Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

Predicting 3D Radiotherapy Dose-Volume Based on Deep Learning

Cancer is one of the most dangerous diseaseswith highmortality.One of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill from doctors and technicians.In our study,we focused on the 3D dose prediction problem in radiotherapy by applying the deeplearning approach to computed tomography(CT)images of cancer patients.Medical image data has more complex characteristics than normal image data,and this research aims to explore the effectiveness of data preprocessing and augmentation in the context of the 3D dose prediction problem.We proposed four strategies to clarify our hypothesis in different aspects of applying data preprocessing and augmentation.In strategies,we trained our custom convolutional neural network model which has a structure inspired by the U-net,and residual blocks were also applied to the architecture.The output of the network is added with a rectified linear unit(Re-Lu)function for each pixel to ensure there are no negative values,which are absurd with radiation doses.Our experiments were conducted on the dataset of the Open Knowledge-Based Planning Challenge which was collected from head and neck cancer patients treatedwith radiation therapy.The results of four strategies showthat our hypothesis is rational by evaluating metrics in terms of the Dose-score and the Dose-volume histogram score(DVH-score).In the best training cases,the Dose-score is 3.08 and the DVH-score is 1.78.In addition,we also conducted a comparison with the results of another study in the same context of using the loss function.

妊娠期高血压疾病继发下肢深静脉血栓患者血清CC趋化因子配体3和D-二聚体水平变化及检测意义

目的:探讨妊娠期高血压疾病(HDP)继发下肢深静脉血栓(DVT)患者血清CC趋化因子配体3(CCL3)和D-二聚体(D-D)水平变化及检测意义。方法:选取HDP患者195例,根据产后6周内是否发生下肢DVT将患者分为DVT组(55例)和非DVT组(140例)。收集两组临床资料,采用酶联免疫吸附试验测定CCL3、D-D水平。采用Logistic回归分析HDP患者发生下肢DVT的影响因素。绘制受试者工作特征(ROC)曲线分析血清CCL3、D-D对HDP患者发生下肢DVT的预测价值。相加模型分析交互作用,计算交互作用相关指标。结果:与非DVT组比较,DVT组舒张压(DBP)、收缩压(SBP)、子痫前期及子痫所占比例、血小板计数(PLT)和静脉血栓栓塞症(VTE)评分较高,活化部分凝血活酶时间(APTT)、凝血酶原时间(PT)较低(均P<0.05)。与非DVT组比较,DVT组血清CCL3、D-D水平升高(均P<0.05)。HDP分期、VTE评分、PLT及血清CCL3、D-D是HDP患者发生下肢DVT的影响因素(均P<0.05)。血清CCL3、D-D联合预测HDP患者发生下肢DVT的曲线下面积(AUC)高于CCL3和D-D分别单独预测的AUC(均P<0.05)。调整混杂因素后,CCL3、D-D同时暴露患者发生下肢DVT风险是CCL3、D-D同时未暴露患者的6.905倍,存在正交互作用,且CCL3、D-D同时暴露患者中有44.28%是因两者交互作用引起,在仅因CCL3、D-D暴露所致下肢DVT发生风险中,有55.19%是因两者交互作用引起。结论:HDP继发下肢DVT患者血清CCL3、D-D水平升高,两者存在正交互作用,联合检测对发生下肢DVT的预测价值较高。

3D Positional and Quantitative Prediction of the Xiaoqinling Gold Ore Belt in Tongguan, Shaanxi, China

Quantitative prediction of deep orebody based on 3D visualization technology is of great significance in mineral exploration. Based on the 2D traditional quantitative predicting method, the geoanomaly theory and the mineral exploration model idea, we constructed 3D models of the topography, strata, structure, magmatite and prospecting engineering of the study area using the commercial 3D modeling software Micromine, delineated eight prospective areas and estimated the gold resources amount with methods of Abundance Estimation and Volume Estimation. Then, we compared and counted the known ore blocks and the predicted blocks, which quantitatively explains this prediction＇s validity. The results show that Xiaoqinling gold belt in Tongguan has convincing potential for gold development and utilization and the prediction method based on 3D visualization technology proves to be effective.

Prediction of Weld Joint Shape and Dimensions in Laser Welding Using a 3D Modeling and Experimental Validation

This paper presents an experimentally validated weld joint shape and dimensions predictive 3D modeling for low carbon galvanized steel in butt-joint configurations. The proposed modelling approach is based on metallurgical transformations using temperature dependent material properties and the enthalpy method. Conduction and keyhole modes welding are investigated using surface and volumetric heat sources, respectively. Transition between the heat sources is carried out according to the power density and interaction time. Simulations are carried out using 3D finite element model on commercial software. The simulation results of the weld shape and dimensions are validated using a structured experimental investigation based on Taguchi method. Experimental validation conducted on a 3 kW Nd: YAG laser source reveals that the modelling approach can provide not only a consistent and accurate prediction of the weld characteristics under variable welding parameters and conditions but also a comprehensive and quantitative analysis of process parameters effects. The results show great concordance between predicted and measured values for the weld joint shape and dimensions.

A simplified hardware-friendly contour prediction algorithm in 3D-HEVC and parallelization design

After the extension of depth modeling mode 4(DMM-4)in 3D high efficiency video coding(3D-HEVC),the computational complexity increases sharply,which causes the real-time performance of video coding to be impacted.To reduce the computational complexity of DMM-4,a simplified hardware-friendly contour prediction algorithm is proposed in this paper.Based on the similarity between texture and depth map,the proposed algorithm directly codes depth blocks to calculate edge regions to reduce the number of reference blocks.Through the verification of the test sequence on HTM16.1,the proposed algorithm coding time is reduced by 9.42%compared with the original algorithm.To avoid the time consuming of serial coding on HTM,a parallelization design of the proposed algorithm based on reconfigurable array processor(DPR-CODEC)is proposed.The parallelization design reduces the storage access time,configuration time and saves the storage cost.Verified with the Xilinx Virtex 6 FPGA,experimental results show that parallelization design is capable of processing HD 1080p at a speed above 30 frames per second.Compared with the related work,the scheme reduces the LUTs by 42.3%,the REG by 85.5%and the hardware resources by 66.7%.The data loading speedup ratio of parallel scheme can reach 3.4539.On average,the different sized templates serial/parallel speedup ratio of encoding time can reach 2.446.

Assimilation of FY-3D MWTS-Ⅱ Radiance with 3D Precipitation Detection and the Impacts on Typhoon Forecasts

Precipitation detection is an essential step in radiance assimilation because the uncertainties in precipitation would affect the radiative transfer calculation and observation errors.The traditional precipitation detection method for microwave only detects clouds and precipitation horizontally,without considering the three-dimensional distribution of clouds.Extending precipitation detection from 2D to 3D is expected to bring more useful information to the data assimilation without using the all-sky approach.In this study,the 3D precipitation detection method is adopted to assimilate Microwave Temperature Sounder-2(MWTS-Ⅱ)onboard the Fengyun-3D,which can dynamically detect the channels above precipitating clouds by considering the near-real-time cloud parameters.Cycling data assimilation and forecasting experiments for Typhoons Lekima(2019)and Mitag(2019)are carried out.Compared with the control experiment,the quantity of assimilated data with the 3D precipitation detection increases by approximately 23%.The quality of the additional MWTS-Ⅱradiance data is close to the clear-sky data.The case studies show that the average root-mean-square errors(RMSE)of prognostic variables are reduced by 1.7%in the upper troposphere,leading to an average reduction of4.53%in typhoon track forecasts.The detailed diagnoses of Typhoon Lekima(2019)further show that the additional MWTS-Ⅱradiances brought by the 3D precipitation detection facilitate portraying a more reasonable circulation situation,thus providing more precise structures.This paper preliminarily proves that 3D precipitation detection has potential added value for increasing satellite data utilization and improving typhoon forecasts.

Germanium-Carbdiyne: A 3D Well-Defined sp-Hybridized Carbon-Based Material with Superhigh Li Storage Property

Carbyne delivers various excellent properties for the existence of the larger number of sp-hybridized carbon atoms.Here,a 3D well-defined porous carbon material germanium-carbdiyne(Ge-CDY)which is comprised of only sp-hybridized carbon atoms bridging by Ge atoms has been developed and investigated.The unique diamond-like structure constructed by linear butadiyne bonds and sp 3-hybridized Ge atoms ensures the stability of Ge-CDY.The large percentage of conjugated alkyne bonds composed of sp-C guarantees the good conductivity and the low band gap,which were further confirmed experimentally and theoretically,endowing Ge-CDY with the potential in electrochemical applications.The well-defined 3D carbon skeleton of Ge-CDY provides abundant uniform nanopores,which is suitable for metal ions storage and diffusion.Further half-cell evaluation also demonstrated Ge-CDY exhibited an excellent performance in lithium storage.All those indicating sp-hybridized carbon-based materials can exhibit great potential to possess excellent properties and be applied in the field of energy,electronic,and so on.

A Data-Intensive FLAC^3D Computation Model:Application of Geospatial Big Data to Predict Mining Induced Subsidence

Although big data are widely used in various fields,its application is still rare in the study of mining subsidence prediction(MSP)caused by underground mining.Traditional research in MSP has the problem of oversimplifying geological mining conditions,ignoring the fluctuation of rock layers with space.In the context of geospatial big data,a data-intensive FLAC3D(Fast Lagrangian Analysis of a Continua in 3 Dimensions)model is proposed in this paper based on borehole logs.In the modeling process,we developed a method to handle geospatial big data and were able to make full use of borehole logs.The effectiveness of the proposed method was verified by comparing the results of the traditional method,proposed method,and field observation.The findings show that the proposed method has obvious advantages over the traditional prediction results.The relative error of the maximum surface subsidence predicted by the proposed method decreased by 93.7%and the standard deviation of the prediction results(which was 70 points)decreased by 39.4%,on average.The data-intensive modeling method is of great significance for improving the accuracy of mining subsidence predictions.

The use of machine learning to predict the effects of cryoprotective agents on the GelMA-based bioinks used in extrusion cryobioprinting

Cryobioprinting has tremendous potential to solve problems to do with lack of shelf availability in traditional bioprinting by combining extrusion bioprinting and cryopreservation.In order to ensure the viability of cells in the frozen state and avoid the possible toxicity of dimethyl sulfoxide(DMSO),DMSO-free bioink design is critical for achieving successful cryobioprinting.A nontoxic gelatin methacryloyl-based bioink used in cryobioprinting is composed of cryoprotective agents(CPAs)and a buffer solution.The selection and ratio of CPAs in the bioink directly affect the survival of cells in the frozen state.However,the development of universal and efficient cryoprotective bioinks requires extensive experimentation.We first compared two commonly used CPA formulations via experiments in this study.Results show that the effect of using ethylene glycol as the permeable CPA was 6.07%better than that of glycerol.Two datasets were obtained and four machinelearning models were established to predict experimental outcomes.The predictive powers of multiple linear regression(MLR),decision tree(DT),random forest(RF),and artificial neural network(ANN)approaches were compared,suggesting an order of ANN>RF>DT>MLR.The final selected ANN model was then applied to another dataset.Results reveal that this machine-learning method can accurately predict the effects of cryoprotective bioinks composed of different CPAs.Outcomes also suggest that the formulations presented here have universality.Our findings are likely to greatly accelerate research and development on the use of bioinks for cryobioprinting.

Dynamic Investigations on the Wear Behavior of a 3D Revolute Joint Considering Time-Varying Contact Stiffness: Simulation and Experiment

The existence of the relative radial and axial movements of a revolute joint’s journal and bearing is widely known.The three-dimensional(3D)revolute joint model considers relative radial and axial clearances;therefore,the freedoms of motion and contact scenarios are more realistic than those of the two-dimensional model.This paper proposes a wear model that integrates the modeling of a 3D revolute clearance joint and the contact force and wear depth calculations.Time-varying contact stiffness is first considered in the contact force model.Also,a cycle-update wear depth calculation strategy is presented.A digital image correlation(DIC)non-contact measurement and a cylindricity test are conducted.The measurement results are compared with the numerical simulation,and the proposed model’s correctness and the wear depth calculation strategy are verified.The results show that the wear amount distribution on the bearing’s inner surface is uneven in the axial and radial directions due to the journal’s stochastic oscillations.The maximum wear depth locates where at the bearing’s edges the motion direction of the follower shifts.These find-ings help to seek the revolute joints’wear-prone parts and enhance their durability and reliability through improved design.

Earlier Detection of Alzheimer’s Disease Using 3D-Convolutional Neural Networks

The prediction of mild cognitive impairment or Alzheimer’s disease(AD)has gained the attention of huge researchers as the disease occurrence is increasing,and there is a need for earlier prediction.Regrettably,due to the highdimensionality nature of neural data and the least available samples,modelling an efficient computer diagnostic system is highly solicited.Learning approaches,specifically deep learning approaches,are essential in disease prediction.Deep Learning(DL)approaches are successfully demonstrated for their higher-level performance in various fields like medical imaging.A novel 3D-Convolutional Neural Network(3D-CNN)architecture is proposed to predict AD with Magnetic resonance imaging(MRI)data.The proposed model predicts the AD occurrence while the existing approaches lack prediction accuracy and perform binary classification.The proposed prediction model is validated using the Alzheimer’s disease Neuro-Imaging Initiative(ADNI)data.The outcomes demonstrate that the anticipated model attains superior prediction accuracy and works better than the brain-image dataset’s general approaches.The predicted model reduces the human effort during the prediction process and makes it easier to diagnose it intelligently as the feature learning is adaptive.Keras’experimentation is carried out,and the model’s superiority is compared with various advanced approaches for multi-level classification.The proposed model gives better prediction accuracy,precision,recall,and F-measure than other systems like Long Short Term Memory-Recurrent Neural Networks(LSTM-RNN),Stacked Autoencoder with Deep Neural Networks(SAE-DNN),Deep Convolutional Neural Networks(D-CNN),Two Dimensional Convolutional Neural Networks(2D-CNN),Inception-V4,ResNet,and Two Dimensional Convolutional Neural Networks(3D-CNN).

RNA structure prediction:Progress and perspective

Many recent exciting discoveries have revealed the versatility of RNAs and their importance in a variety of cellular functions which are strongly coupled to RNA structures. To understand the functions of RNAs, some structure prediction models have been developed in recent years. In this review, the progress in computational models for RNA structure prediction is introduced and the distinguishing features of many outstanding algorithms are discussed, emphasizing three- dimensional （3D） structure prediction. A promising coarse-grained model for predicting RNA 3D structure, stability and salt effect is also introduced briefly. Finally, we discuss the major challenges in the RNA 3D structure modeling.

Three-dimensional Modeling of Ore-forming Elements and Mineralization Prognosis for the Yechangping Mo Deposit,Henan Province,China

Three-dimensional geochemical modeling of ore-forming elements is crucial for predicting deep mineralization.This approach provides key information for the quantitative prediction of deep mineral localization,three-dimensional fine interpolation,analysis of spatial distribution patterns,and extraction of quantitative mineral-seeking markers.The Yechangping molybdenum(Mo)deposit is a significant and extensive porphyry-skarn deposit in the East Qinling-Dabie Mo polymetallic metallogenic belt at the southern margin of the North China Block.Abundant borehole data on oreforming elements underpin deep geochemical predictions.The methodology includes the following steps:(1)Threedimensional geological modeling of the deposit was established.(2)Correlation,cluster,and factor analyses post delineation of mineralization stages and determination of mineral generation sequence to identify(Cu,Pb,Zn,Ag)and(Mo,W,mfe)assemblages.(3)A three-dimensional geochemical block model was constructed for Mo,W,mfe,Cu,Zn,Pb,and Ag using the ordinary kriging method,and the variational function was developed.(4)Spatial distribution and enrichment characteristics analysis of ore-forming elements are performed to extract geological information,employing the variogram and w(Cu+Pb+Zn+Ag)/w(Mo+W)as predictive indicators.(5)Identifying the western,northwestern,and southwestern areas of the mine with limited mineralization potential,contrasted by the northeastern and southeastern areas favorable for mineral exploration.

Spatio-Temporal Variability of Shallow Groundwater Quality in a Hilly Red-Soil Agricultural Catchment in Subtropical Central China

Groundwater quality varies not only in space but also in time. In order to analyze the spatiotemporal variety of ground water quality, the concentration of ammonium nitrogen (NH4N), nitrate nitrogen (NO3N), total nitrogen (TN) and total phosphorus (TP) in very shallow groundwater were investigated in a red-soil catchment in subtropical central China, based on a three-dimensional kriging method. The spatio-temporal analysis demonstrated that NH4N, NO3N and TP presented strong spatio-temporal autocorrelation (with a nugget-to-sill ratio of <25%) and that TN presented a moderate spatio-temporal autocorrelation (with a nugget-to-sill ratio between 25% and 75%). According to the Chinese Groundwater Quality Standards, the ratio of areas contaminated by NH4N, NO3N, TN and TP to the whole catchment was 20.05%, 1.46%, 5.07%, 5.98%, respectively. The 3D delineation of continuously dynamic variation of contaminated area indicated that the catchment’s very shallow groundwater had a moderate contamination by NH4N, slight by TN and TP, and almost non by NO3N. Although the contaminated area was very small, only occurring in small dispersed patches, a close attention should be paid to the shallow groundwater quality because local farmers obtain their domestic drinking water directly from this shallow groundwater without any treatment prior to consuming and the potential health hazard is considerable. The findings from this study highlight the importance of surveillance of the contaminated area over time for decision making to protect public health and maintain sustainable development of the catchment.

3D numerical simulation of heterogeneous in situ stress field in low-permeability reservoirs

Analysis of the in situ stress orientation and magnitude in the No.4 Structure of Nanpu Sag was performed on the basis of data obtained from borehole breakout and acoustic emission measurements.On the basis of mechanical experiments,logging interpretation,and seismic data,a 3 D geological model and heterogeneous rock mechanics field of the reservoir were constructed.Finite element simulation techniques were then used for the detailed prediction of the 3 D stress field.The results indicated that the maximum horizontal stress orientation in the study area was generally NEE-SWW trending,with significant changes in the in situ stress orientation within and between fault blocks.Along surfaces and profiles,stress magnitudes were discrete and the in situ stress belonged to theⅠa-type.Observed inter-strata differences were characterized as five different types of in situ stress profile.Faults were the most important factor causing large distributional differences in the stress field of reservoirs within the complex fault blocks.The next important influence on the stress field was the reservoir’s rock mechanics parameters,which impacted on the magnitudes of in situ stress magnitudes.This technique provided a theoretical basis for more efficient exploration and development of low-permeability reservoirs within complex fault blocks.

A robotic tele-drill system over network using predictive display

Based on a robotic telesurgery system whose function is to liberate doctor from X-ray radiation, a robotic tele-drill system is constructed. The system is in client/server structure. Client part includes main control interface, video-audio interface and predictive display interface. Server part includes robot control server and video, audio server. For applying to teleoperation, a virtual reality environment of the system developed by using Java, Java 3D, Pro/E, etc. is established. The geometry and kinematics model of serial robot MOTOMAN sv3x, parallel robot, C-type arm and X-ray machine, surgery bed and its work environment are fulfilled in it. Simulation engine and its simulation syntax are finished, which made the environment controllable. This environment is used as predictive display interface in the telerobotics in order to tackling the problem in visualization feedback as ambiguous or time delay. Experiments that verified feasibility of the system have been done.

AVS 3D Video Coding Technology and System

Following the success of the audio video standard （AVS） for 2D video coding, in 2008, the China AVS workgroup started developing 3D video （3DV） coding techniques. In this paper, we discuss the background, technical features, and applications of AVS 3DV coding technology. We introduce two core techniques used in AVS 3DV coding： inter-view prediction and enhanced stereo packing coding. We elaborate on these techniques, which are used in the AVS real-time 3DV encoder. An application of the AVS 3DV coding system is presented to show the great practical value of this system. Simulation results show that the advanced techniques used in AVS 3DV coding provide remarkable coding gain compared with techniques used in a simulcast scheme.

ANN Based Predictive Modelling of Weld Shape and Dimensions in Laser Welding of Galvanized Steel in Butt Joint Configurations

The quality assessment and prediction becomes one of the most critical requirements for improving reliability, efficiency and safety of laser welding. Accurate and efficient model to perform non-destructive quality estimation is an essential part of this assessment. This paper presents a structured and comprehensive approach developed to design an effective artificial neural network based model for weld bead geometry prediction and control in laser welding of galvanized steel in butt joint configurations. The proposed approach examines laser welding parameters and conditions known to have an influence on geometric characteristics of the welds and builds a weld quality prediction model step by step. The modelling procedure begins by examining, through structured experimental investigations and exhaustive 3D modelling and simulation efforts, the direct and the interaction effects of laser welding parameters such as laser power, welding speed, fibre diameter and gap, on the weld bead geometry (i.e. depth of penetration and bead width). Using these results and various statistical tools, various neural network based prediction models are developed and evaluated. The results demonstrate that the proposed approach can effectively lead to a consistent model able to accurately and reliably provide an appropriate prediction of weld bead geometry under variable welding conditions.

A Three-Dimensional Numerical Model for Predicting the Weld Bead Geometry Characteristics in Laser Overlap Welding of Low Carbon Galvanized Steel

Laser welding (LW) becomes one of the most economical high quality joining processes. LW offers the advantage of very controlled heat input resulting in low distortion and the ability to weld heat sensitive components. To exploit efficiently the benefits presented by LW, it is necessary to develop an integrated approach to identify and control the welding process variables in order to produce the desired weld characteristics without being forced to use the traditional and fastidious trial and error procedures. The paper presents a study of weld bead geometry characteristics prediction for laser overlap welding of low carbon galvanized steel using 3D numerical modelling and experimental validation. The temperature dependent material properties, metallurgical transformations and enthalpy method constitute the foundation of the proposed modelling approach. An adaptive 3D heat source is adopted to simulate both keyhole and conduction mode of the LW process. The simulations are performed using 3D finite element model on commercial software. The model is used to estimate the weld bead geometry characteristics for various LW parameters, such as laser power, welding speed and laser beam diameter. The calibration and validation of the 3D numerical model are based on experimental data achieved using a 3 kW Nd:Yag laser system, a structured experimental design and confirmed statistical analysis tools. The results reveal that the modelling approach can provide not only a consistent and accurate prediction of the weld characteristics under variable welding parameters and conditions but also a comprehensive and quantitative analysis of process parameters effects on the weld quality. The results show great concordance between predicted and measured values for weld bead geometry characteristics, such as depth of penetration, bead width at the top surface and bead width at the interface between sheets, with an average accuracy greater than 95%.