Methods and decision making on a Mars rover for identification of fossils

A system for automated fusion and interpretation of image data from multiple sensors, including multispectral data from an imaging spectrometer is being developed. Classical artificial intelligence techniques and artificial neural networks are employed to make real time decision based on current input and known scientific goals. Emphasis is placed on identifying minerals which could indicate past life activity or an environment supportive of life. Multispectral data can be used for geological analysis because different minerals have characteristic spectral reflectance in the visible and near infrared range. Classification of each spectrum into a broad class, based on overall spectral shape and locations of absorption bands is possible in real time using artificial neural networks. The goal of the system is twofold: multisensor and multispectral data must be interpreted in real time so that potentially interesting sites can be flagged and investigated in more detail while the rover is near those sites; and the sensed data must be reduced to the most compact form possible without loss of crucial information. Autonomous decision making will allow a rover to achieve maximum scientific benefit from a mission. Both a classical rule based approach and a decision neural network for making real time choices are being considered. Neural nets may work well for adaptive decision making. A neural net can be trained to work in two steps. First, the actual input state is mapped to the closest of a number of memorized states. After weighing the importance of various input parameters, the net produces an output decision based on the matched memory state. Real time, autonomous image data analysis and decision making capabilities are required for achieving maximum scientific benefit from a rover mission. The system under development will enhance the chances of identifying fossils or environments capable of supporting life on Mar

Autonomous exploration system: Techniques for interpretation of multispectral data

An on-board autonomous exploration system that fuses data from multiple sensors, and makes decisions based on scientific goals is being developed using a series of artificial neural networks. Emphasis is placed on classifying minerals into broad geological categories by analyzing multispectral data from an imaging spectrometer. Artificial neural network architectures are being investigated for pattern matching and feature detection, information extraction, and decision making. As a first step, a stereogrammetry net extracts distance data from two gray scale stereo images. For each distance plane, the output is the probable mineral composition of the region, and a list of spectral features such as peaks, valleys, or plateaus, showing the characteristics of energy absorption and reflection. The classifier net is constructed using a grandmother cell architecture: an input layer of spectral data, an intermediate processor, and an output value. The feature detector is a three-layer feed-forward network that was developed to map input spectra to four geological classes, and will later be expanded to encompass more classes. Results from the classifier and feature detector nets will help to determine the relative importance of the region being examined with regard to current scientific goals of the system. This information is fed into a decision making neural net along with data from other sensors to decide on a plan of activity. A plan may be to examine the region at higher resolution, move closer, employ other sensors, or record an image and transmit it back to Earth

Spectral analysis for automated exploration and sample acquisition

Future space exploration missions will rely heavily on the use of complex instrument data for determining the geologic, chemical, and elemental character of planetary surfaces. One important instrument is the imaging spectrometer, which collects complete images in multiple discrete wavelengths in the visible and infrared regions of the spectrum. Extensive computational effort is required to extract information from such high-dimensional data. A hierarchical classification scheme allows multispectral data to be analyzed for purposes of mineral classification while limiting the overall computational requirements. The hierarchical classifier exploits the tunability of a new type of imaging spectrometer which is based on an acousto-optic tunable filter. This spectrometer collects a complete image in each wavelength passband without spatial scanning. It may be programmed to scan through a range of wavelengths or to collect only specific bands for data analysis. Spectral classification activities employ artificial neural networks, trained to recognize a number of mineral classes. Analysis of the trained networks has proven useful in determining which subsets of spectral bands should be employed at each step of the hierarchical classifier. The network classifiers are capable of recognizing all mineral types which were included in the training set. In addition, the major components of many mineral mixtures can also be recognized. This capability may prove useful for a system designed to evaluate data in a strange environment where details of the mineral composition are not known in advance

Risk and Performance Analyses Supporting Closure of WMA C at the Hanford Site in Southeast Washington

The Office of River Protection under the U.S. Department of Energy (DOE) is pursuing closure of the Single-Shell Tank (SST) Waste Management Area 0NMA) C as stipulated by the Hanford Federal Facility Agreement and Consent Order (HFFACO) under federalrequirements and work tasks will be done under the State-approved closure plans and permits. An initial step in meeting the regulatory requirements is to develop a baseline risk assessment representing current conditions based on available characterization data and information collected at the WMA C location. The baseline risk assessment will be supporting a Resource Conservation and Recovery Act of 1976 (RCRA) Field Investigation (RFI)/Corrective Measures Study (CMS) for WMA closure and RCRA corrective action. Complying with the HFFACO conditions also involves developing a long-term closure Performance Assessment (PA) that evaluates human health and environmental impacts resulting from radionuclide inventories in residual wastes remaining in WMA C tanks and ancillary equipment. This PAis being developed to meet the requirements necessary for closure authorization under DOE Order 435.1 and Washington State Hazardous Waste Management Act. To meet the HFFACO conditions, the long-term closure risk analysis will include an evaluation of human health and environmental impacts from hazardous chemical inventories along with other performance Comprehensive Environmental Response, Compensation, and Liability Act Appropriate and Applicable Requirements (CERCLA ARARs) in residualwastes left in WMA C facilities after retrieval and removal. This closure risk analysis is needed to needed to comply with the requirements for permitted closure. Progress to date in developing a baseline risk assessment of WMA C has involved aspects of an evaluation of soil characterization and groundwater monitoring data collected as a part of the RFI/CMS and RCRA monitoring. Developing the long-term performance assessment aspects has involved the construction of detailed numericalmodels of WMA C using the Subsurface Transport Over Multiple Phases (STOMP(C)) computer code, the development of a technical approach for abstraction of a range of representative STOMP(C) simulations into a system-level modelbased on the GoldSim0 system-levelmodelsoftware. The STOMP(C)-based models will be used to evaluate local-scale impacts and closed facility performance over a sufficient range of simulations to allow for development of the system-level model of the WMA C. The GoldSim0-based system-level model will be used to evaluate overall sensitivity of modeled parameters and the estimate the uncertainty in potentialfuture impacts from a closed WMA C facility

Improved Three-Dimensional Resistivity Data Acquisition Capabilities at the Hanford Site - 14146

The recent 3D electrical resistivity characterization at 241_U Tank Farm represents the first full-farm true 3D environmental resistivity deployment in the world. Technological and manufacturing developments by the vendor resulted in a data acquisition system that far surpasses the ability of the previous off-the-shelf systems. The new data acquisition system allows for 180 channels, which enables the full-farm 3D acquisition without the inaccuracies associated with combining multiple datasets. This ultimately leads to a more accurate model of the subsurface and a better understanding of moisture and contaminant distribution within the vadose zone. Additionally, advancements in electrical noise filters and increased output power resulted in better quality data than previously acquired at the site, reducing the amount of poor quality data by more than half. Ultimately, the new, improved system increased the speed of data acquisition and quality of the final results. The system allowed a reduction in field labor and field work duration to half the field budget estimates, resulting in a 25% reduction in overall project costs. The new resistivity data acquisition system represents technological advancements resulting in a greater quantity of data with decreased project costs

Using Time-Resolved Fluorescence to Measure Serum Venom-Specific IgE and IgG

We adapted DELFIA™ (dissociation-enhanced lanthanide fluoroimmunoassay), a time resolved fluorescence method, to quantitate whole venom specific and allergenic peptide-specific IgE (sIgE), sIgG1 and sIgG4 in serum from people clinically allergic to Australian native ant venoms, of which the predominant cause of allergy is jack jumper ant venom (JJAV). Intra-assay CV was 6.3% and inter-assay CV was 13.7% for JJAV sIgE. DELFIA and Phadia CAP JJAV sIgE results correlated well and had similar sensitivity and specificity for the detection of JJAV sIgE against intradermal skin testing as the gold standard. DELFIA was easily adapted for detecting sIgE to a panel of other native ant venoms

Management of anaphylaxis due to COVID-19 vaccines in the elderly

Older adults, especially men and/or those with diabetes, hypertension, and/or obesity, are prone to severe COVID-19. In some countries, older adults, particularly those residing in nursing homes, have been prioritized to receive COVID-19 vaccines due to high risk of death. In very rare instances, the COVID-19 vaccines can induce anaphylaxis, and the management of anaphylaxis in older people should be considered carefully. An ARIA-EAACI-EuGMS (Allergic Rhinitis and its Impact on Asthma, European Academy of Allergy and Clinical Immunology, and European Geriatric Medicine Society) Working Group has proposed some recommendations for older adults receiving the COVID-19 vaccines. Anaphylaxis to COVID-19 vaccines is extremely rare (from 1 per 100,000 to 5 per million injections). Symptoms are similar in younger and older adults but they tend to be more severe in the older patients. Adrenaline is the mainstay treatment and should be readily available. A flowchart is proposed to manage anaphylaxis in the older patients.Peer reviewe

Characterization and Potential Remediation Approaches for Vadose Zone Contamination at Hanford 241-SX Tank Farm

Unplanned releases of radioactive and hazardous wastes have occurred at the 241-SX Tank Farm on the U.S. Department of Energy Hanford Site in southeast Washington State. Interim and long-term mitigation efforts are currently under evaluation for 241-SX Tank Farm. Two contiguous interim surface barriers have been designed for deployment at 241-SX Tank Farm to reduce future moisture infiltration; however, construction of the surface barriers has been deferred to allow testing of alternative technologies for soil moisture reduction and possibly contaminant source term reduction. Previous tests performed by other organizations at the Hanford Site have demonstrated that: vadose zone desiccation using large diameter (greater than 4 inch) boreholes is feasible; under certain circumstances, mobile contaminants may be removed in addition to water vapor; and small diameter (approximately 2 inch) boreholes (such as those placed by the direct push hydraulic hammer) can be used to perform vapor extractions. Evaluation of the previous work combined with laboratory test results have led to the design of a field proof-of-principle test to remove water and possibly mobile contaminants at greater depths, using small boreholes placed with the direct push unit