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.

Recommender systems play a significant role in information filtering and have been utilized in different scenarios, such as e-commerce and social media. With the prosperity of deep learning, deep recommender systems show superior performance by capturing non-linear information and item-user relationships. However, the design of deep recommender systems heavily relies on human experiences and expert knowledge. To tackle this problem, Automated Machine Learning (AutoML) is introduced to automatically search for the proper candidates for different parts of deep recommender systems. This survey performs a comprehensive review of the literature in this field. Firstly, we propose an abstract concept for AutoML for deep recommender systems (AutoRecSys) that describes its building blocks and distinguishes it from conventional AutoML techniques and recommender systems. Secondly, we present a taxonomy as a classification framework containing feature selection search, embedding dimension search, feature interaction search, model architecture search, and other components search. Furthermore, we put a particular emphasis on the search space and search strategy, as they are the common thread to connect all methods within each category and enable practitioners to analyze and compare various approaches. Finally, we propose four future promising research directions that will lead this line of research.

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