Use of improved (biomass) cookstoves (ICs) has been widely proposed as a Black Carbon (BC) mitigation measure with significant climate and health benefits. ICs encompass a range of technologies, including natural draft (ND) stoves, which feature structural modifications to enhance air flow, and forced draft (FD) stoves, which additionally employ an external fan to force air into the combustion chamber. We present here, under Project Surya, the first real-time in situ Black Carbon (BC) concentration measurements from five commercial ICs and a traditional (mud) cookstove for comparison. These experiments reveal four significant findings about the tested stoves. First, FD stoves emerge as the superior IC technology, reducing plume zone BC concentration by a factor of 4 (compared to 1.5 for ND). Indoor cooking-time BC concentrations, which varied from 50 to 1000 μg m(-3) for the traditional mud cookstove, were reduced to 5-100 μg m(-3) by the top-performing FD stove. Second, BC reductions from IC models in the same technology category vary significantly: for example, some ND models occasionally emit more BC than a traditional cookstove. Within the ND class, only microgasification stoves were effective in reducing BC. Third, BC concentration varies significantly for repeated cooking cycles with same stove (standard deviation up to 50% of mean concentration) even in a standardized setup, highlighting inherent uncertainties in cookstove performance. Fourth, use of mixed fuel (reflective of local practices) increases plume zone BC concentration (compared to hardwood) by a factor of 2 to 3 across ICs.
This paper addresses the problem of how to select the optimal number of sensors and how to determine their placement in a given monitored area for multimedia surveillance systems. We propose to solve this problem by obtaining a novel performance metric in terms of a probability measure for accomplishing the task as a function of set of sensors and their placement. This measure is then used to find the optimal set. The same measure can be used to analyze the degradation in system's performance with respect to the failure of various sensors. We also build a surveillance system using the optimal set of sensors obtained based on the proposed design methodology. Experimental results show the effectiveness of the proposed design methodology in selecting the optimal set of sensors and their placement.
Variable pricing cloud resources are the most recent advancement in cloud computing business models. Cloud vendors like Amazon Web Services, a.k.a. Amazon AWS provide a new cloud instance type known as "Spot instance". The distinguishing feature of spot instance is its dynamic pricing. The price of spot instances varies dynamically with time based on demand and supply of cloud resources in the datacenters across the globe. Customers place bids to obtain spot instances using an online auction platform. The auction platform determines the market clearance price, a.k.a. "Spot price" and the users whose bids are above the aforementioned price obtain the instances. Cloud vendors provide current and archived spot price data to assist their customers in bidding process. The major challenge for the customers in this new business model is to predict the spot price before placing their bids. In this paper, we have provided a novel algorithm for spot price prediction. We also have instantiated and demonstrated the proposed algorithm. The results show high accuracy of 9.4% Mean Absolute Percent Error (MAPE) for short term (one day ahead) and less 20% MAPE for long term (five days ahead) forecasting.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.