Empirical Approach to Developing an Optimal Socioeconomic Status Index for Health Surveillance

The association between multi-dimensional deprivation and public health is well established, and many area-based indices have been developed to measure or account for socioeconomic status in health surveillance. The Yost Index, developed in 2001, has been adopted in the US for cancer surveillance and is based on the combination of two heavily weighted (household income, poverty) and five lightly weighted (rent, home value, employment, education and working class) indicator variables. Our objectives were to 1) update indicators and find a more parsimonious version of the Yost Index by examining potential models that included indicators with more balanced weights/influence and reduced redundancy and 2) test the statistical consistency of the factor upon which the Yost Index is based. Despite the usefulness of the Yost Index, a one-factor structure including all seven Yost indicator variables is not statistically reliable and should be replaced with a three-factor model to include the true variability of all seven indicator variables. To find a one-dimensional alternative, we conducted maximum likelihood exploratory factor analysis on a subset of all possible combinations of fourteen indicator variables to find well-fitted one-dimensional factor models and completed confirmatory factor analysis on the resulting models. One indicator combination (poverty, education, employment, public assistance) emerged as the most stable unidimensional model. This model is more robust to extremes in local cost of living conditions, is comprised of ACS variables that rarely require imputation by the end-user and is a more parsimonious solution than the Yost index with a true one-factor structure.

CONSPIRACY BELIEF AND BEHAVIOR IN THE COVID-19 PANDEMIC

This research was aimed at investigating the relationship between belief in conspiracy theory and personality behaviors in quarantine. Belief in conspiracy theory has been hypothesized to predict the degree of adherence to quarantine restrictions (wearing protective equipment, isolation, hygiene) and influences antisocial behavior such as aggression and selfishness, as well as prosocial behavior such as help and altruism. The obtained data of the empirical research indicate the absence of functional connections that could demonstrate the defining role of conspiracy mindset in the manifestations of prosocial and asocial behavior; however, there is still a statistical consistency of changes in indicators. Also, preventive behavior was practically not associated with the personality's conspiracy beliefs about COVID-19 (the regression equation explains less than 1% of the variance). It is emphasized that the data obtained contradict the results of some previous researches, which are devoted to the research of the behavioral consequences of conspiracy mindset and adherence to preventive measures in a pandemic, in particular.

Nonlinear stochastic modelling with Langevin regression

Many physical systems characterized by nonlinear multiscale interactions can be modelled by treating unresolved degrees of freedom as random fluctuations. However, even when the microscopic governing equations and qualitative macroscopic behaviour are known, it is often difficult to derive a stochastic model that is consistent with observations. This is especially true for systems such as turbulence where the perturbations do not behave like Gaussian white noise, introducing non-Markovian behaviour to the dynamics. We address these challenges with a framework for identifying interpretable stochastic nonlinear dynamics from experimental data, using forward and adjoint Fokker–Planck equations to enforce statistical consistency. If the form of the Langevin equation is unknown, a simple sparsifying procedure can provide an appropriate functional form. We demonstrate that this method can learn stochastic models in two artificial examples: recovering a nonlinear Langevin equation forced by coloured noise and approximating the second-order dynamics of a particle in a double-well potential with the corresponding first-order bifurcation normal form. Finally, we apply Langevin regression to experimental measurements of a turbulent bluff body wake and show that the statistical behaviour of the centre of pressure can be described by the dynamics of the corresponding laminar flow driven by nonlinear state-dependent noise.

Stability of the Non-abelian X-ray Transform in Dimension $$\ge 3$$

Non-abelian X-ray tomography seeks to recover a matrix potential $$\Phi :M\rightarrow {\mathbb {C}}^{m\times m}$$ Φ : M → C m × m in a domain M from measurements of its so-called scattering data $$C_\Phi $$ C Φ at $$\partial M$$ ∂ M . For $$\dim M\ge 3$$ dim M ≥ 3 (and under appropriate convexity and regularity conditions), injectivity of the forward map $$\Phi \mapsto C_\Phi $$ Φ ↦ C Φ was established in (Paternain et al. in Am J Math 141(6):1707–1750, 2019). The present article extends this result by proving a Hölder-type stability estimate. As an application, a statistical consistency result for $$\dim M =2$$ dim M = 2 (Monard et al. in Commun Pure Appl Math, 2019) is generalised to higher dimensions. The injectivity proof in (Paternain et al. in Am J Math 141(6):1707–1750, 2019) relies on a novel method by Uhlmann and Vasy (Invent Math 205(1):83–120, 2016), which first establishes injectivity in a shallow layer below $$\partial M$$ ∂ M and then globalises this by a layer stripping argument. The main technical contribution of this paper is a more quantitative version of these arguments, in particular, proving uniform bounds on layer depth and stability constants.

A Multilayer Perceptron Model for Stochastic Synthesis

Time series analysis is a major mathematical tool in hydrology, with the moving average being the most popular model type for this purpose due to its simplicity. During the last 20 years, various studies have focused on an important statistical characteristic, namely the long-term persistence and the simultaneous statistical consistency at all timescales, when different timescales are involved in the simulation. Though these issues have been successfully addressed by various researchers, the solutions that have been suggested are mathematically advanced, which poses a challenge regarding their adoption by practitioners. In this study, a multilayer perceptron network is used to obtain synthetic daily values of rainfall. In order to develop this model, first, an appropriate set of features was selected, and then, a custom cost function was crafted to preserve the important statistical properties in the synthetic time series. This approach was applied to two locations of different climatic conditions that have a long record of daily measurements (more than 100 years for the first and more than 40 years for the second). The results indicate that the suggested methodology is capable of preserving all important statistical characteristics. The advantage of this model is that, once it has been trained, it is straightforward to apply and can be modified easily to analyze other types of hydrologic time series.

Sea–ice concentration at 1 km resolution in summer from merged visible and microwave radiometer observations

<p>Sea–ice concentration, the surface fraction of ice in a given area, is a key component of the Arctic climate system, governing for example the ocean–atmosphere heat exchange. Satellite–based remote sensing offers the possibility for large–scale monitoring of the sea–ice concentration. Using passive microwave measurements, it is possible to observe the sea–ice concentration all year long, almost independently of cloud coverage. The spatial resolution of these measurements is limited to 5 km and coarser. Data from the visible and thermal infrared spectrum offer finer resolutions of 250 m–1 km, but need clear–sky scenes and, in case of visible data, sunlight. In previous work, we developed and analysed a merged dataset of passive microwave and thermal infrared data, combining AMSR2 and MODIS satellite data at 1 km spatial resolution. It has benefits over passive microwave data in terms of the finer spatial resolution and an enhanced potential for lead detection. At the same time, it outperforms thermal infrared data due to its spatially continuous coverage and the statistical consistency with the extensively evaluated passive microwave data. Due to higher surface temperatures in summer, the thermal–infrared based retrieval is limited to winter and spring months. In this contribution, we present first results of extending the existing dataset to summer by using visible data instead of thermal infrared data. The reflectance contrast between ice and water is used for the sea–ice concentration retrieval and results of merging visible and microwave data at 1 km spatial resolution are presented. Difficulties for both, the microwave and visual, data are surface melt processes during summer, which make sea–ice concentration retrieval more challenging. The merged microwave, infrared and visual dataset opens the possibility for a year–long, spatially continuous sea ice concentration dataset at a spatial resolution of 1 km.</p>

A random-walk benchmark for single-electron circuits

Mesoscopic integrated circuits aim for precise control over elementary quantum systems. However, as fidelities improve, the increasingly rare errors and component crosstalk pose a challenge for validating error models and quantifying accuracy of circuit performance. Here we propose and implement a circuit-level benchmark that models fidelity as a random walk of an error syndrome, detected by an accumulating probe. Additionally, contributions of correlated noise, induced environmentally or by memory, are revealed as limits of achievable fidelity by statistical consistency analysis of the full distribution of error counts. Applying this methodology to a high-fidelity implementation of on-demand transfer of electrons in quantum dots we are able to utilize the high precision of charge counting to robustly estimate the error rate of the full circuit and its variability due to noise in the environment. As the clock frequency of the circuit is increased, the random walk reveals a memory effect. This benchmark contributes towards a rigorous metrology of quantum circuits.

Questionnaire on Assessing Individual Vulnerability to Cyberbullying: Development and Preliminary Validation

Cyberbullying is manifested in repeated deliberate acts of aggression by one or more persons who use electronic means of communication to attack a victim who cannot defend him- or herself. Cyberbullying is a form of violence through harassment, insults, bullying, humiliation or intimidation carried out using the Internet, mobile phones and other electronic devices. Cyberbullying is widespread and causes severe harm to its victims, having a negative impact on their mental and physical health. The purpose of this study is to develop a reliable and valid questionnaire on assessing individual vulnerability to cyberbullying. The article constructs such a questionnaire and proves that it meets the standard reliability criteria: internal consistency, homogeneity and retest reliability. It is shown that the questionnaire is valid and satisfies the key validity criteria: validation in the process of designing the questionnaire, substantive, constructive and convergent validity. The questionnaire is standardised, the norms for young men and women are given, presented in average values and standard deviations. The theoretical basis of the study is the model of manipulative influence (which includes cyberbullying) and the psychological mechanism of cyberbullying described by this model. Confirmatory factor analysis showed that the model of the presented questionnaire has good indicators of reliability and statistical consistency. The experimental part of the study involved 307 young men and women at the ages from 17 to 21 years old - students of medical colleges and cadets of the University of the Ministry of Emergencies. The study included two stages of psychodiagnostics of the subjects carried out with an interval of two months. The states of individuals vulnerable to cyberbullying, identified using the questionnaire, are consistent with the results obtained in previous studies. The proposed questionnaire makes it possible to warn an individual about the existing or threatening danger of becoming a victim of cyberbullying. The questionnaire can stimulate the study of cyberbullying in the Russian-speaking society and, in particular, in cross-cultural studies.

Is Fracture Toughness of PUR Foams a Material Property? A Statistical Approach

The published data on the experimentally determined fracture toughness of foams are based on a small number of specimens, having a lack of statistical consistency. The paper proposes a statistical approach on the fracture toughness results of rigid polyurethane (PUR) foams of three different densities. Five types of fracture tests were considered. The results were statistically analyzed using six types of regressions and a meta-analysis to identify the factors influencing the fracture toughness. The statistical analysis indicates that the fracture toughness represents a material property because does not depend on the specimen type. The density plays a major role in the fracture toughness of PUR foams. The irregular shape of the cells induced small anisotropy for low-density foams (100 kg/m3 and 145 kg/m3). This effect could not be observed for the foam with 300 kg/m3 density, for which the cells have a more regular spherical shape. The statistical analysis indicates that the influence of the loading speed is very weak.