Abstract-Sensors capable of sensing phenomena at high data rates on the order of tens to hundreds of thousands of samples per second are now widely deployed in many industrial, civil engineering, scientific, networking, and medical applications. In aggregate, these sensors easily generate several million samples per second that must be processed within milliseconds or seconds. The computation required includes both signal processing and event stream processing. XStream is a stream processing system for such applications.XStream introduces a new data type, the signal segment, which allows applications to manipulate isochronous (regularly spaced in time) collections of sensor samples more conveniently and efficiently than the asynchronous representation used in previous work. XStream includes a memory manager and scheduler optimizations tuned for processing signal segments at high speeds. In benchmark comparisons, we show that XStream outperforms a leading commercial stream processing system by more than three orders of magnitude. On one application, the commercial system processed 72.7 Ksamples/sec, while XStream processed 97.6 Msamples/sec.

State and local policy-makers in the US have shown interest in transitioning electricity systems toward renewable energy sources and in mitigating harmful air pollution. However, the extent to which subnational renewable energy policies can improve air quality remains unclear. To investigate this issue, we develop a systemic modeling framework that combines economic and air pollution models to assess the projected sub-national impacts of Renewable Portfolio Standards (RPSs) on air quality and human health, as well as on the economy and on climate change. We contribute to existing RPS costbenefit literature by providing a comprehensive assessment of economic costs and estimating economy-wide changes in emissions and their impacts, using a general equilibrium modeling approach. This study is also the first to our knowledge to directly compare the health co-benefits of RPSs to those of carbon pricing. We estimate that existing RPSs in the 'Rust Belt' region generate a health co-benefit of $94 per ton CO 2 reduced ($2-477/tCO 2 ) in 2030, or 8¢ for each kWh of renewable energy deployed (0.2-40¢ kWh −1 ) in 2015 dollars. Our central estimate is 34% larger than total policy costs. We estimate that the central marginal benefit of raising renewable energy requirements exceeds the marginal cost, suggesting that strengthening RPSs increases net societal benefits. We also calculate that carbon pricing delivers health co-benefits of $211/tCO 2 in 2030, 63% greater than the health cobenefit of reducing the same amount of CO 2 through an RPS approach.

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Policies must help decarbonize power and transport sectors