Search Results (6)

Logistics services are integral to urban economic activity, and delving into the spatial distribution traits and evolutionary pathways of various kinds of logistics service node facilities (LSNF) is markedly valuable for understanding a city’s functional spatial makeup and refining the spatial layout of logistics services. This study quantitatively and qualitatively analyzes the spatial congregation and spreading characteristics of diverse LSNFs in Wuhan in 2011, 2014, 2017, and 2020, employing kernel density analysis, average nearest neighbor index, mean center, and distance distribution frequency, seeking to characterize the spatial evolution characteristics of LSNF, alongside examining the trends in distances to city cores, principal adjoining roads, and production and consumption sites. The following conclusions were made: (1) Between 2011 and 2020, various types of LSNFs in Wuhan experienced a pattern characterized by the noticeable coexistence of spatial expansion and agglomeration, particularly visible after 2014. The degree of agglomeration is classified in a descending order as follows: CWC, STN, PSN, and PDN. (2) An “absolute diffusion” phenomenon characterizes the distribution of distances between various kinds of LSNFs and city cores or neighboring roads, with the lion’s share of high-frequency distribution zones spreading beyond city cores by 5–10 km, and a majority of the LSNFs being situated within 1 km from adjacent roads. (3) While the LSNF collective exhibits a stronger tendency towards the consumption facet, it reflects a surrounding of industrial production sites on the production facet and locations of manufactured goods consumption on the consumption facet, followed by locations of agricultural product consumption and comprehensive consumption sites. Full article

With expansion of city scale, the issue of public transport systems will become prominent. For single-swipe buses, the traditional method of obtaining section passenger flow is to rely on surveillance video identification or manual investigation. This paper adopts a new method: collecting wireless signals from mobile terminals inside and outside the bus by installing six Wi-Fi probes in the bus, and use machine learning algorithms to estimate passenger flow of the bus. Five features of signals were selected, and then the three machine learning algorithms of Random Forest, K-Nearest Neighbor, and Support Vector Machines were used to learn the data laws of signal features. Because the signal strength was affected by the complexity of the environment, a strain function was proposed, which varied with the degree of congestion in the bus. Finally, the error between the average of estimation result and the manual survey was 0.1338. Therefore, the method proposed is suitable for the passenger flow identification of single-swiping buses in small and medium-sized cities, which improves the operational efficiency of buses and reduces the waiting pressure of passengers during the morning and evening rush hours in the future. Full article

It is well known that dynamical processes on complex networks are influenced by the degree correlations. A common way to take these into account in a mean-field approach is to consider the function $k_{nn}\left(k\right)$ (average nearest neighbors degree). We re-examine the standard choices of $k_{nn}$ for scale-free networks and a new family of functions which is independent from the simple ansatz $k_{nn}\propto k^{\alpha }$ but still displays a remarkable scale invariance. A rewiring procedure is then used to explicitely construct synthetic networks using the full correlation $P(h\mid k)$ from which $k_{nn}$ is derived. We consistently find that the $k_{nn}$ functions of concrete synthetic networks deviate from ideal assortativity or disassortativity at large k. The consequences of this deviation on a diffusion process (the network Bass diffusion and its peak time) are numerically computed and discussed for some low-dimensional samples. Finally, we check that although the $k_{nn}$ functions of the new family have an asymptotic behavior for large networks different from previous estimates, they satisfy the general criterium for the absence of an epidemic threshold. Full article

We generate correlated scale-free networks in the configuration model through a new rewiring algorithm that allows one to tune the Newman assortativity coefficient r and the average degree of the nearest neighbors K (in the range $-1\le r\le 1$ , $K\ge ⟨k⟩$ ). At each attempted rewiring step, local variations $\Delta r$ and $\Delta K$ are computed and then the step is accepted according to a standard Metropolis probability $exp(\pm \Delta r/T)$ , where T is a variable temperature. We prove a general relation between $\Delta r$ and $\Delta K$ , thus finding a connection between two variables that have very different definitions and topological meaning. We describe rewiring trajectories in the r-K plane and explore the limits of maximally assortative and disassortative networks, including the case of small minimum degree ( $k_{min}\ge 1$ ), which has previously not been considered. The size of the giant component and the entropy of the network are monitored in the rewiring. The average number of second neighbors in the branching approximation ${\overline{z}}_{2,B}$ is proven to be constant in the rewiring, and independent from the correlations for Markovian networks. As a function of the degree, however, the number of second neighbors gives useful information on the network connectivity and is also monitored. Full article

High temporal resolution remote sensing satellite data can be used to collect vegetation phenology observations over regional and global scales. Logistic and polynomial functions are the most widely used methods for fitting time series normalized difference vegetation index (NDVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). Furthermore, the maximum in the curvature of the fitted curve is usually considered as the spring green-up date. However, the existing green-up date calculation methods have low accuracy for sparse vegetation. This paper proposes an improved green-up date calculation method using a coupled model and anomalous point detection (CMAPD). This model is based on a combination of logistic and polynomial functions, which is used to fit time series vegetation index. Anomalous values were identified using the nearest neighbor algorithm, and these values were corrected by the combination of growing degree-days (GDD) and land use type. Then, the trends and spatial patterns of green-up date was analyzed in the Sanjiangyuan area. The results show that the coupled model fit the time series data better than a single logistic or polynomial function. Besides, the anomalous point detection method properly controlled the green-up date within the local threshold, and could reflect green-up date more accurately. In addition, a weak statistically significant advance trend for average vegetation green-up date was observed from 2000 to 2016. However, in 10.4% of the study area, the the green-up date has significant advanced. Regression analysis showed that the green-up date is correlated to elevation: the green-up date is clearly later at higher elevations. Full article

Molecular docking is an important method for the research of protein-protein interaction and recognition. A protein can be considered as a network when the residues are treated as its nodes. With the contact energy between residues as link weight, a weighted residue network is constructed in this paper. Two weighted parameters (strength and weighted average nearest neighbors’ degree) are introduced into this model at the same time. The stability of a protein is characterized by its strength. The global topological properties of the protein-protein complex are reflected by the weighted average nearest neighbors’ degree. Based on this weighted network model and these two parameters, a new docking scoring function is proposed in this paper. The scoring and ranking for 42 systems’ bound and unbounded docking results are performed with this new scoring function. Comparing the results obtained from this new scoring function with that from the pair potentials scoring function, we found that this new scoring function has a similar performance to the pair potentials on some items, and this new scoring function can get a better success rate. The calculation of this new scoring function is easy, and the result of its scoring and ranking is acceptable. This work can help us better understand the mechanisms of protein-protein interactions and recognition. Full article