HOP Queue: Hyperspectral Onboard Processing Queue Demonstration

The HOP Queue (Hyperspectral Onboard Processing Queue) demonstration leverages emerging COTS AI accelerators and GPUs to perform on-board processing of hyperspectral imagery data, with the aim of providing near- real time conservation-oriented data and metrics from Low Earth Orbit (LEO). These include forest health, fire detection, and coastal water health. Phase 1 of this project is currently underway, including a completed lab demonstration of this technology and ongoing flight testing. The data from this mission will support Northrop Grumman’s enterprise “Technology for Conservation” campaign and will be provided to NASA’s Surface Biology and Geology (SBG) organization, as well as other conservation efforts

Creating and curating an archive: Bury St Edmunds and its Anglo-Saxon past

This contribution explores the mechanisms by which the Benedictine foundation of Bury St Edmunds sought to legitimise and preserve their spurious pre-Conquest privileges and holdings throughout the Middle Ages. The archive is extraordinary in terms of the large number of surviving registers and cartularies which contain copies of Anglo-Saxon charters, many of which are wholly or partly in Old English. The essay charts the changing use to which these ancient documents were put in response to threats to the foundation's continued enjoyment of its liberties. The focus throughout the essay is to demonstrate how pragmatic considerations at every stage affects the development of the archive and the ways in which these linguistically challenging texts were presented, re-presented, and represented during the Abbey’s history

Optimizing community-level surveillance data for pediatric asthma management

Community-level approaches for pediatric asthma management rely on locally collected information derived primarily from two sources: claims records and school-based surveys. We combined claims and school-based surveillance data, and examined the asthma-related risk patterns among adolescent students.Symptom data collected from school-based asthma surveys conducted in Oakland, CA were used for case identification and determination of severity levels for students (high and low). Survey data were matched to Medicaid claims data for all asthma-related health care encounters for the year prior to the survey. We then employed recursive partitioning to develop classification trees that identified patterns of demographics and healthcare utilization associated with severity.A total of 561 students had complete matched data; 86.1% were classified as high-severity, and 13.9% as low-severity asthma. The classification tree consisted of eight subsets: three indicating high severity and five indicating low severity. The risk subsets highlighted varying combinations of non-specific demographic and socioeconomic predictors of asthma prevalence, morbidity and severity. For example, the subset with the highest class-prior probability (92.1%) predicted high-severity asthma and consisted of students without prescribed rescue medication, but with at least one in-clinic nebulizer treatment. The predictive accuracy of the tree-based model was approximately 66.7%, with an estimated 91.1% of high-severity cases and 42.3% of low-severity cases correctly predicted.Our analysis draws on the strengths of two complementary datasets to provide community-level information on children with asthma, and demonstrates the utility of recursive partitioning methods to explore a combination of features that convey asthma severity. Keywords: Asthma, Classification, Risk stratification, Statistical data analysis, Disease managemen