SEA: A Combined Model for Heat Demand Prediction

Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend decomposition based on LOESS (STL) algorithm can analyze the periodicity of a heat demand series, and decompose the series into seasonal and trend components. Then, predicting the seasonal and trend components respectively, and combining their predictions together as the heat demand prediction is a possible way to predict heat demand. In this paper, STL-ENN-ARIMA (SEA), a combined model, was proposed based on the combination of the Elman neural network (ENN) and the autoregressive integrated moving average (ARIMA) model, which are commonly applied to heat demand prediction. ENN and ARIMA are used to predict seasonal and trend components, respectively. Experimental results demonstrate that the proposed SEA model has a promising performance

Genome Characterization and Potential Risk Assessment of the Novel SARS-CoV-2 Variant Omicron (B.1.1.529)

As the novel coronavirus SARS-CoV-2 spread around the world, multiple waves of variants emerged, thus leading to local or global population shifts during the pandemic. A new variant named Omicron (PANGO lineage B.1.1.529), which was first discovered in southern Africa, has recently been proposed by the World Health Organization to be a Variant of Concern. This variant carries an unusually large number of mutations, particularly on the spike protein and receptor binding domain, in contrast to other known major variants. Some mutation sites are associated with enhanced viral transmission, infectivity, and pathogenicity, thus enabling the virus to evade the immune protective barrier. Given that the emergence of the Omicron variant was accompanied by a sharp increase in infection cases in South Africa, the variant has the potential to trigger a new global epidemic peak. Therefore, continual attention and a rapid response are required to decrease the possible risks to public health

Neural network based algorithm for multi-constrained shortest path problem

Multi-Constrained Shortest Path (MCSP) selection is a fundamental problem in communication networks. Since the MCSP problem is NP-hard, there have been many efforts to develop efficient approximation algorithms and heuristics. In this paper, a new algorithm is proposed based on vectorial Autowave-Competed Neural Network which has the characteristics of parallelism and simplicity. A nonlinear cost function is defined to measure the autowaves (i.e., paths). The M-paths limited scheme, which allows no more than M autowaves can survive each time in each neuron, is adopted to reduce the computational and space complexity. And the proportional selection scheme is also adopted so that the discarded autowaves can revive with certain probability with respect to their cost functions. Those treatments ensure in theory that the proposed algorithm can find an approximate optimal path subject to multiple constraints with arbitrary accuracy in polynomial-time. Comparing experiment results showed the efficiency of the proposed algorithm