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By Parag Jyoti Saikia, University of North Carolina at Chapel Hill 

“Hey Rupam, open the door. Take this fish,” a woman yelled from outside. I was sitting in the kitchen at my friend Rupam’s house in rural northeast India. It was the heart of monsoon season, and rain had been falling since morning. The woman must have been shouting because the noise of the rain on the tin roof muted everything else.

The aunty who lived next door stood outside with a large bowl of Boriala fish. Her husband had gone fishing on the Subansiri River, which flows next to the village, and he had fished all evening. “My husband cannot stay indoors in this weather,” she said in Assamese, the local language. “You can catch a lot of fish during this time.”

The monsoon season has long brought a bounty of fish from May to September for people living downstream.

The Subansiri Lower Hydroelectric Project, under construction, will be one of India’s largest hydropower dams.
Nayan J. Nath, CC BY

However, this is likely to change once the Subansiri Lower Hydroelectric Project, one of the largest hydropower dams in India, is completed upstream. Expected to be fully operational in 2026, the dam will change the natural flow of the river.

For most of the day, the dam will hold back water, letting only a small amount through, roughly equivalent to the region’s dry season. But for about four hours each night, it will release water to generate power, sending a raging river downstream almost like during monsoon season.

The dam will not only block the movement of fish, but also change the way people living downstream experience the river’s flows.

In a 2010 report on the likely impact of the Subansiri Lower Hydroelectric Project on downstream populations, experts from three premier institutes of Assam — the central state of northeast India — identified several concerns for downstream communities, including flood and erosion risk, earthquake risk, the loss of water flow for fishing and groundwater recharge, and the survival of species including river dolphins.

Now, a decade later, as the dam is nearing completion, the central question remains: What will happen to people like Rupam’s neighbor, whose lives and livelihoods depend on the river?

In 2023, I lived in a village next to the Subansiri River. My dissertation research brought me there to study how this hydroelectric dam, under construction since 2005, is affecting communities downstream.

‘Small displacement’ by ‘benign’ dams

Northeast India has been the focus of hydropower dam construction since the beginning of this century. In order to secure the country’s energy future, Central Electricity Authority of India in 2001 identified the Brahmaputra River basin as having the highest hydropower potential – 63,328 megawatts. It proposed constructing a whopping 168 hydropower dams in the region.

This earned the region the nickname “India’s Future Powerhouse.” The Subansiri Lower Hydroelectric Project was the first project.

The government sees the mega-hydro dam initiative as a win-win. It expects the dams to increase India’s energy security while also developing large infrastructure networks in one of India’s contentious borderland regions.

About 80% of the power for “India’s Future Powerhouse” is proposed to be generated in Arunachal Pradesh, the largest state of northeast India. China has repeatedly challenged India’s sovereignty over Arunachal since the latter’s independence in 1947.

Building dams in Arunachal Pradesh has another advantage: very low population density. It has about 17 people per square kilometer, and over 80% of Arunachal’s total territory is forest. That helped the government of India to argue that “there is relatively ‘small displacement’ by submergence as compared to that in other parts of the country and therefore these projects are benign.”

However, these projects are in no way benign to the people who live downstream.

The disruption to lives downstream

The flood plains of the Subansiri are home to people belonging to indigenous communities and lower castes of Hindu caste hierarchy. Mising – the largest indigenous community in the downstream region – call the river “Awanori,” which means “mother river.”

As part of my long-term ethnographic fieldwork, I observed how a range of livelihoods in the downstream region – fishing, agriculture, livestock grazing, recovering driftwood and transporting people by boat in remote areas – are all dependent on the 79-mile Subansiri River. I interviewed people who live there and attended community events to understand how the river plays a big role in everyday life.

Their reliance on the river has been based on natural, uncontrolled flows. Once the dam is completed, the river flows will be controlled by the National Hydroelectric Power Corporation.

Once in operation, the dam will block most of the river’s flow for 20 hours of the day, and then release that water – about 2,560 cubic meters per second – to power turbines that can meet peak electricity demand between 6 p.m. and 10 p.m. every night. During the 20 nonpeak hours, the dam would release less than a tenth as much water.

What happens when the river flow changes?

When the dam was first proposed, there was no plan to release water during the nonpeak 20 hours. Activists argued that cutting the water’s flow would have made it impossible for any aquatic animal to survive downstream.

In 2017, the nonpeak-hour flow proposal was increased to a range of 225 to 250 cubic meters per second. That year, the National Green Tribunal, which resolves civil cases related to the environment, asked the National Hydroelectric Power Corporation to ensure a minimum level of water for the survival of Gangetic dolphin, India’s national aquatic animal. This judgment helped pave the way for restarting the construction after an eight-year delay. However, the tribunal did not address how people living downstream would be affected by the changes.

The calculation of minimum flow only for the survival of one aquatic mammal leaves out numerous ways the flows of the Subansiri matter to people and other animals.

The dam itself threatens the downstream existence of many fish varieties, including the golden mahseer. It will also alter the flow and sediment supply in the river, and the abrupt and powerful flow for four hours each night will have greater scouring capacity and risks eroding the riverbed and banks.

Traditionally, from October to April, the dry riverbed and sandbar islands have been used to grow early-maturing ahu rice and mustard before the monsoon flood waters arrive. People also graze their livestock in the islands and in the fields after crops are harvested.

Once the dam begins flooding the river for four hours a night, however, the riverbed and sand-bar islands will be largely unusable.

The rainy season when the river floods is the most productive time for fishing and collecting driftwood. However the dam will obstruct the movement of fish and trap wood behind the dam. So, even though there will be flood waters every day in the river, fishermen and wood collectors may not benefit from it.

For people like Rupam’s neighbor, the Subansiri River they know will change. They will have to navigate the river more cautiously, and every evening there will be monsoon-season water levels.

Will they be able to catch enough fish to share with their neighbors? Only time will tell.

About the Author:

Parag Jyoti Saikia, Ph.D. Candidate in Socio-Cultural Anthropology, University of North Carolina at Chapel Hill

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Eoin McLaughlin, University College Cork; Cristián Ducoing, Lund University, and Nicholas Hanley, University of Glasgow 

Many commentators believe that the world should move away from measuring economic success in terms of GDP growth. Yes, growth has brought prosperity and untold riches, but it has had significant negative side effects for the planet, including climate change, pollution and species extinction. None of these are captured in GDP data.

A whole “beyond GDP” movement has emerged over the last several decades, arguing that we should adopt a new way of measuring the wealth of nations. There is an ongoing debate about the best alternative, and many indicators have supporters, such as gross national happiness and the genuine progress indicator.

Yet one stands out as having by far the most buy-in from major international institutions. Known as “inclusive wealth”, it expands what we mean by wealth to include things like the natural environment and the abilities of the population. But it comes with a major problem. There’s no agreement around how it should be measured, so different institutions publish very different figures. In our view, this is a major obstacle to its mass adoption.

Inclusive wealth

Inclusive wealth ascribes a value to the assets a nation has produced that generate well-being, and measures how they are changing over time. These assets are:

• Human capital: the knowledge and skills of the population.
• Produced capital: goods and services produced by human endeavour.
• Natural capital: the sum of all nature-based assets from which humans derive well-being, both now and in the future.
• Social capital: the social networks that exist within a society.

There is strong theoretical support for the idea that this approach is a good way of measuring the sustainability of economic development. The key point is that when inclusive wealth per capita is going up, the future wellbeing of the population will go up, which is a necessary condition for sustainable development.

Foundational texts in support of inclusive wealth include Cambridge economist Partha Dasgupta’s 2001 book, Human Well-Being and the Environment, and his Harvard counterpart Martin Weitzman’s 2003 book, Income, Wealth and the Maximum Principle.

Dasgupta carried out a review for the UK government in 2021 into the economics of biodiversity, which similarly advocates for measuring national inclusive wealth instead of national income. There have also been recent calls by academics in this field to use inclusive wealth to help with the global biodiversity framework, a UN-led drive to be “living in harmony with nature” by 2050.

Inclusive wealth is measured by both the World Bank and UN Environment Programme (Unep). The World Bank has been measuring it since the late 1990s, and first published global estimates in a 2006 report called Where is the wealth of nations : measuring capital for the 21st century. It has since published three major updates to this report, including a major revision to the methodology, with another on the way. As for Unep, it began measuring inclusive wealth in 2012.

But there are still some kinks that need ironing out before this indicator can be of any practical use. In a new paper in Ecological Economics, we compare the approaches of the World Bank and Unep and find a big divergence in their calculations.

This may explain why inclusive wealth has yet to be adopted in any serious way by any major economies (all we’ve seen so far is some mentions in policy documents, like this one from New Zealand, and a recent decision by the Biden-Harris administration to start tracking the value of US natural resources at federal level using natural-capital accounting).

The discrepancies relate mainly to natural capital. Both Unep and the World Bank include similar if not identical data from the same components: non-renewables such as fossil fuels and minerals, and renewable elements such as fisheries and forest resources. The problem is that the institutions’ research teams value them differently.

The World Bank approach comes up with a present value for expected future earnings by discounting from what they will eventually be worth. In contrast, Unep uses fixed accounting prices, referred to as “shadow prices”, which are based on market prices today.

This leads to different conclusions about the trajectory of our natural capital, and thus, by implication, of the sustainability of current development paths. This is then exacerbated by another discrepancy around how the institutions measure changes in human capital.

Country differences

In our paper, we highlight the case of Qatar. According to Unep, it is one of the worst performers in terms of the change in inclusive wealth per capita, and so is judged unsustainable. Yet according to World Bank estimates, Qatar’s inclusive wealth per capita is growing positively.

Which is it? If development is unsustainable, some remedial action will be necessary, but if it is sustainable, no problem. How is the Qatari government to decide how to proceed?

We find similar conflicting signals for many other countries. According to the World Bank data, 20 countries’ inclusive wealth per capita is in decline (in other words, unsustainable), while the Unep data has 45 countries in decline. There is also little crossover in terms of these two lists.

As many as 34 of the countries that the World Bank says have growing inclusive wealth per capita are in decline according to Unep.

We agree strongly with the basic proposition that measuring inclusive wealth is key to ensuring the world develops sustainably. But there needs to be a more consistent approach for this signal to achieve enough credibility to be widely adopted. In our experience, the World Bank is much more transparent than Unep about the data in its calculations. Without full Unep transparency, it’s difficult to get to the root of the discrepancy.

Having said that, both broad approaches have merits, so it’s more a question of everyone committing to a single approach than arguing that one is better than the other. Unless this measurement problem can be resolved, it’s difficult to see countries taking inclusive wealth seriously. That could have serious consequences in the battle to make economic development sustainable.

About the Authors:

Eoin McLaughlin, Professor in Economics, University College Cork; Cristián Ducoing, Senior lecturer at Sustainability transformations over time and space, Lund University, and Nicholas Hanley, Chair in Environmental and One Health Economics, University of Glasgow

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Lynne Gauthier, UMass Lowell and Jiabin Shen, UMass Lowell 

Curious Kids is a series for children of all ages. If you have a question you’d like an expert to answer, send it to [email protected].

Why are some people able to visualize scenarios in their minds, with colors and details, and some people are not? – Luiza, age 14, Goiânia, Brazil

Imagine you are in a soccer match, and it’s tied. Each team will begin taking penalty kicks. The crowd is roaring, and whether or not your team wins the game depends on your ability to hit the shot. As you imagine this scene, are you able to picture the scenario with colors and details?

Scientists are hard at work trying to understand why some people can visualize these kinds of scenarios more easily than others can. Even the same person can be better or worse at picturing things in their mind at different times.

As neuroscientists in the fields of physical therapy and psychology, we think about the ways people use mental imagery. Here is what researchers do know so far.

The brain and mental imagery

Mental imagery is the ability to visualize things and scenarios in your mind, without actual physical input.

For example, when you think about your best friends, you may automatically picture their faces in your head without actually seeing them in front of you. When you daydream about an upcoming vacation, you may see yourself on the sunny beach.

People who dream about taking a penalty kick could visualize themselves like they are watching a video of it in their mind. They may even experience the smell of the turf or hear the sounds that fans would make.

Scientists believe your primary visual cortex, located in the back of your brain, is involved in internal visualization. This is the same part of the brain that processes visual information from the eyes and that lets you see the world around you.

Another brain region, located in the very front of the brain, also contributes to mental imagery. This structure, called the prefrontal cortex, is in charge of executive functions – a group of high-level mental skills that allow you to concentrate, plan, organize and reason.

Scientists have found such skills to be, at least to some extent, related to one’s mental imagery ability. If someone is good at holding and manipulating large amounts of information in mind, this person can play with things like numbers or images in their mind on the go.

Experiencing and remembering

Most of the same brain areas are active both while you’re actually experiencing an event and also when you’re visualizing it from a memory in your head. For example, when you behold the beauty of the Grand Canyon, your brain creates a memory of the image. But that memory is not simply stored in a single place in the brain. It’s created when thousands of brain cells across different parts of the brain fire together. Later, when a sound, smell or image triggers the memory, this network of brain cells fires together again, and you may picture the Grand Canyon in your head as clearly as if it were in front of you.

Benefits of mental imagery

The ability to mentally visualize can be helpful.

Notice the look of concentration on a gymnast’s face before competition. The athlete is likely visualizing themselves executing a perfect rings routine in their mind. This visualization activates the same brain regions as when they physically perform on the rings, building their confidence and priming their brain for better success.

Athletes can use visualization to help them acquire skills more quickly and with less wear and tear on their bodies. Engineers and mechanics can use visualization to help them fix or design things.

Mental visualization can also help people relearn how to move their bodies after a brain injury. However, with additional practice, those who do not use visualization will eventually catch up.

Nature-nurture interactions

All is not lost if you have difficulty visualizing. It is possible that the ability to visualize in your mind is a combined effect of both how your individual brain works and your life experiences.

For example, taxi drivers in London need to navigate very complicated streets and, scientists found, experience changes to their brain structures over the course of their careers. In particular, they develop larger hippocampuses, a brain structure related to memory. Scientists believe that the training the taxi drivers went through – having to visualize a map of complex streets across London in daily driving – made them better at mental imagery via changes in their hippocampus.

And watching someone else do a physical action activates the same brain areas as creating your own internal mental imagery. If you want to be able to do something, watching a video of someone else doing it can be just as helpful as visualizing yourself doing it in your head. So even if you struggle with mental visualization, there are still ways to reap its benefits.

Hello, curious kids! Do you have a question you’d like an expert to answer? Ask an adult to send your question to [email protected]. Please tell us your name, age and the city where you live.

And since curiosity has no age limit – adults, let us know what you’re wondering, too. We won’t be able to answer every question, but we will do our best.

About the Authors:

Lynne Gauthier, Associate Professor of Physical Therapy and Kinesiology, UMass Lowell and Jiabin Shen, Assistant Professor of Psychology, UMass Lowell

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Nicholas Card, University of California, Davis 

Brain-computer interfaces are a groundbreaking technology that can help paralyzed people regain functions they’ve lost, like moving a hand. These devices record signals from the brain and decipher the user’s intended action, bypassing damaged or degraded nerves that would normally transmit those brain signals to control muscles.

Since 2006, demonstrations of brain-computer interfaces in humans have primarily focused on restoring arm and hand movements by enabling people to control computer cursors or robotic arms. Recently, researchers have begun developing speech brain-computer interfaces to restore communication for people who cannot speak.

As the user attempts to talk, these brain-computer interfaces record the person’s unique brain signals associated with attempted muscle movements for speaking and then translate them into words. These words can then be displayed as text on a screen or spoken aloud using text-to-speech software.

I’m a reseacher in the Neuroprosthetics Lab at the University of California, Davis, which is part of the BrainGate2 clinical trial. My colleagues and I recently demonstrated a speech brain-computer interface that deciphers the attempted speech of a man with ALS, or amyotrophic lateral sclerosis, also known as Lou Gehrig’s disease. The interface converts neural signals into text with over 97% accuracy. Key to our system is a set of artificial intelligence language models – artificial neural networks that help interpret natural ones.

Casey Harrell, who has ALS, works with a brain-computer interface to turn his thoughts into words.
Nicholas Card

Recording brain signals

The first step in our speech brain-computer interface is recording brain signals. There are several sources of brain signals, some of which require surgery to record. Surgically implanted recording devices can capture high-quality brain signals because they are placed closer to neurons, resulting in stronger signals with less interference. These neural recording devices include grids of electrodes placed on the brain’s surface or electrodes implanted directly into brain tissue.

In our study, we used electrode arrays surgically placed in the speech motor cortex, the part of the brain that controls muscles related to speech, of the participant, Casey Harrell. We recorded neural activity from 256 electrodes as Harrell attempted to speak.

Decoding brain signals

The next challenge is relating the complex brain signals to the words the user is trying to say.

One approach is to map neural activity patterns directly to spoken words. This method requires recording brain signals corresponding to each word multiple times to identify the average relationship between neural activity and specific words. While this strategy works well for small vocabularies, as demonstrated in a 2021 study with a 50-word vocabulary, it becomes impractical for larger ones. Imagine asking the brain-computer interface user to try to say every word in the dictionary multiple times – it could take months, and it still wouldn’t work for new words.

Instead, we use an alternative strategy: mapping brain signals to phonemes, the basic units of sound that make up words. In English, there are 39 phonemes, including ch, er, oo, pl and sh, that can be combined to form any word. We can measure the neural activity associated with every phoneme multiple times just by asking the participant to read a few sentences aloud. By accurately mapping neural activity to phonemes, we can assemble them into any English word, even ones the system wasn’t explicitly trained with.

To map brain signals to phonemes, we use advanced machine learning models. These models are particularly well-suited for this task due to their ability to find patterns in large amounts of complex data that would be impossible for humans to discern. Think of these models as super-smart listeners that can pick out important information from noisy brain signals, much like you might focus on a conversation in a crowded room. Using these models, we were able to decipher phoneme sequences during attempted speech with over 90% accuracy.

From phonemes to words

Once we have the deciphered phoneme sequences, we need to convert them into words and sentences. This is challenging, especially if the deciphered phoneme sequence isn’t perfectly accurate. To solve this puzzle, we use two complementary types of machine learning language models.

The first is n-gram language models, which predict which word is most likely to follow a set of n words. We trained a 5-gram, or five-word, language model on millions of sentences to predict the likelihood of a word based on the previous four words, capturing local context and common phrases. For example, after “I am very good,” it might suggest “today” as more likely than “potato”. Using this model, we convert our phoneme sequences into the 100 most likely word sequences, each with an associated probability.

The second is large language models, which power AI chatbots and also predict which words most likely follow others. We use large language models to refine our choices. These models, trained on vast amounts of diverse text, have a broader understanding of language structure and meaning. They help us determine which of our 100 candidate sentences makes the most sense in a wider context.

By carefully balancing probabilities from the n-gram model, the large language model and our initial phoneme predictions, we can make a highly educated guess about what the brain-computer interface user is trying to say. This multistep process allows us to handle the uncertainties in phoneme decoding and produce coherent, contextually appropriate sentences.

Real-world benefits

In practice, this speech decoding strategy has been remarkably successful. We’ve enabled Casey Harrell, a man with ALS, to “speak” with over 97% accuracy using just his thoughts. This breakthrough allows him to easily converse with his family and friends for the first time in years, all in the comfort of his own home.

Speech brain-computer interfaces represent a significant step forward in restoring communication. As we continue to refine these devices, they hold the promise of giving a voice to those who have lost the ability to speak, reconnecting them with their loved ones and the world around them.

However, challenges remain, such as making the technology more accessible, portable and durable over years of use. Despite these hurdles, speech brain-computer interfaces are a powerful example of how science and technology can come together to solve complex problems and dramatically improve people’s lives.

About the Author:

Nicholas Card, Postdoctoral Fellow of Neuroscience and Neuroengineering, University of California, Davis

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Mohamed-Slim Alouini, King Abdullah University of Science and Technology and Mariette DiChristina, Boston University 

About one-third of the global population, around 3 billion people, don’t have access to the internet or have poor connections because of infrastructure limitations, economic disparities and geographic isolation.

Today’s satellites and ground-based networks leave communications gaps where, because of geography, setting up traditional ground-based communications equipment would be too expensive.

High-altitude platform stations – telecommunications equipment positioned high in the air, on uncrewed balloons, airships, gliders and airplanes – could increase social and economic equality by filling internet connectivity gaps in ground and satellite coverage. This could allow more people to participate fully in the digital age.

One of us, Mohamed-Slim Alouini, is an electrical engineer who contributed to an experiment that showed it is possible to provide high data rates and ubiquitous 5G coverage from the stratosphere. The stratosphere is the second lowest layer of the atmosphere, ranging from 4 to 30 miles above the Earth. Commercial planes usually fly in the lower part of the stratosphere. The experiment measured signals between platform stations and users on the ground in three scenarios: a person staying in one place, a person driving a car and a person operating a boat.

My colleagues measured how strong the signal is in relation to interference and background noise levels. This is one of the measures of network reliability. The results showed that the platform stations can support high-data-rate applications such as streaming 4K resolution videos and can cover 15 to 20 times the area of standard terrestrial towers.

Early attempts by Facebook and Google to commercially deploy platform stations were unsuccessful. But recent investments, technological improvements and interest from traditional aviation companies and specialized aerospace startups may change the equation.

The goal is global connectivity, a cause that brought the platform stations idea recognition in the World Economic Forum’s 2024 Top 10 Emerging Technologies report. The international industry initiative HAPS Alliance, which includes academic partners, is also pushing toward that goal.

An experimental aircraft like this solar-powered airship could someday play a role in providing internet access to rural areas or disaster zones.
Thales Alenia Space via Wikimedia Commons, CC BY-SA

Fast, cost effective, flexible

Platform stations would be faster, more cost effective and more flexible than satellite-based systems.

Because they keep communications equipment closer to Earth than satellites, the stations could offer stronger, higher-capacity signals. This would enable real-time communications speedy enough to communicate with standard smartphones, high-resolution capabilities for imaging tasks and greater sensitivity for sensing applications. They transmit data via free-space optics, or light beams, and large-scale antenna array systems, which can send large amounts of data quickly.

Satellites can be vulnerable to eavesdropping or jamming when their orbits bring them over adversarial countries. But platform stations remain within the airspace of a single country, which reduces that risk.

High-altitude platform stations are also easier to put in place than satellites, which have high launch and maintenance costs. And the regulatory requirements and compliance procedures required to secure spots in the stratosphere are likely to be simpler than the complex international laws governing satellite orbits. Platform stations are also easier to upgrade, so improvements could be deployed more quickly.

Platform stations are also potentially less polluting than satellite mega-constellations because satellites burn up upon reentry and can release harmful metals into the atmosphere, while platform stations can be powered by clean energy sources such as solar and green hydrogen.

The key challenges to practical platform stations are increasing the amount of time they can stay aloft to months at a time, boosting green onboard power and improving reliability – especially during automated takeoff and landing through the lower turbulent layers of the atmosphere.

Beyond satellites

Platform stations could play a critical role in emergency and humanitarian situations by supporting relief efforts when ground-based networks are damaged or inoperative.

The stations could also connect Internet of Things (IoT) devices and sensors in remote settings to better monitor the environment and manage resources.

In agriculture, the stations could use imaging and sensing technologies to help farmers monitor crop health, soil conditions and water resources.

Their capability for high-resolution imaging could also support navigation and mapping activities crucial for cartography, urban planning and disaster response.

The stations could also do double duty by carrying instruments for atmospheric monitoring, climate studies and remote sensing of Earth’s surface features, vegetation and oceans.

From balloons to airplanes

Platform stations could be based on different types of aircraft.

Balloons offer stable, long-duration operation at high altitudes and can be tethered or free-floating. Airships, also known as dirigibles or blimps, use lighter-than-air gases and are larger and more maneuverable than balloons. They’re especially well suited for surveillance, communications and research.

Gliders and powered aircraft can be controlled more precisely than balloons, which are sensitive to variations in wind speed. In addition, powered aircraft, which include drones and fixed-wing airplanes, can provide electricity to communication equipment, sensors and cameras.

Next-generation power

Platform stations could make use of diverse power sources, including increasingly lightweight and efficient solar cells, high-energy-density batteries, green hydrogen internal combustion engines, green hydrogen fuel cells, which are now at the testing stage, and eventually, laser beam powering from ground- or space-based solar stations.

The evolution of lightweight aircraft designs coupled with advancements in high-efficiency motors and propellers enable planes to fly longer and carry heavier payloads. These cutting-edge lightweight planes could lead to platform stations capable of maneuvering in the stratosphere for extended periods.

Meanwhile, improvements in stratospheric weather models and atmospheric models make it easier to predict and simulate the conditions under which the platform stations would operate.

Bridging the global digital divide

Commerical deployment of platform stations, at least for post-disaster or emergency situations, could be in place by the end of the decade. For instance, a consortium in Japan, a country with remote mountainous and island communities, has earmarked US$100 million for solar-powered, high-altitude platform stations.

Platform stations could bridge the digital divide by increasing access to critical services such as education and health care, providing new economic opportunities and improving emergency response and environmental monitoring. As advances in technology continue to drive their evolution, platform stations are set to play a crucial role in a more inclusive and resilient digital future.

About the Authors:

Mohamed-Slim Alouini, Distinguished Professor of Electrical and Computer Engineering, King Abdullah University of Science and Technology and Mariette DiChristina, Dean and Professor of the Practice in Journalism, College of Communication, Boston University

This article is republished from The Conversation under a Creative Commons license. Read the original article.