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The Data & Reasoning Fabric activity, under the Convergent Aeronautics Solutions (CAS) Project has developed a presentation suitable for public outreach. Content and images are expected to be presented publicly, including at EAA Airventure Oshkosh 2022.
The DRF team in collaboration with the California Civil Air Patrol (CAP), executed a field test that identified a preliminary set of data and reasoning service areas that can be enabled and enhanced by DRF to potentially bring forth improved accuracy and reduced latency to CAP’s mission critical decision making, in wildfire detection.
There are multiple opportunities to utilize Data & Reasoning Fabric technologies in wildfire response: (1) Supporting autonomous agents to discover data sources and transact with systems, (2) Fusing data in real-time, and (3) Supporting complex operations in a dynamic real-time environment .
The Data & Reasoning Fabric activity is submitting a response to a request for information answering, "Topic 2: Critical elements of AAM requiring global interoperability and harmonization". If selected this paper will be expected to be presented publicly.
Reasoning and querying over data streams rely on the abil- ity to deliver a sequence of stream snapshots to the processing algo- rithms. These snapshots are typically provided using windows as views into streams and associated window management strategies. Generally, the goal of any window management strategy is to preserve the most im- portant data in the current window and preferentially evict the rest, so that the retained data can continue to be exploited. A simple timestamp- based strategy is rst-in-rst-out (FIFO), in which items are replaced in strict order of arrival. All timestamp-based strategies implicitly assume that a temporal ordering reliably re ects importance to the processing task at hand, and thus that window management using timestamps will maximize the ability of the processing algorithms to deliver accurate interpretations of the stream. In this work, we explore a general no- tion of semantic importance that can be used for window management for streams of RDF data using semantically-aware processing algorithms like deduction or semantic query. Semantic importance exploits the infor- mation carried in RDF and surrounding ontologies for ranking window data in terms of its likely contribution to the processing algorithms. We explore the general semantic categories of query contribution, prove- nance, and trustworthiness, as well as the contribution of domain-specic ontologies. We describe how these categories behave using several con- crete examples. Finally, we consider how a stream window management strategy based on semantic importance could improve overall processing performance, especially as available window sizes decrease.
Rationale Various occupational groups are required to maintain optimal physical and cognitive function during overnight periods of wakefulness, often with less than optimal sleep. Strategies are required to help mitigate the impairments in cognitive function to help sustain workplace safety and productivity. Objectives To test the effectiveness of repeated 200 mg doses of caffeine on cognitive function and live-fire marksmanship with soldiers during three successive nights of sustained wakefulness followed by 4-h afternoon sleep periods. Methods Twenty Special Forces personnel (28.6±4.7 years, 177.6±7.5 cm and 81.2±8.0 kg) were randomly assigned to receive four 200-mg doses of caffeine (n=10) or placebo (n= 10) during the late evening and early morning hours during three successive days. An afternoon 4-h sleep period followed. The psychomotor (PVT) and field (FVT) vigilance, logical reasoning (LRT) tests and a vigilance monitor assessed cognitive function throughout the study. Live-fire marksmanship requiring friend–foe discrimination was assessed. Results Caffeine maintained speed on the PVT (p<0.02), improved detection of events during FVT (p<0.001), increased number of correct responses to stimuli as assessed by the vigilance monitor (p<0.001) and increased response speed during the LRT (p<0.001) throughout the three overnight testing periods. Live-fire marksmanship was not altered by caffeine. Conclusions A total daily dose of 800 mg caffeine during successive overnight periods of wakefulness is an effective strategy to maintain cognitive function when optimal sleep periods during the day are not available.
The present work systematically correlates the strength–ductility relationship of a Ti-Si-modified Fe-Mn-Al-C steel with its microstructural characteristics such as grain size, type and morphology of precipitates, and the transformation-induced plasticity (TRIP) effect during hot deformation. The Ti-Si-modified multicomponent steel developed through melting and casting route is subjected to thermo-mechanical processing such as hot forging (at 1373 K) and subsequent hot rolling (at 1173 K). An excellent combination of ultra-high tensile strength (UTS ~ 1700 ± 20 MPa), reasonable ductility (elongation ~ 11%) and high work hardening behavior (n ~ 0.89) is achieved in the hot-rolled specimen as compared to the hot-forged one (UTS ~ 824 ± 9 MPa, n ~ 0.07) with negligible change in the elongation. The better tensile properties of the hot-rolled specimen in contrast to the hot-forged one are due to the combined effects of grain refinement during rolling, twin–twin interactions, precipitation strengthening by mixture of hexagonal structured Ti{sub 3}(Al,Si)C{sub 2} and Mn-Al-Si-rich carbide precipitates and most significantly the enhanced TRIP effect. The lower critical resolved shear stress value of martensitic transformation instigates the TRIP effect during tensile testing which resulted in the increased hardness and strength of the hot-rolled specimen. The above observation offers a strong support to the proposition that TRIP effect is the dominant plasticity-enhancing mechanism activated during the deformation of the low-stacking fault energy (~ 12.2 mJ/m{sup 2}) Si-Ti-modified, medium-Mn multicomponent steel employed in the present investigation.
Highlights: • Mutation R91G initiates abnormally high flexibility of β-tropomyosin (TM). • Troponin moves R91G-TM to the blocked position at low Ca{sup 2+}. • This mutation does not alter the ability of troponin to switch off actin monomers. • R91G-TM is located near the open position during the ATPase cycle at low Ca{sup 2+}. • Mutation R91G increases the fraction of rigor myosin heads at low Ca{sup 2+}. Substitution of Arg for Gly residue in 91th position in β-tropomyosin caused by a point mutation in TPM2 gene is associated with distal arthrogryposis, characterized by a high Ca{sup 2+}-sensitivity of myofilament and contracture syndrome. To understand the mechanisms of this defect, we studied multistep changes in mobility and spatial arrangement of tropomyosin, actin and myosin heads during the ATPase cycle in reconstituted ghost fibres, using the polarized fluorescence microscopy. The mutation was shown to markedly decrease the bending stiffness of β-tropomyosin in the thin filaments. In the absence of the myosin heads the mutation did not alter the ability of troponin to shift tropomyosin to the blocked position and to switch actin monomers off at low Ca{sup 2+}. During the ATPase cycle the movement of the mutant tropomyosin is restrained, it is located near the open position, which allows strong binding of the myosin heads to actin even at low Ca{sup 2+}. This may be the reason for both high Ca{sup 2+}-sensitivity and contractures associated with the Arg91Gly mutation. The use of reagents that decrease the Ca{sup 2+}sensitivity of the troponin complex may not be appropriate to restore muscle function in patients with this mutation.
Due to the stringent environmental regulatory requirements being imposed by cross-national bodies in recent years, manufacturers have to minimize the environmental impact of their products. Among those environmental impact evaluation tools available, Life Cycle Assessment (LCA) is often employed to quantify the product's environmental impact throughout its entire life cycle. However, owing to the requirements of expert knowledge in environmental science and vast effort for data collection in carrying out LCA, as well as the common absence of complete product information during product development processes, there is a need to develop a more suitable tool for product designers. An evidential reasoning-based approach, which aims at providing a fast-track method to perform design alternative evaluations for non-LCA experts, is therefore introduced as a new initiative to deal with the incomplete or uncertain information. The proposed approach also enables decision makers to quantitatively assess the life cycle phases and design alternatives by comparing their potential environmental impacts, thus effectively and efficiently facilitates the identification of greener designs. A case application is carried out to demonstrate the applicability of the proposed approach.
It was discovered around 250 years ago that some of the rock material in the walls of some hillforts had been subjected to such high temperature that it had vitrified. This prompted a debate as to the reason for it that is still going on today: did the vitrification come about as a result of hostile action, by accident, or for the purpose of constructing the fort? The present paper is based on the prerequisite that hillforts are different, and therefore should be evaluated individually. All identifiable factors of interest should be included, and especially those that might disprove any alternative. Thus, incentives, competence and petrographic aspects were evaluated for the hillfort Broborg (dated Migration Period, in Sweden A.D. 400 - 550), and it is concluded that the vitrification came about for the purpose of constructing the fort. This capability was lost when mortar was introduced half a millennium later.
The stability of the novel Pu( iv ) silicate, Cs 2 PuSi 6 O 15 , was predicted from a combination of crystal chemical reasoning and DFT calculations and confirmed by its synthesis via flux crystal growth.
Recent analysis of the interstellar helium fluxes measured in 2009-2010 at Earth's orbit by the Interstellar Boundary Explorer (IBEX) has suggested that the interstellar velocity (both direction and magnitude) is inconsistent with that derived previously from Ulysses/GAS observations made in the period from 1990 to 2002 at 1.5-5.5 AU from the Sun. Both results are model dependent, and models that were used in the analyses are different. In this paper, we perform an analysis of the Ulysses/GAS and IBEX-Lo data using our state-of-the-art three-dimensional time-dependent kinetic model of interstellar atoms in the heliosphere. For the first time, we analyze Ulysses/GAS data from year 2007, the closest available Ulysses/GAS observations in time to the IBEX observations. We show that the interstellar velocity derived from the Ulysses 2007 data is consistent with previous Ulysses results and does not agree with the velocity derived from IBEX. This conclusion is very robust since, as is shown in the paper, it does not depend on the ionization rates adopted in theoretical models. We conclude that Ulysses data are not consistent with the new local interstellar medium (LISM) velocity vector from IBEX. In contrast, IBEX data, in principle, could be explained with the LISM velocity vector derived from the Ulysses data. This is possible for the models where the interstellar temperature increased from 6300 K to 9000 K. There is a need to perform further studies of possible reasons for the broadening of the helium signal core measured by IBEX, which could be an instrumental effect or could be due to unconsidered physical processes.
With the rise of Large Language Model (LLM) generated code, including in domains like scientific computing, ensuring not only syntactical, but also mathematical correctness, has become a critical task. Traditional formal methods approaches often struggle with the ambiguity of floating-point code, and full symbolic execution is extremely costly and limited. We propose a chain-of-reasoning approach that iteratively lifts basic semantics from code into the SPIRAL system and then establishes numerical equivalency to the desired mathematical operation. Here, we leverage the ample mathematical knowledge already formalized in SPIRAL to enable the system to recognize not just different implementations of the same algorithm but fully separate approaches to solving the given problem. The chain establishes tight error bounds on the output of given code with respect to the true continuous solution it approximates, quantifying all sources of error. We demonstrate this approach by establishing the correctness of a pseudospectral solver for a simple 1-dimensional Poisson problem.
Question Answering (QA) in clinical notes has gained a lot of attention in the past few years. Existing machine reading comprehension approaches in clinical domain can only handle questions about a single block of clinical texts and fail to retrieve information about different patients and clinical notes. To handle more complex questions, we aim at creating knowledge base from clinical notes to link different patients and clinical notes, and performing knowledge base question answering (KBQA). Based on the expert annotations in n2c2, we first created the ClinicalKBQA dataset that includes 8,952 QA pairs and covers questions about seven medical topics through 322 question templates. Then, we proposed an attention-based aspect reasoning (AAR) method for KBQA and investigated the impact of different aspects of answers (e.g., entity, type, path, and context) for prediction. The AAR method achieves better performance due to the well-designed encoder and attention mechanism. In the experiments, we find that both aspects, type and path, enable the model to identify answers satisfying the general conditions and produce lower precision and higher recall. On the other hand, the aspects, entity and context, limit the answers by node-specific information and lead to higher precision and lower recall.
SRS is a web-based and mobile toolkit for information visualization and collaboration. It includes server-side components in the form of web services that provide analytical functions such as data clustering and temporal analysis. The SRS client is accessed via a web browser and uses asynchronous javascript and XML (AJAX) to interact with and visualize the results of these web services. Users of SRS can query data sources that are connected to the SRS backend, and query results are presented in the form of coordinated visual displays in a web browser that depict temporal, topical, and spatial relationships in the results. SRS also includes an early stage graphical reasoning diagram interface that helps analysts express and test hypotheses. This software was developed with funding from the Science and Technology Directorate of the U.S. Department of Homeland Security.
ChemReasoner - Catalyst Discovery via Large Language Model-driven Reasoning
This paper addresses the problem of how to select a sensor set for equipment health monitoring that meets the needs of advanced O&M tasks that target cost reduction. They include maintenance optimization and asset management for the existing fleet and near-autonomous operation as currently envisioned for advanced reactors. The method uses physics-based automated reasoning to provide for a more “explainable” diagnosis. The algorithm is described along with its implementation on a computational cluster. Preliminary results for application to a use case in the current fleet are described.
Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d -1 (95% CI: 1.06 to 1.27 d -1 ), 0.777 d -1 (0.716 to 0.838 d -1 ), and 0.450 d -1 (0.378 to 0.522 d -1 ) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model.