This article contributes to disciplinary histories of International Relations (IR) by revealing a little-known history: how a Nazi diplomat, Curt Max Prüfer, occupied the first chair in IR in India. While the paper documents how Prüfer, a discredited diplomat, landed in Delhi through his connections with peripatetic Indian anti-colonial networks and spent slightly over two years as the first IR chair at Delhi University, it also makes broader claims about how we narrate disciplinary histories. Intellectual genealogies, the predominant way in which disciplinary histories are written, often miss the contingent factors that play a considerable role in the fashioning of the discipline. Contingency-filled narratives also point towards the fact that International Relations/Affairs, at least in its early period of formation, operated as a term of mythical heft – a placeholder to fit anyone with academic or practical expertise in varied fields such as international law, colonial administration, anthropology, diplomacy, history, political economy, and military strategy.

The Vela pulsar (J0835$-$4510) is known to exhibit variations in Faraday rotation and dispersion on multi-decade timescales due to the changing sightline through the surrounding Vela supernova remnant and the Gum Nebula. Until now, variations in Faraday rotation towards Vela have not been studied on timescales less than around a decade. We present the results of a high-cadence observing campaign carried out with the Aperture Array Verification System 2 (AAVS2), a prototype SKA-Low station, which received a significant bandwidth upgrade in 2022. We collected observations of the Vela pulsar and PSR J0630$-$2834 (a nearby pulsar located outside the Gum Nebula), spanning $\sim$1 and $\sim$0.3 yr, respectively, and searched for linear trends in the rotation measure (RM) as a function of time. We do not detect any significant trends on this timescale ($\sim$months) for either pulsar, but the constraints could be greatly improved with more accurate ionospheric models. For the Vela pulsar, the combination of our data and historical data from the published literature have enabled us to model long-term correlated trends in RM and dispersion measure (DM) over the past two decades. We detect a change in DM of $\sim$0.3 $\mathrm{cm}^{-3}\,\mathrm{pc}$ which corresponds to a change in electron density of $\sim$$10^5\,\mathrm{cm}^{-3}$ on a transverse length scale of $\sim$1–2 au. The apparent magnetic field strength in the time-varying region changes from $240^{+30}_{-20}\,\mu\mathrm{G}$ to $-6.2^{+0.7}_{-0.9}\,\mu\mathrm{G}$ over the time span of the dataset. As well as providing an important validation of polarimetry, this work highlights the pulsar monitoring capabilities of SKA-Low stations, and the niche science opportunities they offer for high-precision polarimetry and probing the microstructure of the magneto-ionic interstellar medium.

This article explores the strategic decision making of armed groups during war-to-peace transitions—critical time frames during which militant leaders must reconcile their commitment to armed survival with the imperative of postwar civilian conversion. We specify the internal organizational risks rebel groups confront, as well as the menu of strategies from which they select, in navigating the uncertainty inherent in these perilous periods. Our approach broadens the analysis of war-to-peace transitions, offering new insights into the question of why rebels sometimes successfully integrate into postconflict politics, economies, and society, while at other times they forgo participation in the postconflict state. It represents the first step in a wider research program—one that promises to open a number of new directions in the study of insurgent organizations, transitional societies, and postwar outcomes.

Taku Glacier recently began retreating for the first time since the late 19th century but limited observations of its bed leaves uncertainties on how this retreat will proceed. In this study, we use ground-based gravity measurements to improve the extent of bed-elevation estimates on the Taku by modelling the glacier in 3D. We find the across-flow geometry of the middle to upper reach of the Taku and the Matthes branch has a step-like feature near the edge and a wide, flat bottom. We constrain the ice thickness along flow within uncertainty limits and provide a range of expected values. Along the centre line of our model, we find a maximum ice thickness of 1556 ± 143 m and the deepest bed at 445 ± 166 m below sea level. The along-flow results also delineate two bedrock bumps, which could help stabilise the retreat of the Taku when its terminus is submerged in water. We model the bed to be below sea level until at least 35 km upstream of the terminus where the Matthes branch joins the main branch, improving constraints on how far upstream the Taku would be vulnerable to marine retreat.

Classical sea-ice models in climate model resolution do not resolve the small-scale physics of sea ice. New methods to address this problem include modifications to established viscous-plastic (VP) rheology models, sub-gridscale parameterizations or new rheologies such as the Maxwell elasto-brittle (MEB) rheology. Here, we investigate differences in gridscale dynamics simulated by the VP and MEB models, their dependency on tunable model parameters and their response to added stochastic perturbations of material parameters in a new implementation in the Massachusetts Institute of Technology general circulation model. Idealized simulations are used to demonstrate that material parameters can be tuned so that both VP and MEB rheologies lead to similar cohesive stress states, arching behaviour and heterogeneity in the deformation fields. As expected, simulations with MEB rheology generally show more heterogeneity than the VP model as measured by the number of simulated linear kinematic features (LKFs). For both rheologies, the cohesion determines the emergence of LKFs. Introducing gridscale heterogeneity by random model parameter perturbation, however, leads to a larger increase of LKF numbers in the VP simulations than in the MEB simulations and similar heterogeneity between VP and MEB models.

In this paper, I examine the relationship between community-level exposure to war losses and long-term patterns of electoral behaviour. Using novel data that identifies and geolocates all French soldiers who died during World War I, I show that communities that experienced higher death rates exhibit greater levels of electoral support for the far-right. Subsequently, I provide both theoretical and empirical evidence on how such persistent effects propagate: communities more exposed to the horrors of war develop stronger in-group preferences at the expense of the out-group. In cases like France, where the in-group is defined primarily in terms of the nation, this preference translates into a higher demand for nationalism, which is supplied by far-right political parties.

This paper addresses the optimization of retrieval-augmented generation (RAG) processes by exploring various methodologies, including advanced RAG methods. The research, driven by the need to enhance RAG processes as highlighted by recent studies, involved a grid-search optimization of 23,625 iterations. We evaluated multiple RAG methods across different vectorstores, embedding models, and large language models, using cross-domain datasets and contextual compression filters. The findings emphasize the importance of balancing context quality with similarity-based ranking methods, as well as understanding tradeoffs between similarity scores, token usage, runtime, and hardware utilization. Additionally, contextual compression filters were found to be crucial for efficient hardware utilization and reduced token consumption, despite the evident impacts on similarity scores, which may be acceptable depending on specific use cases and RAG methods.

Complementing readings in International Relations (IR) that understand Covid-19 as an Anthropocene effect, this article observes the pandemic as a laboratory for engagements with Anthropocene experience. It argues that the pandemic turn to dreams renegotiated the conditions of experienceability of Anthropocene temporality. Exploring the scientific, archival, and practical registers on which dreams attracted interest during the pandemic, the article traces how dreams were valued for their promise of capturing the affective exposure of subjects to the pandemic present. This conditioning of experienceability on the limits of the human subject resonates with the relational turn in IR and its affirmation of being-in-relation as a condition for becoming attuned to the Anthropocene. Drawing from Koselleck and Foucault, the article understands this resonance as indicative of a shared archive of experiments in transcending modern accounts of temporality. For this archive, rendering an Anthropocenic present experienceable requires a shift from the distanced account of a modern author-subject to a subject that gauges its own exposure to the present. Despite this ambition of the turn to dreams, the article also flags its constraints, observing how this turn regularly tipped back into reaffirming the modern subject.

Capacity development is crucial for enduring conservation success. Recent scholarship has called for a systems perspective based on input from local stakeholders to better understand and develop conservation capacity. However, few studies have adopted such an approach to explore interactions among capacities or how capacity development needs and priorities evolve. We address this gap through a case study from Bhutan, centred on perceptions from 52 local conservation practitioners, planners, funders and community members. We use mixed methods to identify which capacities have been important for conservation success, which capacities are needed for future success, which capacities are foundational and how capacities interact. We find that capacity needs have shifted from individual-level knowledge and skills to community- and societal-level capacities in response to changing political and economic dynamics. Participants identified political support and leadership, reliable and sufficient funding, strengthening the research base, and increasing community awareness and engagement as critical future needs. Investing in these capacities holds the promise of further augmenting capacity development, thus increasing the value of limited resources. Our results demonstrate that capacity development should be viewed as a dynamic process and supported by strategic investment even in countries with track records of conservation success.

Accounting for 53% of U.S. peanuts (Arachis hypogaea L.), Georgia is the top peanut-producing state, with approximately 1.42 billion kg produced in 2023. Peanut producers often use the acetolactate synthase (ALS) imidazolinone herbicide imazapic, but reduced yellow nutsedge (Cyperus esculentus L.) control was reported in Georgia peanuts after 4 yr of continuous imazapic use. This study aimed to determine the level of resistance (LD50, I50, and GR50) and potential cross-resistance for the suspected resistant population and to identify the associated genetic mutations conferring resistance. A susceptible biotype was treated with 0, 0.0088, 0.0175, 0.035, 0.07, 0.14, 0.28, and 0.56 kg ai ha−1, and a resistant biotype was sprayed with 0, 0.07, 0.14, 0.28, 0.56, 1.13, 2.26, and 4.5 kg ai ha−1 of imazapic. To determine whether the suspected resistant biotype was cross-resistant to halosulfuron-methyl, an ALS herbicide used to control Cyperus spp., both biotypes were treated with 0, 0.0117, 0.0233, 0.0466, 0.0933, 0.187, 0.373, and 0.746 g ai ha−1 of halosulfuron-methyl. Plants were rated for injury at 7, 14, and 28 d after treatment (DAT), and aboveground biomass was harvested at 28 DAT. For imazapic, LD50 was 0.041 and 1.503 kg ai ha−1 and the GR50 was estimated to be 0.0128 and 1.853 kg ha−1 for Sus and Res biotypes, respectively, indicating 36- and 145-fold increase in resistance of the Res biotype for I50 and GR50, respectively. Both biotypes responded similarly to applications of halosulfuron-methyl, with biomass reduction at rates greater than 0.023 kg ai ha−1. Transcriptome profiles revealed a mutation in the target-site gene of the resistant biotype causing an amino acid substitution from alanine to valine at position 205 (Ala-205-Val). Growers should continue to rotate chemistries and implement integrated weed management approaches for control of C. esculentus, as the use of imazapic over consecutive years has led to resistance in C. esculentus.

Force majeure (FM) clauses are designed to free both parties to a contract from liability when an event beyond their control prevents one or both of them from performing the contract. Examples of such events include inclement weather, natural catastrophes, political upheaval, changes in applicable rules or regulations, or unavailability of essential supplies or services. It is common to include FM clauses in manufacturing, construction or transportation contracts, where there is a high risk of such events occurring due to the length of time required to perform the contract, or the complexity of the performance obligations. Often a lot of money is at stake and pre-allocating the risk of such events is crucial to avoid disputes further down the line. While disputes regarding the meaning and effect of FM clauses may not be unusual, such disputes rarely reach the Supreme Court. MUR Shipping was one such rare case.

Emotion recognition in conversation (ERC) faces two major challenges: biased predictions and poor calibration. Classifiers often disproportionately favor certain emotion categories, such as neutral, due to the structural complexity of classifiers, the subjective nature of emotions, and imbalances in training datasets. This bias results in poorly calibrated predictions where the model’s predicted probabilities do not align with the true likelihood of outcomes. To tackle these problems, we introduce the application of conformal prediction (CP) into ERC tasks. CP is a distribution-free method that generates set-valued predictions to ensure marginal coverage in classification, thus improving the calibration of models. However, inherent biases in emotion recognition models prevent baseline CP from achieving a uniform conditional coverage across all classes. We propose a novel CP variant, class spectrum conformation, which significantly reduces coverage bias in CP methods. The methodologies introduced in this study enhance the reliability of prediction calibration and mitigate bias in complex natural language processing tasks.