Optimizing tourist flows through operative carrying capacity assessment: The case of Bakkhali coastal tourism, W.B., India

Carrying capacity assessment of nature-based tourist destinations is important for keeping the consumption of natural resources and anthropogenic pollution levels within environmentally safe and sustainable limits. With the mostly rural character of such destinations, the local community's well-being also needs to be prioritized. Exposure to natural hazards and climate crises have further exacerbated concerns about the long-term sustainability of these locations. The interrelationship between tourism intensity and its impacts clearly reflects in Butler’s Tourism Area Life Cycle model of 1980. The ‘elements of capacity’ and their ‘critical range’ mark a significant threshold in the model that leads us to the concept of Carrying Capacity. The capacity may be physical, spatial, ecological, environmental, social, economic, management, and governance, among others. This is also linked with the quality of touristic experience and satisfaction. In this context, aiming to understand the optimum level of tourist traffic flow in Bakkhali, one of the popular beach destinations of the deltaic island system of the Indian Sundarbans, this study assesses its visitor carrying capacity at three levels—physical, real, and effective. It also briefly introduces the idea of ‘operative’ carrying capacity at the fourth level. The study is based on tourist data till 2019 and adopts the well-established methodological framework of carrying capacity assessment applied widely in several settings. The result suggests that tourism operations at Bakkhali may optimally handle 2040 visitors per day, which may be stretched to a maximum of 2267 visitors per day. This may be used as baseline information for sustainable coastal tourism policy framing in long term while planning for tourism management and infrastructure development in the Sundarban region in immediate terms

Flow Shape Design for Microfluidic Devices Using Deep Reinforcement Learning

Microfluidic devices are utilized to control and direct flow behavior in a wide variety of applications, particularly in medical diagnostics. A particularly popular form of microfluidics -- called inertial microfluidic flow sculpting -- involves placing a sequence of pillars to controllably deform an initial flow field into a desired one. Inertial flow sculpting can be formally defined as an inverse problem, where one identifies a sequence of pillars (chosen, with replacement, from a finite set of pillars, each of which produce a specific transformation) whose composite transformation results in a user-defined desired transformation. Endemic to most such problems in engineering, inverse problems are usually quite computationally intractable, with most traditional approaches based on search and optimization strategies. In this paper, we pose this inverse problem as a Reinforcement Learning (RL) problem. We train a DoubleDQN agent to learn from this environment. The results suggest that learning is possible using a DoubleDQN model with the success frequency reaching 90% in 200,000 episodes and the rewards converging. While most of the results are obtained by fixing a particular target flow shape to simplify the learning problem, we later demonstrate how to transfer the learning of an agent based on one target shape to another, i.e. from one design to another and thus be useful for a generic design of a flow shape.Comment: Neurips 2018 Deep RL worksho