Ning Zhang

Bio: Ning Zhang is an academic researcher from Tsinghua University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 62, co-authored 701 publications receiving 16494 citations. Previous affiliations of Ning Zhang include University of Waikato & Shandong Normal University.

The International Urban Energy Balance Models Comparison Project: First Results from Phase 1

Abstract: A large number of urban surface energy balance models now exist with different assumptions about the important features of the surface and exchange processes that need to be incorporated To date, no comparison of these models has been conducted; in contrast, models for natural surfaces have been compared extensively as part of the Project for Intercomparison of Land-surface Parameterization Schemes Here, the methods and first results from an extensive international comparison of 33 models are presented The aim of the comparison overall is to understand the complexity required to model energy and water exchanges in urban areas The degree of complexity included in the models is outlined and impacts on model performance are discussed During the comparison there have been significant developments in the models with resulting improvements in performance (root-mean-square error falling by up to two-thirds) Evaluation is based on a dataset containing net all-wave radiation, sensible heat, and latent heat flux observations for an industrial area in Vancouver, British Columbia, Canada The aim of the comparison is twofold: to identify those modeling approaches that minimize the errors in the simulated fluxes of the urban energy balance and to determine the degree of model complexity required for accurate simulations There is evidence that some classes of models perform better for individual fluxes but no model performs best or worst for all fluxes In general, the simpler models perform as well as the more complex models based on all statistical measures Generally the schemes have best overall capability to model net all-wave radiation and least capability to model latent heat flux

Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic

Abstract: The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO2) emissions. Here we present daily estimates of country-level CO2 emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO2 emissions (-1551 Mt CO2) in the first half of 2020 compared to the same period in 2019. The magnitude of this decrease is larger than during previous economic downturns or World War II. The timing of emissions decreases corresponds to lockdown measures in each country. By July 1st, the pandemic's effects on global emissions diminished as lockdown restrictions relaxed and some economic activities restarted, especially in China and several European countries, but substantial differences persist between countries, with continuing emission declines in the U.S. where coronavirus cases are still increasing substantially.

Structural decline in China’s CO2 emissions through transitions in industry and energy systems

Abstract: As part of the Paris Agreement, China pledged to peak its CO2 emissions by 2030. In retrospect, the commitment may have been fulfilled as it was being made—China’s emissions peaked in 2013 at a level of 9.53 gigatons of CO2, and have declined in each year from 2014 to 2016. However, the prospect of maintaining the continuance of these reductions depends on the relative contributions of different changes in China. Here, we quantitatively evaluate the drivers of the peak and decline of China’s CO2 emissions between 2007 and 2016 using the latest available energy, economic and industry data. We find that slowing economic growth in China has made it easier to reduce emissions. Nevertheless, the decline is largely associated with changes in industrial structure and a decline in the share of coal used for energy. Decreasing energy intensity (energy per unit gross domestic product) and emissions intensity (emissions per unit energy) also contributed to the decline. Based on an econometric (cumulative sum) test, we confirm that there is a clear structural break in China’s emission pattern around 2015. We conclude that the decline of Chinese emissions is structural and is likely to be sustained if the nascent industrial and energy system transitions continue.

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Abstract: 1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

The European commission

Abstract: 2 1 The innovative transport systems and the CityMobil project 10 1.1 The research questions 10 2 The state of art in the field of automatic transport systems 12 2.1 Case studies and demand studies for innovative transport systems 12 3 The design and implementation of surveys 14 3.1 Definition of experimental design 14 3.2 Questionnaire design and delivery 16 3.3 First analyses on the collected sample 18 4 Calibration of Logit Multionomial demand models 21 4.1 Methodology 21 4.2 Calibration of the “full” model. 22 4.3 Calibration of the “final” model 24 4.4 The demand analysis through the final Multinomial Logit model 25 5 The analysis of interaction between the demand and socioeconomic attributes 31 5.1 Methodology 31 5.2 Application of Mixed Logit models to the demand 31 5.3 Analysis of the interactions between demand and socioeconomic attributes through Mixed Logit models 32 5.4 Mixed Logit model and interaction between age and the demand for the CTS 38 5.5 Demand analysis with Mixed Logit model 39 6 Final analyses and conclusions 45 6.1 Comparison between the results of the analyses 45 6.2 Conclusions 48 6.3 Answers to the research questions and future developments 52