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相关概念视频

Transformers01:26

Transformers

1.0K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
1.0K
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

129
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
129
Types Of Transformers01:16

Types Of Transformers

943
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
943
The Ideal Transformer01:26

The Ideal Transformer

342
In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's...
342
Transformers in Distribution System01:27

Transformers in Distribution System

98
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
98
Structural Classification of Joints01:20

Structural Classification of Joints

3.1K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.1K

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Updated: May 24, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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TransScore:基于变压器卷积网络的姿势评分和亲和度预测的图形模型.

Chuqi Lei, Wenkang Wang, Wei Fan

    IEEE journal of biomedical and health informatics
    |March 3, 2025
    PubMed
    概括
    此摘要是机器生成的。

    新型深度学习模型TransScore增强了用于药物发现的蛋白质化合物相互作用预测. 它提高了姿势得分和亲和力预测,即使在具有挑战性的冷启动场景中,也表现出强大的性能.

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    科学领域:

    • 计算化学是一种计算化学.
    • 人工智能在药物发现中的作用
    • 生物信息学是一种生物信息学.

    背景情况:

    • 分子对接对于通过预测蛋白质化合物相互作用来识别潜在的候选药物至关重要.
    • 基于人工智能的评分功能增强了分子对接,但往往缺乏可扩展性和稳定性,特别是在冷启动场景中.
    • 现有的AI模型通常专注于单个预测任务,限制了它们的适应性.

    研究的目的:

    • 开发一种基于深度学习的新型图形模型,用于强大的蛋白质化合物姿势得分和亲和力预测.
    • 解决当前人工智能驱动的分子对接评分功能的可扩展性和冷启动性能的局限性.
    • 提高预测结合亲和关系及其相对排序的准确性和精度.

    主要方法:

    • 开发了一个使用变压器卷积网络的深度学习图形模型.
    • 这个名为TransScore的模型包含了一个自我注意力机制,以捕捉蛋白质化合物姿势特征.
    • 采用有门的剩余算法来提高各种相关任务的适应性.

    主要成果:

    • 在寒冷和温暖的场景中,TransScore在姿势评分方面表现出卓越的表现,超过了现有的方法.
    • 该模型在不平衡的数据集上表现出强度,并且在热启动和冷启动场景中对亲和力预测的持续改进.
    • 在预测绑定亲和关系及其相对顺序方面,TransScore实现了高准确度和精度.

    结论:

    • TransScore为分子对接提供了强大且可扩展的深度学习解决方案,显著改善了姿势得分和亲和力预测.
    • 该模型处理冷启动场景和各种任务的能力突显了其加速药物发现的潜力.
    • 对二氧化碳炭酶II相互作用的分析表明,TransScore可以阐明蛋白质-联体结合机制,有助于合理的药物设计.