Total Ionizing Dose Tolerance of Micro-SD Cards for Small Satellite Missions

Tests have determined damage thresholds and failure rates as a function of total ionizing dose (TID) of beta radiation for various types of COTS micro-SD cards commonly used for memory storage in space applications. Radiation tolerance of high-density electronics are common critical failure modes for satellites, particularly for small satellites that often use lower shielding and less radiation-hardened COTS components. The tests evaluated SD-card formatting and read/write speeds at nine radiation intervals for up to ~ 1000 Gy TID, equivalent to ~15 times TID typically experienced annually on the unshielded exterior of satellites in Low Earth Orbit. A limited number of failures were observed beginning after ~ 400 Gy TID. Cards experiencing failures were subsequently tested at more rapid interval intervals, and typically recovered their initial read/write speeds after ≤ 24 hrs, except in more severe cases after \u3e 400 Gy TID. These results will facilitate satellite designers’ selection of the appropriate quality and cost of the micro-SD cards for their particular mission, based on reliability and radiation tolerance

Shape variation analyzer: A classifier for temporomandibular joint damaged by osteoarthritis

We developed a deep learning neural network, the Shape Variation Analyzer (SVA), that allows disease staging of bony changes in temporomandibular joint (TMJ) osteoarthritis (OA). The sample was composed of 259 TMJ CBCT scans for the training set and 34 for the testing dataset. The 3D meshes had been previously classified in 6 groups by 2 expert clinicians. We improved the robustness of the training data using data augmentation, SMOTE, to alleviate over-fitting and to balance classes. We combined geometrical features and a shape descriptor, heat kernel signature, to describe every shape. The results were compared to nine different supervised machine learning algorithms. The deep learning neural network was the most accurate for classification of TMJ OA. In conclusion, SVA is a 3D Slicer extension that classifies pathology of the temporomandibular joint osteoarthritis cases based on 3D morphology

A web-based system for statistical shape analysis in temporomandibular joint osteoarthritis

This study presents a web-system repository: Data Storage for Computation and Integration (DSCI) for Osteoarthritis of the temporomandibular joint (TMJ OA). This environment aims to maintain and allow contributions to the database from multi-clinical centers and compute novel statistics for disease classification. For this purpose, imaging datasets stored in the DSCI consisted of three-dimensional (3D) surface meshes of condyles from CBCT, clinical markers and biological markers in healthy and TMJ OA subjects. A clusterpost package was included in the web platform to be able to execute the jobs in remote computing grids. The DSCI application allowed runs of statistical packages, such as the Multivariate Functional Shape Data Analysis to compute global correlations between covariates and the morphological variability, as well as local p-values in the 3D condylar morphology. In conclusion, the DSCI allows interactive advanced statistical tools for non-statistical experts

Zygomaticomaxillary suture maturation: Part IIâ The influence of sutural maturation on the response to maxillary protraction

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137741/1/ocr12191_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137741/2/ocr12191.pd

Diagnostic index: An open-source tool to classify TMJ OA condyles

Osteoarthritis (OA) of temporomandibular joints (TMJ) occurs in about 40% of the patients who present TMJ disorders. Despite its prevalence, OA diagnosis and treatment remain controversial since there are no clear symptoms of the disease, especially in early stages. Quantitative tools based on 3D imaging of the TMJ condyle have the potential to help characterize TMJ OA changes. The goals of the tools proposed in this study are to ultimately develop robust imaging markers for diagnosis and assessment of treatment efficacy. This work proposes to identify differences among asymptomatic controls and different clinical phenotypes of TMJ OA by means of Statistical Shape Modeling (SSM), obtained via clinical expert consensus. From three different grouping schemes (with 3, 5 and 7 groups), our best results reveal that that the majority (74.5%) of the classifications occur in agreement with the groups assigned by consensus between our clinical experts. Our findings suggest the existence of different disease-based phenotypic morphologies in TMJ OA. Our preliminary findings with statistical shape modeling based biomarkers may provide a quantitative staging of the disease. The methodology used in this study is included in an open source image analysis toolbox, to ensure reproducibility and appropriate distribution and dissemination of the solution proposed

Insulated gate and surface passivation structures for GaN-based power transistors

Recent years have witnessed GaN-based devices delivering their promise of unprecedented power and frequency levels and demonstrating their capability as an able replacement for Si-based devices. High-electron-mobility transistors (HEMTs), a key representative architecture of GaN-based devices, are well-suited for high-power and high frequency device applications, owing to highly desirable III-nitride physical properties. However, these devices are still hounded by issues not previously encountered in their more established Si- and GaAs-based devices counterparts. Metal–insulator–semiconductor (MIS) structures are usually employed with varying degrees of success in sidestepping the major problematic issues such as excessive leakage current and current instability. While different insulator materials have been applied to GaN-based transistors, the properties of insulator/III-N interfaces are still not fully understood. This is mainly due to the difficulty of characterizing insulator/AlGaN interfaces in a MIS HEMT because of the two resulting interfaces: insulator/AlGaN and AlGaN/GaN, making the potential modulation rather complicated. Although there have been many reports of low interface-trap densities in HEMT MIS capacitors, several papers have incorrectly evaluated their capacitance–voltage (C–V) characteristics. A HEMT MIS structure typically shows a 2-step C–V behavior. However, several groups reported C–V curves without the characteristic step at the forward bias regime, which is likely to the high-density states at the insulator/AlGaN interface impeding the potential control of the AlGaN surface by the gate bias. In this review paper, first we describe critical issues and problems including leakage current, current collapse and threshold voltage instability in AlGaN/GaN HEMTs. Then we present interface properties, focusing on interface states, of GaN MIS systems using oxides, nitrides and high-κ dielectrics. Next, the properties of a variety of AlGaN/GaN MIS structures as well as different characterization methods, including our own photo-assisted C–V technique, essential for understanding and developing successful surface passivation and interface control schemes, are given in the subsequent section. Finally we highlight the important progress in GaN MIS interfaces that have recently pushed the frontier of nitride-based device technology

Three‐dimensional skeletal mandibular changes associated with Herbst appliance treatment

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136722/1/ocr12154_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136722/2/ocr12154.pd

Osteoarthritis of the Temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning

After chronic low back pain, Temporomandibular Joint (TMJ) disorders are the second most common musculoskeletal condition affecting 5 to 12% of the population, with an annual health cost estimated at $4 billion. Chronic disability in TMJ osteoarthritis (OA) increases with aging, and the main goal is to diagnosis before morphological degeneration occurs. Here, we address this challenge using advanced data science to capture, process and analyze 52 clinical, biological and high-resolution CBCT (radiomics) markers from TMJ OA patients and controls. We tested the diagnostic performance of four machine learning models: Logistic Regression, Random Forest, LightGBM, XGBoost. Headaches, Range of mouth opening without pain, Energy, Haralick Correlation, Entropy and interactions of TGF-β1 in Saliva and Headaches, VE-cadherin in Serum and Angiogenin in Saliva, VE-cadherin in Saliva and Headaches, PA1 in Saliva and Headaches, PA1 in Saliva and Range of mouth opening without pain; Gender and Muscle Soreness; Short Run Low Grey Level Emphasis and Headaches, Inverse Difference Moment and Trabecular Separation accurately diagnose early stages of this clinical condition. Our results show the XGBoost + LightGBM model with these features and interactions achieves the accuracy of 0.823, AUC 0.870, and F1-score 0.823 to diagnose the TMJ OA status. Thus, we expect to boost future studies into osteoarthritis patient-specific therapeutic interventions, and thereby improve the health of articular joints

A web-based system for neural network based classification in temporomandibular joint osteoarthritis

Objective: The purpose of this study is to describe the methodological innovations of a web-based system for storage, integration and computation of biomedical data, using a training imaging dataset to remotely compute a deep neural network classifier of temporomandibular joint osteoarthritis (TMJOA). Methods: This study imaging dataset consisted of three-dimensional (3D) surface meshes of mandibular condyles constructed from cone beam computed tomography (CBCT) scans. The training dataset consisted of 259 condyles, 105 from control subjects and 154 from patients with diagnosis of TMJ OA. For the image analysis classification, 34 right and left condyles from 17 patients (39.9 ± 11.7 years), who experienced signs and symptoms of the disease for less than 5 years, were included as the testing dataset. For the integrative statistical model of clinical, biological and imaging markers, the sample consisted of the same 17 test OA subjects and 17 age and sex matched control subjects (39.4 ± 15.4 years), who did not show any sign or symptom of OA. For these 34 subjects, a standardized clinical questionnaire, blood and saliva samples were also collected. The technological methodologies in this study include a deep neural network classifier of 3D condylar morphology (ShapeVariationAnalyzer, SVA), and a flexible web-based system for data storage, computation and integration (DSCI) of high dimensional imaging, clinical, and biological data. Results: The DSCI system trained and tested the neural network, indicating 5 stages of structural degenerative changes in condylar morphology in the TMJ with 91% close agreement between the clinician consensus and the SVA classifier. The DSCI remotely ran with a novel application of a statistical analysis, the Multivariate Functional Shape Data Analysis, that computed high dimensional correlations between shape 3D coordinates, clinical pain levels and levels of biological markers, and then graphically displayed the computation results. Conclusions: The findings of this study demonstrate a comprehensive phenotypic characterization of TMJ health and disease at clinical, imaging and biological levels, using novel flexible and versatile open-source tools for a web-based system that provides advanced shape statistical analysis and a neural network based classification of temporomandibular joint osteoarthritis