Asymptotic Stability for Relativistic Vlasov-Maxwell-Landau System in Bounded Domain

The control of plasma-wall interaction is one of the keys in a fusion device from both physical and mathematical standpoints. A classical perfect conducting boundary causes the Lorentz force to penetrate inside the domain, which may lead to grazing set singularity in the phase space, preventing the construction of global dynamics for PDEs in any kinetic plasma models. We establish the first global asymptotic stability for the relativistic Vlasov-Maxwell-Landau system for describing a collisional plasma specularly reflected at a perfect conducting boundary.Comment: 110 page

Implementation of Adaptive Partial Discharge Denoising in Resource-Limited Embedded Systems via Efficient Time-Frequency Matrix Factorization

As low-cost Internet-of-Things (IoT)-based partial discharge (PD) monitors for medium-voltage apparatuses in distribution power systems increase, developing an effective PD denoising algorithm is crucial to improve their robustness to onsite noise. Yet, denoising PD signals in the monitoring devices is challenging primarily due to three critical reasons, i.e., high level field noises, uncertain PD waveforms, and limited computing resources. This work describes an adaptive and efficient PD denoising algorithm based on the improved spectral decomposition of the noisy PD signal. PD pulses are accurately extracted from the noisy signal by selecting the dominant components via a low-rank singular value decomposition (SVD) of the time-frequency spectrogram of the signal, thus reducing the size of the involved matrices and the computational complexity. The performance of the proposed denoising algorithm is first demonstrated on a synthetic PD signal and compared with state-of-the-art alternatives implemented on three embedded systems commonly used for PD monitoring. Finally, the strength and the effectiveness of the proposed approach are further validated on experimental data based on the measurement of IoT-based PD monitors for 35-kV switchgears

On-line Partial Discharge Localization of 10-kV Covered Conductor Lines

This paper proposes an innovative partial discharge (PD) location technique for overhead electrical power distribution networks. It is aimed at improving the condition-based maintenance of the network. PD localization is carried out via an improved double-sided traveling-wave method. The method is driven by a hybrid detection technique, which integrates a pulse-based synchronization mechanism and a global positioning system (GPS). The proposed solution offers a number of benefits. It has the nice inherent feature of being immune to varying physical parameters of the transmission line, and it has been proven be offer improved accuracy with respect of the conventional GPS-based location methods. Also, an in-house designed portable and non-invasive test setup is presented and thoroughly discussed, thus demonstrating the feasibility of the proposed method. Moreover, an enhanced algorithm is embedded into the PD location system to improve robustness to high-level noise. Finally, the proposed tool relies on a well-established automatic procedure which requires neither parameter tuning nor any expert intervention. The features and strengths of the method are validated on a real case consisting of a 2125-m long 10-kV overhead covered conductor line

An Automatic Tool for Partial Discharge De-noising via Short Time Fourier Transform and Matrix Factorization

This paper develops a fully automatic tool for the denoising of partial discharge (PD) signals occurring in electrical power networks and recorded in on-site measurements. The proposed method is based on the spectral decomposition of the PD measured signal via the joint application of the short-time Fourier transform and the singular value decomposition. The estimated noiseless signal is reconstructed via a clever selection of the dominant contributions, which allows us to filter out the different spurious components, including the white noise and the discrete spectrum noise. The method offers a viable solution which can be easily integrated within the measurement apparatus, with unavoidable beneficial effects in the detection of important parameters of the signal for PD localization. The performance of the proposed tool is first demonstrated on a synthetic test signal and then it is applied to real measured data. A cross comparison of the proposed method and other state-of-the-art alternatives is included in the study

A Compact Detector for Flexible Partial Discharge Monitoring of 10-kV Overhead Covered Conductor Lines

The availability of accurate and cost-effective solutions for the real-time monitoring of overhead covered conductors (CC) is now becoming an important tool for the reliability and condition assessments of this class of electrical lines. This is even more crucial due to the possibly large number of conductors and the wide geographical spread of the electrical network. This letter proposes a smart and compact detector for partial discharge (PD) based monitoring, matching the above needs and offering a flexible and cost-effective solution with some important features, including a non-invasive sensing, a field energy harvesting function, and a low-power working operation. The detector has been designed and implemented, proving its effectiveness on real cases involving PD-affected 10 kV CC lines

Comparative Study of the Amount of Re-released Hemoglobin from α-Thalassemia and Hereditary Spherocytosis Erythrocytes

Hemoglobin release test (HRT), which is established by our lab, is a new experiment to observe the re-released hemoglobin (Hb) from erythrocytes. In this study, one-dimension HRT, double dimension HRT, and isotonic and hypotonic HRT were performed to observe the re-released Hb from the blood samples of normal adult, hereditary spherocytosis (HS), and α-thalassemia. The results showed that compared with normal adult, the re-released Hb from HS blood sample was decreased significantly; however, the re-released Hb from α-thalassemia blood sample was increased significantly. The mechanism of this phenomenon was speculated to have relation with the abnormal amount of membrane-binding Hb

XRCC3 Thr241Met polymorphism and ovarian cancer risk: a meta-analysis

Genetic polymorphism of X-ray repair crosscomplementing group 3 (XRCC3) Thr241Met has been implicated to alter the risk of ovarian cancer, but the results are controversial. In order to get a more precise result, a meta-analysis was performed. All eligible studies were identified through an extensive search in PubMed, Excerpta Medica Database (Embase), Chinese National Knowledge Infrastructure database, and Chinese Biomedical Literature Database before August 2013. The association between the XRCC3 Thr241Met polymorphism and ovarian cancer risk was conducted by odds ratios (ORs) and 95 % confidence intervals (95 % CIs). Finally, a total of four publications including seven studies with 3,635 cases and 5,473 controls were included in our meta-analysis. Overall, there was no association between XRCC3 Thr241Met polymorphism and risk of ovarian cancer under all five genetic models in overall population (T vs. C: OR = 0.99, 95 % CI = 0.960–1.03, P = 0.752; TT vs. CC: OR = 1.00, 95 % CI = 0.91–1.10, P = 0.943; TC vs. TT: OR = 0.97, 95 % CI = 0.92–1.04, P = 0.396, Fig. 1; TT vs. TC/CC: OR = 1.00, 95 % CI = 0.91–1.12, P = 0.874; TT/TC vs. CC: OR = 0.98, 95 % CI = 0.94–1.03, P = 0.486). In the subgroup analysis according to ethnicity, the results suggested that XRCC3 Thr241Met polymorphism was not associated with the risk of ovarian cancer in Caucasians population. No significant association was found between the XRCC3 Thr241 Met polymorphism and the risk of ovarian cancer. Given the limited sample size and ethnicities included in the meta-analysis, further large scaled and well-designed studies are needed to confirm our results

CSC-Unet: A Novel Convolutional Sparse Coding Strategy Based Neural Network for Semantic Segmentation

It is a challenging task to accurately perform semantic segmentation due to the complexity of real picture scenes. Many semantic segmentation methods based on traditional deep learning insufficiently captured the semantic and appearance information of images, which put limit on their generality and robustness for various application scenes. In this paper, we proposed a novel strategy that reformulated the popularly-used convolution operation to multi-layer convolutional sparse coding block to ease the aforementioned deficiency. This strategy can be possibly used to significantly improve the segmentation performance of any semantic segmentation model that involves convolutional operations. To prove the effectiveness of our idea, we chose the widely-used U-Net model for the demonstration purpose, and we designed CSC-Unet model series based on U-Net. Through extensive analysis and experiments, we provided credible evidence showing that the multi-layer convolutional sparse coding block enables semantic segmentation model to converge faster, can extract finer semantic and appearance information of images, and improve the ability to recover spatial detail information. The best CSC-Unet model significantly outperforms the results of the original U-Net on three public datasets with different scenarios, i.e., 87.14% vs. 84.71% on DeepCrack dataset, 68.91% vs. 67.09% on Nuclei dataset, and 53.68% vs. 48.82% on CamVid dataset, respectively

Association of MDR1 G2677T polymorphism and leukemia risk: evidence from a meta-analysis

In the light of the relationship between the MDR1 G2677T polymorphism and the risk of leukemia remains inclusive or controversial. For better understanding of the effect of MDR1 G2677T polymorphism on leukemia risk, we performed a meta-analysis. Eligible studies were identified through a search of electronic databases such as PubMed, Excerpta Medica Database (Embase), Cochrane Library, and Chinese Biomedical Literature Database (CBM). The association between the MDR1 G2677T polymorphism and leukemia risk was conducted by odds ratios (ORs) and 95 % confidence intervals (95 % CI). A total of seven publications including eight studies with 1,229 cases and 1,097 controls were included in the meta-analysis. There was no association between MDR1 G2677T polymorphism and leukemia risk in all of five models in overall populations (T vs. G: OR = 1.00, 95 % CI = 0.88–1.12, P = 0.914; TT vs. GG: OR = 0.97, 95 % CI = 0.75–1.26, P = 0.812; TG vs. GG: OR = 1.00, 95 % CI = 0.92–1.08, P = 0.939; TT vs. TG/GG: OR = 0.98, 95 % CI = 0.67–1.43, P = 0.906; TT/TG vs. GG: OR = 1.00, 95 % CI = 0.95–1.06, P = 0.994). However, the significant association was found in others (Table 2) under the homozygote model (TT vs. GG: OR = 0.68, 95 % CI = 0.48–0.94, P = 0.020) and recessive model (TT vs. TG/GG: OR = 0.63, 95 % CI = 0.43–0.92, P = 0.016). In the subgroup analysis, according to the type of leukemia, significant association was found between MDR1 G2677T polymorphism and myeloid leukemia but not lymphoblastic leukemia (TT vs. GG: OR = 0.66, 95 % CI = 0.46–0.95, P = 0.026; TT vs. TG/GG: OR = 0.56, 95 % CI = 0.38–0.84, P = 0.005). The results suggested that there was no association between MDR1 G2677T polymorphism and leukemia risk in overall populations, but significant association was found in others populations (Asians and Africans), and myeloid leukemia indicated that G2677T polymorphism might be a protective factor in the susceptibility of myeloid leukemia and in Asians and Africans