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Haibo Chen

Researcher at University of Leeds

Publications -  113
Citations -  1533

Haibo Chen is an academic researcher from University of Leeds. The author has contributed to research in topics: Environmental science & Computer science. The author has an hindex of 17, co-authored 73 publications receiving 1139 citations.

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Real-world emissions of construction mobile machines and comparison to a non-road emission model.

TL;DR: In this paper, the authors implemented real-world tests in Nanjing, China for measuring emission factors (EFs) of air pollutants, including Carbon Monoxide (CO), Hydrocarbon (HC), Nitrogen Oxides (NOx), and Particulate Matter (PM) from ten construction machines in three operational modes (idling, moving, and working) with a portable emission measurement system.
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Exhaust and non-exhaust emissions from conventional and electric vehicles: A comparison of monetary impact values

TL;DR: In this article , monetary impact values of exhaust and non-exhaust emissions emitted from internal combustion engine vehicles (ICEVs) and their equivalent EVs from an economic-environmental perspective, expressed as monetary impact value, were calculated according to the emission factors and damage costs of these pollutants.
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Methodology for field operational tests of automated vehicles

TL;DR: The requirements for a methodology that addresses three types of focus are discussed; centred on the user, the vehicle or the context, and how the current FOT methodology may be adapted or may need to be completely changed is investigated.
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Normalized Solutions for Lower Critical Choquard Equations with Critical Sobolev Perturbation

TL;DR: In this article , normalized solutions for the following Choquard equations with lower critical exponent and a local perturbation were studied and several nonexistence and existence results were proved. But they were not considered for the case of double critical exponents.
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Instrumented city database analysts using multi-agents

TL;DR: The framework that demonstrates the feasibility of using multi-agents as information analysts to process and manage the iC database is described, which can be trained via the interactions with the users to be personalised for individual preferences.