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Just in time for Earth Day, Apple has unveiled a new recycling robot -- and it can disassemble 200 iPhones in a single hour. Daisy can successfully extract parts from nine types of iPhones -- and for every 100,000 devices it can salvage 1,900 kg of aluminum, 770 kg of cobalt, 710 kg of copper and 11 kg of rare earth elements. The robot represents a major step forward in Apple's mission to someday build its devices entirely from recycled materials. "We created Daisy to have a smaller footprint and the capability to disassemble multiple models of iPhones with higher variation compared to Liam" -- an earlier iteration of the company's recycling robotics -- Apple said in its 2018 Environmental Responsibility Report. Ultimately, Apple hopes to develop a closed-loop production system in which every reusable part of older devices is utilized in new ones.

The purpose of this paper is to re-investigate the estimation of multiple factor models by relaxing the convention that the number of factors is small. We first obtain the collection of all possible factors and we provide a simultaneous test, security by security, of which factors are significant. Since the collection of risk factors selected for investigation is large and highly correlated, we use dimension reduction methods, including the Least Absolute Shrinkage and Selection Operator (LASSO) and prototype clustering, to perform the investigation. For comparison with the existing literature, we compare the multi-factor model's performance with the Fama-French 5-factor model. We find that both the Fama-French 5-factor and the multi-factor model are consistent with the behavior of "large-time scale" security returns. In a goodness-of-fit test comparing the Fama-French 5-factor with the multi-factor model, the multi-factor model has a substantially larger adjusted $R^{2}$. Robustness tests confirm that the multi-factor model provides a reasonable characterization of security returns.

Scientists teamed up to use artificial intelligence to discover new alternatives to steel in record time. As a result, they discovered three new blends to form metallic glass and did this 200 times faster than it has ever been done before. Fang Ren, who developed algorithms to analyze data on the fly while a postdoctoral scholar at SLAC, at a Stanford Synchrotron Radiation Lightsource beamline where the system has been put to use. Metallic glass is essentially an alloy of the future. Normally, a few metals can be mixed together so that the ideal properties of each metal are'added' together to make a'super-metal'.

Officials say using the RadPiper robot is safer, tremendously faster and more accurate than the current method of workers taking external measurements. They also say it could save tens of millions of public dollars on cleanups of that site and one near Paducah, Kentucky.

Cunjiang Yu, Bill D. Cook Assistant Professor of mechanical engineering, said potential applications range from surgery and rehabilitation to search and rescue in natural disasters or on the battlefield. Because the robot body changes shape in response to its surroundings, it can slip through narrow crevices to search for survivors in the rubble left by an earthquake or bombing, he said. "They sense the change in environment and adapt to slip through," he said. These soft robots, made of soft artificial muscle and ultrathin deformable sensors and actuators, have significant advantages over the traditional rigid robots used for automation and other physical tasks. The researchers said their work, published in the journal Advanced Materials, took its inspiration from nature.

The paper and packaging industry is in the midst of major change. Print circulation for magazines and newspapers has dropped dramatically in the last decade. Craigslist is replacing the classified section. Google and social media are replacing direct mail advertising. Cloud storage is replacing the office filing cabinet.

Apple has announced the creation of Daisy, a robot specifically designed to quickly disassemble several different iPhone models and recycle parts that can be used again, the company detailed in a Thursday press release. Daisy is actually a bit of a composite itself--the robot is made up of parts from another recycling robot, Liam, that was created in 2016, the release said. Daisy will be used first in the US and Europe and then expand worldwide. According to Apple's release, Daisy will be able take apart nearly 200 iPhones per hour, pushing the company closer to its goal of ending its reliance on mining for vital smartphone materials like cobalt. From every 100,000 iPhones Daisy disassembles, the release said, Apple will be able to harness about 1 kg of gold, 7.5kg of silver, almost two tons of aluminum, and 11kg worth of certain rare-earth elements and minerals like cobalt, palladium, tungsten, tantalum, and tin.

Mining is all about profitability. But tracking that can prove tricky, particularly when you're relying on people on the ground to report on activities. It all comes down to big data: the more data you can collect, the more information you have at your disposal, and the more insight you have into the business. IOT technology is paving the way for mine optimisation initiatives, says Johan Pietersen, MD of Virtualscape Technologies. "Mines are looking for the ability to gather data for various purposes, including improvements in efficiency and safety. They need to be able to monitor activities, assets and people and make better decisions based on real-time data. AI applied to such data is able to provide a realistic view of the mining operation's performance on an hourly basis, allowing mine managers to make pro-active judgment calls on operational execution to their financial benefit."

We live in amazing times. In fact, this entire "thing" of robotics and artificial intelligence (AI) is now being called the next industrial revolution. And it does not come without a lot of fear about a large workforce losing its jobs and means of making a living. When the first industrial revolution hit, factories and mass production drew workers to the cities in droves. Manufacturing put individual craftsmen out of business. Consumers could get products cheaper and faster and that was a good thing.