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Data from repeated measurements of predawn and midday water potentials on Quercus agrifolia and Quercus douglassi trees at Sedgwick Reserve, CA, USA from 2022 - 2024. The data includes the following columns: Column name Description individual_id Unique numeric ID for individual tree site Name of site location, represents a spatially distinct group of trees species Quercus agrifolia and Quercus douglassii date date of data collection, in YYYYMMDD pd_md Indicates whether measurements were taken at predawn (pd, 1-3 hours before sunrise) or midday (md, within 1.5 hours of solar noon) water_potential_mean Mean water potential measurements for each tree/date/time (MPa). water_potential_sd Standard deviation of water potential measurements for each tree/date/time (MPa) water_potential_n Number of water potential measurements for each tree/date/time latitude Location of individual tree, latitude in decimal degrees longitude Location of individual tree, longitude in decimal degrees coord_system EPSG:4326-WGS 84 For details on collection methods, see: Boving I, Allen J, Brodrick PG, Chadwick KD, Trugman A, Anderegg LDL. The Unstable Relationship Between Drought Status and Leaf Water Content Complicates the Remote Sensing of Tree Drought Stress. Glob Chang Biol. 2025 Apr;31(4):e70188. doi: 10.1111/gcb.70188. PMID: 40249004; PMCID: PMC12007071. 

Additive manufacturing creates parts by depositing a preform, typically layer by layer. Subtractive manufacturing involves removing material from a preform to create parts. Hybrid machine tools combine both additive and subtractive processes in the same workspace. They can be used to create parts that meet functional tolerance and surface finish requirements, or to create features that are difficult to produce using additive or subtractive processes alone. This paper describes hybrid metal additive/subtractive machine tools. It covers design considerations, sensors and controls, process management, programming and software, and the impact on the design space. It also identifies future research challenges. 

Recent experimental and computational investigations have shown that trace amounts of surfactants, unavoidable in practice, can critically impair the drag reduction of superhydrophobic surfaces (SHSs), by inducing Marangoni stresses at the air–liquid interface. However, predictive models for realistic SHS geometries do not yet exist, which has limited the understanding and mitigation of these adverse surfactant effects. To address this issue, we derive a model for laminar, three-dimensional flow over SHS gratings as a function of geometry and soluble surfactant properties, which together encompass 10 dimensionless groups. We establish that the grating lengthgis the key geometric parameter and predict that the ratio between actual and surfactant-free slip increases withg². Guided by our model, we perform synergistic numerical simulations and microfluidic experiments, finding good agreement with the theory as we vary surfactant type and SHS geometry. Our model also enables the estimation, based on velocity measurements, of a priori unknown properties of surfactants inherently present in microfluidic systems. For SHSs, we show that surfactant effects can be predicted by a single parameter, representing the ratio between the grating length and the interface length scale beyond which the flow mobilizes the air–water interface. This mobilization length is more sensitive to the surfactant chemistry than to its concentration, such that even trace-level contaminants may significantly increase drag if they are highly surface active. These findings advance the fundamental understanding of realistic interfacial flows and provide practical strategies to maximize superhydrophobic drag reduction.