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"Wang, Robin"
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Yinyang : the way of heaven and earth in Chinese thought and culture
\"The concept of yinyang lies at the heart of Chinese thought and culture. The relationship between these two opposing, yet mutually dependent, forces is symbolized in the familiar black and white symbol that has become an icon in popular culture across the world. The real significance of yinyang is, however, more complex and subtle. This brilliant and comprehensive analysis by one of the leading authorities in the field captures the richness and multiplicity of the meanings and applications of yinyang, including its visual presentations. Through a vast range of historical and textual sources, the book examines the scope and role of yinyang, the philosophical significance of its various layers of meanings and its relation to numerous schools and traditions within Chinese (and Western) philosophy. By putting yinyang on a secure and clear philosophical footing, the book roots the concept in the original Chinese idiom, distancing it from Western assumptions, frameworks and terms, yet also seeking to connect its analysis to shared cross-cultural philosophical concerns\"-- Provided by publisher.
Book
Multi-Scale Surface Treatments of Titanium Implants for Rapid Osseointegration: A Review
The propose of this review was to summarize the advances in multi-scale surface technology of titanium implants to accelerate the osseointegration process. The several multi-scaled methods used for improving wettability, roughness, and bioactivity of implant surfaces are reviewed. In addition, macro-scale methods (e.g., 3D printing (3DP) and laser surface texturing (LST)), micro-scale (e.g., grit-blasting, acid-etching, and Sand-blasted, Large-grit, and Acid-etching (SLA)) and nano-scale methods (e.g., plasma-spraying and anodization) are also discussed, and these surfaces are known to have favorable properties in clinical applications. Functionalized coatings with organic and non-organic loadings suggest good prospects for the future of modern biotechnology. Nevertheless, because of high cost and low clinical validation, these partial coatings have not been commercially available so far. A large number of in vitro and in vivo investigations are necessary in order to obtain in-depth exploration about the efficiency of functional implant surfaces. The prospective titanium implants should possess the optimum chemistry, bionic characteristics, and standardized modern topographies to achieve rapid osseointegration.
Journal Article
The English Debating Self-Efficacy Scale: Scale development, validation, and psychometric properties
The importance of English debate in fostering critical thinking and the role of self-efficacy in enhancing confidence and performance in this domain are widely acknowledged. However, a significant gap exists in the literature regarding the measurement of self-efficacy specifically within English debate. This research seeks to fill this gap by developing and validating an English Debate Self-Efficacy Scale (EDSS). Using a sample of 1,259 participants from an independent college in Hebei Province, China, the study divided participants into two groups: 613 for exploratory factor analysis (EFA) and 646 for confirmatory factor analysis (CFA), with convenience sampling as the chosen methodology. EFA revealed three core dimensions of debate-related self-efficacy: Language proficiency (Cronbach’s Alpha = .894), Debating skills (Cronbach’s Alpha = .861), and Team collaboration (Cronbach’s Alpha = .831). Subsequent CFA validation with an independent sample confirmed the scale’s structure, demonstrating strong structural, convergent, and discriminant validity. Additionally, significant correlations between the English Debate Self-Efficacy Scale and the English Proficiency Self-Efficacy Scale supported the scale’s criterion validity. These findings underscore the scale’s potential as a reliable tool for assessing self-efficacy in English debate contexts, offering valuable insights for research, teaching, and training in educational settings. Limitations related to sample representativeness and research design were also discussed, providing a foundation for future studies to expand upon. In conclusion, the English Debate Self-Efficacy Scale (EDSS) is a reliable and valid instrument for measuring self-efficacy in the context of English debate.
Journal Article
Hyperspectral compressive wavefront sensing
Presented is a novel way to combine snapshot compressive imaging and lateral shearing interferometry in order to capture the spatio-spectral phase of an ultrashort laser pulse in a single shot. A deep unrolling algorithm is utilized for snapshot compressive imaging reconstruction due to its parameter efficiency and superior speed relative to other methods, potentially allowing for online reconstruction. The algorithm’s regularization term is represented using a neural network with 3D convolutional layers to exploit the spatio-spectral correlations that exist in laser wavefronts. Compressed sensing is not typically applied to modulated signals, but we demonstrate its success here. Furthermore, we train a neural network to predict the wavefronts from a lateral shearing interferogram in terms of Zernike polynomials, which again increases the speed of our technique without sacrificing fidelity. This method is supported with simulation-based results. While applied to the example of lateral shearing interferometry, the methods presented here are generally applicable to a wide range of signals, including Shack–Hartmann-type sensors. The results may be of interest beyond the context of laser wavefront characterization, including within quantitative phase imaging.
Journal Article
Energy absorption in the laser-QED regime
A theoretical and numerical investigation of non-ponderomotive absorption at laser intensities relevant to quantum electrodynamics is presented. It is predicted that there is a regime change in the dependence of fast electron energy on incident laser energy that coincides with the onset of pair production via the Breit-Wheeler process. This prediction is numerically verified via an extensive campaign of QED-inclusive particle-in-cell simulations. The dramatic nature of the power law shift leads to the conclusion that this process is a candidate for an unambiguous signature that future experiments on multi-petawatt laser facilities have truly entered the QED regime.
Journal Article
Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data
Objectives
Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients.
Methods
An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19.
Results
A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients’ to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (
p
< 0.0001).
Conclusions
Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment.
Key Point
• AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.
Journal Article
Evaluation of a convolutional neural network for ovarian tumor differentiation based on magnetic resonance imaging
Objectives
There currently lacks a noninvasive and accurate method to distinguish benign and malignant ovarian lesion prior to treatment. This study developed a deep learning algorithm that distinguishes benign from malignant ovarian lesion by applying a convolutional neural network on routine MR imaging.
Methods
Five hundred forty-five lesions (379 benign and 166 malignant) from 451 patients from a single institution were divided into training, validation, and testing set in a 7:2:1 ratio. Model performance was compared with four junior and three senior radiologists on the test set.
Results
Compared with junior radiologists averaged, the final ensemble model combining MR imaging and clinical variables had a higher test accuracy (0.87 vs 0.64,
p
< 0.001) and specificity (0.92 vs 0.64,
p
< 0.001) with comparable sensitivity (0.75 vs 0.63,
p
= 0.407). Against the senior radiologists averaged, the final ensemble model also had a higher test accuracy (0.87 vs 0.74,
p
= 0.033) and specificity (0.92 vs 0.70,
p
< 0.001) with comparable sensitivity (0.75 vs 0.83,
p
= 0.557). Assisted by the model’s probabilities, the junior radiologists achieved a higher average test accuracy (0.77 vs 0.64, Δ = 0.13,
p
< 0.001) and specificity (0.81 vs 0.64, Δ = 0.17,
p
< 0.001) with unchanged sensitivity (0.69 vs 0.63, Δ = 0.06,
p
= 0.302). With the AI probabilities, the junior radiologists had higher specificity (0.81 vs 0.70, Δ = 0.11,
p
= 0.005) but similar accuracy (0.77 vs 0.74, Δ = 0.03,
p
= 0.409) and sensitivity (0.69 vs 0.83, Δ = -0.146,
p
= 0.097) when compared with the senior radiologists.
Conclusions
These results demonstrate that artificial intelligence based on deep learning can assist radiologists in assessing the nature of ovarian lesions and improve their performance.
Key Points
• Artificial Intelligence based on deep learning can assess the nature of ovarian lesions on routine MRI with higher accuracy and specificity than radiologists.
• Assisted by the deep learning model’s probabilities, junior radiologists achieved better performance that matched those of senior radiologists.
Journal Article