For more than a century the Medal of Honor has served as a revered symbol of valor and service to the nation. In the 1990s Japanese American veterans requested a review of their service in World War II to determine whether the U.S. Army has overlooked any of their number for the award. In 1996 a team of historians began a review of all Asian Americans and Pacific Islanders who fought in that war. Their work resulted in the award of twenty-two new Medals of Honor in June 2000. The review was also a revealing journey into the challenges of amending public memory.
A picture is worth a thousand words, and in design metric estimation, a word may be worth a thousand features. Pictures are awarded this worth because they encode a plethora of information. When evaluating designs, we aim to capture a range of information, including usefulness, uniqueness, and novelty of a design. The subjective nature of these concepts makes their evaluation difficult. Still, many attempts have been made and metrics developed to do so, because design evaluation is integral to the creation of novel solutions. The most common metrics used are the consensual assessment technique (CAT) and the Shah, Vargas-Hernandez, and Smith (SVS) method. While CAT is accurate and often regarded as the “gold standard,” it relies on using expert ratings, making CAT expensive and time-consuming. Comparatively, SVS is less resource-demanding, but often criticized as lacking sensitivity and accuracy. We utilize the complementary strengths of both methods through machine learning. This study investigates the potential of machine learning to predict expert creativity assessments from non-expert survey results. The SVS method results in a text-rich dataset about a design. We utilize these textual design representations and the deep semantic relationships that natural language encodes to predict more desirable design metrics, including CAT metrics. We demonstrate the ability of machine learning models to predict design metrics from the design itself and SVS survey information. We show that incorporating natural language processing improves prediction results across design metrics, and that clear distinctions in the predictability of certain metrics exist.
A pilot study was conducted at a day care center for those affected with Alzheimer's disease (AD). The day care center is affiliated with a statewide Department of Mental Health. The purpose ofthe study was to determine the adequacy of caregivers' knowledge of nutrition in general and of the nutritional needs of the AD affected person, specifically. Also studied was the caregivers'perceived amount of burden and the caregivers' levels of depression.It wasfound that the caregivers 'knowledge ofnutrition was below average overall, despite thefact that many were educated beyond high school. A high level of caregiver perceived burden was also discovered. Depression, howevetr did not seem to be afactor with these caregivers.While this was only a pilot study (n= 12), one can begin to see the importance of assisting caregivers with nutrition education. The betterprepared caregivers are to take care of their elderly, the longer it is before nursing home placement becomes a consideration. Health care workers are in an excellent position to assume this teaching role.
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