A hierarchical multi-leadership sine cosine algorithm to dissolving global optimization and data classification: The COVID-19 case study.

The Sine Cosine Algorithm (SCA) is an outstanding optimizer that is appreciably used to dissolve complicated real-world problems. Nevertheless, this algorithm lacks sufficient population diversification and a sufficient balance between exploration and exploitation. So, effective techniques are required to tackle the SCA's fundamental shortcomings. Accordingly, the present paper suggests an improved version of SCA called Hierarchical Multi-Leadership SCA (HMLSCA) which uses an effective hierarchical multi-leadership search mechanism to lead the search process on multiple paths. The efficiency of the HMLSCA has been appraised and compared with a set of famous metaheuristic algorithms to dissolve the classical eighteen benchmark functions and thirty CEC 2017 test suites. The results demonstrate that the HMLSCA outperforms all compared algorithms and that the proposed algorithm provided a promising efficiency. Moreover, the HMLSCA was applied to handle the medicine data classification by optimizing the support vector machine's (SVM) parameters and feature weighting in eight datasets. The experiential outcomes verify the productivity of the HMLSCA with the highest classification accuracy and a gain scoring 1.00 Friedman mean rank versus the other evaluated metaheuristic algorithms. Furthermore, the proposed algorithm was used to diagnose COVID-19, in which it attained the topmost accuracy of 98% in diagnosing the infection on the COVID-19 dataset, which proves the performance of the proposed search strategy.

Effects of topography and soil properties on the distribution and fractionation of REEs in topsoil: A case study in Sichuan Basin, China.

In order to investigate how topographic factors and soil physicochemical properties influenced the distribution and fractionation of rare earth elements (REEs) in soil, Jiangjin district of Sichuan Basin, an area with mountainous topography, was selected as a study area. The concentration of REEs, pH and organic matter (OM) and major elements in 156 topsoil samples were measured and analyzed. The topographic factors considered were elevation, slope, and topographic wetness index (TWI), which were extracted by using the digital elevation model (DEM). The median concentration of total REEs in topsoil of the study area was 147 mg/kg, lower than the Chinese soil background value (164 mg/kg). The concentration of LREEs and HREEs, and the ratio of LREEs/HREEs and LaN/YbN indicated that the distribution and fractionation patterns of REEs in topsoil were LREEs-enriched. Significant Eu negative anomalies and weak Ce negative anomalies were observed in topsoil according to the median values of δEu (0.57) and δCe (0.89). The coefficient of weathering and eluviation (BA), an important factor affecting the distribution and fractionation of REEs, was substantially correlated with δEu (r = 0.344, p < 0.01), δCe (r = -0.252, p < 0.01), ∑REEs (r = 0.135, p < 0.01), and LREEs/HREEs (r = -0.281, p < 0.01) in topsoil. Soil pH and OM had some influence on the distribution and fractionation of REEs. Under the geographical environment of the study area, Ce was positive anomaly with the elevation and slope increasing. The enrichment of LREEs was more significant than HREEs as elevation increased. The findings revealed that topographical attributes and soil physicochemical properties integratedly influenced the distribution and fractionation of REEs in topsoil.

[Temporal and Spatial Distribution Characteristics of PM2.5 in Chongqing Urban Areas].

To explore the diurnal and seasonal characteristics of PM25, hourly PM25 concentration data ol It tixed monitoring sites in Chongqing urban area were collected continuously from June 2014 to May 2015. The result showed that: (1) the seasonal concentration of PM2.5 in different seasons decreased in the order of winter, autumn, spring and summer (P < 0.05), which were 100.2 microg x m(-3), 66.1 microg x m(-3), 45.9 microg x m(-3) and 33.4 microg x m(-3), respectively. (2) Monthly average concentrations of PM2.5 showed a single-peak curve, and the peaks of which occurred in January (P < 0.05, 120.8 microg x m(-3)). (3) For the whole year, daily variation of PM2.5 showed a jaggies curve. (4) Diurnal average concentrations of PM2.5 showed an obviously bimodal curve in winter, autumn and spring, hut it was less obvious in summer. (5) Daily PM25 concentration was significantly positively correlated with SO2, NO2 and CO (P < 0.01), indicating that the concentrations of SO2, NO2 and CO had a significant impact on the concentration of