Post-pandemic green recovery is pivotal in achieving global sustainable development goals by simultaneously revitalizing economies and reducing greenhouse gas emissions, air pollution and improving public welfare. How...
Post-pandemic green recovery is pivotal in achieving global sustainable development goals by simultaneously revitalizing economies and reducing greenhouse gas emissions, air pollution and improving public welfare. However, subnational and city-level understanding of green recovery, its efficacy and its alignment with public health is poorly understood. Here we focus on post-COVID-19 low-carbon recovery—economic growth combined with reduced carbon emissions—and explore health co-benefits in Chinese cities. A novel near-real-time daily carbon emission dataset of 48 cities in China is developed, coupled with detailed health and economic municipal statistics and models. We find that, on average, six low-carbon-recovery cities, mainly megacities, saved 1.2 times as many lives per 100,000 population compared with the 42 other cities, and their annual monetary avoided premature deaths per 100,000 population was 1.5 times more than the 42 other cities. The accumulated monetary health co-benefits for low-carbon-recovery cities were US$ 4.2 billion (95% confidence interval, 2.1–6.3) during the post-COVID-19 period. We show that government spending on electric vehicles increases the likelihood of achieving low-carbon recovery in Chinese cities. Our results underscore the significant health co-benefits of low-carbon recovery, pointing to synergies between advancing local welfare and global environmental objectives.
Memetic algorithms combining with effective constraints handling techniques have been becoming the focus of concern as well as the subject of substantial research issue in decision-making in complex systems. As one of...
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We propose a procedure for identification of reduced-order bilinear models from time-domain data, i.e., sampled values of the control input and of the observed output. To accomplish this, we require two sets of data c...
We propose a procedure for identification of reduced-order bilinear models from time-domain data, i.e., sampled values of the control input and of the observed output. To accomplish this, we require two sets of data corresponding to the response of the system to two differently-scaled step functions, as control inputs. Then, the Markov parameters for two time-varying linear models are extracted, and from this information, we are able to infer a reduced-order bilinear model.
In this paper, the problem of enhancing the virtual reality (VR) experience for wireless users is investigated by minimizing the occurrence of breaks in presence (BIPs) that can detach the users from their virtual wor...
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ISBN:
(数字)9781728109626
ISBN:
(纸本)9781728109633
In this paper, the problem of enhancing the virtual reality (VR) experience for wireless users is investigated by minimizing the occurrence of breaks in presence (BIPs) that can detach the users from their virtual world. To measure the BIPs for wireless VR users, a novel model that jointly considers the VR applications, transmission delay, VR video quality, and users' awareness of the virtual environment is proposed. In the developed model, the base stations (BSs) transmit VR videos to the wireless VR users using directional transmission links so as to increase the data rate of VR users, thus, reducing the number of BIPs for each user. Therefore, the mobility and orientation of VR users must be considered when minimizing BIPs, since the body movements of a VR user may result in blockage of its wireless link. The BIP problem is formulated as an optimization problem which jointly considers the predictions of users' mobility patterns, orientations, and their BS association. To predict the orientation and mobility patterns of VR users, a distributed learning algorithm based on the machine learning framework of deep echo state networks (ESNs) is proposed. The proposed algorithm uses concept from federated learning to enable multiple BSs to locally train their deep ESNs using their collected data and cooperatively build a learning model to predict the entire users' mobility patterns and orientations. Using these predictions, the user association policy that minimizes BIPs is derived. Simulation results demonstrate that the developed algorithm reduces the users' BIPs by up to 16% and 26%, respectively, compared to centralized ESN and deep learning algorithms.
Dynamic simulations have played an important role in assessing the power system dynamic studies. The appropriate numerical model is the key to obtain correct dynamic simulation results. In addition, the appropriate mo...
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This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of he...
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We report a photonic-based radio frequency (RF) arbitrary waveform generator (AWG) using a soliton crystal micro-comb source with a free spectral range (FSR) of 48.9 GHz. We successfully achieve arbitrary shapes inclu...
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The 2011 Thailand Floods heavily impacted 7 industrial complexes, in which 56.7% were Japanese companies. Many notable companies received severe damage until they had to cease their production. Area Business Continuit...
The 2011 Thailand Floods heavily impacted 7 industrial complexes, in which 56.7% were Japanese companies. Many notable companies received severe damage until they had to cease their production. Area Business Continuity Management (Area-BCM) implemented in Thailand stems from this disaster which causes both private and public sectors to think about their business sustainability. The Area-BCM project is an on-going implementation in Thailand aiming to enhance collaboration among stakeholders in industrial areas for coping with upcoming threats. One of the most significant factors before launching a plan is to understand individual attitudes and perceptions pertaining to the Area-BCM project for the best practice, effective and continuous outcomes. This study aims to investigate various factors that affect the perceived usefulness (PU) about implementing Area-BCM. Our proposed research model is developed aligning with the behavioral model and factors influencing flood mitigation consisting of Subjective norms, Experience, Worry about flooding, and Flood hazard knowledge. Questionnaires were distributed to employees in the industrial areas which were flooded in 2011. The developed model was tested by Partial Least Square Structural Equation Modeling (PLS-SEM). The results consequently show that subjective norms and flood hazard knowledge significantly influence perceived usefulness. This can be implied that, in an organization, major thoughts of related people could shape individual perceptions about using a disaster management plan. Moreover, the governmental and local authorities should be a significant force, that helps support plan implementation and educate people about disaster knowledge.
—Driven by the visions of Internet of Things and 5G communications, the edge computing systems integrate computing, storage and network resources at the edge of the network to provide computing infrastructure, enabli...
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