Over the years, power systems have been severely affected by extreme events. This situation has worsened given that climate change has proven to exacerbate their frequency and magnitude. In this context, resilience assessments have proved crucial to prevent and tackle the effects of these events on power systems. Some resilience studies have taken advantage of the so-called fragility curves (FCs) to evaluate the vulnerability of the system components against these natural hazards. Conceptually, FCs provide the failure probability of a particular grid asset according to the intensity of an extreme event, which can be determined based on the hazard intensity inherently dictated by the nature of the event. The probability of failure can be obtained following diverse methodologies and criteria. Thus, the resilience assessment of the event may vary significantly depending on how the probability of failure was determined. This paper provides, for the first time, a comprehensive review of the FCs used to model the vulnerability of the power system components, classifying them according to the physical magnitude and the system element subject to each type of event. Furthermore, a comparison of results obtained applying different FCs is developed to show the relevance of their modelling. The content of this paper can be used as a hands-on guide for researchers and power systems engineers to perform resilience studies.

Gastric cancer (GC) is the fourth most common malignant neoplasm and the second leading cause of cancer death. Identification of key molecular signaling pathways involved in gastric carcinogenesis and progression facilitates early GC diagnosis and the development of targeted therapies for advanced GC patients. Emerging evidence has revealed a close correlation between forkhead box (FOX) proteins and cancer development. However, the prognostic significance of forkhead box S1 (FOXS1) in patients with GC and the function of FOXS1 in GC progression remain undefined. In this study, we found that upregulation of FOXS1 was frequently detected in GC tissues and strongly correlated with an aggressive phenotype and poor prognosis. Functional assays confirmed that FOXS1 knockdown suppressed cell proliferation and colony numbers, with induction of cell arrest in the G0/G1 phase of the cell cycle, whereas forced expression of FOXS1 had the opposite effect. Additionally, forced expression of FOXS1 accelerated tumor growth in vivo and increased cell migration and invasion through promoting epithelial–mesenchymal transition (EMT) both in vitro and in vivo. Mechanistically, the core promoter region of FOXS1 was identified at nucleotides −660~ +1, and NFKB1 indirectly bind the motif on FOXS1 promoters and inhibit FOXS1 expression. Gene set enrichment analysis revealed that the FOXS1 gene was most abundantly enriched in the hedgehog signaling pathway and that GLI1 expression was significantly correlated with FOXS1 expression in GC. GLI1 directly bound to the promoter motif of FOXS1 and significantly decreased FOXS1 expression. Finally, we found that miR-125a-5p repressed FOXS1 expression at the translational level by binding to the 3′ untranslated region (UTR) of FOXS1. Together, these results suggest that FOXS1 can promote GC development and could be exploited as a diagnostic and prognostic biomarker for GC.

Interoperability testing and analysis tools provide a means for achieving and assuring the integrity of multivendor intelligent electronic devices (IEDs) data exchanges. However, the testing and analysis are very time consuming and error prone, and these problems worsen when a substation becomes large and complex during the engineering process, commission, replacement, maintenance, and extension. To address this challenge, this paper presents a virtual digital substation test system (VDSTS) with interoperability analysis tools for assessing and identifying the engineering challenges for the multiple-vendors digital substation. This VDSTS consists of three parts: (i) A virtual digital substation modelling for generating real-time digital substation primary plant operation and fault conditions, (ii) a standard IEC 61850-based substation protection, automation, and control (PAC) system architecture with multivendor IEDs and bay solutions, and (iii) multivendor Substation Configuration description Language (SCL) tools and in-house built data visualisation tool. The study focuses on the interoperability testing of sampled values (SV), generic object-oriented substation events (GOOSE), and manufacturing message specification (MMS) communication services, as defined in IEC 61850. The main issues identified are compatibility issues of SCL tools, protocol implementation issues, different information models, and application limitations. The outcomes will help utilities to reduce the risks associated with the general rollout of digital substations.

To achieve a highly reliable data communication network, Future Intelligent Transmission Network Substation (FITNESS) project has considered three different substation data communication redundancy networks. These include two independent parallel star networks, High‐availability Seamless Redundancy (HSR) network, and Parallel Redundancy Protocol (PRP) network. This paper presents the assessment and comparison analysis of the data flow performance for these three networks. A typical 275 kV substation with the consideration of three redundancy networks is modelled and simulated using OPNET a communication network simulation tool. The data flow capability performance with respect of the network time delay for the typical protection and control equipment requirements (e.g. IEDs and MUs) in one bay are simulated and analysed. Results show that two independent parallel star networks have the highest data flow capacity. The capacity of PRP network is reduced by a half, while the capacity of HSR network is reduced to a quarter. The results also reveal that Ethernet Switch can consume a part of network bandwidth as well. The work would help power utilities to determine an optimal number of devices to be installed and to ensure highly reliable data communication network for protection and control functions in an IEC 61850 based digital substation..

Blockchain‐based crowdsourcing systems can mitigate some known limitations of the centralized crowdsourcing platform, such as single point of failure and Sybil attacks. However, blockchain‐based crowdsourcing systems still endure the issues of privacy and security. Participants’ sensitive information (e.g., identity, address, and expertise) have the risk of privacy disclosure. Sensitive crowdsourcing tasks such as location‐based data collection and labeling images including faces also need privacy‐preserving. Moreover, current work fails to balance the anonymity and public auditing of workers. In this paper, we present a secure blockchain‐based crowdsourcing framework with fine‐grained worker selection, named PrivCrowd which exploits a functional encryption scheme to protect the data privacy of tasks and to select workers by matching the attributes. In PrivCrowd, requesters and workers can achieve both exchange and evaluation fairness by calling smart contracts. Solutions collection also can be done in a secure, sound, and noninteractive way. Experiment results show the feasibility, usability, and efficiency of PrivCrowd.