Nonzero depolarization volumes in electromagnetic homogenization studies

The work of this thesis concerns depolarization regions in the homogenization of random, particulate composites. In conventional approaches to homogenization, the depolarization dyadics which represent the component phase particles are provided by the singularity of the corresponding dyadic Green function. Thereby, the component particles are effectively treated as vanishingly small, point-like entities. However, through neglecting the spatial extent of the depolarization region, important information may be lost, particularly relating to coherent scattering losses. In this thesis, depolarization regions of nonzero volume are considered. In order to estimate the constitutive parameters of homogenized composite materials (HCMs), the strong-property-fluctuation theory (SPFT) is implemented. This is done through a standard procedure involving the calculation of successive corrections to a preliminary ansatz, in terms of statistical cumulants of the spatial distribution of the component phase particles. The influence of depolarization regions of nonzero volume on the zeroth (and first), second and third order SPFT estimates of HCM constitutive parameters is investigated. Both linear and weakly nonlinear HCMs are considered

Communication-efficient Personalized Federated Edge Learning for Massive MIMO CSI Feedback

Deep learning (DL)-based channel state information (CSI) feedback has received significant research attention in recent years. However, previous research has overlooked the potential privacy disclosure problem caused by the transmission of CSI datasets during the training process. In this work, we introduce a federated edge learning (FEEL)-based training framework for DL-based CSI feedback. This approach differs from the conventional centralized learning (CL)-based framework in which the CSI datasets are collected at the base station (BS) before training. Instead, each user equipment (UE) trains a local autoencoder network and exchanges model parameters with the BS. This approach provides better protection for data privacy compared to CL. To further reduce communication overhead in FEEL, we quantize uplink and downlink model transmission into different bits based on their influence on feedback performance. Additionally, since the heterogeneity of CSI datasets in different UEs can degrade the performance of the FEEL-based framework, we introduce a personalization strategy to improve feedback performance. This strategy allows for local fine-tuning to adapt the global model to the channel characteristics of each UE. Simulation results indicate that the proposed personalized FEEL-based training framework can significantly improve the performance of DL-based CSI feedback while reducing communication overhead

Biocompatibility of hydrophilic silica-coated CdTe quantum dots and magnetic nanoparticles

Fluorescent magnetic nanoparticles exhibit great application prospects in biomedical engineering. Herein, we reported the effects of hydrophilic silica-coated CdTe quantum dots and magnetic nanoparticles (FMNPs) on human embryonic kidney 293 (HEK293) cells and mice with the aim of investigating their biocompatibility. FMNPs with 150 nm in diameter were prepared, and characterized by high-resolution transmission electron microscopy and photoluminescence (PL) spectra and magnetometer. HEK293 cells were cultured with different doses of FMNPs (20, 50, and 100μ g/ml) for 1-4 days. Cell viability and adhesion ability were analyzed by CCK8 method and Western blotting. 30 mice were randomly divided into three groups, and were, respectively, injected via tail vein with 20, 60, and 100 μg FMNPs, and then were, respectively, raised for 1, 7, and 30 days, then their lifespan, important organs, and blood biochemical parameters were analyzed. Results show that the prepared water-soluble FMNPs had high fluorescent and magnetic properties, less than 50 μg/ml of FMNPs exhibited good biocompatibility to HEK293 cells, the cell viability, and adhesion ability were similar to the control HEK293 cells. FMNPs primarily accumulated in those organs such as lung, liver, and spleen. Lung exposed to FMNPs displayed a dose-dependent inflammatory response, blood biochemical parameters such as white blood cell count (WBC), alanine aminotransferase (ALT), and aspartate aminotransferase (AST), displayed significant increase when the FMNPs were injected into mice at dose of 100μg. In conclusion, FMNPs exhibit good biocompatibility to cells under the dose of less than 50 μg/ml, and to mice under the dose of less than 2mg/kg body weight. The FMNPs' biocompatibility must be considered when FMNPs are used for in vivo diagnosis and therapy

Lightweight Neural Network with Knowledge Distillation for CSI Feedback

Deep learning (DL) has shown promise in enhancing channel state information (CSI) feedback. However, many studies indicate that better feedback performance often accompanies higher computational complexity. Pursuing better performance-complexity tradeoffs is crucial to facilitate practical deployment, especially on computation-limited devices, which may have to use lightweight autoencoder with unfavorable performance. To achieve this goal, this paper introduces knowledge distillation (KD) to achieve better tradeoffs, where knowledge from a complicated teacher autoencoder is transferred to a lightweight student autoencoder for performance improvement. Specifically, two methods are proposed for implementation. Firstly, an autoencoder KD-based method is introduced by training a student autoencoder to mimic the reconstructed CSI of a pretrained teacher autoencoder. Secondly, an encoder KD-based method is proposed to reduce training overhead by performing KD only on the student encoder. Additionally, a variant of encoder KD is introduced to protect user equipment and base station vendor intellectual property. Numerical simulations demonstrate that the proposed KD methods can significantly improve the student autoencoder's performance, while reducing the number of floating point operations and inference time to 3.05%-5.28% and 13.80%-14.76% of the teacher network, respectively. Furthermore, the variant encoder KD method effectively enhances the student autoencoder's generalization capability across different scenarios, environments, and bandwidths.Comment: 28 pages, 4 figure

Inhibition of chemotherapy resistant breast cancer stem cells by a ROR1 specific antibody.

Breast cancers enduring treatment with chemotherapy may be enriched for cancer stem cells or tumor-initiating cells, which have an enhanced capacity for self-renewal, tumor initiation, and/or metastasis. Breast cancer cells that express the type I tyrosine kinaselike orphan receptor ROR1 also may have such features. Here we find that the expression of ROR1 increased in breast cancer cells following treatment with chemotherapy, which also enhanced expression of genes induced by the activation of Rho-GTPases, Hippo-YAP/TAZ, or B lymphoma Mo-MLV insertion region 1 homolog (BMI1). Expression of ROR1 also enhanced the capacity of breast cancer cells to invade Matrigel, form spheroids, engraft in Rag2-/-[Formula: see text] mice, or survive treatment with paclitaxel. Treatment of mice bearing breast cancer patient-derived xenografts (PDXs) with the humanized anti-ROR1 monoclonal antibody cirmtuzumab repressed expression of genes associated with breast cancer stemness, reduced activation of Rho-GTPases, Hippo-YAP/TAZ, or BMI1, and impaired the capacity of breast cancer PDXs to metastasize or reengraft Rag2-/-[Formula: see text] mice. Finally, treatment of PDX-bearing mice with cirmtuzumab and paclitaxel was more effective than treatment with either alone in eradicating breast cancer PDXs. These results indicate that targeting ROR1 may improve the response to chemotherapy of patients with breast cancer

Association of Aortic Stiffness and Cognitive Decline: A Systematic Review and Meta-Analysis

Background: Increased aortic stiffness has been found to be associated with cognitive function decline, but the evidence is still under debate. It is of great significance to elucidate the evidence in this debate to help make primary prevention decisions to slow cognitive decline in our routine clinical practice.Methods: Electronic databases of PubMed, EMBASE, and Cochrane Library were systematically searched to identify peer-reviewed articles published in English from January 1, 1986, to March 16, 2020, that reported the association between aortic stiffness and cognitive function. Studies that reported the association between aortic pulse wave velocity (PWV) and cognitive function, cognitive impairment, and dementia were included in the analysis.Results: Thirty-nine studies were included in the qualitative analysis, and 29 studies were included in the quantitative analysis. The aortic PWV was inversely associated with memory and processing speed in the cross-sectional analysis. In the longitudinal analysis, the high category of aortic PWV was 44% increased risk of cognitive impairment (OR 1.44; 95% CI 1.24–1.85) compared with low PWV, and the risk of cognitive impairment increased 3.9% (OR 1.039; 95% CI 1.005–1.073) per 1 m/s increase in aortic PWV. Besides, meta-regression analysis showed that age significantly increased the association between high aortic PWV and cognitive impairment risk.Conclusion: Aortic stiffness measured by aortic PWV was inversely associated with memory and processing speed and could be an independent predictor for cognitive impairment, especially for older individuals