Research on the Application of Neural Networks in Construction Engineering Cost

高三英语人工智能单选题40题1. Artificial intelligence is a branch of computer science that aims to create intelligent _____.A.machinesB.devicesC.equipmentsD.instruments答案解析：A。

“machines”通常指机器，在人工智能领域常指具有一定智能的机器；“devices”一般指设备、装置；“equipments”是“equipment”的复数形式，指装备、器材；“instruments”指仪器、工具。

2. In the field of artificial intelligence, algorithms are used to train _____.A.modelsB.patternsC.shapesD.forms答案解析：A。

“models”在人工智能中常指模型，可以通过算法进行训练；“patterns”指模式、图案；“shapes”指形状；“forms”指形式。

3. Deep learning is a powerful technique in artificial intelligence that uses neural _____.worksB.systemsC.structuresanizations答案解析：A。

“networks”指网络，在深度学习中常指神经网络；“systems”指系统；“structures”指结构；“organizations”指组织。

4. Artificial intelligence can process large amounts of data to make accurate _____.A.predictionsB.forecastsC.projectionsD.expectations答案解析：A。

2021年第3期工业仪表与自动化装置-131-基于神经网络和等SCOP算法的中央空调节能控制技术研究顾正宜(中铁上海设计院集团有限公司，上海200070)摘要：为了提高中央空调节能控制系统的通用性和控制精度，该文利用神经网络算法建立了系统各设备的数学模型，并开发了等SCOP算法对模型进行能效最优求解。

该文以某地铁项目冷冻机房设备配置应用为例，利用厂家提供的设备设计性能模型,验证了神经网络模型的精度以及等SCOP算法的可行性。

同时研究也发现，利用设计模型数据训练的神经网络模型直接用于实际项目控制，会带来较大误差。

模型需要根据现场实际数据进行训练，才能提高控制精度。

关键词：等SCOP；神经网络;节能群控；中央空调中图分类号:TU831.3文献标识码：B文章编号#1000-0682(2021)03-0131-03Research on energy saving control technology of central air conditioningbasee on neural network and Equal-SCOP algorithmGU Zhengyi(China Railoay Shanghai Design Institu*Group Co.,Ltd,Shanghai200070,China)Abstracr:In order to improve the versatility and control precision of the central air conditioning en­eray一saving control system,the mathematical model of each device in the system is established by using the neural network algorithm,and the Equal-SCOP algorithm is developed to solve the optimal eneray diciency of the model.In this paper,the application of equipment configuration of a subway project is taken as an example,and the accuracy of the neural n-work model and the feasibility of the Equal一SCOP algorithm are yeriPed by using the equipment design peeormoco model provided by the manufac­turer.At the same time,the study also found that the neural network model trained by the design model datadnaectyused nn theactua\paoeectcontao\wn\banng aagee a oas.Themode\needstobetaanned ac-coadnngtotheactua\datann thetne\d tonmpaovethecontao\paecnsnon.Keywords：EquO-SCOP;neural network；eneray-saving control;central Or conditioning0引言随着我国碳达峰和碳中和目标［1］的提出，节能减排将从经济和市场层面走向法律层面，其重要性和紧迫性将更加显著。

Research on the Construction of Intelligent Innovation and Entrepreneurship Teaching Platform in Universities Based on Neural Network TechnologyTao ZhangSchool of Foreign Languages, Zhengzhou University of Science and Technology, Zhengzhou City, Henan Province, 450064ABSTRACTWith the rapid development of artificial intelligence technology, neuralnetwork technology has become an important branch in the field ofAI. In higher education, neural network technology has also begun tobe applied in the construction of teaching platforms, providing newideas and methods for the development of intelligent innovation andentrepreneurship teaching platforms in universities. This paper aims toexplore the construction path of a university's intelligent innovation andentrepreneurship teaching platform based on neural network technology,providing references for the construction of intelligent innovation andentrepreneurship teaching platforms in universities.KEYWORDSNeural network technology; University; Intelligence; Innovation andentrepreneurship teaching platform; Construction pathDOI: 10.47297/taposatWSP2633-456913.202304011 IntroductionWith the continuous progress and widespread application of information technology, artificial intelligence has become an essential component of today’s society. Neural network technology, as an important branch of artificial intelligence, possesses powerful learning and prediction capabilities and has been widely applied in image recognition, natural language processing, speech recognition, and other fields. In higher education, neural network technology has also begun to be applied in the construction of teaching platforms, offering new ideas and methods for the development of intelligent innovation and entrepreneurship teaching platforms in universities.2 Research Background and Significance(1) The Role of Intelligent Teaching Platforms in Enhancing Innovation and Entrepreneurship EducationIntelligent teaching platforms play a crucial role in enhancing innovation and entrepreneurship education. They enable personalized learning and intelligent guidance, helping students better understand and master the study material, thereby improving learning outcomes and self-confidence. Additionally, these platforms also provide intelligent analysis and management tools for teachers, enabling them to gain insights into students’ learning progress and needs, leading to more preciseTheory and Practice of Science and Technologyteaching and personalized guidance, ultimately enhancing the overall teaching effectiveness and quality.(2) Analyzing the advantages of neural network technology application in the education sectorThe application of neural network technology in education offers various advantages. Firstly, it facilitates personalized learning, tailoring individualized learning plans for each student based on their learning characteristics and progress, thereby meeting their specific learning needs. Secondly, neural network technology enables intelligent guidance, analyzing students’ learning performance and difficulties, and providing them with corresponding learning advice and solutions. Thirdly, it facilitates intelligent assessment, conducting comprehensive and accurate evaluations of students’ learning performance and mastery, offering targeted feedback and improvement measures for both teachers and students. Furthermore, neural network technology can achieve intelligent recommendation, suggesting relevant learning resources and content based on students’ interests and abilities, thereby stimulating students’ learning motivation and engagement. Lastly, the intelligent analysis capabilities of neural network technology help teachers gain a better understanding of students’ learning situations and processes, providing scientific evidence for instructional design and management, and ultimately improving teaching effectiveness and quality.3 Application of Neural Network Technology in the Construction of Intelligent Innovation and Entrepreneurship Teaching Platforms in Universities(1) Personalized teachingUsing neural network technology, personalized learning models can be constructed based on students’ learning habits, abilities, interests, and other factors, providing tailored teaching services to students. For example, by analyzing students’ answer data, students can be categorized, and suitable learning resources can be recommended to them. For hands-on learners, more practical exercises and case analyses can be provided, while for theory-oriented learners, more theoretical knowledge can be offered. This approach better meets students' individual needs and enhances their learning motivation.(2) Intelligent assessmentThrough neural network technology, students' learning outcomes can be intelligently assessed, enabling a better understanding of their learning situation and timely adjustment of teaching strategies. For instance, during exams, neural networks can automatically grade students’ papers, providing quick and accurate scores and error analysis. This not only lightens the workload of teachers but also improves the accuracy and objectivity of assessments. Furthermore, through data analysis of students’ exam scores, trends in their academic performance can be predicted, leading to targeted learning recommendations.(3) Intelligent recommendationUsing neural network technology, students can receive recommendations for suitable courses, majors, and careers based on their learning progress and interests. For example, for students who enjoy programming, relevant learning resources and projects can be recommended to help themVol.4 No.1 2023 further develop their skills. Additionally, by analyzing students’ course selection data, the neural network can suggest courses that are beneficial for their career development.(4) Intelligent interactionLeveraging neural network technology enables intelligent interaction features. Students can interact with the system in real-time through voice, text, images, and other means, facilitating immediate communication and feedback, thus enhancing their learning experience and efficiency. Teachers can also provide real-time learning support and guidance through intelligent interaction. For instance, in programming education, the neural network can analyze students’ code in real-time, offering targeted suggestions and guidance to help students better understand and master the knowledge.4 Construction of Neural Network-based Intelligent Innovation and Entrepreneurship Teaching Platform in Universities(1) Establish data collection systemThe construction of a neural network-based intelligent innovation and entrepreneurship teaching platform in universities requires a substantial amount of data for training and optimization. Therefore, it is essential to establish a comprehensive data collection system. This system can utilize technological means to gather relevant student data, such as learning behavior, academic performance, and social interactions, while ensuring data accuracy and security.(2) Build model training platformThe development of an intelligent innovation and entrepreneurship teaching platform using neural network technology necessitates the construction of a model training platform. Cloud computing technology can be employed to establish a high-performance computing cluster, providing powerful computational support for model training. Additionally, a distributed training framework can be adopted to enable parallel processing of large-scale data. Students can access learning resources, participate in activities, and receive study reminders anytime, anywhere through mobile devices like smartphones and tablets. Moreover, mobile application platforms can facilitate interaction and communication between students and teachers or other students.(3) Formulate intelligent teaching strategiesThe formulation of intelligent teaching strategies is the foundation of constructing an intelligent innovation and entrepreneurship teaching platform in universities. By analyzing students’ learning situations and needs, personalized learning plans and resources that cater to individual students' characteristics can be devised to achieve personalized teaching. Additionally, intelligent assessment and recommendation functionalities can be utilized to provide intelligent teaching services.(4) Establish intelligent teaching environmentThe establishment of an intelligent teaching environment is crucial in the construction of an intelligent innovation and entrepreneurship teaching platform in universities. The creation of facilities such as intelligent classrooms and laboratories can facilitate the development of an intelligent teaching environment. Meanwhile, leveraging intelligent interaction capabilities enables real-timeTheory and Practice of Science and Technologycommunication and feedback between students and the system, enhancing their learning experience and efficiency.(5) Develop intelligent teaching resourcesThe development of intelligent teaching resources is the core of constructing an intelligent innovation and entrepreneurship teaching platform in universities. By developing intelligent textbooks, experimental materials, and other teaching resources, the creation of intelligent teaching resources can be achieved. Additionally, through intelligent recommendation features, students can access learning resources and services that align with their interests and needs.5 Empirical Study(1) Research methods and procedures1) Data CollectionCollect data from the experimental group and the control group. The experimental data comes from students enrolled in an innovation and entrepreneurship course at a certain university, including students’ personal information, learning data, grades, learning behaviors, and teachers’ assessments of students' learning.2) Data preprocessingConduct data cleaning, handle missing values, and perform feature extraction to ensure the accuracy and effectiveness of the data.3) Model trainingSelect suitable neural network models, such as convolutional neural networks, recurrent neural networks, etc., to analyze and model the data, establishing models for personalized teaching, intelligent assessment, intelligent recommendation, and intelligent interaction.4) Model evaluationDivide the processed data into training, validation, and testing sets, and use methods like cross-validation to evaluate the performance and accuracy of the models. Model parameters are adjusted based on student and course characteristics to improve model performance.(2) Analyzing experimental data and resultsBy comparing the performance of different neural network models, the experimental data and results are analyzed to evaluate the effectiveness and contribution of neural network technology in the construction of the innovation and entrepreneurship teaching platform. The advantages of the intelligent teaching platform are found in the following aspects:1) Personalized teachingThe intelligent teaching platform can provide personalized learning content and teaching strategies based on each student's learning data and interests, thereby increasing students’ learning motivation.Vol.4 No.1 20232) Intelligent assessmentThe intelligent teaching platform can provide accurate assessments and feedback by analyzing students’ learning outcomes and practice data, helping students understand their learning progress and areas for improvement, and making timely adjustments and improvements.3) Intelligent recommendationThe intelligent teaching platform can provide intelligent recommendation services to students based on their learning situation and interests, recommending suitable learning resources and activities to expand students’ knowledge and perspectives, thereby enhancing their learning effectiveness and satisfaction.4) Intelligent interactionBy deploying the trained models to practical application scenarios such as the intelligent teaching platform, intelligent interaction is achieved. The system analyzes users’ questions and historical data, uses the trained models for prediction, and returns the most likely answers. Through continuous interaction and learning, the system can gradually improve the accuracy and efficiency of responses, enhancing users’ overall experience.6 ConclusionThrough measures such as establishing a data collection system and constructing model training platforms, the level of construction and the quality of services of the intelligent innovation and entrepreneurship teaching platform in universities can be effectively improved. Neural network technology also provides new ideas and methods for the construction of intelligent innovation and entrepreneurship teaching platforms in universities: by formulating personalized teaching strategies, building intelligent teaching environments, and developing intelligent teaching resources, the construction and application of intelligent innovation and entrepreneurship teaching platforms in universities can be achieved. In the future, with the continuous development and application of neural network technology, the construction of intelligent innovation and entrepreneurship teaching platforms in universities will become more refined and widespread, providing better intelligent teaching services for more students.About the AuthorTao Zhang (1989-), male, Han nationality, native place: Queshan County, Henan Province, professional title: lecturer, postgraduate degree, research direction: employment and entrepreneurship guidance.References[1] Yingshuai Dong. Jiaxuan Qu. Innovative strategies for talent cultivation in universities under the background ofartificial intelligence [J] Industrial Innovation Research, 2022, (18): 193-95.[2] Gengjun Han. Research on the Dual Transformation of the Innovation and Entrepreneurship Education Ecosystem inUniversities under the Empowerment of Artificial Intelligence [J] Technology and Innovation, 2022, (18): 136-38.[3] Jixin He. Huanjun Yao. Gengjun Han. Innovation in the management path of innovation and entrepreneurship servicesin universities in the context of intelligence: from empowerment to empowerment [J] Innovation, 2022, 16 (03): 95-107.[4] Weinan Zheng. Platform-based teaching system construction and teaching model reform for Innovation and EntrepreTheory and Practice of Science and Technologyneurship education [J]. Cultural and Educational Materials, 2021 (23) : 191-94.[5] Qiang Wang.Discussion on the construction of “Innovation and Entrepreneurship” platform based on Co-construction of school and enterprise [J]. Qinghai Transportation Science and Technology,2021,33(04):46-48.。

个人所得税英文参考文献个人所得税英语参考文献一：[1]José Félix Sanz-Sanz. The Laffer curve in schedular multi-rate income taxes with non-genuine allowances: An application to Spain[J]. Economic Modelling,2019,.[2]Craig Brett,John A. Weymark. Voting over selfishly optimal nonlinear income tax schedules[J]. Games and Economic Behavior,2019,.[3]Mónica Unda Gutiérrez. A Tale of Two Taxes: the Diverging Fates of the Federal Property and Income Tax Decrees in post-Revolutionary Mexico[J]. Investigaciones de Historia Económica - Economic History Research,2019,.[4]Sim Choon Ling,Abdullah Osman,Safizal Muhammad,Sin Kit Yeng,Lim Yi Jin. Goods and Services Tax （GST） Compliance among Malaysian Consumers: The Influence of Price, Government Subsidies and Income Inequality[J]. Procedia Economics and Finance,2019,35.[5]Martin Lopez-Daneri. NIT Picking: The Macroeconomic Effects of a Negative Income Tax[J]. Journal of Economic Dynamics and Control,2019,.[6]Tad Miller,Lindsay Miller,Jeffrey Tolin. Provision for income tax expense ASC 740: A teaching note[J]. Journal of Accounting Education,2019,35.[7]Petr David,Lucie Formanová。

生物和环境1. 神经的凋亡Apoptosisi of Neuron2. 肌动蛋白myosin的构象及作用机制The Structure and Function of Myosin3. 钇激光器的发射特性Yb Llaser Radiation Character4. 胰酶分泌素的分泌机制The Secreting Mechanism of Cholecystokinin5. 钙离子在信号传导中的作用The Function of Calcium in Signal Transduction6. 1,5-二磷酸核酮糖羧化酶的进化过程The Evolution of Rubisco7. 质谱技术在生物学中的应用Application of Mass Spectrometry in Biology8. PHB的微生物合成The Synthesis of PHB in Bacteria9. HIV-1 的研究Research on HIV-110.STA T信号通路在人体免疫系统的作用The Function of STA T s (Signal Transducers and Activators of Transcription)Involving the Human Immunological System11.水处理中的反渗透膜Reverse Osmosis in Water Treatment12.水体富营养化研究Research on Water Eutrophication13.饮用水处理和生产Drinking Water Treatment and Production14.废水中重金属的去除Removal of Heavy Metal in Waste Water15.膜分离技术在废水处理中的应用Membrane T echnology in the Use of Waste Water Treatment16.废塑料的生物降解Biodegradation of Wasted Plastics17.有机化合物的生物降解能力的确定方法The Method for the Determination of the Biodegradability of Organic Compounds 18.TiO2光催化氧化技术在环境工程中应用Application of Titanium Dioxide Photocatalysis in Environmental Engineering 19.包装材料的回收利用Reuse (Recycle) of Packaging Materials20.水处理中氮的去除The Removal of Nitrogen in Water-treatment21.污水的生物处理Biological Treatment of Waste Water22.催化还原法去除废气中的氮氧化物(NOx)The Catalytic Processes to Reduce Nitrogen Oxide in Waste Gases23.大气质量模型Atmosphere Environmental Quality Model24.挥发性有机物的测量The Measurement of the V olatile Organic Chemicals25.UASB在废水处理中的应用Application of UASB (Upflow Anaerobic Sludge Blanket) Reactor for the Treatment of Waste Water26.纺织工业废水中染料的去除The Removal of the Dyes from Waste Water of T extile Industry27.复合PCR技术在基因重组中的应用Multiplex PCR in Genetic Rearrangement28.含多环芳香烃废水对环境的污染The Pollution of Waste Water Containing Polycyclic Aromatic Hydrocarbons29.高效生物反应器的发展Development of High Performance Biotreator30.应用高效液相色谱纯化生物分子Purification of Biomolecules by HPLC (High-performance Liquid Chromatography) 31.蓝藻中的膜脂成分分析Analysis of Membrane Lipids in Cyanobacteria32.b-amyloid 在老年痴呆症中对神经的作用Function of b-amyloid on Neuron in Alzheimer's Disease33.小鼠胚胎干细胞的培养Cultivation of Embryonic Stem Cells in Mice34.基质金属蛋白酶的抑制Inhibition of Matrix Metalloproteinase (MMP)35.生物医用亲合吸附剂的研究进展Progress in biomedical affinity adsorbent36.面向环境的土壤磷素测定与表征方法研究进展Review on environmental oriented soil phosphorus testing procedure andinterpreting method37.海水养殖对沿岸生态环境影响的研究进展Review on effects of mariculture on coastal environment38.造纸清洁生产的研究进展Recent studies on cleaning production in paper industry39.深度氧化技术处理有机废水的研究进展Progress on treatment of organic wastewater by advances oxidation processes 40.折流式厌氧反应器（ABR）的研究进展Research advances in anaerobic baffled reactor (ABR)41.膜生物反应器中膜污染研究进展Study progress on the fouling of membrane in membrane bioreactor42.用于水和废水处理的混凝剂和絮凝剂的研究进展Progress on development and application of coagulants and flocculent in water and wastewater treatment43.二氢异香豆素类天然物的研究进展Development of studies on 3,4-dihydroisocoumarins in nature44.天然二萜酚类化合物研究进展Recent advances in the research on natural phenolic diterpenoids45.大气污染化学研究进展Progress in atmospheric chemistry of air pollution46.砷形态分析方法研究进展Development of methods for arsenic speciation47.复合污染的研究进展Advance in the study on compounded pollutions48.生物处理含氯代脂肪烃废水的研究进展Progress in research on the biological treatment of wastewater containingchlorinated aliphatics49.重金属生物吸附剂的应用研究现状Application conditions of heavy metal biosorbent50.两液相培养中有机溶剂对细胞毒性的研究进展Advances in studies on effects of toxicity of organic solvents on cells化学和化工1. 纳米材料的进展及其在塑料中的应用rogress and application of nano-materials in plastics2. 聚硅氯化铝（PASC）混凝剂的混凝特性The Coagulation Property of Polyaluminum Silicate Chlorate (PASC)3. 碳纳米管的制备与研究Preparations and studies of carbon nanotubes4. 纳米材料的制备及其发展动态Synthesis and development of nanosized materials5. 铁（III）核苷酸配位化合物与转铁蛋白的相互作用The interaction between ferric nucleotide coordination compounds and transferrin 6. 原位时间分辨拉曼光谱研究电化学氧化还原和吸附过程In-situ time resolved Raman spectroscopic studied on electrochemical oxidation-reduction and adsorption7. 苯胺电化学聚合机理的研究Study on the mechanism of electrochemical polymerization of aniline8. 沸石新材料研究进展Evolution of novel zeolite materials9. 聚合物共混相容性研究进展Research progress in compatibility of polymer blends10.聚酰亚胺LB膜研究进展Recent advances in polymide langmuir-blodgett films11.聚胺酯液晶研究进展The advances in LC-polyurethanes12.热塑性IPN研究进展及相结构理论Advances in thermoplastic IPN and morphological studies13.酞菁类聚合物功能材料研究进展Progresses in functional materials of phthalocyanine polymers14.有机硒化学研究进展Study progress in organoselenium chemisty15.杯芳烃研究进展Research progress in calixarene chemistry16.木素生物降解的研究进展Research progresses on lignin biodegradation17.甲烷直接催化转化制取芳烃的研究进展Progress research on direct catalytic conversion of methane to aromatics 18.铝基复合材料连接研究进展Advance in joining aluminum metal matrix composites19.现代天然香料提取技术的研究进展New development of the extraction from natural fragrance and flavour20.电泳涂料的研究进展Progress of study on electrodeposition coatings21.防静电涂料研究进展Research progress in antistatic coatings22.壳聚糖开发与应用研究进展Progress in research on the application and production of chitosan23.塑料薄膜防雾化技术的研究进展Research progress of anti-fogging technologies for plastics films24.膜反应器在催化反应中的研究进展Progress in study of films reactors for catalytical reactions25.表面活性剂对结晶过程影响的研究进展The development of studies on the influence of surfactants on crystallization 26.液晶复合分离膜及其研究进展Advances in liquid crystal composite membrane for separation27.高倍吸水树脂研究进展Recent progress in super adsorbent resin28.聚合物光折变的研究进展Progress of the study on photorefractivity in polymers29.微生物聚酯的合成和应用研究进展Progress on the biosynthesis and application of microbial polyesters30.可降解塑料的研究进展Progress in study on degradable plastics31.金属氢研究进展Progress on metallic hydrogen research32.软磁性材料的最新进展Recent advances in hard and soft magnetic materials33.光敏聚酰亚胺的研究进展Development of studies on photosensitive polyimides34.高分子卟啉及其金属配合物的研究进展Advances in polymers of porphyrins and their complexes35.水性聚胺酯研究进展Recent development of waterborne polyurethanes36.C60的研究进展及其在含能材料方面的应用前景Application prospect of C60 in energetic materials37.滤膜溶解富集方法研究进展Progress in investigation of concentration by means of soluble-membrane filter 38.人工晶体研究进展及应用前景The research progress and application prospects of synthetic crystals39.钛硅催化材料的研究进展Development of titanium silicon catalytic materials40.环烯烃聚合物的合成和应用研究进展Progress of polymerization and copolymerization with ethylene of cyclooelfines 41.多孔炭的纳米结构及其解析Nanostructure and analysis of porous carbons42.羰化法合成a-芳基丙酸研究进展Progress in preparation of a-arylpropionic acids through catalytic carbonation 44.组织工程相关生物材料表面工程的研究进展Advances in research on surface engineering of biomaterials for tissueengineering45.表面波在表面活性剂流变学研究中的应用Surface rheological properties of surfactant studied by surface wave technique 46.水溶性高分子聚集行为荧光非辐射能量转移研究进展Development of Fluorescence Nonradiative Energy Transfer in the Research for Aggregation of Water-Soluble Polymers47.两相催化体系中长链烯烃氢甲酰化反应研究进展Advance in the Hydroformylation of Higher Olefin in Two-Phase Catalystic System 48.聚合物膜燃料电池用电催化剂研究进展Progress in the Study of Electrocatalyst for PEMFC49.纳米器件制备的新方法--微接触印刷术New nano-fabrication Method-Microcontact Printing50.智能型水凝胶结构及响应机理的研究进展Recent Development of the Research on the Structure Effects and ResponsiveMechanism of Intelligent Hydrogels51.甲醇蒸馏distillation of methanol电类1. Amplifiers 放大器2. Asynchronous transfer mode(A TM) 异步传输模式3. Aritificial reality 虚拟现实4. Bayesian classification 贝叶斯分类器5. Biped robot 两足机器人6. Cable modem 有线调制解调器7. CDMA mobile communication system 码分多址移动通信系统8. Chaotic neural network 混沌神经网络9. Code optimization 代码优化10. Communication switching 通信交换11. Computer aided design 计算机辅助设计12. Compiler optimisation techniques 编译优化技术13. Computer game design 计算机游戏设计14. Computer graphics 计算机图形学15. Computer network 计算机网络16. Computer simulation 计算机仿真17. Computer vision 计算机视觉18. Continuous speech recognition 连续语音识别19. Corner Detect Operator 边角检测算子20. Database application 数据库应用21. Design of operation system 操作系统设计22. Digital filter 数字滤波器23. Digital image processing 数字图像处理24. Digital integrated circuits 数字集成电路25. Digital satellite communication system 数字卫星通信系统26. Digital signal processing 数字信号处理27. Digital television technology 数字电视技术28. Discrete system simulator programming 离散系统仿真编程29. Distributed interactive learning environment 分布式交互性学习环境30. EDA 数字系统设计自动化31. Electrical vehicles 电动交通工具32. Electricity control system 电力控制系统33. Electromagenetic wave radiation 电磁波辐射34. Face recognition 人脸识别35. Family Automation 家庭自动化36. Fibre bragg gratings 光纤布拉格光栅37. FIR digital filters 有限冲击响应数字滤波器38. Firewall technology 防火墙技术39. Fuzzy control 模糊控制40. Genetic algorithm 遗传算法41. HDTV 高清晰度电视42. High capacity floppy disk 高密度软盘43. High quality speech communication 高质量语音通信44. Image compression 图像压缩45. Image processing and recognition 图像处理和识别46. Image registration 图像配准47. Information retrieval 信息检索48. Intelligent robot 智能机器人49. Intelligent transportation 智能交通50. Internet protocol 因特网协议51. ISDN 综合业务数字网52. Knowledge discovery and data mining 知识发现和数据挖掘53. LAN, MAN and W AN 局域网，城域网和广域网54. Large scale integrated circuits 大规模集成电路55. Laser diode 激光二极管56. Laser measurement 激光测量57. Liner programming 线性规划58. Liner system stability analysis 线性系统稳定性分析59. Local area network security 局域网安全60. Magnetic material and devices 磁介质与设备61. Mass storage systems 海量存储技术62. Microwave devices 微波器件63. Mobile communication systems 移动通信系统64. MOS circuits MOS电路65. Motion control of robot 机器人运动控制66. Multimedia network 多媒体网络67. Network computing and knowledge acquisition 网络计算和知识获取68. Network routing protocol test 网络路由协议测试69. Neural network 神经网络70. Non-linear control 非线性控制71. Optical communication 光通信72. Optical fiber amplifiers 光纤放大器73. Optical hologram storage 光全息存储74. Optical modification 光调制75. Optical sensors 光传感器76. Optical switches 光开关77. Optical waveguides 光波导78. Packet switching technology in networks 网络中的分组交换技术79. Parallel algorithms 并行算法80. Pattern recognition 模式识别81. Photoelectric devices 光电子器件82. Process identificaion 过程辨识83. Programmable DSP chips 可编程数字信号处理芯片84. Programmable logic device 可编程逻辑器件85. Radar antennas 雷达天线86. Radar theory and systems 雷达理论和系统87. RISC architecture 简单指令处理器结构88. Satellite broadcasting 卫星广播89. Self calibration of camera 摄像机自适应校准90. Semiconductor laser 半导体激光器91. Semiconductor quantum well superlattices 半导体量子阱超晶格92. Signal detection and analysis 信号检测和分析93. Signal processing 信号处理94. Software engineering 软件工程95. Solid lasers 固体激光器96. Sound synthesiser 声音合成器97. Speech processing 语音处理98. System architecture design 系统结构设计99. Telecommunication receiving equipment 通信接收设备100. Theory of remote sensing by radar 雷达遥感理论101. Time division multiple access 时分多路访问102. Unix operating system Unix操作系统103. Video encoding and decoding 视频编解码104. Video telecommunication system 视频通信系统105. Wavelength division multiplexing 波分复用106. Wavelet transform 小波变换机械、自动化、物理、力学1. 无电压力传感器Nonelectric Pressure Sensors2. 金属腐蚀Metal Corrosion3. 印刷电路板的设计与制造The Design and Manufactory of Printed Circuit Board4. 分布式操作系统Distributed Operating Systems5. 金属材料的微结构和纳米结构Micro and Nanostructures of metal materials6. 宇宙背景辐射Backgroud Cosmic Radiations7. 非线性规划中的库恩－塔克条件kuhn-Tucker condition in Non-liner Programming8. 气体激光器Gas Laser9. 能量的来源及转化Energy resources and conversion10. 微纳米摩擦学Micro/nano-tribology11. 噪声控制Noise Control12. 空间观测技术Astronomical observation techniques13. 原子钟Atomic Clocks14. 半导体的磁性研究Research for Magnetic of Semiconductors15. 光学图形处理Optical Image Processing16. 液体/气体激光器加工Liquid/Gas Laser Machining17. 太阳能应用Solar Energy Application18. 流动系统中的混沌现象Chaos in Flowing Systems19. 半导体材料及仪器Semiconductor material and devices20. 电场测量研究Electric Field Measurement21. 系统及控制理论Systems and Control Theory22. 机械参量的测试Mechanical variables Measurement23. 光纤Optical Fibres24. 机动目标跟踪Tracking of Maneuvering T argets25. 航天技术Aerospace T echnology26. 导弹跟踪控制系统Missile Tracking System27. 液晶显示器件Liquid Crystal Displays28. CMOS 门电路CMOS Gate Circuits29. 图象采样与处理Image Sampling and Processing30. 光逻辑器件Optical Logic Device31. 信号发生器Signal Generator32. 蛋白质晶体测量Measurement of Protein Crystal Growth33. 有线电视Cables T elevision34. 震动与控制系统Vibration and Control System35. 高压输电系统的安全性研究Stability of High-voltage Power TransmissionSystem36. 电荷Electric Charge37. 电子显微镜及电子光学应用Electron Microscopes an Optics Applications38. 辐射的影响The Effect of Radiation39. 电化学传感器测试装置Electronchemical Sensors T esting Equipment40. 爱因斯坦－麦克斯韦场Einstein-Maxwell Fields41. 柔性角度传感器在生物力学中的应用Biomechanical Application of Flesible Angular Sensor42. 压电材料及应用装置Piezoelectric Materials and Devices43. 超导材料及其应用Superconducting Materials and The Applications of Them44. 光学干涉Optical Interferometry45. 表面测量Surface Measurement46. 等离子体中的电磁波Electromagnetic Waves in Plasma47. 半导体激光器Semiconductor lasers48. 数字人脸辨识Digital Face Recognition49. 光波导Optical Waveguides Theory50. 机械波检测技术Mechanical Waves T esting technology51. 激光调制技术Laser beam Modulation T echnology52. 只读内存Read-only Memory53. 光学显微镜Optical Microscopy54. 光纤位移测量传感器F－O displacement sensors55. 激光扫描Laser Scanners56. 量子论与量子场论Quantum Theory and Quantum Field Theory57. 流体机械Fluid Mechanics58. 地球引力Earth Gravity59. 自动控制系统Automatic Control System60. 静电线性加速器Electrostatic and Linear Accelerators61. 专家系统与网络接口Expert Systems and Network Interface62. 计算机辅助制造Computer aided Manufacture63. 全息存储Holography Storage64. 核能在中国的前景The Future of Nuclear Energy in China65. 机器人运动学和动力学分析The Kinematics an Dynamics of Robots66. 合成材料制品Composite Materials Preparations67. 光的吸收Light Absorption68. 自适应控制系统Self-adjusting Control Systems69. 通信与信息系统Communication and Information Systems70. 数字信号处理芯片Digital Signal Processing Chips71. 虚拟制造Virtual Manufacturing72. 雷达遥感Remote Sensing by Radar73. 晶格理论与点阵统计学Lattice Theory and Statistics74. 面向对象程序设计Object-Oriented Program Development75. 单片机应用及其外围设备The Applications of SCP and Outer Equipment76. 生物医学工程Biomedical Engineering77. 彩色电视设备Color T elevision78. 陶瓷－金属复合材料Ceramics-metallisation Composite Metallisation79. 电子信号的检测与处理Electronic Signal Detection and Processing80. X射线望远镜X- ray T elescope81. 基于网络的分时控制系统Time-varying Control System Based on Network82. 收音机信号传输Radio Broadcasting83. 单壁炭纳米管合成Single-Walled Carbon Nanotube Synthesis84. 无损检测Nondestructive T esting85. 汽车工业Automobile Industry86. 半导体材料与身体健康Semiconductor Materials and Health Physics87. 热辐射Heat Radiation88. 网络拓扑学Network T opology89. 微波的应用The Application of Microwave90. 局域网的设计The Design of Local Area Networks91. 金属元素表面结构Surface Structure of Metallic Elements92. 多媒体系统网络集成Network Synthesis of Multimedia Systems93. 铁氧体微波吸收材料Ferrite Microwave Absorbing Materials94. 炭纤维增强塑料复合材料Carbon Fiber Reinforced Plastic Composite95. 超导材料Superconducting Materials96. 远程定位水质控制Remote and On-site System for Water Quality Control97. 太阳能电力系统Solar Energy Power System98. 卫星接收系统Satellite Broadcasting and Relay System99. 时空对称性与守恒定律Symmetry of Space-time and Conservation Laws 100.邮件系统的体系及应用Application and Schemas for Mailbox System。

大数据技术在应收账款管理中的应用研究闫劲松1张云涛2(1.广西财经学院 广西南宁 530000;2.广西民族大学 广西南宁 530006)摘要：随着大数据、云计算、云会计等技术的发展，财务工作已经逐渐进入智能化时代，财务共享应运而生，已经成为集团企业进行日常财务工作管理的重要手段。

近年来，部分集团公司试图通过建立财务共享中心以自动处理各子公司的繁杂的日常业务，并制定一套规范的流程以进行管理，从而降低成本，但财务共享模式下应收账款管理中仍然存在着集成度与自动化程度不足的问题，而大数据技术的发展将有力地解决上述问题，该文研究企业日常应收账款管理工作中如何实际应用K-Means算法、人工神经网络中的反向传播算法（Backpropagation Algorithm，BP算法）结合机器人流程自动化（Robotics Process Automation，RPA）技术，借此帮助企业更好地管理其应收账款，满足其经营管理的需求。

关键词：应收账款管理 大数据技术 业主信用评级 坏账风险预测中图分类号：F275;F274文献标识码：A 文章编号：1672-3791(2023)20-0252-05 Research on the Application of Big Data Technology inReceivables ManagementYAN Jinsong1ZHANG Yuntao2(1.Guangxi University Of Finance and Economic, Nanning, Guangxi Zhuang Autonomous Region, 530000 China;2.Guangxi Minzu University, Nanning, Guangxi Zhuang Autonomous Region, 530006 China) Abstract:With the development of big data, cloud computing, cloud accounting and other technologies, financial work has gradually entered the era of intelligence, and financial sharing has emerged as the times require, which has become an important means for group enterprises to carry out daily financial work management. In recent years, some group companies are trying to reduce costs by establishing a financial sharing center to automatically process the complicated day-to-day business of each subsidiary and developing a set of standardized process for manage‐ment, but there is still a problem of insufficient integration and automation in receivables management under the fi‐nancial sharing mode, and the development of big data technology will effectively solve the above problems. This paper studies how enterprises apply the K-Mmeans algorithm and the BP algorithm in artificial neural networks and combine with RPA technology in daily receivables management work to help them better manage their receivables and meet their operation and management needs.Key Words: Accounts receivable management; Big data technology; Owners' credit rating; Bad debt risk prediction1 大数据时代下企业财务管理的新要求近年来，中央与地方政府制定了许多措施与方法以保障大数字科技的发展和使用，例如：大力支持5G 技术的开发与完善；在政府部门的日常工作中大力推行物联网、云计算、大数据、人工智能、区块链等新技术的使用等。

基于卷积神经网络的水果图像分类识别研究一、本文概述Overview of this article随着计算机视觉和深度学习技术的飞速发展，图像分类识别在各个领域的应用越来越广泛。

其中，基于卷积神经网络的水果图像分类识别技术，因其高效准确的特性，在农业、食品工业、智能仓储等领域具有重要的实用价值。

本文旨在深入研究卷积神经网络在水果图像分类识别中的应用，探索其性能优化和提升的有效方法。

With the rapid development of computer vision and deep learning technology, the application of image classification and recognition in various fields is becoming increasingly widespread. Among them, fruit image classification and recognition technology based on convolutional neural networks has important practical value in fields such as agriculture, food industry, and intelligent warehousing due to its efficient and accurate characteristics. This article aims to conduct in-depth research on the application of convolutional neural networks in fruit image classification and recognition, andexplore effective methods for optimizing and improving their performance.本文首先概述了卷积神经网络的基本原理和发展历程，分析了其在图像分类识别任务中的优势。

智能科学与技术专业英语一、单词1. Artificial Intelligence (AI)- 英语释义：The theory and development ofputer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision - making, and translation between languages.- 用法：“Artificial Intelligence” is often abbreviated as “AI” and can be used as a subject or in phrases like “AI technology” or “the field of AI”.- 双语例句：- Artificial Intelligence has made great progress in recent years. (近年来，人工智能取得了巨大的进展。

)- Manypanies are investing heavily in artificial intelligence research. (许多公司正在大力投资人工智能研究。

)2. Algorithm- 英语释义：A set ofputational steps and rules for performing a specific task.- 用法：Can be used as a countable noun, e.g. “T his algorithm is very efficient.”- 双语例句：- The new algorithm can solve the problem much faster. (新算法可以更快地解决这个问题。

盛年不重来，一日难再晨。

及时宜自勉，岁月不待人。

一般来说，在精神病学界和临床心理学界，焦虑的定义有三个要件：（1）焦虑是一种烦躁、急切、提心吊胆、紧张不安的心境；（2）焦虑者往往伴有植物神经功能紊乱的症状；（3）焦虑往往是没有相对固定的对象和明确的内容。

但是迄今为止,对于焦虑的定义以及焦虑的本质到底是什么并没有一个统一的说法。

在这里，引用唐海波和邝春霞（2009）对焦虑的定义，即焦虑是个体对即将来临的可能会造成危险或威胁的情境所产生的紧张、不安、忧虑、烦恼等不愉快的复杂情绪状态。

在西方文化史中，正式地、深入地对焦虑进行研究是19世纪末20世纪初，最早深入研究焦虑的人是哲学家克尔凯戈尔（1944），在《恐惧的概念》一书中明确指出，焦虑是人面临自由选择时所必然存在的心理体验，焦虑的产生与人的自我意识的形成和发展有关。

弗洛伊德对焦虑也有系统的研究，他对焦虑症的临床影响仅次于其对癔症的影响。

20世纪曾被者称为“焦虑的时代（AGE OF ANXIETY）”。

对焦虑的神经心理学、心理测量学、心理治疗学、精神药理学的研究都取得了长足的进步。

尤其是对焦虑的行为-认知治疗受到了临床工作者的重视，同时，抗焦虑药物的临床应用也使焦虑的治疗呈现出多样性的局面。

在精神分析之后，其他各大流派也对焦虑做了系统的研究，提出了他们各自的焦虑理论。

弗洛伊德认为，焦虑起源于超我和本我之间的冲突，焦虑是潜意识中存在着危险的一个信号，为了回应这个信号，自我会动用一系列的防御机制，从而防止那些不为人接受的冲动和欲望进入意识层面。

如果作为信号的焦虑不能激发起自我的防御或防御失败，那么就会出现持续的焦虑状态或者其他神经症的症状。

所以，焦虑既是冲突的产物又代表着自我为消除冲突所作的努力。

在弗洛伊德之后，精神分析学派的代表人物霍尼和沙利文也提出了自己的焦虑理论。

霍尼认为，她认为焦虑的形成分成三种：(1)原始焦虑——儿童与父母的分离引起；(2)惊时焦虑——突发性意外、陌生环境、恐怖的电影引起。