Use of the Long Duration Exposure Facility's thermal measurement system for the verification of thermal models

The Long Duration Exposure Facility (LDEF) postflight thermal model predicted temperatures were matched to flight temperature data recorded by the Thermal Measurement System (THERM), LDEF experiment P0003. Flight temperatures, recorded at intervals of approximately 112 minutes for the first 390 days of LDEF's 2105 day mission were compared with predictions using the thermal mathematical model (TMM). This model was unverified prior to flight. The postflight analysis has reduced the thermal model uncertainty at the temperature sensor locations from +/- 40 F to +/- 18 F. The improved temperature predictions will be used by the LDEF's principal investigators to calculate improved flight temperatures experienced by 57 experiments located on 86 trays of the facility

Long duration exposure facility solar illumination data package

A post flight solar illumination data package was created by the LDEF thermal analysis data group in support of the LDEF science office data group. The data presented was prepared with the Thermal Radiation Analysis System (TRASYS) program. Ground tracking data was used to calculate daily orbital beta angles for the calculation of resultant fluxes. This data package will be useful in calculation of solar illumination fluent for a variety of beta angle orbital conditions encountered during the LDEF mission

Long duration exposure facility post-flight thermal analysis: Orbital/thermal environment data package

A post flight mission thermal environment for the Long Duration Exposure Facility was created as part of the thermal analysis data reduction effort. The data included herein is the thermal parameter data used in the calculation of boundary temperatures. This boundary temperature data is to be released in the near future for use by the LDEF principal investigators in the final analysis of their particular experiment temperatures. Also included is the flight temperature data as recorded by the LDEF Thermal Measurements System (THERM) for the first 90 days of flight

Long duration exposure facility post-flight thermal analysis, part 1

Results of the post-flight thermal analysis of the Long Duration Exposure Facility (LDEF) mission are presented. The LDEF mission thermal analysis was verified by comparing the thermal model results to flight data from the LDEF Thermal Measurements System (THERM). Post-flight calculated temperature uncertainties have been reduced to under +/- 18 F from the pre-flight uncertainties of +/- 40 F. The THERM consisted of eight temperature sensors, a shared tape recorder, a standard LDEF flight battery, and an electronics control box. The temperatures were measured at selected locations on the LDEF structure interior during the first 390 days of flight and recorded for post-flight analysis. After the LDEF retrieval from Space on 12 Jan. 1990, the tape recorder was recovered from the spacecraft and the data reduced for comparison to the LDEF predicted temperatures. The LDEF mission temperatures were calculated prior to the LDEF deployment on 7 Apr. 1980, and updated after the LDEF retrieval with the following actual flight parameter data: including thermal fluxes, spacecraft attitudes, thermal coatings degradation, and contamination effects. All updated data used for the calculation of post-flight temperatures is also presented in this document

BI-LAVA: Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis

In the biomedical domain, taxonomies organize the acquisition modalities of scientific images in hierarchical structures. Such taxonomies leverage large sets of correct image labels and provide essential information about the importance of a scientific publication, which could then be used in biocuration tasks. However, the hierarchical nature of the labels, the overhead of processing images, the absence or incompleteness of labeled data, and the expertise required to label this type of data impede the creation of useful datasets for biocuration. From a multi-year collaboration with biocurators and text-mining researchers, we derive an iterative visual analytics and active learning strategy to address these challenges. We implement this strategy in a system called BI-LAVA Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis. BI-LAVA leverages a small set of image labels, a hierarchical set of image classifiers, and active learning to help model builders deal with incomplete ground-truth labels, target a hierarchical taxonomy of image modalities, and classify a large pool of unlabeled images. BI-LAVA's front end uses custom encodings to represent data distributions, taxonomies, image projections, and neighborhoods of image thumbnails, which help model builders explore an unfamiliar image dataset and taxonomy and correct and generate labels. An evaluation with machine learning practitioners shows that our mixed human-machine approach successfully supports domain experts in understanding the characteristics of classes within the taxonomy, as well as validating and improving data quality in labeled and unlabeled collections.Comment: 15 pages, 6 figure

Towards Language Models That Can See: Computer Vision Through the LENS of Natural Language

We propose LENS, a modular approach for tackling computer vision problems by leveraging the power of large language models (LLMs). Our system uses a language model to reason over outputs from a set of independent and highly descriptive vision modules that provide exhaustive information about an image. We evaluate the approach on pure computer vision settings such as zero- and few-shot object recognition, as well as on vision and language problems. LENS can be applied to any off-the-shelf LLM and we find that the LLMs with LENS perform highly competitively with much bigger and much more sophisticated systems, without any multimodal training whatsoever. We open-source our code at https://github.com/ContextualAI/lens and provide an interactive demo

An Autism-Linked Mutation Disables Phosphorylation Control of UBE3A

Deletion of UBE3A causes the neurodevelopmental disorder Angelman syndrome (AS) while duplication or triplication of UBE3A is linked to autism. These genetic findings suggest that the ubiquitin ligase activity of UBE3A must be tightly maintained to promote normal brain development. Here, we found that protein kinase A (PKA) phosphorylates UBE3A in a region outside the catalytic domain, at residue T485, and inhibits UBE3A activity towards itself and other substrates. A de novo autism-linked missense mutation disrupts this phosphorylation site, causing enhanced UBE3A activity in vitro, enhanced substrate turnover in patient-derived cells, and excessive dendritic spine development in the brain. Our study identifies PKA as an upstream regulator of UBE3A activity, and shows that an autism-linked mutation disrupts this phosphorylation control. Moreover, our findings implicate excessive UBE3A activity and the resulting synaptic dysfunction to autism pathogenesis

An Autism-Linked Mutation Disables Phosphorylation Control of UBE3A

Deletion of UBE3A causes the neurodevelopmental disorder Angelman syndrome (AS) while duplication or triplication of UBE3A is linked to autism. These genetic findings suggest that the ubiquitin ligase activity of UBE3A must be tightly maintained to promote normal brain development. Here, we found that protein kinase A (PKA) phosphorylates UBE3A in a region outside the catalytic domain, at residue T485, and inhibits UBE3A activity towards itself and other substrates. A de novo autism-linked missense mutation disrupts this phosphorylation site, causing enhanced UBE3A activity in vitro, enhanced substrate turnover in patient-derived cells, and excessive dendritic spine development in the brain. Our study identifies PKA as an upstream regulator of UBE3A activity, and shows that an autism-linked mutation disrupts this phosphorylation control. Moreover, our findings implicate excessive UBE3A activity and the resulting synaptic dysfunction to autism pathogenesis

The Mobile Agents 2005 Field Test at MDRS: Planning for Exploration

The Mars Society s Desert Research Station (MDRS) Rotation 38, April 3-17, 2005, was dedicated to field tests of NASA's Mobile Agents EVA communications system. MDRS provided an excellent, cost-effective venue for bringing together eighteen scientists and engineers from NASA Ames and Johnson Space Center, in an intensive two weeks of system integration and experiments. The Mobile Agents architecture and collaborative engineering methodology provides a flexible toolkit for configuring extravehicular activity (EVA) components, visualizing and formalizing EVA plans, and automating key supervisory functions

PROMOTING CREATIVITY AND INNOVATION THROUGH AGILE ENGINEERING IN DESIGN EDUCATION: COMPARATIVE ANALYSIS IN LATIN AMERICA

The research bridges the knowledge gap by examining the role of Agile Engineering in enhancing creativity and innovation within Latin American design education, providing a comprehensive comparative assessment. The study's overarching goal is to find effective ways to increase student engagement in the classroom. To test the efficacy of the suggested strategy, researchers examined data from five Latin American countries: Peru, Mexico, the Dominican Republic, Puerto Rico, and Cuba. Some elements considered include student concept support, school debates, student trust in the education system, and student participation. This research proposes an agile education framework for software development courses in music education using deep learning. Each system component is treated as if it were an IoT device because the framework is based on the Internet of Things concept. With an average score of 2.1, significantly higher than the control group under stagnant conditions, the data reveal that the Genetic Algorithm with Randomized Search Method significantly improves classroom arguments. On the other hand, student idea support scored an average of 0.1 on the effectiveness scale in the creative mode. Such findings highlight the potential of innovative educational strategies and offer guidance for educators and policymakers aiming to boost student involvement and creativity in the region