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Cities are scrambling to prevent flooding

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Cities are scrambling to prevent flooding


Urban centers are more prone to flooding than other areas because streets, parking lots, and buildings are impervious, meaning water can’t seep into the ground the way it would in a forest or grassland. Instead, it flows.

Detroit, like many older cities, deals with flowing stormwater by combining it with sewage. This blend is then pumped to treatment plants. During the recent storm, electrical outages and mechanical issues knocked out four of 12 pumps in two major pump stations.

The agency has spent $10 million over the past several years upgrading just these two pump stations, and hundreds of millions more on other improvements. But fully modernizing the sewer system would require building a separate stormwater network at a cost of over $17 billion.

Stormwater infrastructure around the country is aging, and many governments have resorted to Band-Aid solutions instead of building more resilient systems, says Mikhail Chester, an infrastructure and policy researcher at Arizona State University. And mechanical and electrical systems are bound to fail occasionally during major storms, Chester adds.

However, even if the pump stations had worked perfectly, they might not have prevented disastrous flooding.

Outdated models

Detroit’s pumping stations, similar to a lot of stormwater infrastructure, were designed to keep up with a 10-year storm, meaning an amount of rainfall within an hour that has roughly a one in 10 chance of happening in any given year. A 10-year storm in the Detroit area would amount to about 1.7 inches of rainfall in an hour, according to National Weather Service data.

During the June storm, parts of Detroit saw intense levels of rainfall that would be more characteristic of a 1,000-year storm (over 3.7 inches of rain within an hour), far beyond the capacity of the pumping stations, according to the water authority.

But rainfall predictions are based on historical data that might not represent the true odds of major storms, according to Anne Jefferson, a hydrologist at Kent State University. Storms that supposedly have a one in 10 chance of happening in any given year are likely happening more often now because of climate change. And she says few agencies are taking climate change into account in their infrastructure designs.

“We’re locking ourselves into a past climate,” Jefferson says.

Governments hoping to account for climate change when designing infrastructure face uncertainty—should they plan for the best-case emissions scenarios or the worst? And how exactly emissions will affect rainfall is difficult to predict.

Planning for bigger storms is an admirable goal, but it’s also costly. Bigger pumps and pipes are more expensive to build and harder to install, says Chester. And price increases aren’t linear, he adds—a pump or pipe with double the capacity will be more than double the price in most cases.

Fast forward

Coastal cities face even more dire climate threats, and some are investing aggressively to stave them off. Tampa, Florida, spent $27 million upgrading pump stations and other infrastructure after major floods in 2015 and 2016, according to the Tampa Bay Times. Some of the upgrades appear to be working—this year at least, the city avoided floods during major storms like Hurricane Elsa.

However, the rising seas along Tampa’s shoreline may soon cover up the pumps’ outlets. If sea levels reach the spot where water is supposed to exit storm pipes, the system won’t be able to remove water from the city.

Some cities are looking to install other features, like storm ponds and rain gardens, to help manage urban flooding. Grassy areas like rain gardens can reduce the volume and speed of excess water, Jefferson says. If enough of these facilities are built in the right places, they can help prevent smaller floods, she adds, but like other stormwater infrastructure, they’re usually not designed to stop flooding during larger storms.

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This AI could predict 10 years of scientific priorities—if we let it

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This AI could predict 10 years of scientific priorities—if we let it


The survey committee, which receives input from a host of smaller panels, takes into account a gargantuan amount of information to create research strategies. Although the Academies won’t release the committee’s final recommendation to NASA for a few more weeks, scientists are itching to know which of their questions will make it in, and which will be left out. 

“The Decadal Survey really helps NASA decide how they’re going to lead the future of human discovery in space, so it’s really important that they’re well informed,” says Brant Robertson, a professor of astronomy and astrophysics at UC Santa Cruz. 

One team of researchers wants to use artificial intelligence to make this process easier. Their proposal isn’t for a specific mission or line of questioning; rather, they say, their AI can help scientists make tough decisions about which other proposals to prioritize.  

The idea is that by training an AI to spot research areas that are either growing or declining rapidly, the tool could make it easier for survey committees and panels to decide what should make the list.  

“What we wanted was to have a system that would do a lot of the work that the Decadal Survey does, and let the scientists working on the Decadal Survey do what they will do best,” says Harley Thronson, a retired senior scientist at NASA’s Goddard Space Flight Center and lead author of the proposal.  

Although members of each committee are chosen for their expertise in their respective fields, it’s impossible for every member to grasp the nuance of every scientific theme. The number of astrophysics publications increases by 5% every year, according to the authors. That’s a lot for anyone to process. 

That’s where Thronson’s AI comes in.  

It took just over a year to build, but eventually, Thronson’s team was able to train it on more than 400,000 pieces of research published in the decade leading up to the Astro2010 survey. They were also able to teach the AI to sift through thousands of abstracts to identify both low- and high-impact areas from two- and three-word topic phrases like “planetary system” or “extrasolar planet.”  

According to the researchers’ white paper, the AI successfully “backcasted” six popular research themes of the last 10 years, including a meteoric rise in exoplanet research and observation of galaxies.  

“One of the challenging aspects of artificial intelligence is that they sometimes will predict, or come up with, or analyze things that are completely surprising to the humans,” says Thronson. “And we saw this a lot.” 

Thronson and his collaborators think the steering committee should use their AI to help review and summarize the vast amounts of text the panel must sift through, leaving human experts to make the final call.  

Their research isn’t the first to try to use AI to analyze and shape scientific literature. Other AIs have already been used to help scientists peer-review their colleagues’ work.  

But could it be trusted with a task as important and influential as the Decadal Survey? 

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Securing the energy revolution and IoT future

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Securing the energy revolution and IoT future


In early 2021, Americans living on the East Coast got a sharp lesson on the growing importance of cybersecurity in the energy industry. A ransomware attack hit the company that operates the Colonial Pipeline—the major infrastructure artery that carries almost half of all liquid fuels from the Gulf Coast to the eastern United States. Knowing that at least some of their computer systems had been compromised, and unable to be certain about the extent of their problems, the company was forced to resort to a brute-force solution: shut down the whole pipeline.

Leo Simonovich is vice president and global head of industrial cyber and digital security at Siemens Energy.

The interruption of fuel delivery had huge consequences. Fuel prices immediately spiked. The President of the United States got involved, trying to assure panicked consumers and businesses that fuel would become available soon. Five days and untold millions of dollars in economic damage later, the company paid a $4.4 million ransom and restored its operations.

It would be a mistake to see this incident as the story of a single pipeline. Across the energy sector, more and more of the physical equipment that makes and moves fuel and electricity across the country and around the world relies on digitally controlled, networked equipment. Systems designed and engineered for analogue operations have been retrofitted. The new wave of low-emissions technologies—from solar to wind to combined-cycle turbines—are inherently digital tech, using automated controls to squeeze every efficiency from their respective energy sources.

Meanwhile, the covid-19 crisis has accelerated a separate trend toward remote operation and ever more sophisticated automation. A huge number of workers have moved from reading dials at a plant to reading screens from their couch. Powerful tools to change how power is made and routed can now be altered by anyone who knows how to log in.

These changes are great news—the world gets more energy, lower emissions, and lower prices. But these changes also highlight the kinds of vulnerabilities that brought the Colonial Pipeline to an abrupt halt. The same tools that make legitimate energy-sector workers more powerful become dangerous when hijacked by hackers. For example, hard-to-replace equipment can be given commands to shake itself to bits, putting chunks of a national grid out of commission for months at a stretch.

For many nation-states, the ability to push a button and sow chaos in a rival state’s economy is highly desirable. And the more energy infrastructure becomes hyperconnected and digitally managed, the more targets offer exactly that opportunity. It’s not surprising, then, that an increasing share of cyberattacks seen in the energy sector have shifted from targeting information technologies (IT) to targeting operating technologies (OT)—the equipment that directly controls physical plant operations. 

To stay on top of the challenge, chief information security officers (CISOs) and their security operations centers (SOCs) will have to update their approaches. Defending operating technologies calls for different strategies—and a distinct knowledge base—than defending information technologies. For starters, defenders need to understand the operating status and tolerances of their assets—a command to push steam through a turbine works well when the turbine is warm, but can break it when the turbine is cold. Identical commands could be legitimate or malicious, depending on context.

Even collecting the contextual data needed for threat monitoring and detection is a logistical and technical nightmare. Typical energy systems are composed of equipment from several manufacturers, installed and retrofitted over decades. Only the most modern layers were built with cybersecurity as a design constraint, and almost none of the machine languages used were ever meant to be compatible.

For most companies, the current state of cybersecurity maturity leaves much to be desired. Near-omniscient views into IT systems are paired with big OT blind spots. Data lakes swell with carefully collected outputs that can’t be combined into a coherent, comprehensive picture of operational status. Analysts burn out under alert fatigue while trying to manually sort benign alerts from consequential events. Many companies can’t even produce a comprehensive list of all the digital assets legitimately connected to their networks.

In other words, the ongoing energy revolution is a dream for efficiency—and a nightmare for security.

Securing the energy revolution calls for new solutions equally capable of identifying and acting on threats from both physical and digital worlds. Security operations centers will need to bring together IT and OT information flows, creating a unified threat stream. Given the scale of data flows, automation will need to play a role in applying operational knowledge to alert generation—is this command consistent with business as usual, or does context show it’s suspicious? Analysts will need broad, deep access to contextual information. And defenses will need to grow and adapt as threats evolve and businesses add or retire assets.

This month, Siemens Energy unveiled a monitoring and detection platform aimed at resolving the core technical and capability challenges for CISOs tasked with defending critical infrastructure. Siemens Energy engineers have done the legwork needed to automate a unified threat stream, allowing their offering, Eos.ii, to serve as a fusion SOC that’s capable of unleashing the power of artificial intelligence on the challenge of monitoring energy infrastructure.

AI-based solutions answer the dual need for adaptability and persistent vigilance. Machine learning algorithms trawling huge volumes of operational data can learn the expected relationships between variables, recognizing patterns invisible to human eyes and highlighting anomalies for human investigation. Because machine learning can be trained on real-world data, it can learn the unique characteristics of each production site, and can be iteratively trained to distinguish benign and consequential anomalies. Analysts can then tune alerts to watch for specific threats or ignore known sources of noise.

Extending monitoring and detection into the OT space makes it harder for attackers to hide—even when unique, zero-day attacks are deployed. In addition to examining traditional signals like signature-based detection or network traffic spikes, analysts can now observe the effects that new inputs have on real-world equipment. Cleverly disguised malware would still raise red flags by creating operational anomalies. In practice, analysts using the AI-based systems have found that their Eos.ii detection engine was sensitive enough to predictively identify maintenance needs—for example, when a bearing begins to wear out and the ratio of steam in to power out begins to drift.

Done right, monitoring and detection that spans both IT and OT should leave intruders exposed. Analysts investigating alerts can trace user histories to determine the source of anomalies, and then roll forward to see what else was changed in a similar timeframe or by the same user. For energy companies, increased precision translates to dramatically reduced risk – if they can determine the scope of an intrusion, and identify which specific systems were compromised, they gain options for surgical responses that fix the problem with minimal collateral damage—say, shutting down a single branch office and two pumping stations instead of a whole pipeline.

As energy systems continue their trend toward hyperconnectivity and pervasive digital controls, one thing is clear: a given company’s ability to provide reliable service will depend more and more on their ability to create and sustain strong, precise cyber defenses. AI-based monitoring and detection offers a promising start.

To learn more about Siemens Energy’s new AI-based monitoring and detection platform, check out their recent white paper on Eos.ii.

Learn more about Siemens Energy cybersecurity at Siemens Energy Cybersecurity.

This content was produced by Siemens Energy. It was not written by MIT Technology Review’s editorial staff.

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The US is about to kick-start its controversial covid booster campaign

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The US is about to kick-start its controversial covid booster campaign


Disagreements: Booster shots have been controversial. A group of top scientists, including experts at the FDA and WHO, published a review in The Lancet on Monday arguing that booster shots are unnecessary since vaccines are still very effective at preventing severe disease and death. Furthermore, they say, vaccine supplies could save more lives if they’re used for unvaccinated people rather than as boosters for the vaccinated. That’s why the WHO has been pleading with rich countries to stop handing them out until more of the world is vaccinated.

Unequal distribution: The US joins the UK, the UAE, France, Germany, and Israel, which have also launched booster programs. In the UK, for example, a rollout of booster shots to all over-50s is about to begin after officials gave the green light last week. Meanwhile, less than 4% of Africa’s population is fully vaccinated, compared with 70% of adults in the EU. In the US, it’s 55%, a figure that has stubbornly failed to significantly budge in recent weeks. Earlier this week, President Biden announced that the US would buy a further 500 million doses of vaccine to distribute to other parts of the world, bringing its total commitment to more than 1 billion.

Scramble: Millions of Americans are likely to try to get a third shot. A YouGov poll this summer found that three in five vaccinated Americans will get one if it’s available. Given the chaotic nature of the US vaccine rollout, it will be hard to prevent people from gaming the system to get a third shot even if they aren’t technically eligible. 

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