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| 1 | Effects of different dietary energy and protein levels and sex on growth performance,carcass characteristics and meat quality of F1 Angus x Chinese Xiangxi yellow cattle显示文摘Background:The experiment evaluated the effect of nutrition levels and sex on the growth performance,carcass characteristics and meat quality of F1 Angus × Chinese Xiangxi yellow cattle.Methods:During the background period of 184 d,23 steers and 24 heifers were fed the same ration,then put into a2×2×2 factorial arrangement under two levels of- dietary energy(TON:70/80%DM),protein(CP:11.9/14.3%DM)and sex(S:male/female) during the finishing phase of 146 d.The treatments were-(1) high energy/low protein(HELP),(2) high energy/high protein(HEHP),(3) low energy/low protein(LELP) and(4) low energy/high protein(LEHP).Each treatment used 6 steers and 6 heifers,except for HELP- 5 steers and 6 heifers.Results:Growth rate and final carcass weight were unaffected by dietary energy and protein levels or by sex.Compared with the LE diet group,the HE group had significantly lower dry matter intake(DMI,6.76 vs.7.48 kg DM/d),greater chest girth increments(46.1 vs.36.8 cm),higher carcass fat(19.9 vs.16.3%) and intramuscular fat content(29.9 vs.22.8%DM).The HE group also had improved yields of top and medium top grade commercial meat cuts(39.9 vs.36.5%).The dressing percentage was higher for the HP group than the LP group(53.4 vs.54.9%).Steers had a greater length increment(9.0 vs.8.3 cm),but lower carcass fat content(16.8 vs.19.4%) than heifers.The meat quality traits(shear force value,drip loss,cooking loss and water holding capacity) were not affected by treatments or sex,averaging 3.14 kg,2.5,31.5 and 52.9%,respectively.The nutritive profiles(both fatty and amino acid composition) were not influenced by the energy or protein levels or by sex.Conclusions:The dietary energy and protein levels and sex significantly influenced the carcass characteristics and chemical composition of meat but not thegrowth performance,meat quality traits and nutritive profiles. | Lingyan Li Yuankui Zhu Xianyou Wang Yang He Binghai Cao | 2014 | Journal of Animal Science and Biotechnology2014，5，4： | 21 |
| 2 | Dynamic deformation behavior of a FeCrNi medium entropy alloy显示文摘Deformation behavior of a FeCrNi medium entropy alloy(MEA)prepared by powder metallurgy(P/M)method was investigated over a wide range of strain rates.The FeCrNi MEA exhibits high strain-hardening ability,which can be attributed to the multiple deformation mechanisms,including dislocation slip,deformation induced stacking fault and mechanical twinning.The shear localization behavior of the FeCrNi MEA was also analyzed by dynamically loading hat-shaped specimens,and the distinct adiabatic shear band cannot be observed until the shear strain reaches~14.5.The microstructures within and outside the shear band exhibit different characteristics:the grains near the shear band are severely elongated and significantly refined by dislocation slip and twinning;inside the shear band,the initial coarse grains completely disappear,and transform into recrystallized ultrafine equiaxed grains by the classical rotational dynamic recrystallization mechanism.Moreover,microvoids preferentially nucleate in the central areas of the shear band where the temperature is very high and the shear stress is highly concentrated.These microvoids will coalesce into microcracks with the increase of strain,which eventually leads to the fracture of the shear band. | Ao Fu Bin Liu Zezhou Li Bingfeng Wang Yuankui Cao Yong Liu | 2022 | Journal of Materials Science & Technology2022，，5： | 2 |
| 3 | Phase decomposition behavior and its effects on mechanical properties of TiNbTa0.5ZrAl0.5 refractory high entropy alloy显示文摘The mechanical properties of refractory high entropy alloys(RHEAs) strongly depend on their phase structures. In this work, the phase stability of a BCC TiNbTa0.5ZrAl0.5 refractory high entropy alloy subjected to thermomechanical processing was evaluated, and the effects of phase decomposition on room/high temperature mechanical properties were quantitatively studied. It was found that, the thermomechanical processing at 800℃and 1200℃ leads to phase decomposition in the TiNbTa0.5ZrAl0.5 alloy. The phase decomposition is caused by the rapid rising of free energy of the primary BCC phase. The effect of the precipitates on room temperature strength is determined by the competition between the increasing in precipitation strengthening and the decreasing in solid solution strengthening. But at high temperatures(800-1200℃), the phase decomposition causes significant reduction in strength, mainly due to the grain boundary sliding and the decreasing in solid solution strengthening. | Yuankui Cao Weidong Zhang Bin Liu Yong Liu Meng Du Ao Fu | 2021 | Journal of Materials Science & Technology2021，，7： | 2 |
| 4 | A K-nearest Neighbor Model to Predict Early Recurrence of Hepatocellular Carcinoma After Resection显示文摘Background and Aims:Patients with hepatocellular carci-noma(HCC)surgically resected are at risk of recurrence;however,the risk factors of recurrence remain poorly un-derstood.This study intended to establish a novel machine learning model based on clinical data for predicting early re-currence of HCC after resection.Methods:A total of 220 HCC patients who underwent resection were enrolled.Clas-sification machine learning models were developed to predict HCC recurrence.The standard deviation,recall,and preci-sion of the model were used to assess the model’s accura-cy and identify efficiency of the model.Results:Recurrent HCC developed in 89(40.45%)patients at a median time of 14 months from primary resection.In principal compo-nent analysis,tumor size,tumor grade differentiation,por-tal vein tumor thrombus,alpha-fetoprotein,protein induced by vitamin K absence or antagonist-II(PIVKA-II),aspartate aminotransferase,platelet count,white blood cell count,and HBsAg were positive prognostic factors of HCC recurrence and were included in the preoperative model.After compar-ing different machine learning methods,including logistic re-gression,decision tree,naïve Bayes,deep neural networks,and k-nearest neighbor(K-NN),we choose the K-NN model as the optimal prediction model.The accuracy,recall,preci-sion of the K-NN model were 70.6%,51.9%,70.1%,respec-tively.The standard deviation was 0.020.Conclusions:The K-NN classification algorithm model performed better than the other classification models.Estimation of the recurrence rate of early HCC can help to allocate treatment,eventually achieving safe oncological outcomes. | Chuanli Liu Hongli Yang Yuemin Feng Cuihong Liu Fajuan Rui Yuankui Cao Xinyu Hu Jiawen Xu Junqing Fan Qiang Zhu Jie Li | 2022 | Journal of Clinical and Translational Hepatology2022，10，4： | 0 |
| 5 | A novel cobalt-free oxide dispersion strengthened medium-entropy alloy with outstanding mechanical properties and irradiation resistance显示文摘A novel cobalt-free oxide dispersion strengthened(ODS)equiatomic FeCrNi medium entropy alloy(MEA)was successfully fabricated through mechanical alloying and hot extrusion(HE).The ODS FeCrNi MEA is composed of a single face-centered cubic(FCC)matrix,in which highly dispersed oxide nanoparticles,including Y_(2)Ti_(2)O_(7),Y_(2)TiO_(5) and Y_(2)O_(3),are uniformly distributed.Compared with the FeCrNi MEA,the ODS FeCrNi MEA exhibits the improved yield strength(1120 MPa)and ultimate tensile strength(1274 MPa)with adequate ductility retention(12.1%).Theoretical analysis of the strengthening mechanism indicates that the high strength is mainly attributed to the grain-boundary strengthening caused by fine grains and the precipitation strengthening resulted from the oxide nanoparticles.Meanwhile,the matrix that easily activates mechanical twinning during the deformation process is the main reason to ensure moderate ductility.In addition,the introduction of high-density oxide nanoparticles can disperse the defect distri-bution and suppress the defect growth and irradiation-induced segregation,leading to the excellent irra-diation resistance.These findings provide innovative guidance for the development of high-performance structural materials for future nuclear energy applications with balanced strength and ductility. | Ao Fu Bin Liu Bo Liu Yuankui Cao Jian Wang Tao Liao Jia Li Qihong Fang Peter K.Liaw Yong Liu | 2023 | Journal of Materials Science & Technology2023，，21： | 0 |
| 6 | Formation process and mechanical properties in selective laser melted multi-principal-element alloys显示文摘Additive manufacturing is believed to open up a new era in precise microfabrication,and the dynamic microstructure evolution during the process as well as the experiment-simulation correlated study is conducted on a prototype multi-principal-element alloys FeCrNi fabricated using selective laser melting(SLM).Experimental results reveal that columnar crystals grow across the cladding layers and the dense cellular structures develop in the filled crystal.At the micron scale,all constituent elements are evenly distributed,while at the near-atomic scale,Cr element is obviously segregated.Simulation results at the atomic scale illustrate that i)the solid-liquid interface during the grain growth changes from horizontal to arc due to the radial temperature gradient;ii)the precipitates,microscale voids,and stacking faults also form dynamically as a result of the thermal gradient,leading to the residual stress in the SLMed structure.In addition,we established a microstructure-based physical model based on atomic simulation,which indicates that strong interface strengthening exists in the tensile deformation.The present work provides an atomic-scale understanding of the microstructural evolution in the SLM process through the combination of experiment and simulation. | Jing Peng Jia Li Bin Liu Jian Wang Haotian Chen Hui Feng Xin Zeng Heng Duan Yuankui Cao Junyang He Peter K.Liaw Qihong Fang | 2023 | Journal of Materials Science & Technology2023，，2： | 0 |
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