Modelling and vulnerability analysis of cyber-physical power systems based on interdependent networks

The strong coupling between the power grid and communication systems may contribute to failure propagation, which may easily lead to cascading failures or blackouts. In this paper, in order to quantitatively analyse the impact of interdependency on power system vulnerability, we put forward a “degree–electrical degree” independent model of cyber-physical power systems (CPPS), a new type of assortative link, through identifying the important nodes in a power grid based on the proposed index–electrical degree, and coupling them with the nodes in a communication system with a high degree, based on one-to-one correspondence. Using the double-star communication system and the IEEE 118-bus power grid to form an artificial interdependent network, we evaluated and compare the holistic vulnerability of CPPS under random attack and malicious attack, separately based on three kinds of interdependent models: “degree–betweenness”, “degree–electrical degree” and “random link”. The simulation results demonstrated that different link patterns, coupling degrees and attack types all can influence the vulnerability of CPPS. The CPPS with a “degree–electrical degree” interdependent model proposed in this paper presented a higher robustness in the face of random attack, and moreover performed better than the degree–betweenness interdependent model in the face of malicious attack

Automatic Segmentation of Macular Edema in Retinal OCT Images Using Improved U-Net++

The number and volume of retinal macular edemas are important indicators for screening and diagnosing retinopathy. Aiming at the problem that the segmentation method of macular edemas in a retinal optical coherence tomography (OCT) image is not ideal in segmentation of diverse edemas, this paper proposes a new method of automatic segmentation of macular edema regions in retinal OCT images using the improved U-Net++. The proposed method makes full use of the U-Net++ re-designed skip pathways and dense convolution block; reduces the semantic gap of the feature maps in the encoder/decoder sub-network; and adds the improved Resnet network as the backbone, which make the extraction of features in the edema regions more accurate and improves the segmentation effect. The proposed method was trained and validated on the public dataset of Duke University, and the experiments demonstrated the proposed method can not only improve the overall segmentation effect, but also can significantly improve the segmented precision for diverse edema in multi-regions, as well as reducing the error of the number of edema regions

Gut microbial clues to bipolar disorder: State‐of‐the‐art review of current findings and future directions

Abstract Trillions of microorganisms inhabiting in the human gut play an essential role in maintaining physical and mental health. The connections between gut microbiome and neuropsychiatric diseases have been recently identified. The pathogenesis of bipolar disorder, a spectrum of diseases manifesting with mood and energy fluctuations, also seems to be involved in the bidirectional modulation of the microbiome‐gut‐brain (MGB) axis. In this review, we briefly introduce the concept of MGB axis, and then focus on the previous findings in human studies associated with bipolar disorder. These studies provided preliminary evidences on the gut microbial alterations in bipolar disorder. Limitations in these studies and future directions in this research field, such as fecal microbiome transplantation and microbiome‐targeted therapy, were discussed. A research framework linking gut microbiome to determinants and health‐related outcomes in BD was also proposed. Better characterizing and understanding of gut microbial biosignatures in bipolar patients contribute to clarify the etiology of this intractable disease and pave the new way for treatment innovation