Mobile commerce business models and technologies towards success

Mobile commerce is any transaction with a monetary value that is conducted via a mobile telecommunications network. This thesis tries to examine the factors leading to the success of mobile commerce as well as factors that may hinder its success. This research is separated into five parts: In the first part of this thesis, an analysis of wired e-commerce businesses is made; followed by advantages of mobile commerce over wired e-commerce. In the second part of this thesis, new wireless business models that are expected to generate substantial revenue flows as well as some successful examples of these business models are discussed. In the third part of this thesis, advances in wireless technologies that will lead to the success of mobile commerce are discussed. In the fourth part of this thesis, competition strategies and revenue structure of mobile commerce are discussed. And finally, in the fifth part of this thesis, drawbacks of wireless technologies towards the success of mobile commerce as well as how they can be overcome are discussed. The research and the conclusion suggest that although wireless technologies and their related business models are fairly new, they are growing at rapid speed. These are incredible sources of revenue. Once the factors hindering their usability, reliability, development and deployment are overcome, mobile technologies show great potential as revenue generators for both existing and newly developing businesse

Uncertainty Quantification of Microstructural Properties due to Experimental Variations

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143074/1/1.J055689.pd

Utilization of a Linear Solver for Multiscale Design and Optimization of Microstructures

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140693/1/1.j054822.pd

Uncertainty Quantification of Microstructural Properties due to Experimental Variations

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143052/1/6.2017-0815.pd

Linear Solution Scheme for Microstructure Design with Process Constraints

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140696/1/1.J055247.pd

Stochastic Design Optimization of Microstructures with Utilization of a Linear Solver

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143035/1/6.2017-1939.pd

Multi-Scale Design and Optimization of Microstructures under Uncertainties

Research on computational modeling and multiscale design of materials has been garnering a lot of interest due to the demand for high performance materials in electronics, energy and structural applications. The primary goal of the present study is to develop a new computational approach for microstructure design for achieving a set of material properties within a designated level of uncertainty. This thesis combines the methods of uncertainty quantification (UQ) and materials design, using a unique linearization approach that is well-suited for metallic materials modeled using probabilistic descriptors such as the orientation distribution function. An analytical UQ formulation is proposed to model the uncertainties in microstructural features from experimental (electron diffraction) data as well as for inverse modeling the uncertainties in optimal microstructural features from property data. Compared to the widely preferred computational UQ algorithms the analytical model reduces the required computational time significantly as well as capturing the effect of stochasticity in microstructure design accurately. The optimal processing route, which produces materials with optimized texture and/or properties, is identified by developing reduced order models to represent the texture evolution. Examples presented include the performance improvement of Titanium aircraft panels for thermal buckling, and optimization of Fe-Ga alloys for vibration response and identification of optimal processing route for Fe-Ga alloy microstructures.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138534/1/acarp_1.pd

Data Needs for Children with Special Needs in Refugee Populations

This article examines the challenges that affect the identification and assessment of refugee children with special needs in Turkey and provides recommendations related to data collection and assessment of these learners that is broadly relevant in refugee settings

Multi-sensor driver drowsiness monitoring

A system for driver drowsiness monitoring is proposed, using multi-sensor data acquisition and investigating two decision-making algorithms, namely a fuzzy inference system (FIS) and an artificial neural network (ANN), to predict the drowsiness level of the driver. Drowsiness indicator signals are selected allowing non-intrusive measurements. The experimental set-up of a driver-drowsiness-monitoring system is designed on the basis of the soughtafter indicator signals. These selected signals are the eye closure via pupil area measurement, gaze vector and head motion acquired by a monocular computer vision system, steering wheel angle, vehicle speed, and force applied to the steering wheel by the driver. It is believed that, by fusing these signals, driver drowsiness can be detected and drowsiness level can be predicted. For validation of this hypothesis, 30 subjects, in normal and sleep-deprived conditions, are involved in a standard highway simulation for 1.5 h, giving a data set of 30 pairs. For designing a feature space to be used in decision making, several metrics are derived using histograms and entropies of the signals. An FIS and an ANN are used for decision making on the drowsiness level. To construct the rule base of the FIS, two different methods are employed and compared in terms of performance: first, linguistic rules from experimental studies in literature and, second, mathematically extracted rules by fuzzy subtractive clustering. The drowsiness levels belonging to each session are determined by the participants before and after the experiment, and videos of their faces are assessed to obtain the ground truth output for training the systems. The FIS is able to predict correctly 98 per cent of determined drowsiness states (training set) and 89 per cent of previously unknown test set states, while the ANN has a correct classification rate of 90 per cent for the test data. No significant difference is observed between the FIS and the ANN; however, the FIS might be considered better since the rule base can be improved on the basis of new observations