This paper investigates optimal capital taxation in an innovation-driven growth model. We examine how the optimal capital tax rate varies with externalities associated with R&D and innovation. Our results show that the optimal capital tax rate is higher when (i) the “stepping on toes effect” is smaller, (ii) the “standing on shoulders effect” is stronger, or (iii) the extent of creative destruction is smaller. The optimal capital tax rate is more likely to be positive when there is underinvestment in R&D. Moreover, the optimal capital tax rate and the monopolistic markup exhibit an inverted-U relationship. By calibrating our model to the US economy, we find that the optimal capital tax rate is positive, at a rate of around 6.6%. Finally, we consider a number of extensions and find that the result of a positive optimal capital tax is robust.

This paper examines the effect of a tariff on long-run growth and welfare in a two-country innovation-led growth model. We show that although raising the home country’s tariff reduces the growth and GDP of the foreign country, it will backfire by depressing R&D and growth of the home country. The Nash equilibrium tariffs can be positive, and they are larger when the government expenditure is more beneficial to private production and/or when the productivity of innovation is higher. The presence of positive Nash equilibrium tariffs provides a theoretical explanation for why countries have incentives to implement a tariff policy regardless of its negative effect on growth. Finally, the Nash equilibrium tariffs are higher than the globally optimal tariffs, that is, the levels that maximize the joint welfare of both countries.

Obstacle avoidance is an important issue in robotics. In this paper, the particle swarm optimization (PSO) algorithm, which is inspired by the collective behaviors of birds, has been designed for solving the obstacle avoidance problem. Some animals that travel to the different places at a specific time of the year are called migrants. The migrants also represent the particles of PSO for defining the walking paths in this work. Migrants consider not only the collective behaviors, but also geomagnetic fields during their migration in nature. Therefore, in order to improve the performance and the convergence speed of the PSO algorithm, concepts from the migrant navigation method have been adopted for use in the proposed hybrid particle swarm optimization (H-PSO) algorithm. Moreover, the potential field navigation method and the designed fuzzy logic controller have been combined in H-PSO, which provided a good performance in the simulation and the experimental results. Finally, the Federation of International Robot-soccer Association (FIRA) HuroCup Obstacle Run Event has been chosen for validating the feasibility and the practicability of the proposed method in real time. The designed adult-sized humanoid robot also performed well in the 2015 FIRA HuroCup Obstacle Run Event through utilizing the proposed H-PSO.

This article shows the root cause of Cu corrosion and sulfur contamination due sulfite vapor reflow through drainpipe of load cup to the original three-step Cu CMP equipment. Now, we provide a solution which to modify the arrangement of the drainpipe in original three-step Cu CMP equipment. This is an effective implement for reduction the defects of Cu corrosion and sulfur contamination from long-term observations.