Novia University of Applied Sciences

This introduction situates Displaced Families, Dispersed Memories within global and historical contexts of forced migration and family separation. It outlines key concepts, such as family memory, postmemory, embodied memories, transnational and multidirectional memory, and, drawing on the book’s chapters, discusses how mnemonic practices transmit trauma, foster resilience, and challenge hegemonic narratives. To summarize the book’s main contribution, the introduction emphasizes the family as a site of both vulnerability and strength, where silences and storytelling intersect with cultural and political frameworks. Finally, it presents the volume’s three thematic strands, silences and sensory memories, trauma and resilience, and counter-memories, illustrating how interdisciplinary approaches reveal the enduring impact of displacement on families across diverse temporal and geographical contexts.

School coaches are a relatively new professional group in Finland, about which little is known. In the study, we examine the work of school coaches in the light of four social pedagogical work orientations. Our main data were personal interviews with fifteen school coaches in Swedish-speaking educational institutions across the country. The data was analyzed using abductive content analysis.The results show that school coaches emphasize supporting the well-being of students at an individual level, which emphasizes an understanding work orientation. In a few cases also methods in accordance with an adaptive work orientation appear, such as cognitive-behavioral interventions.The study also highlights challenges related to school coaching, like unclear job descriptions and the drawing of boundaries to other school professionals. Reflections are made on the possibilities of school coaching to respond to the challenges of schools and call for a broader discussion about the development needs of schools.

ABSTRACTAnthropogenic noise is an increasing form of environmental change that alters natural soundscapes. Human activities, including road traffic, have led to a notable increase in ambient noise, which may impair how animals use their sensory systems to fulfill vital activities. Previous experiments suggest that noise affects the hunting behavior of nocturnal predators, but it is still scarcely documented how the exposure to anthropogenic noise at night affects the behavior of nocturnal animals throughout their growth. Here, we used captive‐reared Tawny Owls (Strix aluco), nocturnal raptors relying on hearing to locate prey at night, to test the effect of traffic noise on prey detection at two different ages. Our findings show that noise lowers the rate of prey detection and increases the time needed to locate the prey regardless of the age the owls were tested. Although prey detection improved in older owls (i.e., at the subadult stage), it was still impaired by noise. Moreover, we show that the head‐bobbing behavior (i.e., head movements performed to enhance the acoustic localization of a potential prey) was displayed more times by subadults, and it was enhanced by noise regardless of the owls' age. Our study provided new insights about the detrimental effects of noise pollution on the behavior of night‐active animals, showing that noise disrupts prey detection already from early life stages, which implies that both young and adult survival may be negatively impacted. Future studies should examine how these results may be relevant for individual fitness and population dynamics in the wild.

The common crane (Grus grus), like other soaring species of birds, uses a migration strategy to minimise the risk of wind drift over open sea, resulting in aggregation of birds along migratory corridors. With the planned large-scale development of off

ABSTRACT Carbon Capture Utilisation and Storage (CC(U)S) technology faces both technical and non-technical challenges that significantly impact its large-scale deployment. This study focuses on socio-political challenges, particularly in the context of recent developments in Europe, such as the Russian invasion of Ukraine. This research is based on an extensive literature review, a stakeholders’ workshop, and an expert survey, leading to a thematic analysis that identifies major socio-political factors influencing CC(U)S deployment in Europe. Our analysis suggests a conceptual framework that addresses these challenges and identifies two core conceptual domains – socio-political context and background environment – as critical factors for the future development of CC(U)S. The findings indicate that political development and social acceptability are pivotal drivers of CC(U)S deployment. Political development is increasingly linking energy security to national security, prompting a shift towards leveraging domestic resources. Social acceptability beyond social acceptance has the potential to become the focus of future discourses. Moreover, the study highlights the importance of trust- a factor that is prevalently discussed in the literature- in policymakers and industry in post-trust societies affected by major events such as the COVID-19 pandemic and the Russian invasion. Finally, we propose a preliminary evaluation model to assess the socio-political readiness of European countries for CC(U)S deployment. The paper concludes by discussing the political implications and recommendations of this study.

The real-time compliance of a family of movinghorizon simultaneous input-and-state estimation (MH-SISE) problems is assessed. Given the strict real-time requirements of practical applications, the use of numerical optimization techniques for estimation has been limited for systems with fast dynamics. Thus, we explore the use of solver code generation to improve solution times. By generating high-speed solver code tailored to this specific class of moving-horizon problems, substantial improvements in solution times compared to conventional solvers are observed. The proposed methods are evaluated through simulated benchmark tests on lumped-element models of rotating shafts to assess the real-time compliance with respect to measurement sampling rate, model size, and estimation horizon length.

Urban expansion has led to ever increasing noise and light pollution, which impairs the audio-visual perception of wild animals and drives changes in key activities and behaviours. Nocturnal predators may be especially affected in detecting prey, with potentially dramatic consequences for their fitness. However, the combined effects of noise and light pollution on predator performance are still mostly unstudied. We experimentally exposed tawny owls (Strix aluco), nocturnal acoustic raptors, to traffic noise and artificial light. We provided both visual and acoustic prey cues to assess potential non-additive effects among multiple sensory stressors on the ability to detect prey. We found that, in control conditions, owls responded equally to both acoustic and visual prey cues. In contrast, noise and light individually decreased owls' ability to locate acoustic, but not visual, prey cues. When owls were exposed to noise and light combined also visual detection worsened, but not beyond the additive expectation. Conversely, the presence of light seemingly buffered the negative impact of noise on acoustic detection, suggesting an antagonistic interaction. Our findings show that both anthropogenic noise and light affect the hunting behaviour of a nocturnal avian predator, but with a stronger effect on acoustic than visual detection, suggesting that the magnitude of their disruptive impact might depend on the type of prey cue. This implies that sensory pollution might lead to increased reliance on sight-oriented hunting strategies. Importantly, our study shows that the co-occurrence of noise and light can have complex and unexpected impacts on behaviour, underscoring the importance of examining sensory pollution in a multimodal context.

Abstract Maritime simulators are a central tool for the education and training of navigators, allowing them to develop and improve their skills in a controlled and replicable environment. Despite efforts to enhance the simulation training performance assessment, there are few reliable approaches to take advantage of readily available data from simulator logs to inform performance evaluation and training adjustments. Harnessing this data more effectively could enhance the way we assess simulation training and provide a more transparent understanding of learning progress and areas for improvement. To develop a learning analytics dashboard (LAD) for performance assessment in maritime simulation training, we analyse simulator log data with 27 potential input features to predict student performance as the target feature. After filtering down to 13 potential input features using data visualization and expert validation, a cloud artificial intelligence platform is used for predicting student performance. A total of 58 algorithms were trained, of which the eXtreme Gradient Boosted Trees Classifier algorithm is adopted for prediction. The results demonstrate the potential for utilizing machine learning algorithms in analysing maritime navigation training data paving the way for a new direction in simulation training assessment.