Search

We present a new continuous lifetime model with four parameters by combining the Lomax and the Weibull distributions. The extended odd Weibull Lomax (EOWL) distribution is what we'll call it. This new distribution possesses several desirable properties thanks to the simple linear representation of its hazard rate function, moments, and moment -generating function, with stress-strength reliability that are provided in a simple closed forms. The parameters of the EOWL model are estimated using classical methods such as the maximum likelihood (MLE) and the maximum product of spacing (MPS) and estimated also but using a non-classical method such as Bayesian analytical approaches. Bayesian estimation is performed using the Monte Carlo Markov Chain method. Monte Carlo simulation are used to assess the effectiveness of the estimation methods throughout the Metropolis Hasting (MH) algorithm. To illustrate the suggested distribution's effectiveness and suitability for simulating real-world pandemics, we used three existing COVID-19 data sets from the United Kingdom, the United States of America, and Italy which are studied to serve as illustrative examples. We graphed the P-P plots and TTT plots for the proposed distribution proving its superiority in a graphical manner for modelling the three data sets in the paper. [ABSTRACT FROM AUTHOR]

In this article, we present the command cub, which fits ordinal rating data using combination of uniform and binomial (CUB) models, a class of finite mixture distributions accounting for both feeling and uncertainty of the response process. CUB identifies the components that define the mixture in the baseline model specification. We apply maximum likelihood methods to estimate feeling and uncertainty parameters, which are possibly explained in terms of covariates. An extension to inflated CUB models is discussed. We also present a subcommand, scattercub, for visualization of results. We then illustrate the use of cub using a case study on students' satisfaction for the orientation services provided by the University of Naples Federico II in Italy. [ABSTRACT FROM AUTHOR]

For both spiritual and cultural reasons, churches are an essential part of the historical heritage of several countries worldwide, including Europe, Americas and Australasia. The extreme damage that occurred during the 2016–2017 Central Italy seismic swarm highlighted once again the noteworthy seismic vulnerability of unreinforced masonry churches, which exhibited several collapses and caused uncountable losses to the Italian artistic heritage. The seismic performance of 158 affected buildings was analyzed in the aftermath of the main shocks. The failure modes activated by the earthquakes were identified making reference to the local mechanisms currently considered in Italy for post-seismic assessment of churches. The structural damage of the investigated buildings, related to 21 mechanisms rather than to an overall global response, was explained resorting to empirical statistical procedures taking into account ground motion intensity and structural details that can worsen or improve the seismic performance. Finally, parametric fragility curves were derived selecting those structural details that mostly influence the damage by means of the likelihood-ratio test. Developed models can be used in future territorial-scale scenario or risk analyses. [ABSTRACT FROM AUTHOR]

Generally, strong seismic events cause serious damage to cultural heritage, especially in ancient churches characterized by a high vulnerability due to the intrinsic peculiarities of their structural systems which are not able to develop efficient resisting mechanisms. Starting from observed damage of churches and recorded ground motions, it is possible to define empirical response models. In this paper, an empirical predictive model for the seismic damage of churches is proposed. In particular, the damage is expressed by a continuous index and the seismic action is described by a scalar intensity measure. A sample of churches falling into a limited area hit by the Central Italy 2016 seismic sequence, which caused notable damage to the cultural heritage, has been chosen to calibrate the parameters of the proposed predictive model. The considered sample covers a wide range of damage and intensity levels, and includes all the churches of the area. The parameters of the model have been evaluated using the maximum likelihood estimation, considering a double step in the optimization procedure that combines a discrete with a continuous approach. Alternative proposals have been considered for the shape functions used in the numerical approximation and the results are discussed. Finally, the predictive model is applied to evaluate the damage scenario after a single seismic event and to illustrate the potential application of this risk analysis in decision-making processes. In particular, a deterministic event of magnitude 5.9 generated by the Senigallia fault has been considered and the consequences on historical churches reported in the national catalogue, are evaluated. [ABSTRACT FROM AUTHOR]

The first soil map of the whole Aosta Valley Region was produced at the 1:100,000 scale. We used 691 soil profiles, attributed to 16 Cartographic (soil) Units, spatialized using a Maximum Likelihood Estimation model available in ArcGIS software. Six maps were used as base layers, representing the most important soil-forming factors: parent material, vegetation/land use, mean annual precipitation, elevation, absolute aspect and slope angle. The Maximum Likelihood Estimation was followed by an expert-based check that led to a re-assignment of some wrongly attributed cartographic polygons. The validation process revealed that the User's and Producer's Accuracies were rather high (between 47.5% and 84.4% for common soil types). A particularly high pedodiversity, associated to strong geological, vegetational and climatic gradients was observed. [ABSTRACT FROM AUTHOR]

• Applying Non-Homogenous Poisson Process (NHPP) and Homogenous Poisson Process (HPP) to predict the condition of a system. • Presenting the possible differentiation within failure assessment of random processes. • Investigating the effect of utilizing observed data in inter-arrival failure time modelling. • The model was examined on degradation process of the Natural Gas Regulating and Metering Stations (NGRMS). Failure modelling and reliability assessment of repairable systems has been receiving a great deal of attention due to its pivotal role in risk and safety management of process industries. Meanwhile, the level of uncertainty that comes with characterizing the parameters of reliability models require a sound parameter estimator tool. For the purpose of comparison and cross-verification, this paper aims at identifying the most efficient and minimal variance parameter estimator. Hierarchical Bayesian modelling (HBM) and Maximum Likelihood Estimation (MLE) approaches are applied to investigate the effect of utilizing observed data on inter-arrival failure time modelling. A case study of Natural Gas Regulating and Metering Stations in Italy has been considered to illustrate the application of proposed framework. The results highlight that relaxing the renewal process assumption and taking the time dependency of the observed data into account will result in more precise failure models. The outcomes of this study can help asset managers to find the optimum approach to reliability assessment of repairable systems. [ABSTRACT FROM AUTHOR]

Seismic risk assessment has become a crucial issue for optimal management of economic resources allocated to mitigation. For this purpose, in the last decades, several research activities were aimed to update hazard, exposure, and vulnerability models that contribute to seismic risk assessment. From this perspective, the present work focuses on developing new empirical damage fragility curves for census-based typological unreinforced masonry buildings. In particular, damage data observed after the 2009 L'Aquila earthquake, Italy, related to almost 57,000 residential buildings, were used to calibrate the fragility functions. These data were complemented with the census data with the aim of obtaining an accurate estimation of the number of undamaged buildings. Damage fragility curves were identified for typological building classes, defined considering parameters present in both post-earthquake observations and census data with the aim of extending the results to the whole national territory. Six typological classes were defined considering the categories of the construction timespan and of the state of repair parameters. Then, a further distinction of the typological classes considering the number of stories parameter was included where relevant. The fragility curves were defined as a function of peak ground acceleration for five damage states, defined according to the European macroseismic scale. The results confirmed that older buildings are more vulnerable than newer ones and highlighted the crucial role of the state of repair on the damage fragility curves. Finally, the new set of damage fragility functions was uploaded in the Italian Risk Maps information technology platform, used by the Civil Protection Department for risk evaluation, as an exemplification of the potential application of the fragility curves. [ABSTRACT FROM AUTHOR]