润色文字：Additionally, due to the large aspect ratiol and their structural similarity to wire antennas, cables are highly effective radiators.

请问这段话是否正确？可以对其进行润色吗？

这段话的表述是正确的。如果您想要进行润色，可以考虑使用更加清晰明确的语言，例如：

Furthermore, because cables possess a large aspect ratio and bear structural resemblance to wire antennas, they are exceptional radiators of electromagnetic waves.

相关问题

You will be given a dataframe with three columns: student_id, base, and bonus. You should calculate the bonus_percentage column, which contains the percentage of each student's bonus relative to their base grade. The result should be sorted in ascending order by student_id. Additionally, the result should only include students if they have a base that is greater than the average base of all students, and if their bonus is at least the average bonus of all students. Input The first line is a number n, indicating the number of students. The next n lines give the students' information (student_id, base, bonus). Output For each filtered out rows, print the student_id and bonus_percentage (output to 2 decimal places). If there is no row to display, print None. Sample Input 5 12213101 85 3.5 12213103 90 4.0 12213212 80 1.2 12213129 87 0.5 12213301 93 2.1 12213103 4.44 Output: 12213103 4.44 Constraints: The input dataframe will have at most 10^5rows. The student_idcolumn will contain unique non-negative integers. The base and bonus columns will contain non-negative float point numbers. Note: You can use the mean() method to compute the mean of a column.use pandas in python

Sure, I can help you with that. Here's a snippet of Python code that should do the trick using pandas:

import pandas as pd

# read in the input dataframe
n = int(input())
data = []
for i in range(n):
    row = input().split()
    data.append({
        'student_id': int(row[0]),
        'base': float(row[1]),
        'bonus': float(row[2])
    })
df = pd.DataFrame(data)

# calculate the average base and bonus
avg_base = df['base'].mean()
avg_bonus = df['bonus'].mean()

# filter out rows that don't meet the criteria
df = df[(df['base'] > avg_base) & (df['bonus'] >= avg_bonus)]

# calculate the bonus percentage and sort by student_id
df['bonus_percentage'] = df['bonus'] / df['base'] * 100
df = df.sort_values(by='student_id')

# output the filtered rows, or "None" if there are none
if len(df) > 0:
    for index, row in df.iterrows():
        print(row['student_id'], '{:.2f}'.format(row['bonus_percentage']))
else:
    print('None')

You can copy and paste this code into your Python environment and run it to test it out with your input data. Let me know if you have any questions or need further assistance!

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.

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。构成控制药物释放系统的材料被称为“智能材料”，它们可以对内部（pH、氧化还原、酶等）或外部（温度、电/磁、超声和光等）环境的刺激作出反应。在对特定刺激做出反应之前和之后，智能材料的组成或构型会发生变化，破坏释放系统的原有平衡，并释放药物。其中，光控药物释放系统是指通过光来控制药物释放的系统，具有广泛的应用前景，并受到研究人员的广泛关注。近年来，基于不同光敏响应基团的光控药物释放系统已被设计并开发出来，用于精确的光控释药。在这篇综述中，我们介绍了四种光敏响应基团，包括光解基团、光异构基团、光诱导重排基