Top-Antitop cross section measurement in the di-lepton decay channel with ATLAS

We present simulations of the production cross-section measurement of top-antitop pairs in the di-leptonic decay channel with the ATLAS detector. This study uses the Commissioning Service Challenge (CSC) data, which is the latest and centrally produced Monte-Carlo data set to validate the detector simulation before the actual data taking. The signal process was generated with MC@NLO and important background processes were studied. A cut and count method and two likelihood methods were employed to measure the cross section and important systematic effects were investigated. The expected statistical and systematic errors for a luminosity of 100 \ipb are also given.Comment: 3 page

Local Synthesis and Direct Integration of Carbon Nanotubes into Microsystems for Sensor Applications

Carbon nanotubes (CNTs) have been intensively studied since their discovery more than two decades ago. A lot of research has exploited their extraordinary properties and various applications, meanwhile, revealed the challenges of fabricating the CNT-based device. Major challenges concern the high temperature required for the CNT growth, and the difficulty in handling and maneuvering the CNTs. An innovative approach to overcome these challenges is to locally synthesize and directly assemble CNTs into devices. Following such approach, this thesis developed a fabrication process with a high simplicity, a high controllability, and a CMOS/MEMS compatibility for the local synthesis and direct integration of CNTs into Si microsystems. This thesis covers the total process chain: from synthesis and integration of CNTs, to characterization, and to testing of a proof-of-principle gas sensor. The first key finding of this thesis is a simple and robust method to control the temperature for the growth of CNTs by using only electrical signals. During the growth process, a localized hot region for the growth of CNTs is created by locally heating a Si microelectrode (Joule heating). The induced temperature is monitored through in-situ measurements of the electrical resistance of the Si electrode. The measured resistance provides feedback to control the input power for heating the Si electrode. This pure electrical control enables a simple, automated and parallel process to synthesize locally and integrate CNTs directly into microsystems. The second key finding of this thesis is the diameter dependency for the effect of an applied electric field on the growth orientation of CNTs. A statistical analysis of 1100 CNTs showed that small-diameter CNTs (d 10 nm) were curved and did not align. In the transition regime, CNTs were moderately curved, but the average direction was at small angle with the electric field direction. The third key finding of this thesis is the correlation between local temperature and resulting characteristics of CNTs. A high gradient of temperature along the Si microelectrode due to Joule heating allowed for studying the effect of temperature. At the region where the temperature is highest ( 900oC), the nanostructure of CNTs had the highest degree of order, and the average diameter of CNT was smallest. At regions with lower temperatures, CNTs had a higher degree of defects and disorder, and a lower average diameter. The density of CNTs, however, was highest at the moderate-temperature region ( 850oC). The other contribution of this thesis is preliminary results on the development of CNT-based microsystems towards sensor applications. The preliminary results suggest that: (i) contact resistance at the CNT-Si interface could be reduced by both techniques of local annealing and local deposition of Platinum onto the CNT-Si contacts; (ii) thermal evaporation of metals could be used to functionalize the CNTs in a microsystem where CNTs are suspended and span two microelectrodes

Geometric control of myogenic cell fate.

This work combines expertise in stem cell biology and bioengineering to define the system for geometric control of proliferation and differentiation of myogenic progenitor cells. We have created an artificial niche of myogenic progenitor cells, namely, modified extracellular matrix (ECM) substrates with spatially embedded growth or differentiation factors (GF, DF) that predictably direct muscle cell fate in a geometric pattern. Embedded GF and DF signal progenitor cells from specifically defined areas on the ECM successfully competed against culture media for myogenic cell fate determination at a clearly defined boundary. Differentiation of myoblasts into myotubes is induced in growth-promoting medium, myotube formation is delayed in differentiation-promoting medium, and myogenic cells, at different stages of proliferation and differentiation, can be induced to coexist adjacently in identical culture media. This method can be used to identify molecular interactions between cells in different stages of myogenic differentiation, which are likely to be important determinants of tissue repair. The designed ECM niches can be further developed into a vehicle for transplantation of myogenic progenitor cells maintaining their regenerative potential. Additionally, this work may also serve as a general model to engineer synthetic cellular niches to harness the regenerative potential of organ stem cells

White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from Brain Magnetic Resonance Imaging Using Adaptive U-Net and Local Convolutional Neural Network

According to the World Alzheimer Report 2015, 46 million people are living with dementia in the world. The diagnosis of diseases helps doctors treating patients better. One of the signs of diseases is related to white matter, grey matter and cerebrospinal fluid. Therefore, the automatic segmentation of three tissues in brain imaging especially from magnetic resonance imaging (MRI) plays an important role in medical analysis. In this research, we proposed an effective approach to segment automatically these tissues in three-dimensional (3D) brain MRI. First, a deep learning model is used to segment the sure and unsure regions. In the unsure region, another deep learning model is used to classify each pixel. In the experiments, an adaptive U-net model is used to segment the sure and unsure regions, and the Local Convolutional Neural Network (CNN) model with multiple inputs is used to classify each pixel only in the unsure region. Our method was evaluated with a real image database, Internet Brain Segmentation Repository database, with 18 persons (IBSR 18) (https://www.nitrc.org/projects/ibsr) and compared with state of art methods being the results very promising

Geometric effects on mixing performance in a novel passive micromixer with trapezoidal-zigzag channels

A novel passive micromixer, called a trapezoidal-zigzag micromixer (TZM), is reported. A TZM is composed of trapezoidal channels in a zigzag and split–recombine arrangement that enables multiple mixing mechanisms, including splitting–recombining, twisting, transversal flows, vortices, and chaotic advection. The effects of geometric parameters of the TZM on mixing performance are systematically investigated by the Taguchi method and numerical simulations in COMSOL Multiphysics. The number of mixing units, the slope angle of the trapezoidal channel, the height of the constriction element, and the width ratio between the middle-trapezoidal channel and the side-trapezoidal channel are the four parameters under study. The mixing performance of the TZM is investigated at three different Reynolds number (Re) values of 0.5, 5, and 50. The results showed that a TZM with six mixing units, a trapezoidal slope angle of 75°, a constricting height of 100 µm, and a width ratio of 0.5 has the highest mixing efficiency. This optimal TZM has a mixing efficiency greater than 85% for Re values from 0.1 to 80. In particular, for Re  ≤  0.9 and Re  ≥  20, the mixing efficiency of the optimal TZM is greater than 90%. The proposed TZM has a higher mixing efficiency and a smaller footprint than previously reported micromixers