Stochastic Description of Traffic Flow

We propose a traffic model based on microscopic stochastic dynamics. We built a Markov chain equipped with an Arrhenius interaction law. The resulting stochastic process is comprised of both spin-flip and spin-exchange dynamics which models vehicles exiting, entering and interacting in a two-dimensional lattice environment corresponding to a multi-lane highway. The process is further equipped with a novel look-ahead type, anisotropic interaction potential which allows drivers/vehicles to ascertain local fluctuations and advance to new cells forward or sideways. The resulting vehicular traffic model is simulated via kinetic Monte Carlo and examined under both, typical and extreme traffic flow scenarios. The model is shown to correctly predict both qualitative as well as quantitative traffic observables for any highway geometry. Furthermore it also captures interesting multi-scale phenomena in traffic flows after a simulated accident which lead to oscillatory, dissipating, traffic waves with different periods per lane.     

Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction

Software security is a very important aspect for software development organizations who wish to provide high-quality and dependable software to their consumers. A crucial part of software security is the early detection of software vulnerabilities. Vulnerability prediction is a mechanism that facilitates the identification (and, in turn, the mitigation) of vulnerabilities early enough during the software development cycle. The scientific community has recently focused a lot of attention on developing Deep Learning models using text mining techniques for predicting the existence of vulnerabilities in software components. However, there are also studies that examine whether the utilization of statically extracted software metrics can lead to adequate Vulnerability Prediction Models. In this paper, both software metrics- and text mining-based Vulnerability Prediction Models are constructed and compared. A combination of software metrics and text tokens using deep-learning models is examined as well in order to investigate if a combined model can lead to more accurate vulnerability prediction. For the purposes of the present study, a vulnerability dataset containing vulnerabilities from real-world software products is utilized and extended. The results of our analysis indicate that text mining-based models outperform software metrics-based models with respect to their F₂-score, whereas enriching the text mining-based models with software metrics was not found to provide any added value to their predictive performance.     

Schlenk's Legacy—Methyllithium Put under Close Scrutiny

Commercially available stock solutions of organolithium reagents are well-implemented tools in organic and organometallic chemistry. However, such solutions are inherently contaminated with lithium halide salts, which can complicate certain synthesis protocols and purification processes. Here, we report the isolation of chloride-free methyllithium employing K[N(SiMe₃)₂] as a halide-trapping reagent. The influence of distinct LiCl contaminations on the ⁷Li-NMR chemical shift is examined and their quantification demonstrated. The structural parameters of new chloride-free monomeric methyllithium complex [(Me₃TACN)LiCH₃], ligated by an azacrown ether, are assessed by comparison with a halide-contaminated variant and monomeric lithium chloride [(Me₃TACN)LiCl], further emphasizing the effect of halide impurities.