Modeling and Assessing the Sustainability of Dams in the United States

Dam decision-making is often controversial as a choice has to be made between the benefits provided by dams (e.g., recreation, water supply, hydropower) and their potential negative impacts (e.g., effects on natural flow regime, impediment for fish migration). However, our understandings of such tradeoffs under a full range of dam management alternatives remain limited which hinders our ability to make sound and scientifically defensible dam management decisions. The diverse stakeholders involved in the decision-making process with varying perspectives and preferences could further exacerbate the difficulty of decision-making. To advance our knowledge in sustainable dam decision-making, this dissertation developed modeling tools to evaluate dam decisions based on greenhouse gas (GHG) emissions, hydropower generation, sea-run fish population, and management cost from both spatial and temporal perspectives. The developed model was further applied in role-paly simulation workshops to investigate the potential differences between scientifically optimized decisions and the negotiated consensus. The results revealed that although most hydroelectric dams have comparable GHG emissions to other types of renewable energy (e.g., solar, wind energy), electricity produced from tropical reservoir-based dams could potentially have a higher emission rate than fossil-based electricity. It is possible to simultaneously optimize energy, fish, and cost outcomes through strategic dam management actions. Basin-scale management strategies may outperform individual dam management strategies because the former can provide a broader set of solutions for balancing complex tradeoffs than the latter. Furthermore, diversification of management options (e.g., combination of fishway installations, dam removals, and generation capacity) may have the highest potential in balancing fish-energy-cost tradeoffs. Finally, dam management negotiation is helpful in facilitating decisions with more balanced outcomes but not necessary reflect the environmentally optimal outcomes

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 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

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 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

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

Experimental investigations on drag-reduction characteristics of bionic surface with water-trapping microstructures of fish scales

Biological surfaces with unique wettability in nature have provided an enormous innovation for scientists and engineers. More specifically, materials possessing various wetting properties have drawn considerable attention owing to their promising application prospects. Recently, great efforts have been concentrated on the researches on wetting-induced drag-reduction materials inspired by biology because of their ability to save energy. In this work, the drag-reduction characteristics of the bionic surface with delicate water-trapping microstructures of fish Ctenopharyngodon idellus scales were explored by experimental method. Firstly, the resistance of smooth surface and bionic surface experimental sample at different speeds was carefully tested through the testing system for operation resistance. Then, the contact angle (CA) of fish scale surface was measured by means of the contact angle measuring instrument. It was discovered that the bionic surface created a rewarding drag-reduction effect at a low speed, and the drag-reduction rate significantly displayed a downward trend with the increase in flow speed. Thus, when the rate was 0.66 m/s, the drag-reduction effect was at the optimum level, and the maximum drag reduction rate was 2.805%, which was in concordance with the simulated one. Furthermore, a contact angle (CA) of 11.5° appeared on the fish scale surface, exhibiting fine hydrophilic property. It further manifested the spreading-wetting phenomenon and the higher surface energy for the area of apical of fish scales, which played an important role in drag-reduction performance. This work will have a great potential in the engineering and transportation field

Drag reduction mechanism of Paramisgurnus dabryanus loach with self-lubricating and flexible micro-morphology

Underwater machinery withstands great resistance in the water, which can result in consumption of a large amount of power. Inspired by the character that loach could move quickly in mud, the drag reduction mechanism of Paramisgurnus dabryanus loach is discussed in this paper. Subjected to the compression and scraping of water and sediments, a loach could not only secrete a lubricating mucus film, but also importantly, retain its mucus well from losing rapidly through its surface micro structure. In addition, it has been found that flexible deformations can maximize the drag reduction rate. This self-adaptation characteristic can keep the drag reduction rate always at high level in wider range of speeds. Therefore, even though the part of surface of underwater machinery cannot secrete mucus, it should be designed by imitating the bionic micro-morphology to absorb and store fluid, and eventually form a self-lubrication film to reduce the resistance. In the present study, the Paramisgurnus dabryanus loach is taken as the bionic prototype to learn how to avoid or slow down the mucus loss through its body surface. This combination of the flexible and micro morphology method provides a potential reference for drag reduction of underwater machinery

TS-GCN: A novel tumor segmentation method integrating transformer and GCN

As one of the critical branches of medical image processing, the task of segmentation of breast cancer tumors is of great importance for planning surgical interventions, radiotherapy and chemotherapy. Breast cancer tumor segmentation faces several challenges, including the inherent complexity and heterogeneity of breast tissue, the presence of various imaging artifacts and noise in medical images, low contrast between the tumor region and healthy tissue, and inconsistent size of the tumor region. Furthermore, the existing segmentation methods may not fully capture the rich spatial and contextual information in small-sized regions in breast images, leading to suboptimal performance. In this paper, we propose a novel breast tumor segmentation method, called the transformer and graph convolutional neural (TS-GCN) network, for medical imaging analysis. Specifically, we designed a feature aggregation network to fuse the features extracted from the transformer, GCN and convolutional neural network (CNN) networks. The CNN extract network is designed for the image's local deep feature, and the transformer and GCN networks can better capture the spatial and context dependencies among pixels in images. By leveraging the strengths of three feature extraction networks, our method achieved superior segmentation performance on the BUSI dataset and dataset B. The TS-GCN showed the best performance on several indexes, with Acc of 0.9373, Dice of 0.9058, IoU of 0.7634, F1 score of 0.9338, and AUC of 0.9692, which outperforms other state-of-the-art methods. The research of this segmentation method provides a promising future for medical image analysis and diagnosis of other diseases

Diagnostic accuracy of autoverification and guidance system for COVID-19 RT-PCR results

Background: To date, most countries worldwide have declared that the pandemic of COVID-19 is over, while the WHO has not officially ended the COVID-19 pandemic, and China still insists on the personalized dynamic COVID-free policy. Large-scale nucleic acid testing in Chinese communities and the manual interpretation for SARS-CoV-2 nucleic acid detection results pose a huge challenge for labour, quality and turnaround time (TAT) requirements. To solve this specific issue while increase the efficiency and accuracy of interpretation, we created an autoverification and guidance system (AGS) that can automatically interpret and report the COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR) results relaying on computer-based autoverification procedure and then validated its performance in real-world environments. This would be conductive to transmission risk prediction, COVID-19 prevention and control and timely medical treatment for positive patients in the context of the predictive, preventive and personalized medicine (PPPM). Methods: A diagnostic accuracy test was conducted with 380,693 participants from two COVID-19 test sites in China, the Hong Kong Hybribio Medical Laboratory (n = 266,035) and the mobile medical shelter at a Shanghai airport (n = 114,658). These participants underwent SARS-CoV-2 RT-PCR from March 28 to April 10, 2022. All RT-PCR results were interpreted by laboratorians and by using AGS simultaneously. Considering the manual interpretation as gold standard, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were applied to evaluate the diagnostic value of the AGS on the interpretation of RT-PCR results. Results: Among the 266,035 samples in Hong Kong, there were 16,356 (6.15%) positive, 231,073 (86.86%) negative, 18,606 (6.99%) indefinite, 231,073 (86.86%, negative) no retest required and 34,962 (13.14%, positive and indefinite) retest required; the 114,658 samples in Shanghai consisted of 76 (0.07%) positive, 109,956 (95.90%) negative, 4626 (4.03%) indefinite, 109,956 (95.90%, negative) no retest required and 4702 (4.10%, positive and indefinite) retest required. Compared to the fashioned manual interpretation, the AGS is a procedure of high accuracy [99.96% (95%CI, 99.95–99.97%) in Hong Kong and 100% (95%CI, 100–100%) in Shanghai] with perfect sensitivity [99.98% (95%CI, 99.97–99.98%) in Hong Kong and 100% (95%CI, 100–100%) in Shanghai], specificity [99.87% (95%CI, 99.82–99.90%) in Hong Kong and 100% (95%CI, 99.92–100%) in Shanghai], PPV [99.98% (95%CI, 99.97–99.99%) in Hong Kong and 100% (95%CI, 99.99–100%) in Shanghai] and NPV [99.85% (95%CI, 99.80–99.88%) in Hong Kong and 100% (95%CI, 99.90–100%) in Shanghai]. The need for manual interpretation of total samples was dramatically reduced from 100% to 13.1% and the interpretation time fell from 53 h to 26 min in Hong Kong; while the manual interpretation of total samples was decreased from 100% to 4.1% and the interpretation time dropped from 20 h to 16 min at Shanghai. Conclusions: The AGS is a procedure of high accuracy and significantly relieves both labour and time from the challenge of large-scale screening of SARS-CoV-2 using RT-PCR. It should be recommended as a powerful screening, diagnostic and predictive system for SARS-CoV-2 to contribute timely the ending of the COVID-19 pandemic following the concept of PPPM