Side Tributary Distribution of Quasi-Uniform Iterative Binary Tree Networks for River Networks

Self-similarity and plane-filling are intrinsic structure properties of natural river networks. Statistical data indicates that most natural river networks are Tokunaga trees. Researchers have explored to use iterative binary tree networks (IBTNs) to simulate natural river networks. However, the characteristics of natural rivers such as Tokunaga self-similarity and plane-filling cannot be easily guaranteed by the configuration of the IBTN. In this paper, the generator series and a quasi-uniform iteration rule are specified for the generation of nonstochastic quasi-uniform iterative binary tree networks (QU-IBTNs). First, we demonstrate that QU-IBTNs definitely satisfy self-similarity. Second, we show that the constraint for a QU-IBTN to be a Tokunaga tree is that the exterior links must be replaced in the generator series with a neighboring generator that is larger than the interior links during the iterative process. Moreover, two natural river networks are examined to reveal the inherent consistency with QU-IBTN at low Horton-Strahler orders.

Projection of High-Dimensional Genome-Wide Expression on SOM Transcriptome Landscapes

The self-organizing maps portraying has been proven to be a powerful approach for analysis of transcriptomic, genomic, epigenetic, single-cell, and pathway-level data as well as for “multi-omic” integrative analyses. However, the SOM method has a major disadvantage: it requires the retraining of the entire dataset once a new sample is added, which can be resource- and time-demanding. It also shifts the gene landscape, thus complicating the interpretation and comparison of results. To overcome this issue, we have developed two approaches of transfer learning that allow for extending SOM space with new samples, meanwhile preserving its intrinsic structure. The extension SOM (exSOM) approach is based on adding secondary data to the existing SOM space by “meta-gene adaptation”, while supervised SOM portrayal (supSOM) adds support vector machine regression model on top of the original SOM algorithm to “predict” the portrait of a new sample. Both methods have been shown to accurately combine existing and new data. With simulated data, exSOM outperforms supSOM for accuracy, while supSOM significantly reduces the computing time and outperforms exSOM for this parameter. Analysis of real datasets demonstrated the validity of the projection methods with independent datasets mapped on existing SOM space. Moreover, both methods well handle the projection of samples with new characteristics that were not present in training datasets.

Research on public emotional polarization and public opinion evolution of OTC and learning during the COVID-19 epidemic: taking the topic of OTC on Zhihu as an example

PurposeThe outbreak and continuation of COVID-19 have spawned the transformation of traditional teaching models to a certain extent. The Chinese Ministry of Education’s guidance on “keep learning and teaching during class suspension” has made OTC and learning (OTC) become routinized, and the public’s emotional attitudes toward OTC have also evolved over time. The purpose of this study is to segment the emotional text data and introduce it into the topic model to reveal the evolution process and stage characteristics of public emotional polarity and public opinion of OTC topics during public health emergencies in the context of social media participation. The research has important guiding significance for the development of OTC and can influence and improve the efficiency and effect of OTC to a certain extent. The analysis of online public opinion can provide suggestions for the government and media to guide the trend of public opinion and optimize the OTC model.Design/methodology/approachThis paper takes the topic of “OTC” on Zhihu during the COVID-19 epidemic as an example, combined with the characteristics of public opinion changes, chooses Boson emotional dictionary and time series analysis method to build an OTC network public opinion theme evolution analysis framework that integrates emotional analysis and topic mining. Finally, an empirical analysis of the dynamic evolution of the communication network for each stage of the life cycle of a specific topic is realized.FindingsThis paper draws the following conclusions: (1) Through the emotional value table and the change trend chart of the number of comments, the analysis found that the number of positive comments is greater than the number of negative comments, which can be inferred that the public gradually accepts “OTC” and presents a positive emotional state. (2) By observing the changing trend of the average daily emotional value of the public, it is found that the overall emotional value shows a stable development trend after a large fluctuation. From the actual emotional value and the fitted emotional value curve, it can be seen that the overall curve fit is good, so ARIMA (12, 1, 6) can accurately predict the dynamic trend of the daily average emotional value in this paper. Therefore, based on the above-mentioned public opinion, emotional analysis research, relevant countermeasures and suggestions are put forward, which is conducive to guiding the development direction of public opinion in a positive way.Originality/valueTaking the topic of “OTC” in Zhihu as an example, this paper combines Boson emotional dictionary and time series to conduct a series of research analyses. Boson emotional dictionary can analyze the public’s emotional tendency, and time series can well analyze the intrinsic structure and complex features of the data to predict the future values. The combination of the two research methods allows for an adequate and unique study of public emotional polarization and the evolution of public opinion.

TEM sample preparation of microsized LiMn2O4 powder using an ion slicer

The main purpose of this paper is the preparation of transmission electron microscopy (TEM) samples from the microsized powders of lithium-ion secondary batteries. To avoid artefacts during TEM sample preparation, the use of ion slicer milling for thinning and maintaining the intrinsic structure is described. Argon-ion milling techniques have been widely examined to make optimal specimens, thereby making TEM analysis more reliable. In the past few years, the correction of spherical aberration (Cs) in scanning transmission electron microscopy (STEM) has been developing rapidly, which results in direct observation at an atomic level resolution not only at a high acceleration voltage but also at a deaccelerated voltage. In particular, low-kV application has markedly increased, which requires a sufficiently transparent specimen without structural distortion during the sample preparation process. In this study, sample preparation for high-resolution STEM observation is accomplished, and investigations on the crystal integrity are carried out by Cs-corrected STEM.

Hexagonal Ferrites, Synthesis, Properties and Their Applications

We intend to report on possible fabrication routes for all types of hexagonal ferrites which are known for their wide area of use and applications. Hexagonal ferrites have now become an intense topic of research as they are the part of most of magnetic recording and data storage applications globally. Hexagonal or popularly known as ‘Heaxa-ferrites’ are known for their utilization in permanent magnets and their utilization in electrical devices being operated at high frequencies especially at GHz frequencies. We have presented in this chapter all main six types of hexagonal ferrites i.e. M Type, Z-Type, Y-type, W-type, X-Type and U-type hexa-ferrites. Hexaferrites belong to ferromagnetic class of magnetic materials and their properties are purely dependent on intrinsic structure of ferrites. In this chapter, we aim to discuss more on M-type of hexa-ferrites, their properties and their applications. Also, recent advances on M-type ferrites are also a part of this chapter.

Polynomial approximation to manifold learning

Manifold learning plays an important role in nonlinear dimensionality reduction. But many manifold learning algorithms cannot offer an explicit expression for dealing with the problem of out-of-sample (or new data). In recent, many improved algorithms introduce a fixed function to the object function of manifold learning for learning this expression. In manifold learning, the relationship between the high-dimensional data and its low-dimensional representation is a local homeomorphic mapping. Therefore, these improved algorithms actually change or damage the intrinsic structure of manifold learning, as well as not manifold learning. In this paper, a novel manifold learning based on polynomial approximation (PAML) is proposed, which learns the polynomial approximation of manifold learning by using the dimensionality reduction results of manifold learning and the original high-dimensional data. In particular, we establish a polynomial representation of high-dimensional data with Kronecker product, and learns an optimal transformation matrix with this polynomial representation. This matrix gives an explicit and optimal nonlinear mapping between the high-dimensional data and its low-dimensional representation, and can be directly used for solving the problem of new data. Compare with using the fixed linear or nonlinear relationship instead of the manifold relationship, our proposed method actually learns the polynomial optimal approximation of manifold learning, without changing the object function of manifold learning (i.e., keeping the intrinsic structure of manifold learning). We implement experiments over eight data sets with the advanced algorithms published in recent years to demonstrate the benefits of our algorithm.

Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity

Understanding human genetic influences on the gut microbiota helps elucidate the mechanisms by which genetics affects health outcomes. We propose a novel approach, the covariate-adjusted kernel RV (KRV) framework, to map genetic variants associated with microbiome beta-diversity, which focuses on overall shifts in the microbiota. The proposed KRV framework improves statistical power by capturing intrinsic structure within the genetic and microbiome data while reducing the multiple-testing burden. We apply the covariate-adjusted KRV test to the Hispanic Community Health Study/Study of Latinos in a genome-wide association analysis (first gene-level, then variant-level) for microbiome beta-diversity. We have identified an immunity-related gene, IL23R, reported in previous association studies and discovered 3 other novel genes, 2 of which are involved in immune functions or autoimmune disorders. Our findings highlight the value of the KRV as a powerful microbiome GWAS approach and support an important role of immunity-related genes in shaping the gut microbiome composition.

Revalorization of Pleurotus djamor Fungus Culture: Fungus-Derived Carbons for Supercapacitor Application

Currently, there is increasing interest and effort directed to developing sustainable processes, including in waste management and energy production and storage, among others. In this research, corn cobs were used as a substrate for the cultivation of Pleurotus djamor, a suitable feedstock for the management of these agricultural residues. Revalorization of this fungus, as an environmentally friendly carbon precursor, was executed by taking advantage of the intrinsic characteristics of the fungus, such as its porosity. Obtaining fungus-derived porous carbons was achieved by hydrothermal activation with KOH and subsequent pyrolysis at 600, 800, and 1000 °C in an argon atmosphere. The morphologies of the fungal biomass and fungus-derived carbons both exhibited, on their surfaces, certain amorphous similarities in their pores, indicating that the porous base matrix of the fungus was maintained despite carbonization. From all fungus-derived carbons, PD1000 exhibited the largest superficial area, with 612 m2g−1 and a pore size between 3 and 4 nm recorded. Electrochemical performance was evaluated in a three-electrode cell, and capacitance was calculated by cyclic voltammetry; a capacitance of 60 F g−1 for PD1000 was recorded. Other results suggested that PD1000 had a fast ion-diffusion transfer rate and high electronic conductivity. Ultimately, Pleurotus djamor biomass is a suitable feedstock for obtaining carbon in a sustainable way, and it features a defined intrinsic structure for potential energy storage applications, such as electrodes in supercapacitors.