资源简介

科技类热点主题阅读
第一部分 阅读理解
Passage 1：（说明文）
As bird numbers decline globally, scientists and bird lovers are turning to algorithm-powered apps to collect important data on avian species. These AI tools have become game-changers in bioacoustics, making it easier to process large amounts of audio data and support bird conservation.
Each spring, scientists place over 1,600 recorders in the Sierra Nevada forests in the U.S., which record millions of hours of audio, including calls of the endangered California spotted owl. However, processing such a huge amount of data manually is impossible. Thanks to BirdNET, an AI system launched in 2018, this task becomes feasible. Developed by Cornell Lab of Ornithology and Chemnitz University of Technology, BirdNET can identify more than 6,000 bird species worldwide through their calls.
BirdNET and another AI app Merlin work in a similar way. They first convert bird songs into spectrograms—images of soundwaves. Then, their algorithms analyze the unique frequency, timing and amplitude of the calls, which are too subtle for humans to detect. Since its launch, BirdNET has collected about 150 million high-quality bird sounds, while Merlin, with over 3 million active users, also gathers massive acoustic data.
These apps have proven valuable in conservation. For example, with funding from U.S. agencies, scientists used BirdNET to assess spotted owl populations across the Sierra Nevada, providing data to guide restoration efforts. They also help citizen scientists contribute to research, as their data can even replicate known bird migratory routes.
However, the apps have pitfalls. Studies show they may fail to register bird songs or misidentify species, leading to false positives. To reduce these risks, scientists often use statistical models or manual checks to verify data. Despite this, the benefits far outweigh the flaws, as they provide bird-community data that was previously unavailable.
Additionally, these apps make birding more accessible, especially for people with hearing loss. Erin Rollins-Pletsch, who lost 80% of her hearing, uses Merlin to identify birds by recording their calls, which pop up on her phone and bring her great joy.
In short, AI-powered bird sound apps are transforming bird conservation and birding, offering a powerful tool for both scientists and ordinary people to protect avian species. Questions:
What is the main function of BirdNET
To hide recorders in forests to collect audio data.
To identify bird species through their calls with AI.
To help people with hearing loss listen to bird songs.
To study the migratory routes of all North American birds.
How do BirdNET and Merlin process bird sounds
By converting sounds into spectrograms and analyzing their features.
By asking citizen scientists to upload recordings manually.
By comparing bird calls with a database of human voices.
By using statistical models to avoid false positives.
What does the underlined word “pitfalls” in Paragraph 5 probably mean
Advantages B. Improvements C. Functions D. Risks
Which of the following is the best title for the passage
The Decline of Global Bird Populations
How to Use AI Apps for Birding
AI Apps: A Helpful Tool for Bird Conservation
Citizen Scientists and Bird Protection
Passage 2 :（夹叙夹议文）
In a remote village, a health worker struggles to find medical records for a mother and baby with no official ID. This scene is far too common—globally, 850 million people lack official identification, half of whom are unregistered children. Without a medical ID, accessing life-saving treatments becomes a huge challenge, deepening global healthcare inequality.
Traditional paper-based medical IDs are not a solution. They are easily lost, damaged or miskept, and name misspellings often cause confusion. As we take our own medical IDs for granted, we forget their great value: they ensure patients get the right treatment, provide vital health information like blood types and allergies, and help avoid medical errors. For healthcare workers, they save time on admin, allowing them to focus on treating patients.
Fortunately, technology is changing this. In a rural Ghanaian village, a nurse uses a tablet to take a baby’s facial image, creating a biometric digital health ID that will follow the child growing up. This is made possible by Simprints, a company that develops digital health IDs usable anywhere. Its SimprintsID combines mobile devices, on-device AI and secure data sync to solve the problem.
SimprintsID works simply yet effectively. Health workers capture a baby’s biometrics—face, ears or feet—and on-device AI processes the data in seconds, even with poor connectivity. When network is available, records sync securely with central systems. As the child grows, cloud AI updates the ID to match their changing features, making it long-lasting.
Arm-powered technology lies at the core of this solution. It provides efficient computing with low power consumption, critical for remote areas with limited power and connectivity. This efficiency reduces costs, extends battery life, and helps healthcare budgets reach more people. It turns digital IDs into a powerful tool to improve healthcare equity.
Simprints now serves 4 million people in 17 countries and aims to reach 20 million by 2030. It proves that technology can help healthcare leap over traditional barriers. In a world pursuing equitable healthcare, digital medical IDs are not just a small step, but a vital force to ensure no one is left behind.
5.Why are traditional paper-based medical IDs not effective
They are too expensive to make and use.
They are easy to lose, damage or cause confusion.
They can’t be used in remote rural areas.
They don’t contain patients’ health information.
6.How does SimprintsID work for babies
It uses fingerprint data to create a lifelong ID.
It stores biometric data only in cloud servers.
It processes biometric data on devices and updates with the child’s growth.
It requires stable network connectivity to capture data.
7.What can we infer from the passage
A. SimprintsID will soon be used in all countries around the world.
B. Arm-powered technology makes digital IDs practical in poor areas.
C. Paper-based medical IDs will be completely replaced in a few years.
D. All unregistered children can now get digital health IDs easily.
8. Which of the following is the best title for the passage
A. The Importance of Medical Records for Patients
B. How to Solve Healthcare Inequality in Remote Areas
C. Digital Medical ID: Bridging the Gap in Healthcare
D. Simprints: A Leading Company in Medical Technology
Passage 3：(议论文）
AI and Plant Diseases: Fighting Food Threats in a Changing Climate
Climate change is increasing the frequency and severity of crop pests and diseases around the world. Unusual rainfall patterns, milder winters and longer growing seasons create conditions that allow pathogens to survive, spread and strike regions they have never reached before. From locusts in East Africa to potato blight in South Asia, crop failures linked to climate are putting additional pressure on global food security. As the population grows and hunger remains a serious challenge in many parts of the world, protecting crops has become more urgent than ever.
Scientists have long recognized the “disease triangle”: a plant becomes sick only when a virulent pathogen, a susceptible host and favorable weather occur at the same time. Climate change is making the third part — favorable weather — far more common. Higher temperatures help pests expand their habitats. Wetter conditions speed up the growth of fungi and bacteria. In some areas, crops that were once safe now face risks they are not prepared to resist. Historical disasters, such as the Irish potato famine in the 1840s, show just how destructive a single disease can be when conditions turn perfect for pathogens.
Today, technology offers new ways to fight back. Among the most promising tools is artificial intelligence. AI-powered systems can process massive amounts of data from satellites, drones and field sensors far more quickly than humans. They recognize subtle changes in leaf color, stem growth or fruit appearance that signal early infection. Such early identification allows farmers to act before diseases spread into uncontrollable outbreaks. Unlike traditional methods, AI does not depend on guesswork or delayed observation; it provides clear, evidence-based guidance.
AI also supports longer-term solutions. It helps researchers analyze genetic information rapidly, accelerating the development of disease-resistant and drought-tolerant crops. It guides precise pesticide application, reducing waste and environmental harm. It can even separate healthy produce from infected crops during harvesting. Mobile apps now let farmers take photos of plants and receive immediate, on-the-spot analyses, bringing advanced technology within reach of ordinary users.
Yet AI is not without limits. It requires large, high-quality datasets to work reliably. High costs and technical training can prevent small farmers from benefiting fully. Drones and intelligent devices raise questions about rules and safety. Still, experts agree that these challenges can be addressed with investment, education and clear policies.
In a world increasingly threatened by climate shocks, food systems must become more adaptive and prepared. AI will not solve all problems, but it provides a powerful, modern approach to an age-old struggle. By combining technology with care for the environment and support for farmers, humanity can better protect its crops, secure its food supply and build greater resilience for the future.
9.(细节理解) Why does climate change make crop diseases more serious
It reduces the amount of land available for farming.
It creates favorable conditions for pathogens to develop.
It forces farmers to give up traditional planting methods.
D. It increases the cost of crop protection for poor regions.
10. (细节理解) What advantage does AI have in crop protection
A. It completely removes all pests and diseases in a short time.
B. It helps identify crop diseases at an early stage accurately.
C. It changes weather conditions to prevent pathogen growth.
D. It replaces farmers with fully automatic machines.
11.(观点态度) What is the author’s attitude toward AI in fighting crop diseases
A. Doubtful B. Positive C. Cautious D. Critical
12.(主旨大意) What is the main idea of the passage
A. Climate change is the only cause of global food insecurity.
B. Traditional farming methods are better than modern technology.
C. AI plays an important role in fighting crop diseases and ensuring food security.
D. Crop diseases can be solved completely by drones and satellites.
Passage 4:(夹叙夹议文)
Robots Enter Tomato Fields: A New Step in Smart Farming
Farm labor shortages have become a common problem in modern agriculture, pushing the industry to depend more on automation. Harvesting is one of the most labor-intensive tasks, yet many soft fruits and vegetables remain difficult for machines to collect. Tomatoes, for example, often grow thickly in clusters, with ripe and unripe fruit growing closely together. A harvesting machine must pick only the fully grown tomatoes without harming the plant or damaging the unripe produce. This requires not just mechanical power, but careful observation and intelligent decisionmaking.
To deal with this difficulty, Assistant Professor Takuya Fujinaga from Osaka Metropolitan University developed a new robotic system designed especially for tomato harvesting. Unlike traditional machines that only recognize whether a tomato is ripe, his system teaches robots to estimate harvestease—how easy or difficult it will be to pick each tomato successfully before making an attempt.
The system uses image recognition and statistical analysis to decide the best picking angle for each fruit. The robot studies visual details: the color and size of the tomato, the position and shape of its stems, and whether the fruit is hidden or blocked by leaves. Based on these observations, the robot chooses the safest and most effective way to approach and pick the tomato.
This method marks a meaningful step forward. Earlier robotic systems centered only on identifying the presence of fruit. Fujinaga’s idea goes further. It changes the question from “Can the robot pick this tomato ” to “How likely is it to pick this tomato successfully ” This way of thinking is much more useful for realworld farming conditions, where success depends on reliability rather than just function.
In tests, the system achieved an 81% success rate, higher than researchers had expected. Notably, about 25% of successful picks came after the robot changed its approach: when picking from the front failed, it tried from the side and succeeded. This shows the robot can adjust its actions intelligently, just as a human farmer would.
The research shows how many small but important factors influence robotic harvesting: the clustering of tomatoes, stem position, overlapping leaves, and visual blockage. By creating “harvestease” as a measurable standard, the research brings agricultural robots closer to truly intelligent behavior.
Looking ahead, Fujinaga hopes robots will independently decide when and how to harvest crops. He expects a new model of agriculture in which robots and humans work together. Robots will take over easy and repetitive picking jobs, while humans deal with more difficult or delicate tasks. This combination of machine efficiency and human flexibility may shape the future of farming.
13.(细节理解) Why is it difficult for robots to harvest tomatoes
Tomatoes require strict temperature control during harvesting.
Tomatoes grow in clusters with ripe and unripe ones mixed.
Tomatoes are too heavy for mechanical arms.
D. Tomatoes easily go bad after being picked by machines.
14.(细节理解) What is special about Fujinaga’s system
A. It teaches robots to estimate how easy tomatoes are to pick.
B. It completely replaces human farmers in the field.
C. It focuses only on recognizing whether tomatoes are ripe.
D. It picks all tomatoes regardless of their maturity.
15.(观点态度) What is Fujinaga’s attitude toward the future of agricultural robots
A. Cautious B. Uninterested C. Optimistic D. Doubtful
16.(主旨大意) What is the main idea of the text
A. Robots will completely replace humans in all agricultural work.
B. Farm labor shortages have destroyed traditional tomato farming.
C. Tomatoes are the most difficult crop to grow and harvest.
D. A new robotic system improves tomato harvesting with intelligent decisionmaking.
Answers:1. B 2. A 3. D 4. C 5.B 6. C 7. B 8. C 9.B 10.B 11.B 12.C 13.B 14. A 15. C 16. D
第二部分 词汇和短语（背诵版）
生僻词与专业短语（主题专属、认知即可）
鸟类保护类
avian species 鸟类物种 bioacoustics 生物声学 spectrogram 声谱图
frequency 频率 amplitude 振幅 false positives 误判；假阳性
migratory routes 迁徙路线 citizen scientists 公民科学家
endangered species 濒危物种
医疗科技类
biometric 生物识别的 digital health ID 数字健康 ID
on-device AI 端侧 AI data sync 数据同步 healthcare equity 医疗公平
connectivity 网络连接 low power consumption 低功耗
农业 AI 类
pathogen 病原体 fungi 真菌（fungus 复数）
drought-tolerant 耐旱的 disease-resistant 抗病的 pesticide application 农药施用 harvest-ease estimation 采摘难易度评估 visual obstruction 视觉遮挡
cluster 串；簇 stem 茎；果柄
高考核心词汇与短语（必背！阅读 + 写作高频） 动词 / 动词短语
decline 下降；衰退 identify 识别；确认 process 处理
assess 评估 verify 核实 transform 改变；使转型
lack 缺乏 access 获得；使用 ensure 确保
avoid 避免 focus on 专注于 enable 使能够
consume 消耗 expand 扩大；扩展 accelerate 加速
apply 应用；施用 overcome 克服 address 解决（问题）
rely on 依赖 contribute to 促成；有助于
keep track of 追踪 lead to 导致 far outweigh 远超过
conservation 保护 data 数据 feasibility 可行性
population 种群；人口 inequality 不平等 barrier 障碍
efficiency 效率 budget 预算 security 安全
solution 解决方案 error 错误 treatment 治疗
infection 感染 guidance 指导 limitation 局限
resilience 适应力；恢复力 形容词 / 副词 global 全球的
massive 大量的 reliable 可靠的 accessible 可获得的
vital 至关重要的 efficient 高效的 secure 安全的
remote 偏远的 accurate 准确的 previously 之前
increasingly 越来越 precisely 精确地
拓展词汇与短语（阅读提速 + 写作加分）
高级衔接与观点表达
game-changer 颠覆者；突破性事物 prove valuable 证明很有用
guide restoration efforts 指导修复工作 replicate routes 重现路线
reduce risks 降低风险 push for 推动 pursue equity 追求公平
leave no one behind 不让任何人掉队 leap over barriers 跨越障碍
age-old struggle 长久以来的难题 on-the-spot analyses 现场分析 evidence-based guidance 基于证据的指导
科技类高级表达
algorithm-powered app 算法驱动的应用 acoustic data 声学数据
biometric technology 生物识别技术 cloud AI 云端人工智能
arm-powered technology 低功耗算力技术 manual processing 人工处理
statistical model 统计模型 image recognition 图像识别
农业类高级表达
labor-intensive 劳动密集的 labor shortage 劳动力短缺
intelligent decision-making 智能决策 measurable metric 可量化指标
human-robot collaboration 人机协作 precise control 精准控制

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