Discrete element modeling of vibration compaction effect of the vibratory roller in roundtrips on gravels

This paper aims to study the vibration compaction mechanism of the vibratory roller on gravels using a two-dimensional discrete element method. The roadbed model was established by gravel particles with irregular shapes, which was closer to reality. The performance parameters of the vibratory roller, such as operating frequency and rolling velocity, were investigated to explore their influences on the operating efficiency of the vibratory roller in roundtrips. The frequencies of 15 Hz and 17 Hz were proved to be the optimal frequency and resonance frequency in the current simulations, respectively. The vibratory roller could achieve a better vibration compaction effect with less power consumption at the optimal frequency. In addition, the number of roundtrips and power consumption should be considered in the selection of the optimal rolling velocity. The movement direction and the contact force distribution of gravels were illustrated by the displacement field, velocity field, as well as the contact force chains. Our results provide a better understanding of the mechanical behavior of gravel particles and their interactions with the vibratory roller

Vertical Stress and Deformation Characteristics of Roadside Backfilling Body in Gob-Side Entry for Thick Coal Seams with Different Pre-Split Angles

Retained gob-side entry (RGE) is a significant improvement for fully-mechanized longwall mining. The environment of surrounding rock directly affects its stability. Roadside backfilling body (RBB), a man-made structure in RGE plays the most important role in successful application of the technology. In the field, however, the vertical deformation of RBB is large during the panel extraction, which leads to malfunction of the RGE. In order to solve the problem, roof pre-split is employed. According to geological conditions as well as the physical modeling of roof behavior and deformation of surrounding rock, the support resistance of RBB is calculated. The environment of surrounding rock, vertical stress and vertical deformation of the RBB in the RGE with different roof pre-split angles are analyzed using FLAC3D software. With the increase of roof pre-split angle, the vertical stresses both in the coal wall and RBB are minimum, and the vertical deformation of RBB also decreases from 110.51 mm to 6.1 mm. Therefore, based on the results of numerical modeling and field observation, roof pre-split angle of 90° is more beneficial to the maintenance of the RGE

Visual sentiment prediction based on automatic discovery of affective regions

Automatic assessment of sentiment from visual content has gained considerable attention with the increasing tendency of expressing opinions via images and videos online. This paper investigates the problem of visual sentiment analysis, which involves a high-level abstraction in the recognition process. While most of the current methods focus on improving holistic representations, we aim to utilize the local information, which is inspired by the observation that both the whole image and local regions convey significant sentiment information. We propose a framework to leverage affective regions, where we first use an off-the-shelf objectness tool to generate the candidates, and employ a candidate selection method to remove redundant and noisy proposals. Then a convolutional neural network (CNN) is connected with each candidate to compute the sentiment scores, and the affective regions are automatically discovered, taking the objectness score as well as the sentiment score into consideration. Finally, the CNN outputs from local regions are aggregated with the whole images to produce the final predictions. Our framework only requires image-level labels, thereby significantly reducing the annotation burden otherwise required for training. This is especially important for sentiment analysis as sentiment can be abstract, and labeling affective regions is too subjective and labor-consuming. Extensive experiments show that the proposed algorithm outperforms the state-of-the-art approaches on eight popular benchmark datasets

Virtual histological staining of unlabeled autopsy tissue

Histological examination is a crucial step in an autopsy; however, the traditional histochemical staining of post-mortem samples faces multiple challenges, including the inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, as well as the resource-intensive nature of chemical staining procedures covering large tissue areas, which demand substantial labor, cost, and time. These challenges can become more pronounced during global health crises when the availability of histopathology services is limited, resulting in further delays in tissue fixation and more severe staining artifacts. Here, we report the first demonstration of virtual staining of autopsy tissue and show that a trained neural network can rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images that match hematoxylin and eosin (H&E) stained versions of the same samples, eliminating autolysis-induced severe staining artifacts inherent in traditional histochemical staining of autopsied tissue. Our virtual H&E model was trained using >0.7 TB of image data and a data-efficient collaboration scheme that integrates the virtual staining network with an image registration network. The trained model effectively accentuated nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining failed to provide consistent staining quality. This virtual autopsy staining technique can also be extended to necrotic tissue, and can rapidly and cost-effectively generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining.Comment: 24 Pages, 7 Figure

Diagenetic–Porosity Evolution and Reservoir Evaluation in Multiprovenance Tight Sandstones: Insight from the Lower Shihezi Formation in Hangjinqi Area, Northern Ordos Basin

AbstractThe reservoir property of tight sandstones is closely related to the provenance and diagenesis, and multiprovenance system and complex diagenesis are developed in Hangjinqi area. However, the relationship between provenance, diagenesis, and physical characteristics of tight reservoirs in Hangjinqi area has not yet been reported. The Middle Permian Lower Shihezi Formation is one of the most important tight gas sandstone reservoirs in the Hangjinqi area of Ordos Basin. This research compared the diagenesis-porosity quantitative evolution mechanisms of Lower Shihezi Formation sandstones from various provenances in the Hangjinqi area using thin-section descriptions, cathodoluminescence imaging, X-ray diffraction (XRD), scanning electron microscopy (SEM), and homogenization temperature of fluid inclusions, along with general physical data and high-pressure mercury intrusion (HPMI) data. The sandstones mainly comprise quartzarenite, sublitharenite, and litharenite with low porosity and low permeability and display obvious zonation in the content of detrital components as a result of multiprovenance. Pore space of those sandstone mainly consists of primary pores, secondary pores, and microfractures, but their proportion varies in different provenances. According to HPMI, the order of the pore-throat radius from largest to smallest is central provenance, eastern provenance, and western provenance, which is consistent with the change tend of porosity (middle part>northern part>western part) in Hangjinqi region. The diagenetic evolution path of those sandstones is comparable, with compaction, cementation, dissolution, and fracture development. The central provenance has the best reservoir quality, followed by the eastern provenance and the western provenance, and this variation due to the diverse diagenesis (diagenetic stage and intensity) of different provenances. These findings reveal that the variations in detrital composition and structure caused by different provenances are the material basis of reservoir differentiation, and the main rationale for reservoir differentiation is varying degrees of diagenesis during burial process

DEM study of the shear behavior and formation of shear band in biaxial test

International audienc

A new installation technology of large diameter deeply-buried caissons: Practical application and observed performance

The development of installation technologies of open caissons has been lagging behind increasingly complex construction conditions. For such purpose, a new installation technology of large diameter deeply-buried (LDDB) open caissons has been developed and then used for construction of twin LDDB caissons into undrained ground with stiff soils in Zhenjiang, China. To assess the installation effects and filed performance, a monitoring program was presented to document the variations in total jacking forces provided by new shaft driven method, ground water level (GWL) around the caisson shaft, inclination angles of caisson shafts and radial displacements of surrounding soils as well as surface settlements of existing nearby facilities. It is observed that the monitoring data during the installation falls almost entirely within the design criteria, the reported new technology has limited impacts on the induced ground movements, depending on the variation in GWL, interaction between twin caissons and excavation-induced unloading effect. Moreover, the total jacking forces increase approximately in stepwise shape as the installation depth increases; the change law of surface settlements is highly similar to those of GWL, showing their close correlation; the larger inclination angles of caisson shafts are mainly encountered in the earlier installation phase, but well controllable. Further discussion on ground movements caused by various technologies confirms the feasibility of new installation technology. Both the observed and compared results give greater confidence on the use of such the technology in practice.</p

Dynamic trapping and manipulation of biological cells with optical tweezers

Current control techniques for optical tweezers work only when the cell is located in a small neighbourhood around the centroid of the focused light beam. Therefore, the optical trapping fails when the cell is initially located far away from the laser beam or escapes from the optical trap during manipulation. In addition, the position of the laser beam is treated as the control input in existing optical tweezers systems and an open-loop controller is designed to move the laser source. In this paper, we propose a new robotic manipulation technique for optical tweezers that integrates automatic trapping and manipulation of biological cells into a single method. Instead of using open-loop control of the position of laser source as assumed in the literature, a closed-loop dynamic control method is formulated and solved in this paper. We provide a theoretical framework that bridges the gap between traditional robotic manipulation techniques and optical manipulation techniques of cells. The proposed controller allows the transition from trapping to manipulation without any hard switching from one controller to another. Simulation and experimental results are presented to illustrate the performance of the proposed controller.ASTAR (Agency for Sci., Tech. and Research, S’pore)Accepted versio

Hybrid Robotic Grasping with a Soft Multimodal Gripper and a Deep Multistage Learning Scheme

Grasping has long been considered an important and practical task in robotic manipulation. Yet achieving robust and efficient grasps of diverse objects is challenging, since it involves gripper design, perception, control and learning, etc. Recent learning-based approaches have shown excellent performance in grasping a variety of novel objects. However, these methods either are typically limited to one single grasping mode, or else more end effectors are needed to grasp various objects. In addition, gripper design and learning methods are commonly developed separately, which may not adequately explore the ability of a multimodal gripper. In this paper, we present a deep reinforcement learning (DRL) framework to achieve multistage hybrid robotic grasping with a new soft multimodal gripper. A soft gripper with three grasping modes (i.e., enveloping, sucking, and enveloping_then_sucking) can both deal with objects of different shapes and grasp more than one object simultaneously. We propose a novel hybrid grasping method integrated with the multimodal gripper to optimize the number of grasping actions. We evaluate the DRL framework under different scenarios (i.e., with different ratios of objects of two grasp types). The proposed algorithm is shown to reduce the number of grasping actions (i.e., enlarge the grasping efficiency, with maximum values of 161% in simulations and 154% in real-world experiments) compared to single grasping modes