The facility features an activated sludge process system. Additionally, the facility is equipped with an emergency standby diesel generator.
EHRA offered its Landscape Architectural services to complete a Parks and Trails Master Plan for the District.
This project was the second phase of parks implementation outlined in the District's Parks Master Plan, which was completed by EHRA in 2007. Utilizing the site of a recently demolished former wastewater treatment plant provided an opportunity to create a passive park space for District residents.
Identified as a top priority during the development of the District’s Parks Master Plan, this portion of trail was the first phase of over two miles of planned trails to provide connectivity and recreation for District residents.
EHRA worked with the District to create a comprehensive Parks Master Plan, which included recommendations for the development of over two miles of hike/bike trails adjacent to local streets, and within flood control and utility pipeline easements. The District began implementation of the Plan by prioritizing the beautification of West Road, a major arterial street that runs through the District.
A new computer program works smarter, not harder, to solve problems faster than its predecessors. The algorithm is designed to find the best solution to a given problem among all possible options. Whereas other computer programs winnow down the possibilities one at a time, the new program — presented July 12 at the International Conference on Machine Learning in Stockholm — rules out many choices at once.
For instance, imagine a computer is assigned to compile movie recommendations based on a particular film. The ideal recommendation list would include suggestions that are both similar to the original flick — say, in the same genre — yet different enough from each other to give the viewer a variety of choice. A traditional recommendation system would pore over an entire movie library to find films that best met those criteria and add films to its roster of recommendations one by one, a relatively slow and tedious process.
By contrast, the new program starts by randomly picking a bunch of movies from the library. Among that sample, the system keeps the movies that strike the best balance between relevance to the original film and diversity, and discards the rest. From that smaller pool, the algorithm again chooses films at random and keeps only the best of the bunch. That strategy helps the algorithm build its rec list far faster.
The new algorithm, built by Harvard University computer scientists Yaron Singer and Eric Balkanski, compiled movie suggestions more than 10 times as fast as a standard recommender system. In another trial, it devised optimal routes for cabs in New York City about six times as fast as a conventional automated dispatcher.
This program could also speed up data processing for everything from drug discovery to social media analytics, engineering and analyses of genetic data.
Source: Science News