About the project
Objective
Problem statement: Develop future sustainability agendas in an objective and data-driven manner, leveraging large language models (LLMs) to simultaneously address the social, economic and environmental needs of humanity and the planet.
The project has the following goals:
- Effectively utilize the Gemini ecosystem of large language models (LLMs) to analyze thousands of documents, yielding excellent performance in the context of sustainability.
- Perform a detailed analysis of synergies and tradeoffs among the 169 SDG targets, creating a detailed matrix of positive and negative interactions.
- Extend this analysis to find positive and negative connections between the 231 SDG indicators and the 9 Planetary Boundaries.
- Define a new set of goals, addressing the targets from both the SDG and PB frameworks.
Background
The 2030 Agenda for Sustainable Development, adopted by all United Nations Member States in 2015, is an ambitious blueprint for achieving a better and more sustainable future for all. It comprises 17 goals, 169 targets, and 231 measurable indicators that aim to address a wide array of global challenges, including poverty, inequality, climate change, environmental degradation, peace and justice. The intricate and interconnected nature of these goals means that progress in one area can often catalyze advancements in others.
However, this interconnectedness can also lead to unintended consequences where progress on certain goals may inadvertently hinder progress on others. While some of these interactions are straightforward and predictable, many remain complex and difficult to foresee. Nearly a decade after the launch of the 2030 Agenda, we have accumulated a wealth of data and developed new tools that enhance our ability to identify and understand the positive and negative interactions between the Sustainable Development Goals (SDGs). This understanding is crucial as we approach the deadline of the 2030 Agenda and begin to consider the design of goals for the Post-2030 Agenda. This is highlighted in a recent article in Nature by the PIs*. A critical component of this analysis is the Planetary Boundaries (PBs) framework, introduced by the Stockholm Resilience Center in 2009. The PBs provide a science-based framework for monitoring environmental thresholds that define a safe operating space for humanity. These boundaries are quantifiable and offer a valuable tool for assessing environmental sustainability, although they do not directly address the social dimensions that are integral to the SDG Agenda.
*Extending the SDGs to 2050 – a roadmap. Fuso-Nerini et al., Nature (2024)
Crossdisciplinary collaboration
The PIs have a long history of cross-disciplinary collaboration, combining the AI knowledge of Prof. Vinuesa with the sustainability and systems modeling of Prof. Fuso-Nerini.
About the project
Objective
The project aims to develop a system for online rehabilitation of patients with long-term cognitive symptoms after Covid-19.
The project will start with an analysis and concretization of patients’ and clinicians’ demands on the existing pilot system. This information will then be used to develop the system for testing further at the Rehabilitation Medicine University Clinic Stockholm at Danderyd Hospital.
Background
The focus is on patients with severe cognitive problems, e.g. fatigue, concentration problems and memory disorders, after Covid-19. The system to be developed aims to help patients understand more about their symptoms, train to overcome them, and increase their motivation to work with their rehabilitation online.
The project also aims to apply an innovative visualization technology that can support workflows and information flows as well as the integration of applications. The technology can support care personnel so that they can easily receive the results from the patients’ online rehabilitation training and provide support in order to further increase the patients’ motivation.
The project collaborates with the clinical team at Rehabilitation Medicine University Clinic Stockholm, Danderyd Hospital and Visuera Integration.
Crossdisciplinary collaboration
The project partners are Region Stockholm, Danderyd University Hospital, Visuera Integration AB and Telia Company.
About the project
Objective
This seed project aims to support the Digital Futures (DF) Digitalized Industry Working Group by strengthening the international research focus on industrial digitalization, driving sustainability and addressing related complexity from a systems thinking and engineering point of view. The overall objectives of this seed project are (1) to create a European network of leading research institutions, industry and other stakeholders and (2) to write a proposal for a suitable EU project to support this network. We primarily target an EU COST Action but will also consider other instruments.
Background
There are common challenges in sustainability in the manufacturing, transport, energy, water management, and building sectors, as well as related methodologies to deal with the associated socio-technical complexity. There is a fragmentation of competencies in the use of digitalization to support sustainable development and also related to systems thinking/engineering. With the targeted network, we intend to try to overcome this fragmentation.
Cross-disciplinary collaboration
The PI and co-PIs are spread over two different KTH schools. Each PI needs international collaboration as we aim to apply for a proposal to build an EU network. The partners in the ongoing proposal involve eleven countries so far, >50% COSTInclusiveness Target Countries. We have had one in-person meeting at KTH on 12-13 August 2024 (see photo) and plan for the next meeting in person on 14 October 2024. Several online meetings have taken place since the start of the project.
Contacts: Ellen Bergseth, Martin Törngren, Yongkuk Jeong
About the project
Objective
We have two key objectives:
- Understand the extent of diversity at multiple spatial scales in biological systems
- Build an interdisciplinary community in Digital Futures to develop computational models that account for the experimental observed heterogeneities
To this end besides extensive literature surveys we will organize thematic workshops to find synergies among digital future researchers. Besides, we will bring together international experts in the area for a conference at Digital Futures.
Background
Biological systems are shaped by their evolutionary history and adaptation animals acquire throughout their life. This implies that even if two beings exhibit similar behavior the underlying physiological mechanism could be different and vice versa. ‘Degeneracy’ and diversity can be seen at every scale in biological systems: from gene expressions to behavior and can be quantitatively observed in heavy-tailed distribution of physiological parameters. Parameters that describe a biological system usually do not follow the classical Gaussian (or exponential family) distribution.
This biological reality is in stark contrast to how we build computational models of biological systems: Either take a data-driven or normative approach. Data-driven models rely on extensive data but typically we end up with an average model. Normative modeling on the other hand seeks to model biological systems assuming (based on data or some physical/chemical principles) that the system is ‘optimized’ in some sense. Both approaches ignore the fact that cohorts of biological systems do not follow normal distribution and each animal is optimized in their own way.
In this project we want to better understand the structure of the non-normal nature of biological data and how it can be better captured in our computational models.
Cross-disciplinary collaboration
We will focus on three specific spatial scales i.e. molecular level (i.e. genome, transcriptome and proteome), tissue/organ level (i.e. neural networks, cellular physiology, multiscale tissue mechanics, computational human models) and behavior level (i.e. decision-making, motor control). While the data at each level is different we expect there will be some common principles that relate diversity at different levels.
Contacts: Arvind Kumar, Xaiogai Li and Lanie Gutierrez-Farewik
About the project
Objective
This project aims to characterise the brain’s mechanical properties through Magnetic resonance elastography (MRE) in adolescents and older children and correlate them with risk factors for developing anxiety disorders. Our long-term goal is to characterize the brain’s mechanical properties at different ages to improve the diagnosis and treatment of anxiety disorders in the young population. Our long-term aim is to extend this project to characterize the mechanical properties of the brain at different stages of brain development, which can be used to improve the diagnoses of various neuropsychological diseases, especially at early stages where treatments are more likely to have an effect and to track the response of patients to treatments better.
Background
Very little is currently known about the evolution of the mechanical tissue properties of the brain in the first two decades of life. Such information can be valuable in improving the diagnosis of prevalent neuropsychiatric disorders in the young population, particularly anxiety disorders. MRE in the brain is a new technique in which mechanical properties of the brain tissue are estimated non-invasively. MRE has barely been used in children. MRE for the brain is only available in a few sites worldwide. This Spring, KTH will become the only site in Sweden with this technology. This opens a tremendous strategic opportunity for KTH to take the lead in its use for brain diseases.
Crossdisciplinary collaboration
The researchers in the team represent the School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), KTH and the Psychology Department at Stockholm University.
About the project
Objective
This research project aims to realize faster-than-real-time (simulation time less than physical flow time) and high-resolution fluid flow simulation in engineering applications, with indoor climate as a pilot. The expected outcome of this project is a static CNN for super-resolution to achieve fast prediction of steady-state indoor airflow, a hybrid RCNN for super-resolution to achieve faster-than-real-time prediction of transient indoor airflow and standards for low-resolution input data by numerical simulation and experimental data. Besides the indoor flow simulations, this project would open a broad spectrum of engineering accurate computational fluid dynamics (CFD) applications complementary to today’s standard application of RANS turbulence models.
Background
Fast, high-resolution heat and mass transfer prediction are critical in many engineering applications. For example, in creating a desired indoor climate, fast and high-resolution airflow and contaminant transport simulation would help sustain life, reduce cost, and minimize energy consumption. In specific scenarios such as emergency management, conceptual design, heating, ventilation, air conditioning and refrigeration (HVAC&R) system control, faster-than-real-time simulation is desired.
Borrowed from computer vision and image recognition, super-resolution is a promising novel approach for the fast analysis of fluid mechanics problems. Super-resolution refers to techniques that obtain a high-resolution flow image output from a low-resolution flow image input. However, super-resolution implementation in real engineering applications faces two major challenges. Firstly, the input low-resolution flow data could be either obtained by fast and computationally inexpensive simulation, e.g., coarse grid CFD simulation, which might be associated with wrong physics or by experimental measurements, where the least number of sensors needed should be identified. Secondly, for transient indoor/outdoor airflow, super-resolution fails to extrapolate the flow fields that belong to a different statistical distribution. Therefore, this study aims to solve the two challenges that state-of-the-art super-resolution models face.
Crossdisciplinary collaboration
The researchers in the team represent the KTH of Architecture and the Built Environment, Department of Civil and Architectural Engineering, KTH School of Engineering Sciences, and Department of Engineering Mechanics.
About the project
Objective
The project envisions a mobile cyber-physical system where people carrying mobile sensors (e.g., smartphones, smartcards) generate large amounts of trajectory data to sense and monitor human interactions with physical and social environments. The project aims to develop a causal artificial intelligence (AI) methodology to analyze and model human mobility behaviour dynamics (decision-making) using individual travel trajectory data and develop the causal diagrams of human mobility behaviour under disturbances that could help design effective strategies for sustainable and resilient urban mobility systems. The research challenges are learning the complex ‘hidden’ human decision-making mechanism from pervasive ‘observed’ trajectories and developing effective, scalable causal AI models and algorithms.
Background
The ever-changing mobility landscape and climate change continue challenging existing operating models and the responsiveness of city planners, policymakers, and regulators. City authorities have growing investment needs that require more focused operations and management strategies that align mobility portfolios to societal goals. The project targets the root cause of traffic (human) and novel analytic techniques to learn and predict human mobility behaviour dynamics from pervasive mobile sensing data that can help cities meet both sustainability challenges (through predicting congestion, emissions, and energy consumption) and improve urban resilience to disruptive events (such as infrastructure failures, natural disasters, or pandemics).
The human mobility area witnessed active developments in two broad but separate fields: transport and computer science. They work with different data, use different methods, and answer different but overlapping questions, i.e., mobility behaviour modelling using ‘small’ data in transport and mobility pattern analysis using ‘big’ data in computer science. A solid bridge between these is beneficial and needed but is still an open challenge. Mobile sensing and information technology have enabled us to collect much mobility trajectory data from human decision-makers. The predictive AI techniques show the potential to learn and predict human mobility using these trajectory data efficiently. However, they continually run up against the limits of what they observe (correlations, not causal relationships), thus hindering any serious applicability for preparedness and response policies for cities without understanding the causal mobility dynamics.
cAIMBER will bridge the two human mobility research streams in Transport Science and Computer Science. Also, it will develop the causal AI methodology, merging the RL and Causal Inference research fields. Integrating interdisciplinary expertise and techniques will derive generalizable insights about human behaviour dynamics that contribute to the scientific communities’ theoretical conceptualization of travel choices and decision-making mechanisms. Practically, cAIMBER conducts extensive empirical analysis using a comprehensive dataset covering different types of system disturbances for seven years. The accumulated knowledge of human mobility under these situational contexts would help city planners and service operators to make more informed decisions for sustainable and resilient travel.
Crossdisciplinary collaboration
The researchers in the team represent the KTH School of Architecture and Built Environment (ABE), Civil and Architectural Engineering Department, Transport Planning Division and KTH School of Engineering Science (SCI), Mathematics Department, Mathematics for Data and AI Division. Strategic research partners at KTH iMobility Lab and MIT Transit Lab support the project.