Assessment of Construction Risk Management Maturity Using Hybrid Fuzzy Analytical Hierarchy Process and Fuzzy Synthetic Approach: Iraq as Case Study

Knowing and developing the construction organizations' maturity level in risk management is critical to ensure they achieve their strategic objectives. This paper aims to design a new construction organizations' risk-management maturity model (C.ORM3) using new hybrid techniques and a distinct validation strategy based on global and local experience, to assess risk management maturity level in developing countries. A multi-steps methodology was adopted in this research. The study adopted an excessive systematic literature reviews of 22 previous articles on RM maturity and four standards and guidelines for eliciting model components. These components include five attributes with 26 capabilities;24 capabilities identified from literature review and 2 from experts. These capabilities are evaluated against five levels: immature, ad-hoc, standard, managed, and optimized. The authors adopted a new strategy for validating the model by three groups of global and local experts and verifying the proposed model in a realistic-world case study. This study is the first to use a hybrid method based on the Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Synthetic Evaluation (FSE) techniques in evaluating RM maturity (RMM). Iraqi construction organizations validate the practicality of the model. The results showed that the overall RMM level of the Iraqi construction sector is 1.52, between immature and ad-hoc. The model has been converted into a computer template for ease of use by organizations. This study concluded that the suggested C.ORM3 helpful for construction organisations to evaluate their current state of RM and plan for future development © 2023, Mathematical Modelling of Engineering Problems.All Rights Reserved.

Optimum supervised classification algorithm identification by investigating PlanetScope and Skysat multispectral satellite data of Covid lockdown

This research identifies the optimum supervised classification algorithm based on modeling Covid 19 lockdown situations all around the World. The deadly Covid 19 viruses suddenly stopped the fast-moving world and all the commercial and noncommercial activities were stalled for an uncertain period during 2020-2021. In this work, object-based image classification approaches have been used to compare pre-Covid and post-Covid (at the time lockdown) images of the study area. These study areas are Washington DC, USA, Sao Paulo, Brazil, Cairo, Egypt, Afghanistan/Iran border, and Beijing, China. All the study areas possess different geographical conditions but have a similar situation of Covid 19 lockdowns. Six supervised image classification techniques are known as Parallelepiped classification (PPC), Minimum distance classification (MDC), Mahalanobis distance classification (MaDC), Maximum likelihood classification (MLC), Spectral angle mapper classification (SAMC) and Spectral information divergence classification (SIDC) are used to classify the satellite data of the study area. Thus based on classification results and statistical features, it has been observed that PPChas obtained the least significant results. In contrast, the most reliable results and highest classification accuracies are obtained through MDC, MaDC, and MLCclassification algorithms. © 2022 The Author(s)

Optimum supervised classification algorithm identification by investigating PlanetScope and Skysat multispectral satellite data of Covid lockdown

This research identifies the optimum supervised classification algorithm based on modeling Covid 19 lockdown situations all around the World. As the deadly Covid 19 viruses suddenly stopped the fast-moving World. All the commercial and noncommercial activities suddenly stop for an uncertain period during 2020-2021. In this work, object-based image classification approaches have been used to compare pre-Covid and post-Covid (at the time lockdown) images of the study area. These study areas are Washington DC, USA, Sao Paulo, Brazil, Cairo, Egypt, Afghanistan/Iran border, and Beijing, China. All the study areas possess different geographical conditions but have a similar situation of Covid 19 lockdowns. Six supervised image classification techniques are known as Parallelepiped classification (PPC), Minimum distance classification (MDC), Mahalanobis distance classification (MaDC), Maximum likelihood classification (MLC), Spectral angle mapper classification (SAMC) and Spectral information divergence classification (SIDC) are used to classify the satellite data of the study area. Thus based on classification results and statistical features, it has been observed that PPChas obtained the least significant results. In contrast, the most reliable results and highest classification accuracies are obtained through MDC, MaDC, and MLCclassification algorithms.

Severe but plausible – or not?

In light of the Covid-19 crisis, the Federal Reserve (Fed) has carried out stress tests to assess whether major banks have sufficient capital to ensure their viability should a new and perhaps unprecedented crisis emerge. The Fed argues that the scenarios underpinning these stress tests are severe but plausible, yet they have not offered any evidence or framework for measuring the plausibility of their scenarios. If the scenarios are indeed plausible, it makes sense for banks to retain enough capital to with-stand their occurrence. If, however, the scenarios are not reasonably plausible, banks will have deployed capital less productively than they otherwise could have, thereby impairing credit expansion and economic growth. The authors apply a measure of statistical unusualness, called the Mahalanobis distance, to assess the plausibility of the Fed’s stress scenarios. A first pass of this analysis, based on conventional statistical assumptions, reveals that the Fed’s scenarios are not even remotely plausible. However, the authors offer two modifications to their initial analysis that increase the scenarios’ plausibility. First, they show how the Fed can minimally modify their scenarios to render them marginally plausible in a Gaussian world. And second, they show how to evaluate the plausibility of the Fed’s scenarios by replacing the theoretical world of normality with a distribution that is empirically grounded. © 2022 Infopro Digital Risk (IP) Limited.

Cluster Analysis of Coronavirus Sequences using Computational Sequence Descriptors: With Applications to SARS, MERS and SARS-CoV-2 (CoVID-19).

INTRODUCTION: Coronaviruses comprise a group of enveloped, positive-sense single-stranded RNA viruses that infect humans as well as a wide range of animals. The study was performed on a set of 573 sequences belonging to SARS, MERS and SARS-CoV-2 (CoVID-19) viruses. The sequences were represented with alignment-free sequence descriptors and analyzed with different chemometric methods: Euclidean/Mahalanobis distances, principal component analysis and self-organizing maps (Kohonen networks). We report the cluster structures of the data. The sequences are well-clustered regarding the type of virus; however, some of them show the tendency to belong to more than one virus type. BACKGROUND: This is a study of 573 genome sequences belonging to SARS, MERS and SARS-- CoV-2 (CoVID-19) coronaviruses. OBJECTIVES: The aim was to compare the virus sequences, which originate from different places around the world. METHODS: The study used alignment free sequence descriptors for the representation of sequences and chemometric methods for analyzing clusters. RESULTS: Majority of genome sequences are clustered with respect to the virus type, but some of them are outliers. CONCLUSION: We indicate 71 sequences, which tend to belong to more than one cluster.