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Economic valuation of nature’s contribution guides the decision-making process for sustainable resource management. As a pioneer effort to calculate nature’s contribution to the Himalayan plateau, this study uses an inclusive wealth... more
Economic valuation of nature’s contribution guides the decision-making process for sustainable resource management. As a pioneer effort to calculate nature’s contribution to the Himalayan plateau, this study uses an inclusive wealth method and geographic information system. The statistical analysis estimates the natural capital (NC) of Ladakh, India, and discusses the policy to ensure sustainable management of the natural resources. The economic value of nature’s contribution in Ladakh is increasing from the year 1990 to 2018. These economic valuations for nature’s contribution include different attributes, such as forest, agricultural land, animal husbandry, fishery, minerals and fossil fuels. Nature’s contribution to the economy of the study area is mainly from fossil fuels (91%) and minerals (6.9%) driven. This trend is not sustainable for long-term sustainable growth of the area. The results suggest several policies to ensure sustainable NC management in Ladakh, India.
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Path planning for mobile robot swarm is considered as a challenging task due to the complexity it involves. Mobile robot traveling in an unknown environment may face many challenges like the static and dynamic obstacles in the path and... more
Path planning for mobile robot swarm is considered as a challenging task due to the complexity it involves. Mobile robot traveling in an unknown environment may face many challenges like the static and dynamic obstacles in the path and the other robots. The aim of the path planning is to direct the mobile robot toward a target and navigate through the environment without any prior knowledge about the environment, also to avoid the obstacles and other robots. This paper introduces particle swarm optimization (PSO) with dynamic obstacle avoidance technique for robot path planning. Our proposed systems are simulated and tested in Processing IDE, for different environments. We measured the performance of the system by measuring the time for the arrival of 90 percent robot at the target.
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For a swarm of mobile robots in an unknown environment, the most challenging task is to plan the optimal path and also to learn the environmental parameters. Machine learning is one of the solutions to this problem. This paper proposes a... more
For a swarm of mobile robots in an unknown environment, the most challenging task is to plan the optimal path and also to learn the environmental parameters. Machine learning is one of the solutions to this problem. This paper proposes a combination of particle swarm optimization (PSO) and Q-value based reinforcement learning (Q-Learning) for a swarm of mobile robots to find the optimal path in an unknown environment and to learn the environment. Q-learning combined with PSO enable the robots to learn the unknown environment with reward and action selection policy while reducing the time to find the optimal path in the environment using the iterative improvement method of PSO. The proposed algorithm is simulated and found performing faster than Q-learning and PSO performing alone. We also compared with some other established algorithm where the proposed algorithm outperforms each of them in accuracy and speed.
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SummaryThe production of goods and services generates greenhouse gases (GHGs) and air pollution both directly and through the activities of the supply chains on which they depend. The analysis of the latter—called embodied... more
SummaryThe production of goods and services generates greenhouse gases (GHGs) and air pollution both directly and through the activities of the supply chains on which they depend. The analysis of the latter—called embodied emissions—caused by internationally traded goods and services is the subject of this article. We find that trade openness increases embodied emissions in international trade (EET). We also examine the impact of sector trade on EET. By applying a fixed‐effect model using large balanced panel data from 187 countries between 1990 and 2011, we determine that each unit of increase in trade openness results in a 10% to 23% increase in GHGs EET. The sector trade effect is also significant for the embodied emissions of carbon dioxide, methane, nitrous oxide, carbon monoxide, nonmethane volatile organic compounds, particulates and sulfur dioxide. Our findings also clearly indicate that the impact of the gross domestic product (GDP) on the embodied emissions in exports is p...
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ABSTRACT IEEE Standard 1459 defines the power components based on the fast Fourier series. However, Fourier series assumes the signal is periodic in nature, and provides erroneous assessment of the power components in the presence of... more
ABSTRACT IEEE Standard 1459 defines the power components based on the fast Fourier series. However, Fourier series assumes the signal is periodic in nature, and provides erroneous assessment of the power components in the presence of transient signals. Therefore, a new method is proposed for the evaluation of the IEEE Standard 1459 power components based on the time-frequency distribution (TFD) and the cross time-frequency distribution (XTFD) of transient signals. The TFD and XTFD preserve simultaneous time and variable frequency information of transient signals, and estimate the instantaneous power components according to the IEEE Standard 1459. Results of computer simulated and real-world power quality (PQ) disturbance case studies justify the effectiveness of the proposed method for the assessment of instantaneous power components under transient condition. For full access to the article, contact the authors: ghaderi@email.sc.edu
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Abstract Mongolia is a natural resource-rich country, so that its stock market is highly dependent on price fluctuations of commodities, particularly copper and coal. One crucial problem is that the responses of the stock market... more
Abstract Mongolia is a natural resource-rich country, so that its stock market is highly dependent on price fluctuations of commodities, particularly copper and coal. One crucial problem is that the responses of the stock market performance to a shock of the commodity price can be nonlinear or asymmetric. This study addresses such asymmetric linkages by applying nonlinear autoregressive distributed lags (NARDL) models during the period from January 2007 to December 2018. The results show clear evidence of asymmetric long-run relationships between stock price and the commodity price, supporting that different behaviors of stock price in response to positive and negative shocks on the commodity price. Specifically, our analysis presents a positive relationship between copper price and stock price in the case of a positive shock on copper price, but no clear relationship in the case of a negative shock on copper price. The results also reveal a positive relationship between coal price and stock price in the case of a negative shock on coal price, but no clear relationship in the case of a positive shock on coal price. The Mongolian stock market is favorably sensitive to an increase in copper price but vulnerable to a decline in coal price. Possible explanations behind the results are also discussed.