Prediction of three-dimensional ocean temperature in the South China Sea based on time series gridded data and a dynamic spatiotemporal graph neural network

Ocean temperature is an important physical variable in marine ecosystems,and ocean temperature prediction is an important research objective in ocean-related fields.Currently,one of the commonly used methods for ocean temperature prediction is based on data-driven,but research on this method is mostly limited to the sea surface,with few studies on the prediction of internal ocean temperature.Existing graph neural network-based methods usually use predefined graphs or learned static graphs,which cannot capture the dynamic associations among data.In this study,we propose a novel dynamic spatiotemporal graph neural network(DSTGN)to predict threedimensional ocean temperature(3D-OT),which combines static graph learning and dynamic graph learning to automatically mine two unknown dependencies between sequences based on the original 3D-OT data without prior knowledge.Temporal and spatial dependencies in the time series were then captured using temporal and graph convolutions.We also integrated dynamic graph learning,static graph learning,graph convolution,and temporal convolution into an end-to-end framework for 3D-OT prediction using time-series grid data.In this study,we conducted prediction experiments using high-resolution 3D-OT from the Copernicus global ocean physical reanalysis,with data covering the vertical variation of temperature from the sea surface to 1000 m below the sea surface.We compared five mainstream models that are commonly used for ocean temperature prediction,and the results showed that the method achieved the best prediction results at all prediction scales.

Three-Dimensional Ocean Sensor Networks:A Survey

The past decade has seen a growing interest in ocean sensor networks because of their wide applications in marine research,oceanography,ocean monitoring,offshore exploration,and defense or homeland security.Ocean sensor networks are generally formed with various ocean sensors,autonomous underwater vehicles,surface stations,and research vessels.To make ocean sensor network applications viable,efficient communication among all devices and components is crucial.Due to the unique characteristics of underwater acoustic channels and the complex deployment environment in three dimensional(3D) ocean spaces,new efficient and reliable communication and networking protocols are needed in design of ocean sensor networks.In this paper,we aim to provide an overview of the most recent advances in network design principles for 3D ocean sensor networks,with focuses on deployment,localization,topology design,and position-based routing in 3D ocean spaces.

Three-Dimensional Cooperative Localization via Space-Air-Ground Integrated Networks

The space-air-ground integrated network(SAGIN)combines the superiority of the satellite,aerial,and ground communications,which is envisioned to provide high-precision positioning ability as well as seamless connectivity in the 5G and Beyond 5G(B5G)systems.In this paper,we propose a three-dimensional SAGIN localization scheme for ground agents utilizing multi-source information from satellites,base stations and unmanned aerial vehicles(UAVs).Based on the designed scheme,we derive the positioning performance bound and establish a distributed maximum likelihood algorithm to jointly estimate the positions and clock offsets of ground agents.Simulation results demonstrate the validity of the SAGIN localization scheme and reveal the effects of the number of satellites,the number of base stations,the number of UAVs and clock noise on positioning performance.

Discontinuity development patterns and the challenges for 3D discrete fracture network modeling on complicated exposed rock surfaces

Natural slopes usually display complicated exposed rock surfaces that are characterized by complex and substantial terrain undulation and ubiquitous undesirable phenomena such as vegetation cover and rockfalls.This study presents a systematic outcrop research of fracture pattern variations in a complicated rock slope,and the qualitative and quantitative study of the complex phenomena impact on threedimensional(3D)discrete fracture network(DFN)modeling.As the studies of the outcrop fracture pattern have been so far focused on local variations,thus,we put forward a statistical analysis of global variations.The entire outcrop is partitioned into several subzones,and the subzone-scale variability of fracture geometric properties is analyzed(including the orientation,the density,and the trace length).The results reveal significant variations in fracture characteristics(such as the concentrative degree,the average orientation,the density,and the trace length)among different subzones.Moreover,the density of fracture sets,which is approximately parallel to the slope surface,exhibits a notably higher value compared to other fracture sets across all subzones.To improve the accuracy of the DFN modeling,the effects of three common phenomena resulting from vegetation and rockfalls are qualitatively analyzed and the corresponding quantitative data processing solutions are proposed.Subsequently,the 3D fracture geometric parameters are determined for different areas of the high-steep rock slope in terms of the subzone dimensions.The results show significant variations in the same set of 3D fracture parameters across different regions with density differing by up to tenfold and mean trace length exhibiting differences of 3e4 times.The study results present precise geological structural information,improve modeling accuracy,and provide practical solutions for addressing complex outcrop issues.

Polynomials of Degree-Based Indices for Three-Dimensional Mesh Network

In order to study the behavior and interconnection of network devices,graphs structures are used to formulate the properties in terms of mathematical models.Mesh network(meshnet)is a LAN topology in which devices are connected either directly or through some intermediate devices.These terminating and intermediate devices are considered as vertices of graph whereas wired or wireless connections among these devices are shown as edges of graph.Topological indices are used to reflect structural property of graphs in form of one real number.This structural invariant has revolutionized the field of chemistry to identify molecular descriptors of chemical compounds.These indices are extensively used for establishing relationships between the structure of nanotubes and their physico-chemical properties.In this paper a representation of sodium chloride(NaCl)is studied,because structure of NaCl is same as the Cartesian product of three paths of length exactly like a mesh network.In this way the general formula obtained in this paper can be used in chemistry as well as for any degree-based topological polynomials of three-dimensional mesh networks.

A Novel Hydrogen-bonded Three-dimensional Network Complex Containing Nickel

A novel complex, (H 3O) 2[Ni(2,6-pydc) 2]·2H 2O was synthesized in an aqueous solution and characterized by means of single-crystal X-ray diffraction, elemental analyses and IR spectra. The X-ray structural analysis revealed that the novel compound forms three-dimensional(3D) networks by both π-π stacking and hydrogen-bonding interactions. The crystal data for the complex are a=13.853(3) nm, b=9.6892(19) nm, c=13.732(3) nm, α=90.00°, β=115.52(3)°, γ=90.00°, Z=3, R 1=0.0786, wR 2=0.1522.

A New Three-dimensional Network Constructed by Heptamolybdate, Sodium Ions and Hexamethylene Tetramine Cations via Hydrogen Bonds

The crystal structure of the title compound [Na2(OH2)5]2+[C6H12N4H2]2-2+ [Mo7O24]6 ?4H2O, prepared from an aqueous solution of Na2MoO4 ?2H2O in the presence of MoCl3 and hexamethylene tetramine, has been determined by single-crystal X-ray diffraction. The crystal is of orthorhombic, space group Pnma with a = 14.6113(2), b = 18.6833(1), c = 15.3712(2), V = 4196.14(8)3, Z = 4, Mr = 1548.13, F(000) = 3016, = 2.157 mm-1 and Dc = 2.451 g/cm3. The final R factor is 0.0526 for 3818 unique observed reflections (I > 2(I)). The structural analysis reveals that heptamolybdate anions in the title compound consist of seven edge-sharing MoO6 octahedra, and are linked into a three-dimensional framework by sodium ions and hydrogen bonds.

A New Eight-connected Three-dimensional Network Based on a Tetranuclear Zinc Cluster Building Block

One interesting coordination polymer, [Zn2（1,2,4-BTC）（OH）（H2O）2]2·2H2O 1, has been synthesized from 1,2,4-BTC （1,2,4-BTC = 1,2,4-bentricarboxylate） under hydrothermal conditions and characterized by elemental analyses, IR, TG and single-crystal X-ray diffraction. Complex I crystallizes in triclinic, space group P^-1, with a = 6.5200（13）, b = 9,0600（18）, c = 10.968（2） A^°, α = 111.55（3）, β = 92.07（3）,γ= 95.03（3）°, C9H10O10Zn2, Mr = 408.91, V= 598.7（2） A^°^3, Dc = 2.268 g/cm^3, F（000） = 408 and Z = 2. X-ray diffraction analysis reveals that complex 1 is a three-dimensional network built from tetranuclear Zn（Ⅱ） building unit. In this complex, the Zn4 unit is an eight-connected knot, while 1,2,4-BTC a four-connected knot. This results in a CaF2 topology. To the best of our knowledge, such Zn4 unit is the first 8-connected building block built from asymmetry ligand.

A Three-dimensional IP-based Telecom Metropolitan Area Network Model

The Metropolitan Area Network (MAN) has faced serious problems after years of rapid development. The model of three-dimensional IP-based MAN, proposed by ZTE, is a next-generation MAN solution, which not only solves the existing problems but also brings new ideas for the development of next-generation MAN.

Prediction of Salinity Variations in a Tidal Estuary Using Artificial Neural Network and Three-Dimensional Hydrodynamic Models

The simulation of salinity at different locations of a tidal river using physically-based hydrodynamic models is quite cumbersome because it requires many types of data, such as hydrological and hydraulic time series at boundaries, river geometry, and adjusted coefficients. Therefore, an artificial neural network (ANN) technique using a back-propagation neural network (BPNN) and a radial basis function neural network (RBFNN) is adopted as an effective alternative in salinity simulation studies. The present study focuses on comparing the performance of BPNN, RBFNN, and three-dimensional hydrodynamic models as applied to a tidal estuarine system. The observed salinity data sets collected from 18 to 22 May, 16 to 22 October, and 26 to 30 October 2002 (totaling 4320 data points) were used for BPNN and RBFNN model training and for hydrodynamic model calibration. The data sets collected from 30 May to 2 June and 11 to 15 November 2002 (totaling 2592 data points) were adopted for BPNN and RBFNN model verification and for hydrodynamic model verification. The results revealed that the ANN (BPNN and RBFNN) models were capable of predicting the nonlinear time series behavior of salinity to the multiple forcing signals of water stages at different stations and freshwater input at upstream boundaries. The salinity predicted by the ANN models was better than that predicted by the physically based hydrodynamic model. This study suggests that BPNN and RBFNN models are easy-to-use modeling tools for simulating the salinity variation in a tidal estuarine system.

F127 assisted fabrication of Ge/r GO/CNTs nanocomposites with three-dimensional network structure for efficient lithium storage

To solve the volume expansion and poor electrical conductivity of germanium-based anode materials,Ge/rGO/CNTs nanocomposites with three-dimensional network structure are fabricated through the dispersion of polyethylene-polypropylene glycol(F127)and reduction of hydrogen.An interesting phenomenon is discovered that F127 can break GeO_(2)polycrystalline microparticles into 100 nm nanoparticles by only physical interaction,which promotes the uniform dispersion of GeO_(2)in a carbon network structure composed of graphene(rGO)and carbon nanotubes(CNTs).As evaluated as anode material of Lithium-ion batteries,Ge/rGO/CNTs nanocomposites exhibit excellent lithium storage performance.The initial specific capacity is high to 1549.7 mAh/g at 0.2 A/g,and the reversible capacity still retains972.4 mAh/g after 100 cycles.The improved lithium storage performance is attributed to that Ge nanoparticles can effectively slow down the volume expansion during charge and discharge processes,and threedimensional carbon networks can improve electrical conductivity and accelerate lithium-ion transfer of anode materials.

Three-dimensionally interconnected Co9S8/MWCNTs composite cathode host for lithium–sulfur batteries

Several challenging issues,such as the poor conductivity of sulfur,shuttle effects,large volume change of cathode,and the dendritic lithium in anode,have led to the low utilization of sulfur and hampered the commercialization of lithium–sulfur batteries.In this study,a novel three-dimensionally interconnected network structure comprising Co9 S8 and multiwalled carbon nanotubes(MWCNTs)was synthesized by a solvothermal route and used as the sulfur host.The assembled batteries delivered a specific capacity of1154 m Ah g-1 at 0.1 C,and the retention was 64%after 400 cycles at 0.5 C.The polar and catalytic Co9 S8 nanoparticles have a strong adsorbent effect for polysulfide,which can effectively reduce the shuttling effect.Meanwhile,the three-dimensionally interconnected CNT networks improve the overall conductivity and increase the contact with the electrolyte,thus enhancing the transport of electrons and Li ions.Polysulfide adsorption is greatly increased with the synergistic effect of polar Co9 S8 and MWCNTs in the three-dimensionally interconnected composites,which contributes to their promising performance for the lithium–sulfur batteries.

Synthesis and Crystal Structure of a Three-dimensional (3D) Complex Mn(H_2O)_2(HNic)_2 (HNic=2-Hydroxynicotinic Acid)

The title complex Mn（H2O）2（HNic）2 （C22H12MnN2O8, Mr = 367.18） crystallizes in monoclinic, space group P21/c with a = 7.5735（8）, b = 12.5295（13）, c = 7.6466（8）A.β = 101.2790（10）°, Z = 2, V= 711.59（13） A^3, D, = 1.714 g/cm^3,μ（MoKa） = 0.974 mm^-1, F（000） = 374, R1 （1255 observed reflections （Ⅰ 〉 2σ（Ⅰ）） = 0.0250） and wR2 = 0.0662 （all data）. In this paper, we report the complexation of Mn（Ⅱ） by the bidentate ligand 2-hydroxynicotinic acid （HNic）. In the crystal the Mn（Ⅱ） ion exhibits a deformed octahedron structure. The title complex Mn（H2O）2（HNic）2 has a three-dimensional （3D） network structure extended by hydrogen bonds, which are formed by two typical eight-membered hydrogen-bonded rings.

Hydrothermal Synthesis and Crystal Structure of a Three-dimensional Compound {Mn(H_2O)_4(VO)_2(PO_4)_2}_n

The title compound, {Mn(H2O)4(VO)2(PO4)2}n 1, was synthesized by the hydro- thermal reaction of Mn(OAc)2, Na2VO3 and H3PO4 in aqueous solution and its crystal structure was determined by X-ray single-crystal analysis. Crystallographic data for 1: H4MnO14P2V2, tetragonal system, space group I4/mmm, a = 6.251(3), c = 13.410(9) ?, Mr = 446.79, V = 524.0(5) ?3, Z = 2, F(000) = 434, μ = 3.320 mm-1, Dc = 2.832 g/cm3, the final R = 0.0577 for 163 observed reflections (I > 2σ(I)). X-ray crystal structure analysis shows that the vanadium phosphorous oxide layers are further connected by MnII(H2O)4 cations to form a three-dimensional network.

Three-dimensional histology:new visual approaches to morphological changes during neural regeneration

Three-dimensional（3D） histology utilizes tissue clearing techniques to turn intact tissues transparent,allowing rapid interrogation of tissue architecture in three dimensions.In this article,we summarized the available tissue clearing methods and classified them according to their physicochemical principles of operation,which provided a framework for one to choose the best techniques for various research settings.Recent attempts in addressing various questions regarding the degenerating and regenerating nervous system have been promising with the use of 3D histological techniques.

The First Three-dimensional Crown Ether Complex:{[Na(B18-C-6)]_(6)[Pt(SCN)_(6)]}[Pt(SCN)_(6)](SCN)_(2)with Covalent Bond

The novel benzo-18-crown-6(B18-C-6)complex;{[Na(Bl8-C-6)]_(6)[Pt(SCN)_(6)]}[Pt(SCN)_(6)](SCN)_(2)(1)was synthesized and characterized by elemental analysis,IR spectrum and x-ray diffraction analysis.Thr crystal structure belongs to rhomobohedral,space group R-3 with cell dimesions:a=6=1.9933(3),c=2.9760(6)nm,α=β=90,γ=120°,V=10.240(3)nm^(3),Z=3,A,aclcd=1.564 g/cm^(3),F(000)=4908.1 is composed of one{[Na(B18-C-6)]_(6)[Pt(SCN)_(6)]}4+complex cation,one[Pt(SCN)_(6)]^(2-)complex anion and two SCN~anions.{[Na(B18-C-6)]_(6)[Pt(SCN)_(6)3}4+complex cation shows a three-dimensional network structure bridged by Na-O interactions between adjacent[Na(B18-C-6)]+units.The function of[Pt(SCN)_(6)]^(2-)complex anion and two SCN'anions are balancing charge in crystal.

Energy management of hybrid electric propulsion system: Recent progress and a flying car perspective under three-dimensional transportation networks

The hybrid electric propulsion system(HEPS)holds clear potential to support the goal of sustainability in the automobile and aviation industry.As an important part of the three-dimensional transportation network,vehicles and aircraft using HEPSs have the advantages of high fuel economy,low emission,and low noise.To fulfill these advantages,the design of their energy management strategies(EMSs)is essential.This paper presents an in-depth review of EMSs for hybrid electric vehicles(HEVs)and hybrid electric aircraft.First,in view of the main challenges of current EMSs of HEVs,the referenced research is reviewed according to the solutions facing real-time implementation problems,variable driving conditions adaptability problems,and multi-objective optimization problems,respectively.Second,the existing research on the EMSs for hybrid electric aircraft is summarized according to the hybrid electric propulsion architectures.In addition,with the advance in propulsion technology and mechanical manufacturing in recent years,flying cars have gradually become a reality,further enriching the composition of the three-dimensional transportation network.And EMSs also play an essential role in the efficient operation of flying cars driven by HEPSs.Therefore,in the last part of this paper,the development status of flying cars and their future prospects are elaborated.By comprehensively summarizing the EMSs of HEPS for vehicles and aircraft,this review aims to provide guidance for the research on the EMSs for flying cars driven by HEPS and serve as the basis for knowledge transfer of relevant researchers.

Constructal design of a rectangular parallel phase change microchannel in a three-dimensional electronic device

Based on constructal theory,a rectangular parallel phase change microchannel model in a three-dimensional electronic device(TDED)is established with R134a as the cooling fluid.Based on the minimization of a complex function(CF)composed of linear weighting sum of maximum temperature difference and pumping power consumption,constructal design of the TDED is conducted first;and then,maximum temperature difference and pumping power consumption are minimized by non-dominated sorting genetic algorithm-II methods.The results reveal that there exist an optimal mass flow rate(0.0012 kg/s)and a quadratic optimal aspect ratio(AR)(0.39)of the microchannel which lead to quadratic minimum CF(0.817).Compared with the original value,the CF after optimization is reduced by 18.34%.Reducing the inlet temperature of cooling fluid and microchannel number appropriately can help to enhance the overall performance of TDED.By using the artificial neural network and genetic algorithms in the toolboxes of Matlab software,the optimal AR gained in the Pareto solution set is located between 0.2–0.45.The smallest deviation index among three discussed strategies is 0.346,and the corresponding optimal AR is 0.413,which is selected as the optimal design strategy of the microchannel in the TDED under multiple requirements.The findings in this study can serve as theoretical guides for thermal designs of electronic devices.

Generative deep-learning-embedded asynchronous structured light for three-dimensional imaging

Three-dimensional(3D)imaging with structured light is crucial in diverse scenarios,ranging from intelligent manufacturing and medicine to entertainment.However,current structured light methods rely on projector-camera synchronization,limiting the use of affordable imaging devices and their consumer applications.In this work,we introduce an asynchronous structured light imaging approach based on generative deep neural networks to relax the synchronization constraint,accomplishing the challenges of fringe pattern aliasing,without relying on any a priori constraint of the projection system.To overcome this need,we propose a generative deep neural network with U-Net-like encoder-decoder architecture to learn the underlying fringe features directly by exploring the intrinsic prior principles in the fringe pattern aliasing.We train within an adversarial learning framework and supervise the network training via a statisticsinformed loss function.We demonstrate that by evaluating the performance on fields of intensity,phase,and 3D reconstruction.It is shown that the trained network can separate aliased fringe patterns for producing comparable results with the synchronous one:the absolute error is no greater than 8μm,and the standard deviation does not exceed 3μm.Evaluation results on multiple objects and pattern types show it could be generalized for any asynchronous structured light scene.

The variational inequality formulation for unconfined seepage through three-dimensional dense fracture networks

The space block search technology is used to determine a connected three-dimensional fracture network in polygonal shapes,i.e.,seepage paths.After triangulation on these polygons,a finite element mesh for 3D fracture network seepage is obtained.Through introduction of the generalized Darcy's law,conservative equations for both fracture surface and fracture interactions are established.Combined with the boundary condition of Signorini's type,a partial differential equation(PDE) formulation is presented for the whole domain concerned.To solve this problem efficiently,an equivalent variational inequality(VI) formulation is given.With the penalized Heaviside function,a finite element procedure for unconfined seepage problem in 3D fracture network is developed.Through an example in a homogeneous rectangular dam,validity of the algorithm is verified.The analysis of an unconfined seepage problem in a complex fracture network shows that the proposed algorithm is very applicable to complex three-dimensional problems,and is effective in describing some interesting phenomenon usually encountered in practice,such as "preferential flow".