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China's tourism industry developed rapidly in the late 1990s, and its direct result is the continuous and rapid growth of tourism operating income. However, since 2010, China's tourism development has been slow and regional tourism development has been uneven. Even in different years in the same area, the tourism operating income shows great differences. How to select the key factor from many factors, as there is still no recognised method in the theoretical circle. This article combines the theories of econometrics, differential calculus, statistics and other related fields. Through in-depth basic research, the data required for the research is determined, and such data are substituted into the self-constructed econometric differential statistical model. Effective analysis of empirical objects is realised. At the same time, the article uses tourism operating income as an indicator and uses the analysis of variance method to calculate the average coefficient of variation of the same region in different years and different regions in the same year, analyses the trends and characteristics of China's tourism in the temporal and spatial structure, and proposes corresponding results on this basis.

As a basic symmetry of space-time, Lorentz symmetry has played important roles in various fields of physics, and it is a glamorous question whether Lorentz symmetry breaks. Since Einstein proposed special relativity, Lorentz symmetry has withstood very strict tests, but there are still motivations for Lorentz symmetry violation (LV) research from both theoretical consideration and experimental feasibility, that attract physicists to work on LV theories, phenomena and experimental tests with enthusiasm. There are many theoretical models including LV effects, and different theoretical models predict different LV phenomena, from which we can verify or constrain LV effects. Here, we introduce three types of LV theories: quantum gravity theory, space-time structure theory and effective field theory with extra-terms. Limited by the energy of particles, the experimental tests of LV are very difficult; however, due to the high energy and long propagation distance, high-energy particles from astronomical sources can be used for LV phenomenological researches. Especially with cosmic photons, various astronomical observations provide rich data from which one can obtain various constraints for LV researches. Here, we review four common astronomical phenomena which are ideal for LV studies, together with current constraints on LV effects of photons.

Brain function is thought to emerge from the interactions among neuronal populations. Apart from traditional efforts to reproduce brain dynamics from the micro- to macroscopic scales, complementary approaches develop phenomenological models of lower complexity. Such macroscopic models typically generate only a few selected—ideally functionally relevant—aspects of the brain dynamics. Importantly, they often allow an understanding of the underlying mechanisms beyond computational reproduction. Adding detail to these models will widen their ability to reproduce a broader range of dynamic features of the brain. For instance, such models allow for the exploration of consequences of focal and distributed pathological changes in the system, enabling us to identify and develop approaches to counteract those unfavorable processes. Toward this end, The Virtual Brain (TVB) (www.thevirtualbrain.org), a neuroinformatics platform with a brain simulator that incorporates a range of neuronal models and dynamics at its core, has been developed. This integrated framework allows the model-based simulation, analysis, and inference of neurophysiological mechanisms over several brain scales that underlie the generation of macroscopic neuroimaging signals. In this article, we describe how TVB works, and we present the first proof of concept.

A six dimensional manifold of symmetric signature (3,3) is proposed as a space structure for building combined theory of gravity and electromagnetism. Special metric tensor is proposed, yielding the space which combines the properties of Riemann, Weyl and Finsler spaces. Geodesic line equations are constructed where coefficients can be divided into depending on the metric tensor (relating to the gravitational interaction) and depending on the vector field (relating to the electromagnetic interaction). If there is no gravity, the geodesics turn into the equations of charge motion in the electromagnetic field. Furthermore, symmetric six-dimensional electrodynamics can be reduced to traditional four-dimensional Maxwell system, where two additional time dimensions are compactified. A purely geometrical interpretation of the concept of electromagnetic field and point electric charge is proposed.

The space–time fluid–structure interaction (STFSI) methods for 3D parachute modeling are now at a level where they can bring reliable, practical analysis to some of the most complex parachute systems, such as spacecraft parachutes. The methods include the Deforming-Spatial-Domain/Stabilized ST method as the core computational technology, and a good number of special FSI methods targeting parachutes. Evaluating the stability characteristics of a parachute based on how the aerodynamic moment varies as a function of the angle of attack is one of the practical analyses that reliable parachute FSI modeling can deliver. We describe the special FSI methods we developed for this specific purpose and present the aerodynamic-moment data obtained from FSI modeling of NASA Orion spacecraft parachutes and Japan Aerospace Exploration Agency (JAXA) subscale parachutes.

In this paper we suggest the use of ontological structures (OSs) as an appropriate tool for describing the foundations of reality. Every vertex of this structure, representing a fundamental entity (FE) in the universe, is completely and solely characterized by its connections to the other vertices in the structure. The edges of this structure are binary compounds of the FEs, and are identified with the elementary particles. The combinations including more than 2 connected vertices correspond to composite particles. The principles according to which the OSs are designed (Shoshani, in: Tempsky (ed) Philosophy of the natural sciences, VHP Tempsky, Vienna, 1989 ; Shoshani in Phys Essays 4(4):566–576, 1991 ; Shoshani in Phys Essays 11(4):512–520, 1998 ) are discussed in Sect.  2 , and the simplest OSs having the minimal number of vertices, and thus represent the simplest universe, are given in Sect.  3 . This section also describes an OS that includes an infinite number of vertices that might represent the space–time points. This structure imparts a new meaning to space–time, detached from their intuitive grasp (Shoshani in Phys Essays 23(2):285–292, 2010 ). Section  4 is devoted to show how to ascribe intrinsic properties to the fundamental entities by using their inter-connections in the OS. The predictive power and explanatory capacity of this theory, named Apriorics (Shoshani in Phys Essays 27(1):126–130, 2014 ) are briefly described in Sects.  3 and 4 .

The structure of space–time is examined by extending the standard Lorentz connection group to its complex covering group, operating on a 16-dimensional “spinor” frame. A Hamiltonian variation principle is used to derive the field equations for the spinor connection. The result is a complete set of field equations which allow the sources of the gravitational and electromagnetic fields, and the intrinsic spin of a particle, to appear as a manifestation of the space–time structure. A cosmological solution and a simple particle solution are examined. Further extensions to the connection group are proposed.

The ability to predict the runoff response of an urban catchment to rainfall is crucial for managing drainage systems effectively and controlling discharges from urban areas. In this paper we assess the potential of commercial microwave links (MWL) to capture the spatio-temporal rainfall dynamics and thus improve urban rainfall-runoff modelling. Specifically, we perform numerical experiments with virtual rainfall fields and compare the results of MWL rainfall reconstructions to those of rain gauge (RG) observations. In a case study, we are able to show that MWL networks in urban areas are sufficiently dense to provide good information on spatio-temporal rainfall variability and can thus considerably improve pipe flow prediction, even in small subcatchments. In addition, the better spatial coverage also improves the control of discharges from urban areas. This is especially beneficial for heavy rainfall, which usually has a high spatial variability that cannot be accurately captured by RG point measurements.

Human actions are, inherently, structured patterns of body movements. We explore ensembles of hierarchical spatio-temporal trees, discovered directly from training data, to model these structures for action recognition and spatial localization. Discovery of frequent and discriminative tree structures is challenging due to the exponential search space, particularly if one allows partial matching. We address this by first building a concise action word vocabulary via discriminative clustering of the hierarchical space-time segments, which is a two-level video representation that captures both static and non-static relevant space-time segments of the video. Using this vocabulary we then utilize tree mining with subsequent tree clustering and ranking to select a compact set of discriminative tree patterns. Our experiments show that these tree patterns, alone, or in combination with shorter patterns (action words and pairwise patterns) achieve promising performance on three challenging datasets: UCF Sports, HighFive and Hollywood3D. Moreover, we perform cross-dataset validation, using trees learned on HighFive to recognize the same actions in Hollywood3D, and using trees learned on UCF-Sports to recognize and localize the similar actions in JHMDB. The results demonstrate the potential for cross-dataset generalization of the trees our approach discovers.

Current approaches separately analyze concurrently acquired diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) data. The primary limitation of these approaches is that they do not take advantage of the information from DTI that could potentially enhance estimation of resting-state functional connectivity (FC) between brain regions. To overcome this limitation, we develop a Bayesian hierarchical spatiotemporal model that incorporates structural connectivity (SC) into estimating FC. In our proposed approach, SC based on DTI data is used to construct an informative prior for FC based on resting-state fMRI data through the Cholesky decomposition. Simulation studies showed that incorporating the two data produced significantly reduced mean squared errors compared to the standard approach of separately analyzing the two data from different modalities. We applied our model to analyze the resting state DTI and fMRI data collected to estimate FC between the brain regions that were hypothetically important in the origination and spread of temporal lobe epilepsy seizures. Our analysis concludes that the proposed model achieves smaller false positive rates and is much robust to data decimation compared to the conventional approach.