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相关概念视频

Overview of Advanced Functional Groups02:22

Overview of Advanced Functional Groups

29.8K

Functional groups are groups of atoms with specific chemical properties that occur within organic molecules and are sometimes denoted as “R”. Functional groups can “functionalize” a compound by enabling it to adopt different physical and chemical properties.
Types of Advanced Functional Groups
The table below summarizes some of the major functional groups in organic chemistry.
29.8K
Loss of Tumor Suppressor Gene Functions01:12

Loss of Tumor Suppressor Gene Functions

6.1K
Tumor suppressor genes are normal genes that can slow down cell division, repair DNA mistakes, or program the cells for apoptosis in case of irreparable damage. Hence, they play an essential role in preventing the proliferation of damaged cells.
When the tumor suppressor genes develop mutations or are lost, cells start growing out of control, leading to cancer. However, a single functional copy of the tumor suppressor gene is enough for the cells to maintain their normal functions and cell...
6.1K
Precipitation of Ions03:11

Precipitation of Ions

30.3K
Predicting Precipitation
The equation that describes the equilibrium between solid calcium carbonate and its solvated ions is:
30.3K
Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

4.3K
Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
4.3K
Precipitation Reactions03:10

Precipitation Reactions

65.4K
In a precipitation reaction, aqueous solutions of soluble salts react to give an insoluble ionic compound – the precipitate. The reaction occurs when oppositely charged ions in solution overcome their attraction for water and bind to each other, forming a precipitate that separates out from the solution. Since such reactions involve the exchange of ions between ionic compounds in aqueous solution, they are also referred to as double displacement, double replacement, exchange reactions, or...
65.4K
Line Loss01:10

Line Loss

541
The different configurations of source-load connections include wye (star) and delta connections. The relationship between line and phase voltages and currents varies depending on the configuration. When the source is supplying power, it is transmitted through the wires to the load, and during this transmission, some power is absorbed by the wires, leading to line loss.
Line loss impacts power delivery efficiency in a balanced three-phase circuit. The symmetry in such a circuit simplifies the...
541

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相关实验视频

Updated: Feb 1, 2026

Loss- and Gain-of-function Approach to Investigate Early Cell Fate Determinants in Preimplantation Mouse Embryos
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先进的流浪损失函数用于降雨预测.

Jaeho Choi1, Hyeri Kim2,3, Kwang-Ho Kim4

  • 1Chung-Ang University, AI/ML Innovation Research Center, Seoul 06974, Republic of Korea.

Physical review letters
|January 30, 2026
PubMed
概括

准确的降水预测至关重要. 本研究引入了一种先进的流量 (AT) 损失函数,比传统的临界成功指数 (CSI) 方法改进机器学习模型,特别是在干旱时期.

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Large-scale Gene Knockdown in C. elegans Using dsRNA Feeding Libraries to Generate Robust Loss-of-function Phenotypes
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In Vivo Functional Assessment of Rat Masseter Muscle Following Surgical Creation of a Volumetric Muscle Loss (VML) Injury
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Large-scale Gene Knockdown in C. elegans Using dsRNA Feeding Libraries to Generate Robust Loss-of-function Phenotypes
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科学领域:

  • 气象学和气候科学 气象学和气候科学
  • 人工智能和机器学习

背景情况:

  • 准确的降水预测对于适应气候变化至关重要.
  • 机器学习 (ML) 为传统的数值天气预测 (NWP) 提供了替代方案.
  • 现有的ML方法通常使用非最佳损失函数,如关键成功指数 (CSI).

研究的目的:

  • 为降水预测开发一种新的损失函数,克服CSI的局限性.
  • 提高ML模型在降雨预测中的性能,特别是在干旱期间.

主要方法:

  • 一个惩罚表达式被制定并重新解释为一个二次式不受约束的二进制优化 (QUBO) 问题.
  • 通过近似,QUBO公式被放松成一个可微分的先进流浪 (AT) 损失函数.
  • 对AT损失函数的有效性与CSI进行了评估,并通过严格的测试进行了评估.

主要成果:

  • 与现有方法相比,拟议的AT损失函数表现出优越的性能.
  • 评估包括利普希茨常数分析,预测性能指标和一致性实验.
  • 废弃性研究证实了AT损失的有效性,当与操作模型集成.

结论:

  • 新的AT损失函数为优化降水预测模型提供了更强大,更有效的标准.
  • 这一进步对于改善长期干旱期间的预测尤其重要.
  • 这项研究突出了气象ML应用中 QUBO 放松可微分损失函数的潜力.