An official website of the United States government
This paper explores customized scaffolding for pre-service teachers’ problem-solving in technology and engineering discipline. We used clustering analysis to discover natural groupings of scaffolding characteristics which were used in 144 computer-based scaffolding studies from the previous meta-analysis. We first selected input variables based on our research questions which include different scaffolding characteristics, context of use, education level, and effect size. Next, using a two-step clustering algorithm, we found four clusters based on the predominant scaffolding characteristics and profiled each cluster in terms of scaffolding characteristics and their context of use. The resulting cluster solutions indicate what combination of scaffolding characteristics used in different types of problem-centered learning context would be effective for pre-service teachers’ technology- and engineering-related problem-solving.
more »
« less
Machine learning-based design of meta-plasmonic biosensors with negative index metamaterials
In this work, we explore the performance of plasmonic biosensor designs that integrate metamaterials based on machine learning algorithms. The meta-plasmonic biosensors were designed for optimized detection of DNA with a layer of double negative metamaterial modeled by an effective medium. An iterative transfer matrix approach was employed to generate training and test sets of resonance characteristics in the parameter space for machine learning. As a machine learning-based prediction of optical characteristics of a meta-plasmonic biosensor, multilayer perceptron and autoencoder (AE) were used as an algorithm, while the clustering algorithm was constructed by dimensional reduction based on AE and t-Stochastic Neighbor Embedding (t-SNE) as well as k-means clustering. Use of meta-plasmonic structure with analysis based on machine learning has found that enhancement of detection sensitivity by more than 13 times over conventional detection should be achievable with excellent reflectance curves. Further enhancement may be attained by expanding the parameter space.
more »
« less
- Award ID(s):
- 1856165
- PAR ID:
- 10224830
- Date Published:
- Journal Name:
- Biosensors bioelectronics
- Volume:
- 164
- ISSN:
- 1873-4235
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Engineering programs must weave coverage of disciplinary content with the ability of students to apply and extend this content knowledge to new contexts and for use in their professional practice as engineers. It is, therefore, necessary for schools to promote and cultivate additional dispositions within their students that better enable them to adapt and employ their disciplinary knowledge. The concept of an “adaptive expert” (AE) has been previously developed within the learning sciences to describe an individual with deep content knowledge but who also displays additional cognitive characteristics that better enable them to employ their knowledge and skills in practice. Four constructs have been identified in the literature as forming the basis of this adaptive expertise: 1) multiple perspectives (MP), 2) metacognition (META), 3) goals and beliefs (GB), and 4) epistemology (EPIST). Upon entry to an engineering program, it is likely that students will present with different levels of development and awareness within these particular dimensions. Baseline levels must, therefore, be measured in order to assess these levels of development and before research-based practices and activities can be designed to promote growth in these constructs. In this work-in-progress study, the “adaptiveness” of incoming undergraduate STEM students (n=711) is measured using a previously developed validated survey instrument used in other studies to measure levels of adaptive expertise amongst undergraduate students by determining their levels along the four identified dimensions of AE. Based on this survey data, statistically significant differences were found in the AE constructs for men and women, with women outscoring men in three of the four AE subscales (MP, META, EPIST) and men outscoring women in goals and beliefs (GB). White students were found to score statistically higher than Asian students in both multiple perspectives (MP) and goals and beliefs (GB), while no statistically significant differences were observed when White and Black/African American students were compared. The mean epistemology (EPIST) scores for White, non-Hispanic students was statistically higher than Hispanic students, with low-income students scoring lower than non-low-income students on this subscale. This project seeks to provide baseline data concerning the adaptivity of incoming first-year students. A structured mentoring program focusing on elements of AE will then be implemented and student growth in the dimensions of AE assessed through their program of study.more » « less
-
The comprehensive properties of high-entropy alloys (HEAs) are highly-dependent on their phases. Although a large number of machine learning (ML) algorithms has been successfully applied to the phase prediction of HEAs, the accuracies among different ML algorithms based on the same dataset vary significantly. Therefore, selection of an efficient ML algorithm would significantly reduce the number and cost of the experiments. In this work, phase prediction of HEAs (PPH) is proposed by integrating criterion and machine learning recommendation method (MLRM). First, a meta-knowledge table based on characteristics of HEAs and performance of candidate algorithms is established, and meta-learning based on the meta-knowledge table is adopted to recommend an algorithm with desirable accuracy. Secondly, an MLRM based on improved meta-learning is engineered to recommend a more desirable algorithm for phase prediction. Finally, considering poor interpretability and generalization of single ML algorithms, a PPH combining the advantages of MLRM and criterion is proposed to improve the accuracy of phase prediction. The PPH is validated by 902 samples from 12 datasets, including 405 quinary HEAs, 359 senary HEAs, and 138 septenary HEAs. The experimental results shows that the PPH achieves performance than the traditional meta-learning method. The average prediction accuracy of PPH in all, quinary, senary, and septenary HEAs is 91.6%, 94.3%, 93.1%, and 95.8%, respectively.more » « less
-
This paper presents Hit2Flux, a machine learning framework for boiling heat flux prediction using acoustic emission (AE) hits generated through threshold-based transient sampling. Unlike continuously sampled data, AE hits are recorded when the signal exceeds a predefined threshold and are thus discontinuous in nature. Meanwhile, each hit represents a waveform at a high sampling frequency ( 1 MHz). In order to capture the features of both the high-frequency waveforms and the temporal distribution of hits, Hit2Flux involves i) feature extraction by transforming AE hits into the frequency domain and organizing these spectra into sequences using a rolling window to form “sequences-of-sequences,” and ii) heat flux prediction using a long short-term memory (LSTM) network with sequences of sequences. The model is trained on AE hits recorded during pool boiling experiments using an AE sensor attached to the boiling chamber. Continuously sampled acoustic data using a hydrophone were also collected as a reference data set for this study. Results demonstrate that the proposed AE-based method achieves performance comparable to hydrophones, validating its potential for heat flux monitoring. Additionally, it is shown that the inclusion of multiple acoustic emission hits as model inputs leads to higher performance. The Hit2Flux model is also compared to methods pairing various signal preparation techniques with state-of-the-art models. This comparison further highlighted the superior accuracy of the proposed approach. The developed Hi2Flux algorithm can be applied to other transient sampling events, such as structural health monitoring, detection of electromagnetic pulses, among others.more » « less
-
We present an analysis of CO rovibrational emission lines in the 183 infrared spectra of nearby Class II objects obtained with the NIRSPEC instrument on the Keck II telescope over the past two decades. The sample includes a broad range of stellar mass (both T Tauri and Herbig Ae/Be) and disk evolutionary states (from full to debris disks). We find that 53% of the sample has CO rovibrational emission lines present in their spectrum with disk/stellar subtype detection rates of 82% for transition disks, 61% for Herbigs, and 77% for classical T Tauri stars. Although there is no discernible difference between T Tauri and Herbig Ae/Be star CO detection rates, the detection of accretion and of CO are statistically correlated in T Tauri stars but not in Herbig Ae/Be objects. Within the sample of T Tauri stars, we find that no weak-line T Tauri stars have CO rovibrational emission lines. We use slab modeling to analyze the density, temperature, and emitting area of the sample. The retrieval results imply that Herbig Ae/Be objects tend to have cooler and larger CO emitting regions than T Tauri stars. We find that the CO emitting area is not a thin ring as defined by temperature, but a ring of varying size, likely dependent on the structure of the disk. We also present guidelines on how to approach CO rovibrational emission lines in JWST spectra and present methods for linking ground-based observations with JWST spectra. This includes line-to-continuum ratio estimates based on stellar mass and accretion rate.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/https://doi.org/10.1016 /j.bios.2020.112335
