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The application of ecosystem service (ES) knowledge to planning processes and decision-making can lead to more effective climate change adaptation. Despite the increased attention given to the ES concept, its degree of integration and use in spatial planning processes are still below the expectations of those who are promoting this concept. Barriers hindering its operationalisation cover a span of aspects ranging from theoretical to procedural and methodological issues. Overall, there is a general lack of guidance on how and at what point ES knowledge should be integrated into planning processes. This study aims to promote the inclusion of ES knowledge into spatial planning practices and decision-making processes to enhance climate change adaptation. A replicable GIS-based methodology is proposed. First, the potential supply of ESs that can support climate change adaptation (ESCCAs) is defined, mapped, and quantified. Then, a need for an ESCCA supply is identified, and territorial capacities to respond to the expected climate change impacts on natural and socio-economic sectors are assessed. The methodology is applied to the Friuli Venezia Giulia Autonomous Region (Italy) as an illustrative case study. The results reveal that areas with similar geomorphological characteristics tend to respond similarly. Forest ecosystems, inland wetlands and specifically salt marshes can potentially supply a greater variety of ESCCAs. In the case study area, about 62% of the supplied ESCCAs can contribute to reducing the impacts in more than 50% of the impacted sectors. The territory of the study site generally shows good preparedness for expected impacts in most of the analysed sectors; less prepared areas are characterised by agricultural ecosystems. This reading approach based on land cover analyses can thus assist in developing policies to enhance different territorial capacities, ultimately leading to better and more sustainable decision-making. [ABSTRACT FROM AUTHOR]

The authors comment on modernization activities underway at the U.S. Centers for Disease Control and Prevention (CDC) and across the field of public health. Topics discussed include highlighting explicit dependencies among foundational data activities and advanced capabilities, importance of additional support for the shift to a data-ready ecosystem framework, and need to strengthen the public health data science workforce.

Antibiotic overprescribing is a global issue that significantly contributes to increased antimicrobial resistance. Strengthening antimicrobial prescribing practices should be considered a priority. The emergency department (ED) represents a setting where antibiotics are frequently prescribed, but the determinants that influence prescribing choices are complex and multifaceted. We conducted an exploratory qualitative study to investigate the contextual factors that influence antibiotic prescribing choices among clinicians in the ED. The study employed video-reflexive ethnography (VRE) to capture prospective clinical decision-making in situated practice. Data collection involved fieldwork observations, video observations, and delivery of facilitated group reflexive sessions, where clinicians viewed a selection of recorded video snippets relating to antibiotic prescribing. Study was conducted across two EDs within the same health service in Australia. A total of 29 clinical conversations focusing on antibiotic prescribing were recorded. Additionally, 34 clinicians participated in group reflexive sessions. Thematic analysis from the transcribed data yielded four themes: 'importance of clinical judgment', 'usability of prescribing guidelines', 'managing patient expectations', and 'context-dependent disruptions'. Our findings provide insights into the challenges faced by clinicians in navigating complex ED environment, utilising electronic decision-support tools and engaging in discussions about patient treatments with senior clinicians. The findings also indicate that VRE is useful in visualising full complexity of the ED setting, and in initiating meaningful discussions among clinical teams. Integrating the use of VRE in everyday clinical settings can potentially facilitate the implementation of pragmatic solutions for delivering effective antibiotic stewardship practices. [ABSTRACT FROM AUTHOR]

Nature underpins human well-being in critical ways, especially in health. Nature provides pollination of nutritious crops, purification of drinking water, protection from floods, and climate security, among other well-studied health benefits. A crucial, yet challenging, research frontier is clarifying how nature promotes physical activity for its many mental and physical health benefits, particularly in densely populated cities with scarce and dwindling access to nature. Here we frame this frontier by conceptually developing a spatial decision-support tool that shows where, how, and for whom urban nature promotes physical activity, to inform urban greening efforts and broader health assessments. We synthesize what is known, present a model framework, and detail the model steps and data needs that can yield generalizable spatial models and an effective tool for assessing the urban nature-physical activity relationship. Current knowledge supports an initial model that can distinguish broad trends and enrich urban planning, spatial policy, and public health decisions. New, iterative research and application will reveal the importance of different types of urban nature, the different subpopulations who will benefit from it, and nature's potential contribution to creating more equitable, green, livable cities with active inhabitants.

Population in developed countries spend most of their time indoors, whether in their homes, workplaces, stores or leisure areas. Due to the COVID-19 pandemic, this situation worsened and now, more than ever, the importance of a high Indoor Environmental Quality (IEQ) is highlighted. The IEQ is very important in building performance since it is directly related to its occupants’ comfort, health, wellbeing, and productivity and the Sick Building Syndrome (SBS) concept. Therefore, it is essential to develop tools to support designers’ decision-making in the materialization of indoor environments with higher quality. From the state-of-art analysis, it is possible to conclude that the methods to assess the overall building performance already consider the IEQ. Still, most use an approach that does not cover all relevant indicators. In this context, this paper presents the first milestone of a research work that aims to develop a new method to rate the overall IEQ of office buildings in Portugal. The main objective of the present study is to propose a list of IEQ indicators for office buildings, adapted to the Portuguese context, based on the analysis of existing rating methods for buildings and the recommendations of national and international standards.

Finding the optimal land allocation for providing ecosystem services, conserving biodiversity and maintaining rural livelihoods is a key challenge of agricultural management and land-use planning. Agroforestry has been widely discussed as a sustainable land-use solution and as one strategy to improve the provision of multiple ecological and economic functions in agricultural landscapes. In this study, we use the backdrop of agroforestry research to evaluate a method from the multi-criteria decision analysis toolbox: robust multi-objective optimization. The key feature of this modelling approach is its capacity to integrate uncertain ecological and socio-economic data. We illustrate the optimization model with a case study from eastern Panama, showing how the model can bring together scientific and practical knowledge to provide potentially desirable landscape compositions from the perspective of farmers, a public perspective, and a compromise solution. Example results of our case study show how to assess whether agroforestry is a desirable component in a landscape composition to satisfy multiple objectives of different interest groups. Furthermore, we use the model to demonstrate how different objectives influence the optimal area share and type of agroforestry. Due to its parsimonious nature, the model could be used as a starting point of an interactive co-learning process with decision-makers, researchers and other stakeholders. The model, however, is not yet suitable for an exact prediction of future land-use dynamics, for questions of spatially explicit land-use configuration, studies going beyond the regional scale or for socio-economic interactions of agents. Therefore, we outline future research needs and recommendations for other types of models or hybrid approaches.

Population in developed countries spend most of their time indoors, whether in their homes, workplaces, stores or leisure areas. Due to the COVID-19 pandemic, this situation worsened and now, more than ever, the importance of a high Indoor Environmental Quality (IEQ) is highlighted. The IEQ is very important in building performance since it is directly related to its occupants' comfort, health, wellbeing, and productivity and the Sick Building Syndrome (SBS) concept. Therefore, it is essential to develop tools to support designers' decision-making in the materialization of indoor environments with higher quality. From the state-of-art analysis, it is possible to conclude that the methods to assess the overall building performance already consider the IEQ. Still, most use an approach that does not cover all relevant indicators. In this context, this paper presents the first milestone of a research work that aims to develop a new method to rate the overall IEQ of office buildings in Portugal. The main objective of the present study is to propose a list of IEQ indicators for office buildings, adapted to the Portuguese context, based on the analysis of existing rating methods for buildings and the recommendations of national and international standards. [ABSTRACT FROM AUTHOR]

Finding the optimal land allocation for providing ecosystem services, conserving biodiversity and maintaining rural livelihoods is a key challenge of agricultural management and land-use planning. Agroforestry has been widely discussed as a sustainable land-use solution and as one strategy to improve the provision of multiple ecological and economic functions in agricultural landscapes. In this study, we use the backdrop of agroforestry research to evaluate a method from the multi-criteria decision analysis toolbox: robust multi-objective optimization. The key feature of this modelling approach is its capacity to integrate uncertain ecological and socio-economic data. We illustrate the optimization model with a case study from eastern Panama, showing how the model can bring together scientific and practical knowledge to provide potentially desirable landscape compositions from the perspective of farmers, a public perspective, and a compromise solution. Example results of our case study show how to assess whether agroforestry is a desirable component in a landscape composition to satisfy multiple objectives of different interest groups. Furthermore, we use the model to demonstrate how different objectives influence the optimal area share and type of agroforestry. Due to its parsimonious nature, the model could be used as a starting point of an interactive co-learning process with decision-makers, researchers and other stakeholders. The model, however, is not yet suitable for an exact prediction of future land-use dynamics, for questions of spatially explicit land-use configuration, studies going beyond the regional scale or for socio-economic interactions of agents. Therefore, we outline future research needs and recommendations for other types of models or hybrid approaches. [ABSTRACT FROM AUTHOR]

The residential heritage that was built during the great expansion of real estate after the Second World War has severe deficiencies in structural safety, fire resistance, energy efficiency, and accessibility and these cannot be solved with sustainable renovation measures. This study focuses on replacement interventions and promotes a management model that addresses three areas (technical, social, and economic) and it refers to the application of the circularity principle to the construction sector for the goal of climate neutrality by 2050. The final objective is to define a protocol—namely, the guidelines—to reference in a decision-making process that promotes urban regeneration by comparing demolition with reconstruction and renovation. The proposed methodology allows for the determination of suitable areas in Bologna for replacement and the joining of the municipal geodatabase with data from archival research on building permits in 1949–1965 by using GIS software. This digital archive can be implemented in a digital twin for an urban block, which can become a predictive tool for urban planning and the management of the whole life of a building. The main result is the characterization of urban blocks by identifying typical features belonging to specific building libraries that are validated with density analyses. These urban clusters and building archetypes can be used to assess targeted intervention measures by using specific tools, such as predictive maps and 3D city models. [ABSTRACT FROM AUTHOR]

Epidemiological modeling is a key lever for infectious disease control and prevention on farms. It makes it possible to understand the spread of pathogens, but also to compare intervention scenarios even in counterfactual situations. However, the actual capability of decision makers to use mechanistic models to support timely interventions is limited. This study demonstrates how artificial intelligence (AI) techniques can make mechanistic epidemiological models more accessible to farmers and veterinarians, and how to transform such models into user-friendly decision-support tools (DST). By leveraging knowledge representation methods, such as the textual formalization of model components through a domain-specific language (DSL), the co-design of mechanistic models and DST becomes more efficient and collaborative. This facilitates the integration of explicit expert knowledge and practical insights into the modeling process. Furthermore, the utilization of AI and software engineering enables the automation of web application generation based on existing mechanistic models. This automation simplifies the development of DST, as tool designers can focus on identifying users' needs and specifying expected features and meaningful presentations of outcomes, instead of wasting time in writing code to wrap models into web apps. To illustrate the practical application of this approach, we consider the example of Bovine Respiratory Disease (BRD), a tough challenge in fattening farms where young beef bulls often develop BRD shortly after being allocated into pens. BRD is a multi-factorial, multi-pathogen disease that is difficult to anticipate and control, often resulting in the massive use of antimicrobials to mitigate its impact on animal health, welfare, and economic losses. The DST developed from an existing mechanistic BRD model empowers users, including farmers and veterinarians, to customize scenarios based on their specific farm conditions. It enables them to anticipate the effects of various pathogens, compare the epidemiological and economic outcomes associated with different farming practices, and decide how to balance the reduction of disease impact and the reduction of antimicrobial usage (AMU). The generic method presented in this article illustrates the potential of artificial intelligence (AI) and software engineering methods to enhance the co-creation of DST based on mechanistic models in veterinary epidemiology. The corresponding pipeline is distributed as an open-source software. By leveraging these advancements, this research aims to bridge the gap between theoretical models and the practical usage of their outcomes on the field. • AI makes mechanistic epidemiological models usable to support decisions on farms. • Structured text for knowledge representation enhances collaborative tool design. • AI and software engineering automate web application generation for disease control. • The resulting code for creating decision-support tools from models is open-source. [ABSTRACT FROM AUTHOR]

Models are important tools to address sustainability challenges associated with developing aquaculture at farm, regional and global scales. Farm‐scale models (FSMs), which are integrated mathematical models developed to simulate farm operations, can quantify energy, mass or economic input flows and predict a variety of outputs such as fish biomass, waste and by‐products. The variety of farming systems, equations available to build the models, and objectives of applications and intended users has resulted in the publication of wide range of FSMs. We performed a narrative review of 36 fish FSMs published from 1985–2021 to address several questions: Can the main characteristics of these models be defined? How do the farming system studied and the objectives of the study influence model development? What are the main modelling techniques available to simulate the main processes of a fish farm, and what are their advantages and disadvantages? How can FSMs help address sustainability challenges of aquaculture? This review discusses advances, limitations and future lines of research related to FSMs to help select existing models, or develop new ones, that are suitable for their intended use and users. The article is structured according to the main steps of the modelling process: (i) definition of scope and objectives; (ii) process formulation and model selection at individual, cohort and farm levels; (iii) implementation and evaluation; and (iv) applications (e.g. precision fish farming, IMTA modelling, supporting spatial management, life cycle assessment). At each step, recommendations are provided and research needs are stated. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) can be a tool in the diagnosis and acquisition of knowledge, particularly in dentistry, sparking debates on its application in clinical decision-making., Objective: This study aims to evaluate the accuracy, completeness, and reliability of the responses generated by Chatbot Generative Pre-Trained Transformer (ChatGPT) 3.5 in dentistry using expert-formulated questions., Materials and Methods: Experts were invited to create three questions, answers, and respective references according to specialized fields of activity. The Likert scale was used to evaluate agreement levels between experts and ChatGPT responses. Statistical analysis compared descriptive and binary question groups in terms of accuracy and completeness. Questions with low accuracy underwent re-evaluation, and subsequent responses were compared for improvement. The Wilcoxon test was utilized (α = 0.05)., Results: Ten experts across six dental specialties generated 30 binary and descriptive dental questions and references. The accuracy score had a median of 5.50 and a mean of 4.17. For completeness, the median was 2.00 and the mean was 2.07. No difference was observed between descriptive and binary responses for accuracy and completeness. However, re-evaluated responses showed a significant improvement with a significant difference in accuracy (median 5.50 vs. 6.00; mean 4.17 vs. 4.80; p=0.042) and completeness (median 2.0 vs. 2.0; mean 2.07 vs. 2.30; p=0.011). References were more incorrect than correct, with no differences between descriptive and binary questions., Conclusions: ChatGPT initially demonstrated good accuracy and completeness, which was further improved with machine learning (ML) over time. However, some inaccurate answers and references persisted. Human critical discernment continues to be essential to facing complex clinical cases and advancing theoretical knowledge and evidence-based practice., Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. Institutional Research Ethics Committee issued approval 69712923.6.0000.5419. "The research project was approved". Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Molena et al.)

Background and purpose: Volumetric modulated arc therapy (VMAT) planning for head and neck cancer is a complex process. While the lowest achievable dose for each individual organ-at-risk (OAR) is unknown a priori, artificial intelligence (AI) holds promise as a tool to accurately estimate the expected dose distribution for OARs. We prospectively investigated the benefits of incorporating an AI-based decision support tool (DST) into the clinical workflow to improve OAR sparing. Materials and methods: The DST dose prediction model was based on 276 institutional VMAT plans. Under an IRB-approved prospective trial, the physician first generated a custom OAR directive for 50 consecutive patients (physician directive, PD). The DST then estimated OAR doses (AI directive, AD). For each OAR, the treating physician used the lower directive to form a hybrid directive (HD). The final plan metrics were compared to each directive. A dose difference of 3 Gray (Gy) was considered clinically significant. Results: Compared to the AD and PD, the HD reduced OAR dose objectives by more than 3 Gy in 22% to 75% of cases, depending on OAR. The resulting clinical plan typically met these lower constraints and achieved mean dose reductions between 4.3 and 16 Gy over the PD, and 5.6 to 9.1 Gy over the AD alone. Dose metrics achieved using the HD were significantly better than institutional historical plans for most OARs and NRG constraints for all OARs. Conclusions: The DST facilitated a significantly improved treatment directive across all OARs for this generalized H&N patient cohort, with neither the AD nor PD alone sufficient to optimally direct planning.

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Many tools have been generated in recent decades to support decision-makers in understanding and acting on climate science, causing stakeholders in the Gulf of Mexico (Gulf) to repeatedly express the need for guidance when selecting climate resilience tools. The Climate and Resilience Community of Practice (CoP), Gulf of Mexico Alliance (GOMA), and the Northern Gulf of Mexico Sentinel Site Cooperative (Cooperative) developed and implemented an end-user driven process for tool creation. Research has suggested that integrating target end-users throughout tool development improves the probability a tool will be utilized, yet there is little practical guidance available on how to successfully design and implement an end-user driven tool development process. In this study, an end-user driven process and results from implementing the process during tool development are presented. Challenges, successful approaches, and lessons learned to support future tool development, especially for tools focused on sharing climate science are identified. Guidance is provided on needed expertise, timelines for engagement with target end-users, and methods on how to solicit, analyze, and assimilate end-user needs, perspectives, and priorities into a final product. [ABSTRACT FROM AUTHOR]

One of the main goals of weed control is to maintain the weed population density in a equilibrium level that is below economic damages. To achieve this goal, we propose a dynamic optimization model for weed infestation control using herbicides rotation strategy. The objective is to reduce the seed bank and the use of herbicide, maximizing the profit in a pre-determined period of time and minimizing the environmental impacts caused by excessive use of herbicides. The dynamic optimization model takes into account the decreased herbicide efficacy over time, which is due to an increase of weed resistance originated from selective pressure. The dynamic optimization problem involves integer and continuous variables modeled as a mixed-integer nonlinear programming problem (MINLP). The MINLP problem was solved by an implicit enumeration known as branch and bound method. Numerical simulations illustrated the solution of a case study for infestation control of the Bidens subalternans specie in a maize crop by interchanging between two classes of herbicides. The results demonstrate that our optimization model can improve the profit of farmers and has the potential to contribute for further decision-support tools in weed management that considers the resistance dynamics. [ABSTRACT FROM AUTHOR]

• Gulf TREE, a climate resilience tool, saw differences in how it was adopted by different users. • Academic and non-profit users appear to have other pathways for finding climate resilience tools. • Many users appeared to use Gulf TREE between two and three times over the course of the year. • Web tracking analytics, such as Google Analytics can be adapted to better estimate number of uses. • Highly ranked usefulness, usability, and intention to use a tool did not guarantee tool adoption. Increasingly, climate researchers are pressured to generate products and tools from their research that support informed decision-making for increased social and environmental resilience. Despite the goal of these tools to integrate climate science into decision-making, little follow-up study is conducted after climate resilience tools are released to understand their effectiveness or application. It is important as limited resources across federal, state, local, and private sectors are invested in the development of climate resilience tools to understand their efficacy at achieving their intended purpose(s). This study leveraged Gulf TREE, a climate resilience tool released in 2018, to assess diffusion and adoption by intended users for intended purposes. Strategic efforts to enhance Gulf TREE via stakeholder engagement during development and positive evaluations prior to tool release, suggested there would be a high rate of adoption across all potential end-users; however, an end-user's intention to use a tool does not guarantee implementation. To expand the body of knowledge around climate resilience tool development, diffusion, and adoption, the authors explored the following research objectives: 1) Assess if end-users are adopting Gulf TREE; 2) Assess if end-users are adopting Gulf TREE for the intended purpose of finding climate change resilience tools; 3) Assess if end-users from different stakeholder categories are adopting Gulf TREE similarly. The study successfully determined that the climate resilience tool, Gulf TREE, was being adopted for its intended purposes. There were not sufficient data for statistical comparisons of use between stakeholder categories; however, general trends provided some indication of different stakeholder types utilizing Gulf TREE with different frequencies and for different purposes. Further, the study identified variability among sectors for how Gulf TREE was integrated into their existing suite of tools, with federal government and Sea Grant stakeholders using Gulf TREE as their primary resource versus academia and non-profit who appeared to have alternatives on which they continued to rely. Finally, this study identified that usability and usefulness may not be good indicators of tool adoption. This study expands the limited peer-reviewed assessments of a climate resilience tool's use. Continuing to develop this body of knowledge will allow for a better understanding of what constitutes a successful or effective climate resilience tool, how to improve current and future climate resilience tools, and how to best utilize limited resources when attempting to integrate climate science into decision-making. [ABSTRACT FROM AUTHOR]

Abstract Background Ministries of health in low- and middle-income countries often lack timely quality data for data-driven decision making in healthcare networks. We describe the design and implementation of decision-support electronic tools by the Ministry of Health of the State of Chiapas, in Mexico, as part of Salud Mesoamerica Initiative. Methods Three electronic decision-support tools were designed through an iterative process focused on streamlined implementation: 1) to collect and report health facility data at health facilities; 2) to compile and analyze data at health district and central level; and, 3) to support stratified sampling of health facilities. Data was collected for five composite indicators measuring availability of equipment, medicines, and supplies for maternal and child health. Quality Assurance Teams collected data, evaluated results and supported quality improvement. Data was also analyzed at the central level and health districts for decision-making. Results Data from 300 health facilities in four health districts was collected and analyzed (November 2014—June 2015). The first wave revealed gaps on availability of equipment and supplies in more than half of health facilities. Electronic tools provided the ministry of health officers new ways to visualize data, identify patterns and make hypothesis on root-causes. Between the first and second measurement, the number of missing items decreased, and actions performed by quality improvement teams became more proactive. In the final measurement, 89.7—100% of all health facilities achieved all the required items for each indicator. Conclusions Our experience could help guide others seeking to implement electronic decision-support tools in low- and middle-income countries. Electronic decision-support tools supported data-driven decision-making by identifying gaps on heatmaps and graphs at the health facility, subdistrict, district or state level. Through a rapid improvement process, the Ministry of Health met targets of externally verified indicators. Using available information technology resources facilitated prompt implementation and adoption of technology.

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

Policies and legislation for the importation and use of non-native (NN) freshwater fishes have been rapidly evolving in several countries around the world in response to heightened evidence of their adverse ecological and economic impacts. In China, the already large and growing number of NN freshwater fishes being imported from foreign countries or translocated within China has necessitated the development of management strategies supported by policies at both national and regional levels. Despite increasing awareness of the damage caused by NN fishes, such initiatives in China continue to lag behind those already implemented in other countries. This study endeavors to narrow this policy gap by: (i) providing a synoptic overview of the vectors and pathways of NN freshwater fish introductions and dispersal both into and within China; (ii) evaluating existing policies and legislation relative to those currently implemented in Europe, the USA and Canada; (iii) examining the role of decision-support tools for identifying and assessing the risks posed by NN fishes; and (iv) proposing perspectives and possible options for NN fish policies, legislation and management in China. [ABSTRACT FROM AUTHOR]

PurposeTo develop and validate a preliminary machine learning (ML) model aiding in the selection of intracavitary (IC) versus hybrid interstitial (IS) applicators for high-dose-rate (HDR) cervical brachytherapy.MethodsFrom a dataset of 233 treatments using IC or IS applicators, a set of geometric features of the structure set were extracted, including the volumes of OARs (bladder, rectum, sigmoid colon) and HR-CTV, proximity of OARs to the HR-CTV, mean and maximum lateral and vertical HR-CTV extent, and offset of the HR-CTV centre-of-mass from the applicator tandem axis. Feature selection using an ANOVA F-test and mutual information removed uninformative features from this set. Twelve classification algorithms were trained and tested over 100 iterations to determine the highest performing individual models through nested 5-fold cross-validation. Three models with the highest accuracy were combined using soft voting to form the final model. This model was trained and tested over 1,000 iterations, during which the relative importance of each feature in the applicator selection process was determined.ResultsFeature selection indicated that the mean and maximum lateral and vertical extent, volume, and axis offset of the HR-CTV were the most informative features and were thus provided to the ML models. Relative feature importances indicated that the HR-CTV volume and mean lateral extent were most important for applicator selection. From the comparison of the individual classification algorithms, it was found that the highest performing algorithms were tree-based ensemble methods – AdaBoost Classifier (ABC), Gradient Boosting Classifier (GBC), and Random Forest Classifier (RFC). The accuracy of the individual models was compared to the voting model for 100 iterations (ABC = 91.6 ± 3.1%, GBC = 90.4 ± 4.1%, RFC = 89.5 ± 4.0%, Voting Model = 92.2 ± 1.8%) and the voting model was found to have superior accuracy. Over the final 1,000 evaluation iterations, the final voting model demonstrated a high predictive accuracy (91.5 ± 0.9%) and F1 Score (90.6 ± 1.1%).ConclusionThe presented model demonstrates high discriminative performance, highlighting the potential for utilization in informing applicator selection prospectively following further clinical validation.

Purpose: To develop and validate a preliminary machine learning (ML) model aiding in the selection of intracavitary (IC) versus hybrid interstitial (IS) applicators for high-dose-rate (HDR) cervical brachytherapy. Methods: From a dataset of 233 treatments using IC or IS applicators, a set of geometric features of the structure set were extracted, including the volumes of OARs (bladder, rectum, sigmoid colon) and HR-CTV, proximity of OARs to the HR-CTV, mean and maximum lateral and vertical HR-CTV extent, and offset of the HR-CTV centre-of-mass from the applicator tandem axis. Feature selection using an ANOVA F-test and mutual information removed uninformative features from this set. Twelve classification algorithms were trained and tested over 100 iterations to determine the highest performing individual models through nested 5-fold cross-validation. Three models with the highest accuracy were combined using soft voting to form the final model. This model was trained and tested over 1,000 iterations, during which the relative importance of each feature in the applicator selection process was determined. Results: Feature selection indicated that the mean and maximum lateral and vertical extent, volume, and axis offset of the HR-CTV were the most informative features and were thus provided to the ML models. Relative feature importances indicated that the HR-CTV volume and mean lateral extent were most important for applicator selection. From the comparison of the individual classification algorithms, it was found that the highest performing algorithms were tree-based ensemble methods – AdaBoost Classifier (ABC), Gradient Boosting Classifier (GBC), and Random Forest Classifier (RFC). The accuracy of the individual models was compared to the voting model for 100 iterations (ABC = 91.6 ± 3.1%, GBC = 90.4 ± 4.1%, RFC = 89.5 ± 4.0%, Voting Model = 92.2 ± 1.8%) and the voting model was found to have superior accuracy. Over the final 1,000 evaluation iterations, the final voting model demonstrated a high predictive accuracy (91.5 ± 0.9%) and F1 Score (90.6 ± 1.1%). Conclusion: The presented model demonstrates high discriminative performance, highlighting the potential for utilization in informing applicator selection prospectively following further clinical validation. [ABSTRACT FROM AUTHOR]

• BIM is used as a thinking tool for the conceptual design process. • The model is intended for the neutralization of energy consumption, estimating the amount of photovoltaic panels needed to achieve a NZEH. • A new, original methodology is used, with a built BIM prototype to measure energy consumption. • This tool for enriching the world with a new sustainable dimension is implemented in the BIM app as part of the software. • The originality of this paper is to create an embedded simplification tool for evaluating the electrical energy consumption (energy system) inside BIM. BIM is not simply a drafting tool or advanced CAD. BIM is a collaboratively generated and maintained data-rich information source, especially when it comes to the energy-calculation process and beyond. Due to growing awareness around energy conservation, numerous projects and initiatives have occurred to promote the development of low- and net-zero-energy homes. The widespread adoption of photovoltaic rooftop systems has shown that solar homes can be a effective solution to that energy demand; nowadays, the idea that BIM is not just software has been decided upon. In this paper, a new approach was used to calculate energy consumption using a BIM template designed for this purpose. The greatest value derived from BIM was in the template designed to neutralize energy consumption by calculating the number of photovoltaic panels needed to achieve a net-zero-energy house. The BIM prototype developed is considered a useful tool, intended to be used by everyday, non-specialist users to aid and disseminate energy knowledge in order to achieve a zero-energy home, as well as to be used as a decision-support tool, incorporating energy simulation into the early design process of zero-energy buildings in architectural practice. Finally, the tool was tested on a group of users in order to fine-tune and develop the design parameters of the template. [ABSTRACT FROM AUTHOR]

Phalaris minor Retz. (littleseed canarygrass) is the most problematic and herbicide-resistant weed in the rice-wheat cropping system in India. As such, it poses a severe threat to wheat yield and food security. A number of herbicidal and agronomic practices have been identified for the effective control of P. minor. These include crop rotation, crop establishment methods, herbicide spray technology, sowing time, weed seed harvest and effective herbicide mixtures. A population model of P. minor was built based on the life cycle of the species, herbicide resistance mechanisms and the effects of weed control practices. The model simulated the interactions of these factors and provided the best management recommendations for sustainably controlling this noxious weed species. Model results indicate that integration of chemical and non-chemical control methods was the most effective and sustainable strategy. For example, the integration of a happy seeder (a tractor-mounted mulching and sowing machine) with an effective post-emergence herbicide reduced the probability of weed control failure by 32% compared to the scenario with a rotavator and the same herbicide. Similarly, more conventional crop establishment methods such as a rotavator and conventional tillage could be accompanied by pre- or post-emergence applications of herbicide mixtures. Adoption of good herbicide spray technology and weed seed harvest delayed the onset of resistance evolution by up to four years. Furthermore, effective crop rotation such as the inclusion of sugarcane in place of rice in the summer season reduced the risk of resistance evolution by 31% within the 10 year simulation period. In addition to the scenarios using representative parameter values, the variability of model predictions was investigated based on some field experiments. The model provided a powerful tool for promoting Integrated Weed Management and the sustainable use of herbicides. Pragmatic ways of dealing with uncertainty in model prediction are discussed.

• A framework to evaluate urban resilience knowledge products is applied in two cases. • The knowledge product evaluation assesses elements, scales, resilience, and use. • Knowledge product implementation and facilitation strategies may increase use. • Methods that identify actions only in reports likely miss some resilience progress. • The knowledge product evaluation framework can help improve knowledge products. Urban decision-makers are increasingly focused on enhancing community resilience in anticipation of more frequent and intense impacts from climate variability and change. These impacts will manifest in complex and nuanced ways, particularly when coupled with additional social, economic, and environmental shifts that vary across contexts. Given these challenges, urban decision-makers are seeking new knowledge, and new ways of using existing knowledge, to support decision-making processes. In response, a broad range of knowledge products (i.e. decision-support tools, climate services) have been developed for urban areas. Yet, to date, little research has directly evaluated these products. The Knowledge Product Evaluation (KnoPE) framework addresses this gap by providing both conceptual clarity surrounding knowledge products and a structured, generalizable methodology to guide research and support improved knowledge product creation and uptake. The KnoPE Framework combines data and information on knowledge products, their use in decision-making over time, and evidence of tangible actions taken. The KnoPE Framework was developed with two urban resilience knowledge products – the Urban-Climate Adaptation Tool and the Maine Flood Resilience Checklist. Initial testing indicates that the KnoPE Framework can assess the transferability, scalability, and use of knowledge products in urban resilience decision-making. Any evaluation using the KnoPE Framework requires a thorough understanding of the contextual details of each case and understanding what factors may influence knowledge product development and subsequent decision-making processes and outcomes. Yet, as an analytical entry point for the evaluation of knowledge products, the KnoPE Framework can offer insights regarding the extent to which knowledge products influence urban resilience decision-making processes. [ABSTRACT FROM AUTHOR]

Collectively, these included 84 streams, where LCT had been observed by management agencies (occupied streams) and 127 historical streams, where LCT had not been observed in the last several decades (unoccupied streams). Unoccupied streams were assumed to contain LCT habitat because they currently support non-native trout populations (the likely cause of LCT extirpation) or were selected by management partners based on habitat data or resolved historical threats. In streams where LCT co-occurred with non-native trout, we simulated effects of non-native trout eradication by comparing LCT population forecasts with and without non-natives. Eliminating non-natives while simultaneously reducing environmental stochasticity (to the mean for populations without non-native trout or 73% of the mean for populations with non-native trout) substantially reduced extinction risks for most populations, where non-native trout co-occurred with LCT (Fig.). [Extracted from the article]

To inform regional managers of potentially invasive non-native (NN) freshwater fishes in the principal hydrosystem that drains the West Siberian Plain, the River Ob basin, 31 extant and potential future NN fish species were screened using the Aquatic Species Invasiveness Screening Kit (AS-ISK) with respect to current and projected future climate conditions. Calibration of the AS-ISK scores, using receiver operating characteristic curve analysis, identified ‘basic risk assessment’ and ‘climate change assessment’ threshold scores of 27.5 and 34.75, respectively, with which to distinguish species that pose a high risk of being invasive in the Ob basin and those that pose a low-to-medium risk. Of the species screened, 12 ranked as high risk (black bullhead Ameiurus melas, brown bullhead Ameiurus nebulosus, grass carp Ctenopharyngodon idella, common carp Cyprinus carpio, eastern mosquitofish Gambusia holbrooki, silver carp Hypophthalmichthys molitrix, oriental weatherfish Misgurnus anguillicaudatus, rainbow trout Oncorhynchus mykiss, Chinese (Amur) sleeper Perccottus glenii, topmouth gudgeon Pseudorasbora parva, brown trout Salmo trutta, pikeperch Sander lucioperca and rudd Scardinius erythrophthalmus). The remaining species ranked as medium or low risk. Although the risk scores increased in 68% of species under climate change conditions, this affected the risk rankings of only two species: Salmo trutta decreased in rank from high to medium and Sander lucioperca increased in rank from medium to high. The outcomes of the present study, which identified 12 species for which full risk assessments are recommended, serves to inform the development of NN species policy and management in Russia. [ABSTRACT FROM AUTHOR]

Abstract Background The vision of transforming health systems into learning health systems (LHSs) that rapidly and continuously transform knowledge into improved health outcomes at lower cost is generating increased interest in government agencies, health organizations, and health research communities. While existing initiatives demonstrate that different approaches can succeed in making the LHS vision a reality, they are too varied in their goals, focus, and scale to be reproduced without undue effort. Indeed, the structures necessary to effectively design and implement LHSs on a larger scale are lacking. In this paper, we propose the use of architectural frameworks to develop LHSs that adhere to a recognized vision while being adapted to their specific organizational context. Architectural frameworks are high-level descriptions of an organization as a system; they capture the structure of its main components at varied levels, the interrelationships among these components, and the principles that guide their evolution. Because these frameworks support the analysis of LHSs and allow their outcomes to be simulated, they act as pre-implementation decision-support tools that identify potential barriers and enablers of system development. They thus increase the chances of successful LHS deployment. Discussion We present an architectural framework for LHSs that incorporates five dimensions—goals, scientific, social, technical, and ethical—commonly found in the LHS literature. The proposed architectural framework is comprised of six decision layers that model these dimensions. The performance layer models goals, the scientific layer models the scientific dimension, the organizational layer models the social dimension, the data layer and information technology layer model the technical dimension, and the ethics and security layer models the ethical dimension. We describe the types of decisions that must be made within each layer and identify methods to support decision-making. Conclusion In this paper, we outline a high-level architectural framework grounded in conceptual and empirical LHS literature. Applying this architectural framework can guide the development and implementation of new LHSs and the evolution of existing ones, as it allows for clear and critical understanding of the types of decisions that underlie LHS operations. Further research is required to assess and refine its generalizability and methods.

• High throughput scale-down chromatography workflow with decision-support. • Augmentation of design of experiments with the fractionation diagram method. • Automated and systematic high-throughput raw data manipulation. • Resin selection using empirical correlations through design of experiments. • Creation of windows of operation under uncertainty using empirical correlations. Chromatography remains the workhorse in antibody purification; however process development and characterisation still require significant resources. The high number of operating parameters involved requires extensive experimentation, traditionally performed at small- and pilot-scale, leading to demands in terms of materials and time that can be a challenge. The main objective of this research was the establishment of a novel High Throughput Process Development (HTPD) workflow combining scale-down chromatography experimentation with advanced decision-support techniques in order to minimise the consumption of resources and accelerate the development timeframe. Additionally, the HTPD workflow provides a framework to rapidly manipulate large datasets in an automated fashion. The central component of the HTPD workflow is the systematic integration of a microscale chromatography experimentation strategy with an advanced chromatogram evaluation method, design of experiments (DoE) and multivariate data analysis. The outputs of this are leveraged into the screening and optimisation components of the workflow. For the screening component, a decision-support tool was developed combining different multi-criteria decision-making techniques to enable a fair comparison of a number of CEX resin candidates and determine those that demonstrate superior purification performance. This provided a rational methodology for screening chromatography resins and process parameters. For the optimisation component, the workflow leverages insights provided through screening experimentation to guide subsequent DoE experiments so as to tune significant process parameters for the selected resin. The resulting empirical correlations are linked to a stochastic modelling technique so as to predict the optimal and most robust chromatographic process parameters to achieve the desired performance criteria. [ABSTRACT FROM AUTHOR]

This study compared the ability of seven statistical models to distinguish between linked and unlinked crimes. The seven models utilised geographical, temporal, and modus operandi information relating to residential burglaries (n = 180), commercial robberies, (n = 118), and car thefts (n = 376). Model performance was assessed using receiver operating characteristic analysis and by examining the success with which the seven models could successfully prioritise linked over unlinked crimes. The regression‐based and probabilistic models achieved comparable accuracy and were generally more accurate than the tree‐based models tested in this study. The Logistic algorithm achieved the highest area under the curve (AUC) for residential burglary (AUC = 0.903) and commercial robbery (AUC = 0.830) and the SimpleLogistic algorithm achieving the highest for car theft (AUC = 0.820). The findings also indicated that discrimination accuracy is maximised (in some situations) if behavioural domains are utilised rather than individual crime scene behaviours and that the AUC should not be used as the sole measure of accuracy in behavioural crime linkage research. [ABSTRACT FROM AUTHOR]

Climate change adaptation is increasingly concerned with how organisations develop capacity to adapt to uncertain futures. A participatory action research project conducted in Victoria, Australia, examined how health and social service organisations developed their organisational adaptive capacity through the use of adaptation decision-support tools. It can be challenging for any organisation to select and apply a decision-support tool, but this is particularly the case where resources and capacities are limited. For most organisations, climate change is only one of a complex set of dynamic stressors they must consider in meeting organisational goals. This paper shows that while decision-support tools can help co-generate knowledge and facilitate customised organisational adaptation processes, for them to be practically helpful for organisations with limited resources and capacities, intensive collaborative and discursive processes are needed to adjust such tools to fit specific organisational contexts and needs. Facilitators and participatory approaches that enable co-inquiry can play a critical role in supplementing scarce resources and initiating adaptation processes that go well beyond the scope and purpose of the decision-support tool used. Organisations working effectively with decision-support tools to adapt to climate change will need to feel ownership of them and have confidence in modifying them to suit their particular adaptation needs and organisational goals. [ABSTRACT FROM AUTHOR]

Abstract The South Pacific Regional Fisheries Management Organisation (SPRFMO) Convention includes specific provisions to protect vulnerable marine ecosystems (VMEs). The SPRFMO Commission has determined that the interim measures put in place to protect VMEs should be replaced by an improved system of fishable and closed areas. We used the conservation planning tool Zonation to examine the utility of a decision-support tool to develop spatial management options that balance the protection of VMEs with utilisation of high value areas for fishing. Input data included: habitat suitability maps for VME indicator taxa, and uncertainties associated with these model predictions, for an area of the high seas around New Zealand; naturalness condition, represented by two proxy variables using New Zealand trawl effort data; and value to the New Zealand fishery using trawl catch data for two gear types and three time-periods. Running scenario analyses with these data allowed for an understanding of the effect of varying the input data on the spatial prioritisation of areas for VME conservation. The analyses also allowed for the cost to fishing to be determined, in terms of the amount of the trawl catch footprint (normalised to the catch) lost if high priority areas for VME indicator taxa are protected. In most scenarios, the cost to fishing was low given the relatively high proportion of suitable habitat for VME indicator taxa that could be protected. The main outcome of the present study is a demonstration of the practical utility of using available data, including modelled data, and the Zonation decision-support tool to develop future options for the spatial management of the SPRFMO area. Suggestions are also made for improvements in input data for future analyses. Highlights • A decision-support tool was assessed for identifying spatial conservation priorities. • Spatial conservation prioritisation is sensitive to varying the input data. • Balancing the protection of VMEs and use of high value fishing areas is possible. • Cost to fishing was low given amount of VME habitat that could be protected. • Use of a decision-support tool can improve current spatial management measures. [ABSTRACT FROM AUTHOR]

Decision making for climate change management seldom incorporates uncertainty in the analysis that underpins the policy process. First, uncertainty is seldom characterised fully, and attempts to reduce uncertainty—when this is possible—are rare. Second, scientists are ill-equipped to communicate about uncertainty with policy makers, and policy makers most often favour pretended certainty over nuance and detail. Third, the uncertainty analysis that may have been conducted most often fails to actually influence policy in a significant manner. The case is made for (i) characterising and, to the extent possible, reducing uncertainty, (ii) communicating uncertainty, and (iii) reflecting uncertainty in the design of policy initiatives for climate change management. Possible elements for a research agenda on each of these areas are proposed. [ABSTRACT FROM AUTHOR]

Abstract Recreational fishing activity has recovered in the Nerbioi estuary (Northern Spain), after water sanitation and environmental improvement. Recreational fishing is important for the local population; therefore, future management measures that could cause changes in the estuary should also consider the impacts on recreational fishing. Our objective was to analyze the effects that future management decisions and unexpected environmental changes, alone or in combination with climate change effects, can produce in recreational fishing in Nerbioi. The current recreational fishing activity was modelled using a System Dynamics Modelling (SDM). Based on those results, seven future scenarios were simulated. Results suggested that the adoption of future management measures to improve the environmental conditions could lead to additional positive changes for recreational fishing, as after water quality improvement, fish stocks will continue to recover, and these better conditions could attract more fishers and increase their satisfaction. Simulation of temporary and unexpected environmental changes resulted in quick estuarine recovery, without dramatic consequences for recreational fishing. In conclusion, analysing future scenarios on cultural ecosystem services such as recreational fishing, using SDM, can produce valuable information for decision making processes, facilitating the selection between environmental management alternatives. Graphical abstract Image 1 Highlights • System Dynamics Modelling (SDM) used in 7 scenarios affecting recreational fishing. • Chronic pressure removal is more effective than acute events removal in recreation recovery. • SDM assists managers in social-ecological systems and ecosystem services recovery. [ABSTRACT FROM AUTHOR]

Invasive species are a major threat to the economy, the environment, health, and thus human well-being. The international community, including the United Nations’ Global Invasive Species Program (GISP), National Invasive Species Council (NISC), and Center for Invasive Species Management (CISM), has called for a rapid control of invaders in order to minimize their adverse impacts. The effective management of invasive species is a highly complex problem requiring the development of decision tools that help managers prioritize actions most efficiently by considering corresponding bio-economic costs, impacts on ecosystems, and benefits of control. Operations research methods, such as mathematical programming models, are powerful tools for evaluating different management strategies and providing optimal decisions for allocating limited resources to control invaders. In this paper, we summarize the mathematical models applied to optimize invasive species prevention, surveillance, and control. We first define key concepts in invasive species management (ISM) in a framework that characterizes biological invasions, associated economic and environmental costs, and their management. We then present a spatio-temporal optimization model that illustrates various biological and economic aspects of an ISM problem. Next, we classify the relevant literature with respect to modeling methods: optimal control, stochastic dynamic programming, linear programming, mixed-integer programming, simulation models, and others. We further classify the ISM models with respect to the solution method used, their focus and objectives, and the specific application considered. We discuss limitations of the existing research and provide several directions for further research in optimizing ISM planning. Our review highlights the fact that operations research could play a key role in ISM and environmental decision-making, in particular closing the gap between the decision-support needs of managers and the decision-making tools currently available to management. [ABSTRACT FROM AUTHOR]

Abstract Decision-support tools (DSTs) are often produced from collaborations between technical experts and stakeholders to address environmental problems and inform decision making. Studies in the past two decades have provided key insights on the use of DSTs and the importance of bidirectional information flows among technical experts and stakeholders – a process that is variously referred to as co-production, participatory modeling, structured decision making, or simply stakeholder participation. Many of these studies have elicited foundational insights for the broad field of water resources management; however, questions remain on approaches for balancing co-production with uncertainty specifically for watershed modeling decision support tools. In this paper, we outline a simple conceptual model that focuses on the DST development process. Then, using watershed modeling case studies found in the literature, we discuss successful outcomes and challenges associated with embedding various forms of co-production into each stage of the conceptual model. We also emphasize the "3 Cs" (i.e., characterization, calculation, communication) of uncertainty and provide evidence-based suggestions for their incorporation in the watershed modeling DST development process. We conclude by presenting a list of best practices derived from current literature for achieving effective and robust watershed modeling decision-support tools. Highlights • Technical experts and stakeholders oftentimes co-produce decision-support tools. • We discuss co-production and uncertainty in watershed modeling DSTs. • We present a list of best practices derived from watershed modeling case studies. [ABSTRACT FROM AUTHOR]

In weed science and management, models are important and can be used to better understand what has occurred in management scenarios, to predict what will happen and to evaluate the outcomes of control methods. To-date, perspectives on and the understanding of weed models have been disjointed, especially in terms of how they have been applied to advance weed science and management. This paper presents a general overview of the nature and application of a full range of simulation models on the ecology, biology, and management of arable weeds, and how they have been used to provide insights and directions for decision making when long-term weed population trajectories are impractical to be determined using field experimentation. While research on weed biology and ecology has gained momentum over the past four decades, especially for species with high risk for herbicide resistance evolution, knowledge gaps still exist for several life cycle parameters for many agriculturally important weed species. More research efforts should be invested in filling these knowledge gaps, which will lead to better models and ultimately better inform weed management decision making.

Decision-making in the context of urban sustainability is a challenging problem for policy-makers, given its complex nature, demanding the compromise between different and, often, conflicting social, economic and environmental criteria. This multi-factor problem needs to be addressed in a holistic way, on the basis of an integrated transport and urban planning approach, through ad hoc planning for each different area. Land use and mobility policies are characterized by a high level of interaction, a fact that needs to be taken into account in urban and transport planning within the framework of sustainability. Effective land use policies may have a positive impact on sustainable mobility and vice versa: effective mobility policies may have a positive impact on sustainable land use in urban areas. Aiming at developing and testing via a case study a decision-support tool for policy-makers and decision-analysts to promote sustainable urban and transport planning, the application of two hybrid multi-criteria analysis models is proposed in this research work, in order to rank land use and transportation policies in the framework of urban sustainability, especially in terms of public space. The center of Thessaloniki (Greece), an urban area suffering from sustainable mobility and public space problems, is selected as a case study. • Integrated transport and urban planning can promote sustainable development. • New tools are needed to address transport and land use interaction in the local context. • A tool based on MCA is developed to support policy making for sustainable mobility and public space. • The case of Thessaloniki, Greece, a car dominant city, shows the need for safe active transport in upgraded public spaces. [ABSTRACT FROM AUTHOR]

• Organization effect on safety and risk management. • Safety and Risk management in process industries and healthcare. • Human decision-making in safety critical operations. • Design of decision-support tools for effective human decision-making. • Process systems engineering applications in healthcare. Cyber-physical-human systems are ubiquitous entities with varied scales of function. Apart from the systems-level complexity, it also poses a variety of decision-making problems to designers and practitioners. This decision-making is done by humans-in-the-loop, at every level of cyber-physical-human systems, with the help of decision-support tools. For any safety and risk critical cyber-physical-human systems organization, certain key features are useful to assess and compare the organizational functioning. Two such examples are process and healthcare industries, with safety and risk as major objectives. The paper uses the lens of humans-in-the-loop decision-making and decision-support tools to survey how safety and risk are handled in process and healthcare industries. Further, a discussion of current practices in the two domains and how future applications will involve more interdisciplinary aspects is provided. The paper concludes by discussing the ethical and data related concerns of a humans-in-the-loop and algorithm based shared decision-making future. [ABSTRACT FROM AUTHOR]

We modeled and combined outputs for 125 tree species for the eastern United States, using habitat suitability and colonization potential models along with an evaluation of adaptation traits. These outputs allowed, for the first time, the compilation of tree species’ current and future potential for each unit of 55 national forests and grasslands and 469 1 × 1 degree grids across the eastern United States. A habitat suitability model, a migration simulation model, and an assessment based on biological and disturbance factors were used with United States Forest Service Forest Inventory and Analysis data to evaluate species potential to migrate or infill naturally into suitable habitats over the next 100 years. We describe a suite of variables, by species, for each unique geographic unit, packaged as summary tables describing current abundance, potential future change in suitable habitat, adaptability, and capability to cope with the changing climate, and colonization likelihood over 100 years. This resulting synthesis and summation effort, culminating over two decades of work, provides a detailed data set that incorporates habitat quality, land cover, and dispersal potential, spatially constrained, for nearly all the tree species of the eastern United States. These tables and maps provide an estimate of potential species trends out 100 years, intended to deliver managers and publics with practical tools to reduce the vast set of decisions before them as they proactively manage tree species in the face of climate change.

Decision-support tools are increasingly popular for informing policy decisions linked to environmental issues. For example, a number of decision-support tools on transport planning provide information on expected effects of different measures (actions, policies, or interventions) on air quality, often combined with information on noise pollution or mitigation costs. These tools range in complexity and scale of applicability, from city to international, and include one or several polluting sectors. However, evaluation of the need and utility of tools to support decisions on such linked issues is often lacking, especially for tools intended to support local authorities at the city scale. Here we assessed the need for and value of combining air pollution and climate change mitigation measures into one decision-support tool and the existing policy context in which such a tool might be used. We developed a prototype decision-support tool for evaluating measures for coordinated management of air quality and climate change; and administered a survey in which respondents used the prototype to answer questions about demand for such tools and requirements to make them useful. Additionally, the survey asked questions about participants’ awareness of linkages between air pollution and climate change that are crucial for considering synergies and trade-offs among mitigation measures. Participants showed a high understanding of the linkages between air pollution and climate change, especially recognizing that emissions of greenhouse gases and air pollutants come from the same source. Survey participants were: European, predominantly German; employed across a range of governmental, non-governmental and research organizations; and responsible for a diversity of issues, primarily involving climate change, air pollution or environment. Survey results showed a lack of awareness of decision-support tools and little implementation or regular use. However, respondents expressed a general need for such tools while also recognizing barriers to their implementation, such as limited legal support or lack of time, finances, or manpower. The main barrier identified through this study is the mismatch between detailed information needed from such tools to make them useful at the local implementation scale and the coarser scale information readily available for developing such tools. Significant research efforts at the local scale would be needed to populate decision-support tools with salient mitigation alternatives at the location of implementation. Although global- or regional-scale information can motivate local action towards sustainability, effective on-the-ground implementation of coordinated measures requires knowledge of local circumstances and impacts, calling for active engagement of the local research communities.