Bubble formation and growth - Study of the boundary conditions at a liquid-vapor interface through irreversible thermodynamics Quarterly progress report, Mar. - May 1966

Nonequilibrium effect on vapor bubble growth determined in study of boundary conditions at liquid-vapor interfac

Editorial for special issue “Geochemistry and mineralogy of hydrothermal metallic mineral deposits”

The Special Issue of Minerals on Geochemistry and Mineralogy of Hydrothermal Metallic Mineral Deposits presents the results of diverse geochemical and mineralogical research from across the globe. It is aimed to demonstrate that geochemical and mineralogical variation, both within and among hydrothermal ore deposits can be applied to genetic models, to exploration and drilling programs, and more. The eight contributions reflect a wide range of deposits, as well as different types of geochemical and mineralogical research applied to them. While most of these studies are focused on gaining a better understanding of deposit genesis, the results have a far greater application, as highlighted below

Bubble formation and growth - Study of the boundary conditions at a liquid-vapor interface through irreversible thermodynamics Quarterly progress report, Jun. - Aug. 1966

Steady state mercury evaporation experiment to measure transport coefficient in liquid-vapor interface boundary condition stud

Sources of Hydrothermal Fluids Inferred from Oxygen and Carbon Isotope Composition of Calcite, Keweenaw Peninsula Native Copper District, Michigan, USA

The Mesoproterozoic North American Midcontinent Rift hosts the world’s largest accu-mulation of native copper in Michigan’s Keweenaw Peninsula. During a regional metamorpho-genic‐hydrothermal event, native copper was deposited along with spatially zoned main‐stage minerals in a thermal high. This was followed by deposition of late‐stage minerals including minor copper sulfide. Inferences from the oxygen and carbon isotopic composition of main‐stage hydrothermal fluids, as calculated from 296 new and compiled isotopic measurements on calcite, are consistent with existing models that low‐sulfur saline native copper ore‐forming fluids were domi-nantly derived by burial metamorphic processes from the very low sulfur basalt‐dominated rift fill at depth below the native copper deposits. Co‐variation of oxygen and carbon isotopic compositions are consistent with mixing of metamorphic‐derived fluids with two additional isotopically different fluids. One of these is proposed to be evolved seawater that provided an outside source of salinity. This fluid mixed at depth and participated in the formation of a well‐mixed hybrid metamorphic-dominated ore‐forming fluid. Secondary Ion Mass Spectrometry in‐situ isotopic analyses of calcite demonstrate a high degree of variability within samples that is attributed to variable degrees of shallow mixing of the hybrid ore‐forming fluid with sulfur‐poor, reduced evolved meteoric water in the zone of precipitation. The oxygen and carbon isotopic compositions of 100 new and compiled measurements on late‐stage calcite are mostly isotopically different than the main‐stage hydrothermal fluids. The late‐stage hydrothermal fluids are interpreted as various proportions of mixing of evolved meteoric water, main‐stage hybrid ore‐forming fluid, and shallow, evolved seawater in the relatively shallow zone of precipitation

End-to-end deep learning for directly estimating grape yield from ground-based imagery

Yield estimation is a powerful tool in vineyard management, as it allows growers to fine-tune practices to optimize yield and quality. However, yield estimation is currently performed using manual sampling, which is time-consuming and imprecise. This study demonstrates the application of proximal imaging combined with deep learning for yield estimation in vineyards. Continuous data collection using a vehicle-mounted sensing kit combined with collection of ground truth yield data at harvest using a commercial yield monitor allowed for the generation of a large dataset of 23,581 yield points and 107,933 images. Moreover, this study was conducted in a mechanically managed commercial vineyard, representing a challenging environment for image analysis but a common set of conditions in the California Central Valley. Three model architectures were tested: object detection, CNN regression, and transformer models. The object detection model was trained on hand-labeled images to localize grape bunches, and either bunch count or pixel area was summed to correlate with grape yield. Conversely, regression models were trained end-to-end to predict grape yield from image data without the need for hand labeling. Results demonstrated that both a transformer as well as the object detection model with pixel area processing performed comparably, with a mean absolute percent error of 18% and 18.5%, respectively on a representative holdout dataset. Saliency mapping was used to demonstrate the attention of the CNN model was localized near the predicted location of grape bunches, as well as on the top of the grapevine canopy. Overall, the study showed the applicability of proximal imaging and deep learning for prediction of grapevine yield on a large scale. Additionally, the end-to-end modeling approach was able to perform comparably to the object detection approach while eliminating the need for hand-labeling

Effects of thermal fluctuation and the receptor-receptor interaction in bacterial chemotactic signalling and adaptation

Bacterial chemotaxis is controlled by the conformational changes of the receptors, in response to the change of the ambient chemical concentration. In a statistical mechanical approach, the signalling due to the conformational changes is a thermodynamic average quantity, dependent on the temperature and the total energy of the system, including both ligand-receptor interaction and receptor-receptor interaction. This physical theory suggests to biology a new understanding of cooperation in ligand binding and receptor signalling problems. How much experimental support of this approach can be obtained from the currently available data? What are the parameter values? What is the practical information for experiments? Here we make comparisons between the theory and recent experimental results. Although currently comparisons can only be semi-quantitative or qualitative, consistency is clearly shown. The theory also helps to sort a variety of data.Comment: 26 pages, revtex. Journal version. Analysis on another set of data on adaptation time is adde