Improved Stable Indocyanine Green (ICG)‐Mediated Cancer Optotheranostics with Naturalized Hepatitis B Core Particles

In recent years, hepatitis B core protein virus‐like particle (HBc VLP) is an impressive biomaterial, which has attracted considerable attention due to favorable properties such as structural stability, high uptake efficiency, and biocompatibility in biomedical applications. Heretofore, only a few attempts have been made to apply it in physical, chemical, and biological therapy for cancer. In this study, a tumor‐targeting RGD‐HBc VLP is first fabricated through genetic engineering. For image‐guided cancer phototherapy, indocyanine green (ICG) is loaded into RGD‐HBc VLP via a disassembly/reassembly pathway and electrostatic attraction with high efficiency. The self‐assembled stable RGD‐HBc VLP significantly improves body retention (fourfold longer), aqueous stability, and target specificity of ICG. Remarkably, these positive reformations promote more accurate and sensitive imaging of U87MG tumor, as well as prolonged tumor destruction in comparison with free ICG. Moreover, the photothermal and photodynamic effect on tumors are quantitatively differentiated by multiple linear regression analysis. Overall, less‐potent medicinal ICG can be perfectly rescued by bioengineered HBc VLP to realize enhanced cancer optotheranostics.     

基于模糊多模型的堆芯功率控制

传统的堆芯功率PID控制器是基于单一功率水平处的堆芯局部模型设计的，难以准确描述整个堆芯功率水平范围的控制。因此，本文基于5个不同功率水平下的传递函数模型，通过三角隶属度函数加权，建立堆芯模糊多模型，并依据该模型设计堆芯功率模糊PID控制。以TMI型压水堆堆芯为对象，开展不同初始功率水平下的堆芯功率跟踪、堆芯进口温度扰动的控制仿真。结果表明，基于模糊多模型设计的堆芯功率模糊PID控制器可实现对堆芯功率的良好控制。     

Dilated and soft attention-guided convolutional neural network for breast cancer histology images classification

Breast cancer is one of the most common types of cancer in women, and histopathological imaging is considered the gold standard for its diagnosis. However, the great complexity of histopathological images and the considerable workload make this work extremely time-consuming, and the results may be affected by the subjectivity of the pathologist. Therefore, the development of an accurate, automated method for analysis of histopathological images is critical to this field. In this article, we propose a deep learning method guided by the attention mechanism for fast and effective classification of haematoxylin and eosin-stained breast biopsy images. First, this method takes advantage of DenseNet and uses the feature map's information. Second, we introduce dilated convolution to produce a larger receptive field. Finally, spatial attention and channel attention are used to guide the extraction of the most useful visual features. With the use of fivefold cross-validation, the best model obtained an accuracy of 96.47% on the BACH2018 dataset. We also evaluated our method on other datasets, and the experimental results demonstrated that our model has reliable performance. This study indicates that our histopathological image classifier with a soft attention-guided deep learning model for breast cancer shows significantly better results than the latest methods. It has great potential as an effective tool for automatic evaluation of digital histopathological microscopic images for computer-aided diagnosis.     

A tutorial on the segmentation of metallographic images: Taxonomy,new MetalDAM dataset,deep learning-based ensemble model,experimental analysis and challenges

Image segmentation is an important issue in many industrial processes, with high potential to enhance the manufacturing process derived from raw material imaging. For example, metal phases contained in microstructures yield information on the physical properties of the steel. Existing prior literature has been devoted to develop specific computer vision techniques able to tackle a single problem involving a particular type of metallographic image. However, the field lacks a comprehensive tutorial on the different types of techniques, methodologies, their generalizations and the algorithms that can be applied in each scenario. This paper aims to fill this gap. First, the typologies of computer vision techniques to perform the segmentation of metallographic images are reviewed and categorized in a taxonomy. Second, the potential utilization of pixel similarity is discussed by introducing novel deep learning-based ensemble techniques that exploit this information. Third, a thorough comparison of the reviewed techniques is carried out in two openly available real-world datasets, one of them being a newly published dataset directly provided by ArcelorMittal, which opens up the discussion on the strengths and weaknesses of each technique and the appropriate application framework for each one. Finally, the open challenges in the topic are discussed, aiming to provide guidance in future research to cover the existing gaps.     

Efficient Identification of water conveyance tunnels siltation based on ensemble deep learning

The inspection of water conveyance tunnels plays an important role in water diversion projects. Siltation is an essential factor threatening the safety of water conveyance tunnels. Accurate and efficient identification of such siltation can reduce risks and enhance safety and reliability of these projects. The remotely operated vehicle (ROV) can detect such siltation. However, it needs to improve its intelligent recognition of image data it obtains. This paper introduces the idea of ensemble deep learning. Based on the VGG16 network, a compact convolutional neural network (CNN) is designed as a primary learner, called Silt-net, which is used to identify the siltation images. At the same time, the fully-connected network is applied as the meta-learner, and stacking ensemble learning is combined with the outputs of the primary classifiers to obtain satisfactory classification results. Finally, several evaluation metrics are used to measure the performance of the proposed method. The experimental results on the siltation dataset show that the classification accuracy of the proposed method reaches 97.2%, which is far better than the accuracy of other classifiers. Furthermore, the proposed method can weigh the accuracy and model complexity on a platform with limited computing resources.     

The influence of FeNi nanoparticles on the microstructures and soft magnetic properties of FeSi soft magnetic composites

In this work, a new type of FeSi/FeNi soft magnetic powder core (SMPC) was successfully fabricated by coating FeNi nanoparticles on the surface of FeSi micrometer powder. The effects of different contents of FeNi nanoparticles on the micromorphology, internal structures, and soft magnetic properties of SMPCs were studied. The results show that FeNi nanoparticles adhere to the surface of FeSi powder, which can effectively fill the air gap between FeSi powder and is beneficial to the compaction of the powder cores during the pressing process. Thus, the density of the SMPCs is increased. Compared to FeSi SMPCs, the comprehensive soft magnetic properties of FeSi/FeNi SMPCs have been greatly improved. When adding 15 wt% FeNi nanoparticles, the SMPCs exhibit excellent magnetic properties with high effective permeability (increased by 43.8 %) and low core loss (decreased by 22.1 %). The high performance FeSi/FeNi SMPCs prepared in this work are expected to be widely used in power choke coils, uninterruptible power supplies, and boosts and inverter inductors.     

对企业核心竞争力的再思考

目前,国内外关于"企业核心竞争力"的认识比较模糊,可谓仁者见仁,智者见智.美国管理学者Kevin P·Coyne、Stephen J·D·Hall和Patricia Gorman Clifford在麦肯锡高层管理论丛上发表的论文称其为"亦真亦幻的核心竞争力"[1].概念上的模糊,导致了人们认识上的多元化,从而给企业培育、提升核心竞争力制造了障碍,使得企业对如何培育核心竞争力变得无所适从.实际上,核心竞争力是企业竞争力的高级形式,只有一些国际企业发展到了一定层次才能具备.因此,正确理解企业核心竞争力的深刻内涵,弄清企业核心竞争力的形成机理,是指导企业构筑核心竞争力的前提.     

利用岩芯变形测定原始地应力

探讨了利用测量岩芯的直径变形来测定地应力的一种新方法--岩芯变形法，通过预备实验和实际测定确认了岩芯变形法的适用性.预备实验的测定结果表明，平面内的主应力方向和主应力差之值均与所施加的载荷相一致，水压致裂采取到的岩芯的测定结果也表明两种方法的结果相吻合.探讨了岩石的微观裂隙、超声波速度与岩芯变形之间的关系.岩芯变形法不需要对岩芯进行整形，并且在非破坏状态下测定，对各种岩石均适用，为原始地应力测定提供了一种新的方法和途径.