Abstract-Differential Evolution (DE) is

Differential evolution (DE) has been proven to be one of the most powerful global numerical optimization algorithms in the evolutionary algorithm family. The core operator of DE is the differential mutation operator. Generally, the parents in the mutation operator are randomly chosen from the current population. In nature, good species always contain good information, and hence, they have more chance to be utilized to guide other species. Inspired by this phenomenon, in this paper, we propose the ranking-based mutation operators for the DE algorithm, where some of the parents in the mutation operators are proportionally selected according to their rankings in the current population. The higher ranking a parent obtains, the more opportunity it will be selected. In order to evaluate the influence of our proposed ranking-based mutation operators on DE, our approach is compared with the jDE algorithm, which is a highly competitive DE variant with self-adaptive parameters, with different mutation operators. In addition, the proposed ranking-based mutation operators are also integrated into other advanced DE variants to verify the effect on them. Experimental results indicate that our proposed ranking-based mutation operators are able to enhance the performance of the original DE algorithm and the advanced DE algorithms.

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BackgroundThe cynomolgus monkey (Macaca fascicularis) has been increasingly used in biomedical research, making knowledge of its blood-based parameters essential to support the selection of healthy subjects and its use in preclinical research. As age and sex affect these blood-based parameters, it is important to establish baseline indices for these parameters on an age and sex basis and determine the effects of age and sex on these indices.MethodsA total of 917 cynomolgus monkeys (374 males and 543 females) were selected and segregated by age (five groups) and sex. A total of 30 hematological and 22 biochemical parameters were measured, and the effects of age and sex were analyzed.ResultsBaseline indices for hematological and biochemical parameters were separately established by age and sex. Significant effects by age, sex, and age-sex interaction were observed in a number of blood parameters. In the 49–60 months and 61–72 months age groups, red blood cell count, hemoglobulin, and hematocrit showed significantly lower values (P<0.01) in females than males. Serum alkaline phosphatase varied with age in both sexes (P<0.01) and was significantly higher in females than males (P<0.05) in the groups aged 13–24 months and 25–36 months; however, in the three groups aged over 25–36 months, serum alkaline phosphatase was significantly lower in females than males (P<0.01). Creatinine concentration increased with age (P<0.01) in all age groups; specifically in the groups aged 49–60 months and 61–72 months, creatinine was significantly higher (P<0.01) in males than females. Total protein and globulin both increased with age (P<0.01).ConclusionThe baseline values of hematological and biochemical parameters reported herein establish reference indices of blood-based parameters in the cynomolgus monkey by age and sex, thereby aiding researchers in selecting healthy subjects and evaluating preclinical studies using this species.

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Abstract:Carbon sink estimation and ecological assessment of forests require accurate forest type mapping. The traditional survey method is time consuming and labor intensive, and the remote sensing method with high-resolution, multi-spectral commercial satellite images has high cost and low availability. In this study, we explore and evaluate the potential of freely-available multi-source imagery to identify forest types with an object-based random forest algorithm. These datasets included Sentinel-2A (S2), Sentinel-1A (S1) in dual polarization, one-arc-second Shuttle Radar Topographic Mission Digital Elevation (DEM) and multi-temporal Landsat-8 images (L8). We tested seven different sets of explanatory variables for classifying eight forest types in Wuhan, China. The results indicate that single-sensor (S2) or single-day data (L8) cannot obtain satisfactory results; the overall accuracy was 54.31% and 50.00%, respectively. Compared with the classification using only Sentinel-2 data, the overall accuracy increased by approximately 15.23% and 22.51%, respectively, by adding DEM and multi-temporal Landsat-8 imagery. The highest accuracy (82.78%) was achieved with fused imagery, the terrain and multi-temporal data contributing the most to forest type identification. These encouraging results demonstrate that freely-accessible multi-source remotely-sensed data have tremendous potential in forest type identification, which can effectively support monitoring and management of forest ecological resources at regional or global scales.

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