StationRank: Aggregate dynamics of the Swiss railway

Increasing availability and quality of actual, as opposed to scheduled, open transport data offers new possibilities for capturing the spatiotemporal dynamics of the railway and other networks of social infrastructure. One way to describe such complex phenomena is in terms of stochastic processes. At its core, a stochastic model is domain-agnostic and algorithms discussed here have been successfully used in other applications, including Google's PageRank citation ranking. Our key assumption is that train routes constitute meaningful sequences analogous to sentences of literary text. A corpus of routes is thus susceptible to the same analytic tool-set as a corpus of sentences. With our experiment in Switzerland, we introduce a method for building Markov Chains from aggregated daily streams of railway traffic data. The stationary distributions under normal and perturbed conditions are used to define systemic risk measures with non-evident,valuable information about railway infrastructure

Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks

Computational complexity has been the bottleneck of applying physically-based simulations on large urban areas with high spatial resolution for efficient and systematic flooding analyses and risk assessments. To address this issue of long computational time, this paper proposes that the prediction of maximum water depth rasters can be considered as an image-to-image translation problem where the results are generated from input elevation rasters using the information learned from data rather than by conducting simulations, which can significantly accelerate the prediction process. The proposed approach was implemented by a deep convolutional neural network trained on flood simulation data of 18 designed hyetographs on three selected catchments. Multiple tests with both designed and real rainfall events were performed and the results show that the flood predictions by neural network uses only 0.5 % of time comparing with physically-based approaches, with promising accuracy and ability of generalizations. The proposed neural network can also potentially be applied to different but relevant problems including flood predictions for urban layout planning

Facile preparation of a nanostructured functionalized catalytically active organosalt

We report a novel nanostructured organosalt, based on sulfonic acid functionalized pyrazinium {[H-pyrazine–SO3H]Cl2} that was synthesized and characterized by several techniques including Fourier transform infrared (FT- IR) spectroscopy, X-ray diffraction (XRD), thermal gravimetric analysis (TGA), differential thermal gravimetric (DTG) analysis, transmission electron microscopy (TEM), mass spectrometry (MS), proton NMR (1H NMR), carbon-13 NMR (13C NMR) and also electron diffraction (ED) patterns. Results proved that the unprecedented sulfonated pyrizinium organosalt is indeed nanostructured and highly crystalline as supported by TEM, ED and XRD studies, having an average nanoparticle size of 50 nm according to TEM micrographs. The novel nano- organocatalyst was proved to be an efficient catalyst in the synthesis of 1,2,4,5-tetrasubstituted imidazoles by a one-pot multi-component condensation of benzil, a broad range of aldehydes, primary amines and ammonium acetate at 90 °C under solvent-free conditions

Antioxidant activity and ACE-inhibitory of Class II hydrophobin from wild strain Trichoderma reesei

International audienceThere are several possible uses of the Class II hydrophobin HFBII in clinical applications. To fully understand and exploit this potential however, the antioxidant activity and ACE-inhibitory potential of this protein need to be better understood and have not been previously reported. In this study, the Class II hydrophobin HFBII was produced by the cultivation of wild type Trichoderma reesei. The crude hydrophobin extract obtained from the fermentation process was purified using reversed-phase liquid chromatography and the identity of the purified HFBII verified by MALDI-TOF (molecular weight: 7.2 kDa). Subsequently the antioxidant activities of different concentrations of HFBII (0.01–0.40 mg/mL) were determined. The results show that for HFBII concentrations of 0.04 mg/mL and upwards the protein significantly reduced the presence of ABTS+ radicals in the medium, the IC50 value found to be 0.13 mg/mL. Computational modeling highlighted the role of the amino acid residues located in the conserved and exposed hydrophobic patch on the surface of the HFBII molecule and the interactions with the aromatic rings of ABTS. The ACE-inhibitory effect of HFBII was found to occur from 0.5 mg/mL and upwards, making the combination of HFBII with strong ACE-inhibitors attractive for use in the healthcare industry