A Review of Merger and Acquisition Wave Literature: Proposing Future Research in the Restaurant Industry

The purpose of this study is to identify research trends in Merger and Acquisition waves in the restaurant industry and propose future research directions by thoroughly reviewing existing Merger and Acquisition related literature. Merger and Acquisition has been extensively used as a strategic management tool for fast growth in the restaurant industry. However, there has been a very limited amount of literature that focuses on Merger & Acquisition in the restaurant industry. Particular, no known study has been identified that examined M&A wave and its determinants. A good understanding of determinants of M&A wave will help practitioners identify important factors that should be considered before making M&A decisions and predict the optimal timing for successful M&A transactions. This study examined literature on six U.S M&A waves and their determinants and summarized main explanatory factors examined, statistical methods, and theoretical frameworks. Inclusion of unique macroeconomic factors of the restaurant industry and the use of factor analysis are suggested for future research.

The effects of an augmented art curriculum on the attitudes of high school art students toward diversity in art

The purpose of this study was to investigate the effects of an augmented art curriculum on high school art students to determine if those receiving it had more positive attitudes toward diversity in art and used diversity in their art more than students receiving traditional instruction. This study was conducted in a South Florida public high school, with beginning art students. Using a posttest-only control group design and ANOVA (a = .05), it was found that students (n = 54) receiving an augmented art curriculum did not have significantly higher scores on the Attitudes About Art questionnaire than students (n = 57) receiving traditional instruction F (1, 91) =.00, p > .05. However, using the Checklist of Cultural References in Student Art to evaluate student work it was found that there was a significant teacher effect F (1, 30) = 14.14, p

Ph.D. graduate student, Shelby Servais and REU, Julio Pachon conducted collaborative research in the wetland mesocosms during summer 2013.

http://digitalcommons.fiu.edu/fce_lter_photos/1335/thumbnail.jpg

A research platform for artificial neural networks with applications in pediatric epilepsy

This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface. A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function’s slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and nonepileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes. The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth’s parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty. It was demonstrated that (1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and (2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763).

A Research Platform for Artificial Neural Networks with Applications in Pediatric Epilepsy

This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface. A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function’s slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and non-epileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes. The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth’s parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty. It was demonstrated that 1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and 2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763).