Design of a fault diagnosis scheme for a class of singular nonlinear systems

A new fault detection and diagnosis approach is developed in this paper for a class of singular nonlinear systems via the use of adaptive updating rules. Both detection and diagnostic observers are established, where Lyapunov stability theory is used to obtain the required adaptive tuning rules for the estimation of the process faults. This has led to stable observation error systems for both fault detection and diagnosis. A simulated numerical example is included to demonstrate the use of the proposed approach and encouraging results have been obtained.

A Survey on Expert Recommendation in Community Question Answering

Community question answering （CQA） represents the type of Web applications where people can exchange knowledge via asking and answering questions. One significant challenge of most real-world CQA systems is the lack of effective matching between questions and the potential good answerers, which adversely affects the efficient knowledge acquisition and circulation. On the one hand, a requester might experience many low-quality answers without receiving a quality response in a brief time; on the other hand, an answerer might face numerous new questions without being able to identify the questions of interest quickly. Under this situation, expert recommendation emerges as a promising technique to address the above issues. Instead of passively waiting for users to browse and find their questions of interest, an expert recommendation method raises the attention of users to the appropriate questions actively and promptly. The past few years have witnessed considerable efforts that address the expert recommendation problem from different perspectives. These methods all have their issues that need to be resolved before the advantages of expert recommendation can be fully embraced. In this survey, we first present an overview of the research efforts and state-of-the-art techniques for the expert recommendation in CQA. We next summarize and compare the existing methods concerning their advantages and shortcomings, followed by discussing the open issues and future research directions.

PECTATE LYASE-LIKE10 is associated with pollen wall development in Brassica campestris

PECTATE LYASE‐LIKE10（PLL10） was previously identified as one of the differentially expressed genes both in microspores during the late pollen developmental stages and in pistils during the fertilization process in Chinese cabbage（Brassica campestris ssp. chinensis）. Here, antisense‐RNA was used to study the functions of BcPLL10 in Chinese cabbage. Abnormal pollen was identified in the transgenic lines（bcpll10‐4, ‐5, and ‐6）. In fertilization experiments, fewer seeds were harvested when the antisense‐RNA lines were used as pollen donor. In vivo and in vitro pollen germination assays less germinated pollen tubes were observed in bcpll10 lines. Scanning electron microscopy observation verified that the tryphine materials were over accumulated around the pollen surface and sticked them together in bcpll10.Moreover, transmission electron microscopy observation revealed that the internal endintine was overdeveloped and predominantly occupied the intine, and disturbed thenormal proportional distribution of the two layers in the non‐germinal furrow region; and no obvious demarcation existed between them in the germinal furrow region in the bcpll10 pollen. Collectively, this study presented a novel PLL gene that played an important role during the pollen wall development in B. campestris, which may also possess potential importance for male sterility usage in agriculture.

Quantifying predictability of sequential recommendation via logical constraints

The sequential recommendation is a compelling technology for predicting users’next interaction via their historical behaviors.Prior studies have proposed various methods to optimize the recommendation accuracy on different datasets but have not yet explored the intrinsic predictability of sequential recommendation.To this end,we consider applying the popular predictability theory of human movement behavior to this recommendation context.Still,it would incur serious bias in the next moment measurement of the candidate set size,resulting in inaccurate predictability.Therefore,determining the size of the candidate set is the key to quantifying the predictability of sequential recommendations.Here,different from the traditional approach that utilizes topological constraints,we first propose a method to learn inter-item associations from historical behaviors to restrict the size via logical constraints.Then,we extend it by 10 excellent recommendation algorithms to learn deeper associations between user behavior.Our two methods show significant improvement over existing methods in scenarios that deal with few repeated behaviors and large sets of behaviors.Finally,a prediction rate between 64%and 80%has been obtained by testing on five classical datasets in three domains of the recommender system.This provides a guideline to optimize the recommendation algorithm for a given dataset.