•This study innovatively constructs a multi-layer statistical model of panel ELES by closely integrating the ELES model, utility functions and external habit formation theory.•Using urban residents' ...consumption data with a nested structure to empirically analyse the comparison effect of urban residents in various types of consumption expenditure and their differences.•A multi-layer statistical theory model is used to analyse the causes of the comparison effect of urban residents in various types of consumer expenditure.
As an important component of gross domestic product, expanded consumer spending is essential for promoting economic growth. To provide a realistic basis to support effective policy formulation to meet diversified consumption needs and enhance the fundamental role of consumption in economic development, this study constructs a multi-layer panel extended linear expenditure system (ELES) model that includes external habit formation (comparison effect). The study empirically analyses the comparison effect and causes using nested consumption data of urban residents from 31 provinces in China from 2002 to 2019. The results show that the comparison effect is relatively large for urban residents’ subsistence consumption expenditure. In contrast, the comparison effect in development and enjoyment consumption expenditure is smaller overall. Second, a widening income gap increases food and clothing comparison effects. Increased educational attainment reduces comparison effects on housing, health care and transport. Significant geographical differences in the comparison effect on various types of consumer spending are evident based on the degree of regional economic development. Finally, the study proposes policy recommendations of increasing residents’ income, raising educational levels and promoting balanced regional development. The most significant contribution of this study is the innovative multi-layer panel ELES statistical model which incorporates external habit formation theory to systematically analyse comparison effects and causes.
Did you know that any straight-line drawing on paper can be folded so that the complete drawing can be cut out with one straight scissors cut? That there is a planar linkage that can trace out any ...algebraic curve, or even 'sign your name'? Or that a 'Latin cross' unfolding of a cube can be refolded to 23 different convex polyhedra? Over the past decade, there has been a surge of interest in such problems, with applications ranging from robotics to protein folding. With an emphasis on algorithmic or computational aspects, this treatment gives hundreds of results and over 60 unsolved 'open problems' to inspire further research. The authors cover one-dimensional (1D) objects (linkages), 2D objects (paper), and 3D objects (polyhedra). Aimed at advanced undergraduate and graduate students in mathematics or computer science, this lavishly illustrated book will fascinate a broad audience, from school students to researchers.
String theory is one of the most exciting and challenging areas of modern theoretical physics. This book guides the reader from the basics of string theory to recent developments. It introduces the ...basics of perturbative string theory, world-sheet supersymmetry, space-time supersymmetry, conformal field theory and the heterotic string, before describing modern developments, including D-branes, string dualities and M-theory. It then covers string geometry and flux compactifications, applications to cosmology and particle physics, black holes in string theory and M-theory, and the microscopic origin of black-hole entropy. It concludes with Matrix theory, the AdS/CFT duality and its generalizations. This book is ideal for graduate students and researchers in modern string theory, and will make an excellent textbook for a one-year course on string theory. It contains over 120 exercises with solutions, and over 200 homework problems with solutions available on a password protected website for lecturers at www.cambridge.org/9780521860697.
Typhoon forecast holds significant importance in preventing wind-related disasters within coastal urban areas. Besides ensuring accuracy, there's a pressing need to expedite the forecasting process ...to allow an adequate response time before typhoon's arrives. Therefore, an efficient computing framework is presently proposed, comprising a mesoscale-Urban Canopy Model (UCM) coupling approach and a mesoscale-microscale coupling approach. Within this framework, the Weather Research &Forecast (WRF) -UCM coupled model is applied to provide local wind profiles and directions for microscale domain configurations. Subsequently, an embedded Large Eddy Simulation (ELES) model is used to simulate fluctuating results within the designated area and temporal-averaged wind results across other urban regions, thus saving computational cost. The present framework is tested by forecasting urban flows during typhoon Hagupit (2020). The typhoon's track and intensity are accurately simulated, as well as a comprehensive prediction of urban typhoon wind field and building aerodynamics using the ELES model with recommended inner LES domain arrangements. Moreover, results highlight the high sensitivity of urban wind fields to inflow conditions, emphasizing the necessity to conduct simulations encompassing the entire typhoon duration. Notably, the case study completed more than 20 h before the typhoon's arrival, demonstrating its promising prospects for forecasting urban wind fields.
•Use the WRF-UCM model to improve typhoon's regional atmospheric characteristics.•The first use of the ELES model in a real urban area with building clusters.•Predict urban wind fields and building pressure during the whole typhoon event.•Enabling coupled meso-micro forecasts to finish before the typhoon's arrival.
Following the release of a harmful substance within an urban environment, buildings and street canyons create complex flow regimes that affect dispersion and localized effluent concentrations. While ...some fast-response dispersion models can capture the effects caused by individual buildings, further research is required to refine urban characterizations such as plume channeling and spreading, and initial dispersion, especially within the presence of a nonhomogeneous array of structures. Field, laboratory, and modeling experiments that simulate urban or industrial releases are critical in advancing current dispersion models. This project leverages the configuration of buildings used in a full-scale, mock urban field study to examine the concentrations of a neutrally buoyant tracer in a series of wind tunnel and Embedded Large Eddy Simulation (ELES) experiments. The behavior, propagation, and magnitude of the plumes were examined and compared to identify microscale effects.
After demonstrating excellent quantitative and qualitative comparisons between the wind tunnel and ELES via lateral and vertical concentration profiles, we show that a nonlinear least squares fit of the Gaussian plume equation well represents these profiles, even within the array of buildings and network of street canyons. The initial plume dispersion depended strongly on the structures immediately adjacent to the release, and consequently, the near-surface plume spread very rapidly in the first few street canyons downwind of the source. The ELES modeling showed that under slightly oblique incoming wind directions of 5° and 15°, an additional 5° and 14° off-axis channeling of the plume occurred at ground level, respectively. This indicates how building structures can cause considerable plume drift from the otherwise expected centerline axis, especially with greater wind obliquity. Additionally, AERMOD was used to represent the class of fast-running, Gaussian dispersion models to inform where these types of models may be usefully applied within urban areas or groups of buildings. Using an urban wind speed profile and other parameters that may be locally available after a release, AERMOD was shown to qualitatively represent the ground-level plume while somewhat underestimating peak concentrations. It also overestimated the lateral plume spread and was challenged in the very near-field to the source. Adding a turbulence profile from the ELES data into AERMOD's meteorological input improved model estimates of lateral plume spread and centerline concentrations, although peak concentration values were still underestimated in the far field. Finally, we offer some observations and suggestions for Gaussian dispersion modeling based on this mock urban modeling exercise.
•Laboratory dispersion experiments were conducted within a mock urban environment.•Wind tunnel tracer concentrations compared well with corresponding CFD simulations.•Lateral and vertical concentration profiles remained Gaussian despite buildings.•Oblique incoming wind directions increased off-axis channeling effects.•Improvements to Gaussian dispersion model performance are proposed and tested.
This study presents an assessment of the capabilities of various turbulence modelling approaches —ELES, SAS, URANS and RANS—to predict the aerodynamic flow around a double-stacked freight wagon, both ...in isolation and within a train. The numerical predictions are compared with experimental measurements at the same Reynolds number to determine the accuracy of each model. Specifically, aerodynamic drag, front and rear surface pressures, planar velocity fields and skin friction lines are validated against the wind tunnel results. In particular, predictions from the ELES and SAS models show good agreement with the wind tunnel data, both qualitatively and quantitatively. Indeed, ELES predicts both the range and distribution of the rear-face surface pressure very closely, indicating that the separated flow is also likely to be well predicted. Both SAS and ELES predict the pressure drag of the multi-wagon configuration to within 2% of the experimental value. However, the steady RANS model predicts the trends in pressure drag in line with the experiments as the front and rear gaps are varied, even though individual drag predictions are considerably worse. Overall, the set of results establishes the benefits and deficiencies of using particular turbulence models to capture different aspects of freight train aerodynamics.
Abstract
Water pricing is the key to maximizing the economic and social benefits of the water transfer project. In this study, we propose the extended linear expenditure system-water price tolerance ...index (ELES-WPTI) model that combines the ELES model and the WPTI method for water pricing. Firstly, the ELES model is used to estimate the price elasticity of water demand and the basic demand for farmers of different income levels. Secondly, the WPTI method is used to simulate and analyze the affordability of farmers of different income levels for agricultural water under the dynamic change of water price standards. Finally, the ELES-WPTI model is applied to the Yinda-Jihuang (YJ) Project, China, to determine the appropriate agricultural water price. The results reveal that the farmers in the DH district have slightly higher affordability of water price than that in HL District. As water consumption should account for less than 15% of the total production cost and 10% of the net income, the affordable water price is determined to be 237 $/hm² in the DH district and 205 $/hm² in the HL district, respectively.
Non-isothermal stratification conditions can alter the airflow pattern and pollutant dispersion process within urban areas. The present study is focused on the impact of various stratification ...conditions, namely, stable, isothermal (neutral), and unstable, on the airflow and concentration fields around an isolated high-rise building. Zonal Reynolds-averaged Navier-Stokes (RANS)-large eddy simulation (LES), also known as embedded large eddy simulation (ELES), is employed for simulating the airflow and concentration fields under non-isothermal boundary layers in order to make a balance between computational costs and accuracy. Comparing the results predicted by the present ELES with an available LES study, with almost similar computational settings (i.e., the inflow turbulence generation method, grid resolution, etc.) shows better performance of ELES in predicting the concentration field. The findings also illustrate that the impact of the unstable stratification condition on turbulence statistics is more pronounced than that of the stable stratification condition. The present article also investigates the effect of thermal stratification conditions on the mechanisms of pollutant dispersion, namely, convective and turbulent diffusion fluxes. The findings reveal that an increase in turbulence kinetic energy (TKE), caused by the unstable thermal stratification condition, increases the concentration fluctuations, which causes the pollutant concentration to be decreased. Furthermore, spectral and proper orthogonal decomposition (POD) analyses are performed for all stratification scenarios. The results show that by altering the thermal condition from isothermal to non-isothermal, either stable or unstable conditions, the contribution of the primary dominant modes to total TKE increases.
•Embedded LES (ELES) is used to model dispersion under various stratification conditions.•Impact of thermal stratification conditions on turbulence statistics is analyzed.•Impact of thermal stratification conditions on pollutant dispersion mechanisms are analyzed.•Spectral and POD analyses of flow field are performed under various stratification conditions.
Given a data set, you can fit thousands of models at the push of a button, but how do you choose the best? With so many candidate models, overfitting is a real danger. Is the monkey who typed Hamlet ...actually a good writer? Choosing a model is central to all statistical work with data. We have seen rapid advances in model fitting and in the theoretical understanding of model selection, yet this book is the first to synthesize research and practice from this active field. Model choice criteria are explained, discussed and compared, including the AIC, BIC, DIC and FIC. The uncertainties involved with model selection are tackled, with discussions of frequentist and Bayesian methods; model averaging schemes are presented. Real-data examples are complemented by derivations providing deeper insight into the methodology, and instructive exercises build familiarity with the methods. The companion website features Data sets and R code.
This book is a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that ...model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The treatment of exact algorithms covers the main inference paradigms based on elimination and conditioning and includes advanced methods for compiling Bayesian networks, time-space tradeoffs, and exploiting local structure of massively connected networks. The treatment of approximate algorithms covers the main inference paradigms based on sampling and optimization and includes influential algorithms such as importance sampling, MCMC, and belief propagation. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.
