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The Tor network allows individuals to secure their online identities by encrypting their traffic, however it is vulnerable to fingerprinting attacks that threaten users' online privacy. In this paper, the authors develop a new video fingerprinting model to explore how well video streaming can be fingerprinted in Tor. They found that their model could distinguish which one of 50 videos a user was hypothetically watching on the Tor network with 85% accuracy, demonstrating that video fingerprinting is a serious threat to the privacy of Tor users.

While some believe that ride-hailing services offer reduced CO₂ emissions compared to individual driving, studies have found that driving without passengers on ride-hailing trips or "deadheading" prevents this. Here, with a mathematical model, the authors investigated if the use of electric vehicles as ride-hailing vehicles could offer reduced CO₂ emissions. They found that the improved vehicle efficiency and cleaner generation could in fact lower emissions compared to the use of personal gas vehicles.

The COVID-19 pandemic demonstrated the depth and significance of healthcare inequality in the United States. Xiao, Xiao, and Gong examine healthcare disparities in the Richmond (Virginia) metropolitan area by analyzing whether people from disadvantaged populations must travel for longer to reach healthcare facilities.

This article explores the largely unstudied impact of noise pollution on plant life. By exposing Arabidopsis thaliana seedlings to urban traffic noise, the study found a significant increase in seedling growth, alongside substantial changes in gene expression. This research reveals critical insights into how noise pollution affects plant physiology and contributes to a broader understanding of its ecological impacts, helping to guide future efforts in ecosystem conservation.

In this paper, we measured the privacy budgets and utilities of different differentially private mechanisms combined with different machine learning models that forecast traffic congestion at future timestamps. We expected the ANNs combined with the Staircase mechanism to perform the best with every value in the privacy budget range, especially with the medium high values of the privacy budget. In this study, we used the Autoregressive Integrated Moving Average (ARIMA) and neural network models to forecast and then added differentially private Laplacian, Gaussian, and Staircase noise to our datasets. We tested two real traffic congestion datasets, experimented with the different models, and examined their utility for different privacy budgets. We found that a favorable combination for this application was neural networks with the Staircase mechanism. Our findings identify the optimal models when dealing with tricky time series forecasting and can be used in non-traffic applications like disease tracking and population growth.

This study investigates the feasibility of using long-range radio communication in a busy city environment in order to begin better understanding how the Internet of Things might be implemented into smart cities.

In this study, the authors investigate whether antibiotic-resistant bacteria can be found in everyday locations. To do this, they collected samples from multiple high-trafficked areas in Cambridge, MA and grew them in the presence and absence of antibiotics. Interestingly, they grew bacterial colonies from many locations' samples, but not all could grow in the presence of ampicillin. These findings are intriguing and relevant given the rising concern about antibiotic-resistant bacteria.