Development of optical diaphragm deflection sensors

The objective of this project was to develop high-temperature pressure sensors using non-metallic components and optical sensing methods. The sensors are to operate over a temperature range from room temperature approx. 20C to 540C, to respond to internal pressure up to 690 kPa, to respond to external pressure up to 690 kPa, and to withstand external overpressure of 2070 kPa. Project tasks include evaluating sensing techniques and sensor systems. These efforts include materials and sensing method selection, sensor design, sensor fabrication, and sensor testing. Sensors are tested as a function of temperature, pressure, overpressure, and vibration. The project results show that high-temperature pressure sensors based on glass components and optical sensing methods are feasible. The microbend optical diaphragm deflection sensor exhibits the required sensitivity and stability for use as a pressure sensor with temperature compensation. for the microbend sensor, the 95% confidence level deviation of input pressure from the pressure calculated from the overall temperature-compensated calibration equation is 3.7% of full scale. The limitations of the sensors evaluated are primarily due to the restricted temperature range of suitable commercially available optical fibers and the problems associated with glass-to-metal pressure sealing over the entire testing temperature range

GRNsight: a web application and service for visualizing models of small- to medium-scale gene regulatory networks

GRNsight is a web application and service for visualizing models of gene regulatory networks (GRNs). A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. The original motivation came from our efforts to perform parameter estimation and forward simulation of the dynamics of a differential equations model of a small GRN with 21 nodes and 31 edges. We wanted a quick and easy way to visualize the weight parameters from the model which represent the direction and magnitude of the influence of a transcription factor on its target gene, so we created GRNsight. GRNsight automatically lays out either an unweighted or weighted network graph based on an Excel spreadsheet containing an adjacency matrix where regulators are named in the columns and target genes in the rows, a Simple Interaction Format (SIF) text file, or a GraphML XML file. When a user uploads an input file specifying an unweighted network, GRNsight automatically lays out the graph using black lines and pointed arrowheads. For a weighted network, GRNsight uses pointed and blunt arrowheads, and colors the edges and adjusts their thicknesses based on the sign (positive for activation or negative for repression) and magnitude of the weight parameter. GRNsight is written in JavaScript, with diagrams facilitated by D3.js, a data visualization library. Node.js and the Express framework handle server-side functions. GRNsight’s diagrams are based on D3.js’s force graph layout algorithm, which was then extensively customized to support the specific needs of GRNs. Nodes are rectangular and support gene labels of up to 12 characters. The edges are arcs, which become straight lines when the nodes are close together. Self-regulatory edges are indicated by a loop. When a user mouses over an edge, the numerical value of the weight parameter is displayed. Visualizations can be modified by sliders that adjust the force graph layout parameters and through manual node dragging. GRNsight is best-suited for visualizing networks of fewer than 35 nodes and 70 edges, although it accepts networks of up to 75 nodes or 150 edges. GRNsight has general applicability for displaying any small, unweighted or weighted network with directed edges for systems biology or other application domains. GRNsight serves as an example of following and teaching best practices for scientific computing and complying with FAIR principles, using an open and test-driven development model with rigorous documentation of requirements and issues on GitHub. An exhaustive unit testing framework using Mocha and the Chai assertion library consists of around 160 automated unit tests that examine nearly 530 test files to ensure that the program is running as expected. The GRNsight application (http://dondi.github.io/GRNsight/) and code (https://github.com/dondi/GRNsight) are available under the open source BSD license

Thermodynamic parameters of bonds in glassy materials from viscosity-temperature relationships

Doremus's model of viscosity assumes that viscous flow in amorphous materials is mediated by broken bonds (configurons). The resulting equation contains four coefficients, which are directly related to the entropies and enthalpies of formation and motion of the configurons. Thus by fitting this viscosity equation to experimental viscosity data these enthalpy and entropy terms can be obtained. The non-linear nature of the equation obtained means that the fitting process is non-trivial. A genetic algorithm based approach has been developed to fit the equation to experimental viscosity data for a number of glassy materials, including SiO2, GeO2, B2O3, anorthite, diopside, xNa2O–(1-x)SiO2, xPbO–(1-x)SiO2, soda-lime-silica glasses, salol, and α-phenyl-o-cresol. Excellent fits of the equation to the viscosity data were obtained over the entire temperature range. The fitting parameters were used to quantitatively determine the enthalpies and entropies of formation and motion of configurons in the analysed systems and the activation energies for flow at high and low temperatures as well as fragility ratios using the Doremus criterion for fragility. A direct anti-correlation between fragility ratio and configuron percolation threshold, which determines the glass transition temperature in the analysed materials, was found

GRNsight: a web application and service for visualizing models of small- to medium-scale gene regulatory networks

GRNsight is a web application and service for visualizing models of gene regulatory networks (GRNs). A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. The original motivation came from our efforts to perform parameter estimation and forward simulation of the dynamics of a differential equations model of a small GRN with 21 nodes and 31 edges. We wanted a quick and easy way to visualize the weight parameters from the model which represent the direction and magnitude of the influence of a transcription factor on its target gene, so we created GRNsight. GRNsight automatically lays out either an unweighted or weighted network graph based on an Excel spreadsheet containing an adjacency matrix where regulators are named in the columns and target genes in the rows, a Simple Interaction Format (SIF) text file, or a GraphML XML file. When a user uploads an input file specifying an unweighted network, GRNsight automatically lays out the graph using black lines and pointed arrowheads. For a weighted network, GRNsight uses pointed and blunt arrowheads, and colors the edges and adjusts their thicknesses based on the sign (positive for activation or negative for repression) and magnitude of the weight parameter. GRNsight is written in JavaScript, with diagrams facilitated by D3.js, a data visualization library. Node.js and the Express framework handle server-side functions. GRNsight’s diagrams are based on D3.js’s force graph layout algorithm, which was then extensively customized to support the specific needs of GRNs. Nodes are rectangular and support gene labels of up to 12 characters. The edges are arcs, which become straight lines when the nodes are close together. Self-regulatory edges are indicated by a loop. When a user mouses over an edge, the numerical value of the weight parameter is displayed. Visualizations can be modified by sliders that adjust the force graph layout parameters and through manual node dragging. GRNsight is best-suited for visualizing networks of fewer than 35 nodes and 70 edges, although it accepts networks of up to 75 nodes or 150 edges. GRNsight has general applicability for displaying any small, unweighted or weighted network with directed edges for systems biology or other application domains. GRNsight serves as an example of following and teaching best practices for scientific computing and complying with FAIR principles, using an open and test-driven development model with rigorous documentation of requirements and issues on GitHub. An exhaustive unit testing framework using Mocha and the Chai assertion library consists of around 160 automated unit tests that examine nearly 530 test files to ensure that the program is running as expected. The GRNsight application (http://dondi.github.io/GRNsight/) and code (https://github.com/dondi/GRNsight) are available under the open source BSD license

Elastic properties of TeO2-B2O3-Ag2O glasses.

A series of glasses [(TeO2) x (B2O3)1−x ]1−y [Ag2O] y with x = 70 and y = 10, 15, 20, 25 and 30 mol% were synthesised by rapid quenching. Longitudinal and shear ultrasonic velocity were measured at room temperature and at 5 MHz frequency. Elastic properties, Poisson's ratio, microhardness, softening temperature and Debye temperature have been calculated from the measured density and ultrasonic velocity at room temperature. The experimental results indicate that the elastic constants depend upon the composition of the glasses and the role of the Ag2O inside the glass network is discussed. Estimated parameters based on Makishima–Mackenzie theory and bond compression model were calculated in order to analyse the experimental elastic moduli. Comparison between the experimental elastic moduli data obtained in the study and the calculated theoretically by the mentioned above models has been discussed