Three-dimensional numerical analysis of plant-soil hydraulic interactions on pore water pressure of vegetated slope under different rainfall patterns

Understanding the pore water pressure distribution in unsaturated soil is crucial in predicting shallow landslides triggered by rainfall,mainly when dealing with different temporal patterns of rainfall intensity.However,the hydrological response of vegetated slopes,especially three-dimensional(3D)slopes covered with shrubs,under different rainfall patterns remains unclear and requires further investigation.To address this issue,this study adopts a novel 3D numerical model for simulating hydraulic interactions between the root system of the shrub and the surrounding soil.Three series of numerical parametric studies are conducted to investigate the influences of slope inclination,rainfall pattern and rainfall duration.Four rainfall patterns(advanced,bimodal,delayed,and uniform)and two rainfall durations(4-h intense and 168-h mild rainfall)are considered to study the hydrological response of the slope.The computed results show that 17%higher transpiration-induced suction is found for a steeper slope,which remains even after a short,intense rainfall with a 100-year return period.The extreme rainfalls with advanced(PA),bimodal(PB)and uniform(PU)rainfall patterns need to be considered for the short rainfall duration(4 h),while the delayed(PD)and uniform(PU)rainfall patterns are highly recommended for long rainfall durations(168 h).The presence of plants can improve slope stability markedly under extreme rainfall with a short duration(4 h).For the long duration(168 h),the benefit of the plant in preserving pore-water pressure(PWP)and slope stability may not be sufficient.

Ecological network analysis reveals complex responses of tree species life stage interactions to stand variables

Tree interactions are essential for the structure,dynamics,and function of forest ecosystems,but variations in the architecture of life-stage interaction networks(LSINs)across forests is unclear.Here,we constructed 16 LSINs in the mountainous forests of northwest Hebei,China based on crown overlap from four mixed forests with two dominant tree species.Our results show that LSINs decrease the complexity of stand densities and basal areas due to the interaction cluster differentiation.In addition,we found that mature trees and saplings play different roles,the first acting as“hub”life stages with high connectivity and the second,as“bridges”controlling information flow with high centrality.Across the forests,life stages with higher importance showed better parameter stability within LSINs.These results reveal that the structure of tree interactions among life stages is highly related to stand variables.Our efforts contribute to the understanding of LSIN complexity and provide a basis for further research on tree interactions in complex forest communities.

Impact of different interaction behavior on epidemic spreading in time-dependent social networks

We investigate the impact of pairwise and group interactions on the spread of epidemics through an activity-driven model based on time-dependent networks.The effects of pairwise/group interaction proportion and pairwise/group interaction intensity are explored by extensive simulation and theoretical analysis.It is demonstrated that altering the group interaction proportion can either hinder or enhance the spread of epidemics,depending on the relative social intensity of group and pairwise interactions.As the group interaction proportion decreases,the impact of reducing group social intensity diminishes.The ratio of group and pairwise social intensity can affect the effect of group interaction proportion on the scale of infection.A weak heterogeneous activity distribution can raise the epidemic threshold,and reduce the scale of infection.These results benefit the design of epidemic control strategy.

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.

Opinion consensus incorporating higher-order interactions in individual-collective networks

In the current information society, the dissemination mechanisms and evolution laws of individual or collective opinions and their behaviors are the research hot topics in the field of opinion dynamics. First, in this paper, a two-layer network consisting of an individual-opinion layer and a collective-opinion layer is constructed, and a dissemination model of opinions incorporating higher-order interactions(i.e. OIHOI dissemination model) is proposed. Furthermore, the dynamic equations of opinion dissemination for both individuals and groups are presented. Using Lyapunov's first method,two equilibrium points, including the negative consensus point and positive consensus point, and the dynamic equations obtained for opinion dissemination, are analyzed theoretically. In addition, for individual opinions and collective opinions,some conditions for reaching negative consensus and positive consensus as well as the theoretical expression for the dissemination threshold are put forward. Numerical simulations are carried to verify the feasibility and effectiveness of the proposed theoretical results, as well as the influence of the intra-structure, inter-connections, and higher-order interactions on the dissemination and evolution of individual opinions. The main results are as follows.(i) When the intra-structure of the collective-opinion layer meets certain characteristics, then a negative or positive consensus is easier to reach for individuals.(ii) Both negative consensus and positive consensus perform best in mixed type of inter-connections in the two-layer network.(iii) Higher-order interactions can quickly eliminate differences in individual opinions, thereby enabling individuals to reach consensus faster.

Expression Pattern, Interaction Network, and Functional Analysis of the Arabidopsis Botrytis Susceptible1 Interactor

E3 ubiquitin ligases are participated in numerous processes, regulating the response to biotic and abiotic stresses. Botrytis susceptible1 interactor (BOI) is a RING (Really Interesting New Gene)-type E3 ligase that mediates the ubiquitination of BOS1 (Botrytis susceptible1), a transcription factor involved in stress and pathogen responses. Although BOI is an E3 ligase, there are reports to show that BOI interacts with target proteins such as DELLAs or CONSTANS to repress gibberellin responses and flowering without the degradation of the target proteins. In this article, we utilize diversified methods to comprehensively analyze the expression pattern, interaction network and function of BOI gene. Firstly, 1800 bp upstream region of BOI gene from Arabidopsis thaliana (Arabidopsis) genome was isolated, and fused GUS reporter gene. The resulting expression cassette was introduced into wild-type Arabidopsis through Agrobacterium-mediated transformation. The result demonstrated that BOI gene was expressed predominantly in leaves, siliques, young roots, and flowering tissues, indicating that BOI gene may be involved in multiple processes in plant growth and development in Arabidopsis. Besides, eight candidate interacting proteins were obtained from the Arabidopsis cDNA library via yeast two-hybrid technology, including EXO70E2 (AT5G61010), WRKY7 (AT4G24240), WRKY11 (AT4G31550), WRKY17 (AT2G24570), UBP20 (AT4G17895), L5 (AT1G12290), SAUR9 (AT4G36110) and TCP21 (AT5G08330). Functional analysis of these candidate interacting proteins manifested that they related to multiple pathways, including biological and abiotic stress, programmed cell death, protein degradation, material metabolism and transcriptional regulation. In addition, the results of the transient assay proclaimed that BOI protein affects the protein stability of EXO70E2 and L5 through its E3 ubiquitin ligase activity. Our results provide novel clues for a better understanding of molecular mechanisms underlying BOI-mediated regulations.

Interactions of penny-shaped cracks in three-dimensional solids

The interaction of arbitrarily distributed penny-shaped cracks in three-dimensional solids is analyzed in this paper. Using oblate spheroidal coordinates and displacement functions, an analytic method is devel- oped in which the opening and the sliding displacements on each crack surface are taken as the basic unknown functions. The basic unknown functions can be expanded in series of Legendre polynomials with unknown coefficients. Based on superposition technique, a set of governing equations for the unknown coefficients are formulated from the traction free conditions on each crack surface. The boundary collocation procedure and the average method for crack-surface tractions are used for solving the governing equations. The solution can be obtained for quite closely located cracks. Numerical examples are given for several crack problems. By comparing the present results with other existing results, one can conclude that the present method provides a direct and efficient approach to deal with three-dimensional solids containing multiple cracks.

Three-dimensional Holographic Vector of Atomic Interaction Field(3D-HoVAIF) for the QSPR/QSAR of Polychlorinated Naphthalenes

Three-dimensional holographic vector of atomic interaction field（3D-HoVAIF） is used to describe the chemical structures of polychlorinated naphthalenes（PCNs）.After variable screening by stepwise multiple regression（SMR） technique,the liner relationships between gas-chromatographic relative retention time（RRT）,298 K supercooled liquid pressures（logPL）,n-octanol/air partition coefficient（logKOA）,n-octanol/water partition coefficient（logKOW）,aqueous solubilities（logSW）,relative in vitro potency values（-logEROD） of PCNs and 3D-HoVAIF descriptors have been established by partial least-square（PLS） regression.The result shows that the 3D-HoVAIF descriptors can be well used to express the quantitative structure-property（activity） relationships of PCNs.Predictive capability of the models has also been demonstrated by leave-one-out cross-validation.Moreover,the predicted values have been presented for those PCNs which are lack of experimentally physico-chemical properties and biological activity by the optimum models.

Strengthening the three-dimensional comprehensive observation system of multi-layer interaction on the Tibetan Plateau to cope with the warming and wetting trend

Changes in the water cycle on the Tibetan Plateau(TP)have a significant impact on local agricultural production and livelihoods and its downstream regions.Against the background of widely reported warming and wetting,the hydrological cycle has accelerated and the likelihood of extreme weather events and natural disasters occurring(i.e.,snowstorms,floods,landslides,mudslides,and ice avalanches)has also intensified,especially in the highelevation mountainous regions.Thus,an accurate estimation of the intensity and variation of each component of the water cycle is an urgent scientific question for the assessment of plateau environmental changes.Following the transformation and movement of water between the atmosphere,biosphere and hydrosphere,the authors highlight the urgent need to strengthen the three-dimensional comprehensive observation system(including the eddy covariance system;planetary boundary layer tower;profile measurements of temperature,humidity,and wind by microwave radiometers,wind profiler,and radiosonde system;and cloud and precipitation radars)in the TP region and propose a practical implementation plan.The construction of such a three-dimensional observation system is expected to promote the study of environmental changes and natural hazards prevention.

Study on the Binding Interaction of Nanomaterials Tio_2 with Bovine Serum Albumin by Three-Dimensional Fluorescence Spectrometry (TDFS)

Although scientific and policy bodies have stated that nanomaterials are not intrinsically toxic, there is interest in evaluating if and how many engineered nanomaterials may do harm to the health of mankind and the ecological environment. The interaction between nano-TiO2 and bovine serum albumin (BSA) was studied by using TDFS and UV methods in this research.

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.

Integrated network analysis of transcriptomic and protein-protein interaction data in taurine-treated hepatic stellate cells

BACKGROUND Studies show that the antifibrotic mechanism of taurine may involve its inhibition of the activation and proliferation of hepatic stellate cells(HSCs). Since the molecular mechanism of taurine-mediated antifibrotic activity has not been fully unveiled and is little studied, it is imperative to use "omics" methods to systematically investigate the molecular mechanism by which taurine inhibits liver fibrosis.AIM To establish a network including transcriptomic and protein-protein interaction data to elucidate the molecular mechanism of taurine-induced HSC apoptosis.METHODS We used microarrays, bioinformatics, protein-protein interaction(PPI) network,and sub-modules to investigate taurine-induced changes in gene expression in human HSCs(LX-2). Subsequently, all of the differentially expressed genes(DEGs) were subjected to gene ontology function and Kyoto encyclopedia of genes and genomes pathway enrichment analysis. Furthermore, the interactions of DEGs were explored in a human PPI network, and sub-modules of the DEGs interaction network were analyzed using Cytoscape software.RESULTS A total of 635 DEGs were identified in taurine-treated HSCs when compared with the controls. Of these, 304 genes were statistically significantly up-regulated, and 331 down-regulated. Most of these DEGs were mainly located on the membrane and extracellular region, and are involved in the biological processes of signal transduction, cell proliferation, positive regulation of extracellular regulated protein kinases 1(ERK1) and ERK2 cascade, extrinsic apoptotic signaling pathway and so on. Fifteen significantly enriched pathways with DEGs were identified, including mitogen-activated protein kinase(MAPK) signaling pathway, peroxisome proliferators-activated receptor signaling pathway,estrogen signaling pathway, Th1 and Th2 cell differentiation, cyclic adenosine monophosphate signaling pathway and so on. By integrating the transcriptomics and human PPI data, nine critical genes, including MMP2, MMP9, MMP21,TIMP3, KLF10, CX3CR1, TGFB1, VEGFB, and EGF, were identified in the PPI network analysis.CONCLUSION Taurine promotes the apoptosis of HSCs via up-regulating TGFB1 and then activating the p38 MAPK-JNK-Caspase9/8/3 pathway. These findings enhance the understanding of the molecular mechanism of taurine-induced HSC apoptosis and provide references for liver disorder therapy.

Protein interaction network related to Helicobacter pylori infection response

AIM:To understand the complex reaction of gastric inflammation induced by Helicobacter pylori(H pylori) in a systematic manner using a protein interaction network. METHODS:The expression of genes significantly changed on microarray during H pylori infection was scanned from the web literary database and translated into proteins.A network of protein interactions was constructed by searching the primary interactions of selected proteins.The constructed network was mathematically analyzed and its biological function was examined.In addition,the nodes on the network were checked to determine if they had any further functional importance or relation to other proteins by extending them. RESULTS:The scale-free network showing the relationship between inflammation and carcinogenesis was constructed.Mathematical analysis showed hub and bottleneck proteins,and these proteins were mostly related to immune response.The network contained pathways and proteins related to H pylori infection,such as the JAK-STAT pathway triggered by interleukins.Activation of nuclear factor (NF)-κB,TLR4,and other proteins known to function as core proteins of immune response were also found. These immune-related proteins interacted on the network with pathways and proteins related to the cell cycle,cell maintenance and proliferation,andtranscription regulators such as BRCA1,FOS,REL,and zinc finger proteins.The extension of nodes showed interactions of the immune proteins with cancer- related proteins.One extended network,the core network,a summarized form of the extended network, and cell pathway model were constructed. CONCLUSION:Immune-related proteins activated by H pylori infection interact with proto-oncogene proteins.The hub and bottleneck proteins are potential drug targets for gastric inflammation and cancer.

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.

Three-dimensional visualization interactive system for digital twin workshop

To improve the human-physical-virtual coordination and integration of the digital twin workshop,3D visual monitoring and human-computer interaction of the digital twin workshop was studied.First,a novel 6D model of the 3D visualization interactive system for digital twin workshops is proposed.As the traditional 5D digital twin model ignores the importance of human-computer interaction,a new dimension of the user terminal was added.A hierarchical real-time data-driven mapping model for the workshop production process is then proposed.Moreover,a real-time data acquisition method for the industrial Internet of things is proposed based on OPC UA(object linking and embedding for process control unified architecture).Based on the 6D model of the system,the process of creating a 3D visualization virtual environment based on virtual reality is introduced,in addition to a data-driven process based on the data management cloud platform.Finally,the 6D model of the system was confirmed using the blade rotor test workshop as the object,and a 3D visualization interactive system is developed.The results show that the system is more transparent,real-time,data-driven and more efficient,as well as promotes the coordination and integration of human-physical-virtual,which has practical significance for developing digital twin workshops.

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.

LncRNAs exert indispensable roles in orchestrating the interaction among diverse noncoding RNAs and enrich the regulatory network of plant growth and its adaptive environmental stress response

With the advent of advanced sequencing technologies,non-coding RNAs(ncRNAs)are increasingly pivotal and play highly regulated roles in the modulation of diverse aspects of plant growth and stress response.This includes a spectrum of ncRNA classes,ranging from small RNAs to long non-coding RNAs(lncRNAs).Notably,among these,lncRNAs emerge as significant and intricate components within the broader ncRNA regulatory networks.Here,we categorize ncRNAs based on their length and structure into small RNAs,medium-sized ncRNAs,lncRNAs,and circle RNAs.Furthermore,the review delves into the detailed biosynthesis and origin of these ncRNAs.Subsequently,we emphasize the diverse regulatory mechanisms employed by lncRNAs that are located at various gene regions of coding genes,embodying promoters,5’UTRs,introns,exons,and 3’UTR regions.Furthermore,we elucidate these regulatory modes through one or two concrete examples.Besides,lncRNAs have emerged as novel central components that participate in phase separation processes.Moreover,we illustrate the coordinated regulatory mechanisms among lncRNAs,miRNAs,and siRNAs with a particular emphasis on the central role of lncRNAs in serving as sponges,precursors,spliceosome,stabilization,scaffolds,or interaction factors to bridge interactions with other ncRNAs.The review also sheds light on the intriguing possibility that some ncRNAs may encode functional micropeptides.Therefore,the review underscores the emergent roles of ncRNAs as potent regulatory factors that significantly enrich the regulatory network governing plant growth,development,and responses to environmental stimuli.There are yet-to-be-discovered roles of ncRNAs waiting for us to explore.

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.

Human Interaction Recognition in Surveillance Videos Using Hybrid Deep Learning and Machine Learning Models

Human Interaction Recognition(HIR)was one of the challenging issues in computer vision research due to the involvement of multiple individuals and their mutual interactions within video frames generated from their movements.HIR requires more sophisticated analysis than Human Action Recognition(HAR)since HAR focuses solely on individual activities like walking or running,while HIR involves the interactions between people.This research aims to develop a robust system for recognizing five common human interactions,such as hugging,kicking,pushing,pointing,and no interaction,from video sequences using multiple cameras.In this study,a hybrid Deep Learning(DL)and Machine Learning(ML)model was employed to improve classification accuracy and generalizability.The dataset was collected in an indoor environment with four-channel cameras capturing the five types of interactions among 13 participants.The data was processed using a DL model with a fine-tuned ResNet(Residual Networks)architecture based on 2D Convolutional Neural Network(CNN)layers for feature extraction.Subsequently,machine learning models were trained and utilized for interaction classification using six commonly used ML algorithms,including SVM,KNN,RF,DT,NB,and XGBoost.The results demonstrate a high accuracy of 95.45%in classifying human interactions.The hybrid approach enabled effective learning,resulting in highly accurate performance across different interaction types.Future work will explore more complex scenarios involving multiple individuals based on the application of this architecture.

Spatial Interaction Network Analysis of Crude Oil Trade Relations between Countries along the Belt and Road

Based on the theories and methods of complex network,crude oil trade flows between countries along the Belt and Road(B&R,hereafter)are inserted into the Geo-space of B&R and form a spatial interaction network which takes the countries as nodes and takes the trade relations as edges.The networked mining and evolution analysis can provide important references for the research on trade relations among the B&R countries and the formulation of trade policy.This paper researches and discusses the construction,statistical analysis,top networks and stability of the crude oil trade network between the B&R countries from 2001 to 2020 from the perspectives of Geo-Computation for Social Sciences(GCSS)and spatial interaction.Firstly,evolutions of out-degree,in-degree,out-strength and in-strength of the top 10 countries in the crude oil trade network are computed and analyzed.Secondly,the top network method is used to explore the evolution characteristics of hierarchical structures.And finally,the sequential evolution characteristics of the crude oil trade network stability are analyzed utilizing the network stability measure method based on the trade relationship autocorrelation function.The analysis results show that Russia has the largest out-degree and out-strength,and China has the largest in-degree and in-strength.The crude oil trade volume of the top 10 import and export networks between 2001—2020 accounts for over 90%of the total trade volume of the crude oil trade network,and the proportion remains relatively stable.However,the stability of the network showed strong fluctuations in 2009,2012 and 2014,which may be closely related to major international events in these years,which could furtherly be used to build a correlation model between network volatility and major events.This paper explores how to construct and analyze the spatial interaction network of crude oil trade and can provide references for trade relations research and trade policy formulation of B&R countries.