The allelopathic effects of fresh tissue, dry powder and aqueous extracts of three macroalgae, Ulva pertusa, Corallina pilulifera and Sargassum thunbergii, on the growth of the dinoflagellates Heterosigma akashiwo and Alexandrium tamarense were evaluated using coexistence culture systems in which concentrations of the three macroalga were varied. The results of the coexistence assay showed that the growth of the two microalgae was strongly inhibited by using fresh tissue, dry powder and aqueous extracts of the three macroalga; the allelochemicals were lethal to H. akashiwo at relatively higher concentrations of the three macroalga. The macroalgae showing the most allelopathic effect on H. akashiwo and A. tamarense using fresh tissue were U. pertusa and S. thunbergii, using dry powder were S. thunbergii and U. pertusa, and using aqueous extracts were U. pertusa and C. pilulifera. We also examined the potential allelopathic effect on the two microalgae of culture filtrate of the three macroalga; culture medium filtrate initially exhibited no inhibitory effects when first added but inhibitory effects became apparent under semi-continuous addition, which suggested that continuous release of small quantities of rapidly degradable allelochemicals from the fresh macroalgal tissue were essential to effectively inhibit the growth of the two microalgae.

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Fault detection is the research focus and priority in this study to ensure the high reliability of a proposed three-level inverter. Kernel principal component analysis (KPCA) has been widely used for feature extraction because of its simplicity. However, highlighting useful information that may be hidden under retained KPCs remains a problem. A weighted KPCA is proposed to overcome this shortcoming. Variable contribution plots are constructed to evaluate the importance of each KPC on the basis of sensitivity analysis theory. Then, different weighting values of KPCs are set to highlight the useful information. The weighted statistics are evaluated comprehensively by using the improved feature eigenvectors. The effectiveness of the proposed method is validated. The diagnosis results of the inverter indicate that the proposed method is superior to conventional KPCA.

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The Swarm Intelligence (SI) algorithms have been proved to be a comprehensive method to solve complex optimization problems by simulating the emergence behaviors of biological swarms. Nowadays, data science is getting more and more attention, which needs quick management and analysis of massive data. Most traditional methods can only be applied to continuous and differentiable functions. As a set of population-based approaches, it is proven by some recent research works that the SI algorithms have great potential for relevant tasks in this field. In order to gather better insight into the utilization of these methods in data science and to provide a further reference for future researches, this paper focuses on the relationship between data science and swarm intelligence. After introducing the mainstream swarm intelligence algorithms and their common characteristics, both the theoretical and real-world applications in the literature which utilize the swarm intelligence to the related domains of data analytics are reviewed. Based on the summary of the existing works, this paper also analyzes the opportunities and challenges in this field, which attempts to shed some light on designing more effective algorithms to solve the problems in data science for real-world applications.