Do Bars Trigger Activity in Galactic Nuclei?

We investigate the connection between the presence of bars and AGN activity, using a volume-limited sample of $\sim$9,000 late-type galaxies with axis ratio $b/a>0.6$ and $M_{r} < -19.5+5{\rm log}h$ at low redshift ($0.02\le z\lesssim 0.055$), selected from Sloan Digital Sky Survey Data Release 7. We find that the bar fraction in AGN-host galaxies (42.6%) is $\sim$2.5 times higher than in non-AGN galaxies (15.6%), and that the AGN fraction is a factor of two higher in strong-barred galaxies (34.5%) than in non-barred galaxies (15.0%). However, these trends are simply caused by the fact that AGN-host galaxies are on average more massive and redder than non-AGN galaxies because the fraction of strong-barred galaxies (\bfrsbo) increases with $u-r$ color and stellar velocity dispersion. When $u-r$ color and velocity dispersion (or stellar mass) are fixed, both the excess of \bfrsbo in AGN-host galaxies and the enhanced AGN fraction in strong-barred galaxies disappears. Among AGN-host galaxies we find no strong difference of the Eddington ratio distributions between barred and non-barred systems. These results indicate that AGN activity is not dominated by the presence of bars, and that AGN power is not enhanced by bars. In conclusion we do not find a clear evidence that bars trigger AGN activity.Comment: 13 pages, 11 figures, accepted for publication in Ap

Determination of Refrigerant Path Number for Fin-tube Condenser Considering Heat Transfer Performance and Pumping Power

Fin-tube heat exchangers are widely used in air-conditioners and heat pumps, which are constructed with a lot of tubes. Refrigerant circuit of heat exchanger with numerous pipe can be constructed by many methods. Refrigerant circuit design is usually determined designer’s experience and case by case test without guides. The number of path affects largely on heat exchanger performance. In this paper, design methodology for optimum number of path is suggested by relating convective thermal resistance and pumping power. Suggested methodology is described through an example and verified by various refrigerant circuit simulation results

A Small Animal Optical Tomographic Imaging System with Omni-Directional, Non-Contact, Angular-Resolved Fluorescence Measurement Capabilities

The overall goal of this thesis is to develop a new non-contact, whole-body, fluorescence molecular tomography system for small animal imaging. Over the past decade, small animal in vivo imaging has led to a better understanding of many human diseases and improved our ability to develop and test new drugs and medical compounds. Among various imaging modalities, optical imaging techniques have emerged as important tools. In particular, fluorescence and bioluminescence imaging systems have opened new ways for visualizing many molecular pathways inside living animals including gene expression and protein functions. While substantial progress has been made in available prototype and commercial optical imaging systems, there still exist areas for further improvement in the outcome of existing instrumentations. Currently, most small animal optical imaging systems rely on 2D planar imaging that provides limited ability to accurately locate lesions deep inside an animal. Furthermore, most existing tomographic imaging systems use a diffusion model of light propagation, which is of limited accuracy. While more accurate models using the equation of radiative transfer have become available, they have not been widely applied to small animal imaging yet. To overcome the limitations of existing optical small animal imaging systems, a novel imaging system that makes use of the latest hardware and software advances in the field was developed. At the heart of the system is a new double-conical-mirror-based imaging head that enables a single fixed position camera to capture multi-directional views simultaneously. Therefore, the imaging head provides 360-degree measurement data from an entire animal surface in one step. Another benefit provided by this design is the substantial reduction of multiple back-reflections between the animal and mirror surfaces. These back reflections are common in existing mirror-based imaging heads and tend to degrade the quality of raw measurement data. Furthermore, the conical-mirror design offers the capability to measure angular-resolved data from the animal surface. To make full use of this capability, a novel equation of radiative transfer-based ray-transfer operator was introduced to map the spatial and angular information of emitted light on the animal surface to the captured image data. As a result, more data points are involved into the image reconstructions, which leads to a higher image resolution. The performance of the imaging system was evaluated through numerical simulations, experiments using a well-defined tissue phantom, and live-animal studies. Finally, the double reflection mirror scheme presented in this dissertation can be cost-effectively employed with all camera-based imaging systems. The shapes and sizes of mirrors can be varied to accommodate imaging of other objects such as larger animals or human body parts, such as the breast, head, or feet

A hierarchical heuristic approach for machine loading problems in a partially grouped environment

The loading problem in a Flexible Manufacturing System (FMS) lies in the allocation of operations and associated cutting tools to machines for a given set of parts subject to capacity constraints. This dissertation proposes a hierarchical approach to the machine loading problem when the workload and tool magazine capacity of each machine are restrained. This hierarchical approach reduces the maximum workload of the machines by partially grouping them. This research deals with situations where different groups of machines performing the same operation require different processing times and this problem is formulated as an integer linear problem. This work proposes a solution that is comprised of two phases. In the first phase (Phase I), demand is divided into batches and then operations are allocated to groups of machines by using a heuristic constrained by the workload and tool magazine capacity of each group. The processing time of the operation is different for each machine group, which is composed of the same identical machines; however, these machines can perform different sets of operations if tooled differently. Each machine and each group of machines has a limited time for completing an operation. Operations are allocated to groups based on their respective workload limits. In the second phase (Phase II), demand is divided into batches again and operations are assigned to machines based on their workload and tool magazine capacity defined by Longest Processing Time (LPT) and Multifit algorithms. In Phase II, like Phase I, partial grouping is more effective in balancing the workload than total grouping. In partial grouping, each machine is tooled differently, but they can assist one another in processing each individual operation. Phase I demonstrates the efficiency of allocating operations to each group. Phase II demonstrates the efficiency of allocating operations to each machine within each group. This two-phase solution enhances routing flexibility with the same or a smaller number of machines through partial grouping rather than through total grouping. This partial grouping provides a balanced solution for problems involving a large number of machines. Performance of the suggested loading heuristics is tested by means of randomly generated tests

Towards Semi-Supervised Learning of Automatic Post-Editing: Data-Synthesis by Infilling Mask with Erroneous Tokens

Semi-supervised learning that leverages synthetic training data has been widely adopted in the field of Automatic post-editing (APE) to overcome the lack of human-annotated training data. In that context, data-synthesis methods to create high-quality synthetic data have also received much attention. Considering that APE takes machine-translation outputs containing translation errors as input, we propose a noising-based data-synthesis method that uses a mask language model to create noisy texts through substituting masked tokens with erroneous tokens, yet following the error-quantity statistics appearing in genuine APE data. In addition, we propose corpus interleaving, which is to combine two separate synthetic data by taking only advantageous samples, to further enhance the quality of the synthetic data created with our noising method. Experimental results reveal that using the synthetic data created with our approach results in significant improvements in APE performance upon using other synthetic data created with different existing data-synthesis methods