Why contests improve philanthropy

Knight Foundation has supported nearly a dozen open contests, reviewed almost 25,000 applications and chosen more than 400 winning ideas. This report discusses what the Foundation has learnt from this experience about how good contests work, what they can do, and what the challenges are

Producing Temperature Sensitive Paint In-House

The Temperature Sensitive Paint (TSP) is a widely used method in measuring and visualizing flow separation. Compared to the cost and time consumption needed for digital methods, such as pitot tubes, temperature sensitive paint provides a cheaper alternative. Due to high usage in College of Engineering research projects, it was determined that in house fabrication of temperature sensitive paint would reduce time and cost limitations. For initial stages, literature research was performed to determine the raw luminophore and polymer binder optimum for the ranges of temperatures. Europium III thenoyltrifluoroacetonate was determined to be an effective luminophore to create a solvent for jet impingement research. Standard operating procedure was also created such that it met the environmental risk factors for fabrication of paint. The testing of the solvent is yet to be performed. However, the goal is to compare and develop an automated image sensing calibration system based on reference imaging. Control data from Dr. Mark Ricklick and Dr. Ebenezer Gnanamanickam will be utilized as a comparison for effectiveness of paint to be validated and implemented in Gas Turbine Lab Research

Why Contests Improve Philanthropy: Six Lessons on Designing Public Prizes for Impact

Since 2007, Knight foundation has run or funded nearly a dozen open contests, many over multiple years, choosing some 400 winners from almost 25,000 entries, and granting more than $75 million to individuals, businesses, schools and nonprofits. The winners believe, as we do, that democracy thrives when people and communities are informed and engaged. The contests reflect the full diversity of our program areas: journalism and media innovation, engaging communities and fostering the arts. Over the past seven years, we have learned a lot about how good contests work, what they can do, and what the challenges are. Though contests represent less than 20 percent of our grant-making, they have improved our traditional programs in myriad ways. A 2009 McKinsey & Company Report, "and the winner is....," put it this way: "Every leading philanthropist should consider the opportunity to use prizes to help achieve their mission, and to accept the challenge of fully exploiting this powerful tool." But of America's more than 76,000 grant-making foundations, only a handful, maybe 100 at most, have embraced the use of contests. That means 99.9 percent do not. Sharing these lessons here is an invitation to others to consider how contests, when appropriate, might widen their networks, deepen the work they already do, and broaden their definition of philanthropic giving. Before you launch and manage your own contests, you might want to consider the six major lessons we've learned about how contests improved our philanthropy

Brain Tumor Detection Using MRI Images

When abnormal cells develop within the brain, a tumor is formed. Early tumor detection improves the likelihood of a patient\u27s recovery. Compared to CT scan pictures, magnetic resonance imaging (MRI) is a trustworthy method for finding malignancies. In this project, we will use deep learning methods to detect tumors faster with higher accuracy using MRI images. Specifically, we will investigate the performance of transfer learning models based on convolutional neural networks (CNN) structures on the tumor detection problem. A machine learning approach called transfer learning uses a model already trained for the present task. The advantage of this technique is that we do not need to train the model from scratch, which will save time and increase accuracy. With the help of the Visual Geometry Group (VGG 16), Inception V3, and Resnet 50, this study attempts to identify brain tumors. It also uses a methodical approach for hyperparameter tuning to improve the trained models\u27 accuracy. The main objective is to develop a practical approach for detecting brain tumors using MRIs to make quick, efficient, and precise decisions regarding the patients\u27 conditions. Our suggested methodology is evaluated on the Kaggle dataset, taken from BRATS 2015 for brain tumor diagnosis using MRI images, including 3700 MRI brain images, with 3300 showing tumors. The simulation results show that training the deep learning models could achieve an accuracy of 96.0% for VGG-16, 94% for Resnet50, and 90.7% for the InceptionV3 model. In order to improve the accuracy even further, Bayesian Optimization is leveraged as a hyperparameter tuning technique to obtain the best set of parameters. We could achieve the accuracy of 97.5% for VGG-16, 95% for Resnet50, and 91.5% for InceptionV3

How the Public Perceives Community Information Systems

Based on surveys in three cities, analyzes links between perceptions of the local government's transparency and residents' satisfaction with its performance, the community, and local information ecosystem, as well as sense of civic empowerment

On the false-diffusion problem in the numerical modelling of convection-diffusion processes

This thesis is concerned with the classification and evaluation of various numerical schemes that are available for computing solutions for fluid-flow problems, and secondly, with the development of an improved numerical discretisation scheme of the finite-volume type for solving steady-state differential equations for recirculating flows with and without sources. In an effort to evaluate the performance of the various numerical schemes available, some standard test cases were used. The relative merits of the schemes were assessed by means of one-dimensional laminar flows and two-dimensional laminar and turbulent flows, with and without sources. Furthermore, Taylor series expansion analysis was also utilised to examine the limitations that were present. The outcome of this first part of the work was a set of conclusions, concerning the accuracy of the numerous schemes tests, vis-a-vis their stability, ease of implementation, and computational costs. It is hoped that these conclusions can be used by `computational fluid-dynamics' practitioners in deciding on an optimum choice of scheme for their particular problem. From the understanding gained during the first part of the study, and in an effort to combine the attributes of a successful discretisation scheme, eg positive coefficients. conservation and the elimination of 'false-diffusion', a new flow-oriented finite-volume numerical scheme was devised and applied to several test cases in order to evaluate its performance. The novel approach in formulating the new CUPID* scheme (for Corner UPw^nDing) underlines the idea of focussing attention at the control-volume corners rather than at the control-volume cell-faces. In two-dimensions, this leads to an eight neighbour influence for the central grid point value, depending on the flow-directions at the corners of the control-volume. In the formulation of the new scheme, false-diffusion is considered from a pragmatic perspective, with emphasis on physics rather than on strict mathematical considerations such as the order of discretisation, etc. The accuracy of the UPSTREAM scheme (for JJPwind in STREAMIines) indicates that although it is formally only first-order accurate, it considerably reduces 'false-diffusion'. Scalar transport calculations (without sources) show that the UPSTREAM scheme predicts bounded solutions which are more accurate than the upwind-difference scheme and the unbounded skew-upstream-difference scheme. Furthermore, for laminar and turbulent flow calculations, improved results are obtained when compared with the performances of the other schemes. The advantage of the UPSTREAM-difference scheme is that all the influence coefficients are always positive and thus the coefficient matrices are suitable for iterative solution procedures. Finally, the stability and convergence characteristics are similar to those of the upwind-difference scheme, eg converged solutions are guaranteed. What cannot be guaranteed, however, is the conservatism of the scheme and it is recommended that future work should be directed towards improving that disadvantage

Studies on Heterocyclic Compounds

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Effects of Trigger Point Dry Needling in Conjunction with Intramuscular Electrical Stimulation for a Patient with Lumbar Radiculopathy: A Case Report

The purpose of this case was to see the benefits of Trigger Point Dry Needling in conjunction with Intramuscular Electrical Stimulation for the treatment of lumbar radiculopathy regarding the range of motion, strength, sensation, reduction of pain, improve functional ability, and quality of life. Traditional interventions for this diagnosis include strengthening exercises, modalities such as transcutaneous electrical stimulation, and even surgery as a last resort. There is limited research available regarding Trigger Point Dry Needling in conjunction with Intramuscular Electrical Stimulation for lumbar radiculopathy.4 This case report supports the idea that this physical therapy treatment intervention has positive outcomes in a short duration improving quality of life and functional ability.https://soar.usa.edu/flsafall2018/1013/thumbnail.jp

Delirium-a letter update

Delirium is essentially acute brain failure that results from stressors that surpass the brain's homeostatic reserve. Delirium is caused by a combination of risk factors that are both predisposing and precipitating. Advanced age, frailty, medication exposure or withdrawal, sedation depth, and sepsis are all known risks. Stressors most likely have coordinated rather than independent effects, and the systems they affect are linked rather than separate. Changes in the blood-brain barrier and the central nervous system's de novo synthesis or elaboration of inflammatory mediators account for the pathophysiology of delirium. It also seems that neuro-inflammatory activity contributes to the dysregulation of neurotransmitters