Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

In this study, the authors aimed to study an effective intelligent method for employment stability prediction in order to provide a reasonable reference for postgraduate employment decision and for policy formulation in related departments. First, this paper introduces an enhanced slime mould algorithm (MSMA) with a multi-population strategy. Moreover, this paper proposes a prediction model based on the modified algorithm and the support vector machine (SVM) algorithm called MSMA-SVM. Among them, the multi-population strategy balances the exploitation and exploration ability of the algorithm and improves the solution accuracy of the algorithm. Additionally, the proposed model enhances the ability to optimize the support vector machine for parameter tuning and for identifying compact feature subsets to obtain more appropriate parameters and feature subsets. Then, the proposed modified slime mould algorithm is compared against various other famous algorithms in experiments on the 30 IEEE CEC2017 benchmark functions. The experimental results indicate that the established modified slime mould algorithm has an observably better performance compared to the algorithms on most functions. Meanwhile, a comparison between the optimal support vector machine model and other several machine learning methods on their ability to predict employment stability was conducted, and the results showed that the suggested the optimal support vector machine model has better classification ability and more stable performance. Therefore, it is possible to infer that the optimal support vector machine model is likely to be an effective tool that can be used to predict employment stability.

Sparse Signal Reconstruction on Fixed and Adaptive Supervised Dictionary Learning for Transient Stability Assessment

Fixed and adaptive supervised dictionary learning (SDL) is proposed in this paper for wide-area stability assessment. Single and hybrid fixed structures are developed based on impulse dictionary (ID), discrete Haar transform (DHT), discrete cosine transform (DCT), discrete sine transform (DST), and discrete wavelet transform (DWT) for sparse features extraction and online transient stability prediction. The fixed structures performance is compared with that obtained from transient K-singular value decomposition (TK-SVD) implemented while adding a stability status term to the optimization problem. Stable and unstable dictionary learning are designed based on datasets recorded by simulating thousands of contingencies with varying faults, load, and generator switching on the IEEE 68-bus test system. This separate supervised learning of stable and unstable scenarios allows determining root mean square error (RMSE), useful for online stability status assessment of new scenarios. With respect to the RMSE performance metric in signal reconstruction-based stability prediction, the present analysis demonstrates that [DWT], [DHT|DWT] and [DST|DHT|DCT] are better stability descriptors compared to K-SVD, [DHT], [DCT], [DCT|DWT], [DHT|DCT], [ID|DCT|DST], and [DWT|DHT|DCT] on test datasets. However, the K-SVD approach is faster to execute in both off-line training and real-time playback while yielding satisfactory accuracy in transient stability prediction (i.e., 7.5-cycles decision window after fault-clearing).

Hydrological and mechanical properties of loess landslides under different vegetation restoration types

Loess landslide is a common geological disaster in northern Shaanxi, which seriously affects people's life and property safety and social and economic development. The research on vegetation restoration types and hydrological and mechanical properties of loess landslides can provide basic data support for landslide stability prediction, and further provide reference for landslide prevention and treatment. In the present study, the loess landslide point of Zhang Zi Gou in Gan Quan County, Yan’an City was taken as the research object. On the basis of the existing natural condition data, the basic physical and mechanical properties and hydrological characteristics were obtained by collecting field landslide soil samples for indoor experimental analysis. The indoor analysis shows that the landslide is mainly distributed in dry land, medium coverage and low coverage grassland, indicating that the surface vegetation coverage can affect the stability of landslide. The worse the vegetation coverage, the more landslides occur. The void ratio and porosity of landslide soil decrease with the increase of dry density. The cohesion of natural soil is obviously higher than that of saturated soil, and the internal friction angle of natural soil is slightly lower than that of saturated soil. In general, due to the influence of water content, the shear strength of natural soil samples is higher than that of saturated soil samples. Therefore, in order to improve the accuracy of prediction and early warning system, it is necessary to consider the response of hydrological and mechanical properties of loess to vegetation restoration. The results provide basic data support for the establishment of loess landslide stability prediction system and provide reference for geological disaster management. Bangladesh J. Bot. 50(3): 795-801, 2021 (September) Special