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The traditional neighborhood rough set model has low noise immunity and computational efficiency for mixed data. This paper establishes a local variable-precision neighborhood rough set model with dynamic change of attributes based on matrix theory. Firstly, on the basis of the updating law of local diagonal matrix and transition matrix, the dynamic updating mechanism of approximation under the local variable-precision neighborhood rough set of mixed information system is constructed; secondly, according to this dynamic updating mechanism, a new dynamic algorithm of local variable-precision neighborhood rough set with attribute change is proposed; finally, through the experimental analysis, it can be seen that the proposed dynamic algorithm has a high computational efficiency and a good robustness. [ABSTRACT FROM AUTHOR]

The efficiency of beach recovery during a time of moderate waves following storm waves is closely related to the interaction between dynamics, sediment, and the landform. The existing studies mainly focus on the description of erosion and accretion characteristics, while the response and feedback mechanism of beach berm sediment have not been elucidated. The main controlling factors of recovery efficiency are not clear. In this paper, field observation and the XBeach numerical model are utilized on the sandy beach in Puqian Bay, China, to capture high-frequency cross-shore data during the post-storm recovery period. The variation characteristics and rules of berm elements, including berm ridge height and slope on two sides of the berm ridge, are analyzed. It is observed that the berm constantly changes to adapt to dynamic conditions. Additionally, a correlation between volume change and certain landform parameters is proposed, leading to the identification of a new relationship in wave run-up. The new forum reflects berm influence and considers the berm ridge and berm width. [ABSTRACT FROM AUTHOR]

This paper addresses the current existence of attribute reduction algorithms for incomplete hybrid decision-making systems, including low attribute reduction efficiency, low classification accuracy and lack of consideration of unlabeled data types. To address these issues, this paper first redefines the weakly labeled relative neighborhood discernibility degree and develops a non-dynamic attribute reduction algorithm. In addition, this paper proposes an incremental update mechanism for weakly tagged relative neighborhood discernibility degree and introduces a new dynamic attribute reduction algorithm for increasing the set of objects based on it. Meanwhile, this paper also compares and analyses the improved algorithm proposed in this study with two existing attribute reduction algorithms using 8 data sets in the UCI database. The results show that the dynamic attribute reduction algorithm proposed in this paper achieves higher attribute reduction efficiency and classification accuracy, which further validates the effectiveness of the algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]