To Invest or Not to Invest: Using Vocal Behavior to Predict Decisions of Investors in an Entrepreneurial Context

Entrepreneurial pitch competitions have become increasinglypopular in the start-up culture to attract prospective investors. As theultimate funding decision often follows from some form of social interaction,it is important to understand how the decision-making processof investors is influenced by behavioral cues. In this work, we examinewhether vocal features are associated with the ultimate funding decisionof investors by utilizing deep learning methods.We used videos of individualsin an entrepreneurial pitch competition as input to predict whetherinvestors will invest in the startup or not. We proposed models that combinedeep audio features and Handcrafted audio Features (HaF) and feedthem into two types of Recurrent Neural Networks (RNN), namely LongShort-Term Memory (LSTM) and Gated Recurrent Units (GRU). Wealso trained the RNNs with only deep features to assess whether HaFprovide additional information to the models. Our results show that it ispromising to use vocal behavior of pitchers to predict whether investorswill invest in their business idea. Different types of RNNs yielded similarperformance, yet the addition of HaF improved the performance

To Invest or Not to Invest: Using Vocal Behavior to Predict Decisions of Investors in an Entrepreneurial Context

Entrepreneurial pitch competitions have become increasinglypopular in the start-up culture to attract prospective investors. As theultimate funding decision often follows from some form of social interaction,it is important to understand how the decision-making processof investors is influenced by behavioral cues. In this work, we examinewhether vocal features are associated with the ultimate funding decisionof investors by utilizing deep learning methods.We used videos of individualsin an entrepreneurial pitch competition as input to predict whetherinvestors will invest in the startup or not. We proposed models that combinedeep audio features and Handcrafted audio Features (HaF) and feedthem into two types of Recurrent Neural Networks (RNN), namely LongShort-Term Memory (LSTM) and Gated Recurrent Units (GRU). Wealso trained the RNNs with only deep features to assess whether HaFprovide additional information to the models. Our results show that it ispromising to use vocal behavior of pitchers to predict whether investorswill invest in their business idea. Different types of RNNs yielded similarperformance, yet the addition of HaF improved the performance

Healthy Nutrition and Physical Activity in Childcare: Views from Childcare Managers, Childcare Workers and Parents on Influential Factors

Childhood obesity is an important public health issue influenced by both personal and environmental factors. The childcare setting plays an important role in children’s energy balance-related behaviours (EBRB), such as physical activity, sedentary behaviour and healthy nutrition. This study aimed to explore facilitators and barriers of healthy EBRB in childcare in a comprehensive way, from the perspective of three crucial stakeholders: childcare managers, childcare workers and parents. A qualitative study was performed using semi-structured interviews. Content analysis was performed using the ‘Environmental Research framework for weight Gain prevention’ (EnRG framework) to guide the analysis. Forty-eight interviews were held with a total of 65 participants (9 childcare managers, 23 childcare workers and 33 parents). Influential factors in all types of environment (physical, sociocultural, economic and political) were mentioned. Although a need for change was not always expressed, the interviews revealed opportunities for improvement of healthy EBRB in childcare. These opportunities were related to the sociocultural, physical and political environment. Childcare workers and managers expressed an influence of the home setting on the childcare setting, resulting in a need for more congruence between these settings. There are opportunities for improvement in the childcare setting to promote healthy EBRB in young children in the Netherlands. It appears important to align intervention components between the childcare and home setting

Accuracy in Diagnosis of Celiac Disease Without Biopsies in Clinical Practice

The guidelines of the European Society of Pediatric Gastroenterology, Hepatology, and Nutrition allow for diagnosis of celiac disease without biopsies in children with symptoms and levels of immunoglobulin A against tissue-transglutaminase (TGA-IgA) 10-fold or more the upper limit of normal (ULN), confirmed by detection of endomysium antibodies (EMA) and positivity for HLA-DQ2/DQ8. We performed a large, international prospective study to validate this approach