Centrifuge modeling of a large-scale surcharge on adjacent foundation

This study investigates the ground and structural response of adjacent raft foundations induced by largescale surcharge by ore in soft soil areas through a 130g centrifuge modeling test with an innovative layered loading device.The prototype of the test is a coastal iron ore yard with a natural foundation of deep soft soil.Therefore,it is necessary to adopt some measures to reduce the influence of the large-scale surcharge on the adjacent raft foundation,such as installing stone columns for foundation treatment.Under an acceleration of 130 g,the model conducts similar simulations of iron ore,stone columns,and raft foundation structures.The tested soil mass has dimensions of 900 mm×700 mm×300 mm(lengthwidthdepth),which is remodeled from the soil extracted from the drilling holes.The test conditions are consistent with the actual engineering conditions and the effects of four-level loading conditions on the composite foundation of stone columns,unreinforced zone,and raft foundations are studied.An automatic layer-by-layer loading device was innovatively developed to simulate the loading process of actual engineering more realistically.The composite foundation of stone columns had a large settlement after the loading,forming an obvious settlement trough and causing the surface of the unreinforced zone to rise.The 12 m surcharge loading causes a horizontal displacement of 13.19 cm and a vertical settlement of 1.37 m in the raft foundation.The stone columns located on both sides of the unreinforced zone suffered significant shear damage at the sand-mud interface.Due to the reinforcement effect of stone columns,the sand layer below the top of the stone columns moves less.Meanwhile,the horizontal earth pressure in the raft foundation zone increases slowly.The stone columns will form new drainage channels and accelerate the dissipation of excess pore pressure.

Microstructural and thermal properties of coal measure sandstone subjected to high temperatures

To study the microscopic structure,thermal and mechanical properties of sandstones under the influence of temperature,coal measure sandstones from Southwest China are adopted as the research object to carry out high-temperature tests at 25℃-1000℃.The microscopic images of sandstone after thermal treatment are obtained by means of polarizing microscopy and scanning electron microscopy(SEM).Based on thermogravimetric(TG)analysis and differential scanning calorimetric(DSC)analysis,the model function of coal measure sandstone is explored through thermal analysis kinetics(TAK)theory,and the kinetic parameters of thermal decomposition and the thermal decomposition reaction rate of rock are studied.Through the uniaxial compression experiments,the stress‒strain curves and strength characteristics of sandstone under the influence of temperature are obtained.The results show that the temperature has a significant effect on the microstructure,mineral composition and mechanical properties of sandstone.In particular,when the temperature exceeds 400℃,the thermal fracture phenomenon of rock is obvious,the activity of activated molecules is significantly enhanced,and the kinetic phenomenon of the thermal decomposition reaction of rock appears rapidly.The mechanical properties of rock are weakened under the influence of rock thermal fracture and mineral thermal decomposition.These research results can provide a reference for the analysis of surrounding rock stability and the control of disasters caused by thermal damage in areas such as underground coal gasification(UCG)channels and rock masses subjected to mine fires.

腺苷/A2AR信号通路阻断在肿瘤治疗中的应用

腺苷是一种在肿瘤微环境中高水平产生的代谢产物,主要通过在免疫细胞上表达的腺苷A2A受体(adenosine A2A receptor,A2AR)有效地抑制炎症组织中的免疫反应,抑制抗肿瘤免疫应答。因此阻断腺苷信号通路是促进抗肿瘤免疫的重要方向。在这篇文章中,我们将简介腺苷信号通路,总结腺苷/A2AR在抑制肿瘤免疫中的作用,并介绍腺苷/A2AR通路阻断在抗肿瘤治疗中的研究现状。临床前研究数据已揭示了抑制A2AR治疗肿瘤的潜在效力,A2AR拮抗剂从基础研究向临床应用的转化也正在进行当中。临床试验的初步结果显示,A2AR拮抗剂在肿瘤患者中安全性良好,无论单药还是联合其他疗法使用均有一定的疗效。未来仍需进一步探索如何选取生物标志物对潜在获益人群进行筛选,以及如何联用A2AR拮抗剂和其他治疗方案以实现最优的抗肿瘤策略。

Deep learning based classification of rock structure of tunnel face

The automated interpretation of rock structure can improve the efficiency,accuracy,and consistency of the geological risk assessment of tunnel face.Because of the high uncertainties in the geological images as a result of different regional rock types,as well as in-situ conditions(e.g.,temperature,humidity,and construction procedure),previous automated methods have limited performance in classification of rock structure of tunnel face during construction.This paper presents a framework for classifying multiple rock structures based on the geological images of tunnel face using convolutional neural networks(CNN),namely Inception-ResNet-V2(IRV2).A prototype recognition system is implemented to classify 5 types of rock structures including mosaic,granular,layered,block,and fragmentation structures.The proposed IRV2 network is trained by over 35,000 out of 42,400 images extracted from over 150 sections of tunnel faces and tested by the remaining 7400 images.Furthermore,different hyperparameters of the CNN model are introduced to optimize the most efficient algorithm parameter.Among all the discussed models,i.e.,ResNet-50,ResNet-101,and Inception-v4,Inception-ResNet-V2 exhibits the best performance in terms of various indicators,such as precision,recall,F-score,and testing time per image.Meanwhile,the model trained by a large database can obtain the object features more comprehensively,leading to higher accuracy.Compared with the original image classification method,the sub-image method is closer to the reality considering both the accuracy and the perspective of error divergence.The experimental results reveal that the proposed method is optimal and efficient for automated classification of rock structure using the geological images of the tunnel face.

Machine learning-based classification of rock discontinuity trace:SMOTE oversampling integrated with GBT ensemble learning

This paper presents a hybrid ensemble classifier combined synthetic minority oversampling technique(SMOTE),random search(RS)hyper-parameters optimization algorithm and gradient boosting tree(GBT)to achieve efficient and accurate rock trace identification.A thirteen-dimensional database consisting of basic,vector,and discontinuity features is established from image samples.All data points are classified as either‘‘trace”or‘‘non-trace”to divide the ultimate results into candidate trace samples.It is found that the SMOTE technology can effectively improve classification performance by recommending an optimized imbalance ratio of 1:5 to 1:4.Then,sixteen classifiers generated from four basic machine learning(ML)models are applied for performance comparison.The results reveal that the proposed RS-SMOTE-GBT classifier outperforms the other fifteen hybrid ML algorithms for both trace and nontrace classifications.Finally,discussions on feature importance,generalization ability and classification error are conducted for the proposed classifier.The experimental results indicate that more critical features affecting the trace classification are primarily from the discontinuity features.Besides,cleaning up the sedimentary pumice and reducing the area of fractured rock contribute to improving the overall classification performance.The proposed method provides a new alternative approach for the identification of 3D rock trace.

Machine learning-based automatic control of tunneling posture of shield machine

For a tunnel driven by a shield machine,the posture of the driving machine is essential to the construction quality and environmental impact.However,the machine posture is controlled by the experienced driver of shield machine by setting hundreds of tunneling parameters empirically.Machine learning(ML)algorithm is an alternative method that can let the computer to learn from the driver’s operation and try to model the relationship between parameters automatically.Thus,in this paper,three ML algorithms,i.e.multi-layer perception(MLP),support vector machine(SVM)and gradient boosting regression(GBR),are improved by genetic algorithm(GA)and principal component analysis(PCA)to predict the tunneling posture of the shield machine.A set of the parameters for shield tunneling is extracted from the construction site of a Shanghai metro.In total,53,785 pairwise data points are collected for about 373 d and the ratio between training set,validation set and test set is 3:1:1.Each pairwise data point includes 83 types of parameters covering the shield posture,construction parameters,and soil stratum properties at the same time.The test results show that the averaged R2 of MLP,SVM and GBR based models are 0.942,0.935 and 0.6,respectively.Then the automatic control for the posture of shield tunnel is illustrated with an application example of the proposed models.The proposed method is proved to be helpful in controlling the construction quality with optimized construction parameters.

Pore structure characterization and seepage analysis of ionic rare earth orebodies based on computed tomography images

Pore network structure of ore body is a diffusion channel of leaching agent solution that exerts a significant influence on seepage.The ore body structure,pore distribution,pore and throat size,and pore network characteristics of topsoil,weathered,and semiweathered layers of ionic rare earth ore in southern Jiangxi Province were explored in this study.The effect of leaching operation on the pore structure was investigated,and main factors affecting the seepage were analyzed.Results showed that the semiweathered layer presents a dense structure and a small number of unconnected pores.Pores of topsoil and weathered layers are mainly long and narrow column openings with some planar fractures.Even pore distribution and large size span were observed.Compared with the weathered layer,the topsoil layer demonstrates larger voids,smaller average pore volume and equivalent radius,and fewer coordination throats;however,the average equivalent radius of the throat in the topsoil layer is larger and largescale channels exist through ore body vertically.Hence,permeability of the topsoil layer is significantly higher than that of the weathered layer.Colloidal clay minerals migrate easily and the occurrence of silting in the small porosity blocks the throat and significantly decreases the permeability of the ore body in the leaching process.The equivalent radius of the throat is the key to the seepage.Reducing the migration of fine particles is an effective measure to protect the throat and shorten the leaching period.

Deep learning-based evaluation of factor of safety with confidence interval for tunnel deformation in spatially variable soil

The random finite difference method(RFDM) is a popular approach to quantitatively evaluate the influence of inherent spatial variability of soil on the deformation of embedded tunnels.However,the high computational cost is an ongoing challenge for its application in complex scenarios.To address this limitation,a deep learning-based method for efficient prediction of tunnel deformation in spatially variable soil is proposed.The proposed method uses one-dimensional convolutional neural network(CNN) to identify the pattern between random field input and factor of safety of tunnel deformation output.The mean squared error and correlation coefficient of the CNN model applied to the newly untrained dataset was less than 0.02 and larger than 0.96,respectively.It means that the trained CNN model can replace RFDM analysis for Monte Carlo simulations with a small but sufficient number of random field samples(about 40 samples for each case in this study).It is well known that the machine learning or deep learning model has a common limitation that the confidence of predicted result is unknown and only a deterministic outcome is given.This calls for an approach to gauge the model’s confidence interval.It is achieved by applying dropout to all layers of the original model to retrain the model and using the dropout technique when performing inference.The excellent agreement between the CNN model prediction and the RFDM calculated results demonstrated that the proposed deep learning-based method has potential for tunnel performance analysis in spatially variable soils.

Face stability analysis of circular tunnels in layered rock masses using the upper bound theorem

An analysis of tunnel face stability generally assumes a single homogeneous rock mass.However,most rock tunnel projects are excavated in stratified rock masses.This paper presents a two-dimensional(2D)analytical model for estimating the face stability of a rock tunnel in the presence of rock mass stratification.The model uses the kinematical limit analysis approach combined with the block calculation technique.A virtual support force is applied to the tunnel face,and then solved using an optimization method based on the upper limit theorem of limit analysis and the nonlinear Hoek-Brown yield criterion.Several design charts are provided to analyze the effects of rock layer thickness on tunnel face stability,tunnel diameter,the arrangement sequence of weak and strong rock layers,and the variation in rock layer parameters at different positions.The results indicate that the thickness of the rock layer,tunnel diameter,and arrangement sequence of weak and strong rock layers significantly affect the tunnel face stability.Variations in the parameters of the lower layer of the tunnel face have a greater effect on tunnel stability than those of the upper layer.

SSD Real-Time Illegal Parking Detection Based on Contextual Information Transmission

With the improvement of the national economic level,the number of vehicles is still increasing year by year.According to the statistics of National Bureau of Statics,the number is approximately up to 327 million in China by the end of 2018,which makes urban traffic pressure continues to rise so that the negative impact of urban traffic order is growing.Illegal parking-the common problem in the field of transportation security is urgent to be solved and traditional methods to address it are mainly based on ground loop and manual supervision,which may miss detection and cost much manpower.Due to the rapidly developing deep learning sweeping the world in recent years,object detection methods relying on background segmentation cannot meet the requirements of complex and various scenes on speed and precision.Thus,an improved Single Shot MultiBox Detector(SSD)based on deep learning is proposed in our study,we introduce attention mechanism by spatial transformer module which gives neural networks the ability to actively spatially transform feature maps and add contextual information transmission in specified layer.Finally,we found out the best connection layer in the detection model by repeated experiments especially for small objects and increased the precision by 1.5%than the baseline SSD without extra training cost.Meanwhile,we designed an illegal parking vehicle detection method by the improved SSD,reaching a high precision up to 97.3%and achieving a speed of 40FPS,superior to most of vehicle detection methods,will make contributions to relieving the negative impact of illegal parking.

A hybrid attention deep learning network for refined segmentation of cracks from shield tunnel lining images

This research developed a hybrid position-channel network (named PCNet) through incorporating newly designed channel and position attention modules into U-Net to alleviate the crack discontinuity problem in channel and spatial dimensions. In PCNet, the U-Net is used as a baseline to extract informative spatial and channel-wise features from shield tunnel lining crack images. A channel and a position attention module are designed and embedded after each convolution layer of U-Net to model the feature interdependencies in channel and spatial dimensions. These attention modules can make the U-Net adaptively integrate local crack features with their global dependencies. Experiments were conducted utilizing the dataset based on the images from Shanghai metro shield tunnels. The results validate the effectiveness of the designed channel and position attention modules, since they can individually increase balanced accuracy (BA) by 11.25% and 12.95%, intersection over union (IoU) by 10.79% and 11.83%, and F1 score by 9.96% and 10.63%, respectively. In comparison with the state-of-the-art models (i.e. LinkNet, PSPNet, U-Net, PANet, and Mask R–CNN) on the testing dataset, the proposed PCNet outperforms others with an improvement of BA, IoU, and F1 score owing to the implementation of the channel and position attention modules. These evaluation metrics indicate that the proposed PCNet presents refined crack segmentation with improved performance and is a practicable approach to segment shield tunnel lining cracks in field practice.

Plastic supported platinum modified nickel electrode and its high electrocatalytic activity for sodium borohydride electrooxidation

A novel plastic/multi-walled carbon nanotube（MWNTs）-nickel（Ni）-platinum（Pt） electrode（PMNP） is prepared by chemical-reducing Pt onto the surface of Ni film covered plastic/MWNTs（PM） substrate. The MWNTs are adhered by a piece of commercial double faced adhesive tape on the surface of plastic paper and the Ni film is prepared by a simple electrodeposition method. The morphology and phase structure of the PMNP electrode are characterized by scanning electron microscopy,transmission electron microscope and X-ray diffractometer. The catalytic activity of the PMNP electrode for Na BH4 electrooxidation is investigated by means of cyclic voltammetry and chronoamperometry. The catalyst combines tightly with the plastic paper and exhibits a good stability. MWNTs serve as both conductive material and hydrogen storage material and the Ni film and Pt are employed as electrochemical catalysts. The PMNP electrode exhibits a high electrocatalytic performance and the oxidation current density reaches to 10.76 A/（mg·cm） in 0.1 mol/dm3 Na BH4at0 V,which is much higher than those in the previous reports. The using of waste plastic reduces the discarding of white pollution and consumption of metal resources.

Clinical Study on the Relationship between the HCG Levels during the Late Pregnancy and the Delivery Mechanism

Objective： The relationship between the HCG levels during the late pregnancy and the delivery mechanism was discussed. Method： If the HCG levels during the late pregnancy were related to the delivery mechanism was studied by using the β -HCG changes of 100 women pregnant for 36 weeks, 37 weeks, 38 weeks, 39 weeks, 40 weeks, and 41 weeks, and also the [3 -HCG changes when their uterine orifice was opened for 3cm near the time of labor as the clinical data. All these cases were found to suffer no clinical complications. Result： The difference in HCG changes during the late pregnancy was of no statistical significance （P〉0.05）. Conclusion： The β-HCG levels change during the late pregnancy is not significantly correlated with the labor onset time, and the labor onset time is unpredictable with the monitoring of the HCG levels change during the late pregnancy.

Book Review

Uncertainty,Modeling,and Decision Making in Geotechnics Edited by Kok-Kwang Phoon,Takayuki Shuku,and Jianye Ching,CRC Press,ISBN:978-1-032-36750-7,https://doi.org/10.1201/9781003333586 The pervasive challenge of inherent uncertainty stands as one of the most widely acknowledged hurdles in geotechnical and rock engineering.Instances of failures in geotechnics are frequently reported worldwide,often stemming from unpredictable ground properties during design,inadequate quality control measures,or erroneous decision-making processes.

Spatially resolved metabolomics visualizes heterogeneous distribution of metabolites in lung tissue and the anti-pulmonary fibrosis effect of Prismatomeris connate extract

Pulmonary fibrosis (PF) is a chronic progressive end-stage lung disease. However, the mechanisms underlying the progression of this disease remain elusive. Presently, clinically employed drugs are scarce for the treatment of PF. Hence, there is an urgent need for developing novel drugs to address such diseases. Our study found for the first time that a natural source of Prismatomeris connata Y. Z. Ruan (Huang Gen, HG) ethyl acetate extract (HG-2) had a significant anti-PF effect by inhibiting the expression of the transforming growth factor beta 1/suppressor of mothers against decapentaplegic (TGF-β1/Smad) pathway. Network pharmacological analysis suggested that HG-2 had effects on tyrosine kinase phosphorylation, cellular response to reactive oxygen species, and extracellular matrix (ECM) disassembly. Moreover, mass spectrometry imaging (MSI) was used to visualize the heterogeneous distribution of endogenous metabolites in lung tissue and reveal the anti-PF metabolic mechanism of HG-2, which was related to arginine biosynthesis and alanine, asparate and glutamate metabolism, the downregulation of arachidonic acid metabolism, and the upregulation of glycerophospholipid metabolism. In conclusion, we elaborated on the relationship between metabolite distribution and the progression of PF, constructed the regulatory metabolic network of HG-2, and discovered the multi-target therapeutic effect of HG-2, which might be conducive to the development of new drugs for PF.

Resilience of city underground infrastructure under multi-hazards impact:From structural level to network level

Underground infrastructure(UI)plays a great important role in the urbanization and modernization of megacities in the world.However,the intensive development of the UI during the past decades has posed great risks to the safety of city infrastructures under the impact of multi-hazards,especially with the condition of global climate change.In this paper,a general conceptualized framework to assess the resilience of UI in cities under multihazards impact is proposed.The urban tunnel system,e.g.,metro tunnel,road tunnel etc.,is selected as the typical underground infrastructure discussed with the emphasis both on the structural level in terms of mechanical behaviors and system level in terms of network efficiency.The hazards discussed in this paper include the natural hazards and human-related ones,with emphasis on earthquake,flood,and aggressive disturbances caused by human activities.After the general framework proposed for resilience of the structural and network behavior of the UI,two application examples are illustrated.The structural resilience of the shield tunnel under earthquake impact is analyzed by using the proposed resilience model,and the network resilience of the road tunnel system under the flood impact due to climate change is analyzed,respectively.The resilience enhancement by using the adaptive design strategy of real-time observational method is mathematically presented in this case.Some other practical engineering recovery measures are briefly discussed at the end of this application example.The findings in the application examples should be helpful to enhance the resilience-based design of the structural and network of tunnels from the component to the system level.

New dammarane-type saponins from the leaves of Panax notoginseng

Two new triterpenoid saponins named notoginsenoside-Ng3(1) and notoginsenoside-Ng4(2) along with three known saponins (3-5), were isolated from a water extract of the leaves of Panax notoginseng. Their structures were elucidated by HRESIMS, NMR, X-ray techniques and acid hydrolysis. Moreover,compound 2 was characterized with the conjugated double bonds side-chain, which was rarely found in this plant. The absolute configuration of notoginsenoside Fa (3) with five sugars was confirmed by the single-crystal X-ray diffraction for the first time. Acetylcholinesterase inhibitory activity experiments were also conducted, all the isolated saponins showed weak inhibitory activities in the final concentration of 0.16 mmol/L.

Flexibility Improvement of CHP Unit for Wind Power Accommodation

Improving the flexibility of combined heat and power(CHP)units is an important way to solve the problem of wind power accommodation in northern China.Firstly,this paper analyzes the principle of an extraction-type CHP unit,calculates its safe operation range,and analyzes its contradiction between heating and peaking.Secondly,the safe operation ranges of the CHP unit with several flexibility modifications are further calculated,which involve two-stage bypass,low-pressure cylinder(LPC)removal,heat storage tank,and electric boiler.Finally,based on the safe operation ranges,their effects on improving the capabilities of deep peak shaving and wind power accommodation are compared,and their adaptabilities to different wind scenarios are analyzed.The results show that:①all flexibility modifications can improve the deep peak shaving capability of the CHP unit,especially for the two-stage bypass and the electric boiler;②LPC removal modification can accommodate wind power to some extent,but most of wind power is still abandoned;③heat storage tank modification is unstable in different wind scenarios,which is determined by the surplus heating capability during the daytime.

Uniformly grown PtCo-modified C03O4 nanosheets as a highly efficient catalyst for sodium borohydride electrooxidation

A facile hydrothermal synthetic method, followed by in situ reduction and galvanic replacement processes, is used to prepare PtCo-modified Co304 nanosheets （PtCo/C0304 NSs） supported on Ni foam. The prepared nanomaterial is used as an electrocatalyst for NaBH4 oxidation in alkaline solution. The morphology and phase composition of PtCo/C0304 NSs are characterized by scanning electron microscopy （SEM）, transmission electron microscopy （TEM）, X-ray diffraction （XRD）, and X-ray photoelectron spectroscopy （XPS）. The catalytic performance of PtCo/Co3O4 NSs is investigated by cyclic voltammetry （CV） and chronoamperometry （CA） in a standard three-electrode system. Current densities of 70 and 850 mA·cm^-2 were obtained at -0.4 V for Co/Co3O4 and PtCo/Co3O4 NSs, respectively, in a solution containing 2 mol·L^-1 NaOH and 0.2 mol·L^-1 NaBH4. The use of a noble metal （Pt） greatly enhances the catalytic activity of the transition metal （Co） and Co3O4. Besides, both Co and Co3O4 exhibit good B-H bond breaking ability （in NaBH4）, which leads to better electrocatalytic activity and stability of PtCo/Co3O4 NSs in NaBH4 electrooxidation compared to pure Pt. The results demonstrate that the as-prepared PtCo/Co3O4 NSs can be a promising electrocatalyst for borohydride oxidation.

Two new phenylpropanoid glycosides from the aerial parts of Lespedeza cuneata

Two new phenylpropanoid glycosides named cuneataside E (1) and cuneataside F (2), were isolated from the aerial parts of Lespedeza cuneata (Dum. Cours.) G. Don, whose structures were E and Z isomer, respectively. Their structures were elucidated on the basis of comprehensive spectroscopic analysis (UV, IR, HR-ESI-MS, 1D and 2D NMR). In in vitro bioassays at 10 mu mol/L, compound 1 showed moderate hepatoprotective activity against N-acetyl-p-aminophenol (APAP)-induced toxicity in HeG2 cells. (C) 2016 Chinese Pharmaceutical Association and Institute of Materia Medica, Chinese Academy of Medical Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND