![]() Specimens prepared from 0.030 inch thick graphite-polyethersulfone are also available for future induction fastening evaluation.Ĭompany Reinvesting and Corporate ROI Rob Holder HP-DOJ/DOS Account Executive Report Documentation Page Form ApprovedOMB No. Continued testing shows that the interface screening must be well impregnated with resin to ensure proper flow when bonding graphite-acrylic lap shear samples. Tests of the LARC prototype induction welder is continuing in an instrumented test stand comprised of a Dumore drill press (air over oil feed for variable applied loads) and a dynamometer to measure actual welding loads. The induction fastening effort remains within cost and schedule constraints. The design and analysis of flight weight primary and secondary beam builder structures proceeded satisfactorily but remains curtailed until further funding is made available to complete the work. The development effort on the composite beam cap fabricator was completed within cost and close to abbreviated goals. Describes the first true zero E-scale home in a hot-dry climate with ducts inside, R-50 attic insulation, roof-mounted photovoltaic power system, and solar thermal water heating. Utilizing the Naval Supplyīuilders Challenge High Performance Builder Spotlight - Artistic Homes, Albuquerque, NMīuilding America Builders Challenge fact sheet on Artistic Homes of Albuquerque, New Mexico. benefits are being realized in RFID initiatives that are not being captured by traditional Return on Investment analysis. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Naval. AUTHOR(S) Shane Guilford and Mark Kutis 5. NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA MBA PROFESSIONAL REPORT RFID Benefits Looking Beyond ROI By: Shane. Finally, the guide tells how you can participate and answers many of the questions asked by builders about the Super Good Cents program. It explains the program standards and the typical building techniques used by Super Good Cents builders. This Builder`s guide describes the Super Good Cents program and the benefits available to participating builders. OSU Extension Energy Program United States. Treasure Homes, Inc., achieved a HERS rating of 46 without PV on its prototype “Gem†home, located on the shores of Lake Michigan in northern Indiana, thanks in part to training received from a Building America partner, the National Association of Home Builders Research Center.Ī Builder`s Guide to Super Good Cents Contruction and Sales. But the sensitivity, specificity, and AUC are the most important to report over the training/validation data.High Performance Builder Spotlight: Treasure Homes Inc. Regarding the confusion matrix you still can calculate for the test data by calculating the prediction errors for each class over a sample of the test data. Note that the AUC calculated is named in the output ROC, yet it is the AUC. In addition, in the Train function in the caret package there is a paramter called metric, if you place "ROC" in the metric parameter, it will automatically report the sensitivity, specificity and AUC. Thus for the Caret package, you will need do define the method parameter in the train function while using caret to point out for the library you want to use ( tree, gradient boosting, random forest or adaboost trees). In addition, you can use the Caret package, which is is a mother package for using many other machile learning tools. You can use the package mentioned by Arjun R. In case your problem is binary classification, you can go as mentioned for R, this is much easier. ![]() I feel this method is quite subjective, likely to create great variabilities between images. ROIs) to cover all the GFP expressed areas in the original image, measure the mean value (3), then subtract (2) from (3). Now comes the question – how do I create ROIs from the thresholded image to cover all black areas (magic wand only highlight individual white areas)? Or is there a way to do the “mapping”? Then, if possible, I could “map” the black areas to my original image to measure the mean value from the “proper” protein expressed area (let’s assume that the autofluorescence is comparable between 2 groups). The white areas then have value of 0 and black areas 255. Threshold the image using range of 0 to value (2) as measured above to get the Thresholded Image.Easy to achieve, and seems reasonably logical. ![]()
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