Re-examination of phase relations between solid solutions in R4Al2O9-R4Si2N2O7-R4Si2O10 (R=Y,Gd, Yb) systems

Samples in Si–Al-R-O-N (R = Y, Gd, Yb) systems were prepared by solid-state reactions using R₂O₃, Al₂O₃, SiO₂ and Si₃N₄ powders as starting materials. X-ray diffraction was done to investigate RAM-J(R) solid solutions [RAM = R₄Al₂O₉, J(R) = R₄Si₂N₂O₇] formation and their equilibrium with RSO (R₄Si₂O₁₀). Phase relations between RAM, J(R) and RSO at 1700 °C were summarized in a phase diagram. It was determined that a limited solid solution of RAM and RSO could be formed along RAM-RSO tie-line, while RAM and J(R) form a continuous solid solution along RAM-J(R) tie-line. In RAM-J(R)-RSO ternary systems, the RAM-J(R) tie-lines were extended towards the RSO corner to form a continuous solid solution area of JRAMss (R = Y, Gd, Yb). The established phase relations in the Si–Al-R-O-N (R = Y, Gd, Yb) systems may facilitate compositional selections for developing JRAMss as monolithic ceramics or for SiC/Si₃N₄ based composites using the solid-solutions as a second refractory phase.     

Underwater image super-resolution using multi-stage information distillation networks

Recently, single image super-resolution (SISR) has been widely applied in the fields of underwater robot vision and obtained remarkable performance. However, most current methods generally suffered from the problem of a heavy burden on computational resources with large model sizes, which limited their real-world underwater robotic applications. In this paper, we introduce and tackle the super resolution (SR) problem for underwater robot vision and provide an efficient solution for near real-time applications. We present a novel lightweight multi-stage information distillation network, named MSIDN, for better balancing performance against applicability, which aggregates the local distilled features from different stages for more powerful feature representation. Moreover, a novel recursive residual feature distillation (RRFD) module is constructed to progressively extract useful features with a modest number of parameters in each stage. We also propose a channel interaction & distillation (CI&D) module that employs channel split operation on the preceding features to produce two-part features and utilizes the inter channel-wise interaction information between them to generate the distilled features, which can effectively extract the useful information of current stage without extra parameters. Besides, we present USR-2K dataset, a collection of over 1.6K samples for large-scale underwater image SR training, and a testset with an additional 400 samples for benchmark evaluation. Extensive experiments on several standard benchmark datasets show that the proposed MSIDN can provide state-of-the-art or even better performance in both quantitative and qualitative measurements.     

WavNet — Visual saliency detection using Discrete Wavelet Convolutional Neural Network

In the recent advancements in image and video analysis, the detection of salient regions in the image becomes the initial step. This plays a crucial role in deciding the performance of such algorithms. In this work, a Multi-Resolution Feature Extraction (MRFE) technique that makes use of Discrete Wavelet Convolutional Neural Network (DWCNN) for generating features is employed. An Enhanced Feature Extraction (EFE) module extracts additional features from the high level features of the DWCNN, which are used to frame both channel as well as spatial attention models for yielding contextual attention maps. A new hybrid loss function is also proposed, which is a combination of Balanced Cross Entropy (BCE) loss and Edge based Structural Similarity (ESSIM) loss that effectively identifies and segments the salient regions with clear boundaries. The method is tested exhaustively with five different benchmark datasets and is proved superior to the existing state-of-the-art methods with a minimum Mean Absolute error (MAE) of 0.03 and F-measure of 0.956.     

Portrait of Cancer Stem Cells on Colorectal Cancer: Molecular Biomarkers,Signaling Pathways and miRNAome

Colorectal cancer (CRC) is a leading cause of cancer death worldwide, and about 20% is metastatic at diagnosis and untreatable. Increasing evidence suggests that the heterogeneous nature of CRC is related to colorectal cancer stem cells (CCSCs), a small cells population with stemness behaviors and responsible for tumor progression, recurrence, and therapy resistance. Growing knowledge of stem cells (SCs) biology has rapidly improved uncovering the molecular mechanisms and possible crosstalk/feedback loops between signaling pathways that directly influence intestinal homeostasis and tumorigenesis. The generation of CCSCs is probably connected to genetic changes in members of signaling pathways, which control self-renewal and pluripotency in SCs and then establish function and phenotype of CCSCs. Particularly, various deregulated CCSC-related miRNAs have been reported to modulate stemness features, controlling CCSCs functions such as regulation of cell cycle genes expression, epithelial-mesenchymal transition, metastasization, and drug-resistance mechanisms. Primarily, CCSC-related miRNAs work by regulating mainly signal pathways known to be involved in CCSCs biology. This review intends to summarize the epigenetic findings linked to miRNAome in the maintenance and regulation of CCSCs, including their relationships with different signaling pathways, which should help to identify specific diagnostic, prognostic, and predictive biomarkers for CRC, but also develop innovative CCSCs-targeted therapies.     

基于卷积神经网络的安全防御模式研究

云计算、大数据、5G通信等技术的快速发展，有力的促进了计算机网络在多个领域的普及和使用，人们已经进入到了“互联网+”时代，实现了工作、生活和学习的数字化、智能化和共享化。计算机网络在为人们提供各种便利服务的同时也面临着许多的安全威胁，比如木马、病毒等，不法分子攻击网络数据中心，破坏用户服务终端及服务器，为人们带来严重的威胁。网络安全学者或企业机构也提出了许多安全防御技术，比如入侵检测技术、深度包过滤技术、防火墙技术、数据加密技术等，一定程度上提高了网络安全防御的能力，但是随着互联网数据流量的增多，需要引入更加先进的人工智能技术，以便能够快速的采集、分析网络数据流，确定数据流中是否存在病毒或木马，以便及时的对其进行查杀，进一步提高网络安全防御的主动性、积极性     

Electrochemical performance of in situ LiFePO4 modified by N-doped graphene for Li-ion batteries

LiFePO₄ modified by N-doped graphene (NG) with a three-dimensional conductive network structure was synthesized via a one-step in situ hydrothermal method. The effects of N amount of NG on the phase structure, morphology, and electrochemical properties of LiFePO₄ are investigated in this study. X-ray diffraction (XRD) results show that doping suitable N amounts in NG do not alter the crystal structure of LiFePO_4, and scanning electron microscopy (SEM) images show that NG can slightly reduce the particle size of LiFePO₄. The high-resolution transmission electron microscopy (HRTEM) results show that the LiFePO₄ particles are well covered and connected by NG. The electrochemical performance confirms that LiFePO₄ modified by 20% N-doped graphene (named LFP/NG-4) displays a perfect specific capacity of 166.6 mAh·g^?1 at a rate of 0.2C and can reach 125 mAh·g^?1 at a rate of 5 C. Electrochemical impedance spectroscopy (EIS) results illustrate that the charge transfer resistance value of the LFP/NG-4 composite is only 58.6 Ω, which is very low compared with LiFePO₄. Cyclic voltammetry (CV) tests indicate that the addition of 20% N-doped graphene can effectively reduce electrode polarization and improve reversibility. The LFP/NG-4 composite with a three-dimensional conductive network structure can be regarded as a promising cathode material for Li-ion batteries.     

The P-graph approach for systematic synthesis of wastewater treatment networks

Wastewater treatment consists of three or four sequential stages: preliminary, primary, secondary, and tertiary. Each stage can comprise multiple alternative technologies that can perform the same tasks with different efficiencies, operating times, and costs. Thus, we propose a systematic approach for designing wastewater treatment networks by utilizing principles of mathematical modeling and generating an exhaustive enumeration of all the possible technologies and their connections during the early stages of designing a treatment facility. Some of these structures are nonintuitive and include recycling, reprocessing, bypasses, and multiple technologies in parallel or series to remove the same contaminant. The nonintuitive structures with multiple technologies may provide a measure of resilience compared to typical heuristic designs. Thus, the combination of P-graph methodology and the sequence of treatment technologies predicted via the optimization algorithm from the maximal structure is based on holistic considerations and does not lead to suboptimal solutions.     

5G技术在医院智能服务中的应用

纵观当今的发展格局,是科学技术高速发展的时代。随着5G时代的来临,网络技术在各个领域中扮演着越来越重要的角色。特别是在医疗方面,为医院的日常工作奠定了坚实的基础。今天就5G网络技术在医院只能服务中的应用展开谈论。     

基于GA-BP的汽车风振噪声声品质预测模型

目前对于汽车风振噪声的优化研究主要以声压级（Sound pressure level，SPL）作为单一评价指标，既不能全面反映噪声的物理属性，也无法考虑人耳对噪声的主观认知过程。为准确评价风振噪声，引入声品质，运用大涡模拟（Large eddy simulation，LES）对风振噪声进行数值仿真，根据实车道路试验判断仿真的准确性；对仿真结果进行声品质客观评价与主观评价，综合声品质客观评价参数与声品质主观评价试验结果建立BP神经网络预测模型；利用遗传算法（Genetic algorithm，GA），进一步对BP神经网络的结构参数进行优化，建立GA-BP声品质预测模型。研究结果表明，GA-BP声品质预测模型在训练速度和预测精度上都优于BP神经网络预测模型。预测模型基于声品质主客观评价结果，其预测值可以代替传统的声压级评价指标，为风振噪声提供更为准确合理的评价。     

基于PMU的电力系统监控体系结构优化设计

文中提出了一种利用有限数量的相量测量单元(PMU)和相量数据集中器(PDC)设计最优监控结构的方法。通过在大量的设定值场景下,使电力系统可观测性曲线的期望值最大化,同时使通信基础设施成本最小化,最终确定PMU和PDC的最佳位置。提出了一种非线性动态扩展卡尔曼滤波(EFK)状态观测器。这种状态观测器可以将暂态行为转换为由代数微分方程描述的广域电力系统,而无需非线性反演技术。最后以IEEE-5电力系统为例,说明了该方法的有效性。