This study examines the status of mixed-methods research (MMR) in computer-assisted language learning (CALL). A total of 204 studies employing MMR were analyzed. Manual coding was carried out to reveal MMR purposes, designs, features, and rhetorical justifications. Findings indicate CALL authors mostly adopt MMR for triangulation and complementarity purposes. Core designs are more favored in CALL MMR research articles, compared to complex designs. Moderate size random sampling prevails in the data, where data sources are sequentially collected and analyzed using parametric tests. Symptomatic argumentative schemes are found to be the most common justification of MMR. Based on the findings, it is evident that most CALL researchers employ conventional MMR designs. The study concludes with implications for CALL stakeholders and authors.

A meta-conjecture of Coulson, Keevash, Perarnau, and Yepremyan [12] states that above the extremal threshold for a given spanning structure in a (hyper-)graph, one can find a rainbow version of that spanning structure in any suitably bounded colouring of the host (hyper-)graph. We solve one of the most pertinent outstanding cases of this conjecture by showing that for any $1\leq j\leq k-1$, if $G$ is a $k$-uniform hypergraph above the $j$-degree threshold for a loose Hamilton cycle, then any globally bounded colouring of $G$ contains a rainbow loose Hamilton cycle.

Political polarization is a group phenomenon in which opposing factions, often of unequal size, exhibit asymmetrical influence and behavioral patterns. Within these groups, elites and masses operate under different motivations and levels of influence, challenging simplistic views of polarization. Yet, existing methods for measuring polarization in social networks typically reduce it to a single value, assuming homogeneity in polarization across the entire system. While such approaches confirm the rise of political polarization in many social contexts, they overlook structural complexities that could explain its underlying mechanisms. We propose a method that decomposes existing polarization and alignment measures into distinct components. These components separately capture polarization processes involving elites and masses from opposing groups. Applying this method to Twitter discussions surrounding the 2019 and 2023 Finnish parliamentary elections, we find that (1) opposing groups rarely have a balanced contribution to observed polarization, and (2) while elites strongly contribute to structural polarization and consistently display greater alignment across various topics, the masses, too, have recently experienced a surge in alignment. Our method provides an improved analytical lens through which to view polarization, explicitly recognizing the complexity of and need to account for elite-mass dynamics in polarized environments.

Fault analysis at the early design stages of an engineering system is crucial for ensuring reliability and safety during operation. Given the limited information on system components and configurations available at this stage, such analysis heavily relies on historical data and expert knowledge. Traditional methods like Fault Tree, Bayesian networks, and Markov chains depend on manually established causality models for system failures. In complex systems with numerous components, creating these causality models becomes increasingly time-consuming and susceptible to human-error in identifying potential causal relationships. One of the major reasons for the modeling errors is that the causality models lack the support of physics. To address these limitations, this article introduces a novel approach for formally establishing causal relations between faults and system failures and calculating the probabilities of each cause. Even at conceptual design stage, the proposed method can automatically deduce all possible causes and fault propagation paths for each system failure corresponding to the physics modeled by the analyst. The entire approach is divided into two major steps: the first step identifies the system trajectories for a known condition using qualitative physics and symbolic AI, and the second step calculates the conditional probabilities of the causes outlined in the first step for a given initial condition. Knowledge about the probabilistically weighted causes of system failures allows designers to identify potential issues that have a relatively high likelihood of occurrence and severe consequences. The article demonstrates the method’s application by analyzing a simplified secondary loop of a nuclear power plant.