Statistical regularities can be acquired from usage. To examine language speakers’ statistical metacognition about multiword expressions (MWEs), we collected ratings for frequency, dispersion, and directional association strength of English binomials from L1, advanced and intermediate L2 speakers. Mixed-effects modeling showed all speakers had limited speaker-to-corpus consistency but significant sensitivity to statistical regularities of language, supporting usage-based (Gries & Ellis, 2015) and statistical learning theories (Christiansen, 2019). Their statistical metacognition was also shaped by word-level cues, consistent with dual-route model (Carrol & Conklin, 2014). Despite similarities, frequency metacognition showed the strongest speaker-to-corpus consistency, while dispersion metacognition was the hardest to develop. Advanced L2 speakers showed the greatest speaker-to-corpus consistency and sensitivity, while lower-proficiency speakers relied more on word-level cues in metacognitive judgments, supporting the shallow-structure hypothesis (Clahsen & Felser, 2006). Overall, L1 and L2 speakers develop diverse statistical metacognition, with L2 speakers not necessarily inferior, suggesting that statistical metacognition is not solely shaped by usage-based experience.

We extend the notion of the J-invariant to arbitrary semisimple linear algebraic groups and provide complete decompositions for the normed Chow motives of all generically quasi-split twisted flag varieties. Besides, we establish some combinatorial patterns for normed Chow groups and motives and provide some explicit formulae for values of the J-invariant.

This article sheds light on the understudied significance of Islam, Communism, and global politics in defining what constituted an acceptable “religion” (shūkyō 宗教) in wartime Japan. An analysis of the Japanese Imperial Diet’s debates on the place of Islam in the Religious Organizations Law of 1939, which defined state-sanctioned religious organizations, reveals that Muslim attention from around the world, international politics, the global spread of Communism, and the relatively short history of Islam in Japan, affected politicians’ decision not to mention Islam as a religious organization in the law. While previous literature on the Religious Organizations Law has not adequately addressed the significance of international and non-Euro-American transnational influences, this article argues that lawmakers viewed the power of transnational Muslim and Communist networks as crucial when defining both officially acceptable “religion” and the Shrine (jinja 神社), or Shrine Shinto, as the national core to be protected under this law. The debates surrounding Islam offer fertile ground for examining the significance of global affairs in determining acceptable forms of “religion” in Japan, as well as the broader implications of what Japanese state officials called “religion” and “thought” (shisō 思想) in wartime Japanese and world politics.

The paper explores the accuracy of WiFi-Round Trip Timing (RTT) positioning in indoor environments. Filtering techniques are applied to WiFi-RTT positioning in indoor environments, enhanced by Residual Signal Strength Indicator (RSSI)-based outlier detection. A Genetic and Grid filter are compared with a Particle filter and single-epoch least-squares across a range of test scenarios. In static scenarios, 67% of trials had sub-metre accuracy and 90.5% had a root mean square error (RMSE) below 2 m. In Non-Line-of-Sight (NLOS) conditions, 38% of trials had sub-metre accuracy, whereas for environments with full Line-of-Sight (LOS) conditions, 95.2% of trials had sub-metre accuracy. In scenarios with motion, 22.2% of trials had sub-metre accuracy. RSSI-based outlier detection in NLOS conditions, provided an average improvement of 41.3% over no outlier detection across all algorithms in the static and 14% in the dynamic tests. The Genetic filter achieved a mean improvement of 49.2% in the static and 47% in the dynamic tests compared with least squares.

Negative dependence in tournaments has received attention in the literature. The property of negative orthant dependence (NOD) was proved for different tournament models with a special proof for each model. For general round-robin tournaments and knockout tournaments with random draws, Malinovsky and Rinott (2023) unified and simplified many existing results in the literature by proving a stronger property, negative association (NA). For a knockout tournament with a non-random draw, they presented an example to illustrate that ${\boldsymbol{S}}$ is NOD but not NA. However, their proof is not correct. In this paper, we establish the properties of negative regression dependence (NRD), negative left-tail dependence (NLTD), and negative right-tail dependence (NRTD) for a knockout tournament with a random draw and with players being of equal strength. For a knockout tournament with a non-random draw and with equal strength, we prove that ${\boldsymbol{S}}$ is NA and NRTD, while ${\boldsymbol{S}}$ is, in general, not NRD or NLTD.

Political ideology has regained prominence in political science and psychology. On the one hand, most of the literature recognizes that ideology is not characterized by a single dimension. On the other hand, recent scholarship has returned to Converse's classic conceptualization of ideology as a belief system: a network of interconnected political beliefs. Using survey data collected after the 2022 Italian general elections, I examine the dimensionality of political attitudes and compare latent and network conceptualizations. Results confirm that Italian political attitudes are bidimensional, and that a partial correlation network model captures their structure very well. I then apply Correlational Class Analysis to identify three distinct belief system types. Political orientations (left-right self-placement and vote) emerge as the strongest individual-level predictors of class membership. I explain these findings through an extension of Converse's theory: while he argued that belief systems primarily vary in tightness (internal consistency), I show that conflicting partisan cues might foster low belief consensus: disagreement over which attitudes should be held together.

The treatment of longstanding severe eating disorders is a public concern amid rising service pressures and legal cases. These cases raise complex issues about the interface between legislative schemes, restrictive practices, best interests, treatment refusal and potential interaction with assisted dying legislation, when patients lack capacity yet clearly express wishes.

Large language models (LLMs) like OpenAI’s ChatGPT, Google’s Gemini and Anthropic’s Claude can be useful tools in psychiatric practice, helping with tasks such as searching for information, managing administrative work and supporting education. This article demystifies how these tools work by explaining their core operational principles and noting their key limitations, including the risks of confabulation (fabricating information), sycophancy and knowledge cut-offs. It provides practical guidance on mitigating these risks through structured ‘prompt engineering’ and offers a safety framework for integrating LLMs into low-risk administrative and educational workflows. The article stresses the importance of approaching these technologies with caution by independently verifying information, adhering to UK data protection laws and upholding the principles of best practice in patient care. The goal is to help clinicians use these powerful but fallible technologies wisely, ensuring that patient safety and professional responsibility remain paramount as they explore these new tools.

In this article, a circularly polarized dielectric resonator antenna (DRA) array with conformal characteristics and improved specific absorption rate (SAR) has been proposed for X-band applications. The proposed structure has been fed through the corporate feed network which excites a radiating mode inside DRA, i.e., $TE_{1\delta1}$. This mode has been utilized to enhance the impedance bandwidth which is below −10 dB for both the E- and H-plane so as to meet the requirements of next-generation defense communication and low-cost satellite systems. To generate the axial ratio (AR), the extended off-set feed has been employed to provide the required 90$^{\circ}$ phase shift. Further, in order to enhance the gain and reduce the SAR, an electromagnetic band gap structure has been used as a reflector. Furthermore, multiple arrays have been introduced to extend the coverage area through beam-forming. The proposed design has been fabricated for the experimental validation. The measured IBW and ARBW is 34.74% and 12.2%, respectively. The gain is 10.1 dBic throughout the band of operation along with the radiation efficiency above 85% in various bending conditions. The SAR is much below the permissible limit of 1.6 W/kg. Thus, the proposed array is compact, and it clearly achieves a smaller footprint, better IBW, ARBW and a low SAR with potential prospect for X-band purposes.

In this paper, we investigate a competitive market involving two agents who consider both their own wealth and the wealth gap with their opponent. Both agents can invest in a financial market consisting of a risk-free asset and a risky asset, under conditions where model parameters are partially or completely unknown. This setup gives rise to a nonzero-sum differential game within the framework of reinforcement learning (RL). Each agent aims to maximize his own Choquet-regularized, time-inconsistent mean-variance objective. Adopting the dynamic programming approach, we derive a time-consistent Nash equilibrium strategy in a general incomplete market setting. Under the additional assumption of a Gaussian mean return model, we obtain an explicit analytical solution, which facilitates the development of a practical RL algorithm. Notably, the proposed algorithm achieves uniform convergence, even though the conventional policy improvement theorem does not apply to the equilibrium policy. Numerical experiments demonstrate the robustness and effectiveness of the algorithm, underscoring its potential for practical implementation.

This article examines the relational self-care practices of migrant Chinese women working as massage workers in the United States (hereinafter referred to as Chinese massage workers). Threading both the bodily and the intimate, Chinese massage workers offer care and relaxation for their clients through the modality of touch and quiet comfort. A wealth of scholarly work highlights the complexities of migrant massage workers’ daily lives and their paid labor of care. Thus far, the study of migrant massage workers focuses mainly on their romantic, familial, and work relationships. Little is known about the relational self-care practices that migrant massage workers engage in. Drawing on 20 months of ethnographic fieldwork, this article examines the practice of Chinese massage workers caring for one another through the intellectual genealogy of self-care in Black feminist scholarship. Through an examination of relational self-care performed by and for Chinese massage workers, this article shifts the focus from analyzing the expected performances of care-giving by migrant women massage workers within the economy of racial capitalism to a reconceptualization of self-care as a relational practice.