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The observation ofγrays from the decay of⁴⁴Ti in the remnants of core-collapse supernovae (CCSNe) provides crucial information regarding the nucleosynthesis occurring in these events, as⁴⁴Ti production is sensitive to CCSNe conditions. The final abundance of⁴⁴Ti is also sensitive to specific nuclear input parameters, one of which is the⁵⁷Ni(p,γ)⁵⁸Cu reaction rate. A precise rate for⁵⁷Ni(p,γ)⁵⁸Cu is thus critical if⁴⁴Ti production is to be an effective probe into CCSNe. To experimentally constrain the⁵⁷Ni(p,γ)⁵⁸Cu rate, the structure properties of⁵⁸Cu were measured via the⁵⁸Ni(³He,t)⁵⁸Cu*(γ) reaction using GODDESS (GRETINA ORRUBA Dual Detectors for Experimental Structure Studies) at Argonne National Laboratory’s ATLAS facility. Details of the experiment, ongoing analysis, and plans are presented.

 

The digitization of legacy infrastructure constitutes an important component of smart cities. While most cities worldwide possess digital maps of their transportation infrastructure, few have accurate digital information on their electric, natural gas, telecom, water, wastewater, and district heating and cooling systems. Digitizing data on legacy infrastructure systems comes with several challenges such as missing data, data conversion issues, data inconsistency, differences in the data format, spatio-temporal resolutions, structure, semantics and syntax, difficulty in providing controlled access to the datasets, and so on. Therefore, we introduce GUIDES, a new data conversion and management framework for urban infrastructure systems, which is comprised of big data analytics, efficient data management techniques, semantic web technologies, methods to ensure information security, and tools that aid visual analytics. The proposed framework facilitates: (i) mapping of urban infrastructure systems; (ii) integration of heterogeneous geospatial data; (iii) a secured way of storing, analyzing and querying data while preserving the semantics; (iv) qualitative and quantitative analysis over several spatio-temporal resolutions; and (v) visualization of static (e.g., land use) and dynamic (e.g., road traffic) information.