A novel method to determine droop coefficients of DC voltage control for VSC-MTDC system

For droop control in voltage source converter based multi-terminal HVDC systems, the determination of droop coefficients is a key issue, which directly affects both power distribution and DC control performances. This paper proposes a novel design of droop coefficients considering the requirements of power distribution, DC voltage control and system stability. Considering the power margins of different converters, the ratio relationship among droop coefficients is established. Converters with larger power margins take bigger portion of power mismatch to avoid overload problem. Furthermore, the integral square error of converters DC voltage is adopted as the DC voltage control performance index, and optimization of droop coefficients to achieve coordinated DC voltage control of steady-state deviation and transient variation, is derived. Finally, the constraint of droop coefficients is established to guarantee the DC system stability after power disturbance. Case studies are conducted on the Nordic 32 system with an embedded 4-terminal DC grid to demonstrate the feasibility and effectiveness of the proposed droop control scheme

Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images

Focusing on the complicated pathological features, such as blurred boundaries, severe scale differences between symptoms, background noise interference, etc., in the task of retinal edema lesions joint segmentation from OCT images and enabling the segmentation results more reliable. In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network, which can provide accurate segmentation results with reliability assessment. Specifically, aiming at improving the model's ability to learn the complex pathological features of retinal edema lesions in OCT images, we develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module of our newly designed. Meanwhile, to make the segmentation results more reliable, a novel uncertainty segmentation head based on the subjective logical evidential theory is introduced to generate the final segmentation results with a corresponding overall uncertainty evaluation score map. We conduct comprehensive experiments on the public database of AI-Challenge 2018 for retinal edema lesions segmentation, and the results show that our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches. The code will be released on: https://github.com/LooKing9218/ReliableRESeg

Nonlinear dynamic analysis on maglev train system with flexible guideway and double time-delay feedback control

In this paper, the dynamic behavior of time-delayed feedback control for maglev train system with double discrete time delays is considered with flexible guideway. Considering the maglev guideway as Beroulli-Euler beam, the mathematical model of maglev system with flexible guideway is constructed. The time delay of the two state feedback signals in the maglev system occurs simultaneously, and the values are different. The present treatment method only considers one single feedback delay, which are insufficiency. Thus, the Hopf bifurcation with double time-delay feedback of maglev train running on the flexible guideway is analyzed considering time-delayed position feedback signal τ1 and velocity feedback signal τ2. A novel method is presented to develop the double-parametric Hopf bifurcation diagram in relation to τ1 and τ2. Sufficient numerical simulations are provided to illustrate the complex dynamical behavior of the discrete delays τ1 and τ2 for maglev system and we verify the obtained theoretical analysis. Finally, the field experiments are carried out to validate the effectiveness of the Hopf bifurcation analytical method preliminarily

Efficacy and safety of novel oral anticoagulants versus vitamin K antagonists in the treatment of venous thromboembolism

The aim of this meta-analysis was to systematically evaluate the efficacy and safety of novel oral anticoagulants and vitamin K antagonists in the treatment of venous thromboembolism. A total of 6 studies met the inclusion criteria and a total of 19,350 patients with venous thromboembolism were included. Among them, rivaroxaban (3 RCTs, n=90/3,449/4,832); dabigatran (2 RCTs, n=200/2,539); edoxaban (1 RCT, n=8,240). The results of meta-analysis showed that the total bleeding rate after treatment with the vitamin K antagonist group was higher than with the new oral anticoagulant group (OR=0.82, 95% confidence interval 0.75-0.90, p<0.0001), and the difference was highly statistically significant. Overall, new oral anticoagulants are compara-ble to vitamin K antagonists, but new oral anticoagulants can reduce the occurrence of bleeding events and the safety was superior to vitamin K antagonists

Causal association between air pollution and autoimmune diseases: a two-sample Mendelian randomization study

BackgroundIn recent years, an increasing number of observational studies have reported the impact of air pollution on autoimmune diseases (ADs). However, no Mendelian randomization (MR) studies have been conducted to investigate the causal relationships. To enhance our understanding of causality, we examined the causal relationships between particulate matter (PM) and nitrogen oxides (NOx) and ADs.MethodsWe utilized genome-wide association study (GWAS) data on PM and NOx from the UK Biobank in European and East Asian populations. We also extracted integrated GWAS data from the Finnish consortium and the Japanese Biobank for two-sample MR analysis. We employed inverse variance weighted (IVW) analysis to assess the causal relationship between PM and NOx exposure and ADs. Additionally, we conducted supplementary analyses using four methods, including IVW (fixed effects), weighted median, weighted mode, and simple mode, to further investigate this relationship.ResultsIn the European population, the results of MR analysis suggested a statistically significant association between PM2.5 and psoriasis only (OR = 3.86; 95% CI: 1.89–7.88; PIVW &lt; 0.00625), while a potential association exists between PM2.5–10 and vitiligo (OR = 7.42; 95% CI: 1.02–53.94; PIVW &lt; 0.05), as well as between PM2.5 and systemic lupus erythematosus (OR = 68.17; 95% CI: 2.17–2.1e+03; PIVW &lt; 0.05). In East Asian populations, no causal relationship was found between air pollutants and the risk of systemic lupus erythematosus and rheumatoid arthritis (PIVW &gt; 0.025). There was no pleiotropy in the results.ConclusionOur results suggest a causal association between PM2.5 and psoriasis in European populations. With the help of air pollution prevention and control, the harmful progression of psoriasis may be slowed

Abnormal Mammary Gland Development and Growth Retardation in Female Mice and MCF7 Breast Cancer Cells Lacking Androgen Receptor

Phenotype analysis of female mice lacking androgen receptor (AR) deficient (AR−/−) indicates that the development of mammary glands is retarded with reduced ductal branching in the prepubertal stages, and fewer Cap cells in the terminal end buds, as well as decreased lobuloalveolar development in adult females, and fewer milk-producing alveoli in the lactating glands. The defective development of AR−/− mammary glands involves the defects of insulin-like growth factor I–insulin-like growth factor I receptor and mitogen-activated protein kinase (MAPK) signals as well as estrogen receptor (ER) activity. Similar growth retardation and defects in growth factor–mediated Ras/Raf/MAPK cascade and ER signaling are also found in AR−/− MCF7 breast cancer cells. The restoration assays show that AR NH2-terminal/DNA-binding domain, but not the ligand-binding domain, is essential for normal MAPK function in MCF7 cells, and an AR mutant (R608K), found in male breast cancer, is associated with the excessive activation of MAPK. Together, our data provide the first in vivo evidence showing that AR-mediated MAPK and ER activation may play important roles for mammary gland development and MCF7 breast cancer cell proliferation

Significantly improved high-temperature energy storage performance of commercial BOPP films by utilizing ultraviolet grafting modification

Commercial biaxially oriented polypropylene (BOPP) film capacitors have been widely applied in the fields of electrical and electronic engineering. However, due to the sharp increase in electrical conduction loss as the temperature rises, the energy storage performance of BOPP films seriously degrades at elevated temperatures. In this study, the grafting modification method is facile and suitable for large-scale industrial manufacturing and has been proposed to increase the high-temperature energy storage performance of commercial BOPP films for the first time. Specifically, acrylic acid (AA) as a polar organic molecular is used to graft onto the surface of commercial BOPP films by using ultraviolet irradiation (abbreviated as BOPP−AA). The results demonstrate that the AA grafting modification not only slightly increases the dielectric constant, but also significantly reduces the leakage current density at high-temperature, greatly improving the high-temperature energy storage performance. The modified BOPP−AA films display a discharged energy density of 1.32 J/cm3 with an efficiency of >90% at 370 kV/mm and 125 °C, which is 474% higher than that of the pristine BOPP films. This work manifests that utilizing ultraviolet grafting modification is a very efficient way to improve the high-temperature energy storage performance of commercial BOPP films as well as provides a hitherto unexplored opportunity for large-scalable production applications

Simple quantitative measurement based on DWI to objectively judge DWI-FLAIR mismatch in a canine stroke model

PURPOSEDiffusion-weighted imaging (DWI) - fluid attenuated inversion recovery (FLAIR) mismatch was proven useful to time the onset of wake-up stroke; however, identifying the status of FLAIR imaging has been mostly subjective. We aimed to evaluate the value of relative DWI signal intensity (rDWI), and relative apparent diffusion coefficient (rADC) in identifying the FLAIR status in the acute period.METHODSAutologous clot was used to embolize left middle cerebral artery in 20 dogs. Magnetic resonance imaging was performed 3–6 hours and 24 hours after embolization. DWI-FLAIR mismatch was defined as hyperintense signal detected on DWI, but not on FLAIR. The mean values of rDWI or rADC of FLAIR- and FLAIR+ lesions were compared and the critical cutoff values of rDWI and rADC for identifying the FLAIR status were determined.RESULTSStroke models were successfully established in all animals. DWI+ lesions were found in all 20 dogs from three hours, while FLAIR+ lesions were found in three, 11, 16, 19, and 20 dogs at five time points after embolization, respectively. The mean rDWI values were significantly different between FLAIR- and FLAIR+ lesions (P < 0.001), but rADC values were not (P = 0.73). Using rDWI=1.90 as the threshold value, excellent diagnostic efficacy was achieved (AUC, 0.88; sensitivity, 0.77; specificity, 0.88). However, rADC appeared not useful (AUC, 0.48; sensitivity, 0.52; specificity, 0.58) in identifying the FLAIR status.CONCLUSIONIn our embolic canine stroke model, rDWI was useful to identify FLAIR imaging status in the acute period, while rADC was not

Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification

Failure to recognize samples from the classes unseen during training is a major limit of artificial intelligence (AI) in real-world implementation of retinal anomaly classification. To resolve this obstacle, we propose an uncertainty-inspired open-set (UIOS) model which was trained with fundus images of 9 common retinal conditions. Besides the probability of each category, UIOS also calculates an uncertainty score to express its confidence. Our UIOS model with thresholding strategy achieved an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set, external testing set and non-typical testing set, respectively, compared to the F1 score of 92.20%, 80.69% and 64.74% by the standard AI model. Furthermore, UIOS correctly predicted high uncertainty scores, which prompted the need for a manual check, in the datasets of rare retinal diseases, low-quality fundus images, and non-fundus images. This work provides a robust method for real-world screening of retinal anomalies