Research on understanding the effects of language experiences upon executive control processes has turned away from static measures of language use to using more continuous measures such as proficiency, language switching and exposure. The present work utilizes language entropy, a measure that indexes the social and linguistic diversity of daily-life contexts (e.g., a classroom, cafeteria, home) of language use, to delineate the mechanisms through which contextual and social effects influence executive control. Results from existing studies utilizing entropy primarily examine bilingual contexts; however, this study focuses on multilingual university students in Ahmedabad, India. Participants (N = 56) provided entropy data from the Language History and Background Questionnaire and executive control measures from the AX-CP Task for proactive control and the n-back Task for working memory. Entropy measures proved very predictive for participants’ current language use patterns, but did not significantly predict any aspect of AX-CPT or n-back Task performance. Implications for context-specific stimulus categorization and the adaptive control hypothesis are discussed.

Pretesting for exogeneity has become routine in many empirical applications involving instrumental variables (IVs) to decide whether the ordinary least squares or IV-based method is appropriate. Guggenberger (2010a, Econometric Theory, 26, 369–382) shows that the second-stage test – based on the outcome of a Durbin-Wu-Hausman-type pretest in the first stage – exhibits extreme size distortion, with asymptotic size equal to 1 when the standard critical values are used. In this paper, we first show that both conditional and unconditional on the data, standard wild bootstrap procedures are invalid for two-stage testing. Second, we propose an identification-robust two-stage test statistic that switches between OLS-based and weak-IV-robust statistics. Third, we develop a size-adjusted wild bootstrap approach for our two-stage test that integrates specific wild bootstrap critical values with an appropriate size-adjustment method. We establish uniform validity of this procedure under conditional heteroskedasticity or clustering in the sense that the resulting tests achieve correct asymptotic size, regardless of whether the identification is strong or weak. Our procedure is especially valuable for empirical researchers facing potential weak identification. In such settings, its power advantage is notable: whereas weak-IV-robust methods maintain correct size but often suffer from relatively low power, our approach achieves better performance.

Digital solutions are seen as ways to improve citizens’ access to public services and raise their trust. Yet, the specific impact of digital public services for migrants, remains understudied. Therefore, this study investigates migrants’ use of digital public services and examines the impact of such services on migrants’ satisfaction with migration agencies. We rely on original data from an online survey (N = 22,659) in Sweden consisting of migrants who received decisions from the migration agency regarding a variety of applications. Our results show that online applications are not related to higher satisfaction among migrant groups when measured as satisfaction during general contact. However, with more specific measurements, such as satisfaction when visiting the migration agency, online applications are related to higher satisfaction. We also find that satisfaction with the migration agency is stratified across different types of applications, with asylum-seekers being the least satisfied in their contact with the migration agency.

With the widespread application of smart antennas in 5G communication and radar detection, adaptive beamforming technology based on deep learning has become a research focus for improving the anti-interference performance of antenna arrays due to its powerful nonlinear modeling capability. It can transform the beamforming problem into a neural network regression problem, enabling the model to rapidly output an approximately optimal beamforming weight vector without prior information. Aiming at the issues of poor adaptability to dynamic interference and high computational complexity of traditional algorithms, this paper proposes IRDSNet, a novel adaptive beamforming algorithm based on Inception-ResNet-dual-pool Squeeze-and-Excitation Network (DP-SENet), to optimize the performance of uniform circular array antennas. IRDSNet integrates the Inception structure, depthwise separable convolution, and Ghost convolution to construct a multi-scale feature extraction module, enhancing the model’s feature extraction capabilities while maintaining a low parameter count. By introducing an improved DP-SENet, the model’s ability to focus on key features is enhanced, while the incorporation of residual modules optimizes feature transmission efficiency. Simulation results demonstrate that the IRDSNet algorithm achieves a null depth exceeding −90 dB at various interference angles, with an output Signal-to-Interference-plus-Noise Ratio (SINR) consistently above 23 dB and a short inference time, demonstrating excellent interference suppression performance.

This Spotlight argues that Holocaust historiography stands at a critical impasse. Decades of groundbreaking research have produced an era of unprecedented empirical richness – but also profound fragmentation: a tension masterfully documented in The Cambridge History of the Holocaust (2025). As the field has matured – dethroning German exceptionalism, re-centring victim experiences and expanding its temporal and methodological horizons – the frameworks that once provided coherence, from Berlin-centrism to national containers, have been exhausted. In response, this article proposes a new methodological scaffolding: relational Europeanism. This approach shifts the analytical focus from where events occurred to how they unfolded, privileging interaction over location, proximity over typology and the methodological practice of entanglement. By tracing these dynamics horizontally across borders, vertically through scales and temporally through pre-war and post-war periods, relational Europeanism rethinks the Holocaust as a continental process woven from irreducibly local contexts. It offers a viable path to hold the field’s dazzling plurality together without imposing a new synthesis. In an age of nationalist memory politics and eroding historical knowledge, this method is not merely an academic exercise but also an ethical imperative – providing the connective tissue to write European histories as transnational as the experiences themselves.

In three empirical studies, we compare one syntactic and one semantic approach to agreement preferences in so-called pancake constructions (pcs) in Swedish, as in Senap är starkt ‘Mustard is strong’. pcs are either substance-denoting, naming an inherent property of the subject, or situation-denoting, naming a property of the subject that is linked to some event. These two types were found to differ in predicative agreement patterns when their subjects were modified (e.g. Skånsk senap är … ‘Scanian mustard is’). The studies also indicate that the presence of a modal verb can affect agreement patterns differently in the two types: substance-denoting pcs were affected by modification and modality to a much larger extent than situation-denoting ones. We conclude that the two approaches can explain some patterns, but leave others unexplained, and the results lend partial support to analyses that make a syntactic difference between the two types of pcs.

Creative thinking is a crucial step in the design ideation process, where analogical reasoning plays a vital role in expanding the design concept space. The emergence of Generative AI has brought a significant revolution in co-creative systems, with a growing number of studies on Design-by-Analogy support tools. However, there is a lack of studies investigating the creative performance of Large Language Model (LLM)-generated analogical content and benchmarking of language models in creative tasks such as design ideation. Through this study, we aim to (i) investigate the effect of creativity heuristics by leveraging LLMs to generate analogical stimuli for novice designers in ideation tasks and (ii) evaluate and benchmark language models across analogical creative tasks. We developed a support tool based on the proposed conceptual framework and validated it by conducting controlled ideation experiments with 24 undergraduate design students. Groups assisted with the support tool generated higher-rated ideas, thus validating the proposed framework and the effectiveness of analogical reasoning for augmenting creative output with LLMs. Benchmarking of the models revealed significant differences in the creative performance of analogies across various language models, suggesting that future studies should focus on evaluating language models across creative, subjective tasks.

Obesity and overweight in pregnant women increase pregnancy and neonatal morbidity with a risk of metabolic syndrome for children in later life. Maternal preconceptional bariatric surgery reduces maternal and paediatric outcomes but may induce fetal nutritional deficiencies and intrauterine growth restriction through placental reprogramming. The aim of this study was to describe feto-placental unit modifications induced by obesity, and the effect of bariatric surgery performed before gestation, on a diet-induced obese rat model. One month after surgery, rats of ‘control’, ‘obese’ and ‘bariatric surgery’ groups were mated and then sacrificed at D19 of gestation. Clinical description, immuno-histochemistry and molecular analyses were performed on feto-placental units. Obesity induces placental modifications including lipid accumulations, increased inflammation and oxidative stress. Some of these modifications are partially restored by maternal preconceptional bariatric surgery. On the other hand, a reduction in the expression of markers of glucose transport, insulin function and amino acid transport, after bariatric surgery was observed. This phenotype may lead to fetal caloric restriction, adoption of a ‘thrifty phenotype’ and subsequently fetal growth restriction. These preliminary findings highlight the importance of a close follow-up of women who have undergone bariatric surgery and their children.

This article critically examines the integration of artificial intelligence (AI) into nuclear decision-making processes and its implications for deterrence strategies in the Third Nuclear Age. While realist deterrence logic assumes that the threat of mutual destruction compels rational actors to act cautiously, AI disrupts this by adding speed, opacity and algorithmic biases to decision-making processes. The article focuses on the case of Russia to explore how different understandings of deterrence among nuclear powers could increase the risk of misperceptions and inadvertent escalation in an AI-influenced strategic environment. I argue that AI does not operate in a conceptual vacuum: the effects of its integration depend on the strategic assumptions guiding its use. As such, divergent interpretations of deterrence may render AI-supported decision making more unpredictable, particularly in high-stakes nuclear contexts. I also consider how these risks intersect with broader arms race dynamics. Specifically, the pursuit of AI-enabled capabilities by global powers is not only accelerating military modernisation but also intensifying the security dilemma, as each side fears falling behind. In light of these challenges, this article calls for greater attention to conceptual divergence in deterrence thinking, alongside transparency protocols and confidence-building measures aimed at mitigating misunderstandings and promoting stability in an increasingly automated military landscape.

The second Trump administration has shaken the foundations of US leadership in global health, with this column assessing rapid shifts in global health governance. By analyzing how the administration’s anti-science ethos, foreign assistance cuts, and multilateral disengagement have undermined global solidarity, the column considers the destabilizing impacts on global health and examines how other states, regional bodies, and international organizations are responding to this US decline. This examination reveals both strains for global health promotion and resilience within a changed governance landscape.

Military decision-making institutions face new challenges and opportunities from increasing artificial intelligence (AI) integration. Military AI adoption is incentivized by competitive pressures and expanding national security needs; thus, we can expect increased complexity due to AI proliferation. Governing this complexity is urgent but lacks clear precedents. This discussion critically re-examines key concerns that AI integration into resort-to-force decision-making organizations introduces. Beside concerns, this article draws attention to new, positive affordances that AI proliferation may introduce. I then propose a minimal AI governance standard framework, adapting private sector insights to the defence context. I argue that adopting AI governance standards (e.g., based on this framework) can foster an organizational culture of accountability, combining technical know-how with the cultivated judgment needed to navigate contested governance concepts. Finally, I hypothesize some strategic implications of the adoption of AI governance programmes by military institutions.

Integrating AI into military decision processes on the resort to force raises new moral challenges. A key question is: How can we assign responsibility in cases where AI systems shape the decision-making process on the resort to force? AI systems do not qualify as moral agents, and due to their opaqueness and the “problem of many hands,” responsibility for decisions made by a machine cannot be attributed to any one individual. To address this socio-technical responsibility gap, I suggest establishing “proxy responsibility” relations. Proxy responsibility means that an actor takes responsibility for the decisions made by another actor or synthetic agent who cannot be attributed with responsibility for their decisions. This article discusses the option to integrate an AI oversight body to establish proxy responsibility relations within decision-making processes regarding the resort to force. I argue that integrating an AI oversight body creates the preconditions necessary for attributing proxy responsibility to individuals.