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Lipinski's Rule of Five and Ghose filter are empirical guidelines for evaluating the drug-likeness of a compound, suggesting that orally active drugs typically fall within specific ranges for molecular descriptors such as hydrogen bond donors and acceptors, weight, and lipophilicity. We revisit these practices and offer a more analytical perspective using the Dominance-based Rough Set Approach (DRSA). By analyzing representative samples of drug and non-drug molecules and focusing on the same molecular descriptors, we derived decision rules capable of distinguishing between these two classes systematically and reproducibly. This way, we reduced human bias and enabled efficient knowledge extraction from available data. The performance of the DRSA model was rigorously validated against traditional rules and available machine learning (ML) approaches, showing a significant improvement over empirical rules while achieving comparable predictive accuracy to more complex ML methods. Our rules remain simple and interpretable while being characterized by high sensitivity and specificity. [ABSTRACT FROM AUTHOR]

Over the last few decades, researches related to the impact of production and industrial manufacturing on environmental sustainability, coupled with multi-dimensional multi-criteria decision analysis and performance evaluation has consistently gained importance. However, no significant work in which an effort to optimize the total production process with the consideration of the positive and negative impacts of each input and output on Environmental sustainability has been noticed. The main aim of our paper is to address this gap. In this paper, an effort has been made to develop a realistic optimization model wherein a manufacturing firm has been considered to function with several decision-making units taking into account the effects of factors like profit, employment generation, social development, environmental pollution and sustainability. The various inputs employed and outputs produced by the firm are responsible for having negative impacts on the environment, besides creative value generation. With due consideration to both these values instead of mere profit, the model determines and explores the firm's decision considering different objectives. The optimization problem has been solved by a GA developed by us and demonstrated with numerical examples. The solutions of the numerical examples point towards some interesting revelations and indicate some prospective future research directions which contributes to this field of research. [ABSTRACT FROM AUTHOR]

Geotechnical engineering, being preliminary in any construction project, is of paramount importance for achieving sustainability. One of the important domains of sustainable development in geotechnics is sustainability assessments as it is essential to determine the impact of engineering activities on built environment. An indicator-based sustainability assessment tool called Geotechnical Sustainability Assessment Tool (Geo-SAT) was developed in this regard. The indicators used in Geo-SAT are generic in nature and judged on equal weightages, hence create skewness. Furthermore, the essentiality of these indicators varies across different projects and their life cycle stages. So, the tool requires scale normalization to remove this exaggeration of results. In current research 32 technical indicators are sampled out of 171 indicators of Geo-SAT using Delphi technique. The weightages of these indicators have been then normalized using Multiple Criteria Decision Analysis (MCDA). Multiple Attribute Value Theory (MAVT), a sub-technique under MCDA, is used for analysis as it compares between different criteria (i.e., project life cycle stages, indicators, and expert's opinion) and computes the weighted averages. For this purpose, a questionnaire has been developed and distributed among 52 relevant field experts having a predefined experience criteria set by authors, to record their opinion regarding the importance of indicators across project life cycle stages. 50 responses were accepted based on their reliability and completeness. As a result, the sustainability indices for all indicators are then calculated using MAVT based on weights of importance and relative weights attained from the questionnaires. Furthermore, it is established that the most critical indicators for the dam's sustainability assessment are "quality control during construction", "construction material wastage" and "feasibility reports", with highest weightages of 4.8%, 4.3%, and 4.2%, respectively. It is concluded that inculcating these computed weightages to Geo-SAT will increase the reliability and reduce the skewness in the sustainability assessment of dam projects. The current research is an effort to develop a sustainability assessment tool for dams (as no such assessment tool exists for dams exclusively) by improving Geo-SAT in terms of scale normalization. The normalized version of Geo-SAT, which is based on weighted averages of indicators, is a way forward for future development of certification system. [ABSTRACT FROM AUTHOR]

When a group of decision-makers faces the problem of ranking a set of decision alternatives, it is usually necessary to reach a consensus among the group members on ranking alternatives. However, before that, we should minimize inconsistencies between the fuzzy outranking relation and the final ranking at the individual and group levels in the outranking approach of multiple criteria decision analysis. This paper proposes an order-based consensus model that performs from the search for consistency between preference relations to achieving consensus. For this task, consensus and proximity measures are defined. These measures guide and aid the group dialog stage of the consensus procedure. To validate the pertinency and rationality of the proposed consensus model, a descriptive example is addressed concerning the ranking of possible locations of an electricity power plant in the European Union from the best to the worst. [ABSTRACT FROM AUTHOR]

The surging demand for electricity, propelled by the widespread adoption of intelligent grids and heightened consumer interaction with electricity demand and pricing, underscores the imperative for precise prognostication of optimal power plant utilization. To confront this challenge, a dataset centered on issue-centric power plans is meticulously crafted. This dataset encapsulates pivotal facets indispensable for attaining sustainable power generation, including meager gas emissions, installation cost, low maintenance cost, elevated power generation, and copious resource availability. The selection of an optimal power plant entails a multifaceted decision-making process, demanding a systematic approach. Our research advocates the amalgamation of multiple-criteria decision-making (MCDM) models with self-organizing maps to gauge the efficacy of diverse sustainable energy systems. The examination discerns solar energy as the preeminent MCDM criterion, securing the apex position with a score of 83.4%, attributable to its ample resource availability, considerable energy generation, nil greenhouse gas emissions, and commendable efficiency. Wind and hydroelectric power closely trail, registering scores of 75.3% and 74.5%, respectively, along with other energy sources. The analysis underscores the supremacy of the renewable energy sources, particularly solar and wind, in fulfilling sustainability objectives and scrutinizing factors such as cost, resource availability, and the environmental impact. The proposed methodology empowers stakeholders to make judicious decisions, accentuating facets that are required for more sustainable and resilient power infrastructure. [ABSTRACT FROM AUTHOR]

Background There are increasing demands on Emergency Medical Services. More efficient treatment pathways are required to support conveyance decision making and patient referral in prehospital care. Point of Care testing is increasingly available and utilised across the NHS to support optimal ways of working. We aimed to design and conduct a Multiple Criteria Decision Analysis to prioritise in vitro point of care tests and use cases for inclusion in a platform trial of in vitro point of care testing in UK Emergency Medical Services. Methods We designed a Multiple Criteria Decision Analysis that included systematic scoping reviews stakeholder recruitment, two stakeholder surveys and two stakeholder workshops to scope the use cases, explore criteria and map use cases, evaluate the criteria and measure the use cases against the criteria. Results We recruited 32 stakeholders. We developed a scoring matrix with 4 criteria for scoring the use cases and 8 criteria for scoring the point of care tests and applied weighting determined from survey results. Use cases were scored by the stakeholders against 4 criteria. The 3 highest scoring use cases were point of care troponin testing in: possible Acute Myocardial Infarction, lactate testing in suspected sepsis and in trauma. We developed the process for scoring the point of care tests to be completed close to a proposed trial to allow for a changes in technology. Conclusions We successfully designed a Multiple Criteria Decision Analysis to identify use cases and candidate tests for inclusion in a future platform trial of in vitro point of care testing in UK Emergency Medical Services. We identified 3 use cases for evaluation in a platform trial of in vitro point of care testing: troponin testing in possible acute myocardial infarction, lactate testing in suspected sepsis and lactate testing to identify occult haemorrhage in trauma.

Lipinski’s Rule of Five and Ghose filter are empirical guidelines for evaluating the drug-likeness of a compound, suggesting that orally active drugs typically fall within specific ranges for molecular descriptors such as hydrogen bond donors and acceptors, weight, and lipophilicity. We revisit these practices and offer a more analytical perspective using the Dominance-based Rough Set Approach (DRSA). By analyzing representative samples of drug and non-drug molecules and focusing on the same molecular descriptors, we derived decision rules capable of distinguishing between these two classes systematically and reproducibly. This way, we reduced human bias and enabled efficient knowledge extraction from available data. The performance of the DRSA model was rigorously validated against traditional rules and available machine learning (ML) approaches, showing a significant improvement over empirical rules while achieving comparable predictive accuracy to more complex ML methods. Our rules remain simple and interpretable while being characterized by high sensitivity and specificity.

In the current context of global warming, where district cooling systems are increasingly burdening power grids worldwide, it is important to design and operate alternative systems that are not only environmentally friendly, but also cost effective for sustainable energy transition. This paper investigates the potential of two, previously unexplored, solar cooling systems while considering economic and environmental criteria: a Solar Thermal and Electrical Cooling System (STECS) that relies solely on solar energy, offering a significant environmental advantage, and a Hybrid Solar Cooling System (HSCS) that combines a compression chiller and an absorption chiller for improved efficiency. First, novel Mixed-Integer Linear Programming models are proposed to aid in generating cost-effective designs and operations for each of STECS and HSCS. Second, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is adapted to evaluate both systems and benchmark them against conventional cooling systems previously studied in the literature. The proposed framework allows decision makers to assess the potential of these systems using three criteria; namely, cost, carbon dioxide emissions, and noise levels, which are crucial for ensuring the social acceptance of district cooling technologies worldwide. A computational study is presented to show the practical relevance of the proposed framework using real-demand data obtained from an educational district in the State of Qatar. Results show that the integration of solar energy into conventional cooling systems has the potential to significantly reduce the annual total net cost by 61%, CO2 emissions by 60%, and noise levels by 53%, on average. Additionally, the benchmark results demonstrate that the zero-carbon dioxide emissions solar cooling system achieves the highest overall performance score when all criteria are considered together.

The literature on the extreme value theory threshold optimization problem for multiple time series analysis does not consider determining a single optimal tail probability for all marginal distributions. With multiple tail probabilities, their discrepancy results in a differing number of exceedances, which may favour a particular marginal series. In this study, we propose a single optimal tail probability by integrating trade-offs among multiple time series within an MOO framework. Mathematically, our approach links the peaks-over-threshold technique and goal programming technique by developing a set of regression functions, which represent continuous paths of possible tail areas for multiple time series, and we formulate them at the desired levels within a multiobjective optimization framework. The optimal solution is found as the minimum Chebyshev variant weighted value. Our approach advances the development of the peaks-over-threshold method by considering the characteristics of a group of time series collectively instead of independently. The proposed optimal tail probability can be considered an optimal reference point for practical risk investment portfolio analysis that employs an identical tail size across multiple time series data. The daily log returns of four U.S. stock market indices, namely, S&P 500, NASDAQ Composite, NYSE Composite, and Russell 2000, from 1 July 1992 to 30 June 2022 are studied empirically. [ABSTRACT FROM AUTHOR]

The commercial real estate (CRE) sector concerns a diverse set of actors that specialize in real estate activities, such as brokerage, valuation, construction, and asset management. CRE activity is a key indicator for the level of corporate growth and the economic development of a country. However, investments in CRE face a volatile global environment, which is characterized by deep uncertainties and cyclical behavior. In this context, decision support tools that facilitate the monitoring and assessment of CRE investments are of major importance for various stakeholders, including investors and policy makers. The objective of this study is to present a comprehensive approach for assessing CRE investments based on a multicriteria evaluation framework. The analysis is based on a sample of commercial assets taken from the portfolio of a real estate investment company in the United States. The proposed multicriteria approach considers a rich set of evaluation criteria, including macroeconomic indicators, information about the local CRE markets, as well as the characteristics of the assets under consideration. Using analytical methodologies from the family of ELECTRE outranking methods, the assets are ranked and rated according to their overall performance. Different scenarios are explored for the weightings of the appraisal criteria, thus enabling the analysis of the robustness of the results. [ABSTRACT FROM AUTHOR]

Sustainable development of cities constitutes nowadays a worldwide goal. Therefore, the related urban and architectural choices must fulfil sustainable objectives. In this context, sustainability assessment presents itself as a key and fundamental element to guide decision‐making processes, orienting choices towards actions that make the built environment more sustainable. Among the several existing assessment tools and methods, multi‐criteria decision analysis (MCDA) are part of the most widely used approaches to support sustainable decisions. Therefore, this article aims to understand what makes sustainability assessment through MCDA suitable or unsuitable to support decision‐making processes in the context of sustainable urban and architectural design and observe how available MCDA methods support this purpose. To do this, a literature review related to MCDA methods in sustainable urban and architectural context has been performed. Descriptive statistics and tables are provided to point out the main trends according to specific research questions. In this sense, it is intended to highlight some potential gaps and points of reflection for future research developments that can support sustainable urban and architectural development. [ABSTRACT FROM AUTHOR]

The surging demand for electricity, propelled by the widespread adoption of intelligent grids and heightened consumer interaction with electricity demand and pricing, underscores the imperative for precise prognostication of optimal power plant utilization. To confront this challenge, a dataset centered on issue-centric power plans is meticulously crafted. This dataset encapsulates pivotal facets indispensable for attaining sustainable power generation, including meager gas emissions, installation cost, low maintenance cost, elevated power generation, and copious resource availability. The selection of an optimal power plant entails a multifaceted decision-making process, demanding a systematic approach. Our research advocates the amalgamation of multiple-criteria decision-making (MCDM) models with self-organizing maps to gauge the efficacy of diverse sustainable energy systems. The examination discerns solar energy as the preeminent MCDM criterion, securing the apex position with a score of 83.4%, attributable to its ample resource availability, considerable energy generation, nil greenhouse gas emissions, and commendable efficiency. Wind and hydroelectric power closely trail, registering scores of 75.3% and 74.5%, respectively, along with other energy sources. The analysis underscores the supremacy of the renewable energy sources, particularly solar and wind, in fulfilling sustainability objectives and scrutinizing factors such as cost, resource availability, and the environmental impact. The proposed methodology empowers stakeholders to make judicious decisions, accentuating facets that are required for more sustainable and resilient power infrastructure.

In multiple criteria decision-making (MCDM) problems, ranking alternatives is usually the end. However, in real life, it is rarely an end in itself and serves as a means to an end. Further, in real-life problems, the selection of alternatives is usually made with the aid of unique experts, and thus minimizing the influence of the uncertainty of their subjective judgements on the optimality of the decisions is a critical issue. The current study proposes a novel Interval Ordinal Priority Approach to objectively solve these and other issues by allowing uncertainty analysis and quantification. The study argues that when the model's input contains uncertainty (even if represented by crisp numbers), expecting the output to be free from uncertainty is an unrealistic conjecture. Therefore, unlike the conventional MCDM models producing crisp weights, the proposed approach yields interval weights with the length of the interval representing the uncertainty (inconsistency among the experts' judgements). Also, instead of resorting to the subjective measurement of thresholds to qualify or disqualify a set of inputs based on the degree of uncertainty, a novel objective measure of threshold is put forward. The validity of the proposed method is demonstrated through illustrative examples and comparative analysis. Later, the study is concluded with the implications for real-world decision-making. [ABSTRACT FROM AUTHOR]

In this paper, we are interested in decision aiding problems, aiming at rating a set of objects with respect to several dimensions, called criteria. A rating problem statement consists on partitioning a set of objects into predefined ordered equivalence classes, called categories, identified by ratings. Rating problems are widely studied in the literature, either based on the utility theory, rough sets or the majority principle. The existing methods based on the majority principle present some disadvantages potentially leading to an unconvincing rating because challenged by contradictory pairwise comparisons. In this work, we present a new method providing a "convincing" (challenge-free) rating over a set of studied objects, based on the aggregation of positive and negative reasons, respectively supporting and opposing a rating. The method exploits comparisons among the objects and the profiles characterizing the categories as well as comparisons among the objects. [ABSTRACT FROM AUTHOR]

Modern distribution grids are transforming due to the increasing amount of load and generation based on DC technologies, whose interconnection is facilitated by the upcoming AC-DC distribution networks. Their fault management is challenged by new issues related to the power transferred among AC and DC sub-networks. In light of this, the present paper proposes a novel Service Restoration (SR) algorithm, specifically tailored for AC-DC distribution grids, that optimizes the re-energization by establishing priorities of disconnected bus groups and computing, as candidate solutions, hierarchical combinations of normally open and normally closed switches. To overcome the limitations of existing approaches, a Multiple Criteria Decision Analysis (MCDA) is proposed to combine and objectively prioritize different SR goals (i.e., the use of telecontrolled switches, the minimization of power losses, and the applicability of the proposed solutions in a defined time horizon), and ultimately make the grid operator benefit from the possibility to flexibly tune different operational objectives. The proposed algorithm allows to effectively discriminating competing solutions, i.e., whence and how to re-energize disconnected buses, and complies with the time requirements for field implementation. The results prove the crucial role of DC sub-networks, associated to the control of power injection from AC-DC converters, in enhancing the SR by improving its targets and increasing the number of re-energized loads. Ultimately, the effect of MCDA comparison parameters on the SR outcomes is quantitatively investigated via global sensitivity analysis, whose adoption is recommended for supporting the grid operator in the algorithm implementation.

Abstract Background Applicability of comprehensive assessment of integrated care services in real world settings is an unmet need. To this end, a Triple Aim evaluation of Hospital at Home (HaH), as use case, was done. As ancillary aim, we explored use of the approach for monitoring the impact of adoption of integrated care at health system level in Catalonia (Spain). Methods Prospective cohort study over one year period, 2017–2018, comparing hospital avoidance (HaH-HA) with conventional hospitalization (UC) using propensity score matching. Participants were after the first episode directly admitted to HaH-HA or the corresponding control group. Triple Aim assessment using multiple criteria decision analysis (MCDA) was done. Moreover, applicability of a Triple Aim approach at health system level was explored using registry data. Results HaH-HA depicted lower: i) Emergency Room Department (ER) visits (p

The multicriteria decision aiding field offers many methods to support decision makers in comparing a list of alternatives. Among these, outranking methods such as ELECTRE are appreciated for avoiding full compensation among criteria, but outranking relations are difficult to exploit due to incompleteness and lack of transitivity. This work focuses on choice problems, proposing a stochastic exploitation method to select the most preferred alternative. It builds on the concept of Markov solution, which has become popular to select a winner in tournaments and voting problems. The proposed method can be used to exploit crisp outranking relations, valued outranking relations, or stochastic outranking relations. This can be a valuable addition to the toolbox for exploiting outranking relations as this work shows that solutions can be computed without much effort and guarantee some essential properties. [ABSTRACT FROM AUTHOR]

• Three ISD setups were developed: without, with, and with fins inserted PCMES unit. • Bitter gourd slices dried in 12 h from 92% to 12% moisture content on a wet basis. • The Weibull model is chosen as the best model for predicting drying behaviour. • Energy and economic analyzes of three ISD setups are performed and compared. • From hybrid MCDA approaches, the ISD with fins inserted PCMES unit is ranked first. As the food processing industry needs a lot of heat energy to remove moisture from agricultural products, there is a greater emphasis on solar energy. In this study, the energy analysis and economic feasibility of Indirect Solar Dryer (ISD) comprises a blower, three different corrugated absorber plate solar collector setups (without Phase Change Material Energy Storage (PCMES), with PCMES, and with fins inserted PCMES). The drying time of the bitter gourd slices in the developed ISD setup is significantly decreased compared to natural drying. The Weibull model is selected as the best mathematical model for predicting drying behavior based on statistical variables. The ISD's energy analysis reported the thermal efficiencies, energy consumption, and moisture extraction rate. The ISD with fins inserted PCMES performed better than the other two setups in energy performances. However, because of the usage of fins, the same setup has a slightly higher (3.1%) capital cost than the setup without fins. ISD with fins inserted PCMES is chosen as the best setup based on energy and economic parameters from the multiple criteria decision-making techniques. The selected setup produced a robust and high-quality product; and, thus, recommended for drying agricultural products in food drying industries. [ABSTRACT FROM AUTHOR]

The escalating anthropogenic activities in recent decades are constantly affecting the aquatic ecology and posing a threat to freshwater species. In this study, a two‐dimensional ecohydraulic model and multiple criteria decision analysis (MCDA) coupled framework is employed in the Dikhow River, India, to investigate the favourable habitat condition for the nearly threatened Notopterus chitala fish locally known as Chital fish. The habitat residence suitability and habitat suitability indices are calculated at the observed and ecological flows. The ecological flow rates are computed from flow duration curve shifting and flow duration curve analysis methods. The hydrodynamic computations are carried out using a 1D‐2D coupled MIKE FLOOD model. The habitat assessment of the targeted species is carried out by constructing the suitability curves from the computed hydraulic parameters at different environmental management conditions. Results from the habitat assessment curves indicate that the maximum habitat suitability index is found for the targeted fish species at average annual flow. Based on the MCDA analysis, the outer banks of the meander zones are identified as not suitable residence regions for the target species. This study revealed that the coupled approach could be useful to regulate the stream restoration strategies and identify potential habitat preference locations in the domain. [ABSTRACT FROM AUTHOR]

Data on past and current decisions can be utilised to enhance the decision-making process by automating decisions or making problem solving more intuitive. Data is either extracted from distributed sources and repositories, or obtained with the regression analysis from holistically assessed alternatives and human judgements. One of possible advanced approaches to encourage intuitive decision-making aims at inferring criteria weights of the decision model with regard to correlations between preferential parameters, in such a way that objective inner information on alternatives is consolidated with personal knowledge and experience. This is relevant because the task of specifying criteria weights is cognitively demanding and represents a key aspect of each decision model. The paper first discusses the notation and infrastructure to exchange decision models and handle preferential information underlying the mechanisms of indirect weight derivation. As the main contribution of the research, a method for the inference of criteria weights from veto-related information is proposed, with which selective strengths of veto degrees are calculated to compare the magnitudes of veto, while strengths of veto assessments are used to determine the influence of veto on the deterioration of rankings or categories into which alternatives are sorted, respectively. Strengths of non-compensatory veto criteria are then projected into compensatory weights. The experimental study reveals the characteristics of indirectly derived criteria weights and the influence of veto. Several quality factors are considered, such as the validity of weights, accuracy of results, richness of information and ability to discriminate conflicting alternatives. Weights are also compared to standard ROC and RS surrogate weights. The approach is generalised to both common decision-making problematics of ranking and sorting. [ABSTRACT FROM AUTHOR]

Taxpayers and operators worldwide have significant current liabilities associated with decommissioning of offshore Oil & Gas (O&G) assets. Consequently, decommissioning is at the forefront of industrial, governmental, and non-governmental agendas. Decommissioning is a highly complex activity with health, safety, environmental, social, economic, and technical implications. Increasing scientific evidence supports that manmade subsea structures create hard, artificial reef habitats that provide ecological and social benefits to society. Given the significant uncertainty regarding how subsea structures should be retired at the end of their operational lifetimes, it is necessary for governments, taxpayers, and operators to understand the risks and benefits associated with potential decommissioning options. Currently, the North Sea decommissioning process is based on the policies and direction of the Oslo and Paris Convention’s (OSPAR) Decision 98/3 and follow comparative assessment (CA) multiple-criteria decision analysis (MCDA) guidelines to determine the best overall strategy for decommissioning subsea structures; however, CA MCDA processes can be biased, ambiguous, difficult to use, interpret, and replicate, and limited in their consideration of multigenerational benefits. Consequently, to assist decision-makers in understanding and evaluating options and associated benefits for decommissioning subsea structures, this study adapted the net environmental benefit analysis (NEBA) framework to supplement and strengthen the CA process for evaluating decommissioning options for offshore O&G facilities. The net environmental benefit analysis based comparative assessment (NEBA-CA) framework is presented that addresses the growing need for a practical, quantitative, scientifically robust, defendable, and transparent MCDA approach to determine optimized decommissioning strategies for subsea assets. Increased transparency in CAs will provide an additional layer of credibility with regulators and society. The approach is data driven and a desktop analysis mainly relying on existing data. Using a North Sea case study, this work demonstrates the ability of NEBA-CA to resolve inherent complexity in comparing decommissioning options, thereby supporting operators in working with regulators to decommission assets in a way that maximizes ecosystem service benefits to society while managing site-related risks and costs. The NEBA-CA framework supplements and strengthens the standard CA process by 1) incorporating quantified metrics including multigenerational ecosystem service benefits and risks, 2) excluding front ranking (scoring) or weighting of metrics, and 3) providing consistent graphical displays to support visual differentiation of options and metrics.

Due to its location in arid and semi-arid climates, Iran is one of the countries with the highest water stress, and the need for a study that states a solution for using salt water is felt more than ever. For the first time in this paper, an indirect method is presented to identify areas prone to salty water in the entire country of Iran, which simultaneously have the potential of wind energy to supply electricity for reverse osmosis desalination devices. Simultaneously economic, environmental, technical, and geological parameters have been considered. The proposed methodology provides a structured decision aid, which can be used in other countries to identify the mentioned areas and produce fresh water using reverse osmosis devices. Geographic Information Systems (GIS) are used to generate layers of data and to apply the elimination criteria and constraints. Multiple Criteria Decision Analysis (MCDA) is then used to rank and sort the grids via the identified evaluation criteria. Seventy-eight cities in Iran, mainly located in the east of the country, were identified as potential areas, and 16 cities with the best conditions for using wind energy in the supply of reverse osmosis energy were identified, which are areas with operational potential. For each cubic meter of saltwater desalination at 25 °C, about 0.38 kWh of electricity is consumed, the amount of water that can be desalinated depends on the amount of electricity produced by wind turbines. If the total amount of water that can be operationally extracted through 550 kW turbines can be exploited, it can supply 63.8475% of the country's water needs . About 11658552.6321 cubic meters of water can be desalinated and used. Torbat-e-Jam, located in Khorasan Razavi province, can produce 2244868.421 cubic meters of fresh water, the most prone among other areas.

When a public administration wishes to implement policies, there is a previous need of comparing different options to assess their social attractiveness. A fair policy assessment process should consider the ethical obligation of taking a plurality of social values, perspectives and interests into account; there is no doubt that Impact Assessment (IA) is then multidimensional in nature. For example, the European Commission current practice on IA considers three main objectives i.e. efficiency, effectiveness (including proportionality) and coherence and it is based on the assessment of various broad impacts such as economic, environmental and social ones. In empirical assessment of public policies and publicly provided goods, Multiple Criteria Decision Analysis is an appropriate policy tool, since it allows taking into account a wide range of assessment criteria (e.g. environmental impact, income distribution, social inclusion, and so on) and not simply profit maximization, as a private economic agent might do. This paper deals with the importance of using appropriate multi-criteria mathematical aggregation rules to guarantee consistency and transparency of results. An illustrative example dealing with a recent EC IA on modernising VAT for cross-border B2C e-Commerce is presented too. [ABSTRACT FROM AUTHOR]

People with various skill sets and backgrounds are usually found working on projects and thus, group decision-making (GDM) is one of the most important functions within any project. However, when projects concern healthcare or other critical services for proletariat or general public (especially during COVID19), the importance of GDM can hardly be overstated. Measuring the performance of healthcare construction projects is a critical activity and should be gauged based on the input from a large number of stakeholders. Such problems are usually recognized as large-scale group decision-making (LSGDM). In the current study, we aim to propose a decision support system for measuring the performance of healthcare construction projects against a large number of experts using ordinal data. The study identifies several key indicators from literature and recorded the observations of a large number of experts about these indicators. After that, the acceptable range of complexity is specified, the Silhouette plot is provided to find the optimal number of clusters, and the ordinal K-means method is employed to cluster the experts' opinions. Later, the confidence level is measured using a novel Weighted Kendall's W for the optimal number of the clusters, and the threshold is checked. Finally, the conventional problem is solved using the Group Weighted Ordinal Priority Approach (GWOPA) model in multiple attributes decision making (MADM), and the performance of the projects is determined. The validity of the proposed approach is confirmed through a comparative analysis. Also, a real-world case is solved, and the performance of some healthcare construction projects in China is gauged with a comprehensive sensitivity analysis. [ABSTRACT FROM AUTHOR]

Background: Applicability of comprehensive assessment of integrated care services in real world settings is an unmet need. To this end, a Triple Aim evaluation of Hospital at Home (HaH), as use case, was done. As ancillary aim, we explored use of the approach for monitoring the impact of adoption of integrated care at health system level in Catalonia (Spain).Methods: Prospective cohort study over one year period, 2017-2018, comparing hospital avoidance (HaH-HA) with conventional hospitalization (UC) using propensity score matching. Participants were after the first episode directly admitted to HaH-HA or the corresponding control group. Triple Aim assessment using multiple criteria decision analysis (MCDA) was done. Moreover, applicability of a Triple Aim approach at health system level was explored using registry data.Results: HaH-HA depicted lower: i) Emergency Room Department (ER) visits (p < .001), ii) Unplanned re-admissions (p = .012); and iii) costs (p < .001) than UC. The weighted aggregation of the standardized values of each of the eight outcomes, weighted by the opinions of the stakeholder groups considered in the MCDA: i) enjoyment of life; ii) resilience; iii) physical functioning; iv) continuity of care; v) psychological wellbeing; (vi) social relationships & participation; (vii) person-centeredness; and (viii) costs, indicated better performance of HaH-HA than UC (p < .05). Actionable factors for Triple Aim assessment of the health system with a population-health approach were identified.Conclusions: We confirmed health value generation of HaH-HA. The study identified actionable factors to enhance applicability of Triple Aim assessment at health system level for monitoring the impact of adoption of integrated care.Registration: ClinicalTrials.gov (26/04/2017; NCT03130283). [ABSTRACT FROM AUTHOR]

The Pythagorean fuzzy sets conveniently capture unreliable, ambiguous, and uncertain information, especially in problems involving multiple and opposing criteria. Pythagorean fuzzy sets are one of the popular generalizations of the intuitionistic fuzzy sets. They are instrumental in expressing and managing hesitant under uncertain environments, so they have been involved extensively in a diversity of scientific fields. This paper proposes a new Pythagorean entropy for Multi-Criteria Decision-Analysis (MCDA) problems. The entropy measures the fuzziness of two fuzzy sets and has an influential position in fuzzy functions. The more comprehensive the entropy, the more inadequate the ambiguity, so the decision-making established on entropy is beneficial. The COmplex PRoportional ASsessment (COPRAS) method is used to tackle uncertainty issues in MCDA and considers the singularity of one alternative over the rest of them. This can be enforced to maximize and minimize relevant criteria in an assessment where multiple opposing criteria are considered. Using the Pythagorean sets, we represent a decisional problem solution by using the COPRAS approach and the new Entropy measure. [ABSTRACT FROM AUTHOR]

One of the long-term challenges for policy makers in host countries of migrants is the optimal geographical allocation of the migrant population so as to strengthen integration outcomes and serve the crucial goal of social inclusion. The political debate on the appropriate placement policy of newcomers has continued for years after the large-scale inflows of asylum seekers in Greece. This paper focuses on the evaluation of the dispersal policy of asylum seekers in Greece as implemented under the reception and accommodation scheme. Furthermore, it provides decision makers with an alternative dispersal policy framework driven by the integration outcomes of the already established migrant population in the country. The research encompasses Eurostat NUTS 2 annual data for a choice upon availability of the Zaragoza Integration Indicators and the immigrant population indicator for the thirteen Greek regions. The data cover a ten-year period from 2010 to 2019, and they are analysed with the multiple criteria decision-making method PROMETHEE. In addition to desk research, fieldwork has been conducted to illuminate the results of the model. The paper extends the literature on the governance of migration and contributes to the planning of migrant integration policies in their host countries. [ABSTRACT FROM AUTHOR]

Facing multiple and often considered as conflicting stakes, either economical, migratory, or environmental, policy-making may struggle to identify and implement relevant policy action allowing for balanced and joint completion of such challenges. Addressing this important public issue, we develop a multi-criteria two-stage Goal Programming (GP) model to identify optimal policy paths towards the Europe 2020 strategy on economic growth, employment levels and environmental sustainability. The model is calibrated on current contributions of economic sectors in all European countries to each policy objective: contribution to economic output (GDP), emissions of Green House Gas, electric consumption and number of jobs. First, we study the optimal allocation of workers within economic sectors of each European country to maximize the joint achievement of Europe 2020 multi criteria sustainability targets. We then extend the model to allow cooperation between states, namely allowing internal migrations of workers between countries. We highlight how supranational allocation schemes of surplus workers improve the satisfaction of national sustainability objectives. Finally, we consider extra-European migrants regional integration and study the consequences of such opening over EU2020 targets satisfaction and per capita GDP. Simulation results highlight countries performance comparison, and sheds light on significant benefits from such cooperation for the majority of countries. Improved integration of internal and external workforce generally improves the achievement of EU2020 objectives, while keeping per capita GDP at least constant. Moreover, we expose the relevance of cooperative work-flows allocation strategies across Europe and emphasize the importance of workers mobility in order to ensure more sustainable common development. [ABSTRACT FROM AUTHOR]

The present study aims to propose an axiomatic evolution of the method, called THOR 2, based on the analysis of the original algorithm. It was proposed, in the evolution, the distinction in the attribution of weights in the sum of scores as well as the multiplication of the value of the criterion weight by the fuzzy-rough index in all preference relations. This functionality allows that, in the absence of data to fill in the classification of alternatives and weights in the decision matrix, it is possible to estimate the data and assign a low pertinence value for attributing that data, thus avoiding the elimination of the alternative or criterion due to the absence of the data. In order to validate the pertinence function proposed for THOR 2, an analysis of the ranking of alternatives in three different scenarios was carried out. In this way, the scenarios were simulated in which there was an absence of data in the original decision matrix. The analysis aimed to compare the result of the ranking of the alternatives when there is no data with the situation that the decision matrix is complete (all data are available), observing the impact on the ranking of the alternatives. In all scenarios that used data estimated in conjunction with the pertinence function, the ranking was kept in line with the ranking in the initial situation. However, when it was decided to exclude the criteria, the ordering was different from the ordering in the situation of origin.

The goal of this work is to apply Multiple Criteria Decision Analysis tools, both theoretical and practical, to analyse, support and possibly enhance composite indexes, in particular those related to sustainability assessment. In this context, the Sustainable Development of Energy, Water and Environment Systems Index represents a paradigmatic example and an emerging reference point, thus it is specifically addressed throughout the work. On the theoretical side, the focus is on the property of “independence”, i.e., of evaluating an alternative independently of the others. It is argued that this property can be appealing for an index that is conceived to address, over time, an increasing number of inherently evolving systems. A viable and theoretically grounded approach for devising a version of the index fulfilling independence is proposed. On the practical side, the contribution concerns visual support tools. A well-known projective method is adapted to work with the index, and a new tool with comparable expressive capabilities is proposed. The new representation is more focused on the index, technically simpler, and less sensitive to changes in the input data. The features of the visual tools are illustrated exploiting currently available (partially aggregated) index data. In particular, the new tool is used to illustrate two issues addressed in the scientific literature on the index, namely, the use of scenario analysis as a predictive tool, and the decoupling of energy usage and carbon dioxide emissions.

With the expansion of coronavirus in the World, the search for technology solutions based on the analysis and prospecting of diseases has become constant. The paper addresses a machine learning algorithms analysis used to predict and identify infected patients. For analysis, we use a multicriteria approach using the PROMETHEE-GAIA method, providing the structuring of alternatives respective to a set of criteria, thus enabling the obtaining of their importance degree under the perspective of multiple criteria. The study approaches a sensitivity analysis, evaluating the alternatives using the PROMETHEE I and II methods, along with the GAIA plan, both implemented by the Visual PROMETHEE computational tool, exploring numerical and graphical resources. The analysis model proves to be effective, guaranteeing the ranking of alternatives by inter criterion evaluation and local results with intra criterion evaluation, providing a transparent analysis concerning the selection of prediction algorithms to combat the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Regarding the scope of project portfolio management, the correct prioritization of actions leads to the achievement of the organizational strategic objectives. Based on the strategic planning of a technology consulting company, the paper presents an alternative model for project prioritization from the moment that the project selection decision is a vital process in technological associations in two ways. First, the software development project budget requires a large investment and must be in alignment with the strategic objectives of the company. Second, the multidimensionality of organizational returns on projects is naturally risky in terms of the expected outcome. This study considers the project analysis based on a multicriteria approach by the THOR method, providing to make a transparent evaluation, highlighting the most important variables concerning the problem, and making the identification of the most favorable solution. [ABSTRACT FROM AUTHOR]

With the advent of technological evolution, drones have been providing to improve services and operations by their wide range of applications, both in civil and military scopes. In this context, the paper addresses a multicriteria analysis based on the drone assessment to support public security. For the evaluation process, the PROMETHEE-SAPEVO-M1 systematic is carried out structuring the preferences and exposing the set of the most favorable solution in the light of quantitative and qualitative data, upon inter-criterion and intra-criterion analysis. For the implementation of the modeling, its computational web platform is performed, exploring the results by numerical and graphical analysis. The systematic approached proves to be effective, guaranteeing the identification of the most favorable solution, providing a complete and robust analysis regarding the improvement of public security in the world. [ABSTRACT FROM AUTHOR]

The wide variety of available mobile devices, associated with the increase in wireless communication network availability, enables an increasing offer of mobile and ubiquitous technologies. In this sense, this article proposes an approach called Helix-SIoT, which explores the Social Internet of Things concepts to support Blind People (BP). The objective is to enhance the treatment of the different sensors that integrate the assistance of these people with SIoT use. For this, the proposal employs Context Awareness and Multiple Criteria Decision Analysis, helping people's autonomy in terms of mobility, response times, and the choice of the Caregiver in the most transparent way possible. The functional architecture of the Helix approach operates integrated with the Adaptation and Context Recognition Subsystem of the EXEHDA middleware, which provides support for the acquisition, storage, and processing of context information used by the mobile Helix-SIoT applications. [ABSTRACT FROM AUTHOR]

To enhance preparedness for diverse pandemic situations we aim to predict the performance of various pharmaceutical intervention strategies. We gathered domain experts and ran a series of decision conferences where a scenario‐based multi‐criteria decision analysis (MCDA) model was interactively defined and implemented. Assuming an influenza pandemic, a micro simulation model was used to estimate societal health impact, a health‐economic model was used to estimate economic losses, and expert preferences were elicited to define trade‐offs between multiple criteria and synthesize various estimates. Sensitivity analysis to address various forms of uncertainty was also conducted. Nine intervention strategies, including the baseline "no interventions" strategy, were evaluated and ranked under five pandemic scenarios for Sweden's population. We conclude that a scenario‐based MCDA approach relying on multiple models for assessment of consequences is instrumental in defining robust interventions and support decision‐making at the pre‐pandemic and pandemic situations. [ABSTRACT FROM AUTHOR]

Background: There are increasing demands on Emergency Medical Services. More efficient treatment pathways are required to support conveyance decision making and patient referral in prehospital care. Point of Care testing is increasingly available and utilised across the NHS to support optimal ways of working. We aimed to design and conduct a Multiple Criteria Decision Analysis to prioritise in vitro point of care tests and use cases for inclusion in a platform trial of in vitro point of care testing in UK Emergency Medical Services., Methods: We designed a Multiple Criteria Decision Analysis that included systematic scoping reviews stakeholder recruitment, two stakeholder surveys and two stakeholder workshops to scope the use cases, explore criteria and map use cases, evaluate the criteria and measure the use cases against the criteria., Results: We recruited 32 stakeholders. We developed a scoring matrix with 4 criteria for scoring the use cases and 8 criteria for scoring the point of care tests and applied weighting determined from survey results. Use cases were scored by the stakeholders against 4 criteria. The 3 highest scoring use cases were point of care troponin testing in: possible Acute Myocardial Infarction, lactate testing in suspected sepsis and in trauma. We developed the process for scoring the point of care tests to be completed close to a proposed trial to allow for a changes in technology., Conclusions: We successfully designed a Multiple Criteria Decision Analysis to identify use cases and candidate tests for inclusion in a future platform trial of in vitro point of care testing in UK Emergency Medical Services. We identified 3 use cases for evaluation in a platform trial of in vitro point of care testing: troponin testing in possible acute myocardial infarction, lactate testing in suspected sepsis and lactate testing to identify occult haemorrhage in trauma., Competing Interests: Competing interests: AT reports consultancy fees from Siemens Healthineers and Perspectum. RB reports research grants with Roche Diagnostics, Siemens Healthineers and Abbott Point of Care; consultancy with Roche Diagnostics, Siemens Healthineers, Abbott Point of Care, Beckman Coulter, Radiometer, LumiraDx, Aptamer Group and Psyros Diagnostics (money to institution); speaker fees with EMCREG (Emergency Medicine Cardiac Research and Education Group) International (personal honoraria). support for travel/accommodation to conferences: Siemens Healthineers, Roche Diagnostics, Astra Zeneca. No other competing interests were disclosed., (Copyright: © 2024 Kirby K et al.)