Permeability and seepage stability of coal-reject and clay mix

AbstractThis paper presents an experimental investigation into the permeability and seepage stability of the granular coal-reject mixed with clay, which is affected by clay content, axial pressure, and the use of geotextile. Laboratory seepage tests were performed to determine the coefficients of permeability under both Darcy’s and non-Darcy’s flow conditions. The critical hydraulic gradients of coal-reject with different percentages of clay, axial pressures and geotextile were also tested. The results indicate that the coefficients of permeability, k, and global permeability, K, decrease while the critical hydraulic gradient increases with increasing clay content and axial pressure. The permeability and the critical hydraulic gradient start their radical changes when the clay content exceeds 10% of the total dry weight of the sample. The placing of a layer of geotextile on the bottom of specimen can decrease the permeability and significantly improve the seepage stability of the coal-reject and clay mixture

Mapping the Spatial-Temporal Dynamics of Vegetation Response Lag to Drought in a Semi-Arid Region

Drought, as an extreme climate event, affects the ecological environment for vegetation and agricultural production. Studies of the vegetative response to drought are paramount to providing scientific information for drought risk mitigation. In this paper, the spatial-temporal pattern of drought and the response lag of vegetation in Nebraska were analyzed from 2000 to 2015. Based on the long-term Daymet data set, the standard precipitation index (SPI) was computed to identify precipitation anomalies, and the Gaussian function was applied to obtain temperature anomalies. Vegetation anomaly was identified by dynamic time warping technique using a remote sensing Normalized Difference Vegetation Index (NDVI) time series. Finally, multilayer correlation analysis was applied to obtain the response lag of different vegetation types. The results show that Nebraska suffered severe drought events in 2002 and 2012. The response lag of vegetation to drought typically ranged from 30 to 45 days varying for different vegetation types and human activities (water use and management). Grasslands had the shortest response lag (~35 days), while forests had the longest lag period (~48 days). For specific crop types, the response lag of winter wheat varied among different regions of Nebraska (35–45 days), while soybeans, corn and alfalfa had similar response lag times of approximately 40 days

PointHuman: Reconstructing Clothed Human from Point Cloud of Parametric Model

It is very difficult to accomplish the 3D reconstruction of the clothed human body from a single RGB image, because the 2D image lacks the representation information of the 3D human body, especially for the clothed human body. In order to solve this problem, we introduced a priority scheme of different body parts spatial information and proposed PointHuman network. PointHuman combines the spatial feature of the parametric model of the human body with the implicit functions without expressive restrictions. In PointHuman reconstruction framework, we use Point Transformer to extract the semantic spatial feature of the parametric model of the human body to regularize the implicit function of the neural network, which extends the generalization ability of the neural network to complex human poses and various styles of clothing. Moreover, considering the ambiguity of depth information, we estimate the depth of the parameterized model after point cloudization, and obtain an offset depth value. The offset depth value improves the consistency between the parameterized model and the neural implicit function, and accuracy of human reconstruction models. Finally, we optimize the restoration of the parametric model from a single image, and propose a depth perception method. This method further improves the estimation accuracy of the parametric model and finally improves the effectiveness of human reconstruction. Our method achieves competitive performance on the THuman dataset

Investigating Neural Substrates of Individual Independence and Interdependence Orientations via Efficiency-based Dynamic Functional Connectivity : A Machine Learning Approach

Fundings: Beihang University and Capital Medical University Advanced Innovation Center for Big DataBased Precision Medicine Plan; 10.13039/501100001809-National Natural Science Foundation of China; 10.13039/501100000275-Leverhulme Trust;Peer reviewedPostprin

Static detection of control-flow-related vulnerabilities using graph embedding

© 2019 IEEE. Static vulnerability detection has shown its effectiveness in detecting well-defined low-level memory errors. However, high-level control-flow related (CFR) vulnerabilities, such as insufficient control flow management (CWE-691), business logic errors (CWE-840), and program behavioral problems (CWE-438), which are often caused by a wide variety of bad programming practices, posing a great challenge for existing general static analysis solutions. This paper presents a new deep-learning-based graph embedding approach to accurate detection of CFR vulnerabilities. Our approach makes a new attempt by applying a recent graph convolutional network to embed code fragments in a compact and low-dimensional representation that preserves high-level control-flow information of a vulnerable program. We have conducted our experiments using 8,368 real-world vulnerable programs by comparing our approach with several traditional static vulnerability detectors and state-of-the-art machine-learning-based approaches. The experimental results show the effectiveness of our approach in terms of both accuracy and recall. Our research has shed light on the promising direction of combining program analysis with deep learning techniques to address the general static analysis challenges

Inhibitory effects of adenovirus mediated tandem expression of RhoA and RhoC shRNAs in HCT116 cells

<p>Abstract</p> <p>Background</p> <p>RhoA and RhoC are deregulated by over expression in many human tumors, including colorectal cancer. Some reports show that they play a pivotal role in the carcinogenesis, tumor development and infiltration metastasis. In this study, for the first time we constructed recombinant adenovirus to investigate the inhibitory effects of RhoA and RhoC shRNAs in tandem expression on the cell proliferation and invasion of colorectal cancer HCT116 cells.</p> <p>Methods</p> <p>The recombinant adenovirus carrying RhoA and RhoC shRNAs in tandem expression was transfected into HCT116. The mRNA transcription and protein expressions of RhoA and RhoC were examined by RT-FQPCR and Western blot respectively. Cellular proliferation inhibitory activity was determined by methyl thiazolyl tetrazolium (MTT) assay and invasive and migrating potential was detected through in vitro Matrigel coated invasion and migration assay.</p> <p>Results</p> <p>Both mRNA and proteins Levels of RhoA and RhoC were significantly reduced in HCT116 cells transfected with Ad-A1+A2+C1+C2 than those in Ad-HK group and control one. The relative RhoA and RhoC mRNA transcriptions were decreased to 40% and 36% (P < 0.05), while proteins expression reducing 42% and 35%, respectively (P < 0.05). Growth curves analysis showed that alive cell number in the Ad-A1+A2+C1+C2 group was lower than others in the third to sixth day and transwell chamber analysis showed that migration/invasion activity was significantly suppressed in Ad-A1+A2+C1+C2 group.</p> <p>Conclusion</p> <p>Our results indicate recombinant adenovirus carrying RhoA and RhoC shRNAs in tandem expression may inhibit the growth and invasion of HCT116 cells. Application of such vector to inhibit one or more genes may be a new method to cancer therapy.</p

北沙参茎叶提取物对小鼠半数溶血值(HC50)的影响

Objective: To study the effect of Umbelliferae littoralis leaf extract on the Hemolytic Value (HC50) of mice, and to provide the basis for the development and utilization medicinal resources and edible resources. Methods: Prepare littoralis leaf water extract and alcohol extract, and set different dose treatment groups and blank control group, and continuously deliver American ginseng capsule for 15 days. Inject sRBC according to the weight on the tenth day. Take the blood serum from eyeball blood after 5 days. Put supernatant of 1ml and Dulbecco's reagent of 3ml in the test tube, and mix the 10% sRBC of 0.25ml and Dulbecco's reagent of 4ml together in another test tube, and measure absorbance at 540nm fine control (SA liquid) tubing as blank, HC50 value were calculated. Results: Different extracts of stems and littoralis leaf were given to the mice for 15 days, and hemolytic value of the mice in water extract 4.68g/kg dose group, alcohol extract 4.68g/kg dose group and American ginseng capsule group significantly increased while comparing with the blank control group (P&lt;0.05). Conclusion: Littoralis Leaf plays an important role in regulating human immunity.目的  研究伞形科植物北沙参茎叶提取物对小鼠半数溶血值(HC50)的影响，为扩大药用资源和食用资源及开发利用提供依据。 方法  制备北沙参茎叶水提取物和醇提取物，设置给药组的不同剂量与空白对照组、西洋参胶囊组连续给药15天，第10天按体重注射 sRBC。5天后取眼球血分离血清。取上清液1ml和都氏试剂3ml于试管内，同时取10% sRBC 0.25ml,加都氏试剂至4ml,于另一支试管内充分混匀，于540nm处以对照（SA液）管作空白,测定吸光度值计算HC50。 结果  经口给予小鼠不同剂量的北沙参茎叶提取物15天，与对照组比较，水提取物4.68g/kg剂量组、醇提取物4.68g/kg剂量组、西洋参胶囊组小鼠半数溶血值明显升高（P＜0.05）。 结论  北沙参茎叶具有调节体液免疫的作用

DeepWukong: Statically Detecting Software Vulnerabilities Using Deep Graph Neural Network

Static bug detection has shown its effectiveness in detecting well-defined memory errors, e.g., memory leaks, buffer overflows, and null dereference. However, modern software systems have a wide variety of vulnerabilities. These vulnerabilities are extremely complicated with sophisticated programming logic, and these bugs are often caused by different bad programming practices, challenging existing bug detection solutions. It is hard and labor-intensive to develop precise and efficient static analysis solutions for different types of vulnerabilities, particularly for those that may not have a clear specification as the traditional well-defined vulnerabilities. This article presents DeepWukong, a new deep-learning-based embedding approach to static detection of software vulnerabilities for C/C++ programs. Our approach makes a new attempt by leveraging advanced recent graph neural networks to embed code fragments in a compact and low-dimensional representation, producing a new code representation that preserves high-level programming logic (in the form of control-and data-flows) together with the natural language information of a program. Our evaluation studies the top 10 most common C/C++ vulnerabilities during the past 3 years. We have conducted our experiments using 105,428 real-world programs by comparing our approach with four well-known traditional static vulnerability detectors and three state-of-the-art deep-learning-based approaches. The experimental results demonstrate the effectiveness of our research and have shed light on the promising direction of combining program analysis with deep learning techniques to address the general static code analysis challenges

Global model of an atmospheric-pressure capacitive discharge in helium with air impurities from 100 to 10000 ppm

Helium is a common working gas for cold atmospheric plasmas (CAPs) and this is often mixed with other gases, such as oxygen and nitrogen, to increase its reactivity. Air is often found in these plasmas and it can be either introduced deliberately as a precursor or entrapped in systems that operate in open atmosphere. In either case, the presence of small traces of air can cause a profound change on the composition of the plasma and consequently its application efficacy. In this paper, a global model for He+Air CAPs is developed, in which 59 species and 866 volume reactions are incorporated, and a new boundary condition is used for the mass transport at the interface between the plasma and its surrounding air gas. The densities of reactive species and the power dissipation characteristics are obtained as a function of air concentrations spanning from 100 to 10000 ppm. As the air concentration increases, the dominant cation changes from O2 + to NO+ and then to NO2 + , the dominant anion changes from O2 - to NO2 - and then to NO3 - , the dominant ground state reactive oxygen species changes from O to O3, and the dominant ground state reactive nitrogen species changes from NO to HNO2. O2(a) is the most abundant metastable species and its density is orders of magnitude larger than other metastable species for all air concentrations considered in the study. Ion Joule heating is found important due to the electronegative nature of the plasma, which leads to the fast decrease of electron density when the air concentration is larger than 1000 ppm. The generation and loss pathways of important biologically relevant reactive species such as O, O2 - , O3, OH, H2O2, NO, HNO2, HNO3 are discussed and differences with the pathways observed in He+O2, He+H2O, Ar+Air and pure air plasmas are highlighted. Based on the simulation results, a simplified chemistry set with 47 species and 109 volume reactions is proposed. This simplified model greatly reduces the computational load while maintaining the accuracy of the simulation results within a factor of 2. The simplified chemistry model is computationally much less intensive, facilitating its integration into multidimensional fluid models for the study of the spatio-temporal evolution of He+Air CAPs