This study evaluated the potential of biochar derived from the brown macroalga, Eisenia bicyclis, which is widely distributed along the Japanese coast, as a functional soil amendment and microbial attachment substrate. Marine macroalgae are characterized by rapid growth and high photosynthetic capacity, contributing to effective CO₂ fixation; however, stranded algae often accumulate as an environmental burden. Conversion of macroalgal biomass into biochar represents a promising strategy for the sustainable utilization of marine algal resources. Accordingly, biochars derived from E. bicyclis were produced at three pyrolysis temperatures (350, 450, and 550 °C), and their properties were evaluated in comparison with lignocellulosic biochars obtained from wood and rice husk, using elemental analysis and Fourier-transform infrared (FTIR)-based surface functional group characterization. Subsequently, to assess functional performance in soil, the biochars were added to agricultural field-collected soil and incubated for 28 days, after which microbial attachment was quantified using ATP-based biomass measurements and visualized by SEM. The results showed that algae-derived biochars contained higher concentrations of nitrogen and mineral elements than lignocellulosic biochars. Among the tested conditions, biochar produced at 450 ℃ exhibited recalcitrance comparable to that of wood- and rice husk–derived biochars. When biochars with comparable recalcitrance were applied, microbial attachment levels were similar regardless of feedstock type, and microbial aggregates were consistently observed on the biochar surfaces. These findings demonstrate that E. bicyclis–derived biochar is a nutrient-rich and persistent material that supports soil microbial colonization, highlighting the value of marine macroalgal biomass for agricultural and environmental applications.
Despite growing interest in task engagement, few studies have examined how it contributes to L2 development. This longitudinal study examined how task engagement related to gains in L2 Japanese comprehensibility among nine UK-based university students participating in a semester-long, video-mediated eTandem exchange with Japanese partners. Using 17 engagement indicators, we first provided a descriptive overview of associations between engagement and development. The strongest association was observed for Japanese partners' use of cultural questions. Guided by these results, we then employed interactional sociolinguistics concepts to understand how cultural questions facilitated comprehensibility development. The discourse analysis revealed that cultural questions shifted interactional frames and footings (Goffman, 1974, 1981), prompting L2 learners to take extended turns and practice making their speech comprehensible. The findings highlight the value of qualitative perspectives in task engagement research, showing that L2 comprehensibility development emerges not only from learners' own engagement but also through their partners' interactive behaviors.
This classroom study explored the effects of vocabulary support on collocation learning and affective responses in task-based language teaching (TBLT) among English-as-a-foreign-language (EFL) learners at a Japanese university. For this purpose, 68 EFL learners completed two interactive information-gap tasks under either vocabulary-support or task-only condition, in which the first group received a list of collocations, whereas the second did not. Collocation learning was assessed through a pretest and a posttest, and learners' affective responses to the tasks were measured using a posttask questionnaire. Results revealed that the vocabulary-support group outperformed the task-only group in collocation learning. However, the task-only group demonstrated higher positive psychological states, including enjoyment, pride, and perceived partners' collaborativeness, as well as lower boredom. Higher-proficiency learners in the task-only group exhibited stronger pride than those in the vocabulary-support group. Furthermore, collocation learning gains were correlated with enjoyment, boredom, and perceived collaborativeness in the vocabulary-support condition only. These findings underscore the potential link between emotions and lexical learning, suggesting that vocabulary support improves collocation learning but may diminish positive affect during tasks. Thus, further research on the relationship between emotion and cognition in TBLT is needed to seek an optimal balance between linguistic support and affective dimensions.
Identifying the precise moment when a quantum channel undergoes a change is a fundamental problem in quantum information theory. We study how accurately one can determine the time at which a channel transitions to another. We investigate the quantum limit of the average success probability in unambiguous discrimination, in which errors are completely avoided by allowing inconclusive results with a certain probability. This problem can be viewed as a quantum process discrimination task, where the process consists of a sequence of quantum channels; however, obtaining analytical solutions for quantum process discrimination is generally extremely challenging. In this paper, we propose a method to derive lower and upper bounds on the maximum average success probability in unambiguous discrimination. In particular, when the channels before and after the change are unitary, we show that the maximum average success probability can be analytically expressed in terms of the length of the channel sequence and the discrimination limits for the two channels.
Current embodied world models are primarily optimized for predictive objectives, limiting their ability to generalize under distribution shifts and reason systematically about unseen situations and hypothetical interventions. We argue that embodied intelligence should move beyond predictive world modeling toward self-evolving cognitive systems that continually construct and refine internal causal representations through interaction with the environment. To this end, we propose a self-evolving cognitive framework via causal world modeling for embodied scientific intelligence, which integrates three complementary components: causal world modeling, intervention-driven causal reasoning, and continual cognitive refinement. The proposed framework continuously revises and expands its internal causal world model through causal discovery, intervention-driven feedback, and counterfactual reasoning, supporting continual cognitive refinement and enabling cognition itself to evolve over time. Furthermore, we reinterpret embodied interaction not merely as a means of trajectory optimization, but as an epistemic process for causal hypothesis generation, intervention-driven experimentation, and continual knowledge acquisition. This work provides a conceptual and theoretical foundation for a transition from predictive intelligence toward epistemic intelligence, in which intelligence emerges through the continual construction, revision, and refinement of causal world models via interaction with the environment. Accordingly, an intervention-driven causal-epistemic benchmarking paradigm is suggested for evaluating self-evolving embodied scientific intelligence.
