How to Measure Carbon Equity: Carbon Gini Index Based on Historical Cumulative Emission Per Capita

This paper uses Lorenz Curve and Gini Index with adjustment to per capita historical cumulative emission and constructs Carbon Gini Index to measure inequality in climate change area. The analysis using Carbon Gini Index shows that 70% of carbon space in the atmosphere has been used for unequal distribution, which is almost the same as that of income in the country with the biggest gap between rich and poor in the world. The carbon equity should be an urgency and priority in the climate agenda. Carbon Gini Index established in this paper can be used to measure inequality in the distribution of carbon space and provide a quantified indicator for measurement of carbon equity among different proposals.Climate Change, Carbon Equity, Long-term Mitigation Goal, Cumulative Emission Per Capita, Carbon Gini Index

Carbon emissions in China: How far can new efforts bend the curve?

While China is on track to meet its global climate commitments through 2020, China’s post-2020 CO2 emissions trajectory is highly uncertain, with projections varying widely across studies. Over the past year, the Chinese government has announced new policy directives to deepen economic reform, protect the environment, and limit fossil energy use in China. To evaluate how new policy directives could affect energy and climate change outcomes, we simulate two levels of policy effort—a Continued Effort scenario that extends current policies beyond 2020 and an Accelerated Effort scenario that reflects newly announced policies—on the evolution of China’s energy and economic system over the next several decades. Importantly, we find that both levels of policy effort would bend down the CO2 emissions trajectory before 2050 without undermining economic development, although coal use and CO2 emissions peak about 10 years earlier in the Accelerated Effort scenario

Literary destination familiarity and inbound tourism: evidence from mainland China

Destination familiarity is an important non-economic determinant of tourists’ destination choice that has not been adequately studied. This study posits a literary dimension to the concept of destination familiarity —that is, the extent to which tourists have gained familiarity with a given destination through literature—and seeks to investigate the impact of this form of familiarity on inbound tourism to Mainland China. Employing the English fiction dataset of the Google Books corpus, the New York Times annotated corpus, and the Time magazine corpus, we construct two types of destination familiarity based on literary texts: affection-based destination familiarity and knowledge-based destination familiarity. The results from dynamic panel estimation (1994–2004) demonstrate that the higher the degree of affection-based destination familiarity with a province in the previous year, the larger the number of inbound tourists the following year. Examining the influence of literature and its consumption on tourism activities sheds light on the dynamics of sustainable tourism development in emerging markets

Driving Control Research for Longitudinal Dynamics of Electric Vehicles with Independently Driven Front and Rear Wheels

This paper takes the electric off-road vehicle with separated driven axles as the research object. To solve the longitudinal dynamics driving control problems, vehicle dynamics model, and control strategies were studied and the corresponding simulation was carried out. An 8-DOF vehicle dynamics model with separated driven axles was built. The driving control strategies on the typical roads were put forward. The recognition algorithm of the typical road surfaces based on the wheels’ slip rates was proposed. And the two control systems were designed including the pedal opening degree adjustment control system based on PI algorithm and the interaxle torque distribution control system based on sliding mode control algorithm. The driving control flow of the proposed vehicle combining the pedal adjustment control system with the interaxle torque distribution control system was developed. And the driven control strategies for the typical roads were simulated. Simulation results show that the proposed drive control strategies can adapt to different typical road surfaces, limit the slip rates of the driving wheels within the stable zone, and ensure the vehicle driving safely and stably in accordance with the driver's intention

FabricFolding: Learning Efficient Fabric Folding without Expert Demonstrations

Autonomous fabric manipulation is a challenging task due to complex dynamics and potential self-occlusion during fabric handling. An intuitive method of fabric folding manipulation first involves obtaining a smooth and unfolded fabric configuration before the folding process begins. However, the combination of quasi-static actions such as pick & place and dynamic action like fling proves inadequate in effectively unfolding long-sleeved T-shirts with sleeves mostly tucked inside the garment. To address this limitation, this paper introduces an improved quasi-static action called pick & drag, specifically designed to handle this type of fabric configuration. Additionally, an efficient dual-arm manipulation system is designed in this paper, which combines quasi-static (including pick & place and pick & drag) and dynamic fling actions to flexibly manipulate fabrics into unfolded and smooth configurations. Subsequently, keypoints of the fabric are detected, enabling autonomous folding. To address the scarcity of publicly available keypoint detection datasets for real fabric, we gathered images of various fabric configurations and types in real scenes to create a comprehensive keypoint dataset for fabric folding. This dataset aims to enhance the success rate of keypoint detection. Moreover, we evaluate the effectiveness of our proposed system in real-world settings, where it consistently and reliably unfolds and folds various types of fabrics, including challenging situations such as long-sleeved T-shirts with most parts of sleeves tucked inside the garment. Specifically, our method achieves a coverage rate of 0.822 and a success rate of 0.88 for long-sleeved T-shirts folding

IEEE Access Special Section Editorial: Security and Trusted Computing for Industrial Internet of Things: Research Challenges and Opportunities

Industrial IoT (IIoT) interconnects critical devices and sensors in critical infrastructure sectors with existing Internet of Things (IoT) devices and applications. Generally, IIoT deployment allows organizations and users to gain invaluable insights into industrial processes and achieve high-productivity gains while reducing cost. Their role will be increasingly important as we move toward Industry 5.0. Hence, it is also crucial to understand and address any security and privacy risks that may arise, including those discussed in the articles accepted in this Special Section

Prolonged exposure of cortical neurons to oligomeric amyloid-β impairs NMDA receptor function via NADPH oxidase-mediated ROS production: protective effect of green tea (–)-epigallocatechin-3-gallate

Excessive production of Aβ (amyloid β-peptide) has been shown to play an important role in the pathogenesis of AD (Alzheimer's disease). Although not yet well understood, aggregation of Aβ is known to cause toxicity to neurons. Our recent study demonstrated the ability for oligomeric Aβ to stimulate the production of ROS (reactive oxygen species) in neurons through an NMDA (N-methyl-d-aspartate)-dependent pathway. However, whether prolonged exposure of neurons to aggregated Aβ is associated with impairment of NMDA receptor function has not been extensively investigated. In the present study, we show that prolonged exposure of primary cortical neurons to Aβ oligomers caused mitochondrial dysfunction, an attenuation of NMDA receptor-mediated Ca2+ influx and inhibition of NMDA-induced AA (arachidonic acid) release. Mitochondrial dysfunction and the decrease in NMDA receptor activity due to oligomeric Aβ are associated with an increase in ROS production. Gp91ds-tat, a specific peptide inhibitor of NADPH oxidase, and Mn(III)-tetrakis(4-benzoic acid)-porphyrin chloride, an ROS scavenger, effectively abrogated Aβ-induced ROS production. Furthermore, Aβ-induced mitochondrial dysfunction, impairment of NMDA Ca2+ influx and ROS production were prevented by pre-treatment of neurons with EGCG [(−)-epigallocatechin-3-gallate], a major polyphenolic component of green tea. Taken together, these results support a role for NADPH oxidase-mediated ROS production in the cytotoxic effects of Aβ, and demonstrate the therapeutic potential of EGCG and other dietary polyphenols in delaying onset or retarding the progression of AD

Exploring the Group Difference in the Nonlinear Relationship between Commuting Satisfaction and Commuting Time

Analyzing commuting-time satisfaction could help to improve the subjective well-being of society. This study aimed to explore the nonlinear relationship between commuting satisfaction and commuting times in different groups and its influencing factors. An empirical study was conducted in Kunming, China. Firstly, applying a random forest algorithm revealed that there was a nonlinear relationship between commuting satisfaction and commuting time. Secondly, the k-means clustering algorithm was used to divide the respondents into three types of commuter: short-duration-tolerant (group 1), medium-duration-tolerant (group 2), and long-duration-tolerant (group 3). It was found that the commuting-time satisfaction of these three clustered groups had different threshold effects. Specifically, the commuting satisfaction of group 1 showed a nonlinear downward trend, which decreased significantly at 12 and 28 min, respectively; the commuting satisfaction of group 2 rapidly decreased at 35 min; the commuting satisfaction of group 3 first increased in the range of 20–30 min, decreased significantly after 45 min, and decreased sharply above 70 min. These time thresholds were consistent with the ideal commuting times (ICTs) and tolerance thresholds of the commuting times (TTCTs) of the three clustered groups, which indicates that the ICT and TTCT had significant effects on commuting satisfaction. Lastly, the results of the multinominal logistic model showed that variables such as the commuting mode, job–housing distance, income, and educational background had significant effects on the three clustered groups. The policy implications of the study are that commuting circles should be planned with the TTCT as a constraint boundary and ICT as the optimal goal; in addition, different strategies should be adopted for different commuting groups to improve commuting satisfaction