International Business Machines Corporation

Thomas Henry Osiecki

Ying Liu

Lucas Correia Villa Real

Hans-Jurgen Eickelman

Chin Ngai Sze

Sani Richard Nassif

William Evan Speight

Damir Anthony Jamsek

Anne Elizabeth Gattiker

Mark William Stephenson

Jan Simon Rellermeyer

Min-Yu Tsai

Aggregating and transforming data, and performing analytics thereupon, for application-specific optimization based on multiple data sources. The data is preferably ingressed automatically, and may originate from various public and/or private data sources. Data transformation preferably aligns the data aggregated from the various sources, to thereby allow meaningful referencing. Complex and non-aligned data can therefore be consolidated, such that it is readily digestible by simulation (or other) software. In an embodiment, risk of flooding for a supply chain is computed from the aggregated and transformed data, using data analytics based on physical computation for flood risk assessment, allowing the supply chain to be optimized with regard to threat of flooding and/or actual flooding. In another embodiment, risk of wild fire may be assessed. Other types of risk may also be assessed.

Aggregation and analytics for application-specific optimization based on multiple data sources

Simulating particle beam interactions includes identifying a set of n functions F, F, . . . , Fcorresponding to a plurality of different physical aspects of a particle beam, performing simulations of each Fusing a full physics model, selecting for each Fa distribution function fthat models relevant behavior and reducing computation of the full physics model for each Fby replacing Fwith a distribution function f. The computation reduction includes comparing a set of simulations wherein each freplaces its respective Fto determine if relevant behavior is accurately modeled and selecting one of for Ffor each n, for a Monte Carlo simulation based on a runtime and accuracy criteria.

Fast and accurate proton therapy dose calculations

A method, system, and computer program product for random number generation using a network of mobile devices are provided in the illustrative embodiments. From a set of mobile devices, a corresponding set of data packets is received. A presence of raw sensor data is detected in a first data packet received from a first mobile device in the set of mobile devices. The raw sensor data comprises data corresponding to changing value of an output of a sensor in a set of sensors installed in the first mobile device. The raw sensor data is separated from the first data packet, resulting in an original data packet. A first random number is generated using the raw sensor data.

Random number generation using a network of mobile devices

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Austin, TX, United States of America

Thomas Henry Osiecki