Investigation of meso-failure behaviors of Jinping marble using SEM with bending loading system

In th is study, th e m eso-failure m ech an ism an d fracture surface o f Jinping m arble w ere investigated bym ean s o f scanning electro n m icroscope （SEM） w ith ben d in g loading system and laser-scanner equipment. The Y antang an d B aishan m arbles specim ens from Jinping II hyd ro p o w er sta tio n w ere used. Testresu lts show th a t th e fracture to u g h n ess and m echanical behaviors o f Y antang m arble w ere basicallyh ig h er th a n th o se o f Baishan m arble. This is m ainly d u e to th e fact th a t Baishan m arble co n tain s a largep ercen tag e o f d o lom ite an d m in o r mica. Crack pro p ag atio n p a th and fracture m orphology in d icated th a tth e d irection o f ten sile stress has a significant effect on th e m echanical behaviors an d fracture toughnesso f B aishan m arble. For Yantang an d B aishan m arbles, a large n u m b e r o f m icrocracks a ro u n d th e m aincrack tip w ere observed w h e n th e directio n o f ten sile stress w as parallel to th e bed d in g plane.C onversely, few m icrocracks o ccurred w h e n th e directio n o f ten sile stress w as p erp en d icu lar to th ebed d in g plane. The presen ce o f a large n u m b e r o f m icrocracks a t th e m ain crack tip d ecreased th e globalfracture to u g h n ess o f m arble. The results o f th re e -p o in t ben d in g te sts show ed th a t th e average bearingcapacity o f intact m arble is 3.4 tim es th e notch ed m arble, b u t th e ductility p ro p e rty o f th e defectivem arble afte r p eak load is b e tte r th a n th a t o f th e intact m arble. H ence, large d efo rm atio n m ay beg en erated before failure o f in tact m arbles a t Jinping II h y d ro p o w er station. The fractal d im en sio n o ffracture surface w as also calculated by th e cube covering m eth o d . O bservational resu lt show ed th a t th elargest fractal dim en sio n o f Y antang m arble is cap tu red w h e n th e directio n o f ten sile stress is parallel toth e bedding plane. H ow ever, th e fractal d im en sio n o f fracture surface o f Yantang an d Baishan m arblesw ith ten sile stress vertical to th e bed d in g plane is relatively sm all. The fractal d im en sio n can also be usedto characterize th e ro ughness o f fracture surface o f rock m aterials.

槟榔与细胞内活性氧及自噬的关系

槟榔作为一级致癌物,其与口腔癌的关系受到广泛关注。活性氧(ROS)及自噬水平与肿瘤的发生发展密切相关,研究发现槟榔可诱发细胞内ROS及自噬水平变化。本文结合相关研究进展,对槟榔、细胞内ROS以及自噬之间的关系进行综述。

Fault Diagnosis of Motor in Frequency Domain Signal by Stacked De-noising Auto-encoder

With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big data.Deep learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities,this also brings influence to the mechanical fault diagnosis field.Therefore,according to the characteristics of motor vibration signals(nonstationary and difficult to deal with)and mechanical‘big data’,combined with deep learning,a motor fault diagnosis method based on stacked de-noising auto-encoder is proposed.The frequency domain signals obtained by the Fourier transform are used as input to the network.This method can extract features adaptively and unsupervised,and get rid of the dependence of traditional machine learning methods on human extraction features.A supervised fine tuning of the model is then carried out by backpropagation.The Asynchronous motor in Drivetrain Dynamics Simulator system was taken as the research object,the effectiveness of the proposed method was verified by a large number of data,and research on visualization of network output,the results shown that the SDAE method is more efficient and more intelligent.

Deciphering bioactive compounds of complex natural products by tandem mass spectral molecular networking combined with an aggregation-induced emission based probe

Natural products are great treasure troves for the discovery of bioactive components.Current bioassay guided fractionation for identification of bioactive components is time-and workload-consuming.In this study,we proposed a robust and convenient strategy for deciphering the bioactive profile of natural products by mass spectral molecular networking combined with rapid bioassay.As a proof-of-concept,the strategy was applied to identify angiotensin converting enzyme(ACE)inhibitors of Fangjihuangqi decoction(FJHQD),a traditional medicine clinically used for the treatment of heart failure.The chemical profile of FJHQD was comprehensively revealed with the assistance of tandem mass spectral molecular networking,and a total of 165 compounds were identified.With characterized constituents,potential clinical applications of FJHQD were predicted by Bioinformatics Analysis Tool for Molecular mech ANism of Traditional Chinese Medicine,and a range of cardiovascular related diseases were significantly enriched.ACE inhibitory activities of FJHQD and its constituents were then investigated with an aggregation-induced emission based fluorescent probe.FJHQD exhibited excellent ACE inhibitory effects,and a bioactive molecular network was established to elucidate the ACE inhibitory profile of constituents in FJHQD.This bioactive molecular network provided a panoramic view of FJHQD’s ACE inhibitory activities,which demonstrated that flavones from Astragali Radix and Glycyrrhizae Radix et Rhizoma,saponins from Astragali Radix,and sesquiterpenoids from Atractylodis Macrocephalae Rhizoma were principal components responsible for this effect of FJHQD.Among them,four novel ACE inhibitors were the first to be reported.Our study indicated that the proposed strategy offers a useful approach to uncover the bioactive profile of traditional medicines and provides a pragmatic workflow for exploring bioactive components.

Weak Fault Diagnosis of Rolling Bearing Based on Improved Stochastic Resonance

Stochastic resonance can use noise to enhance weak signals,effectively reducing the effect of noise signals on feature extraction.In order to improve the early fault recognition rate of rolling bearings,and to overcome the shortcomings of lack of interaction in the selection of SR(Stochastic Resonance)method parameters and the lack of validation of the extracted features,an adaptive genetic random resonance early fault diagnosis method for rolling bearings was proposed.compared with the existing methods,the AGSR(Adaptive Genetic Stochastic Resonance)method uses genetic algorithms to optimize the system parameters,and further optimizes the parameters while considering the interaction between the parameters.This method can effectively extract the weak fault features of the bearing.In order to verify the effect of feature extraction,the feature signal extracted by AGSR method was input into the Fully connected neural network for fault diagnosis.the practicality of the algorithm is verified by simulation data and rolling bearing experimental data.the results show that the proposed method can effectively detect the early weak features of rolling bearings,and the fault diagnosis effect is better than the existing methods.

STM study on micromorphology of pyrite and dynamic significance of ore-formation

A careful scanning tunneling microscope (STM) observation has been made on the micromorphology structure of the crystal faces for a group of pyrites which include two types of pyrite specimens: natural pyrites, from the different stage of hydrothermal ore deposits and artificial crystal of pyrite. It has been discovered that there is a set of micromorphological structures on the surfaces of pyrite crystals, including pisolitic hillocks, lotus root-like hillocks and spiral steps. This study reveals that the micromorphology of pyrite crystals, which are closely related to thermodynamic conditions and dynamic environment of the ore-forming systems, carries a lot of genetic information.