Water & Waste Management

How Machine Learning Helps Identify Toxicity In Potential Drugs


The team believe that being able to determine the atomic structure of protein molecules will play a huge role in understanding how they work, and how they may respond to drug therapies. The drugs typically work by binding to a protein molecule, and then changing its shape and thus altering how it works.

Here's Why Water in the West Goes from Drought to Deluge

Mother Jones

Jerry Brown declared over on April 7--this winter's record-breaking wet weather has recharged surface water supplies across California: By the end of February, reservoirs in about 80 percent of the state's river basins were above historical average capacity. This year's plentiful precipitation is pushing water-forecasting models to their limits as analysts predict summer water supply based on winter rain and snowfall amounts outside historical norms. The late March or early April California snowpack reading is crucial for the managers that allocate water to irrigation districts and customers downstream of Trinity Lake, the third largest reservoir in the state, says Donald Bader, manager of the Northern California Area Office of the Bureau of Reclamation, which manages about a dozen California reservoirs. Blue Mesa, for example, the largest reservoir in Colorado, was at 69 percent capacity by the end of March; that's 126 percent of the historical average for that time of year.

[Report] Giant viruses with an expanded complement of translation system components


Some giant viruses encode a genome larger than that of some bacteria, but their evolutionary history is a mystery. Examining the genomes within a sample from a wastewater treatment plant in Austria, Schulz et al. assembled a previously undiscovered giant virus genome, which they used to mine genetic databases for related viruses. The authors thus identified a group of giant viruses with more genes encoding components of the protein translation machinery, including aminoacyl transfer RNA synthetases, than in other giant viruses.

4 questions with Rush CIO Dr. Shafiq Rab


Dr. Shafiq Rab, CIO of Rush University Medical Center in Chicago, uses his background in public health to inform his IT vision. For almost 20 years, he has served as a healthcare CIO at institutions like Hackensack (N.J.) University Health Network; Greater Hudson Valley Health System in Middletown, N.Y.; and St. Mary's Hospital in Passaic, N.J. In January, Dr. Rab brought his public health and IT expertise to Chicago after being named CIO and senior vice president of Rush University Medical Center. Question: How does your commitment to public health impact your work as a CIO?

Smart Water: EMAGIN is Using Artificial Intelligence to Make Managing Water Utilities and Industries Safer and Greener


By combining artificial intelligence with water utilities and industries, EMAGIN wants to shift the paradigm from reacting manually to proactively controlling how water utilities are operated and managed. EMAGIN's innovative, artificial intelligence-driven optimization and analytics platform is the Hybrid Adaptive Real-Time Virtual Intelligence, or HARVI. With HARVI, EMAGIN wants to leverage artificial intelligence to create an intelligent water system that connects to its natural and built environment. "It's an honour to be ranked one the top data-driven startups globally in the water sector," reveals Mohamad.

Kitchener startup applies artificial intelligence to water management


A startup that has developed artificial intelligence to better manage city water systems is among 10 companies from around the world admitted to a San Francisco accelerator focused on turning drought, leaky pipes and pollution into business opportunities. After graduating from the University of Waterloo with a degree in environmental engineering, Gaffoor hooked up with Vedut, who graduated from the University of Ontario Institute of Technology with a degree in software engineering. Two Ontario municipalities are using the startup's artificial intelligence to help operate drinking water and wastewater systems. Gaffoor, Emagin's chief executive officer, and Vedut, its chief operating officer, say the application of artificial intelligence to municipal water systems is an emerging area.

Immobots Take Control

AITopics Original Links

"There are lots of systems in the world that have sensors and actuators, but don't look like traditional mobile robots," says Brian Williams, a former NASA researcher who coinvented Deep Space One's autonomous software and is now a professor at MIT's Space Systems and Artificial Intelligence Laboratories. Once programmed with immobotic software, Williams explains, these systems "have a commonsense model of the physics of their internal components and can reason from that model to determine what is wrong and to know how to act." But in truth, the stuck power switch wasn't an authentic crisis: ground controllers deliberately misled the craft's control software so they could see how Remote Agent, the system developed by Williams and his NASA colleagues, would respond. That's why engineers at Xerox and its recently spun-off Palo Alto Research Center (PARC) have begun to build immobot intelligence into high-end copy machines.

The day the world ends

AITopics Original Links

Kurzweil and his supporters, such as the mathematician Vernor Vinge and the Bletchley Park computer scientist Jack Good, saw the coming age of silicon dominance not as a threat but as a promise. Over the space of just three hours, artificial intelligence literally evolved itself, creating ever more sophisticated programmes that turned the Earth into the home of a new lifeform - a huge, powerful global electronic super-intelligence. By 4pm, 90 per cent of the world's power stations, including the new fusion plants, were quietly, and without fuss, shutting down. Back in 1965, Jack Good, whose cryptographic work at Bletchley Park was a key part in the defeat of the Nazis, wrote that "the first ultraintelligent machine is the last invention that man need ever make".

knowledge-based system for assessing exposure to hazardous materials

AITopics Original Links

The Knowledge-based approach allowed the system to be implemented as three separate modules: inference engine, knowledge base, and user interface. Initially required to run under MS-DOS on a PC AT equivalent with 640K of RAM, a second release to run under Windows 3.1 reused the inference engine and knowledge base, requiring only a revised user interface. Enhancements made to the inference engine and the knowledge base were immediately available to both environments.

Advantages of Synthetic Noise and Machine Learning for Analyzing Radioecological Data Sets


We propose that analysis of small radioecological data sets by GLMs and/or machine learning can be made more informative by using the following techniques: (1) adding synthetic noise variables to provide benchmarks for distinguishing the performances of valuable predictors from irrelevant ones; (2) adding noise directly to the predictors and/or to the outcome to test the robustness of analysis results against random data fluctuations; (3) adding artificial effects to selected predictors to test the sensitivity of the analysis methods in detecting predictor effects; (4) running a selected machine learning method multiple times (with different random-number seeds) to test the robustness of the detected "signal"; (5) using several machine learning methods to test the "signal's" sensitivity to differences in analysis techniques. Here, we applied these approaches to simulated data, and to two published examples of small radioecological data sets: (I) counts of fungal taxa in samples of soil contaminated by the Chernobyl nuclear power plan accident (Ukraine), and (II) bacterial abundance in soil samples under a ruptured nuclear waste storage tank (USA). Specifically, our approach identified a negative effect of radioactive contamination in data set I, and suggested that in data set II stable chromium could have been a stronger limiting factor for bacterial abundance than the radionuclides 137Cs and 99Tc. This new information, which was extracted from these data sets using the proposed techniques, can potentially enhance the design of radioactive waste bioremediation.