Light ion fusion in deformation model

Experiments with heavy ions at moderate energies show the importance of deformation in heavy ion collisions. A deformation model which takes deformation dynamically into account is developed. Having described fusion and deep inelastic collision for a very heavy system (Xe + Bi) and a medium heavy system (Ar + Th) at various energies successfully, we turn to some comparatively lighter heavy ions where fusion is the most dominant feature. Fusion cross-sections for six pairs of lighter systems (³⁵Cl +¹¹⁶Sn,⁵⁸Ni+⁶²Ni,³⁵Cl+⁶²Ni,³²S+²⁴Mg,²⁴Mg+²⁴Mg and¹²C+²⁷Al) have been obtained using our deformation model which agree well with experiment. The two-slope-behaviour of fusion excitation function which is an important feature of light ion fusion systematics is also obtained, in our model calculations for all the systems studied.     

Quantum chromodynamics corrections to polarised deep-inelastic electron-nucleon scattering

Quantum chromodynamics corrections to orderα _s (the running coupling constant), using the quark-parton approach are calculated for the spin-dependent structure functions in deep-inelastic polarised electron-nucleon scattering. Consequences of these corrections for the Bjorken sum rule and the asymmetry in the case of longitudinally polarised (with respect to the beam) nucleons is discussed which could provide possible tests of quantum chromodynamics. Comparison of our results with the moments of the flavour non-singlet contribution to the structure functions obtained using operator product expansion is also given. An erratum to this article is available at .     

深海内波非线性薛定谔方程的研究

考虑以跃层为界的两层分层流体,在弱非线性条件下,从分层流体的动力学基本方程组出发,应用多重尺度方法推导出描述深海内波的非线性薛定谔方程,分析方程中频散和非线性系数,求出非线性薛定谔方程的孤子解,并通过数值实验验证了理论的正确性.     

CdTe太阳电池中起因于Cu的深能级

在CdTe太阳电池中,易引入并形成Cu深能级中心. 本文采用深能级瞬态谱测试法研究了ZnTe背接触和石墨背接触CdTe太阳电池的部分深能级中心. 研究中运用密度泛函相关理论,分析闪锌矿结构CdTe,Cd空位体系和掺Cu体系的电子态密度,计算得出T_d场和C_3v场下Cu²⁺ d轨道的分裂情况. 计算结果表明,CdTe太阳电池中的E_v+0206 eV和E_v+0122 eV两个深中心来源于Cu替代Cd原子. 计算结果还表明,掺入Cu可降低CdTe体系能量. 关键词： 深能级瞬态谱 第一性原理 CdTe Cu杂质     

Highlights from the COMPASS experiment

The Compass experiment at CERN is studying the nucleon spin structure with a 160 GeV polarized muon beam and polarized targets as well as hadron structure with 190 GeV pion, kaon and proton beams. The paper gives an overview of the results for the helicity and transverse spin structure of the nucleon. A first result from the spectroscopy experiments, the observation of a resonance with exotic JPC = 1- + quantum numbers at 1660 MeV is also presented. The paper ends with an outlook to future measurements.     

Quasi in situ Ni K‐edge EXAFS investigation of the spent NiMo catalyst from ultra‐deep hydrodesulfurization of gas oil in a commercial plant

Ni species on the spent NiMo catalyst from ultra‐deep hydrodesulfurization of gas oil in a commercial plant were studied by Ni K‐edge EXAFS and TEM measurement without contact of the catalysts with air. The Ni–Mo coordination shell related to the Ni–Mo–S phase was observed in the spent catalyst by quasi in situ Ni K‐edge EXAFS measurement with a newly constructed high‐pressure chamber. The coordination number of this shell was almost identical to that obtained by in situ Ni K‐edge EXAFS measurement of the fresh catalyst sulfided at 1.1 MPa. On the other hand, large agglomerates of Ni₃S₂ were observed only in the spent catalyst by quasi in situ TEM/EDX measurement. MoS₂‐like slabs were sintered slightly on the spent catalyst, where they were destacked to form monolayer slabs. These results suggest that the Ni–Mo–S phase is preserved on the spent catalyst and Ni₃S₂ agglomerates are formed by sintering of Ni₃S₂ species originally present on the fresh catalyst.     

大数据背景下基于5S的城市交通拥堵评价模型研究

近几年来,城市交通拥堵问题日益突出,极大制约了城市发展。在大数据背景下,为了准确掌握交通实时拥堵状况,改善城市交通,便利市民出行,本文深入挖掘城市交通拥堵的影响因素,构建了基于交通5S要素的城市拥堵理论模型,运用径向基函数神经网络方法工具,以上海静安寺、上海站、陆家嘴周围三大拥堵路段的交通数据集为例,验证了该模型的有效性。实验结果表明,由该模型获得的城市交通拥堵预测值与上海实际交通路况具有较好的拟合效果,表明交通5S模型与方法能够准确有效地评价城市交通拥堵。     

Real-Time Tool Detection for Workflow Identification in Open Cranial Vault Remodeling

Deep learning is a recent technology that has shown excellent capabilities for recognition and identification tasks. This study applies these techniques in open cranial vault remodeling surgeries performed to correct craniosynostosis. The objective was to automatically recognize surgical tools in real-time and estimate the surgical phase based on those predictions. For this purpose, we implemented, trained, and tested three algorithms based on previously proposed Convolutional Neural Network architectures (VGG16, MobileNetV2, and InceptionV3) and one new architecture with fewer parameters (CranioNet). A novel 3D Slicer module was specifically developed to implement these networks and recognize surgical tools in real time via video streaming. The training and test data were acquired during a surgical simulation using a 3D printed patient-based realistic phantom of an infant’s head. The results showed that CranioNet presents the lowest accuracy for tool recognition (93.4%), while the highest accuracy is achieved by the MobileNetV2 model (99.6%), followed by VGG16 and InceptionV3 (98.8% and 97.2%, respectively). Regarding phase detection, InceptionV3 and VGG16 obtained the best results (94.5% and 94.4%), whereas MobileNetV2 and CranioNet presented worse values (91.1% and 89.8%). Our results prove the feasibility of applying deep learning architectures for real-time tool detection and phase estimation in craniosynostosis surgeries.     

Increased Entropic Brain Dynamics during DeepDream-Induced Altered Perceptual Phenomenology

In recent years, the use of psychedelic drugs to study brain dynamics has flourished due to the unique opportunity they offer to investigate the neural mechanisms of conscious perception. Unfortunately, there are many difficulties to conduct experiments on pharmacologically-induced hallucinations, especially regarding ethical and legal issues. In addition, it is difficult to isolate the neural effects of psychedelic states from other physiological effects elicited by the drug ingestion. Here, we used the DeepDream algorithm to create visual stimuli that mimic the perception of hallucinatory states. Participants were first exposed to a regular video, followed by its modified version, while recording electroencephalography (EEG). Results showed that the frontal region’s activity was characterized by a higher entropy and lower complexity during the modified video, with respect to the regular one, at different time scales. Moreover, we found an increased undirected connectivity and a greater level of entropy in functional connectivity networks elicited by the modified video. These findings suggest that DeepDream and psychedelic drugs induced similar altered brain patterns and demonstrate the potential of adopting this method to study altered perceptual phenomenology in neuroimaging research.     

Compression Helps Deep Learning in Image Classification

The impact of JPEG compression on deep learning (DL) in image classification is revisited. Given an underlying deep neural network (DNN) pre-trained with pristine ImageNet images, it is demonstrated that, if, for any original image, one can select, among its many JPEG compressed versions including its original version, a suitable version as an input to the underlying DNN, then the classification accuracy of the underlying DNN can be improved significantly while the size in bits of the selected input is, on average, reduced dramatically in comparison with the original image. This is in contrast to the conventional understanding that JPEG compression generally degrades the classification accuracy of DL. Specifically, for each original image, consider its 10 JPEG compressed versions with their quality factor (QF) values from {100,90,80,70,60,50,40,30,20,10}. Under the assumption that the ground truth label of the original image is known at the time of selecting an input, but unknown to the underlying DNN, we present a selector called Highest Rank Selector (HRS). It is shown that HRS is optimal in the sense of achieving the highest Top k accuracy on any set of images for any k among all possible selectors. When the underlying DNN is Inception V3 or ResNet-50 V2, HRS improves, on average, the Top 1 classification accuracy and Top 5 classification accuracy on the whole ImageNet validation dataset by 5.6% and 1.9%, respectively, while reducing the input size in bits dramatically—the compression ratio (CR) between the size of the original images and the size of the selected input images by HRS is 8 for the whole ImageNet validation dataset. When the ground truth label of the original image is unknown at the time of selection, we further propose a new convolutional neural network (CNN) topology which is based on the underlying DNN and takes the original image and its 10 JPEG compressed versions as 11 parallel inputs. It is demonstrated that the proposed new CNN topology, even when partially trained, can consistently improve the Top 1 accuracy of Inception V3 and ResNet-50 V2 by approximately 0.4% and the Top 5 accuracy of Inception V3 and ResNet-50 V2 by 0.32% and 0.2%, respectively. Other selectors without the knowledge of the ground truth label of the original image are also presented. They maintain the Top 1 accuracy, the Top 5 accuracy, or the Top 1 and Top 5 accuracy of the underlying DNN, while achieving CRs of 8.8, 3.3, and 3.1, respectively.