Practical aspects of primal-dual nonlinear rescaling method with dynamic scaling parameter update

Primal-dual nonlinear rescaling method with dynamic scaling parameter update (PDNRD) is an optimization method from a class of nonlinear rescaling techniques. Previous work does not discuss practical aspects of PDNRD method such as the explanation and the setting of the parameters. To complete this framework, the parameters were described. Moreover, PDNRD method was applied on two quadratic programming problems with quadratic constraints and recommendations about the setting of the parameters were made

Relative energy inequality and weak-strong uniqueness for an isothermal non-Newtonian compressible fluid

Our paper deals with three-dimensional nonsteady Navier-Stokes equations for non-Newtonian compressible fluids. It contains a de­ri­va­tion of the relative energy inequality for the weak solutions to these equations. We show that the standard energy inequality implies the relative energy inequality. Consequently, the relative energy inequality allows us to achieve a weak-strong uniqueness result. In other words, we present that the weak solution of the Navier-Stokes system coincides with the strong solution emanated from the same initial conditions as long as the strong solution exists. For this purpose, a new assumption on the coercivity of the viscous stress tensor was introduced along with two natural examples satisfying it

Nonlinear Rescaling Method and Self-concordant Functions

summary:Nonlinear rescaling is a tool for solving large-scale nonlinear programming problems. The primal-dual nonlinear rescaling method was used to solve two quadratic programming problems with quadratic constraints. Based on the performance of primal-dual nonlinear rescaling method on testing problems, the conclusions about setting up the parameters are made. Next, the connection between nonlinear rescaling methods and self-concordant functions is discussed and modified logarithmic barrier function is recommended as a suitable nonlinear rescaling function

ROCA - An ArcGIS toolbox for road alignment identification and horizontal curve radii computation.

We present the ROCA (ROad Curvature Analyst) software, in the form of an ESRI ArcGIS Toolbox, intended for vector line data processing. The software segments road network data into tangents and horizontal curves. Horizontal curve radii and azimuth of tangents are then automatically computed. Simultaneously, additional frequently used road section characteristics are calculated, such as the sinuosity of a road section (detour ratio), the number of turns along an individual road section and the average cumulative angle for a road section. The identification of curves is based on the naïve Bayes classifier and users are allowed to prepare their own training data files. We applied ROCA software to secondary roads within the Czech road network (9,980 km). The data processing took less than ten minutes. Approximately 43% of the road network in question consists of 42,752 horizontal curves. The ROCA software outperforms other existing automatic methods by 26% with respect to the percentage of correctly identified curves. The segmented secondary roads within the Czech road network can be viewed on the roca.cdvgis.cz/czechia web-map application. We combined data on road geometry with road crashes database to develop the crash modification factors for horizontal curves with various radii. We determined that horizontal curves with radii of 50 m are approximately 3.7 times more hazardous than horizontal curves with radii accounting for 1000 m. ROCA software can be freely downloaded for noncommercial use from https://roca.cdvinfo.cz/ website

A spatiotemporal analysis of ungulate–vehicle collision hotspots in response to road construction and realignment

Although roads are central to human society, they have many negative environmental impacts and create risk for traveling motorists. Our aim was to evaluate the spatiotemporal evolution of ungulate–vehicle collision (UVC) hotspots in response to major road construction. We examined two different locations and scales in the province of Alberta, Canada: (1) a highway bypass adjacent to a large city with 4.5 km of wildlife mitigation measures (wildlife fencing and two underpasses) and (2) 55 km of rural highway that was converted from a two-lane to a four-lane divided highway. Using government police collision and carcass data (2000–2021), before-after and control-impact analyses were used to assess changes in UVC rates. Our approach is novel in that we tested the paired use of a clustering method known as kernel density estimation plus and a spatiotemporal stepwise modification of this method to monitor UVC hotspots. By monitoring UVCs over space and time, we could identify stable vs. ephemeral UVC hotspots, a fence-end effect, and a barrier effect due to traffic volume, and we could explore hotspot stability before and after construction. The wildlife mitigation measures along the highway bypass resulted in 86% fewer UVCs compared to an unmitigated highway. At a larger scale, however, net benefits were affected by road density. The construction of a four-lane divided highway with no wildlife mitigation measures and an increase in the posted speed limit resulted in a slight increase in UVCs and the reemergence of the majority of historical UVC hotspots. Our analysis highlighted the need to incorporate wildlife considerations at a variety of scales throughout the transportation planning and mitigation evaluation process

Application of statistical techniques to proportional loss data: Evaluating the predictive accuracy of physical vulnerability to hazardous hydro-meteorological events

Knowledge about the cause of differential structural damages following the occurrence of hazardous hydrometeorological events can inform more effective risk management and spatial planning solutions. While studies have been previously conducted to describe relationships between physical vulnerability and features about building properties, the immediate environment and event intensity proxies, several key challenges remain. In particular, observations, especially those associated with high magnitude events, and studies designed to evaluate a comprehensive range of predictive features are both limited. To build upon previous developments, we described a workflow to support the continued development and assessment of empirical, multivariate physical vulnerability functions based on predictive accuracy. Within this workflow, we evaluated several statistical approaches, namely generalized linear models and their more complex alternatives. A series of models were built 1) to explicitly consider the effects of dimension reduction, 2) to evaluate the inclusion of interaction effects between and among predictors, 3) to evaluate an ensemble prediction method for applications where data observations are sparse, 4) to describe how model results can inform about the relative importance of predictors to explain variance in expected damages and 5) to assess the predictive accuracy of the models based on prescribed metrics. The utility of the workflow was demonstrated on data with characteristics of what is commonly acquired in ex-post field assessments. The workflow and recommendations from this study aim to provide guidance to researchers and practitioners in the natural hazards community

COVID-19 related travel restrictions prevented numerous wildlife deaths on roads: A comparative analysis of results from 11 countries

Millions of wild animals are killed annually on roads worldwide. During spring 2020, the volume of road traffic was reduced globally as a consequence of the COVID-19 pandemic. We gathered data on wildlife-vehicle collisions (WVC) from Czechia, Estonia, Finland, Hungary, Israel, Norway, Slovenia, Spain, Sweden, and for Scotland and England within the United Kingdom. In all studied countries WVC statistics tend to be dominated by large mammals (various deer species and wild boar), while information on smaller mammals as well as birds are less well recorded. The expected number of WVC for 2020 was predicted on the basis of 2015–2019 WVC time series representing expected WVC numbers under normal traffic conditions. Then, the forecasted and reported WVC data were compared. The results indicate varying levels of WVC decrease between countries during the COVID-19 related traffic flow reduction (CRTR). While no significant change was determined in Sweden, where the state-wide response to COVID-19 was the least intensive, a decrease as marked as 37.4% was identified in Estonia. The greatest WVC decrease, more than 40%, was determined during the first weeks of CRTR for Estonia, Spain, Israel, and Czechia. Measures taken during spring 2020 allowed the survival of large numbers of wild animals which would have been killed under normal traffic conditions. The significant effects of even just a few weeks of reduced traffic, help to highlight the negative impacts of roads on wildlife mortality and the need to boost global efforts of wildlife conservation, including systematic gathering of roadkill data

COVID-19 related travel restrictions prevented numerous wildlife deaths on roads: A comparative analysis of results from 11 countries

Millions of wild animals are killed annually on roads worldwide. During spring 2020, the volume of road traffic was reduced globally as a consequence of the COVID-19 pandemic. We gathered data on wildlife-vehicle collisions (WVC) from Czechia, Estonia, Finland, Hungary, Israel, Norway, Slovenia, Spain, Sweden, and for Scotland and England within the United Kingdom. In all studied countries WVC statistics tend to be dominated by large mammals (various deer species and wild boar), while information on smaller mammals as well as birds are less well recorded. The expected number of WVC for 2020 was predicted on the basis of 2015–2019 WVC time series representing expected WVC numbers under normal traffic conditions. Then, the forecasted and reported WVC data were compared. The results indicate varying levels of WVC decrease between countries during the COVID-19 related traffic flow reduction (CRTR). While no significant change was determined in Sweden, where the state-wide response to COVID-19 was the least intensive, a decrease as marked as 37.4% was identified in Estonia. The greatest WVC decrease, more than 40%, was determined during the first weeks of CRTR for Estonia, Spain, Israel, and Czechia. Measures taken during spring 2020 allowed the survival of large numbers of wild animals which would have been killed under normal traffic conditions. The significant effects of even just a few weeks of reduced traffic, help to highlight the negative impacts of roads on wildlife mortality and the need to boost global efforts of wildlife conservation, including systematic gathering of roadkill data