Quantitative models for microstructure and thermal conductivity of vermicular graphite cast iron cylinder block based on cooling rate

The relationships of cooling rate with microstructure and thermal conductivity of vermicular graphite cast iron(VGI) cylinder block were studied, which are important for design and optimization of the casting process of VGI cylinder blocks. Cooling rates at different positions in the cylinder block were calculated based on the cooling curves recorded with a solidification simulation software. The metallographic structure and thermal conductivity were observed and measured using optical microscopy(OM), scanning electrical microscopy(SEM) and laser flash diffusivity apparatus, respectively. The effects of the cooling rate on the vermicularity, total and average areas of all graphite particles, and the pearlite fraction in the VGI cylinder block were investigated. It is found that the vermicularity changes in parabola trend with the increase of cooling rate. The total area of graphite particles and the cooling rate at eutectoid stage can be used to predict pearlite fraction well. Moreover, it is found that the thermal conductivity at room temperature is determined by the average area of graphite particles and pearlite fraction when the range of vermicularity is from 80% to 93%. Finally, the quantitative models are established to calculate the vermicularity, pearlite fraction, and thermal conductivity of the VGI cylinder block.

Calibration of quantitative rescattering model for simulating vortex high-order harmonic generation driven by Laguerre–Gaussian beam with nonzero orbital angular momentum

We calibrate the macroscopic vortex high-order harmonic generation(HHG)obtained by the quantitative rescattering(QRS)model to compute single-atom induced dipoles against that by solving the time-dependent Schr?dinger equation(TDSE).We show that the QRS perfectly agrees with the TDSE under the favorable phase-matching condition,and the QRS can accurately predict the main features in the spatial profiles of vortex HHG if the phase-matching condition is not good.We uncover that harmonic emissions from short and long trajectories are adjusted by the phase-matching condition through the time-frequency analysis and the QRS can simulate the vortex HHG accurately only when the interference between two trajectories is absent.This work confirms that it is an efficient way to employ the QRS model in the single-atom response for precisely simulating the macroscopic vortex HHG.

Quantitative Modelling of Multiphase Lithospheric Stretching and Deep Thermal History of Some Tertiary Rift Basins in Eastern China

The stretching process of some Tertiary rift basins in eastern China is characterized by multiphase rifting. A multiple instantaneous uniform stretching model is proposed in this paper to simulate the formation of the basins as the rifting process cannot be accurately described by a simple (one episode) stretching model. The study shows that the multiphase stretching model, combined with the back-stripping technique, can be used to reconstruct the subsidence history and the stretching process of the lithosphere, and to evaluate the depth to the top of the asthenosphere and the deep thermal evolution of the basins. The calculated results obtained by applying the quantitative model to the episodic rifting process of the Tertiary Qiongdongnan and Yinggehai basins in the South China Sea are in agreement with geophysical data and geological observations. This provides a new method for quantitative evaluation of the geodynamic process of multiphase rifting occurring during the Tertiary in eastern China.

Quantitative evaluation of multi-process collaborative operation in steelmaking–continuous casting sections

The quantitative evaluation of multi-process collaborative operation is of great significance for the improvement of production planning and scheduling in steelmaking–continuous casting sections(SCCSs). However, this evaluation is difficult since it relies on an in-depth understanding of the operating mechanism of SCCSs, and few existing methods can be used to conduct the evaluation, due to the lack of full-scale consideration of the multiple factors related to the production operation. In this study, three quantitative models were developed, and the multiprocess collaborative operation level was evaluated through the laminar-flow operation degree, the process matching degree, and the scheduling strategy availability degree. Based on the evaluation models for the laminar-flow operation and process matching levels, this study investigated the production status of two steelmaking plants, plants A and B, based on actual production data. The average laminar-flow operation(process matching) degrees of SCCSs were obtained as 0.638(0.610) and 1.000(0.759) for plants A and B, respectively, for the period of April to July 2019. Then, a scheduling strategy based on the optimization of the furnace-caster coordinating mode was suggested for plant A. Simulation experiments showed higher availability than the greedy-based and manual strategies. After the proposed scheduling strategy was applied,the average process matching degree of the SCCS of plant A increased by 4.6% for the period of September to November 2019. The multi-process collaborative operation level was improved with fewer adjustments and interruptions in casting.

Quantitative Metal Magnetic Memory Reliability Modeling for Welded Joints

Metal magnetic memory（MMM） testing has been widely used to detect welded joints. However, load levels, environmental magnetic field, and measurement noises make the MMM data dispersive and bring difficulty to quantitative evaluation. In order to promote the development of quantitative MMM reliability assessment, a new MMM model is presented for welded joints. Steel Q235 welded specimens are tested along the longitudinal and horizontal lines by TSC-2M-8 instrument in the tensile fatigue experiments. The X-ray testing is carried out synchronously to verify the MMM results. It is found that MMM testing can detect the hidden crack earlier than X-ray testing. Moreover, the MMM gradient vector sum K_（vs） is sensitive to the damage degree, especially at early and hidden damage stages. Considering the dispersion of MMM data, the K_（vs） statistical law is investigated, which shows that K_（vs） obeys Gaussian distribution. So K_（vs） is the suitable MMM parameter to establish reliability model of welded joints. At last, the original quantitative MMM reliability model is first presented based on the improved stress strength interference theory. It is shown that the reliability degree R gradually decreases with the decreasing of the residual life ratio T, and the maximal error between prediction reliability degree R_1 and verification reliability degree R_2 is 9.15%. This presented method provides a novel tool of reliability testing and evaluating in practical engineering for welded joints.

Quantitative modeling, optimization, and verification of ^(63)Nipowered betavoltaic cells based on three-dimensional ZnO nanorod arrays

Betavoltaic cells(BCs)are promising self-generating power cells with long life and high power density.However,the low energy conversion efficiency(ECE)has limitations in practical engineering applications.Widebandgap semiconductors(WBGSs)with three-dimensional(3-D)nanostructures are ideal candidates for increasing the ECE of BCs.This paper proposes hydrothermally grown ZnO nanorod arrays(ZNRAs)for ^(63)Ni-powered BCs.A quantitative model was established for simulation using the parameter values of the dark characteristics,which were obtained from the experimental measurements for a simulated BC based on a Ni-incorporated ZNRAs structure.Monte Carlo(MC)modeling and simulation were conducted to obtain the values of the β energy deposited in ZNRAs with different nanorod spacings and heights.Through the simulation and optimization of the 3-D ZNRAs and 2-D ZnO bulk structures,the performance of the ^(63)Ni-powered BCs based on both structures was evaluated using a quantitative model.The BCs based on the 3-D ZNRAs structure and 2-D ZnO bulk structure achieved a maximum ECE of 10.1%and 4.69%,respectively,which indicates the significant superiority of 3-D nanostructured WBGSs in increasing the ECE of BCs.

Quantitative Model for the Surface-related Electron Transfer in CdS Quantum Dots

The influence of surface S^2- dangling bonds and surface doped ions（Se^2-, Cu^2＋, and Hg^2＋） on the photoluminescence of Cd^2＋-rich CdS QDs was investigated. A quantitative model was proposed to understand the complex transfer processes of excited electrons in CdS QDs. The transfer of excited electrons from either the conduction band or the Cd^2＋-related trap-state to the surface S^2-related shallow hole trap-state is effective. However, the trap of excited electrons by surface doped ion trap-states from the Cd^2＋-related trap-state is more effective than that from the conduction band. The efficiency of trapping electrons from both the conduction band and the Cd^2＋-related trap-state can be quantitatively understood with the help of the proposed model. The results show that the transfer efficiency of excited electrons is dependent on the location of the energy-level of the relevant surface-related trap-state. The trap of excited electrons by the surface trap-state with energy-level closer to that of the conduction band is more effective, especially for the trap of excited electrons from Cd^2＋-related trap-state.

STUDIES ON OPTIMUM MODELS FOR CULTIVATING DAHURIAN LARCH SEEDLINGS ON FRIGID REGIONS

The optimum models for cultivationg the seedlings of Dahurian Larch (Larix gmelim Rupr. ) were studied in the central nursery of Tahe Forestery Bureau from 1989 to 1993. The resultswerc as follows The optimum models of the main morphological standards of the seedlings were30000 kg /hm2 of' decomposed' ash for improviong soil, 70,000 kg/hm2 of excrement and 1,500 kg/hm2of' calcium superphosphate as basal fertilizer , and 500 kg/hm2 of (NH4)2HPO4 and 220 kg/hm2 of'urea as top dressing. During growth perlod of the seedlings, the growth was greatly checked when thesoil water suction (SWS) was uithin 50 ~ 73. 3 kPa or below 10 kPa but greatest when SWS waswithin 30 ~ 40 kPa in initial growth stage and within 10-20kPa in last growth stage. The method for determining the optimum sowing time is to measure the depth of soil thawing, accumulated temperature abovc 5℃ both at 5 cm of the depth of of soil and in atmosphere. In Tahe region, the optimumsowing trme for Dahurian larch for Dahuria larch is about on May 10 when the depth of soil thawing, the accumulatedtemperature above 5℃ at 5 cm of the depth and the accumulated temperature above 5℃ in atmosphere reaches 40 cm. abovc 46℃ and abovae 56℃ respectively. The optimum seedlings density forthis region is 600-800 individuals/m2.

An Approach for Integrating Quantitative Decision Model with Qualitative Judgment

In this paper, decision making in complex environment is considered and an approach integrating quantitative decision model with qualitative judgment is proposed. The concept of belief degree for quantitative decision model in a complex environment is presented. The integration in formulation and reasoning of quantitative model with qualitative judgment is studied. The combination of various belief degree generated by quantitative model and qualitative judgment is discussed. A decision rule of tradeoff between optimality and belief degree of optimality is proposed.

Quantitative rescattering theory for nonsequential double ionization

We review the recently improved quantitative rescattering theory for nonsequential double ionization, in which the lowering of threshold due to the presence of electric field at the time of recollision has been taken into account. First,we present the basic theoretical tools which are used in the numerical simulations, especially the quantum theories for elastic scattering of electron as well as the processes of electron impact excitation and electron impact ionization. Then,after a brief discussion about the properties of the returning electron wave packet, we provide the numerical procedures for the simulations of the total double ionization yield, the double-to-single ionization ratio, and the correlated two-electron momentum distribution.

Study on original position statistic quantitative method for the content of Al in inclusions from steels

The inclusions of Al in steels have great influence on the properties and the quality of steels.But there are some difficulties on the content determination of Al inclusions with accuracy and rapidity.Conventional quantity analysis of stable inclusions is electrolysis method with the disadvantages of long analytical period,trivial operation and a little loss of some fine and instable oxides.The content of insoluble aluminum can be obtained by the subtraction between the content of total Al and acid soluble Al determined by chemical methods with the disadvantage of complicate procedure.Original position statistic distribution analysis(OPA) can be used for the determination of inclusions by collecting and discriminating the signals produced by single spark discharge in the way of no pre-sparking,scanning and emitting continuously.In this study the element of Al in some spectrum certified reference materials of carbon and medium or low-alloyed steels was analyzed by original opposition statistic distribution technique.Two quantitative mathematical models used by original position distribution analysis technology for the analysis of the content of Al inclusions have been investigated and been applied to the analysis of the content of Al inclusions in many kinds of spectrum certified reference materials and some medium and low-alloyed steel samples.The scope of application of the models and their limited conditions were discussed.It was found that the results calculated by OPA had great difference with the value by chemical method for some samples with the total content of Al above 0.2%.The reason is that some intensity of abnormal sparks have gone beyond the range of the largest intensity that can be determined by the instrument so that there is a certain deviation on the identification of abnormal sparks and the calculation of threshold value.It was found that the size distribution of Al inclusions had a great influence on the application of the two models.For most of certified reference materials for spectrum analysis and real steel samples the results of the content of Al in inclusions calculated by model 1 was satisfied and had good coincidence with the value of insoluble Al by chemical method. But for the samples with so many large inclusions of Al existed,the influence of intensity should be considered because the signal increase did not vary in the form of simple function.So model 2 was more suitable for the content calculation of Al in inclusions in this case.In order to choose suitable model to be used it is suggested that the size distribution of inclusions should be determined by OPA before content determination of Al in inclusions. The results of the content of Al inclusions calculated by the selected mathematical model were satisfied and had good coincidence with the value of insoluble Al obtained by the subtraction between the content of total Al and soluble Al determined by chemical methods when the content of total aluminum was below 0.2%.

Machine Learning-Based Quantitative Structure-Activity Relationship and ADMET Prediction Models for ERα Activity of Anti-Breast Cancer Drug Candidates

Breast cancer is presently one of the most common malignancies worldwide,with a higher fatality rate.In this study,a quantitative structure-activity relationship(QSAR)model of compound biological activity and ADMET(Absorption,Distribution,Metabolism,Excretion,Toxicity)properties prediction model were performed using estrogen receptor alpha(ERα)antagonist information collected from compound samples.We first utilized grey relation analysis(GRA)in conjunction with the random forest(RF)algorithm to identify the top 20 molecular descriptor variables that have the greatest influence on biological activity,and then we used Spearman correlation analysis to identify 16 independent variables.Second,a QSAR model of the compound were developed based on BP neural network(BPNN),genetic algorithm optimized BP neural network(GA-BPNN),and support vector regression(SVR).The BPNN,the SVR,and the logistic regression(LR)models were then used to identify and predict the ADMET properties of substances,with the prediction impacts of each model compared and assessed.The results reveal that a SVR model was used in QSAR quantitative prediction,and in the classification prediction of ADMET properties:the SVR model predicts the Caco-2 and hERG(human Ether-a-go-go Related Gene)properties,the LR model predicts the cytochrome P450 enzyme 3A4 subtype(CYP3A4)and Micronucleus(MN)properties,and the BPNN model predicts the Human Oral Bioavailability(HOB)properties.Finally,information entropy theory is used to validate the rationality of variable screening,and sensitivity analysis of the model demonstrates that the constructed model has high accuracy and stability,which can be used as a reference for screening probable active compounds and drug discovery.

Relationship between rock uniaxial compressive strength and digital core drilling parameters and its forecast method

The rock uniaxial compressive strength(UCS)is the basic parameter for support designs in underground engineering.In particular,the rock UCS should be obtained rapidly for underground engineering with complex geological conditions,such as soft rock,fracture areas,and high stress,to adjust the excavation and support plan and ensure construction safety.To solve the problem of obtaining real-time rock UCS at engineering sites,a rock UCS forecast idea is proposed using digital core drilling.The digital core drilling tests and uniaxial compression tests are performed based on the developed rock mass digital drilling system.The results indicate that the drilling parameters are highly responsive to the rock UCS.Based on the cutting and fracture characteristics of the rock digital core drilling,the mechanical analysis of rock cutting provides the digital core drilling strength,and a quantitative relationship model(CDP-UCS model)for the digital core drilling parameters and rock UCS is established.Thus,the digital core drilling-based rock UCS forecast method is proposed to provide a theoretical basis for continuous and quick testing of the surrounding rock UCS.

A new hybrid framework of site selection for groundwater recharge

Since incorrect site selection has sometimes led to the failure of artificial recharge projects,it is necessary to increase the effectiveness of such projects and minimize their failure by employing new techniques.Therefore,the present research used a combination of decision-making models,numerical groundwater modeling and clustering technique to determine suitable sites for implementation of an artificial recharge project.This hybrid approach was employed for the Yasouj aquifer located in southwestern Iran.In the first stage,by employing an AHP decision-making model,hydraulic conductivity,specific yield,slope,land use,depth to groundwater,and aquifer thickness were selected from 21 criteria used in previous research.The selected criteria were then entered as input into the classical k-means clustering model.Using the output,aquifer was divided into seven different regions or clusters.These clusters were then matched with the land use map,and some of the abandoned land areas were selected as the final option for implementing the artificial recharge project.Finally,the MODFLOW code in the GMS software was used to simulate the groundwater level and cluster the sites selected,with regards to increase in groundwater level.Results indicated that the most significant increases in groundwater level(43 and 27 cm)were those of Clusters 2 and 6 in the northern and western parts of the aquifer,respectively.Therefore,this approach can be used in other similar aquifers in arid and semi-arid regions to select the best sites for artificial recharge and to prevent loss of floodwaters.

Review on the study of topographic effect on seismic ground motion

Topographic effect study is a very important research topic in seismology, seismic engineering,earthquake engineering, engineering earthquake construction and engineering seismology. This paper focuses on its present development status. Post-earthquake investigation has found that the existence of topography caused more serious earthquake damage. The actual seismographs also recorded the topographic amplification effect of 6 to 7 times and even more than 10 times. Numerical simulation is an important technique to study topographic effect, which complements the lack of observed records. However researches on 3-D topographic effect are not enough and need to be studied deeper. To find the main influence factors and the quantitative relationship between topography and ground motion are required very urgently. Obviously the achievements not only can be applied in the earthquake resistant design, but also can provide the quantitative pre-earthquake disaster prediction and quantitative post-earthquake disaster evaluation.

Research and Implementation of Architecture for Distributed Service Performance Management

An architecture for online discovery quantitative models system of service performance management is proposed. The system is capable of constructing the quantitative models without prior knowledge of the managed elements. The model can be updated continuously in response to the changes made in provider configurations and the evolution of business demands. Due to the existence of strong correlation between the distributed service metrics and response times, linear and hyper-linear quantitative models are constructed, which respectively, use stepwise multiple linear regression algorithm. The simulation results show the effectiveness of a quantitative model constructing system and model constructing algorithms.

Effects of local neighbourhood diversity on crown structure and productivity of individual trees in mature mixed-species forests

Background:Species-specific genotypic features,local neighbourhood interactions and resource supply strongly influence the tree stature and growth rate.In mixed-species forests,diversity-mediated biomass allocation has been suggested to be a fundamental mechanism underlying the positive biodiversity-productivity relationships.Empirical evidence,however,is rare about the impact of local neighbourhood diversity on tree characteristics analysed at a very high level of detail.To address this issue we analysed these effects on the individual-tree crown architecture and tree productivity in a mature mixed forest in northern Germany.Methods:Our analysis considers multiple target tree species across a local neighbourhood species richness gradient ranging from 1 to 4.We applied terrestrial laser scanning to quantify a large number of individual mature trees(N=920)at very high accuracy.We evaluated two different neighbour inclusion approaches by analysing both a fixed radius selection procedure and a selection based on overlapping crowns.Results and conclusions:We show that local neighbourhood species diversity significantly increases crown dimension and wood volume of target trees.Moreover,we found a size-dependency of diversity effects on tree productivity(basal area and wood volume increment)with positive effects for large-sized trees(diameter at breast height(DBH)>40 cm)and negative effects for small-sized(DBH<40 cm)trees.In our analysis,the neighbour inclusion approach has a significant impact on the outcome.For scientific studies and the validation of growth models we recommend a neighbour selection by overlapping crowns,because this seems to be the relevant scale at which local neighbourhood interactions occur.Because local neighbourhood diversity promotes individual-tree productivity in mature European mixed-species forests,we conclude that a small-scale species mixture should be considered in management plans.

Evidence of potential change in nonsequential double ionization

Recently,the quantitative rescattering model(QRS)for nonsequential double ionization(NSDI)is modified by taking into account the potential change(PC)due to the presence of electric field at the time of recollision.Using the improved QRS model,we simulate the longitudinal momentum distributions of doubly charged ions He2+by projecting the correlated two-electron momentum distributions for NSDI of He onto the main diagonal.The obtained results are compared directly with the experimental data at different intensities.It is found that when the PC is considered,the width of momentum distributions reduces and the agreement between theory and experiment is improved.

The optimization method of industrial robot arm structure based on green manufacturing technology

In order to realize the optimal design of the industrial robot arm structure,an optimization method of the industrial robot arm structure based on green manufacturing technology is proposed.The stability of arm structure parameter acquisition can be controlled.The quantitative adjustment model of structural optimization parameters is constructed.The differential fusion control of the arm structure is realized.This paper analyzes the structure parameter law of the robot arm.We use dynamic parameter prediction and output torque parameter compensation method to control the arm structure.According to the adaptive iterative processing results,the arm structure parameter identification is realized.According to the identification results,the cutting parameter optimization method is adopted for the analytical control of the arm structure,and finally the optimized design of the industrial robot arm structure is realized through the green manufacturing technology.The simulation test results show that for the accuracy of the industrial robot arm structure design,this method is better,the output stability is higher,and the arm motion trajectory has a low deviation from the actual motion trajectory,which improves the optimization control and design capabilities of the industrial robot arm structure.

In Silico Investigation of Agonist Activity of a Structurally Diverse Set of Drugs to hPXR Using HM-BSM and HM-PNN

The human pregnane X receptor（hPXR） plays a critical role in the metabolism, transport and clearance of xenobiotics in the liver and intestine. The hPXR can be activated by a structurally diverse of drugs to initiate clinically relevant drug-drug interactions. In this article, in silico investigation was performed on a structurally diverse set of drugs to identify critical structural features greatly related to their agonist activity towards h PXR. Heuristic method（HM）-Best Subset Modeling（BSM） and HM-Polynomial Neural Networks（PNN） were utilized to develop the linear and non-linear quantitative structure-activity relationship models. The applicability domain（AD） of the models was assessed by Williams plot. Statistically reliable models with good predictive power and explain were achieved（for HM-BSM, r^2=0.881, q^2_（LOO）=0.797, q^2_（EXT）=0.674; for HM-PNN, r^2=0.882, q^2_（LOO）=0.856, q^2_（EXT）=0.655）. The developed models indicated that molecular aromatic and electric property, molecular weight and complexity may govern agonist activity of a structurally diverse set of drugs to h PXR.