Particle Swarm Algorithm to Optimize LSTM Short-Term Load Forecasting

Accurate load forecasting is of great significance for national and grid planning and management. In order to improve the accuracy of short-term load forecasting, an LSTM prediction model based on particle swarm optimization (PSO)algorithm is proposed. LSTM has the characteristics of avoiding gradient disappearance and gradient explosion, but there is a problem that parameters are difficult to select. Therefore, particle swarm optimization algorithm is used to help it select parameters. The experimental results show that the optimized LSTM has higher prediction accuracy

An unsupervised approach to Geographical Knowledge Discovery using street level and street network images

Recent researches have shown the increasing use of machine learn-ing methods in geography and urban analytics, primarily to extract features and patterns from spatial and temporal data using a supervised approach. Researches integrating geographical processes in machine learning models and the use of unsupervised approacheson geographical data for knowledge discovery had been sparse. This research contributes to the ladder, where we show how latent variables learned from unsupervised learning methods on urbanimages can be used for geographic knowledge discovery. In particular, we propose a simple approach called Convolutional-PCA(ConvPCA) which are applied on both street level and street network images to find a set of uncorrelated and ordered visual latentcomponents. The approach allows for meaningful explanations using a combination of geographical and generative visualisations to explore the latent space, and to show how the learned representation can be used to predict urban characteristics such as streetquality and street network attributes. The research also finds that the visual components from the ConvPCA model achieves similaraccuracy when compared to less interpretable dimension reduction techniques.Comment: SigSpatial 2019 GeoA

Transcriptomic and metabolomic analyses to study the key role by which Ralstonia insidiosa induces Listeria monocytogenes to form suspended aggregates

Ralstonia insidiosa can survive in a wide range of aqueous environments, including food processing areas, and is harmful to humans. It can induce Listeria monocytogenes to form suspended aggregates, resulting from the co-aggregation of two bacteria, which allows for more persistent survival and increases the risk of L. monocytogenes contamination. In our study, different groups of aggregates were analyzed and compared using Illumina RNA sequencing technology. These included R. insidiosa under normal and barren nutrient conditions and in the presence or absence of L. monocytogenes as a way to screen for differentially expressed genes (DEGs) in the process of aggregate formation. In addition, sterile supernatants of R. insidiosa were analyzed under different nutrient conditions using metabolomics to investigate the effect of nutrient-poor conditions on metabolite production by R. insidiosa. We also undertook a combined analysis of transcriptome and metabolome data to further investigate the induction effect of R. insidiosa on L. monocytogenes in a barren environment. The results of the functional annotation analysis on the surface of DEGs and qPCR showed that under nutrient-poor conditions, the acdx, puuE, and acs genes of R. insidiosa were significantly upregulated in biosynthetic processes such as carbon metabolism, metabolic pathways, and biosynthesis of secondary metabolites, with Log2FC reaching 4.39, 3.96, and 3.95 respectively. In contrast, the Log2FC of cydA, cyoB, and rpsJ in oxidative phosphorylation and ribosomal pathways reached 3.74, 3.87, and 4.25, respectively. Thirty-one key components were identified while screening for differential metabolites, which mainly included amino acids and their metabolites, enriched to the pathways of biosynthesis of amino acids, phenylalanine metabolism, and methionine metabolism. Of these, aminomalonic acid and Proximicin B were the special components of R. insidiosa that were metabolized under nutrient-poor conditions