Depicting urban boundaries from a mobility network of spatial interactions: A case study of Great Britain with geo-located Twitter data

Existing urban boundaries are usually defined by government agencies for administrative, economic, and political purposes. Defining urban boundaries that consider socio-economic relationships and citizen commute patterns is important for many aspects of urban and regional planning. In this paper, we describe a method to delineate urban boundaries based upon human interactions with physical space inferred from social media. Specifically, we depicted the urban boundaries of Great Britain using a mobility network of Twitter user spatial interactions, which was inferred from over 69 million geo-located tweets. We define the non-administrative anthropographic boundaries in a hierarchical fashion based on different physical movement ranges of users derived from the collective mobility patterns of Twitter users in Great Britain. The results of strongly connected urban regions in the form of communities in the network space yield geographically cohesive, non-overlapping urban areas, which provide a clear delineation of the non-administrative anthropographic urban boundaries of Great Britain. The method was applied to both national (Great Britain) and municipal scales (the London metropolis). While our results corresponded well with the administrative boundaries, many unexpected and interesting boundaries were identified. Importantly, as the depicted urban boundaries exhibited a strong instance of spatial proximity, we employed a gravity model to understand the distance decay effects in shaping the delineated urban boundaries. The model explains how geographical distances found in the mobility patterns affect the interaction intensity among different non-administrative anthropographic urban areas, which provides new insights into human spatial interactions with urban space.Comment: 32 pages, 7 figures, International Journal of Geographic Information Scienc

Remanufacturing process for used automotive electronic control components in China

China's recycling roadmap and technology scheme for used automotive electronic control components are investigated. The mathematical analysis model of the remanufacturing process is established on the basis of stochastic network technology, as well as on the graphical evaluation and review technique (GERT). In addition, the calculation method used for estimating single-product remanufacturing time is examined. The objective of this study is to determine the probability of success for the remanufacturing of used automotive electronic control components and remanufacturing time. On the basis of experimental parameters, we simulate the remanufacturing process using the Monte Carlo simulation in Crystal Ball. Compared with the result of the GERT model (8.5114 h), the simulation error rate is 0.225%. This consistency in results indicates that both the stochastic network model and Crystal Ball can accurately simulate the remanufacturing process of used automotive electronic control components, making these techniques feasible approaches for such processes. Aside from numerical experiments on and sensitivity analyses of key processes, the relationship between total remanufacturing time and five influencing factors is identified. Total remanufacturing time can be significantly reduced by optimizing the key processes. The optimization methods are also investigated

SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery

As an unsupervised dimensionality reduction method, principal component analysis (PCA) has been widely considered as an efficient and effective preprocessing step for hyperspectral image (HSI) processing and analysis tasks. It takes each band as a whole and globally extracts the most representative bands. However, different homogeneous regions correspond to different objects, whose spectral features are diverse. It is obviously inappropriate to carry out dimensionality reduction through a unified projection for an entire HSI. In this paper, a simple but very effective superpixelwise PCA approach, called SuperPCA, is proposed to learn the intrinsic low-dimensional features of HSIs. In contrast to classical PCA models, SuperPCA has four main properties. (1) Unlike the traditional PCA method based on a whole image, SuperPCA takes into account the diversity in different homogeneous regions, that is, different regions should have different projections. (2) Most of the conventional feature extraction models cannot directly use the spatial information of HSIs, while SuperPCA is able to incorporate the spatial context information into the unsupervised dimensionality reduction by superpixel segmentation. (3) Since the regions obtained by superpixel segmentation have homogeneity, SuperPCA can extract potential low-dimensional features even under noise. (4) Although SuperPCA is an unsupervised method, it can achieve competitive performance when compared with supervised approaches. The resulting features are discriminative, compact, and noise resistant, leading to improved HSI classification performance. Experiments on three public datasets demonstrate that the SuperPCA model significantly outperforms the conventional PCA based dimensionality reduction baselines for HSI classification. The Matlab source code is available at https://github.com/junjun-jiang/SuperPCAComment: 13 pages, 10 figures, Accepted by IEEE TGR

Cooperative "folding transition" in the sequence space facilitates function-driven evolution of protein families

In the protein sequence space, natural proteins form clusters of families which are characterized by their unique native folds whereas the great majority of random polypeptides are neither clustered nor foldable to unique structures. Since a given polypeptide can be either foldable or unfoldable, a kind of "folding transition" is expected at the boundary of a protein family in the sequence space. By Monte Carlo simulations of a statistical mechanical model of protein sequence alignment that coherently incorporates both short-range and long-range interactions as well as variable-length insertions to reproduce the statistics of the multiple sequence alignment of a given protein family, we demonstrate the existence of such transition between natural-like sequences and random sequences in the sequence subspaces for 15 domain families of various folds. The transition was found to be highly cooperative and two-state-like. Furthermore, enforcing or suppressing consensus residues on a few of the well-conserved sites enhanced or diminished, respectively, the natural-like pattern formation over the entire sequence. In most families, the key sites included ligand binding sites. These results suggest some selective pressure on the key residues, such as ligand binding activity, may cooperatively facilitate the emergence of a protein family during evolution. From a more practical aspect, the present results highlight an essential role of long-range effects in precisely defining protein families, which are absent in conventional sequence models.Comment: 13 pages, 7 figures, 2 tables (a new subsection added

Novel Phases of Semi-Conducting Silicon Nitride Bilayer: A First-Principle Study

In this paper, we have predicted the stabilities of several two-dimensional phases of silicon nitride, which we name as \alpha-phase, \beta-phase, and \gamma-phase, respectively. Both \alpha- and \beta-phases has formula Si$_{2}$N$_{2}$, and are consisted of two similar layer of buckled SiN sheet. Similarly, \gamma-phase is consisted of two puckered SiN sheets. For these phases, the two layers are connected with Si-Si covalent bonds. Transformation between \alpha- and \beta-phases is difficult because of the high energy barrier. Phonon spectra of both \alpha- and \beta-phase suggest their thermodynamic stabilities, because no phonon mode with imaginary frequency is present. By Contrast, \gamma-phase is unstable because phonon modes with imaginary frequencies are found along \Gamma-Y path in the Brilliouin zone. Both \alpha- and \beta-phase are semiconductor with narrow fundamental indirect band gap of 1.7eV and 1.9eV, respectively. As expected, only s and p orbitals in the outermost shells contribute the band structures. The p$_{z}$ orbitals have greater contribution near the Fermi level. These materials can easily exfoliate to form 2D structures, and may have potential electronic applications.Comment: 9 pages, 6 figure

Microbial responses to inorganic nutrient amendment overridden by warming: Consequences on soil carbon stability.

Eutrophication and climate warming, induced by anthropogenic activities, are simultaneously occurring worldwide and jointly affecting soil carbon stability. Therefore, it is of great interest to examine whether and how they interactively affect soil microbial community, a major soil carbon driver. Here, we showed that climate warming, simulated by southward transferring Mollisol soil in agricultural ecosystems from the cold temperate climate zone (N) to warm temperate climate (C) and subtropical climate zone (S), decreased soil organic matter (SOM) by 6%-12%. In contrast, amendment with nitrogen, phosphorus and potassium enhanced plant biomass by 97% and SOM by 6% at the N site, thus stimulating copiotrophic taxa but reducing oligotrophic taxa in relative abundance. However, microbial responses to nutrient amendment were overridden by soil transfer in that nutrient amendment had little effect at the C site but increased recalcitrant carbon-degrading fungal Agaricomycetes and Microbotryomycetes taxa derived from Basidiomycota by 4-17 folds and recalcitrant carbon-degrading genes by 23%-40% at the S site, implying a possible priming effect. Consequently, SOM at the S site was not increased by nutrient amendment despite increased plant biomass by 108%. Collectively, we demonstrate that soil transfer to warmer regions overrides microbial responses to nutrient amendment and weakens soil carbon sequestration

Modelling and Backstepping Motion Control of the Aircraft Skin Inspection Robot

Aircraft skin health concerns whether the aircraft can fly safely. In this paper, an improved mechanical structure of the aircraft skin inspection robot was introduced. Considering that the aircraft skin surface is a curved environment, we assume that the curved environment is equivalent to an inclined plane with a change in inclination. Based on this assumption, the Cartesian dynamics model of the robot is established using the Lagrange method. In order to control the robot’s movement position accurately, a position backstepping control scheme for the aircraft skin inspection robot was presented. According to the dynamic model and taking into account the problems faced by the robot during its movement, a position constrained controller of the aircraft skin inspection robot is designed using the barrier Lyapunov function. Aiming at the disturbances in the robot, we adopt a fuzzy system to approximate the unknown dynamics related with system states. Finally, the simulation results of the designed position constrained controller were compared with the sliding mode controller, and prove the validity of the position constrained controller