【推荐2】Humans are developing new places to live in. In the south of Silicon Valley sits the Monterey Peninsula, where you’ll find a moveable community that’s being designed as a rest region for the region’s tech elites(精英).

Walden Monterey was founded in 2016 by developer Nick Jekogian, who set out to turn the 609-acre land into a coastal “agrihood” community, a growing trend among the tech elites in which they avoid the idea of belonging to golf communities, unlike what the previous generations have done, and instead enjoy agricultural neighborhoods that focus on nature, farms, and outdoor living.

The property plans to build 22 homes in total, with the lots they sit costing about $1 million each(three lots have been sold, as of September 2018). After the sales are made, buyers can work with a team of more than 20 architects assembled by Jekogian to then pay additional millions each for home construction.

A key step in the buying process involves potential buyers actually visiting the land. Jekogian invites people to stay in “moveable rooms” or small moveable glass houses, which allow them to experience what living on the land would actually be like.

But now, Walden Monterey will soon provide a new way to try out the land. The design studio DFA, founded by Laith Sayigh, was approached by Walden Monterey to design a house for potential buyers to stay each in while they think about purchase decisions.

The 3D-printed houses, named Galini Sleeping Pods, are 300 square feet in size, can be moved anywhere, are powered by solar panels, wind turbines and Tesla batteries, and will cost about $250,000 each. Sayigh told Business Insider that they’ re not just a future staple for the Walden Monterey community, but that they represent the next generation of construction technology.

【推荐1】As the carbon countdown clock ticks on, cities must be more imaginative and cooperative, a lively round table hosted in Newham by a non-profit organization Friends of the Earth said.

“Net zero is around the corner,” said Mike Wake, Director of Friends of the Earth. “The UK has cut greenhouse gas emission (排放) by 40% to date mostly through the growth of large renewable generation on the grid (输电网), but further cuts will be needed as London tries to find new ways of using energy.

“While Newham has lower greenhouse gas emission per head than the UK, it has high levels of fuel poverty, poor air quality and leaky homes. Vastly improved energy efficiency, especially on older and leakier homes, along with more efficient forms of heating and an expansion of low carbon heat networks, will help,” said Wake.

“But the future requires a shift to a more communal solution,” the representatives said. District heating, which pipes hot water from a central source to connected homes or businesses, is often a cheaper and lower-carbon method of heating highly populated areas. “It’s the lowest cost and low carbon solution for the future,” Wake added.

“There are new technologies to use wasted heat,” Wake said. “We worked with businesses to decarbonise (使环保) energy supply. Rather than wasting, spare heat generated by manufacturing and other activities can be captured and used to heat nearby properties. Friends of the Earth has already helped save Newham thousands of tonnes of emissions through district heating plans, solar PV and more energy efficient measures in the homes.”

But the challenges are substantial. Newham has London’s second-highest rate of fuel poverty. “Anyway, I’m confident and hopeful about the collective challenge,” said Wake. “But for Newham to be at the forefront of a green economy, to solve fuel poverty and deal with structural equalities, we must spare no effort.”

【推荐2】They come suddenly, without warning, and the question in your mind “Why now?” melts into the depressing realization that this could mean the end of your life. Until now.

A new machine-learning system of artificial intelligence（AI）can successfully predict the risk of cardiac arrest （心脏骤停）using timing and weather data, according to a new study published in the journal Heart.

The study found that the out-of-hospital risk of cardiac arrest was highest on Sundays, Mondays, during sharp drops in temperature during or between days, and on public holidays. The new findings could serve as an early warning system, lowering the risk of deadly episodes and raising survival chances, in addition to improving emergency medical services’ ability to prepare for serious situations. This is significant because out-of- hospital cardiac arrests are common globally, and are generally linked to low survival rates.

To deepen the research, the scientists evaluated the capacity of machine learning to anticipate daily out-of-hospital cardiac arrests via timing, as 1n the year, season, day of the week, hour of day, and daily weather, like snowfall, temperature, wind speed, and atmospheric pressure readings. Between 2005 and 2013, 1,299, 784 cases happened, and machine learning was implemented for 525,374 cases using timing data, weather, or both for a training dataset. These results were then contrasted with 135,678 cases that happened between 2014 and 2015, to examine the model’s capacity for accuracy for anticipating the statistical number of daily cardiac arrests in other years.

“This predictive model may be useful for preventing out of hospital cardiac arrest,” said the researchers. However, it’s critically important to note that this study does not promise a way to predict when people will really die from cardiac arrest. As a leading cause of death, the new machine learning AI merely predicts when the risk of having a heart attack is high.