Approaching Disordered Quantum Dot Systems by Complex Networks with Spatial and Physical-Based Constraints

This paper focuses on modeling a disordered system of quantum dots (QDs) by using complex networks with spatial and physical-based constraints. The first constraint is that, although QDs (=nodes) are randomly distributed in a metric space, they have to fulfill the condition that there is a minimum inter-dot distance that cannot be violated (to minimize electron localization). The second constraint arises from our process of weighted link formation, which is consistent with the laws of quantum physics and statistics: it not only takes into account the overlap integrals but also Boltzmann factors to include the fact that an electron can hop from one QD to another with a different energy level. Boltzmann factors and coherence naturally arise from the Lindblad master equation. The weighted adjacency matrix leads to a Laplacian matrix and a time evolution operator that allows the computation of the electron probability distribution and quantum transport efficiency. The results suggest that there is an optimal inter-dot distance that helps reduce electron localization in QD clusters and make the wave function better extended. As a potential application, we provide recommendations for improving QD intermediate-band solar cells.

Constructing banking networks under decreasing costs of link formation

The structure of networks plays a central role in the behavior of financial systems and their response to policy. Real-world networks, however, are rarely directly observable: banks’ assets and liabilities are typically known, but not who is lending how much and to whom. This paper adds to the existing literature in two ways. First, it shows how to simulate realistic networks that are based on balance-sheet information. To do so, we introduce a model where links cause fixed-costs, independent of contract size; but the costs per link decrease the more connected a bank is (scale economies). Second, to approach the optimization problem, we develop a new algorithm inspired by the transportation planning literature and research in stochastic search heuristics. Computational experiments find that the resulting networks are not only consistent with the balance sheets, but also resemble real-world financial networks in their density (which is sparse but not minimally dense) and in their core-periphery and disassortative structure.

Comparitive Analysis of Link Prediction in Complex Networks

The most attractive aspect of data mining is link prediction in a complex network. Link prediction is the behavior of the network link formation by predicting missed and future relationships among elements based on current observed connections. Link prediction techniques can be categorized into probabilistic, similarity, and dimension reduction based. In this paper six familiar link predictors are applied on seven different network datasets to provide directory to users. The experimental results of multiple prediction algorithms were compared and analyzed on the basis of proposed comparative link prediction model. The results revealed that Jaccard coefficient and Hub promoted performed well on most of the datasets. Different applied methods are arranged on the basis of accuracy. Moreover, the shortcomings of different techniques are also presented.

A synthetic 5,3-cross-link in the cell wall of rod-shaped Gram-positive bacteria

Gram-positive bacteria assemble a multilayered cell wall that provides tensile strength to the cell. The cell wall is composed of glycan strands cross-linked by nonribosomally synthesized peptide stems. Herein, we modify the peptide stems of the Gram-positive bacterium Bacillus subtilis with noncanonical electrophilic d-amino acids, which when in proximity to adjacent stem peptides form novel covalent 5,3-cross-links. Approximately 20% of canonical cell-wall cross-links can be replaced with synthetic cross-links. While a low level of synthetic cross-link formation does not affect B. subtilis growth and phenotype, at higher levels cell growth is perturbed and bacteria elongate. A comparison of the accumulation of synthetic cross-links over time in Gram-negative and Gram-positive bacteria highlights key differences between them. The ability to perturb cell-wall architecture with synthetic building blocks provides a novel approach to studying the adaptability, elasticity, and porosity of bacterial cell walls.