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.

Probing deactivation by coking in catalyst pellets for dry reforming of methane using a pore network model

Dry reforming of methane(DRM) is an attractive technology for utilizing the greenhouse gases(CO_(2) and CH_(4)) to produce syngas. However, the catalyst pellets for DRM are heavily plagued by deactivation by coking, which prevents this technology from commercialization. In this work, a pore network model is developed to probe the catalyst deactivation by coking in a Ni/Al_(2)O_(3) catalyst pellet for DRM. The reaction conditions can significantly change the coking rate and then affect the catalyst deactivation. The catalyst lifetime is higher under lower temperature, pressure, and CH_(4)/CO_(2) molar ratio, but the maximum coke content in a catalyst pellet is independent of these reaction conditions. The catalyst pellet with larger pore diameter, narrower pore size distribution and higher pore connectivity is more robust against catalyst deactivation by coking, as the pores in this pellet are more difficult to be plugged or inaccessible.The maximum coke content is also higher for narrower pore size distribution and higher pore connectivity, as the number of inaccessible pores is lower. Besides, the catalyst pellet radius only slightly affects the coke content, although the diffusion limitation increases with the pellet radius. These results should serve to guide the rational design of robust DRM catalyst pellets against deactivation by coking.

Modelling a Fused Deep Network Model for Pneumonia Prediction

Deep Learning(DL)is known for its golden standard computing paradigm in the learning community.However,it turns out to be an extensively utilized computing approach in the ML field.Therefore,attaining superior outcomes over cognitive tasks based on human performance.The primary benefit of DL is its competency in learning massive data.The DL-based technologies have grown faster and are widely adopted to handle the conventional approaches resourcefully.Specifically,various DL approaches outperform the conventional ML approaches in real-time applications.Indeed,various research works are reviewed to understand the significance of the individual DL models and some computational complexity is observed.This may be due to the broader expertise and knowledge required for handling these models during the prediction process.This research proposes a holistic approach for pneumonia prediction and offers a more appropriate DL model for classification purposes.This work incorporates a novel fused Squeeze and Excitation(SE)block with the ResNet model for pneumonia prediction and better accuracy.The expected model reduces the human effort during the prediction process and makes it easier to diagnose it intelligently as the feature learning is adaptive.The experimentation is carried out in Keras,and the model’s superiority is compared with various advanced approaches.The proposed model gives 90%prediction accuracy,93%precision,90%recall and 89%F1-measure.The proposed model shows a better trade-off compared to other approaches.The evaluation is done with the existing standard ResNet model,GoogleNet+ResNet+DenseNet,and different variants of ResNet models.

The Evolving Bipartite Network and Semi-Bipartite Network Models with Adjustable Scale and Hybrid Attachment Mechanisms

The bipartite graph structure exists in the connections of many objects in the real world, and the evolving modeling is a good method to describe and understand the generation and evolution within various real complex networks. Previous bipartite models were proposed to mostly explain the principle of attachments, and ignored the diverse growth speed of nodes of sets in different bipartite networks. In this paper, we propose an evolving bipartite network model with adjustable node scale and hybrid attachment mechanisms, which uses different probability parameters to control the scale of two disjoint sets of nodes and the preference strength of hybrid attachment respectively. The results show that the degree distribution of single set in the proposed model follows a shifted power-law distribution when parameter r and s are not equal to 0, or exponential distribution when r or s is equal to 0. Furthermore, we extend the previous model to a semi-bipartite network model, which embeds more user association information into the internal network, so that the model is capable of carrying and revealing more deep information of each user in the network. The simulation results of two models are in good agreement with the empirical data, which verifies that the models have a good performance on real networks from the perspective of degree distribution. We believe these two models are valuable for an explanation of the origin and growth of bipartite systems that truly exist.

Research on runoff variations based on wavelet analysis and wavelet neural network model： A case study of the Heihe River drainage basin （1944-2005）

The Heihe River drainage basin is one of the endangered ecological regions of China. The shortage of water resources is the bottleneck, which constrains the sustainable development of the region. Many scholars in China have done researches concerning this problem. Based on previous researches, this paper analyzed characteristics, tendencies, and causes of annual runoff variations in the Yingluo Gorge （1944-2005） and the Zhengyi Gorge （1954-2005）, which are the boundaries of the upper reaches, the middle reaches, and the lower reaches of the Heihe River drainage basin, by wavelet analysis, wavelet neural network model, and GIS spatial analysis. The results show that： （1） annual runoff variations of the Yingluo Gorge have principal periods of 7 years and 25 years, and its increasing rate is 1.04 m^3/s.10y; （2） annual runoff variations of the Zhengyi Gorge have principal periods of 6 years and 27 years, and its decreasing rate is 2.25 m^3/s.10y; （3） prediction results show that： during 2006-2015, annual runoff variations of the Yingluo and Zhengyi gorges have ascending tendencies, and the increasing rates are respectively 2.04 m^3/s.10y and 1.61 m^3/s.10y; （4） the increase of annual runoff in the Yingluo Gorge has causal relationship with increased temperature and precipitation in the upper reaches, and the decrease of annual runoff in the Zhengyi Gorge in the past decades was mainly caused by the increased human consumption of water resources in the middle researches. The study results will provide scientific basis for making rational use and allocation schemes of water resources in the Heihe River drainage basin.

Application of Bayesian regularized BP neural network model for analysis of aquatic ecological data—A case study of chlorophyll-a prediction in Nanzui water area of Dongting Lake

Bayesian regularized BP neural network（BRBPNN） technique was applied in the chlorophyll-α prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of the network were obtained. After the selection of input variables using stepwise/multiple linear regression method in SPSS i1.0 software, the BRBPNN model was established between chlorophyll-α and environmental parameters, biological parameters. The achieved optimal network structure was 3-11-1 with the correlation coefficients and the mean square errors for the training set and the test set as 0.999 and 0.000？8426, 0.981 and 0.0216 respectively. The sum of square weights between each input neuron and the hidden layer of optimal BRBPNN models of different structures indicated that the effect of individual input parameter on chlorophyll- α declined in the order of alga amount 〉 secchi disc depth（SD） 〉 electrical conductivity （EC）. Additionally, it also demonstrated that the contributions of these three factors were the maximal for the change of chlorophyll-α concentration, total phosphorus（TP） and total nitrogen（TN） were the minimal. All the results showed that BRBPNN model was capable of automated regularization parameter selection and thus it may ensure the excellent generation ability and robustness. Thus, this study laid the foundation for the application of BRBPNN model in the analysis of aquatic ecological data（chlorophyll-α prediction） and the explanation about the effective eutrophication treatment measures for Nanzui water area in Dongting Lake.

Artificial neural network models predicting the leaf area index:a case study in pure even-aged Crimean pine forests from Turkey

Background: Leaf Area Index(LAI) is an important parameter used in monitoring and modeling of forest ecosystems. The aim of this study was to evaluate performance of the artificial neural network(ANN) models to predict the LAI by comparing the regression analysis models as the classical method in these pure and even-aged Crimean pine forest stands.Methods: One hundred eight temporary sample plots were collected from Crimean pine forest stands to estimate stand parameters. Each sample plot was imaged with hemispherical photographs to detect the LAI. The partial correlation analysis was used to assess the relationships between the stand LAI values and stand parameters, and the multivariate linear regression analysis was used to predict the LAI from stand parameters. Different artificial neural network models comprising different number of neuron and transfer functions were trained and used to predict the LAI of forest stands.Results: The correlation coefficients between LAI and stand parameters(stand number of trees, basal area, the quadratic mean diameter, stand density and stand age) were significant at the level of 0.01. The stand age, number of trees, site index, and basal area were independent parameters in the most successful regression model predicted LAI values using stand parameters(R_(adj)~2=0.5431). As corresponding method to predict the interactions between the stand LAI values and stand parameters, the neural network architecture based on the RBF 4-19-1 with Gaussian activation function in hidden layer and the identity activation function in output layer performed better in predicting LAI(SSE(12.1040), MSE(0.1223), RMSE(0.3497), AIC(0.1040), BIC(-77.7310) and R^2(0.6392)) compared to the other studied techniques.Conclusion: The ANN outperformed the multivariate regression techniques in predicting LAI from stand parameters. The ANN models, developed in this study, may aid in making forest management planning in study forest stands.

Numerical simulation of neuronal spike patterns in a retinal network model

This study utilized a neuronal compartment model and NEURON software to study the effects of external light stimulation on retinal photoreceptors and spike patterns of neurons in a retinal network Following light stimulation of different shapes and sizes, changes in the spike features of ganglion cells indicated that different shapes of light stimulation elicited different retinal responses. By manipulating the shape of light stimulation, we investigated the effects of the large number of electrical synapses existing between retinal neurons. Model simulation and analysis suggested that interplexiform cells play an important role in visual signal information processing in the retina, and the findings indicated that our constructed retinal network model was reliable and feasible. In addition, the simulation results demonstrated that ganglion cells exhibited a variety of spike patterns under different light stimulation sizes and different stimulation shapes, which reflect the functions of the retina in signal transmission and processing.

Pore network modeling of water block in low permeability reservoirs

A pore network model was used in this paper to investigate the factors, in particular, throat radius, wettability and initial water saturation, causing water block in low permeability reservoirs. A new term - ＇relative permeability number＇ （RPN） was firstly defined, and then used to describe the degree of water block. Imbibition process simulations show that the RPN drops in accordance with the extension of the averaged pore throat radius from 0.05 to 1.5 μm, and yet once beyond that point of 1.5 μm, the RPN reaches a higher value, indicating the existence of a critical pore throat radius where water block is the maximum. When the wettability of the samples changes from water-wet to weakly water-wet, weakly gas-wet, or gas（oil）-wet, the gas RPN increases consistently, but this consistency is disturbed by the RPN dropping for weakly water-wet samples for water saturations less than 0.4, which means weakly waterwet media are more easily water blocked than water-wet systems. In the situation where the initial water saturation exceeds 0.05, water block escalates along with an increase in initial water saturation.

Projected change in precipitation forms in the Chinese Tianshan Mountains based on the Back Propagation Neural Network Model

In the context of global warming,precipitation forms are likely to transform from snowfall to rainfall with a more pronounced trend.The change in precipitation forms will inevitably affect the processes of regional runoff generation and confluence as well as the annual distribution of runoff.Most researchers used precipitation data from the CMIP5 model directly to study future precipitation trends without distinguishing between snowfall and rainfall.CMIP5 models have been proven to have better performance in simulating temperature but poorer performance in simulating precipitation.To overcome the above limitations,this paper used a Back Propagation Neural Network(BNN)to predict the rainfall-to-precipitation ratio(RPR)in months experiencing freezing-thawing transitions(FTTs).We utilized the meteorological(air pressure,air temperature,evaporation,relative humidity,wind speed,sunshine hours,surface temperature),topographic(altitude,slope,aspect)and geographic(longitude,latitude)data from 28 meteorological stations in the Chinese Tianshan Mountains region(CTMR)from 1961 to 2018 to calculate the RPR and constructed an index system of impact factors.Based on the BNN,decision-making trial and evaluation laboratory method(BP-DEMATEL),the key factors driving the transformation of the RPR in the CTMR were identified.We found that temperature was the only key factor affecting the transformation of the RPR in the BP-DEMATEL model.Considering the relationship between temperature and the RPR,the future temperature under different representative concentration pathways(RCPs)(RCP2.6/RCP4.5/RCP8.5)provided by 21 CMIP5 models and the meteorological factors from meteorological stations were input into the BNN model to acquire the future RPR from 2011 to 2100.The results showed that under the three scenarios,the RPR in the number of months experiencing FTTs during 2011-2100 will be higher than that in the historical period(1981-2010)in the CTMR.Furthermore,in terms of spatial variation,the RPR values on the south slope will be larger than those on the north slope under the three emission scenarios.Moreover,the RPR values exhibited different variation characteristics under different emission scenarios.Under the low-emission scenario(RCP2.6),as time passed,the RPR values changed slightly at more stations.Under the mediumemission scenario(RCP4.5),the RPR increased in the whole CTMR and stabilized on the north slope by the end of this century.Under the high-emission scenario(RCP8.5),the RPR values increased significantly through the 21 st century in the whole CTMR.This study may help to provide a scientific management basis for agricultural production and hydrology.

Ripple-effect analysis for operational architecture of air defense systems with supernetwork modeling

In order to solve the problem that the ripple-effect analy- sis for the operational architecture of air defense systems （OAADS） is hardly described in quantity with previous modeling approaches, a supernetwork modeling approach for the OAADS is put for- ward by extending granular computing. Based on that operational units and links are equal to different information granularities, the supernetwork framework of the OAADS is constructed as a “four- network within two-layer” structure by forming dynamic operating coalitions, and measuring indexes of the ripple-effect analysis for the OAADS are given combining with Laplace spectral radius. In this framework, via analyzing multidimensional attributes which inherit relations between operational units in different granular scales, an extended granular computing is put forward integrating with a topological structure. Then the operation process within the supernetwork framework, including transformation relations be- tween two layers in the vertical view and mapping relations among functional networks in the horizontal view, is studied in quantity. As the application case shows, comparing with previous modeling approaches, the supernetwork model can validate and analyze the operation mechanism in the air defense architecture, and the ripple-effect analysis can be used to confirm the key operational unit with micro and macro viewpoints.

Assessing the Forecasting of Comprehensive Loss Incurred by Typhoons:A Combined PCA and BP Neural Network Model

This paper develops a joint model utilizing the principal component analysis(PCA)and the back propagation(BP)neural network model optimized by the Levenberg Marquardt(LM)algorithm,and as an application of the joint model to investigate the damages caused by typhoons for a coastal province,Fujian Province,China in 2005-2015(latest).First,the PCA is applied to analyze comprehensively the relationship between hazard factors,hazard bearing factors and disaster factors.Then five integrated indices,overall disaster level,typhoon intensity,damaged condition of houses,medical rescue and self-rescue capability,are extracted through the PCA;Finally,the BP neural network model,which takes the principal component scores as input and is optimized by the LM algorithm,is implemented to forecast the comprehensive loss of typhoons.It is estimated that an average annual loss of 138.514 billion RMB occurred for 2005-2015,with a maximum loss of 215.582 in 2006 and a decreasing trend since 2010 though the typhoon intensity increases.The model was validated using three typhoon events and it is found that the error is less than 1%.These results provide information for the government to increase medical institutions and medical workers and for the communities to promote residents’self-rescue capability.

A Collaboration Network Model with Multiple Evolving Factors

To describe the empirical data of collaboration networks, several evolving mechanisms have been proposed, which usually introduce different dynamics factors controlling the network growth. These models can reasonably reproduce the empirical degree distributions for a number of we11-studied real-world collaboration networks. On the basis of the previous studies, in this work we propose a collaboration network model in which the network growth is simultaneously controlled by three factors, including partial preferential attachment, partial random attachment and network growth speed. By using a rate equation method, we obtain an analytical formula for the act degree distribution. We discuss the dependence of the act degree distribution on these different dynamics factors. By fitting to the empirical data of two typical collaboration networks, we can extract the respective contributions of these dynamics factors to the evolution of each networks.

A MOLECULAR NETWORK MODEL TO DESCRIBE THE NONLINEAR VISCOELASTIC BEHAVIOR OF POLYMERS

A transient molecular network model is built to describe the non- linear viscoelasticity of polymers by considering the effect of entanglement loss and regeneration on the relaxation of molecular strands. It is an extension of previous network theories. The experimental data on three thermoplastic polymers (ABS, PVC and PA6) obtained under various loading conditions are used to test the model. Agreement between the theoretical and experimental curves shows that the suggested model can describe successfully the relaxation behavior of the thermoplastic polymers under different loading rates by using relatively few relaxation modes. Thus the mi- cromechanism responsible for strain-rate dependence of relaxation process and the origin of nonlinear viscoelasticity may be disclosed.

RECURRENT NEURAL NETWORK MODEL BASED ON PROJECTIVE OPERATOR AND ITS APPLICATION TO OPTIMIZATION PROBLEMS

The recurrent neural network （RNN） model based on projective operator was studied. Different from the former study, the value region of projective operator in the neural network in this paper is a general closed convex subset of n-dimensional Euclidean space and it is not a compact convex set in general, that is, the value region of projective operator is probably unbounded. It was proved that the network has a global solution and its solution trajectory converges to some equilibrium set whenever objective function satisfies some conditions. After that, the model was applied to continuously differentiable optimization and nonlinear or implicit complementarity problems. In addition, simulation experiments confirm the efficiency of the RNN.

Artificial Neural Network Model for Discrimination of Stability of Ancient Landslide in Impounding Area of Three Gorges Project, China

The factors of geomorphology, geological setting, effect of ground water and environment dynamic factors (e.g. rainfall and artificial water recharge) should be integrated in the discrimination of the stability of the ancient landslide. As the criterion of landslide stability has been studied, the artificial neural network model was then applied to discriminate the stability of the ancient landslide in the impounding area of the Three Gorges project on the Yangtze River, China. The model has the property of self adaptive identifying and integrating complex qualitative factors and quantitative factors. The results of the artificial neural network model are coincided well with what were gained by classical limit equilibrium analysis (the Bishop method and Janbu method) and by other comprehensive discrimination methods.

Designing and optimizing a parallel neural network model for predicting the solubility of diosgenin in n-alkanols

Accurate estimation of the solubility of a chemical compound is an important issue for many industrial proce sses.To overcome the defects of some thermodynamic models and simple correlations,a parallel neural network(PNN) model was conceived and optimized to predict the solubility of diosgenin in seven n-alkanols(C_(1)-C_(7)).The linear regression analysis of the parity plots indicates that the PNN model can give more accurate descriptions of the solubility of diosgenin than the ordinary neural network(ONN) model.The comparison of the average root mean square deviation(RMSD) shows that the suggested model has a slight advantage over the thermodynamic NRTL model in terms of the calculating precision.Moreover,the PNN model can reflect the effects of the temperature and the chain length of the alcohol solvent on the solution behavior of diosgenin correctly and can estimate its solubility in the n-alkanols with more carbon atoms.

Force-constant-decayed anisotropic network model:An improved method for predicting RNA flexibility

RNA is an important biological macromolecule,which plays an irreplaceable role in many life activities.RNA functions are largely determined by its tertiary structure and the intrinsic dynamics encoded in the structure.Thus,how to effective extract structure-encoded dynamics is of great significance for understanding RNA functions.Anisotropic network model(ANM)is an efficient method to investigate macromolecular dynamical properties,which has been widely used in protein studies.However,the performance of the conventional ANM in describing RNA flexibility is not as good as that on proteins.In this study,we proposed a new approach,named force-constant-decayed anisotropic network model(fcdANM),to improve the performance in investigating the dynamical properties encoded in RNA structures.In fcd-ANM,nucleotide pairs in RNA structure were connected by springs and the force constant of springs was decayed exponentially based on the separation distance to describe the differences in the inter-nucleotide interaction strength.The performance of fcd-ANM in predicting RNA flexibility was evaluated using a non-redundant structure database composed of 51 RNAs.The results indicate that fcd-ANM significantly outperforms the conventional ANM in reproducing the experimental B-factors of nucleotides in RNA structures,and the Pearson correlation coefficient between the predicted and experimental nucleotide B-factors was distinctly improved by 21.05%compared to the conventional ANM.Fcd-ANM can serve as a more effective method for analysis of RNA dynamical properties.

Action Recognition Based on CSI Signal Using Improved Deep Residual Network Model

In this paper,we propose an improved deep residual network model to recognize human actions.Action data is composed of channel state information signals,which are continuous fine-grained signals.We replaced the traditional identity connection with the shrinking thresholdmodule.Themodule automatically adjusts the threshold of the action data signal,and filters out signals that are not related to the principal components.We use the attention mechanism to improve the memory of the network model to the action signal,so as to better recognize the action.To verify the validity of the experiment more accurately,we collected action data in two different environments.The experimental results show that the improved network model is much better than the traditional network in recognition.The accuracy of recognition in complex places can reach 92.85%,among which the recognition rate of raising hands is up to 96%.We combine the improved residual deep network model with channel state information action data,and prove the effectiveness of our model for classification through experimental data.