资源简介

高考英语热点阅读
文章选自Science Daily
第一部分 阅读理解
Passage 1(夹叙夹议文）
Researchers have developed a new kind of programmable smart skin using hydrogel, a soft, water rich material. Unlike traditional man made materials that have fixed properties, this smart skin can be carefully tuned to behave in different ways. Its appearance, flexibility, surface texture and shape can all be adjusted when it meets external triggers such as heat, special liquids or physical stress.
The idea comes from cephalopods like octopuses. These animals can quickly change the color and texture of their skin to hide in the environment or send messages to each other. Their skin is controlled by a complex combination of muscles and nerves. Inspired by such natural abilities, the research team created a 4D printing system to make similar dynamic effects in a man made soft material.
The team used a method called halftone encoded printing. This technology turns image and texture information into digital signals and stores them directly inside the hydrogel. The method is similar to the dot pattern used in printed pictures. By designing these hidden patterns, the researchers can program how different parts of the material will react to heat, liquids or pressure. Some parts may expand, soften or change color more than others, allowing total control over the material’s performance.
In tests, the researchers encoded the Mona Lisa into a thin hydrogel sheet. The sheet looked completely clear when washed with ethanol. However, the hidden image slowly showed up when the material was placed in cold water or gently heated. Such a function can be used for camouflage or information encryption, where secret pictures or messages remain hidden until certain conditions are met.
The smart skin also has excellent flexibility. It can change from a flat sheet into complex 3D shapes without using extra layers or materials. All shape and texture changes are controlled only by the printed patterns inside the material. The team even made samples that changed into 3D forms while showing hidden images at the same time, just like how octopuses control both body shape and skin appearance at once.
This new technology builds on earlier studies of 4D printed hydrogels. The researchers have added more functions into one single material. In the future, they hope to develop a wider, scalable platform that can program different abilities into adaptive materials. This cross field research may bring new chances for smart materials, biomimetic devices, information safety and biomedical tools.
(细节理解) What inspired the researchers to develop the smart skin
The beautiful colors of the Mona Lisa
The skin-changing ability of cephalopods
Traditional materials with fixed properties
3D-printed machines with high speed
(细节理解) How is the information stored in the smart skin
By drawing pictures directly on the surface
By using halftone-encoded printing
By adding several different materials together
By connecting it to a computer system
(推理判断) What can be inferred about the smart skin from the text
It can show hidden images under certain conditions
It has been widely used in biomedical devices
It needs many layers to change into 3D shapes
It can only change its color in bright light
(主旨大意) What is the main idea of the passage
A new method to protect endangered sea animals
The history and development of 3D printing technology
Researchers create 4D smart skin inspired by nature
Different ways to use encrypted information
Passage 2(议论文）
Robot Lip-Sync: Bridging the Uncanny Valley
When people communicate face to face, almost half their attention goes to lip movements. Yet for humanoid robots, making natural and believable lip motions remains extremely difficult. Even the most advanced machines often show stiff, puppet-like movements. Humans are highly sensitive to unnatural facial expressions, especially unrealistic lip movements, which largely cause the “Uncanny Valley” — a feeling that robots are eerie and emotionally lifeless. However, new research may soon change this situation.
On January 15, a team from Columbia Engineering introduced a breakthrough robot. For the first time, a robot can learn realistic lip movements for speaking and singing by observing humans, rather than following preset rules. The robot has 26 facial motors and learns by watching itself in a mirror and studying hours of human videos. It can pronounce words in different languages and even “sing” along with sounds.
Creating natural lip movement is challenging for two key reasons. First, it requires flexible facial materials and many tiny, quiet, highly coordinated motors. Second, lip movements must closely match fast-changing speech sounds. Most robots have rigid faces controlled by fixed rules, leading to unnatural expressions. The Columbia robot solves this by self-learning through a “vision-to-action” model, gradually mastering how each motor shapes its face.
The robot still faces limitations. It struggles with certain sounds like “B” and lip-puckering sounds such as “W”. Nevertheless, the team is confident that performance will improve over time.
Researchers emphasize that realistic facial expressions are vital for human-robot communication. “When lip-sync is combined with conversational AI, it greatly deepens the emotional connection between robots and humans,” says Yuhang Hu, the study’s leader. Hod Lipson, director of the lab, adds, “Facial expression is just as important as movement of legs and hands. Without natural eyes and lips, robots will remain stuck in the Uncanny Valley.”
The team notes that emotional facial expressions are a major missing part of modern robotics. As robots enter fields like education, healthcare and elder care, realistic communication will become increasingly necessary. The researchers also remind people to consider ethical issues as machines become more emotionally engaging.
“This technology will be powerful. We must proceed carefully to maximize benefits and reduce risks,” Lipson says. He believes that robots should learn human expressions by observation, not rigid programming. “There is something truly touching when a robot smiles simply by watching humans,” he adds.
5.(细节理解) What mainly causes the “Uncanny Valley” effect in robots
Heavy and rigid body structure
Awkward walking gestures
Unnatural lip and facial movements
Limited ability to understand languages
6.(细节理解) How did the new robot learn lip movements
A. By receiving muscle control surgery
B. By observing itself and human videos
C. By communicating directly with doctors
D. By following fixed preset rules
7.(推理判断) What can we infer from the text
A. The new technology has no ethical concerns at all
B. Realistic facial expressions will improve human-robot trust
C. Robots will completely replace humans in care work soon
D. The robot can already speak all languages perfectly
8.(观点态度) What is Lipson’s attitude toward robot facial expression technology
A. Fearful
B. Uninterested
C. Supportive
D. Doubtful
Passage 3(议论文）
Generative AI is proving to be a powerful tool in medical research. In a recent study by UC San Francisco and Wayne State University, scientists found that AI could process huge medical datasets far faster than human teams and sometimes achieve even better results. Human experts had spent months on the same tasks that AI finished in a short time.
The research focused on predicting preterm birth, a leading cause of newborn death. Researchers gave identical tasks to different groups. Some used only human effort, while others worked with AI. Even a master’s student and a high school student built effective prediction models with AI support. AI could generate useful code in minutes, which usually takes experienced programmers days.
Eight AI systems were tested, but only four produced usable code. Yet the successful ones worked without requiring large teams of experts. Because of this high speed, junior researchers finished their work, proved their findings, and sent their results to a journal in just a few months.
“These AI tools can relieve one of the biggest bottlenecks in data science: building analysis pipelines,” said Marina Sirota, a leading professor. “The speed-up couldn’t come sooner for patients who need help now.”
Preterm birth affects about 1,000 babies daily in the United States and can lead to long-term health problems. Researchers studied data from over 1,200 pregnant women to find risk factors. Earlier similar research took nearly two years to publish, but the AI-assisted project took only six months.
AI analyzed vaginal microbiome data to recognize signs of preterm birth and tested blood samples to estimate pregnancy age. In some cases, AI models performed better than human-built models.
However, scientists warn that AI still needs careful human supervision. It can produce misleading results, and professional knowledge remains necessary. Still, AI lets researchers spend less time fixing code and more time studying results and asking key scientific questions.
“Thanks to generative AI, researchers with limited data science skills can focus on answering important biomedical questions instead of debugging code,” said Adi L. Tarca, another lead scientist. The study shows AI will greatly speed up medical progress and help more patients in need.
9.(细节理解) What is the major advantage of generative AI in medical research
It can cure preterm birth completely.
It processes data much faster than human teams.
It replaces all human experts in laboratories.
It works well without any supervision.
10.(细节理解) How many of the tested AI systems produced usable code
Eight B. Six C. Four D.Two
11.(观点态度) What is Sirota’s attitude toward AI in medical research
A. Doubtful B. Uncaring C. Critical D. Supportive
12.(主旨大意) What is the main idea of the passage
Generative AI helps speed up medical research on preterm birth.
Preterm birth has become the biggest threat to babies worldwide.
Human researchers are no longer important in medical studies.
D. All AI systems can work perfectly without human guidance.
Passage 4(说明文)
Human tissue is complex and three-dimensional. However, pathologists commonly use thin 2D slices to diagnose diseases, which provides only a limited view of the tissue’s real structure. In recent years, there has been a growing trend to study tissue in its complete 3D form. Yet 3D pathology images hold hundreds of times more data than 2D ones, making manual analysis nearly impossible for humans.
To solve this problem, researchers from Mass General Brigham and the University of Washington have developed a new deep-learning system called TriPath. The team used two high-resolution 3D imaging methods to scan carefully prepared prostate cancer samples. They then trained the AI model to predict the risk of cancer recurrence by analyzing the full 3D structure of tissue biopsies.
The test results were impressive. By fully capturing the 3D shapes and details of entire tissue samples, TriPath outperformed human pathologists and traditional AI models that only use 2D image slices. Its ability to analyze complete 3D information led to more accurate and reliable predictions. The findings were published in the top scientific journal Cell.
Although TriPath still needs to be tested on larger datasets before being widely used in hospitals, the researchers hold a positive attitude toward its future value.
Andrew H. Song, the lead author, said, “Our study shows that analyzing the full 3D volume of tissue is key to making accurate patient risk predictions. This is the core feature of TriPath and can only be achieved through 3D pathology.”
Faisal Mahmood, a co-author, added, “With advances in AI and 3D spatial biology, TriPath offers a new framework to support clinical decisions and may help discover new biological markers for prognosis and treatment.”
Jonathan Liu from the University of Washington also noted that earlier 3D pathology studies focused only on clear structures, but TriPath is the first model that uses deep learning to find hidden 3D features for risk classification. This breakthrough shows great potential for guiding important medical treatment choices.
In the future, with more validation and development, TriPath could become a powerful tool to help pathologists make faster, more accurate decisions, greatly improving the quality of cancer diagnosis and treatment.
(细节理解) Why is 3D pathology challenging for traditional analysis
3D images are too expensive to produce.
3D datasets contain far more information.
3D machines are too difficult to operate.
D. 3D images are less clear than 2D ones.
14.(细节理解) What is special about TriPath compared with traditional models
A. It depends entirely on manual observation.
B. It uses 3D tissue data for risk prediction.
C. It can cure prostate cancer in a short time.
D. It replaces all doctors in clinical work.
15.(观点态度) What is the researchers’ attitude toward TriPath’s future
A. Doubtful B. Uncaring C.Critical D.Optimistic
16.(最佳标题) Which of the following is the best title for the text
A. TriPath: An AI Model for 3D Pathology Analysis
B. Differences Between 2D and 3D Medical Images
C. New Ways to Treat Prostate Cancer
D. The History of Computational Pathology
Answers:1.B 2. B 3. A 4. C 5. C 6. B 7. B 8. C 9. B 10. C 11. D 12. A 13.B 14. B 15. D 16. A
第二部分 词汇和短语
高考英语科技类阅读
Day 1（Passage 1 智能仿生皮肤）
一、生僻 / 专业词汇（必认）
hydrogel 水凝胶 cephalopod 头足类动物 octopus 章鱼
halftone encoded 半色调编码的 ethanol 乙醇 camouflage 伪装
encryption 加密 biomimetic 仿生的 scalable 可扩展的
texture 纹理；质地
二、高频重点词汇（必考）
programmable 可编程的 flexible 灵活的 flexibility 灵活性
adjust 调整 external 外部的 trigger 触发因素
inspire 启发；鼓舞 dynamic 动态的 expand 膨胀；扩张
soften 变软 encode 编码 hidden 隐藏的
complex 复杂的 adaptive 适应的 biomedical 生物医学的
三、重点短语 / 搭配（完形 + 阅读常考）
be tuned to 被调整以适应 fixed properties 固定属性 physical stress 物理压力
be inspired by 受…… 启发 4D printing system 4D 打印系统store information 存储信息
digital signals 数字信号 react to 对…… 做出反应
information encryption 信息加密information safety 信息安全 change into 变成
without extra layers 无需额外层 build on 以…… 为基础
cross field research 跨领域研究
四、同义替换（写作加分）
adjust = change = modify 调整
hidden = invisible 隐藏的
complex = complicated 复杂的
develop = create = invent 研发
Day 2（Passage 2 机器人唇形同步）
一、生僻 / 专业词汇
lip sync 唇语同步 uncanny valley 恐怖谷 humanoid 人形的
coordinated 协调的 rigid 僵硬的 puckering 噘嘴的
ethical 伦理的 eerie 怪异的
emotionally lifeless 无情感的
二、高频重点词汇
communicate 交流 believable 逼真的 stiff 僵硬的
sensitive 敏感的 unrealistic 不真实的 breakthrough 突破
observe 观察 preset 预设的 limitation 局限
vital 至关重要的 emotional 情感的 connection 联系
maximize 最大化 benefit 益处 risk 风险
三、重点短语 / 搭配
face to face 面对面 lip movements 唇部动作 facial expressions 面部表情
cause the Uncanny Valley 引发恐怖谷效应 learn by doing 通过做来学习
vision to action model 视觉 动作模型 be vital for 对…… 至关重要
emotional connection 情感连接 conversational AI 对话人工智能
elder care 养老护理 ethical issues 伦理问题
maximize benefits 最大化收益 reduce risks 降低风险
be stuck in 被困在
四、同义替换
difficult = challenging 困难的
important = vital = crucial 重要的
solve = deal with 解决
improve = enhance 提升
Day 3（Passage 3 + 4 医疗 AI / 3D 病理）
一、生僻 / 专业词汇
generative AI 生成式 AI preterm birth 早产 microbiome 微生物组
pathology 病理学 pathologist 病理学家 biopsy 活组织检查
recurrence 复发 prognosis 预后 prostate 前列腺
validation 验证 three dimensional 三维的 diagnose 诊断
二、高频重点词汇
process 处理 dataset 数据集 predict 预测
effective 有效的 supervision 监督 misleading 误导的
accurate 准确的 reliable 可靠的 potential 潜力
analyze 分析 analysis 分析 estimate 估计
complex 复杂的 limited 有限的 framework 框架
三、重点短语 / 搭配
medical research 医学研究 process data 处理数据 speed up 加速
risk factors 风险因素 blood samples 血液样本
human supervision 人工监督 misleading results 误导性结果
focus on 专注于 diagnose diseases 诊断疾病
deep learning system 深度学习系统 high resolution 高分辨率的
cancer recurrence 癌症复发 clinical decisions 临床决策
biological markers 生物标志物 treatment choices 治疗选择
四、同义替换
speed up = accelerate 加速
predict = forecast 预测
accurate = precise 准确的
analyze = study 分析

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