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Exploring Alternatives and Unfolding Possibilities : A Futures Perspective on Carbon Capture and Storage in the Nordic Region
No full textCarbon Capture and Storage (CCS) is increasingly recognised as a bridging technology for achieving a low-carbon future. However, its deployment faces significant uncertainties across technical, social, political, and economic dimensions. This study explores the possible futures of CCS in the Nordic region by addressing two key questions: What are the main drivers and uncertainties in the deployment of CCS-related technologies? And what generic scenarios can be envisioned for CCS by 2050?
Based on a literature review and expert survey, and using structural analysis, this study identifies interdependencies among 15 key factors. The analysis highlights three primary driving forces: social acceptability, political development, and funding and business models, alongside technological innovations, which is a presumed driver, each encompassing distinct uncertainties.
Using Dator’s Four Generic Futures framework, this paper develops scenario archetypes that outline four distinct pathways: Seamless deployment, a sustained growth path prioritising CCS for emission mitigation; Obstacle-ridden implementation, a disciplined path with constrained deployment due to systemic barriers; Stagnation, a collapse trajectory for CCS favouring zero-emission alternatives; and Negative emissions boom, a transformative scenario in which technologies such as BECCS and DACCS gains prominence.
The study concludes with policy implications, reflecting the uncertainties and complexities surrounding CCS deployment in the Nordic context.peerReviewe
Trajectories of Belonging Among Nordic Rural Youth : Strengthening, Weakening, or Conflicting Belonging over Time
No full textThis chapter investigates what constitutes Nordic young people’s sense of belonging—or not belonging—in their rural communities. Belonging is approached as a concept referring to a sense of connection, membership, and security, here infused with individual and collective histories and shaped by everyday practices. Despite the recent interest in the concept of belonging in youth studies (cf. Harris et al., 2021), longitudinal research on rural youth belonging is rare, and we know little about what shapes belonging over time. Drawing on extensive and comparable qualitative longitudinal data from Norway, Finland, Sweden, and Denmark (n = 196), the chapter investigates what constitutes young people’s sense of belonging in rural regions, along with how belonging changes over time. Drawing on both the ongoing theoretical discussion and Nordic data, the chapter distinguishes between three central dimensions of youth belonging—the social, spatial, and cultural—while identifying three common trajectories of belonging: (1) In strengthening belonging, all three dimensions of belonging are balanced in supporting transition to adulthood in rural areas with ease: strong connectedness to local communities, place attachment, but also appreciation of values, traditions, and lifestyles characteristic of their region. (2) In weakening belonging, different dimensions of unbelonging work together to weaken belonging over time. This may be resolved by moving, but this is not always possible. (3) In conflicting belonging, some dimensions of belonging are in conflict with each other. Young people may express a strong belonging to nature but experience social unbelonging, which results in trajectories where easy solutions are not available. Longitudinal data allow for a reflection on the dynamics between different dimensions of belonging: Earlier bonds of belonging may loosen, while others are reinforced over time in different phases of life. The chapter contributes to the ongoing discussion of youth belonging by showing that the way different dimensions of belonging intertwine and change have consequences for the life trajectories of rural young people. Belonging is often partial, involves negotiation and struggle, and is a result of the continuous recreation and repetition of performative practices.peerReviewe
Artificial Intelligence : Using Machine Learning to Classify Students and Predict Low Achievers
No full textThis chapter addresses the classification of at-risk students in educational settings using machine learning approaches in R. Transitioning from regression-based predictions of students’ grades covered by the previous chapter, the focus here shifts to identifying broader categories of academic performance, such as low achievers or potential dropouts. Early identification of such students enables timely interventions, one of the main goals of learning analytics. The process is first illustrated through a Random Forest classifier, using engagement indicators to classify students into high and low achievers. The chapter demonstrates the complete modeling workflow, including data preparation, model training, and evaluation using performance metrics. Additionally, the tidymodels framework is explored as a more modern alternative that enables easy comparison with other AI / machine learning algorithms like Naive Bayes or Support Vector Machine.peerReviewe
Tuning cation ordering and Mn3+ content in non-stoichiometric LiNi0.5-Mn1.5+O4- (LNMO) for enhanced cathode stability in lithium-ion batteries
No full textRapidly increasing demand for high-energy, long-cycle-life lithium-ion batteries (LIBs), particularly in electric vehicles and grid-scale applications, has highlighted the need for advanced cathode materials. High-voltage LiNi0.5Mn1.5O4 (LNMO) has attracted considerable attention owing to its elevated working potential, reduced reliance on nickel, and cobalt-free composition. In this work, a scalable co-precipitation method is employed to synthesize non-stoichiometry LNMO cathodes with varying particle size, enabling precise control over particle morphology, cation ordering and Mn3+ content. Comprehensive structural and electrochemical evaluations reveal that reducing the Ni content in LNMO elevates the Mn3+ concentration and promotes the degree if cation disorder, which facilitates a single-phase, solid-solution reaction mechanism during lithiation and de-lithiation. In such a mechanism, Li+ are inserted and extracted uniformly throughout the material without the formation of distinct phase boundaries, thereby significantly reducing kinetic barriers and polarization. Furthermore, although Mn3+ typically induces local Jahn-Teller distortions, in a highly disordered lattice these distortions are more uniformly distributed, which minimizes local stress accumulation and enhances structural stability during cycling. This uniform distribution not only supports rapid Li+ diffusion through continuous and well-connected pathways but also improves electronic conductivity by optimizing the local electronic structure. Consequently, LNMO with the highest cation disorder and Mn3+ content, exhibits superior electrochemical performance, delivering 119.6 mAh·g−1 at 2C, retaining 70.3 % of its capacity after 1000 cycles and demonstrating the best kinetics among the samples.peerReviewe
Cost of Artificial Intelligence : A Survey in Finnish Software Companies
No full textArtificial intelligence (AI), particularly deep learning (DL) and large language models (LLMs), has rapidly gained prominence across industries. Training and serving deep neural networks (DNNs)—the core of LLMs—requires substantial computational resources, often provided by costly specialized hardware such as Graphics Processing Units. Cloud computing enables flexible access to such hardware but raises cost concerns. This study reports the significance of AI-related costs for Finnish software companies, based on a thematic subset of the 2025 Finnish Software Industry Survey. Of the 411 respondents, 64 reported developing or fine-tuning AI models, with 61% considering AI costs a significant or very significant concern. Respondents estimated AI-related expenses to rise from under 10% of cloud or hardware costs in 2025 to 10–25% in 2026 and 25–50% by 2028. These findings indicate that AI is expected to become a major driver of infrastructure costs, highlighting the importance of cost-efficient adoption strategies.peerReviewe
Change and transmission of students' and teachers’ lesson-specific emotions
No full textBackground
Although it is known that positive emotions in school benefit both students and teachers, students' emotions and teachers’ emotions are rarely examined together. Furthermore, emotions are rarely examined as lesson-specific, that is, as state-like that vary from one lesson to another.
Aims
Building on the emotion transmission literature, the aim of the present study was to examine the change in Finnish primary school students' and teachers' emotions during a school day. We furthermore aimed to investigate, how students' and teachers’ positive and negative emotions transmit from teachers to students and from students to teachers.
Sample
The participants were 20 primary school teachers (85 % females; Mage = 45 years) and their 258 students (49.2 % females; Mage = 10 years).
Methods
The participants reported their lesson-specific emotions 1–4 times using a mobile-based instrument during one school day. The data were analyzed using cross-classified multilevel modeling.
Results
The results showed that teachers' negative emotions decreased during the school day. Furthermore, teachers' higher negative emotions predicted lower positive emotions in students, and students’ higher positive emotions predicted lower negative emotions in teachers.
Conclusions
These results provide novel information about the dynamic and interrelated nature of emotions in real-time classroom settings and generate new openings for interventions of emotional regulation for both teachers and students.peerReviewe
Behavior-related changes in canine heart rate and heart rate variability during short-term measurement
No full textCardiac function is influenced by both physiological and emotional factors, with heart rate variability (HRV) serving as a key indicator of health and wellbeing. Despite the growing interest in utilizing short-term cardiac measures in canine science, the behavioral aspects of HRV in domestic dogs (Canis familiaris) remain largely unexplored. This study aimed to provide reference values for short-term HRV to aid future research on canine behavior in medium-sized, mesocephalic and dolichocephalic dogs, to examine differences in HRV during typical canine behaviors, and to develop practical tools for the analysis of canine cardiac function. We assessed heart rate, HRV, and physical activity of 29 dogs across five behavioral states (Resting, Playing, Panting, Spontaneous sniffing and Food searching) and investigated how these behaviors influenced time-domain and frequency-domain HRV parameters. The impact of physical activity, sex, neutered status, age, height, and weight on these parameters within the specific sample was also assessed. Both time-domain and frequency domain parameters were affected by the behaviors. Precisely, HRV generally decreased with behavior-related physical activity (root mean square of successive differences, RMSSD; Resting vs. Playing, p < 0.001; and Resting vs. Searching for food, p < 0.001). However, RMSSD was significantly lower during Searching for food compared to Spontaneous sniffing (p = 0.012), despite similar activity levels, indicating higher emotional arousal when searching for food. Overall, the high-frequency component (HF power) and RMSSD differentiated well between the distinct canine behaviors. Also, physical activity (measured as 3D acceleration) was the most influential background variable in this highly specific sample, correlating with HRV parameters and depending on the behavior.peerReviewe
Twice upon a home : Energy use, emissions and inequality across primary and second homes
No full textResidential energy consumption plays an essential role in mitigating climate change. An energy transition has the potential to alleviate socioeconomic disparities, although it can also lead to an unfair distribution of costs and benefits. The residential energy transition is already progressing in many countries, yet there remains a research gap regarding the participation of diverse population groups and the contribution of second homes to overall household energy consumption and emissions. We applied a novel approach, integrating multiple data sources, to calculate residential energy consumption and emissions for a sample of Finnish households, including those arising from second homes. Regression analysis was then employed to examine the influence of household and building-specific factors on energy consumption and emissions. Regression models initially indicated a positive association between income and per-capita energy use and emissions. However, after accounting for building characteristics, income was negatively associated with per-capita energy consumption. These findings suggest that middle- and high-income households tend to occupy more energy-efficient dwellings than low-income households. Furthermore, while rural households exhibited higher per-capita energy consumption than urban households, differences in emissions were less pronounced. Second homes significantly increased energy use and emissions. The results indicate that certain population groups and areas may be excluded from the benefits of the residential energy transition, warranting further research into the specific circumstances of diverse groups. Future policies aimed at promoting residential energy transition should prioritize support for low-income households and incorporate measures addressing energy consumption and emissions from second homes.peerReviewe
Mirror energy differences between 43Ti and 43Sc : a direct insight into the nuclear wave functions
No full textThe structure of the 43 22Ti21 nucleus has been explored employing correlations between delayed and prompt γ rays, leading to a substantial extension of its positive-parity yrast band up to 25/2+. New states were were identified using the isomer-tagging technique together with γ-γ coincidences and were compared to the analogue states in the mirror nucleus 43 21Sc22. The Mirror Energy Differences (MED) between analogue states have been extracted and compared to the predictions from the Large-Scale Shell-Model calculations in two main shells. For the positive-parity states which involve particle-hole excitations from the sd to the f p shell, a striking correlation between the measured MED and the type of nucleons excited across the shell gap is observed. This is reinforced by the systematic behavior of non-natural parity MED for nuclei across the entire f7/2 shell, which puts in evidence that experimental MED serve as direct, sensitive probes of the underlying microscopic structure of nuclear wave function.peerReviewe
Linear formulations and a hybrid large neighborhood search algorithm for the tool indexing problem
No full textIn this article, one of the most commonly encountered problems in manufacturing systems, known as the Tool Indexing Problem (TIP), is considered. TIP involves allocating cutting tools to different slots in a tool magazine of a Computer Numerically Controlled (CNC) machine to reduce the processing time of jobs on the machine. In this article, three mixed-integer linear programming formulations of single-objective TIP without tool duplication and lifespan are presented. A comparative study of these three linear formulations of TIP is also included in this article. During the study, it was found that the exact solver CPLEX with these linear formulations struggles to find optimal solutions in a reasonable time for larger instances. Therefore, a Hybrid Large Neighborhood Search with Local Search (HLNS-LS) algorithm, which is a metaheuristic approach, is proposed for solving TIP. The LNS phase iteratively destroys and repairs solutions to explore different regions of the solution space, while the LS phase intensifies the search by applying multiple neighborhood operators, such as swap, insert, shift, and customized 2-Opt and 3-Opt, to refine solutions further. This hybrid approach balances diversification and intensification, aiming to find high-quality solutions that minimize the total turret rotation cost associated with tool indexing. The performance of the proposed HLNS-LS algorithm is evaluated against an improved Harmony Search algorithm, a Weighted Superposition Attraction-based algorithm, and a Constraint Programming model across 85 small-, medium-, and large-sized benchmark instances from the existing TIP literature.peerReviewe