The development height of a gas conducting fracture zone (GFZ) in the gob overlying strata is crucial to the gas drainage and safe production of a coal mine. In order to address the issues of ...excessive gas concentration and uncertain GFZ height in No. 7435 Face overlying strata of Kongzhuang Coal Mine, China, the caving characteristics of overlying strata were explored using both physical experiments on similar materials and numerical simulations of Particle Flow Code (PFC) software and verified each other. The relationship of cracks development to porosity changing characteristics was introduced to quantitatively determine the height of the local GFZ. The quantified GFZ heights were compared with those measured using the in-situ drilling flow method. The results showed that 1) PFC software could accurately simulate the overlying strata caving behaviors, thus saving manpower, materials and financial resources needed for related physical experiments, and 2) the temporospatial distribution characteristics of porosity could be used to forecast GFZ height, and are of significant importance for determination of GFZ. Overall, the conclusions are of engineering significance for accurate arrangement of boreholes for gas drainage and reduction of mine gas disasters.
•Experiments using similar materials proved that the particle flow numerical simulation is reliable.•The development height of the mining-affected fracture zone was quantitatively studied based on porosity distribution.•The porosity-divided gas-conducting fracture zone is verified by the water leakage through boreholes method•Quantified porosity distribution is of significance for flow field research.
Precise identification of three-dimensional genome organization, especially enhancer-promoter interactions (EPIs), is important to deciphering gene regulation, cell differentiation and disease ...mechanisms. Currently, it is a challenging task to distinguish true interactions from other nearby non-interacting ones since the power of traditional experimental methods is limited due to low resolution or low throughput.
We propose a novel computational framework EP2vec to assay three-dimensional genomic interactions. We first extract sequence embedding features, defined as fixed-length vector representations learned from variable-length sequences using an unsupervised deep learning method in natural language processing. Then, we train a classifier to predict EPIs using the learned representations in supervised way. Experimental results demonstrate that EP2vec obtains F1 scores ranging from 0.841~ 0.933 on different datasets, which outperforms existing methods. We prove the robustness of sequence embedding features by carrying out sensitivity analysis. Besides, we identify motifs that represent cell line-specific information through analysis of the learned sequence embedding features by adopting attention mechanism. Last, we show that even superior performance with F1 scores 0.889~ 0.940 can be achieved by combining sequence embedding features and experimental features.
EP2vec sheds light on feature extraction for DNA sequences of arbitrary lengths and provides a powerful approach for EPIs identification.
Contact force chains (CFCs) in the heterogeneous granular materials are often considered to be structured physical systems that play a key role in their mechanical properties such as stiffness, ...strength, stability and flowability. In this context, quantitatively estimating the evolution of CFCs in a quasi-statically sheared granular system is essential for advancing our understanding of the mechanics of granular materials. In this paper, based on discrete element method (DEM) simulation data, an artificial neural network (ANN) is developed and applied to predict the anisotropy of the CFCs in two types of idealized granular materials with different initial relative densities undergoing triaxial shearing. Five features including particle size, coordination number and particle displacement (i.e., x-, y- and z-components of the particle displacement) at the particle-scale and the meso-scale each are used to train and test the established ANN model. The results of model prediction show that the 3D orientational distributions of the CFCs from the ANN predictions match very well the DEM simulation results during the whole shearing progress. It is found that for both dense and loose samples, the combined set of particle-scale and meso-scale features have a dominating influence on the CFC evolutions but the ANN model performs better in the CFCs estimation at the strain increment of 0–7% than at the strain increment of 7%–14%. The outcome of this study shows that machine learning is a promising tool for studying the complex mechanical behavior of granular materials.
•Constructing a multi-layer feedforward ANN model based on a detailed set of 3D DEM simulation data.•Estimating the anisotropy of CFC in a quasi-statically sheared granular system with a high accuracy.•Demonstrating the effect of shear band on the ANN model performance.•Investigating the initial relative density on ANN predictions.
Understanding the catalytic mechanism of bimetallic nanocatalysts remains challenging. Here, we adopt an adsorbate mediated thermal reduction approach to yield monodispersed AuPd catalysts with ...continuous change of the Pd-Au coordination numbers embedded in a mesoporous carbonaceous matrix. The structure of nanoalloys is well-defined, allowing for a direct determination of the structure-property relationship. The results show that the Pd single atom and dimer are the active sites for the base-free oxidation of primary alcohols. Remarkably, the d-orbital charge on the surface of Pd serves as a descriptor to the adsorbate states and hence the catalytic performance. The maximum d-charge gain occurred in a composition with 33-50 at% Pd corresponds to up to 9 times enhancement in the reaction rate compared to the neat Pd. The findings not only open an avenue towards the rational design of catalysts but also enable the identification of key steps involved in the catalytic reactions.
Quantitative detection of histone modifications has emerged in the recent years as a major means for understanding such biological processes as chromosome packaging, transcriptional activation, and ...DNA damage. However, high-throughput experimental techniques such as ChIP-seq are usually expensive and time-consuming, prohibiting the establishment of a histone modification landscape for hundreds of cell types across dozens of histone markers. These disadvantages have been appealing for computational methods to complement experimental approaches towards large-scale analysis of histone modifications.
We proposed a deep learning framework to integrate sequence information and chromatin accessibility data for the accurate prediction of modification sites specific to different histone markers. Our method, named DeepHistone, outperformed several baseline methods in a series of comprehensive validation experiments, not only within an epigenome but also across epigenomes. Besides, sequence signatures automatically extracted by our method was consistent with known transcription factor binding sites, thereby giving insights into regulatory signatures of histone modifications. As an application, our method was shown to be able to distinguish functional single nucleotide polymorphisms from their nearby genetic variants, thereby having the potential to be used for exploring functional implications of putative disease-associated genetic variants.
DeepHistone demonstrated the possibility of using a deep learning framework to integrate DNA sequence and experimental data for predicting epigenomic signals. With the state-of-the-art performance, DeepHistone was expected to shed light on a variety of epigenomic studies. DeepHistone is freely available in https://github.com/QijinYin/DeepHistone .
Based on a 'shortcut-to-adiabaticity' (STA) scheme, we theoretically design and experimentally realize a set of high-fidelity single-qubit quantum gates in a superconducting Xmon qubit system. ...Through a precise microwave control, the qubit is driven to follow a fast 'adiabatic' trajectory with the assistance of a counter-diabatic field and the correction of derivative removal by adiabatic gates. The experimental measurements of quantum process tomography and interleaved randomized benchmarking show that the process fidelities of our STA quantum gates are higher than 94.9% and the gate fidelities are higher than 99.8%, very close to the state-of-art gate fidelity of 99.9%. An alternate of high-fidelity quantum gates is successfully achieved under the STA protocol.
The monitoring of ship pipeline valve leaks holds immense significance as it serves to enhance ship safety, mitigate energy and material losses, and protect the health and sustainability of the ...marine environment. In comparison to traditional expert-based fault diagnosis methods, this study presents a deep learning-based fault feature extraction approach for end-to-end valve leak fault diagnosis. To address the challenge of incomplete leak fault description by a single sensor, a Multi-Channel Multi-Scale Convolutional Neural Network (MCMS-CNN) model is established. Diverging from existing methods, the proposed MCMS-CNN model automatically extracts fault features from grayscale images obtained from two sensors and performs feature-level fusion for fault classification, thereby mitigating the impact of data redundancy and noise and enhancing fault recognition accuracy. The effectiveness of the proposed approach is verified through valve leak experiments, and comparisons are conducted with different models and information fusion methods. The results demonstrate that the proposed MCMS-CNN model exhibits advantages in terms of accuracy and robustness, confirming its practicality in ship pipeline valve leak fault diagnosis.
•Using deep learning methods to automatically extract raw signal features, avoiding the influence of expert experience and human factors.•Fuses fault information from multiple sensors to improve the accuracy of leakage identification of ship pipeline valves.•Studied the performance difference between MCMS-CNN, BP neural network and traditional CNN.•Studied the effectiveness of feature-level data fusion methods in ship pipeline valve leakage.
Low concentration strontium (LC-Sr) can promote the growth of plants. In order to explore its promoting mechanism from the aspect of photosynthesis, the leaf characteristics, CO2 assimilation and ...chlorophyll (Chl) a fluorescence kinetics were investigated with hydroponically LC-Sr-treated Chinese cabbage seedlings. After a 28-d treatment to SrCl2 at different concentrations (0.1, 0.2, 0.5, and 1.0 mmol L−1), we observed an increase in the specific leaf weight (SLW) of Chinese cabbage compared with the control group. Notably, as the strontium concentration increased, a more pronounced improvement trend in the contents of Chl and protein in the leaves was observed, contributing to the enhancement of photosynthesis. However, the statistical differences in Pn among various LC-Sr treatments were not significant. Nevertheless, the leaf starch content exhibited a significant increase after LC-Sr treatments. Additionally, Chl a fluorescence transient has been used as a sensitive indicator of the promotional effect of LC-Sr on photosynthesis. The results of fluorescence parameters showed that LC-Sr treatments accelerated the light reaction speed of leaves (Tfm, dV/dto, dVG/dto), improved the energy utilization efficiency of photosystem (PSI and PSII) (ETo/CSo, ψET,ψRE, δRo, φRo), and ultimately enhanced the photosynthetic performance of leaves (PIabs, SFIabs, DFabs). The increased RCs/CSo and Sm contributed to the enhancement of the light reaction activity of strontium-treated leaves. The LC-Sr treatments had no interference with the calcium absorption, and notably enhanced the photosynthetic capacity of Chinese cabbage, shedding light on potential benefits of LC-Sr for crop cultivation.
•Strontium at 0.1–1.0 mmol L−1 was beneficial to the growth of Chinese cabbage.•Strontium at 0.1–1.0 mmol L−1 had no interference with the calcium absorption.•The photosynthesis of Chinese cabbage was promoted by 0.1–1.0 mmol L−1 SrCl2.•Strontium had a direct stimulating effect on photosynthesis.
Current colorimetric methods for quantitative determination of seed viability (SV) with 2,3,5-triphenyl tetrazolium chloride (TTC) have been plagued by issues of being cumbersome and time-consuming ...during the experimental process, slow in extraction and staining, and exhibiting inconsistent results. In this work, we introduced a new approach that combines TTC-staining with high-temperature extraction using dimethyl sulfoxide (DMSO). The optimization of the germination stage, TTC-staining method, and 1,3,5-triphenylformazan (TTF) extraction method were meticulously carried out as follows: When the majority of wheat seeds had grown the radicle, and the length of radicles was approximately equal to the seed length (24 h-germination), 2 g germinating seeds were placed into a beaker (20 mL) containing 5 mL 10 g·L−1 TTC solution. The seeds were stained with TTC in the dark at 25 °C for 1 h. Following the staining, 1 mL 1 mol·L−1 H2SO4 was added to stop the reaction for 5 min. The H2SO4 solution was then removed, and the seeds were gently rinsed with deionized water. Subsequently, the TTF produced in the seeds was extracted directly with 5 mL DMSO solution at 55 °C for 1 h. The absorbance of the extract was measured at 483 nm, and the index of SV was calculated according to a predetermined TTC calibration curve and expressed by mg TTC·g−1 (seed)·h−1. The new method has been demonstrated to be rapid, stable, and highly sensitive, as evidenced by the accurate measurement of seed viability with different aging degrees.
The development of thermal barrier coating has been limited by calcium–magnesium–alumina–silicate (CMAS) corrosion. ~13 wt% YSZ coating under partial penetration of CMAS attacking is used as ...materials of thermal cycling experiment to exclude the influence of phase transition. Failure modes at boundary of CMAS permeable and impermeable zone at 1100 ℃ and 1250 ℃ are not completely consistent. The boundary of two conditions is inclined to occur vertical, transverse and oblique cracks. However, step-type cracks developed by the vertical crack tip are formed at 1100 ℃, and straight oblique penetrating cracks are formed at 1250 ℃.
•Thermal cycling experiments are conducted at 1100 °C and 1250 °C.•Three kinds of cracks are formed at boundary of TBCs coated with and without CMAS.•The stepped oblique cracks are formed in 1100 °C thermal cycling samples.•The oblique cracks formed at 1250 °C are continuous penetrating cracks.•CMAS enrich at the interface between TC and TGO to form anorthite layer at 1250 °C.
PDF
