Flexible Multifunctional Sensors for Wearable and Robotic Applications

This review provides an overview of the current state-of-the-art of the emerging field of flexible multifunctional sensors for wearable and robotic applications. In these application sectors, there is a demand for high sensitivity, accuracy, reproducibility, mechanical flexibility, and low cost. The ability to empower robots and future electronic skin (e-skin) with high resolution, high sensitivity, and rapid response sensing capabilities is of interest to a broad range of applications including wearable healthcare devices, biomedical prosthesis, and human–machine interacting robots such as service robots for the elderly and electronic skin to provide a range of diagnostic and monitoring capabilities. A range of sensory mechanisms is examined including piezoelectric, pyroelectric, piezoresistive, and there is particular emphasis on hybrid sensors that provide multifunctional sensing capability. As an alternative to the physical sensors described above, optical sensors have the potential to be used as a robot or e-skin; this includes sensory color changes using photonic crystals, liquid crystals, and mechanochromic effects. Potential future areas of research are discussed and the challenge for these exciting materials is to enhance their integration into wearables and robotic applications.</p

Two-dimensional stress field prediction using deep learning technique and relative frequency equalized data augmentation method

This paper presents a data augmentation method for generating a surrogate model of numerical analysis results. The proposed method focuses on the relative frequency of learning data for generating a learning model using deep learning techniques. Generally, data augmentation techniques are known to be useful for improving prediction accuracy. Adding noise and data duplication are commonly used for predicting numerical simulation results, but it is essential to carefully consider the amount of noise or choose a duplication target. However, these techniques are not appropriate for generating surrogate models. The reason is that the numerical analysis results mostly have high data imbalance, and no specific solution has been presented. The method proposed in this paper solves this problem and aims to be a simple and highly versatile data augmentation method. This paper describes the application of the proposed method to predict two-dimensional stress fields. It was confirmed that by increasing the number of data augmentations using the proposed method, the prediction errors were reduced for three different stress components stably. Additionally, it was confirmed that the prediction accuracy improved 5.81 to 27.0% compared to that of the data augmentation by simple duplication

Prediction of fatigue crack growth using convolutional neural network (2nd Report, Prediction of crack propagation on different levels)

In this paper, the prediction of crack propagation with two cracks using machine learning is described. The analysis results of crack propagation by s-version FEM (s-FEM), which combines the automatic mesh generation technique, are used for generation of training and validation datasets. Plural crack propagation with the different vertical distance between the two cracks as a variable are analyzed. The analysis cases are divided into training and validation datasets. In training process, the input parameters are the coordinates of 4 crack tip, the output data are crack propagation vectors, the number of cycles for crack propagation of 0.25 mm. Initial crack configurations should be specified. After the specification, the predictor iteratively predicts crack propagation direction and the number of loading cycles. A prediction accuracy depends on the training datasets, which contains 0.25 mm length of each crack propagation in this study. To improve prediction accuracy, the data augmentation is effectively applied. In case of plural crack interaction, when the crack tips close each other, the accuracy gets worse and worse. Reducing datasets which satisfy the crack coalescence condition, it is shown that the prediction accuracy is improved. Even if training datasets are not enough number for accurate prediction, it is shown that the prediction accuracy is improved by the data augmentation

Distinct cell clusters touching islet cells induce islet cell replication in association with over-expression of Regenerating Gene (REG) protein in fulminant type 1 diabetes.

BACKGROUND: Pancreatic islet endocrine cell-supporting architectures, including islet encapsulating basement membranes (BMs), extracellular matrix (ECM), and possible cell clusters, are unclear. PROCEDURES: The architectures around islet cell clusters, including BMs, ECM, and pancreatic acinar-like cell clusters, were studied in the non-diabetic state and in the inflamed milieu of fulminant type 1 diabetes in humans. RESULT: Immunohistochemical and electron microscopy analyses demonstrated that human islet cell clusters and acinar-like cell clusters adhere directly to each other with desmosomal structures and coated-pit-like structures between the two cell clusters. The two cell-clusters are encapsulated by a continuous capsule composed of common BMs/ECM. The acinar-like cell clusters have vesicles containing regenerating (REG) Iα protein. The vesicles containing REG Iα protein are directly secreted to islet cells. In the inflamed milieu of fulminant type 1 diabetes, the acinar-like cell clusters over-expressed REG Iα protein. Islet endocrine cells, including beta-cells and non-beta cells, which were packed with the acinar-like cell clusters, show self-replication with a markedly increased number of Ki67-positive cells. CONCLUSION: The acinar-like cell clusters touching islet endocrine cells are distinct, because the cell clusters are packed with pancreatic islet clusters and surrounded by common BMs/ECM. Furthermore, the acinar-like cell clusters express REG Iα protein and secrete directly to neighboring islet endocrine cells in the non-diabetic state, and the cell clusters over-express REG Iα in the inflamed milieu of fulminant type 1 diabetes with marked self-replication of islet cells

Pathological features of the pancreas affected by fulminant type 1 diabetes (FT1DM).

<p><b>A:</b> CD8 + T cells (red) infiltrate from outside the islet, disrupting vascular BMs (green, arrows) through the interstitial space between the vasculature and islets. Arrowheads indicate BMs (green) of exocrine pancreatic cells. <b>B:</b> BMs and ECM surrounding islets (green) are markedly disrupted and punctuated (arrows) in FT1DM. The vasculatures of the islets show marked dilation, and the vascular BMs have lost the human-specific double membrane profile <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0095110#pone.0095110-Virtanen1" target="_blank">[15]</a> (arrowheads). <b>C:</b> Acinar-like cell cluster touching Langerhans islets with thin interstitial surrounding (ATLANTIS) shows marked expression of REG Iα (green) in FT1DM. BMs (red, arrows) encapsulating the islet beta cells (blue) and ATLANTIS (green) are disrupted and discontinuous in some parts. <b>D:</b> Double immunostaining for amylase (red) and REG Iα (green) shows that amylase expression in the ATLANTIS (in circle) in inflamed FT1DM becomes faint in inverse relation to REG Iα over-expression. I: Islet, PAC: pancreatic acinar cells. <b>E:</b> Triple immunostaining for REG Iα (green), glucagon + somatostatin (SS) + pancreatic polypeptide (PP) (red), and insulin (blue) demonstrates that REG Iα-positive cells are not beta, glucagon, SS, or PP cells. <b>F:</b> Serum levels of REG Iα are increased in the patients with FT1DM of duration less than 2 weeks. **p<0.01 vs. controls.</p