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Especially in the energy supply of multi-family houses, a wide variety of stakeholders are involved, from owners, to users, to energy service providers and society. They usually have different requirements and understandings of optimality, but ultimately have to make joint decisions and thus sensible compromises. In Germany in particular, there are a large number of multi-family houses and, at the same time, many government restrictions and subsidies in terms of energy supply. This makes it difficult to make clear recommendations for the choice of an energy supply concept that takes all stakeholder interests into account. We first identify the relevant stakeholders and define their objectives. In order to relate these with one another, we present a methodology based on energy system simulation and TOPSIS to make energy concepts objectively evaluable. A generic multi-family house with 40 residential units is examined, combining different energy technologies and insulation standards. There is no energy concept that satisfies all stakeholders equally and it is difficult to build coalitions between them. The best results are achieved by air-source heat pumps in combination with photovoltaic. [ABSTRACT FROM AUTHOR]

Abstract Background Policymakers, who are constantly discussing growing health expenditures, should know whether the health system is efficient. We can provide them with such information through international health system efficiency evaluations. The main objectives of this study are: (a) to evaluate the efficiency of health systems in 28 developed countries by multiple-criteria decision analysis (MCDA) and data envelopment analysis (DEA) and (b) to identify reasonable benchmark countries for the Czech Republic, for which we collect information on the relative importance of health system inputs and outputs. Methods We used MCDA and DEA to evaluate the efficiency of the health systems of 28 developed countries. The models included four health system inputs (health expenditure as a relative share of GDP, the number of physicians, nurses, and hospital beds) and three health system outputs (life expectancy at birth, healthy life expectancy, and infant mortality rate). The sample covers 27 OECD countries and Russia, which is also included in the OECD database. To determine the input and output weights, we used a questionnaire sent to health policy experts in the Czech Republic. Results We obtained subjective information on the relative importance of the health system inputs and outputs from 27 Czech health policy experts. We evaluated health system efficiency using four MCDA and two DEA models. According to the MCDA models, Turkey, Poland, and Israel were found to have efficient health systems. The Czech Republic ranked 16th, 19th, 15th, and 17th. The benchmark countries for the Czech Republic’s health system were Israel, Estonia, Luxembourg, Italy, the UK, Spain, Slovenia, and Canada. The DEA model with the constant returns to scale identified four technically efficient health systems: Turkey, the UK, Canada, and Sweden. The Czech Republic was found to be one of the worst-performing health systems. The DEA model with the variable returns to scale identified 15 technically efficient health systems. We found that efficiency results are quite robust. With two exceptions, the Spearman rank correlations between each pair of models were statistically significant at the 0.05 level. Conclusions During the model formulation, we investigated the pitfalls of efficiency measurement in health care and used several practical solutions. We consider MCDA and DEA, above all, as exploratory methods, not methods providing definitive answers.

Background: Policymakers, who are constantly discussing growing health expenditures, should know whether the health system is efficient. We can provide them with such information through international health system efficiency evaluations. The main objectives of this study are: (a) to evaluate the efficiency of health systems in 28 developed countries by multiple-criteria decision analysis (MCDA) and data envelopment analysis (DEA) and (b) to identify reasonable benchmark countries for the Czech Republic, for which we collect information on the relative importance of health system inputs and outputs. Methods: We used MCDA and DEA to evaluate the efficiency of the health systems of 28 developed countries. The models included four health system inputs (health expenditure as a relative share of GDP, the number of physicians, nurses, and hospital beds) and three health system outputs (life expectancy at birth, healthy life expectancy, and infant mortality rate). The sample covers 27 OECD countries and Russia, which is also included in the OECD database. To determine the input and output weights, we used a questionnaire sent to health policy experts in the Czech Republic. Results: We obtained subjective information on the relative importance of the health system inputs and outputs from 27 Czech health policy experts. We evaluated health system efficiency using four MCDA and two DEA models. According to the MCDA models, Turkey, Poland, and Israel were found to have efficient health systems. The Czech Republic ranked 16th, 19th, 15th, and 17th. The benchmark countries for the Czech Republic's health system were Israel, Estonia, Luxembourg, Italy, the UK, Spain, Slovenia, and Canada. The DEA model with the constant returns to scale identified four technically efficient health systems: Turkey, the UK, Canada, and Sweden. The Czech Republic was found to be one of the worst-performing health systems. The DEA model with the variable returns to scale identified 15 technically efficient health systems. We found that efficiency results are quite robust. With two exceptions, the Spearman rank correlations between each pair of models were statistically significant at the 0.05 level. Conclusions: During the model formulation, we investigated the pitfalls of efficiency measurement in health care and used several practical solutions. We consider MCDA and DEA, above all, as exploratory methods, not methods providing definitive answers. [ABSTRACT FROM AUTHOR]

Bioenergy from energy plants, an alternative fuel, is projected to increasingly meet future worldwide energy demands as well as those of a developing nation like Pakistan. Jatropha curcas (JC) has been found to possess many desirable qualities, including high oil content seeds (27–40 %), rapid growth, ease of growing, tolerance to drought, ability to grow on poor soil and wasteland, needing less nutrient input and management, and not interfering with current food crops, insects, or pest resistance. Hence, growing JC in highly appropriate barren lands could reduce the global reliance on fossil fuels. However, an evaluation of the suitability of the land would be required for tree establishment to be effective. In this study, we integrated an Analytical Hierarchy Process (AHP) with the ArcGIS tool for assessing suitable available sites to cultivate JC across Pakistan. Using geospatial technologies, this study intended to assess and map possible areas appropriate for JC bioenergy plantations with the necessary climate, soil type, and topography factors. Pakistan was classified into 3 levels of suitability for JC plantations based on the results of a spatial analysis that integrated various data sets and varied evaluation criteria: Most Suitable, Moderately Suitable, and Less Suitable. Our results showed that out of the total area of Pakistan, around 54,392,075 hectares (57 %), 22,516,700 hectares (23 %), and 19,594,100 hectares (20 %) were identified as highly suitable, moderately suitable, and less suitable, respectively, for JC bioenergy plantations. We conclude that the methods used in this study provided a considerably reliable estimate of suitable sites for JC production in Pakistan. It can assist smallholder-based initiatives to promote JC cultivation on farmer-owned to enhance their living circumstances. Multi-criteria decision analysis (MCDA) was found to be a beneficial tool in breaking down the issues of identifying and classification of the locations for afforestation in the management of forests.

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Additive manufacturing (AM) technologies have transformed manufacturing, by providing greater control over material deposition and consumption. Thanks to greater customization and their high strength-to-mass ratio, the AM of composite materials has significantly grown over the past few years. The main focus in this research area is improving printing precision and higher production rates. However, there is a lack of thorough analysis on the energy consumption of fused filament fabrication (FFF) machines for composite manufacturing, especially when associated with mechanical and economic aspects. We designed an experimental method, based on flow analysis for measuring the impact of temperature parameters on total cost, energy consumption, and tensile resistance of composite parts made by FFF. The user should be able to improve FFF efficiency regarding economic, energy, and technical aspects and obtain recommendations for setting up and using the machine. This study confirms that combining traditional economic and technical indicators (total cost and tensile resistance) with emerging energy indicators (specific energy consumption) can be successfully applied to additive manufacturing to provide an overview of printing parameters impact. Results are yielding information to support optimization investigations depending on the need and goal. For example, two tested parameter combinations that offer similar tensile properties (4% reduction in tensile resistance compared to the best combination) show a 20% difference with lower energy consumption. [ABSTRACT FROM AUTHOR]

Especially in the energy supply of multi-family houses, a wide variety of stakeholders are involved, from owners, to users, to energy service providers and society. They usually have different requirements and understandings of optimality, but ultimately have to make joint decisions and thus sensible compromises. In Germany in particular, there are a large number of multi-family houses and, at the same time, many government restrictions and subsidies in terms of energy supply. This makes it difficult to make clear recommendations for the choice of an energy supply concept that takes all stakeholder interests into account. We first identify the relevant stakeholders and define their objectives. In order to relate these with one another, we present a methodology based on energy system simulation and TOPSIS to make energy concepts objectively evaluable. A generic multi-family house with 40 residential units is examined, combining different energy technologies and insulation standards. There is no energy concept that satisfies all stakeholders equally and it is difficult to build coalitions between them. The best results are achieved by air-source heat pumps in combination with photovoltaic.

Pharmaceutical industries in most developing countries with limited resources are expected to encounter several barriers while incorporating Industry 4.0 to transform into Pharma 4.0. With limited resources, a developing country must prioritize the barriers consider their impacts, and make a resource utilization plan accordingly. In this study, We employed a hierarchical multiple criteria decision analysis (MCDM) technique to identify potential barriers to Pharma 4.0 in developing countries and examine their effects to generate a prioritization inventory. Firstly, we extracted the likely barriers using a systematic literature study and used an expert opinion-based Delphi Method to choose the most pertinent barriers. Subsequently, we analyzed the correlation and influence of the selected barriers on each other by formulating a hierarchical multi-criteria model integrating Interpretive Structural Modelling (ISM) and the Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). As an outcome, we found three distinct categories of the selected 12 barriers: Prominent (4 of 12), Influencing (5 of 12), and Resulting (3 of 12). The results of this study are intended to assist the government in developing a solid adoption strategy for Pharma 4.0 and supply chain strategists in ensuring optimum resource utilization by resolving the examined barriers during the deployment of Pharma 4.0. The study is the first of its kind to discover barriers to Pharma 4.0 adoption and create hierarchical correlations within the context of the pharmaceutical sector from the perspective of a developing country.

The relationship with bike infrastructure is missing in the current examination of user perception, which places a great deal of emphasis on visual aesthetic quality or surveys. Italy is experiencing the great development of cycling infrastructures, and the related plans, in line with sustainability objectives, have the task of examining users in various aspects. The purpose of this study is to examine users' criteria based on perceptions of the landscape in the presence of cycling infrastructures and to analyze the current infrastructures and understand their alignment with sustainable plans. One of the multiple-criteria decision analysis (MCDA) methods based on fuzzy comprehensive evaluation (FCE) was utilized, and it entails identifying the factors that constitute an index and assessing the priority of these factors in relation to other components. The findings demonstrate that the primary variables are distinct aspects of the landscape that are objective. We discovered that these components perform better for urban than metropolitan sectors in this study, as we divided the scenarios into metropolitan and general (urban, suburban, and coastal urban) scenarios. [ABSTRACT FROM AUTHOR]

The synergy between Distributed Ledger Technologies, like blockchain technology and circular economy, can lead to economic, environmental, and social sustainability through protecting natural resources, optimizing processes, fostering system effectiveness, designing out waste, and also providing job opportunities. Nevertheless, industries and their supply chain need to consider several requirements for blockchain implementation to realize a circular economy. To achieve this goal, requirements for implementing blockchain technology in construction supply chain were prioritized based on circular economy attributes. For multiple-criteria decision analysis and making our preference decision, we employed the new-founded multi-attribute decision-making (MADM) method, the "Fuzzy Ordinal Priority Approach (Fuzzy OPA)." Under uncertainty conditions, this method can determine fuzzy weights of circular economy attributes and fuzzy scores of blockchain requirements using linguistic variables. The results obtained from the case study show that three high-ranked requirements for implementing blockchain are (1) developing circular economy attributes on platforms, (2) intra-organizational considerations, and (3) technological requirements and collaboration infrastructure. To discuss the circular economy, we used the ReSOLVE framework, consisting of six attributes "Regenerate, Share, Optimize, Loop, Virtualize, and Exchange." According to research findings, blockchain implementation can move the construction industry toward a circular economy through (1) regenerating environment, (2) optimizing systems performance, and (3) looping and circulating products. [ABSTRACT FROM AUTHOR]

The trend towards high-performance residential buildings with new building regulations necessitates fundamental changes in the residential market, which is currently driven by low initial investment costs and dominated by weak innovative cycles. This change involves a difficult decision-making process that must consider the multiple and generally conflicting objectives regarding optimal retrofitting for residential buildings. This study aimed to develop an approach that would provide feedback about a building's energy and economic performance in relation to the decision-making process to ensure that the complex residence retrofitting process is more efficient. For this purpose, a performance-oriented decision support workflow is recommended for a typical multifamily apartment block within a hypothetical settlement context in Istanbul Province, which includes (i) an automated parametric energy simulation through the coupling of EnergyPlus and MATLAB® to determine differences between retrofit alternatives in relation to the building envelope, energy systems and renewable energy systems, and (ii) a multiple-criteria decision analysis to determine the retrofit alternatives by which the optimal performance can be achieved, taking into account the conflicting nature of key performance indicators (primary energy saving and life-cycle cost saving). Architects and residence owners—who are the main decision makers—can use this proposed workflow to explore effective retrofit alternatives and to make informed decisions about performance-based retrofitting by comparing the energy and economic performance of these alternatives. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) technologies have transformed manufacturing, by providing greater control over the material deposition. Owing to its versatility and high strength-to-mass ratio, AM with composite materials has grown exponentially, founding many applications in industries. Further implementation of AM with composite materials would require the user to be able to improve FFF (Fused Filament Fabrication) efficiency regarding energy and technical aspects. For this purpose, it is necessary to assess the energetic and mechanical effects of printing parameters. In this paper, five printing parameters are evaluated: bed heating strategy, bed temperature, nozzle temperature, layer thickness and deposition speed regarding energy consumption during printing phase and ultimate tensile strength (UTS). A decision-making tool based on Ashby's material selection tool is also implemented to further discriminate different solutions. Different weights and a Pareto front have been used to illustrate the methodology's potential. Among the tested parameters, bed heating strategy has proven to be the most impactful parameter while bed temperature shows little to no effect. This study proposes a methodology as starting point for efficient FFF printing. [ABSTRACT FROM AUTHOR]

Managed retreat may be a necessity for coastal communities as sea levels rise due to climate change. Selecting the right policy decisions and timing is difficult given the vested interests of communities and stakeholder groups and requires careful balancing of the benefits and risks associated with each management alternative. State and federal agencies often employ single‐objective optimization frameworks such as cost‐benefit analysis to analyze coastal relocation alternatives, but such methods are limited in their ability to balance competing value considerations and stakeholder demands. The use of a multicriteria decision analysis (MCDA) methodology allows for such considerations to be quantified and evaluated, thereby improving planning and decision‐making for coastal retreat policies. This paper provides a strategic MCDA framework to evaluate coastal retreat policy that could be leveraged by at‐risk coastal communities. The MCDA is applied to a hypothetical coastal retreat scenario to visualize policy preferences and differing value considerations among stakeholders. This model can be used by government agencies to foster more sound, acceptable, and implementable coastal retreat policies and streamline the incorporation of this climate adaptation mechanism, which may be necessary for the near future. Integr Environ Assess Manag 2023;19:83–98. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). KEY POINTS: This paper presents a multicriteria decision analysis tool that can assist in the development and evaluation of coastal retreat policy and decision‐making, and demonstrates the tool's functionality in a hypothetical coastal retreat scenario. [ABSTRACT FROM AUTHOR]

The harmonious interaction between humans and the biosphere determines how sustainable existence and coexistence are. However, when social challenges appear, sustainability is heavily compromised. Health is one of the areas affected by such challenges, with the delivery of health services being ensured by health systems. Consequently, understanding how sustainable health services are, especially those that consume the majority of resources—hospitals—is indispensable for a sustainable future. For this reason, we propose using a hybrid data envelopment analysis (DEA) approach to study hospital sustainability in Portugal under environmental, social, and economic perspectives, in cooperation with the Portuguese Ministry of Health. In particular, the proposed methodology incorporates the preference information of decision makers (via the construction of utility scales and the determination of Möbius coefficients) and criteria interactivity, due to the integration of the Choquet multiple criteria preference aggregation model in the DEA approach. In the end, despite approximately 30% of the sampled 29 assessed hospitals were deemed as efficient across the three perspectives in 2018, only 1 was entirely sustainable. [ABSTRACT FROM AUTHOR]

In the context of the multiple-criteria decision aid (MCDA), several fuzzy integrals concerning capacities (non-additive measures) have been introduced by various researchers in the last sixty years. Recently, Lehrer has proposed a new integral for capacities known as concave integral. The concave integral is based on the decomposition of random variables into simple ingredients. The concave integral concerning capacity is defined as the maximum value obtained among all its decompositions. The paper aims to model a new integration based on the decomposition of random variables into simple ingredients for multi-criteria decision making support when underlying scales are bipolar. This paper proposes a generalization of the concave integral in terms of decompositions of the integrated function to be suitable for bipolar scales. We show that the random variable is analyzed as a combination of indicators, where each allowed decomposition has a value determined by the bi-capacity. Lastly, we illustrate our framework by a practical example.

A comprehensive assessment of chemical alternatives (ACA) is necessary to avoid regrettable substitution. In a preceding study, an analysis of six hazard assessment methods found that none of them are fully aligned with the hazard assessment criteria of Article 57 of the European REACH regulation, indicating a need for a method better reflecting hazard assessment schemes in European chemical regulations. This paper presents a multiple-criteria decision analysis (MCDA) method for the ACA that takes the criteria of Article 57 of REACH into account. Investigated and presented are objective hierarchies, the aggregation of objectives, the curvature of the value functions, weights, and the introduction of a classification threshold. The MCDA-ACA method allows for the aggregation of hazards in such a way that poor performance in one hazard cannot be compensated for by good performance in another hazard. The method parameters were developed and tested using two data sets with the aim to classify chemical alternatives into acceptable (nonregrettable) and unacceptable (regrettable) alternatives according to the regulations set in Europe. The flexibility of the general method was explored by adapting the method to align with two hazard assessment schemes, Article 57 of REACH and GreenScreen. The results show that MCDA-ACA is so flexible and transparent that it can easily be adapted to various hazard assessment schemes.

The studies on complex business issues in management described by multiple characteristics (indicators), use a variety of methodological approaches. Diverse decision support methods are used to solve problems in this area (depending on the situation considered). These methods include TOPSIS and VMCM. There are methodological similarities between them, but there are also several differences. VMCM eliminates some limitations of TOPSIS regarding, inter alia, the inclusion of non-typical objects, real-world patterns, and time-varying dynamics. Methodological considerations supplemented with an exemplary application are presented in this paper. [ABSTRACT FROM AUTHOR]

The relationship with bike infrastructure is missing in the current examination of user perception, which places a great deal of emphasis on visual aesthetic quality or surveys. Italy is experiencing the great development of cycling infrastructures, and the related plans, in line with sustainability objectives, have the task of examining users in various aspects. The purpose of this study is to examine users’ criteria based on perceptions of the landscape in the presence of cycling infrastructures and to analyze the current infrastructures and understand their alignment with sustainable plans. One of the multiple-criteria decision analysis (MCDA) methods based on fuzzy comprehensive evaluation (FCE) was utilized, and it entails identifying the factors that constitute an index and assessing the priority of these factors in relation to other components. The findings demonstrate that the primary variables are distinct aspects of the landscape that are objective. We discovered that these components perform better for urban than metropolitan sectors in this study, as we divided the scenarios into metropolitan and general (urban, suburban, and coastal urban) scenarios.

Integrated planning and informed decision-making require an understanding of the interactions between coastal and rural social-ecological systems and quantification of the impacts of adaptive management across sectors. The Mar Menor lagoon (SE-Spain) provides an example of how a lack of integrated planning can lead to environmental and socio-economic crises. Here we present a systems approach using participatory modelling to identify optimal solutions for the sustainable development of this social-ecological system. Through a participatory process, we co-developed causal loop diagrams that formed the starting point of a System Dynamics model. Model development and its application helped facilitating knowledge exchange between stakeholders, identify challenges and opportunities for sustainable development, and evaluate the socio-economic and environmental impacts of different management and policy solutions. Model results were evaluated using multi-criteria analysis demonstrating its potential application as a decision support system to identify an optimal set of solutions. • Lack of integrated planning in coastal-rural areas hinders sustainable development. • Participatory modelling facilitates knowledge exchange among stakeholders. • System dynamic models allowed quantifying impacts of co-created solutions. • Multicriteria decision analysis helped identify an optimal set of solutions. • Participatory modelling facilitated system understanding and better-informed decisions. [ABSTRACT FROM AUTHOR]

Social vulnerability is deeply affected by the increase in hazardous events such as earthquakes and floods. Such hazards have the potential to greatly affect communities, including in developed countries. Governments and stakeholders must adopt suitable risk reduction strategies. This study is aimed at proposing a qualitative multi-hazard risk analysis methodology in the case of combined seismic and flood risk using PROMETHEE, a Multiple-Criteria Decision Analysis technique. The present case study is a multi-hazard risk assessment of the Ferrara province (Italy). The proposed approach is an original and flexible methodology to qualitatively prioritize urban centers affected by multi-hazard risks at the regional scale. It delivers a useful tool to stakeholders involved in the processes of hazard management and disaster mitigation. [ABSTRACT FROM AUTHOR]

Value in drugs do not always lies simply in them curing diseases: they may also be used to prolong life, improve its quality, and relieve patients' families. How much these elements matter should weigh on reimbursement decisions. Rare diseases often lack strong clinical evidence; hence, overlooking other elements that society values may lead to suboptimal decisions. We examine the preferences for treatments of non‐oncological rare diseases among 16 experts in Poland with PAPRIKA—a choice‐based elicitation method. Among seven attributes, clinical effect was found to be the most important, followed by returning the patient's family to normal functioning. Whether a treatment is used in children or any alternative treatment is available were the least important attributes. The respondents have a non‐linear, diminishing preference for clinical effects, and an almost linear (negative) preference for cost. We develop a new method of sensitivity analysis that accounts for the non‐uniqueness of part‐worth utilities and the possibility of respondents answering inattentively to some questions, and we find our results to be robust. When looking at heterogeneity, three types of respondents deviating from mean‐preferences emerge, in terms of what particular factors they place emphasis on: (a) cost and effectiveness, (b) to whom technology is offered (prognosis and availability of alternative), (c) the impact on family, safety, and caring for children. The part‐worth utilities identified can be used to support multiple criteria decision‐making in Poland. [ABSTRACT FROM AUTHOR]

The whole interconnected European network is involved in the energy transition towards power systems based on renewable power electronics interfaced generation. In this context, the major concerns for both network planning and operation are the inertia reduction and the frequency control due to the progressive decommissioning of thermal power plants with synchronous generators. This paper investigates the impact of different frequency control constraints on the unit commitment of power plants resulting from market simulations. The market outputs are compared in terms of system costs, and of frequency stability performance evaluated on the basis of the rate of change of frequency and the maximum frequency excursion. The best compromise solution is found using a multiple-criteria decision analysis method, depending on the choice of the decision maker's weighting factors. The proposed approach is tested on a real case taken from one of the most relevant future scenarios of the Italian transmission system operator. The results show how the best compromise solution that can be found depends on the decision maker preference towards cost-based or frequency stability-based criteria.

In the past decade, the use of multiple-criteria decision analysis technology has dramatically increased in solving complex real-world problems in solid waste management. Likewise, many municipalities have paid attention to finding feasible solutions for disposal and recycling of solid waste due to the increase in waste generation rates worldwide. Therefore, policy-makers must determine which recycling program to be implemented among various recycling program options. In this paper, a new approach to select a recycling program for recovered paper and pulp recyclables was proposed using analytic hierarchy process–Technique for Order Preference by Similarity to Ideal Solution (AHP-TOPSIS) techniques. A set of essential parameters of the decision-making system were identified, and a numerical case to illustrate the procedure was conducted. Our findings show very encouraging results to use a combined model between AHP and TOPSIS to select a suitable recycling program for different recovered recyclable materials. [ABSTRACT FROM AUTHOR]

This paper proposes a method for monitoring the structural health of concrete bridges in Iran. In this method, the bridge condition index (BCI) of bridges is determined by the analytical hierarchy process (AHP). BCI constitutes eight indices that are scored based on the experts' views, including structural, hydrology and climate, safety, load impact, geotechnical and seismicity, strategic importance, facilities, and traffic and pavement. Experts' views were analyzed by Expert Choice software, and the relative importance (weight) of all eight indices were determined using AHP. Moreover, the scores of indices for various conditions were extracted from experts' standpoints. BCI defines as the sum of weighted scores of indices. Bridge inspectors can examine the bridge, determine the scores of indices, and compute BCI. Higher values of BCI indicate better conditions. Therefore, bridges with lower BCI take priority in maintenance activities. As the case studies, the authors selected five bridges in Iran. Successful implementation of the proposed method for these case studies verified that this method can be applied as an easy-to-use optimization tool in health monitoring and prioritizing programs. [ABSTRACT FROM AUTHOR]

This study aims at the sustainability assessment of maize silage cultivation according to levels of nitrogen fertilization. Based on data provided by a field experiment, economic, production and environmental criteria were evaluated for three levels of nitrogen fertilization: 80, 120 and 160 kg/ha. The environmental impact of maize cultivation was evaluated by the Life Cycle Assessment (LCA) methodology. In a multi-criteria assessment, the weights of sub-criteria were evaluated on the basis of scientists, agricultural advisers and farmers’ responses through AHP methodology. Based on the mean opinion of the respondents, economic criteria have the greatest impact on overall sustainability evaluation (54%), while the impact of environmental criteria (30%) and production criteria (16%) is much smaller. Analysis of economic subcriteria proved to have the greatest impact of gross margin (61%) on sustainability assessment, followed by the economic efficiency index (31%), while the smallest impact (8%) was obtained for the economic independence index. Among the analysed production subcriteria, the level of production and complexity of agrotechnical operations (44% each) had the greatest impact on sustainability assessment, and the least – labour use (12%). The obtained results showed the best economic evaluation for 120 kg N/ha, while from a production and an environmental point of view the best alternative is the application of 80 kg N/ha. The overall evaluation, with estimated criteria weights, shows the best sustainability performance for an 80 kg/ha fertilization dose.

Objectives: This study aims to develop a framework for establishing priorities in the regional health service of Murcia, Spain, to facilitate the creation of a comprehensive multiple criteria decision analysis (MCDA) framework. This framework will aid in decision-making processes related to the assessment, reimbursement, and utilization of high-impact health technologies., Method: Based on the results of a review of existing frameworks for MCDA of health technologies, a set of criteria was proposed to be used in the context of evaluating high-impact health technologies. Key stakeholders within regional healthcare services, including clinical leaders and management personnel, participated in a focus group (n = 11) to discuss the proposed criteria and select the final fifteen. To elicit the weights of the criteria, two surveys were administered, one to a small sample of healthcare professionals (n = 35) and another to a larger representative sample of the general population (n = 494)., Results: The responses obtained from health professionals in the weighting procedure exhibited greater consistency compared to those provided by the general public. The criteria more highly weighted were "Need for intervention" and "Intervention outcomes." The weights finally assigned to each item in the multicriteria framework were derived as the equal-weighted sum of the mean weights from the two samples., Conclusions: A multi-attribute function capable of generating a composite measure (multicriteria) to assess the value of high-impact health interventions has been developed. Furthermore, it is recommended to pilot this procedure in a specific decision context to evaluate the efficacy, feasibility, usefulness, and reliability of the proposed tool.

Abstract: Due to the current climatic conditions and concerns about the energy generation by renewable sources, wind energy becomes an alternative to meet the existing energy demand. This paper aims to analyze the most promising regions of the State of Rio de Janeiro for the implementation of wind farms for electricity generation. As an analysis tool, we intend to use the Analytic Hierarchy Process (AHP), due to its practicality, to assist in the state’s wind power atlas regions evaluation, identifying generating farms installation feasibility based on logistical, technical and economic aspects. Three suitable regions for wind farms installation were evaluated according to the following criteria: potencial for generation, land cost, interconnection cost to the grid, implementing zones e terrain-landform logistics. It was found that, for the Serrana Region, it is indicated the installation of a wind farm that operates at 50 meters in height, because it only has enough potential for equipment with this height. In the Lakes Region, it is appropriate to use wind turbines of 75 meters in height, highlighting it as the most promising for this equipment height, besides having the best logistics capacity of the three regions. Because it has a greater potential area than the other, the North Region provides a greater total production, also taking into account its greater efficiency in the aerogenerators to 100 meters of height, considering this region as the most appropriate. It is worth highlighting that, in this region, there is potential for installation in all the heights presented, scrutinizing the results obtained.

Social vulnerability is deeply affected by the increase in hazardous events such as earthquakes and floods. Such hazards have the potential to greatly affect communities, including in developed countries. Governments and stakeholders must adopt suitable risk reduction strategies. This study is aimed at proposing a qualitative multi-hazard risk analysis methodology in the case of combined seismic and flood risk using PROMETHEE, a Multiple-Criteria Decision Analysis technique. The present case study is a multi-hazard risk assessment of the Ferrara province (Italy). The proposed approach is an original and flexible methodology to qualitatively prioritize urban centers affected by multi-hazard risks at the regional scale. It delivers a useful tool to stakeholders involved in the processes of hazard management and disaster mitigation.

Abstract In this paper, instead of the classical approach to the multi-criteria location selection problem, a new approach was presented based on selecting a portfolio of locations. First, the indices affecting the selection of maintenance stations were collected. The K-means model was used for clustering the maintenance stations. The optimal number of clusters was calculated through the Silhouette index. The efficiency of each cluster of stations was determined using the Charnes, Cooper and Rhodes input-oriented data envelopment analysis model. A bi-objective zero one programming model was used to select a Pareto optimal combination of rank and distance of stations. The Pareto solutions for the presented bi-objective model were determined using the invasive weed optimization method. Although the proposed methodology is meant for the selection of repair and maintenance stations in an oil refinery Company, it can be used in multi-criteria decision-making problems.

The paper develops an analytical formulation that quantifies the effect of vertical grade on vehicle fuel consumption and then illustrates the use of the developed procedure in the identification of the fuel efficient freeway layouts. Specifically, the Virginia Tech Comprehensive Power-based Fuel consumption Model (VT-CPFM) was used to develop the formulation and then applied using 2015's 10 most-sold vehicles in the U.S. and Europe to quantify the vertical grade effect on vehicle fuel consumption rates. An increase in fuel consumption of approximately 140% was found when the roadway grade increased from 0.5% to 6%. The proposed selection procedure uses Geographical Information System (GIS) applications in the design phase to evaluate possible freeway layouts. A multi-criteria analysis is performed to rank the feasible alternatives. The yearly fuel consumed by cars traveling on each feasible layout is then predicted and the alternatives are sorted in ascendingly. If the alternative selected by the multi-criteria analysis gives the least yearly fuel consumption, then that alternative should be constructed. If not, then the alternative that results in the least yearly fuel consumption should be re-evaluated with respect to the one selected by the multi-criteria analysis tool. The proposed procedure is validated using a real case study involving the construction of a new freeway in Cameroon. As much as a 12% difference in fuel consumption was found between the alternative with the least estimated yearly fuel consumption and that selected based on a multiple-criteria decision analysis. [ABSTRACT FROM AUTHOR]

Background. Reviewing drugs to determine coverage or reimbursement level is a complex process that involves significant time and expertise. Review boards gather evidence from the submission provided, input from clinicians and patients, and results of clinical and economic reviews. This information consists of assessments on multiple criteria that often conflict with one another. Multiple-criteria decision analysis (MCDA) includes methods to address complex decision making problems with conflicting objectives and criteria. We propose an MCDA approach that infers a utility model based on reviews of previously submitted drugs. Methods. We use a recent extension of the UTilitiés Additives DIScriminantes approach, UTADISGMS. This disaggregation approach deconstructs a portfolio of elements such as a set of drugs that have been reviewed and for which a decision has been made. It derives global and marginal utility functions that are consistent with the preferences exhibited by the review boards in their recommendations. We apply the method to oncology drugs reviewed in Canada between 2011 and 2017. We also illustrate how to conduct scenario analyses and predict the coverage decisions for new drugs. Results. Applying the method yields a utility value for each submission along with a set of thresholds that partition the utility values based on the submission outcomes. Scenario analyses illustrate the predictive ability of the method. Conclusion. Preference disaggregation is an indirect way of eliciting an additive global utility value function. It requires less of a cognitive effort from the decision making bodies because it infers preferences from the data rather than relying on direct assessments of model parameters. We illustrate how it can be applied to validate existing decisions and to predict the recommendation of a new drug. [ABSTRACT FROM AUTHOR]

The nature of enveloped virus-like particles (VLPs) has triggered high interest in their application to different research fields, including vaccine development. The baculovirus expression vector system (BEVS) has been used as an efficient platform for obtaining large amounts of these complex nanoparticles. To date, most of the studies dealing with VLP production by recombinant baculovirus infection utilize indirect detection or quantification techniques that hinder the appropriate characterization of the process and product. Here, we propose the application of cutting-edge quantification methodologies in combination with advanced statistical designs to exploit the full potential of the High Five/BEVS as a platform to produce HIV-1 Gag VLPs. The synergies between CCI, MOI, and TOH were studied using a response surface methodology approach on four different response functions: baculovirus infection, VLP production, VLP assembly, and VLP productivity. TOH and MOI proved to be the major influencing factors in contrast with previous reported data. Interestingly, a remarkable competition between Gag VLP production and non-assembled Gag was detected. Also, the use of nanoparticle tracking analysis and flow virometry revealed the existence of remarkable quantities of extracellular vesicles. The different responses of the study were combined to determine two global optimum conditions, one aiming to maximize the VLP titer (quantity) and the second aiming to find a compromise between VLP yield and the ratio of assembled VLPs (quality). This study provides a valuable approach to optimize VLP production and demonstrates that the High Five/BEVS can support mass production of Gag VLPs and potentially other complex nanoparticles. [ABSTRACT FROM AUTHOR]