Role-Play Simulations and System Dynamics for Sustainability Solutions around Dams in New England

Research has shown that much of the science produced does not make its way to the decision-making table. This leads to a gap between scientific and societal progress, which is problematic. This study tests a novel science-based negotiation simulation that integrates role-play simulations (RPSs) with a system dynamic model (SDM). In RPSs, stakeholders engage in a mock decision-making process (reflecting real-life institutional arrangements and scientific knowledge) for a set period. By playing an assigned role (different from the participant’s real-life role), participants have a safe space to learn about each other’s perspectives, develop shared understanding about a complex issue, and collaborate on solving that issue. System Dynamic Models (SDMs) are visual tools used to simulate the interactions and feedback with a complex system. We test the integration of the two approaches toward problem-solving with real stakeholders in New Hampshire and Rhode Island via a series of two consecutive workshops in each state. The workshops are intended to engage representatives from diverse groups who are interested in dam related issues to foster dialogue, learning, and creativity. Participants will discuss a hypothetical (yet realistic) dam-decision scenario to consider scientific information and explore dam management options that meet one another\u27s interests. In the first workshop participants will contribute to the design of the fictionalized dam decision scenario and the SDM, for which we have presented drafts based on a literature review, stakeholder interviews, and expert knowledge. In the second workshop, participants will assume another representative\u27s role and discuss dam management options for the fictionalized scenario. We will report results related to the effectiveness to which this new knowledge production process leads to more innovative and collaborative decision-making around New England dams

Acting out our dam future: science-based role-play simulations as mechanisms for learning and natural resource planning

Science often does not make its way into decisions, leading to a problematic gap between scientific and societal progress. To tackle this issue, our research tests a novel science-based negotiation simulation that integrates a role-play simulation (RPS) with a system dynamics model (SDM). In RPSs, stakeholders engage in a mock decision-making process (reflecting real-life institutional arrangements and scientific knowledge) for a set period. System dynamics models (SDMs) are visual tools used to simulate the interactions and feedback within a complex system. We test the integration of the two approaches with stakeholders in New England via a series of two consecutive workshops across two states. The workshops engage stakeholders from diverse groups to foster dialogue, learning, and creativity. Participants discuss a hypothetical (yet realistic) decision scenario to consider scientific information and explore dam management options that meet one another\u27s interests. In the first workshop, participants contributed to the design of the fictionalized dam decision scenario and the SDM. In the second workshop, participants assumed another representative\u27s role and discussed dam management options for the fictionalized scenario. This presentation will briefly report on the practical design of this science-based role-play, and particularly emphasize preliminary results of workshop outcomes, which were evaluated using debriefing sessions, surveys, concept mapping exercises, and interviews. Results will determine the extent to which this new knowledge production process leads to learning, use of science, and more collaborative decision-making about dams in New England and beyond

Pearl River Negotiation Simulation: Negotiating the Future of Dams

The role-play included in this packet is a facilitated, multi-issue negotiation simulation for eight or nine participants about the management of five dams in the hypothetical Pearl River basin. This role-play is meant to be used in conjunction with a system dynamics model, which simulates potential environmental and economic outcomes under different dam management alternatives in the Pearl River basin. The user interface for the system dynamics model can be accessed at: https://ddc.unh.edu/dam-system-dynamics/. The science-based role-play negotiation simulation provides opportunity for discussion of complex topics surrounding human-environment interactions, use of scientific data and modeling in environmental decision-making under uncertainty, and the mutual gains approach to negotiations over water resources. This PDF includes the following materials: (1) Teaching instructions, (2) Presentation slides, (3) Table place cards for each role, (4) General instructions for all players, which describe the setting of the Pearl River Basin, provide details on the status of the five dams in the basin, and outline the three decisions to be negotiated, and (5) Confidential instructions for the eight roles, which provide background information about each role, including about the role’s specific interests and constraints. A video introducing the role-play is available at: https://scholars.unh.edu/nh_epscor/3/. William Winslow of the UNH Data Discovery Center helped with developing the web-based user interface

Acting and Modeling the Future of Dams: Knowledge Production Processes in Sustainability Science

Sustainability scientists are developing new knowledge production processes (KPPs) based on findings that science has a greater impact on decision-making when it (1) adopts an interdisciplinary systems approach, and (2) is participatory and, therefore, perceived as more salient, legitimate, and credible by users. This presentation will discuss the findings from a review of the literature on the intersection of two KPP methods: systems dynamics (SD) and role-play simulations (RPS). SD is a powerful approach for modeling dynamic, complex systems to improve understanding of system behaviors in coupled social-ecological systems. It can capture complex biophysical phenomena and trade-offs, while also representing feedbacks and thresholds from social and institutional systems. It incorporates both qualitative and quantitative information. Unlike static models, SD is explicitly dynamic. It is well suited to group modeling efforts and informing consensus-based decisions. RPSs are experiential, scenario-based tools that help participants learn about how science is used in policy-making decisions, learn about others\u27 preferences and priorities regarding a public policy decision, develop and evaluate innovative options for addressing critical challenges, and contribute to building consensus among diverse and interdependent stakeholders. Although both approaches aim to improve the basis for decision-making, they are rarely discussed together. This presentation considers the literature on each method and their intersection by analyzing: (1) each method\u27s objectives and functions, (2) the steps in their processes for incorporating participation and interdisciplinary, systems-based knowledge, (3) approaches for evaluating outcomes, (4) strengths and weaknesses, (5) opportunities and challenges for integrations, and identifies recommendations for future research. A version of the presentation with an attached transcript can be found here

Can science-informed, consensus-based stakeholder negotiations achieve optimal dam decision outcomes?

Integrating science and decision-making in dam management is needed to address complex tradeoffs among environmental, economic, and social outcomes across varied geographic scales and diverse stakeholder interests. In this study, we introduce an approach that integrates system dynamics modeling (SDM) and role-play simulation (RPS) to facilitate use of the best available knowledge in dam decision-making. Using a hypothetical dam decision context in the New England region of the United States, this research investigates: (1) How do science-informed, negotiated outcomes compare to Pareto-optimal outcomes produced by a scientific model that balance selected system performance tradeoffs?; and (2) How do science-informed, negotiated outcomes compare to the status quo outcome? To our knowledge, this research is the first effort to combine SDM and RPS to support dam decisions and compare science-informed, consensus-based outcomes and optimized system outcomes. Our analyses show Pareto-optimal solutions usually involve a multi-dam management approach with diversified management options. Although all negotiated outcomes produced a net loss compared with at least one of the Pareto-optimal solutions balanced across tradeoffs, some yielded benefits close to or better than specific Pareto-optimal solutions. All negotiated outcomes yielded improvements over the status quo outcome. Our findings highlight the potential for science-informed, stakeholder-engaged approaches to inform decision-making and improve environmental and economic outcomes

SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting

RNN-based methods have faced challenges in the Long-term Time Series Forecasting (LTSF) domain when dealing with excessively long look-back windows and forecast horizons. Consequently, the dominance in this domain has shifted towards Transformer, MLP, and CNN approaches. The substantial number of recurrent iterations are the fundamental reasons behind the limitations of RNNs in LTSF. To address these issues, we propose two novel strategies to reduce the number of iterations in RNNs for LTSF tasks: Segment-wise Iterations and Parallel Multi-step Forecasting (PMF). RNNs that combine these strategies, namely SegRNN, significantly reduce the required recurrent iterations for LTSF, resulting in notable improvements in forecast accuracy and inference speed. Extensive experiments demonstrate that SegRNN not only outperforms SOTA Transformer-based models but also reduces runtime and memory usage by more than 78%. These achievements provide strong evidence that RNNs continue to excel in LTSF tasks and encourage further exploration of this domain with more RNN-based approaches. The source code is coming soon

Exploring TSPAN4 promoter methylation as a diagnostic biomarker for tuberculosis

Background:Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), is a persistent infectious disease threatening human health. The existing diagnostic methods still have significant shortcomings, including a low positivity rate in pathogen-based diagnoses and the inability of immunological diagnostics to detect active TB. Hence, it is urgent to develop new techniques to detect TB more accurate and earlier. This research aims to scrutinize and authenticate DNA methylation markers suitable for tuberculosis diagnosis. Concurrently, Providing a new approach for tuberculosis diagnosis.Methods:Blood samples from patients with newly diagnosed tuberculosis and healthy controls (HC) were utilized in this study. Examining methylation microarray data from 40 whole blood samples (22TB + 18HC), we employed two procedures: signature gene methylated position analysis and signature region methylated position analysis to pinpoint distinctive methylated positions. Based on the screening results, diagnostic classifiers are constructed through machine learning, and validation was conducted through pyrosequencing in a separate queue (22TB + 18HC). Culminating in the development of a new tuberculosis diagnostic method via quantitative real-time methylation specific PCR (qMSP).Results:The combination of the two procedures revealed a total of 10 methylated positions, all of which were located in the promoter region. These 10 signature methylated positions facilitated the construction of a diagnostic classifier, exhibiting robust diagnostic accuracy in both cross-validation and external test sets. The LDA model demonstrated the best classification performance, achieving an AUC of 0.83, specificity of 0.8, and sensitivity of 0.86 on the external test set. Furthermore, the validation of signature methylated positions through pyrosequencing demonstrated high agreement with screening outcomes. Additionally, qMSP detection of 2 potential hypomethylated positions (cg04552852 and cg12464638) exhibited promising results, yielding an AUC of 0.794, specificity of 0.720, and sensitivity of 0.816.Conclusion:Our study demonstrates that the validated signature methylated positions through pyrosequencing emerge as plausible biomarkers for tuberculosis diagnosis. The specific methylation markers in the TSPAN4 gene, identified in whole blood samples, hold promise for improving tuberculosis diagnosis. This approach could significantly enhance diagnostic accuracy and speed, offering a new avenue for early detection and treatment

Tautomerism unveils a self-inhibition mechanism of crystallization

Modifiers are commonly used in natural, biological, and synthetic crystallization to tailor the growth of diverse materials. Here, we identify tautomers as a new class of modifiers where the dynamic interconversion between solute and its corresponding tautomer(s) produces native crystal growth inhibitors. The macroscopic and microscopic effects imposed by inhibitor-crystal interactions reveal dual mechanisms of inhibition where tautomer occlusion within crystals that leads to natural bending, tunes elastic modulus, and selectively alters the rate of crystal dissolution. Our study focuses on ammonium urate crystallization and shows that the keto-enol form of urate, which exists as a minor tautomer, is a potent inhibitor that nearly suppresses crystal growth at select solution alkalinity and supersaturation. The generalizability of this phenomenon is demonstrated for two additional tautomers with relevance to biological systems and pharmaceuticals. These findings offer potential routes in crystal engineering to strategically control the mechanical or physicochemical properties of tautomeric materials

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat