Generalized Artificial Life Structure for Time-dependent Problems

In recent years, more attention has been paid on artificial life researches. Artificial life（AL） is a research on regulating gene parameters of digital organisms under complicated problematic environments through natural selections and evolutions to achieve the final emergence of intelligence. Most recent studies focused on solving certain real problems by artificial life methods, yet without much address on the AL life basic mechanism. The real problems are often very complicated, and the proposed methods sometimes seem too simple to handle those problems. This study proposed a new approach in AL research, named ＂generalized artificial life structure（GALS）＂, in which the traditional ＂gene bits＂ in genetic algorithms is first replaced by ＂gene parameters＂, which could appear anywhere in GALS. A modeling procedure is taken to normalize the input data, and AL ＂tissue＂ is innovated to make AL more complex. GALS is anticipated to contribute significantly to the fitness of AL evolution. The formation of ＂tissue＂ begins with some different AL basic cells, and then tissue is produced by the casual selections of one or several of these cells. As a result, the gene parameters, represented by ＂tissues＂, could become highly diversified. This diversification should have obvious effects on improving gene fitness. This study took the innovative method of GALS in a stock forecasting problem under a carefully designed manipulating platform. And the researching results verify that the GALS is successful in improving the gene evolution fitness.

Prediction of Superconductivity for Oxides Based on Structural Parameters and Artificial Neural Network Method

Superconductive properties for oxides were predicted by artificial neural network (ANN) method with structural and chemical parameters as inputs. The predicted properties include superconductivity for oxides, distributed ranges of the superconductive transition temperature (Tc) for complex oxides, and Tc values for cuprate superconductors. The calculated results indicated that the adjusted ANN can be used to predict superconductive properties for unknown oxides.

Life-Inspired Endogenous Dynamic Behavior of Lipid Droplet-like Microcompartments in Artificial Adipocyte-like Structures

Chemical systems that can replicate cellular behaviors are gaining increasing attention and are being used to study various biological processes.Here,a protein-or amylose-based assembly at an oil/water interface was employed to construct a large compartmentalized adipocyte-like structure,and a lipid droplet-like microcompartment(amylose-polymerstabilized 2-ethyl-1-hexanol microcompartment),which exhibits floating behaviors driven by the amylose-polymer micelle solubilization,was introduced inside these adipocyte-like structures.When the lipase-based catalytic reaction and the external oil composition were controlled and another lipid droplet-like microcompartment(lipase-stabilized tributyrin microcompartment)was introduced,then a series of dynamic behaviors were exhibited by the two types of lipid droplet-like microcompartments,including growth,self-sorting,and fusion within the adipocyte-like structures.Therefore,it is anticipated that this autonomous generation of hierarchical multicompartments represents a breakthrough compared to the traditional bottom-up strategies,and the artificial life systems we studied could contribute a chemical-based strategy of understanding the process of lipid droplet growth and fusion inside a living adipocyte cell.

Relationship between atomic structure and crystal type studied by artificial neural network

We have used chemical bond parameters and pattern recognition method to investigatethe regularities of the crystal type of alloy phase,and achieved good results.Theparameters used,however,are semi-empirical paramters,which are not very strict fromtheoretical viewpoint.In this letter,we use the numbers describing atomic structure(thenumbers of valence electrons Z1,Z2,the principal quantum numbers of valence electrons n1,

Emergent quantum phenomena in atomically engineered iridate heterostructures

Over the last few years,researches in iridates have developed into an exciting field with the discovery of numerous emergent phenomena,interesting physics,and intriguing functionalities.Among the studies,iridate-based artificial structures play a crucial role owing to their extreme flexibility and tunability in lattice symmetry,chemical composition,and crystal dimensionality.In this article,we present an overview of the recent progress regarding iridate-based artificial structures.We first explicitly introduce several essential concepts in iridates.Then,we illustrate important findings on representative SrIrO_(3)/SrTiO_(3) superlattices,heterostructures comprised of SrIrO3 and magnetic oxides,and their response to external electric-field stimuli.Finally,we comment on existing problems and promising future directions in this exciting field.