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In order to study the bonding strength of the joint surface of the double-sided laminated shear wall, a double-sided laminated shear wall specimen was designed and fabricated, and then the the bars were planted in the precast layer and the middle post pouring layer of the specimen, and the normal tensile bond strength of the joint surface was tested by the drilling core planting bar drawing method. The results show that the bond strength of the joint surface of double-sided laminated shear wall can be detected by the method of drilling core planting bar drawing, and according to the cross section of the core sample, it can be known that the joint surface is indeed the weak part of the laminated member, and it is suggested that the bond strength of the joint surface of the double-sided laminated shear wall should be tested by planting steel bars in the middle post-cast layer.

This paper proposes a method to optimize the conflicting tram signal priority requests at a multi-lines (M-lines) interchange intersection. The method is formulated on the basis of the spatiotemporal logic of complex switch area operations and the characteristics of multiple conflicting tram priority requests. Our study first depicts the interlocking logic of tram system operation according to the phases design and the spatial rules. Furthermore, it classifies the tram priority requests by the intersection entrances, priority phases and tram routes. Finally, it optimizes the serving sequences under the conditions of tram travel delay and switch operation delay. A microscopic traffic simulator, VISSIM, was used to simulate tram system and collect vehicle data. An external application program interface, VISSIM's COM-interface, was used to implement the proposed optimization method. The results show that the proposed method significantly outperforms the no-priority and the conventional first-come-first-served (FCFS) policy in terms of reducing tram delays. More specifically, the average tram delay is reduced by more than 26.6% over the no-priority policy and 13.0% on the FCFS policy in the initial operation. In addition, we find that the average delay which is caused by the switch operation delay increases with the tramline frequency. But the benefits of the proposed method are greater than those of the no-priority strategies.

Detailed mechanistic studies of a Pd-catalyzed asymmetric C–N coupling led to a rational design of a new series of bis-phosphine mono-oxides ligated Pd(ii) pre-catalysts that allow for reliable and complete catalyst activation., Significant catalyst loading reduction and increased reaction robustness have been achieved for a Pd-catalyzed asymmetric intramolecular C–N coupling through comprehensive mechanistic studies. Detailed kinetic, spectroscopic, and crystallographic analyses revealed that the mono-oxidation of the bis-phosphine ligand is critical for a successful transformation. 31P NMR studies provided an understanding of the inefficient activation of the Pd(OAc)2/(R,R)-QuinoxP* pre-catalyst to form the active bis-phosphine mono-oxide–Pd(0) catalyst with competitive formation of a less active (R,R)-QuinoxP*·PdBr2 complex. Based on these detailed mechanistic studies, a new series of bis-phosphine mono-oxides (BPMO)-ligated Pd(ii) pre-catalysts have been rationally developed that allow for reliable and complete catalyst activation which should have general utility in academic and industrial settings.

Automatic segmentation of fine-grained brain structures remains a challenging task. Current segmentation methods mainly utilize 2D and 3D deep neural networks. The 2D networks take image slices as input to produce coarse segmentation in less processing time, whereas the 3D networks take the whole image volumes to generated fine-detailed segmentation with more computational burden. In order to obtain accurate fine-grained segmentation efficiently, in this paper, we propose an end-to-end Feature-Fused Context-Encoding Network for brain structure segmentation from MR (magnetic resonance) images. Our model is implemented based on a 2D convolutional backbone, which integrates a 2D encoding module to acquire planar image features and a spatial encoding module to extract spatial context information. A global context encoding module is further introduced to capture global context semantics from the fused 2D encoding and spatial features. The proposed network aims to fully leverage the global anatomical prior knowledge learned from context semantics, which is represented by a structure-aware attention factor to recalibrate the outputs of the network. In this way, the network is guaranteed to be aware of the class-dependent feature maps to facilitate the segmentation. We evaluate our model on 2012 Brain Multi-Atlas Labelling Challenge dataset for 134 fine-grained structure segmentation. Besides, we validate our network on 27 coarse structure segmentation tasks. Experimental results have demonstrated that our model can achieve improved performance compared with the state-of-the-art approaches.

Introduction: It is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's disease (AD) dementia. Methods: A deep learning method is developed and validated based on MRI scans of 2146 subjects (803 for training and 1343 for validation) to predict MCI subjects' progression to AD dementia in a time-to-event analysis setting. Results: The deep learning time-to-event model predicted individual subjects' progression to AD dementia with a concordance index (C-index) of 0.762 on 439 ADNI testing MCI subjects with follow-up duration from 6 to 78 months (quartiles: [24, 42, 54]) and a C-index of 0.781 on 40 AIBL testing MCI subjects with follow-up duration from 18-54 months (quartiles: [18, 36,54]). The predicted progression risk also clustered individual subjects into subgroups with significant differences in their progression time to AD dementia (p, Accepted for publication in Alzheimer's & Dementia