The impacts of the COVID-19 pandemic on multimodal human mobility in London: A perspective of decarbonizing transport

Decarbonizing transport is one of the core tasks for achieving Net Zero targets, but the COVID-19 pandemic disrupts human mobility and the established transport development strategies. Although existing research has explored the relationship between virus transmission, human mobility, and restrictions policies, few have studied the responses of multimodal human mobility to the pandemic and their impacts on the achievement of decarbonizing transport. This paper employs 32 consecutive biweekly observations of mobile phone application data to understand the influences of the pandemics on multimodal human mobility from February 2020 to April 2021 in London. We here illustrate that multimodal travel behavior and traffic flows significant changed after the pandemic and related lockdowns, but the decline or recovery varies across different travel modes and lockdowns. The car mode has shown the most resilience throughout the pandemic, but the travel modes in the public transit sector were hit hard. Cycle and walk modes remained high at the beginning of the pandemic, but the trend did not continue as the pandemic developed and the season changed. Our findings suggest that the COVID-19 pandemic brought more challenges to travel mode shifting and the achievement of decarbonizing transport rather than opportunities. This analysis will assist transport authorities to optimize the established transport policies and to redistribute limited resources for accelerating the achievement of decarbonizing transport

The Influence of Low Traffic Neighbourhood Scheme on Multimodal Traffic Flow in London

This study aims to investigate the influence of Low Traffic Neighbourhood (LTN) Scheme deployed since the COVID-19 pandemic on multimodal traffic flow in London. We adopt a mobile phone application dataset to investigate the changes in multimodal traffic flow generated by the general public following the introduction of LTNs. Three LTNs located in London are explored between 4 th May and 30th August 2020. The analysis approved that LTN scheme could encourage residents to take cycling and restrict through-traffic, but the influence varies across areas, travel modes and groups and may affect by specific measures

Kinetic Monte Carlo simulation of sintering behavior of additively manufactured stainless steel powder particles using reconstructed microstructures from synchrotron X-ray microtomography

In this study, the sintering behavior of additively manufactured stainless steel powder particles is simulated using a three-dimensional kinetic Monte Carlo (kMC) model. The initial microstructure of powder particles is reconstructed using micro-CT images from the Argonne National Laboratory’s synchrotron X-ray microtomography facility. Using the model, the sintering characteristics of the powder, including its relative density, neck growth, and grain coarsening, are quantitatively analyzed. Sintering temperature directly affects the rate of densification and grain growth and coarsening. Higher temperature results in faster densification and grain growth. Additionally, the relationship between grain coarsening and densification is analyzed. It is observed that when the relative density is below 0.70, the powder particles undergo densification; whereas when the relative density is higher than 0.70, grain coarsening is the main mechanism

Revised Interpretation of Energy Consumption Per Unit Product of Electrolytic Aluminum and Alumina

To meet the needs of China's green, low carbon and high quality development combined with the revision of the unit energy consumption standards for electrolytic aluminum and aluminum oxide products in the relevant industries, energy consumption levels, statistical scope and other content, in accordance with enterprise research, departmental regulations, industry needs to integrate and revise the original standards.  It is conducive to guiding the aluminum smelting industry to increase energy saving and carbon reduction efforts, eliminate backward production capacity and process equipment, promote the research and development and promotion of energy-saving technology for aluminum smelting, and provide technical support for the implementation of energy-saving and carbon reduction goals for the aluminum smelting industry.

A Physics-Informed Auto-Learning Framework for Developing Stochastic Conceptual Models for ENSO Diversity

Understanding ENSO dynamics has tremendously improved over the past decades. However, one aspect still poorly understood or represented in conceptual models is the ENSO diversity in spatial pattern, peak intensity, and temporal evolution. In this paper, a physics-informed auto-learning framework is developed to derive ENSO stochastic conceptual models with varying degrees of freedom. The framework is computationally efficient and easy to apply. Once the state vector of the target model is set, causal inference is exploited to build the right-hand side of the equations based on a mathematical function library. Fundamentally different from standard nonlinear regression, the auto-learning framework provides a parsimonious model by retaining only terms that improve the dynamical consistency with observations. It can also identify crucial latent variables and provide physical explanations. Exploiting a realistic six-dimensional reference recharge oscillator-based ENSO model, a hierarchy of three- to six-dimensional models is derived using the auto-learning framework and is systematically validated by a unified set of validation criteria assessing the dynamical and statistical features of the ENSO diversity. It is shown that the minimum model characterizing ENSO diversity is four-dimensional, with three interannual variables describing the western Pacific thermocline depth, the eastern and central Pacific sea surface temperatures (SSTs), and one intraseasonal variable for westerly wind events. Without the intraseasonal variable, the resulting three-dimensional model underestimates extreme events and is too regular. The limited number of weak nonlinearities in the model are essential in reproducing the observed extreme El Ni\~nos and nonlinear relationship between the eastern and western Pacific SSTs

Customer Profiling Based on Mobile Apps GPS Data : A Case Study on Westfield Shopping Malls

In order to provide a personalised experience to customers, it’s essential for shopping centers to understand its customer base and their shopping behaviors. Building a well-developed customer profile is critical for improving marketing efficiency, expanding market share, and building long-term, stable business ties with trading partners. Currently most shopping malls or retail business use footfall or customer surveys to grasp the customer behaviors, which are insufficient to obtain accurate and representative information about the customers. This study aims to provide a detailed customer profile for shopping centers using GPS datasets. We choose the two Westfield shopping malls in London as the case study area. In order to uncover additional customer information, this study focuses four research questions:(1) Origin places of customers; (2) Their transportation mode to the mall; (3) The average dwell time of customers; (4) The pattern of return visitors. According to the results, malls can develop a range of marketing initiatives to provide a better shopping experience for customers and attract more of them

Three-Dimensional Finite Element Study on Stress Generation in Synchrotron X-Ray Tomography Reconstructed Nickel-Manganese-Cobalt Based Half Cell

In this study, the stress generation caused by phase transitions and lithium intercalation of nickel-manganese-cobalt (NMC) based half cell with realistic 3D microstructures has been studied using finite element method. The electrochemical properties and discharged curves under various C rates are studied. The potential drops significantly with the increase of C rates. During the discharge process, for particles isolated from the conductive channels, several particles with no lithium ion intercalation are observed. For particles in the electrochemical network, the lithium ion concentration increases during the discharge process. The stress generation inside NMC particles is calculated coupled with lithium diffusion and phase transitions. The results show the stresses near the concave and convex regions are the highest. The neck regions of the connected particles can break and form several isolated particles. If the isolated particles are not connected with the electrically conductive materials such as carbon and binder, the capacity loses in battery. For isolated particles in the conductive channel, cracks are more likely to form on the surface. Moreover, stresses inside the particles increase dramatically when considering phase transitions. The phase transitions introduce an abrupt volume change and generate the strain mismatch, causing the stresses increase