Ni(OH)2 cluster-modified TiO2 (Ni(OH)2/TiO2) nanocomposite photocatalysts were fabricated by a simple precipitation method using Degussa P25 TiO2 powder (P25) as support and Ni(NO3)2 as precursor. The effect of Ni(OH)2 cluster loading content on the photocatalytic hydrogen production rates of the as-prepared samples in methanol aqueous solution was investigated. The results showed that the photocatalytic H2-production activity of TiO2 was significantly enhanced by loading Ni(OH)2 clusters. The optimal Ni(OH)2 loading content was found to be 0.23 mol %, giving a H2-production rate of 3056 μmol h−1 g−1 with quantum efficiency (QE) of 12.4%, exceeding that on pure TiO2 by more than 223 times. This high photocatalytic H2-production activity is due to the deposition of Ni(OH)2 clusters on the surface of TiO2. The enhanced mechanism is because the potential of Ni2+/Ni (Ni2+ + 2e− = Ni, E o = −0.23 V) is slightly lower than conduction band (CB) (−0.26 V) of anatase TiO2, meanwhile higher than the reduction potential of H+/H2 (2H+ + 2e− = H2, E o = −0.00 V), which favors the electron transfer from CB of TiO2 to Ni(OH)2 and the reduction of partial Ni2+ to Ni0. The function of Ni0 is to help the charge separation and to act as cocatalyst for water reduction, thus enhancing the photocatalytic H2-production activity.

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ObjectivesTo evaluate the performance of a novel three-dimensional (3D) joint convolutional and recurrent neural network (CNN-RNN) for the detection of intracranial hemorrhage (ICH) and its five subtypes (cerebral parenchymal, intraventricular, subdural, epidural, and subarachnoid) in non-contrast head CT.MethodsA total of 2836 subjects (ICH/normal, 1836/1000) from three institutions were included in this ethically approved retrospective study, with a total of 76,621 slices from non-contrast head CT scans. ICH and its five subtypes were annotated by three independent experienced radiologists, with majority voting as reference standard for both the subject level and the slice level. Ninety percent of data was used for training and validation, and the rest 10% for final evaluation. A joint CNN-RNN classification framework was proposed, with the flexibility to train when subject-level or slice-level labels are available. The predictions were compared with the interpretations from three junior radiology trainees and an additional senior radiologist.ResultsIt took our algorithm less than 30 s on average to process a 3D CT scan. For the two-type classification task (predicting bleeding or not), our algorithm achieved excellent values (≥ 0.98) across all reporting metrics on the subject level. For the five-type classification task (predicting five subtypes), our algorithm achieved > 0.8 AUC across all subtypes. The performance of our algorithm was generally superior to the average performance of the junior radiology trainees for both two-type and five-type classification tasks.ConclusionsThe proposed method was able to accurately detect ICH and its subtypes with fast speed, suggesting its potential for assisting radiologists and physicians in their clinical diagnosis workflow.Key Points • A 3D joint CNN-RNN deep learning framework was developed for ICH detection and subtype classification, which has the flexibility to train with either subject-level labels or slice-level labels. • This deep learning framework is fast and accurate at detecting ICH and its subtypes. • The performance of the automated algorithm was superior to the average performance of three junior radiology trainees in this work, suggesting its potential to reduce initial misinterpretations. Electronic supplementary materialThe online version of this article (10.1007/s00330-019-06163-2) contains supplementary material, which is available to authorized users.

We study the problem of generating keyphrases that summarize the key points for a given document. While sequence-to-sequence (seq2seq) models have achieved remarkable performance on this task (Meng et al., 2017), model training often relies on large amounts of labeled data, which is only applicable to resource-rich domains. In this paper, we propose semi-supervised keyphrase generation methods by leveraging both labeled data and large-scale unlabeled samples for learning. Two strategies are proposed. First, unlabeled documents are first tagged with synthetic keyphrases obtained from unsupervised keyphrase extraction methods or a selflearning algorithm, and then combined with labeled samples for training. Furthermore, we investigate a multi-task learning framework to jointly learn to generate keyphrases as well as the titles of the articles. Experimental results show that our semi-supervised learning-based methods outperform a state-of-the-art model trained with labeled data only. * Work was done while visiting Northeastern University. Document:In this paper, we consider an enthalpy formulation for a two-phase Stefan problem arising from the solidification of aluminum during process. We solve this free boundary problem in a time varying threedimensional domain and consider convective heat transfer in the liquid phase. The resulting equations are discretized using a characteristics method in time and a method in space, and we propose a numerical algorithm to solve the obtained nonlinear discretized problem. Finally, numerical results are given which are compared with industrial experimental measurements.

4 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2018.