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Abram Terentyev
Abram Terentyev

Search Results For V-ray (14) ((INSTALL))


How do we prepare your SketchUp model for a V-RAY rendering? It depends on your process of work. For example, if you directly build all of the models in the SketchUp file and apply the materials while modeling, you can use the V-RAY at any time, even at the beginning of the modeling. Using a completed Revit model for better rendering results, you can use V-RAY after building all of the models and then applying materials and lighting through V-RAY. This tutorial will demonstrate how you create a rendering based on the Eames House 3D model from the Revit model. I only added SketchUp Glass on the model. In the previous lecture, I showed how to import the Revit model to the SketchUp model and apply the SketchUp material. Please refer to the previous lecture for the information.




Search results for v-ray (14)


Download: https://www.google.com/url?q=https%3A%2F%2Furluso.com%2F2uduOq&sa=D&sntz=1&usg=AOvVaw3XpIO6nEjP8tRpRR0fnmvb



This tip is a trick of V-Ray rendering. Adjusting the intensity of the Sun on V-Ray is not enough to illuminate the interior space. V-Ray users add Rectangular lighting sources on windows to make the space brighter and better render results. This may minimize the noise effects on the render results.


Choosing an item from citations and headings will bring you directly to the content. Choosing an item from full text search results will bring you to those results. Pressing enter in the search box will also bring you to search results.


Unless the airplane has the following equipment, no person may conduct a flag or supplemental operation or a domestic operation within the States of Alaska or Hawaii over an uninhabited area or any other area that (in its operations specifications) the Administrator specifies required equipment for search and rescue in case of an emergency:


Each Category has a Search Scene tab on the top of the list. You can search for assets from the category selected. You can select more than one category or asset at a time with Ctrl+select(Cmd+select) or Shift+select.


Custom grouping scene assets of any type is now possible in the V-Ray Assets Outliner. A new Tag View button allows toggling tags visibility on or off. It is located at the top-right corner of the Asset Outliner, next to the search field. Tags help to organize and work more efficiently in complex projects. Upon creation each tag is assigned a random color.


We found that Amazon disproportionately placed its own products in the top search result. Despite making up only 5.8 percent of products in our sample, Amazon gave its own products and exclusives the number one spot 19.5 percent of the time overall. By comparison, competing brands (those that are not Amazon brands or exclusive products) were given the number one spot at a nearly identical rate but comprised more than 13 times as many products at 76.9 percent.


We created a Firefox desktop emulator using Selenium. The emulator visited Amazon.com and made each of the 11,342 searches on Jan. 21, 2021. The search emulator was forwarded through IP addresses in a single location, Washington, D.C., in order to reduce variation in search results (which typically vary by location).


In addition to the above, we collected the individual product pages for the 125,769 products that appeared in the first page of our 3,492 top searches in order to analyze the buy box information. The buy box displays the price, return policy, default seller, and default shipper for a product.


To gather the product pages, we used Amazon Web Services and the same Selenium emulator we made for collecting the search result pages. The emulator visited the hyperlink for each product and saved a screenshot and the source code.


We analyzed the rate of products that received the top search result relative to the proportion of products of the same category that appeared in our sample. We found that Amazon brands and exclusives were disproportionately given the number one search result relative to their small proportion among all products.


We used two straightforward measures for our analysis. First, we calculated a population metric using the percentage of products belonging to each category among products from all the search pages. To do this, we divided the number of products per category that occupy search result slots compared to all product slots in our sample. This included duplicates.


We chose to focus on that top left spot because Amazon changes the number of items across the first row based on screen size, and some searches return only a single item per row, so the top left spot is the only one to remain the same across all search results in our data.


In a majority of the searches in our data, 59.7 percent, Amazon sold the top spot to a sponsored product (17.3 percent of all product slots). The bulk of our analysis concerns the remaining 40.3 percent.


When the same product that is an Amazon brand or exclusive appeared more than once in the same search, we considered it labeled if any of the listings were labeled. This gives Amazon the benefit of the doubt by assuming that a customer will understand that the disclaimer applies to duplicate listings. Therefore, our metrics for disclosure are the lower bound.


We commissioned the market research group YouGov to conduct a nationally representative survey of 1,000 U.S. adults on the internet, to contextualize our findings. It revealed that 76 percent of respondents correctly identified Amazon Basics as being owned by Amazon and 51 percent correctly identified Whole Foods.


We compared the star ratings (a rough proxy for quality) and number of reviews (a rough proxy for sales volume) of the Amazon Brands that the company placed in the number one spot on the product search results page with other products on the same page.


We found that in two-thirds (65.3 percent) of the instances where Amazon placed its own products before competitor brands, the products that were Amazon brands and exclusives had lower star ratings than competing brands placed lower in the search results. Half of the time (51.7 percent) that the company placed its own products first, these items had fewer reviews than competing products the company chose to place lower on the search results page.


We took our original dataset of 3,492 search results with at least one Amazon brand or exclusive, filtered out sponsored products, and generated a dataset of product comparisons. Each product comparison is between the number one product and number two product on the same search page. The random forest used these attributes to predict a yes or no (boolean) category: which product among the pair was given the top search result (placed_higher).


The product comparisons encode the differences in star ratings (stars_delta) and number of reviews (reviews_delta); whether the product appeared among the top three clicked products from one million popular searches in 2020 from Amazon Seller Central (is_top_clicked); and whether the product was sold by Amazon (is_amazon_sold), shipped by Amazon (is_amazon_shipped), or was an Amazon brand or exclusive (is_amazon). We also used a randomly generated number as a control (random_noise). Distributions of each of these features is available on GitHub.


We used grid search with five-fold cross-validation to determine optimal hyperparameters (parameters we control versus those that arise from learning cycles): 500 decision trees in each forest, and a maximum of three questions each decision tree can ask the data. By asking more questions, each tree becomes deeper. But that also means that the trees are more likely to memorize the data. The more trees we train, the more resources it takes to run our experiment. Grid search trains and evaluates models with an exhaustive list of combinations of these hyperparameters to determine the best configuration.


We systematically removed each feature and retrained and reevaluated the model (called an ablation study) in order to isolate the importance of each individual feature. We used the accuracy of the model trained on all seven features as a baseline to compare each newly evaluated model (see results in Change of Accuracy in table above).


When we compared additional product pairs with the number one spot and those of lower-ranked products beyond just the number two spot, is_amazon remained the most predictive feature out of those we tested (results in our GitHub).


We used predictive models to show that being an Amazon brand or exclusive was the most influential feature among those we tested in determining which products Amazon chose to place at the top of search results.


The two datasets we created are small in comparison to the full catalog of products for sale on Amazon.com, for which there are no reliable estimates. However, we sought to examine searches and products that generate significant sales, not every product or every search.


When we collected product pages in February, about 3.9 percent of them were no longer available or the product had been removed from the Amazon Marketplace altogether since we gathered the search pages in January. We removed these products from any calculations involving the seller or shipper.


Because the buy box remained largely unchanged during a 12-week gap in this representative subsample of our data, we find that our buy box findings are reliable, despite the three- to four-week gap between when we gathered search results and product pages.


This seemed to signal a change from previous research. So we went further to determine whether the buy box had become more stable since the 2016 Northeastern University study. That study was limited to products with multiple sellers. When we did the same, it brought the sample size down to 1,209. Looking only at products with multiple sellers, we found Amazon changed the buy box seller for only 23.5 percent of products. In addition, among products with multiple sellers, Amazon gave itself the buy box for 40.0 percent of them. 041b061a72


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