Singapore has called on the tech industry to pitch for S$30 million (US$22.68 million) worth of grants aimed at enhancing the country's water treatment processes and operations. Its national water agency PUB on Monday announced three Requests for Proposals (RFPs), inviting technology providers and researchers to develop applications that would help ensure water sustainability. It noted that water demand was projected to double by 2060, when non-domestic sector would account for 70 percent of overall demand. By then, energy-intensive sources such as its recycling system NEWater and desalinated water would generate up to 85 percent of Singapore's water needs, it added. Five NEWater plants currently supplied up to 40 percent of Singapore's current water needs, which would climb to 55 percent by 2060.
Using electrodes made of carbon nanotubes (CNTs) can significantly improve the performance of devices ranging from capacitors and batteries to water desalination systems. But figuring out the physical characteristics of vertically aligned CNT arrays that yield the most benefit has been difficult. Now an MIT team has developed a method that can help. By combining simple benchtop experiments with a model describing porous materials, the researchers have found they can quantify the morphology of a CNT sample, without destroying it in the process. In a series of tests, the researchers confirmed that their adapted model can reproduce key measurements taken on CNT samples under varying conditions.
This article discusses our experience building and running an intelligent control system during a three-year period for a National Aeronautics and Space Administration advanced life support (ALS) system. "We'll have to go with 4 two-head pumps for the nitrifier." "That pump doesn't give me any feedback for speed, so we can't be sure it's responding to commands." "It'll have to do," said a woman at the far end of the conference table. By 1995, the AI controls team had been working with several groups in the Crew and Thermal Systems Division (CTSD), building AI control systems in support of CTSD's investigations in advanced life support (ALS).
This paper attacks this problem (crafting adversarial samples to fool toxic content analyzers). Social media platforms provide an environment where people can freely engage in discussions. Unfortunately, they also enable several problems, such as online harassment. Recently, Google and Jigsaw started a project called Perspective, which uses machine learning to automatically detect toxic language. A demonstration website has been also launched, which allows anyone to type a phrase in the interface and instantaneously see the toxicity score .
Before getting a second life, metals, plastics, cardboard, and other solid recyclable materials often embark on a long journey that touches two continents. Every day, about 1,500 shipping containers of recycled materials are loaded onto cargo ships in the U.S. and dispatched to China. In fact, since 2007, recyclable waste has been one of the U.S.' main exports to China, a partnership that fueled the Asian nation's manufacturing boom. As China's economy increasingly shifts to services, reducing its dependence on the polluting manufacturing industry, its government is getting more serious about environmental protection. While recycling is obviously beneficial for the environment, practically nobody is perfect at it.
At Instagram, we have the world's largest deployment of the Django web framework, which is written entirely in Python. We began using Python early on because of its simplicity, but we've had to do many hacks over the years to keep it simple as we've scaled. Last year we tried dismissing the Python garbage collection (GC) mechanism (which reclaims memory by collecting and freeing unused data), and gained 10% more capacity. However, as our engineering team and number of features have continued to grow, so has memory usage. Eventually, we started losing the gains we had achieved by disabling GC.
Microscopes enhanced with artificial intelligence (AI) could help in the quick and accurate diagnosis of the deadly blood infections, which may improve patients' odds of survival, according to a study. The bacteria that most often cause bloodstream infections include the rod-shaped bacteria including Escherichia coli or E.coli, the round clusters of Staphylococcus species, and the pairs or chains of Streptococcus species. Rapid identification and delivery of antibiotic medications is the key to treating bloodstream infections, which can kill up to 40 percent of patients who develop them. In the study, led by scientists Beth Israel Deaconess Medical Centre (BIDMC) in Boston, used an automated microscope designed to collect high-resolution image data from microscopic slides. A convolutional neural network (CNN) -- a class of artificial intelligence modelled on the mammalian visual cortex and used to analyse visual data -- was trained to categorise bacteria based on their shape and distribution.
A daunting challenge faced by environmental regulators in the U.S. and other countries is the requirement that they evaluate the potential toxicity of a large number of unique chemicals that are currently in common use (in the range of 10,000–30,000) but for which little toxicology information is available. The time and cost required for traditional toxicity testing approaches, coupled with the desire to reduce animal use is driving the search for new toxicity prediction methods [1–3]. Several efforts are starting to address this information gap by using relatively inexpensive, high throughput screening approaches in order to link chemical and biological space [1, 4–21]. The U.S. EPA is carrying out one such large screening and prioritization experiment, called ToxCast, whose goal is to develop predictive signatures or classifiers that can accurately predict whether a given chemical will or will not cause particular toxicities . This program is investigating a variety of chemically-induced toxicity endpoints including developmental and reproductive toxicity, neurotoxicity and cancer.
Envirobot - a robotic eel that can swim through contaminated water to find the source of pollution - is being developed at EPFL in Switzerland. A link has been posted to your Facebook feed. Envirobot - a robotic eel that can swim through contaminated water to find the source of pollution - is being developed at EPFL in Switzerland.
Smart Wind and Solar Power Big data and artificial intelligence are producing ultra-accurate forecasts that will make it feasible to integrate much more renewable energy into the power grid. Researchers around the world are collecting wind speed and output data from wind turbines. The result: wind power forecasts of unprecedented accuracy are making it possible to use far more renewable energy, at lower cost, than utilities ever thought possible. While solar power generation lags wind power production, researchers are furiously working around the world to better harness the sun's abundant power.