Quantitative microstructure characterization for a red-bed soft rock in the Three Gorges Reservoir area

In this paper, a novel algorithm for microscopic color cell image edge detection based on Pulse Coupled Neural Networks (PCNN) is presented. Microscopic color image edge detection has proven to be a difficult task due to uneven brightness, cross-color existing between cell boundary and background caused by dyeing and noise. To solve these problems, this paper originally employs an improved PCNN model called multi-dimensional PCNN to implement edge detection of microscopic color cell image. PCNN is an advanced approach which aims at processing color images parellelly rather than separately dispose signal to every channel because it is characterized by synchronous neuronal burst and multi-dimensional convolution. To test the feasibility and effectiveness of multi-dimensional PCNN on edge detection of microscopic color cell images, experiments of several microscopic cell images are carried out. Empirical results show that multi-dimensional PCNN outperforms classical methods in terms of anti-noise and the accuracy of weak edge detection.

Three Gorges Reservoir in China was running since June 2003, and its impacts on soil erosion (SE) and soil conservation (SC) have attracted major public attentions. We quantified the soil conservation service of ecosystems in the Three Gorges Reservoir Area (TGRA) based on a GIS platform using the universal soil loss equation. We revealed the changes of spatial and temporal patterns of soil conservation (SC) and soil erosion (SE) after project construction as well as impact factors on local SE and SC. Results showed that the total amounts and mean capacity of soil conservation services in the TGRA were 15.38-billion t a−1 and 2134.73 t ha−1 a−1, respectively. Northeast reservoir area owned better services than the southwest, and the regions with a capacity of >5000 t ha−1 a−1 were primary located in mountain areas. An increasing trend in SC appeared in the TRGA and “with increasing SC” totaled 22690.5 km2 (38.9%), while the areas “with decreasing SC” amounted to 3460.4 km2 (5.9%) between 2000 and 2010. Moreover, the pattern of changing SC was continuous in this area. The spatial characteristics of soil conservation service in the TGRA were primarily affected by slope, climate and terrain features. In addition, the reforestation and/or forest protection would contribute to soil erosion control in the TGRA. The results revealed a great spatial heterogeneity of soil conservation service in this region, which may provide useful suggestions for land management, soil erosion control and ecosystem protection in the TGRA in China.

During the storage period of the Three Gorges Reservoir, the bank erosion caused by wind-induced wave is getting more serious, especially for the crushed stone soil bank. The continuous wave scouring and erosion destabilize the bank slope and even induce the occurrence of landslides. Based on the energy conservation theory, the bank slope will reach a stable state after continuous wave erosion. Regarding this, this paper derives the prediction formula of the stable slope angle and erosion width when the wave erodes the crushed stone soil bank slope. Model tests on bank slopes with different dry densities and different crushed stone contents under the action of waves were conducted to observe the erosion process of wave-induced crushed stone soil bank slope and verify the proposed prediction formula. The results show that under the action of wave erosion, erosion ridge and collapse steep angle would form at the upper edge of the bank slope and the controlling factors of this process are the dry density of the crushed stone soil and the wave energy. When the content of crushed stone soil gradually increases, the ability of the bank slope to resist wave erosion weakens at first and then gradually strengthens. Comparing the erosion stable slope angle obtained from the model tests with the calculated results using the proposed prediction formula, a linear relationship could be observed, indicating that the proposed prediction formula is reliable for further analysis.

Study on Measuring Microfiber Diameter in Melt-blown WebBased on Image Analysis

We present a new annotated microscopic cellular image dataset to improve the effectiveness of machine learning methods for cellular image analysis. Cell counting is an important step in cell analysis. Typically, domain experts manually count cells in a microscopic image. Automated cell counting can potentially eliminate this tedious, time-consuming process. However, a good, labeled dataset is required for training an accurate machine learning model. Our dataset includes microscopic images of cells, and for each image, the cell count and the location of individual cells. The data were collected as part of an ongoing study investigating the potential of electrical stimulation to modulate stem cell differentiation and possible applications for neural repair. Compared to existing publicly available datasets, our dataset has more images of cells stained with more variety of antibodies (protein components of immune responses against invaders) typically used for cell analysis. The experimental results on this dataset indicate that none of the five existing models under this study are able to achieve sufficiently accurate count to replace the manual methods. The dataset is available at https://figshare.com/articles/dataset/Dataset/21970604.

A microstructure-based failure criterion for Aminadav dolomites

Strength estimation of granular rocks using a microstructure-based empirical model

Fused Filament Fabrication (FFF) is the most popular and widely used additive manufacturing process for printing polymer and composite products. Various production factors influenced the strength and stiffness of the part manufactured by 3D printing. A comprehensive experimental analysis was conducted in this study to examine the effect of FFF process parameters (infill density, pattern, and layer thickness) on mechanical properties and failure mechanism. The tensile, flexural, and impact test specimens were printed using ABS and carbon fibre reinforced ABS filaments in accordance with ASTM standards. Furthermore, dynamic properties are studied using dynamic mechanical analysis to estimate the loss factor and glass transition temperature under the impact of temperature and frequency in addition to static properties. Further, the results showed the addition of carbon fiber in ABS increases the mechanical properties. The failure modes are studied using optical microscopy and Scanning Electron Microscopy images and it has been visualized that due to improper layer deposition, poor bonding between the previous layer and low infill density creates a void in the specimen which results in poor mechanical properties. The Dynamic Mechanical Analysis showed that at higher frequency the molecular movement decreases which in turn stabilizes the composite behavior and reduces the loss factor.

Landslide susceptibility zonation is one of the research hotspots in the field of landslide research. It is important in the process of disaster prevention and land planning. Select Wan Zhou as study area; it is one portion of the Three Gorges Reservoir Area. According to existing landslide statistical data, select lithology, slope gradient, slope structure and reservoir grade as impact factors. Use analytic hierarchy process and information method to calculate weighted value of each factor respectively, and then combine them. The landslide susceptibility zonation was done by ArcGIS. This method can unite the advantages of the disaster data and expert experience. According to statistics, result accuracy rate is 90.3%.The key area of landslide disaster prevention can be obtained from the landslide susceptibility zonation map.

Recognition of white blood cells (WBCs) is the first step to diagnose some particular diseases such as acquired immune deficiency syndrome, leukemia, and other blood-related diseases that are usually done by pathologists using an optical microscope. This process is time-consuming, extremely tedious, and expensive and needs experienced experts in this field. Thus, a computer-aided diagnosis system that assists pathologists in the diagnostic process can be so effective. Segmentation of WBCs is usually a first step in developing a computer-aided diagnosis system. The main purpose of this paper is to segment WBCs from microscopic images. For this purpose, we present a novel combination of thresholding, k-means clustering, and modified watershed algorithms in three stages including (1) segmentation of WBCs from a microscopic image, (2) extraction of nuclei from cell’s image, and (3) separation of overlapping cells and nuclei. The evaluation results of the proposed method show that similarity measures, precision, and sensitivity respectively were 92.07, 96.07, and 94.30% for nucleus segmentation and 92.93, 97.41, and 93.78% for cell segmentation. In addition, statistical analysis presents high similarity between manual segmentation and the results obtained by the proposed method.

Microstructure of shale formation is the key to understanding its petrophysical and chemical properties. Optical microscopy, scanning electron microscopy and micro-computed tomography (μ-CT) have been combined for characterization of microstructure of Longmaxi (LMX) shale from Shizhu area, Sichan Basin. The results indicate that laminated LMX shale consists of mineral matrix-rich layers and organic matter (OM)-rich layers at micrometer scale in two and three dimensions. Mineral matrix layers, mainly consisting of interparticle pores and intraplatelet pores, are approximately parallel to the bedding plane. Pyrite-rich layer, mainly containing intercrystalline pores, shows a strong preferred orientation parallel to the bedding plane. OM-rich layer, mainly containing OM pores, seems to be discontinuous. In addition, intercrystalline pores are enriched in some layers, while OM pores are distributed irregularly in matrix layers. This vertical heterogeneity of pore microscopic structures in LMX shale is of great importance to understand its petrophysical and chemical properties.

Inverse dynamical photon scattering (IDPS), an artificial neural network based algorithm for three-dimensional quantitative imaging in optical microscopy, is introduced. Because the inverse problem entails numerical minimization of an explicit error metric, it becomes possible to freely choose a more robust metric, to introduce regularization of the solution, and to retrieve unknown experimental settings or microscope values, while the starting guess is simply set to zero. The regularization is accomplished through an alternate directions augmented Lagrangian approach, implemented on a graphics processing unit. These improvements are demonstrated on open source experimental data, retrieving three-dimensional amplitude and phase for a thick specimen.

Solid-state NMR as a method to reveal structure and membrane-interaction of amyloidogenic proteins and peptides

Wound healing problem requires the analysis of tens of images from different microscopic systems. We describe a set of semi-automatic algorithms to analyze a variety of microscopy images used to study the wound healing process. The proposed suite, beside the phase contrast images, allows analyzing fluorescent microscopy images, inverted light microscopy images at different magnification and staining methods, or images obtained by scanning electron microscopy. The proposed software is designed in Matlab®. It is suggested to integrate it into the CellProfilerTM software, thus introducing new functionalities without losing the CellProfiler existing capabilities. The approach is efficient, easy-to-use, and enables biologists to comprehensively and quantitatively address many questions of the wound healing problem.Keywordswound healingimage analysisCellProfilerimage processing

We discuss recently emerging applications of the state-of-art deep learning methods on optical microscopy and microscopic image reconstruction, which enable new transformations among different modes and modalities of microscopic imaging, driven entirely by image data. We believe that deep learning will fundamentally change both the hardware and image reconstruction methods used in optical microscopy in a holistic manner.