Denmark Technical College

As climate change increasingly impacts ecosystems, economic activity, and human well-being, Europe is accelerating the development of coherent and effective climate action under the umbrella of the Paris Agreement and the EU Green Deal. This research evaluates the dynamics and effectiveness of existing mitigation and adaptation policies and governance frameworks at the European Union level and within eight European countries: Austria, the Czech Republic, Denmark, France, Germany, Italy, Romania, and the United Kingdom.This work combines an extensive review of primary EU and national policy documents in force in 2025, with insights from 42 semi-structured interviews with key informants, including experts from European institutions, national decision-makers, research organisations, and civil society representatives. The interviews provide critical insights into actual implementation dynamics, political priorities and challenges, and the institutional barriers that shape climate action beyond formal planning commitments.The analysis focuses on the core pillars of climate policy, examining objectives, timeframes, and sectoral coverage across key socio-economic sectors such as energy, transport, agriculture, health, infrastructure, and water management. A focal point is the tension between sustainable development, mitigation and adaptation, as mitigation policies are relatively mature and supported by robust EU monitoring, while adaptation remains heterogeneous and under-prioritised or under-financed. The findings reveal significant gaps in the operationalisation of climate strategies, short-termism and polarisation or weak institutional cooperation, despite the steady development of the formal strategic settings, the limited but useful involvement of civil society, the limited actual focus on societal issues and legislative gaps, among others.Our findings inform the development of the CROSSEU Decision Support System, ensuring the tool reflects real-world governance constraints and stakeholder needs. By identifying where synergies emerge and where institutional bottlenecks constrain progress, the research supports the translation of climate-socio-economic risks data into actionable, socially-informed decision pathways. Furthermore, the strong national specificities highlight the need for understanding the local context and the relevance of comparative research for fostering equitable and resilient climate strategies at the European scale.This research was funded by the "Cross-sectoral Framework for Socio-Economic Resilience to Climate Change and Extreme Events in Europe (CROSSEU)" project, under the European Union’s Horizon Europe Programme (Grant agreement No. 101081377).

There are not so many off-the-shelf ITS services and solutions which makes purchasing/implementing such services and solutions challenging for public procurers. Challenges public procurers face when purchasing ITS solutions range from uncertainty in costs and time, risks in failed commercialisation of innovation, to difficulties of reviewing or extending contracts. However, many of the challenges can be addressed by innovation procurement procedures such as Competitive Dialogues. City of Copenhagen has used various procurement procedures to implement innovative and sustainable transport solutions which has set an outstanding example to other cities and authorities of using public procurement as an instrument for implementation of ITS. Analyses of the cases of using various procurement procedures have been carried out in order to identify how the city overcome the barriers, and benefits and lessons learnt from the procedures. The analyses aim to provide comprehensive references to other public authorities.

Maintenance planning is critical for managing road infrastructure, particularly in densely populated urban areas. This paper presents a data-driven framework that integrates predictive analytics, spatial clustering, and optimization techniques to support more effective maintenance strategies. The proposed approach begins by evaluating individual pavement condition metrics that incorporate threshold-based maintenance triggers. Identified maintenance needs are then grouped into geographically coherent clusters using a density-based clustering algorithm. For each cluster, standardized maintenance probability scores and aggregated cost estimates are computed. A cluster-level optimization model is subsequently formulated to prioritize interventions within budget limits. Experimental results highlight the effectiveness of the proposed approach in enhancing resource allocation and minimizing risk in highpriority zones, enabling more proactive and spatially-informed infrastructure management.

The aim of this study was to compare in vivo and ex vivo Photon Counting CT (PCCT) of subchondral bone features in patients with knee osteoarthritis (KOA). Pre-surgery in vivo and post-surgery ex vivo PCCT of the tibial plateau from participants with severe KOA referred to arthroplasty surgery from January 2022 through September 2023 were compared. Linear regression and Bland-Altman plots were used to assess correlation and agreement between in vivo and ex vivo measures of bone volume fraction (BV/TV), trabecular thickness (Tb.Th.) and attenuation in healthy and sclerotic trabecular bone. Delineated areas of bone sclerosis were compared using the Dice coefficient and Hausdorff distance. 18 in vivo/ex vivo PCCT scans were included. Strong correlations were found for BV/TV, R2 = 0.82 and attenuation; healthy, R2 = 0.89, and sclerotic, R2 = 0.79, bone, while a moderate correlation was found for Tb.Th., R2 = 0.55. Bias for BV/TV and Tb.Th. was -4.1% and -0.598 mm, respectively, and -41.4 HU and -81.1 HU for healthy and sclerotic bone, respectively. A proportional bias was observed for Tb.Th. and BV/TV. There was excellent agreement between the segmentations of sclerotic areas (Dice coefficient = 0.91, Hausdorff distance = 0.11 mm). In patients with severe KOA, BV/TV and attenuation can be obtained with a high correlation and small bias between in vivo and ex vivo scans, while Tb.Th. showed moderate correlation and larger bias. Longitudinal studies using in vivo PCCT are feasible, but caution may be advised when measuring Tb.Th. The key OA feature of subchondral bone sclerosis is well translated from ex vivo to in vivo PCCT. Question Bone changes occur with osteoarthritis development; the role of these changes is unclear, and no method for visualising bone microstructure in vivo exists. Findings Photon-counting CT showed a strong correlation between in vivo and ex vivo subchondral density measures, while a moderate correlation was found for trabecular thickness. Clinical relevance Photon-counting CT is feasible for in vivo longitudinal evaluation of bone in patients with knee osteoarthritis, allowing studies into the earlier stages of the disease.

Basal insulin has been and remains a common and cost-effective intensification step from insufficient oral antidiabetic drug (OAD) treatment for people with type 2 diabetes (T2D), but individualized intensification alternatives are rapidly increasing. Recently, decision support tools to assist healthcare professionals based on machine learning (ML) algorithms are becoming more popular. By means of ML, the aim of this pilot study is to explore to what extent patient characteristics and continuous glucose monitoring (CGM) data enhance the ability to predict a successful basal insulin treatment outcome beyond what can be predicted based on hemoglobin A1C (HbA1c) alone at treatment initiation. Clinical data were acquired from four different trials with a total of 222 poorly regulated (HbA1c ≥ 7% ) patients with T2D on OAD initiating basal insulin treatment. HbA1c, patient characteristics, and consensus CGM metrics (based on three days) were available and systematically added as input to three classification models, respectively, based on logistic regression and Gaussian process (GP) classification with linear and both linear and nonlinear kernels. Classification models predicted a binarized HbA1c value after six months as either acceptable (HbA1c < 7%) or suboptimal (HbA1c≥7%) using a repeated stratified cross-validation setup. The consensus metrics based on only three days of CGM show a trend towards slightly improved performance when added on top of HbA1c. However, it appears difficult to accurately predict a binarized HbA1c outcome based on the considered patient information to a satisfactory level for clinical use. Future research should consider the outlined limitations associated with this study and suggested considerations for improvement. However, this pilot study can be considered an initial attempt towards leveraging the potential of ML and CGM data for personalised and cost-effective treatment decision-support for basal insulin initiation.

Today, power systems are faced with fast changes in the distribution grid level. The attention to distributed and renewable energy resources is growing, traditional controllers are being replaced by smart energy management systems in buildings, and the share of electric vehicles (EVs) in transportation is increasing. These changes are creating new challenges for the reliable operation of distribution grids such as voltage drop issues and overloading of transformers and lines. Demand response is known as one of the best solutions for solving grid issues. To make the best use of the loads flexibility, aggregation and coordination mechanisms are needed. In this paper, a new load aggregation method is proposed that is based on installing low voltage grid management units (LVGMUs) in different areas of the distribution grid for collecting the power consumption, flexibility capacity data, and interaction with the distribution system operator (DSO). Algorithms are proposed to define the interaction between DSO, LVGMUs, and customers. Heat pumps, PV/battery setups, and EVs are chosen as flexible loads. The method is evaluated by applying it to the IEEE-33Bus test system. Simulation results confirm the effectiveness of the proposed method in providing voltage regulation service.

This work investigates how the damages in cross-sectoral climate change risk hotspots can be assessed drawing on methodologies developed and applied to eight case studies conducted within the EU CROSSEU project. Hotspot analysis represents several key challenges in the assessment of the impacts of climate hazards and the focus here is on extreme climate events rather than on the impacts of gradual climate change. Hotspots are defined as areas where climate events are likely to generate high potential damages. The hotspot identification methodology provides a framework for identification of context specific vulnerabilities due to a combination of factors, including the magnitude of extreme climate events (physical aspects), the presence of critical infrastructure and vulnerable populations (socio-economic aspects), and the sector specific vulnerabilities as well as interconnectedness of different sectors (cross-sectoral aspects). The identification of hotspots is based on a combination of quantitative and qualitative data, including climate projections, socio-economic data, and stakeholder consultations.The hotspot methodological framework is applied to a range of case study sectors and geographical settings. The case studies cover heat waves in Czech Republic and United Kingdom; drought in regions of Germany, Czech Republic, Poland, and Romania; floods in Denmark, Germany, and Italy; and snow avalanches in the Alps and Carpathian Mountains. While three other case studies addressed climate change impacts and spillover effects in the Lower Danube region and across Europe- particularly on renewable energy infrastructure and agriculture.In terms of physical vulnerabilities, the case studies demonstrate that Prague and Southern Moravia in the Czech Republic, and London in the UK, are hotspots for heat-related mortality and morbidity, and specific social and structural vulnerabilities in these areas are related to high population densities, aging populations, and the urban heat island effect.&amp;#160; Several regions in Germany, Czech Republic, Poland, and Romania are identified as hotspots for drought. The economic vulnerability of these regions is primarily due to the reliance of agriculture on rainfed water sources. Coastal cities in Southern Denmark and Northern Germany are vulnerable to storm surges, impacting thousands of residents by disrupting daily life, socioeconomic activities, restricting movement and even necessitating temporary relocation. The mountainous areas of the Trentino Alto Adige region in Italy are hotspots for debris flows and flash floods, and are vulnerable due to their low-lying coastal areas, high population densities, and critical infrastructure. The Italian Alps and the F&amp;#259;g&amp;#259;ra&amp;#537; Mountains in the Romanian Carpathians are hotspots for snow avalanches with potential high economic losses for tourism. The Lower Danube region is a hotspot for both droughts and floods, posing significant risks to a unique biodiversity ecosystem, as well as to agriculture, energy infrastructure, and human settlements.This hotspot analysis in the CROSSEU project provides key comparative risk assessment measures, contributing to the establishment of effective adaptation strategies in the EU and also at regional levels.This research received funds from the project &amp;#8220;Cross-sectoral Framework for Socio-Economic Resilience to Climate Change and Extreme Events in Europe (CROSSEU)&amp;#8221; funded by the European Union Horizon Europe Programme, under Grant agreement n&amp;#176; 101081377.

Abstract Concrete with fiber reinforcement is stronger and more ductile than concrete without reinforcement. Significant efforts have been made to demonstrate the properties and enhancements of concrete after reinforcement with various types and shapes of fibers. However, the issue of optimization in the reinforcement process is still unanswered. There is no academic study in the literature now available that can pinpoint the ideal fiber type, quantity, and shape and, more crucially, the overall technical viability of the reinforcement. The parametric analysis in this study determines the ideal shape, size, and proportion of fibers. The input and output parameters were separated from the optimization design variables. Input parameters included assessment of samples of fresh and mechanical concrete properties and the influence of type, length, and percentage of fiber on concrete performance. The aim was to establish the most efficient relationship between fiber dose and dimension to optimize the combined responses of workability and splitting tensile, flexural, and compressive strength. The mechanical and fresh properties of concrete reinforced with four different fibers, PFRC‐1, PFRC‐2, SFRC‐1, and SFRC‐2, were tested. The analysis showed that SFRC‐2‐20 mm‐1%, with compressive, split tensile, flexural, and workability values of 44.7 MPa, 3.64 MPa, 5.3 MPa, and 6.5 cm respectively, was the most effective combination among the materials investigated. The optimization technique employed in this study offers new, important insights into how input and output parameters relate to one another.

Electro-mechanical oscillations between interconnected synchronous generators and oscillations in system frequency are an inherent part of the operation of large power systems. Very Low Frequency (VLF) oscillations are usually classified as oscillations in the 0.01-0.1 Hz range. With the move towards variable renewable energy sources and low-inertia power systems, VLF oscillations are being observed with increasing regularity in many small and island grids. If left undamped, these can present a threat to system stability. However, finding the root cause and source(s) of VLF oscillations is an extremely challenging task for network operators. Recent work has identified a need for improved tools for identifying and characterising VLF oscillations, in order to determine the combination of system conditions that can be used as predictors for VLF events. A suitable small signal model is also required in order to enable verification of the root cause of VLF events and study of mitigation measures. Accordingly, this paper presents a new approach for detrending and characterizing system frequency oscillations using an adapted Zhou algorithm. The paper also describes a method for applying this algorithm for the detection/location of oscillations, and for their detrending and characterization. Finally, an approach for relating detected oscillation events to power system operating conditions for diagnostic purposes is described. The effectiveness of the proposed approach is demonstrated using a single frequency power system model, and using system frequency oscillations recorded from the Irish power system.