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Objective This study investigated the impact of driving styles of drivers and automated vehicles (AVs) on drivers' perception of automated driving maneuvers and quantified the relationships among drivers' perception of AV maneuvers, driver trust, and acceptance of AVs. Background Previous studies on automated driving styles focused on the impact of AV's global driving style on driver's attitude and driving performance. However, research on drivers' perception of automated driving maneuvers at the specific driving style level is still lacking. Method Sixteen aggressive drivers and sixteen defensive drivers were recruited to experience twelve driving scenarios in either an aggressive AV or a defensive AV on the driving simulator. Their perception of AV maneuvers, trust, and acceptance was measured via questionnaires, and driving performance was collected via the driving simulator. Results Results revealed that drivers' trust and acceptance of AVs would decrease significantly if they perceived AVs to have a higher speed, larger deceleration, smaller deceleration, or shorter stopping distance than expected. Moreover, defensive drivers perceived significantly greater inappropriateness of these maneuvers from aggressive AVs than defensive AVs, whereas aggressive drivers didn't differ significantly in their perceived inappropriateness of these maneuvers with different driving styles. Conclusion The driving styles of automated vehicles and drivers influenced drivers' perception of automated driving maneuvers, which influence their trust and acceptance of AVs. Application This study suggested that the design of AVs should consider drivers' perceptions of automated driving maneuvers to avoid undermining drivers' trust and acceptance of AVs.

Objective This study investigated drivers' subjective feelings and decision making in mixed traffic by quantifying driver's driving style and type of interaction. Background Human-driven vehicles (HVs) will share the road with automated vehicles (AVs) in mixed traffic. Previous studies focused on simulating the impacts of AVs on traffic flow, investigating car-following situations, and using simulation analysis lacking experimental tests of human drivers. Method Thirty-six drivers were classified into three driver groups (aggressive, moderate, and defensive drivers) and experienced HV-AV interaction and HV-HV interaction in a supervised web-based experiment. Drivers' subjective feelings and decision making were collected via questionnaires. Results Results revealed that aggressive and moderate drivers felt significantly more anxious, less comfortable, and were more likely to behave aggressively in HV-AV interaction than in HV-HV interaction. Aggressive drivers were also more likely to take advantage of AVs on the road. In contrast, no such differences were found for defensive drivers indicating they were not significantly influenced by the type of vehicles with which they were interacting. Conclusion Driving style and type of interaction significantly influenced drivers' subjective feelings and decision making in mixed traffic. This study brought insights into how human drivers perceive and interact with AVs and HVs on the road and how human drivers take advantage of AVs. Application This study provided a foundation for developing guidelines for mixed transportation systems to improve driver safety and user experience.

Objectives: The COVID-19 pandemic has caused a profound impact on health and well-being of populations. However, there are limited studies that have investigated the psychological aspects of vulnerable groups including pregnant women amid the COVID-19 pandemic. Therefore, we aimed to assess the psychological impact of the COVID-19 pandemic among Chinese pregnant women from February 2020 until March 2020. Methods: Our study was conducted using a modified validated online questionnaire comprising of sociodemographic, the Impact of Event Scale (IES), attitude and mental health-related questions towards COVID-19. Results: A total of 560 women were included. The overall mean age and IES of women was 25.8 ± 2.7 years and 31.4 ± 13.7. Moreover, 67.1% of them had IES ⩾26. Psychological impact seemed to be more severe in women in second trimester of pregnancy (the highest IES) ( p = .016). There was a significant association between trimesters of pregnancy and some indicators of negative health impacts (including increased stress from work, increased stress from home, feeling apprehensive and helpless during the early stages of the COVID-19 pandemic) (all p < .05). Conclusions: Our results reported moderate-to-severe stressful impact among Chinese pregnant women. We recommend that appropriate measures should be taken to address the maternal mental health issues.

Micas have recently attracted the interest of materials scientists to be used as flexible substrates for the growth of nanomaterials. Synthetic fluorophlogopite is particularly appealing due to its high thermal stability and a significantly lower level of impurities compared to natural mica. Perhaps because of such high purity, little attention has been given to its magnetic properties, assumed to be diamagnetic. Herein, we report an in-depth study of the magnetic properties of commercial fluorophlogopite mica crystals as a function of magnetic field and temperature. Our study shows that the magnetic response of mica comprises diamagnetic and paramagnetic components. The latter is quite significant at low temperatures and can be analyzed within the framework of Curie's law and the Langevin theory, which indicate that it originates from isolated magnetic cations. Hence, special care must be taken to study the magnetic properties of mica-supported nanostructures. We propose a protocol to correct the magnetic contributions of mica substrates which can be useful in future developments of flexible magnetic devices. ; The authors thank Michael Mikhov for his useful comments regarding the analysis of the data. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. [819623]). We also acknowledge financial support from the Spanish Ministerio de Ciencia, Innovación y Universidades (MINCIU), through Project No. RTI2018-098537-B-C21, cofunded by ERDF from EU, and "Severo Ochoa" Programme for Centres of Excellence in R&D (Grant No. SEV-2015-0496) and from Generalitat de Catalunya through the projects 2017SGR00765 and 2017SGR1632. The authors also acknowledge Bernat Bozzo, Maite Simón, Vega Lloveras, Francisco Javier Campos and Anna Crespi from the Scientific Services at ICMAB-CSIC and Ignasi Villarroya from the Chemical Analysis Service at UAB. Z. M. was financially supported by China Scholarship Council (CSC) with No. 201606070094. Z. M.'s work was done as a part of the PhD program in Materials Science at Universitat Autònoma de Barcelona. ; Peer reviewed

Coping with climate change requires promoting low-carbon energy transition (LCET) in cities. However, the planning method of LCET for rapidly growing cities deserves further study because it involves dynamicity and interactions of multiple factors. This paper aims to put forward a comprehensive methodology to fill that gap. First, a theoretical framework of "energy system–sustainability–governance–operation of rapidly growing cities" is put forward to explain the general mechanism of LCET. Second, a three step method is built for LCET planning, including energy system analysis based on low emissions analysis platform modeling, operation analysis applying multilevel perspective and stakeholder theories, and governance evaluation by policy review. To verify this method, a city in Western China, Chengdu, was selected as the case study. The results show that Chengdu's energy related CO2 emissions are expected to peak in 2025 under timely and aggressive measures. The main obstacles lie in three aspects: techno–economic insufficiencies, lack of social cognition, and problems of institution and policy. To realize this scenario, Chengdu must incorporate the target and pathway of a carbon peak as soon as possible into its policy system and enhance the coordination among governmental departments.

Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Energy and Smart Cities -- Multi-agent Based Simulation for Investigating Electric Vehicle Adoption and Its Impacts on Electricity Distribution Grids and CO2 Emissions -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Multi-agent Based Simulation Development -- 4.1 Danish Electric Vehicle Adoption Curve -- 4.2 EV Models -- 4.3 Driving Patterns -- 4.4 EV Charging CO2-eq. emission -- 5 Case Study -- 6 Scenario Design -- 7 Results -- 7.1 Baseline Scenario Results -- 7.2 Various Electric Vehicle Adoption Curves Scenario Results -- 8 Discussion -- 9 Conclusion -- References -- Brazilian Utilities' Invoice Data Understanding: Extraction, Data Standardization and Consumption Overview from Partner Universities -- 1 Introduction -- 2 Brazilian Utilities Market -- 2.1 Brazilian Energy Market and Contract -- 2.2 Data Extraction and Storage -- 3 Results -- 4 Conclusion -- References -- Distributed Energy Resources and EV Charging Stations Expansion Planning for Grid-Connected Microgrids -- 1 Introduction -- 1.1 Motivation -- 1.2 Literature Review -- 1.3 Paper Contributions and Organization -- 2 Planning Strategy for Grid-Connected MGs with Fast Charging Stations -- 2.1 Objective Function -- 2.2 Constraints -- 2.3 Uncertainty Model -- 3 Numerical Results -- 4 Conclusion -- References -- Energy Forecasting -- Comparison of Inputs Correlation and Explainable Artificial Intelligence Recommendations for Neural Networks Forecasting Electricity Consumption -- 1 Introduction -- 2 Methodology -- 3 Case Study -- 4 Results -- 4.1 Forecasting -- 4.2 Explanations -- 5 Conclusions -- References -- Deep Learning Models to Estimate and Predict the Solar Irradiation in Brazil -- 1 Introduction -- 2 Artificial Neural Network Techniques for Solar Irradiation.

International audience ; The global consequences of climate change calls for adoption of new technologies across all sectors of society. However, it is important to know how fast new technologies are adopted to reach the political climate goals. This paper presents a method for designing agent-based simulations for studying technology adoption. The method includes the design of simulation population, agent adoption logic, and external factors influencing agents' adoption decisions. A case study of the Danish commercial greenhouses' adoption of a smart energy solution is investigated. The result is presented with Roger's technology adoption curve which can be used to demonstrate the desired adoption rate by regulating different simulation parameters. The adoption rate of 50% with the return on the investment of 3 and 5 years is investigated. The greenhouse segment of growing pot plants with an area of more than 20,000 m2 is found to be the innovators. The simulation result shows that the population categories of the 'innovators', 'early adopters', and 'early majority' don't change between the return on investment time of 3 and 5 years. Furthermore, the relation of the return on investment time and the initial cost is close to linear. Hence, a lower/higher initial cost will result in a shorter/longer return on investment time.

International audience ; The global consequences of climate change calls for adoption of new technologies across all sectors of society. However, it is important to know how fast new technologies are adopted to reach the political climate goals. This paper presents a method for designing agent-based simulations for studying technology adoption. The method includes the design of simulation population, agent adoption logic, and external factors influencing agents' adoption decisions. A case study of the Danish commercial greenhouses' adoption of a smart energy solution is investigated. The result is presented with Roger's technology adoption curve which can be used to demonstrate the desired adoption rate by regulating different simulation parameters. The adoption rate of 50% with the return on the investment of 3 and 5 years is investigated. The greenhouse segment of growing pot plants with an area of more than 20,000 m2 is found to be the innovators. The simulation result shows that the population categories of the 'innovators', 'early adopters', and 'early majority' don't change between the return on investment time of 3 and 5 years. Furthermore, the relation of the return on investment time and the initial cost is close to linear. Hence, a lower/higher initial cost will result in a shorter/longer return on investment time.

International audience ; The global consequences of climate change calls for adoption of new technologies across all sectors of society. However, it is important to know how fast new technologies are adopted to reach the political climate goals. This paper presents a method for designing agent-based simulations for studying technology adoption. The method includes the design of simulation population, agent adoption logic, and external factors influencing agents' adoption decisions. A case study of the Danish commercial greenhouses' adoption of a smart energy solution is investigated. The result is presented with Roger's technology adoption curve which can be used to demonstrate the desired adoption rate by regulating different simulation parameters. The adoption rate of 50% with the return on the investment of 3 and 5 years is investigated. The greenhouse segment of growing pot plants with an area of more than 20,000 m2 is found to be the innovators. The simulation result shows that the population categories of the 'innovators', 'early adopters', and 'early majority' don't change between the return on investment time of 3 and 5 years. Furthermore, the relation of the return on investment time and the initial cost is close to linear. Hence, a lower/higher initial cost will result in a shorter/longer return on investment time.