3D Printed Embedded Force Sensors

Additive Manufacturing and 3D printing has opened the door to an endless amount of opportunities, including recent advances in conductive and resistive circuit printing. Taking advantage of these new technologies, we have designed a 3D printed insole with embedded plantar pressure sensor arrays. The customizable aspect of 3D printing allowed us to uniquely design a multitude of sensors. With the use of a dual extrusion printer we were able to produce a model that printed both the resistive circuit and complete insole simultaneously. These distinctive technologies have given us the capability to capture valuable pressure data from the sole of the foot. Analog signals sent from the pressure sensor arrays are received and processed through an attached multiplexer designed specifically for this application. The signal is then digitized and transmitted over the SPI transfer protocol to a processor and wirelessly communicated, via Bluetooth Low Energy, to a mobile android device to allow the user to easily record and interpret the array\u27s pressure data in real-time. The android device houses a pressure mapping view to show the gradient of force throughout the insole. With the capabilities of this insole we have provided an avenue for physicians and physical therapists to gather quantifiable insight into their patient\u27s progression throughout the rehabilitation process. With more intelligent and personalized data the applications of this technology are countless.https://scholarscompass.vcu.edu/capstone/1147/thumbnail.jp

Crude Oil Prices and US Crop Exports: Exploring the Secondary Links between the Energy and Ag Markets

As the biofuel industry has developed, there has been a lot of discussion about the linkages between the energy and agricultural markets. The growth of the ethanol and biodiesel sectors bolstered the connection among the oil, gas, and crop markets. As crop-based biofuels compete in the energy market, crop prices are directly impacted not only by the relative standing of biofuels in the fuel hierarchy, but also by general shifts in energy supplies and demands. However, there is another distinct way energy markets can impact crop markets—many US international trade partners are reliant on the energy sector as a major source of income. Thus, energy market swings can translate into significant income movements for those countries, influencing their ability to purchase US agricultural products. In this article, we examine the robustness of treating a key energy commodity—crude oil—as an indicator for income for those oil-reliant countries and investigate how that affects their demand for US crop exports

Are Agricultural Professionals’ Farmland Value and Crop Price Forecasts Consistent?

Using agricultural professionals’ forecasts of future farmland values and corn and soybean cash prices for their service area, we analyze whether their land and corresponding crop price expectations are consistent. We find that changes in expected land prices over time are positively correlated with expected crop price changes, suggesting these two forecasts are somewhat consistent. More importantly, we find that the linkage between these two forecasts is significantly stronger in the medium- and long-term as opposed to the short-term, as well as a substantially stronger correlation for districts that have heavier reliance on crop production as a net farm income source

Choosing Wavelet Methods, Filters, and Lengths for Functional Brain Network Construction

Wavelet methods are widely used to decompose fMRI, EEG, or MEG signals into time series representing neurophysiological activity in fixed frequency bands. Using these time series, one can estimate frequency-band specific functional connectivity between sensors or regions of interest, and thereby construct functional brain networks that can be examined from a graph theoretic perspective. Despite their common use, however, practical guidelines for the choice of wavelet method, filter, and length have remained largely undelineated. Here, we explicitly explore the effects of wavelet method (MODWT vs. DWT), wavelet filter (Daubechies Extremal Phase, Daubechies Least Asymmetric, and Coiflet families), and wavelet length (2 to 24) - each essential parameters in wavelet-based methods - on the estimated values of network diagnostics and in their sensitivity to alterations in psychiatric disease. We observe that the MODWT method produces less variable estimates than the DWT method. We also observe that the length of the wavelet filter chosen has a greater impact on the estimated values of network diagnostics than the type of wavelet chosen. Furthermore, wavelet length impacts the sensitivity of the method to detect differences between health and disease and tunes classification accuracy. Collectively, our results suggest that the choice of wavelet method and length significantly alters the reliability and sensitivity of these methods in estimating values of network diagnostics drawn from graph theory. They furthermore demonstrate the importance of reporting the choices utilized in neuroimaging studies and support the utility of exploring wavelet parameters to maximize classification accuracy in the development of biomarkers of psychiatric disease and neurological disorders.Comment: working pape