In this paper, the historical power load data from the National Electricity Market (Australia) is used to analyze the characteristics and regulations of electricity (the average value of every eight hours). Then, considering the inverse of Euclidean distance as the weight, this paper proposes a novel short-term load forecasting model based on the weighted k-nearest neighbor algorithm to receive higher satisfied accuracy. In addition, the forecasting errors are compared with the back-propagation neural network model and the autoregressive moving average model. The comparison results demonstrate that the proposed forecasting model could reflect variation trend and has good fitting ability in short-term load forecasting.

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Authorship attribution is to identify the most likely author of a given sample among a set of candidate known authors. It can be not only applied to discover the original author of plain text, such as novels, blogs, emails, posts etc., but also used to identify source code programmers. Authorship attribution of source code is required in diverse applications, ranging from malicious code tracking to solving authorship dispute or software plagiarism detection. This paper aims to propose a new method to identify the programmer of Java source code samples with a higher accuracy. To this end, it first introduces back propagation (BP) neural network based on particle swarm optimization (PSO) into authorship attribution of source code. It begins by computing a set of defined feature metrics, including lexical and layout metrics, structure and syntax metrics, totally 19 dimensions. Then these metrics are input to neural network for supervised learning, the weights of which are output by PSO and BP hybrid algorithm. The effectiveness of the proposed method is evaluated on a collected dataset with 3,022 Java files belong to 40 authors. Experiment results show that the proposed method achieves 91.060% accuracy. And a comparison with previous work on authorship attribution of source code for Java language illustrates that this proposed method outperforms others overall, also with an acceptable overhead.

To provide secure communication, the authentication-and-key-agreement scheme plays a vital role in multi-server environments, Internet of Things (IoT), wireless sensor networks (WSNs), etc. This scheme enables users and servers to negotiate for a common session initiation key. Our proposal first analyzes Amin et al.’s authentication scheme based on RSA and proves that it cannot provide perfect forward secrecy and user un-traceability, and is susceptible to offline password guessing attack and key-compromise user impersonation attack. Secondly, we provide that Srinivas et al.’s multi-server authentication scheme is not secured against offline password guessing attack and key-compromise user impersonation attack, and is unable to ensure user un-traceability. To remedy such limitations and improve computational efficiency, we present a multi-server two-factor authentication scheme using elliptic curve cryptography (ECC). Subsequently, employing heuristic analysis and Burrows–Abadi–Needham logic (BAN-Logic) proof, it is proven that the presented scheme provides security against all known attacks, and in particular provides user un-traceability and perfect forward security. Finally, appropriate comparisons with prevalent works demonstrate the robustness and feasibility of the presented solution in multi-server environments.

BackgroundCOVID-19 is a respiratory illness caused by SARS-CoV-2. The most recent variant is Omicron (line B.1.1.529), which was first identified in South Africa in November 2021. The concern with this variant is the ineffectiveness of vaccines currently available. We aim to systematically evaluate the effectiveness of the currently available COVID-19 vaccines and boosters for the Omicron variant.MethodsWe searched the PubMed, Embase, the Cochrane Library and Web of Science databases from inception to June 5th, 2022. Studies that examined the effectiveness of SARS-CoV-2 vaccines against the Omicron variant infection were included. Random-effects model was used to estimate the pooled vaccine effectiveness against the Omicron variant.ResultsA total of 13 studies were included to evaluate the effectiveness of the vaccine against the Omicron variant, and 11 studies were included to compare the effectiveness between the two-dose and three-dose (booster) vaccinations. Full vaccination (two-dose with or without booster) showed a protective effect against the Omicron variant compared to no vaccination (OR = 0.62, 95% CI: 0.56–0.69), while the effectiveness decreased significantly over 6 months after the last dose. The two-dose vaccination plus booster provided better protection against the Omicron variant compared to the two-dose vaccination without booster (OR = 0.60, 95% CI: 0.52–0.68). Additional analysis was performed for the most commonly used vaccines in the United Staes: BNT162b2(Pfizer) (OR = 0.65, 95% CI: 0.52–0.82) and mRNA-1273(Moderna) (OR = 0.67, 95% CI: 0.58–0.88) vaccines in the US, which showed similar effectiveness compared to no vaccination.ConclusionsThe full dose of SARS-CoV-2 vaccination effectively reduces infection from the SARS-CoV-2 Omicron variant; however, the effectiveness wanes over time. The booster vaccine provides additional protection against the Omicron variant.