Deciphering the generating rules and functionalities of complex networks

Network theory helps us understand, analyze, model, and design various complex systems. Complex networks encode the complex topology and structural interactions of various systems in nature. To mine the multiscale coupling, heterogeneity, and complexity of natural and technological systems, we need expressive and rigorous mathematical tools that can help us understand the growth, topology, dynamics, multiscale structures, and functionalities of complex networks and their interrelationships. Towards this end, we construct the node-based fractal dimension (NFD) and the node-based multifractal analysis (NMFA) framework to reveal the generating rules and quantify the scale-dependent topology and multifractal features of a dynamic complex network. We propose novel indicators for measuring the degree of complexity, heterogeneity, and asymmetry of network structures, as well as the structure distance between networks. This formalism provides new insights on learning the energy and phase transitions in the networked systems and can help us understand the multiple generating mechanisms governing the network evolution.

Self-organized multiscale structures in thermally relativistic electron-positron-ion plasmas

The self-organization of a thermally relativistic magnetized plasma comprising of electrons, positrons and static ions is investigated. The self-organized state is found to be the superposition of three distinct Beltrami fields known as triple Beltrami (TB) state. In general, the eigenvalues associated with the multiscale self-organized vortices may be a pair of complex conjugate and real one. It is shown that all the eigenvalues become real when thermal energy increases or the positron density decreases. The impact of relativistic temperature and positron density on the formation of self-organized structures is investigated. The self-organized field and flow vortices may vary simultaneously on vastly different length scales. The disparate variation of self-organized vortices is important in the context of dynamo theory. The present work is useful to study the formation of multiscale vortices and dynamo mechanisms in multi-species thermally relativistic plasmas.

Characteristics Evolution of Multiscale Structures in Deep Coal under Liquid Nitrogen Freeze-Thaw Cycles

Liquid nitrogen freeze-thaw fracturing has attracted more and more attention in improving the coal reservoir permeability. In order to reveal the impact of liquid nitrogen freeze-thaw on the multiscale structure of deep coal, the multiscale structure evolution law of deep and shallow coal samples from the same seam in the Qinshui coalfield during the liquid nitrogen freeze-thaw cycling was investigated using NMR T 2 spectrum, NMRI, and SEM. The connectivity between mesopores and macropores in deep and shallow coal is improved after liquid nitrogen freeze-thaw cycles. The influence of liquid nitrogen freeze-thaw cycles on the structure evolution of deep and shallow coal is the formation and expansion of microscopic fractures. The initial NMR porosity of deep coal is lower than that of shallow coal from the same coalfield and coal seam. The NMR porosity of both the deep and shallow coal samples increases with the increase of the number of freeze-thaw cycles, and the NMR porosity growth rate of the deep sample is lower than that of the shallow sample.

ADDITIVE MANUFACTURING OF HIGH-LOADING POLYMER NANOCOMPOSITES WITH MULTISCALE ALIGNMENT

Polymer nanocomposites have advantages in mechanical, electrical, and optical properties compared to individual components. These unique properties of the nanocomposites have attracted attention in many applications, including electronics, robotics, biomedical fields, automotive industries. To achieve their high performance, it is crucial to control the orientation of nanomaterials within the polymer matrix. For example, the electric conductivity will be maximized in the ordered direction of conductive nanomaterials such as graphene and carbon nanotubes (CNTs). Conventional fabrication methods are commonly used to obtain polymer nanocomposites with the controlled alignment of nanomaterials using electric or magnetic fields, fluid flow, and shear forces. Such approaches may be complex in preparing a manufacturing system, have low fabrication rate, and even limited structure scalability and complexity required for customized functional products. Recently, additive manufacturing (AM), also called 3D printing, has been developed as a major fabrication technology for nanocomposites with aligned reinforcements. AM has the ability to control the orientation of nanoparticles and offers a great way to produce the composites with cost-efficiency, high productivity, scalability, and design flexibility. Herein, we propose a manufacturing process using AM for the architected structure of polymer nanocomposites with oriented nanomaterials using a polylactic acid polymer as the matrix and graphite and CNTs as fillers. AM can achieve the aligned orientation of the nanofillers along the printing direction. Thus, it enables the fabrication of multifunctional nanocomposites with complex shapes and higher precision, from micron to macro scale. This method will offer great opportunities in the advanced applications that require complex multiscale structures such as energy storage devices (e.g., batteries and supercapacitors) and structural electronic devices (e.g., circuits and sensors).

Fast Two-Scale Analysis via Clustering

Both man-made and natural materials exhibit heterogeneous properties at smaller observation scales. The multiscale analysis allows the inclusion of fine-scale information in coarse-scale simulations. One of the commonly used methods is homogenization, replacing the detailed fine-scale structures with their locally homogeneous effective material properties. When fine-scale material structures are stationary, representative volume elements (RVE) are often identified for their effective material properties to be applied over the entire structure. However, in non-stationary material structures, it is inappropriate to assume a single representative material. In this case, homogenization is often required for every individual cell, resulting in significant increases in computational cost. We propose a stiffness-based clustering algorithm that reduces the total number of homogenization computations needed for multiscale analysis. Cells with similar effective stiffness tensors are clustered together such that only a single homogenization is required for each cluster. Specifically, the clustering algorithm is based on the novel concept of Eigenstiffness, which represents the relative directional stiffness of a given material structure. The rotation invariant property of Eigenstiffness allows material structure with similar intrinsic stiffness but different orientations to be clustered together, further decreasing the number of clusters required for the multiscale analysis. Without a priori knowledge of the accurate homogenized material properties, approximated elasticity tensors and Eigenstiffness estimated through FFT-based homogenization methods are used for rapid clustering. The effectiveness of the method is verified by numerical simulations on various multiscale structures, including Voronoi foams and fiber-reinforced composites.

PDBeCIF: an open-source mmCIF/CIF parsing and processing package

Background Biomacromolecular structural data outgrew the legacy Protein Data Bank (PDB) format which the scientific community relied on for decades, yet the use of its successor PDBx/Macromolecular Crystallographic Information File format (PDBx/mmCIF) is still not widespread. Perhaps one of the reasons is the availability of easy to use tools that only support the legacy format, but also the inherent difficulties of processing mmCIF files correctly, given the number of edge cases that make efficient parsing problematic. Nevertheless, to fully exploit macromolecular structure data and their associated annotations such as multiscale structures from integrative/hybrid methods or large macromolecular complexes determined using traditional methods, it is necessary to fully adopt the new format as soon as possible. Results To this end, we developed PDBeCIF, an open-source Python project for manipulating mmCIF and CIF files. It is part of the official list of mmCIF parsers recorded by the wwPDB and is heavily employed in the processes of the Protein Data Bank in Europe. The package is freely available both from the PyPI repository (http://pypi.org/project/pdbecif) and from GitHub (https://github.com/pdbeurope/pdbecif) along with rich documentation and many ready-to-use examples. Conclusions PDBeCIF is an efficient and lightweight Python 2.6+/3+ package with no external dependencies. It can be readily integrated with 3rd party libraries as well as adopted for broad scientific analyses.

Adhesive Deposition Process Characterization for Microstructure Assembly

Recent advancements in additive manufacturing such as Direct Write Inkjet printing introduced novel tools that allow controlled and precise deposition of fluid in nano-liter volumes, enabling fabrication of multiscale structures with submillimeter dimensions. Applications include fabrication of flexible electronics, sensors, and assembly of Micro-Electro-Mechanical Systems (MEMS). Critical challenges remain in the control of fluid deposition parameters during Inkjet printing to meet specific dimensional footprints at the microscale necessary for the assembly process of microscale structures. In this paper we characterize an adhesive deposition printing process with a piezo-electric dispenser of nano-liter volumes. Applications include the controlled delivery of high viscosity Ultraviolet (UV) and thermal curable adhesives for the assembly of the MEMS structures. We applied the Taguchi Design of Experiment (DOE) method to determine an optimal set of process parameters required to minimize the size of adhesive printed features on a silicon substrate with good reliability and repeatability of the deposition process. Experimental results demonstrate repeatable deposition of UV adhesive features with 150 μm diameter on the silicon substrate. Based on the observed wettability effect of adhesive printed onto different substrates we propose a solution for further reduction of the deposit-substrate contact area for microassembly optimization.

Comparing World City Networks by Language: A Complex-Network Approach

City networks are multiplex and diverse rather than being regarded as part of a single universal model that is valid worldwide. This study contributes to the debate on multiple globalizations by distinguishing multiscale structures of world city networks (WCNs) reflected in the Internet webpage content in English, German, and French. Using big data sets from web crawling, we adopted a complex-network approach with both macroscale and mesoscale analyses to compare global and grouping properties in varying WCNs, by using novel methods such as the weighted stochastic block model (WSBM). The results suggest that at the macro scale, the rankings of city centralities vary across languages due to the uneven geographic distribution of languages and the variant levels of globalization of cities perceived in different languages. At the meso scale, the WSBMs infer different grouping patterns in the WCNs by language, and the specific roles of many world cities vary with language. The probability-based comparative analyses reveal that the English WCN looks more globalized, while the French and German worlds appear more territorial. Using the mesoscale structure detected in the English WCN to comprehend the city networks in other languages may be biased. These findings demonstrate the importance of scrutinizing multiplex WCNs in different cultures and languages as well as discussing mesoscale structures in comparative WCN studies.