Estimation of the anisotropy of hydraulic conductivity through 3D fracture networks using the directional geological entropy

With an extension of the geological entropy concept in porous media,the approach called directional entrogram is applied to link hydraulic behavior to the anisotropy of the 3D fracture networks.A metric called directional entropic scale is used to measure the anisotropy of spatial order in different directions.Compared with the traditional connectivity indexes based on the statistics of fracture geometry,the directional entropic scale is capable to quantify the anisotropy of connectivity and hydraulic conductivity in heterogeneous 3D fracture networks.According to the numerical analysis of directional entrogram and fluid flow in a number of the 3D fracture networks,the hydraulic conductivities and entropic scales in different directions both increase with spatial order(i.e.,trace length decreasing and spacing increasing)and are independent of the dip angle.As a result,the nonlinear correlation between the hydraulic conductivities and entropic scales from different directions can be unified as quadratic polynomial function,which can shed light on the anisotropic effect of spatial order and global entropy on the heterogeneous hydraulic behaviors.

Design,progress and challenges of 3D carbon-based thermally conductive networks

The advent of the 5G era has stimulated the rapid development of high power electronics with dense integration.Three-dimensional(3D)thermally conductive networks,possessing high thermal and electrical conductivities and many different structures,are regarded as key materials to improve the performance of electronic devices.We provide a critical overview of carbonbased 3D thermally conductive networks,emphasizing their preparation-structure-property relationships and their applications in different scenarios.A detailed discussion of the microscopic principles of thermal conductivity is provided,which is crucial for increasing it.This is followed by an in-depth account of the construction of 3D networks using different carbon materials,such as graphene,carbon foam,and carbon nanotubes.Techniques for the assembly of two-dimensional graphene into 3D networks and their effects on thermal conductivity are emphasized.Finally,the existing challenges and future prospects for 3D carbon-based thermally conductive networks are discussed.

Quantification of 3D macropore networks in forest soils in Touzhai valley(Yunnan,China)using X-ray computed tomography and image analysis

The three dimensional （3D） geometry of soil macropores largely controls preferential flow, which is a significant infiltrating mechanism for rainfall in forest soils and affects slope stability. However, detailed studies on the 3D geometry of macropore networks in forest soils are rare. The intense rainfall-triggered potentially unstable slopes were threatening the villages at the downstream of Touzhai valley （Yunnan, China）. We visualized and quantified the 3D macropore networks in undisturbed soil columns （Histosols） taken from a forest hillslope in Touzhai valley, and compared them with those in agricultural soils （corn and soybean in USA; barley, fodder beet and red fescue in Denmark） and grassland soils in USA. We took two large undisturbed soil columns （250 mm^25o mmxsoo mm）, and scanned the soil columns at in-situ soil water content conditions using X-ray computed tomography at a voxel resolution of 0.945 × 0.945 × 1.500o mm^3. After reconstruction and visualization, we quantified the characteristics of macropore networks. In the studied forest soils, the main types of maeropores were root channels, inter-aggregate voids, maeropores without knowing origin, root-soil interfaee and stone-soil interface. While maeropore networks tend to be more eomplex, larger, deeper and longer. The forest soils have high maeroporosity, total maeropore wall area density, node density, and large maeropore volume, hydraulie radius, mean maeropore length, angle, and low tortuosity. The findings suggest that maeropore networks in the forest soils have high inter- connectivity, vertical continuity, linearity and less vertically oriented.

Biomass-Derived Nitrogen and Sulfur Co-Doped 3D Carbon Networks with Interconnected Meso-Microporous Structure for High-Performance Supercapacitors

Three-dimensional(3D)carbon networks have been explored as promising capacitive materials thanks to their unique structural features such as large ion-accessible surface area and interconnected porous networks,thus enhancing both ions and electrons transport.Here,sustainable bacterial cellulose(BC)is used both precursor and template for facile synthesis of free-standing N,S-codoped 3Dcarbon networks(a-NSC)by the pyrolysis and activation of polyrhodanine coated BC.The synthesized a-NSC shows highly conductive interconnected porous networks(24S·cm^(-1)),large surface area(1 420m^2·g^(-1))with hierarchical meso-microporosity,and high-level heteroatoms codoping(N:3.1%in atom,S:3.2%in atom).Benefitting from these,a-NSC as binder-free electrode exhibits an ultrahigh specific capacitance of 340F·g^(-1)(24μF·cm^(-2))at the current density of 0.5A·g^(-1)in 6MKOH electrolyte,high-rate capability(71%at 20A·g^(-1))and excellent cycle stability.Furthermore,the assembled symmetrical supercapacitor displays a much short time constant of 0.35sin 1MTEABF4/AN electrolyte,obtaining a maximum energy density of 32.1W·h·kg^(-1 )at power density of 637W·kg^(-1).The in situ multi-heteroatoms doping enables biocellulose-derived carbon networks to exploit its full potentials in energy storage applications,which can be extended to other dimensional carbon nanostructures.

Integrating artificial neural networks and geostatistics for optimum 3D geological block modeling in mineral reserve estimation:A case study

In this research, a method called ANNMG is presented to integrate Artificial Neural Networks and Geostatistics for optimum mineral reserve evaluation. The word ANNMG simply means Artificial Neural Network Model integrated with Geostatiscs, In this procedure, the Artificial Neural Network was trained, tested and validated using assay values obtained from exploratory drillholes. Next, the validated model was used to generalize mineral grades at known and unknown sampled locations inside the drilling region respectively. Finally, the reproduced and generalized assay values were combined and fed to geostatistics in order to develop a geological 3D block model. The regression analysis revealed that the predicted sample grades were in close proximity to the actual sample grades, The generalized grades from the ANNMG show that this process could be used to complement exploration activities thereby reducing drilling requirement. It could also be an effective mineral reserve evaluation method that could oroduce optimum block model for mine design.

Novel Poly m eric Transition Metal Co m plexes Having1D Chain and 3D Networks Structures

The syntheses and structures of eleven novel polymeric transition metal complexes having one dimensional chain structures or three dimensional networks are summarized. They are prepared from the controlled assembling reactions in organic solvents and characterized by X ray diffraction analyses. The spectroscopic or magnetic properties of some complexes are studied.

RSSI-Based 3D Wireless Sensor Node Localization Using Hybrid T Cell Immune and Lotus Optimization

Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding regions.The sensor nodes are responsible for accumulating and exchanging information.Generally,node local-ization is the process of identifying the target node’s location.In this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization models.Initially,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D WSN.The position of the anchor nodes is fixed for detecting the location of the target.Further,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node localization.In the regular and irregular surfaces,this hybrid algorithm effectively performs the localization process.The suggested hybrid algorithm converges very fast in the three-dimensional(3D)environment.The accuracy of the proposed node localization process is 94.25%.

A Novel Hierarchical Porous 3D Structured Vanadium Nitride/Carbon Membranes for High-performance Supercapacitor Negative Electrodes

Transition-metal nitrides exhibit wide potential windows and good electrochemical performance, but usually experience imbalanced practical applications in the energy storage field due to aggregation, poor circulation stability, and complicated syntheses. In this study, a novel and simple multiphase polymeric strategy was developed to fabricate hybrid vanadium nitride/carbon(VN/C) membranes for supercapacitor negative electrodes, in which VN nanoparticles were uniformly distributed in the hierarchical porous carbon 3D networks. The supercapacitor negative electrode based on VN/C membranes exhibited a high specific capacitance of 392.0 F g^(-1) at 0.5 A g^(-1) and an excellent rate capability with capacitance retention of 50.5% at 30 A g^(-1). For the asymmetric device fabricated using Ni(OH)_2//VN/C membranes, a high energy density of 43.0 Wh kg^(-1) at a power density of800 W kg^(-1) was observed. Moreover, the device also showed good cycling stability of 82.9% at a current density of 1.0 A g^(-1) after 8000 cycles. This work may throw a light on simply the fabrication of other high-performance transition-metal nitridebased supercapacitor or other energy storage devices.

Ex vivo 3D osteocyte network construction with primary murine bone cells

Osteocytes reside as three-dimensionally（3D） networked cells in the lacunocanalicular structure of bones and regulate bone and mineral homeostasis. Despite of their important regulatory roles, in vitro studies of osteocytes have been challenging because：（1） current cell lines do not sufficiently represent the phenotypic features of mature osteocytes and（2） primary cells rapidly differentiate to osteoblasts upon isolation. In this study, we used a 3D perfusion culture approach to：（1） construct the 3D cellular network of primary murine osteocytes by biomimetic assembly with microbeads and（2） reproduce ex vivo the phenotype of primary murine osteocytes, for the first time to our best knowledge. In order to enable 3D construction with a sufficient number of viable cells, we used a proliferated osteoblastic population of healthy cells outgrown from digested bone chips. The diameter of microbeads was controlled to：（1） distribute and entrap cells within the interstitial spaces between the microbeads and（2） maintain average cell-to-cell distance to be about 19 mm. The entrapped cells formed a 3D cellular network by extending and connecting their processes through openings between the microbeads. Also, with increasing culture time, the entrapped cells exhibited the characteristic gene expressions（SOST and FGF23） and nonproliferative behavior of mature osteocytes. In contrast, 2D-cultured cells continued their osteoblastic differentiation and proliferation. This 3D biomimetic approach is expected to provide a new means of：（1） studying flow-induced shear stress on the mechanotransduction function of primary osteocytes,（2） studying physiological functions of 3D-networked osteocytes with in vitro convenience,and（3） developing clinically relevant human bone disease models.

Behavior recognition algorithm based on the improved R3D and LSTM network fusion

Because behavior recognition is based on video frame sequences,this paper proposes a behavior recognition algorithm that combines 3D residual convolutional neural network(R3D)and long short-term memory(LSTM).First,the residual module is extended to three dimensions,which can extract features in the time and space domain at the same time.Second,by changing the size of the pooling layer window the integrity of the time domain features is preserved,at the same time,in order to overcome the difficulty of network training and over-fitting problems,the batch normalization(BN)layer and the dropout layer are added.After that,because the global average pooling layer(GAP)is affected by the size of the feature map,the network cannot be further deepened,so the convolution layer and maxpool layer are added to the R3D network.Finally,because LSTM has the ability to memorize information and can extract more abstract timing features,the LSTM network is introduced into the R3D network.Experimental results show that the R3D+LSTM network achieves 91%recognition rate on the UCF-101 dataset.

A Novel 3D Supramolecular Network Constructed from [Cu(4,4'-bipyridine)(O_2CMe)_2]_2 Molecular Ladders by Hydrogen Bonding

The title complex, {[Cu2（4,4＇-bipyridine）2（μ-O2CMe）2（O2CMe）2],H2O}n 1, was synthesized and structurally characterized by X-ray crystallography. It crystallizes in monoclinic, space group C2/c with a = 13.4474（5）, b = 11.7566（2）, c = 19.5380（6）A, β = 92.930（2）°, V = 3084.84（16） A^3, Z = 4, Cu2C28N409H30, Mr = 693.64, Dc = 1.494 g/cm^3, F（000） = 1424 and μ（MoKα） = 1.436 mm^-1. With the use of 2062 observed reflections （I 〉 2σ（I））, the structure was refined to R = 0.0769 and wR = 0.2154. In complex 1, the dimeric copper acetate units are linked through 4,4’-bipyridine to yield ID molecular ladders. These ladders are connected via O-H…O hydrogen bonds to generate 2D layers, which are further linked through C-H…O hydrogen bonds to give a 3D supramolecular network.

A New 3D Supramolecular Network Formed by [Zn_3(2,6-Pyridinedicarboxylate)_3Cl_4]^(4-)and 4-Carboxypyridinium

A new compound [Zn3（C7NO4H3）3Cl4]·[C6NO2H6]4·4H2O （I） has been synthesized and structurally characterized by X-ray crystallography.It crystallizes in monoclinic,space group C2/c with a=16.9018（14）,b=12.6902（10）,c=25.1170（2）,β=90.54°,V=5387.0（8）3,Z=4,Zn3C45H41Cl4N7O24,Mr=1401.76,Dc=1.728 g/cm3,F（000）=2840,μ（MoKa）=1.615 mm-1,the R= 0.0758 and wR=0.2060 for 3468 observed reflections （I 〉 2σ（I））.Analysis of single-crystal X-ray diffraction data shows that compound I displays an interesting example of 3D supramolecular networks with perfect neutral and ionic hydrogen bonding array.

The Deformation Analysis of the 3D Alignment Control Network Based on the Multiple Congruence Models

In the construction and maintenance of particle accelerators,all the accelerator elements should be installed in the same coordinate system,only in this way could the devices in the actual world be consistent with the design drawings.However,with the occurrence of the movements of the reinforced concrete cover plates at short notice or building deformations in the long term,the control points upon the engineering structure will be displaced,and the fitness between the subnetwork and the global control network may be irresponsible.Therefore,it is necessary to evaluate the deformations of the 3D alignment control network.Different from the extant investigations,in this paper,to characterize the deformations of the control network,all of the congruent models between the points measured in different epochs have been identified,and the congruence model with the most control points is considered as the primary or fundamental model,the remaining models are recognized as the additional ones.Furthermore,the discrepancies between the primary S-transformation parameters and the additional S-transformation parameters can reflect the relative movements of the additional congruence models.Both the iterative GCT method and the iterative combinatorial theory are proposed to detect multiple congruence models in the control network.Considering the actual work of the alignment,it is essential to identify the competitive models in the monitoring network,which can provide us a hint that,even the fitness between the subnetwork and the global control network is good,there are still deformations which may be ignored.The numerical experiments show that the suggested approaches can describe the deformation of the 3D alignment control network roundly.

Ten-Minute Synthesis of a New Redox-Active Aqueous Binder for Flame-Retardant Li-S Batteries

As a critical role in battery systems,polymer binders have been shown to efficiently suppress the lithium polysulfide shuttling and accommodate volume changes in recent years.However,preparation processes and safety,as the key criterions for Li-S batteries'practical applications,still attract less attention.Herein,an aqueous multifunction binder(named PEI-TIC)is prepared via an easy and fast epoxy-amine ring-opening reaction(10 min),which can not only give the sulfur cathode a stable mechanical property,a strong chemical adsorption and catalytic conversion ability,but also a fire safety improvement.The Li-S batteries based on the PEI-TIC binder display a high discharge capacity(1297.8 mAh g^(-1)),superior rate performance(823.0 mAh g^(-1)at 2 C),and an ultralow capacity decay rate of 0.035%over more than 800 cycles.Even under 7.1 mg cm^(-2)S-loaded,the PEI-TIC electrode can also achieve a high areal capacity of 7.2 mA h g^(-1)and excellent cycling stability,confirming its application potential.Moreover,it is also noted that TG-FTIR test is performed for the first time to explore the flame-retardant mechanism of polymer binders.This work provides an economically and environmentally friendly binder for the practical application and inspires the exploration of the flame-retardant mechanism of all electrode components.

Tungsten oxide/nitrogen-doped carbon nanotubes composite catalysts for enhanced redox kinetics in lithium-sulfur batteries

The sluggish redox kinetics of polysulfides in lithium-sulfur(Li-S)batteries are a significant obstacle to their widespread adoption as energy storage devices.However,recent studies have shown that tungsten oxide(WO_(3))can facilitate the conversion kinetics of polysulfides in Li-S batteries.Herein,we fabricated host materials for sulfur using nitrogen-doped carbon nanotubes(N-CNTs)and WO_(3).We used low-cost components and simple procedures to overcome the poor electrical conductivity that is a disadvantage of metal oxides.The composites of WO_(3) and N-CNTs(WO_(3)/N-CNTs)create a stable framework structure,fast ion diffusion channels,and a 3D electron transport network during electrochemical reaction processes.As a result,the WO_(3)/N-CNT-Li2S6 cathode demonstrates high initial capacity(1162 mA·h·g^(-1) at 0.5℃),excellent rate performance(618 mA·h·g^(-1) at 5.5℃),and a low capacity decay rate(0.093%up to 600 cycles at 2℃).This work presents a novel approach for preparing tungsten oxide/carbon composite catalysts that facilitate the redox kinetics of polysulfide conversion.

Image-Based Flow Prediction of Vocal Folds Using 3D Convolutional Neural Networks

In this work,a three dimensional(3D)convolutional neural network(CNN)model based on image slices of various normal and pathological vocal folds is proposed for accurate and efficient prediction of glottal flows.The 3D CNN model is composed of the feature extraction block and regression block.The feature extraction block is capable of learning low dimensional features from the high dimensional image data of the glottal shape,and the regression block is employed to flatten the output from the feature extraction block and obtain the desired glottal flow data.The input image data is the condensed set of 2D image slices captured in the axial plane of the 3D vocal folds,where these glottal shapes are synthesized based on the equations of normal vibration modes.The output flow data is the corresponding flow rate,averaged glottal pressure and nodal pressure distributions over the glottal surface.The 3D CNN model is built to establish the mapping between the input image data and output flow data.The ground-truth flow variables of each glottal shape in the training and test datasets are obtained by a high-fidelity sharp-interface immersed-boundary solver.The proposed model is trained to predict the concerned flow variables for glottal shapes in the test set.The present 3D CNN model is more efficient than traditional Computational Fluid Dynamics(CFD)models while the accuracy can still be retained,and more powerful than previous data-driven prediction models because more details of the glottal flow can be provided.The prediction performance of the trained 3D CNN model in accuracy and efficiency indicates that this model could be promising for future clinical applications.

Regulating the non-effective carriers transport for high-performance lithium metal batteries

The absence of control over carriers transport during electrochemical cycling,accompanied by the deterioration of the solid electrolyte interphase(SEI)and the growth of lithium dendrites,has hindered the development of lithium metal batteries.Herein,a separator complexion consisting of polyacrylonitrile(PAN)nanofiber and MIL-101(Cr)particles prepared by electrospinning is proposed to bind the anions from the electrolyte utilizing abundant effective open metal sites in the MIL-101(Cr)particles to modulate the transport of non-effective carriers.The binding effect of the PANM separator promotes uniform lithium metal deposition and enhances the stability of the SEI layer and long cycling stability of ultra-high nickel layered oxide cathodes.Taking PANM as the Li||NCM96 separator enables high-voltage cycling stability,maintaining 72%capacity retention after 800 cycles at a charging and discharging rate of 0.2 C at a cut-off voltage of 4.5 V and 0°C.Meanwhile,the excellent high-rate performance delivers a specific capacity of 156.3 mA h g^(-1) at 10 C.In addition,outstanding cycling performance is realized from−20 to 60°C.The separator engineering facilitates the electrochemical performance of lithium metal batteries and enlightens a facile and promising strategy to develop fast charge/discharge over a wide range of temperatures.

Mural Anomaly Region Detection Algorithm Based on Hyperspectral Multiscale Residual Attention Network

Mural paintings hold significant historical information and possess substantial artistic and cultural value.However,murals are inevitably damaged by natural environmental factors such as wind and sunlight,as well as by human activities.For this reason,the study of damaged areas is crucial for mural restoration.These damaged regions differ significantly from undamaged areas and can be considered abnormal targets.Traditional manual visual processing lacks strong characterization capabilities and is prone to omissions and false detections.Hyperspectral imaging can reflect the material properties more effectively than visual characterization methods.Thus,this study employs hyperspectral imaging to obtain mural information and proposes a mural anomaly detection algorithm based on a hyperspectral multi-scale residual attention network(HM-MRANet).The innovations of this paper include:(1)Constructing mural painting hyperspectral datasets.(2)Proposing a multi-scale residual spectral-spatial feature extraction module based on a 3D CNN(Convolutional Neural Networks)network to better capture multiscale information and improve performance on small-sample hyperspectral datasets.(3)Proposing the Enhanced Residual Attention Module(ERAM)to address the feature redundancy problem,enhance the network’s feature discrimination ability,and further improve abnormal area detection accuracy.The experimental results show that the AUC(Area Under Curve),Specificity,and Accuracy of this paper’s algorithm reach 85.42%,88.84%,and 87.65%,respectively,on this dataset.These results represent improvements of 3.07%,1.11%and 2.68%compared to the SSRN algorithm,demonstrating the effectiveness of this method for mural anomaly detection.

基于块编码特点的压缩视频质量增强算法

针对现有压缩视频质量增强算法未能充分利用压缩视频特点的问题,研究了视频编码与压缩视频质量增强任务之间的本质关系,并针对性地设计了一种基于三维卷积神经网络(3D convolutional neural network, 3D-CNN)的非对齐压缩视频质量增强算法。实验结果表明:相较于高效视频编码(high efficiency video coding, HEVC)标准H.265,所提算法在低延迟(low delay, LD)配置下且量化参数(quantization parameter, QP)为37时,峰值信噪比(peak signal-to-noise ratio, PSNR)提升了0.465 2 dB;相较于数据压缩会议(data compression conference, DCC)中提出的多帧引导的注意力网络(multi-frame guided attention network, MGANet)方法,该算法PSNR的增长量提升了15.1%。

Optimization of Plasma Etching Parameters and Mask for Silica Optical Waveguides

Optical waveguides in silica-on-silicon are one of the key elements in optical communications.The processes of deep etching silica waveguides using resist and metal masks in RIE plasma are investigated.The etching responses,including etching rate and selectivity as functions of variation of parameters,are modeled with a 3D neural network.A novel resist/metal combined mask that can overcome the single-layer masks’ limitations is developed for enhancing the waveguides deep etching and low-loss optical waveguides are fabricated at last.