The effect of binder phase content on WC-AlCoCrFeNiTi_(0.2) high entropy cemented carbides microstructure and mechanical properties

Thiswork aims to explore WC-G201 high entropy cemented carbides properties fabricated by spark plasma sintering with AlCoCrFeNiTi_(0.2)and WC as binder phase and hard phase respectively.AlCoCrFeNiTi_(0.2)powder and WC powder were mixed at the mass ratio of 5:95,10:90 and 15:85 respectively,and then via SPS at 1300℃for 5min.The results show that the binder phase has good wettability to WC,and with the increase of binder phase content,the hardness of the alloy decreases gradually,fracture toughness increases first and then decreases,the maximum value is 7.88 MPa m1/2.It is estimated that the comprehensive mechanical property of cemented carbides is the best when the binder content is between 5wt%and 10wt%.

Roles of effective stabilizers in improving oral bioavailability of naringenin nanocrystals:Maintenance of supersaturation generated upon dissolution by inhibition of drug dimerization

Nanocrystals(NCs),a colloidal dispersion system formulated with stabilizers,have attracted widespread interest due to their ability to effectively improve the oral bioavailability of poorly water-soluble drugs.The stabilizer plays a key role because it can affect the physical stability and even the oral bioavailability of NCs.However,how stabilizers affect the bioavailability of NCs remains unknown.In this study,F68,F127,HPMC,and PVP were each used as a stabilizer to formulate naringenin NCs.The NCs formulated with PVP exhibited excellent release behaviors,cellular uptake,permeability,oral bioavailability,and anti-inflammatory effects.The underlying mechanism is that PVP effectively inhibits the formation of naringenin dimer,which in turn improves the physical stability of the supersaturated solution generated when NC is dissolved.This finding provides insights into the effects of stabilizers on the in vivo performances of NCs and supplies valuable knowledge for the development of poorly water-soluble drugs.

Susceptibility of patients with chronic obstructive pulmonary disease to heart rate difference associated with the short-term exposure to metals in ambient fine particles:A panel study in Beijing,China

Susceptibility of patients with chronic obstructive pulmonary disease(COPD)to cardiovascular autonomic dysfunction asso-ciated with exposure to metals in ambient fine particles(PM2.5,particulate matter with aerodynamic diameter≤2.5µm)remains poorly evidenced.Based on the COPDB(COPD in Beijing)panel study,we aimed to compare the associations of heart rate(HR,an indicator of cardiovascular autonomic function)and exposure to metals in PM2.5 between 53 patients with COPD and 82 healthy controls by using linear mixed-effects models.In all participants,the HR levels were significantly associated with interquartile range increases in the average concentrations of Cr,Zn,and Pb,but the strength of the associations differed by exposure time(from 1.4%for an average 9 days(d)Cr exposure to 3.5%for an average 9 d Zn exposure).HR was positively associated with the average concentrations of PM2.5 and certain metals only in patients with COPD.Associations between HR and exposure to PM2.5,K,Cr,Mn,Ni,Cu,Zn,As,and Se in patients with COPD significantly differed from those in health controls.Furthermore,association between HR and Cr exposure was robust in COPD patients.In conclusion,our findings indicate that COPD could exacerbate difference in HR following exposure to metals in PM2.5.

Optimal CNN-based semantic segmentation model of cutting slope images

This paper utilizes three popular semantic segmentation networks,specifically DeepLab v3+,fully convolutional network(FCN),and U-Net to qualitively analyze and identify the key components of cutting slope images in complex scenes and achieve rapid image-based slope detection.The elements of cutting slope images are divided into 7 categories.In order to determine the best algorithm for pixel level classification of cutting slope images,the networks are compared from three aspects:a)different neural networks,b)different feature extractors,and c)2 different optimization algorithms.It is found that DeepLab v3+with Resnet18 and Sgdm performs best,FCN 32s with Sgdm takes the second,and U-Net with Adam ranks third.This paper also analyzes the segmentation strategies of the three networks in terms of feature map visualization.Results show that the contour generated by DeepLab v3+(combined with Resnet18 and Sgdm)is closest to the ground truth,while the resulting contour of U-Net(combined with Adam)is closest to the input images.