Automation, Information, and Management Systems (United States)

Outcome measures currently used to assess function in Angelman Syndrome (AS) may be affected by participant anxiety in the clinic, use of a measure outside of age norms, and a reliance on caregiver report. This study aimed to leverage the process used to develop the Duchenne Video Assessment to develop, pilot, and evaluate tasks to be recorded in AS to lay the foundation for the Angelman Syndrome Video Assessment (ASVA), which is being developed for the assessment of everyday functional skills in individuals with AS in their home environment. The task list was informed by published conceptual disease models identifying four domains of interest (i.e. communication, motor skills (fine and gross), self-care and executive functioning) and determined by a team of experts based on family and clinician input. The study was conducted remotely in the home environment, with families capturing videos of everyday activities and participating in an exit interview about their experience. Videos were evaluated, and caregiver interview transcripts were coded and analyzed to determine whether each task would be included in the finalized measure. Eleven dyads (i.e., caregiver and individual with AS) completed the study. Individuals with AS between 4 and 39 years of age were enrolled. The video completion rate was 96%, with 99% of the submitted videos meeting quality standards. Caregivers endorsed the value of assessing these individuals in their home environment. The video capture list was reduced to 13 tasks for ambulatory individuals and 12 tasks for minimally ambulatory individuals. The ASVA is a novel tool that captures data on the daily functioning of individuals with AS in their home environment. If it is validated in the future, this tool presents a promising new approach to evaluate participant functioning in clinical trials and for clinical care.

Purpose: This paper proposes a novel Artificial Intelligence (AI)-driven model to enhance Emotional Intelligence (EI) among Generation Z (Gen Z), aiming to improve their emotional adaptability in academic, professional, and social environments. Design/Methodology/Approach: A mixed-method research design was employed, comprising quantitative surveys using qualitative semi-structured interviews. Findings: Preliminary results indicate that AI can effectively assess and enhance EI competencies, especially empathy, emotional regulation, and social skills, within Gen Z. Post-intervention results indicated a notable enhancement in Emotional Intelligence scores among the experimental participants. Research Limitations/Implications: The study is limited by its reliance on secondary data and sample representativeness. Long-term effectiveness and generalizability require further research. Practical Implications: The AI-based model can be deployed in educational institutions and workplace settings to support Gen Z’s emotional development. Originality/Value: This research is among the first to propose a comprehensive AIpowered EI enhancement model tailored specifically for the digital-native Gen Z cohort.

Abstract Recent advances in geostationary imaging have enabled the derivation of high spatiotemporal‐resolution cloud‐motion winds for the study of mesoscale unsteady flows. Due to the general absence of ground truth, the quality assessment of satellite winds is challenging. In the current limited practice, straightforward plausibility checks on the smoothness of the retrieved wind field or tests on aggregated trends such as the mean velocity components are applied for quality control. In this study, we demonstrate additional diagnostic tools based on feature extraction from the retrieved velocity field. Lagrangian Coherent Structures (LCS), such as vortices and transport barriers, guide and constrain the emergence of cloud patterns. Evaluating the alignment of the extracted LCS with the observed cloud patterns can potentially serve as a test of the retrieved wind field to adequately explain the time‐dependent dynamics. We discuss the suitability and expressiveness of direct, geometry‐based, texture‐based, and feature‐based flow visualization methods for the quality assessment of high spatiotemporal‐resolution winds through the real‐world example of an atmospheric Kármán vortex street and its laboratory archetype, the 2D cylinder flow.

To estimate the number of individuals in the United States diagnosed with cancer as children (ages 0-19 years) as of 2005, with a focus on those surviving for >30 years. To estimate the national prevalence of survivors of childhood cancers, we used data from the Surveillance Epidemiology and End Results program from 1975 to 2004. Long-term childhood cancer survivors, diagnosed before 1975, were estimated using incidence and survival models extrapolated into years before 1975. We estimated that there are a total of 328,652 survivors of childhood cancer in the United States as of January 1, 2005, of these, 24% have survived >30 years since diagnosis. The cancer sites with the largest number of survivors are brain (51,650), acute lymphoblastic leukemia (49,271), germ cell tumors (34,169), and Hodgkin lymphoma (31,598). Sites with higher proportions of survivors diagnosed >30 years ago are germ cell (43%), soft tissue (38%), renal (34%), and bone (26%). Historical trends from Connecticut data show major improvements in survival for all of the childhood cancer sites. The number of survivors of childhood cancers is expected to increase in the future consequent to the lifesaving advances in treatment introduced after 1970, especially for acute lymphoblastic leukemia. Because this population is at increased risk for illness-related morbidity and mortality, appreciating the number of survivors who were treated as children is important both to determining the national cancer burden and planning for the future health care needs of these individuals.

A class of nonlinear state-space models, characterized by a single source of randomness, is introduced. A special case, the model underpinning the multiplicative Holt-Winters method of forecasting, is identified. Maximum likelihood estimation based on exponential smoothing instead of a Kalman filter, and with the potential to be applied in contexts involving non-Gaussian disturbances, is considered. A method for computing prediction intervals is proposed and evaluated on both simulated and real data.