Search results for: Ashley Votta
4 Aggressive Driving in Young Motorists
Authors: Suneel M. Agerwala, Ashley Votta, Briana Hogan, John Yannocone, Steven Samuels, SheilaChiffriller
Abstract:
Road rage is an increasingly prevalent expression of aggression in our society. Its dangers are apparent and understanding its causes may shed light on preventative measures. This study involved a fifteen-minute survey administered to 147 undergraduate students at a North Eastern suburban university. The survey consisted of a demographics section, questions regarding financial investment in respondents- vehicles, experience driving, habits of driving, experiences witnessing role models driving, and an evaluation of road rage behavior using the Driving Vengeance Questionnaire. The study found no significant differences in driving aggression between respondents who were financially invested in their vehicle compared to those who were not, or between respondents who drove in heavy traffic hours compared to those who did not, suggesting internal factors correlate with aggressive driving habits. The study also found significant differences in driving aggression between males versus females, those with more points on their license versus fewer points, and those who witnessed parents driving aggressively very often versus rarely or never. Additional studies can investigate how witnessing parents driving aggressively is related to future driving behaviors.Keywords: Aggression, college, driving, road rage.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17003 Emergency Health Management and Student Hygiene at a South African University
Authors: Kudzai Ashley Tagwira, Michelle Marle Marais, Tracy Anne Ludwig, Rutendo Precious Chidziva, Mavis Nyaradzo Munodawafa, Wendy M. Wrench, Roman Tandlich
Abstract:
Risk of infectious disease outbreaks is related to the hygiene among the population. To assess the actual risks and modify the relevant emergency procedures if necessary, a hygiene survey was conducted among undergraduate students on the Rhodes University campus. Soap was available to 10.5% and only 26.8% of the study participants followed proper hygiene in relation to food consumption. This combination increases the risk of infectious disease outbreaks at the campus. Around 83.6% were willing to wash their hands if soap was provided. Procurement and availability of soap in undergraduate residences on campus should be improved, as the total cost is estimated at only 2000 USD per annum. Awareness campaigns about food-related hygiene and the need for regular handwashing with soap should be run among Rhodes University students. If successful, rates of respiratory and hygiene-related diseases will be decreased and emergency health management simplified.Keywords: Awareness, Food hygiene, Infectious disease spread, Undergraduate students.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21372 Investigating the Demand for Short-shelf Life Food Products for SME Wholesalers
Authors: Yamini Raju, Parminder S. Kang, Adam Moroz, Ross Clement, Ashley Hopwell, Alistair Duffy
Abstract:
Accurate forecasting of fresh produce demand is one the challenges faced by Small Medium Enterprise (SME) wholesalers. This paper is an attempt to understand the cause for the high level of variability such as weather, holidays etc., in demand of SME wholesalers. Therefore, understanding the significance of unidentified factors may improve the forecasting accuracy. This paper presents the current literature on the factors used to predict demand and the existing forecasting techniques of short shelf life products. It then investigates a variety of internal and external possible factors, some of which is not used by other researchers in the demand prediction process. The results presented in this paper are further analysed using a number of techniques to minimize noise in the data. For the analysis past sales data (January 2009 to May 2014) from a UK based SME wholesaler is used and the results presented are limited to product ‘Milk’ focused on café’s in derby. The correlation analysis is done to check the dependencies of variability factor on the actual demand. Further PCA analysis is done to understand the significance of factors identified using correlation. The PCA results suggest that the cloud cover, weather summary and temperature are the most significant factors that can be used in forecasting the demand. The correlation of the above three factors increased relative to monthly and becomes more stable compared to the weekly and daily demand.Keywords: Demand Forecasting, Deteriorating Products, Food Wholesalers, Principal Component Analysis and Variability Factors.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 33681 Early Depression Detection for Young Adults with a Psychiatric and AI Interdisciplinary Multimodal Framework
Authors: Raymond Xu, Ashley Hua, Andrew Wang, Yuru Lin
Abstract:
During COVID-19, the depression rate has increased dramatically. Young adults are most vulnerable to the mental health effects of the pandemic. Lower-income families have a higher ratio to be diagnosed with depression than the general population, but less access to clinics. This research aims to achieve early depression detection at low cost, large scale, and high accuracy with an interdisciplinary approach by incorporating clinical practices defined by American Psychiatric Association (APA) as well as multimodal AI framework. The proposed approach detected the nine depression symptoms with Natural Language Processing sentiment analysis and a symptom-based Lexicon uniquely designed for young adults. The experiments were conducted on the multimedia survey results from adolescents and young adults and unbiased Twitter communications. The result was further aggregated with the facial emotional cues analyzed by the Convolutional Neural Network on the multimedia survey videos. Five experiments each conducted on 10k data entries reached consistent results with an average accuracy of 88.31%, higher than the existing natural language analysis models. This approach can reach 300+ million daily active Twitter users and is highly accessible by low-income populations to promote early depression detection to raise awareness in adolescents and young adults and reveal complementary cues to assist clinical depression diagnosis.
Keywords: Artificial intelligence, depression detection, facial emotion recognition, natural language processing, mental disorder.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1176