Variable interaction in multi-objective optimization problems

14th International Conference on Parallel Problem Solving from Nature – PPSN XIV, 2016-09-17, 2016-09-21, Edinburgh, UK, pp. 399 - 409This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.The final publication is available at link.springer.comVariable interaction is an important aspect of a problem, which reflects its structure, and has implications on the design of efficient optimization algorithms. Although variable interaction has been widely studied in the global optimization community, it has rarely been explored in the multi-objective optimization literature. In this paper, we empirically and analytically study the variable interaction structures of some popular multi-objective benchmark problems. Our study uncovers nontrivial variable interaction structures for the ZDT and DTLZ benchmark problems which were thought to be either separable or non-separable

ON SOME INEQUALITIES FOR -MEASURABLE OPERATORS

This paper deals with the Choi’s inequality for measurable operators affiliated with a given von Neumann algebra. Some Young and Cauchy-Schwarz type inequalities for -measurable operators are also given

Food and nutrition literacy status and its correlates in Iranian senior high-school students

Background: Planning interventions to promote food and nutrition literacy (FNL) require a better understanding of the FNL status of the target group and its correlates. Aims: This study aimed to examine the FNL status and its determinants in Iranian senior high-school students. Methods: In this cross-sectional study, FNL and its components (food and nutrition knowledge, functional skills, interactive skills, advocacy, critical analysis of information, and food label reading skill) were evaluated by a locally designed and validated, self-administered questionnaire. Besides, socioeconomic, demographic, anthropometric measures, as well as academic performance of 626 senior high-school students were assessed. Results: The mean ± SD of the total FNL score (within potential range of 0 to 100) was 52.1 ± 10.96, which is below the minimum adequate level of 60. The probability of high FNL knowledge score was significantly higher among students who majored in Natural Sciences (OR = 1.73, CI = 1.09�2.75), had better school performance (OR = 1.13, CI = 1.06�1.20) and higher SES score (OR = 1.20, CI = 1.01�1.44). The score for food label reading was significantly lower in girls (OR = 0.45, CI = 0.31�0.67), while those who had a family member with the nutrition-related disease were more likely to have a higher score of food label reading skill (OR = 1.48, CI = 1.01�1.64). Conclusion: The level of FNL in senior high-school students in Tehran was relatively low. These findings have key messages for the education system and curriculum designers to have more consideration for food and nutrition-related knowledge and skills in schools. © 2021, The Author(s)

Gis-based gully erosion susceptibility mapping: a comparison of computational ensemble data mining models

Gully erosion destroys agricultural and domestic grazing land in many countries, especially those with arid and semi-arid climates and easily eroded rocks and soils. It also generates large amounts of sediment that can adversely impact downstream river channels. The main objective of this research is to accurately detect and predict areas prone to gully erosion. In this paper, we couple hybrid models of a commonly used base classifier (reduced pruning error tree, REPTree) with AdaBoost (AB), bagging (Bag), and random subspace (RS) algorithms to create gully erosion susceptibility maps for a sub-basin of the Shoor River watershed in northwestern Iran. We compare the performance of these models in terms of their ability to predict gully erosion and discuss their potential use in other arid and semi-arid areas. Our database comprises 242 gully erosion locations, which we randomly divided into training and testing sets with a ratio of 70/30. Based on expert knowledge and analysis of aerial photographs and satellite images, we selected 12 conditioning factors for gully erosion. We used multi-collinearity statistical techniques in the modeling process, and checked model performance using statistical indexes including precision, recall, F-measure, Matthew correlation coefficient (MCC), receiver operatic characteristic curve (ROC), precision-recall graph (PRC), Kappa, root mean square error (RMSE), relative absolute error (PRSE), mean absolute error (MAE), and relative absolute error (RAE). Results show that rainfall, elevation, and river density are the most important factors for gully erosion susceptibility mapping in the study area. All three hybrid models that we tested significantly enhanced and improved the predictive power of REPTree (AUC=0.800), but the RS-REPTree (AUC= 0.860) ensemble model outperformed the Bag-REPTree (AUC= 0.841) and the AB-REPTree (AUC= 0.805) models. We suggest that decision makers, planners, and environmental engineers employ the RS-REPTree hybrid model to better manage gully erosion-prone areas in Iran

Radiative recombination of bare Bi83+: Experiment versus theory

Electron-ion recombination of completely stripped Bi83+ was investigated at the Experimental Storage Ring (ESR) of the GSI in Darmstadt. It was the first experiment of this kind with a bare ion heavier than argon. Absolute recombination rate coefficients have been measured for relative energies between ions and electrons from 0 up to about 125 eV. In the energy range from 15 meV to 125 eV a very good agreement is found between the experimental result and theory for radiative recombination (RR). However, below 15 meV the experimental rate increasingly exceeds the RR calculation and at Erel = 0 eV it is a factor of 5.2 above the expected value. For further investigation of this enhancement phenomenon the electron density in the interaction region was set to 1.6E6/cm3, 3.2E6/cm3 and 4.7E6/cm3. This variation had no significant influence on the recombination rate. An additional variation of the magnetic guiding field of the electrons from 70 mT to 150 mT in steps of 1 mT resulted in periodic oscillations of the rate which are accompanied by considerable changes of the transverse electron temperature.Comment: 12 pages, 14 figures, to be published in Phys. Rev. A, see also http://www.gsi.de/ap/ and http://www.strz.uni-giessen.de/~k

Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm

Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and extended multi-attribute profiles (EMAPs) is presented for coastal wetland mapping using Sentinel-2 multispectral instrument (MSI) imagery. In the proposed method, the morphological attribute profiles (APs) are firstly extracted using four attribute filters based on the characteristics of wetlands in each band from Sentinel-2 imagery. These APs form a set of EMAPs which comprehensively represent the irregular wetland objects in multiscale and multilevel. The EMAPs and original spectral features are then classified with a new multilayer perceptron (MLP) classifier whose parameters are optimized by a stability-constrained adaptive alpha for a gravitational search algorithm. The performance of the proposed method was investigated using Sentinel-2 MSI images of two coastal wetlands, i.e., the Jiaozhou Bay and the Yellow River Delta in Shandong province of eastern China. Comparisons with four other classifiers through visual inspection and quantitative evaluation verified the superiority of the proposed method. Furthermore, the effectiveness of different APs in EMAPs were also validated. By combining the developed EMAPs features and novel MLP classifier, complicated wetland types with high within-class variability and low between-class disparity were effectively discriminated. The superior performance of the proposed framework makes it available and preferable for the mapping of complicated coastal wetlands using Sentinel-2 data and other similar optical imagery