A genetic algorithm for slope stability analyses with concave slip surfaces using custom operators

Heuristic methods are popular tools to find critical slip surfaces in slope stability analyses. A new genetic algorithm (GA) is proposed in this work that has a standard structure but a novel encoding and generation of individuals with custom-designed operators for mutation and crossover that produce kinematically feasible slip surfaces with a high probability. In addition, new indices to assess the efficiency of operators in their search for the minimum factor of safety (FS) are proposed. The proposed GA is applied to traditional benchmark examples from the literature, as well as to a new practical example. Results show that the proposed GA is reliable, flexible and robust: it provides good minimum FS estimates that are not very sensitive to the number of nodes and that are very similar for different replications

Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices

The diversity of bibliometric indices today poses the challenge of exploiting the relationships among them. Our research uncovers the best core set of relevant indices for predicting other bibliometric indices. An added difficulty is to select the role of each variable, that is, which bibliometric indices are predictive variables and which are response variables. This results in a novel multioutput regression problem where the role of each variable (predictor or response) is unknown beforehand. We use Gaussian Bayesian networks to solve the this problem and discover multivariate relationships among bibliometric indices. These networks are learnt by a genetic algorithm that looks for the optimal models that best predict bibliometric data. Results show that the optimal induced Gaussian Bayesian networks corroborate previous relationships between several indices, but also suggest new, previously unreported interactions. An extended analysis of the best model illustrates that a set of 12 bibliometric indices can be accurately predicted using only a smaller predictive core subset composed of citations, g-index, q2-index, and hr-index. This research is performed using bibliometric data on Spanish full professors associated with the computer science area.

Does the natural "microcosm" created by Tuber aestivum affect soil microarthropods? A new hypothesis based on Collembola in truffle culture

microarthropods play an important role in fungi dispersion, but little is still known about the interaction between truffle and soil microarthropods. The aim of this study was to investigate the ability of the truffle Tuber aestivum to modify soil biogeochemistry (i.e. create a zone of scarce vegetation around the host plant, called a burn or brûlé) and to highlight the effects of the brûlé on the soil fauna community. We compared soil microarthropod communities found in the soil inside versus outside the T. aestivum brûlé with the chemistry of soil collected inside versus outside the brûlé. The study was carried out in three Mediterranean areas, two in Italy and one in Spain. The results confirmed the ability of T. aestivum to modify soil biogeochemistry in the brûlé: pH was higher and total organic carbon tended to be lower inside the brûlé compared to outside. Soil fauna communities showed some interesting differences. Some groups, such as Symphyla and Pauropoda, adapted well to the soil; some Collembolan families, and biodiversity and soil quality indices were generally higher outside the brûlé. Folsomia sp. showed higher abundance in the soil of the brûlé compared to outside. The results suggest that some Collembola groups may be attracted by the fungal metabolites produced by T. aestivum, while other Collembola and other microarthropods may find an unfavourable environment in the soil of the brûlé. The next steps will be to confirm this hypothesis and to extend the study to other keys groups such as nematodes and earthworms and to link fluctuations of soil communities with the biological phases of truffle growth.