R. Obiała, B.H.V. Topping, G.M. Seed and D.E.R. Clark
This paper describes how non‐orthogonal geometric models may be transformed into orthogonal polyhedral models. The main purpose of the transformation is to obtain a geometric…
Abstract
Purpose
This paper describes how non‐orthogonal geometric models may be transformed into orthogonal polyhedral models. The main purpose of the transformation is to obtain a geometric model that is easy to describe and further modify without loss of topological information from the original model.
Design/methodology/approach
The transformation method presented in this paper is based on fuzzy logic (FL). The idea of using FL for this type of transformation was first described by Takahashi and Shimizu. This paper describes both philosophy and techniques behind the transformation method as well as its application to some example 2D and 3D models. The problem in this paper is to define a transformation technique that will change a non‐orthogonal model into a similar orthogonal model. The orthogonal model is unknown at the start of the transformation and will only be specified once the transformation is complete. The model has to satisfy certain conditions, i.e. it should be orthogonal.
Findings
The group of non‐orthogonal models that contain triangular faces such as tetrahedra or pyramids cannot be successfully recognized using this method. This algorithm fails to transform these types of problem because to do so requires modification of the structure of the model. It appears that only when the edges are divided into pieces and the sharp angles are smoothed then the method can be successfully applied. Even though the method cannot be applied to all geometric models many successful examples for 2D and 3D transformation are presented. Orthogonal models with the same topology, which make them easier to describe, are obtained.
Originality/value
This transformation makes it possible to apply simple algorithms to orthogonal models enabling the solution of complex problems usually requiring non‐orthogonal models and more complex algorithms.
Details
Keywords
Shwetank Avikal, Rohit Singh, Anurag Barthwal and Mangey Ram
The aim of the present work is to develop a method to find the preventive measures for COVID-19 and their priorities. These preventive measures are prioritized according to the…
Abstract
Purpose
The aim of the present work is to develop a method to find the preventive measures for COVID-19 and their priorities. These preventive measures are prioritized according to the expert opinion.
Design/methodology/approach
An integrated method using the Kano model and Fuzzy-AHP is used to achieve the study objectives. First, the preventive measures are identified according to the expert. Next, the Kano model is used to determine the different Kano categories for remedial activities that are identified by the World Health Organization (WHO) and other medical authorities. Finally, Fuzzy-AHP is applied to determine the weights of these activities and assign the priorities according to their impact.
Findings
It is observed that the activity Avoid Travelling is the most important classification or category with the highest weight as compared to the other activities and sub-activities. It is also noticed that the category packed food items get the lowest weight and is the least important classification or category. In this work, two different approaches, designed for different purposes, provide similar results and verify each other.
Originality/value
Research contributing to the classification and prioritization of preventive activities using Kano and Fuzzy-AHP methods is not available. In the critical time of COVID-19, when governments are obliged to deal with many infected patients and a high number of deaths, one can focus on different preventive activities according to their classification, weights and ranks.