坦佩雷大学

Background Emotions play an important role in learning. Activating teaching methods, such as flipped learning, can provide positive learning experiences but also pose emotional challenges to students. Aims This intensive longitudinal study investigated 1) how students’ emotional valence before a learning event, learning mode (individual, peer, teacher-led), teaching implementation (lecture-based vs. flipped), and physiological arousal (electrodermal activity, EDA) during the learning event related to emotional valence after a learning event, and 2) how EDA varies across within-event segments and teaching implementations during learning events. Sample Participants were 69 students enrolled in an engineering mathematics course implemented either as lecture-based (n = 34) or flipped learning (n = 35). Methods Students reported their emotional states via a smartphone application and wore smart rings to measure EDA. A series of multilevel binomial logistic regression and linear mixed-effects models were analyzed across 546 learning events, totaling 827 h of data. Results Positive pre-event emotional valence predicted positive post-event emotional valence. Teacher-led events were associated with more positive post-event emotional valence than individual events, particularly in the lecture-based group. The main effect of the teaching implementation was not significant. EDA did not show robust associations with post-event emotional valence; however, lecture-based events showed a clearer decline in EDA across event segments than flipped learning events. Conclusions The results highlight the importance of supporting social and individual learning processes and examining physiological arousal as a temporal learning-event process rather than a simple proxy for post-event emotional valence.

Municipal wastewater treatment plants face increasing pressure to improve energy recovery while reducing sludge volumes and operational costs. In this context, low-input process intensification strategies are of growing interest. This study evaluated a continuous side-stream magnetization strategy applied during 44 days of mesophilic anaerobic digestion (37 +/- 2 degrees C) of sewage sludge, where digesting sludge was continuously recirculated through a tubular rare-earth-material magnetic polarizer generating a low-intensity static magnetic field (20 mT). The experimental results revealed a substantial increase in AD performance, with the SMF-exposed reactor achieving a 48.3% increase in specific biomethane production and a 15% higher reduction in volatile solids compared to the control reactor. Additionally, the digestate exposed to the SMF showed an improved dewaterability, evidenced by a 26.8% reduction in capillary suction time compared to the non-magnetized digestate. Microbial community analysis at the end of the AD process indicated that continuous exposure to SMF significantly enhanced the abundance of key methanogens, including Methanosarcina and Methanobacterium, which contributed to the higher biomethane production observed during AD. These findings indicate that continuous SMF exposure during AD can significantly enhance biomethane production and can be regarded as a promising approach to improve energy recovery from sewage sludge.

Engineering and intelligent systems increasingly require decision-making under heterogeneous evidence. These sources include multi-source data, predictive models, competing objectives, feasibility constraints, and uncertainty. Evolutionary algorithms (EAs) are widely used in such settings, yet the literature is typically organised by algorithmic lineage, which obscures how and where information is fused within the evolutionary process. This survey reframes EAs as adaptive information fusion architectures. We introduce a fusion-centric taxonomy spanning data-level integration of heterogeneous observations, model/feature-level integration through surrogate and learning components, objective-level integration through multi- and many-objective formulations, constraint handling as feasibility-signal integration, and decision-level integration through ensembles, distributed (island) evolution, and multi-run aggregation. Building on this perspective, we synthesise major EA families according to their dominant fusion mechanisms and review fusion-driven applications across structural and mechanical design, energy and smart grids, robotics and control, communications, healthcare engineering, and neural architecture search. To complement this qualitative synthesis, we propose lightweight quantitative indicators—integration depth, integration diversity, and decision outcome entropy—to characterise algorithm–fusion–domain alignment and to identify recurring success and failure modes, including surrogate bias, over-aggregation, and fusion over-complexity. We conclude with practical design guidelines and discuss emerging directions, including federated evolutionary fusion and reliability considerations in learning- and large language model (LLM)-assisted fusion.

Reports of resurfaced repressed memories during psychedelic experiences have circulated for decades and still emerge today. However, the veracity of repressed memories remains debated, and the mechanisms through which psychedelics might recover alleged repressed memories are unclear. This scoping review aimed to provide an overview of the literature on repressed memory in the context of psychedelics. It examined how repressed memory was defined, which substances were predominantly discussed, which mechanisms were proposed to explain their effects, and whether these mechanisms were empirically supported. A scoping review was conducted in line with PRISMA-ScR guidelines. Web of Science, PubMed, PsycINFO and Google Scholar were searched for relevant publications. Fifty-three sources met eligibility criteria. Data were charted on study design, psychedelic substance, definitions of repressed memory, results, and proposed mechanisms. Most publications focused on lysergic acid diethylamide (LSD) in relation to repressed memory. Few sources provided a definition of repressed memory. Proposed mechanisms on how psychedelics might influence repressed memory included psychoanalytical reductions of defensive memory blockades and neurobiological alterations of executive control. However, empirical support for these mechanisms was limited. The included literature did not offer a coherent explanation on how psychedelics could recover repressed memories, nor consistent evidence that they did so reliably. Future work should provide clear definitions of repressed memory in the context of psychedelics, test proposed effects of psychedelics on memory and executive control across multiple psychedelic substances, include placebo-controlled designs, and account for the potential occurrence of false memories.

PurposeThis paper aims to investigate how entrepreneurial small and medium-sized enterprises (SMEs) develop resilience strategies when faced with crises by leveraging interactions within business networks. Using the Industrial Marketing and Purchasing (IMP) perspective, this study explores entrepreneurial SME responses to disruptive events moving towards resilience, robustness and antifragility.Design/methodology/approachThe study adopts a qualitative, abductive approach using six illustrative case studies from diverse sectors across four countries. Data collection involved semi-structured interviews, direct observations, secondary sources and event-based narrative analysis. The study analyses resilience strategies across three interrelated levels, organisational, dyadic and network.FindingsEntrepreneurial SMEs build resilience through six relationally embedded strategies across three levels: (1) Organisational strategies (Frame and Reclaim, Adapt and Advance) manage internal vulnerabilities through data-driven relationship management and business model innovation. (2) Dyadic strategies (Diversify and Thrive, Reconnect and Protect) stabilise relationships through portfolio diversification and emotional capital cultivation. (3) Network strategies (Bridge and Bond, Ally and Amplify) enable collective responses through cross-sector partnerships and institutional alliances. The study finds that the level at which a crisis originates does not determine the level at which firms develop their resilience responses.Practical implicationsThe framework provides entrepreneurial SMEs with strategic options tailored to their network position, resource constraints and crisis type, demonstrating how firms can progress from basic recovery to antifragile growth.Originality/valueTo the best of the authors' knowledge, this study offers the first systematic, multi-level typology of resilience strategies in entrepreneurial SME networks, extending IMP theorising to capture how resilience strategies are co-constructed across organisational, dyadic and network levels.