RESUMEN
Global warming has created problems for human life, and it has been increasing for a few years. All the developing and developed countries are establishing policies to attain zero carbon status. This study extends the ongoing debate on carbon emissions. It examines the effect of natural resources and RE (Biofuel and other renewable sources) on greenhouse gas (CO2 emission and PM2.5) emissions while using data over 22 years (1999-2021) from G7 countries. In addition, this study has investigated the effect of carbon taxes, financial development, and environmental policies on carbon neutrality. The cross-sectional-ARDL, the Common correlated effect means group (CCEMG), and the Augmented mean group (AMG) cutting-edge model have been employed. Quantile regression has been employed for robustness. The study results demonstrate that biofuel and other renewable energy (RE) sources, carbon taxes, environmental policy, and eco-innovation decrease greenhouse gas emissions (CO2 emissions). Meanwhile, financial development, and natural resource dependence positively impact carbon neutrality. The robustness result also verifies the findings from CS-ARDL, AMG, and CCEMG methods. The empirical findings are used to infer policy implications for G7 economies.
RESUMEN
We reported a new technology for transforming N-containing organic wastes (such as aniline, adiponitrile, and tar from preoxidized PAN fibers) into N-doped carbon nanotubes (N-CNTs) with nanosized iron or nickel catalysts in two consecutive reactors at 650-800 °C. Most of the reactants were converted, and N-CNTs in large amounts were produced in the first stage of the reactor, while the reactants could be completely removed in the second stage of the reactor. The doping content of N-species on CNTs easily approached 10-22 wt %, much higher than most of the earlier reported methods. The results were potentially useful for the treatment of toxic cyano-organics and the production of high-end products simultaneously.
RESUMEN
The Assistive Robotic Arm Extender (ARAE) is an upper limb assistive and rehabilitation robot that belongs to the end-effector type, enabling it to assist patients with upper limb movement disorders in three-dimensional space. However, the problem of gravity compensation for the human upper limb with this type of robot is crucial, which directly affects the deployment of the robot in the assistive or rehabilitation field. This paper presents an adaptive gravity compensation framework that calculates the compensated force based on the estimated human posture in 3D space. First, we estimated the human arm joint angles in real-time without any wearable sensors, such as inertial measurement unit (IMU) or magnetic sensors, only through the kinematic data of the robot and established human model. The performance of the estimation method was evaluated through a motion capture system, which validated the accuracy of joint angle estimation. Second, the estimated human joint angles were input to the rigid link model to demonstrate the support force profile generated by the robot. The force profile showed that the support force provided by the developed ARAE robot could adaptively change with human arm postures in 3D space. The adaptive gravity compensation framework can improve the usability and feasibility of the 3D end-effector rehabilitation or assistive robot.