Nontrivial topological defects of micro-rods immersed in nematics and their phototuning

Combinations of different geometry and the surface anchoring conditions give rise to the diversity of topological structures in nematic colloid systems. Tuning these parameters in a single system offers possibilities...

Highly Conjugated Three-Dimensional van der Waals Heterostructures Based Nanocomposite Films for Ultrahigh-Responsivity TEA Gas Sensors at Room Temperature

Covalent organic frameworks (COFs) have emerged as next-generation materials with predesigned π-electronic skeletons and highly ordered topological structures, which are promising for rapid gas sensing detection. COFs combined with carbon...

Mathematical model of structural and topological optimization of logistics networks

The subject of research in the article is the topological structures of closed-loop logistics networks. The goal of the article is to increase the efficiency of centralized logistics networks by developing a mathematical model for a two-criteria problem of optimizing topological structures in the process of their reengineering. The article solves the following tasks: analysis of the current state of the problem of structural and topological optimization of logistics networks; formalization of the problem of optimization of logistics networks as geographically distributed objects; synthesis of objective functions of the mathematical model of a two-criterion optimization problem for centralized three-level topological structures of closed logistics networks at the reengineering stage; development of a system of constraints of the mathematical model of the problem of optimizing centralized three-level topological structures of closed logistics networks; a function for evaluating the overall utility of options based on the Kolmogorov-Gabor polynomial is offered. The following methods are used: methods of systems theory, methods of utility theory, optimization and operations research. The following results were obtained: the analysis of the current state of the problem of system optimization of logistics networks, mathematical models and methods for its solution was carried out; formalization of the problem of structural and topological optimization of logistics networks as geographically distributed objects; a mathematical model of a two-criterion task of reengineering of three-level topological structures of logistics networks in terms of costs and efficiency with integrated points of production and processing has been developed (originality). Conclusions: Based on the results of the analysis of the problem of optimizing the topological structures of logistics systems, it has been established that the problems of direct and reverse logistics are still considered as conditionally independent, which does not allow obtaining effective global solutions. In the context of expanding the network of consumers, changes in delivery volumes, the introduction of environmental restrictions, it is proposed to reengineer the networks, which provides for their radical redesign. The formulated statement and the developed mathematical model of a two-criterion (in terms of cost and efficiency) optimization problem for three-level topological structures for combined production and processing points will increase the efficiency of logistics networks with reverse flows by reducing the cost of reengineering (practical value).

Multilayer Batch Learning Growing Neural Gas for Learning Multiscale Topologies

Hierarchical topological structure learning methods are expected to be developed in the field of data mining for extracting multiscale topological structures from an unknown dataset. However, most methods require user-defined parameters, and it is difficult for users to determine these parameters and effectively utilize the method. In this paper, we propose a new parameter-less hierarchical topological structure learning method based on growing neural gas (GNG). First, we propose batch learning GNG (BL-GNG) to improve the learning convergence and reduce the user-designed parameters in GNG. BL-GNG uses an objective function based on fuzzy C-means to improve the learning convergence. Next, we propose multilayer BL-GNG (MBL-GNG), which is a parameter-less unsupervised learning algorithm based on hierarchical topological structure learning. In MBL-GNG, the input data of each layer uses parent nodes to learn more abstract topological structures from the dataset. Furthermore, MBL-GNG can automatically determine the number of nodes and layers according to the data distribution. Finally, we conducted several experiments to evaluate our proposed method by comparing it with other hierarchical approaches and discuss the effectiveness of our proposed method.

Comparison of Fuel Cell DC/DC Power Supply Topologies

With the development of fuel cell products and technology, power electronics researchers have proposed a variety of fuel cell DC/DC power supply topologies for the output characteristics of fuel cells. This article compares and analyzes several existing non-isolated and isolated topological structures, summarizes the respective advantages and disadvantages of different topological structures and applicable scenarios, and provides references for further indepth discussion of related issues.

Proteins mediating different DNA topologies block RNAP elongation with different efficiency

Many DNA-binding proteins induce topological structures such as loops or wraps through binding to two or more sites along the DNA. Such topologies may regulate transcription initiation and may also be roadblocks for elongating RNA polymerase (RNAP). Remarkably, a lac repressor protein bound to a weak binding site (O2) does not obstruct RNAP in vitro but becomes an effective roadblock when securing a loop of 400 bp between two widely separated binding sites. To investigate whether topological structures mediated by proteins bound to closely spaced binding sites and interacting cooperatively also represent roadblocks, we compared the effect of the lambda CI and 186 CI repressors on RNAP elongation. Dimers of lambda CI can bind to two sets of adjacent sites separated by hundreds of bp and form a DNA loop via the interaction between their C-terminal domains. The 186 CI protein can form a wheel of seven dimers around which specific DNA binding sequences can wrap. Atomic force microscopy (AFM) was used to image transcription elongation complexes of DNA templates that contained binding sites for either the lambda or 186 CI repressor. While RNAP elongated past lambda CI on unlooped DNA, as well as past 186 CI-wrapped DNA, it did not pass the lambda CI-mediated loop. These results may indicate that protein-mediated loops with widely separated binding sites more effectively block transcription than a wrapped topology with multiple, closely spaced binding sites.

The impact of network topological structures on systematic technology adoption and carbon emission reduction

This paper investigates how the topological structure of the technological spillover network among agents affects the adoption of a new clean technology and the reduction of system’s carbon emissions. Through building a systematic technology adoption model with technological spillover effect among agents from the network perspective, this paper first illustrates how the new technology diffuses from the earlier adopters to the later adopters under different network topological structures. Further, this paper examines how the carbon emission constraints imposed on pilot agents affect the carbon emissions of other agents and the entire system under different network topological structures. Simulation results of our study suggest that, (1) different topological structures of the technological spillover network have great influence on the adoption and diffusion of a new advanced technology; (2) imposing carbon emission constraints on pilot agents can reduce carbon emissions of other agents and thereby the entire system. However, the effectiveness of the carbon emission constraints is also largely determined by the network topological structures. Our study implies that the empirical research of the network topological structure among the participating entities is a pre-requisite to evaluate the real effectiveness of a carbon emission reduction policy from the system perspective.

Pointwise k-Pseudo Metric Space

In this paper, the concept of a k-(quasi) pseudo metric is generalized to the L-fuzzy case, called a pointwise k-(quasi) pseudo metric, which is considered to be a map d:J(LX)×J(LX)⟶[0,∞) satisfying some conditions. What is more, it is proved that the category of pointwise k-pseudo metric spaces is isomorphic to the category of symmetric pointwise k-remote neighborhood ball spaces. Besides, some L-topological structures induced by a pointwise k-quasi-pseudo metric are obtained, including an L-quasi neighborhood system, an L-topology, an L-closure operator, an L-interior operator, and a pointwise quasi-uniformity.