Synthesizing a Gel Polymer Electrolyte for Supercapacitors, Assembling a Supercapacitor Using a Coin Cell, and Measuring Gel Electrolyte Performance.

Supercapacitors (SC) have attracted attention as energy storage devices due to their high density and long cycle performance. SCs used in devices operating in stretchable systems require stretchable electrolytes. Gel polymer electrolytes (GPEs) are an ideal replacement for liquid electrolytes.

VAC-CNN: A Visual Analytics System for Comparative Studies of Deep Convolutional Neural Networks.

The rapid development of Convolutional Neural Networks (CNNs) in recent years has triggered significant breakthroughs in many machine learning (ML) applications. The ability to understand and compare various CNN models available is thus essential. The conventional approach with visualizing each model's quantitative features, such as classification accuracy and computational complexity, is not sufficient for a deeper understanding and comparison of the behaviors of different models.

A Deep Generative Model for Reordering Adjacency Matrices.

Depending on the node ordering, an adjacency matrix can highlight distinct characteristics of a graph. Deriving a "proper" node ordering is thus a critical step in visualizing a graph as an adjacency matrix. Users often try multiple matrix reorderings using different methods until they find one that meets the analysis goal.

Evaluating the Electrochemical Properties of Supercapacitors using the Three-Electrode System.

The three-electrode system is a basic and general analytical platform for investigating the electrochemical performance and characteristics of energy storage systems at the material level. Supercapacitors are one of the most important emergent energy storage systems developed in the past decade. Here, the electrochemical performance of a supercapacitor was evaluated using a three-electrode system with a potentiostat device.

Elaborate Control of Inkjet Printer for Fabrication of Chip-based Supercapacitors.

There are tremendous efforts in various fields to apply the inkjet printing method for the fabrication of wearable devices, displays, and energy storage devices. To get high-quality products, however, sophisticated operation skills are required depending on the physical properties of the ink materials. In this regard, optimizing the inkjet printing parameters is as important as developing the physical properties of the ink materials.

Serum Peptide Immunoglobulin G Autoantibody Response in Patients with Different Central Nervous System Inflammatory Demyelinating Disorders.

Previous efforts to discover new surrogate markers for the central nervous system (CNS) inflammatory demyelinating disorders have shown inconsistent results; moreover, supporting evidence is scarce. The present study investigated the IgG autoantibody responses to various viral and autoantibodies-related peptides proposed to be related to CNS inflammatory demyelinating disorders using the peptide microarray method. We customized a peptide microarray containing more than 2440 immobilized peptides representing human and viral autoantigens.

Urban green space and happiness in developed countries.

Unlabelled: Urban green space is thought to contribute to citizen happiness by promoting physical and mental health. Nevertheless, how urban green space and happiness are related across many countries with different socioeconomic conditions has not been explored. By measuring the urban green space score (UGS) from high-resolution satellite imagery of 90 global cities covering 179,168 km and 230 million people in 60 developed countries, we find that the amount of urban green space and GDP are correlated with a nation's happiness level.

Serum FAM19A5 in neuromyelitis optica spectrum disorders: Can it be a new biomarker representing clinical status?

Background: Neuromyelitis optica spectrum disorder (NMOSD) targets astrocytes and elevates the levels of astrocyte-injury markers during attacks. FAM19A5, involved in reactive gliosis, is secreted by reactive astrocytes following central nervous system (CNS) damage.

Objective: To investigate the significance of serum FAM19A5 in patients with NMOSD.

A Deep Generative Model for Graph Layout.

Different layouts can characterize different aspects of the same graph. Finding a "good" layout of a graph is thus an important task for graph visualization. In practice, users often visualize a graph in multiple layouts by using different methods and varying parameter settings until they find a layout that best suits the purpose of the visualization.

Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning.

Dimensionality reduction (DR) is frequently used for analyzing and visualizing high-dimensional data as it provides a good first glance of the data. However, to interpret the DR result for gaining useful insights from the data, it would take additional analysis effort such as identifying clusters and understanding their characteristics. While there are many automatic methods (e.

What Would a Graph Look Like in this Layout? A Machine Learning Approach to Large Graph Visualization.

Using different methods for laying out a graph can lead to very different visual appearances, with which the viewer perceives different information. Selecting a "good" layout method is thus important for visualizing a graph. The selection can be highly subjective and dependent on the given task.

A Study of Layout, Rendering, and Interaction Methods for Immersive Graph Visualization.

Information visualization has traditionally limited itself to 2D representations, primarily due to the prevalence of 2D displays and report formats. However, there has been a recent surge in popularity of consumer grade 3D displays and immersive head-mounted displays (HMDs). The ubiquity of such displays enables the possibility of immersive, stereoscopic visualization environments.

The influence of elastic modulus of base material on the marginal adaptation of direct composite restoration.

This study compared the marginal adaptation of direct composites under base materials with different elastic moduli. MOD cavities were prepared in 30 teeth. The cervical margin was placed 1 mm above the cementoenamel junction (CEJ) in one side and 1 mm below the CEJ in dentin in the other.

Centroid neural network with a divergence measure for GPDF data clustering.

An unsupervised competitive neural network for efficient clustering of Gaussian probability density function (GPDF) data of continuous density hidden Markov models (CDHMMs) is proposed in this paper. The proposed unsupervised competitive neural network, called the divergence-based centroid neural network (DCNN), employs the divergence measure as its distance measure and utilizes the statistical characteristics of observation densities in the HMM for speech recognition problems. While the conventional clustering algorithms used for the vector quantization (VQ) codebook design utilize only the mean values of the observation densities in the HMM, the proposed DCNN utilizes both the mean and the covariance values.