FULLERENE GEOMETRY PREDICTION WITH MOLECULAR MECHANICS CALCULATIONS

A serial of fullerenes had been built and the optimized geome- tries had been obtained with the energy minimization of molecular mechanics calculations according to the fact that the pentagonal number is exactly 12 in the fullerenes which consist of pentagons and hexagons.The fullerene geometry prediction could facilitate further theoretical and synthetical studies in the near future.

Distinct gut microbiomes in Thai patients with colorectal polyps

BACKGROUND Colorectal polyps that develop via the conventional adenoma-carcinoma sequence[e.g.,tubular adenoma(TA)]often progress to malignancy and are closely associated with changes in the composition of the gut microbiome.There is limited research concerning the microbial functions and gut microbiomes associated with colorectal polyps that arise through the serrated polyp pathway,such as hyperplastic polyps(HP).Exploration of microbiome alterations asso-ciated with HP and TA would improve the understanding of mechanisms by which specific microbes and their metabolic pathways contribute to colorectal carcinogenesis.AIM To investigate gut microbiome signatures,microbial associations,and microbial functions in HP and TA patients.METHODS Full-length 16S rRNA sequencing was used to characterize the gut microbiome in stool samples from control participants without polyps[control group(CT),n=40],patients with HP(n=52),and patients with TA(n=60).Significant differences in gut microbiome composition and functional mechanisms were identified between the CT group and patients with HP or TA.Analytical techniques in this study included differential abundance analysis,co-occurrence network analysis,and differential pathway analysis.RESULTS Colorectal cancer(CRC)-associated bacteria,including Streptococcus gallolyticus(S.gallolyticus),Bacteroides fragilis,and Clostridium symbiosum,were identified as characteristic microbial species in TA patients.Mediterraneibacter gnavus,associated with dysbiosis and gastrointestinal diseases,was significantly differentially abundant in the HP and TA groups.Functional pathway analysis revealed that HP patients exhibited enrichment in the sulfur oxidation pathway exclusively,whereas TA patients showed dominance in pathways related to secondary metabolite biosynthesis(e.g.,mevalonate);S.gallolyticus was a major contributor.Co-occurrence network and dynamic network analyses revealed co-occurrence of dysbiosis-associated bacteria in HP patients,whereas TA patients exhibited co-occurrence of CRC-associated bacteria.Furthermore,the co-occurrence of SCFA-producing bacteria was lower in TA patients than HP patients.CONCLUSION This study revealed distinct gut microbiome signatures associated with pathways of colorectal polyp development,providing insights concerning the roles of microbial species,functional pathways,and microbial interactions in colorectal carcinogenesis.

PREDICTION OF MECHANICAL PROPERTY OF WHISKER REINFORCED METAL MATRIX COMPOSITE: PART-I. MODEL AND FORMULATION

Based on study of strain distribution in whisker reinforced metal matrix composites, an explicit precise stiffness tensor is derived. In the present theory, the effect of whisker orientation on the macro property of composites is considered, but the effect of random whisker position and the complicated strain field at whisker ends are averaged. The derived formula is able to predict the stiffness modulus of composites with arbitrary whisker orientation under any loading condition. Compared with the models of micro mechanics, the present theory is competent for modulus prediction of actual engineering composites. The verification and application of the present theory are given in a subsequent paper published in the same issue

Artificial neural network prediction of mechanical properties of hot rolled low carbon steel strip

Conventionally, direct tensile tests are employed to measure mechanical properties of industrially pro- duced products. In mass production, the cost of sampling and labor is high, which leads to an increase of total pro- duction cost and a decrease of production efficiency. The main purpose of this paper is to develop an intelligent pro- gram based on artificial neural network （ANN） to predict the mechanical properties of a commercial grade hot rolled low carbon steel strip, SPHC. A neural network model was developed by using 7 x 5 x 1 back-propagation （BP） neural network structure to determine the multiple relationships among chemical composition, product pro- cess and mechanical properties. Industrial on-line application of the model indicated that prediction results were in good agreement with measured values. It showed that 99.2 % of the products＇ tensile strength was accurately pre- dicted within an error margin of ~ 10 %, compared to measured values. Based on the model, the effects of chemical composition and hot rolling process on mechanical properties were derived and the relative importance of each in- put parameter was evaluated by sensitivity analysis. All the results demonstrate that the developed ANN models are capable of accurate predictions under real-time industrial conditions. The developed model can be used to sub- stitute mechanical property measurement and therefore reduce cost of production. It can also be used to control and optimize mechanical properties of the investigated steel.

Prediction of Henry Constants and Adsorption Mechanism of Volatile Organic Compounds on Multi-Walled Carbon Nanotubes by Using Support Vector Regression

Support vector regression （SVR） combined with particle swarm optimization for its parameter optimization is employed to establish a model for predicting the Henry constants of multi-walled carbon nanotubes （MWNTs） for adsorption of volatile organic compounds （VOCs）. The prediction performance of SVR is compared with those of the model of theoretical linear salvation energy relationship （TLSER）. By using leave-one-out cross validation of SVR test Henry constants for adsorption of 35 VOCs on MWNTs, the root mean square error is 0.080, the mean absolute percentage error is only 1.19~, and the correlation coefficient （R2） is as high as 0.997. Compared with the results of the TLSER model, it is shown that the estimated errors by SVR are ali smaller than those achieved by TLSER. It reveals that the generalization ability of SVR is superior to that of the TLSER model Meanwhile, multifactor analysis is adopted for investigation of the influences of each molecular structure descriptor on the Henry constants. According to the TLSER model, the adsorption mechanism of adsorption of carbon nanotubes of VOCs is mainly a result of van der Waals and interactions of hydrogen bonds. These can provide the theoretical support for the application of carbon nanotube adsorption of VOCs and can make up for the lack of experimental data.

Analysis of Hot Rolling Routes of AZ31B Magnesium Alloy and Prediction of Tensile Property of Hot-rolled Sheets

At the initial rolling temperature of 250 to 400 ℃, AZ31B magnesium alloy sheets were hot rolled by four different rolling routes. Microstructures and mechanical properties of the hot-rolled magnesium alloy sheets were analyzed by optical microscope and tensile tests respectively. Based on the Hall-Petch relation, considering the average grain size and grain size distribution, the nonlinear fitting analysis between the tensile strength and average grain size was carried on, and then the prediction model of tensile strength of hot-rolled AZ31B magnesium alloy sheet was established. The results indicate that, by rolling with multi-pass cross rolling, uniform, fine and equiaxial grain microstructures can be produced, the anisotropy of hot-rolled magnesium sheet can also be effectively weakened. Strong correlation was observed between the average grain size and tensile property of the hot-rolled magnesium alloy sheet. Grain size distribution coefficient d（CV） was introduced to reflect the dispersion degree about a set of grain size data, and then the Hall-Petch relation was perfected. Ultimately, the prediction accuracy of tensile strength of multi-pass hot-rolled AZ31B magnesium alloy was improved, and the prediction of tensile property can be performed by the model.

Theoretical Prediction of the Photovoltaic Properties of BFBPD-PC61 BM System as a Promising Organic Solar Cell

In this work,the photovoltaic properties of BFBPD-PC61 BM system as a promising high-performance organic solar cell（OSC） were theoretically investigated by means of quantum chemistry and molecular dynamics calculations coupled with the incoherent charge-hopping model.Moreover,the hole carrier mobility of BFBPD thin-film was also estimated with the aid of an amorphous cell including 100 BFBPD molecules.Results revealed that the BFBPD-PC61 BM system possesses a middle-sized open-circuit voltage of 0.70 V,large short-circuit current density of 17.26 mA ·cm^-2,high fill factor of 0.846,and power conversion efficiency of 10%.With the Marcus model,in the BFBPD-PC61 BM interface,the exciton-dissociation rate,kdis,was predicted to be 2.684×10^13 s^-1,which is as 3-5 orders of magnitude large as the decay（radiative and non-radiative） one（10-8-10^10s^-1）,indicating a high exciton-dissociation efficiency of 100% in the BFBPD-PC61 BM interface.Furthermore,by the molecular dynamics simulation,the hole mobility of BFBPD thin-film was predicted to be as high as 1.265 × 10^-2 cm-2·V^-1·s^-1,which can be attributed to its dense packing in solid state.

Bi-variable damage model for fatigue life prediction of metal components

Based on the theory of continuum damage mechanics,a bi-variable damage mechanics model is developed,which,according to thermodynamics,is accessible to derivation of damage driving force,damage evolution equation and damage evolution criteria. Furthermore,damage evolution equations of time rate are established by the generalized Drucker＇s postulate. The damage evolution equation of cycle rate is obtained by integrating the time damage evolution equations,and the fatigue life prediction method for smooth specimens under repeated loading with constant strain amplitude is constructed. Likewise,for notched specimens under the repeated loading with constant strain amplitude,the fatigue life prediction method is obtained on the ground of the theory of conservative integral in damage mechanics. Thus,the material parameters in the damage evolution equation can be obtained by reference to the fatigue test results of standard specimens with stress concentration factor equal to 1,2 and 3.

Deep learning for predictive mechanical properties of hot-rolled strip in complex manufacturing systems

Higher requirements for the accuracy of relevant models are put throughout the transformation and upgrade of the iron and steel sector to intelligent production.It has been difficult to meet the needs of the field with the usual prediction model of mechanical properties of hotrolled strip.Insufficient data and difficult parameter adjustment limit deep learning models based on multi-layer networks in practical applications;besides,the limited discrete process parameters used make it impossible to effectively depict the actual strip processing process.In order to solve these problems,this research proposed a new sampling approach for mechanical characteristics input data of hot-rolled strip based on the multi-grained cascade forest(gcForest)framework.According to the characteristics of complex process flow and abnormal sensitivity of process path and parameters to product quality in the hot-rolled strip production,a three-dimensional continuous time series process data sampling method based on time-temperature-deformation was designed.The basic information of strip steel(chemical composition and typical process parameters)is fused with the local process information collected by multi-grained scanning,so that the next link’s input has both local and global features.Furthermore,in the multi-grained scanning structure,a sub sampling scheme with a variable window was designed,so that input data with different dimensions can get output characteristics of the same dimension after passing through the multi-grained scanning structure,allowing the cascade forest structure to be trained normally.Finally,actual production data of three steel grades was used to conduct the experimental evaluation.The results revealed that the gcForest-based mechanical property prediction model outperforms the competition in terms of comprehensive performance,ease of parameter adjustment,and ability to sustain high prediction accuracy with fewer samples.

Filling Pattern of Volcanostratigraphy of Cenozoic Volcanic Rocks in the Changbaishan Area and Possible Future Eruptions

The Cenozoic volcanostratigraphy in the Changbaishan area had complex building processes.Twenty-two eruption periods have been determined from the Wangtian＇e, Touxi, and Changbaishan volcanoes. The complex volcanostratigraphy of the Changbaishan area can be divided into four types of filling patterns from bottom to top. They are lava flows filling in valleys（LFFV）, lava flows filling in platform（LFFP）, lava flows formed the cone（LFFC）, and pyroclastic Flow filling in crater or valleys（PFFC/V）. LFFV has been divided into four layers and terminates as a lateral overlap. The topography of LFFV, which is controlled by the landform, is lens shaped with a wide flat top and narrow bottom.LFFP has been divided into three layers and terminates as a lateral downlap. The topography of LFFP is sheet and tabular shaped with a narrow top and wide bottom. It has large width to thickness ratio. It was built by multiple eruptive centers distributed along the fissure. The topography of LFFC, which is located above the LFFP, has a hummocky shape with a narrow sloping top and a wide flat bottom. It terminates as a later downlap or backstepping. It has large width to thickness ratio. It was built by a single eruptive center. The topography of PFFC/V, which located above the LFFC, LFFP, or valley, has the shape of fan and terminates as a lateral downlap or overlap. It has a small width to thickness ratio and was built by a single eruptive center. The filling pattern is controlled by temperature, SiO_2 content,volatile content, magma volume, and the paleolandform. In the short term, the eruptive production of the Changbaishan area is comenditic ash or pumice of a Plinian type eruption. The eruptive volume in future should be smaller than that of the Baguamiao period, and the filling pattern should be PFFC/V,which may cause huge damage to adjacent areas.

Modeling hot strip rolling process under framework of generalized additive model

This research develops a new mathematical modeling method by combining industrial big data and process mechanism analysis under the framework of generalized additive models(GAM)to generate a practical model with generalization and precision.Specifically,the proposed modeling method includes the following steps.Firstly,the influence factors are screened using mechanism knowledge and data-mining methods.Secondly,the unary GAM without interactions including cleaning the data,building the sub-models,and verifying the sub-models.Subsequently,the interactions between the various factors are explored,and the binary GAM with interactions is constructed.The relationships among the sub-models are analyzed,and the integrated model is built.Finally,based on the proposed modeling method,two prediction models of mechanical property and deformation resistance for hot-rolled strips are established.Industrial actual data verification demonstrates that the new models have good prediction precision,and the mean absolute percentage errors of tensile strength,yield strength and deformation resistance are 2.54%,3.34%and 6.53%,respectively.And experimental results suggest that the proposed method offers a new approach to industrial process modeling.

Fast determination of meso-level mechanical parameters of PFC models

To solve the problems of blindness and inefficiency existing in the determination of meso-level mechanical parameters of particle flow code (PFC) models, we firstly designed and numerically carried out orthogonal tests on rock samples to investigate the correlations between macro-and meso-level mechanical parameters of rock-like bonded granular materials. Then based on the artificial intelligent technology, the intelligent prediction systems for nine meso-level mechanical parameters of PFC models were obtained by creating, training and testing the prediction models with the set of data got from the orthogonal tests. Lastly the prediction systems were used to predict the meso-level mechanical parameters of one kind of sandy mudstone, and according to the predicted results the macroscopic properties of the rock were obtained by numerical tests. The maximum relative error between the numerical test results and real rock properties is 3.28% which satisfies the precision requirement in engineering. It shows that this paper provides a fast and accurate method for the determination of meso-level mechanical parameters of PFC models.

Network Pharmacology Approach to Uncover the Mechanism Governing the Effect of Polygonum cuspidatum on Hepatoma

Background:As a popular Chinese herbal medicine,polygonum cuspidatum is widely used to treat hepatoma in China.Network pharmacology targets biological networks and analyzes the links between drugs,targets,and diseases in these networks.In this study,network pharmacology was utilized to reveal the potential pharmacological mechanisms of polygonum cuspidatum on hepatoma.Methods:The chemical constituents of polygonum cuspidatum were searched from TCMSP data and target gene names were extracted from UniProt database.The GeneCard,OMIM,PharmGkb,Therapeutic Targets database,and DrugBank database were used to establish a database of hepatoma targets.Common targets for drugs and diseases were obtained from Venn online tools.The protein-protein interaction network was constructed with the STRING database to analyze the related protein interaction relationship.The clusterProfiler R software package was used to enrich the common target proteinsof GO and KEGG to obtained the related functions and pathways.Cytoscape 3.7.2 was used to build a“drug-compound-target-disease”network.Finally,docking of the active components with the core target was carried out.Results:Ten active components of polygonum cuspidatum were obtained,and 108 potential targets for hepatoma were identified.The biological functions of the common target genes of polygonum cuspidatum and hepatoma are shown in GO analysis.Pathways involved in the treatment of hepatoma include virus-related signaling pathways,IL-17 signaling pathways,apoptosis signaling pathways,and PI3K/AKT signaling pathways.Conclusions:The network pharmacology directly shows the“drug-compound-target-disease”effects of polygonum cuspidatum on hepatoma,and provides a basis for studying the mechanism of the effect of polygonum cuspidatum on hepatoma.

Molecular mechanism of treatment of pneumonia in children with Mori cortex-Lycii cortex based on network pharmacology

Objective:To study the molecular mechanism of Mori cortex-Lycii cortex in the treatment of pneumonia in children based on network pharmacology. Methods:TCMSP and BATMAN-TCM online prediction database were used to screen and collect the active ingredients and targets of Mori cortex-Lycii cortex based on oral bioavailability and drug-like. Predictive analysis of disease targets was conducted through PubMed,GeneCards and DrugBank databases. The component-target regulation network was constructed by using Cytoscape 3.7.2 software,and the network topology of the core target was analyzed. Finally,the Bioconductor platform and R language were used for GO function analysis and KEGG pathway enrichment analysis,and the target-key pathway network diagram was constructed. Results:A total of 43 active components,including quercetin,kaempferol,acacetin,and beta-sitosterol,were identified with 242 potential targets. There were 3 271 pneumonia targets in children,among which the key targets were IL-6,AKT1,MAPK8,etc. There were 31 common targets of MMP9,TNF,AKT1 and so on. GO biological processes included the response to lipopolysaccharides,molecule of bacterial origin,metal ions,regulation of apoptotic signaling pathway,and T cell activation. The KEGG signaling pathways involved mainly included TNF,PI3 K/AKT and MAPK signaling pathway. Conclusion:Quercetin,kaempferol,beta-sitosterol and acacetin in Mori cortex-Lycii cortex may act on several signal transduction pathways such as TNF,PI3 K/AKT,MAPK signal pathways through AKT1,MAPK8,IL-6 and MMP9 targets,then treat children pneumonia via antiinflammation action. The results can provide references for the further study on the treatment of pneumonia in children with Mori Ccortex-Lycii cortex.

Prediction of mechanical properties of cold rolled strip based on improved extreme random tree

Taking the 2130 cold rolling production line of a steel mill as the research object,feature dimensionality reduction and decoupling processing were realized by fusing random forest and factor analysis,which reduced the generation of weak decision trees while ensured its diversity.The base learner used a weighted voting mechanism to replace the traditional average method,which improved the prediction accuracy.Finally,the analysis method of the correlation between steel grades was proposed to solve the problem of unstable prediction accuracy of multiple steel grades.The experimental results show that the improved prediction model of mechanical properties has high accuracy:the prediction accuracy of yield strength and tensile strength within the error of±20 MPa reaches 93.20%and 97.62%,respectively,and that of the elongation rate under the error of±5%has reached 96.60%.

Mechanical Property Prediction of Strip Model Based on PSO-BP Neural Network

Mechanical property prediction of hot rolled strip is one of the hotspots in material processing research. To avoid the local infinitesimal defect and slow constringency in pure BP algorithm, a kind of global optimization algorithm-particle swarm optimization （PSO） is adopted. The algorithm is combined with the BP rapid training algorithm, and then, a kind of new neural network （NN） called PSO-BP NN is established. With the advantages of global optimization ability and the rapid constringency of the BP rapid training algorithm, the new algorithm fully shows the ability of nonlinear approach of multilayer feedforward network, improves the performance of NN, and provides a favorable basis for further online application of a comprehensive model.

Prediction of Mechanical Properties of Hot Rolled Strip by Using Semi-Parametric Single-Index Model

A semi-parametric single-index model based approach was proposed for prediction of mechanical properties of hot rolled strip. Based on industrial production data, a semi-parametric single-index model was developed by choo-sing the appropriate kernel function and window width to predict the yield strength, tensile strength and elongation. When data samples are limited, compared with regression method and neural network method, the prediction results show that the semi-parametric single-index model based method is more adaptive and the prediction performance is superior to those by both regression and neural network methods.

Mechanism of Key Ingredient of Astragalus membranaceus on Lung Adenocarcinoma via PI3K/AKT Signaling Clarifled by Utilizing Network Pharmacology Approach and Experimental Validation

Objective:To investigate the mechanism of the effect of Astragalus membranaceus(A.membranaceus)on lung adenocarcinoma at the molecular level to elucidate the specific targets according to the network pharmacology approach.Methods:The active components of A.membranaceus and their potential targets were col ected from the Traditional Chinese Medicine Systems Pharmacology Database.Lung adenocarcinoma-associated genes were acquired based on Gene Cards,Online Mendelian Inheritance in Man(OMIM),Pharm GKB,and Therapeutic Targets databases.The PI3K/AKT signaling pathway-related genes were obtained using Reactome portal.Networks of"ingredient-target"and"ingredient-target-pathway-disease"were constructed using the Cytoscape3.6.0 software.The relationships among targets were analyzed according protein-protein interaction(PPI)network.Finally,molecular docking was applied to construct the binding conformation between active ingredients and core targets.Cel counting kit 8(CCK8)and Western blot assays were performed to determine the mechanism of the key ingredient of A.membranaceus.Results:A total of 20 active components and their 329 targets,and 7,501 lung adenocarcinoma-related genes and 130 PI3K/AKT signaling pathway-related genes were obtained.According to Venn diagram and PPI network analysis,2 mainly active ingredients,including kaempferol and quercetin,and 6 core targets,including TP53,MAPK1,EGF,AKT1,ERBB2,and EGFR,were identified.The two important active ingredients of A.membranaceus,kaempferol and quercetin,exert the therapeutic effect in lung adenocarcinoma partly by acting on the 6 core targets(TP53,MAPK1,EGF,AKT1,ERBB2,and EGFR)of PI3K/AKT signaling pathway.Expressions of potential targets in lung adenocarcinoma and normal samples were analyzed by using UALCAN portal and found that ERBB2 was overexpressed in lung adenocarcinoma tissues and upregulation of it correlated with clinicopathological characteristics.Final y,quercetin repressed viabilities of lung adenocarcinoma cells by targeting ERBB2 on PI3K/AKT signaling confirmed by CCK8 and Western blot.Conclusion:Our finding unraveled that an active ingredient of A.membranaceus,quercetin,significantly inhibited the lung adenocarcinoma cel s proliferation by repressing ERBB2 level and inactivating the PI3K/AKT signaling pathway.

A physically based methodology for predicting anisotropic creep properties of Ni-based superalloys

This paper is focused on developing suitable methodology for predicting creep characteristics （i.e., the minimum creep strain rate, stress rupture life and time to a specified creep strain） of typical Ni-based directionally solidified （DS） and single-crystal （SC） superalloys. A modem method with high accuracy on simulating wide ranging creep properties was fully validated by a sufficient amount of experimental data, which was then developed to model anisotropic creep characteristics by introducing a simple orientation factor defined by the ultimate tensile strength （UTS）. Physical confidence on this methodology is provided by the well-predicted transitions of creep defor- mation mechanisms. Meanwhile, this method was further adopted to innovatively evaluate the creep properties of different materials from a relative perspective.

Safety Analysis for Thread Pair Clearance of Safety Mechanism of Three Gorges Project Ship Lift

The Three Gorges Project（TGP） ship lift employs 4 safety mechanisms, of which one consists of a rotary locking screw and a nut jaw column. The thread pair clearance（TPC） of safety mechanisms is set at 60 mm. Owing to influential factors,the TPC changes randomly in the upward/downward-stroke of the ship chamber. If it diminished to 0, the safety mechanism would be jammed, thus resulting in disastrous accidents. By the bearing test of the drive system, 7 influential factors have been studied; 15 other influential factors（including 8 factors of manufacture and installation deviation, 3 factors of chamber offset, 2 factors of external load and 2 factors of wear） have been analyzed based on the design data. Results by the limit superposition reveal that the TPC change varies from -43.8 mm to ＋48.4 mm when the water level of the chamber ranges from 3.4 m to 3.6 m. According to the Gaussian distribution, the probability of the TPC change varied from -53.7 mm to ＋58.8 mm in the most detrimental status is99.74%, therefore, the TPC remains in a safe condition. This paper puts forward that two-phase operation of the drive system should be adopted so as to reduce the maximum TPC change to -44.6 mm.