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This study proposes a set of key decision-making features of the contaminated site remediation process to assist in selecting the most appropriate decision support method(s). Using a case study consistent with the requirements of the U.S. regulation for contaminated sites management, this article shows that suitable Multiple Criteria Decision Analysis methods can be selected based on a dynamic and evolving problem structuring. The selected methods belong to the family of PROMETHEE methods and can provide ranking recommendations of the considered alternatives using variable structures of the criteria, evaluation of the alternatives and exploitation of the preference model. It was found that in order to support a quick and up-to-date application of powerful decision support techniques in the process of remediation of contaminated sites, decision analysts and stakeholders should interact and co-develop the process. This research also displays how such interactions can guarantee a transparent and traceable decision recommendation so that stakeholders can better understand why some alternatives perform comprehensively better than others when a multitude of inputs is used in the decision-making process.

Background: To date (April 2021), medical device (MD) design approaches have failed to consider the contexts where MDs can be operationalised. Although most of the global population lives and is treated in Low- and Middle-Income Countries (LMCIs), over 80% of the MD market share is in high-resource settings, which set de facto standards that cannot be taken for granted in lower resource settings. Using a MD designed for high-resource settings in LMICs may hinder its safe and efficient operationalisation. In the literature, many criteria for frameworks to support resilient MD design were presented. However, since the available criteria (as of 2021) are far from being consensual and comprehensive, the aim of this study is to raise awareness about such challenges and to scope experts' consensus regarding the essentiality of MD design criteria., Results: This paper presents a novel application of Delphi study and Multiple Criteria Decision Analysis (MCDA) to develop a framework comprising 26 essential criteria, which were evaluated and chosen by international experts coming from different parts of the world. This framework was validated by analysing some MDs presented in the WHO Compendium of innovative health technologies for low-resource settings., Conclusions: This novel holistic framework takes into account some domains that are usually underestimated by MDs designers. For this reason, it can be used by experts designing MDs resilient to low-resource settings and it can also assist policymakers and non-governmental organisations in shaping the future of global healthcare.

A web-based software, called MCDA Index Tool (https://www.mcdaindex.net/), is presented in this paper. It allows developing indices and ranking alternatives, based on multiple combinations of normalization methods and aggregation functions. Given the steadily increasing importance of accounting for multiple preferences of the decision-makers and assessing the robustness of the decision recommendations, this tool is a timely instrument that can be used primarily by non-multiple criteria decision analysis (MCDA) experts to dynamically shape and evaluate their indices. The MCDA Index Tool allows the user to (i) input a dataset directly from spreadsheets with alternatives and indicators performance, (ii) build multiple indices by choosing several normalization methods and aggregation functions, and (iii) visualize and compare the indices' scores and rankings to assess the robustness of the results. A novel perspective on uncertainty and sensitivity analysis of preference models offers operational solutions to assess the influence of different strategies to develop indices and visualize their results. A case study for the assessment of the energy security and sustainability implications of different global energy scenarios is used to illustrate the application of the MCDA Index Tool . Analysts have now access to an index development tool that supports constructive and dynamic evaluation of the stability of rankings driven by a single score while including multiple decision-makers' and stakeholders' preferences., (© The Author(s) 2020.)

Decision making is a complex task that involves a multitude of perspectives, constraints, and variables. Multiple Criteria Decision Analysis (MCDA) is a process that has been used for several decades to support decision making. It includes a series of steps that systematically help Decision Maker(s) (DM(s)) and stakeholders in structuring a decision making problem, identifying their preferences, and building a decision recommendation consistent with those preferences. Over the last decades, many studies have demonstrated the conduct of the MCDA process and how to select an MCDA method. Until now, there has not been a review of these studies, nor a proposal of a unified and comprehensive high-level representation of the MCDA process characteristics (i.e., features), which is the goal of this paper. We introduce a review of the research that defines how to conduct the MCDA process, compares MCDA methods, and presents Decision Support Systems (DSSs) to recommend a relevant MCDA method or a subset of methods. We then synthesize this research into a taxonomy of characteristics of the MCDA process, grouped into three main phases, (i) problem formulation, (ii) construction of the decision recommendation, and (iii) qualitative features and technical support. Each of these phases includes a subset of the 10 characteristics that helps the analyst implementing the MCDA process, while also being aware of the implication of these choices at each step. By showing how decision making can be split into manageable and justifiable steps, we reduce the risk of overwhelming the analyst, as well as the DMs/stakeholders during the MCDA process. A questioning strategy is also proposed to demonstrate how to apply the taxonomy to map MCDA methods and select the most relevant one(s) using real case studies. Additionally, we show how the DSSs for MCDA method recommendation can be grouped into three main clusters. This proposal can enhance a traceable and categorizable development of such systems., Competing Interests: Declaration of Competing Interest None.

Nanomaterials (materials at the nanoscale, 10 -9 m) are extensively used in several industry sectors due to the improved properties they empower commercial products with. There is a pressing need to produce these materials more sustainably. This paper proposes a MCDA approach to assess the implementation of green chemistry principles as applied to the protocols for nanoparticles synthesis. In the presence of multiple green and environmentally oriented criteria, decision aiding is performed with a synergy of ordinal regression methods; preference information in the form of desired assignment for a subset of reference protocols is accepted. The classification models, indirectly derived from such information, are composed of an additive value function and a vector of thresholds separating the pre-defined and ordered classes. The method delivers a single representative model that is used to assess the relative importance of the criteria, identify the possible gains with improvement of the protocol's evaluations and classify the non-reference protocols. Such precise recommendation is validated against the outcomes of robustness analysis exploiting the sets of all classification models compatible with all maximal subsets of consistent assignment examples. The introduced approach is used with real-world data concerning silver nanoparticles. It is proven to effectively resolve inconsistency in the assignment examples, tolerate ordinal and cardinal measurement scales, differentiate between inter- and intra-criteria attractiveness and deliver easily interpretable scores and class assignments. This work thoroughly discusses the learning insights that MCDA provided during the co-constructive development of the classification model, distinguishing between problem structuring, preference elicitation, learning, modeling and problem-solving stages.

This paper proposes a robustness analysis based on Multiple Criteria Decision Aiding (MCDA). The ensuing model was used to assess the implementation of green chemistry principles in the synthesis of silver nanoparticles. Its recommendations were also compared to an earlier developed model for the same purpose to investigate concordance between the models and potential decision support synergies. A three-phase procedure was adopted to achieve the research objectives. Firstly, an ordinal ranking of the evaluation criteria used to characterize the implementation of green chemistry principles was identified through relative ranking analysis. Secondly, a structured selection process for an MCDA classification method was conducted, which ensued in the identification of Stochastic Multi-Criteria Acceptability Analysis (SMAA). Lastly, the agreement of the classifications by the two MCDA models and the resulting synergistic role of decision recommendations were studied. This comparison showed that the results of the two models agree between 76% and 93% of the simulation set-ups and it confirmed that different MCDA models provide a more inclusive and transparent set of recommendations. This integrative research confirmed the beneficial complementary use of MCDA methods to aid responsible development of nanosynthesis, by accounting for multiple objectives and helping communication of complex information in a comprehensive and traceable format, suitable for stakeholders and/or decision-makers with diverse backgrounds.

Substantial limitations and uncertainties hinder the exposure assessment of engineered nanomaterials (ENMs). The present deficit of reliable measurements and models will inevitably lead in the near term to qualitative and uncertain exposure estimations, which may fail to support adequate risk assessment and management. Therefore it is necessary to complement the current toolset with user-friendly methods for near-term nanosafety evaluation. This paper proposes an approach for relative exposure screening of ENMs. For the first time, an exposure model explicitly implements quantitative weight of evidence (WoE) methods and utilises expert judgement for filling data gaps in the available evidence-base. Application of the framework is illustrated for screening of exposure scenarios for nanoscale titanium dioxide, carbon nanotubes and fullerenes, but it is applicable to other nanomaterials as well. The results show that the WoE-based model overestimates exposure for scenarios where expert judgement was substantially used to fill data gaps, which suggests its conservative nature. In order to test how variations in input data influence the obtained results, probabilistic Monte Carlo sensitivity analysis was applied to demonstrate that the model performs in stable manner.

Statement of Problem: Debonding is a common cause of failure encountered with fiber posts and usually occurs along the post space-dentin adhesive interface. The ideal thickness of the resin cement needed to improve retentive bond strength is unknown., Purpose: The purpose of this study was to evaluate the effect of 4 different post-space diameters and related resin cement film thicknesses on the bond strength between fiber posts and root dentin., Material and Methods: Eighty human maxillary central incisors with similar root lengths were selected, sectioned at the cemento-enamel junction, and the roots were treated endodontically. The roots were divided into 4 groups (n=20), and the post spaces were prepared to a depth of 8 mm using 1 of 4 different drills with a different diameter: Torpan drill ISO 90 for the control group (D90); and Torpan Drill ISO 100 (D100), Torpan Drill ISO 120 (D120), or Enapost Drill ISO 140 (D140) for the 3 test groups. Quartz fiber posts (Endo Lightpost), 0.9 mm in apical diameter, were luted (Panavia 21) in each specimen after dentin bonding procedures. The specimens were then subjected to a pullout test using a universal testing machine at a crosshead speed of 1.0 mm/min, and the maximum force required to dislodge each post was recorded (N). Data were analyzed using 1-way ANOVA and Tukey multiple comparisons tests (a=.05)., Results: The mean bond strength values (SD) in N for the experimental groups D100 (181.7 (55.3)) and D120 (210.7 (55.0)) were higher (P=.036 and <.001, respectively) than that obtained for the D90 control group (138.5 (49.2)). The lowest mean value was recorded for the D140 group (91.1 (36.0)). All experimental groups showed significantly different results, when compared to the control group. A multiple comparison between experimental groups showed significant differences (P<.001). Only the comparison between D100 versus D120 was not significant., Conclusions: Data suggest that the resin cement film thickness influences the pullout strengths of fiber-reinforced posts. For the selected quartz fiber-reinforced post and resin luting cement, the highest bond strength values were not obtained when the thinnest cement layer was tested (D90) but when oversized post spaces were used (D100-D120). However, when the cement film thickness was too great (D140), the dislocation resistance of fiber-reinforced posts was significantly decreased.

This study evaluated the effect of three post surface treatments on quartz-fiber post (a) retention and (b) morphology. A control group was not treated. Different treatments were performed for the other posts: silanization; etching by hydrofluoric acid 9.5%; sandblasting with 50 microm Al2O3. Two specimens of each group were submitted to a qualitative scanning electron microscopy (SEM) analysis. Remaining specimens were luted in prepared root canals (n = 10) and retentions were determined using a universal testing machine. Retentive post bond strengths were significantly enhanced with any tested post surface pretreatment. The increase in retentive strength was more remarkable in sandblasted and etched posts. Significant changes of post surfaces were SEM observed after different conditioning treatments.