1,727,102 research outputs found
Xuefei Yang, guitar
Full text linkIssac AlbenizAgustin BarriosJoaquin RodrigoStephen GossEnrique Granados, arr. Xuefei Yangtraditional, arr. Jorge MorelAntonio LauroLeo Brouwe
Chinese cities are too powerful for their own good
No full textChinese cities have too much power and too many responsibilities. Over-powerful cities have exacerbated regional disparity, says Xuefei Ren (Michigan State University)
Datasets for Stealthy False Data Injection Attacks (SFDIA) against Smart Grid
No full textThis database contains two FDIA datasets, ‘FDIA-1-HV-mixed–0-no_sw’ and ‘FDIA-1-EHV-mixed–0-no_sw’, generated on two power grids, ‘1-HV-mixed–0-no_sw’ and ‘1-EHV-mixed–0-no_sw’, respectively. The ‘1-HV-mixed–0-no_sw’ is a high voltage level grid with 110 KV transmission lines, denoted by Grid-HV, which is monitored by 355 measurements, with 35,136 profiles for dynamical power load and generation. The ‘1-EHV-mixed–0-no_sw’ is an extra-high voltage level grid with 220-380 KV transmission lines, denoted by GridEHV, which is monitored by 3,952 measurements, with 35,136 profiles for dynamical power load and generation
Sensitivity analysis and machine learning for computationally challenging computer codes
Full text linkMicrotask crowdsourcing can both empower and marginalise workers
No full textCrowd workers’ voices must be heard, write Xuefei (Nancy) Deng, K.D. Joshi and Robert D. Gallier
Global sensitivity analysis in epidemiological modeling
No full textOperations researchers worldwide rely extensively on quantitative simulations to model alternative aspects of the COVID-19 pandemic. Proper uncertainty quantification and sensitivity analysis are fundamental to enrich the modeling process and communicate correctly informed insights to decision-makers. We develop a methodology to obtain insights on key uncertainty drivers, trend analysis and interaction quantification through an innovative combination of probabilistic sensitivity techniques and machine learning tools. We illustrate the approach by applying it to a representative of the family of susceptible-infectious-recovered (SIR) models recently used in the context of the COVID-19 pandemic. We focus on data of the early pandemic progression in Italy and the United States (the U.S.). We perform the analysis for both cases of correlated and uncorrelated inputs. Results show that quarantine rate and intervention time are the key uncertainty drivers, have opposite effects on the number of total infected individuals and are involved in the most relevant interactions
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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