We study how Spanish equity investors assessed firms’ exposure to political risk during the regime change of the 1930s. We show that shifts in political uncertainty regularly predicted a general deterioration of future investment opportunities in the stock market. However, we also find that firms differed in their sensitivity to uncertainty, reflecting important differences in their perceived exposures to political risk. The negative impact of uncertainty was significantly milder for firms with political connections to republican parties. The price of some stocks increased in periods of heightened uncertainty, thus allowing investors to hedge against reinvestment risk. In the case of firms that became targets of hostile political actions, we observe that investors frequently adjusted their assessment of individual stocks to changes in firm-specific political circumstances. Over the whole period of the Second Republic, investors’ systematic preference for safer equity hedges led to a continuous decline in the price of stocks perceived as more exposed to political risk.

Low iron (Fe) stores at birth may adversely influence child cognitive and motor development. The aims of this study were to assess cord blood Fe levels and explore maternal and neonatal factors associated with Fe status. Cord blood specimens (n 46) were obtained from the BC Children’s Hospital BioBank in Vancouver, Canada. The primary outcome was cord plasma ferritin, measured using sandwich-ELISA. Predictors of interest included maternal age, gestational age, gravidity, infant sex, birth weight and delivery method. Median (interquartile range (IQR)) maternal age and gestational age at delivery was 33·5 (29·3–35·8) years and 36·5 (30·0–39·0) weeks, respectively, and 44 % of infants were female. Median (IQR) cord ferritin was 100·4 (75·7–128·9) µg/l, and 26 % had low Fe status (ferritin <76 µg/l). Among preterm deliveries, a 1-week increase in gestational age was associated with a 6·22 (95 % CI (1·10, 9·52)) µg/l increase in median cord ferritin. However, among term deliveries, a negative trend was observed (–2·38 µg/l per week of gestation (95 % CI (–34·8, 0·78))), indicating a potential non-linear relationship between gestational age and cord ferritin. Female term infants had higher cord ferritin compared with males (β (95 % CI): 30·3 (18·4, 57·9) µg/l), suggesting sex-specific differences in Fe transfer, acquisition and utilisation. Cord ferritin was higher with vaginal deliveries compared with caesarean sections (β (95 % CI): 39·1 (29·0, 51·5) µg/l). Low Fe status may be a concern among infants in Canada; however, further research is needed to inform appropriate thresholds to define optimal Fe status in cord blood.

Interest in studies examining the effect of temperament types on nutrition has recently increased. The aim of this study was to evaluate the relationship between nine types of temperament, anthropometric measurements, and nutrition in adults. This study was conducted on 1317 individuals aged between 18 and 55 years. Descriptive information, dietary habits and anthropometric measurements of the participants were questioned. The Nine Types of Temperament Scale was administered to the individuals and food consumption records were obtained with a 24-hour retrospective reminder method. Type 2 scores of obese participants were higher than those of underweight and normal body weight; Type 8 scores of overweight participants were higher than those of normal body weight. Daily dietary intake of protein, riboflavin, folate, vitamins K, C, calcium, iron, and cholesterol were negatively associated with Type 1 score; protein, magnesium, iron, zinc intake, and water consumption were negatively associated with Type 2 score. Type 3 score was negatively associated with dietary CHO (%), dietary magnesium, iron, and zinc intake and positively associated with water consumption. The results of the study indicate significant relationships between temperament types, dietary habits, and anthropometric measures. In this context, considering temperament types when planning dietary patterns of individuals may be a new approach.

A cylindrical cascade on $\mathbb {T}^d\times \mathbb {R}^r$ can be seen as a deterministic random walk on $\mathbb {R}^r$ driven by an observable over the irrational toral translation on the base torus. We prove that, when the observable is the indicator function of a generic (straight) rectangle in $\mathbb {T}^2$, the cascade on $\mathbb {T}^2\times \mathbb {R}$ is ergodic for a $G_{\delta }$-dense set of translation vectors. We also provide examples of ergodic cylindrical cascades in higher dimensions with more restrictive conditions on the side lengths of the rectangles.

In order to take on arbitrary geometries, shape-changing arrays must introduce gaps between their elements. To enhance performance, this unused area can be filled with meta-material inspired switched passive networks on flexible sheets in order to compensate for the effects of increased spacing. These flexible meta-gaps can easily fold and deploy when the array changes shape. This work investigates the promise of meta-gaps through the measurement of a 5-by-5 λ-spaced array with 40 meta-gap sheets and 960 switches. The optimization and measurement problems associated with such a high-dimensional phased array are discussed. Simulated and in-situ optimization experiments are conducted to examine the differential performance of metaheuristic algorithms and characterize the underlying optimization problem. Measurement results demonstrate that in our implementation meta-gaps increase the average main beam power within the field of view (FoV) by 0.46 dB, suppress the average side lobe level within the FoV by 2 dB, and enhance the field-of-view by 23.5∘ compared to a ground-plane backed array.

Glaciers play a crucial role in the Asian Water Tower, underscoring the necessity of accurately assessing their mass balance and ice volume to evaluate their significance as sustainable freshwater resources. In this study, we analyzed ground-penetrating radar (GPR) measurements from a 2020 survey of the Xiao Dongkemadi Glacier (XDG) to determine ice thickness, and we extended the glacier’s volume-change record to 2020 by employing multi-source remote-sensing data. Our findings show that the GPR-derived mean ice thickness of XDG in 2020 was 54.78 ± 3.69 m, corresponding to an ice volume of 0.0811 ± 0.0056 km3. From 1969 to 2020, the geodetic mass balance was −0.19 ± 0.02 m w.e. a−1, and the glacier experienced area and ice volume losses of 16.38 ± 4.66% and 31.01 ± 4.59%, respectively. The long-term mass-balance reconstruction reveals weak fluctuations occurred from 1967 to 1993 and that overall mass losses have occurred since 1994. This ongoing shrinkage and ice loss are mainly associated with the temperature increases in the warm season since the 1960s. If the climate trend across the central Tibetan Plateau follows to the SSP585 scenario, then XDG is at risk of disappearing by the end of the century.

Atmospheric models used for weather and climate prediction are traditionally formulated in a deterministic manner. In other words, given a particular state of the resolved scale variables, the most likely forcing from the subgrid scale processes is estimated and used to predict the evolution of the large-scale flow. However, the lack of scale separation in the atmosphere means that this approach is a large source of error in forecasts. Over recent years, an alternative paradigm has developed: the use of stochastic techniques to characterize uncertainty in small-scale processes. These techniques are now widely used across weather, subseasonal, seasonal, and climate timescales. In parallel, recent years have also seen significant progress in replacing parametrization schemes using machine learning (ML). This has the potential to both speed up and improve our numerical models. However, the focus to date has largely been on deterministic approaches. In this position paper, we bring together these two key developments and discuss the potential for data-driven approaches for stochastic parametrization. We highlight early studies in this area and draw attention to the novel challenges that remain.

The EU Return Directive demands that immigrant detention be as short as possible, but, by logical implication, this also means that detention can be as long as necessary. What concerns the maximum length of detention, the Return Directive is remarkably generous: Immigrants can be detained for a period of up to eighteen months—a deprivation of liberty that is otherwise justified only as punishment for serious crimes. The practice of such long-term detention, now burgeoning, is highly questionable for moral, practical, and—our focus—legal reasons.

The European Convention of Human Rights (ECHR) provides the relevant yardstick. While discussions on the legality of immigrant detention have focused on requirements of necessity, we shift attention towards the surprisingly absent question of maximum duration. Our analysis delves into the drafting context of the ECHR to reveal that it only authorizes the pre-removal detention of immigrants for markedly short periods. Picking up the interpretative canon of the regime, we note that meanings can of course change, but we argue that it is a legal mistake to consider that long-term detention is now sanctioned by the Convention.

Tightly focused proton beams generated from helical coil targets have been shown to be highly collimated across small distances, and display characteristic spectral bunching. We show, for the first time, proton spectra from such targets at high resolution via a Thomson parabola spectrometer. The proton spectral peaks reach energies above 50 MeV, with cutoffs approaching 70 MeV and particle numbers greater than 10${}^{10}$. The spectral bunch width has also been measured as low as approximately 8.5 MeV (17% energy spread). The proton beam pointing and divergence measured at metre-scale distances are found to be stable with the average pointing stability below 10 mrad, and average half-angle beam divergences of approximately 6 mrad. Evidence of the influence of the final turn of the coil on beam pointing over long distances is also presented, corroborated by particle tracing simulations, indicating the scope for further improvement and control of the beam pointing with modifying target parameters.

Stochastic generators are useful for estimating climate impacts on various sectors. Projecting climate risk in various sectors, e.g. energy systems, requires generators that are accurate (statistical resemblance to ground-truth), reliable (do not produce erroneous examples), and efficient. Leveraging data from the North American Land Data Assimilation System, we introduce TemperatureGAN, a Generative Adversarial Network conditioned on months, regions, and time periods, to generate 2 m above ground atmospheric temperatures at an hourly resolution. We propose evaluation methods and metrics to measure the quality of generated samples. We show that TemperatureGAN produces high-fidelity examples with good spatial representation and temporal dynamics consistent with known diurnal cycles.

Airborne radar sensors capture the profile of snow layers present on top of an ice sheet. Accurate tracking of these layers is essential to calculate their thicknesses, which are required to investigate the contribution of polar ice cap melt to sea-level rise. However, automatically processing the radar echograms to detect the underlying snow layers is a challenging problem. In our work, we develop wavelet-based multi-scale deep learning architectures for these radar echograms to improve snow layer detection. These architectures estimate the layer depths with a mean absolute error of 3.31 pixels and 94.3% average precision, achieving higher generalizability as compared to state-of-the-art snow layer detection networks. These depth estimates also agree well with physically drilled stake measurements. Such robust architectures can be used on echograms from future missions to efficiently trace snow layers, estimate their individual thicknesses, and thus support sea-level rise projection models.

Climate models are biased with respect to real-world observations. They usually need to be adjusted before being used in impact studies. The suite of statistical methods that enable such adjustments is called bias correction (BC). However, BC methods currently struggle to adjust temporal biases. Because they mostly disregard the dependence between consecutive time points. As a result, climate statistics with long-range temporal properties, such as the number of heatwaves and their frequency, cannot be corrected accurately. This makes it more difficult to produce reliable impact studies on such climate statistics. This article offers a novel BC methodology to correct temporal biases. This is made possible by rethinking the philosophy behind BC. We will introduce BC as a time-indexed regression task with stochastic outputs. Rethinking BC enables us to adapt state-of-the-art machine learning (ML) attention models and thereby learn different types of biases, including temporal asynchronicities. With a case study of number of heatwaves in Abuja, Nigeria and Tokyo, Japan, we show more accurate results than current climate model outputs and alternative BC methods.