A method for evaluating Dry Eye Disease (“DED”) in a human or animal subject is provided. Thread thinning dynamics of a tear sample of the subject are determined using an acoustically-driven microfluidic extensional rheometry instrument. At least one physical parameter value of the tear sample is calculated based at least in part on the determined thread thinning dynamics. DED is evaluated based at least in part on the at least one calculated physical parameter value of the tear sample. A device for evaluating Dry Eye Disease (DED) in a human or animal subject is also provided. The device includes an acoustically-driven microfluidic extensional rheometry instrument and a processing device configured to evaluate DED based at least in part on the calculated at least one physical parameter value of the tear sample.

A water analytics (or actional stormwater services) platform is disclosed wherein the platform allows for keeping up with the most recent data and industry standards and gives flexibility to meet client (e.g., citizens, city managers, stormwater/conveyance system operators) needs. The platform evaluates the site-specific and collective impacts of individual flood events and presents value risk assessments. The platform estimates direct physical damages and provides related analysis for implementation and assessment by end-users. Some preferred embodiments include additional modules for direct and indirect loss of public service and their impacts to the service population.

A water analytics (or actional stormwater services) platform is disclosed wherein the platform allows for keeping up with the most recent data and industry standards and gives flexibility to meet client (e.g., citizens, city managers, stormwater/conveyance system operators) needs. The platform evaluates the site-specific and collective impacts of individual flood events and presents value risk assessments. The platform estimates direct physical damages and provides related analysis for implementation and assessment by end-users. Some preferred embodiments include additional modules for direct and indirect loss of public service and their impacts to the service population.

A comprehensive machine-learning algorithm, namely a Multi-Fidelity Neural Network (MFNN) architecture, is disclosed for data-driven constitutive meta-modelling of complex fluids. The physics-based neural networks are informed by underlying rheological constitutive models through synthetic generation of low-fidelity model-based data points.

A system includes a curing assembly for low temperature curing and residual stress relief of material substrates. The curing assembly includes a first exposure chamber configured to expose the material substrate to UV radiation, and a second exposure chamber configured to expose the material substrate to Gamma radiation. In some embodiments, a mixing apparatus may mix nano-filler particles into the material substrate prior to exposure to Gamma radiation. The cure assembly may also include a control system for determining exposure dosages and exposure times based at least in part, on the material properties of the material substrate.

A system includes a curing assembly for low temperature curing and residual stress relief of material substrates. The curing assembly includes a first exposure chamber configured to expose the material substrate to UV radiation, and a second exposure chamber configured to expose the material substrate to Gamma radiation. In some embodiments, a mixing apparatus may mix nano-filler particles into the material substrate prior to exposure to Gamma radiation. The cure assembly may also include a control system for determining exposure dosages and exposure times based at least in part, on the material properties of the material substrate.

A method is provided for determining the surface viscosity of a liquid in which a thread is formed from a drop of the liquid. The thread is lengthened and its minimum radius h.sub.0 is determined at multiple times between the thread formation and thread pinch-off. The minimum radius and associated time values are used to determine a linear relationship of minimum radius and time, with the coefficient of the linear relationship, or the slope X of the line in the linear relationship, corresponding to the surface viscosity μ.sub.s of the liquid according to one of the following equations:

[00001]$\begin{array}{cc}x=\frac{0.0709}{1+5B_{\mathrm{s0}/3h_0}},& \left(1\right)\end{array}$

where B.sub.s0=μ.sub.s/μR in which h.sub.0 is defined as above, R is the dimension of the feature on which the drop is provided and μ is the bulk viscosity of the liquid, or

[00002]$\begin{array}{cc}x=\frac{0.0304}{\mathrm{Oh}\left(1+5b_{\mathrm{s0}/3h_0}\right)},& \left(2\right)\end{array}$

in which Oh=μ/√{square root over (ρRσ)}, where μ and R are as defined above, ρ is the density of the liquid, and σ is the surface tension of the liquid without surfactants.

A first series of images of a first fluid is received. A first set of fluid characteristics of the first fluid is determined from the first series of images. A second series of images of a second fluid is received. A second set of fluid characteristics of the second fluid is determined from the second series of images. A match is determined to be found between the first set of fluid characteristics and the second set of fluid characteristics. The second fluid is identified based upon determining that the first set of fluid characteristics matches the second set of fluid characteristics.

A first series of images of a first fluid is received. A first set of fluid characteristics of the first fluid is determined from the first series of images. A second series of images of a second fluid is received. A second set of fluid characteristics of the second fluid is determined from the second series of images. A match is determined to be found between the first set of fluid characteristics and the second set of fluid characteristics. The second fluid is identified based upon determining that the first set of fluid characteristics matches the second set of fluid characteristics.

In order to determine a rheological property of a fluid, the fluid is conveyed with a constant volume flow rate through a nozzle and the fluid strand thereby generated is deposited on a substrate. A relative movement takes place between the nozzle and the substrate at a forward feed velocity value. A contour of the liquid strand between the nozzle and the substrate is optically measured, and an extensional viscosity as a rheological property is deduced from knowledge of the volume flow rate, the forward feed velocity value and the contour of the fluid strand.