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Young People’s Projective Imagination Growing Up in Rural Nordic Countries
No full textIn research, policy, and the media, urban young people have been a symbol of the future. However, rarely have young people living in rural areas been seen as such; rather, rurality has been portrayed as old-fashioned, backward, and associated with the past (Farrugia & Ravn, 2022), particularly if they stayed and imagined building a life where they grew up (Østergaard et al., 2024; Vehkalahti et al., 2024). In this chapter, we unfold young people’s future imaginations and compare among those who grew up in different rural areas in four Nordic countries: Finland, Sweden, Norway, and Denmark. We reveal how they imagine their future and also how this imagination affects them, not only in terms of their future lives but also their everyday lives (Adams, Futures imperfect: A reflection on challenges. Sociology, 57(2): 279–287. https://doi.org/10.1177/00380385221113478, 2023; Adam & Groves, Future matters: Action, knowledge, ethics. Brill, 2007; Beckert & Suckert, The future as a social fact. The analysis of perceptions of the future in sociology. Poetics, 84. https://doi.org/10.1016/j.poetic.2020.101499, 2021).peerReviewe
Capturing the Breadth and Dynamics of the Temporal Processes with Frequency Transition Network Analysis : A Primer and Tutorial
No full textThis chapter presents Frequency-Based Transition Network Analysis (FTNA), a novel method to model the relational dynamics and the transitions between states or events based on the frequency of occurrence of transitions. Compared to TNA based on Markov models, FTNA is well-suited when the research focus is on describing, summarizing, or visually analyzing the observed data without the probabilistic assumptions and constraints. Compared to process mining, FTNA leverages statistical techniques such as pruning, bootstrapping and permutation to validate and compare models. Moreover, FTNA employs networks as a lens to represent and analyze transitions, which provides a rich family of metrics and analyses such as centrality measures, communities and patterns. In this chapter, we offer an introduction to the method and its main features, along with a step-by-step tutorial in R using a case study in group collaboration.peerReviewe
Unsupervised linear discrimination using skewness
No full textIt is well-known that, in Gaussian two-group separation, the optimally discriminating projection direction can be estimated without any knowledge on the group labels. In this work, we gather several such unsupervised estimators based on skewness and derive their limiting distributions. As one of our main results, we show that all affine equivariant estimators of the optimal direction have proportional asymptotic covariance matrices, making their comparison straightforward. Two of our four estimators are novel and two have been proposed already earlier. We use simulations to verify our results and to inspect the finite-sample behaviors of the estimators.peerReviewe
Defensive colouration is not a reliable indicator of fungal infection in aposematic poison frogs
No full textThe expression of visual signals such as colouration can be altered by parasitic or pathogenic infections through multiple pathways, including resource reallocation, impaired tissue structure, and reduced pigment acquisition. These effects may compromise the functions of colouration and overall fitness. Conversely, the link between pigments and immunological defences can aid differently coloured individuals in coping with infection. While the pigmentation-condition association has been widely studied in the context of sexual selection, far less is known about how pathogens affect defensive colouration, such as aposematic signals. Here, we investigated whether infection by the fungal pathogen Batrachochytrium dendrobatidis (Bd) is reflected in characteristics of the melanin- and/or carotenoid-based colouration of the aposematic poison frog Dendrobates tinctorius in the wild. Using ddPCR to identify the frogs’ infection status, and multispectral digital imaging to quantify their colouration traits, we show that neither type of colouration is a reliable indicator of Bd infection. Instead, body size influenced both infection outcomes and colouration, with sex-specific patterns suggesting potential ontogenetic or life-history trade-offs. Our findings highlight that the links between colour expression and condition are more context- and taxa-dependent than often assumed, and suggest that, in D. tinctorius, defensive signals may remain stable despite pathogen exposure.peerReviewe
Runtime Composition in Dynamic System of Systems : A Systematic Review of Challenges, Solutions, Tools, and Evaluation Methods
No full textContext
Modern Systems of Systems (SoSs) increasingly operate in dynamic environments (e.g., smart cities, autonomous vehicles) where runtime composition—the on-the-fly discovery, integration, and coordination of constituent systems (CSs)—is crucial for adaptability. Despite growing interest, the literature lacks a cohesive synthesis of runtime composition in dynamic SoSs.
Objective
This study synthesizes research on runtime composition in dynamic SoSs and identifies core challenges, solution strategies, supporting tools, and evaluation methods.
Methods
We conducted a Systematic Literature Review (SLR), screening 1,774 studies published between 2019 and 2024 and selecting 80 primary studies for thematic analysis (TA).
Results
Challenges fall into four categories: modeling and analysis, resilient operations, system orchestration, and heterogeneity of CSs. Solutions span seven areas: co-simulation and digital twins, semantic ontologies, integration frameworks, adaptive architectures, middleware, formal methods, and AI-driven resilience. Service-oriented frameworks for composition and integration dominate tooling, while simulation platforms support evaluation. Interoperability across tools, limited cross-toolchain workflows, and the absence of standardized benchmarks remain key gaps. Evaluation approaches include simulation-based, implementation-driven, and human-centered studies, which have been applied in domains such as smart cities, healthcare, defense, and industrial automation.
Conclusions
The synthesis reveals tensions, including autonomy versus coordination, the modeling-reality gap, and socio-technical integration. It calls for standardized evaluation metrics, scalable decentralized architectures, and cross-domain frameworks. The analysis aims to guide researchers and practitioners in developing and implementing dynamically composable SoSs.peerReviewe
Synthesis-Controlled Polymorphism in bis(Benzylammonium) Tetrathiocyanatocobaltate(II) : Distinct Crystal Packing (C2/c vs. P21) Dictates Band Gap Energy and Superior Antimicrobial Performance with computational investigation
No full textThe synthesis, crystal structure, and multifaceted properties of two distinct polymorphs of bis(benzylammonium) tetrathiocyanatocobaltate(II), namely Compound (I) and Compound (II), is described here. The polymorphs were controlled through the choice of thiocyanate source, with the centrosymmetric C2/c needle polymorph (I) synthesized using KSCN, and the denser, non-centrosymmetric P2₁ block form (II) produced from HSCN. The single crystal X-ray diffraction studies reveal that they possess different structures which can be associated with different packing arrangements enhanced by charge-assisted N-H...S hydrogen bonding between the [Co(NCS)₄]²⁻ anions and benzylammonium cations. The spectroscopic and thermal studies are in agreement with the structural features, with IR spectra pointing to primary S-coordination, and DTA-TG profiles showing different decomposition behaviors. The UV-Vis spectra resulted in calculated band gaps of 3.49 eV for polymorph (II) and 4.20 eV for polymorph (I)both as wide band gap semiconductors with favorable characteristics for optical and electronic applications. The biological studies showed that Compound (II) had greatly improved antimicrobial activities against E. coli, S. aureus, and C. albicans than Compound (I) and the reference drug. The experimental findings were confirmed by molecular docking and 100 ns molecular dynamics (MD) simulations. Compound (II) offered a significantly more stable protein-ligand complex with lower root mean squared deviation (1.6 Å), lower fluctuations of residues, and only a single dominating basin of free-energy in principal component analysis (PCA) suggesting increased rigidity and conformational stability. These findings were more strongly supported by the MM/GBSA calculations, revealing stronger binding free energy (ΔG_bind = –22.01 kcal mol⁻¹) and stabilizing residues (Asn43, Asp46, Ser5, and Asn44) that might explain the improved bioactivity.The integrated results from computational and experimental studies show that structural polymorphism influences biological potency and electronic performance with Compound (II) being identified as the more stable, active, and multifunctional polymorph which can potentially be used for antimicrobial and optical material applications.peerReviewe
Coupled Nonnegative CANDECOMP/PARAFAC Decomposition for Multi-block Tensor Analysis
No full textNonnegative tensor decomposition imposes nonnegative constraints on its latent factors, providing a part-based tensor representation that can extract meaningful and convincing information. This approach has been used widely across applications like signal processing, neuroscience, and other areas. For multi-block tensor group analysis, including multiple-subject or multiple-modal medical data, traditional single tensor decomposition fails to maintain feature comparability or explore the coupled information across tensors. This study introduces a novel coupled CANDECOMP/PARAFAC tensor decomposition method using the non-negativity constraints and the alternating proximal gradient strategy, termed CoNCPD-APG. The proposed algorithm enables the group analysis of two or more tensors that are fully- or partially-coupled, allowing for the simultaneous acquisition of shared, individual information, and core tensors. Experiment results of synthetic and real event-related potential data confirm the effectiveness of the proposed coupled tensor decomposition algorithm in discovering meaningful latent patterns and relationships from/among complex multi-block tensors.peerReviewe
Teacher observations of loneliness and ostracism among five-year-olds : Associations with social–emotional functioning, vocabulary, and language background
No full textBeing included in play and forming positive peer relationships are critical for children to meet their need to belong in early childhood education and care (ECEC). Loneliness and ostracism, then, threaten meeting this need. In this study, five-year-old children’s (N = 31,169) loneliness and ostracism were examined through ECEC teacher observations. About one-sixth of the children were evaluated as lonely and close to one-tenth as ostracized often to very often. Groups of children were then formed, based on different combinations in these threats to belonging: 1) rare threats (80.2%), 2) frequently lonely (rarely ostracized) (10.5%), 3) accumulated threats (7.7%), and 4) frequently ostracized (rarely lonely) (1.6%). Multinomial regression analysis with pairwise comparisons suggested differences between the groups in children’s social-emotional functioning, vocabulary, and language background but not in gender. It is vital to equip ECEC teachers with competencies to observe and address loneliness and ostracism, to build safe and inclusive peer communities for all children, and to develop children’s social-emotional skills. Implications for ensuring that every child can build peer relationships, access play, and learn to positively include their diverse peers are discussed.peerReviewe
Mapping Relational Dynamics with Transition Network Analysis : A Primer and Tutorial
No full textThis chapter presents Transition Network Analysis (TNA) that captures the full breadth of the relational dynamics of a temporal process. TNA models transitions between events as a weighted directed network. In doing so, TNA brings the wealth of network analysis to the modeled process which include graph, node and edge level metrics. TNA also enables the detection of recurring patterns such as dyads or triads, and communities and clusters. More importantly, TNA allows researchers to statistically validate the findings using bootstrapping, permutation, and case dropping techniques to verify if and when the research conclusions are correct. Furthermore, TNA allows researchers to include covariates that would explain why certain patterns emerge or examine the differences across subgroups. Such statistical rigor that brings validation and hypothesis testing at each step of the analysis offers a method for researchers to build, verify and advance existing theories and develop new ones on the basis of a robust scientific approach. This chapter offers a step-by-step tutorial using the tna R package, illustrating all the TNA features in a case study about group regulation.peerReviewe