Terms Of Spatial Dimensions Research Articles

Analysis of the Evolution Pattern and Regional Conservation of Cultural Heritage from the Perspective of Urban Sustainable Transformation: The Case of Xiamen, China

This study focuses on the cultural heritage of Xiamen and uses the spatial analysis method of ArcGIS to analyze the spatiotemporal evolution and distribution characteristics of cultural heritage. Through analysis, we aim to optimize the structure of cultural heritage protection areas and tourism routes in urban planning by analyzing the spatial structure characteristics of Xiamen’s cultural heritage types in various historical periods. The results show that in terms of the time dimension, the existing cultural heritage in Xiamen was mainly built between 1840 and 1949, and the center of cultural heritage shows an overall trend of a “north-south direction”. In terms of spatial dimension, the existing cultural height in Xiamen shows a clustering distribution pattern, with clusters located in the Gulangyu and Xiagang areas of Siming District. According to the period, there is a clear spatial correlation pattern, showing a spatial clustering trend of “strong first and then weak”.

A Sparse Sensor Placement Strategy Based on Information Entropy and Data Reconstruction for Ocean Monitoring

Sparse sensor placement strategies are applied to reconstruct a region’s full-state data conditioned to a limited number of sensors, particularly crucial to ocean monitoring systems. In maritime systems, existing sparse sensor placement methods consider the reconstruction error of data or rely on specific requirements. Considering how sensors acquire essential information for monitoring systems, the utilization of entropy from information theory becomes quite interesting. In this article, we show that entropy measurements on different quantities of information are sensitive to indicate the border areas, thus requiring a balance between the number of sensors needed and the amount of information collected by them in coastal areas. Due to such, we propose (i) a novel sparse sensor placement strategy based on entropy, where the entropy measurements in temporal dimension are utilized for sample selection, so portions of samples selected are utilized for training data, significantly improving the training efficiency without sacrificing accuracy of subsequent data reconstruction. In the proposed strategy, (ii) we use orthogonal triangle decomposition from linear algebra, where a low-cost sensor is employed as a pivot. In terms of spatial dimension, the entropy of each location is adopted as entropy weight to reconstruct full-state data. Additionally, (iii) the strategy employs a greedy algorithm of weighted column pivoting for the orthogonal triangle decomposition, which is designed to suit yet effectively seek additional information and minimal reconstruction error in each iteration processing step. Experimental results using Sea Surface Temperature (SST) data show that the proposed strategy outperforms existing methods, acquiring more information, ensuring higher efficiency, and reducing costs while minimizing reconstruction errors.

EXPRESS: Influence of Different Spatial Representations on the SNARC Effect for Letters: Electrophysiological Evidence.

Studies have previously demonstrated that different spatial representations may affect the spatial-numerical association of response codes (SNARC) effect for numbers; however, limited studies have assessed the SNARC effect for letters. In this study, event-related potentials (ERPs) were used to measure the influence of two spatial representation modes (ruler and clock) on the SNARC effect. The ruler produced a SNARC-like effect; i.e., the left hand reacted faster than the right to the letters that appear before N in the alphabet; the right hand reacted faster than the left to the letters after N, whereas the clock produced a reverse SNARC effect. In addition, the ERP data showed that the SNARC-like effect for letters in both representations induced significant activation in the frontal and parietal regions, indicating that the same brain areas are involved in processing letters and numbers in terms of spatial dimensions. This study further identified the conditions for the SNARC effect and proved that the SNARC effect is attributed to the simultaneous participation of brain regions for sequence and spatial information processing.

Population Exposure Changes to One Heat Wave and the Influencing Factors Using Mobile Phone Data—A Case Study of Zhuhai City, China

The frequent occurrence of extreme high temperature weather and heat waves has greatly affected human life. This paper analyzes population exposure and its influencing factors during a heat wave incident in Zhuhai from 6 to 12 September 2021 based on real-time mobile phone data and meteorological data. The results show that the most areas of Zhuhai are affected by high temperature during this heat wave incident. The hourly population exposure is directly proportional to hourly heat wave coverage. In terms of time dimension, the overall population exposure shows a trend of decreasing and then increasing. In terms of spatial dimensions, high population exposure is concentrated in areas such as primary and secondary schools, colleges and universities, office buildings, and residential areas. Low exposure is distributed in most of the mountainous areas along the southern coast. In addition, the leading factors that cause changes in population exposure in different periods of the heat wave cycle are different, which rely more on either climatic factors or population factors.

AGG: A Novel Intelligent Network Traffic Prediction Method Based on Joint Attention and GCN-GRU

Timely and accurate network traffic prediction is a necessary means to realize network intelligent management and control. However, this work is still challenging considering the complex temporal and spatial dependence between network traffic. In terms of spatial dimension, links connect different nodes, and the network traffic flowing through different nodes has a specific correlation. In terms of spatial dimension, not only the network traffic at adjacent time points is correlated, but also the importance of distant time points is not necessarily less than the nearest time point. In this paper, we propose a novel intelligent network traffic prediction method based on joint attention and GCN-GRU (AGG). The AGG model uses GCN to capture the spatial features of traffic, GRU to capture the temporal features of traffic, and attention mechanism to capture the importance of different temporal features, so as to realize the comprehensive consideration of the spatial-temporal correlation of network traffic. The experimental results on an actual dataset show that, compared with other baseline models, the AGG model has the best performance in experimental indicators, such as root mean square error (RMSE), mean absolute error (MAE), accuracy (ACC), determination coefficient ( R 2 ), and explained variance score (EVS), and has the ability of long-term prediction.

Spatial-temporal evolution and influencing factors of total factor productivity in China's logistics industry under low-carbon constraints.

Behind the rapid development of China's logistics industry, there are problems of high energy consumption and high pollution. Under the dual constraints of resources and environment, promoting the low-carbon transformation of the logistics industry is the key to achieving sustainable development of the logistics industry. This paper applies the epsilon-based measure (EBM) model which considers undesirable output and global Malmquist-Luenberger (GML) index to measure the logistics efficiency under the low-carbon constraints of 30 provinces in China from 2005 to 2017, that is, the green total factor productivity (GTFP), and characterizes its temporal and spatial evolution characteristics through visualization and spatial analysis methods. Then, this paper uses the geographically weighted regression (GWR) model to analyze the influence of industrial agglomeration level, informatization level, foreign direct investment, logistics energy intensity, traffic network density, and technological innovation capability on the GTFP of the logistics industry. The findings of this paper show that (1) during the inspection period, the overall average GTFP of the logistics industry was 0.992, which did not reach the effective level, and the spatial differentiation showed that the average GTFP of eastern was greater than that of in central, and that of in central was greater than that of in western. (2) The GTFP of the logistics industry has experienced an alternating process of rising and falling in time, with large fluctuations. Also, in terms of spatial dimension, there is a trend that high-level areas gradually gather to the southeast, and there is significant spatial autocorrelation. (3) For the logistics industry, high-efficiency areas and high-output areas show significant spatial homogeneity. (4) The estimation results of the GWR show that the direction and intensity of the multi-dimensional driving factors on the GTFP of the logistics industry are different in different regions, showing obvious spatial non-stationary characteristics.

Existence and Nonexistence of the Solutions to the Cauchy Problem of Quasilinear Parabolic Equation with a Gradient Term

This paper deals with the existence and non-xistence of the solutions to the Cauchy problem of a class of quasilinear parabolic equations with a gradient term. We establish Fujita-type blowup theorems and determine the critical Fujita exponent in terms of spatial dimension, the asymptotic behavior of the coefficients of the gradient term at infinity, the exponents of spatial positions in the coefficients of the time-derivative term, and the source term. In particular, we classify the critical case as the blowup case.

Spatial–temporal fuzzy information granules for time series forecasting

The prediction of time series in multi-steps is of significance in reality. However, considering the uncertainty and high noise existing in time series, the long-term forecasting is still an open problem. By means of granular computing, in this article, a novel spatial–temporal fuzzy information granule (STFIG) model is proposed to achieve the multi-step forecasting of time series. From the perspective of time dimension, by using unequal division method, time series is converted into generalized time-varying fuzzy information granules, where the trend information and dispersion degree of sequence data can be quantitatively described. Moreover, in terms of spatial dimension, the fluctuation information of time series is also calculated and involved into information granules, which can further enhance the semantic representation of sequential data. In order to improve the ability of dealing with uncertainties and fuzziness in time series, the interval type-2 fuzzy set is applied in the granules model. By using synthetic data and real-life time series, experiments are carried out to verify the effectiveness of the proposed scheme, where abundant semantic information and better long-term predictive performance can be obtained.

Topological insulators and superconductors from string theory

Topological insulators and superconductors in different spatial dimensions and with different discrete symmetries have been fully classified recently, revealing a periodic structure for the pattern of possible types of topological insulators and superconductors, both in terms of spatial dimensions and in terms of symmetry classes. It was proposed that K theory is behind the periodicity. On the other hand, D-branes, a solitonic object in string theory, are also known to be classified by K theory. In this paper, by inspecting low-energy effective field theories realized by two parallel D-branes, we establish a one-to-one correspondence between the K-theory classification of topological insulators/superconductors and D-brane charges. In addition, the string theory realization of topological insulators and superconductors comes naturally with gauge interactions, and the Wess-Zumino term of the D-branes gives rise to a gauge field theory of topological nature, such as ones with the Chern-Simons term or the $\ensuremath{\theta}$ term in various dimensions. This sheds light on topological insulators and superconductors beyond noninteracting systems, and the underlying topological field theory description thereof. In particular, our string theory realization includes the honeycomb lattice Kitaev model in two spatial dimensions, and its higher-dimensional extensions. Increasing the number of D-branes naturally leads to a realization of topological insulators and superconductors in terms of holography (AdS/CFT).

Particle number counting statistics in ideal Bose gases

We discuss the exact particle number counting statistics of degenerate ideal Bose gases in the microcanonical, canonical, and grand-canonical ensemble, respectively, for various trapping potentials. We then invoke the Maxwell's Demon ensemble [Navez et el., Phys. Rev. Lett. (1997)] and show that for large total number of particles the root-mean-square fluctuation of the condensate occupation scales n0 / [T=Tc] r N s with scaling exponents r = 3=2, s = 1=2 for the3D harmonic oscillator trapping potential, and r = 1,s= 2=3 for the 3D box. We derive an explicit expression for r and s in terms of spatial dimension D and spectral index sigma of the single-particle energy spectrum. Our predictions also apply to systems where Bose-Einstein condensation does not occur. We point out that the condensate fluctuations in the microcanonical and canonical ensemble respect the principle of thermodynamic equivalence.

Polarity in the Social Sciences and in Physics

In the social sciences, data are measured in terms of spatial dimensions, and according to the technique employed such measurement results in scales or factors. Some social scientists however, especially psychologists, are too satisfied with mere units of length; they overlook directionality in measurement, and are insufficiently aware of problems of polarity. This is not particularly true of sociologists. But in psychology, bi-polar measurement would often be more appropriate where, instead, measurement is used [13]; for, whether or bi-polar measurement should be employed, ought to be determined in part by the data. It is important to know when, and with what kind of data, a minus sign is essential and when it is not. Holistic activities such as gestalt perception require a plus and a minus sign but a factor measuring eyeblink can be regarded as unidirectional and extending from little to much. In the social sciences measurement, when used to excess, can lead to euphemistic thinking and overlooking such problems as backwardness and mental deficiency in education [9]. Unidirectionality is incapable of expressing complex relationships in linguistics [12] and is inappropriate in studying ethical and aesthetic values in philosophy [14]. It is frequently possible to express measure functions in either bi-polar or in form; and in the social sciences one of the crucial problems is to distinguish between bi-polar measures which can be converted into measures without destroying an antithesis, from those which cannot be thus converted without loss in transformation [10]. There is a similar problem in physics. There is widespread disagreement in physics regarding the representation of space and time in connection with relativity which raises issues that, although not identical with those which have been raised in the social sciences, are very similar from the point of view of measurement theory. Philosophers frequently have arguments concerning the isotropy and anisotropy of space [8], arguments about why there are only three dimensions of physical space (as distinct from hypothetical space in geometry, where for the purpose of calculation there can be any number of spatial dimensions, orthogonal