Soil salinity assessment using the EM38: Field operating instructions and data interpretation

The Geonics EM38® is a portable instrument designed to take in situ field measurements of soil conductivity to about 1.5 m depth. If used correctly, the EM38 allows rapid, reliable estimates of soil salinity to be obtained from large areas without intensive soil sampling. The meter is very useful for delineating the extent and relative severity of saline areas. Although soil salinity has the dominant effect on the EM38 signal, other soil physical factors such as clay content, moisture content and temperature can affect the response. If the EM38 measurements are to be related to plant performance and used for detailed diagnostic purposes over a range of soil types, they must be calibrated with laboratory determined salinity measurements. This report describes the correct operating, surveying and calibration procedures for the EM38

Groundwater chemistry of the Weaber Plain (Goomig Farmlands): baseline results 2010–13

The Ord River Irrigation Area (ORIA) is located in the north-east of the Kimberley region of Western Australia, near the town of Kununurra. The irrigation area was established in 1963 and over time developed to the current extent of 14 000 hectares (ha). The Weaber Plain (Goomig Farmlands) area is located north-north-east of the existing irrigation area, 30km from Kununurra, and has been identified as being suitable for irrigated agriculture for many decades. However, it was not until 2009, with state government support, that the 7400ha project commenced, with construction starting in 2010. State and Australian government environmental approvals required the proponent to install a groundwater monitoring network and develop a groundwater management plan. The environmental approvals required seasonal monitoring of groundwater to establish baseline groundwater chemistry conditions. The monitoring bores were sampled for up to three years and showed a large variation in water type and water quality across the Weaber and Knox Creek plains

New reactive fluorophores in the 1,2,3-trianze series

A one-pot synthesis of new fluorescent 2,5-dihydro-1,2,3-triazines with reactive functional groups and a large Stokes shift of 200 nm is described

New reactive fluorophores in the 1,2,3-trianze series

A one-pot synthesis of new fluorescent 2,5-dihydro-1,2,3-triazines with reactive functional groups and a large Stokes shift of 200 nm is described

SOFIA and ALMA Investigate Magnetic Fields and Gas Structures in Massive Star Formation: The Case of the Masquerading Monster in BYF 73

We present SOFIA+ALMA continuum and spectral-line polarisation data on the massive molecular cloud BYF 73, revealing important details about the magnetic field morphology, gas structures, and energetics in this unusual massive star formation laboratory. The 154$\mu$m HAWC+ polarisation map finds a highly organised magnetic field in the densest, inner 0.55$\times$0.40 pc portion of the cloud, compared to an unremarkable morphology in the cloud's outer layers. The 3mm continuum ALMA polarisation data reveal several more structures in the inner domain, including a pc-long, $\sim$500 M$_{\odot}$ "Streamer" around the central massive protostellar object MIR 2, with magnetic fields mostly parallel to the east-west Streamer but oriented north-south across MIR 2. The magnetic field orientation changes from mostly parallel to the column density structures to mostly perpendicular, at thresholds $N_{\rm crit}$ = 6.6$\times$10$^{26}$ m$^{-2}$, $n_{\rm crit}$ = 2.5$\times$10$^{11}$ m$^{-3}$, and $B_{\rm crit}$ = 42$\pm$7 nT. ALMA also mapped Goldreich-Kylafis polarisation in $^{12}$CO across the cloud, which traces in both total intensity and polarised flux, a powerful bipolar outflow from MIR 2 that interacts strongly with the Streamer. The magnetic field is also strongly aligned along the outflow direction; energetically, it may dominate the outflow near MIR 2, comprising rare evidence for a magnetocentrifugal origin to such outflows. A portion of the Streamer may be in Keplerian rotation around MIR 2, implying a gravitating mass 1350$\pm$50 M$_{\odot}$ for the protostar+disk+envelope; alternatively, these kinematics can be explained by gas in free fall towards a 950$\pm$35 M$_{\odot}$ object. The high accretion rate onto MIR 2 apparently occurs through the Streamer/disk, and could account for $\sim$33% of MIR 2's total luminosity via gravitational energy release.Comment: 33 pages, 32 figures, accepted by ApJ. Line-Integral Convolution (LIC) images and movie versions of Figures 3b, 7, and 29 are available at https://gemelli.spacescience.org/~pbarnes/research/champ/papers

A Method to Calculate Adherence to Inhaled Therapy That Reflects the Changes in Clinical Features of Asthma.

Rationale Currently studies on adherence to inhaled medications report Average Adherence over time. This measure does not account for variations in the interval between doses nor for errors in inhaler use. Objectives We investigated whether adherence calculated as a single Area Under the concentration-time Curve (AUC) measure, incorporating the interval between doses and inhaler technique, was more reflective of patient outcomes than current methods of assessing adherence. Methods We attached a digital audio device (INCATM) to a dry powder inhaler. This recorded when the inhaler was used and analysis of the audio data indicated if the inhaler had been used correctly. These aspects of inhaler use were combined to calculate adherence over time, as an AUC measure. Over a 3 month period a cohort of asthma patients were studied. Adherence to a twice-daily inhaler preventer therapy using this device and clinical measures were assessed. Measurements and Results Recordings from 239 patients with severe asthma were analysed. Average Adherence, based on the dose counter was 84.4%, whereas the ratio of expected to observed accumulated AUC, Actual Adherence, was 61.8% (

Connecting Mission Profiles and Radiation Vulnerability Assessment

Radiation vulnerability assessment early in spacecraft development is cheaper and faster than in late development phases. RGENTIC and SEAM are two software platforms that can be coupled to provide this type of early assessment. Specifically, RGENTIC is a tool that outputs descriptions of radiation risks based on a selected mission environment and the system’s electronic part portfolio, while SEAM models how radiation-induced faults in electronic parts propagate through a system. In this work, we propose a spacecraft evaluation flow where RGENTIC’s outputs, which are radiation vulnerabilities of electronic parts for a given mission, become inputs to SEAM, resulting in an automatic part-type template palette presented to users so that they can easily begin modeling the occurrence and propagation of radiation-induced faults in their spacecraft. In this context, fault propagation modeling shows how radiation effects impact the spacecraft’s electronics. The interface between these platforms can be streamlined through the creation of a SEAM global part-type library with templates based on radiation effects in part-type families such as sensors, processors, voltage regulators, and so forth. Several of the part-types defined in RGENTIC have been integrated into SEAM templates. Ultimately, all 66+ part-types from the RGENTIC look-up table will be included in the SEAM global part library. Once accomplished, the part templates can be used to populate each project-specific part library in SEAM, ensuring all RGENTIC’s part-types are represented, and the radiation effects are consistent between the two. The harmonization process between RGENTIC and SEAM begins as follows: designers input a detailed knowledge of their system and mission into RGENTIC, which then outputs a generic part-type list that associates each part-type with potential radiation concerns. The list is then downloaded in a SEAM-readable file, which SEAM uses to populate the initially blank project with the part templates that correspond to RGENTIC’s output. The final product is a system fault model using a project-specific radiation effect part library. The radiation effects considered in the part library are associated with three categories of radiation-environment issues: single event effects (SEE), total ionizing dose (TID), and displacement damage dose (DDD). An example part-type is the discrete LED, which has been functionally decomposed into input power and output light. It has a single possible radiation-induced fault that is associated with DDD, which causes degraded brightness and is observed on the output. Overall, designers will benefit from a coordination of these two tools because it simplifies the initial definition of the project in SEAM. This is especially the case for new users, since the necessary radiation models for their parts are available before modeling commences. Furthermore, starting from a duplicate of an existing project decreases the amount of time and effort required to develop project-specific models. Incorporating RGENTIC’s table of part-types resolves these issues and provides a streamlined process for creating system radiation fault models. Consequently, spacecraft designers can identify radiation problems early in the design cycle and fix them with lower cost and less effort than in later design stages

Quantifying particle size and turbulent scale dependence of dust uplift in the Sahara using aircraft measurements

The first size-resolved airborne measurements of dust fluxes and the first dust flux measurements from the central Sahara are presented and compared with a parameterization by Kok (2011a). High-frequency measurements of dust size distribution were obtained from 0.16 to 300 µm diameter, and eddy covariance fluxes were derived. This is more than an order of magnitude larger size range than previous flux estimates. Links to surface emission are provided by analysis of particle drift velocities. Number flux is described by a −2 power law between 1 and 144 µm diameter, significantly larger than the 12 µm upper limit suggested by Kok (2011a). For small particles, the deviation from a power law varies with terrain type and the large size cutoff is correlated with atmospheric vertical turbulent kinetic energy, suggesting control by vertical transport rather than emission processes. The measured mass flux mode is in the range 30–100 µm. The turbulent scales important for dust flux are from 0.1 km to 1–10 km. The upper scale increases during the morning as boundary layer depth and eddy size increase. All locations where large dust fluxes were measured had large topographical variations. These features are often linked with highly erodible surface features, such as wadis or dunes. We also hypothesize that upslope flow and flow separation over such features enhance the dust flux by transporting large particles out of the saltation layer. The tendency to locate surface flux measurements in open, flat terrain means these favored dust sources have been neglected in previous studies

Methodology for Correlating Historical Degradation Data to Radiation-Induced Degradation System Effects in Small Satellites

When constructing a system-level fault tree to demonstrate device-to-system level radiation degradation, reliability engineers need relevant, device-level failure probabilities to incorporate into reliability models. Deriving probabilities from testing can be expensive and time-consuming, especially if the system is complex. This methodology offers an alternative means of deriving device-level failure probabilities. It uses Bayesian analysis to establish links between historical radiation datasets and failure probabilities. A demonstration system for this methodology is provided, which is a TID response of a linear voltage regulator at 100 krad(SiO2). Data fed into the Bayesian model is derived from literature on the components found within a linear voltage regulator. An example is presented with data pertaining to the device’s bipolar junction transistor (BJT)’s gain degradation factor (GDF). Kernel density estimation is used to provide insight into the dataset’s general distribution shape. This guides the engineer into picking the appropriate distribution for device-level Bayesian analysis. Failure probabilities generated from the Bayesian analysis are incorporated into a LTspice model to derive a system failure probability (using Monte Carlo) of the regulator’s output. In our demonstration system, a 96.5% likelihood of system degradation was found in the assumed environment