Detecting the Spatially Non-Stationary Relationships between Housing Price and Its Determinants in China: Guide for Housing Market Sustainability

Given the rapidly developing processes in the housing market of China, the significant regional difference in housing prices has become a serious issue that requires a further understanding of the underlying mechanisms. Most of the extant regression models are standard global modeling techniques that do not take spatial non-stationarity into consideration, thereby making them unable to reflect the spatial nature of the data and introducing significant bias into the prediction results. In this study, the geographically weighted regression model (GWR) was applied to examine the local association between housing price and its potential determinants, which were selected in view of the housing supply and demand in 338 cities across mainland China. Non-stationary relationships were obtained, and such observation could be summarized as follows: (1) the associations between land price and housing price are all significant and positive yet having different magnitudes; (2) the relationship between supplied amount of residential land and housing price is not statistically significant for 272 of the 338 cities, thereby indicating that the adjustment of supplied land has a slight effect on housing price for most cities; and (3) the significance, direction, and magnitude of the relationships between the other three factors (i.e., urbanization rate, average wage of urban employees, proportion of renters) and housing price vary across the 338 cities. Based on these findings, this paper discusses some key issues relating to the spatial variations, combined with local economic conditions and suggests housing regulation policies that could facilitate the sustainable development of the Chinese housing market

On the Research and Innovation of Sanxingdui Culture

The archaeological achievements of Sanxingdui culture excavation have been reported frequently. Each stage of Sanxindui excavation progress can attract the attention of the world and become the “top stream” of archaeology. The ancient Shu civilization, represented by Sanxingdui, has constantly collided, exchanged, integrated and learned from the outside world, and has become an important historical witness of the pluralistic, inclusive and continuous Chinese civilization. The numerous unprecedented artifacts newly discovered by Sanxingdui archaeology provide excellent research materials for exploring the origin of ancient Shu civilization. This paper briefly combines the course of Sanxingdui archaeological discovery, which helps readers deeply comprehends the cultural connotation hidden behind it, and then studies the significance of Sanxingdui culture to Bashu culture and Chinese civilization. The purpose is to follow the trend and put forward new methods and paths for the development of Sanxingdui culture, promote the innovative development of Sanxingdui culture at home and abroad

Information entropy-based intention prediction of aerial targets under uncertain and incomplete information

© 2020 by authors. To improve the effectiveness of air combat decision-making systems, target intention has been extensively studied. In general, aerial target intention is composed of attack, surveillance, penetration, feint, defense, reconnaissance, cover and electronic interference and it is related to the state of a target in air combat. Predicting the target intention is helpful to know the target actions in advance. Thus, intention prediction has contributed to lay a solid foundation for air combat decision-making. In this work, an intention prediction method is developed, which combines the advantages of the long short-term memory (LSTM) networks and decision tree. The future state information of a target is predicted based on LSTM networks from real-time series data, and the decision tree technology is utilized to extract rules from uncertain and incomplete priori knowledge. Then, the target intention is obtained from the predicted data by applying the built decision tree. With a simulation example, the results show that the proposed method is effective and feasible for state prediction and intention recognition of aerial targets under uncertain and incomplete information. Furthermore, the proposed method can make contributions in providing direction and aids for subsequent attack decision-makin

Salient Object Detection in RGB-D Videos

Given the widespread adoption of depth-sensing acquisition devices, RGB-D videos and related data/media have gained considerable traction in various aspects of daily life. Consequently, conducting salient object detection (SOD) in RGB-D videos presents a highly promising and evolving avenue. Despite the potential of this area, SOD in RGB-D videos remains somewhat under-explored, with RGB-D SOD and video SOD (VSOD) traditionally studied in isolation. To explore this emerging field, this paper makes two primary contributions: the dataset and the model. On one front, we construct the RDVS dataset, a new RGB-D VSOD dataset with realistic depth and characterized by its diversity of scenes and rigorous frame-by-frame annotations. We validate the dataset through comprehensive attribute and object-oriented analyses, and provide training and testing splits. Moreover, we introduce DCTNet+, a three-stream network tailored for RGB-D VSOD, with an emphasis on RGB modality and treats depth and optical flow as auxiliary modalities. In pursuit of effective feature enhancement, refinement, and fusion for precise final prediction, we propose two modules: the multi-modal attention module (MAM) and the refinement fusion module (RFM). To enhance interaction and fusion within RFM, we design a universal interaction module (UIM) and then integrate holistic multi-modal attentive paths (HMAPs) for refining multi-modal low-level features before reaching RFMs. Comprehensive experiments, conducted on pseudo RGB-D video datasets alongside our RDVS, highlight the superiority of DCTNet+ over 17 VSOD models and 14 RGB-D SOD models. Ablation experiments were performed on both pseudo and realistic RGB-D video datasets to demonstrate the advantages of individual modules as well as the necessity of introducing realistic depth. Our code together with RDVS dataset will be available at https://github.com/kerenfu/RDVS/

Shadow Thermodynamics of AdS Black Hole with the Nonlinear Electrodynamics Term

In this paper, we have creatively employed the shadow radius to study the thermodynamics of a charged AdS black hole with a nonlinear electrodynamics(NLED) term. First, the connection between the shadow radius and event horizon is constructed with the aid of the geodesic analysis. It turns out that the black hole shadow radius shows a positive correlation as a function of the event horizon radius. Then in the shadow context, we found that the black hole temperature and heat capacity can be presented by the shadow radius. And further analysis shows that the shadow radius can do as well as the event horizon in revealing black hole phase transition process. In this sense, we constructed the thermal profile of the charged AdS black hole with inclusion of the NLED effect. In P < Pc case, it is found that the N-type trend of the temperature given by the shadow radius is always coincide with that obtained by using the event horizon. So, we can concluded for the charged AdS black hole that the phase transition process can be intuitively presented as the thermal profile in the shadow context. Finally, the effects of NLED have been carefully analysed through out the paper.Comment: 17 pages, 21 figure

Transversal loading sensor based on tunable beat frequency of a dual-wavelength fiber laser

Microwave signal generation by using the photonic beating from a phase-shift fiber Bragg grating (PS-FBG)-based dual-wavelength laser is proposed and experimentally demonstrated. The dual-wavelength laser is formed by a linear cavity, in which a PS-FBG is used as a dual-wavelength selective component. Transversal loading on the PS-FBG enhances the birefringence of the optical fiber and consequently makes the transmission peak of the PS-FBG splitting into two sharp transmission peaks of orthogonal polarizations. The wavelength spacing between the two transmission peaks increases with the transversal loading on the PS-FBG, thus making the polarization beating frequency increase. This property is exploited in a transversal loading sensor

Defect formation of lytic peptides in lipid membranes and their influence on the thermodynamic properties of the pore environment

We present an experimental study of the pore formation processes of small amphipathic peptides in model phosphocholine lipid membranes. We used atomic force microscopy to characterize the spatial organization and structure of alamethicin- and melittin- induced defects in lipid bilayer membranes and the influence of the peptide on local membrane properties. Alamethicin induced holes in gel DPPC membranes were directly visualized at different peptide concentrations. We found that the thermodynamic state of lipids in gel membranes can be influenced by the presence of alamethicin such that nanoscopic domains of fluid lipids form close to the peptide pores, and that the elastic constants of the membrane are altered in their vicinity. Melittin-induced holes were visualized in DPPC and DLPC membranes at room temperature in order to study the influence of the membrane state on the peptide induced hole formation. Also differential scanning calorimetry was used to investigate the effect of alamethicin on the lipid membrane phase behavior.Comment: 11 pages, 7 figures, 1 tabl