Runtime Composition in Dynamic System of Systems : A Systematic Review of Challenges, Solutions, Tools, and Evaluation Methods

Context 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

Coupled Nonnegative CANDECOMP/PARAFAC Decomposition for Multi-block Tensor Analysis

Nonnegative 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

Trade-offs between fairness and performance in educational AI : Analyzing post-processing bias mitigation on the OULAD

Context: AI-driven educational tools often face a trade-off between fairness and performance, particularly when addressing biases across sensitive demographic attributes. While fairness metrics have been developed to monitor and mitigate bias, optimizing all of these metrics simultaneously is mathematically infeasible, and adjustments to fairness often result in a decrease in overall system performance. Objective: This study investigates the trade-off between predictive performance and fairness in educational AI systems, focusing on gender and disability as sensitive attributes. We evaluate whether post-processing fairness interventions can mitigate group-level disparities while preserving model usability. Method: Using the Open University Learning Analytics Dataset, we trained four machine learning models to predict student outcomes. We applied the equalized odds post-processing technique to mitigate bias and assessed model performance with accuracy, F1-score, and AUC, alongside fairness metrics including statistical parity difference (SPD) and equal opportunity difference (EOD). Statistical significance of changes was tested using the Wilcoxon signed-rank test. Results: All models achieved strong baseline predictive performance, with RF performing best overall. However, systematic disparities were evident, particularly for students with disabilities, showing that high accuracy does not necessarily ensure equitable outcomes. Post-processing reduced group-level disparities substantially, with SPD and EOD values approaching zero, though accuracy and F1-scores decreased slightly but significantly. RF and ANN were more resilient to fairness adjustments. Conclusion: This study highlights the importance of fairness-aware machine learning, such as post-processing interventions, and suggests that appropriate mitigation methods should be used to ensure benefits are distributed equitably across diverse learners, without favoring any particular fairness metric.peerReviewe

Free-living physical activity levels in children with cerebral palsy

Background Cerebral Palsy (CP) is a common motor disorder in children, leading to reduced physical activity (PA) and increased health risks. To complement traditional PA methods (e.g. accelerometers, self-reports), electromyography (EMG) provides physiologically relevant information on muscle activity during free-living. This study used EMG for assessing daily muscle activity in individuals with CP and their typically developing (TD) peers. Methods Shorts with embedded EMG electrodes and hip-worn tri-axial accelerometer recorded daily PA in 8 children with spastic CP (mean age 14y 7mo, Gross Motor Function Classification System (GMFCS) I (n = 5), III (n = 3)) and 6 TD children (mean age 15y 4mo) during free-living. Daily EMG activity levels are reported as a percentage of mean EMG amplitude during the 6MWT. Inactivity time and light, moderate and vigorous PA are reported relative to recording time using established cut-off values for accelerometry, and EMG amplitude categorized based on a two-minute average from the middle of 6MWT. Findings Free-living EMG inactivity (CP: 58.4 %, TD: 50.4 %) and activity levels did not differ statistically between CP and TD groups. Accelerometry showed a greater inactivity time than EMG in CP (p = 0.021) and TD (p = 0.010) groups. In CP, few statistically significant differences were observed between legs, muscles, and GMFCS levels. Interpretation Free-living EMG monitoring did not reveal excessive muscle activity in individuals with CP during daily activities compared to TD peers. EMG detects light PA that accelerometry may underestimate, offering a more detailed view of daily muscle use.peerReviewe

Lost or Found in Transitions? : Mobile Media Identities and Life Transitions in Later Life

This chapter explores the intersection of mobile media use and identity formation during later life. While early research focused on youth, this work addresses the gap in understanding older adults’ mobile media identities. With the rise of smartphone use among older adults, we argue for a systematic examination of how major life changes—both predictable and unpredictable—influence mobile media appropriation and identity. We propose that life transitions provide a context for nonlinear changes in media use.peerReviewe

General introduction to the special issue on resilience in learning

In this introduction to the special issue ‘Resilience in Learning: In Search of Protective Factors and Compensatory Mechanisms’ we provide an overview of the seven articles featured in this special issue and address important themes in this relatively new area of research. These seven articles each have their own foci and form an introduction into the emerging field of academic resilience in the context of education. They cover a theoretical paper, scoping review, several original empirical studies, and an in-depth reflection on the emerging evidence for protective factors and compensatory mechanisms in the context of learning. As this special issue should only be considered as a starting point, we end with suggesting future directions to advance the field.nonPeerReviewe

Uniqueness and nonuniqueness of p-harmonic Green functions on weighted R and metric spaces

We study uniqueness of p-harmonic Green functions in domains Ω in a complete metric space equipped with a doubling measure supporting a p-Poincaré inequality, with 1<p><∞. For bounded domains in unweighted Rn, the uniqueness was shown for the p-Laplace operator Δp and all p by Kichenassamy and Véron (1986) [25], while for p = 2 it is an easy consequence of the linearity of the Laplace operator Δ. Beyond that, uniqueness is only known in some particular cases, such as in Ahlfors p-regular spaces, as shown by Bonk et al. (2022) [10]. When the singularity x0 has positive p capacity, the Green function is a particular multiple of the capacitary potential for capp({x0},Ω) and is therefore unique. Here we give a sufficient condition for uniqueness in metric spaces, and provide an example showing that the range of p for which it holds (while x0 has zero p-capacity) can be a nondegenerate interval. In the opposite direction, we give the first example showing that uniqueness can fail in metric spaces, even for p = 2.</p>peerReviewe

Rethinking social media and mental health : The role of emotion regulation difficulties

Research, on the whole, does not suggest that time spent on social media is associated with risks to mental health, although it is possible there are more nuances about how people use social media. Further, evidence suggests that individuals with emotion regulation difficulties may be drawn to certain social media behaviours as a means of coping with distress. The present study aimed to examine whether emotion regulation difficulties predict patterns of social media use and, in turn, symptoms of depression and anxiety. We examined four distinct types of social media use: (1) image management-based, (2) social comparison-based, (3) negative engagement-based, and (4) passive consumption-based. Sampling 548 adults aged 18–84 years (Mage = 33.16, SD = 17.37; 401 (73.2 %) female; 128 (23.2 %) male), we tested a structural equation model to examine whether the four distinct types of social media use mediated links between difficulties in emotion regulation at Time 1 and depression and anxiety symptomology at Time 2, one week later. Results suggested that, when controlling for age, difficulties in emotion regulation significantly predicted all types of social media use and symptoms of depression and anxiety over one week. Only comparison-based social media use predicted anxiety symptoms over time. The model explained 50.1 % and 52.1 % of the variance in depression and anxiety symptoms, respectively. Taken together, these findings suggest the critical importance of emotion regulation in predicting mental health. By contrast, with the exception of social comparison and anxiety, no form of social media use predicted mental health outcomes.peerReviewe

Politicians in Representative Politics : An Introduction to an Ambiguous Conceptual History

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‘Politician’ and ‘Statesman’ in the Age of Revolutions : Sweden, the Netherlands, France and Britain, 1770–1815

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