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Search Results: 1 - 10 of 118 matches for " Zhengyi "
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Urban Housing Prices, Labor Mobility and the Development of Urban High-Tech Industries
—An Empirical Analysis Based on Panel Data in the Pearl River Delta Region
 [PDF]

Zhengyi Niu
Modern Economy (ME) , 2019, DOI: 10.4236/me.2019.103070
Abstract: This paper proposes two hypotheses that urban housing prices inhibit urban labor mobility and urban high-tech industry development by introducing the extended CP model of housing prices. On this basis, using the data of the 9-city data of the Pearl River Delta from 2001 to 2015 to conduct empirical analysis and testing through the fixed-effects model, it is found that the increase of relative housing prices in cities will promote urban labor inflows and inhibit the development of urban high-tech industries. Factors such as education level, medical level, and government budget expenditure will, to a certain extent, strengthen the role of relative housing prices in promoting labor inflows and weaken the inhibition of urban housing prices on high-tech industries.
biodiversityasamajorroleinhumanecology
wu,zhengyi
生物多样性 , 1994,
Abstract:
Evolution of Internal Crack in BCC Fe under Compressive Loading  [PDF]
Dongbin Wei, Zhengyi Jiang, Jingtao Han
Journal of Modern Physics (JMP) , 2012, DOI: 10.4236/jmp.2012.310197
Abstract: A molecular dynamics model has been developed to investigate the evolution of the internal crack of nano scale during heating or compressive loading in BCC Fe. The initial configuration does not contain any pre-existing dislocations. In the case of heating, temperature shows a significant effect on crack evolution and the critical temperature at which the crack healing becomes possible is 673 K. In the case of compressive loading, the crack can be healed at 40 K at a loading rate 0.025 × 1018 Pa·m1/2/s in 6 × 10-12 s. The diffusion of Fe atoms into the crack area results in the healing process. However, dislocations and voids appear during healing and their positions change continuously.
Symmetry Reduction and Explicit Solutions of the (2 + 1)-Dimensional DLW Equation  [PDF]
Zhengyi Ma, Jinxi Fei, Yuanming Chen
Applied Mathematics (AM) , 2014, DOI: 10.4236/am.2014.520304
Abstract: Utilizing the Clarkson-Kruskal direct method, the symmetry of the (2 + 1)-dimensional dispersive long wave equation is derived. From which, through solving the characteristic equations, four types of the explicit reduction solutions that related the hyperbolic tangent function are obtained. Finally, several soliton excitations are depicted from one of the solutions.
Sufficient and Necessary Center Conditions for the Poincaré Systems
Jinya Xu,Zhengyi Lu
Journal of Applied Mathematics , 2011, DOI: 10.1155/2011/762762
Abstract: We obtain sufficient and necessary center conditions for the Poincaré system . The necessity of the condition is derived from the first 2 focal values by symbolic computation with Maple, and the sufficiency is proved by Volokitin's method. 1. Introduction Research on Hilbert’s sixteenth problem [1, 2] in general usually proceeds by the investigation on specific classes of polynomial systems. Much effort has been devoted in recent years to the investigation of various systems with cubic or quintic polynomials for the center problem [3–9]. We are interested in a certain family of polynomial systems of the form with , and , where is a homogeneous polynomial of degree . The centers of these systems are called uniformly isochronous centers [10]. The case in which is of degree has been investigated in [10, 11]. In the nonhomogeneous case [12], the pioneering studies mainly focus on the systems with [13], [14], [15], ( and only one not equal to zero, for ) [16]. In this paper, we consider the system (1.1) with , where and , are real constants. We call the systems above as . In [17, 18], the authors prove that in the system the uniformly isochronous centers are time reversible, which was done by imposing the existence of a transversal commutator. However, for any concrete , it is difficult to obtain explicit form of the conditions for the origin to be a center for the system due to increasing expansion of computation during the management of large expressions. Sufficient and necessary conditions for the origin to be a center are obtained in Volokitin [19, 20] for and 3 and in Xu and Lu [21] for . In this paper, we shall consider the system and obtain sufficient and necessary conditions for the origin to be a center. In Section 2, the main result and the basic method are stated, and the necessary and sufficient center conditions are proved in Sections 3 and 4, respectively. In Section 5, sufficient and necessary center conditions for systems and are given in tight form of polynomial systems. 2. Center Condition In this section, the technique in [22] is adopted, and a recursive formula for focal values is obtained. For , system takes the form In polar coordinate system, system (2.1) can be written as where and are the functions of : Let be the solution of (2.2) with . For small enough, we write where . Substituting (2.4) into (2.2) and equating the coefficients of , we obtain For (2.5), we can easily obtain . Thus we can deduce where The value of is called the th focal value. Let ; then the origin is a center for system (2.1) if and only if In fact, we have where
Tetra-μ3-iodido-tetrakis{[ethyl 2-(1H-benzimidazol-1-yl)acetate-κN3]copper(I)}
Lili Yang,Zhengyi Zhang
Acta Crystallographica Section E , 2012, DOI: 10.1107/s160053681202257x
Abstract: The complex molecule of the tetranuclear cubane-type title compound, [Cu4I4(C11H12N2O2)4], has crystallographically imposed fourfold inversion symmetry. The CuI ions are coordinated in a distorted tetrahedral geometry by an N atom of a benzimidazole ring system and three μ3-iodide ions, forming a Cu4I4 core. In the crystal, complex molecules are connected into a three-dimensional network by C—H...O hydrogen bonds involving H and O atoms of adjacent ethoxycarbonyl groups.
Predicting Ambulance Demand: a Spatio-Temporal Kernel Approach
Zhengyi Zhou,David S. Matteson
Statistics , 2015,
Abstract: Predicting ambulance demand accurately at fine time and location scales is critical for ambulance fleet management and dynamic deployment. Large-scale datasets in this setting typically exhibit complex spatio-temporal dynamics and sparsity at high resolutions. We propose a predictive method using spatio-temporal kernel density estimation (stKDE) to address these challenges, and provide spatial density predictions for ambulance demand in Toronto, Canada as it varies over hourly intervals. Specifically, we weight the spatial kernel of each historical observation by its informativeness to the current predictive task. We construct spatio-temporal weight functions to incorporate various temporal and spatial patterns in ambulance demand, including location-specific seasonalities and short-term serial dependence. This allows us to draw out the most helpful historical data, and exploit spatio-temporal patterns in the data for accurate and fast predictions. We further provide efficient estimation and customizable prediction procedures. stKDE is easy to use and interpret by non-specialized personnel from the emergency medical service industry. It also has significantly higher statistical accuracy than the current industry practice, with a comparable amount of computational expense.
Predicting Melbourne Ambulance Demand using Kernel Warping
Zhengyi Zhou,David S. Matteson
Statistics , 2015,
Abstract: Predicting ambulance demand accurately in fine resolutions in space and time is critical for ambulance fleet management and dynamic deployment. Typical challenges include data sparsity at high resolutions and the need to respect complex urban spatial domains. To provide spatial density predictions for ambulance demand in Melbourne, Australia as it varies over hourly intervals, we propose a predictive spatio-temporal kernel warping method. To predict for each hour, we build a kernel density estimator on a sparse set of the most similar data from relevant past time periods (labeled data), but warp these kernels to a larger set of past data irregardless of time periods (point cloud). The point cloud represents the spatial structure and geographical characteristics of Melbourne, including complex boundaries, road networks, and neighborhoods. Borrowing from manifold learning, kernel warping is performed through a graph Laplacian of the point cloud and can be interpreted as a regularization towards, and a prior imposed, for spatial features. Kernel bandwidth and degree of warping are efficiently estimated via cross-validation, and can be made time- and/or location-specific. Our proposed model gives significantly more accurate predictions compared to a current industry practice, an unwarped kernel density estimation, and a time-varying Gaussian mixture model.
Fast Detection Method in Cooperative Cognitive Radio Networks
Zhengyi Li,Lin Liu,Chi Zhou
International Journal of Digital Multimedia Broadcasting , 2010, DOI: 10.1155/2010/160462
Abstract: Cognitive Radio (CR) technology improves the utilization of spectrum highly via opportunistic spectrum sharing, which requests fast detection as the spectrum utilization is dynamic. Taking into consideration the characteristic of wireless channels, we propose a fast detection scheme for a cooperative cognitive radio network, which consists of multiple CRs and a central control office. Specifically, each CR makes individual detection decision using the sequential probability ratio test combined with Neyman Pearson detection with respect to a specific observation window length. The proposed method upper bounds the detection delay. In addition, a weighted out of fusion rule is also proposed for the central control office to reach fast global decision based on the information collected from CRs, with more weights assigned for CRs with good channel conditions. Simulation results show that the proposed scheme can achieve fast detection while maintaining the detection accuracy. 1. Introduction In the traditional management of licensed spectrum, users usually pay and have the exclusive access of spectrum with a certain level of Quality of Service (QoS) guarantee. On one hand, the spectrum is getting more and more crowded as the number of wireless devices increases drastically. However, on the other hand, the utilization of spectrum at any given time is low. Figure 1 shows a measurement of 30M–3GHz spectrum utilization. We can see that a lot of spectrum bands are vacant. Therefore, it would be efficient to allow unlicensed users to share spectrum with licensed users by using a vacant frequency band. Figure 1: A measurement of 30M–3GHz spectrum utilization. Cognitive Radio technology is developed to utilize these white spaces intelligently [1, 2]. FCC Spectrum Policy Task Force published a new spectrum management policy, open access or license exempted model, in 2002, to allow unlicensed user to use the opportunistic spectrum. As the transition from analog to digital television is complete, there are vacant channels (white spaces) in every media market [3]. Accordingly, the FCC announced a Notice of Proposed Rule Making (NPRM) on 13 May 2004, which proposed “to allow unlicensed radio transmitters to operate in the broadcast TV spectrum at locations where that spectrum is not being used”. Seen as the secondary user, the cognitive radio (CR) must avoid interfering with primary user (PU), that is, licensed user, while sharing the licensed band with the PU. Therefore, cognitive radio needs to sense the spectrum to detect the existence of PU, identify the white spaces
Backpropagation-Based Cooperative Localization of Primary User for Avoiding Hidden-Node Problem in Cognitive Networks
Lin Liu,Zhengyi Li,Chi Zhou
International Journal of Digital Multimedia Broadcasting , 2010, DOI: 10.1155/2010/905321
Abstract: Cognitive radio (CR) is a technology to implement opportunistic spectrum sharing to improve the spectrum utilization. However, there exists a hidden-node problem, which can be a big challenge to solve especially when the primary receiver is passive listening. We aim to provide a solution to the hidden-node problem for passive-listening receiver based on cooperation of multiple CRs. Specifically, we consider a cooperative GPS-enabled cognitive network. Once the existence of PU is detected, a localization algorithm will be employed to first estimate the path loss model for the environment based on backpropagation method and then to locate the position of PU. Finally, a disable region is identified taking into account the communication range of both the PU and the CR. The CRs within the disabled region are prohibited to transmit in order to avoid interfering with the primary receiver. Both analysis and simulation results are provided. 1. Introduction As more devices go wireless, spectrum becomes more and more crowded. Study of spectrum utilization, however, reveals that not all the spectrum is in use for all the time. Enforcement Bureau of Federal Communications Commission (FCC) measures the spectrum usage in Atlanta, Chicago, and so forth, and the study shows that only 5%–10% of the spectrum is used (up to 100?GHz) on the average. DARPA study reveals that only 2% of the allocated spectrum is used at any given time. Therefore, there is a potential to make efficient use of the unused spectrum without interfering with primary users (PUs) so that the spectrum utilization can be improved and more users can be supported. Cognitive radio (CR) is a technology to implement opportunistic spectrum sharing to improve the spectrum utilization [1–3]. CR can be applied in civilian applications, law enforcement, as well as military applications. For CR, spectrum sensing is the first step but very crucial to the success. Only when the electromagnetic environment is thoroughly understood, it can be decided over which frequency to transmit and how to transmit. As the cognitive radio is seen as the secondary user to share the licensed band with the PU, they must avoid or control the interference to potential PU. However, as a radio device, a single CR may suffer severe shadowing or multipath fading with respect to primary transmitter so that it cannot detect the existence of primary transmitter even in its vicinities. In addition, there exists a hidden-node problem, in which a CR may be too far from the transmitter to detect the existence of the PU, but close to the primary
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