Comparison of Full-Wave and Ray-Tracing Analysis of Mode Conversion in Plasmas

This dissertation reports on the first direct comparison between the results of ray-based and full-wave calculations for mode conversion in plasma. This study was motivated by the modular method originally developed by Ye and Kaufman to treat a magnetosonic wave crossing a cold minority-ion gyroresonance layer. We start with the cold plasma fluid model and introduce a system of evolution equations for electrons and two ion species: deuterium and hydrogen. We first study this system of equations for uniform plasma by Fourier methods, which gives the dispersion relations. We discuss how the traditional approach---which eliminates all other dynamical variables in terms of the electric field---leads to singular denominators at the resonances. We then introduce the Kaufman & Ye approach, which retains the ion velocities as dynamical variables. In this formulation, the ion resonances appear as \u27avoided crossings\u27 between the familiar \u27fast wave\u27 and a zero-group-velocity ion \u27mode\u27 associated with the particle velocities. We then extend our problem to nonuniform plasma where the resonance is localized in space. Away from the resonance, WKB methods apply, but they break down in the vicinity of the resonance. In this region, we introduce the notion of \u27uncoupled modes\u27 and discuss how to use them to systematically carry out a simplification of the problem. This leads directly to the modular method of Kaufman & Ye in the mode conversion region, and provides the connection coefficients for the WKB solutions across the resonance layer. We specialize to an incoming wave packet and use the full-wave equations and the reduced 2x2 form to numerically study the wave packet conversion. This allows us to observe the emission of the reflected wave packet after a time delay (the linear \u27ion-cyclotron echo\u27). We calculate the incoming, transmitted and reflected wave packet energies. We compare them to the transmission and reflection coefficients predicted by the S matrix approach of Kaufman and Ye for a wide range of ion density ratios and find good agreement

PhotoRedshift-MML: a multimodal machine learning method for estimating photometric redshifts of quasars

We propose a Multimodal Machine Learning method for estimating the Photometric Redshifts of quasars (PhotoRedshift-MML for short), which has long been the subject of many investigations. Our method includes two main models, i.e. the feature transformation model by multimodal representation learning, and the photometric redshift estimation model by multimodal transfer learning. The prediction accuracy of the photometric redshift was significantly improved owing to the large amount of information offered by the generated spectral features learned from photometric data via the MML. A total of 415,930 quasars from Sloan Digital Sky Survey (SDSS) Data Release 17, with redshifts between 1 and 5, were screened for our experiments. We used |{\Delta}z| = |(z_phot-z_spec)/(1+z_spec)| to evaluate the redshift prediction and demonstrated a 4.04% increase in accuracy. With the help of the generated spectral features, the proportion of data with |{\Delta}z| < 0.1 can reach 84.45% of the total test samples, whereas it reaches 80.41% for single-modal photometric data. Moreover, the Root Mean Square (RMS) of |{\Delta}z| is shown to decreases from 0.1332 to 0.1235. Our method has the potential to be generalized to other astronomical data analyses such as galaxy classification and redshift prediction. The algorithm code can be found at https://github.com/HongShuxin/PhotoRedshift-MML .Comment: 10 pages, 8 figures, accepted for publication in MNRA

Microglia Prevent Beta-Amyloid Plaque Formation in the Early Stage of an Alzheimer\u27s Disease Mouse Model with Suppression of Glymphatic Clearance

BACKGROUND: Soluble beta-amyloid (Aβ) can be cleared from the brain through various mechanisms including enzymatic degradation, glial cell phagocytosis, transport across the blood-brain barrier, and glymphatic clearance. However, the relative contribution of each clearance system and their compensatory effects in delaying the pathological process of Alzheimer\u27s disease (AD) are currently unknown. METHODS: Fluorescent trace, immunofluorescence, and Western blot analyses were performed to compare glymphatic clearance ability and Aβ accumulation among 3-month-old APP695/PS1-dE9 transgenic (APP/PS1) mice, wild-type mice, aquaporin 4 knock out (AQP4−/−) mice, and AQP4−/−/APP/PS1 mice. The consequence of selectively eliminating microglial cells, or downregulating apolipoprotein E (apoE) expression, on Aβ burden, was also investigated in the frontal cortex of AQP4−/−/APP/PS1 mice and APP/PS1 mice. RESULTS: AQP4 deletion in APP/PS1 mice significantly exaggerated glymphatic clearance dysfunction, and intraneuronal accumulation of Aβ and apoE, although it did not lead to Aβ plaque deposition. Notably, microglia, but not astrocytes, increased activation and phagocytosis of Aβ in the cerebral cortex of AQP4−/−/APP/PS1 mice, compared with APP/PS1 mice. Selectively eliminating microglia in the frontal cortex via local injection of clodronate liposomes resulted in deposition of Aβ plaques in AQP4−/−/APP/PS1 mice, but not APP/PS1 mice. Moreover, knockdown of apoE reduced intraneuronal Aβ levels in both APP/PS1 mice and AQP4−/−/APP/PS1 mice, indicating an inhibitory effect of apoE on Aβ clearance. CONCLUSION: The above results suggest that the glymphatic system mediated Aβ and apoE clearance and microglia mediated Aβ degradation synergistically prevent Aβ plague formation in the early stages of the AD mouse model. Protecting one or both of them might be beneficial to delaying the onset of AD

Comparison of ultrasound−based ADNEX model with magnetic resonance imaging for discriminating adnexal masses: a multi-center study

ObjectivesThe ADNEX model offered a good diagnostic performance for discriminating adnexal tumors, but research comparing the abilities of the ADNEX model and MRI for characterizing adnexal tumors has not been reported to our knowledge. The aim of this study was to evaluate the diagnostic accuracy of the ultrasound-based ADNEX (Assessment of Different NEoplasias in the adneXa) model in comparison with that of magnetic resonance imaging (MRI) for differentiating benign, borderline and malignant adnexal masses.MethodsThis prospective study included 529 women with adnexal masses who underwent assessment via the ADNEX model and subjective MRI analysis before surgical treatment between October 2019 and April 2022 at two hospitals. Postoperative histological diagnosis was considered the gold standard.ResultsAmong the 529 women, 92 (17.4%) masses were diagnosed histologically as malignant tumors, 67 (12.7%) as borderline tumors, and 370 (69.9%) as benign tumors. For the diagnosis of malignancy, including borderline tumors, overall agreement between the ADNEX model and MRI pre-operation was 84.9%. The sensitivity of the ADNEX model of 0.91 (95% confidence interval [CI]: 0.85–0.95) was similar to that of MRI (0.89, 95% CI: 0.84–0.94; P=0.717). However, the ADNEX model had a higher specificity (0.90, 95% CI: 0.87–0.93) than MRI (0.81, 95% CI: 0.77–0.85; P=0.001). The greatest sensitivity (0.96, 95% CI: 0.92–0.99) and specificity (0.94, 95% CI: 0.91–0.96) were achieved by combining the ADNEX model and subjective MRI assessment. While the total diagnostic accuracy did not differ significantly between the two methods (P=0.059), the ADNEX model showed greater diagnostic accuracy for borderline tumors (P&lt;0.001).ConclusionThe ultrasound-based ADNEX model demonstrated excellent diagnostic performance for adnexal tumors, especially borderline tumors, compared with MRI. Accordingly, we recommend that the ADNEX model, alone or with subjective MRI assessment, should be used for pre-operative assessment of adnexal masses

Using grey Holt-Winters model to predict the air quality index for cties in China

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkThe randomness, non-stationarity and irregularity of air quality index series bring the di fficulty of air quality index forecasting. To enhance forecast accuracy, a novel model combining grey accumulated generating technique and Holt-Winters method is developed for air quality index forecasting in this paper. The grey accumulated generating technique is utilized to handle non-stationarity of random and irregular data series and Holt-Winters method is employed to deal with the seasonal e ects. To verify and validate the proposed model, two monthly air quality index series from January in 2014 to December in 2016 collected from Shijiazhuang and Handan in China are taken as the test cases. The experimental results show that the proposed model is remarkably superior to conventional Holt-Winters method for its higher forecast accuracy