Finding overlapping communities in multilayer networks.

Finding communities in multilayer networks is a vital step in understanding the structure and dynamics of these layers, where each layer represents a particular type of relationship between nodes in the natural world. However, most community discovery methods for multilayer networks may ignore the interplay between layers or the unique topological structure in a layer. Moreover, most of them can only detect non-overlapping communities. In this paper, we propose a new community discovery method for multilayer networks, which leverages the interplay between layers and the unique topology in a layer to reveal overlapping communities. Through a comprehensive analysis of edge behaviors within and across layers, we first calculate the similarities for edges from the same layer and the cross layers. Then, by leveraging these similarities, we can construct a dendrogram for the multilayer networks that takes both the unique topological structure and the important interplay into consideration. Finally, by introducing a new community density metric for multilayer networks, we can cut the dendrogram to get the overlapping communities for these layers. By applying our method on both synthetic and real-world datasets, we demonstrate that our method has an accurate performance in discovering overlapping communities in multilayer networks

Optical Biosensor Based on Graphene and Its Derivatives for Detecting Biomolecules

Graphene and its derivatives show great potential for biosensing due to their extraordinary optical, electrical and physical properties. In particular, graphene and its derivatives have excellent optical properties such as broadband and tunable absorption, fluorescence bursts, and strong polarization-related effects. Optical biosensors based on graphene and its derivatives make nondestructive detection of biomolecules possible. The focus of this paper is to review the preparation of graphene and its derivatives, as well as recent advances in optical biosensors based on graphene and its derivatives. The working principle of face plasmon resonance (SPR), surface-enhanced Raman spectroscopy (SERS), fluorescence resonance energy transfer (FRET) and colorimetric sensors are summarized, and the advantages and disadvantages of graphene and its derivatives applicable to various types of sensors are analyzed, and the methods of surface functionalization of graphene and its derivatives are introduced; these optical biosensors can be used for the detection of a range of biomolecules such as single cells, cellular secretions, proteins, nucleic acids, and antigen-antibodies; these new high-performance optical sensors are capable of detecting changes in surface structure and biomolecular interactions with the advantages of ultra-fast detection, high sensitivity, label-free, specific recognition, and the ability to respond in real-time. Problems in the current stage of application are discussed, as well as future prospects for graphene and its biosensors. Achieving the applicability, reusability and low cost of novel optical biosensors for a variety of complex environments and achieving scale-up production, which still faces serious challenges

virtualtanksdivisionandcapacitycalculationbasedonnurbsshipform

在船体曲面、甲板面NURBS表达和内底内壳多面体表达，有效的平面与曲面求交算法和几何特性计算方法基础上，给出了任意形状舱室的舱容计算方法。根据围闭舱室的几何元素（舱壁、甲板、船体曲面或内底和内壳）不同，将舱室分为12种基本形状，只需要船体曲面的型值信息、舱壁位置或折点信息以及内底和内壳的折点位置信息，即可进行参数化虚拟分舱和舱容计算，避免繁琐的舱室型值信息的人工输入，减少人工工作量，提高工作效率。该方法可以计算任意液面包括任意倾斜液面下的舱容，计算结果精确，方法简洁，为船舶3D参数化设计奠定基础

Advanced Nanostructured MXene-Based Materials for High Energy Density Lithium–Sulfur Batteries

Lithium–sulfur batteries (LSBs) are one of the most promising candidates for next-generation high-energy-density energy storage systems, but their commercialization is hindered by the poor cycling stability due to the insulativity of sulfur and the reaction end products, and the migration of lithium polysulfide. MXenes are a type of emerging two-dimensional material and have shown excellent electrochemical properties in LSBs due to their high conductivity and large specific surface area. Herein, several synthetic strategies developed for MXenes since their discovery are summarized alongside discussion of the excellent properties of MXenes for LSBs. Recent advances in MXene-based materials as cathodes for LSBs as well as interlayers are also reviewed. Finally, the future development strategy and prospect of MXene-based materials in high-energy-density LSBs are put forward

Effect of Irrigation on Growth, Yield, and Chemical Composition of Two Green Bean Cultivars

A study was conducted in an environmentally controlled greenhouse to evaluate two green bean cultivars, ‘Bronco’ and ‘Paulista’, under three application volumes of irrigation water based on replacing 100, 80, and 60% of evapotranspiration (ET). The experiment was in a split-plot design with three replications, recording vegetative growth, yield, pod parameters, water use efficiency (WUE), and chemical content of pods. The results showed that there were no differences between 80% ET and 100% ET for most parameters. In addition, 80% of ET increased the pod yield and improved the pod parameters and chemical composition. Therefore, this irrigation treatment can increase green bean productivity and improve pod quality. Reducing water application from 100 to 60% of ET progressively increased WUE. The ‘Bronco’ cultivar had a higher plant height, pod yield, WUE, pod weight, pod diameter, and total fiber amount than ‘Paulista’, while the ‘Paulista’ cultivar was superior in total chlorophyll, number of pods per plant, pod length, P, Ca, Mg, Fe, Cu, protein, vitamin C, titratable acid, and soluble sugar

How <i>α</i> affected the cross-layer similarity.

<p>How <i>α</i> affected the cross-layer similarity.</p

Weakness of our method.

<p>Weakness of our method.</p

Finding overlapping communities in multilayer networks - Fig 2

<p>A toy example of a node pair that is reachable in multilayer networks: the information can start with node <i>A</i> and finally reach node <i>D</i>. By using the same name node <i>B</i> in layer 2, the information can reach node <i>C</i> in layer 2. And then, using the same node <i>C</i> in layer 3, this information will reach node <i>D</i> in layer 3 at last.</p

Recall rate comparison of our method and two other methods on synthetic multilayer networks.

<p>Recall rate comparison of our method and two other methods on synthetic multilayer networks.</p

Comparison of different values of <i>α</i> on the cross layers.

<p>Comparison of different values of <i>α</i> on the cross layers.</p