Purpose: There are growing signs that the COVID‐19 virus has started to spread to rural areas and can impact the rural health care system that is already stretched and lacks resources. To aid in the legislative decision process and proper channelizing of resources, we estimated and compared the county‐level change in prevalence rates of COVID‐19 by rural‐urban status over 3 weeks. Additionally, we identified hotspots based on estimated prevalence rates.Methods: We used crowdsourced data on COVID‐19 and linked them to county‐level demographics, smoking rates, and chronic diseases. We fitted a Bayesian hierarchical spatiotemporal model using the Markov Chain Monte Carlo algorithm in R‐studio. We mapped the estimated prevalence rates using ArcGIS 10.8, and identified hotspots using Gettis‐Ord local statistics.Findings: In the rural counties, the mean prevalence of COVID‐19 increased from 3.6 per 100,000 population to 43.6 per 100,000 within 3 weeks from April 3 to April 22, 2020. In the urban counties, the median prevalence of COVID‐19 increased from 10.1 per 100,000 population to 107.6 per 100,000 within the same period. The COVID‐19 adjusted prevalence rates in rural counties were substantially elevated in counties with higher black populations, smoking rates, and obesity rates. Counties with high rates of people aged 25‐49 years had increased COVID‐19 prevalence rates.Conclusions: Our findings show a rapid spread of COVID‐19 across urban and rural areas in 21 days. Studies based on quality data are needed to explain further the role of social determinants of health on COVID‐19 prevalence.

In this paper, we describe a method for transforming and migrating data schemas developed for RDBMS to HBase. The method consists of a set of HBase-organization guidelines and a four-step data-schema transformation process that HBase application developers may follow during the migration of their application data from RDBMSs to HBase. The method also considers data-access paths extracted from query logs, in order to improve the quality of the transformation and the eventual access efficiency of the HBase repository. We illustrate and validate the method with a case study.

This paper reports on the ohmic contact formed by the conductive glass layer found at the interface of Ag/Si contacts on lowly doped emitter silicon solar cells due to the presence of semimetal nanoparticles. The scanning electron microscopy and scanning transmission electron microscope analyses revealed an interface glass layer (IGL) thickness of greater than 380 nm, which was enriched with micro-sized alloys composed of semimetal nanoparticles. This IGL was conductive as confirmed by conductive atomic force microscopy (C-AFM). The presence of these semimetal nanoparticles, identified as Ag${}_2$ Te and PbTe, was both endowed with low bandgap energies as confirmed by Raman spectroscopy and energy dispersive x-ray spectroscopy. These semimetal nanoparticles were found only in the IGL and formed a “bridge” to connect the Ag gridline and Si emitter for carrier transport. Based on the modified Fowler–Nordheim tunneling process, the modeled C-AFM I–V characteristic curve showed a barrier height of 0.1 eV corresponding to an IGL thickness of only 18 nm. Thus, the carrier transport mechanism “through the conductive bridge” was formed by the semimetal nanoparticles embedded in the IGL. Therefore, the high conductivity of the interface glass led to the specific contact resistance to be independent of the emitter peak doping concentration.

The analysis of network data has become an increasingly prominent and demanding field across multiple research fields including data science, health, and social sciences, requiring the development of robust models and efficient computational methods. One well-established and widely employed modeling approach for network data is the Exponential Random Graph Model (ERGM). Despite its popularity, there is a recognized necessity for further advancements to enhance its flexibility and variable selection capabilities. To address this need, we propose a novel hierarchical Bayesian adaptive lasso model (BALERGM), which builds upon the foundations of the ERGM. The BALERGM leverages the strengths of the ERGM and incorporates the flexible adaptive lasso technique, thereby facilitating effective variable selection and tackling the inherent challenges posed by high-dimensional network data. The model improvements have been assessed through the analysis of simulated data, as well as two authentic datasets. These datasets encompassed friendship networks and a respondent-driven sampling dataset on active and healthy lifestyle awareness programs.