Waterfall Algorithm as a tool of investigation the geometrical features of granular porous media

The paper describes the so-called Waterfall Algorithm, which may be used to calculate a set of parameters characterising the spatial structure of granular porous media, such as shift ratio, collision density ratio, consolidation ratio, path length and minimum tortuosity. The study is performed for 1800 different two-dimensional random pore structures. In each geometry, 100 individual paths are calculated. The impact of porosity and the particle size on the above-mentioned parameters is investigated. It was stated in the paper, that the minimum tortuosity calculated by the Waterfall Algorithm cannot be used directly as a representative tortuosity of pore channels in the Kozeny or the Carman meaning. However, it may be used indirect by making the assumption that a unambiguous relationship between the representative tortuosity and the minimum tortuosity exists. It was also stated, that the new parameters defined in the present study are sensitive on the porosity and the particle size and may be therefore applied as indicators of the geometry structure of granular media. The Waterfall Algorithm is compared with other methods of determining the tortuosity: A-Star Algorithm, Path Searching Algorithm, Random Walk technique, Path Tracking Method and the methodology of calculating the hydraulic tortuosity based on the Lattice Boltzmann Method. A very short calculation time is the main advantage of the Waterfall Algorithm, what meant, that it may be applied in a very large granular porous media.

Investigating the Potential of Using POI and Nighttime Light Data to Map Urban Road Safety at the Micro-Level: A Case in Shanghai, China

The way in which the occurrence of urban traffic collisions can be conveniently and precisely predicted plays an important role in traffic safety management, which can help ensure urban sustainability. Point of interest (POI) and nighttime light (NTL) data have always been used for characterizing human activities and built environments. By using a district of Shanghai as the study area, this research employed the two types of urban sensing data to map vehicle–pedestrian and vehicle–vehicle collision risks at the micro-level by road type with random forest regression (RFR) models. First, the Network Kernel Density Estimation (NKDE) algorithm was used to generate the traffic collision density surface. Next, by establishing a set of RFR models, the observed density surface was modeled with POI and NTL variables, based on different road types and periods of the day. Finally, the accuracy of the models and the predicted outcomes were analyzed. The results show that the two datasets have great potential for mapping vehicle–pedestrian and vehicle–vehicle collision risks, but they should be carefully utilized for different types of roads and collision types. First, POI and NTL data are not applicable to the modeling of traffic collisions that happen on expressways. Second, the two types of sensing data are quite suitable for estimating the occurrence of traffic collisions on arterial and secondary trunk roads. Third, while the two datasets are capable of predicting vehicle–pedestrian collision risks on branch roads, their ability to predict vehicle safety on branch roads is limited.

Use of Open Data to Assess Cyclist Safety in London

This study develops a predictive model for cycling collisions in London. Specifically, the effects of bus lanes, parking or loading facilities, and multilane roads on the risk of cycling collisions are considered. To the best of the authors’ knowledge, this is the first such predictive collision model that develops covariates to measure the characteristics of different types of road infrastructure within zones. A kernel density estimator is used to identify 90 collision hotspots. Each hotspot is populated with information regarding the highway infrastructure within it. A multiple linear regression model tests for the statistical significance of the infrastructure variables. Bus lanes, multilane roads, and 30-mph speed limits are found to affect cycle collision counts, whereas junction density has the largest impact on collision density. Speed limits of 20 mph affect collision counts to a lesser degree than 30 mph, indicating potential safety improvement from reducing speed limits. One-way roads are found to reduce the risk of collisions, along with the provision of priority junctions. This infers that other junction types, such as roundabouts and signalized junctions, present a higher collision risk. The models produce conflicting results on parking or loading provision. The models are expanded to include sociodemographic variables, such as population and employment. The combined model offers no performance improvement over the infrastructure-only model, although a potential link between public transport provision and reducing cycle collisions warrants further investigation.

Electronic Thermal Conductivity Effects of Nanoscale Conductors in a Gaseous Flow Environment

ABSTRACTThe surrounding ambient introduces a gaseous boundary to many nanotechnology applications such as nanosensors, nanoelectromechanical systems and nanocoatings. Despite the large surface area to volume ratio of nanostructures, a formal study of the surface scattering effects induced by a gaseous boundary has received little attention. In this work, we consider the perturbing effects to the electron cloud or jellium of conducting nanostructures when submitted to a gaseous interface of varying interaction energies. Specifically, we incorporate the novel experimental method of Dynamic Electron Scattering (DES) to measure electronic thermal conductivity of 30 nm thick Au and Cu metal films in He and Ar atmospheres. The gas particle impact energy is varied by changing the flow speed from stationary (non-moving gas field) to high speed flow over the metal films. The scattering effects of each gas are clearly observable through electronic thermal conductivity reductions as the gas impact energy increases. We find the high collision density of He to induce greater reductions in thermal conductivity than the much heavier Ar with lower collision density. The perturbed transport properties of the Au and Cu thin films are explained by kinetic surface scattering mechanisms that dominate the scattering landscape of high surface area to volume ratio materials as suggested by comparative measurements on bulk Cu.