Self-compassion is related to better mental health among Syrian refugees

Teaching self-compassion could be an efficient intervention in the future to boost the mental health of displaced individuals with limited access to health care.

University of California - San Diego

 

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Syrian refugees walking in Jordan.

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Credit: United Nations High Commissioner for Refugees (UNHCR)/ Shawkat Alharfosh

Displaced individuals experience high rates of emotional distress, depression and anxiety resulting from trauma and stress from displacement and loss. Their mental health may suffer further due to a lack of resources, language barriers, and discrimination during resettlement.

A new study by University of California San Diego researchers reports that displaced Syrian refugees with higher reported self-compassion were less likely to report poor mental health outcomes. The study was published in PLOS ONE on September 19, 2024.

Sarah Alsamman, a student at UC San Diego School of Medicine, and Wael Al-Delaimy, M.D., Ph.D., professor of public health at the Herbert Wertheim School of Public Health and Human Longevity Science, along with local partners, surveyed 272 displaced Syrians residing in Amman, Jordan about their history of trauma and mental health symptoms. The participants were recruited through community organizations providing aid and educational opportunities to refugees.

“In spending time with these communities, I learned about the complex network of stressors they faced, including severe unemployment, limited access to health care, and separation from family,” said Alsamman. 

The researchers also asked participants about their level of self-compassion. This could include practicing kindness and tenderness toward themselves when going through a difficult time instead of judging themselves harshly, engaging in non-judgmental mindfulness toward painful thoughts, and recognizing that they are not alone, but part of a larger human experience.

The participants also rated their perceived level of resilience in the face of adversity. 

“Resilience reflects their belief in themselves, their community, their family tradition, or social support,” said Al-Delaimy.

Key findings of the survey data revealed:

• More than 75 percent of the refugees experienced anxiety, emotional distress or depression.
• Respondents reporting higher levels of self-compassion experienced more than 80% lower symptoms of depression and anxiety.
• While self-compassion and resilience may interact with each other to protect mental health, self-compassion plays a more powerful role in mitigating mental health.

Previous studies have documented the capacity of resilience to limit mental health stressors experienced by refugees, but this is the first time self-compassion has been shown to potentially moderate mental illness in this population.

Al-Delaimy says unlike resilience, self-compassion is a self-taught, modifiable practice that can be increased through training, and thinks health care professionals could use this to promote positive mental health outcomes among refugees who typically have limited access to mental health care.

“This could become an innovative way to empower displaced communities processing an incredibly unjust life experience. Our goal is to shift to a strength-based approach aimed at identifying and cultivating factors that protect from negative mental health outcomes,” said Al-Delaimy.

Syrian refugees account for more than one-third of all displaced persons around the globe, with over 14 million forced to flee their homes during more than a decade of ongoing conflict. The researchers plan to extend their study by testing the impact of self-compassion interventions on mental health in a larger group of displaced Syrians living in Southern California.

“That's another aspect that we are trying to address: Is there a difference among those who are outside the country and people who have been resettled here?” Al-Delaimy said.

Rana Dajani of The Hashemite University in Jordan and the MIT Refugee Action Hub (ReACT) co-authored the study.

The T. Denny Sanford Institute for Empathy and Compassion at UC San Diego provided support for the first author, Sarah Alsamman.

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Journal

PLoS ONE

DOI

10.1371/journal.pone.0309051 

 

Deforestation in the Amazon is driven more by domestic demand than by the export market

A study by the University of São Paulo shows that expansion of cattle ranching to meet growing domestic demand has contributed more than any other driver to the elimination or degradation of the Amazon’s original vegetation

Fundação de Amparo à Pesquisa do Estado de São Paulo

 

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Almost a quarter of Legal Amazonia has been deforested and over 1 million km2 are degraded

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Credit: IBAMA

Brazilian Legal Amazonia (BLA) – which comprises the entirety of the Amazon Basin located in Brazil and vast adjacent swathes of the Cerrado, spanning nine states – is more than 5 million square kilometers (km2) in area and corresponds to almost 60% of the country’s land mass. Almost a quarter of this area (23%) has been deforested, and over 1 million km2 are degraded, so that the region risks reaching an ecological tipping point at which ecosystems collapse and billions of tons of carbon are released into the atmosphere. Some parts of BLA, especially borderlands of the Cerrado and the so-called “Deforestation Arc”, are now net carbon emitters. Conservation of the virgin forest areas and rehabilitation of degraded areas are urgently needed, and members of the global community are taking action in this regard.

Foreign demand for commodities is often considered the main driver of deforestation. It is certainly significant, but domestic markets exert far greater pressure, according to a study by Eduardo Haddad and collaborators published in the journal Nature Sustainability.

“Deforestation is often evaluated from the supply standpoint, meaning the analysis focuses on the productive sectors that are promoting replacement of forest by other land uses, such as growing of crops and raising of livestock. The methodology we used enabled us to see the phenomenon of deforestation from the demand perspective as well, identifying the sources of the economic stimuli that get productive sectors involved in deforestation. Based on this criterion, our study shows that 83.17% of deforestation was driven by demand from outside Amazonia and only 16.83% by demand from the region. Breaking down that 83.17%, we found that demand from other parts of Brazil accounted for 59.68% and foreign demand for 23.49%,” Haddad said.

Haddad is full professor at the University of São Paulo’s School of Economics, Administration, Accounting and Actuarial Science (FEA-USP), and a consultant to multilateral development finance organizations such as the World Bank, Inter American Development Bank (IDB), Organization for Economic Cooperation and Development (OECD), United Nations Development Program (UNDP) and Joint Africa Institute (JAI).

The methodology used in the study was based mainly on the input-output matrix model developed by Russian-born American economist Wassily Leontief (1906-1999). The model represents the relations among economic sectors as a matrix, showing how inputs in one industry produce outputs for consumption or for use as inputs by another industry, and how changes in production of goods or services affect demand for inputs.

“In Brazil, the most recent input-output matrix was produced by IBGE [the national statistics bureau] in 2015. It hasn’t been updated since then, owing to mathematical complexity and restricted access to data for millions of companies and their business structures. Using data for 2015 would be inadequate if not for the unfortunate fact that the structure of the Brazilian economy has changed very little in the meantime.

The 2010s were the worst decade for GDP in the 120-year time series, with growth of only 0.3% per year on average. We used the 2015 input-output matrix adapted for BLA, combined with sectoral and regional deforestation data and greenhouse gas emissions, to measure the direct and indirect impact of domestic and foreign demand for BLA’s inputs and outputs, focusing on deforestation-intensive sectors such as agriculture,” Haddad explained.

Land-use changes

The Amazon has undergone enormous changes in the last half-century. Technical innovations, investment in infrastructure and political changes have facilitated the expansion of soybean farming from the central Cerrado to vast portions of BLA. Local production of soybeans, which was less than 200 metric tons in 1974, or a mere 0.02% of the national total, reached 50 million mt in 2022, accounting for 41.5% of the total. Livestock farming has expanded just as vertiginously, from 8.9 million head of cattle in 1974 (9.5% of the national total) to 104.3 million in 2022 (44.5% of the total).

“The expansion of cattle ranching was driven mainly by growth in consumption of beef, dairy and leather goods in other parts of Brazil. In line with the rise in per capita income and rapid urbanization, meat consumption rose faster than the world average after the 1960s. Of the 1.4 million hectares deforested to make way for cattle pasture, 61.63% responded directly or indirectly to domestic demand from outside the Amazon and 21.06% to foreign demand. Deforestation to make way for crops displayed a different pattern, with 58.38% responding to demand for exports and 41.62% to domestic demand,” Haddad said.

The article on the study in Nature Sustainability notes that deforestation in Brazil has been concentrated geographically in BLA, affecting different biomes. In 2015, BLA accounted for 65.7% of total deforestation nationwide. Cattle ranching was the main immediate cause (with 93.4% of the regional total), followed by farming of crops, mainly soybeans, corn and cotton (6.4%), and mining (0.2%). Construction of infrastructure and intensive urbanization were among the anthropic drivers directly linked to the elimination or degradation of the original plant cover in the Amazon Rainforest and Cerrado biomes.

“Illegal activities such as grilagem [misappropriation of government land via falsification of title deeds] are highly relevant in this context. A recent study shows that half of all the deforestation seen in BLA in the last two decades took place on government land illegally occupied by grileiros. Litigation over land ownership lasts decades and doesn’t prevent most illegal areas or illegal deforestation on private property from participating in both the market for land and the production process,” Haddad said.

This latest study by Haddad et al. shows that economic demand from Brazil’s most developed regions (Southeast, Center-West and South) is an even stronger driver of deforestation in the Amazon than the export market. This finding is an important contribution to policymaking and action by civil society to conserve or regenerate such areas. Moreover, because land-use changes via cattle ranching and monoculture are still the main sources of CO2 emissions in Brazil, control of deforestation and degradation is imperative if Brazil is to achieve its greenhouse gas emission reduction targets.

Haddad is the corresponding author of the article, whose last author is Carlos Afonso Nobre. The other co-authors are Inácio Fernandes de Araújo Junior, Rafael Feltran Barbieri, Fernando Salgueiro Perobelli, Ademir Rocha, and Karina Simone Sass.

FAPESP supported the study via two projects (14/50848-9 and 21/12397-9).

About São Paulo Research Foundation (FAPESP)

The São Paulo Research Foundation (FAPESP) is a public institution with the mission of supporting scientific research in all fields of knowledge by awarding scholarships, fellowships and grants to investigators linked with higher education and research institutions in the State of São Paulo, Brazil. FAPESP is aware that the very best research can only be done by working with the best researchers internationally. Therefore, it has established partnerships with funding agencies, higher education, private companies, and research organizations in other countries known for the quality of their research and has been encouraging scientists funded by its grants to further develop their international collaboration. You can learn more about FAPESP at www.fapesp.br/en and visit FAPESP news agency at www.agencia.fapesp.br/en to keep updated with the latest scientific breakthroughs FAPESP helps achieve through its many programs, awards and research centers. You may also subscribe to FAPESP news agency at http://agencia.fapesp.br/subscribe.

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Journal

Nature Sustainability

DOI

10.1038/s41893-024-01387-7 

Article Title

Economic drivers of deforestation in the Brazilian Legal Amazon

Capturing Ecology – Winning images from the British Ecological Society photography competition announced

British Ecological Society



image:

This photo was captured on a sunny summer day in Canberra. Bearded dragons (Pogona barbata) love to bask under the sun in their eucalypt woodland habitat. I used fish-eye lens to capture the habitat and the sun in the composition to have a detailed portrayal of the lizards ecology.view more

Credit: Damien Esquerre. British Ecological Society

Lanterns 

During a walk through the high-altitude forest in Ecuador, I came across a beautiful flowering shrub (Abutilon megapotamicum) at the edge. I spent some time photographing the light coming through the flowers and then saw this perfect branch displaying flowers at all ages. It was a composition I couldn't resist.

Credit

Danni Thompson. British Ecological Society.

 

Study among Dutch people adds nuance to link between brain structure and ideology

Universiteit van Amsterdam

For a long time, the claim has been made that the brains of conservative people are different than those of progressives. Using MRI scans of almost 1,000 Dutch people, researchers from the University of Amsterdam (UvA) show that there is indeed a connection between brain structure and ideology. However, the connection is smaller than expected. Nevertheless, the researchers find it remarkable that differences in the brain are linked to something as abstract as ideology.

The claim that brain structure and ideology are linked was fueled by a 2011 study of 90 English students that found this connection. Scientists at the UvA have now conducted the largest replication study to date to further investigate the relationship between ideology and brain structure.

The researchers analyzed the MRI scans of 975 Dutch people aged 19 to 26, representing a cross-section of the Dutch population in terms of education and political preference. They linked these scans to questionnaires about ideology. 'You can see ideology as a series of positions on different themes or as an identity,' explains first author Gijs Schumacher. 'You can also distinguish between ideological ideas about socio-cultural issues such as women's and LGBTIQ rights, and about economic issues such as income inequality.'

The amygdala is slightly larger

The scientists found, just as in the English study, that the amygdala of conservative people is slightly larger. ‘It is remarkable that we also found this result in our much larger and more representative sample. For example, the English sample did not contain any extremely conservative participants, while ours did,’ says Schumacher.

The scientists also found that there is no relationship between another brain area - the anterior cingulate cortex - and ideology, something that the original study did find.

Difference of a sesame seed

The difference in the amygdala was the size of a sesame seed. ‘The amygdala of the average conservative voter is 157 sesame seeds in size and that of the average progressive voter is 156 sesame seeds. That is a small difference, but significant. It suggests that there is a connection between brain anatomy and ideology at some level, but that it is very indirect,’ explains co-author Steven Scholte. ‘Our expectation was therefore to find no effect at all.’

‘However, we do not know exactly how conservatism and the size of the amygdala are related,’ adds Diamantis Petropoulos Petalas (also associated with this study but now working at The American College of Greece). ‘The amygdala has mainly been studied in relation to threatening situations and fear, but seems to respond much more broadly to emotions in general and to divergent information. There may be a connection where the amygdala is larger in individuals who react more strongly to information, which could sometimes result in more conservative ideas in politics.'

No simple dichotomy

However, the research suggests there is no simple dichotomy regarding political ideology in the brain. ‘People sometimes speak of blue (Democratic) and red (Republican) brains in the American context. This metaphor is tempting, but completely misplaced,’ says Schumacher. ‘We argue that ideology should be seen as a much broader concept and show that there are fewer connections between brain and ideology than have been found in previous studies.’

Ideology itself is also more complex than was assumed in previous research. As an example, Schumacher mentions how participants who voted for the SP, a Dutch political party with radical left-wing economic positions but more conservative social values, had a larger amygdala on average than participants who identified with more progressive parties. ‘Ideology is therefore much more complex than just identification on socio-cultural themes.’

Other brain areas

The researchers then extended their analysis to find connections between ideology and other brain areas. For example, they found a connection between the volume of the right fusiform gyrus, an area of ​​the brain important for facial recognition, and more right-wing positions on social and economic issues. The reason for this remains to be seen.

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Journal

iScience

DOI

10.1016/j.isci.2024.110532 

Article Title

Is political ideology correlated with brain structure? A preregistered replication

Political ideology is associated with differences in brain structure, but less than previously thought

Cell Press

 

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This is the graphical abstract for the iScience paper "Is political ideology correlated with brain structure? A preregistered replication."

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Credit: Petropoulos Petalas et al./iScience

Conservative voters have slightly larger amygdalas than progressive voters—by about the size of a sesame seed. In a replication study publishing September 19 in the Cell Press journal iScience, researchers revisited the idea that progressive and conservative voters have identifiable differences in brain morphology, but with a 10x larger and more diverse sample size than the original study. Their results confirmed that the size of a person’s amygdala is associated with their political views but failed to find a consistent association between politics and the anterior cingulate cortex (ACC). Anatomical differences in both the amygdala and ACC varied depending on a person’s economic and social ideology—which aren’t necessarily aligned—indicating that relationships between political ideology and brain structure are nuanced and multidimensional.

“It was really a surprise that we replicated the amygdala finding,” says first author and political psychology and neuroscience researcher Diamantis Petropoulos Petalas (@diamond_petalas) of The American College of Greece and member of the @HotPoliticsLab at the University of Amsterdam. “Quite honestly, we did not expect to replicate any of these findings.”

The new study, which aimed to replicate a widely shared 2011 study that was based on 90 UK university students, used pre-existing MRI brain scans from 928 individuals aged 19-26 whose levels of education and political identities were representative of the Dutch population. Because the Netherlands has a multi-party political system, the study was also able to compare brain structures along the continuum from left- to rightwing, in contrast to the two-party UK system. In addition, the researchers looked at the participants’ “ideology” from various angles, including their political identity and stance on socioeconomic issues, which allowed them to compare brain structures along different dimensions of progressivism and conservativism.

The researchers paired the brain data with a questionnaire about the participants’ politics, which included questions about their social and economic identity (e.g., how they view themselves on a sliding scale of progressive to conservative, and which political party they identify with), and questions pertaining to their social and economic ideology (e.g., where they stand on different social and economic issues, such as women’s and LGBQT rights, income inequality, and profit sharing).

“We see ideology as a complex, multidimensional product; that includes different attitudes on social and economic matters, as well as identification with progressive or conservative ideals; it’s really not just about the left or the right,” says Petropoulos.

In agreement with the original study, the researchers found an association between conservatism and the volume of gray matter in the amygdala; however, this association was three times weaker compared to the original study.

“The amygdala controls for the perception and the understanding of threats and risk uncertainty, so it makes a lot of sense that individuals who are more sensitive towards these issues have higher needs for security, which is something that typically aligns with more conservative ideas in politics,” says Petropoulos.

The association between amygdala size and conservatism also depended on the political party that the individual identified with—for example, participants who identified with the socialist party, which has radically left-wing economic policies but more conservative social values, had on average more gray matter in the amygdala compared to other progressive parties.

“The Netherlands has a multi-party system, with different parties representing a spectrum of ideologies, and we find a very nice positive correlation between the parties’ political ideology and the amygdala size of that person,” says Petropoulos. “That speaks to the idea that we're not talking about a dichotomous representation of ideology in the brain, such as Republicans versus Democrats as in the US, but we see a more fine-grained spectrum of how political ideology can be reflected in the brain’s anatomy.”

However, in contrast to the original study, the team did not find any association between conservatism and a smaller volume of gray matter in the ACC, a brain region involved in error detection, impulse control, and emotional regulation.

The researchers also extended their analysis to examine potential associations between political identity and other regions of the brain. This analysis uncovered a positive association between the gray matter volume in the right fusiform gyrus, a region in the temporal lobe that is essential for visual and cognitive functions, and economic and social conservatism.

“These regions have to do with facial recognition, so it makes sense that they might be involved when one is thinking about political issues, because political issues often remind us of the political personas that represent ideology on those issues,” says Petropoulos. “Just the memory of the face of a politician, for instance, might make the fusiform gyrus light up a little.”

The MRI scans used in this study provide information only on the anatomy of different brain regions, but the researchers say that future work should integrate information about the functional connections between the amygdala and different parts of the brain.

“I think the future of this endeavor to identify political orientations in the brain will be to look more towards functional connectivity network and neural synchrony studies—how brain networks organize and synchronize between individuals, and whether there are differences in this synchronization when individuals with difference political ideologies consume similar content,” says Petropoulos.

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The authors declare no competing interests.

iScience, Petropoulos Petalas et al., “Is Political Ideology Correlated with Brain Structure? A Preregistered Replication” https://www.cell.com/iscience/fulltext/S2589-0042(24)01757-7  

iScience (@iScience_CP) is an open-access journal from Cell Press that provides a platform for original research and interdisciplinary thinking in the life, physical, and earth sciences. The primary criterion for publication in iScience is a significant contribution to a relevant field combined with robust results and underlying methodology. Visit http://www.cell.com/iscience. To receive Cell Press media alerts, contact press@cell.com.  

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Journal

iScience

DOI

10.1016/j.isci.2024.110532 

Method of Research

Observational study

Subject of Research

People

Article Title

Is Political Ideology Correlated with Brain Structure? A Preregistered Replication

Article Publication Date

19-Sep-2024

 

How can we make the best possible use of large language models for a smarter and more inclusive society?

New article outlines the ways large language models can help and hurt collective intelligence and proposes recommendations for action

Max Planck Institute for Human Development

What do you do if you don't know a term like "LLM"? You probably quickly google it or ask your team. We use the knowledge of groups, known as collective intelligence, as a matter of course in everyday life. By combining individual skills and knowledge, our collective intelligence can achieve outcomes that exceed the capabilities of any individual alone, even experts. This collective intelligence drives the success of all kinds of groups, from small teams in the workplace to massive online communities like Wikipedia and even societies at large. 

LLMs are artificial intelligence (AI) systems that analyze and generate text using large datasets and deep learning techniques. The new article explains how LLMs can enhance collective intelligence and discusses their potential impact on teams and society. "As large language models increasingly shape the information and decision-making landscape, it's crucial to strike a balance between harnessing their potential and safeguarding against risks. Our article details ways in which human collective intelligence can be enhanced by LLMs, and the various harms that are also possible," says Ralph Hertwig, co-author of the article and Director at the Max Planck Institute for Human Development, Berlin. 

Among the potential benefits identified by the researchers is that LLMs can significantly increase accessibility in collective processes. They break down barriers through translation services and writing assistance, for example, allowing people from different backgrounds to participate equally in discussions. Furthermore, LLMs can accelerate idea generation or support opinion-forming processes by, for example, bringing helpful information into discussions, summarizing different opinions, and finding consensus. 

Yet the use of LLMs also carries significant risks. For example, they could undermine people’s motivation to contribute to collective knowledge commons like Wikipedia and Stack Overflow. If users increasingly rely on proprietary models, the openness and diversity of the knowledge landscape may be endangered. Another issue is the risk of false consensus and pluralistic ignorance, where there is a mistaken belief that the majority accepts a norm. "Since LLMs learn from information available online, there is a risk that minority viewpoints are unrepresented in LLM-generated responses. This can create a false sense of agreement and marginalize some perspectives," points out Jason Burton, lead author of the study and assistant professor at Copenhagen Business School and associate research scientist at the MPIB.  

“The value of this article is that it demonstrates why we need to think proactively about how LLMs are changing the online information environment and, in turn, our collective intelligence—for better and worse,” summarizes co-author Joshua Becker, assistant professor at University College London. The authors call for greater transparency in creating LLMs, including disclosure of training data sources, and suggest that LLM developers should be subject to external audits and monitoring. This would allow for a better understanding of how LLMs are actually being developed and mitigate adverse developments.    

In addition, the article offers compact information boxes on topics related to LLMs, including the role of collective intelligence in the training of LLMs. Here, the authors reflect on the role of humans in developing LLMs, including how to address goals such as diverse representation. Two information boxes with a focus on research outline how LLMs can be used to simulate human collective intelligence, and identify open research questions, like how to avoid homogenization of knowledge and how credit and accountability should be apportioned when collective outcomes are co-created with LLMs. 

Key Points: 

• LLMs are changing how people search for, use, and communicate information, which can affect the collective intelligence of teams and society at large. 
• LLMs offer new opportunities for collective intelligence, such as support for deliberative, opinion-forming processes, but also pose risks, such as endangering the diversity of the information landscape. 
• If LLMs are to support rather than undermine collective intelligence, the technical details of the models must be disclosed, and monitoring mechanisms must be implemented. 

Participating institutes 

• Department of Digitalization, Copenhagen Business School, Frederiksberg, DK 
• Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, DE 
• Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, DE 
• Humboldt-Universität zu Berlin, Department of Psychology, Berlin, DE 
• Center for Cognitive and Decision Sciences, University of Basel, Basel, CH 
• Google DeepMind, London, UK 
• UCL School of Management, London, UK 
• Centre for Collective Intelligence Design, Nesta, London, UK 
• Bonn-Aachen International Center for Information Technology, University of Bonn, Bonn, DE 
• Lamarr Institute for Machine Learning and Artificial Intelligence, Bonn, DE 
• Collective Intelligence Project, San Francisco, CA, USA 
• Center for Information Technology Policy, Princeton University, Princeton, NJ, USA 
• Department of Computer Science, Princeton University, Princeton, NJ, USA 
• School of Sociology, University College Dublin, Dublin, IE 
• Geary Institute for Public Policy, University College Dublin, Dublin, IE 
• Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA 
• Department of Psychological Sciences, Birkbeck, University of London, London, UK 
• Science of Intelligence Excellence Cluster, Technische Universität Berlin, Berlin, DE 
• School of Information and Communication, Insight SFI Research Centre for Data Analytics, University College Dublin, Dublin, IE 
• Oxford Internet Institute, Oxford University, Oxford, UK 
• Deliberative Democracy Lab, Stanford University, Stanford, CA, USA 
• Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA, USA 

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Journal

Nature Human Behaviour

DOI

10.1038/s41562-024-01959-9 

Method of Research

Commentary/editorial

Subject of Research

People

Article Title

How large language models can reshape collective intelligence

Article Publication Date

20-Sep-2024

COI Statement

M.A.B. is employed by Google DeepMind. S. Huang and D.S. are employed by the Collective Intelligence Project. L.F. is affiliated with the Lamarr Institute of ML and AI, an associated partner in the OpenGPT-X project via Fraunhofer IAIS. The other authors declare no competing interests.

Korean research team proposes AI-powered approach to establishing a 'carbon-neutral energy city’

An energy management algorithm and system have been implemented to address the stability issues of urban power grids.

National Research Council of Science & Technology



Researchers group photo (from left to right Dr. Jong-Kyu Kim, Dr. Min-Hwi Kim, Dr. Gwangwoo Han, student researcher Dong Eun Jung, Dr. Young-Sub An, and Dr. Hong-Jin Joo)

Credit: KOREA INSTITUTE OF ENERGY RESEARCH

A joint research team from the Renewable Energy System Laboratory and the Energy ICT Research Department at the Korea Institute of Energy Research (KIER) has developed key technologies to realize "Urban Electrification" using artificial intelligence (AI).

Urban electrification aims to reduce the use of fossil fuels and introduce renewable energy sources, such as building-integrated solar technology, to transform urban energy systems. While this concept is relatively unfamiliar in the Republic of Korea, it is being promoted as a key strategy in the U.S. and Europe for achieving carbon neutrality and creating sustainable urban environments.

In traditional urban models, energy supply can be easily adjusted using fossil fuels to meet electricity demand. However, in electrified cities, the high dependence on renewable energy leads to greater variability in energy supply due to weather changes. This causes mismatches in electricity demand across buildings and makes the stable operation of the power grid more challenging.

In particular, Low-Probability High-Impact Events (LPHI), such as sudden cold snaps or extreme heat waves, can cause a sharp increase in energy demand while limiting energy production. These events pose a significant threat to the stability of the urban power grid, potentially leading to large-scale blackouts.
*Low-Probability High-Impact Event (LPHI) refers to incidents that have a low likelihood of occurring but can have significant consequences when they do. These events are difficult to predict and can cause substantial economic and social damage when they occur.

The research team developed an energy management algorithm based on AI analysis to address power grid stability issues and implemented it into a system. The demonstration of the developed system showed an 18% reduction in electricity costs compared to conventional methods.

The research team first used AI to analyze energy consumption patterns by building type and renewable energy production patterns. They also unraveled how complex variables, such as weather, human behavior patterns, and the scale and operational status of renewable energy facilities, affect the power grid. Notably, they discovered that Low-Probability High-Impact Events, which occur on average only 1.7 days per year (around 0.5% of the time), have a decisive impact on the overall stability of the power grid and its operational costs.

The analyzed content is developed into an algorithm and a system. The developed algorithm optimizes energy sharing between buildings and effectively manages peak demand and peak energy production. In addition to maintaining daily energy balance, the system is designed to respond to Low-Probability High-Impact Events, ensuring the stability of the power grid even in extreme situations.

When the developed system was applied to a community-scale real-world environment replicating urban electrification, it achieved an energy self-sufficiency rate* of 38% and a self-consumption rate** of 58%. This is a significant improvement compared to the 20% self-sufficiency and 30% self-consumption rate of buildings without the system. This application also resulted in an 18% reduction in electricity costs and greatly improved the stability of the power grid.
*Energy Self-Sufficiency Rate: This indicates the extent to which a building can meet its electricity demand through its own power generation. A higher value means lower dependence on external power grids, thereby reducing the burden on the grid.
**Energy Self-Consumption Rate: This refers to the proportion of electricity produced by a building that is used directly on-site rather than exported to the power grid. A higher rate contributes to the stable operation of the power grid.

Particularly, the annual energy consumption applied in the demonstration was 107 megawatt-hours (MWh), which is seven times larger than simulation-based studies conducted by leading international institutions. This significantly enhances the potential for applying the system in real urban environments.

Dr. Gwangwoo Han, the lead author of the paper and a researcher at the Energy ICT Research Department, stated, "The results of this study demonstrate that AI can enhance the efficiency of urban electrification and address power grid stability issues, while also highlighting the importance of managing Low-Probability High-Impact Events." He further predicted that "by applying this system to various urban environments in the future, we can improve energy efficiency and enhance grid stability, ultimately making a significant contribution to achieving carbon neutrality."

This research was conducted as part of the Korea Institute of Energy Research’s (KIER) R&D projects. The findings have been published online in the internationally renowned journal Sustainable Cities and Society (Impact Factor 10.7, top 2.7% in JCR rankings) in the field of building studies.

The operational data used in the demonstration study.

Credit

KOREA INSTITUTE OF ENERGY RESEARCH

AI Energy Monitoring System. (VIDEO)

Journal

Sustainable Cities and Society

DOI

10.1016/j.scs.2024.105648

Article Title

Analysis of grid flexibility in 100% electrified urban energy community: A year-long empirical study

Article Publication Date

15-Oct-2024

 

AI is learning to read your emotions, and here’s why that can be a good thing

SAYS THE CHINESE STATE

Peer-Reviewed Publication

Tsinghua University Press

 

image: 

Using both contemporary psychological methods and AI tools can help achieve a clearer path to emotion quantification through artificial intelligence

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Credit: Feng Liu, East China Normal University

Using a fusion of traditional and novel technological methods, researchers are hoping to better quantify emotions to transform the face of the emotion quantification field

 

Human emotions are complex and are not always easily able to be boiled down to a recognizable pattern. Determining one’s emotional state can be difficult human-to-human, and the many nuances of existence as an emotional entity seem impossible to train a non-human entity to understand, identify and learn from. However, a considerable amount of work and research has been put into training artificial intelligence (AI) to observe, quantify and recognize various states of emotion in humans. The fusion of tried and true psychological methods combined with the intelligence and trainability of AI can make emotion recognition technology invaluable in fields such as healthcare and education.

 

Results were published in CAAI Artificial Intelligence Research on August 21, 2024.

 

Where conventional techniques are limited, AI can improve. Through the use of a multitude of developments, such as gesture recognition technology, facial emotion recognition (FER) and multi-modal emotional recognition, emotional recognition technology stands a chance to be transformational for many individuals and fields of study as a whole. 

 

“This technology has the potential to transform fields such as healthcare, education, and customer service, facilitating personalized experiences and enhanced comprehension of human emotions,” said Feng Liu, author and researcher of the review.

 

An artificial intelligence that understands human emotion and can appropriately interact given the emotional input of the human can be revolutionary for human-computer interactions and can be a key in assessing the mental health status of an individual. This isn’t done through just one form of input, but instead can also take physiology into account. For example, some techniques can take input from the electrical activity of the brain through an EEG scan and combine that with eye movement technology to monitor people’s expressions. Other measurements of emotional arousal such as heart-rate variability and electrical skin response are also tools that are used to convert the intangible “emotion” into patterns and recognizable, readable data for AI to learn from and improve.

 

Multi-modal emotion recognition similarly combines different perceptual channels, such as sight, hearing and touch to gain a more complete picture of what emotions can entail. The combination of different fields and techniques is necessary to create an accurate and well-rounded representation of the complexities of human emotion.

 

“It is believed that interdisciplinary collaboration between AI, psychology, psychiatry and other fields will be key in achieving this goal and unlocking the full potential of emotion quantification for the benefit of society,” said Liu.

 

Having AI be able to correctly recognize human emotions can be especially useful in a world where mental health is quickly becoming a top priority. Emotion quantification AI can help in monitoring an individual’s mental health and create personalized experiences for that individual, all without having to entangle another person in the process.

 

Successful use of emotion recognition and quantification AI requires a few major components. One concern that would need to be addressed is safety and transparency, especially as it relates to more sensitive topics such as medical and psychological counseling. Data handling practices and privacy measures taken by the entities using this type of AI will have to be stringent. Additionally, ensuring the AI can adapt to the nuances of cultures is of utmost importance, as this will maintain the integrity and reliability of the AI for future referencing and learning.

 

Feng Liu of the School of Computer Science and Technology at East China Normal University is the author and researcher of this study.

 

The Beijing Key Laboratory of Behavior and Mental Health supported this research.

 

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About CAAI Artificial Intelligence Research

CAAI Artificial Intelligence Research (CAAI AIR) is an Open Access, peer-reviewed scholarly journal, published by Tsinghua University Press, released exclusively on SciOpen. CAAI AIR aims to publish the state-of-the-art achievements in the field of artificial intelligence and its applications, including knowledge intelligence, perceptual intelligence, machine learning, behavioral intelligence, brain and cognition, AI chips and applications, etc. Original research and review articles on but not limited to the above topics are welcome. The journal is completely Open Access with no article processing fees for authors.

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Journal

CAAI Artificial Intelligence Research

DOI

10.26599/AIR.2024.9150040 

Article Title

Artificial Intelligence in Emotion Quantification : A Prospective Overview