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Errors in variable subscripts, equations and Fig. 8 in Section 3.2 of the article by Lotze et al. [(2024). J. Synchrotron Rad. 31, 42–52] are corrected.

Local lattice distortion (LLD) is a salient feature of bcc-structured refractory high-entropy alloys (RHEAs), closely associated with their mechanical properties. To quantify the extent of LLDs in RHEAs, the pair distribution function (PDF) analysis has been identified as a promising approach. However, the commonly observed chemical segregation within these alloys introduces challenges in accurately determining LLDs. In this study, the effect of chemical segregation on LLD quantification was investigated through fitting simulated two-phase composite PDFs, representing segregated microstructures, with a single-phase model and evaluating the errors to assess the accuracy and reliability of small-box analysis in this context. The results show that the errors introduced by chemical segregation increase with increasing lattice parameter difference, and the fitting quality gradually deteriorates to a point where it no longer adequately describes the data. We found that the lattice parameter difference should be below 1% for precise and reliable LLD measurements in bcc-structured RHEAs. Additionally, we observed that while the scattering length variation due to segregation does affect LLD quantification, its effect is comparatively minor.

In Sweden, as elsewhere, migrants’ language knowledge has become a debated topic and a cornerstone in restrictive migration policies. In this paper, we investigate how the metaphor ‘language as a key’ to society is used by migrant language learners and how it relates to the participants’ experiences of learning Swedish. The study draws on interviews with 174 migrants enrolled in three different state-subsidized language educational contexts providing tuition in basic Swedish. The results highlight how the metaphor was recurrently used and how it constructed certain time-space relations. It positioned migrant language learners as outsiders to a national imaginary, and the temporal aspects of the metaphor both pointed to what was needed to be done in the present, that is, learning Swedish, and how language learning was associated with future-oriented possibilities. While the migrant students reproduced a simplified discourse on the relationship between language learning and integration, their language learning experiences simultaneously subversively challenged the simplified metaphor.

Pressure-induced polymorphism has recently been demonstrated in several high entropy alloys. This offers a new window into the much-debated issue of phase selection and stability in these systems. Here, we examine the effect of cryogenic temperatures on the pressure-induced transition from face centered cubic to hexagonal close-packed structures of the prototype CoCrFeMnNi (Cantor) alloy. We observe a reduction in the critical pressure for the onset of the polymorphic transition as the temperature decreases, confirming the progressive stabilization of the hexagonal phase with decreasing temperature previously predicted by ab initio calculations accounting for magnetic interactions. We argue that in situ high-pressure experiments at cryogenic temperatures, which suppress time-dependent transformation triggered at higher temperatures, present a unique opportunity to significantly improve our understanding of these complex alloys. [ABSTRACT FROM AUTHOR]

This study developed a random forest approach for downscaling the coarse-resolution (36 km) soil moisture measured by The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission to 1 km spatial resolution, utilizing airborne remotely sensed data (radar backscatter and radiometer retrieved soil moisture), vegetation characteristics (normalized difference vegetation index), soil properties, topography, and ground soil moisture measurements from before the launch of SMAP for training a random forest model. The 36 km SMAP soil moisture product was then downscaled by the trained model to 1 km resolution using the information from SMAP. The downscaled soil moisture was evaluated using airborne retrieved soil moisture observations and ground soil moisture measurements. Considering the airborne retrieved soil moisture as a reference, the results demonstrated that the proposed random forest model could downscale the SMAP radiometer product to 1 km resolution with a correlation coefficient of 0.97, unbiased Root Mean Square Error of 0.048 m3 m−3 and bias of 0.016 m3 m−3. Accordingly, the downscaled soil moisture captured the spatial and temporal heterogeneity and demonstrated the potential of the proposed machine learning model for soil moisture downscaling.

Estimating soil moisture from microwave brightness temperature is extremely challenging in densely vegetated areas. The soil moisture retrieved from the Soil Moisture Active Passive (SMAP) measurements tends to be consistently overestimated, sometimes exceeding the saturation level of mineral soils. Therefore, the retrieved soil moisture cannot detect or monitor climate extremes, such as floods and droughts for forests, natural resource management, and climate change research. We hypothesize that the main issue is that the scattering albedo (ω) and the optical depth (τ) are parameterized solely with NDVI (Normalized Difference Vegetation Index), neglecting the polarization characteristics from vegetation structure. This study proposes a weighting factor between scattering and optical thickness, a function of MPDI (Microwave Polarization Difference Index), and applies it to both parameters simultaneously to increase the scattering effect and decrease the attenuation effect in high MPDI. The validation results based on the Climate Reference Network revealed that considering MPDI is critical in reducing soil moisture overestimation errors and obtaining more accurate soil moisture over forested regions. This results in correlation improving from 0.36 to 0.44, a decrease in ubRMSE from 0.179 to 0.125 cm³cm−³, and bias lowering from 0.127 to 0.060 cm³cm−³ in comparison with the SMAP measurements over forested regions. Editor highlights: • Optical depth increases with high NDVI and low MPDI. • Scattering albedo increases with high NDVI and high MPDI. • Increasing scattering albedo decreases brightness temperature in forward simulation. • Decreased brightness temperature in simulation resolved overestimation of SMAP SM. • L-band (1.4 GHz) radiometer measurements can assess forest soil moisture (SM).

In many European countries, Sweden included, social inclusion of adult migrants has come to mean second language learning and labour market establishment. This understanding of social inclusion has been problematised by previous research as it reinforces a deficit discourse where migrants are depicted as lacking skills or incentives, and social inclusion is seen merely as a matter of adjusting to society. This study aims to examine migrants’ positioning in relation to language learning and social inclusion. It is based on a longitudinal interview study with adult migrants, first when being enrolled in second language education, and later in the continuing process of making a life in a new country. We analyse five migrant narratives, drawing on the concepts of positioning, agency, rights, duties, and capital in relation to their past, present, and future aspirations. The results show that the position of the “good migrant” taking responsibility for language learning and job seeking is prominent. At the same time, positioning is also constructed in relation to individual aspirations and opportunities, depending on one’s circumstance of life and capital, such as previous education or social networks. Thus, inclusion is closely related to being recognised, not primarily as a migrant, but as the person one strives to be, both professionally and personally.

Abstract A theory of maximum entropy production (MEP) for electromagnetic wave propagation in dielectric materials is proposed and applied to simultaneously retrieving soil moisture (SM) and vegetation water content (VWC) from L‐band microwave brightness temperature (TB). One representation of the MEP principle states that a non‐equilibrium system corresponds to such a configuration of energy fluxes that minimizes a dissipation function under the constraint of energy conservation. The dissipation function for radiative transfer is formulated as an analogy of that for heat transfer. A new physical parameter, radiative inertia as an analogy of thermal inertia, is introduced to characterize radiative attenuation in dielectric media. The radiative inertia is parameterized in terms of the penetration depth of electromagnetic waves as a function of the complex dielectric constant. The MEP based retrieval algorithm predicts SM and VWC by minimizing the dissipation function under the constraint of the conservation of radiative energy. The retrievals of SM and VWC based on the MEP theory were validated against field observations in tropical and temperate forested regions of the Amazon and North America. The proof‐of‐concept analysis demonstrates the capability of the MEP algorithm for simultaneous retrievals of SM and VWC even for dense canopy (e.g., VWC > 5 kg m−2). The MEP method is a new theoretical framework for developing innovative remote sensing algorithms of the Earth system not limited to microwave observations.

Satellite microwave sensors are well suited for monitoring landscape freeze-thaw (FT) transitions owing to the strong brightness temperature (TB) or backscatter response to changes in liquid water abundance between predominantly frozen and thawed conditions. The FT retrieval is also a sensitive climate indicator with strong biophysical importance. However, retrieval algorithms can have difficulty distinguishing the FT status of soils from that of overlying features such as snow and vegetation, while variable land conditions can also degrade performance. Here, we applied a deep learning model using a multilayer convolutional neural network driven by AMSR2 and SMAP TB records, and trained on surface (~0–5 cm depth) soil temperature FT observations. Soil FT states were classified for the local morning (6 a.m.) and evening (6 p.m.) conditions corresponding to SMAP descending and ascending orbital overpasses, mapped to a 9 km polar grid spanning a five-year (2016–2020) record and Northern Hemisphere domain. Continuous variable estimates of the probability of frozen or thawed conditions were derived using a model cost function optimized against FT observational training data. Model results derived using combined multi-frequency (1.4, 18.7, 36.5 GHz) TBs produced the highest soil FT accuracy over other models derived using only single sensor or single frequency TB inputs. Moreover, SMAP L-band (1.4 GHz) TBs provided enhanced soil FT information and performance gain over model results derived using only AMSR2 TB inputs. The resulting soil FT classification showed favorable and consistent performance against soil FT observations from ERA5 reanalysis (mean percent accuracy, MPA: 92.7%) and in situ weather stations (MPA: 91.0%). The soil FT accuracy was generally consistent between morning and afternoon predictions and across different land covers and seasons. The model also showed better FT accuracy than ERA5 against regional weather station measurements (91.0% vs. 86.1% MPA). However, model confidence was lower in complex terrain where FT spatial heterogeneity was likely beneath the effective model grain size. Our results provide a high level of precision in mapping soil FT dynamics to improve understanding of complex seasonal transitions and their influence on ecological processes and climate feedbacks, with the potential to inform Earth system model predictions.