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According to the demand of sports athletes' scientific training, combined with data mining technology, a set of sports training model which is suitable for China's national conditions and has a higher level of scientific research is established. Starting from the data mining technology, this paper expounds its connotation and several common methods in detail. Based on the demand of sports training, this paper studies the quality control of sports training and the statistical system of tactical combat. On this basis, an improved output method of Aprilio algorithm is introduced to enhance the correctness of systematic evaluation. On this basis, three algorithms, classic, DC-Apriori and modified Apriori, are simulated and compared. The results show that the improved Apriori algorithm studied in this project can provide better decision support for the training model. [ABSTRACT FROM AUTHOR]

Coastal ecosystems are complex and often support a broad spectrum of functions with competing objectives. In addition to their ecological value, they offer socio-economic benefits (i.e., ecosystem services) to coastal communities. One potential way to help address this complexity is to use decision support systems to help natural resources managers understand system dynamics and evaluate strategies to maintain the health and integrity of these ecosystems. This paper presents a roadmap and detailed application of co-production strategies where managers and researchers are fully engaged in a collaborative manner in the design of a decision support tool for coastal ecosystems. It also emphasizes the importance of capturing end-users' (i.e., natural resource managers) priorities to refine the conceptual design of the decision support tool, while maintaining a sound scientific and modeling framework. The case study presented here centers on the Northern Gulf of Mexico, but the concept can be exported globally to other systems. This effort highlights foundational co-production strategies, including transdisciplinary team assembly, a knowledge sharing workshop, Toolbox Dialogue Initiative workshops to facilitate working across disciplines, core team and focus group meetings, and design charrettes. Further, this paper articulates the benefits and difficulties of executing a co-production process through virtual collaborations. • Coastal ecosystem management by co-productive, interdisciplinary design methods. • Co-production design strategies for decision support systems. • Northern Gulf of Mexico natural resource management decision support tool. [ABSTRACT FROM AUTHOR]

Methods inspired by an observation of biological organisms constitute a dynamically growing area of research. Such approaches were proved to be efficient in solving many real-life tasks. This paper introduces an application of two Swarm Intelligence methods for solving the ship collision avoidance problem. The Firefly Algorithm population-based approach has been compared with the Ant Colony Optimization-based method, developed as a constructive algorithm. The definition of the problem to be solved, an explanation of the methods and simulation tests results have been presented in the paper and followed by a discussion of the obtained solutions. The methods can be applied in the Decision Support Systems of manned ships or in the Autonomous Navigation Systems of Unmanned Surface Vessels or fully autonomous ships - Maritime Autonomous Surface Ships. [ABSTRACT FROM AUTHOR]

The pursuit of environmental sustainability and resource conservation has underscored the growing significance of the circular economy paradigm across industries. This transformation is particularly pertinent within the automotive industry, necessitating a profound restructuring of the value chain, with a specific focus on end-of-life considerations. Significant potential for circular economy arises from the thorough examination of end-of-life vehicles and processes, involving strategies such as component reuse, material recycling and primary resource reduction. Providing process-, product-, component-, and material-specific information at the end-of-life of automotive vehicles can improve decision-making regarding circular strategies. It also demands for new digital solutions. This paper addresses an integrated approach of data flow analysis and Life Cycle Assessment (LCA) to enable a comprehensive monitoring of the environmental impact of these processes. First, the use case of end-of-life dismantling processes is presented - including pyrotechnics neutralization, vehicle dehydration, disassembly of reuse components as well as vehicle compaction. Secondly, the data demand respective information and material flows is depicted. As a main result, the LCA data flow model specifically tailored to the end-of-life vehicle dismantling processes is described. This model serves as a fundamental basis for analyzing the environmental impact of end-of-life processes combined with the underlying data flow. [ABSTRACT FROM AUTHOR]

The manufacturing industry faces numerous challenges: increasing energy and material costs, limited resources availability, customers' and legal requirements on product quality, climate action, social standards. An example for a political and regulative push is the European Green Deal and the Industry 5.0 strategy announced by the European Commission, which requires manufacturing enterprises to take actions to improve sustainability. Especially small and medium enterprises (SMEs) require support to identify potentially suitable actions to implement. This paper proposes a guided decision support method for SMEs to help them on the sustainability pathway. With a multi-step procedure, the current status of a specific company regarding sustainability actions, their development goals, challenging fields of action, plans and interests in application of advanced technologies and social or environmental services can be identified. Starting from a questionnaire-based self-assessment, relevant fields of actions are derived and potential advanced technologies, social innovations and environmental services are identified. The developed approach combines evaluation of the assessment results, as well as online workshops with SMEs to refine the obtained information, align the suggested set of actions with needs of the companies and prioritize them. As result of this procedure, participating SMEs receive an advanced sustainability action plan (ASAP), which helps them to prioritize available measures to implement on their individual sustainability pathway. The ASAP has been implemented for 41 companies within an online community hub system designed to match and join technology and service providers with manufacturing SMEs. "Processes and Production system" was identified as the most important action field for 41% of SMEs, highlighting the need for efficiency improvement in production processes. [ABSTRACT FROM AUTHOR]

With the EU Green Deal and the UN Sustainable Developments Goals, the vision of a greenhouse gas-neutral and resource-efficient economy is already firmly anchored in world politics. In this context, the automotive industry faces many challenges including the increasing scarcity of natural resources, a rising demand in terms of sustainable vehicle design, production and materials sourcing. Due to all this, end-of-life decisions regarding dismantling have become increasingly important. A high proportion of secondary materials will be required in the vehicles of the future. To response to these challenges, companies have turned their focus towards the circular economy as a central approach to close material loops. In the German research project "Catena-X" a new data ecosystem with digital twins is one enabler that is being developed. The digital twins represent a promising approach to the circular economy by ensuring transparent, product-specific and end-to-end data exchange throughout the entire product lifecycle, from the material sourcing to the eventual dismantling and recycling. As one particular and unique solution, a decision framework that facilitates the best circular strategy at the end of a vehicle's life is developed and presented in this paper. The underlying data-driven decision support framework is based on circular economy KPIs. This includes material, components and specific vehicle KPIs to best identify the most suitable circular strategy. The framework was methodologically developed by an interdisciplinary team of partners, who are stakeholders throughout the value chain and participants in the Catena-X project. An integrated approach of user-centered design, an adapted version of the V-model and the Scaled Agile Framework were used for the methodology in the development of the solution. The paper presents the concept of a digital twin for a decision support system, that includes a central decision logic that also includes the relevant KPIs and a survey for evaluating the decision logic utilised with a chosen dismantling company. [ABSTRACT FROM AUTHOR]

This paper aims to perform a literature review on the use of multi-criteria methods for assessing agricultural sustainability, focusing on the distribution of papers according to year, journal, and countries, the most productive authors, the most frequently used multi-criteria methods and their characteristics, the type of system and the type of agriculture in which these methods are being performed, the methodological approaches and assessment types, and the sustainability dimensions considered. The data collection has been carried out through the Web of Science™ platform on September 3rd, 2021. After a refinement process, 41 papers were selected. The descriptive analysis was carried out through Bibliometrix tool, while content analysis was performed using Nvivo. The descriptive analysis shows that from 2016 to 2021 the scientific production addressing multi-criteria methods to assess agricultural sustainability started to grow markedly in a very rapid matter, reaching an average of 6 papers per year. France and China are the most scientifically productive countries. The content analysis points out that the most used multi-criteria method is the AHP that was used 11 times. The outranking methods, instead, were used only 3 times. In 68% of the papers the Triple Bottom Line was used as dimensions, and in 41% of the papers the spatial applicability was the farming system. The results highlight that doesn't exist many MCDM/A methods for assessing agricultural sustainability, and most of them are compensatory. These results point out that the use of multi-criteria methods in assessing agricultural sustainability is still underexplored and can be improved. [Display omitted] • Multicriteria methods are powerful tools for assessing agricultural sustainability. • A literature review on agricultural sustainability evaluation was performed. • The number of studies has been increasing in the last five years. • Most of the approaches are based on compensatory multicriteria methods. [ABSTRACT FROM AUTHOR]

Establishing insulin dosages for diabetic patients is getting a challenge right after a patient's admission to a hospital. This is mainly due to the physicians' limited information on the current health state of a newly diagnosed patient. Specifically, for juvenile patients, high variability of body response to insulin causes the necessity of continuous adjustment of dosages during the patient treatment. To support the physicians in applying the initial insulin dose and adjusting the following doses, we propose a forecasting approach in this paper. We propose a forecasting model based on a specifically designed neural network to predict basal insulin. In the experimental part of this paper, we provide evidence for the high efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of dealing with epistemic, i.e. non-probabilistic, uncertainties in strategic energy planning modelling. Current models have limited consideration of this type of uncertainty compared to probabilistic uncertainty, and also typically lead to overly conservative results. To address this issue, the contribution of the paper is to propose a novel decision support method which combines two decision-making methodologies into a single, internally consistent algorithm, and to show its applicability to real-size energy planning studies. Robust optimization is applied to address constraint uncertainties, while the minimax regret criterion is utilized for uncertainties in the objective function. This approach facilitates energy modelling exercises that can be more closely aligned with decision-makers' preferences for both feasibility and optimality. To demonstrate its effectiveness, the method is applied to a real-size strategic energy planning model, and the algorithm is shown to be able to provide detailed solutions in reasonable times. Ex-post evaluations confirm that this approach maintains robust optimization performance by effectively reducing the occurrence and magnitude of infeasibilities, while satisfying the minimax regret criterion across the entire range of uncertainties. Therefore, this integration preserves the distinct advantages of each methodology without any adverse effects when used together. • Robustness framework for decision-making in the face of epistemic uncertainty. • Decision support method combines robust optimization with min-max regret criterion. • Methodology accounts for decision-makers' feasibility and optimality preferences. • Real-size energy planning model proves applicability in other models or disciplines. [ABSTRACT FROM AUTHOR]

This paper presents an indoor navigation support system based on the Building Information Models (BIM) paradigm. Although BIM is initially defined for the Architecture, Engineering and Construction/Facility Management (AEC/FM) industry, the authors believe that it can provide added value in this context. To this end, the authors will focus on the Industry Foundation Classes (IFC) standard for the formal representation of BIM. The approach followed in this paper will be based on the ifcOWL ontology, which translates the IFC schemas into Ontology Web Language (OWL). Several modifications of this ontology have been proposed, consisting of the inclusion of new items, SWRL rules and SQWRL searches. This way of expressing the elements of a building can be used to code information that is very useful for navigation, such as the location of elements related to the actions desired by the user. It is important to note that this design is intended to be used as a complement to other well-known tools and techniques for indoor navigation. The proposed modifications have been successfully tested in a variety of simulated and real scenarios. The main limitation of the proposal is the immense amount of information contained in the ifcOWL ontology, which causes difficulties involving its processing and the time necessary to perform operations on it. Those elements that are considered important have been selected, removing those that seem secondary to navigation. This procedure will result in a significant reduction in the storage and semantic processing of the information. Thus, for a system with 1000 individuals (in the ontological sense), the processing time is about 90 s. The authors regard this time as acceptable, since in most cases the tasks involved can be considered part of the system initialization, meaning they will only be executed once at the beginning of the process. [ABSTRACT FROM AUTHOR]

The use of occupational exoskeletons (OE) promises improved ergonomics, empowerment of users and prevention of musculoskeletal disorders. However, selecting an OE for a specific application is difficult, the process is complex and a holistic approach that will guide decision-makers in companies is missing. The goal of this paper is to describe a framework that will assist the selection of an OE for a given use case. Based on a systematic literature review existing implementation plans for OE are identified and 99 relevant criteria collected. As the criteria are described in different level of abstractions, a categorisation is proposed, that include human and task-centred dimensions (Task & Workplace, User, Human-Machine-Interface) as well as macro-economic dimensions based on the PEST framework (Political, Economic, Socio-cultural, Technological). The derived framework with its criteria and dimension can be implemented in existing selection processes and provides a key step for increasing the market penetration of OE. [ABSTRACT FROM AUTHOR]

This paper described the application of a terminology server (TS) within a health institution in Uruguay, whose architecture is based on SNOMED CT and graph databases (NoSQL). The aim of this project is to apply the use of terminological service from a clinical, statistical, managerial, decision support and research perspective, among others, without compromising performance. Particularly, this article exposes the use of the TS as support for alert systems associated to diabetes mellitus patients. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) has been applied in many situation as the development of visualization and communication technologies. In coal mine industry, it is essential to improve the decision making process in the underground front line with the assistance of AR. In this paper, we propose a framework to implement an AR based decision support system, which includes knowledge support, real time expert connection and AR communication platform. The proposed framework has been applied in a coal mine in China and the user feedback indicate that the proposed framework can improve the communication and knowledge access efficiency in the coal mine industry. [ABSTRACT FROM AUTHOR]

The paper presents a decision-making approach applied to marine navigation. The issue is to emulate the behaviour of a human expert in the task of determining a safe manoeuvre or a set of manoeuvres, that is a safe trajectory for a smart ship, when a collision situation is detected by navigational sensors. Discrete planning algorithms are applied to solve this problem. A comparative analysis of deterministic and heuristic algorithms based upon simulation results is given. All of the tested algorithms return a feasible solution in near-real time. Results of the carried out experiments prove that the best results in terms of both quality of a solution and run time is accomplished by a deterministic approach utilizing a database of candidate solutions (trajectories) - the Trajectory Base Algorithm (TBA). The paper also presents solutions from all ship' perspectives received with the use of the TBA. Results of this verification demonstrate that trajectories calculated from different ships' perspectives for the same encounter situation do not lead to a collision situation between the ships. Such outcome validates the problem-solving capability of the algorithm and its applicability in a real system on-board the ship. [ABSTRACT FROM AUTHOR]

The paper describes an intellectual model of clinical risk management (CRM) for a general hospital based on Failure Mode and Effects Analysis (FMEA), Route Cause Analysis (RCA) and Systems Engineering Initiative in Patient Safety (SEIPS) model. Input and output parameters of the CRM model, management resources and corrective interventions were determined. The main output parameter was a cumulative burden of healthcare-related adverse events (HAEs) that reflects both the severity and the rate of each HAE. The model was tested in a 300-bed urban hospital. It was shown that effectiveness of interventions used to prevent HAE may vary depending on the HAE type. In resource-constrained environment, implementation of the CRM model in routine practice allows for the selection of interventions aimed at preventing healthcare safety failures that will provide the greatest benefit both for patients and for the hospital. It opens up prospects for cognitive support of patient safety improvements and for the healthcare costs reduction. [ABSTRACT FROM AUTHOR]

[Display omitted] The United Nations 2030 Agenda for Sustainable Development provides an important framework for economic, social, and environmental action. A comprehensive indicator system to aid in the systematic implementation and monitoring of progress toward the Sustainable Development Goals (SDGs) is unfortunately limited in many countries due to lack of data. The availability of a growing amount of multi-source data and rapid advancements in big data methods and infrastructure provide unique opportunities to mitigate these data shortages and develop innovative methodologies for comparatively monitoring SDGs. Big Earth Data, a special class of big data with spatial attributes, holds tremendous potential to facilitate science, technology, and innovation toward implementing SDGs around the world. Several programs and initiatives in China have invested in Big Earth Data infrastructure and capabilities, and have successfully carried out case studies to demonstrate their utility in sustainability science. This paper presents implementations of Big Earth Data in evaluating SDG indicators, including the development of new algorithms, indicator expansion (for SDG 11.4.1) and indicator extension (for SDG 11.3.1), introduction of a biodiversity risk index as a more effective analysis method for SDG 15.5.1, and several new high-quality data products, such as global net ecosystem productivity, high-resolution global mountain green cover index, and endangered species richness. These innovations are used to present a comprehensive analysis of SDGs 2, 6, 11, 13, 14, and 15 from 2010 to 2020 in China utilizing Big Earth Data, concluding that all six SDGs are on schedule to be achieved by 2030. [ABSTRACT FROM AUTHOR]

In literature, many studies try to analyze healthcare usage and generated decision support systems. In this paper, the aim is to generate a decision support system for insurance companies in the United States according to the federal dataset. The data set contains variables from the United States Healthcare Administration formal data group and they are; group of age, group of healthcare insurance, group of nationality, Corporate Social Responsibility (CSR) usage, and Advance Premium Tax Credit (APTC) usage. The best model for each state in the United States is determined with the dataset from 2014 November to 2015 February. In this study, various statistical models are attempted to generate independent and optimal models for each state. In the model selection process, three different metrics are used. Accuracy rate, f1score and precision score are used in the model selection process and models are compared to each other according to these three metrics for each state uniquely. Consequently, the proposed model is used with a decision support scheme to support customer service workers in healthcare insurance companies. The decision support system under consideration can facilitate a substantial decrease in the duration of interactions between customer service representatives and clients. Additionally, this system can generate more logically sound and efficient customer offers. [ABSTRACT FROM AUTHOR]

The re-energization of electrical distribution systems in a post-disaster scenario is of grave importance as most modern infrastructure systems rely heavily on the presence of electricity. This paper introduces a method to coordinate the field teams for the optimal energization of an electrical distribution system after an earthquake-induced blackout. The proposed method utilizes a Markov Decision Process (MDP) to create an optimal energization strategy, which aims to minimize the expected time to energize each distribution system component. The travel duration of each team and the possible outcomes of the energization attempts are considered in the state transitions. The failure probabilities of the system components are computed using the fragility curves of structures and the Peak Ground Acceleration (PGA) values which are encoded to the MDP model via transition probabilities. Furthermore, the proposed solution offers several methods to determine the non-optimal actions during the construction of the MDP and eliminate them in order to improve the run-time performance without sacrificing the optimality of the solution. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper describes development of a nitrate decision support tool for groundwater wells (GW-NDST) that combines nitrate leaching and groundwater lag-times to compute well concentrations. The GW-NDST uses output from support models that simulate leached nitrate, groundwater age distributions, and nitrate reduction rates. The support models are linked through convolution to simulate nitrate transport to wells. Spatially distributed parameters were adjusted through calibration to 34,255 nitrate sample targets. Prediction uncertainty is illustrated via Monte Carlo realizations informed during calibration. Over 78% of target concentrations were within the simulated range of results from 450 realizations. An example forecasting scenario illustrates that a range of feasible outcomes exist and should be considered when interpreting forecasts for decision making. Uncertainty in forecasting is unavoidable; the intent of characterizing uncertainty in the GW-NDST is to facilitate decision making by increasing insight into the response of nitrate contamination to physical and chemical processes. • This Decision Support Tool assists resource managers improve drinking water quality. • The flexible Reduced-Complexity framework matched nitrate concentrations in wells. • An Iterative Ensemble Smoother (IES) generated 450 posterior parameter realizations. • IES realizations were informed through Monte Carlo methods during calibration. • Six leaching reduction scenarios use uncertainty in nitrate concentration forecasts. [ABSTRACT FROM AUTHOR]

Climate services are integral to climate change adaptation in agriculture. Inclusive innovation prioritises inclusion of marginalised actors, grassroots knowledge and accessibility of use. Yet many innovation processes are multi-stakeholder, complex and may not prioritise inclusion from the outset. In these circumstances, researchers and designers may seek to influence existing projects towards greater inclusivity. This paper explores efforts to increase the inclusivity of existing innovation processes, focusing on the design of My Climate View, an online Australian climate services platform for agriculture. Findings relate to contemporary issues in inclusive innovation literature, including: (1) Who should be included in innovation processes (within given resourcing)?; (2) How requirements for inclusion or exclusion can emerge through the design and use of a technology; and (3) The influence of program level structures upon inclusivity and design. Our approach is informed by Responsible Innovation and Inclusive Innovation theory and enacted through user-centred design. Findings are synthesised from analysis of 60 semi-structured interviews with farmers, advisors and extension officers. We additionally draw on observations as researchers in the multi-actor My Climate View development team, where extensive engagement separate to research activities has included agricultural and Indigenous stakeholders. Engaging with a broad sample of potential target users enabled diverse and sometimes unexpected use cases. Customisation of interface parameters allowed the incorporation of tacit knowledge and positively affected the inclusivity of My Climate View, extending the value of the platform to growers of niche commodities outside of the original scope. Inclusivity in innovation can be impacted by diverse factors, including design team decisions, structural factors, budget and external factors. Requirements for inclusion and exclusion can emerge and evolve throughout a design process, supporting Responsible Innovation's framing of inclusivity alongside principles of reflexivity and responsiveness. The findings enrich the evidence base for climate services and broader agricultural technology development processes by providing insights for those seeking greater impact by practicing inclusivity in innovation. We provide four implications for increasing inclusivity in the design of climate services: (1) Maximising customisation and avoiding prescriptive interpretation enables greater flexibility of use; (2) Sampling broadly within target users to uncover unanticipated values and opportunities for inclusion and exclusion; (3) It is important to embed efforts towards inclusion alongside processes of reflexivity; and (4) Responsible and inclusive approaches to climate services design are valuable even if brought in once development has commenced. [Display omitted] • Explores the challenges of steering a largescale climate services innovation project towards greater inclusivity. • Maximising customisation options for users and avoiding prescriptive interpretation enables greater flexibility of use. • Responsible and inclusive approaches to climate services design are valuable even if adopted once development has commenced. [ABSTRACT FROM AUTHOR]

The present paper introduces a new risk-informed based tool that can support disaster management authorities in making decisions in response to earthquakes at local and regional scales by using estimated physical and socio-economic losses. The risk-informed based tool is building upon existing risk assessment methods, and multi-criteria decision-making (MCDM) methodology. The integrated risk assessment methods are adapted from the earthquake loss estimation tool SELENA. The integrated MCDM methodology, allowing selection of optimal mitigation measures by taking into account the priorities and preferences of disaster management authorities, was developed within the EU Horizon 2020 TURNkey project [1]. The new tool presented in this study also allows easy integration and incorporation of existing database and models that can be openly available. The tool is characterized with user-friendly interface and flexible format of the input and output data, that can be easily understood by different actors in disaster risk management. [ABSTRACT FROM AUTHOR]

Companies aim to spur quality, effectiveness, efficiency, and support growth. At the same time, however, conditions change significantly, imposing demands on companies to be flexible and agile with regard to their production environment. Changes involved are based on the business strategy, internal and external stressors, and impact operational and tactical level. The adaptability of the digital infrastructure determines the flexibility, control, and possibility of supporting decision-making in production processes. This paper specifies requirements and a blueprint for an adaptable digital infrastructure. Furthermore, components such as middleware are described that are needed to enable not only a flexible digital infrastructure, but to get the right information, at the right moment, to the right entity, system, person, and decision-maker. [ABSTRACT FROM AUTHOR]

Due to the increasing implementation of cyber physical systems and the internet of things in industry, there is a trend towards flexible, multi-variant production with a very high degree of automation to strengthen the resilience of the company. Each variant is subject to high quality requirements that must be checked to ensure quality. A common means for this is quality assurance through visual inspections, which are carried out manually. Visual inspections are time-consuming yet prone to wrong decisions, since they are subjective, inconsistent, and susceptible to uncertainties. The quality of the inspection relies heavily on the experience of the personnel. This work addresses this issue through the concept and design of a system for objective decision making in visual inspection by integrating Deep Learning (DL) models with a Belief Rule Based Expert System (BRBES) inside a smart devices application. Smart devices like tablets and smartphones serve to generate information by recording and evaluating image material of the components being inspected. Based on this data, DL models are trained and used to classify defects on new image material to automate part of the inspection process. Furthermore, smart devices serve to provide context-dependent decision recommendations in the visual inspection process, which were calculated by the BRBES with the inclusion of uncertainties. The knowledge base of the BRBES is fed by the expert knowledge of experienced visual inspectors using knowledge elicitation techniques. In this way, the system can enable optimized and objectified visual inspection based on the data-driven and knowledge-driven approaches used. This paper outlines the concept of integrating DL and BRBES into a smart devices decision support system. [ABSTRACT FROM AUTHOR]

Energy storage systems (ESS) are becoming more prevalent and indispensable in modern electrical infrastructure. The process of choosing the proper type of ESS technology for the application is the first step in designing the best performing ESS. However, the selection process involves a variety of factors, and currently there lacks a sophisticated and systematic framework for convenient energy storage selection. This paper develops a data-driven optimization framework for selecting energy storage systems for general energy system applications. In the framework, a supervised classification machine learning method is proposed for the first time to quantify the technical suitability of energy storage technologies for different applications. The DOE Global Energy Storage Database provided the basic information for machine learning, and the Random Forest Classifier had the best prediction performance for this dataset. The probability of technical suitability can be predicted and further incorporated in a multi-objective optimization for technology recommendation based on integrated technical, economic, and environmental criteria. As a demonstration of the methodology, the prediction of technical suitability and recommendation of technology selection were conducted for eleven common energy storage applications. The result showed that the prediction of technical suitability and the multi-objective optimization yielded reasonable recommendations compared with the literature. In conclusion, the proposed data-driven decision-making approach can be used as a convenient tool to help decision-makers make informed decisions when choosing the energy storage system for general applications. [Display omitted] • A data-driven decision-support framework for energy storage selection is developed. • Data exploration and visualization of the DOE energy storage project database are presented. • Novel quantification of energy storage technical suitability using machine learning is proposed. • Random forest classifier has the best prediction performance among the tested models. [ABSTRACT FROM AUTHOR]

Digitalized processes offer various advantages: uniform information flow, traceability of process progress, and enhanced analyzability. While these represent initial benefits from the digitalization of the process, a recurring evaluation and optimization of the process are required to sustain and improve process efficiency. For both non-digitalized and digitalized processes, the challenges for process owners are comparable. However, for digitalized processes, convenient tools facilitate process modifications. Particularly no-code digitalized processes exploit the opportunity for quick and agile process adaption with low efforts but require a structured and systematic method to ensure a quality-driven approach. This paper conceptualizes a rule-based decision support model for process improvement that takes advantage of digital traces recorded during process execution of digitalized processes. On the one hand, process mining techniques support data analysis and assist the extraction of process performance indicators as precursors to predict low process performance. On the other hand, process experts compile a list of recommendations for action in workshops and interviews to counteract low process efficiency. The approach to rule-based decision support developed in the scope of this research merges these two dimensions and constitutes an assistive tool for the continuous improvement of digitalized processes. Initial results of the model in practical application support this assessment and demonstrate further research needs. [ABSTRACT FROM AUTHOR]

Abstract Approaching water, energy, and food, as interconnected system of systems, as an alternative to traditional silo-based resources planning and management approaches continues to fall short of expectations of its research-backed benefits. The lack of nexus applications in policy and decision making can be related to numerous factors, with the main barrier being the complex nature of "nexus" systems combined with the disarray of tools attempting to model its interconnections. The paper aims to provide a method for comparing the perceived complexity of nexus tools identified by international organizations as well as primary literature sources. Eight separate criteria are introduced and discussed as measures of a tool " complexity index " and used to score the relative simplicity, or complexity, of a given tool. The result of this process is used to identify trends within existing nexus-assessment tools while guiding potential users towards appropriate tool(s) best-suited for their case study needs and objectives. The main objectives of this paper are to: 1) categorize nexus assessment tools according to a criteria-set which allows for suitable tool selection; 2) identify a method for rapid evaluation of the trade-offs for choosing different tools (simple-complex spectrum). The results of the comparative analysis of the selected nexus assessment tools concur with literature citing a growing gap between nexus research and applications in actual policy and decision-making settings. Furthermore, results suggest that tools receiving higher complexity scores, while being able to capture details to specific resource interactions, are unable to cover a larger number of interactions and system components simultaneously, as compared to lower complexity score tools. Lastly, the outcome of the analysis point towards the need for integrating more preliminary assessment capabilities, i.e. diagnostics, guidelines, and capacity building, into existing tools that improve the communication and translation of model outputs into policy and decision-making. Graphical abstract Unlabelled Image Highlights • Water-energy-food nexus assessment tools vary in complexity and applications. • A complexity index is created as a measure of complexity for nexus assessment tools. • Scatter-plots are created that categorize tools and quantify their complexity. • Such plots serve as a guideline to select tools specific to user objectives. [ABSTRACT FROM AUTHOR]

Abstract Many papers have been written on financial indicators to assess the use of a maintenance policy based on Total Productive Maintenance, while others have compared results showing the impact of criteria such as the Mean Time Between Failures. This paper provides the maintenance managers indicators which can assess the relevance of the actions carried out as well as readjustment of the planned maintenance program. In long term, this indicators knowledge may lead them to review their maintenance policy. To reach this aim, we propose several indicators for reliability, diagnosis and prognosis to assess and improve the maintenance policy based on Total Productive Maintenance. Methods used to obtain these indicators are only based on operating maintenance activities. These latter were extracted from a database produced by a Computerized Maintenance Management Information System. This work focuses on a maintained process based on total productive maintenance in which available sensor data are not indicative of degradation level achieved by the system. Indicators presented were obtained using Survival Laws, Hidden Markov Model and Support Vector Machine. As a concrete case study, an alloy foundry process is used. [ABSTRACT FROM AUTHOR]

Abstract Given the Swedish national goals for transport and infrastructure, economic efficiency is essential also in public transport. In 2003, Ljungberg (2007) sought to answer to which extent PTAs use Cost-Benefit analyses (CBA), a methodology to assess economic efficiency, in their planning of operations and infrastructure. It was found that CBA was seldom used. This paper tries to answer the same question, but for year 2016. The aim of this paper is to see to what extent PTAs are using CBA at the time of the survey, and investigate whether there are any changes with the previous study. A survey was sent to all Swedish PTAs with questions regarding current, previous, and projected future use of CBA. Questions about knowledge of reference materials and why the organization use (or do not use) CBA was asked. The main result is that most PTAs are not using CBA as decision support. For those that do, the method is used mostly for investments in payment systems and major line or traffic changes. The PTAs seem not regard a lack of economic resources a reason for not using CBA. Rather, lack of knowledge and more reliance on other types of decision support are the reasons. [ABSTRACT FROM AUTHOR]

Orientation is increasingly in demand, especially today in the age of digital and recent technologies, with the disappearance of some jobs and the emergence of others, and with the variety of choices in front of the students who have led professionals to seek a new method and digital solution to help them solve these issues and achieve their personal and professional success. There are a lot of institutions and associations that are organized orientation sessions for students and try to fill the gaps in that area, but it is always a poor fit in the face of a lot of students. And to make an effective decision related to guidance, we need to consider several factors such as grades, preferences, interests, and personality characteristics... which gives a sharp vision of the student and helps to know which category he belongs to. The purpose of this paper is to present a review of studies on distinctive approaches used in the orientation field such as the K-Nearest-Neighbor Method (KNN), Naïve Bayes, Fuzzy logic, Fuzzy Analytic Hierarchy Process (FAHP), and the Internet of things (IoT) in order to discover the best approach which helps students to pick and succeed in their orientation. [ABSTRACT FROM AUTHOR]

• We propose the optimal resources allocation consensus model (ORACM). • We present a method for constructing utility functions in ORACM. • We discuss the connection between ORACM and minimum cost consensus model. • We justified the advantages of ORACM through theoretical and simulation analyses. In group decision making (GDM), the minimum cost consensus model (MCCM) to assist a group to reach a consensus with the minimum cost has gained widespread attention. However, determining the unit costs for adjusting decision makers' opinions in the MCCM is a challenging problem that limits its practical applications. Meanwhile, the MCCM is not modeled as a resources allocation problem in an explicit manner, and the opinions in the MCCM do not represent utilities/satisfactions, leading to the unclear implications of opinions' adjustments. To overcome these limitations of the MCCM, this paper proposes the optimal resources allocation consensus model (ORACM) to assist the moderator to allocate resources without determining unit costs to support consensus reaching, through the introduction of the resources allocation problem and utility functions in its modeling. Furthermore, we present a theoretical analysis framework to reveal the properties of the ORACM and the connection between the ORACM and the MCCM, justifying the theoretical advantages of the ORACM. Moreover, the ORACM is applied to the transboundary river pollution control negotiations of Sichuan's Tuojiang River, and the effectiveness and feasibility of the ORACM are further validated with detailed simulation and comparison analyses. [ABSTRACT FROM AUTHOR]

Mixed enterprise farming systems that integrate more than one production system are important in agricultural production world-wide. Understanding and improving them can be made easier by modelling them with software tools. Modelling mixed enterprise farming systems can be a complex task as the interaction between the enterprises will introduce many dependencies. There are many software tools available that can model single enterprise systems, while there are few with the ability to model the biophysical systems in mixed farming. AusFarm has been designed and used to model mixed enterprise farming systems, integrating livestock, pasture, and crop models in one software tool and allowing flexible management of the whole farm. This paper demonstrates some key techniques that have been used for building and simulating mixed enterprise Australian farm systems in AusFarm. Examples of how to structure a cropping system and a livestock system are given. Key livestock and crop management tasks are implemented using flexible management rules. • Component based modelling in AusFarm can represent many types of mixed farming systems. • AusFarm's flexible management allows modelling of many types of farming practices. • Multi-dimensional experiments can be constructed using AusFarm for examining comparable scenarios. • AusFarm is designed to encapsulate a workflow for building, testing and analysing farming systems. [ABSTRACT FROM AUTHOR]

Despite the increased use of Simulation based Optimization, the design of facility layout is challenged by high levels of uncertainty associated with new production processes. Addressing this issue, this paper aims to understand the conceptual modeling activities of Simulation-based Optimization for facility layout design in conditions of high uncertainty. Based on three in-depth case studies, the results of this paper show how characterization criteria of production systems can be used in conceptual modelling to reduce uncertainty. These results may be essential to support managers and stakeholders during the introduction of new production processes in the design of facility layouts. [ABSTRACT FROM AUTHOR]

Introduction: Traditionally, paper based observation charts have been used to identify deteriorating patients, with emerging recent electronic medical records allowing electronic algorithms to risk stratify and help direct the response to deterioration.Objective(s): We sought to compare the Between the Flags (BTF) calling criteria to the Modified Early Warning Score (MEWS), National Early Warning Score (NEWS) and electronic Cardiac Arrest Risk Triage (eCART) score.Design and Participants: Multicenter retrospective analysis of electronic health record data from all patients admitted to five US hospitals from November 2008-August 2013.Main Outcome Measures: Cardiac arrest, ICU transfer or death within 24h of a score RESULTS: Overall accuracy was highest for eCART, with an AUC of 0.801 (95% CI 0.799-0.802), followed by NEWS, MEWS and BTF respectively (0.718 [0.716-0.720]; 0.698 [0.696-0.700]; 0.663 [0.661-0.664]). BTF criteria had a high risk (Red Zone) specificity of 95.0% and a moderate risk (Yellow Zone) specificity of 27.5%, which corresponded to MEWS thresholds of >=4 and >=2, NEWS thresholds of >=5 and >=2, and eCART thresholds of >=12 and >=4, respectively. At those thresholds, eCART caught 22 more adverse events per 10,000 patients than BTF using the moderate risk criteria and 13 more using high risk criteria, while MEWS and NEWS identified the same or fewer.Conclusion(s): An electronically generated eCART score was more accurate than commonly used paper based observation tools for predicting the composite outcome of in-hospital cardiac arrest, ICU transfer and death within 24h of observation. The outcomes of this analysis lend weight for a move towards an algorithm based electronic risk identification tool for deteriorating patients to ensure earlier detection and prevent adverse events in the hospital. [ABSTRACT FROM AUTHOR]

This paper examines a kind of explainable AI, centered around what we term pro-hoc explanations , that is a form of support that consists of offering alternative explanations (one for each possible outcome) instead of a specific post-hoc explanation following specific advice. Specifically, our support mechanism utilizes explanations by examples , featuring analogous cases for each category in a binary setting. Pro-hoc explanations are an instance of what we called frictional AI , a general class of decision support aimed at achieving a useful compromise between the increase of decision effectiveness and the mitigation of cognitive risks, such as over-reliance, automation bias and deskilling. To illustrate an instance of frictional AI, we conducted an empirical user study to investigate its impact on the task of radiological detection of vertebral fractures in x-rays. Our study engaged 16 orthopedists in a 'human-first, second-opinion' interaction protocol. In this protocol, clinicians first made initial assessments of the x-rays without AI assistance and then provided their final diagnosis after considering the pro-hoc explanations. Our findings indicate that physicians, particularly those with less experience, perceived pro-hoc XAI support as significantly beneficial, even though it did not notably enhance their diagnostic accuracy. However, their increased confidence in final diagnoses suggests a positive overall impact. Given the promisingly high effect size observed, our results advocate for further research into pro-hoc explanations specifically, and into the broader concept of frictional AI. • Pro-hoc AI explanations aim to reduce over-reliance, not just give advice. • Study: 16 orthopedists assess pro-hoc explanations in fracture detection. • Introducing Frictional AI: Systems embedding cognitive friction for better decisions. • Pro-hoc XAI aids doctors, enhancing decision-making for novices without accuracy loss. • Research needed on pro-hoc explanations, Frictional AI for decision-making balance. [ABSTRACT FROM AUTHOR]

In recent years, consortium blockchains have attracted considerable interest from the business communities and academia. To satisfy specific application requirements, appropriately configuring or adjusting the parameters is essential but challenging for developers when building the consortium blockchain. In this paper, the parameter configuration and adjustment for the consortium blockchain is transformed as a multi-objective optimization problem, and a novel explainable deep reinforcement learning (DRL) algorithm is proposed to solve the problem. On considering that existing DRL algorithms cannot be directly used in the consortium blockchain as they suffer from lacking of explainability, a causal model for configuring and adjusting the consortium blockchain's parameters is proposed and integrated into the DRL algorithm. The causal model can be used to derive causal explanations of the DRL algorithm to increase its trustworthiness. Furthermore, the causal model-based DRL (C-DRL) algorithm can perform causal inference before taking action to eliminate unreasonable exploration and improve the DRL algorithm's performance. The experimental results demonstrate the proposed algorithm provides the consortium blockchain with adaptive parameter configuration and adjustment to achieve sustainable high performance and security. Furthermore, the proposed algorithm increases the convergence speed of the vanilla algorithm by 49.3% and is more trustworthy than the vanilla DRL algorithm. • To achieve sustainable high performance and security, we transform the parameter configuration and adjustment for the consortium blockchain as a multi-objective optimization problem and provide a formal representation of the problem. • We propose a causal model of consortium blockchain's parameters using the structural causal model (SCM) formalism. • We propose a causal model-based DRL (C-DRL) algorithm to solve the multi-objective optimization problem. The C-DRL algorithm is equipped with the causal model of consortium blockchain's parameters. • The agent can use the causal model to derive the causal explanation for its action to provide trustworthiness. Furthermore, the agent with the causal model can utilize causal inference to eliminate unreasonable exploration and improve the convergence speed of the current DRL algorithm. • Extensive experiments are conducted to validate our proposal. The simulation results show that the agent running the C-DRL algorithm has more reasonable behavior than that running the model-free DRL algorithm. Moreover, the C-DRL algorithm converges faster than the model-free DRL algorithm. [ABSTRACT FROM AUTHOR]

Payments for Ecosystem Services (PES) are incentives offered to land owners or managers in exchange for practices that benefit society with ecological goods and/or services. For the success of PES schemes, the involvement of stakeholders, their needs, knowledge, and perspectives has been found beneficial. This paper presents a new approach in engaging stakeholders based on an iterative transdisciplinary framework for a Social Multi-Criteria Evaluation (SMCE) decision-making process. After a comprehensive review of existing options, the paper describes the development and application of a new software, OPTamos, to allow for efficient handling and processing of large volumes of quantitative and qualitative data generated from stakeholder workshops. Applied to a land use related decision in the Cuitzmala watershed, in the state of Jalisco in Mexico, the outcomes from this approach resulted in acceptable land management options for the entire area of the watershed that satisfied criteria generated by the stakeholders themselves and to finally develop an action plan. [ABSTRACT FROM AUTHOR]

We review several papers that have afforded insights into determinants of positive outcomes (e.g. the adoption of tools, improved learning and/or collaboration) from modelling projects. From a subsequent internet search in the environmental domain we identified 33 such factors that are then invoked in a transferable survey-based method to facilitate structured reflections by model developers on 15 projects. Four factors were considered most necessary to realize overall success for any modelling project. Three factors related to aspects of stakeholder engagement in the modelling process; the other to critical thinking around problem framing and the role(s) of models. The latter factor was considered reasonably well-achieved across the projects. Harder to control were the stakeholder engagement factors which, along with project management considerations, can constrain or enable achievement of other factors. The paper provides further evidence of the critical need to consider non-technical aspects in the design and implementation of modelling projects. [ABSTRACT FROM AUTHOR]

Smart agriculture offers the potential to analyse agricultural data at a scale not previously possible. Researchers argue that the combination of rich data and intelligent decision support has the potential to improve productivity and profitability in agriculture, whilst also improving sustainability. We argue that achieving this potential requires not just on technological advancement, it also requires a detailed understanding of factors that impact technology acceptance in smart agriculture. Acceptance is necessary if technical advances are to translate into real-world impact. However, technology acceptance is complex and often poorly understood. This systematic review focuses on technology acceptance in prediction and decision support systems in crop production. Major databases were searched to identify papers that formally address technology acceptance and include detailed data. 16 papers met the inclusion criteria and were included in the final analysis. Common facilitators and barriers are identified, and papers are mapping against the Theoretical Framework of Acceptability. This analysis showed that constructs including perceived effectiveness are addressed frequently, but others such as opportunity costs and burden have received less attention. The findings suggest the necessity for greater application of formal methods and the need for standardized, domain-specific methods to support this assessment. • Presents a systematic review of technology acceptance in smart agriculture, focusing on decision support in crop farming. • Increased productivity, access to information, and support for training are amongst the most common facilitators of acceptance. • Complexity, infrastructure requirements, increased burden, and not directly addressing farming needs are common burdens. • While effectiveness has been widely addressed, issues such as burden, opportunity costs and ethicality have received limited attention. • The development of a validated models of technology acceptance, and associated instruments, should be prioritised. [ABSTRACT FROM AUTHOR]

Tackling societal challenges relating to sustainability requires both an understanding of the underlying complex socio-ecological systems and participation of scientists as well as relevant stakeholders, such as practice experts, decision makers, and citizens. This paper introduces the Decision Theatre Triangle, a method which combines empirical information, mathematical modelling and simulation, and a format for dialogue between scientists and stakeholders. While it builds on previous Decision Theatre work, the new structuring into these three elements emphasizes what is needed for setting up a Decision Theatre for a given challenge. Based on experience with a specific example – sustainable mobility in Germany – it is argued that agent-based models are particularly suitable for Decision Theatres and that the method is useful not only for decision support but also for science communication and co-creation of a deeper knowledge of the system under discussion. As a step towards facilitating a broader use of the Decision Theatre Triangle method, the paper then sketches research needs for each of its three elements, with a focus on mathematical modelling and simulation. [Display omitted] • The Decision Theatre Triangle combines data, models, and stakeholder dialogues. • It enables decision support, science communication and co-production of research. • Agent-based modelling shows benefits in the Decision Theatre Triangle method. • Setting up Decision Theatre Triangles for societal challenges needs model structures. [ABSTRACT FROM AUTHOR]

• By constructing the application framework of corrosion prediction, this paper indicates the necessity of data preprocessing and feature analysis. • Random forest and deep learning have extensive application prospects in this field. • Machine learning can effectively achieve intelligent corrosion prediction and improve corrosion control effect. As the principal means of oil and natural gas transportation, oil and gas pipeline systems suffer from common corrosion problems, accurate corrosion prediction of oil and gas pipelines has an essential influence on pipe material selection, remaining useful life prediction, maintenance planning, etc. At present, a large number of corrosion monitoring techniques are applied in oil and gas pipeline systems. The monitored data have the characteristics of multidimensional quantities, noise interference, non-linearity, etc. Machine learning can effectively solve the limitations of relying solely on mathematical models to achieve intelligent corrosion prediction and improve the corrosion control effect. Considering the corrosion prediction problems in oil and gas pipeline systems, the application of machine learning methods in corrosion rate prediction, oil and gas pipeline leakage and defect assessment, and corrosion image recognition were focused on in this paper. By constructing the application framework of machine learning in the field of oil and gas pipeline corrosion prediction, the necessity of data preprocessing and feature correlation analysis are indicated in this paper. Furthermore, random forest and deep learning have extensive application prospects in this field. Finally, the application prospects of machine learning were discussed. [ABSTRACT FROM AUTHOR]

Accurately estimating soil moisture at fine resolutions – scales approaching a few meters – has been a target of a broad community involved in soil moisture research and application development, from agriculture support, improving land-atmosphere interaction in weather and climate prediction, and hydrologic modeling, to a variety of military applications. The US Army interest in high spatial-resolution soil moisture products is based on the need to better understand soil impacts on broad range of applications, including but not limited to movement and maneuver and logistics. The Geospatial Weather-Affected Terrain Conditions and Hazards (GeoWATCH) application was developed to support Army needs for high resolution soil moisture products. This paper describes the GeoWATCH application, development of the high-resolution soil moisture analysis and prediction capability, and linkages to downstream Army applications that deliver weather-informed content to support decision making. This paper also describes product evaluation and validation efforts to understand application performance. • GeoWATCH was developed to provide real-time, downscaled, globally available soil moisture products. • GeoWATCH calculates high resolution soil moisture from elevation, land use and soils data with a water redistribution model. • The GeoWATCH web user interface provides easy access to view and download model results. • Model evaluation suggests the downscaling improves accuracy of soil moisture products from coarser resolution inputs. [ABSTRACT FROM AUTHOR]

The paper deals with a decision-making method applied in the navigation of ships. It presents results of research on collision avoidance and path-planning for unmanned and fully autonomous ships. The continuation of earlier studies are presented. The research was concentrated on the evaluation of a deterministic real-time path planning algorithm using multi-ship encounter situations registered on-board a ship Horyzont II. Data were obtained during the vessel's voyage to Spitsbergen. The method applied, called the Trajectory Base Algorithm (TBA), searches through a database of candidate solutions - trajectories, in order to find the final best solution to the considered encounter situation. The aim of the study was to further evaluate the TBA applicability in a practical industrial application, in the Collision Avoidance Module of the Autonomous Navigation System for unmanned and autonomous vessels. Obtained results confirmed that the method is reliable, returns safe solutions in near-real time and can be applied in order to solve the considered problem. [ABSTRACT FROM AUTHOR]

The paper introduces a Decision Support System for ships, developed to solve a problem of collision avoidance with static and dynamic obstacles. The system maps the decision making capability of a human (navigation) expert to solve the path planning problem for a ship in a complex navigation environment. It can be further developed to provide automatic control of a ship. It utilizes a new, fast and effective, deterministic method, called the Trajectory Base Algorithm, to calculate a safe, optimal path for a ship. The system structure, a detailed explanation of a new method, followed by results of simulation tests are all presented in the paper. The results proof a successful application of the method to solve a path planning problem for ships with the consideration of both static and dynamic obstacles in the environment, marine traffic regulations and dynamic properties of a ship, what makes this approach applicable in commercial systems. The approach can also be adapted for application in mobile robots path planning. The experimental results and ability of the system to achieve a new functionality of full autonomy show significance of this contribution to the development of Expert and Intelligent Systems domain. The author believes that autonomous systems constitute the future of Expert and Intelligent Systems. [ABSTRACT FROM AUTHOR]

The route planning before tourists going to an unfamiliar city is relatively subjective, which can hardly provide maximum motive benefits. Aimed at the problem, tourism route decision support based on neural net buffer analysis model is brought up in this paper. Firstly, sight spots in Zhengzhou city are taken as examples to ensure research objects. GIS buffer analysis and manual neural net are taken as basic model to ensure research method, on which base, motive iterative function transmission model related to neural net transmission chain and service facility in sight spots buffer is set up. And then three downtown sight spots are taken as algorithm example to research the feasibility of the model to calculate motive iterative function output value of different tourism routes with the same sight spots, which provides objective tourism route decision support for tourists. Algorithm example proves that the model brought up in this paper is feasible and valuable, and can provide effective decision support for tourists. [ABSTRACT FROM AUTHOR]

Sustainability criteria used for decision support in the product innovation process and aligned throughout the design process is one key element to efficiently introduce a sustainability perspective early in product development. The aim of this paper is to present an approach for identifying such sustainability criteria and to suggest a process for how these can be developed in any manufacturing company. The sustainability criteria are presented in a set of matrices, separating the criteria into product life-cycle phases and socio-ecological sustainability principles. In addition the paper presents a qualitative measurement scale for the criteria, called a sustainability compliance index that indicates to what degree a product or process concept performs in relation to a sustainable solution. The sustainability criteria were tested in different settings at a case company within the aerospace industry to give a first indication and evaluation of the ability to give guidance and support in bringing in a sustainability perspective when developing, evaluating and selecting different concepts in the early phases of product development. [ABSTRACT FROM AUTHOR]

• The risk of subsidence damage induced by leakage was assessed. • Mitigation measures were evaluated for hydrogeological risk management. • Cost-benefit analysis was used for the risk evaluation. • Expert elicitation, data and empirical models were combined. Leakage of groundwater into underground facilities can subsequently cause groundwater drawdown, subsidence and subsidence damages to the built-up environment. In order to reduce the risk of damage, measures to mitigate the risks must often be implemented. The aim of this paper is to describe and demonstrate a probabilistic cost-benefit analysis approach to assess the economic profitability of investing in different risk mitigation alternatives. Since underground construction is always associated with uncertainties, the analysis uses probability distribution functions for uncertain parameters and Monte Carlo simulations to quantify probabilities of damage and implementation costs. The proposed approach is exemplified with a case study, the road tunnel project Bypass (Förbifart) Stockholm in eastern Sweden, for which four risk mitigation alternatives were evaluated. In conclusion, the approach helps to highlight the economic effects of different risk mitigation approaches and constitute a transparent support for decisions on implementation of risk mitigation. For the case study, the analysis indicates that the implementation costs of ∼ 7000 MSEK (700 million EUR) for risk mitigation needed to fulfil the legal requirements, from the Swedish Land- and Environmental court, in the form of ambitious sealing strategies are disproportionate relative to the benefits of ∼ 50 MSEK (5 million EUR) gained in the form of reduced damage risk for the built-up environment. In other words, billions SEK of taxpayers' money are spent on unnecessary expenses to fulfill legal requirements without societal benefits. The novelty of the paper constitutes the coupling of models and combination of established methods for management of hydrogeological risks. [ABSTRACT FROM AUTHOR]

This paper demonstrates an energy signal tool to assess the system-level and whole-building energy use of an office building in downtown Denver, Colorado. The energy signal tool uses a traffic light visualization to alert a building operator to energy use which is substantially different from expected. The tool selects which light to display for a given energy end-use by comparing measured energy use to expected energy use, accounting for uncertainty. A red light is only displayed when a fault is likely enough, and abnormal operation costly enough, that taking action will yield the lowest cost result. While the theoretical advances and tool development were reported previously, it has only been tested using a basic building model and has not, until now, been experimentally verified. Expected energy use for the field demonstration is provided by a compact reduced-order representation of the Alliance Center, generated from a detailed DOE-2.2 energy model. Actual building energy consumption data is taken from the summer of 2014 for the office building immediately after a significant renovation project. The purpose of this paper is to demonstrate a first look at the building following its major renovation compared to the design intent. The tool indicated strong under-consumption in lighting and plug loads and strong over-consumption in HVAC energy consumption, which prompted several focused actions for follow-up investigation. In addition, this paper illustrates the application of Bayesian inference to the estimation of posterior parameter probability distributions to measured data. Practical discussion of the application is provided, along with additional findings from further investigating the significant difference between expected and actual energy consumption. [ABSTRACT FROM AUTHOR]

We are increasingly confronted with severe social and economic impacts of environmental degradation all over the world. From a valuation perspective, environmental problems and conflicts originate from trade-offs between values. The urgency and importance to integrate nature's diverse values in decisions and actions stand out more than ever. Valuation, in its broad sense of ‘assigning importance’, is inherently part of most decisions on natural resource and land use. Scholars from different traditions -while moving from heuristic interdisciplinary debate to applied transdisciplinary science- now acknowledge the need for combining multiple disciplines and methods to represent the diverse set of values of nature. This growing group of scientists and practitioners share the ambition to explore how combinations of ecological, socio-cultural and economic valuation tools can support real-life resource and land use decision-making. The current sustainability challenges and the ineffectiveness of single-value approaches to offer relief demonstrate that continuing along a single path is no option. We advocate for the adherence of a plural valuation culture and its establishment as a common practice, by contesting and complementing ineffective and discriminatory single-value approaches. In policy and decision contexts with a willingness to improve sustainability, integrated valuation approaches can be blended in existing processes, whereas in contexts of power asymmetries or environmental conflicts, integrated valuation can promote the inclusion of diverse values through action research and support the struggle for social and environmental justice. The special issue and this editorial synthesis paper bring together lessons from pioneer case studies and research papers, synthesizing main challenges and setting out priorities for the years to come for the field of integrated valuation. [ABSTRACT FROM AUTHOR]

Scenarios are valuable tools that could support decision making under deep uncertainty, nevertheless, their potential remains untapped. The paper explores whether participatory scenario construction in the form of stories coupled with the Intergovernmental Panel on Climate Change (IPCC) 2100 projections may contribute to increased utility of the scenarios and projections for climate-induced risk reduction, in the Nile Basin. The Nile River is 6,695 kilometres long and covers a basin area of 3.18 million square kilometres (one-tenth of the African land mass). The basin is highly susceptible to climate-change induced disasters. According to the IPCC, there is high confidence that the Basin will suffer from severe shifts in biome distribution, compounded water stress, degradation of marine life and reduced crop productivity. There is also medium confidence that the Nile Basin will experience: severe decline in livestock, significant increase in vector and water-borne diseases, undernutrition, increased migration and sea level rise. The basin is already experiencing some of these key risks, on the other hand, their impact in the next 30 to 35 years is deeply uncertain. The findings of this paper are based on four scenarios, namely: Kazuri, Miskeen, Umoja and EjoHeza and data collected from two forums that were held in Jinja, Uganda and Nairobi, Kenya. The forum participants were a multi-disciplinary team of national and international stakeholders. The paper concludes that coupling Nile Basin storylines with the IPCC 2100 projections, proved to be an effective tool in increasing the utility of the scenarios and projections, for purposes of disaster risk reduction. Future work will entail analysing the uptake of the scenarios to resolve deadlocks and enhance cooperation. [ABSTRACT FROM AUTHOR]