Twisted-mode VECSEL with intracavity second harmonic generation.

A vertical external cavity surface emitting laser (VECSEL) that uses intracavity second harmonic generation (SHG) and the twisted mode technique for power scaling was explored. The effects of the twisted mode technique are shown and discussed by examining the mode structure and spectrum of the fundamental mode. The maximum SHG output was 1 W at 458 nm while the fundamental was lasing in the TEM mode.

Outdoor Adventure and Experiential Education and COVID-19: What Have We Learned?

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) now known as COVID-19 changed the world and the outdoor adventure and experiential education (OAEE) fields were not immune. These changes significantly impacted various OAEE programs in multiple ways and at different levels of intensity. The purpose of this study was to ascertain the impacts of the COVID-19 pandemic on the OAEE fields and identify how OAEE organizations have responded to those impacts.

High output power, single mode, and TEM operation of a multiple gain chip VECSEL using a twisted-mode configuration.

A vertical external cavity surface emitting laser (VECSEL) has been developed for a sodium guide star application. Stable single frequency operation with 21 W of output power near 1178 nm with multiple gain elements while lasing in the TEM mode has been achieved. Higher output power results in multimode lasing.

GEORGE®: A Pilot Study of a Smartphone Application for Huntington's Disease.

Background: Current Huntington's disease (HD) measures are limited to subjective, episodic assessments conducted in clinic. Smartphones can enable the collection of objective, real-world data but their use has not been extensively evaluated in HD.

Objective: Develop and evaluate a smartphone application to assess feasibility of use and key features of HD in clinic and at home.

An Electronic Medical Record-Based Discharge Disposition Tool Gets Bundle Busted: Decaying Relevance of Clinical Data Accuracy in Machine Learning.

Background: Determining discharge disposition after total joint arthroplasty (TJA) has been a challenge. Advances in machine learning (ML) have produced computer models that learn by example to generate predictions on future events. We hypothesized a trained ML algorithm's diagnostic accuracy will be better than that of current predictive tools to predict discharge disposition after primary TJA.