Thermal images generated by a line-scanning technique: statistical properties

The analysis of statistical parameters of computer-generated therfial images by a line-scanning technique indicates an approach to efficient image transmission.     

Mode-selective dynamic light scattering: theory versus experimental realization

We present a quantitative experimental comparison of fiber-based, single- and few-mode dynamic light scattering with the classical pinhole-detection optics. The recently presented theory of mode-selective dynamic light scattering [Appl. Opt. 32, 2860 (1993)] predicts a collection efficiency and a signal-tobaseline ratio superior to that of a classical pinhole setup. These predictions are confirmed by our experiments. Using single-mode optical fibers with different cutoff wavelengths and commercially available mechanical components, we have constructed a mode-selective detection optics in a simple and compact dynamic light-scattering spectrometer that permits an optimal compromise between signal intensity and dynamical resolution.     

The influence of temperature and grain size on substructure evolution in stainless steel 316L

The flow stress of polycrystals is controlled by the processes occurring in the grain interior as well as in the mantle, i.e. at the grain boundary and its immediate vicinity. The early stages of evolution of dislocation substructure in these two regions with strain in 316L stainless steel polycrystals have been studied at 293 K, 673 K and 1123 K representing the low temperature thermal, the intermediate temperature athermal and the high temperature thermal regimes respectively. Specimens with grain sizes of 4 and 12 m were employed to determine the effect of grain size.Transmission electron microscopy studies on deformed specimens show the different roles of grain boundary and grain interior in different temperature regimes. In the low temperature regime grain boundaries act as obstacles to moving dislocations and as such high density of dislocation is found in the grain boundary vicinity. In the intermediate temperature regime the dislocations which are easily spread into the grain interior rearrange to form cell walls. In the high temperature regime grain boundaries transform to the equilibrium state and do not contain any grain boundary dislocations, and the distribution of dislocations within grains is homogeneous at all strains. Significantly higher values of dislocation densities in the vicinity as well as in the grain interior were found in the finer grain size material in the whole strain region employed.     

基于深度强化学习的居民实时自治最优能量管理策略

随着居民分布式资源的普及,如何考虑用户多类型设备的运行特性,满足实时自治能量管理需求以达到用户侧经济性最优成为亟待解决的课题。传统基于模型的最优化方法在模型精准构建和应对多重不确定性等方面存在局限性,为此提出一种无模型的基于深度强化学习的实时自治能量管理优化方法。首先,对用户设备进行分类,采用统一的三元组描述其运行特性,并确定相应的能量管理动作;接着,采用长短期记忆神经网络提取环境状态中多源时序数据的未来走势;进而,基于近端策略优化算法,赋能在多维连续-离散混合的动作空间中高效学习最优能量管理策略,在最小化用电成本的同时提升策略对不确定性的适应性;最后,通过实际情境对比现有方法的优化决策效果,验证所提方法的有效性。     

Evolutionary design of neural network architectures: a review of three decades of research

We present a comprehensive review of the evolutionary design of neural network architectures. This work is motivated by the fact that the success of an Artificial Neural Network (ANN) highly depends on its architecture and among many approaches Evolutionary Computation, which is a set of global-search methods inspired by biological evolution has been proved to be an efficient approach for optimizing neural network structures. Initial attempts for automating architecture design by applying evolutionary approaches start in the late 1980s and have attracted significant interest until today. In this context, we examined the historical progress and analyzed all relevant scientific papers with a special emphasis on how evolutionary computation techniques were adopted and various encoding strategies proposed. We summarized key aspects of methodology, discussed common challenges, and investigated the works in chronological order by dividing the entire timeframe into three periods. The first period covers early works focusing on the optimization of simple ANN architectures with a variety of solutions proposed on chromosome representation. In the second period, the rise of more powerful methods and hybrid approaches were surveyed. In parallel with the recent advances, the last period covers the Deep Learning Era, in which research direction is shifted towards configuring advanced models of deep neural networks. Finally, we propose open problems for future research in the field of neural architecture search and provide insights for fully automated machine learning. Our aim is to provide a complete reference of works in this subject and guide researchers towards promising directions.

     

Multifunctional Gadolinium‐Doped Mesoporous TiO2 Nanobeads: Photoluminescence,Enhanced Spin Relaxation,and Reactive Oxygen Species Photogeneration,Beneficial for Cancer Diagnosis and Treatment

Materials with controllable multifunctional abilities for optical imaging (OI) and magnetic resonant imaging (MRI) that also can be used in photodynamic therapy are very interesting for future applications. Mesoporous TiO₂ sub‐micrometer particles are doped with gadolinium to improve photoluminescence functionality and spin relaxation for MRI, with the added benefit of enhanced generation of reactive oxygen species (ROS). The Gd‐doped TiO₂ exhibits red emission at 637 nm that is beneficial for OI and significantly improves MRI relaxation times, with a beneficial decrease in spin–lattice and spin–spin relaxation times. Density functional theory calculations show that Gd³⁺ ions introduce impurity energy levels inside the bandgap of anatase TiO₂, and also create dipoles that are beneficial for charge separation and decreased electron–hole recombination in the doped lattice. The Gd‐doped TiO₂ nanobeads (NBs) show enhanced ability for ROS monitored via ^?OH radical photogeneration, in comparison with undoped TiO₂ nanobeads and TiO₂ P25, for Gd‐doping up to 10%. Cellular internalization and biocompatibility of TiO₂@x Gd NBs are tested in vitro on MG‐63 human osteosarcoma cells, showing full biocompatibility. After photoactivation of the particles, anticancer trace by means of ROS photogeneration is observed just after 3 min irradiation.     

Cross-property connections for copper–graphite composites

Copper–graphite composite materials in the range of 0–10 vol% of carbon phase were prepared from the mixture of copper and graphite powders by hot isostatic pressing. The microstructure, mechanical (tensile strength, elongation to fracture) and physical (electrical and thermal conductivity) properties of composite samples were investigated, and the cross-property connections were calculated. It was shown that electrical and thermal conductivity cross-property (Lorenz number) is almost constant and increases only slightly (no more than 10 % increase was observed). This implies that in the investigated composition range the Lorenz number of a copper–graphite composite system behaves according to Franz–Wiedemann law for pure metals at constant temperature. On the contrary, the conductivity to tensile strength cross-property connections showed significant linear increase (over 200 % in the investigated composition range) for both electrical conductivity and thermal conductivity of composite materials. The cross-property connections of conductivity to the elongation to fracture exhibit a nonlinear dependence on the volume fraction of graphite.