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Presenter Notes: Sandia's Very Expensive Lesson (Don't Let This Happen To You...).
Abstract not provided.
Aneides aeneus (Green Salamander). Geographic distribution note (new county record: Roane, Tennessee, USA)
The individual was found in atypical habitat that consisted of a low-lying wet weather conveyance in second growth forest, which lacked any nearby rock outcrops or other consolidated rock larger than 25 cm in greatest length. Despite repeated searches during summer 2019, additional individuals were not found at this site.
Lampropeltis rhombomaculata (Northern Mole Kingsnake). Geographic distribution note (new county record: Loudon, Tennessee, USA).
This article describes a specimen of Northern Mole Kingsnake located in Loudon County, Tennessee.
Comparison of Expert Vocabulary Usage Patterns Between Mental Health and Nonmental Health Clinicians When Diagnosing Pediatric Anxiety Disorders
Objective: To compare the utilization patterns of expert vocabulary (EVo) in diagnosing pediatric anxiety between mental health and non-mental health clinical notes from electronic health records to understand the role of Evo in informing classification and decision-making in anxiety diagnoses. Study design: We conducted a retrospective study using a cohort less than age 25 from Cincinnati Children's Hospital including 897 685 patients with 61 586 446 notes. We analyzed EVo, collected from mental health clinicians, in both mental and nonmental health notes. We compared classification accuracy using EVo-based patient-level embedding from all clinical notes, mental-health notes, and nonmental health notes for 2 tasks: 1) pre-vs postdiagnosis anxiety patients, and 2) prediagnosis anxiety vs nonanxiety patients. Results: EVo usage was highest in prediagnosis anxiety, lower in nonanxiety, and lowest in post-diagnosis. Classification models using EVo features from all, mental-health, and non-mental health notes showed similar F1 scores for prediagnosis anxiety (0.70 ± 0.2 for 2 categories). For anxiety vs nonanxiety classification, all clinical and nonmental health notes had better F1 scores than mental-health notes (above 0.90 for 3 categories). There was a notable difference in class-wise performance across both tasks. Conclusions: There are significant differences in anxiety EVo use between mental health and nonmental health clinicians. Despite less anxiety-specific terminology, non-mental health notes still captured key aspects of patient presentations, emphasizing the importance of including all clinicians' notes in analysis. EVo's utility for anxiety classification is most effective in prediagnostic phases, suggesting the need for a dedicated diagnostic lexicon and further study before incorporating EVo into classification models.
INGENIOUS Thermal Conductivity Measurement Source Categorization
Thermal conductivity (TC) data taken for different wells at a specified drill depth. This is an abridged version of the complete SMU heat flow database, downloaded from the SMU node of the NGDS at the beginning of INGENIOUS (approximately April 2021), and filtered to the INGENIOUS study area. This National Geothermal Data System (NGDS) project aggregates geothermal data collected and curated by the SMU Geothermal Laboratory and its partner organizations. All columns in this database are the same as the SMU database, except for 2 additions associated with this project. Repeated columns are for data correlation purposes. Column descriptions and data types are the same as previous iterations of the SMU database. The new values that are the addition are two new columns developed as part of the INGENIOUS project: INGENIOUS TC Value | INGENIOUS notes INGENIOUS notes are individual notes that were written for specific data points during the analysis process. There are not always notes associated with each input value. INGENIOUS TC Value includes 4 values: 1. Assumed Measured These are values that are assumed to be measured thermal conductivity values, either within a specific well or within the same study region. Many of these have either a published reference, a reported standard deviation, or a unique thermal conductivity value. 2. Data release - assumed measured These are values in the SMU database that are from proprietary data that were added to the SMU database and are labeled as data release for their reference. These values were searched for in person at the SMU Geothermal Laboratory as well as virtual examination of data available on the NGDS. For many of these, there are reported thermal conductivity values associated with the heat flow data in the database, but no specific table or reference to measurements in the original data release files. 3. Known measured These are values that have a reported measurement, either as an original file in the SMU data files on the NGDS or a reported table in a publication. In the rare circumstances, Maria Richards or David Blackwell confirmed measurement. Confirmation of measurement would be written in the INGENIOUS notes column. 4. Unmeasured Unmeasured values are those that are known to be unmeasured, either estimated from another report or no information given. In the SMU database, there are wells that have a heat flow but no thermal conductivity. These are categorized as unmeasured. There are also heat flow values that are stated to have estimated or generalized average thermal conductivity values for the region and rock type. Because these are known to be unmeasured, they are categorized as such. 5. Blank Blank values are either A quality or X quality. These quality values are stated in the INGENIOUS notes. These values were not going to change associated with the heat flow analysis, so these were not examined.
Leveraging large language models to automate the identification of healthcare access barriers for veterans
Objective: To develop and evaluate an automated system for identifying healthcare barriers focusing on transportation issues in veterans’ clinical notes using large language models (LLMs) and to assess the impact of different prompting strategies on classification performance and explanation consistency. Methods: We developed a hybrid system combining pattern matching for templated notes with LLM analysis for free-text notes. Using 2000 manually annotated clinical notes, we compared four prompting strategies (dual-role short, dual-role long, analysis-first, analysis-only) across Mistral-7B and Llama-3.1 models. We evaluated classification performance using standard metrics and assessed explanation consistency through embedding similarity analysis. Results: The analysis-first strategy achieved superior performance, with Mistral-7B reaching an F1 score of 0.914, outperforming traditional machine learning approaches (GBM: 0.786, BERT: 0.811). LLMs demonstrated higher explanation consistency within models (mean cosine similarity 0.887–0.908) compared to cross-model similarities (0.767–0.872). Pattern matching successfully handled 6.7% of templated notes deterministically. Mistral-7B showed greater internal consistency but higher abstention rates compared to Llama-3.1. Conclusion: Requiring LLMs to analyze evidence before classification improves both accuracy and explanation consistency for identifying transportation barriers in clinical notes. This approach enables automated barrier detection at scale while providing clinically relevant explanations, supporting both population-level healthcare planning and individual patient care decisions.
ASMT-783246, Assessment Report for Department 635 Analysis of Fiscal Year 2021 ES&H Events
This assessment analyzes Environment, Safety, and Health (ES&H) occurrences and Non-Occurrence Trackable Events (NOTEs) from fiscal year (FY) 2021. For this report, assessors used three primary methods for categorizing occurrence and NOTE data: issue categorization, DOE reporting criteria groups, and DOE cause codes. The FY 2021 Q1 occurrence and NOTE total was the lowest since this type of analysis began in FY 2018 Q4, following a downward trend from the FY 2019 Q3 high point. The FY 2021 Q2 occurrence and NOTE total was nearly double the FY 2021 Q1 total; occurrence totals declined slightly in Q3 and Q4, while NOTE totals remained the same. NOTEs in each of the final three quarters were more than double the amount from Q1. This assessment resulted in one observation. As COVID-19 vaccination rates increase and COVID 19 impacts on operations decrease, the number of workers on-site and the amount of activity-level work will increase. With these changes, focused attention on the following areas related to work planning and controls may reduce the probability of future events, hazard identification and analysis, compliance with standards, formality of operations, and job scoping.
Apparatus for using a time interval counter to measure frequency stability
An apparatus for measuring the relative stability of two signals is disclosed comprising a means for mixing the two signals down to a beat note sine wave and for producing a beat note square wave whose upcrossings are the same as the sine wave. A source of reference frequency is supplied to a clock divider and interval counter to synchronize them and to generate a picket fence for providing a time reference grid of period shorter than the beat period. An interval counter is employed to make a preliminary measurement between successive upcrossings of the beat note square wave for providing an approximate time interval therebetween as a reference. The beat note square wave and the picket fence are then provided to the interval counter to provide an output consisting of the time difference between the upcrossing of each beat note square wave cycle and the next picket fence pulse such that the counter is ready for each upcrossing and dead time is avoided. A computer containing an algorithm for calculating the exact times of the beat note upcrossings then computes the upcrossing times.
Start Up Application Concerns with Field Programmable Gate Arrays (FPGAs)
This note is being published to improve the visibility of this subject, as we continue to see problems surface in designs, as well as to add additional information to the previously published note for design engineers. The original application note focused on designing systems with no single point failures using Actel Field Programmable Gate Arrays (FPGAs) for critical applications. Included in that note were the basic principles of operation of the Actel FPGA and a discussion of potential single-point failures. The note also discussed the issue of startup transients for that class of device. It is unfortunate that we continue to see some design problems using these devices. This note will focus on the startup properties of certain electronic components, in general, and current Actel FPGAs, in particular. Devices that are "power-on friendly" are currently being developed by Actel, as a variant of the new SX series of FPGAs. In the ideal world, electronic components would behave much differently than they do in the real world, The chain, of course, starts with the power supply. Ideally, the voltage will immediately rise to a stable V(sub cc) level, of course, it does not. Aside from practical design considerations, inrush current limits of certain capacitors must be observed and the power supply's output may be intentionally slew rate limited to prevent a large current spike on the system power bus. In any event, power supply rise time may range from less than I msec to 100 msec or more.
Passive Endwall Treatments for Enhancing Stability
These lecture notes were presented at the von Karman Institutes lecture series on Advances in Axial Compressor Aerodynamics, May 2006. They provide a fairly extensive overview of what's been learned from numerous investigations of various passive casing endwall technologies that have been proposed for alleviating the stall limiting physics associated with the compressor endwall flow field. The lecture notes are organized to give an appreciation for the inventiveness and understanding of the earliest compressor technologists and to provide a coherent thread of understanding that has arisen out of the early investigations. As such the lecture notes begin with a historical overview of casing treatments from their infancy through the earliest proposed concepts involving blowing, suction and flow recirculation. A summary of lessons learned from these early investigations is provided at the end of this section. The lecture notes then provide a somewhat more in-depth overview of recent advancements in the development of passive casing treatments from the late 1990's through 2006, including advancements in understanding the flow mechanism of circumferential groove casing treatments, and the development of discrete tip injection and self-recirculating casing treatments. At the conclusion of the lecture notes a final summary of lessons learned throughout the history of the development of passive casing treatments is provided. Finally, a list of future needs is given. It is hoped that these lecture notes will be a useful reference for future research endeavors to improve our understanding of the fluid physics of passive casing treatments and how they act to enhance compressor stability, and that they will perhaps provide a springboard for future research activities in this area of interest
HarDWR - Harmonized Water Rights Records
A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - Recalculated based on data sourced from WestDAAT - Changed using a Site ID column to identify unique records to using aa combination of Site ID and Allocation ID - Removed the Water Management Area (WMA) column from the harmonized records. The replacement is a separate file which stores the relationship between allocations and WMAs. This allows for allocations to contribute to water right amounts to multiple WMAs during the subsequent cumulative process. - Added a column describing a water rights legal status - Added "Unspecified" was a water source category - Added an acre-foot (AF) column - Added a column for the classification of the right's owner v1.02 - Added a .RData file to the dataset as a convenience for anyone exploring our code. This is an internal file, and the one referenced in analysis scripts as the data objects are already in R data objects. v1.01 - Updated the names of each file with an ID number less than 3 digits to include leading 0s v1.0 - Initial public release Description Here we present an updated database of Western U.S. water right records. This database provides consistent unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of seven broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of inter-sectoral water allocation changes though water markets (Grogan et al., *in review*). Specifically, the data were formatted for use as input to a process-based hydrologic model, Water Balance Model (WBM), with a water rights module (Grogan et al., *in review*). While this specific study motivated the development of the database presented here, water management in the U.S. West is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al., 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. West. We produced the water rights database presented here in four main steps: (1) data collection, (2) data quality control, (3) data harmonization, and (4) generation of cumulative water rights curves. Each of steps (1)-(3) had to be completed in order to produce (4), the final product that was used in the modeling exercise in Grogan et al. (*in review*). All data in each step is associated with a spatial unit called a Water Management Area (WMA), which is the unit of water right administration utilized by the state in which the right came from. Steps (2) and (3) required use to make assumptions and interpretation, and to remove records from the raw data collection. We describe each of these assumptions and interpretations below so that other researchers can choose to implement alternative assumptions an interpretation as fits their research aims. Motivation for Changing Data Sources The most significant change has been a switch from collecting the raw water rights directly from each state to using the water rights records presented in WestDAAT, a product of the Water Data Exchange (WaDE) Program under the Western States Water Council (WSWC). One of the main reasons for this is that each state of interest is a member of the WSWC, meaning that WaDE is partially funded by these states, as well as many universities. As WestDAAT is also a database with consistent categorization, it has allowed us to spend less time on data collection and quality control and more time on answering research questions. This has included records from water right sources we had previously not known about when creating v1.0 of this database. The only major downside to utilizing the WestDAAT records as our raw data is that further updates are tied to when WestDAAT is updated, as some states update their public water right records daily. However, as our focus is on cumulative water amounts at the regional scale, it is unlikely most records updates would have a significant effect on our results. The structure of WestDAAT led to several important changes to how HarWR is formatted. The most significant change is that WaDE has calculated a field known as `SiteUUID`, which is a unique identifier for the Point of Diversion (POD), or where the water is drawn from. This separate from `AllocationNativeID`, which is the identifier for the allocation of water, or the amount of water associated with the water right. It should be noted that it is possible for a single site to have multiple allocations associated with it and for an allocation to be able to be extracted from multiple sites. The site-allocation structure has allowed us to adapt a more consistent, and hopefully more realistic, approach in organizing the water right records than we had with HarDWR v1.0. This was incredibly helpful as the raw data from many states had multiple water uses within a single field within a single row of their raw data, and it was not always clear if the first water use was the most important, or simply first alphabetically. WestDAAT has already addressed this data quality issue. Furthermore, with v1.0, when there were multiple records with the same water right ID, we selected the largest volume or flow amount and disregarded the rest. As WestDAAT was already a common structure for disparate data formats, we were better able to identify sites with multiple allocations and, perhaps more importantly, allocations with multiple sites. This is particularly helpful when an allocation has sites which cross WMA boundaries, instead of just assigning the full water amount to a single WMA we are now able to divide the amount of water between the number of relevant WMAs. As it is now possible to identify allocations with water used in multiple WMAs, it is no longer practical to store this information within a single column. Instead the stAllocationToWMATab.csv file was created, which is an allocation by WMA matrix containing the percent Place of Use area overlap with each WMA. We then use this percentage to divide the allocation's flow amount between the given WMAs during the cumulation process to hopefully provide more realistic totals of water use in each area. However, not every state provides areas of water use, so like HarDWR v1.0, a hierarchical decision tree was used to assign each allocation to a WMA. First, if a WMA could be identified based on the allocation ID, then that WMA was used; typically, when available, this applied to the entire state and no further steps were needed. Second was the spatial analysis of Place of Use to WMAs. Third was a spatial analysis of the POD locations to WMAs, with the assumption that allocation's POD is within the WMA it should belong to; if an allocation still had multiple WMAs based on its POD locations, then the allocation's flow amount would be divided equally between all WMAs. The fourth, and final, process was to include water allocations which spatially fell outside of the state WMA boundaries. This could be due to several reasons, such as coordinate errors / imprecision in the POD location, imprecision in the WMA boundaries, or rights attached with features, such as a reservoir, which crosses state boundaries. To include these records, we decided for any POD which was within one kilometer of the state's edge would be assigned to the nearest WMA. Other Changes WestDAAT has Allowed In addition to a more nuanced and consistent method of assigning water right's data to WMAs, there are other benefits gained from using the WestDAAT dataset. Among those is a consistent categorization of a water right's legal status. In HarDWR v1.0, legal status was effectively ignored, which led to many valid concerns about the quality of the database related to the amounts of water the rights allowed to be claimed. The main issue was that rights with legal status' such as "application withdrawn", "non-active", or "cancelled" were included within HarDWR v1.0. These, and other water rights status' which were deemed to not be in use have been removed from this version of the database. Another major change has been the addition of the "unspecified water source category. This is water that can come from either surface water or groundwater, or the source of which is unknown. The addition of this source category brings the total number of categories to three. Due to reviewer feedback, we decided to add the acre-foot (AF) column so that the data may be more applicable to a wider audience. We added the ownerClassification column so that the data may be more applicable to a wider audience. File Descriptions The dataset is a series of various files organized by state sub-directories. In addition, each file begins with the state's name, in case the file is separate from its sub-directory for some reason. After the state name is the text which describes the contents of the file. Here is each file described in detail. Note that st is a placeholder for the state's name. stFullRecords_HarmonizedRights.csv: A file of the complete water records for each state. The column headers for each of this type of file are: state - The name of the state to which the allocations belong to. FIPS - The two digit numeric state ID code. siteID - The site location ID for POD locations. A site may have multiple allocations, which are the actual amount of water which can be drawn. In a simplified hypothetical, a farm stead may have an allocation for "irrigation" and an allocation for "domestic" water use, but the water is drawn from the same pumping equipment. It should be noted that many of the site ID appear to have been added by WaDE, and therefore may not be recognized by a given state's water rights database. allocationID - The allocation ID for the water right. For most states this is the water right ID, and what is recommended to use should a right be looked up on a given state's water rights database. The water amounts associated with these IDs tend to be finer scaled than those associated with siteID. It should be noted that some allocations may be extracted from multiple sites, particularly for larger Places of Use. ownerClassification - A classification of the types of owners for water rights. The most common is `Private` which incorporates a wide range of entities. Several classifications would be grouped into a government category, most of which are for the U.S. Federal Government. These allocations could be listed as "Federal", "United States of America", or as the names of any number of federal agencies. The last major grouping of entities is for "Native American"s. priorityDate - The date we use as the water right priority date for our modeling analysis. This is the legal priority date when it is available. However, for some rights, specifically from California and New Mexico, we used a pseudo priority date (e.g. well completion date or start of well drilling date) when a legal priority date was not available. The most questionable dates come from New Mexico, where the only date associated with certain water right records was the date the allocation was recorded in the database. As the allocation record creation tended to be within a few months of the filing of the application of the water right, from manually double checking the water rights, and our analysis focuses on aggregating water rights on the timescale of years, we determined it was acceptable to use such dates to include as many records as possible. primaryBeneficialUse - From the numerous state water use categories, WaDE categorized them into 21 categories WestDAAT. This column is the original WaDE category for the primary water use at the PoD site. allocationBeneficialUse - From the numerous state water use categories, WaDE categorized them into 21 categories for WestDAAT. This column is the original WaDE category
Allowable exposure limits for carbon dioxide during extravehicular activity
The intent was to review the research pertaining to human exposure to carbon dioxide (CO2) and to recommend allowable exposure limits for extravehicular activity (EVA). Respiratory, renal, and gastrointestinal systems may be adversely affected by chronic low dose CO2 exposure. Ventilation was increased 15 percent with 1 percent CO2 and 50 percent with 2 percent CO2. Chronic exposure to less than 2 percent CO2 led to 20 day cycles of uncompensated and compensated respiratory acidosis. Acid-base changes were small. Histopathologic changes in guinea pig lungs have been noted with long term exposure to 1 percent CO2. No changes were seen with exposure to 0.5 percent CO2. Cycling of bone calcium stores with associated changes in blood and urinary calcium levels occurs with long term CO2 exposure. Histologic changes in bone have been noted in guinea pigs exposed to 1 percent CO2. Renal calcification has been noted in guinea pigs with exposure to as low as 0.5 percent CO2. An increase in gastric acidity was noted in subjects with long term exposure to 1 percent CO2. Cardiovascular and neurologic function were largely unaffected. A decrease in the incidence of respiratory, renal, and gastrointestinal disease was noted in submariners coincident with a decrease in ambient CO2 from 1.2 percent to 0.8-0.9 percent. Oxygen (O2) and CO2 stimulate respiration independently and cumulatively. The addition of CO2 to high dose O2 led to the faster onset of seizure activity in mice. Experiments evaluating the physiologic responses to intermittent, repetitive exposures to low dose CO2 and 100 percent O2 mixtures should be performed. A reduction in the current NASA standard for CO2 exposure during EVA of 1 percent (7.6 mmHg) for nominal and 2 percent (15.2 mmHg) for heavy exertion to 0.5 percent (3.8 mmHg) for nominal and 1 percent (7.6 mmHg) for heavy exertion may be prudent. At a minimum, the current NASA standard should not be liberalized.
Manifestation of quasi-biennial oscillation in ozone vertical distribution
The quasi-biennial oscillations (QBO) in ozone and temperature vertical distributions are studied on the basis of ozonesonde data of 21 stations. Maximum QBO amplitudes in ozone are noted in the 16-20 kn layer over Resolute (75N), Aspendale (38S) and in the northern mid-latitude band, but in the 20-24 km layer in the northern subtropical band. In the upper layers the QBO effect is less evident. In the tropospheric layer it is difficult to note the QBO-related effect in all the groups of the data. In all the layers where the QBO effect is noted the positive deviations precede, but the negative deviations follow the time of maximum of the easterly equatorial wind at 50 mb level. No essential differences in phase or amplitude characteristics of the ozone QBO were noted for the Aspendale data compared with that for the Northern Hemisphere data. The QBO-effect is not noted in the temperature data in the mid-latitudes. Above Resolute and in subtropics the ozone and temperature effects are roughly in phase each with other, except in the 28-32 km layer over subtropics, where they are opposite each to other.
An Enabling Technology for New Planning and Scheduling Paradigms
The Night Projects Directorate at NASA's Marshall Space Flight Center is developing a new planning and scheduling environment and a new scheduling algorithm to enable a paradigm shift in planning and scheduling concepts. Over the past 33 years Marshall has developed and evolved a paradigm for generating payload timelines for Skylab, Spacelab, various other Shuttle payloads, and the International Space Station. The current paradigm starts by collecting the requirements, called ?ask models," from the scientists and technologists for the tasks that are to be scheduled. Because of shortcomings in the current modeling schema, some requirements are entered as notes. Next, a cadre with knowledge of vehicle and hardware modifies these models to encompass and be compatible with the hardware model; again, notes are added when the modeling schema does not provide a better way to represent the requirements. Finally, the models are modified to be compatible with the scheduling engine. Then the models are submitted to the scheduling engine for automatic scheduling or, when requirements are expressed in notes, the timeline is built manually. A future paradigm would provide a scheduling engine that accepts separate science models and hardware models. The modeling schema would have the capability to represent all the requirements without resorting to notes. Furthermore, the scheduling engine would not require that the models be modified to account for the capabilities (limitations) of the scheduling engine. The enabling technology under development at Marshall has three major components: (1) A new modeling schema allows expressing all the requirements of the tasks without resorting to notes or awkward contrivances. The chosen modeling schema is both maximally expressive and easy to use. It utilizes graphical methods to show hierarchies of task constraints and networks of temporal relationships. (2) A new scheduling algorithm automatically schedules the models without the intervention of a scheduling expert. The algorithm is tuned for the constraint hierarchies and the complex temporal relationships provided by the modeling schema. It has an extensive search algorithm that can exploit timing flexibilities and constraint and relationship options. (3) An innovative architecture allows multiple remote users to simultaneously model science and technology requirements and other users to model vehicle and hardware characteristics. The architecture allows the remote users to submit scheduling requests directly to the scheduling engine and immediately see the results. These three components are integrated so that science and technology experts with no knowledge of the vehicle or hardware subsystems and no knowledge of the internal workings of the scheduling engine have the ability to build and submit scheduling requests and see the results. The immediate feedback will hone the users' modeling skills and ultimately enable them to produce the desired timeline. This paper summarizes the three components of the enabling technology and describes how this technology would make a new paradigm possible.
Enabling a New Planning and Scheduling Paradigm
The Flight Projects Directorate at NASA's Marshall Space Flight Center is developing a new planning and scheduling environment and a new scheduling algorithm to enable a paradigm shift in planning and scheduling concepts. Over the past 33 years Marshall has developed and evolved a paradigm for generating payload timelines for Skylab, Spacelab, various other Shuttle payloads, and the International Space Station. The current paradigm starts by collecting the requirements, called "tasks models," from the scientists and technologists for the tasks that they want to be done. Because of shortcomings in the current modeling schema, some requirements are entered as notes. Next a cadre with knowledge of vehicle and hardware modifies these models to encompass and be compatible with the hardware model; again, notes are added when the modeling schema does not provide a better way to represent the requirements. Finally, another cadre further modifies the models to be compatible with the scheduling engine. This last cadre also submits the models to the scheduling engine or builds the timeline manually to accommodate requirements that are expressed in notes. A future paradigm would provide a scheduling engine that accepts separate science models and hardware models. The modeling schema would have the capability to represent all the requirements without resorting to notes. Furthermore, the scheduling engine would not require that the models be modified to account for the capabilities (limitations) of the scheduling engine. The enabling technology under development at Marshall has three major components. (1) A new modeling schema allows expressing all the requirements of the tasks without resorting to notes or awkward contrivances. The chosen modeling schema is both maximally expressive and easy to use. It utilizes graphics methods to show hierarchies of task constraints and networks of temporal relationships. (2) A new scheduling algorithm automatically schedules the models without the intervention of a scheduling expert. The algorithm is tuned for the constraint hierarchies and the complex temporal relationships provided by the modeling schema. It has an extensive search algorithm which can exploit timing flexibilities and constraint and relationship options. (3) A web-based architecture allows multiple remote users to simultaneously model science and technology requirements and other users to model vehicle and hardware characteristics. The architecture allows the users to submit scheduling requests directly to the scheduling engine and immediately see the results. These three components are integrated so that science and technology experts with no knowledge of the vehicle or hardware subsystems and no knowledge of the internal workings of the scheduling engine have the ability to build and submit scheduling requests and see the results. The immediate feedback will hone the users' modeling skills and ultimately enable them to produce the desired timeline. This paper summarizes the three components of the enabling technology and describes how this technology would make a new paradigm possible.
Detection of High Velocity Absorption Components in the He I Lines of Eta Carinae near the Time of Periastron
We have obtained a total of 58 high spectral resolution (R90,000) spectra of the massive binary star eta Carinae since 2012 in an effort to continue our orbital and long-term echelle monitoring of this extreme binary (Richardson et al. 2010, AJ, 139, 1534) with the CHIRON spectrograph on the CTIO 1.5 m telescope (Tokovinin et al. 2013, PASP, 125, 1336) in the 45507500A region. We have increased our monitoring efforts and observation frequency as the periastron event of 2014 has approached. We note that there were multiple epochs this year where we observe unusual absorption components in the P Cygni troughs of the He I triplet lines. In particular, we note high velocity absorption components related to the following epochs for the following lines: He I 4713: HJD 2456754- 2456795 (velocity -450 to -560 kms) He I 5876: HJD 2456791- 2456819 (velocity -690 to -800 kms) He I 7065: HJD 2456791- 2456810 (velocity -665 to -730 kms) Figures: Note that red indicates a high-velocity component noted above. He I 4713: http:www.astro.umontreal.carichardson4713.png He I 5876: http:www.astro.umontreal.carichardson5876.png He I 7065: http:www.astro.umontreal.carichardson7065.png These absorptions are likely related to the wind-wind collision region and bow shock, as suggested by the high-velocity absorption observed by Groh et al. (2010, AA, 519, 9) in the He I 10830 Atransition. In these cases, we suspect that we look along an arm of the shock cone and that we will see a fast absorption change from the other collision region shortly after periastron. We suspect that this is related to the multiple-components of the He II 4686 line that was noted by Walter (ATel6334), and is confirmed in our data. Further, high spectral resolution data are highly encouraged,especially for resolving powers greater than 50,000.These observations were obtained with the CTIO 1.5 m telescope, operated by the SMARTS Consortium, and were obtained through both SMARTS and NOAO programs 2012A-0216,2012B-0194, and 2013b-0328). We thank Emily MacPherson (Yale) for her efforts in scheduling the observations that we have and will obtain in the coming weeks.
Tonal Emergence: An agent-based model of tonal coordination
Humans have a remarkable capacity for coordination. Our ability to interact and act jointly in groups is crucial to our success as a species. Joint Action (JA) research has often concerned itself with simplistic behaviors in highly constrained laboratory tasks. But there has been a growing interest in understanding complex coordination in more open-ended contexts. In this regard, collective music improvisation has emerged as a fascinating model domain for studying basic JA mechanisms in an unconstrained and highly sophisticated setting. A number of empirical studies have begun to elucidate coordination mechanisms underlying joint musical improvisation, but these empirical findings have yet to be cached out in a working computational model. The present work fills this gap by presenting TonalEmergence, an idealized agent-based model of improvised musical coordination. TonalEmergence models the coordination of notes played by improvisers to generate harmony (i.e., tonality), by simulating agents that stochastically generate notes biased towards maximizing harmonic consonance given their partner’s previous notes. Here, the model replicates an interesting empirical result from a previous study of professional jazz pianists: feedback loops of mutual adaptation between interacting agents support the production of consonant harmony. The model is further explored to show how complex tonal dynamics, such as the production and dissolution of stable tonal centers, are supported by agents that are characterized by (i) a tendency to strive toward consonance, (ii) stochasticity, and (iii) a limited memory for previously played notes. TonalEmergence thus provides a grounded computational model to simulate and probe the coordination mechanisms underpinning one of the more remarkable feats of human cognition: collective music improvisation.