424 research outputs found
Design and fabrication of a prototype aluminum nitride-based pressure sensor with finite element analysis and validation
Since 1985 when the first robot PUMA 560 was employed to place a needle during a brain CT biopsy, surgical robots have become ubiquitous in clinical surgeries. Despite its advantages and success in surgeries, the interactions between the robot and the surgeons remain deficient, especially for the pressure sensing which plays an important role. Inspired by our previous work on bacterial sensing, in the current work I have designed, fabricated, analyzed, and evaluated an innovative prototype pressure sensor based on Aluminum Nitride (AlN) Surface Acoustic Wave (SAW) and Shear Horizontal (SH)-SAW. This AlN-based device has unique superiority over other SAW devices, including relatively lower cost, higher sensitivity, intrinsically higher reliability, more compact size, and faster response. In this novel design a sandwich-like structure is adopted and the AlN thin film on the top is used as the insulated layer to make the device applicable in aqueous environment. The delta function analysis and structural mechanics analysis have been performed to validate the proposed design scheme qualitatively. So as to make a quantitative and comprehensive analysis, the numerical computational analysis using finite element method (FEM) has been carried out using the software package COMSOL Multiphysics®. The 2D plane-strain simulation and 3D simplified model simulation have been executed to analyze the device performance with or without insulator. A good agreement has been achieved between the simulation and the experimental measurements, which validates the design scheme and establishes the effectiveness of the device. This SAW/SH-SAW device has been fabricated in the WSU SSIM clean room. The crystalline AlN thin film is deposited on A-plane sapphire with 2 µm thickness using the PSMBE system. The aluminum interdigital transducer (IDT) is evaporated on the AlN thin film with predefined delay-line pattern using the BJD-1800 vacuum deposition system. Another layer of AlN thin film with 1 µm thickness is deposited on the top of the IDT area with some customized masks to make the device insulated.
Furthermore, the differential frequency measurement system has been set up using electronic components to evaluate the system. Several signal processing algorithms are developed and compared to acquire system output. The thermal stability of the differential system is also studied and temperature compensation is developed to improve system robustness. The portable electrical circuit involving the frequency measurement system is finally designed and evaluated. Such a sensor could serve as a key component in artificial skin or be equipped on the end of a surgical robotic arm in the future
Identification and mechanical control of ferroelastic domain structure in rhombohedral CaMnO
We report on observation of ferroelastic domain structure in single crystals
of multiferroic CaMnO at room temperature. Two types of ferroelastic
domain wall are found, consistent with the material's rhombohedral symmetry
that is reduced from cubic symmetry at higher temperatures. Using Raman
spectroscopy along with other measurements, we develop a systematic method to
determine the microscopic domain orientation. Moreover, we find a switching
behavior of the domains, which allows us to detwin the crystals conveniently at
room temperature using a moderate uniaxial compression. Our result paves the
way for further spectroscopic study and domain engineering in CaMnO.Comment: 7 pages, 4 figure
Soft vibrational mode associated with incommensurate orbital order in multiferroic CaMnO
We report inelastic light scattering measurements of lattice dynamics related
to the incommensurate orbital order in . Below the
ordering temperature , we observe extra
phonon peaks as a result of Brillouin-zone folding, as well as a soft
vibrational mode with a power-law -dependent energy, . This temperature dependence demonstrates the
second-order nature of the transition at , and it indicates that
the soft mode can be regarded as the amplitude excitation of the composite
order parameter. Our result strongly suggests that the lattice degrees of
freedom are actively involved in the orbital-ordering mechanism.Comment: 7 pages, 8 figure
SSL Framework for Causal Inconsistency between Structures and Representations
The cross-pollination of deep learning and causal discovery has catalyzed a
burgeoning field of research seeking to elucidate causal relationships within
non-statistical data forms like images, videos, and text. Such data, often
being named `indefinite data', exhibit unique challenges-inconsistency between
causal structure and representation, which are not common in conventional data
forms. To tackle this issue, we theoretically develop intervention strategies
suitable for indefinite data and derive causal consistency condition (CCC).
Moreover, we design a self-supervised learning (SSL) framework that considers
interventions as `views' and CCC as a `philosophy' with two implement examples
on Supervised Specialized Models (SSMs) and Large Language Models (LLMs),
respectively. To evaluate pure inconsistency manifestations, we have prepared
the first high-quality causal dialogue dataset-Causalogue. Evaluations are also
performed on three other downstream tasks. Extensive experimentation has
substantiated the efficacy of our methodology, illuminating how CCC could
potentially play an influential role in various fields
A Systematic Literature Review: The Modalities, Pedagogies, Benefits, and Implications of Storytelling Approaches in Early Childhood Education Classroom
Abstract
The purpose of this systematic literature review was to investigate and synthesize several aspects of storytelling in the reviewed scholarly research, providing a holistic summary and potential insights for early childhood educators. The study asked: (1) What are the various forms, modes and media, and involved pedagogies that storytelling in early childhood education can take? (2) What are the reported benefits of storytelling in early childhood education? (3) Based on the literature, what understandings and pedagogical implications are enriched for early childhood educators to utilize storytelling in their pedagogies? Using a theoretical framework based in multimodal literacy and sociocultural theory, data for the study were derived from 33 screened articles that had been published in the last 10 years. The findings showcase that educators use diverse storytelling approaches with multimodal ensembles in early childhood education, and storytelling was found to provide children a variety of different opportunities to make meaning of the world and express it. By being immersed in storytelling, children were documented in the literature as benefiting from considerable immediate and long-term effects. This study offers understandings of a diversity of forms of storytelling and instructional implications for engaging children through multimodal participation. Additionally, this study may provide baseline knowledge for teacher education to improve storytelling strategies and corresponding multimodal scaffolding feedback, which may provide insights into supporting young children’s storytelling experiences
Machine Learning Approaches to Predict PM2.5 Using Satellite Images
According to World Health Organization, air pollution is considered to be one of the greatest environmental health threats. PM2.5, fine particles with a diameter that is generally 2.5 micrometers and smaller, is inhalable into the lungs and can induce adverse health effects. In order to mitigate the effects of PM2.5 on health outcomes, it is crucial to make accurate predictions of ambient concentrations. In the past, most studies developed traditional linear models from meteorological data or Environmental Protection Agency (EPA) ambient air quality monitors. However, the non-linear relationship between PM2.5 and other factors impacts the effectiveness of the models. Some other barriers are the sparseness of air quality monitors and the limited data sources, which also hinder researchers’ ability to get accurate predictions. Recently, advanced technologies in satellite remote sensing have been widely used to estimate PM2.5 concentrations. The implementation of machine learning approaches has improved computational efficiency and accuracy. In this study, we used daily satellite imagery from January 2017 to October 2021 in 25 U.S. locations, and implemented an eXtreme Gradient Boosting (XGBoost) algorithm, a deep Convolutional Neural Network (CNN), and a CNN-XGBoost pipeline to make predictions based on the extracted features from each satellite image and atmospheric information along with meteorological conditions. To evaluate the performance of each model, we used daily EPA Federal Reference Method PM2.5 measurements as the validation data, and calculated the corresponding root mean squared error (RMSE) and the coefficient of determinant (R^2). After combining each daily observation from 25 locations, the XGBoost approach demonstrated the highest performance with an RMSE of 3.98 microgram per cubic meter and an R^2 of 0.65. The CNN-XGBoost pipeline, tending to overestimate PM2.5 concentrations, had an RMSE of 5.87 microgram per cubic meter and an R^2 of 0.37. In conclusion, our study showed that XGBoost achieved reasonable PM2.5 prediction performance, indicating that the application of satellite remote sensing data and machine learning approaches has significant potential use in PM2.5 concentrations prediction. The data and R code used in this thesis is available on GitHub (https://github.com/sindydu0904/Satellite_pmPredict)
Semantic Feature Learning for Universal Unsupervised Cross-Domain Retrieval
Cross-domain retrieval (CDR), as a crucial tool for numerous technologies, is
finding increasingly broad applications. However, existing efforts face several
major issues, with the most critical being the need for accurate supervision,
which often demands costly resources and efforts. Cutting-edge studies focus on
achieving unsupervised CDR but typically assume that the category spaces across
domains are identical, an assumption that is often unrealistic in real-world
scenarios. This is because only through dedicated and comprehensive analysis
can the category spaces of different domains be confirmed as identical, which
contradicts the premise of unsupervised scenarios. Therefore, in this work, we
introduce the problem of Universal Unsupervised Cross-Domain Retrieval (U^2CDR)
for the first time and design a two-stage semantic feature learning framework
to address it. In the first stage, a cross-domain unified prototypical
structure is established under the guidance of an instance-prototype-mixed
contrastive loss and a semantic-enhanced loss, to counteract category space
differences. In the second stage, through a modified adversarial training
mechanism, we ensure minimal changes for the established prototypical structure
during domain alignment, enabling more accurate nearest-neighbor searching.
Extensive experiments across multiple datasets and scenarios, including closet,
partial, and open-set CDR, demonstrate that our approach significantly
outperforms existing state-of-the-art CDR works and some potentially effective
studies from other topics in solving U^2CDR challenges.Comment: 18 pages, 4 figures, ongoing wor
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