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This study aims to analyze the impact of COVID-19 on housing price within four major metropolitan areas in Texas: Austin, Dallas, Houston and San Antonio. The analysis intends to understand economic and mobility drivers behind the housing market under the inclusion of fixed and random effects.

This study used a linear mixed effects model to assess the socioeconomic and housing and transport-related factors contributing to median home prices in four major cities in Texas and to capture unobserved factors operating at spatial and temporal level during the COVID-19 pandemic.

The regression results indicated that an increase in new COVID-19 cases resulted in an increase in housing price. Additionally, housing price had a significant and negative relationship with the following variables: business cycle index, mortgage rate, percent of single-family homes, population density and foot traffic. Interestingly, unemployment claims did not have a significant impact on housing price, contrary to previous COVID-19 housing market related literature.

Previous literature analyzed the housing market within the first phase of COVID-19, whereas this study analyzed the effects of the COVID-19 throughout the entirety of 2020. The mixed model includes spatial and temporal analyses as well as provides insight into how quantitative-based mobility behavior impacted housing price, rather than relying on qualitative indicators such as shutdown order implementation.

This study aims to analyze the impact of technology-based corporation relocation on housing price indices during COVID-19 within the metropolitan areas of Austin, Texas and Seattle/Bellevue, Washington.The corporations under observation were Tesla and Amazon, respectively. The analysis intends to understand economic drivers behind the housing market and the radius of its effect while including fixed and random effects.

This study used a difference-in-difference (DID) method to evaluate changes in housing price index near and further away from Tesla’s and Amazon’s new corporate locations. The DID method allows for the capture of unique regional characteristics, as it requires a treatment and control group: housing price index and 5-mile and 10-mile search radii centered from the new corporate location.

The results indicated that corporate relocation announcements had a positive effect on housing price index post-pandemic. Specifically, the effect of Tesla’s relocation in Austin on the housing price index was not concentrated near the relocation site, but beyond the 5- and 10-mile radii. For Seattle/Bellevue, the effect of Amazon’s relocation announcement on housing price index was concentrated near the relocation site as well as beyond a 10-mile radius. Interestingly, these findings suggest housing markets incorporate speculation of prospective economic expansion linked with a corporate relocation.

Previous literature assessed COVID-19 housing market conditions and the economic effects of corporate relocation separately, whereas this study analyzed the housing price effects of corporate relocation during COVID-19. The DID method includes spatial and temporal analyses that allow for the impact of housing price to be observed across specified radii rather than a city-wide impact analysis.

This study aims to explore the impacts of Airbnb listings on land values in the Austin, Texas, USA area, particularly on single-family homes. The goal of the analysis is to shed light on how greatly and in what direction Airbnb is affecting the housing market, with an emphasis on the spatial distribution of its effects.

The analysis in this paper is performed using three distinct models on a data set of land parcel data within Travis County: an ordinary least squares regression model, a geographically weighted regression (GWR) aimed at detecting the influence of variables at the census tract level, and a Bayesian approach, which describes spatial and temporal effects on the data.

The findings of the analysis indicate that across the years 2013 to 2019, higher numbers of Airbnb listings were associated with lower percentage increases in land value in certain tracts in the northern and eastern parts of the city. Additionally, the results of the Bayesian model indicated that much of the change in land value can be attributed to unobserved factors within census tracts.

The contribution of this study to the existing literature is its analysis of the spatial and temporal analysis of the effects of Airbnb listings on land value using a GWR and a Bayesian model. Also, as the negative correlation found in the study departs from previous research, this paper may provide policymakers insight into the complex spatial distribution and conflicting effects of Airbnb listings across distinct parts of cities.

The purpose of this paper is to investigate nonlinear vibrations of triple-walled carbon nanotubes buried within Pasternak foundation carrying viscous fluids.

Considering the geometry of nanotubes, the governing equations were initially derived using Timoshenko and modified couple stress theories and by taking into account Von-Karman expressions. Then, by determining boundary conditions, type of fluid motion, Knudsen number and, ultimately, fluid viscosity, the principal equation was solved using differential quadrature method, and linear and nonlinear nanotube frequencies were calculated.

The results indicated that natural frequency is decreased as the fluid velocity and aspect ratio increase. Moreover, as the aspect ratio is increased, the results converge for simple and fixed support boundary conditions, and the ratio of nonlinear to linear frequencies approaches. Natural frequency of vibrations and critical velocity increase as Pasternak coefficient and characteristic length increase. As indicated by the results, by assuming a non-uniform velocity for the fluid and a slip boundary condition at Kn = 0.05, reductions of 10.714 and 28.714% were observed in the critical velocity, respectively. Moreover, the ratio of nonlinear to linear base frequencies decreases as the Winkler and Pasternak coefficients, maximum deflection of the first wall and characteristic length are increased in couple stress theory.

This paper is a numerical investigation of nonlinear vibration analysis for triple-walled carbon nanotubes conveying viscous fluid.

Hepatitis C is one of the major health issues in both developed and developing countries. Hepatitis C virus (HCV) infection is more common in prisoners than in the general population. The purpose of this study was to determine the prevalence of HCV and its associated risk factors in Iranian male prisoners in Tehran.

In this cross-sectional study, the authors investigated the frequency and risk factors of hepatitis C infection among male prisoners in the Great Tehran Prison. Information on risk factors including the length of imprisonment, previous history of imprisonment, history of drug injection, history of tattooing, history of piercing, history of high-risk sex and family history of hepatitis C were extracted from patients’ records. To evaluate HCV status, blood samples were collected and tested.

In this study, 179 participants were included. Nine participants (5.0%, 95% CI, 2.3-9.3) were positive for hepatitis C. HCV infection was not significantly associated with age, marital status, education, previous history of imprisonment, length of imprisonment, piercing and high-risk sex; however, there was a significant association between a history of tattooing and a history of injecting drug use and Hepatitis C.

The prevalence of hepatitis C among male prisoners in Great Tehran Prison was 5% in this study, similar to recent studies on prisoners in Tehran. A history of drug injections as well as tattooing were the most important risk factors for hepatitis C in male prisoners.

One of the recent applications of fabrics is to use them for sound insulation. Accordingly, due to their low production cost and low relative density, fabrics have drawn attention in some of the industries such as the automotive and aircraft industries. The present study is aimed to investigate the effects of the fiber cross-section, porosity, thickness of samples and fuzzing of the knitted fabric on the sound absorption coefficient.

In the present study, fabrics with three different stitch densities were knitted by yarns consist of three different forms of fiber cross-section shapes (circular, elliptical and plus-shaped). In this work, the sound absorption coefficient of knitted fabrics was investigated with regard to the different fiber cross-sections and structural parameters using an impedance tube.

As indicated by the obtained results, the cross-section, porosity, thickness and mass per unit area of the fabrics were the determinant factors for the sound absorption coefficient. In addition to, the sound absorption coefficient and porosity were shown to have an inverse relationship.

A section of the present paper has been allocated to the investigation of the effect of the fiber cross-section and fuzzing of fabric on the sound absorption of plain knitted fabrics.