压轴题01 阅读理解CD篇（科技创新发明类）（原卷版）-2024年高考英语压轴题专项训练（新高考通用）

这是一份压轴题01 阅读理解CD篇（科技创新发明类）（原卷版）-2024年高考英语压轴题专项训练（新高考通用），共10页。试卷主要包含了说明文的解题技巧等内容，欢迎下载使用。


说明文基本规律及解题要领
高考中科普类阅读理解一般不给标题，反而经常要求考生选择最佳标题。说明文一般采用如下四部分：
首段：一般即是文章的主题段，开门见山点明新发明或研究对象。
背景: 交代问题的现状或研究的起因。
主干: 部分介绍研究所取得的突破，作者往往会详细介绍研究对象、研究方法、研究理论或具体的实验、统计等过程。
结尾: 通常会再次对中心进行概括、重述研究成果、预计的市场未来等与主题呼应。
二、说明文的解题技巧
1. 运用语篇结构（text structure），了解文章大意
科普说明文主题鲜明、脉络清晰，行文结构模式较为固定。弄清文本结构有助于把握文章主旨和阅读重点。科技创新发明类文章通常是介绍一种新产品、新技术，更多运用描述法从功能、用途、材料和市场前景等方面进行介绍。 结构上一般采用上述四个部分，说明手法上常使用以下说明方法：描述法（包括举例子、下定义、列数据等）、因果法、问题与对策法。
实验研究型文章一般会以实验的过程进展为线索，多用描述法、问题与对策法等方法，通过列数据、做对比等来说明新的科学研究发现及其产生的影响。
阅读时，首先用略读法快速浏览每段的首尾句，根据英语说明文思维模式特征，作者一般都会开门见山，直奔主题。结尾通常也是中心思想的概括，并与导语相呼应。因此在做主旨大意、写作意图和最佳标题等题目时，需要重点关注首尾段落里面高频复现的词汇和内容。
2. 定位标志词，分析长难句，进行逻辑推理判断
每一个问题，在原文中，都要有一个定位。然后精读，找出那个标志词或者中心句。根据题干要求，用查读法快速定位到相关段落。再利用标志词所提供的逻辑关系找到细节信息，如列数据、举例子、原因和结果等。如果句子成分复杂，有生词，也不要烦躁退缩，分析主句和从句或非谓语动词之间的关系，一些出现在术语、抽象概念、长难句前后的同义词、近义词等，都是用以理解文章的语境线索。通过这些对长句进行层层剖析，露出主干部分，就能明晰句意，弄懂作者的真实意图。
关注某人说到或推断观点态度题
某人说过的话，有时并不是题眼，但可以从侧面或某个角度来反映作者的观点，也就是作者想表达的，正确答案都是和这样的观点相一致的。要把握关键词，有感情色彩的词。
4.关注转折关系的逻辑词
说明文中常会出现表示转折意义的词，如hwever, but, yet,while等。这些词后面才是作者真正想表达的意思，常常会在此处命题。
5. 熟悉选项设置规律，关注细节
正确选项：文中内容的“同义替换”或者“归纳概括”。
干扰项：“张冠李戴”、“偷梁换柱”、“无中生有”和“以偏概全”四种类型。

01 科技创新发明类
1.（2024·河北·二模）
There’re plenty f fresh fruits and vegetables available in lcal markets. But while thse red juicy strawberries lk fresh, cnsumers have n way f knwing hw lng the fruit can be stred at hme. The same ges fr distributin centers and supermarkets.
Nw, the fd technlgy startup OneThird, lcated in the Netherlands, is lking t change that with an infrared (红外线) scanner that can accurately predict hw lng fresh fruits and vegetables will last. The startup is named OneThird because ne-third f fd is wasted due t spilage (变质) every year.
The startup’s funders were inspired by a UK cmpany that uses this type f technlgy in the medical field and decided t see if it was applicable fr fd. “I lked at the challenges in the fd-supply chain and fund ut that 40 percent f fd waste is fresh prduce. One f the biggest causes f waste is that nbdy knws shelf life.” funder and CEO f OneThird, Marc Snikkers said.
Quality inspectins at farms and distributin centers are dne manually (手动地). An inspectr checks the fruits and vegetables and makes ntes abut the size and quality. Then the fd is sent t cnsumers withut cnsidering travel time r hw lng the prduce will remain usable.
Using the infrared scanner at the distributin center means that inspectrs can use the infrmatin t apprve shipments that will ensure the prduce can be distributed n a timely basis. This means that a shipment f rip e tmates will nt be sent lng distances away.
OneThird’s scanner cmbines the technlgy f ptical scanners, image mdeling, and Artificial Intelligence t prvide accurate shelf-life predictins.
The startup fund that the technlgy can reduce up t 25 percent prduce waste that was caused by spilage. “Glbal fd waste has an enrmus envirnmental impact; reducing glbal fd waste cuts glbal greenhuse gas emissins and prmtes glbal fd security,” said Jacb Smith, a climate expert frm the University f Maine.
1．What prblem des OneThird aim t slve?
A．The high cst f string fresh fruits and vegetables.
B．Inefficient quality inspectins at distributin centers.
C．Fd waste caused by uncertainty abut its shelf life.
D．Cnsumers’ difficulty judging the freshness f prduce.
2．What inspired OneThird t cme up with the idea f using infrared scanner?
A．The use f the device in anther field.B．Observatin f the fd-supply chain.
C．Cnsumer demands fr fresher prduce.D．Experts’advice n fd waste reductin.
3．What can we learn abut the manual quality inspectins?
A．They are time-cnsuming and cstly.B．They are nt perfrmed at a regular time.
C．The inspectrs tend t make wrng judgement.D．The shipping time is nt taken int cnsideratin.
4．What’s Jacb’s attitude t OneThird’s effrt?
A．Apprving.B．Dubtful.C．Tlerant.D．Dismissive.
2.（2024·山东枣庄·二模）
Even if yu haven’t held a cnversatin with Siri r Alexa, yu’ve likely encuntered a chatbt nline. They ften appear in a chat windw that pps up with a friendly greeting: Thank yu fr visiting ur site.Hw can I help yu tday? Depending n the site, the chatbt is prgrammed t respnd accrdingly and even ask fllw-up questins.
Chatbts are a frm f cnversatinal AI designed t simplify human interactin with cmputers. They are prgrammed t simulate human cnversatin and exhibit intelligent behavir that is equivalent t that f a human.
Chatbts cmmunicate thrugh speech r text. Bth rely n artificial intelligence technlgies like machine learning and natural language prcessing (NLP), which is a branch f artificial intelligence that teaches machines t read, analyze and interpret human language. This technlgy gives chatbts a baseline fr understanding language structure and meaning. NLP, in essence, allws the cmputer t understand what yu are asking and hw t apprpriately respnd.
With develpments in deep learning and reinfrcement learning, chatbts can interpret mre cmplexities in language and imprve the dynamic nature f cnversatin between human and machine. Essentially, a chatbt tries t match what yu’ve asked t an intent that it understands. The mre a chatbt cmmunicates with yu, the mre it understands and the mre it learns t cmmunicate like yu and thers with similar questins. Yur psitive respnses reinfrce its answers, and then it uses thse answers again.
Frm custmer service chatbts nline t persnal assistants in ur hmes,chatbts have started t enter ur lives. In almst every industry, cmpanies are using chatbts t help custmers easily navigate their websites, answer simple questins and direct peple t the relevant pints f cntact. Persnal assistants like Siri and Alexa are designed t respnd t a wide range f scenaris and queries, frm current weather and news updates t persnal calendars, music selectins and randm questins.
5．Why des the authr mentin Siri and Alexa in Paragraph 1?
A．T explain hw a chatbt wrks.B．T shw where t find a chatbt.
C．T give examples f chatbts.D．T cmpare different chatbts.
6．What is the basis f chatbts?
A．Language study.B．Data transmissin.
C．Scial interactin.D．Natural language prcessing.
7．What des the underlined wrd “reinfrce” in paragraph 4 mean?
A．Inspire.B．Strengthen.C．Organize.D．Match.
8．What is the last paragraph mainly abut?
A．The future trend f chatbts.B．The authr’s predictins.
C．The effects f chatbts.D．The applicatins f chatbts.
3.（2024·江苏南京·二模）
Since the last ice age, humans have cleared nearly half f the earth’s frests and grasslands fr agriculture. With the wrld ppulatin expanding, there’s ever-increasing pressure n farmland t prduce nt nly mre fd but als clean energy. In places such as Yakima Cunty, Washingtn, it’s created cmpetitin fr space as land-hungry slar panels (板) cnsume available fields. Last mnth, the state apprved plans t cver 1,700 acres f agricultural land with slar panels, fueling cncerns ver the lng-term impacts f lsing crpland.
A recent study frm the University f Califrnia, hwever, shws hw farmers may sn harvest crps and energy tgether. One researcher, Majdi Abu Najm, explains that visible light spectrum (光谱) can be separated int blue and red light waves, and their phtns (光子) have different prperties. Blue nes have higher energy than red nes. While that gives blue light what is needed t generate pwer, it als results in higher temperatures. “Frm a plant angle, red phtns are the efficient nes,” says Abu Najm. “They dn’t make the plant feel ht.”
A gal f the study is t create a new generatin f slar panels. He sees ptential in the rganic slar cells, which cme frm carbn-based materials. Thin and transparent, the cells are applied like a film nt varius surfaces. This new technlgy culd be used t develp special slar panels that blck blue light t generate pwer, while passing the red light n t crps planted directly belw. These panels culd als prvide shade fr heat-sensitive fruits during the httest part f the day.
By 2050, we’ll have tw billin mre peple, and we’ll need mre fd and mre energy. By maximizing the slar spectrum, “we’re making full use f an endlessly sustainable resurce,” says Abu Najm. “If a technlgy kicks in that can develp these panels, then the sky is the limit n hw efficient we can be.”
9．What prblem des the first paragraph fcus n?
A．Lsing crpland t slar panels.
B．Distributin f the wrld ppulatin.
C．Reductin in frests and grasslands.
D．Cmpeting fr land between farmers.
10．What des the underlined wrd “that” in paragraph 2 refer t?
A．Generatin f slar pwer.
B．Ht weather increasing efficiency.
C．Blue phtns having higher energy.
D．Separatin f visible light spectrum.
11．What d we knw abut the rganic slar cells?
A．They make fruits heat-sensitive.
B．They can cl dwn in ht days,
C．They allw red light t pass thrugh.
D．They can stre carbn-based materials.
12．What des Abu Najm think f the future f the new slar panels?
A．Limited.B．Prmising.
C．Uncertain.D．Challenging.
4.（2024·山东·一模）
With the help frm an artificial language (AL) mdel, MIT neurscientists have discvered what kind f sentences are mst likely t fire up the brain’s key language prcessing centers. The new study reveals that sentences that are mre cmplex, because f either unusual grammar r unexpected meaning, generate strnger respnses in these language prcessing centers. Sentences that are very straightfrward barely engage these regins, and meaningless rders f wrds dn’t d much fr them either.
In this study, the researchers fcused n language-prcessing regins fund in the left hemisphere (半球) f the brain. By cllecting a set f 1,000 sentences frm varius surces, the researchers measured the brain activity f participants using functinal magnetic resnance imaging (fMRI) while they read the sentences. The same sentences were als fed int a large language mdel, similar t ChatGPT, t measure the mdel’s activatin patterns. Once the researchers had all f thse data, they trained the mdel t predict hw the human language netwrk wuld respnd t any new sentence based n hw the artificial language netwrk respnded t these 1,000 sentences.
The researchers then used the mdel t determine 500 new sentences that wuld drive highest brain activity and sentences that wuld make the brain less active, and their findings were cnfirmed in subsequent human participants. T understand why certain sentences generate strnger brain respnses, the mdel examined the sentences based n 11 different language characteristics. The analysis revealed that sentences that were mre surprising resulted in greater brain activity. Anther linguistic (语言的) aspect that crrelated with the brain’s language netwrk respnses was the cmplexity f the sentences, which was determined by hw well they fllwed English grammar rules and bw lgically they linked with each ther.
The researchers nw plan t see if they can extend these findings in speakers f languages ther than English. They als hpe t explre what type f stimuli may activate language prcessing regins in the brain’s right hemisphere.
13．What sentences make ur brain wrk harder?
A．Lengthy.B．Lgical.
C．Straightfrward.D．Cmplicated.
14．What is the functin f the AL mdel in the research?
A．T examine language netwrk.B．T reduce language cmplexity.
C．T lcate language prcessing area.D．T identify language characteristics.
15．Hw did the researchers carry ut their study?
A．By cnducting interviews.B．By cllecting questinnaires.
C．By analyzing experiment data.D．By reviewing previus studies.
16．Which f the fllwing is a suitable title fr the text?
A．AL Mdel Stimulates Brain Activities
B．AL Mdel Speeds Up Language Learning
C．AL Mdel Reveals the Secrets f Brain Activatin
D．AL Mdel Enhances Brain Prcessing Capacity
5.（2024·山东·一模）
Cafeterias have been filled with challenges — right frm planning, purchasing, and preparing, t reducing waste, staying n budget, managing gds, and training staff. Thrugh the tedius prcess, restaurateurs lacked a unified platfrm fr efficient management. T bring cnsistency t the unrganised catering (餐饮) industry, childhd friends Arjun Subramanian and Raj Jain, wh shared a passin fr innvatin, decided t partner in 2019 t explre pprtunities in the cafeteria industry.
In May 2020, they c-funded Plats, a ne-stp slutin fr restaurants with a custm technlgy kit t streamline all aspects f cafeteria management. The cmpany ffers end-t-end cafeteria management, staff selectin and fd trials t ensure smth peratins and cnsistent service. “We believe startups slve real prblems and Plats is ur sht at making daily wrkplace fd enjyable again. We aim t simplify the dining experience, prviding a cnvenient and efficient slutin that benefits bth restaurateurs and custmers and creating a cnnected ecsystem,” says Subramanian, CEO and c-funder.
Plats guarantees that a technlgy-driven cafeteria allws custmers t rder, pay, pick up, and prvide ratings and feedback. It als ffers gds and menu management t effectively perfrm daily peratins. Additinally, its applicatins cnnect all sharehlders fr a smart cafeteria experience. “We help businesses that are int catering n cnditin that they have access t an industrial kitchen setup where they’re making fd accrding t certain standards,” Jain states.
Since the beginning, Plats claims t have transfrmed 45 cafeterias acrss eight cities in the cuntry. Currently, it has ver 45,000 mnthly users placing mre than 200,000 rders. Despite facing challenges in launching cafeterias acrss majr cities in the initial stages, Plats has experienced a 15% increase in its mnth-ver-mnth prfits.
As fr future plans, the startup is lking t raise $1 millin frm investrs as strategic partners, bringing in capital, expertise, and netwrks. “Finding the right lead investr is the cmpass that pints yur startup tward success,” Subramanian says.
17．What des the underlined wrd “tedius” in Paragraph 1 mean?
A．Time-cnsuming.B．Breath-taking.
C．Heart-breaking.D．Energy-saving.
18．What is the purpse f funding Plats?
A．T cnnect custmers with a greener ecsystem.
B．T ensure fd security and variety in cafeterias.
C．T imprve cafeteria management with technlgy.
D．T make staff selectin mre efficient and enjyable.
19．What can we learn frm the statistics in Paragraph 4?
A．Plats has achieved its ultimate financial gal.
B．Plats has gained impressive marketing prgress.
C．Challenges in fd industry can be easily vercme.
D．Tech-driven cafeterias have cvered mst urban areas.
20．What is Subramanian’s future plan fr Plats?
A．T reduce csts.B．T increase prfits.
C．T seek investment.D．T innvate technlgy.
6.（2024·湖北·二模）
Neurengineer Silvestr Micera develps advanced technlgical slutins t help peple regain sensry and mtr functins that have been lst due t injury events r neurlgical disrders. Until nw, he has never befre wrked n strengthening the human bdy and cgnitin with the help f technlgy.
Nw in a study published in Science Rbtics, Micera and his team reprt n hw diaphragm (隔膜) mvement can be mnitred fr successful cntrl f an extra arm, essentially augmenting a healthy individual with a third-rbtic-arm.
Fr further explratin, the researchers first built a virtual envirnment t test a healthy user’s capacity t cntrl a virtual arm using mvement f his r her diaphragm. They fund that diaphragm cntrl des nt interfere with actins like cntrlling ne’s physilgical (生理的) arms, ne’s speech r gaze.
In this virtual reality setup, the user is equipped with a belt that measures diaphragm mvement. Wearing a virtual reality headset, the user sees three arms: the right arm and hand, the left arm and hand, and a third arm between the tw with a symmetric (对称), six-fingered hand.
In the virtual envirnment, the user is then hinted t reach ut with either the left hand, the right hand, r in the middle with the symmetric hand. In the real envirnment, the user hlds nt an exskeletn (外骨骼) with bth arms, which allws fr cntrl f the virtual left and right arms. Mvement detected by the belt arund the diaphragm is used fr cntrlling the virtual middle, symmetric arm. The setup was tested n 61 healthy subjects (受试者) in ver 150 sessins.
Previus studies regarding the cntrl f rbtic arms have been fcused n helping the disabled. The latest Science Rbtics study is a step beynd repairing the human bdy twards augmentatin. “Our next step is t explre the use f mre cmplex rbtic devices using ur varius cntrl strategies, t perfrm real-life tasks, bth inside and utside f the labratry. Only then will we be able t grasp the real ptential f this apprach,” cncludes Micera.
21．What des the authr intend t d in Paragraph 2?
A．T intrduce the tpic.B．T shw an evidence.
C．T summarize the general idea.D．T ffer sme backgrund.
22．What are the furth and fifth paragraphs prbably abut?
A．A virtual reality game.B．A new medical device.
C．A new treatment methd.D．An experiment n animals.
23．Hw des the authr supprt the theme f the text?
A．By listing sme related data.B．By ffering sme examples.
C．By making sme cmparisns.D．By describing research prcesses.
24．What is prbably cntinued with the text?
A．Hw t expand cntrllable rbtic devices.
B．Where t find new and exciting pprtunities.
C．Hw t further develp the rbt market ptential.
D．Why t balance inside and utside f the labratry.
命题预测
分析近几年高考阅读理解C、D篇可知，高考命题中科普说明文一直都是以压轴题的形式存在，着重考查考生对于语篇的理解能力以及信息处理能力。 题材多样，语篇主要来源于英美主流报刊、杂志和网站。内容涉及科技创新发明、人工智能类、医疗健身健康类、社会与文化研究报告、观念事理类、环境与保护类、动植物研究等多种领域，具有较强的思想性、趣味性、实际功用性和较强的时代感。
从近年全国卷和各地高考试卷中科普类阅读命题的统计来看，高考阅读理解科普类文章的理论性和逻辑性强、生词多、句式结构复杂。六种命题类型都有所体现。命题尊重语篇的文体特征和行文特点，考查了考生理解说明文语篇的能力，以及灵活运用各种阅读策略提取、归纳所读信息的能力，尤其加大了对概括能力和推断能力等高阶思维能力的考查。预测2024年高考对于科普说明文的考查仍然是重点。
高频考法
推理判断题
标题归纳题
细节理解题
词义猜测题