Somatosensory neurons integrate the geometry of skin deformation and mechanotransduction channels to shape touch sensing.

Touch sensation hinges on force transfer across the skin and activation of mechanosensitive ion channels along the somatosensory neurons that invade the skin. This skin-nerve sensory system demands a quantitative model that spans the application of mechanical loads to channel activation. Unlike prior models of the dynamic responses of touch receptor neurons in Caenorhabditis elegans (Eastwood et al., 2015), which substituted a single effective channel for the ensemble along the TRNs, this study integrates body mechanics and the spatial recruitment of the various channels. We demonstrate that this model captures mechanical properties of the worm's body and accurately reproduces neural responses to simple stimuli. It also captures responses to complex stimuli featuring non-trivial spatial patterns, like extended or multiple contacts that could not be addressed otherwise. We illustrate the importance of these effects with new experiments revealing that skin-neuron composites respond to pre-indentation with increased currents rather than adapting to persistent stimulation

Survival Analysis with Multivariate adaptive Regression Splines

Multivariate adaptive regression splines (MARS) are a useful tool to identify linear and nonlinear effects and interactions between two covariates. In this dissertation a new proposal to model survival type data with MARS is introduced. Martingale and deviance residuals of a Cox PH model are used as response in a common MARS approach to model functional forms of covariate effects as well as possible interactions in a data-driven way. Simulation studies prove that the new method yields a better fit to the data than the traditional Cox PH approach. The analysis of real data of the German Heart Center on survivors of an acute myocardial infarction also documents the good performance of the method

Microeconometric essays on migration, trust and Satisfaction

One of the important findings is that very little assumptions need to be imposed to identify the economic performance of immigrants who leave a region when such departures are not observed in panel data sets. It is also shown that the evaluation of the determinants of an individual's trust and trustworthiness can greatly benefit from the combination of survey and experimental data. This combination is shown to reveal interesting insights on how individual actions do not necessarily match their stated attitudes. Finally, the final essay demonstrates how stated answers to questions on income satisfaction allow the identification and estimation of household equivalence scales using estimation techniques relaxing conventional statistical assumptions

Discrete element and artificial intelligence modeling of rock properties and formation failure in advance of shovel excavation

Rock tests are performed before the start of every mining or civil engineering project as part of a detailed feasibility study. The feasibility study is costly and it comprises drilling, sample collection, sample handling and laboratory testing. Numerical modeling techniques, such as Particle Flow Code (PFC), can be used to provide reliable estimates of rock strength values. The numerical models for unconfined compressive strength (UCS), direct tension, and Brazilian tests were developed in PFC, and validated using data from literature. A particle size range of 3-5 mm with Dmax/Dmin = 1.67 gave the best results. The numerical errors were in the range of 6-22% for UCS, 21-80% for direct tension, and 5- 10% for Brazilian tests. About 1,800 confined compression tests were also performed in PFC to obtain formation material properties. However, the PFC algorithm takes a very long computational time to complete the process, and thus, there is a need for more efficient and faster methods. In this research, the author uses artificial intelligence methods including, Artificial Neural Network, Mamdani Fuzzy Logic, and Hybrid neural Fuzzy Inference System (HyFIS) to solve this problem. These methods, along with the Multiple Linear Regression method, were used for the predictive analysis. Based on R2 and RMSE statistics for the testing phase, HyFIS is the best predictive model. This study is the first attempt to develop self-learning artificial intelligent models for predicting formation material properties. In addition, this research study investigates the shovel excavation process using the discrete element technique in PFC to examine the shovel digging phase. The shovel excavation simulator provides a tool for optimizing strategies for maximizing its performance that provides a major breakthrough in the shovel excavation frontier --Abstract, page iii