CATL 2019：动力电池技术前瞻，驱动电动车未来

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"CATL（宁德时代）在2019年的《电池：电动汽车关键技术视角》报告中，探讨了电动车动力系统的关键技术——电池包。作为全球领先的电池制造商，CATL对未来电池技术发展有着深远影响。电池包在电动汽车中的重要性主要体现在以下几个方面： 1. 能源效率与排放减少：电池是电动汽车的核心组成部分，它通过高效的化学储能技术（如锂离子电池）显著提升了电动汽车的能效，有助于降低碳排放，实现可持续出行。 2. 主导能量存储：电池包在电动车中的占据主导地位，负责存储并转换电能，是决定车辆行驶范围和性能的关键因素。 3. 空间与重量：由于电池包的体积庞大，它占据了车辆相当大的空间，并对车辆的整体设计和重量分配产生重大影响。 4. 成本影响：电池包作为车辆的主要成本之一，其价格和性能直接影响电动车的售价和竞争力。 5. 技术进步：电池包技术包括化学成分、封装设计、控制算法以及应用层面的创新，这些都是推动电动车普及和优化的关键要素。 报告还详细介绍了CATL的细胞生产和模块生产路线图，强调了电池安全性、性能、寿命、成本和环境影响的五重考量。其中，安全性的核心在于高能量密度电池的滥用性能测试，例如耐穿刺、短路、过充和过放等极限情况下的表现，这些测试符合欧洲汽车工业协会（EUCAR）的严格标准。 CATL秉承着一套黄金法则哲学，致力于在确保安全的前提下，持续提升电池的性能、寿命和环境友好性。电池技术的发展不仅关乎电动车产业的进步，也是实现绿色交通转型的关键驱动力。随着CATL这样的领军企业不断推动技术创新，我们期待看到更加高效、可靠且环保的动力电池包解决方案在未来几年内的广泛应用。"

eclipse-java-2020-12-R-win32-x86_64_2

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2. `plugins` 文件夹：包含各种插件，这些插件扩展了Eclipse的功能，如Java开发工具（JDT）、透视图（ Perspectives ）和视图（ Views ）等。 3. `features` 文件夹：包含了所有已安装特性的描述和配置信息。 4. `...

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1. **工作台（Workbench）**：Eclipse的工作台是一个可视化界面，它由多个视图（Views）、编辑器（Editors）和透视图（Perspectives）组成，提供了开发环境的主要交互界面。 2. **透视图（Perspectives）**：透视图...

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润色下面英文：The controlled drug delivery systems, due to their precise control of drug release in spatiotemporal level triggered by specific stimulating factors and advantages such as higher utilization ratio of drug, less side-effects to normal tissues and so forth, provide a new strategy for the precise treatment of many serious diseases, especially tumors. The materials that constitute the controlled drug delivery systems are called “smart materials” and they can respond to the stimuli of some internal (pH, redox, enzymes, etc.) or external (temperature, electrical/magnetic, ultrasonic and optical, etc.) environments. Before and after the response to the specific stimulus, the composition or conformational of smart materials will be changed, damaging the original balance of the delivery systems and releasing the drug from the delivery systems. Amongst them, the photo-controlled drug delivery systems, which display drug release controlled by light, demonstrated extensive potential applications, and received wide attention from researchers. In recent years, photo-controlled drug delivery systems based on different photo-responsive groups have been designed and developed for precise photo-controlled release of drugs. Herein, in this review, we introduced four photo-responsive groups including photocleavage groups, photoisomerization groups, photo-induced rearrangement groups and photocrosslinking groups, and their different photo-responsive mechanisms. Firstly, the photocleavage groups represented by O-nitrobenzyl are able to absorb the energy of the photons, inducing the cleavage of some specific covalent bonds. Secondly, azobenzenes, as a kind of photoisomerization groups, are able to convert reversibly between the apolar trans form and the polar cis form upon different light irradiation. Thirdly, 2-diazo-1,2-naphthoquinone as the representative of the photo-induced rearrangement groups will absorb specific photon energy, carrying out Wolff rearrangement reaction. Finally, coumarin is a promising category photocrosslinking groups that can undergo [2＋2] cycloaddition reactions under light irradiation. The research progress of photo-controlled drug delivery systems based on different photo-responsive mechanisms were mainly reviewed. Additionally, the existing problems and the future research perspectives of photo-controlled drug delivery systems were proposed.

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控制药物释放系统是指可以控制药物空间和时间释放的系统，可以通过一些特定的刺激因素激活，具有药物利用率更高、对正常组织的副作用更小等优势，为精确治疗许多严重疾病，尤其是肿瘤提供了新的策略。...

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The article also raises some potential challenges and criticisms of school-based curriculum development, such as concerns about teacher expertise and the potential for narrow or biased perspectives to...

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Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798-1828. 2. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep ...

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As an AI language model, I do not have personal beliefs or opinions. However, I am programmed to provide unbiased and informative responses to the best of my abilities This allows me to assist users ...

Compared with homogeneous network-based methods, het- erogeneous network-based treatment is closer to reality, due to the different kinds of entities with various kinds of relations [22– 24]. In recent years, knowledge graph (KG) has been utilized for data integration and federation [11, 17]. It allows the knowledge graph embedding (KGE) model to excel in the link prediction tasks [18, 19]. For example, Dai et al. provided a method using Wasser- stein adversarial autoencoder-based KGE, which can solve the problem of vanishing gradient on the discrete representation and exploit autoencoder to generate high-quality negative samples [20]. The SumGNN model proposed by Yu et al. succeeds in inte- grating external information of KG by combining high-quality fea- tures and multi-channel knowledge of the sub-graph [21]. Lin et al. proposed KGNN to predict DDI only based on triple facts of KG [66]. Although these methods have used KG information, only focusing on the triple facts or simple data fusion can limit performance and inductive capability [69]. Su et al. successively proposed two DDIs prediction methods [55, 56]. The first one is an end-to-end model called KG2ECapsule based on the biomedical knowledge graph (BKG), which can generate high-quality negative samples and make predictions through feature recursively propagating. Another one learns both drug attributes and triple facts based on attention to extract global representation and obtains good performance. However, these methods also have limited ability or ignore the merging of information from multiple perspectives. Apart from the above, the single perspective has many limitations, such as the need to ensure the integrity of related descriptions, just as network-based methods cannot process new nodes [65]. So, the methods only based on network are not inductive, causing limited generalization [69]. However, it can be alleviated by fully using the intrinsic property of the drug seen as local information, such as chemical structure (CS) [40]. And a handful of existing frameworks can effectively integrate multi-information without losing induction [69]. Thus, there is a necessity for us to propose an effective model to fully learn and fuse the local and global infor- mation for improving performance of DDI identification through multiple information complementing.是什么意思

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这段文字讨论了异质网络方法相对于同质网络方法更接近现实的原因，并介绍了知识图谱和知识图谱嵌入模型在链接预测任务中的应用。作者提出了一些方法来解决多信息融合和归纳能力限制等问题，包括使用药物的局部信息如...

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1. "Learning from Demonstrations for Real World Reinforcement Learning" - 一篇介绍基于数据的强化学习的论文，讨论了如何从演示中学习来指导强化学习。链接：https://arxiv.org/abs/1803.11347 2. "Deep ...

Do unto others as you would have them do unto you, for example, write a composition about human relations.

2023-04-24 上传
Certainly, the phrase "Do unto others as you would have them do unto you" is a timeless principle that speaks to the essence of how we ought to treat our fellow human beings. At its core, this maxim ...
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