Tallinn University of Technology

Hydrogen is a key pillar in the transition to carbon neutrality, with green hydrogen production through electrolysis playing a vital role in decarbonizing industry, transport, and heating. Since electrolysis generates significant waste heat, capturing and reusing this energy provides an underused renewable source for district heating networks (DHNs), improving overall energy efficiency and economic feasibility. This study reviews projected hydrogen demand and electrolysis capacities for 2030 and up to 2050 across IEA DHC member countries to assess country-specific waste heat potentials. A stakeholder survey (n = 71) involving district heating (DH) operators, electrolyser operators, researchers, and policymakers identifies key barriers and opportunities for integration into DHNs, providing a basis for targeted policy recommendations. The analysis revealed that waste heat from alkaline electrolysis (AEL) and proton exchange membrane electrolysis (PEM-EL) at low to moderate temperatures can serve as a valuable heat source for low-temperature DHNs. Integration with conventional DHNs, which operate at higher supply temperatures, would require temperature upgrading, such as the use of supplementary heat pumps. Main implementation barriers include the need for additional infrastructure and high investment costs, proximity constraints between electrolysers and DHNs, limited awareness of the concept, and regulatory hurdles. Future strategic coordination between the hydrogen production and DH sectors, supported by targeted infrastructure planning, financial mechanisms, and regulatory frameworks, is essential to realize this potential and advance the decarbonization of the heating sector. • Country-specific projections of electrolysis capacity developments. • Quantification of waste heat potential by country. • Stakeholder survey identifying barriers and integration opportunities. • Evidence-based policy guidance to enable district heating integration.

Developing a circular economy (CE) for construction and demolition waste (CDW) presents a promising pathway to decarbonize the construction industry by reducing reliance on carbon-intensive primary material production. However, most existing studies rely on life cycle assessment (LCA) approaches and treat CDW flows as isolated processes, overlooking the broader economy-wide emission consequences and distributional effects arising from complex inter-sectoral and inter-regional material and energy flows. This limitation risks underestimating the full carbon mitigation potential of CDW circularity, thereby hindering progress toward the Net-Zero targets pledged by most global economies. This study introduces a novel analytical framework integrating LCA with environmentally extended input-output (EEIO) analysis to quantify the environmental impacts of CDW circularity, explicitly accounting for sectoral and regional trading linkages. Applying the framework to the United Kingdom (UK), this study estimates carbon emissions and potential savings under multiple CDW circularity scenarios for the year 2018. The results indicate that material use for domestic final demand and exports generates approximately 159 and 170 million tonnes of CO₂, respectively, while CDW circularity achieves only modest emission savings of 2–3 million tonnes (<1%). Reuse scenarios deliver greater reductions than recycling, with the most significant benefits observed in secondary and tertiary industries, particularly from the circular use of metallic and wood materials. Despite the UK's high CDW recovery rate, inefficient treatment pathways and weak alignment between recovered-material supply and industrial demand constrain the net-zero potential of CDW circularity. Enhancing recovery efficiency, advancing cleaner technologies, and improving material productivity are therefore critical for supporting the UK's net-zero transition. This study is novel in pioneering one of the first LCA–EEIO analytical frameworks for evaluating the economy-wide environmental impacts of CDW circularity, providing a scalable methodological foundation and system-level evidence for global economies seeking to accelerate their pathways toward Net-Zero targets. • Developed a framework to assess carbon impacts of CDW circularity and trading. • Integrated environmentally extended input–output (EEIO) analysis and LCA. • The UK was used as a case to offer policy implications and validate the framework. • Carbon savings from CDW circularity and trading are rather limited in UK (<1%). • Greater carbon savings from reuse than recycling, and from metals than others.

SummarySecreted mucins are the major component of the mucus layer that protects intestinal epithelial surfaces by blocking excessive interactions with the microbiota. Mucins are complex glycoproteins decorated with over 100 differentO-glycans. Some bacteria can utilize mucins and excessive degradation has been associated with disruption of the mucus barrier and inflammation. Despite the importance of mucins, a detailed enzymatic pathway by which gut bacteria degrade colonic mucinO-glycans and the impact of this process on gut colonization are unknown. Here, we identified >100 genes that are expressed by the symbiontBacteroides thetaiotaomicronduring growth on differentO-glycan substrates, revealing effects of glycan structure on gene expression. The characterization of 33 glycoside hydrolase enzymes revealed the pathway for colonicO-glycan degradation by this bacterium.In vivocompetition experiments show that multiple exo-acting enzymes targeting mucin capping structures are central to gut colonization and may provide targets to inhibit bacterial mucin degradation.

T his special issue aims to unpack what competencies public managers, policy makers, and public leaders need to manage and cope with the digital transition and what this implies for teaching digital governance. We situate digital governance into a wider context of socio-technical change, by using a multilevel perspective based on transition scholarship and the recent debate on directionality. This allows us to capture the public sector capabilities needed for governing the different spheres of digital transition, as well as the individual skills and knowledge required from public officials. We situate the six studies in this special issue within our multilevel perspective. The contributions provide concrete examples of skills and knowledge needed and what it implies for public management programs. Furthermore, based on our multilevel framework we identify avenues for future research into organizational and strategic capabilities for the digital transition and the operational, reflective, and political skills needed to foster these capabilities.

Financial fraud poses a growing threat to the stability and public trust of United States financial institutions, yet existing detection systems frequently fail to balance predictive accuracy with the transparency and accountability that regulatory frameworks demand. This study evaluates how standardized algorithmic governance frameworks and explainable artificial intelligence architectures can substantiate public trust in automated fraud detection systems operating within the U.S. financial infrastructure. A mixed-methods research design was employed, which combines systematic literature review, comparative policy analysis, and empirical data synthesis from real-time monitoring environments to assess how formal verification workflows, drift monitoring protocols, and third-party audit mechanisms translate into measurable institutional accountability. The findings demonstrate that hybrid ensemble models augmented with post-hoc interpretability tools such as SHAP and LIME achieve F1-scores of 92.2% and AUC-ROC values of 0.97, as well as maintaining high regulatory alignment, which outperform opaque deep learning architectures across both predictive and compliance dimensions. Institutions deploying layered governance architectures incorporating federated learning, cryptographic audit trails, and continuous drift monitoring record compliance scores of 94%, compared to 41% for systems with no formal governance structure. Furthermore, the integration of contestable AI mechanisms and stakeholder inclusion protocols significantly reduces perceived institutional bias and strengthens procedural fairness. This study concludes that the convergence of explainable AI, proactive bias mitigation, and rigorous governance architecture is essential for reconciling fraud detection efficacy with the democratic accountability that modern financial oversight requires. Keywords: Financial Fraud Detection, Explainable Artificial Intelligence, Algorithmic Governance, Public Trust, Regulatory Compliance, Institutional Accountability.

The aim of this study was to develop crop-specific thresholds for common insect pests in cereals. Threshold levels were updated during a three-year (2022–2024) test period conducted in spring barley, spring wheat, and oats. The main insect pests of cereals were aphids (Aphididae) and cereal leaf beetles (Oulema melanopus, O. gallaeciana). In areas with high and prolonged aphid activity, control measures should be initiated from the stem elongation stage, as this is the optimal time to maintain aphid populations below economically damaging levels. An aphicide application should be considered when aphid colonies are detected, as they may carry Barley Yellow Dwarf Virus (BYDV) and facilitate its spread within the crop. When aphid activity occurs later in the season, treatment may be required from the beginning of heading, with the final assessment conducted before the early milk stage (growth stage 71), after which aphicide application is generally no longer economically justified. Based on observations of cereal leaf beetle larvae and adults across several cereal crops, insecticide treatment is recommended when the threshold of one larva per shoot or 20% leaf damage is exceeded. Integrated control measures are recommended for the management of common cereal insect pests.

Wind waves are inherently irregular and random, making the goal of finding a fully deterministic description practically impossible. However, knowing their probabilistic properties is crucial for engineering applications and for understanding ocean dynamics. To deepen this understanding and build more efficient wind-wave models, machine-learning approaches are likely to become increasingly valuable. Recent progress in physics-informed machine learning (PIML) has transformed fluid mechanics by combining data-driven approaches with physical fundamental equations, enabling more robust and generalizable models.In our study, we apply PIML techniques to identify probabilistic characteristics of wind waves. Our research is based on learning probability distributions directly from data, which allows us to avoid restrictive assumptions or classical approximations.We utilize field measurements collected in Skulte, Latvia, during August–September 2022. The dataset includes pressure time series and 3D velocity profiles, providing a detailed description of wave dynamics. Building upon existing PIML architectures, we developed a framework capable of inferring an accurate and efficient probabilistic model of wind waves. Preliminary results show promising agreement with theoretical expectations and previous studies.The dataset was provided by Kevin Parnell and colleagues from Tallinn University of Technology (TalTech), together with the Latvian Institute of Aquatic Ecology. Our findings highlight the potential of PIML for improving probabilistic wave modelling and set the foundation for future applications in coastal engineering and environmental monitoring.

Sedimentary phosphorites are the primary sources of nitrogen-phosphorus-potassium fertilisers, and they have recently been highlighted as a potential economic source of rare earth elements (REE). The growing need for clean technologies strongly influences the demand for REE, and in Europe, most deposits have not been investigated in detail since the 1970-1980s.Lower-Ordovician shelly phosphorites in Estonia are among Europe's most extensive phosphate rock reserves, with a tonnage of approximately three billion tons. The ore consists of sandstone rich in phosphatic brachiopod fragments deposited in a shallow marine peritidal environment of the Baltic Paleobasin. Mineralisation is carried out carbonate fluorapatite (CFA), an apatite with a highly diverse chemical composition [Ca10-a-bNaaMgb(PO4)6-x(CO3)x-y-z(CO3⋅F)x-y-z(SO4)zF2]. The shells themselves are complex objects, with apatite originating from the crystallisation of organic tissues and the precipitation of secondary phosphate during sediment burial. The partitioning and uptake of the individual REEs in them depend on many factors, including input from marine sources, the oxygenation state of the sedimentary column, and the precursors carriers phases of REEs that may have different affinities for each rare earth.In the REMHub project, investigations were conducted on three deposits: Toolse, Aseri, and Ülgase; representing a dataset of 630 ablations up to date. The LA-ICP-MS imaging technique developed by Drost (2018), addressed elemental distribution as raster maps, allowing identification and discrimination by integrating semi-quantitative data through elements' stepwise distribution. Diagenetic stages and compositions were evaluated using the following pathfinders as pooling channels. Sr, U, and Ce. On average, apatites present homogeneous REE patterns, MREE-enriched up to 15-folds compared to PAAS, with Y-Ce anomalies indicative of early-digenetic overprinting. However, the degree of overprint varied. In Ülgase, authigenic concretions and shells presented depleted REE signals, close to coastal signature. However, concretions showed a lower enrichment (∑REE 400-800ppm) compared to shells (REE 1500-3000 ppm). In Toolse, shells presented intermediate recrystallised textures, with Sr-U-depleted stages allowing the tracing of pristine signals, and U-rich stages presenting marked Gd-U and La anomalies. The average REE grade is 1966ppm. In Aseri, U-sorting reveals a second, alteration-driven enrichment in which the fragment edges present a ΣREE up to 12 754ppm (120 folds).Overall, investigations demonstrated a progressive evolution of REE signals during early diagenesis, highly influenced by redox cycles in shallow sediments, authigenic recrystallisation, organic matter decomposition within the shells, and possibly late distal alteration fluids.

Abstract Math walks are informal, place‑based activities in which learners go on walks and notice and wonder about how mathematics appears in the world, including at community centers, museums, and after‑school clubs. Engaging families and learners in math walks can be beneficial, as math walks connect mathematics to the real world and familiar places. Our approach draws on theories of place‑based learning, interest development, and mathematical problem‑posing and is a partnership of a local university with a non‑profit entity. Research and evaluation results are presented, highlighting the most recent development of the MathFinder mobile app.