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   https://doi.org/10.1016/j.jweia.2019.06.001  

   Helgedagsrud, Tore A.; Bazilevs, Yuri; Mathisen, Kjell M.; Øiseth, Ole A. (August 2019, Journal of Wind Engineering and Industrial Aerodynamics)
2. Local and Global Optimum Design in Aero-Structural Optimization of Long-Span Bridges Considering Flutter

   https://doi.org/10.2139/ssrn.5038974  

   Cid_Montoya, Miguel; Hernandez, Santiago; Jurado, José Ángel (December 2024, SSRN Electronic Journal)

   The aero-structural design of bridges is mainly controlled by the deck cross-section design. Design modifications on bridge decks impact the deck aerodynamics and the deck mechanical contribution, which also affect the bridge aeroelastic responses. The nonlinear inherent nature of bluff body aerodynamics combined with the nonlinearities of multimodal aeroelastic analyses result in complex relationships between the full bridge aeroelastic responses and deck shape design variables. This fact impacts the design process as it may lead to the development of complex feasible design regions in the chosen design domain, including disjoint feasible regions that may cause local minima. Given the limitations of metaheuristic optimization methods to deal with optimization problems with large sets of design variables, as required in holistic bridge design problems, gradient-based optimization algorithms can be recast to address global optimization problems. In this study, we propose the use of tunneling optimization methods to address this challenge. 

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3. Predictive processing models and affective neuroscience

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5. Circular and unified analysis in network neuroscience

   https://doi.org/10.7554/eLife.79559  

   Rubinov, Mika (November 2023, eLife)

   Genuinely new discovery transcends existing knowledge. Despite this, many analyses in systems neuroscience neglect to test new speculative hypotheses against benchmark empirical facts. Some of these analyses inadvertently use circular reasoning to present existing knowledge as new discovery. Here, I discuss that this problem can confound key results and estimate that it has affected more than three thousand studies in network neuroscience over the last decade. I suggest that future studies can reduce this problem by limiting the use of speculative evidence, integrating existing knowledge into benchmark models, and rigorously testing proposed discoveries against these models. I conclude with a summary of practical challenges and recommendations. 

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