In this study, we focus on European immigration attitudes in the perspective of deservingness perceptions and political orientation. Our data are conducted from the European Social Survey (2016) database, which contains 21 European countries and 39,400 participants. We used the multilevel method to study the relationship between immigration attitudes and deservingness perceptions. The results demonstrate that the more negative deservingness perceptions are, the more negative immigration attitudes more likely become. Moreover, country-level political orientation moderate the relationship between immigration attitudes and deservingness perceptions. Deservingness perceptions have a greater role in explaining immigration attitudes on countries with political left-context, which gives us a new perspective to understand the public debates about immigration.

We propose a family of weighted statistics based on the CUSUM process of the WLS residuals for the online detection of changepoints in a Random Coefficient Autoregressive model, using both the standard CUSUM and the Page-CUSUM process. We derive the asymptotics under the null of no changepoint for all possible weighing schemes, including the case of the standardized CUSUM, for which we derive a Darling–Erdös-type limit theorem; our results guarantee the procedure-wise size control under both an open-ended and a closed-ended monitoring. In addition to considering the standard RCA model with no covariates, we also extend our results to the case of exogenous regressors. Our results can be applied irrespective of (and with no prior knowledge required as to) whether the observations are stationary or not, and irrespective of whether they change into a stationary or nonstationary regime. Hence, our methodology is particularly suited to detect the onset, or the collapse, of a bubble or an epidemic. Our simulations show that our procedures, especially when standardising the CUSUM process, can ensure very good size control and short detection delays. We complement our theory by studying the online detection of breaks in epidemiological and housing prices series.

This paper proposes a compact coplanar waveguide-fed slot antenna for ultra-wideband wearable applications, featuring six notch bands. The antenna utilizes a flexible liquid crystal polymer substrate with a thickness of 0.1 mm. The antenna achieves six band-notched characteristics by incorporating a split concentric ring etched on the radiating patch and L-shaped branches loaded on the ground plane. The proposed flexible antenna has dimensions of 30 × 30 mm2(0.65λ0*0.65λ0,λ0 is the free space wavelength at 6.5 GHz.). Measurement results show an impedance bandwidth ranging from 2.37 to 13.7 GHz and a fractional bandwidth of 134%. The notch bands cover 2.9–3.77 GHz for WiMAX applications, 4.14–4.9 GHz for the ARN band, 5.09–5.55 GHz for the WLAN downlink band, 5.86–6.46 GHz for the C-band uplink band, 6.66–7.39 GHz for the C-band/INSAT/super-extended band, and 7.93–8.43 GHz for ITU-8 GHz. The maximum gain in the operating band is 6.5 dBi. The performances of the flexible antenna are analyzed under bending conditions. The ANSYS HFSS electromagnetic simulator was used for the design and simulation of the proposed antenna. The flexible antenna is suitable for wearable applications.

Eye gaze tracking is increasingly popular due to improved technology and availability. In the domain of assistive device control, however, eye gaze tracking is often used in discrete ways (e.g., activating buttons on a screen), and does not harness the full potential of the gaze signal. In this article, we present a method for collecting both reactionary and controlled eye gaze signals, via screen-based tasks designed to isolate various types of eye movements. The resulting data allows us to build an individualized characterization for eye gaze interface use. Results from a study conducted with participants with motor impairments are presented, offering insights into maximizing the potential of eye gaze for assistive device control. Importantly, we demonstrate the potential for incorporating direct continuous eye gaze inputs into gaze-based interface designs; generally seen as intractable due to the ‘Midas touch’ problem of differentiating between gaze movements for perception versus for interface operation. Our key insight is to make use of an individualized measure of smooth pursuit characteristics to differentiate between gaze for control and gaze for environment scanning. We also present results relating to gaze-based metrics for mental workload and show the potential for the concurrent use of eye gaze for control input as well as assessing a user’s mental workload both offline and in real-time. These findings might inform the development of continuous control paradigms using eye gaze, as well as the use of eye tracking as the sole input modality to systems that share control between human-generated and autonomy-generated inputs.

This letter presents an improved analytical model for analyzing probe-fed microstrip antennas loaded by metallic vias with lumped terminations. The proposed formulation is based on the resonant cavity model and enables efficient analysis of such perturbed radiators for various types of terminations. The model is validated through the analysis of two antennas: one operating in a TM00 mode and the other with four capacitive terminations to produce circular polarization. Moreover, a reconfigurable RHCP/LHCP antenna based on the patch with capacitive terminations has been manufactured and tested, showing a broadside axial ratio below 0.5 dB at 1.575 GHz.

Ambient air pollution remains a global challenge, with adverse impacts on health and the environment. Addressing air pollution requires reliable data on pollutant concentrations, which form the foundation for interventions aimed at improving air quality. However, in many regions, including the United Kingdom, air pollution monitoring networks are characterized by spatial sparsity, heterogeneous placement, and frequent temporal data gaps, often due to issues such as power outages. We introduce a scalable data-driven supervised machine learning model framework designed to address temporal and spatial data gaps by filling missing measurements within the United Kingdom. The machine learning framework used is LightGBM, a gradient boosting algorithm based on decision trees, for efficient and scalable modeling. This approach provides a comprehensive dataset for England throughout 2018 at a 1 km2 hourly resolution. Leveraging machine learning techniques and real-world data from the sparsely distributed monitoring stations, we generate 355,827 synthetic monitoring stations across the study area. Validation was conducted to assess the model’s performance in forecasting, estimating missing locations, and capturing peak concentrations. The resulting dataset is of particular interest to a diverse range of stakeholders engaged in downstream assessments supported by outdoor air pollution concentration data for nitrogen dioxide (NO2), Ozone (O3), particulate matter with a diameter of 10 μm or less (PM10), particulate matter with a diameter of 2.5 μm or less PM2.5, and sulphur dioxide (SO2), at a higher resolution than was previously possible.

We report the first shock-tube experiments on Richtmyer–Meshkov instability at a single-mode light–heavy interface accelerated by a strong shock wave with Mach number higher than 3.0. Under the proximity effect of the transmitted shock and its induced secondary compression effect, the interface profile is markedly different from that in weakly compressible flows. For the first time, the validity of the compressible linear theory and the failure of the impulsive model in predicting the linear amplitude evolution in highly compressible flows are verified through experiments. Existing nonlinear and modal models fail to accurately describe the perturbation evolution, as they do not account for the shock proximity and secondary compression effects on interface evolution. The shock proximity effect manifests mainly in the early stages when the transmitted shock remains close to the interface, while the effect of secondary compression manifests primarily at the period when interactions of transverse shocks occur at the bubble tips. Based on these findings, we propose an empirical model capable of predicting the bubble evolution in highly compressible flows.

Acoustic resonances in cascade structures may cause structural damage and instability problems in aero-engines and other industrial plants; thus, developing corresponding prediction methods is important. However, works published in the open literature mostly focus on the special case of the stationary Parker modes and provide little knowledge into the rotating resonances in annular cascades, especially in the presence of non-zero background mean flows. This paper develops a three-dimensional semi-analytic model to study the acoustic resonances in an annular cascade in the presence of axial mean flow. The model applies an unsteady cascade response based on the three-dimensional lifting surface method to construct a matrix equation. Characteristic frequencies are solved in the complex domain by numerically searching for singular points. Both the oscillation frequency and the growth rate of the three-dimensional resonance modes are theoretically calculated for the first time under non-zero mean flow conditions. The results reveal an organised distribution with varying inter-blade phase angle and show obvious change with the background flow speed. It is found that the unsteady vortex shedding from the trailing edges of the cascade is a key factor influencing the dissipation rate of the resonance modes. In addition, the important effects of acoustic scattering by the cascade during resonances are examined, which qualitatively corroborate some previous experimental observations.

Measurement error undermines the accuracy of dietary intake data. The 24-hour dietary recall (24HR) is the standard data collection method in nutrition surveillance. Several neurocognitive processes underpin the act of recall, and individuals differ in their performance of these processes. This study aimed to investigate whether variation in neurocognitive processes, measured using four cognitive tasks, was associated with variation in measurement error of 24HRs. Participants (n 139) completed the Trail Making Test, the Wisconsin Card Sorting Test, the Visual Digit Span, and the Vividness of Visual Imagery questionnaire. During a controlled feeding study, participants completed three technology-assisted 24HR; the Automated Self-Administered Dietary Assessment Tool (ASA24), Intake24, and an Interviewer-Administered Image-Assisted 24HR (IA-24HR) one week apart. Percentage error between reported and true energy intakes was calculated. Using linear regression, the association between cognitive task scores and absolute percentage error in estimated energy intake was assessed. Longer time spent completing the Trail Making Test, an indicator of visual attention and executive functioning, was associated with greater error in energy intake estimation using ASA24 (B 0.13, 95% CI 0.04, 0.21) and Intake24 (B 0.10, 95% CI 0.02, 0.19). Regression models explained 13.6% (ASA24) and 15.8% (Intake24) of the variance in energy estimation error. No cognitive task scores were associated with error using IA-24HR. This study demonstrates that variation between individuals in neurocognitive processes explains some of the variation in 24HR error. Further investigation into the role of neurocognitive processes in 24HR and their role in the reliability of dietary intake data is warranted.

What political imaginaries have existed beyond the nation-state? What might the misfitting (queer?) materials of the past—those unamenable to inclusion in narratives of national resistance—teach us about colonial and apartheid pasts? What alternatives to the colony and its contemporary forms might we imagine now? To respond to these questions, this essay assembles an archive of twentieth-century Capetonian queenliness, placing the historical Queen Elizabeth in proximity with textual renderings of the queer queens of apartheid Cape Town. A fictional, tongue-in-cheek, book review, published in Drum magazine in 1977, figures as a paradigmatic text of a mid-century popular textual genre that is animated by the sensibility that I call “camp royalist.” The critical impetus that animates camp royalism provokes us to reconsider how we represent colonial and apartheid pasts and invites us to think about possible future, nonnational, political collectivities and critiques.

For a continuous-time phase-type (PH) distribution, starting with its Laplace–Stieltjes transform, we obtain a necessary and sufficient condition for its minimal PH representation to have the same order as its algebraic degree. To facilitate finding this minimal representation, we transform this condition equivalently into a non-convex optimization problem, which can be effectively addressed using an alternating minimization algorithm. The algorithm convergence is also proved. Moreover, the method we develop for the continuous-time PH distributions can be used directly for the discrete-time PH distributions after establishing an equivalence between the minimal representation problems for continuous-time and discrete-time PH distributions.

This article presents a bioinspired pneumatic soft actuator designed to mimic the flexo-extension movement of the human finger, with a particular focus on stiffness modulation through granular jamming. Three-chamber geometries – honeycomb, rectangular, and half-round – were evaluated to optimize curvature performance, utilizing Mold Star 15 Slow elastomer for actuator fabrication. Granular jamming, both passive and active, was implemented within the inextensible layer using chia and quinoa grains to enhance stiffness modulation. Experimental results revealed that the honeycomb geometry most closely aligned with the natural index finger trajectory. Stiffness evaluations demonstrated a range of 0–0.47 N/mm/° for quinoa and 0–0.9 N/mm/° for chia. The actuator’s force output increased by 16% for quinoa and 71% for chia compared to the nonjammed configuration. This enhanced performance is particularly beneficial for applications such as hand rehabilitation, where adaptive stiffness and force modulation are critical. Granular jamming, especially with active chia, provided superior adaptability for tasks requiring variable stiffness and resistance, making it a promising candidate for wearable robotic applications in rehabilitation.

We study linear random walks on the torus and show a quantitative equidistribution statement, under the assumption that the Zariski closure of the acting group is semisimple.