浅谈如何保护好患者的隐私权

The discussion on patient’s privacy in doctor-patient relationship is attracting more and more attention, as people’s legal and self-protection awareness increased gradually. This paper refers to how to protect the patient’s privacy in medical activities.随着人们法律意识和自我保护意识的逐渐增强，医患关系中关于患者隐私权的讨论也越来越多。本文就医疗活动中如何保护好患者的隐私权浅谈下个人看法

STEADY-STATE ANALYSIS OF THE GI/M/1 QUEUE WITH MULTIPLE VACATIONS AND SET-UP TIME

In this paper, we consider a GI/M/1 queueing model with multiple vacations and set-up time. We derive the distribution and the generating function and the stochastic decomposition of the steady-state queue length, meanwhile, we get the waiting time distributions. Key words: multiple vacations, set-up time, stochastic decompositio

1,2-Bis(3-hydroxy­benzyl­idene)diazane

The asymmetric unit of the title compound, C14H12N2O2, which was synthesized unexpectedly by refluxing an ethano­lic solution of isonicotinic hydrazide and 3-hydroxy­benzaldehyde, contains one half-mol­ecule with the center of the N—N bond lying on a crystallographic center of inversion. In the crystal structure, mol­ecules are linked by inter­molecular O—H⋯N hydrogen bonds into an infinite layer structure parallel to (110)

Tris(5,6-dimethyl-1H-benzimidazole-κN 3)(pyridine-2,6-dicarboxyl­ato-κ3 O 2,N,O 6)nickel(II)

The title mononuclear complex, [Ni(C7H3NO4)(C9H10N2)3], shows a central NiII atom which is coordinated by two carboxyl­ate O atoms and the N atom from a pyridine-2,6-dicarboxyl­ate ligand and by three N atoms from different 5,6-dimethyl-1H-­benzimidazole ligands in a distorted octa­hedral geometry. The crystal structure shows intermolecular N—H⋯O hydrogen bonds

Topological Dirac states beyond π\pi orbitals for silicene on SiC(0001) surface

The discovery of intriguing properties related to the Dirac states in graphene has spurred huge interest in exploring its two-dimensional group-IV counterparts, such as silicene, germanene, and stanene. However, these materials have to be obtained via synthesizing on substrates with strong interfacial interactions, which usually destroy their intrinsic $\pi$($p_z$)-orbital Dirac states. Here we report a theoretical study on the existence of Dirac states arising from the $p_{x,y}$ orbitals instead of $p_z$ orbitals in silicene on 4H-SiC(0001), which survive in spite of the strong interfacial interactions. We also show that the exchange field together with the spin-orbital coupling give rise to a detectable band gap of 1.3 meV. Berry curvature calculations demonstrate the nontrivial topological nature of such Dirac states with a Chern number $C = 2$, presenting the potential of realizing quantum anomalous Hall effect for silicene on SiC(0001). Finally, we construct a minimal effective model to capture the low-energy physics of this system. This finding is expected to be also applicable to germanene and stanene, and imply great application potentials in nanoelectronics.Comment: 6 Figures , Accepted by Nano Letter

Tris(1H-benzimidazole-κN 3)(pyridine-2,6-dicarb­oxy­lato-κ3 O 2,N,O 6)nickel(II)

In the title complex, [Ni(C7H3NO4)(C7H6N2)3], the NiII ion is coordinated by two carboxyl­ate O atoms and the N atom from a pyridine-2,6-dicarboxyl­ate ligand and by three N atoms from three benzimidazole ligands to form a slightly distorted octa­hedral geometry. In the crystal, mol­ecules are linked by N—H⋯O hydrogen bonds to form a three-dimensional network

Res2Net: A New Multi-scale Backbone Architecture

Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs) continually demonstrate stronger multi-scale representation ability, leading to consistent performance gains on a wide range of applications. However, most existing methods represent the multi-scale features in a layer-wise manner. In this paper, we propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. The proposed Res2Net block can be plugged into the state-of-the-art backbone CNN models, e.g., ResNet, ResNeXt, and DLA. We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e.g., CIFAR-100 and ImageNet. Further ablation studies and experimental results on representative computer vision tasks, i.e., object detection, class activation mapping, and salient object detection, further verify the superiority of the Res2Net over the state-of-the-art baseline methods. The source code and trained models are available on https://mmcheng.net/res2net/.Comment: 11 pages, 7 figure