The technology of 3D bioprinting has gained significant interest in biomedical engineering, regenerative medicine, and the pharmaceutical industry. Providing a new scope in tissue and organ printing, 3D bioprinters are becoming commercialized for biological processes. However, the current technology is costly, ranging from USD$9,000-$30,000 and is limited to customized extrusion methods. Multiple microfluidic pump systems for bioink extrusion are commercially available at USD$30,000. Additionally, the use of Cartesian systems for 3D printing restricts the user to three axes of movement and makes multi-material modeling a challenge. Consequently, it was proposed to design a cost effective robotic 3D bioprinting system, compatible with peptide bioinks which were developed at KAUST Laboratory for Nanomedicine. The components of the system included a programmable robotic arm, an extruder for bioprinting, and multiple microfluidic pumps. The extruder was designed using a coaxial nozzle made of three inlets and one outlet. The programmable microfluidic pumps transported the peptide bioink, phosphate buffer saline (PBS) and human skin fibroblast cells (in cell culture media solution) through the nozzle to extrude a peptide nanogel thread. Model cell structures were printed and monitored for a period of two weeks and subsequently found to be alive and healthy. The system was kept well under a budget of USD$3,500. Future modifications of the current system will include adding a custom bioprinting arm to allow multi-material printing which can fully integrate and synchronize between the pumps and the robotic arm. This system will allow the production of a more advanced robotic arm-based 3D bioprinting system in the future.
Houborg, Rasmus; McCabe, Matthew; Angel, Yoseline; Middleton, Elizabeth M.(Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII, SPIE-Intl Soc Optical Eng, 2016-10-25)[Conference Paper]
Temporally rich hyperspectral time-series can provide unique time critical information on within-field variations in vegetation health and distribution needed by farmers to effectively optimize crop production. In this study, a dense timeseries of images were acquired from the Earth Observing-1 (EO-1) Hyperion sensor over an intensive farming area in the center of Saudi Arabia. After correction for atmospheric effects, optimal links between carefully selected explanatory hyperspectral vegetation indices and target vegetation characteristics were established using a machine learning approach. A dataset of in-situ measured leaf chlorophyll (Chll) and leaf area index (LAI), collected during five intensive field campaigns over a variety of crop types, were used to train the rule-based predictive models. The ability of the narrow-band hyperspectral reflectance information to robustly assess and discriminate dynamics in foliar biochemistry and biomass through empirical relationships were investigated. This also involved evaluations of the generalization and reproducibility of the predictions beyond the conditions of the training dataset. The very high temporal resolution of the satellite retrievals constituted a specifically intriguing feature that facilitated detection of total canopy Chl and LAI dynamics down to sub-weekly intervals. The study advocates the benefits associated with the availability of optimum spectral and temporal resolution spaceborne observations for agricultural management purposes.
McCabe, Matthew; Houborg, Rasmus; Lucieer, Arko(Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII, SPIE-Intl Soc Optical Eng, 2016-10-25)[Conference Paper]
With global population projected to approach 9 billion by 2050, it has been estimated that a 40% increase in cereal production will be required to satisfy the worlds growing nutritional demands. Any such increases in agricultural productivity are likely to occur within a system that has limited room for growth and in a world with a climate that is different from that of today. Fundamental to achieving food and water security, is the capacity to monitor the health and condition of agricultural systems. While space-Agency based satellites have provided the backbone for earth observation over the last few decades, many developments in the field of high-resolution earth observation have been advanced by the commercial sector. These advances relate not just to technological developments in the use of unmanned aerial vehicles (UAVs), but also the advent of nano-satellite constellations that offer a radical shift in the way earth observations are now being retrieved. Such technologies present opportunities for improving our description of the water, energy and carbon cycles. Efforts towards developing new observational techniques and interpretative frameworks are required to provide the tools and information needed to improve the management and security of agricultural and related sectors. These developments are one of the surest ways to better manage, protect and preserve national food and water resources. Here we review the capabilities of recently deployed satellite systems and UAVs and examine their potential for application in precision agriculture.
Crop height measured from UAVs fitted with commercially available RGB cameras provide an affordable alternative to retrieve field scale high resolution estimates. The study presents an assessment of between flight reproducibility of Crop Surface Maps (CSM) extracted from Digital Surface Maps (DSM) generated by Structure from Motion (SfM) algorithms. Flights were conducted over a centre pivot irrigation system covered with an alfalfa crop. An important step in calculating the absolute crop height from the UAV derived DSM is determining the height of the underlying terrain. Here we use automatic thresholding techniques applied to RGB vegetation index maps to classify vegetated and soil pixels. From interpolation of classified soil pixels, a terrain map is calculated and subtracted from the DSM. The influence of three different thresholding techniques on CSMs are investigated. Median Alfalfa crop heights determined with the different thresholding methods varied from 18cm for K means thresholding to 13cm for Otsu thresholding methods. Otsu thresholding also gave the smallest range of crop heights and K means thresholding the largest. Reproducibility of median crop heights between flight surveys was 4-6cm for all thresholding techniques. For the flight conducted later in the afternoon shadowing caused soil pixels to be classified as vegetation in key locations around the domain, leading to lower crop height estimates. The range of crop heights was similar for both flights using K means thresholding (35-36cm), local minimum thresholding depended on whether raw or normalised RGB intensities were used to calculate vegetation indices (30-35cm), while Otsu thresholding had a smaller range of heights and varied most between flights (26-30cm). This study showed that crop heights from multiple survey flights are comparable, however, they were dependent on the thresholding method applied to classify soil pixels and the time of day the flight was conducted.
Angel, Yoseline; Houborg, Rasmus; McCabe, Matthew(Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII, SPIE-Intl Soc Optical Eng, 2016-10-25)[Conference Paper]
Hyperspectral remote sensing images are usually affected by atmospheric conditions such as clouds and their shadows, which represents a contamination of reflectance data and complicates the extraction of biophysical variables to monitor phenological cycles of crops. This paper explores a cloud removal approach based on reflectance prediction using multitemporal data and spatio-Temporal statistical models. In particular, a covariance model that captures the behavior of spatial and temporal components in data simultaneously (i.e. non-separable) is considered. Eight weekly images collected from the Hyperion hyper-spectrometer instrument over an agricultural region of Saudi Arabia were used to reconstruct a scene with the presence of cloudy affected pixels over a center-pivot crop. A subset of reflectance values of cloud-free pixels from 50 bands in the spectral range from 426.82 to 884.7 nm at each date, were used as input to fit a parametric family of non-separable and stationary spatio-Temporal covariance functions. Applying simple kriging as an interpolator, cloud affected pixels were replaced by cloud-free predicted values per band, obtaining their respective predicted spectral profiles at the same time. An exercise of reconstructing simulated cloudy pixels in a different swath was conducted to assess the model accuracy, achieving root mean square error (RMSE) values per band less than or equal to 3%. The spatial coherence of the results was also checked through absolute error distribution maps demonstrating their consistency.
Soil moisture is a key component of the hydrologic cycle, influencing processes leading to runoff generation, infiltration and groundwater recharge, evaporation and transpiration. Generally, the measurement scale for soil moisture is found to be different from the modeling scales for these processes. Reducing this mismatch between observation and model scales in necessary for improved hydrological modeling. An innovative approach to downscaling coarse resolution soil moisture data by combining continuous data assimilation and physically based modeling is presented. In this approach, we exploit the features of Continuous Data Assimilation (CDA) which was initially designed for general dissipative dynamical systems and later tested numerically on the incompressible Navier-Stokes equation, and the Benard equation. A nudging term, estimated as the misfit between interpolants of the assimilated coarse grid measurements and the fine grid model solution, is added to the model equations to constrain the model's large scale variability by available measurements. Soil moisture fields generated at a fine resolution by a physically-based vadose zone model (HYDRUS) are subjected to data assimilation conditioned upon coarse resolution observations. This enables nudging of the model outputs towards values that honor the coarse resolution dynamics while still being generated at the fine scale. Results show that the approach is feasible to generate fine scale soil moisture fields across large extents, based on coarse scale observations. Application of this approach is likely in generating fine and intermediate resolution soil moisture fields conditioned on the radiometerbased, coarse resolution products from remote sensing satellites.
Hanafy, Sherif M.; Schuster, Gerard T.(74th EAGE Conference and Exhibition incorporating EUROPEC 2012, EAGE Publications, 2014-12-03)[Conference Paper]
Elastic seismic simulations and field data tests are used to validate the theory of a seismic scanning tunneling macroscope (SSTM). For nearfield elastic simulation, the SSTM results show superresolution to be better than λ/8 if the only scattered data are used as input data. If the direct P and S waves are muted then the resolution of the scatterer locations are within about λ/5. Seismic data collected in an Arizona tunnel showed a superresolution limit of at least λ/19. These test results are consistent with the theory of the SSTM and suggest that the SSTM can be a tool used by geophysicists as a probe for near-field scatterers.
We model high molecular weight homopolymers in semidilute concentration via Dissipative Particle Dynamics (DPD). We show that in model reduction methodologies for polymers it is not enough to preserve system properties (i.e., density ρ, pressure p, temperature T, radial distribution function g(r)) but preserving also the characteristic shape and length scale of the polymer chain model is necessary. In this work we apply a DPD-model-reduction methodology for linear polymers recently proposed; and demonstrate why the applicability of this methodology is limited upto certain maximum polymer length, and not suitable for solvent coarse graining.
A variational multi scale approach to model blood flow through arteries is proposed. A finite element discretization to represent the coarse scales (macro size), is coupled to smoothed dissipative particle dynamics that captures the fine scale features (micro scale). Blood is assumed to be incompressible, and flow is described through the Navier Stokes equation. The proposed cou- pling is tested with two benchmark problems, in fully coupled systems. Further refinements of the model can be incorporated in order to explicitly include blood constituents and non-Newtonian behavior. The suggested algorithm can be used with any particle-based method able to solve the Navier-Stokes equation.
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