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Circular economy in the extractive frontier : Tensions and pathways for transformative change in mining
No full textThe mining sector, like other sectors of the economy, is under increasing pressure to adopt circular economy (CE) principles across its value chains and core operations. This paper offers a critical and conceptually grounded contribution to understanding how CE can support systemic transformation in one of the most resource-intensive and path-dependent sectors of the global economy. It examines the structural and institutional conditions that shape the adoption of CE in mining and identifies key tensions that constrain or enable transformative change. In parallel, the paper explores emerging pathways informed by technological innovation, shifts in production routines, and the rise of new circular business models. These insights are synthesised into a multi-level framework that captures the dynamic interactions between micro-, meso-, and macro-level processes shaping CE transitions. In addition to offering a diagnostic perspective, the framework outlines concrete action points for advancing systemic change. Funding Agencies|Marianne and Marcus Wallenberg Foundation [MMW 2023.0056]; Swedish Energy Agency (Energimyndigheten) [P2022-01062]</p
A Framework for Road Authorities to Assess Their Readiness to Support Connected and Automated Driving
No full textConnected and Automated Driving (CAD) will bring disruption to individuals, economies, and society. Most forms of CAD require some level of support from the infrastructure for their safe operation, in particular communications. However, additional infrastructure services to support CAD could improve safety and robustness and bring further benefits such as increased capacity. However, the infrastructure requirements of vehicle Original Equipment Manufacturers (OEMs) are not always clear, and it is therefore difficult for National Road Authorities (NRA) to prepare future levels of support for CAD, given rapidly evolving technology and uncertain projections of future CAD demand. There is a need to articulate those requirements, bringing stakeholders together to formulate a structured approach, and a roadmap that will advance safe and smart roads that support CAD. This paper presents the DiREC project (consortium partners: TRL, ARUP, TU Delft, VTT, VTI and FEHRL) funded by the CEDR Transnational Road Research Programme Call 2020 with funding provided by CEDR members of Belgium (Flanders), Denmark, Ireland, Israel, Netherlands, Norway, Sweden, Switzerland and the United Kingdom. DiREC is seeking to address the above challenge. The project has established a CAV-Readiness Framework (CRF) based on a level of service approach to understand the needs of CAD, and to define the infrastructure and services that NRAs could provide to support these needs.Research funding provided by the CEDR Transnational Road Research Programme Call 2020.Digital Road for Evolving Connected and Automated Driving (DiREC
Thermo-elasticity problems with evolving microstructures
No full textWe consider the mathematical analysis and homogenization of a moving boundary problem posed for a highly heterogeneous, periodically perforated domain. More specifically, we are looking at a one-phase thermo-elasticity system with phase transformations where small inclusions, initially periodically distributed, are growing or shrinking based on a kinetic under-cooling-type law and where surface stresses are created based on the curvature of the phase interface. This growth is assumed to be uniform in each individual cell of the perforated domain. After transforming to the initial reference configuration (utilizing the Hanzawa transformation), we use the contraction mapping principle to show the existence of a unique solution for a possibly small but ε independent time interval (ε is here the scale of heterogeneity). In the homogenization limit, we recover a macroscopic thermo-elasticity problem which is strongly non-linearly coupled (via an internal parameter called height function) to local changes in geometry. As a direct by-product of the mathematical analysis work, we present an alternative equivalent formulation which lends itself to an effective pre-computing strategy that is very much needed as the limit problem is computationally expensive
AI for colon cancer : A focus on classification, detection, and predictive modeling
No full textPurpose: Artificial Intelligence (AI) is increasingly recognized for its potential in improving the detection, classification, prediction, and segmentation of colon cancer. Yet, the reliability of these applications depends on the quality and completeness of the underlying studies. This systematic review evaluates the current state of AI applications in colon cancer research, focusing on their impact on diagnostic accuracy, treatment planning, and patient outcomes. Methods: A comprehensive search was conducted in PubMed, Scopus, and Web of Science for articles published between 2020 and 2024. The quality of the included studies was assessed using standardized criteria. A meta-analysis was performed where applicable, and a subgroup analysis was conducted based on the type of AI technology (e.g., deep learning, machine learning) and its application (detection, classification, etc.). Additionally, we recorded whether each study incorporated Explainable AI (XAI) techniques or Generative AI (e.g., GANs) as part of its methodology. Results: In 80 articles, AI models showed significant improvements in diagnostic accuracy, particularly in polyp detection during colonoscopies and histopathological analysis. Deep learning approaches often outperformed traditional methods. However, clinical integration remains challenging due to data and validation gaps. Conclusion: AI holds great promise in colon cancer diagnosis and treatment. Future work should focus on integrating AI tools into clinical workflows through explainable models and standardized validation.
Hazardous alcohol use among patients with psychiatric disorders : Findings from phosphatidylethanol (PEth) screening in healthcare
No full textIdentifying hazardous alcohol use among patients with psychiatric disorders is important for improving treatment outcomes. This study aimed to describe the prevalence of hazardous alcohol use across 20 psychiatric diagnoses using phosphatidylethanol (PEth) blood sampling. We conducted a cross-sectional analysis of PEth samples collected between 2017 and 2023 from a regional Swedish healthcare database, linked to clinician-assessed psychiatric diagnoses. Participants were adults (≥18 years) with a psychiatric disorder (N = 18,240). Hazardous alcohol use was defined as a PEth concentration > 0.30 µmol/L (∼210 µg/L). Marginal risk ratios (RR) were estimated using logistic regression adjusted for sex, age, comorbidity, season, and healthcare setting. In the sample (mean age: 40 years; 58.7 % female), 7.4 % screened positive for hazardous use. The highest prevalence was observed in patients with substance use disorder (12 %, RR: 1.87, 95 % CI: 1.57–2.23) and panic disorder (8.8 %, RR: 1.43, 95 % CI: 1.19–1.70). Lower prevalence was seen in patients with exhaustion syndrome (5.4 %), ADHD (5.0 %), and obsessive–compulsive disorder (4.2 %). Panic disorder remained significantly associated with hazardous use after adjustment. Male sex (RR: 1.79, 95 % CI: 1.58–2.02) and age, with the highest risk observed around 60 years, were both strongly associated with increased risk. Hazardous alcohol use is prevalent among patients with psychiatric disorders when assessed with PEth. Targeted screening, especially among older men and patients with panic disorder, may improve identification and intervention.Correction in: ADDICTIVE BEHAVIORS, Volume 172DOI: 10.1016/j.addbeh.2025.108512</p
Environmental assessment in Estonia – A quest for an effective EA system
No full textThe effectiveness of Environmental Assessment (EA) has been intensively discussed in academia and practice. However, few studies provided a longitudinal analysis of a country or examined in-depth the interactions with the institutional context. This article aims to contribute to the understanding of EA-effectiveness by examining the interaction of EA with planning and decision-making institutions. Therefore, we present an in-depth historical analysis of how EA together with the planning and decision-making system evolved over time, using Estonia as a case. We developed an analytical framework to analyse the various effectiveness dimensions of EA (procedural, substantive, transactive, legitimacy, and knowledge& learning) and their interactions with the broader institutional setting of planning and decision-making, and conducted document analysis (of regulations, policies and evaluations), interviews and a focus group, reviewing the period between 1988 and 2024. Important findings include that the dominance of the effectiveness dimensions is dynamic characterized by the interaction between the EA-system and the broader institutional setting of the planning system. Furthermore, there is interaction between the different dimensions of effectiveness over time. This means that EA effectiveness cannot be fully understood by examining one single effectiveness dimension nor by considering EA in isolation; the broader institutional context must be considered. To improve EA effectiveness, it is crucial to acknowledge this, and to address multiple effectiveness dimensions as well as the broader institutional setting. Perhaps the key to enhancing EA effectiveness lies beyond EA itself, which aligns with its original role as an instrument to aid decision-making and planning.
Status overview of the torrefaction technology, its limitations, and prospective solutions: A reactor design and performance viewpoint
No full textThe goal of torrefaction is to transform lignocellulosic biomass into a solid product with better characteristics than raw biomass. Torrefaction is especially helpful when converting biomass into a final energy carrier via thermochemical processes like gasification and direct combustion. However, even though torrefied biomass has demonstrated advantageous quality, the prevailing torrefaction methods are not yet considered sufficiently advanced to overcome certain operational difficulties such as finding the proper balance between salient independent variables like temperature, reaction time and particle size; hence, much of the torrefied products obtained exhibit inconsistent properties. The technique also lacks the ability to efficiently handle a wide range of biomass feedstocks and exhibits scale-up issues that could be linked to poor temperature control and non-uniform heat distribution. Many of the existing torrefaction systems have been developed and validated for use in the processing of wood-based materials. Studies performed with non-woody materials yielded unsatisfactory results due to their inclination to ignite and carbonize readily during torrefaction. This review, therefore, presents a status overview of torrefaction technology and discusses the limitations of the technique from a reactor design perspective, as well as identifies system-level research that could potentially help to address the technology’s constraints. The basic principles of torrefaction and the mechanisms that affect the quality of torrefied products, including key process variables and kinetics are also discussed. Other expounded aspects include reactor design concepts, process monitoring and control, and specific technological barriers thought to hamper the technology’s attempt to achieve commercial success.Validerad;2025;Nivå 2;2025-12-01 (u2);Full text license: CC BY</p
Global ocean indicators : Marking pathways at the science-policy nexus
No full textOcean knowledge is crucial for shaping policies that enable sustainable development, adaptation, and well-being at all levels, as everyone-either directly or indirectly-depends on the ocean, which today faces escalating threats from climate change, pollution, and biodiversity loss, pushing us beyond critical planetary boundaries. Ocean indicators are crucial for translating ocean science and data into practical metrics, guidance, and tools informing on the state and health of the ocean that can be directly applied by policymakers, practitioners, and the public. Despite their critical importance, ocean indicators trail behind those for continental areas, limiting effective monitoring and policy integration. Developing reliable, comparable, and regularly updated ocean indicators, backed by a unified international framework, is essential for delivering coherent, actionable insights that can guide global goals and protect the ocean's future. This paper establishes a scientific foundation for ocean indicators through international and multidisciplinary collaboration, presenting defined criteria and a set of pilot indicators for the ocean's physical, biogeochemical, biodiversity, and ecosystem aspects. The proposed framework offers a solid foundation for generating indicators that not only track the ocean state but also provide outputs for application in informing policy and decision-making
Physics-Informed Neural Network Framework for Wheel-Rail Contact Analysis: Toward an Intelligent Maintenance Workflow in Railway Systems
No full textRailway transportation traffic is rapidly growing, which demands a more effective and efficient generation analysis for more reliable predictive maintenance planning. This is achievable if prognostic indicators are known, e.g. stress, deformation and displacement fields. These parameter values unlock the underlying physics knowledge about fault modes and mechanisms to solve the wheel-rail deterioration. Commercial multi-physics software lack source code accessibility, flexibility and interoperability between computing platforms. On the other hand, physics-informed neural networks (PINNs), which belong to the second AI revolution and scientific ML (SciML) that combines physical and machine learning models, show promise in computational fluid dynamics and electrodynamics; however, their application to railwayremains largely unexplored. This study addresses these research gaps through a comprehensive open-source and reproducible PINN PhysicsNeMo framework for 3D wheel-rail contact analysis as proof of concept. Current railway maintenance often relies on reactive approaches; this PhysicsNeMo framework supports integration by providing predictive stress analyses. The aim is to explore the PhysicsNeMo simulations for railway, establishing a foundation for an interpretable, explainable and trustworthy AI. Results demonstrate detailed and intuitive 3D wheel visualisations of stress distributions and displacement fields, with insights into damage mechanisms for railway designers and maintainers, facilitating more efficient maintenance workflows.Full text license: CC BY 4.0Academics4Rai
Exposure to a mixture of endocrine disrupting chemicals and thyroid function tests in pregnant women in the SELMA study
No full textBackground: Based on experimental and human studies, endocrine disrupting chemicals (EDCs) can disrupt the thyroid hormone system. However, their association with thyroid function tests when considered as part of a chemical mixture is unknown. Methods: We used data of 1970 pregnant women from the Swedish Environmental Longitudinal Mother and Child, Asthma and Allergy (SELMA) study to investigate the cross-sectional association between exposure to 26 chemical compounds with maternal thyroid function tests in early pregnancy, using Weighted Quantile Sum (WQS) regression. Results: Higher exposure to EDCs mixtures was associated with a lower FT3 [WQS Estimate per an IQR increase (95 % CI): −0.09 (−0.16 to −0.01), mostly driven by PCBs] and a lower TT3 [WQS Estimate per an IQR increase (95 % CI): −0.05 (−0.09 to −0.01), mostly driven by PFOS]. In addition, higher exposure to a mixture of short lived urinary based compounds was associated with a lower TT4/TT3 ratio while higher exposure to a mixture of persistent serum based compounds was associated with a higher TT4/TT3 ratio. Conclusions: In this proof-of-principle analysis, we show that there could be an added benefit of analyzing thyroid hormone system disrupting EDCs using a mixture-based analysis approach. Our findings pave the way and provide hypotheses for future experimental and human studies to investigate the effects of EDCs as a mixture on the thyroid hormone system, revealing information on potential biological mechanisms explaining the associations from observational data