Search for: All records

This paper introduces Helping Interdisciplinary Vocabulary Engineering for Materials Science (HIVE-4-MAT), an automatic linked data ontology application. The paper provides contextual background for materials science, shared ontology infrastructures, and knowledge extraction applications. HIVE-4-MAT's three key features are reviewed: 1) Vocabulary browsing, 2) Term search and selection, and 3) Knowledge Extraction/Indexing, as well as the basics of named entity recognition (NER). The discussion elaborates on the importance of ontology infrastructures and steps taken to enhance knowledge extraction. The conclusion highlights next steps surveying the ontology landscape, including NER work as a step toward relation extraction (RE), and support for better ontologies. 

Touch is often omitted or viewed as unnecessary in digital learning. Lack of touch feedback limits the accessibility and multimodal capacity of digital educational content. Touchscreens with vibratory, haptic feedback are prevalent, yet this kind of feedback is often under-utilized. This work provides initial investigations into the design, development, and use of vibratory feedback within multimodal, interactive, educational simulations on touchscreen devices by learners with and without visual impairments. The objective of this work is to design and evaluate different haptic paradigms that could support interaction and learning in educational simulations. We investigated the implementation of four haptic paradigms in two physics simulations. Interviews were conducted with eight learners (five sighted learners; three learners with visual impairments) on one simulation and initial results are shared. We discuss the learner outcomes of each paradigm and how they impact design and development moving forward. 

Biodiversity image repositories are crucial sources of training data for machine learning approaches to biological research. Metadata, specifically metadata about object quality, is putatively an important prerequisite to selecting sample subsets for these experiments. This study demonstrates the importance of image quality metadata to a species classification experiment involving a corpus of 1935 fish specimen images which were annotated with 22 metadata quality properties. A small subset of high quality images produced an F1 accuracy of 0.41 compared to 0.35 for a taxonomically matched subset of low quality images when used by a convolutional neural network approach to species identification. Using the full corpus of images revealed that image quality differed between correctly classified and misclassified images. We found the visibility of all anatomical features was the most important quality feature for classification accuracy. We suggest biodiversity image repositories consider adopting a minimal set of image quality metadata to support future machine learning projects. 

One of the fundamental goals of chemistry is to determine how molecular structure influences interactions and leads to different reaction products. Studies of isomer-selected and resolved chemical reactions can shed light directly on how form leads to function. In the following, we present the results of gas-phase reactions between acetylene cations (C 2 D 2 + ) with two different isomers of C 3 H 4 : propyne (DC 3 D 3 ) and allene (H 2 C 3 H 2 ). Our highly controlled, trapped-ion environment allows for precise determination of reaction products and kinetics. From these results, we can infer details of the underlying reaction dynamics of C 2 H 2 + + C 3 H 4 . Through the synergy of experimental results and high-level quantum chemical potential energy surface calculations, we are able to identify distinct reaction mechanisms for the two isomers. We find long-range charge exchange with no complex formation is favored for allene, whereas charge exchange leads to an intermediate reaction complex for propyne and thus, different products. Therefore, this reaction displays a pronounced isomer-selective bi-molecular reactive process. 

Snow disappearance date (SDD) affects the ecohydrological dynamics of montane forests, by altering water availability, forest fire regime, and the land surface energy budget. The forest canopy modulates SDD through competing processes; dense canopy intercepts snowfall and enhances longwave radiation while shading snowpack from shortwave radiation and sheltering it from the wind. Limited ground‐based observations of snow presence and absence have restricted our ability to unravel the dominant processes affecting SDD in montane forests. We apply a lidar‐derived method to estimate fractional snow cover area (fSCA) at two relatively warm sites in the Sierra Nevada and two colder sites in the Rocky Mountains, which we link to SDD. With the exception of late season snowpack and low fSCA, snow retention is longer under low vegetation density than under high vegetation density in both warm and cold sites. Warm forests consistently have longer snow retention in open areas compared to dense under canopy areas, particularly on south‐facing slopes. Cold forests tend to have longer snow retention under lower density canopy compared to open areas, particularly on north‐facing slopes. We use this empirical analysis to make process inferences and develop an initial framework to predict SDD that incorporates the role of topography and vegetation structure. Building on our framework will be necessary to provide better forest management recommendations for snowpack retention across complex terrain and heterogenous canopy structure.