In this work,the structure,viscosity and ion-exchange process of Na_(2)O-MgO-Al_(2)O_(3)-SiO_(2) glasses with different Al_(2)O_(3)/SiO_(2) molar ratios were investigated.The results showed that,with increasing Al_(2)...In this work,the structure,viscosity and ion-exchange process of Na_(2)O-MgO-Al_(2)O_(3)-SiO_(2) glasses with different Al_(2)O_(3)/SiO_(2) molar ratios were investigated.The results showed that,with increasing Al_(2)O_(3)/SiO_(2) ratio,the simple structural units Q_(1) and Q_(2) transformed into highly aggregated structural units Q_(3) and Q_(4),indicating the increase of polymerization degree of glass network.Meanwhile,the coefficient of thermal expansion decreased from 9.23×10^(-6)℃^(-1) to 8.88×10^(-6)℃^(-1).The characteristic temperatures such as melting,forming,softening and glass transition temperatures increased with the increase of Al_(2)O_(3)/SiO_(2) ratio,while the glasses working temperature range became narrow.The increasing Al_(2)O_(3)/SiO_(2) ratio and prolonging ion-exchange time enhanced the surface compressive stress(CS)and depth of stress layer(DOL).However,the increase of ion exchange temperature increased the DOL and decreased the CS affected by stress relaxation.There was a good linear relationship between stress relaxation and surface compressive stress.Chemical strengthening significantly improved the hardness of glasses,which reached the maximum value of(622.1±10)MPa for sample with Al_(2)O_(3)/SiO_(2) ratio of 0.27 after heat treated at 410℃for 2 h.展开更多
目的构建基于乳腺X线多视图的深度学习框架(Network based on mammography multiple views,MMV-Net),评价模型对乳腺良性和恶性肿块的分类效能。方法回顾性分析2018-2020年哈尔滨医科大学附属肿瘤医院1585例乳腺X线图像数据集,其中良性...目的构建基于乳腺X线多视图的深度学习框架(Network based on mammography multiple views,MMV-Net),评价模型对乳腺良性和恶性肿块的分类效能。方法回顾性分析2018-2020年哈尔滨医科大学附属肿瘤医院1585例乳腺X线图像数据集,其中良性806例,恶性779例,按8∶2分为训练集(n=1268)和测试集(n=317),并按照5折交叉验证对训练集进行分层,采用集成的DDSM数据集和INBreast数据集作为外部测试集(n=1645)来评估模型性能。输入层每个病例包含4个视图,通过删除ResNet22网络模型的最后两层网络结构并加入平均池化层作为特征提取层,以及分别加入全连接层和softmax激活函数作为决策层构建MMV-Net模型,使用贝叶斯超参数优化。比较MMV-Net、MFA-Net和集成Inception V4模型在AUC值、准确率、精确率、召回率和F1分数上的表现。结果MMV-Net模型在测试集上区分良性和恶性肿块的AUC值为0.913,MFA-Net的AUC为0.882,Inception V4的AUC为0.865;MMV-Net模型的准确率和精确率等评估指标也高于其他两种模型。结论基于乳腺X线多视图的深度学习MMV-Net模型有助于乳腺良性和恶性肿块的分类。展开更多
基金Funded by National Natural Science Foundation of China(Nos.52172019 and 52072148)Shandong Provincial Youth Innovation Team Development Plan of Colleges and Universities(No.2022K1100)。
文摘In this work,the structure,viscosity and ion-exchange process of Na_(2)O-MgO-Al_(2)O_(3)-SiO_(2) glasses with different Al_(2)O_(3)/SiO_(2) molar ratios were investigated.The results showed that,with increasing Al_(2)O_(3)/SiO_(2) ratio,the simple structural units Q_(1) and Q_(2) transformed into highly aggregated structural units Q_(3) and Q_(4),indicating the increase of polymerization degree of glass network.Meanwhile,the coefficient of thermal expansion decreased from 9.23×10^(-6)℃^(-1) to 8.88×10^(-6)℃^(-1).The characteristic temperatures such as melting,forming,softening and glass transition temperatures increased with the increase of Al_(2)O_(3)/SiO_(2) ratio,while the glasses working temperature range became narrow.The increasing Al_(2)O_(3)/SiO_(2) ratio and prolonging ion-exchange time enhanced the surface compressive stress(CS)and depth of stress layer(DOL).However,the increase of ion exchange temperature increased the DOL and decreased the CS affected by stress relaxation.There was a good linear relationship between stress relaxation and surface compressive stress.Chemical strengthening significantly improved the hardness of glasses,which reached the maximum value of(622.1±10)MPa for sample with Al_(2)O_(3)/SiO_(2) ratio of 0.27 after heat treated at 410℃for 2 h.
文摘目的构建基于乳腺X线多视图的深度学习框架(Network based on mammography multiple views,MMV-Net),评价模型对乳腺良性和恶性肿块的分类效能。方法回顾性分析2018-2020年哈尔滨医科大学附属肿瘤医院1585例乳腺X线图像数据集,其中良性806例,恶性779例,按8∶2分为训练集(n=1268)和测试集(n=317),并按照5折交叉验证对训练集进行分层,采用集成的DDSM数据集和INBreast数据集作为外部测试集(n=1645)来评估模型性能。输入层每个病例包含4个视图,通过删除ResNet22网络模型的最后两层网络结构并加入平均池化层作为特征提取层,以及分别加入全连接层和softmax激活函数作为决策层构建MMV-Net模型,使用贝叶斯超参数优化。比较MMV-Net、MFA-Net和集成Inception V4模型在AUC值、准确率、精确率、召回率和F1分数上的表现。结果MMV-Net模型在测试集上区分良性和恶性肿块的AUC值为0.913,MFA-Net的AUC为0.882,Inception V4的AUC为0.865;MMV-Net模型的准确率和精确率等评估指标也高于其他两种模型。结论基于乳腺X线多视图的深度学习MMV-Net模型有助于乳腺良性和恶性肿块的分类。