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As human pressures on water resources increase, the data and decision support (DS) tools used in the governance, development and management of transboundary rivers are likely to become increasingly...

Air traffic administration requires evidence when promoting new technology or a new concept of operation. Therefore, when decision support tools are applied, it is necessary to analyze the costs and benefits quantitatively. This paper focuses on the evaluation of Key Performance Indicators (KPIs) correlated with the improvement of arrival operations after the implementation of the Arrival Management (AMAN) system and Wake Turbulence Re-categorization in China (RECAT-CN). Firstly, we give an overview of the implementation of the AMAN system and RECAT in China. Secondly, the KPIs related to the arrival operation are established according to the characteristics of AMAN and RECAT-CN, based on the existing KPI systems in the field of Air Traffic Management (ATM). The proposed KPIs are: airport acceptance rate; final approach interval; flight time within the terminal area (TMA); and taxi-in time. Thirdly, arrival operation within the TMA around Guangzhou International Airport is used as an example to carry out the quantitative analysis. The region and time range were defined for the performance comparison, and external factors were also examined. Finally, using descriptive and inferential statistics, the proposed KPIs’ comparison results are presented and visualized. Such results indicate a significant improvement in arrival operation with the AMAN system and RECAT-CN at Guangzhou International Airport.

PurposeThis paper investigates the suitability of fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.Design/methodology/approachTwo fuzzy-logic-based support tools are developed together with experts from a Swedish manufacturing firm. The first uses a complete rule base and the second a reduced rule base. Sixteen inference settings are used in both of the support tools.FindingsThe findings show that fuzzy-logic-based support tools are suitable for initial screening of manufacturing reshoring decisions. The developed support tools are capable of suggesting whether a reshoring decision should be further evaluated or not, based on six primary competitiveness criteria. In contrast to existing literature this research shows that it does not matter whether a complete or reduced rule base is used when it comes to accuracy. The developed support tools perform similarly with no statistically significant differences. However, since the interpretability is much higher when a reduced rule base is used and it require fewer resources to develop, the second tool is more preferable for initial screening purposes.Research limitations/implicationsThe developed support tools are implemented at a primary-criteria level and to make them more applicable, they should also include the sub-criteria level. The support tools should also be expanded to not only consider competitiveness criteria, but also other criteria related to availability of resources and strategic orientation of the firm. This requires further research with regard to multi-stage architecture and automatic generation of fuzzy rules in the manufacturing reshoring domain.Practical implicationsThe support tools help managers to invest their scarce time on the most promising reshoring projects and to make timely and resilient decisions by taking a holistic perspective on competitiveness. Practitioners are advised to choose the type of support tool based on the available data.Originality/valueThere is a general lack of decision support tools in the manufacturing reshoring domain. This paper addresses the gap by developing fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.

An overview of existing decision-making support software and systems for weakly structured subject domains, using expert information has been performed. Existing tools are analyzed and compared with each other from the standpoint of their mathematical ware and functionality, particularly in the area of strategic planning problem solution. It is outlined the key decision-making support tools development trends of the last few decades. On the one hand, the decision-making support system concept is often loosely used, and different kinds of statistical tools, and whole development environments are marketed as decision support software. On the other, many decision support tools are designed for specific problems and subject domains. In spite of complexity of decision problems, manufacturers of decision support software products should keep universality and flexibility requirements in mind. It has been shown that existing automated decision-making support tools are characterized by a set of functional drawbacks and limitations. None of the decision support systems listed in the article allows users to automate the whole strategic planning cycle (from strategic goal formulation to its decomposition into sub-goals and projects to optimal distribution of limited resources among these projects).Based on conducted analysis, a set of relevant requirements to modern decision-making support tools under present-day realities has been suggested. These include universality (irrespectively of the particular subject domain), expert-friendly scale-agnostic interface, opportunity for group expert session organization in remote mode, and others.Based on these requirements, it is obtained specific recommendations for decision-making support means selection in the process of strategic planning (i.e. which systems work better for specific strategic planning-related problems), and for further improvement of decision support tools. Tabl.: 1. Refs: 33 titles.

Despite the widespread opinion that the traditional finance is exclusively interested in the monetary return, in the last ten years this sector has been affected by a contamination of the public principles related to the social impacts. The global development and the spread of “win-win” financial instruments such as the Social Impact Bonds (SIBs) outlines a growing interest in making an investment that aims at generating benefits for all the subjects involved, always guaranteeing a monetary return to the private investor. The complexity of identifying the social impact sectors to be preferred, in a context characterized by different social needs, represents a critical issue in the SIBs investment. This research defines a model that can constitute a decision support tool for the public and private subjects in the preliminary phases concerning the resource allocation for a social program. The proposed algorithm allows to define a temporal priority of the social impact sectors that are simultaneously able to maximize the conveniences for all the subjects involved. Through the model, the public and private subjects will be able to determine the best allocation of financial resources according to the real social needs, contributing to an effective spread of SIBs both in Italy and abroad.

Open dumps are places where solid waste is disposed directly on the ground, without any environmental assessment, resulting in environmental, societal and economic damages. In Cape Verde, most of the municipalities dispose their solid waste on open dumps. Cape Verde's new policies on solid waste management determined the closure and the remediation of all the dumps around the country. In this context, this research diagnosed the conditions of tree dumps in Santiago Island and one dump in Fogo Island, in order to rank them for remediation priorities. Also, remediation scenarios and actions for each of the dumps were proposed, using Decision Support Tools (DST). Data were collected by visiting the dumps and applying field questionnaires. The results demonstrated a similarity between Santiago’s open dumps (Santa Cruz, Santa Catarina and Praia Municipal Dumps), having a "Medium" impact level. Sao Felipe Municipal Dump located in Fogo Island has the highest impact level and it is the priority for remediation actions. The decision support tool usage proved to be an important instrument to aid decision making for managing areas contaminated by Municipal Solid Waste (MSW) in Cape Verde. Key words: Open dumps, diagnosis, remediation scenarios.

Improving situational awareness and crisis response are key priorities in reducing potential risks associated with sea ice and environmental-related hazards in the Arctic. This research explores th...

Peer support specialists (i.e., lay interventionists representing one of the fastest-growing mental health workforce) are increasingly using technologies to support individuals with mental health challenges between clinical encounters. The use of technology by peers has been significantly increased During COVID-19. Despite the wide array of technologies available, there is no framework designed specifically for peer support specialists and service users to select technologies to support their personal recovery. The objective of the study was to develop a Decision-Support Tool for Peer Support Specialists and Service Users to facilitate shared decision-making when choosing technologies to support personal recovery. The study used an iterative co-production process, including item formulation and a series of group cognitive interviews with peer support specialists and service users (n=9; n=9, n=4). The total sample included 22 participants: peer support specialists (n=18, 81.8%) and service users (n=4, 18.2%). The final version of the Decision-Support Tool for Peer Support Specialists and Service Users (D-SPSS), includes 8 domains: (1) privacy and security; (2) cost; (3) usability; (4) accessibility; (5) inclusion and equity; (6) recovery principles; (7) personalized for service users’ needs; and (8) device set-up. Our study found that involving peer support specialists and service users in the design and co-production phase of a decision-support tool is feasible and has the potential to empower both peer support specialists and service users, and potentially increase engagement in the use of technologies that support individuals’ recovery from traditional clinical encounters. Experience Framework This article is associated with the Innovation & Technology lens of The Beryl Institute Experience Framework (https://www.theberylinstitute.org/ExperienceFramework). Access other PXJ articles related to this lens. Access other resources related to this lens

Over the last few decades, Urban Heat Stress (UHS) has become a crucial concern of scientists and policy-makers. Many projects have been implemented to mitigate Urban Heat Island (UHI) effects using nature-based solutions. However, decision-making and selecting an adequate framework are difficult because of complex interactions between natural, social, economic and built environments. This paper contributes to the UHI issue by: (i) identifying the most important key factors of a Decision Support Tool (DST) used for urban heat mitigation, (ii) presenting multi-criteria methods applied to urban heat resilience, (iii) reviewing existing spatial and non-spatial DSTs, (iv) and analyzing, classifying and ranking DSTs. It aims to help decision-makers through an overview of the pros and cons of existing DSTs and indicate which tool is providing maximum support for choosing and planning heat resilience measures from the designing phase to the heat mitigation phase. This review shows that Multi-Criteria Decision Analysis (MCDA) can be used for any pilot site and the criteria can be adapted to the given location accordingly. It also highlights that GIS-based spatial tools have an effective decision support system (DSS) because they offer a quick assessment of interventions and predict long-term effects of urban heat. Through a comparative study using specific chosen criteria, we conclude that the DSS tool is well suited and fulfils many prerequisites to support new policies and interventions to mitigate UHS.

Hospital information systems are increasingly used as part of decision support tools for planning at strategic, tactical and operational decision levels. Clinical pathways are an effective and efficient approach in standardising the progression of treatment, to support patient care and facilitate clinical decision making. This literature review proposes a taxonomy of problems related to clinical pathways and explores the intersection between Information Systems (IS), Operational Research (OR) and industrial engineering. A structured search identified 175 papers included in the taxonomy and analysed in this review. The findings suggest that future work should consider industrial engineering integrated with OR techniques, with an aim to improving the handling of multiple scopes within one model, while encouraging interaction between the disjoint care levels and with a more direct focus on patient outcomes. Achieving this would continue to bridge the gap between OR, IS and industrial engineering, for clinical pathways to aid decision support.

Background. A multifactorial decision support system (mDSS) is a tool designed to improve the clinical decision-making process, while using clinical inputs for an individual patient to generate case-specific advice. The study provides an overview of the literature to analyze current available mDSS focused on prostate cancer (PCa), in order to better understand the availability of decision support tools as well as where the current literature is lacking. Methods. We performed a MEDLINE literature search in July 2018. We divided the included studies into different sections: diagnostic, which aids in detection or staging of PCa; treatment, supporting the decision between treatment modalities; and patient, which focusses on informing the patient. We manually screened and excluded studies that did not contain an mDSS concerning prostate cancer and study proposals. Results. Our search resulted in twelve diagnostic mDSS; six treatment mDSS; two patient mDSS; and eight papers that could improve mDSS. Conclusions. Diagnosis mDSS is well represented in the literature as well as treatment mDSS considering external-beam radiotherapy; however, there is a lack of mDSS for other treatment modalities. The development of patient decision aids is a new field of research, and few successes have been made for PCa patients. These tools can improve personalized medicine but need to overcome a number of difficulties to be successful and require more research.

The fundamental goal of any business is to create value for its owners. In shipping, the value is not only created with freight income, but also with the trade of the vessel itself. A ship has a limited lifetime and can be traded in different markets. The lowest value it will ever receive is its scrap price. An owner may decide to sell a vessel to scrap due to various reasons together with her physical condition and age. In this paper, a fuzzy Analytic Hierarchy Process based decision model is used to provide practitioners with a decision support tool for demolition sale versus further trading of a vessel. The usage of the tool is further illustrated with five actual cases.

Industrial Symbiotic Relations (ISRs), as bilaterally cooperative industrial practices, are emerging relations for exchanging reusable resources among production processes of originally distinct firms. In ISRs, firms can enjoy mutual environmental, social, and economic benefits. Due to similarities in aim and functionality of ISRs and the concept of Circular Economy (CE), it is expected that ISRs play a major role in implementing CE in the context of industrial production. However, industrial firms generally lack analytical tools tailored to support their decisions whether – and based on what priority – to negotiate a particular ISR opportunity, selected from a set of potential alternatives. This question is the main focus of the decision support method developed in this paper, that we call the “industrial symbiosis opportunity filtering” problem. The key economic factor that influences the decision of firms to reject or negotiate an ISR in real-life scenarios, is the total cost-reduction/benefit that they may enjoy in case the ISR would be implemented. In case they evaluate that a sufficient benefit is obtainable, they see the opportunity as a promising one and pursue to contract negotiations. Following this observation, we take an operations-oriented stance and provide a Formal Industrial Symbiosis Opportunity Filtering method ( FISOF in short) that: (1) takes into account the key operational aspects of ISRs, (2) formalizes ISRs as industrial institutions using semantic structures adopted from multi-agent systems literature, and (3) enables evaluating ISR opportunities using implementable decision support algorithms. In practice, the FISOF method and its algorithms can be integrated into industrial symbiosis frameworks to support firms in the process of ISR evaluation. We also illustrate how information sharing enables the use of collective strategies to overcome epistemic limitations and provide a decision support algorithm that is able to capture all the mutually promising ISR implementations.

This paper presents a case study comparing text analysis approaches used to classify the current status of a patient to inform scheduling. It aims to help one of the UKs largest healthcare providers systematically capture patient outcome information following a clinic attendance, ensuring records are closed when a patient is discharged and follow-up appointments can be scheduled to occur within the time-scale required for safe, effective care. Analysing patient letters allows systematic extraction of discharge or follow-up information to automatically update a patient record. This clarifies the demand placed on the system, and whether current capacity is a barrier to timely access. Three approaches for systematic information capture are compared: phrase identification (using lexicons), word frequency analysis and supervised text mining. Approaches are evaluated according to their precision and stakeholder acceptability. Methodological lessons are presented to encourage project objectives to be considered alongside text classification methods for decision support tools.

The article was devoted to the research of methodological and practical aspects of the development and application of information support for decision-making tools to improve the competitiveness of enterprises, which together constitute a system for supporting decision-making and managing competitiveness. The algorithm for working with decision support tools in the competitiveness management process was developed and implemented, which includes the solution of the following tasks: analysis of the market situation, data collection, selection and analysis of competitor data based on BigData technology, selection of competitive pairs, modeling and forecasting of enterprises , analysis, assessment and selection of factors of influence on competitiveness, synthesis of scenarios of enterprise development, predictive modeling of dynamics of competitive indicators, the analysis of simulation results, the selection vectors factor values for the optimal development strategy, the selection of recommendations for achieving optimal values of the indicators of competitiveness, the synthesis of the recommendations of the selection rules, the provision of advice to decision-makers.

Robust environmental decision support tools are critical to maximise the ecological benefit of management decisions. However the models that underpin these rarely undergo rigorous evaluation. Here, we evaluated components of a scenario-based habitat suitability model, the Murray Flow Assessment Tool, by correlating model outputs against fish monitoring data collected since its development. Overall, we detected a low correlation between habitat suitability scores for fish and fish assemblages during low-flow conditions, including when lags in fish response to hydrological inputs were introduced. Scores specific to fish functional groups were also poorly correlated with data for those groups. Finally, model outcomes were highly sensitive to methods used to combine both individual indices and weightings for each component. Thus, we recommend using constant weightings, simple and consistent combination methods and reconsidering the number of fish functional groups as simplifications to this model and in the development of similar habitat suitability models elsewhere.

With the emergence of the Internet of Things (IoT), there has been an exponential growth in the number of sensors and the data streams they produce. This sensor data is being used in a wide range of primary industries in Australia to enable smarter decisions at many levels of production. This data is only useful if it is accessible to decision makers in a timely manner. This paper examines how a framework that is applicable across multiple domains can be developed to enable rapid development of decision support tools. This aims to avoid development duplication and enable businesses to save time and money. The framework is aimed at rapidly producing Decision Support Systems (DSS) that can aggregate heterogeneous data streams into a single flexible architecture and visualize the data in a way that assists experts to make decisions.

International audience; Several activities can cause unpleasant odours: waste storage and treatment, refinery, animal breeding, slaughterhouse, sewage treatment plant, pulp mill, fertilizer plant… Even if these unpleasant odours very rarely have toxic effects, they have an impact on the image of the emitting activity, the quality of life and, to a larger extent, on the functioning of the territory. It is this last observation, and the population's growing demand for the quality of its living environment that has led decision-makers and industrialists to address the problem of odour emissions.Until now, there is no standard method for assessing odour nuisance on a territory. Several methods exist and their use depends on the objectives and resources of the stakeholders. Few models have been developed to predict odour nuisance on a territory.The purpose of this article is to explain the interest of predictive methods in the evaluation of odour nuisance on a territory. For this purpose, this article presents an example of a method that anticipates and maps the levels of odour nuisance on a territory and the economic consequences that it causes. Based on a risk analysis method, the model uses AHP method, a multi-criteria approach, to assess the effect (nuisance) of the exposure on receptors. (Popa 2013). This method provides a paradigm shift from a posteriori measurement of odour nuisance to a preventive approach used as a decision support tool.

The increase in uptake of electric vehicles (EV) is accompanied by an increasingly complex charging service ecosystem characterised by variability in charging tariffs, charging powers, and availability of charging infrastructure in space and time. This increases the necessity of decision support tools (DSTs) that can minimise operating costs by utilizing best charging opportunities, and ensuring all mobility needs are met. We formulate and solve an optimization problem resulting in optimal charging amounts at each stay location based on the users’ intended travel itinerary to inform their decision making. We apply this to a representative sample of London drivers’ full day travel itineraries. We analyse the resulting distribution of cost reduction compared to dumb charging. We analyse factors that affect the cost reductions, such as driving mileage, which shows the types of users for whom such tools can be most beneficial, and thus appropriately prioritise its adoption. The preliminary results gave average daily benefits of £0.382 and cost reduction of 43.5% for each user, with the average price of energy being 3 times cheaper in £/kWh. The tool can ease decision making for EV users effectively, by navigating for them the large amount of information. The results showed that even relatively short, uncomplicated travel itineraries could benefit from the DST, with even more benefits for more complicated itineraries. Thus, this optimization could be suitable and beneficial for the majority of BEV users.

Marine oil spills pose a significant threat to ocean and coastal ecosystems. In addition to costs incurred by response activities, an economic burden could be experienced by stakeholders dependent on coastal resources. Decision support tools for oil spill response (OSR-DSTs) have been playing an important role during oil spill response operations. This paper aims to provide an insight into the status of research on OSR-DSTs and identify future directions. Specifically, a systematic review is conducted including an examination of the advantages and limitations of currently applied and emerging decision support techniques for oil spill response. In response to elevated environmental concerns for protecting the polar ecosystem, the review includes a discussion on the use of OSR-DSTs in cold regions. Based on the analysis of information acquired, recommendations for future work on the development of OSR-DSTs to support the selection and implementation of spill response options are presented.

Este trabalho tem por objetivo verificar se valores estimados com os modelos propostos pelos autores Sharpe (1964) e Fama e French (1993) e 2015a) são eficientes para orientar os investidores. Para alcançar o objetivo proposto, realizou-se uma analise comparativa dos retornos efetuados e estimados das ações de 60 empresas brasileiras, no período de 2000 a 2018, através do cálculo das variações entre os retornos reais e estimados, identificando-se se os retornos realizados são próximos dos calculados. O trabalho mostra-se relevante, pois o estudo realizado testou e verificou se os modelos de precificação de ativos, clássicos da literatura de finanças, realmente são eficientes e devem ser utilizados pelos investidores como ferramenta de apoio a tomada de decisão em renda variável. A partir dos resultados da pesquisa, é possível responder quais modelos tendem a ser mais confiáveis e quais os cuidados que os investidores devem ter ao fazer uso desses modelos. Além disso, o artigo visa a preencher a lacuna de pesquisa sobre a eficiência dos modelos de precificação de ativos no mercado acionário brasileiro, abrangendo tanto uma quantidade maior de ações a serem analisadas quanto um período de pesquisa mais amplo, o que agrega diversos cenários econômicos (expansão, estagnação, crises econômicas e recessão). Os resultados obtidos a partir de análise comparativa dos retornos reais e estimados empregando o modelo de precificação de ativo de Sharpe (1964) e o modelo de três e cinco fatores proposto por Fama e French (1993) e (2015a) indicam que apenas um pequeno percentual dos retornos calculados ocorreu conforme os retornos efetuados no período de análise, o que evidencia que tais modelos devem ser empregados com outras ferramentas para apoio de decisão em investimentos em renda variáveis.

Leaf wetness duration is an essential input in disease prediction models and decision support systems in Florida and elsewhere. Incorrect installation or lack of regular maintenance of leaf wetness sensors may lead to errors in plant disease risk monitoring and negative impacts on yield. This 7-page publication provides detailed guidelines for the proper installation and maintenance of leaf wetness sensors and describes the most common problems found in field installations as well as potential solutions. Written by T. B. Onofre, C. W. Fraisse, N. A. Peres, and J. McNair, and published by the UF/IFAS Department of Agricultural and Biological Engineering, February 2020.

Introduction Decision support tools (DST) may aid compliance of teams with the Neonatal Resuscitation Program (NRP) algorithm but have not been adequately tested in this population. Furthermore, the optimal team size for neonatal resuscitation is not known. Our aim was to determine whether use of a tablet-based DST or team size altered adherence to the NRP algorithm in teams of healthcare providers (HCPs) performing simulated neonatal resuscitation. Method One hundred nine HCPs were randomized into a team of 2 or 3 and into using a DST or memory alone while performing 2 simulation scenarios. The primary outcome was NRP compliance, assessed by the modified Neonatal Resuscitation Performance Evaluation (NRPE). Secondary outcomes were the subcomponents of the NRPE score, cumulative time error (the cumulative time in seconds to perform resuscitation tasks in error, early or late, from NRP guidelines), and the interaction between DST and team size. Results Decision support tool use improved total NRPE score when compared with memory alone (p = 0.015). There was no difference in NRPE score within teams of 2 compared with 3 HCPs. Cumulative time error was decreased with DST use compared with memory alone but was not significant (p = 0.057). Team size did not affect time error. Conclusions Teams with the DST had improved NRP adherence compared with teams relying on memory alone in 1 of 2 scenarios. Two and 3 HCP teams performed similarly. Given the positive results observed in the simulated environment, further testing the DST in the clinical environment is warranted.

Veterinarians and farmers must contend with the development of drug resistance and climate variability, which threaten the sustainability of current parasite control practices. Field trials evaluating competing strategies for controlling parasites while simultaneously slowing the development of resistance are time consuming and expensive. In contrast, modelling studies can rapidly explore a wide range of scenarios and have generated an array of decision support tools for veterinarians and farmers such as real-time weather-dependent infection risk alerts. Models have also been valuable for predicting the development of anthelmintic resistance, evaluating the sustainability of current parasite control practices and promoting the responsible use of novel anthelmintics.

The CoSoTIA (Concentrated Solar Technologies for Industrial Applications) project initiated by the CRTI center in collaboration with the URMPE unit concerns the study and development of CSP solar concentrators for industrial applications. In the present work, we present decision support tools for the choice of a solar concentration technology for sites in Algeria. They will be used for the comparison between different technologies of solar concentration e.g.: cylindro-parabolic, parabolic, solar tower, etc. The models used include project costs and site characteristics; they incorporate also engineering knowledge (economic, social, technical and environmental). The information needed for decision-making produced by these tools is: the total cost of the project, indicators with economic, social, technical and environmental aspects. The case studies presented were conducted under the SAM Advisor environment, which was developed to evaluate the capacities to implement CSP project in order to produce expertise for the different actors through an application on a given site (by the client). Four case sites in Algeria and for two types of solar concentrator plant are studied and presented. A comparative study was conducted and for each site the best CSP was deduced and commented.

Decision Support Systems (DSS) for Sustainable Urban Drainage Systems (SUDS) are a valuable aid for SUDS widespread adoption. These tools systematize the decision-making criteria and eliminate the bias inherent to expert judgment, abridging the technical aspect of SUDS for non-technical users and decision-makers. Through the collection and careful assessment of 120 papers on SUDS models and SUDS-DSS, this review shows how these tools are built, selected, and used to assist decision-makers questions. The manuscript classifies the DSS based on the question they assist in answering, the spatial scale used, the software selected, among other aspects. SUDS-DSS aspects that require more attention are identified, including environmental and social considerations, SUDS trains performance and criteria for selection, stochasticity of rainfall, and future scenarios impact. Suggestions for SUDS-DSS are finally offered to better equip decision-makers in facing emerging stormwater challenges in urban centers. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). This work was supported by the Instituto Colombiano de Credito Educativo y Estudios Tecnicos en el Exterior (ICETEX) under programm Pasaporte a la Ciencia granted to the first author under the number 5334506.

Mermaid is a new decision support tool for managing shellfish growing areas. It is written in Visual Basic for Application (VBA) language and uses Microsoft Excel for input, calculation, and output modules. The program automatically imports the regulatory agency's data and generates scattergrams that can be used as decision support tools to help decide which shellfish growing areas should be closed and which ones should be open for harvest. The Mermaid program uses the equations developed by the Pearl model that provide more sensitive and accurate measures of sanitation safety for consumption of shellfish, and are more accurate than the U.S. National Standards.

Constant challenges, environmental threats, and rapid changes of living conditions on the earth make it necessary to seriously take up the topic of resilience and sustainability. The interdisciplinary and holistic approach is more important than ever before, and engineering science is required to adapt to global conditions. This article presents the results of research aimed at the identification of sustainability-related parameters for kinetic green façades in the preliminary design phase and evaluation of current decision support tools. The authors carried out the comparative analysis of existing decision support methods and tools for sustainable development, used in fields and disciplines such as architectural design, environmental engineering, and structural design. The particular focus of the research was on the preliminary concept design of kinetic green façades. Specific methods such as forecasting and backcasting linked to post-occupancy evaluation tools were also taken into account. Parametric modeling based on optimization algorithms was recognized as the most adequate method. As a result of the conducted research, the steps to be taken at the early design stage for sustainable façade design were identified based on the example of the innovative system of kinetic green façade. The first step is to determine the design criteria of the façade considering the factors related to climate, culture, environment, and special design requirements. In the next step, the design parameters of the façade system are defined depending on the aforementioned criteria. In the third step, system design and modeling are done. Finally, the performance of the façade system is evaluated. If the desired performance is not achieved, the designer returns to the 2nd and 3rd steps. These last three steps of the preliminary design stage of sustainable façade systems are critical since they allow us for the façade design optimization, which in turn has a significant influence on the whole building performance and sustainability parameters.

Existing assessment and decision support tools have limited application to real-world food-energy-water (FEW) Nexus challenges. Integrated assessment approaches are often discipline-specific or highly theoretical, lacking grounding in real-world FEW issues. FEW systems require application of integrated techniques that address multiple attributes of trade-off analyses, dynamic and disparate datasets, and difficult decision contexts. Research must enable: appropriate tool sets matched with FEW Nexus hotspots; customizing existing tools to fit local specifics; compatibility between collected data and integrative nexus assessment tool needs; evaluation of these assessments through incorporation of stakeholder input and guidance forward for solution implementation. The core challenge is identification and design of a set of strategies that are robust under various future conditions (scenarios). Successful strategies must address natural, technological, and human system settings. Approaches that clarify the range of beneficial and potentially adverse trade-offs will support the identification of decisions and intervention options.

Digestate is a byproduct of anaerobic digestion, which can be considered waste or a product of potential use for the chemical industry or agriculture. In either case, the digestate must usually be treated prior to being disposed of or valorized. This review describes digestate processing technologies and their specific characteristics. Nutrient recovery and removal from digestate can be achieved through mechanical, physicochemical or biological processes. Available and potential digestate treatment techniques are presented. The complexities of the technologies available, legislation, the agronomical value of the digestate and the economic value of the process mean a decision support tool is required to help managers choose the best digestate processing technology. To ensure adequate analysis, the whole biomethanization project should be integrated in the use of these decision support tools. The objectives and limits of some of the currently available tools are analyzed at the end of this review.

This is the second of two papers reporting the results of a major study considering 'operator models' for municipal solid waste management (MSWM) in emerging and developing countries. Part A documents the evidence base, while Part B presents a four-step decision support system for selecting an appropriate operator model in a particular local situation. Step 1 focuses on understanding local problems and framework conditions; Step 2 on formulating and prioritising local objectives; and Step 3 on assessing capacities and conditions, and thus identifying strengths and weaknesses, which underpin selection of the operator model. Step 4A addresses three generic questions, including public versus private operation, inter-municipal co-operation and integration of services. For steps 1-4A, checklists have been developed as decision support tools. Step 4B helps choose locally appropriate models from an evidence-based set of 42 common operator models ( coms); decision support tools here are a detailed catalogue of the coms, setting out advantages and disadvantages of each, and a decision-making flowchart. The decision-making process is iterative, repeating steps 2-4 as required. The advantages of a more formal process include avoiding pre-selection of a particular com known to and favoured by one decision maker, and also its assistance in identifying the possible weaknesses and aspects to consider in the selection and design of operator models. To make the best of whichever operator models are selected, key issues which need to be addressed include the capacity of the public authority as 'client', management in general and financial management in particular.

Marine protected areas (MPAs) are an important marine conservation strategy; however, managing MPAs is challenging due to threats such as coastal development, non-compliance and recreational pressures. We conducted semi-structured interviews with MPA managers and rangers in Queensland, New South Wales and Victoria to discuss the opportunity to apply models to support management decision-making in MPAs. The utilisation of models for risk analyses, social learning and forecasting was considered of high relevance for decision makers. However, the utility of a tool depends on the nature of the management problem at hand. For instance, conceptual models can bring a systems perspective to management whereas probabilistic models may indicate the likelihood of a management outcome. We recommend selecting a tool based on decision support requirements. Furthermore, participating MPA managers recommended the need to collaborate with scientists to develop and apply decision support tools to address routine as...

Structures that block movement of fish through river networks are built to serve a variety of societal needs, including transportation, hydroelectric power, and exclusion of exotic species. Due to their abundance, road crossings and dams reduce the amount of habitat available to fish that migrate from the sea or lakes into rivers to breed. The benefits to fish of removing any particular barrier depends on its location within the river network, its passability to fish, and the relative position of other barriers within the network. Balancing the trade-offs between ecological and societal values makes choosing among potential removal projects difficult. To facilitate prioritization of barrier removals, we developed an online decision support tool (DST) with three functions: (1) view existing barriers at various spatial scales; (2) modify information about barriers, including removal costs; and (3) run optimization models to identify portfolios of removals that provide the greatest amount of habitat access for a given budget. A survey of available DSTs addressing barrier removal prioritization indicates that barrier visualization is becoming widespread but few tools allow dynamic calculation of connectivity metrics, scenario analysis, or optimization. Having these additional functions, our DST enables organizations to develop barrier removal priorities based on\ud cost-effectiveness in restoring aquatic connectivity.

We review a sequence of strategic planning efforts over time in the United States, all involving processes to prioritize new vaccine candidates. The Institute of Medicine of the National Academies of Sciences, Engineering, and Medicine has been involved in three priority setting processes, each using different metrics and methodologies: infant mortality equivalents (1985–1986), cost-effectiveness (2000), and more recently, the implementation of a software system based on a broader multi-criteria systems approach that can include either of the earlier metrics among other various considerations (2015). The systems approach offers users the flexibility to select, combine, rank, weigh and evaluate different attributes representing their perspectives, assumptions, and particular needs. This approach also overcomes concerns relating to the previous single-metric ranking approaches that yielded lists that, once published, were static, and could not readily accommodate new information about emerging pathogens, new scientific advances, or changes in the costs and performance features of interventions. We discuss the rationale and reasoning behind the design of this multi-criteria decision support approach, stakeholder feedback about the tool, and highlight the potential advantages from using this expanded approach to better inform and support vaccine policies.

Machine-learning methods can assist with the medical decision-making processes at the both the clinical and diagnostic levels. In this article, we first review historical milestones and specific applications of computer-based medical decision support tools in both veterinary and human medicine. Next, we take a mechanistic look at 3 archetypal learning algorithms—naive Bayes, decision trees, and neural network—commonly used to power these medical decision support tools. Last, we focus our discussion on the data sets used to train these algorithms and examine methods for validation, data representation, transformation, and feature selection. From this review, the reader should gain some appreciation for how these decision support tools have and can be used in medicine along with insight on their inner workings.

Mantle cell lymphoma (MCL) is a rare cancer with diverse management options. Although clinical practice guidelines have become ubiquitous across medicine, the utility of guidelines for MCL management is limited by provider awareness and the lack of a definitive standard of care. We sought to determine whether expert recommendations, delivered as an online decision support tool, impacted practitioners' therapeutic decisions with MCL. Participants were more likely than the experts to select aggressive regimens for both newly diagnosed and relapsed/refractory MCL. After seeing the expert recommendations, participants revealed that the expert opinion impacted their treatment choices in 103 of 365 clinical scenarios, suggesting that online decision support tools may increase the number of clinicians making treatment decisions for patients with MCL that are concordant with expert consensus recommendations.

Objective To compare differences in patient-provider communication among patients who, prior to contraceptive counseling, used or did not use a decision support tool (“My Birth Control”) which has educational and interactive modules and produces a provider printout with the patient’s preferences. Methods As part of a cluster-randomized trial of the tool in four San Francisco safety net clinics, we collected and thematically analyzed 70 audio recordings of counseling visits (31 pre- and 39 post-tool implementation) from 15 providers randomized to the intervention. Results Without the tool, most providers began by asking participants what method they were considering and focused counseling on that method or on directing patients towards long-acting reversible contraception; with the tool, most focused on reviewing and discussing multiple methods of interest to the participant as indicated on the printout. Discussion of patients’ preferences for specific method features was not observed in pre-implementation recordings but was part of several post-implementation recordings. Several participants explicitly noted they had gained knowledge from the tool. Conclusion Observed counseling differences suggest the tool may have a positive impact on patient-centeredness of contraceptive counseling, consistent with findings from the main study. Practice implications My Birth Control shows potential for improving patient-centeredness in counseling without extensive provider training.

This chapter explores the development of geographic information systems (GIS)/multi-criteria evaluation (MCE) systems on the web and evaluates their utility as mechanisms for improving public participation in spatial decision making. The rise of the Internet and the World Wide Web has created many opportunities for those involved in GIS research. The main experimental objective of the open spatial decision-making system is to investigate the potential of web-based GIS/MCE decision support tools for improving public understanding of and participation in important environmental decisions. In designing web-based GIS/MCE systems for participatory decision support it is possible to draw on a long and varied history of research into decision making and decision support systems. Use of intelligent spatial agents may be important in ensuring new users and those unfamiliar with spatial science and GIS do not use inappropriate datasets and/or analyses.

Decisions for improving motor function in human subjects both with disability, injury prevention, and performance improvements are made through clinical and movement analysis. Currently, this analysis facilitates identifying abnormalities in an individual’s neuro-musculoskeletal motor function. However definitively identifying the underlying cause or long-term consequences of a specific abnormality in the individual’s movement pattern is difficult since this requires information from multiple sources and formats across different times and currently relies on the experience and intuition of the expert. In addition, this data must be persistent for longitudinal outcome studies. Therefore a multimedia informatics system, comparable to an electronic patient record (ePR) system, that integrates multiple sources of informatics data could have a significant impact on decision support within this analysis workflow. In this chapter, the methodology, design, and architecture of such an informatics system including use case data types that need integration in order to develop relevant decision support tools are discussed. The end result is a new frontier area of imaging informatics research that supports improvement in performance.

Improving waste and resource management entails working on interrelations between different material flows, territories and groups of actors. This calls for new decision support tools for translating the complex information on flows into accessible knowledge usable by stakeholders in the spatial planning process. This article describes an open source tool based on the geodesign approach, which links the co-creation of design proposals together with stakeholders, impact simulations informed by geographic contexts, systems thinking, and digital technology—the Geodesign Decision Support Environment. Though already used for strategic spatial planning, the potential of geodesign for waste management and recycling is yet to be explored. This article draws on empirical evidence from the pioneering application of the tool to promote spatially explicit circular economy strategies in the Amsterdam Metropolitan Area.

Online mental health treatment has the potential to meet the increasing demand for mental health treatment. But low adherence to the treatment remains a problem that endangers treatment outcomes and their cost-effectiveness. This literature review compares predictors of adherence and outcome for clinical and online treatment of mental disorders to identify ways to improve the efficacy of online treatment and increase clients’ adherence. Personalization of treatment and client improvement tracking appears to provide the most potential to improve clients’ outcome and increase the cost-effectiveness of online treatment. Overall, it was noticed that decision support tools to improve online treatment are commonly not utilized and that their influence on treatment is unknown. However, integration of statistical methods into online treatment and research of their influence on the client has begun. Decision support systems derived from predictors of adherence might be required for personalization of online treatments and to improve outcome and cost-effectiveness to ease the burden of mental disorders.

Making public buildings smarter and more energy efficient requires tailored solutions to overcome the barriers in implementing suitable technology options in a large variety of building types. The PrioritEE project aimed to strengthen the policy-making and strategic planning competencies of local and regional public authorities in the energy management of Municipal Public Buildings, gaining experience in five local pilots in the Mediterranean area. This paper focuses on the PrioritEE toolbox, describing both the “soft components” and the technical components. These components are widely applicable and replicable in all European cities for supporting local administrations in improving energy efficiency and the use of renewable energy sources in municipal public buildings. The Energy Technologies and Building Retrofit Database is a technology repository of energy efficiency measures and renewable energy technologies, providing the main reference data on costs, technical performance and lifetime. With the Decision Support Tool users can assess current building stock energy performance, evaluate different efficiency and renewable interventions, and compare, rank, and prioritize technical options through a set of key performance indicators. A sample of the results to date in the five partner regions are also presented, comparing the main individual characteristics in order to derive replicable solutions for local authorities. This project also highlighted how crucial stakeholder involvement is for tool improvement, capacity building and enabling knowledge transfer. info:eu-repo/semantics/publishedVersion

Decision support tools can aid decision making by systematically incorporating information, accounting for uncertainties, and facilitating evaluation between alternatives. Without user buy-in, however, decision support tools can fail to influence decision-making processes. We surveyed fishery researchers, managers, and fishers affiliated with the Lake Whitefish Coregonus clupeaformis fishery in the 1836 Treaty Waters of Lakes Huron, Michigan, and Superior to assess opinions of current and future management needs to identify barriers to, and opportunities for, developing a decision support tool based on Lake Whitefish recruitment projections with climate change. Approximately 64% of 39 respondents were satisfied with current management, and nearly 85% agreed that science was well integrated into management programs. Though decision support tools can facilitate science integration into management, respondents suggest that they face significant implementation barriers, including lack of political will to cha...

Despite a compendium of pharmacotherapies available for treating psychiatric illnesses, suboptimal response to these therapies is typical and thought to be in part a result of genetic variation. This notion has sparked a personalised psychiatry movement, which has in turn led to the development of several commercial pharmacogenetic-based decision support tools marketed to psychiatrists as an alternative to typical, trial-and-error, prescribing. However, there is considerable uncertainty about the validity and usefulness of these tools and whether there is sufficient evidence to support their adoption. In this Personal View, we provide an introduction to these tools and assess their potential usefulness in psychiatry practice. We conclude with clinical considerations and development strategies for improving future pharmacogenetic-based decision support tools for clinical use.

Dashboard systems for clinical decision support integrate data from multiple sources. These systems, the newest in a long line of dose calculators and other decision support tools, utilize Bayesian approaches to fully individualize dosing using information gathered through therapeutic drug monitoring. In the treatment of inflammatory bowel disease patients with infliximab, dashboards may reduce therapeutic failures and treatment costs. The history and future development of modern Bayesian dashboard systems is described. Refereed/Peer-reviewed

Prediction markets are a form of group decision support system which uses a market mechanism to elicit and aggregate information from large numbers of individuals. The literature recognises their potential as decision support tools, but also notes several issues of concern regarding their utility in an organisational setting. One critical concern is the possibility that prediction markets may be subject to malicious manipulation. This paper presents a field experiment which examines the effect of such manipulations on prediction market performance. We divide a sample of 72 contracts into a control group and an experimental group. Contracts in the experimental group are manipulated by a trader with a malicious motivation. The study demonstrates that manipulations do have an effect on prediction market accuracy, but that these effects are rapidly ameliorated by rational traders and shows that fear of malicious manipulation should not preclude the use of prediction markets as organisational decision making tools.

Several studies have focused on methods of increasing system and uncertainty knowledge for socio-economic and environmental policies; however, the nonlinearity and dynamism of real world increase the gap between uncertainty depiction and its evaluation in policy strategies. This work attempts to implement a methodology that is able to minimise uncertainty in decision support tools related to rural planning and management. Fuzzy Cognitive Maps, the Dempster–Shafer theory and nonlinear optimisation were applied to achieve the above-mentioned goal. The method was tested to describe suitable policies and intervention strategies to address the effects of the recent economic crisis in the agricultural sector.

The business model (BM) concept which has emerged in the scientific literature and business world over the past fifteen years is a new management concept that, although characterised by fuzzy boundaries, can be synthesized as “the rationale of how an organisation creates, delivers and captures value”. Many authors see the BM as an excellent tool for the strategic evaluation of business ideas, but the literature review shows a lack of efficient quali-quantitative tools for evaluating potential BM alternatives and selecting the best BM solution from among them. In this paper, we propose a Business Model Decision Support Tool (BM-DST) based on a quali-quantitative methodology of Multiple Criteria Decision Analysis (MCDA) called Analytic Hierarchy Process (AHP) and specifically based on a modified AHP procedure called Value-Analytic Hierarchy Process (V-AHP). A case study with a numerical example was carried out in the field of industrial plants, specifically in the context of the Industrial Product-Service Systems, in order to select the best BM solution from among three potential BM alternatives usually taken into account by an Original Equipment Manufacturer (OEM) and Engineering-Procurement- Construction (EPC) company: Transactional Project Deliveries, Project Led Solutions and Life-Cycle Solutions.

About half of people who take antidepressants do not respond and many experience adverse effects. These detrimental outcomes are in part a result of the impact of an individual’s genetic profile on pharmacokinetics and pharmcodynamics. If known and made available to clinicians, this could improve decision-making and antidepressant therapy outcomes. This has spurred the development of numerous pharmacogenetic-based decision support tools. In this article, we provide an overview of pharmacogenetic decision support tools, with particular focus on tools relevant to antidepressants. We briefly describe the evolution and current state of antidepressant pharmacogenetic decision support tools in clinical practice, followed by the evidence-base for their use. Finally, we present a series of considerations for clinicians contemplating use of these tools and discuss the future of antidepressant pharmacogenetic decision support tools.