Additional language speakers (ALSs) often experience anxiety due to challenges posed by their nonstandard pronunciation. Building on these insights, this paper introduces an instrument, the Accent Anxiety Scale (AAS), specifically designed to assess three sources of anxiety that are experienced by ALSs, including (a) apprehension about negative evaluations from other individuals due to their distinctive speech style, (b) concerns about rejection from the target language community because of their “foreign” pronunciation, and (c) anxieties over potential communication hurdles attributed to the intelligibility of their pronunciation. We evaluated the psychometric robustness of the AAS by analyzing data from a total of 474 immigrant and international student ALSs at a predominantly English-speaking Canadian university. Study 1 focused on immigrants (N = 203) and employed exploratory factor and correlational analyses to isolate a concise number of internally consistent and valid items for each subscale. Study 2 extended these analyses to international students (N = 153) and employed confirmatory factor and correlation analyses to further validate the AAS in this population. Study 3 examined international students (N = 118) at two time points to establish the AAS’s temporal stability. These studies yielded robust psychometric evidence for the factor structure, reliability, and validity of the AAS. The findings not only support the use of the AAS as a research instrument but also offer implications for pedagogical strategies aimed at alleviating ALSs’ accent anxiety.

In this paper, we provide a systematic review of existing artificial intelligence (AI) regulations in Europe, the United States, and Canada. We build on the qualitative analysis of 129 AI regulations (enacted and not enacted) to identify patterns in regulatory strategies and in AI transparency requirements. Based on the analysis of this sample, we suggest that there are three main regulatory strategies for AI: AI-focused overhauls of existing regulation, the introduction of novel AI regulation, and the omnibus approach. We argue that although these types emerge as distinct strategies, their boundaries are porous as the AI regulation landscape is rapidly evolving. We find that across our sample, AI transparency is effectively treated as a central mechanism for meaningful mitigation of potential AI harms. We therefore focus on AI transparency mandates in our analysis and identify six AI transparency patterns: human in the loop, assessments, audits, disclosures, inventories, and red teaming. We contend that this qualitative analysis of AI regulations and AI transparency patterns provides a much needed bridge between the policy discourse on AI, which is all too often bound up in very detailed legal discussions and applied sociotechnical research on AI fairness, accountability, and transparency.

Pouch-type actuators have recently garnered significant interest and are increasingly utilized in diverse fields, including soft wearable robotics and prosthetics. This is largely due to their lightweight, high output force, and low cost. However, the inherent hysteresis behavior markedly affects the stability and force control of pouch-type driven systems. This study proposes a modified generalized Prandtl–Ishlinskii (MGPI) model, which includes generalized play operators, the tangent envelope function, and one-sided dead-zone operators, to describe the asymmetric and non-convex hysteresis characteristics of pouch-type actuators. Compared to a classical Prandtl–Ishlinskii (PI) model incorporating one-sided dead-zone functions, the MGPI model exhibits smaller relative errors at six different air pressures, demonstrating its capability to accurately describe asymmetric and non-convex hysteresis curves. Subsequently, the MGPI hysteresis model is integrated with displacement sensing technology to establish a load compensation control system for maintaining human posture. Four healthy subjects are recruited to conduct a 1 kg load compensation test, achieving efficiencies of 85.84%, 84.92%, 83.63%, and 68.86%, respectively.

This study investigated whether nonnative speakers of English would be able to identify the meanings of ambiguous English noun-noun compounds, focusing on semantic relation between the modifier and head. English noun-noun compounds with varying degrees of ambiguity were selected through an analysis of contexts from COCA. The participants were two groups of college students, who were natives peakers of Arabic (n = 20) and Chinese (n = 20). The participants thought-aloud the meanings (more common vs. less common) of the English noun-noun compounds shown in the contexts. The overall accuracy was comparable between the groups, but by-item accuracy showed some differences in the meanings the groups identified more accurately.

In our previous paper, we gave a presentation of the torus-equivariant quantum K-theory ring $QK_{H}(Fl_{n+1})$ of the (full) flag manifold $Fl_{n+1}$ of type $A_{n}$ as a quotient of a polynomial ring by an explicit ideal. In this paper, we prove that quantum double Grothendieck polynomials, introduced by Lenart-Maeno, represent the corresponding (opposite) Schubert classes in the quantum K-theory ring $QK_{H}(Fl_{n+1})$ under this presentation. The main ingredient in our proof is an explicit formula expressing the semi-infinite Schubert class associated to the longest element of the finite Weyl group, which is proved by making use of the general Chevalley formula for the torus-equivariant K-group of the semi-infinite flag manifold associated to $SL_{n+1}(\mathbb {C})$.