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Collaborative filtering is a widely used recommendation algorithm, but problems such as low efficiency and data sparseness still exist. In order to solve these problems, we present an improved clustering recommendation algorithm. The algorithm introduces a cloud model, in which the expectation, entropy and hyper entropy are calculated according to the item attributes and user attributes dimensions. To build up a user interest model, the influence of rating time, rating level and rating habits are also taken into account. Then the similarities of user interests are compared by the corrected similarity measurement based on cloud model, and the K-means algorithm is adopted to perform clustering. Finally, the recommendation results of the public projects are merged by using the proportion of the participants who will make predictions. Experiment results on the MovieLens show that the algorithm can not only solve the problem of low efficiency and data sparseness but also improve the accuracy of the recommendation results. [ABSTRACT FROM AUTHOR]

It studies the task scheduling in cloud computing, and proposes a hybrid scheduling algorithm (ACOSA) combined with ant colony algorithm and simulated annealing algorithm for the MapReduce programming framework of cloud computing. This algorithm aims at minimizing the scheduling time and introduces the task and resource matching factors and load balance. Firstly, the ant colony algorithm was used to get the optimal solution to a set of tasks and resources. Then, the path was optimized, and the pheromone of solution was updated by the simulated annealing algorithm. Lastly, they were recompiled by extending Cloudsim cloud computing simulation platform, and the ACOSA algorithm was achieved. The experimental results show that the algorithm has a good performance in scheduling time and load balancing. [ABSTRACT FROM AUTHOR]

By combining the PageRank algorithm with the features of intensive noise-data to improve the noise-data recognition rate of DBSCAN in environments with intensive Noise-Point distribution, it structured the inner-cluster mapping function for voting, and proposed the inter-cluster voting noise recognition algorithm-NoiseRank. Experimental results show that in environments with intensive Noise-Point distribution, the Noise-data recognition rate of NoiseRank is much higher than that of DBSCAN. [ABSTRACT FROM AUTHOR]

Recent studies suggest that up-regulated HSF1 possesses metabolic phenotypes switch and chemoresistance in cancer cells. However, the mechanism in which these characteristics are still ambiguous. Our study aims to identify how HSF1 confers chemoresistance through regulating metabolic pathway in hepatocellular carcinoma (HCC). Oxaliplatin (OXA)-resistant HCC cells (HCC-OXR) in both of abundant glucose (AG; 25 mM) and low glucose (LG; 5.5 mM) conditions were constructed; then glucose consumption, lactate production, intracellular ATP level and oxygen consumption of parental and OXA-resistant cells were determined by using the associated detected kits. Moreover, HSF1 was knocked down to analyze its effects on metabolic phenotypes alteration and chemoresistance formation in HCC cells. Compared to cells in AG condition, HCC cells delayed to form chemoresistance to OXA in LG condition; and OXA-resistant cells underwent a metabolic switch from glycolysis to oxidative phosphorylation (OXPHOS), which presented decreased glucose uptake and lactate production with increased levels of oxygen consumption and intercellular ATP; interestingly, this energy-producing pathway was blocked in HSF1-knockdown OXA-resistant cells, especially in LG condition. Analysis on previous data revealed that AMPK pathway was a critical regulator in the metabolism of OXA-resistance HCC cells. Furthermore, AMPKα2 was identified as an important factor regulated by HSF1 to achieve metabolic phenotype switch in OXA-resistance HCC cells. Consequently, these results suggest that combining restrictive glucose uptake and targeting HSF1/AMPKα2 is an attractive strategy to prevent chemoresistance to OXA in HCC patients., Competing Interests: None., (AJCR Copyright © 2019.)