51 resultados para Next-App


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Do you pronounce the /r/ in 'arm'? Do you call a shelf a 'sheuf'? And what on earth is a 'hoddy-doddy'? There is extensive variation in English dialects: this is why your answers to such questions will allow this app to localize your broader dialect region on a map of England. Did your home dialect change over time? Our algorithm is based on historical data from the Survey of English Dialects. If it guesses where you are from correctly, your home dialect has probably remained stable over the past decades. If the guess is far off, however, it is probably because of dialect change. - Can we localize your dialect based on your pronunciation of 26 words? - Record your dialect and listen to recordings of other users and to historical dialect recordings! - Choose a pronunciation variant, e.g. 'sheuf', and discover where in England it is used...or choose a place and explore its dialect!

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Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference. Objective: The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires. Methods: The study was conducted at the Bern University Hospital, “Inselspital” (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital’s restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user’s experience with GoCARB. Results: The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use. Conclusions: This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.

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To increase our understanding of the formation of students' intentions to found an own firm, research needs to systematically integrate theory of planned behavior, resource-based view, and family business literature. To date, however, an explicit and systematic integration of these perspectives cannot be found. We attempt to close this gap by explicitly investigating founding intentions of students with family business background. More specifically, we examine how the provision of human, social, and financial resources by the family affects students' desirability and feasibility perceptions, and ultimately founding intentions. Our analysis based on a sample of 14'290 students from 26 countries reveals that both desirability and feasibility perceptions mediate the relationships between all three types of resources and founding intentions. Interestingly, the provision of financial resources is negatively related to both desirability and feasibility perceptions. These findings illustrate the research potential of a combination of theory of planned behavior with the resource-based view, especially in the family business context. Our study thus offers valuable contributions to literature on career choices, theory of planned behavior, and family business, as well as to practice.