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Trust is crucial for ensuring the safety, security, and widespread adoption of automated vehicles (AVs), and if trust is lacking, drivers and the general public may hesitate to embrace this technology. This research seeks to investigate contextualized trust profiles in order to create personalized experiences for drivers in AVs with varying levels of reliability. A driving simulator experiment involving 70 participants revealed three distinct contextualized trust profiles (i.e., confident copilots, myopic pragmatists, and reluctant automators) identified through K-means clustering, and analyzed in relation to drivers' dynamic trust, dispositional trust, initial learned trust, personality traits, and emotions. The experiment encompassed eight scenarios where participants were requested to take over control from the AV in three conditions: a control condition, a false alarm condition, and a miss condition. To validate the models, a multinomial logistic regression model was constructed using the shapley additive explanations explainer to determine the most influential features in predicting contextualized trust profiles, achieving an F1-score of 0.90 and an accuracy of 0.89. In addition, an examination of how individual factors impact contextualized trust profiles provided valuable insights into trust dynamics from a user-centric perspective. The outcomes of this research hold significant implications for the development of personalized in-vehicle trust monitoring and calibration systems to modulate drivers' trust levels, thereby enhancing safety and user experience in automated driving. 

Rivers regulate land-ocean total phosphorus (TP) fluxes critical to ecosystem health and food security, yet global dynamics remain poorly understood due to limited observations. Here, we develop a machine learning framework integrating multimodal data and 280,000 TP measurements to reconstruct TP flux patterns over 1980–2019 across 420 major rivers. Results reveal a deceptive global equilibrium. While TP flux declines in the Northern Hemisphere, driven by dam trapping, particularly in Western Europe (−16.2%) and Eastern Asia (−8.7%), it rises in the Southern Hemisphere due to increased fertilizer use and deforestation, especially in Southern Africa (+15%) and the Malay Archipelago (+20.3%). Notably, the number of small rivers with rising TP flux is nearly double that of large rivers. This growing TP export from small rivers and Southern Hemisphere basins may intensify eutrophication, expand hypoxic zones, and threaten fishery yields. Our findings highlight a shifting global phosphorus landscape and underscore the need for more targeted, sustainable phosphorus management strategies. 

Trust calibration poses a significant challenge in the interaction between drivers and automated vehicles (AVs) in the context of human-automation collaboration. To effectively calibrate trust, it becomes crucial to accurately measure drivers’ trust levels in real time, allowing for timely interventions or adjustments in the automated driving. One viable approach involves employing machine learning models and physiological measures to model the dynamic changes in trust. This study introduces a technique that leverages machine learning models to predict drivers’ real-time dynamic trust in conditional AVs using physiological measurements. We conducted the study in a driving simulator where participants were requested to take over control from automated driving in three conditions that included a control condition, a false alarm condition, and a miss condition. Each condition had eight takeover requests (TORs) in different scenarios. Drivers’ physiological measures were recorded during the experiment, including galvanic skin response (GSR), heart rate (HR) indices, and eye-tracking metrics. Using five machine learning models, we found that eXtreme Gradient Boosting (XGBoost) performed the best and was able to predict drivers’ trust in real time with an f1-score of 89.1% compared to a baseline model of K -nearest neighbor classifier of 84.5%. Our findings provide good implications on how to design an in-vehicle trust monitoring system to calibrate drivers’ trust to facilitate interaction between the driver and the AV in real time. 

Abstract Nitrogen (N) deposition is a significant nutrient input to cropland and consequently important for the evaluation of N budgets and N use efficiency (NUE) at different scales and over time. However, the spatiotemporal coverage of N deposition measurements is limited globally, whereas modeled N deposition values carry uncertainties. Here, we reviewed existing methods and related data sources for quantifying N deposition inputs to crop production on a national scale. We utilized different data sources to estimate N deposition input to crop production at national scale and compared our estimates with 14 N budget datasets, as well as measured N deposition data from observation networks in 9 countries. We created four datasets of N deposition inputs on cropland during 1961–2020 for 236 countries. These products showed good agreement for the majority of countries and can be used in the modeling and assessment of NUE at national and global scales. One of the datasets is recommended for general use in regional to global N budget and NUE estimates. 

Nanoscale industrial robots have potential as manufacturing platforms and are capable of automatically performing repetitive tasks to handle and produce nanomaterials with consistent precision and accuracy. We demonstrate a DNA industrial nanorobot that fabricates a three-dimensional (3D), optically active chiral structure from optically inactive parts. By making use of externally controlled temperature and ultraviolet (UV) light, our programmable robot, ~100 nanometers in size, grabs different parts, positions and aligns them so that they can be welded, releases the construct, and returns to its original configuration ready for its next operation. Our robot can also self-replicate its 3D structure and functions, surpassing single-step templating (restricted to two dimensions) by using folding to access the third dimension and more degrees of freedom. Our introduction of multiple-axis precise folding and positioning as a tool/technology for nanomanufacturing will open the door to more complex and useful nano- and microdevices.