Materials consisting of a single layer of atoms, often called two dimensional (2D) materials have many promising applications, due to their extraordinary physical properties. These properties, however, depend on the presence, density, and kind of structural defects present in the perfect 2D crystalline lattice. Electrons with energies falling into the allowed band, described by Bloch waves in quantum mechanics, propagate freely in a perfect crystal, but defects act as scattering centers for the Bloch waves. We studied the influence of different structural defects on the transport properties of a graphene lattice by calculating the scattering of electronic wave packets. We applied and compared two different methods. Within the first method, we describe the atomic lattice and the electronic structure of graphene by an atomic pseudopotential, then calculate the Bloch functions and corresponding E(k_Bloch) energies, where k_Bloch is the Bloch wave vector. The defect is represented by a local potential, then we compute the scattering by the time development of a wave packet composed of the Bloch waves. In the second method, however, we don’t need to calculate the wave functions, thus we also don’t need the graphene potential, because we incorporate the E(k) dispersion relation directly into the kinetic energy operator and the defect is still represented by a local potential. The dispersion relation can be a simple tight-binding (TB) dispersion relation, but for a more accurate representation of the electronic structure, we can utilize E(k) relations from an ab-initio DFT calculation.

Abstract: A wound is specifically referring to a sharp injury which damages the epidermis of the skin. The reasons of that surgery, burns, trauma and chronic diseases. An essential part of medical and pharmaceutical wound care is dressing. The wound dressing or patch perform the role of protection barrier from infection, keeps in appropriate moisture and pH levels, and promotes the healing process. The addition of components such as biologically active molecules and nanoparticles to the dressing matrix helps prevent or treat infection, detect a number of parameters, such as moisture level or the presence of a pathogen, etc., and can also accelerate wound healing through biological pathways. This study aims to 3D print multifunctional hydrogel wound dressings with programmable passive release of antimicrobial and healing agents like growth factor. Aluminum boehmite is dispersed into the hydrogel matrix, which promotes wound healing. The system also includes a gradient distribution of fibroblast growth factor (FGF-2), which in turn helps to accelerate the healing process. The work consists of chemical, engineering, and biological parts. Optimization of the hydrogel composition (modification of the synthesis of chitosan, the addition of other hydrogels, for example, CNC, PEG), as well as the selection of printing parameters (pressure, printing plate temperature, printing speed) and fixing the resulting pattern (glutaraldehyde, UV crosslinking) are the stages of chemical engineering parts. Biology includes in vitro cytological studies, as well as the study of the effect of the uniform and gradient presence of FGF on the rate of wound healing in vivo.

Acknowledgments: This work was supported by the Ministry of Education and Science of Russia (project No. 075-15-2019-1896).

Aim: Primary teeth are subjected to a continuous process of de- and re-mineralization. To prevent loss of dental tissues, fluoride-containing toothpastes have been successfully applied as a global solution to mainly prevent dental caries and promote tooth re-mineralization. However, the swallowing of a high amount of fluoride in younger children would lead to potential risk of fluorosis. Newly developed biomimetic toothpastes have provided promising results in preventive den tistry, therefore, the aim of the present study was to analyse the ability of commercial toothpastes to diffuse into deciduous enamel layer to remineralize the crystal habitat.

Materials and Methods: Previously extracted primary teeth (n=8) were manually brushed for 15 days (3 times/day), using 3 different pediatric toothbrushes: (a) commercial toothpaste containing fluorine 500 ppm; (b) commercial toothpaste containing fluorine 1400 ppm; (c) toothpaste containing hydroxyapatite nanocrystal. Elements used as control (n = 2) were subjected to the manual brushing with water. Then, specimens were assessed by means of variable pressure scanning electron microscopy (VP-SEM).

Results: Toothpaste containing nanocrystals of hydroxyapatite seemed to better diffuse through the enamel layer of deciduous teeth. This biomimetic toothpaste might contribute to remineralize the loss of the mineral component and play a central role in the prevention of dental caries.

Conclusion: Biomimetic toothpastes would be considered a reliable alternative to fluoride-containing toothpaste. These preliminary results not only would improve the synthesis of novel biomaterials for deciduous teeth, but also would represent a positive global economic impact since the wide prevalence of dental caries affecting primary teeth.

Aim: Calcium silicate-based cements represent safe and predictable materials widely used in different fields of endodontics. They can be applied as pulp dressing agents during vital pulp therapy (VPT) of carious-affected deciduous or permanent teeth with immature roots as well as endodontic cements in case of root perforation or regenerative endodontic procedures. Therefore, it’s crucial to demonstrate biocompatible and antibiofilm properties of bioactive cements (i.e. MTA and Biodentine) in order to support their successful use in the clinical field.

Materials and Methods: Biocompatibility of ProRootMTA and Biodentine specimens was assessed through cell culture of Saos-2 cells and both cement extracts by viability assay, oxidative stress analysis and immunofluorescence evaluation; on the other hand, antibiofilm efficacy was assessed by evaluating the biofilm forming ability of Streptococcus mutans onProRootMTA and Biodentine disks using Crystal Violet assay.

Results: Cells exposed to ProRootMTA and Biodentine showed a good cell viability, slightly better in presence of the first; moreover, cells seeded on ProRootMTA presented a higher degree of biocompatibility compared to Biodentine. Accordingly, Biodentine demonstrated lightly fewer promising outcomes in terms of oxidative stress and focal adhesions of cells than ProRoot MTA, although the differences were not statistically significant. Inhibition of superficial colonization as well as biofilm forming ability of S. mutants were successfully obtained with both evaluated cements, even though ProRootMTA demonstrated a more efficient time-dependent antibiofilm effect than Biodentine.

Conclusion: Bioactive cements proved to be biocompatible and to possess antibiofilm properties. When compared, MTA would seem to perform slightly better and could be considered as the gold standard material in the endodontic procedures.

Aiming at the low precision and heavy workload of manual measurement, as well as the high cost and complex operation of high-precision measuring instruments, a precision measuring instrument and method of fertilizer shape characteristics are proposed. In this method, the fertilizer shape characteristics are calculated by image acquisition, gray and gamma correction, and edge detection. Firstly, the measuring instrument works in an intermittent collection mode, which can acquire the top and side images of fertilizer at the same time. Secondly, gamma correction is performed on the top and side grayscale images to improve the image contrast, after the fertilizer image collection is completed. Finally, the edge detection algorithm based on the orientation gradient is proposed to extract the top and side contour images of the fertilizer accurately, and the shape characteristics are calculated from the contour images. The shape characteristics of the compound fertilizer, the organic fertilizer, and the biological fertilizer are measured by using the three-dimensional scanning method and the method proposed in this paper. The significant difference test between the two methods is evaluated by the Grubbs test, F test, and t test. The results show that there is no significant difference between the two measuring methods of fertilizer shape characteristics. The proposed measuring instrument and method proposed in this paper can quickly and accurately measure the fertilizer shape characteristics, which can lay a solid foundation for fertilizer production and quality inspection.

In recent years, entropy measures, and more specifically, spectral entropy have emerged as an efficient method for the damage assessment of both mechanical systems and civil structures. In the present work, entropy measures are applied as a damage-sensitive feature for the real-time structural health monitoring of buried pipelines. The management of these underground Fluids Distribution Systems (FDSs) is critical for supplying clean water, oil, gas, and other goods. However, the health state of these systems tends to deteriorate over time so that they become more vulnerable to leaks or catastrophic failure events. Maintenance surveys and visual inspections are expensive and labour-intensive, due to the difficulties in accessing buried pipelines. Thus, Vibration-Based Inspection (VBI) techniques and continuous monitoring would be perfectly suited for the task. The approach is validated numerically on the soil-structure models of a typical pipeline structure (i.e. Steel Pipes - SPs).

Load-carrying capability and evolving crack damage of a cube-shaped concrete specimen have been assessed during a laboratory compression test carried up to fracture. Damage assessment has been carried by Acoustic Emission (AE) monitoring technique, through a network of six resonant PZT transducers. Besides classical methods of AE data analysis, including 3D AE source location and b-value analysis, the application of a recently proposed approach based on Natural Time (NT) analysis is herein proposed [1,2]. The present study focuses on identifying the entrance of the system into a critical condition, through the definition of a critical NT parameter, to be extracted from the AE signal time series, as a pre-failure indicator. The numerical simulation of this test using a version of the Discrete Element method [3,4] allowed to understand some aspect of the damage evolution in the specimen regions, close to the formation of the critical cracks, that led to the collapse.

[1] Varotsos PA, N.V. Sarlis NV and Skordas ES, 2011 Natural Time Analysis: The New View of Time (Springer, Berlin).

[2] Potirakis SM and Mastrogiannis D, Critical features revealed in acoustic and electromagnetic emissions during fracture experiments on LiF, 2017 Physica A 485, 11–22.

[3] Iturrioz I, Lacidogna G, Carpinteri A (2014). Acoustic emission detection in concrete specimens: Experimental analysis and lattice model simulations. International Journal of Damage Mechanics, 23: 327-358.

[4]Iturrioz I, Birck G, Riera JD (2018) Numerical DEM simulation of the evolution of damage and AE preceding failure of structural components. Engineering Fracture Mechanics, doi.org/10.1016/j.engfracmech.2018.02.023.

High frequency eddy current testing has been reported that this method is applicable to detecting the electrical properties change in non-electrical conductive materials such as glass fiber reinforced plastics (GFRPs) [1-2]. This method induces displacement current into the specimen and detects the change of the electromagnetic field from this current. In this study, this method is called as electromagnetic induction testing because this method does not detect the change of the electromagnetic filed from eddy current.

Electromagnetic induction testing can conduct measurements at high speed and non-contact. Furthermore, in the previous research from our group, we proposed the new method to use Driver Field Lens (DFL) with electromagnetic induction testing to measure the crack angle for GFRP. Although DFL has the advantage of detecting the crack angle, it can reduce the sensitivity of detecting the crack existence.

In order to overcome this disadvantage of DFL, autoencoder is combined with electromagnetic induction testing. Autoencoder is unsupervised learning in artificial neural network and it can extract features of input data. Autoencoder can decode by using the former extracted features and it can detect abnormal signals comparing the decoded data. We construct the autoencoder to judge the defect existence in GFRP and possibility to use of it combined with electromagnetic induction testing for non-destructive testing is discussed.

[1] Koichi Mizukami, et al., "Desigin of eddy current-based dielectric constant meter for defect detection in glass fiber reinforsed plastics " NDT&E International, Vol. 74 (2015), pp. 24–32.

[2] S. Gäbler, et al., "Measuring and Imaging Permittivity of Insulators Using High-Frequency Eddy-Current Devices”, IEEE Transaction on Instrumentation and Measurement, Vol. 64, No. 8 (2015), pp. 2227-2238.

In this paper, we establish a graph imaging technique to manifest local stabilization within atomic systems of multilevel atoms. Specifically, we address the interrelation between local stabilization and image entropy. As an example, we consider the mutual interaction of two-pair of pulses propagation in a double-Λ configuration as a dynamical graph-model with four nodes. The dynamic transition-matrix describes the connectivity matrix in the static graph-modal. Recently, we have obtained the transition matrix associated with the graph-model for the light interaction with multilevel atoms [1]. It is to be emphasized that the graph and its image have the same transition matrix. Mainly, the graph-model exposes the stabilization in terms of the singular-value decomposition of energies for the transition matrix. That is, and irrespective of the structure of the transition matrix. The image-model of the graph displays the details of the matrix structure in terms of rows and columns probabilities. Therefore, it will enable us to study conditional probabilities and mutual information inherent in the network of the graph. Furthermore, the graph imaging provides the main row/column contribution to the transition-matrix in terms of image entropy. Our results show that image entropy exposes spatial dependence which is irrelevant to graph entropy.

[1] Alhasan, A.M. Graph Entropy Associated with Multilevel Atomic Excitation. Proceedings 2020, 46(1), 9.

The goal of deep anomaly detection is to identify abnormal data by utilizing a deep neural network trained by a normal training dataset. Real-world industrial anomaly detection problems generally distinguish normal and abnormal data through small morphological differences, such as crack and stain. Nevertheless, most existing algorithms focused on capturing not morphological features but semantic features of normal data. Therefore, they suffer poor performance on real-world industrial anomaly detection problems, even though they show their superiority on simulations with representative image classification datasets. To solve this problem, we propose a novel deep anomaly detection method that encourages understanding salient morphological features of normal data. The main idea behind our algorithm is to train a multi-class model to classify between dozens of morphological transformations applied to all the given data. To this end, the proposed algorithm utilizes a self-supervised learning strategy, which makes unsupervised learning straightforwardly. Additionally, we present a kernel size loss to enhance the proposed neural networks' morphological feature representation power. This objective function is defined as the loss between predicted kernel size and label kernel size via morphological transformed images with the label kernel. In all experiments on the industrial dataset, the proposed method demonstrates superior performance. For instance, in the MVTec anomaly detection task, our model achieves the AUROC of 72.92% that is 8.74% higher than the semantic feature-based deep anomaly detection.