Search

Increasing number of personal vehicles and cargo transportation requires the development of a well-planned transport policy within states and states associations. The existing normative acts give different directions in the field of regulation of transportation processes. One of the most important aspects in assessing the existing state transport arteries and planning new ones is the analysis of their capacity, which could be performed in various ways. The classical approach defines capacity as a certain value that is constant for the entire period under consideration. However, this value is changing at any given point in time depending on various factors (weather, psychological, accidental), and this fact determines the stochastic nature of this indicator. The paper presents an overview of existing approaches and a perspective approach that defines capacity as a random variable. The paper also presents the method to model highway capacity, which delivers greater accuracy compared to traditional approaches used in Russia and Germany. Using the proposed approach it is possible to obtain adequate values of highway capacity, which is important in assessing the quality of the functioning of existing highways and planning new ones. [ABSTRACT FROM AUTHOR]

This research study deals with the probabilistic analysis of the failure of three models of a homogeneous soil embankment slope and it will be compared with deterministic or classical methods. This analysis is used to evaluate the probability of rupture of these slopes based on the kinematic method of the rigid solids (cutting of the mass likely to slip in rigid blocks). This method allows establishing the equation R-S=0 and its introduction thereafter in a software of the probabilistic functions entitled "ZUVAN" based on the theory of reliability of the 1st order developed at the university of WEIMAR in Germany leading to the values of the index of safety ß and the functions of probability Pf. The method adopted is the FORM. The random variables considered are the effective soil shear parameters, the surcharge, and the volume weight ?. The distribution of the cohesion and the friction angle values is represented by the log-normal distribution law, the surcharge is represented by the extreme law, and the volume weight by the normal distribution law. The results of the probabilistic approach can be compared with those obtained by the deterministic calculation using the Bishop, Fellenius, jumbo, and perturbation methods using the geoslope software. [ABSTRACT FROM AUTHOR]

Germany has been officially free of bovine tuberculosis since 1996. However, in the last years there has been an increase of bovine tuberculosis cases, particularly in the southern part of Germany, in the Allgäu region. As a consequence a one-time tuberculosis surveillance program was revisited with different premortal and postmortal tests. The aim of this paper was to estimate diagnostic sensitivities and specificities of the different tests used within this surveillance program. In the absence of a perfect test with 100% sensitivity and 100% specificity, thus in the absence of a gold standard, a Bayesian latent class approach with two different datasets was performed. The first dataset included 389 animals, tested with single intra-dermal comparative cervical tuberculin (SICCT) test, PCR and pathology; the second dataset contained 175 animals, tested with single intra-dermal cervical tuberculin (SICT) test, Bovigam® assay, pathology and culture. Two-way conditional dependencies were considered within the models. Additionally, inter-laboratory agreement (five officially approved laboratories) of the Bovigam® assay was assessed with Cohen's kappa test (21 blood samples). The results are given in posterior means and 95% credibility intervals. The specificities of the SICT test, SICCT test, PCR and pathology ranged between 75.8% [68.8–82.2%] and 99.0% [96.8–100%]. The Bovigam® assay stood out with a very low specificity (6.9% [3.6–11.1%]), though it had the highest sensitivity (95.7% [91.3–99.2%]). The sensitivities of the SICCT test, PCR, SICT test, pathology and culture varied from 57.8% [48.0–67.6%] to 88.9% [65.5–99.7%]. The prevalences were 19.8% [14.6–26.5%] (three-test dataset) and 7.7% [4.2–12.3%] (four-test dataset). Among all pairwise comparisons the highest agreement was 0.62 [0.15–1]). In conclusion, the specificity of the Bovigam® assay and the inter-laboratory agreement were lower than expected. [ABSTRACT FROM AUTHOR]

The reliability of the machine learning model prediction for a given input can be assessed by comparing it against the actual output. However, in hydrological studies, machine learning models are often adopted to predict future or unknown events, where the actual outputs are unavailable. The prediction accuracy of a model, which measures its average performance across an observed data set, may not be relevant for a specific input. This study presents a method based on metamorphic testing (MT), adopted from software engineering, to assess the prediction reliability where the actual outputs are unknown. In this method, the predictions for a group of related inputs are considered consistent only if the input and output follow certain relations that are deduced from the properties of the system being modeled. For instance, the predicted runoff volume should increase in a rainfall‐runoff model as the rainfall magnitude of an input increases. In this study, the MT‐based method was applied to assess the predictions made by various machine learning models that were trained to predict the magnitude of flood events in Germany. Surprisingly, the prediction accuracy of a model and its ability to provide consistent predictions were found to be uncorrelated. This study further investigated the factors influencing the assessment result of a given input, such as its similarity to observed data. Overall, this research shows that MT is an effective and simple method for detecting inconsistent model predictions and is recommended when a model is employed to making predictions under new conditions. Plain Language Summary: In hydrological studies, machine learning models are often built to learn the statistical correlations between random variables of interests. In these models, predictions are made based on learned statistical correlations, and the involved hydrological processes are not explicitly considered. Thus, it is difficult to examine the mechanisms that lead to the prediction made for a given input. Metamorphic testing (MT) is a technique to assess the correctness of the predicted output for a given input that does not require knowledge of the actual output. In MT, the relation among a group of related inputs and the corresponding outputs is examined, and the predictions are considered consistent if this relation satisfies certain criteria that are related to the properties of the system being modeled. This study applied MT to assess various machine learning models that predicted the magnitude of flood events in Germany. MT was found to be very effective in detecting inconsistent predictions. Many models failed to capture the positive correlation between precipitation magnitude and the magnitude of flood events, even for those with high prediction accuracies. The MT‐based method presented in this study is useful for assessing the reliability of model predictions, especially when the actual outcomes are unknown. Key Points: This study presents a metamorphic testing‐based method to assess the reliability of model predictions when the actual outcomes are unknownSpecific tests can be designed using hydrological knowledge to check if a model preserves certain properties of the system being modeledSome models with high prediction accuracies can often respond incorrectly when input changes and thereby produce inconsistent predictions [ABSTRACT FROM AUTHOR]

The dissimilarity of ordinal categories can be expressed with a distance measure. A unified approach relying on expected distances is proposed to obtain well-interpretable measures of location, dispersion, or symmetry of random variables, as well as measures of serial dependence within a given process. For special types of distance, these analytic tools lead to known approaches for ordinal or real-valued random variables. We also analyze the sample counterparts of the proposed measures and derive asymptotic results for practically important cases in ordinal data and time series analysis. Two real applications about the economic situation in Germany and the credit rating of European countries are presented. for this article are available online. [ABSTRACT FROM AUTHOR]

If k < n, can one express the majority of n bits as the majority of at most k majorities, each of at most k bits? We prove that such an expression is possible only if k gtrsim n4/5 = n0.8. This improves on a bound proved by Kulikov and Podolskii [Computing majority by constant depth majority circuits with low fan-in gates, in Proceedings of the 34th STACS, Schloss Dagstuhl, Wadern, Germany, 2017, 49], who showed that k gtrsim n0.7+o(1). Our proof is based on ideas originating in discrepancy theory as well as a strong concentration bound for sums of independent Bernoulli random variables and a strong anticoncentration bound for the hypergeometric distribution. [ABSTRACT FROM AUTHOR]