Conceptual design of a closed loop nutrient solution delivery system for CELSS implementation in a micro-gravity environment

Described here are the results of a study to develop a conceptual design for an experimental closed loop fluid handling system capable of monitoring, controlling, and supplying nutrient solution to higher plants. The Plant Feeder Experiment (PFE) is designed to be flight tested in a microgravity environment. When flown, the PFX will provide information on both the generic problems of microgravity fluid handling and the specific problems associated with the delivery of the nutrient solution in a microgravity environment. The experimental hardware is designed to fit into two middeck lockers on the Space Shuttle, and incorporates several components that have previously been flight tested

Condensation and Clustering in the Driven Pair Exclusion Process

We investigate particle condensation in a driven pair exclusion process on one- and two- dimensional lattices under the periodic boundary condition. The model describes a biased hopping of particles subject to a pair exclusion constraint that each particle cannot stay at a same site with its pre-assigned partner. The pair exclusion causes a mesoscopic condensation characterized by the scaling of the condensate size $m_{\rm con}\sim N^\beta$ and the number of condensates $N_{\rm con}\sim N^\alpha$ with the total number of sites $N$. Those condensates are distributed randomly without hopping bias. We find that the hopping bias generates a spatial correlation among condensates so that a cluster of condensates appears. Especially, the cluster has an anisotropic shape in the two-dimensional system. The mesoscopic condensation and the clustering are studied by means of numerical simulations.Comment: 4 pages, 5 figure

Auction market placement and a rest stop during transportation affect the respiratory bacterial microbiota of beef cattle

BackgroundBovine respiratory disease (BRD) is a significant health problem in beef cattle production, resulting in considerable economic losses due to mortalities, cost of treatment, and reduced feed efficiency. The onset of BRD is multifactorial, with numerous stressors being implicated, including transportation from farms to feedlots. In relation to animal welfare, regulations or practices may require mandatory rest times during transportation. Despite this, there is limited information on how transportation and rest stops affect the respiratory microbiota.ResultsThis study evaluated the effect of cattle source (ranch-direct or auction market-derived) and rest stop duration (0 or 8 h of rest) on the upper respiratory tract microbiota and its relationship to stress response indicators (blood cortisol and haptoglobin) of recently weaned cattle transported for 36 h. The community structure of bacteria was altered by feedlot placement. When cattle were off-loaded for a rest, several key bacterial genera associated with BRD (Mannheimia, Histophilus, Pasteurella) were increased for most sampling times after feedlot placement for the ranch-direct cattle group, compared to animals given no rest stop. Similarly, more sampling time points had elevated levels of BRD-associated genera when auction market cattle were compared to ranch-direct. When evaluated across time and treatments several genera including Mannheimia, Moraxella, Streptococcus and Corynebacterium were positively correlated with blood cortisol concentrations.ConclusionThis is the first study to assess the effect of rest during transportation and cattle source on the respiratory microbiota in weaned beef calves. The results suggest that rest stops and auction market placement may be risk factors for BRD, based solely on increased abundance of BRD-associated genera in the upper respiratory tract. However, it was not possible to link these microbiota to disease outcome, due to low incidence of BRD in the study populations. Larger scale studies are needed to further define how transportation variables impact cattle health

Creating an administrative fellowship at the CSU Health Network

College of Business; CSU Health Network.Includes bibliographical references.The CSU Health Network Administrative Fellowship seeks to mirror the model of the approximately 75+ other such programs in the United States. Administrative fellowships are found in many health systems and hospitals, including locally at UCHealth and Children’s Hospital Colorado, and at institutions as well known as Duke, the Mayo Clinic, and Johns Hopkins. Traditionally, an administrative fellowship is completed immediately after the awarding of an MBA or MHA (Master of Healthcare Administration). At CSUHN, we specifically sought to provide a similar experience for a practicing mid-career clinician (physician, PA, or NP) who also held or was near completion of an MBA or MHA. For the inaugural year of the CSUHN fellowship, the incumbent was a senior MBA student at CSU and a full-time PA in the medical clinic. The curriculum of the CSUHN fellowship placed the fellow as a de facto member of the organization’s leadership team for one year, allowing access to the highest levels of decision making and strategic planning. The fellow attended the weekly leadership team meetings and had weekly one-on-one sessions with the Director of Medical Services, who served as the mentor for the fellowship. This high-level exposure gave the fellow the opportunity to become involved in the operations of every part of this large and diverse clinic, applying the knowledge, skills, and abilities learned in the MBA program. The “primary benefit the fellow receives is exposure to senior management— something that cannot be duplicated any other way, both in the depth of senior management and breadth of the organization.”(1)College of Business - Dean's Award for Research Excellence

Supervised Classification of Multisensor Remotely Sensed Images Using a Deep Learning Framework

In this paper, we present a convolutional neural network (CNN)-based method to efficiently combine information from multisensor remotely sensed images for pixel-wise semantic classification. The CNN features obtained from multiple spectral bands are fused at the initial layers of deep neural networks as opposed to final layers. The early fusion architecture has fewer parameters and thereby reduces the computational time and GPU memory during training and inference. We also propose a composite fusion architecture that fuses features throughout the network. The methods were validated on four different datasets: ISPRS Potsdam, Vaihingen, IEEE Zeebruges and Sentinel-1, Sentinel-2 dataset. For the Sentinel-1,-2 datasets, we obtain the ground truth labels for three classes from OpenStreetMap. Results on all the images show early fusion, specifically after layer three of the network, achieves results similar to or better than a decision level fusion mechanism. The performance of the proposed architecture is also on par with the state-of-the-art results