Evidence for suppressed mid-Holocene northeastern Australian monsoon variability from coral luminescence

Summer monsoon rainfall in northeastern (NE) Australia exhibits substantial interannual variability resulting in highly variable river flows. The occurrence and magnitude of these seasonal river flows are reliably recorded in modern inshore corals as luminescent lines. Here we present reconstructed annual river flows for two ~120 year mid-Holocene windows based on luminescence measurements from five cores obtained from three separate coral colonies. We were able to cross-date the luminescence signatures in four cores from two of the colonies, providing confidence in the derived reconstruction. Present-day NE Australian rainfall and river flow are sensitive to El Niño–Southern Oscillation (ENSO) variability, with La Niña (El Niño) events typically associated with wetter (drier) monsoon seasons. Thus, our replicated and annually resolved coral records provide valuable insights into the northern Australian summer monsoon and ENSO variability at a key period (6 ka) when greenhouse gas levels and ice sheet cover were comparable to the preindustrial period but orbital forcing was different. Average modern and mid-Holocene growth characteristics were very similar, suggesting that sea surface temperatures off NE Australia at 6 kyr were also close to present values. The reconstructed river flow record suggests, however, that the mid-Holocene Australian summer monsoon was weaker, less variable from year to year (possibly indicative of reduced ENSO variability), and characterized by more within-season flood pulses than present. In contrast to today, the delivery of moisture appears to have been dominated by eastward propagating convective coupled waves associated with the Madden-Julian Oscillation

NASA Products to Enhance Energy Utility Load Forecasting

Existing energy load forecasting tools rely upon historical load and forecasted weather to predict load within energy company service areas. The shortcomings of load forecasts are often the result of weather forecasts that are not at a fine enough spatial or temporal resolution to capture local-scale weather events. This project aims to improve the performance of load forecasting tools through the integration of high-resolution, weather-related NASA Earth Science Data, such as temperature, relative humidity, and wind speed. Three companies are participating in operational testing one natural gas company, and two electric providers. Operational results comparing load forecasts with and without NASA weather forecasts have been generated since March 2010. We have worked with end users at the three companies to refine selection of weather forecast information and optimize load forecast model performance. The project will conclude in 2012 with transitioning documented improvements from the inclusion of NASA forecasts for sustained use by energy utilities nationwide in a variety of load forecasting tools. In addition, Battelle has consulted with energy companies nationwide to document their information needs for long-term planning, in light of climate change and regulatory impacts

Zonation investigation Strawberry Hill intertidal region

Keywords: Zoo451; Strawberry Hill; Zonation; Distribution; Postelsia palmaeformis; Verongia thiona; Lissodendoryx noxiosa; Halichondria panacea; Oplitospongia pennata; Flustrella cervicus; Allopora porphyra; Pisaster ochraceous; Tonicella lineata; Pachycheles rudis; Balanus Pachycheles; Ophlitaspongia pennata; Flustrella cervicis; Pisaster ochraceu

Predicting Defects in Laser Powder Bed Fusion using In-Situ Thermal Imaging Data and Machine Learning

Variation in the local thermal history during the Laser Powder Bed Fusion (LPBF) process in Additive Manufacturing (AM) can cause micropore defects, which add to the uncertainty of the mechanical properties (e.g., fatigue life, tensile strength) of the built materials. In-situ sensing has been proposed for monitoring the AM process to minimize defects, but successful minimization requires establishing a quantitative relationship between the sensing data and the porosity, which is particularly challenging with a large number of variables (e.g., laser speed, power, scan path, powder property). Physics-based modeling can simulate such an in-situ sensing-porosity relationship, but it is computationally costly. In this work, we develop Machine Learning (ML) models that can use in-situ thermographic data to predict the micropore of LPBF stainless steel materials. This work considers two identified key features from the thermal histories: the time above the apparent melting threshold (τ) and the maximum radiance (Tmax). These features are computed, stored for each voxel in the built material, and then used as inputs. The binary state of each voxel, either defective or normal, is the output. Different ML models are trained and tested for the binary classification task. In addition to using the thermal features of each voxel to predict its own state, the thermal features of neighboring voxels are also included as inputs. This is shown to improve the prediction accuracy, which is consistent with thermal transport physics around each voxel contributing to its final state. Among the models trained, the F1 scores on test sets reach above 0.96 for Random Forests. Feature importance analysis based on the ML models shows that Tmax is more important to the voxel state than τ. The analysis also finds that the thermal history of the voxels above the present voxel is more influential than those beneath it. Our study significantly extends the capability of using in-situ thermographic data to predict porosity in LPBF materials. Since ML models are fast, they may play integral roles in the optimization and control of such AM technologies

An economic survey of New Zealand wheatgrowers : survey no. 2

This is survey number 2. No. 1 was know as "National wheatgrowers' survey"This Report is the second in an annual series of economic surveys of New Zealand wheatgrowing farms. These surveys have been undertaken by the Agricultural Economics Research Unit at Lincoln College on behalf of Wheat Growers Sub-Section of Federated Farmers of New Zealand Inc. Specific attention has been focused on the physical characteristics of wheatgrowing farms, the area of wheat and other crops sown, wheat yields, cultural practices and costs and returns for the 1977/78 wheat crop. An attempt has also been made to allocate plant and machinery overhead costs to the wheat enterprise on both an historical and current cost basis. The need for current and detailed information from the Survey involved two visits to the farms in the sample; one in the spring following drilling and the second in the autumn after harvest

Local Prediction of Laser Powder Bed Fusion Porosity by Short-Wave Infrared Imaging Thermal Feature Porosity Probability Maps

Local thermal history can significantly vary in parts during metal Additive Manufacturing (AM), leading to local defects. However, the sequential layer-by-layer nature of AM facilitates in-situ part voxelmetric observations that can be used to detect and correct these defects for part qualification and quality control. The challenge is to relate this local radiometric data with local defect information to estimate process error likelihood in future builds. This paper uses a Short-Wave Infrared (SWIR) camera to record the temperature history for parts manufactured with Laser Powder Bed Fusion (LPBF) processes. The porosity from a cylindrical specimen is measured by ex-situ micro-computed tomography (μCT). Specimen data from the SWIR camera, combined with the μCT data, are used to generate thermal feature-based porosity probability maps. The porosity predictions made by various SWIR thermal feature-porosity probability maps of a specimen with a complex geometry are scored against the true porosity obtained via μCT. The receiver operating characteristic curves constructed from the predictions for the complex sample demonstrate the porosity probability mapping methodology\u27s potential for in-situ based porosity detection

Frequency Domain Measurements of Melt Pool Recoil Force using Modal Analysis

Recoil pressure is a critical factor affecting the melt pool dynamics during Laser Powder Bed Fusion (LPBF) processes. Recoil pressure depresses the melt pool. When the recoil pressure is low, thermal conduction and capillary forces may be inadequate to provide proper fusion between layers. However, excessive recoil pressure can produce a keyhole inside the melt pool, which is associated with gas porosity. Direct recoil pressure measurements are challenging because it is localized over an area proportionate to the laser spot size producing a force in the mN range. This paper reports a vibration-based approach to quantify the recoil force exerted on a part in a commercial LPBF machine. The measured recoil force is consistent with estimates from high speed synchrotron imaging of entrained particles, and the results show that the recoil force scales with applied laser power and is inversely related to the laser scan speed. These results facilitate further studies of melt pool dynamics and have the potential to aid process development for new materials

Health trajectories reveal the dynamic contributions of host genetic resistance and tolerance to infection outcome

Resistance and tolerance are two alternative strategies hosts can adopt to survive infections. Both strategies may be genetically controlled. To date, the relative contribution of resistance and tolerance to infection outcome is poorly understood. Here, we use a bioluminescent Listeria monocytogenes (Lm) infection challenge model to study the genetic determination and dynamic contributions of host resistance and tolerance to listeriosis in four genetically diverse mouse strains. Using conventional statistical analyses, we detect significant genetic variation in both resistance and tolerance, but cannot capture the time-dependent relative importance of either host strategy. We overcome these limitations through the development of novel statistical tools to analyse individual infection trajectories portraying simultaneous changes in infection severity and health. Based on these tools, early expression of resistance followed by expression of tolerance emerge as important hallmarks for surviving Lm infections. Our trajectory analysis further reveals that survivors and non-survivors follow distinct infection paths (which are also genetically determined) and provides new survival thresholds as objective endpoints in infection experiments. Future studies may use trajectories as novel traits for mapping and identifying genes that control infection dynamics and outcome. A Matlab script for user-friendly trajectory analysis is provided