Electrospun Nanofiber Yarns for Nanofluidic Applications

This dissertation is centered on the development and characterization of electrospun nanofiber probes. These probes are envisioned to act like sponges, drawing up fluids from microcapillaries, small organisms, and, ideally, from a single cell. Thus, the probe performance significantly depends on the materials ability to readily absorb liquids. Electrospun nanofibers gained much attention in recent decades, and have been applied in biomedical, textile, filtration, and military applications. However, most nanofibers are produced in the form of randomly deposited non-woven fiber mats. Recently, different electrospinning setups have been proposed to control alignment of electrospun nanofibers. However, reproducibility of the mechanical and transport properties of electrospun nanofiber yarns is difficult to achieve. Before this study, there were no reports demonstrating that the electrospun yarns have reproducible transport and mechanical properties. For the probe applications, one needs to have yarns with identical characteristics. The absorption properties of probes are of the main concern. These challenges are addressed in this thesis, and the experimental protocol and characterization methods are developed to study electrospun nanofiber yarns

Using Hidden Markov Model for Stock Day Trade Forecasting

Around the world, the Hidden Markov Models (HMM) are the most popular methods in the machine learning and statistics for modeling sequences, especially in speech recognition domain. According to the number of patent applications for speech recognition technology form 1988 to 1998, the trend shows that this method has become very mature. In this thesis, we will make a new use of the HMM and apply it on day trading stock forecast. However, the HMM is based on probability and statistics theory. In a statistics framework, the HMM is a composition of two stochastic processes, a Hidden Markov chain, which accounts for temporal variability, and an observable process, which accounts for spectral variability. The combination contains uncertainly status just likes the stock walk trace. Therefore, the HMM and the stock walk trace have the same idea by coincidence. In this thesis, we will try to learn the stock syntax; just like how the HMM model was used in speech recognition in different languages, and the take the next step ahead in price prediction. Additionally, the stock market is the reflection of the economy. The stock trace is impacted by many factors such as policy, psychology, microeconomics, economics, and capital, etc. There, in this thesis, the TAIFEX Taiwan index futures (TX) and day trade are used to avoid all the uncertainty factors. After the all experiments, it is proven that the HMM is better than the benchmark methodRandom Walk method and the Investment Trust & Consulting Association method- Modified Trading method. Moreover, the result is very conspicuous by the statistics testing of significance