Analysis of Smartphone Text Data Related to mpox from a U.S. Sample of Gay, Bisexual, and Other Men Who Have Sex with Men During the 2022 Outbreak.

Purpose: We sought to understand technology-based communication regarding mpox (monkeypox) among gay, bisexual, and other men who have sex with men (GBMSM) during the global outbreak in 2022. Methods: Forty-four GBMSM (Mage = 25.3 years, 68.2% cisgender, 43.2% non-White) living in the United States participated. From May 2022 to August 2022, all text data related to mpox (174 instances) were downloaded from the smartphones of GBMSM. Text data and smartphone app usage were analyzed. Results: Content analysis revealed 10 text-based themes and 7 app categories. GBMSM primarily used search and browser, texting, and gay dating apps to share vaccine updates, seek mpox vaccination, find general mpox information, share mpox information with other GBMSM, and discuss links between mpox and gay culture. Data visualizations revealed that changes in communication themes and app usage were responsive to major milestones in the mpox outbreak. Conclusion: GBMSM used apps to facilitate a community-driven mpox response.

Generative Imputation and Stochastic Prediction.

In many machine learning applications, we are faced with incomplete datasets. In the literature, missing data imputation techniques have been mostly concerned with filling missing values. However, the existence of missing values is synonymous with uncertainties not only over the distribution of missing values but also over target class assignments that require careful consideration. In this paper, we propose a simple and effective method for imputing missing features and estimating the distribution of target assignments given incomplete data. In order to make imputations, we train a simple and effective generator network to generate imputations that a discriminator network is tasked to distinguish. Following this, a predictor network is trained using the imputed samples from the generator network to capture the classification uncertainties and make predictions accordingly. The proposed method is evaluated on CIFAR-10 and MNIST image datasets as well as five real-world tabular classification datasets, under different missingness rates and structures. Our experimental results show the effectiveness of the proposed method in generating imputations as well as providing estimates for the class uncertainties in a classification task when faced with missing values.

Numerical assessment of induced ELF currents in the human head due to the battery current of a digital mobile phone.

In this study, the extremely low frequency (ELF) fields induced in the human head by the battery currents of a mobile phone are considered. The magnetic field induced by the phone was measured, and this data was used to calculate the resulting currents induced in the human head and brain. Both the finite element method (FEM) and finite integration technique (FIT) were used for numerical computations. The computed current density values were then compared with the guidelines given by the International Commission on Non-Ionising Radiation Protection (ICNIRP). The comparison showed that the computed exposure is well within the limits of those guidelines.