The emergence of 5G enables a broad set of diversified and heterogeneous services with complex and potentially conflicting demands. For networks to be able to satisfy those needs, a flexible, adaptable, and programmable architecture based on network slicing is being proposed. Moreover, a softwarization and cloudification of the communications networks is required, where network functions (NFs) are being transformed from programs running on dedicated hardware platforms to programs running over a shared pool of computational and communication resources. This architectural framework allows the introduction of resource elasticity as a key means to make an efficient use of the computational resources of 5G systems, but adds challenges related to resource sharing and efficiency. In this paper, we propose Artificial Intelligence (AI) as a built-in architectural feature that allows the exploitation of the resource elasticity of a 5G network. Building on the work of the recently formed Experiential Network Intelligence (ENI) industry specification group of the European Telecommunications Standards Institute (ETSI) to embed an AI engine in the network, we describe a novel taxonomy for learning mechanisms that target exploiting the elasticity of the network as well as three different resource elastic use cases leveraging AI. This work describes the basis of a use case recently approved at ETSI ENI.

Power curve measurements provide a conventional and effective means of assessing the performance of a wind turbine, both commercially and technically. Increasingly high wind penetration in power systems and offshore accessibility issues make it even more important to monitor the condition and performance of wind turbines based on timely and accurate wind speed and power measurements. Power curve data from Supervisory Control and Data Acquisition (SCADA) system records, however, often contain significant measurement deviations, which are commonly produced as a consequence of wind turbine operational transitions rather than stemming from physical degradation of the plant. Using such raw data for wind turbine condition monitoring purposes is thus likely to lead to high false alarm rates, which would make the actual fault detection unreliable and would potentially add unnecessarily to the costs of maintenance. To this end, this paper proposes a probabilistic method for excluding outliers, developed around a copula-based joint probability model. This approach has the capability of capturing the complex non-linear multivariate relationship between parameters, based on their univariate marginal distributions; through the use of a copula, data points that deviate significantly from the consolidated power curve can then be removed depending on this derived joint probability distribution. After filtering the data in this manner, it is shown how the resulting power curves are better defined and less subject to uncertainty, whilst broadly retaining the dominant statistical characteristics. These improved power curves make subsequent condition monitoring more effective in the reliable detection of faults.

Zinc finger protein 667‐antisense RNA 1 (ZNF667‐AS1), located on human chromosome 19q13.43, is a member of the C2H2 zinc finger protein family. Herein, we aimed to analyze the interactions between ZNF667‐AS1, microRNA‐93‐3p (miR‐93‐3p), and paternally expressed gene 3 (PEG3) and to explore their roles in the tumorigenesis of cervical cancer (CC). Differentially expressed long noncoding RNAs and miRNAs related to CC were determined using gene expression datasets sourced from the Gene Expression Omnibus database. Subsequently, the regulatory relationships between ZNF667‐AS1 and miR‐93‐3p and between miR‐93‐3p and PEG3 were identified using the dual‐luciferase reporter gene assay. In addition, the expression of miR‐93‐3p and ZNF667‐AS1 was up‐ or downregulated in CC cells (HeLa), in order to assess their effects on cell cycle distribution and cell invasion in vitro, and tumor growth and metastasis in vivo. MiR‐93‐3p was found to be highly expressed, while ZNF667‐AS1 and PEG3 were poorly expressed in CC. ZNF667‐AS1 could competitively bind to miR‐93‐3p, which targeted PEG3. In addition, miR‐93‐3p downregulation and ZNF667‐AS1 overexpression led to increased expression of PEG3, tissue inhibitor of metalloproteinases, and p16 and decreased expression of cyclin D1, matrix metalloproteinase‐2 and ‐9. MiR‐93‐3p inhibition and ZNF667‐AS1 elevation also inhibited cell cycle entry and cell invasion in vitro, but repressed tumor growth and metastasis in vivo. These key findings demonstrated that upregulation of ZNF667‐AS1 could suppress the progression of CC via the modulation of miR‐93‐3p‐dependent PEG3, suggesting a potential therapeutic target for the treatment of CC.