Search

In the dual context of global aging and the cleaner energy transition, the impact of home-based care for the aged on cooking energy preferences in rural areas is important. This paper selects CHARLS 2018 data, uses the Probit model, and constructs a theoretical framework with Fogg's Behavior Model to demonstrate the effects of care for the aged. The results showed that older persons with family care in rural areas were more likely to use traditional cooking energy. The disincentive is reinforced by the utilization of traditional cooking energy by neighbors in the community. And those with personal care were more likely to use clean cooking energy. The ability to guarantee clean cooking energy being adopted reinforces the facilitating effect of personal care. In order to promote and use clean energy, it is vital to understand the unique influence of different kinds of care for the aged. Efforts in policy and practice should focus on boosting the motivation and ability to adopt clean energy. The main contributions are: (1) The paper included different kinds of care for the aged as an influencing factor in using clean cooking energy; (2) Through its analysis of motivation and ability factors influencing behavioral choices, it exhaustively explores the inherent mechanisms; (3) This paper can bring policy implications for the promotion of clean cooking energy in rural areas of developing countries. • Family care for the aged inhibits the choice of clean cooking energy, while personal care promotes the usage. • The neighborhood effect and feasible ability can regulate the influence of the two kinds of care respectively. • Fogg's Behavior Model Provides a whole new research framework. • Enhancing motivation and feasible ability can promote clean cooking energy in developing countries' rural areas. [ABSTRACT FROM AUTHOR]

• Cooperative decision-making of multiple autonomous vehicles can be realized using a coalition game-based model. • The driving risk from the perspective of different manual vehicles is quantified, thereby guiding the decision-making of autonomous vehicles in the lane-changing process. • The impact of manual vehicles' randomness on the cooperative decision-making of multiple autonomous vehicles was analyzed. • A conflict decoupling algorithm for conflicts in a connected mixed traffic environment is proposed. Advances in vehicle-networking technologies have enabled vehicles to cooperate in mixed traffic. However, realizing the cooperative decision-making of multiple connected autonomous vehicles (CAVs) when influenced by the presence of connected manual vehicles (CMVs) is a challenging area in current research. In this study, we propose a coalition game-based (CG-based) model for multi-CAV cooperative decision-making in a connected mixed traffic environment. First, the model integrates the perceived risk field theory, quantifying the driving risk from the perspective of different CMVs; this risk is used to determine the uncertainty of the motion state of CMVs. Second, the model can identify the conflicts caused by multiple lane-changing vehicles and decouple the conflict problem into multiple two-vehicle lane-changing games, including a cooperative game between two CAVs and a non-cooperative game between a CAV and a CMV. To test the proposed model, four scenarios that blocked the passage of multiple CAVs were set up; in these scenarios, the average speed of the CG-based model was 21.05, 16.76, 23.17, and 12.55% higher than that of the LC2013 model. The simulation results showed that the CG-based model could improve the efficiency of multiple CAVs while ensuring safety in a mixed traffic flow. [ABSTRACT FROM AUTHOR]

Drivers eye movement data in simulated construction conflicts at different speeds were collected and analyzed to find the relationship between the drivers’ eye movement and the construction conflict. On the basis of the relationship between the drivers’ eye movement and the construction conflict, the peak point of wavelet processed pupil diameter, the first point on the left side of the peak point and the first blink point after the peak point are selected as key points for locating construction conflict periods. On the basis of the key points and the GSA, a construction conflict recognition method so called the CCFRM is proposed. And the construction conflict recognition speed and location accuracy of the CCFRM are verified. The good performance of the CCFRM verified the feasibility of proposed key points in construction conflict recognition. [ABSTRACT FROM AUTHOR]

Abstract: An integrated, clean, facile and ecologically friendly approach of biodiesel production from Eruca Sativa Gars (ESG) vegetable oil focused on lab scaling up was reported in this study. Transesterification of ESG oil was heterogeneously catalyzed by Cs2.5H0.5PW12O40 heteropolyacid salt. The properties of biodiesel from ESG were comparable to conventional diesel fuel and comply with the US Standard for Biodiesel (ASTM 6751). Using ESG biodiesel instead of conventional diesel fuel reduces emissions. The results illustrate that the Cs2.5H0.5PW12O40 is an environmentally benign solid acid catalyst and ESG biodiesel is a kind of nontoxic and biodegradable renewable alternative fuel. [Copyright &y& Elsevier]

This paper quantifies the impact of driving time and roadside landscape colors on the driver’s mean heart rate (MHR). The data used in the paper was collected based on real vehicle experiments conducted on Jilin-Hunchun freeway, China. A texture analysis algorithm is implemented to segment roadside landscape images, where the K-means clustering method is applied to extract color values of images. Models of relationships between the driving time, landscape colors, and MHR are developed respectively. To study the impact of both driving time and landscape colors on the MHR, an integrated model is proposed. Landscape colors and driving time are used as independent variables to explain the MHR. The level of sensitivity of the MHR to landscape colors and driving time is studied using the single factor sensitive analysis. The paper shows that landscape colors are negatively correlated with the MHR, where with increase in color brightness the passive fatigue level of the driver decreases. Driving time is positively correlated with the MHR, where with increase in driving time the MHR increases as well, implying that the active fatigue level of the driver becomes worse. It is also found that the MHR is more sensitive to driving time than landscape colors. The paper represents a fundamental study of the models of drivers’ fatigue levels and landscape colors, which can improve the design of roadside landscapes. [ABSTRACT FROM AUTHOR]

Turning on the heating, ventilation and air-conditioning (HVAC) system is one effective measure to improve the thermal comfort of passengers in an electric bus (EB). However, it will also increase the consumption of the battery power. For an EB with limited battery capacity and that can only be charged at night, it is impossible to keep the HVAC on for all-day trips. This study aims to develop a daily HVAC operation planning model for an EB to maximize the thermal comfort of the passengers under the constraint of battery capacity. Firstly, we developed a cabin temperature estimation model to quantify the thermal comfort of the passengers. Secondly, considering the stochastic volatility of trip energy consumption, the chance-constrained programming based HVAC planning model is established, to determine the optimal HVAC gear of each trip. Finally, a real EB is taken as an example and field operational data collected in three different seasons are used in the case study. Results show that passenger's thermal comfort can be improved in different seasons after applying the proposed optimization method while avoiding the service interruption due to running down of battery power. • A daily HVAC system usage optimization model for an EB is proposed. • Estimation models for cabin temperature and trip energy consumption are developed. • Passenger thermal comfort is maximized considering limited battery capacity. • Real EB operational data in three seasons are used to validate the proposed models. [ABSTRACT FROM AUTHOR]

Despite the impressive efficacy of chimeric antigen receptor (CAR) T-cell therapy, adverse effects, including cytokine release syndrome and neurotoxicity, impede its therapeutic application, thus making the modulation of CAR T-cell activity a priority. The destabilizing domain mutated from Escherichia coli dihydrofolate reductase (DHFR) is inherently unstable and degraded by proteasomes unless it is stabilized by its chemical ligand trimethoprim (TMP), a Food and Drug Administration-approved drug. Here the authors reveal a strategy to modulate CAR T-cell activity at the protein level by employing DHFR and TMP as a chemical switch system. First, the system was demonstrated to work in human primary T cells. To introduce the system to CAR T cells, DHFR was genetically fused to the carboxyl terminal of a third-generation CAR molecule targeting CD19 (CD19-CAR), constructing the CD19-CAR-DHFR fusion. The CD19-CAR-DHFR molecule level was shown to be modulated by TMP. Importantly, the incorporation of DHFR had no impact on the recognition specificity and normal function of the CAR molecule. Little adverse effect on cell proliferation and apoptosis was detected. It was proved that TMP could regulate cytokine secretion and the in vitro cytotoxicity of CD19-CAR-DHFR T cells. Furthermore, the in vivo anti-tumor efficacy was demonstrated to be controllable through the manipulation of TMP administration. The approach to control CD19-CAR also succeeded in 19-BBZ(71), another CD19-targeting CAR with a different structure. The proposed approach based on DHFR and TMP provides a facile strategy to bring CAR T-cell therapy under conditional user control, and the strategy may have the potential to be transplantable. [Display omitted] [ABSTRACT FROM AUTHOR]