This paper proposes a novel unified prediction approach for both small-signal and transient rotor angle stability as opposed to other studies that have only addressed transient rotor angle stability. Deep learning techniques are employed in this paper to train an online prediction model for rotor angle stability (RAS) using the voltage phasor measurements which are collected across the entire system. As a result, the trained model provides a fast yet accurate prediction of the transient stability status when a power system is subjected to a disturbance. Also, if the system is transiently stable, the prediction model updates the power system operator concerning the damping of low-frequency local and inter-area modes of oscillations. Therefore, the presented approach provides information concerning the transient stability and oscillatory dynamic response of the system such that proper control actions are taken. To achieve these objectives, advanced deep learning techniques are employed to train the online prediction model using a dataset which is generated through extensive time domain simulations for wide range of operating conditions. A convolutional neural network (CNN) transient stability classifier is trained to operate on the transient response of the phasor voltages across the entire system and provide a binary stability label. In tandem, a long-short term memory (LSTM) network is trained to learn the oscillatory response of a predicted stable system to capture the step-by-step dynamic evolution of the critical poorly damped low-frequency oscillations. The superior performance of the proposed model is tested using the New-England 10-machine, 39-bus, IEEE 16-machine, 68-bus, 5-area and IEEE 50-machine, 145-bus test systems and is verified with time domain simulation.

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Demand response (DR) programs have emerged as a potential key enabling ingredient in the context of smart grid (SG). Nevertheless, the rising concerns over privacy issues raised by customers subscribed to these programs constitute a major threat towards their effective deployment and utilization. This has driven extensive research to resolve the hindrance confronted, resulting in a number of methods being proposed for preserving customers' privacy. While these methods provide stringent privacy guarantees, only limited attention has been paid to their computational efficiency and performance quality. Under the paradigm of differential privacy, this paper initiates a systematic empirical study on quantifying the trade-off between privacy and optimality in centralized DR systems for maximizing cumulative customer utility. Aiming to elucidate the factors governing this trade-off, we analyze the cost of privacy in terms of the effect incurred on the objective value of the DR optimization problem when applying the employed privacy-preserving strategy based on Laplace mechanism. The theoretical results derived from the analysis are complemented with empirical findings, corroborated extensively by simulations on a 4-bus MG system with up to thousands of customers. By evaluating the impact of privacy, this pilot study serves DR practitioners when considering the social and economic implications of deploying privacy-preserving DR programs in practice. Moreover, it stimulates further research on exploring more efficient approaches with bounded performance guarantees for optimizing energy procurement of MGs without infringing the privacy of customers on demand side.

Since the declaration of SARS-CoV-2 outbreak as a pandemic, the United Arab Emirates (UAE) public health authorities have adopted strict measures to reduce transmission as early as March 2020. As a result of these measures, flight suspension, nationwide RT-PCR and surveillance of viral sequences were extensively implemented. This study aims to characterize the epidemiology, transmission pattern, and emergence of variants of concerns (VOCs) and variants of interests (VOIs) of SARS-CoV-2 in the UAE, followed by the investigation of mutations associated with hospitalized cases. A total of 1274 samples were collected and sequenced from all seven emirates between the period of 25 April 2020 to 15 February 2021. Phylogenetic analysis demonstrated multiple introductions of SARS-CoV-2 into the UAE in the early pandemic, followed by a local spread of root clades (A, B, B.1 and B.1.1). As the international flight resumed, the frequencies of VOCs surged indicating the January peak of positive cases. We observed that the hospitalized cases were significantly associated with the presence of B.1.1.7 (p < 0.001), B.1.351 (p < 0.001) and A.23.1 (p = 0.009). Deceased cases are more likely to occur in the presence of B.1.351 (p < 0.001) and A.23.1 (p = 0.022). Logistic and ridge regression showed that 51 mutations are significantly associated with hospitalized cases with the highest proportion originated from S and ORF1a genes (31% and 29% respectively). Our study provides an epidemiological insight of the emergence of VOCs and VOIs following the borders reopening and worldwide travels. It provides reassurance that hospitalization is markedly more associated with the presence of VOCs. This study can contribute to understand the global transmission of SARS-CoV-2 variants.

BackgroundGiven the current influx of 16S rRNA profiles of microbiota samples, it is conceivable that large amounts of them eventually are available for search, comparison and contextualization with respect to novel samples. This process facilitates the identification of similar compositional features in microbiota elsewhere and therefore can help to understand driving factors for microbial community assembly.ResultsWe present Visibiome, a microbiome search engine that can perform exhaustive, phylogeny based similarity search and contextualization of user-provided samples against a comprehensive dataset of 16S rRNA profiles environments, while tackling several computational challenges. In order to scale to high demands, we developed a distributed system that combines web framework technology, task queueing and scheduling, cloud computing and a dedicated database server. To further ensure speed and efficiency, we have deployed Nearest Neighbor search algorithms, capable of sublinear searches in high-dimensional metric spaces in combination with an optimized Earth Mover Distance based implementation of weighted UniFrac. The search also incorporates pairwise (adaptive) rarefaction and optionally, 16S rRNA copy number correction. The result of a query microbiome sample is the contextualization against a comprehensive database of microbiome samples from a diverse range of environments, visualized through a rich set of interactive figures and diagrams, including barchart-based compositional comparisons and ranking of the closest matches in the database.ConclusionsVisibiome is a convenient, scalable and efficient framework to search microbiomes against a comprehensive database of environmental samples. The search engine leverages a popular but computationally expensive, phylogeny based distance metric, while providing numerous advantages over the current state of the art tool.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1763-0) contains supplementary material, which is available to authorized users.