Surface temperature distribution along a thin liquid layer due to thermocapillary convection

The surface temperature distributions due to thermocapillary convections in a thin liquid layer with heat fluxes imposed on the free surface were investigated. The nondimensional analysis predicts that, when convection is important, the characteristics length scale in the flow direction L, and the characteristic temperature difference delta T sub o can be represented by L and delta T sub o approx. (A2Ma)/1/4 delta T sub R, respectively, where L sub R and delta sub R are the reference scales used in the conduction dominant situations with A denoting the aspect ratio and Ma the Marangoni number. Having L and delta sub o defined, the global surface temperature gradient delta sub o/L, the global thermocapillary driving force, and other interesting features can be determined. Numerical calculations involving a Gaussian heat flux distribution are presented to justify these two relations

Multivariable Repetitive-predictive Controllers using Frequency Decomposition

Repetitive control is a methodology for the tracking of a periodic reference signal. This paper develops a new approach to repetitive control systems design using receding horizon control with frequency decomposition of the reference signal. Moreover, design and implementation issues for this form of repetitive predictive control are investigated from the perspectives of controller complexity and the effects of measurement noise. The analysis is supported by a simulation study on a multi-input multi-output robot arm where the model has been constructed from measured frequency response data, and experimental results from application to an industrial AC motor

Directed polymers in random media under confining force

The scaling behavior of a directed polymer in a two-dimensional (2D) random potential under confining force is investigated. The energy of a polymer with configuration $\{y(x)\}$ is given by H\big(\{y(x)\}\big) = \sum_{x=1}^N \exyx + \epsilon \Wa^\alpha, where $\eta(x,y)$ is an uncorrelated random potential and \Wa is the width of the polymer. Using an energy argument, it is conjectured that the radius of gyration $R_g(N)$ and the energy fluctuation $\Delta E(N)$ of the polymer of length $N$ in the ground state increase as $R_g(N)\sim N^{\nu}$ and $\Delta E(N)\sim N^\omega$ respectively with $\nu = 1/(1+\alpha)$ and $\omega = (1+2\alpha)/(4+4\alpha)$ for $\alpha\ge 1/2$. A novel algorithm of finding the exact ground state, with the effective time complexity of \cO(N^3), is introduced and used to confirm the conjecture numerically.Comment: 9 pages, 7 figure

An ectopic hamartomatous thymoma compressing left jugular vein

Ectopic hamartomatous thymoma (EHT) is an extremely rare benign neoplasm. It is usually found at the root of the neck (frequently on the left) and does not usually impact adjacent tissues in clinically significant ways. While EHT manifests distinct pathological features, the lesion is either asymptomatic or may show nonspecific clinical features. We report one case of EHT which was assumed to be of low malignant potential since it severely compressed the inlet of left internal jugular vein as seen by computed tomography scan. To the best of our knowledge, this clinical finding of EHT is very rare. After the diagnosis and treatment of this patient, we believe that EHT or suspected EHT should be treated less invasively.Key words: Ectopia, harmatoma, mini‑sternotomy, thymom

JPL Energy Consumption Program (ECP) documentation: A computer model simulating heating, cooling and energy loads in buildings

The engineering manual provides a complete companion documentation about the structure of the main program and subroutines, the preparation of input data, the interpretation of output results, access and use of the program, and the detailed description of all the analytic, logical expressions and flow charts used in computations and program structure. A numerical example is provided and solved completely to show the sequence of computations followed. The program is carefully structured to reduce both user's time and costs without sacrificing accuracy. The user would expect a cost of CPU time of approximately $5.00 per building zone excluding printing costs. The accuracy, on the other hand, measured by deviation of simulated consumption from watt-hour meter readings, was found by many simulation tests not to exceed + or - 10 percent margin

Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships

Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships

Modeling gap seeking behaviors for agent-based crowd simulation

Research on agent-based crowd simulation has gained tremendous momentum in recent years due to the increase of computing power. One key issue in this research area is to develop various behavioral models to capture the microscopic behaviors of individuals (i.e., agents) in a crowd. In this paper, we propose a novel behavior model for modeling the gap seeking behavior which can be frequently observed in real world scenarios where an individual in a crowd proactively seek for gaps in the crowd flow so as to minimize potential collision with other people. We propose a two-level modeling framework and introduce a gap seeking behavior model as a proactive conflict minimization maneuver at global navigation level. The model is integrated with the reactive collision avoidance model at local steering level. We evaluate our model by simulating a real world scenario. The results show that our model can generate more realistic crowd behaviors compared to the classical social-force model in the given scenario

How Catastrophic Innovation Failure Affects Organizational and Industry Legitimacy: The 2014 Virgin Galactic Test Flight Crash

We examine how catastrophic innovation failure affects organizational and industry legitimacy in nascent sectors by analyzing the interactions between Virgin Galactic and stakeholders in the space community in the aftermath of the firm’s 2014 test flight crash. Following catastrophic innovation failure, we find that industry participants use their interpretations of the failure to either uphold or challenge the legitimacy of the firm while maintaining the legitimacy of the industry. These dynamics yield two interesting effects. First, we show that, in upholding the legitimacy of the industry, different industry participants rhetorically redraw the boundaries of the industry to selectively include players they consider legitimate and exclude those they view as illegitimate: detracting stakeholders constrain the boundaries of the industry by excluding the firm or excluding the firm and its segment, whereas the firm and supporting stakeholders amplify the boundaries of the industry by including firms in adjacent high-legitimacy sectors. Second, we show that, in assessing organizational legitimacy, the firm and its stakeholders differ in the way they approach distinctiveness between the identities of the industry and the firm. Detracting stakeholders differentiate the firm from the rest of the industry and isolate it, whereas the firm and supporting stakeholders reidentify the firm with the industry, embedding the firm within it. Overall, our findings illuminate the effects that catastrophic innovation failure has over high-order dynamics that affect the evolution of nascent industries

How Firms Frame Catastrophic Innovation Failure

We examine how catastrophic innovation failure affects organizational and industry legitimacy in nascent sectors by analyzing the interactions between Virgin Galactic and stakeholders in the space community in the aftermath of the firm’s 2014 test flight crash. Following catastrophic innovation failure, we find that industry participants use their interpretations of the failure to either uphold or challenge the legitimacy of the firm, while maintaining the legitimacy of the industry. These dynamics yield two interesting effects. First, we show that, in upholding the legitimacy of the industry, different industry participants rhetorically re-draw the boundaries of the industry to selectively include players they consider ‘legitimate’ and exclude those they view as ‘illegitimate:’ detracting stakeholders constrain the boundaries of the industry by excluding the firm or excluding the firm and its segment, while the firm and supporting stakeholders amplify the boundaries of the industry by including firms in adjacent high-legitimacy sectors. Second, we show that, in assessing organizational legitimacy, the firm and its stakeholders differ in the way they approach distinctiveness between the identities of the industry and the firm. Detracting stakeholders differentiate the firm from the rest of the industry and isolate it, while the firm and supporting stakeholders re-identify the firm with the industry, embedding the firm within it. Overall, our findings illuminate the effects that catastrophic innovation failure has over high-order dynamics that affect the evolution of nascent industries