Employing dynamic fuzzy membership functions to assess environmental performance in the supplier selection process

The proposed system illustrates that logic fuzzy can be used to aid management in assessing a supplier's environmental performance in the supplier selection process. A user-centred hierarchical system employing scalable fuzzy membership functions implement human priorities in the supplier selection process, with particular focus on a supplier's environmental performance. Traditionally, when evaluating supplier performance, companies have considered criteria such as price, quality, flexibility, etc. These criteria are of varying importance to individual companies pertaining to their own specific objectives. However, with environmental pressures increasing, many companies have begun to give more attention to environmental issues and, in particular, to their suppliers’ environmental performance. The framework presented here was developed to introduce efficiently environmental criteria into the existing supplier selection process and to reflect on its relevant importance to individual companies. The system presented attempts to simulate the human preference given to particular supplier selection criteria with particular focus on environmental issues when considering supplier selection. The system considers environmental data from multiple aspects of a suppliers business, and based on the relevant impact this will have on a Buying Organization, a decision is reached on the suitability of the supplier. This enables a particular supplier's strengths and weaknesses to be considered as well as considering their significance and relevance to the Buying OrganizationPeer reviewe

A simulation‐based framework for earthquake risk‐informed and people‐centred decision making on future urban planning

Numerous approaches to earthquake risk modelling and quantification have already been proposed in the literature and/or are well established in practice. However, most of these procedures are designed to focus on risk in the context of current static exposure and vulnerability, and are therefore limited in their ability to support decisions related to the future, as yet partially unbuilt, urban landscape. We propose an end-to-end risk modelling framework that explicitly addresses this specific challenge. The framework is designed to consider the earthquake (ground-shaking) risks of tomorrow’s urban environment, using a simulation-based approach to rigorously capture the uncertainties inherent in future projections of exposure as well as physical and social vulnerability. The framework also advances the state-of-practice in future disaster risk modelling by additionally: (1) providing a harmonised methodology for integrating physical and social impacts of disasters that facilitates flexible characterisation of risk metrics beyond physical damage/asset losses; and (2) incorporating a participatory, people-centred approach to risk-informed decision making. The framework is showcased using the physical and social environment of an expanding synthetic city. This example application demonstrates how the framework may be used to make policy decisions related to future urban areas, based on multiple, uncertain risk drivers

Modelling and quantifying tomorrow's risks from natural hazards

Understanding and modelling future risks from natural hazards is becoming increasingly crucial as the climate changes, human population grows, asset wealth accumulates, and societies become more urbanised and interconnected. This need is recognised by the 2015-2030 Sendai Framework for Disaster Risk Reduction, which emphasises the importance of preparing for the disasters that our world may face tomorrow through strategies/policies that aim to minimise uncontrolled development in hazardous areas. While the vast majority of natural-hazard risk-assessment frameworks have so far focused on static impacts associated with current conditions and/or are influenced by historical context, some authors have sought to provide decision makers with risk-quantification approaches that can be used to cultivate a sustainable future. This Review documents these latter efforts, explicitly examining work that has modelled and quantified the individual components that comprise tomorrow's risk, i.e., future natural hazards affected by climate change, future exposure (e.g., in terms of population, land use, and the built environment), and the evolving physical vulnerabilities of the world's infrastructure. We end with a discussion on the challenges faced by modellers in determining the risks that tomorrow's world may face from natural hazards, and the constraints these place on the decision-making abilities of relevant stakeholders

A Novel People-Centered Approach to Modeling and Decision Making on Future Earthquake Risk

Numerous approaches to earthquake risk modeling and quantification have already been proposed in the literature and/or are well established in practice. However, most of these procedures are designed to focus on risk in the context of current static exposure and vulnerability, and are therefore limited in their ability to support decisions related to the future, as yet partially unbuilt, urban landscape. This paper outlines an end-to-end risk modeling framework that explicitly addresses this specific challenge. The framework is designed to consider the earthquake risks of tomorrow's urban environment, using a simulationbased approach to rigorously capture the uncertainties inherent in future projections of exposure as well as physical and social vulnerability. The framework also advances the state-of-practice in future disaster risk modeling by additionally: (1) providing a harmonized methodology for integrating physical and social impacts of disasters that facilitates flexible characterization of risk metrics beyond physical damage/asset losses; and (2) incorporating a participatory, people-centered approach to riskinformed decision making. It can be used to support decision making on policies related to future urban planning and design, accounting for various stakeholder perspectives on risk

Using metal-ligand binding characteristics to predict metal toxicity: quantitative ion character-activity relationships (QICARs).

Ecological risk assessment can be enhanced with predictive models for metal toxicity. Modelings of published data were done under the simplifying assumption that intermetal trends in toxicity reflect relative metal-ligand complex stabilities. This idea has been invoked successfully since 1904 but has yet to be applied widely in quantitative ecotoxicology. Intermetal trends in toxicity were successfully modeled with ion characteristics reflecting metal binding to ligands for a wide range of effects. Most models were useful for predictive purposes based on an F-ratio criterion and cross-validation, but anomalous predictions did occur if speciation was ignored. In general, models for metals with the same valence (i.e., divalent metals) were better than those combining mono-, di-, and trivalent metals. The softness parameter (sigma p) and the absolute value of the log of the first hydrolysis constant ([symbol: see text] log KOH [symbol: see text]) were especially useful in model construction. Also, delta E0 contributed substantially to several of the two-variable models. In contrast, quantitative attempts to predict metal interactions in binary mixtures based on metal-ligand complex stabilities were not successful

Applying Binomial Statistics to Weighted Monte Carlo

Weighted Monte Carlo calculations requiring a uniform sampling of the problem-space can suffer from diminished statistical significance because many, if not most, of the randomly-chosen sampling points contribute only slightly to the desired result. Their contribution is reduced in size due the variable-size of the weighting terms. In contrast, none of the randomly-chosen points which are favored by variable size weighting terms will have their statistical significance enhanced beyond that of just one random point in the Monte Carlo sampling. A Monte Carlo analysis was used in earlier work to verify both Gauss\u27 Law and Newton\u27s Shell Theorem. Both examples suffered from statistical difficulties since each Monte Carlo sampling point has a weight inversely proportional to the square of the distance between source and field points. The present work analyzes the diminished significance in weighted Monte Carlo for the specific example of Newton\u27s Shell Theorem, describing the geometry in terms of closest approach distance of the spherical mass shell to the field point. Binomial Statistics is used to remedy this diminished statistical significance by providing a prescription for increasing the value of the Monte Carlo sample size needed to assure that the chosen precision remains invariant as the mass-shell geometry is changed

Towards the production of radiotherapy treatment shells on 3D printers using data derived from DICOM CT and MRI: preclinical feasibility studies

Background: Immobilisation for patients undergoing brain or head and neck radiotherapy is achieved using perspex or thermoplastic devices that require direct moulding to patient anatomy. The mould room visit can be distressing for patients and the shells do not always fit perfectly. In addition the mould room process can be time consuming. With recent developments in three-dimensional (3D) printing technologies comes the potential to generate a treatment shell directly from a computer model of a patient. Typically, a patient requiring radiotherapy treatment will have had a computed tomography (CT) scan and if a computer model of a shell could be obtained directly from the CT data it would reduce patient distress, reduce visits, obtain a close fitting shell and possibly enable the patient to start their radiotherapy treatment more quickly. Purpose: This paper focuses on the first stage of generating the front part of the shell and investigates the dosimetric properties of the materials to show the feasibility of 3D printer materials for the production of a radiotherapy treatment shell. Materials and methods: Computer algorithms are used to segment the surface of the patient’s head from CT and MRI datasets. After segmentation approaches are used to construct a 3D model suitable for printing on a 3D printer. To ensure that 3D printing is feasible the properties of a set of 3D printing materials are tested. Conclusions: The majority of the possible candidate 3D printing materials tested result in very similar attenuation of a therapeutic radiotherapy beam as the Orfit soft-drape masks currently in use in many UK radiotherapy centres. The costs involved in 3D printing are reducing and the applications to medicine are becoming more widely adopted. In this paper we show that 3D printing of bespoke radiotherapy masks is feasible and warrants further investigation