Moralizing Immigration: Political Framing, Moral Conviction, and Polarization in the United States and Denmark

Morally charged rhetoric is commonplace in political discourse on immigration but scholars have not examined how it affects divisions over the issue among the public. To address this gap, we employ preregistered survey experiments in two countries where anti-immigration rhetoric has been prominent: the United States and Denmark. We demonstrate that exposure to moralized messages leads respondents to place greater moral weight on their existing immigration opinions and become more averse to political leaders and, in the United States, social interaction partners who espouse opposite beliefs. This suggests that political moralization contributes to moral conflict and affective polarization. We find no evidence, however, that moral framing produces attitudinal polarization—that is, more extreme immigration opinions. Our study helps make sense of the heightened intensity of anti-immigrant politics even when attitudes are stable. It also suggests a promising avenue for comparative research on affective polarization by shifting the focus from partisanship to the moralization of existing issue disagreements.

Molecular Paleobiology of the Echinoderm Skeleton

Molecular paleobiology provides a promising avenue to merge data from deep time, molecular biology and genomics, gaining insights into the evolutionary process at multiple levels. The echinoderm skeleton is a model for molecular paleobioloogical studies. I begin with an overview of the skeletogenic process in echinoderms, as well as a discussion of what gene regulatory networks are, and why they are of interest to paleobiologists. I then highlight recent advances in the evolution of the echinoderm skeleton from both paleobiological and molecular/functional genomic perspectives, highlighting examples where diverse approaches provide complementary insight and discussing potential of this field of research.

High-fidelity quantum gates for OAM single qudits on quantum memory

The application of high-dimensional quantum systems (qudits) in quantum computing and communications seems to be a promising avenue due to the possibility of increasing the amount of information encoded in one physical carrier. In this work, we propose a method for implementing single-qudit gates for qudits based on light modes with orbital angular momentum (OAM). Method for logical qudits encoding, which ensures the quasi-cyclicity of operations, is introduced. Based on the protocol for converting the OAM of light in the Raman quantum memory scheme (Vashukevich et al 2020 Phys. Rev. A 101 033830), we show that the considered gates provide an extremely high level of fidelity of single-qudit transformations. We also compare quantum gates’ properties for systems of different dimensions and find the optimal conditions for carrying out transformations in the protocol under consideration.

Assessing the effect of the electrode orientation on the performance of soil microbial fuel cells

Soil microbial fuel cells (SMFCs) are a sub-class of the microbial fuel cells family, in which the soil acts as the electrolyte, and as the source of microorganisms and organic fuel. Given the great simplicity of the system design, SMFCs show a promising avenue for energy generation in remote areas. In this study, we investigate the influence that geometrical factors, such as the electrode orientation, have on the electrochemical performance of SMFCs. Two types of electrode orientations: horizontal and vertical, were tested. Additionally, the influence of anode and cathode immersion in soil was explored too. Our results demonstrate that vertical positioning of the cathode in soil is not a viable option. The increase in cathodic immersion leads to a more rapid performance decay, attributed to more anaerobic conditions along soil’s depth. The increase in anode immersion has a positive effect on the evolution of the negative electrode potential. However, with the increase in electrode spacing, the performance drops due to a greater internal resistance.

The Unified Narcissism Scale: Moving towards an integrated measure of narcissism

Narcissism as a psychological construct has had a contentious past both in its conceptualization and measurement. There is an emerging consensus that narcissism consists of grandiose and vulnerable subtypes, which share a common core. In the present research (N = 1002), we constructed a new measure of unified narcissism that reflects these contemporary understandings using items from the most widely used measures of grandiose and vulnerable narcissism: the Narcissistic Personality Inventory (NPI; Raskin & Terry, 1988, https://doi.org/10.1037/0022-3514.54.5.890), and the Pathological Narcissism Inventory (PNI; Pincus et al., 2009, https://doi-org/10.1037/a0016530). We used classical test theory and item response theory approaches to devise a 29-item Unified Narcissism Scale. The scale showed good internal consistency, and convergent and discriminant validity, and showed evidence of measurement invariance between men and women. This research gave strong support for the structure, reliability, and validity of the unified measure, which offers a promising avenue for further enhancing our knowledge of narcissism.

Hospitalization Due to Fire-Induced Pollution in the Brazilian Legal Amazon from 2005 to 2018

Fire is widely used in the Amazon as a ubiquitous driver of land management and land cover change. Regardless of their purpose, fires release a considerable amount of pollutants into the atmosphere, with severe consequences for human health. This paper adds to the extant literature by measuring the causal effect of fires on hospitalizations, using the approach of instrumental variables, whose validity is assessed with multiple statistical tests. A wide range of confounders are added as covariates, seizing on the accuracy enhancement potential of a broad and fine-grained dataset that covers 14 years of the whole Amazon territory at a municipal–monthly level. The results reveal a positive effect of fire on hospitalizations due to respiratory illnesses in general, and particularly in those due to asthma. A 1% increase in pollution concentration would increase hospitalizations by 0.14% at a municipality–monthly level. A total of 5% of respiratory hospitalizations were estimated to be attributable to fire-induced pollution, corresponding to 822 cases per month. The analysis demonstrates that the coupling of econometrics and remote sensing data is a promising avenue towards the assessment of impacts caused by fires, which may be applied to other regions of the world subjected to anthropogenic fires.

Objective Measurement of Hyperactivity Using Mobile Sensing and Machine Learning (Preprint)

UNSTRUCTURED Although hyperactivity is a core symptom of ADHD, there are no objective measures that are widely used in clinical settings. We describe the development of a smartwatch application to measure hyperactivity in school-age children. The LemurDx prototype is a software system for smartwatches that uses wearable sensor technology and machine learning (ML) to measure hyperactivity, with the goal of differentiating children with ADHD combined presentation or predominantly hyperactive/impulsive presentation from children with typical levels of activity. In this pilot study, we recruited 30 children (ages 6-11) to wear the smartwatch with the LemurDx app for two days. Parents also provided activity labels for 30-minute intervals to help train the algorithm. Half the sample had ADHD combined presentation or predominantly hyperactive/impulsive presentation (n = 15) and half were healthy controls (n = 15). Results indicated high usability scores and an overall diagnostic accuracy of .89 (sensitivity = .93; specificity = .86) when the motion sensor output was paired with the activity labels, suggesting that state-of-the-art sensors and ML may provide a promising avenue for the objective measurement of hyperactivity.

Importance of Kernel Bandwidth in Quantum Machine Learning

Quantum kernel methods are considered a promising avenue for applying quantum computers to machine learning problems. However, recent results overlook the central role hyperparameters play in determining the performance of machine learning methods. In this work we identify the hyperparameter controlling the bandwidth of a quantum kernel and show that it controls the expressivity of the resulting model. We use extensive numerical experiments with multiple quantum kernels and classical datasets to show consistent change in the model behavior from underfitting (bandwidth too large) to overfitting (bandwidth too small), with optimal generalization in between. We draw a connection between the bandwidth of classical and quantum kernels and show analogous behavior in both cases. Furthermore, we show that optimizing the bandwidth can help mitigate the exponential decay of kernel values with qubit count, which is the cause behind recent observations that the performance of quantum kernel methods decreases with qubit count. We reproduce these negative results and show that if the kernel bandwidth is optimized, the performance instead improves with growing qubit count and becomes competitive with the best classical methods.

The Impact of Behavioral Economics on the Law: Introduction

Adopting the paradigms, findings and tools of behavioral economics has opened a promising avenue for legal research. This article sketches the broader framework within which the papers assembled in this special issue may be placed.

Wideband mid-infrared thermal emitter based on stacked nanocavity metasurfaces

Efficient thermal radiation in the mid-infrared (M-IR) region is of supreme importance for many applications including thermal imaging and sensing, thermal infrared light sources, infrared spectroscopy, emissivity coatings, and camouflage. The capability of controlling light makes metasurface an attractive platform for infrared applications. Recently, different metamaterials have been proposed to achieve high thermal radiation. To date, broadening of the radiation bandwidth of metasurface emitter (meta-emitter) has become a key goal to enable extensive applications. We experimentally demonstrate a broadband M-IR thermal emitter using stacked nanocavity metasurface consisting of two pairs of circular-shaped dielectric (Si3N4) – metal (Au) stacks. A high thermal radiation can be obtained by engineering the geometry of nanocavity metasurface. Such a meta-emitter provides wideband and broad angular absorptance of both p- and s-polarized light, offering a wideband thermal radiation with an average emissivity of more than 80% in the M-IR atmospheric window of 8–14 μm. The experimental illustration together with theoretical framework places a basis for designing broadband thermal emitters, which, as anticipated, will initiate a promising avenue to M-IR source.