Separating component parts of soil respiration under Robinia pseudoacacia plantation in the Taihang Mountains,China

Partitioning the respiratory components of soil surface CO2 efflux is important in understanding carbon turnover and in identifying the soil carbon sink/source function in response to land-use change. The sensitivities of soil respiration components on changing climate patterns are currently not fully understood. We used trench and isotopic methods to separate total soil respiration into autotrophic （RA） and heterotrophic components （RH）. This study was undertaken on a Robinia pseudoacacia L. plantation in the southern Taihang Mountains, China. The fractionation of soil ^13CO2 was analyzed by comparing the δ^13C of soil CO2 extracted from buried steel tubes with results from Gas Vapor Probe Kits at a depth of 50 cm.at the preliminary test （2.03‰）. The results showed that the contribution of autotrophic respiration （fRA） increased with increasing soil depth.The contribution of heterotrophic respiration （fR/4） declined with increasing soil depth. The contribution of autotrophic respiration was similar whether estimated by the trench method （fRA, 23.50%） or by the isotopic method in which a difference in value of ^13C between soil and plant prevailed in the natural state （RC, 21.03%）. The experimental error produced by the trench method was insignificant as compared with that produced by the isotopic method, providing a technical basis for further investigations.

Seasonal variations in temperature sensitivity of soil respiration in a larch forest in the Northern Daxing’an Mountains in Northeast China

Temperature sensitivity of respiration of forest soils is important for its responses to climate warming and for the accurate assessment of soil carbon budget. The sensitivity of temperature (T_(i)) to soil respiration rate (R_(s)), and Q_(10) defined by e^(10(lnRs−lna)/Ti) has been used extensively for indicating the sensitivity of soil respiration. The soil respiration under a larch (Larix gmelinii) forest in the northern Daxing’an Mountains, Northeast China was observed in situ from April to September, 2019 using the dynamic chamber method. Air temperatures (T_(air)), soil surface temperatures (T_(0cm)), soil temperatures at depths of 5 and 10 cm (T_(5cm) and T_(10cm), respectively), and soil-surface water vapor concentrations were monitored at the same time. The results show a significant monthly variability in soil respiration rate in the growing season (April–September). The Q_(10) at the surface and at depths of 5 and 10 cm was estimated at 5.6, 6.3, and 7.2, respectively. The Q_(10@10 cm) over the period of surface soil thawing (Q_(10@10 cm, thaw) = 36.89) were significantly higher than that of the growing season (Q_(10@10 cm, growth )= 3.82). Furthermore, the Rs in the early stage of near-surface soil thawing and in the middle of the growing season is more sensitive to changes in soil temperatures. Soil temperature is thus the dominant factor for season variations in soil respiration, but rainfall is the main controller for short-term fluctuations in respiration. Thus, the higher sensitivity of soil respiration to temperature (Q_(10)) is found in the middle part of the growing season. The monthly and seasonal Q_(10) values better reflect the responsiveness of soil respiration to changes in hydrometeorology and ground freeze-thaw processes. This study may help assess the stability of the soil carbon pool and strength of carbon fluxes in the larch forested permafrost regions in the northern Daxing’an Mountains.

A sub-grid scale model for Burgers turbulence based on the artificial neural network method

The present study proposes a sub-grid scale model for the one-dimensional Burgers turbulence based on the neuralnetwork and deep learning method.The filtered data of the direct numerical simulation is used to establish thetraining data set,the validation data set,and the test data set.The artificial neural network(ANN)methodand Back Propagation method are employed to train parameters in the ANN.The developed ANN is applied toconstruct the sub-grid scale model for the large eddy simulation of the Burgers turbulence in the one-dimensionalspace.The proposed model well predicts the time correlation and the space correlation of the Burgers turbulence.

Analysis and discussion of different methods of artificial ice-high specimen preparation

Because ice-high foundation soil is widely distributed in permafrost regions,the correct preparation of ice-high specimens is of critical interest in engineering design for foundation stability.Past research has shown that the uniaxial compression strength of ice-high frozen soils changes as the ice or total water content increases; the differences of different methods of specimen preparation are analyzed here and the advantages and disadvantages of them are presented.It is confirmed that the role of crushed ice is significantly different from that of naturally frozen ice in frozen soils,and the size and amount of crushed ice will influence the strength and deformation mechanism of frozen soils.Therefore,it is strongly recommended that when a ice-high specimen is artificially prepared,the ice should be frozen through natural means and not be replaced with crushed ice.

Estimation of Tsunami Run-up Height by Three Artificial Neural Network Methods

Tsunami ran-up height is a significant parameter for dimensions of coastal structures. In the present study, tsunami run-up heights are estimated by three different Artificial Neural Network （ANN） models, i.e. Feed Forward Back Propagation （FFBP）, Radial Basis Functions （RBF） and Generalized Regression Neural Network （GRNN）. As the input for the ANN configuration, the wave height （H） values are employed. It is shown that the tsunami ran-up height values are closely approximated with all of the applied ANN methods. The ANN estimations are slightly superior to those of the empirical equation. It can be seen that the ANN applications are especially significant in the absence of adequate number of laboratory experiments. The results also prove that the available experiment data set can be extended with ANN simulations. This may be helpful to decrease the burden of the experimental studies and to supply results for comparisons.

Deciphering gastric inflammation-induced tumorigenesis through multi-omics data and AI methods

Gastric cancer(GC), the fifth most common cancer globally, remains the leading cause of cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process in GC development;therefore, systematic research in this area should improve understanding of the biological mechanisms that initiate GC development and promote cancer hallmarks. Here, we summarize biological knowledge regarding gastric inflammation-induced tumorigenesis, and characterize the multi-omics data and systems biology methods for investigating GC development. Of note, we highlight pioneering studies in multi-omics data and state-of-the-art network-based algorithms used for dissecting the features of gastric inflammation-induced tumorigenesis, and we propose translational applications in early GC warning biomarkers and precise treatment strategies. This review offers integrative insights for GC research, with the goal of paving the way to novel paradigms for GC precision oncology and prevention.

The improved artificial bee colony algorithm for mixed additive and multiplicative random error model and the bootstrap method for its precision estimation

To solve the complex weight matrix derivative problem when using the weighted least squares method to estimate the parameters of the mixed additive and multiplicative random error model(MAM error model),we use an improved artificial bee colony algorithm without derivative and the bootstrap method to estimate the parameters and evaluate the accuracy of MAM error model.The improved artificial bee colony algorithm can update individuals in multiple dimensions and improve the cooperation ability between individuals by constructing a new search equation based on the idea of quasi-affine transformation.The experimental results show that based on the weighted least squares criterion,the algorithm can get the results consistent with the weighted least squares method without multiple formula derivation.The parameter estimation and accuracy evaluation method based on the bootstrap method can get better parameter estimation and more reasonable accuracy information than existing methods,which provides a new idea for the theory of parameter estimation and accuracy evaluation of the MAM error model.

Simultaneous Determination of Iron and Manganese in Water Using Artificial Neural Network Catalytic Spectrophotometric Method

A new analytical method using Back-Propagation (BP) artificial neural network and kinetic spectrophotometry for simultaneous determination of iron and magnesium in tap water, the Yellow River water and seawater is established. By conditional experiments, the optimum analytical conditions and parameters are obtained. Levenberg-Marquart (L-M) algorithm is used for calculation in BP neural network. The topological structure of three-layer BP ANN network architecture is chosen as 15-16-2 (nodes). The initial value of gradient coefficient μ is fixed at 0.001 and the increase factor and reduction factor of μ take the default values of the system. The data are processed by computers with our own programs written in MATLAB 7.0. The relative standard deviation of the calculated results for iron and manganese is 2.30% and 2.67% respectively. The results of standard addition method show that for the tap water, the recoveries of iron and manganese are in the ranges of 98.0%-104.3% and 96.5%-104.5%, and the RSD is in the range of 0.23%-0.98%; for the Yellow River water (Lijin district of Shandong Province), the recoveries of iron and manganese are in the ranges of 96.0%-101.0% and 98.7%-104.2%, and the RSD is in the range of 0.13%-2.52%; for the seawater in Qingdao offshore, the recoveries of iron and manganese are in the ranges of 95.3%-104.8% and 95.3%-104.7%, and the RSD is in the range of 0.14%-2.66%. It is found that 21 common cations and anions do not interfere with the determination of iron and manganese under the optimum experimental conditions. This method exhibits good reproducibility and high accuracy in the determination of iron and manganese and can be used for the simultaneous determination of iron and manganese in tap water and natural water. By using the established ANN- catalytic spectrophotometric method, the iron and manganese concentrations of the surface seawater at 11 sites in Qingdao offshore are determined and the level distribution maps of iron and manganese are drawn.

An Artificial Neural Network-Based Response Surface Method for Reliability Analyses of c-φ Slopes with Spatially Variable Soil

This paper presents an artificial neural network（ANN）-based response surface method that can be used to predict the failure probability of c-φslopes with spatially variable soil.In this method,the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model;the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties;and finally,an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables.The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability.As a result,the obtained approximate function can be used as an alternative to the specific analysis process in c-φslope reliability analyses.

Calculation method of ship collision force on bridge using artificial neural network

Ship collision on bridge is a dynamic process featured by high nonlinearity and instantaneity. Calculating ship-bridge collision force typically involves either the use of design-specification-stipulated equivalent static load, or the use of finite element method (FEM) which is more time-consuming and requires supercomputing resources. In this paper, we proposed an alternative approach that combines FEM with artificial neural network (ANN). The radial basis function neural network (RBFNN) employed for calculating the impact force in consideration of ship-bridge collision mechanics. With ship velocity and mass as the input vectors and ship collision force as the output vector, the neural networks for different network parameters are trained by the learning samples obtained from finite element simulation results. The error analyses of the learning and testing samples show that the proposed RBFNN is accurate enough to calculate ship-bridge collision force. The input-output relationship obtained by the RBFNN is essentially consistent with the typical empirical formulae. Finally, a special toolbox is developed for calculation efficiency in application using MATLAB software.

Two-phase Method without Any Artificial Variable

A method is provided to achieve an initial basic feasible solution of a linear programming in this paper. This method dose not need introducing any artificial variable, but needs only solving an auxiliary linear programming. Compared with the traditional two-phase method, it has advantages of saving the memories and reducing the computational efforts.

Smart Society and Artificial Intelligence:Big Data Scheduling and the Global Standard Method Applied to Smart Maintenance

The implementation of artificial intelligence(AI)in a smart society,in which the analysis of human habits is mandatory,requires automated data scheduling and analysis using smart applications,a smart infrastructure,smart systems,and a smart network.In this context,which is characterized by a large gap between training and operative processes,a dedicated method is required to manage and extract the massive amount of data and the related information mining.The method presented in this work aims to reduce this gap with near-zero-failure advanced diagnostics(AD)for smart management,which is exploitable in any context of Society 5.0,thus reducing the risk factors at all management levels and ensuring quality and sustainability.We have also developed innovative applications for a humancentered management system to support scheduling in the maintenance of operative processes,for reducing training costs,for improving production yield,and for creating a human–machine cyberspace for smart infrastructure design.The results obtained in 12 international companies demonstrate a possible global standardization of operative processes,leading to the design of a near-zero-failure intelligent system that is able to learn and upgrade itself.Our new method provides guidance for selecting the new generation of intelligent manufacturing and smart systems in order to optimize human–machine interactions,with the related smart maintenance and education.

Prediction of Superconductivity for Oxides Based on Structural Parameters and Artificial Neural Network Method

Superconductive properties for oxides were predicted by artificial neural network (ANN) method with structural and chemical parameters as inputs. The predicted properties include superconductivity for oxides, distributed ranges of the superconductive transition temperature (Tc) for complex oxides, and Tc values for cuprate superconductors. The calculated results indicated that the adjusted ANN can be used to predict superconductive properties for unknown oxides.

Seismic wave input method for three-dimensional soil-structure dynamic interaction analysis based on the substructure of artificial boundaries

The method of inputting the seismic wave determines the accuracy of the simulation of soil-structure dynamic interaction. The wave method is a commonly used approach for seismic wave input, which converts the incident wave into equivalent loads on the cutoff boundaries. The wave method has high precision, but the implementation is complicated, especially for three-dimensional models. By deducing another form of equivalent input seismic loads in the fi nite element model, a new seismic wave input method is proposed. In the new method, by imposing the displacements of the free wave fi eld on the nodes of the substructure composed of elements that contain artifi cial boundaries, the equivalent input seismic loads are obtained through dynamic analysis of the substructure. Subsequently, the equivalent input seismic loads are imposed on the artifi cial boundary nodes to complete the seismic wave input and perform seismic analysis of the soil-structure dynamic interaction model. Compared with the wave method, the new method is simplifi ed by avoiding the complex processes of calculating the equivalent input seismic loads. The validity of the new method is verifi ed by the dynamic analysis numerical examples of the homogeneous and layered half space under vertical and oblique incident seismic waves.

Application of the Artificial Compression Method to the Simulation of Two-Dimensional Frontogenesis

The artificial compression method (ACM) that is generally used to capture the contact discontinuity in nonviscous flows is used here in the simulation of quasi-geostrophic ideal frontogenesis in two dimensions. A comparison is made among the result of the ACM, the simulation result of Cullen, and the exact solution of the semi-geostrophic equations. The simulated front in this paper is more prominent than Cullen′s and is much closer to the exact solution.

An Improved Minimum Distance Method Based on Artificial Neural Networks

MDM (minimum distance method) is a very popular algorithm in staterecognition. But it has a presupposition, that is, the distance within one class must be shorterenough than the distance between classes. When this presupposition is not satisfied, the method isno longer valid. In order to overcome the shortcomings of MDM, an improved minimum distance method(IMDM) based on ANN (artificial neural networks) is presented. The simulation results demonstratethat IMDM has two advantages, that is, the rate of recognition is faster and the accuracy ofrecognition is higher compared with MDM.

Preparation of Microcapsules Containing Artificial Diet for Tropical Fishes with Spray Gelling Method

The microcapsules containing the artificial diet for tropical fishes were prepared with the spray gelling method in order to prevent water environmental pollution. The carboxymethyl cellulose sodium aqueous solution, in which α-tocopherol droplets containing the powdery artificial diet were dispersed, was dropped or sprayed into the chitosan aqueous solution. Microcapsules were prepared by forming polyionic complex shell made from chitosan and carboxymethyl cellulose sodium. In the experiment, the concentration of carboxymethyl cellulose sodium (CMCNa) was mainly changed to investigate the effect on the diameters of microcapsules, the content and the microencapsulation efficiency. The microcapsules couldn’t be prepared with the concentration of carboxymethyl cellulose sodium less than 3.0 wt%. The microcapsules were the core-shell type. The diameters of microcapsules were increased with the concentration of CMCNa and the microencapsulation efficiency of ca. 100% could be obtained by the preparation method presented in this study. The microcapsules were found to be eaten well by tropical fishes and to prevent water environmental pollution.

Study on the comprehensive advantage evaluation method of high-tech enterprises based on RBF artificial neural network

This paper designs an intelligent evaluation approach using a Radial Basis Function （RBF） Artificial Neural Network. We based our approach on establishing a comprehensive advantage evaluating index system that offers scientific substance for creating a development plan and the strategic management of high-tech industry and regional clusters of high-tech enterprises. Furnhermore, this paper selects some typical high-tech enterprises＇ data to make comprehensive training on the network system. Meanwhile, the paper chooses some enterprises as testing samples to test the method, the result of which proves that this method is truly effective. The research of this paper provides a comprehensive advantage evaluating and managing method for high-tech enterprise.

Purification of heavy metal chromium in saturated sand by artificial freezing:Mechanism and method optimization

Heavy metal pollution of soil has become one of the most common hazards in human development.The artificial freezing method,especially the progressive freezing method,can reduce heavy metal pollutants in the soil and promises to be an effective in-situ treatment of contaminated sites.This study analyzes the freezing purification mechanism of heavy metal contaminants in saturated sand and identifies three main factors that impact the effects of purification:freezing rate,initial concentration,and diffusion coefficient.Moreover,one-dimensional freezing tests are carried out by different freezing modes.The experimental results show that the heavy metal chromium could only be removed effectively with a slow freezing rate.By optimizing the freezing mode and freezing rate,a long section of soil was frozen and purified,with the maximum purification rate reaching 65.8%.This study shows that it is feasible to treat contaminated saturated sand by a gradual-cooling freezing method.

Determination of penetration depth at high velocity impact using finite element method and artificial neural network tools

Determination of ballistic performance of an armor solution is a complicated task and evolved significantly with the application of finite element methods(FEM) in this research field.The traditional armor design studies performed with FEM requires sophisticated procedures and intensive computational effort,therefore simpler and accurate numerical approaches are always worthwhile to decrease armor development time.This study aims to apply a hybrid method using FEM simulation and artificial neural network(ANN) analysis to approximate ballistic limit thickness for armor steels.To achieve this objective,a predictive model based on the artificial neural networks is developed to determine ballistic resistance of high hardness armor steels against 7.62 mm armor piercing ammunition.In this methodology,the FEM simulations are used to create training cases for Multilayer Perceptron(MLP) three layer networks.In order to validate FE simulation methodology,ballistic shot tests on 20 mm thickness target were performed according to standard Stanag 4569.Afterwards,the successfully trained ANN(s) is used to predict the ballistic limit thickness of 500 HB high hardness steel armor.Results show that even with limited number of data,FEM-ANN approach can be used to predict ballistic penetration depth with adequate accuracy.