1,419 research outputs found
Templates for magnetic symmetry and altermagnetism in hexagonal MnTe
The symmetry of long-range magnetic order in manganese telluride (alpha-MnTe)
is unknown. Likewise, its standing as an altermagnet. To improve the situation,
we present symmetry informed Bragg diffraction patterns based on a primary
magnetic order parameter for antiferromagnetic alignment between Mn dipoles. It
does not break translation symmetry in a centrosymmetric structure, in keeping
with an accepted definition of altermagnetism. Our templates serve x-ray
diffraction that benefits from signal enhancement using a Mn atomic resonance,
and neutron scattering. Even rank multipoles in magnetic neutron diffraction
reflect a core requirement of altermagnetism, because they are zero for strong
spin-orbit coupling. Symmetry in the templates demands that nuclear and
magnetic contributions possess the same phase, which enables standard neutron
polarization analysis on Bragg spots with overlapping contributions. However,
three of the four templates generate Bragg spots that do not appear in the
lattice (nuclear) diffraction pattern, i.e., Bragg spots that are
basis-forbidden and purely magnetic in origin. On the other hand, identical
symmetry demands a 90 deg phase shift between magnetic (time-odd) and
charge-like (time-even, Templeton-Templeton) contributions to x-ray scattering
amplitudes. Consequently, circular polarization in the primary beam of x-rays
is rotated. The difference in the intensities of a Bragg spot measured with
right- and left-handed circular primary polarization defines a chiral
signature. Further tests include predictions in three out four templates of
zero intensity in a specified channel of x-ray polarization. Diffraction
properties of a template are radically different from those of a parity-time
(PT)-symmetric antiferromagnet, for its symmetry allows a linear ME effect and
prohibits both a PM effect and a chiral signature
The privacy calculus in the context of novel health technology for diagnosing and tracking infectious diseases:The role of disease severity and technology's evidence base for effectiveness in adoption and voluntary health data-sharing
In the past decades, accelerated by the recent COVID pandemic, the field of healthcare has faced technological advancements, such as wearables and mobile applications, that collect personal or health data. However, such tools are ineffective if they are not adopted by a large part of the population or if relevant health data, collected by the application, are not (voluntarily) shared. This study assessed the role of disease severity and evidence base for the effectiveness of the technology in the Privacy Calculus risk-benefit trade-off to contribute or hinder technology acceptance and data sharing. A large-scale 2 × 2 × 2 online vignette experiment (n = 822) was carried out, where participants were presented with a hypothetical scenario describing a novel health technology for diagnosing and tracking of infectious diseases. The results indicated that participants’ privacy concerns negatively affected their intention to use the technology and willingness to share data, and that a high severity of the disease weakened this relationship. None of the other expected effects on intentions to use, willingness to share data or privacy concerns, were significant. These findings highlight the role of privacy as a barrier to technology acceptance, and suggest disease severity plays a role in the Privacy Calculus risk-benefit trade off by weakening the negative effect of privacy concerns on adoption in contexts where disease severity is high.</p
The privacy calculus in the context of novel health technology for diagnosing and tracking infectious diseases:The role of disease severity and technology's evidence base for effectiveness in adoption and voluntary health data-sharing
In the past decades, accelerated by the recent COVID pandemic, the field of healthcare has faced technological advancements, such as wearables and mobile applications, that collect personal or health data. However, such tools are ineffective if they are not adopted by a large part of the population or if relevant health data, collected by the application, are not (voluntarily) shared. This study assessed the role of disease severity and evidence base for the effectiveness of the technology in the Privacy Calculus risk-benefit trade-off to contribute or hinder technology acceptance and data sharing. A large-scale 2 × 2 × 2 online vignette experiment (n = 822) was carried out, where participants were presented with a hypothetical scenario describing a novel health technology for diagnosing and tracking of infectious diseases. The results indicated that participants’ privacy concerns negatively affected their intention to use the technology and willingness to share data, and that a high severity of the disease weakened this relationship. None of the other expected effects on intentions to use, willingness to share data or privacy concerns, were significant. These findings highlight the role of privacy as a barrier to technology acceptance, and suggest disease severity plays a role in the Privacy Calculus risk-benefit trade off by weakening the negative effect of privacy concerns on adoption in contexts where disease severity is high.</p
Spin and orbital moments of ultra-thin Fe films on various semiconductor surfaces
The magnetic moments of ultrathin Fe films on three different III-V semiconductor substrates, namely GaAs, InAs and In0.2Ga0.8As have been measured with X-ray magnetic circular dichroism at room temperature to assess their relative merits as combinations suitable for next-generation spintronic devices. The results revealed rather similar spin moments and orbital moments for the three systems, suggesting the relationship between film and semiconductor lattice parameters to be less critical to magnetic moments than magnetic anisotropy
Semiparametric theory and empirical processes in causal inference
In this paper we review important aspects of semiparametric theory and
empirical processes that arise in causal inference problems. We begin with a
brief introduction to the general problem of causal inference, and go on to
discuss estimation and inference for causal effects under semiparametric
models, which allow parts of the data-generating process to be unrestricted if
they are not of particular interest (i.e., nuisance functions). These models
are very useful in causal problems because the outcome process is often complex
and difficult to model, and there may only be information available about the
treatment process (at best). Semiparametric theory gives a framework for
benchmarking efficiency and constructing estimators in such settings. In the
second part of the paper we discuss empirical process theory, which provides
powerful tools for understanding the asymptotic behavior of semiparametric
estimators that depend on flexible nonparametric estimators of nuisance
functions. These tools are crucial for incorporating machine learning and other
modern methods into causal inference analyses. We conclude by examining related
extensions and future directions for work in semiparametric causal inference
High-order Dy multipole motifs observed in DyB2C2 with resonant soft x-ray Bragg diffraction
Resonant soft x-ray Bragg diffraction at the Dy M4,5 edges has been exploited
to study Dy multipole motifs in DyB2C2. Our results are explained introducing
the intra-atomic quadrupolar interaction between the core 3d and valence 4f
shell. This allows us to determine for the first time higher order multipole
moments of dysprosium electrons and to draw their precise charge density.
The Dy hexadecapole and hexacontatetrapole moment have been estimated at -20%
and +30% of the quadrupolar moment, respectively. No evidence for the lock-in
of the orbitals at T_N has been observed, in contrast to earlier suggestions.
The multipolar interaction and the structural transition cooperate along c but
they compete in the basal plane explaining the canted structure along [110].Comment: 4 pages, 3 figure
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