Web service adaptation: A decade’s overview

With the exponential growth of communication and information technologies, adaptation has gained a significant attention as it becomes a key feature of service-based systems, allowing them to operate and evolve in highly dynamic and uncertain environments. Although several Web service standards and frameworks have been proposed and extended, existing solutions do not provide a suitable architecture, in which all aspects of monitoring and adaptation (e.g., proactive, cross-layer, and autonomic adaptation) can be expressed. In addition, the emergence of new computing environments to host and execute various types of services (Web/cloud services, big data-intensive services, mobile services, microservices, etc.) raises the need for more efficient monitoring and adaptation systems. This survey aims to bring a synthesis and a road-map to the adaptation of service-based systems. We also discuss adaptation solutions in emerging service models, such as cloud services and big services. Based on an adaptation taxonomy which we extracted from the surveyed approaches, and by identifying the main requirements and goals of service adaptation in Web, cloud and big data environments, detailed analysis and discussions, as well as the open issues, are provided.     

MAML2: meta reinforcement learning via meta-learning for task categories

Meta-learning has been widely applied to solving few-shot reinforcement learning problems, where we hope to obtain an agent that can learn quickly in a new task. However, these algorithms often ignore some isolated tasks in pursuit of the average performance, which may result in negative adaptation in these isolated tasks, and they usually need sufficient learning in a stationary task distribution. In this paper, our algorithm presents a hierarchical framework of double meta-learning, and the whole framework includes classification, meta-learning, and re-adaptation. Firstly, in the classification process, we classify tasks into several task subsets, considered as some categories of tasks, by learned parameters of each task, which can separate out some isolated tasks thereafter. Secondly, in the meta-learning process, we learn category parameters in all subsets via meta-learning. Simultaneously, based on the gradient of each category parameter in each subset, we use meta-learning again to learn a new meta-parameter related to the whole task set, which can be used as an initial parameter for the new task. Finally, in the re-adaption process, we adapt the parameter of the new task with two steps, by the meta-parameter and the appropriate category parameter successively. Experimentally, we demonstrate our algorithm prevents the agent from negative adaptation without losing the average performance for the whole task set. Additionally, our algorithm presents a more rapid adaptation process within re-adaptation. Moreover, we show the good performance of our algorithm with fewer samples as the agent is exposed to an online meta-learning setting.     

智慧水文应用数字孪生5G专网用户自运营管理实践研究--以曲靖市5G智慧水文面店示范站项目为例

中国移动助力云南省曲靖市落地5G智慧水文面店示范站项目实践,融合5G、北斗、云计算、物联网、数字孪生等新技术,基于中国移动OneLink平台和OneNET平台定制开发了曲靖水文5G数字孪生运用管理云平台,提供软硬件总集成服务成功打造曲靖市5G+智慧水文面店示范站。该实践树立了5G专网用户自运营管理的成功标杆,为水文行业以及对5G专网用户自运营管理有需求的政府及企事业单位起到成功案例借鉴复制作用。     

基于无人机影像的电网绝缘子自爆识别

电网因其在电能传输方面的关键性作用,在我国民生项目建设领域一直扮演着至关重要的角色。电网杆塔上的绝缘子一旦发生自爆(也称“缺陷”),绝缘子会自动剥落,输电线路就会产生安全隐患,严重时会降低输电线路的运行寿命,甚至会引发供电中断,发生大范围的停电事故,造成巨大的财产损失。目前,主流的巡检方法为人工巡检,该方法不仅耗时耗力,而且也存在一定主观出错率,已不适用于目前电路巡检的实际情况。本设计采用YOLO V5网络模型,对无人机航拍影像中绝缘子串及绝缘子自爆进行自动识别。首先通过平移、翻转、裁剪等,对航拍绝缘子影像数据集进行数据增广,并对增广后的数据集在LabelImg中进行标注,然后利用YOLO V5网络模型对绝缘子串及绝缘子自爆进行识别,最后采用PyQt5框架在PyCharm中设计了绝缘子自爆识别的系统界面,对模型进行调用,实现了绝缘子串及绝缘子自爆识别。本设计采用从网络上下载、国家电网提供、数据增广所得到的500张无人机航拍影像作为数据集,对所得数据集进行人工标注,再使用YOLO V5网络模型进行训练和测试,结果表明YOLO V5网络模型对绝缘子串具有较高的识别精度,最高识别精度为90.2%,对绝缘子自爆的最高检测精度为80.8%。这说明了YOLO V5网络模型在绝缘子串识别方面有较好的表现,但是由于训练集中绝缘子自爆的样本影像数量有限,所以该网络模型对绝缘子的自爆识别存在一定局限性,本实验能够部分代替人力实现电网绝缘子智能巡检,提高了检测效率。     

Phase Separation Derived Core/Shell Structured Cu11V6O26/V2O5 Microspheres: First Synthesis and Excellent Lithium‐Ion Anode Performance with Outstanding Capacity Self‐Restoration

Novel amorphous vanadium oxide coated copper vanadium oxide (Cu₁₁V₆O₂₆/V₂O₅) microspheres with 3D hierarchical architecture have been successfully prepared via a microwave‐assisted solution method and subsequent annealing induced phase separation process. Pure Cu₁₁V₆O₂₆ microspheres without V₂O₅ coating are also obtained by an H₂O₂ solution dissolving treatment. When evaluated as an anode material for lithium‐ion batteries (LIBs), the as‐synthesized hybrid exhibits large reversible capacity, excellent rate capability, and outstanding capacity self‐recovery. Under the condition of high current density of 1 A g^?1, the 3D hierarchical Cu₁₁V₆O₂₆/V₂O₅ hybrid maintains a reversible capacity of ≈1110 mA h g^?1. Combined electrochemical analysis and high‐resolution transmission electron microscopy observation during cycling reveals that the amorphous V₂O₅ coating plays an important role on enhancing the electrochemical performances and capacity self‐recovery, which provides an active amorphous protective layer and abundant grain interfaces for efficient inserting and extracting of Li‐ion. As a result, this new copper vanadium oxide hybrid is proposed as a promising anode material for LIBs.     

Redox and pH Dual Responsive Polymer Based Nanoparticles for In Vivo Drug Delivery

Responsive nanomaterials have emerged as promising candidates as drug delivery vehicles in order to address biomedical diseases such as cancer. In this work, polymer‐based responsive nanoparticles prepared by a supramolecular approach are loaded with doxorubicin (DOX) for the cancer therapy. The nanoparticles contain disulfide bonds within the polymer network, allowing the release of the DOX payload in a reducing environment within the endoplasm of cancer cells. In addition, the loaded drug can also be released under acidic environment. In vitro anticancer studies using redox and pH dual responsive nanoparticles show excellent performance in inducing cell death and apoptosis. Zebrafish larvae treated with DOX‐loaded nanoparticles exhibit an improved viability as compared with the cases treated with free DOX by the end of a 3 d treatment. Confocal imaging is utilized to provide the daily assessment of tumor size on zebrafish larva models treated with DOX‐loaded nanoparticles, presenting sustainable reduction of tumor. This work demonstrates the development of functional nanoparticles with dual responsive properties for both in vitro and in vivo drug delivery in the cancer therapy.     

Revolving Vernier Mechanism Controls Size of Linear Homomultimer

A new kind of the Vernier mechanism that is able to control the size of linear assembly of DNA origami nanostructures is proposed. The mechanism is realized by mechanical design of DNA origami, which consists of a hollow cylinder and a rotatable shaft in it connected through the same scaffold. This nanostructure stacks with each other by the shape complementarity at its top and bottom surfaces of the cylinder, while the number of stacking is limited by twisting angle of the shaft. Experiments have shown that the size distribution of multimeric assembly of the origami depends on the twisting angle of the shaft; the average lengths of the multimer are decamer, hexamer, and tetramer for 0°, 10°, and 20° twist, respectively. In summary, it is possible to affect the number of polymerization by adjusting the precise shape and movability of a molecular structure.     

Carbon Nanodot‐Sensitized Modulation of Alzheimer's β‐Amyloid Self‐Assembly,Disassembly, and Toxicity

The self‐assembly of amyloidogenic peptides into β‐sheet‐rich aggregates is a general feature of many neurodegenerative diseases, including Alzheimer's disease, which signifies the need for the effective attenuation of amyloid aggregation toward alleviating amyloid‐associated neurotoxicity. This study reports that photoluminescent carbon nanodots (CDs) can effectively suppress Alzheimer's β‐amyloid (Aβ) self‐assembly and function as a β‐sheet breaker disintegrating preformed Aβ aggregates. This study synthesizes CDs using ammonium citrate through one‐pot hydrothermal treatment and passivates their surface with branched polyethylenimine (bPEI). The bPEI‐coated CDs (bPEI@CDs) exhibit hydrophilic and cationic surface characteristics, which interact with the negatively charged residues of Aβ peptides, suppressing the aggregation of Aβ peptides. Under light illumination, bPEI@CDs display a more pronounced effect on Aβ aggregation and on the dissociation of β‐sheet‐rich assemblies through the generation of reactive oxygen species from photoactivated bPEI@CDs. The light‐triggered attenuation effect of Aβ aggregation using a series of experiments, including photochemical and microscopic analysis, is verified. Furthermore, the cell viability test confirms the ability of photoactivated bPEI@CDs for the suppression of Aβ‐mediated cytotoxicity, indicating bPEI@CDs' potency as an effective anti‐Aβ neurotoxin agent.