Consumption-Based CO2 Emissions Accounting And Scenario Simulation In Asia And Pacific Region

This study uses a consumer-based accounting approach to evaluate CO2 emission factors of 17 major Asia and Pacific countries that distribute all emissions in the supply chain to the commodity up to the final consumption location due to the influence of a country's consumption patterns. In addition, the number of emissions connected with each country's consumption of products and services, mainly in Asia and the Pacific countries, has received little attention. This study contributes to understand the effects of the country's consumption of products and services on carbon emission peaks and formulate efficient carbon-mitigation plans for governments and decision-makers. The accelerating economic growth and industrialization have posed significant challenges to global carbon-mitigation efforts and climate change response; as a result, each country has been provided a higher emphasis on CO2 emission. The Monte Carlo simulation technique has been used to create a dynamic scenario simulation model to investigate possible future peaks of Asia and Pacific countries' carbon emissions, considering the uncertainties of factors. The result shows that total consumption-based CO2 emissions are remarkable in the three Asian countries, including China (387451.95 metric tons (Mt) CO2), Japan (185259.60 Mt CO2), and India (100720.46 Mt CO2). In South Korea, Brunei, and Taiwan, annual consumption emissions are 1.77, 1.62, and 1.49 tons of CO2 per person. In terms of final consumption, the household sector is the supreme noteworthy donor to consumption-based emissions, accounting for 27–56%. The household sector probably peak at 19.7 Gt CO2 as per the dynamic scenario simulation. As for three other types of final demand, the government expenditure will possibly reach at highest 44.0 Gt CO2 by the next 30 years while the capital formation will probably hit its highest emissions at 149.5 Gt CO2.

Spiral Bevel Gears Nonlinear Vibration Having Radial and Axial Misalignments Effects

In gear transmissions, vibration causes noise and malfunction. In actual applications, misalignments contribute to intensifying the destructive effect of vibrations. In this paper, the nonlinear dynamics of a spiral bevel gear pair, with small helix angle, considering different misalignments, are deeply investigated. Axial misalignment, radial misalignment, and the combination of these two types are considered in this study. The governing equation is numerically solved through an implicit Runge–Kutta scheme. Since the main goal of this study is the analysis of the dynamic scenario, the mesh stiffness of the gear pair is obtained from the literature. The dynamical system is nonlinear and time-varying; it is analyzed through time responses, phase portraits, Poincaré maps, and bifurcation diagrams. Results show that, among the considered three cases with different types of misalignments, the spiral bevel gear with axial misalignment is the worst destructive case; aperiodic, subharmonic, and multiperiod responses are observable for this case. It is interesting that the chaotic responses for the case, having both types of misalignments, are less likely for the case with axial misalignment, only.

A MULTI-AGENT REINFORCEMENT LEARNING FRAMEWORK FOR INTELLIGENT MANUFACTURING WITH AUTONOMOUS MOBILE ROBOTS

Intelligent manufacturing (IM) embraces Industry 4.0 design principles to advance autonomy and increase manufacturing efficiency. However, many IM systems are created ad hoc, which limits the potential for generalizable design principles and operational guidelines. This work offers a standardizing framework for integrated job scheduling and navigation control in an autonomous mobile robot driven shop floor, an increasingly common IM paradigm. We specifically propose a multi-agent framework involving mobile robots, machines, humans. Like any cyberphysical system, the performance of IM systems is influenced by the construction of the underlying software platforms and the choice of the constituent algorithms. In this work, we demonstrate the use of reinforcement learning on a sub-system of the proposed framework and test its effectiveness in a dynamic scenario. The case study demonstrates collaboration amongst robots to maximize throughput and safety on the shop floor. Moreover, we observe nuanced behavior, including the ability to autonomously compensate for processing delays, and machine and robot failures in real time.

Exploring Factors Influencing Scenarios Evolution of Waste NIMBY Crisis: Analysis of Typical Cases in China

As the impact factors of the waste Not-In-My-Back Yard (NIMBY) crisis are complex, and the scenario evolution path of it is diverse. Once the crisis is not handled properly, it will bring adverse effects on the construction of waste NIMBY facilities, economic development and social stability. Consequently, based on ground theory, this paper takes the waste NIMBY crisis in China from 2006 to 2019 as typical cases, through coding analysis, scenario evolution factors of waste NIMBY crisis are established. Furtherly, three key scenarios were obtained, namely, external situation (E), situation state (S), emergency management (M), what is more, scenario evolution law of waste NIMBY crisis is revealed. Then, the dynamic Bayesian network theory is used to construct the dynamic scenario evolution network of waste NIMBY crisis. Finally, based on the above models, Xiantao waste NIMBY crisis is taken as a case study, and the dynamic process of scenario evolution network is visually displayed by using Netica. The simulation results show that the scenario evolution network of Xiantao waste NIMBY crisis is basically consistent with the actual incident development process, which confirms the effectiveness and feasibility of the model.

A Fuzzy Gain-Based Dynamic Ant Colony Optimization for Path Planning in Dynamic Environments

Path planning can be perceived as a combination of searching and executing the optimal path between the start and destination locations. Deliberative planning capabilities are essential for the motion of autonomous unmanned vehicles in real-world scenarios. There is a challenge in handling the uncertainty concerning the obstacles in a dynamic scenario, thus requiring an intelligent, robust algorithm, with the minimum computational overhead. In this work, a fuzzy gain-based dynamic ant colony optimization (FGDACO) for dynamic path planning is proposed to effectively plan collision-free and smooth paths, with feasible path length and the minimum time. The ant colony system’s pheromone update mechanism was enhanced with a sigmoid gain function for effective exploitation during path planning. Collision avoidance was achieved through the proposed fuzzy logic control. The results were validated using occupancy grids of variable size, and the results were compared against existing methods concerning performance metrics, namely, time and length. The consistency of the algorithm was also analyzed, and the results were statistically verified.