Addressing climate change depends on large-scale system changes, which require public advocacy. Here, we identified and tested 17 expert-crowdsourced theory-informed behavioral interventions designed to promote public, political, and financial advocacy in a large quota-matched sample of US residents (n = 31,324). The most consistently effective intervention emphasized both the collective efficacy and emotional benefits of climate action, increasing advocacy by up to 10 percentage points. This was also the top intervention among participants identifying as Democrats. Appealing to binding moral foundations, such as purity and sanctity, was also among the most effective interventions, showing positive effects even among participants identifying as Republicans. These findings provide critical insights to policymakers and practitioners aiming to galvanize the public behind collective action and advocacy on climate change with affordable and scalable interventions.
The credibility revolution in psychology and related sciences contributed to the adoption of large-scale research initiatives known as Big Team Science (BTS). BTS has made significant advances in addressing issues of replication, statistical power, and diversity through the use of larger samples and more representative cross-cultural data. However, while these collaborations hold great potential, they also introduce unique challenges related to their scale. Drawing on experiences from successful BTS projects, we identified and outlined key strategies for overcoming diversity, volunteering, and capacity challenges. We emphasize the need for clear role definitions, structured and preregistered workflows, centralized project management, and transparent decision documenting to prevent common pitfalls. Ultimately, we call for reflection on the strengths and limitations of BTS to enhance the quality, generalizability, and impact of research across disciplines. This work complements existing BTS guides by offering experientially-grounded, discipline-specific strategies and addressing underexplored logistical, ethical, and epistemological challenges in large-scale collaborations.
Determination of expansion terms for the displacement field discretization in energy methods, ensuring the satisfaction of all natural boundary conditions of the system, remains a significant challenge for researchers in nonlinear mechanics. This study initially outlines modeling techniques for closed circular cylindrical shells, then presents the discretization approach of the displacement field to satisfy the boundary conditions, and gives a criterion for its completeness. An energy extremization method is employed to derive the system's equations of motion. The study facilitates nonlinear dynamic analysis of shells with different boundary conditions and expansion terms, and the method is validated through commercial finite element simulations, assessing the adequacy of the expansion in satisfying natural boundary conditions. It was found that for certain boundary conditions, such as movable simply supported and also cantilevered shells, both the corresponding beam eigenfunction and its first spatial derivative must be incorporated into the displacement expansion. Conversely, for some other cases, such as immovable simply supported ends, the inclusion of terms with the first derivative of the eigenfunction is unnecessary, and the natural boundary conditions are inherently satisfied through the variational approach.
We performed an integrated investigation of normal faults in the Brushy Canyon Formation near the eastern edge of Cenozoic Basin and Range/Rio Grande Rift deformation to understand the origin, timing, and fluid history of normal faulting in the western Delaware Basin. Two roadcut exposures in the southern Guadalupe Mountains along the eastern edge of the Salt Flat Graben reveal (i) steep normal faults in subhorizontal layers, and (ii) tilted layering and antithetic normal faults in a hanging-wall rollover of the asymmetrical graben. Normal fault orientations match the orientations of abundant opening-mode fractures in well-cemented turbidite sandstone beds. In horizontal layers, opening-mode fractures and faults are near-vertical, and where bedding is tilted 20-30 degrees, fractures and faults remain bed-perpendicular. We interpret that progressive rotation of vertical opening-mode fractures during layer tilting increased slip tendency driving shear displacement. Faults with 1-50 cm displacements in sandstone abruptly terminate in claystone beds (similar to 60-95 wt % clay) due to ductile flowage, compartmentalizing deformation. Minor normal displacement occurred along weak laminae in sandstone beds, offsetting bed-perpendicular calcite-filled opening-mode fractures. U-Pb dating of calcite from normal faults and opening-mode fractures yield ages of 20.5-4.0 Ma, consistent with regional timing for Basin and Range extension. Oil inclusion fluorescence in fault and opening-mode-fracture hosted calcite veins indicate similar to 29 to 32 API gravity oil. Very low delta C-13 and delta O-18 stable isotope values are consistent with hydrocarbon oxidation. These results document progressive structural evolution and fluid movement in the Delaware Basin, providing context for hydrocarbon migration and fault reactivation by hydraulic fracturing, hydrocarbon production, and wastewater injection.
Unmanned aerial vehicles (UAVs) have seen breakthroughs in forming Aerial Edge Computing (AEC), which executes computationally intensive tasks generated by Internet of Things (IoT) devices, thanks to their ease of deployment, especially in scenarios where traditional terrestrial base stations are damaged and unable to process tasks due to natural disasters. However, an AEC faces significant challenges due to the limited battery capacity of UAVs and the need for efficient collaboration among them to execute tasks. Existing studies often overlook fine-grained task prioritization and balanced load distribution across UAVs, leading to inefficiencies in energy usage and service delay. In this paper, we have developed an optimization framework for efficiently offloading computationally intensive IoT tasks in a three-stage Digital Twin-enabled multi-UAV-based AEC network environment, which jointly minimizes service latency and energy consumption while ensuring the expected load distribution among the UAVs. The formulated framework is a Mixed-Integer Nonlinear Programming (MINLP) problem, which is inherently NP-hard. To address this, we design GLEMATO, a scalable GTrXL-assisted MADDPG framework that learns high-quality offloading policies through memory-aware task prioritization and cooperative multi-agent decision-making in dynamic AEC scenarios. In GLEMATO, while the GTrXL model ensures adaptive task prioritization by considering factors such as task generation time, energy budget, and application deadlines, while the MADDPG enables decentralized policy learning through sharing cooperative state-actions among UAVs. The experimental results, carried out on the OpenAI Gym simulator platform, demonstrate that the developed GLEMATO framework reduces average energy consumption and service latency by 21.8% and 23.3%, respectively, and increases the average task completion ratio by up to 20.1% for computationally intensive tasks compared to the state-of-the-art approaches.
