Optimal Concentration of the Bubble Drainage Agent in Foam Drainage Gas Recovery Applications

Foam drainage is theflow of liquid through the interstitial spaces between bubbles driven by capillarity and grav-ity and resisted by viscous damping.The so-called foam drainage gas recovery technology is a technique tradi-tionally used to mitigate the serious bottom-hole liquid loading in the middle and late stages of gas well production.In this context,determining the optimal concentration of the bubble drainage agent is generally cru-cial for the proper application of this method.In this study,a combination of indoor experiments and theoretical analysis have been used to determine the pressure drop related to the foam-carrying capacity in a representative gasfield.Dynamic and static experiments were designed with a bubble drainage agent concentration varying in the range 0.3%–0.6%.Using thefield formation water data,the optimal soaking agent concentration was obtained and pressure drop test experiments on the foam carrying capacity were conducted accordingly.These tests have revealed that the optimal foam displacement agent concentration is 0.5%,and the foam quality at the optimum concentration is between 0.78–0.98.A theoretical method for calculating the pressure drop at the optimum soak-away concentration based on experimental data has also been introduced.The error of the proposed method is within 15%compared to the experimental measured value,demonstrating that it is highly accurate and simple.

Degradation of Mg-Zn-Y-Nd alloy intestinal stent and its effect on the growth of intestinal endothelial tissue in rabbit model

Biodegradable magnesium alloys have excellent properties with respect to biodegradability, biocompatibility, and biomechanics, which may indicate a possibility of its application in intestinal stents. Investigation of Mg-Zn-Y-Nd alloy’s application in intestinal stents has been performed. This study aims to investigate the degradation behavior of Mg-Zn-Y-Nd alloy intestinal stents coated with poly(L-lactide)/paclitaxel in the intestinal environment and its biocompatibility with intestinal tissue. In this paper, Mg-Zn-Y-Nd alloy’s corrosion properties were evaluated by the immersion test in human feces, SEM and XRD, and animal tests. In vitro results showed that when the Mg-Zn-Y-Nd alloy was immersed in human feces for two weeks, its corrosion resistance could be improved by micro arc oxidation(MAO) and poly-l-lactide(PLLA) dual coating. Additionally, this result was also confirmed in vivo experiments by rabbit model. And animal tests also demonstrated that the Mg-Zn-Y-Nd alloy with MAO/PLLA/paclitaxel dual coating drug-eluting stents could inhibit the proliferation of local intestinal tissue around the stents. However, in vivo studies illustrated that the intestinal stents gradually degraded in rabbit model within 12 days.Considering the degradation rate of the stent was faster than expected in rabbits, the support performance of the scaffold requires further improvement.

Standardized Cultivation Technology of Maize Jinhai 5

It is very important to adopt standardized production technology in the process of maize cultivation,which is conducive to giving full play to the high-yield,high-quality and multi-resistant characteristics of Jinhai 5.This paper standardized the cultivation technology of Jinhai 5 from the aspects of maize seed quality,fertilization before sowing,sowing,seedling management,mid-period management,later management,disease and pest control,and harvesting,so as to provide reference for the standardized production of maize Jinhai 5.

Multi-Sensor Ensemble Classifier for Activity Recognition

This paper presents a multi-sensor ensemble classifier (MSEC) for physical activity (PA) pattern recognition of human subjects. The MSEC, developed for a wearable multi-sensor integrate measurement system (IMS),combines multiple classifiers based on different sensor feature sets to improve the accuracy of PA type identification.Experimental evaluation of 56 subjects has shown that the MSECis more effectivein assessing activities of varying intensitiesthan the traditional homogeneous classifiers. It is able to correctly recognize 6 PA types with an accuracy of 93.50%, which is 7% higher than the non-ensemble support vector machine method. Furthermore, the MSECis effective in reducing the subject-to-subject variabilityin activity recognition.

Electrical manipulation of lightwaves in the uniaxially strained photonic honeycomb lattices under a pseudomagnetic field

The realization of pseudomagnetic fields for lightwaves has attained great attention in the field of nanophotonics.Like real magnetic fields,Landau quantization could be induced by pseudomagnetic fields in the strainengineered graphene.We demonstrated that pseudomagnetic fields can also be introduced to photonic crystals by exerting a linear parabolic deformation onto the honeycomb lattices,giving rise to degenerate energy states and flat plateaus in the photonic band structures.We successfully inspire the photonic snake modes corresponding to the helical state in the synthetic magnetic heterostructure by adopting a microdisk for the unidirectional coupling.By integrating heat electrodes,we can further electrically manipulate the photonic density of states for the uniaxially strained photonic crystal.This offers an unprecedented opportunity to obtain on-chip robust optical transports under the electrical tunable pseudomagnetic fields,opening the possibility to design Si-based functional topological photonic devices.

A deep Q-learning network based active object detection model with a novel training algorithm for service robots

This paper focuses on the problem of active object detection(AOD).AOD is important for service robots to complete tasks in the family environment,and leads robots to approach the target ob ject by taking appropriate moving actions.Most of the current AOD methods are based on reinforcement learning with low training efficiency and testing accuracy.Therefore,an AOD model based on a deep Q-learning network(DQN)with a novel training algorithm is proposed in this paper.The DQN model is designed to fit the Q-values of various actions,and includes state space,feature extraction,and a multilayer perceptron.In contrast to existing research,a novel training algorithm based on memory is designed for the proposed DQN model to improve training efficiency and testing accuracy.In addition,a method of generating the end state is presented to judge when to stop the AOD task during the training process.Sufficient comparison experiments and ablation studies are performed based on an AOD dataset,proving that the presented method has better performance than the comparable methods and that the proposed training algorithm is more effective than the raw training algorithm.

AIAP: A Quality Control and Integrative Analysis Package to Improve ATAC-seq Data Analysis

Assay for transposase-accessible chromatin with high-throughput sequencing(ATAC-seq) is a technique widely used to investigate genome-wide chromatin accessibility. The recently published Omni-ATAC-seq protocol substantially improves the signal/noise ratio and reduces the input cell number. High-quality data are critical to ensure accurate analysis.Several tools have been developed for assessing sequencing quality and insertion size distribution for ATAC-seq data;however, key quality control(QC) metrics have not yet been established to accurately determine the quality of ATAC-seq data. Here, we optimized the analysis strategy for ATAC-seq and defined a series of QC metrics for ATAC-seq data,including reads under peak ratio(RUPr), background(BG), promoter enrichment(Pro En), subsampling enrichment(Sub En), and other measurements. We incorporated these QC tests into our recently developed ATAC-seq Integrative Analysis Package(AIAP) to provide a complete ATAC-seq analysis system, including quality assurance, improved peak calling, and downstream differential analysis. We demonstrated a significant improvement of sensitivity(20%–60%) in both peak calling and differential analysis by processing paired-end ATAC-seq datasets using AIAP. AIAP is compiled into Docker/Singularity, and it can be executed by one command line to generate a comprehensive QC report. We used ENCODE ATAC-seq data to benchmark and generate QC recommendations, and developed q ATACViewer for the userfriendly interaction with the QC report. The software, source code, and documentation of AIAP are freely available at https://github.com/Zhang-lab/ATAC-seq_QC_analysis.