration ◽  
Growth Effect ◽  
Continuous Growth ◽  
Urban Agglomerations ◽  
The Core

This paper employed dynamic generalized method of moment methods to measure the growth effect of 202 prefecture-level cities covered by 14 national urban agglomerations in China from 2007 to 2016. Based on this, this paper further explored the main factors affecting the growth of urban agglomeration and the path to achieving sustainable growth from the aspects of system, technology, structure, and influencing factors, and used the dynamic panel data (DPD) model and threshold panel data to empirically test the growth effect of urban agglomerations. The empirical results showed the following. (1) From the perspective of influencing factors, the improvement of technology and the increase in technology expenditure had a good growth effect on urban agglomeration, and this growth effect became more and more significant as the economic development level within the urban agglomeration narrowed; moreover, the increase of the agglomeration degree could alleviate the negative externality caused by the expansion of the urban scale and produce the dispersion effect to relieve the pressure of urban agglomeration. (2) From the results of the growth effect of urban agglomerations, the growth effect of multi-core urban agglomerations was more significant than that of single-core and dual-core urban agglomerations, and technology, agglomeration degree, foreign direct investment and human capital all significantly promoted the growth of urban agglomerations. Compared with trans-provincial urban agglomerations, provincial urban agglomerations have less resistance due to administrative jurisdiction, and the growth effect was obvious. (3) From the perspective of regional differences, the growth momentum of urban agglomerations in the eastern region was significantly stronger than that in the central and western regions, and the growth effect of agglomeration degree, technology, and human capital on urban agglomeration were all stronger than that in the central and western regions. Considering that the spatial distance between the edge cities and the central cities of the urban agglomeration will have an important impact on the overall growth of the urban agglomeration, this paper then used the panel threshold method to deeply discuss the influence mechanism and path dependence of the agglomeration degree on the growth of urban agglomerations. The results showed that within a certain spatial scale, a higher agglomeration degree of an urban agglomeration creates a stronger radiation effect of the core city and more obvious growth momentum of the urban agglomeration. In the future development of urban agglomerations, it is necessary to clarify the functions of the core city, vigorously develop new technologies, strengthen the construction of the core city as well as maximize its radiation and driving effect on the surrounding cities. Meanwhile, the government should improve transportation, increase the construction of urban expressways and railways, strengthen the connection between cities, strengthen regional integration and cooperation, and give play to the role of human capital in promoting growth to achieve the stable and continuous growth of urban agglomerations.

So-Far Incompatibilism and the So-Far Consequence Argument

2006 ◽  
Vol 73 (1) ◽  
pp. 163-178
Author(s):  
Stephen Hetherington
Keyword(s):  
Laws Of Nature ◽  
Main Idea ◽  
Causal Determinism ◽  
Essential Point ◽  
Consequence Argument ◽  
Alfred Mele ◽  
The Core ◽  
Freedom Of Action

The consequence argument is at the core of contemporary incompatibilism about causal determinism and freedom of action. Yet Helen Beebee and Alfred Mele have shown how, on a Humean conception of laws of nature, the consequence argument is unsound. Nonetheless, this paper describés how, by generalising their main idea, we may restore the essential point and force (whatever that might turn out to be) of the consequence argument. A modified incompatibilist argument — which will be called the consequence argument — may thus be derived.

scholarly journals An Eigenvector Centrality for Multiplex Networks with Data

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 763 ◽  
Author(s):  
Francisco Pedroche ◽  
Leandro Tortosa ◽  
José F. Vicent
Keyword(s):  
Complex Systems ◽  
Network Theory ◽  
Essential Feature ◽  
Eigenvector Centrality ◽  
Centrality Measure ◽  
Multiplex Networks ◽  
Primitive Matrix ◽  
The Core ◽  
Proposed Model ◽  
Node Classification

Networks are useful to describe the structure of many complex systems. Often, understanding these systems implies the analysis of multiple interconnected networks simultaneously, since the system may be modelled by more than one type of interaction. Multiplex networks are structures capable of describing networks in which the same nodes have different links. Characterizing the centrality of nodes in multiplex networks is a fundamental task in network theory. In this paper, we design and discuss a centrality measure for multiplex networks with data, extending the concept of eigenvector centrality. The essential feature that distinguishes this measure is that it calculates the centrality in multiplex networks where the layers show different relationships between nodes and where each layer has a dataset associated with the nodes. The proposed model is based on an eigenvector centrality for networks with data, which is adapted according to the idea behind the two-layer approach PageRank. The core of the centrality proposed is the construction of an irreducible, non-negative and primitive matrix, whose dominant eigenpair provides a node classification. Several examples show the characteristics and possibilities of the new centrality illustrating some applications.

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