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Chinese Chemical Letters 2024年第7期35卷 200-205页

作者： Shiqi Peng Yongfang Rao Tan li yufei zhang {2.-ji Cao Shuncheng Lee yu Huang Key Laboratory of Aerosol Chemistry&Physics State Key Laboratory of Loess and Quaternary Geology(SKLLQG)Institute of Earth EnvironmentChinese Academy of Sciences(CAS)Xi’an 710061China school of Human Settlements and civil {2. Xi’an Jiaotong UniversityXi’an 710049China Department of Environmental science and engineering Xi’an Jiaotong UniversityXi’an 710049China Faculty of Chemical engineering Kunming University of Science and TechnologyKunming 650500China school of Environment and Energy South China University of TechnologyGuangzhou 51006China school of Environmental and Municipal engineering Xi’an University of Architecture and TechnologyXi’an 710055China institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100190China Department of civil and Environmental engineering The Hong Kong Polytechnic UniversityHong KongChina CAS Center for Excellence in Quaternary science and Global Change Xi’an 710061China

Developing low-loading single-atom catalysts with superior catalytic activity and selectivity in formaldehyde(HCHO)oxidation at room temperature remains ***,ZrO_(2.nanoparticles coupled low-loading Ir single atoms in N-doped carbon(Ir_(1)-N-C/ZrO_(2.)was *** optimal Ir_(1)-N-C/ZrO_(2.with 0.2. wt%Ir loading delivers the high HCHO removal and conversion efficiency(>95%)at 2.℃,which is higher than that over Ir_(1)-N-C with the same Ir *** specific rate can reach 12.5.6 mmol gIr^(-1)h^(-1),surpassing the Ir based catalysts reported to *** functional theory calculation results and electron spin resonance spectra indicate that the introduction of Zr O_(2.nanoparticles modulate the electronic structure of the Ir single atoms,promoting O_(2.activation to·O_(2.^(–).Moreover,the Ir-C-Zr channel is favorable for the dissociation of·O_(2.^(–)to active oxygen atom(*O),and further accelerates the transformation of HCHO and intermediates(dioxymethylene and formates)to CO_(2.and H_(2.*** work provides a facile strategy to design low-loading single-atom catalysts with high catalytic activity toward HCHO oxidation.

关键词： ZrO_(2.nanoparticles Ir single atoms Electronic structure Formaldehyde Catalytic oxidation

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An attention-based teacher-student model for multivariate short-term landslide displacement prediction incorporating weather forecast data

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Journal of Mountain science 2024年第8期21卷 2739-2753页

作者： CHEN jun HU Wang zhang {2. QIU Hongzhi WANG Renchao Sichuan Artificial Intelligence Research institute Yibin 644000China school of computer {2. and engineering University of Electronic Science and Technology of ChinaChengdu 611731China school of architecture and {2. engineering Chengdu UniversityChengdu 610106China

Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and ***,traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction,while a single structural model cannot handle input sequences of different lengths at the same *** order to solve these limitations,in this study,a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient(MIC),long short-term memory network(LSTM),and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement *** MIC,a suitable input sequence length is selected for the LSTM *** investigate the influence of rainfall on landslides during different seasons,a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different *** teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as China Meteorological Administration(CMA)and Reliable Prognosis(https://***)to improve the model’s expression capability,and the student model learns sequence information from other time *** attention module is then designed to integrate different sequence information to derive a context vector,representing seasonal temporal attention ***,the predicted displacement is obtained through a linear *** proposed method demonstrates superior prediction accuracies,surpassing those of the support vector machine(SVM),LSTM,recurrent neural network(RNN),temporal convolutional network(TCN),and LSTM-Attention *** achieves a mean absolute error(MAE)of 0.072.mm,root mean square error(RMSE)of 0.096 mm,and pearson correlation coefficients(PC

关键词： Landslide prediction MIC LSTM Attention mechanism Teacher Student model Prediction stability and interpretability

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STUDY ON APPliCATION OF MATHEMATICAL MODEL COMPUTATION AND MEASUREMENT OF SIliCA POWDER SIZE DISTRIBUTION BY LASER DIFFRACTION

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STUDY ON APPLICATION OF MATHEMATICAL MODEL COMPUTATION AND M...

The Bluetooth Body Centre Network Channel Character For Wearable computer

2.03年全国粉体设备—技术—产品信息交流会

作者： zhang jun Gai Guosheng Song Shouzhi Sun {2.hai school of Resource and {2. engineering , Northeast university, Shenyang 110004 Department of Materials science and engineering , Tsinghua university, beijing 100084

＜正＞ -In this paper, we have applied the computer technology to regulate neatly middle particle D85, D90, D97 etc of faculative milling products size and particle distributing bound, by Laser Particle Sizer measure to obtain data of frequency distributing （calculous）, cumulating distribution （integral） and build mathematic model. It is combining with the research of milling system of high-pure ultra-fine silica in Laboratory of Powder technology Department of Materials science and engineering Tsinghua university, according to requirement of industry. The method classify the data which get from Laser Particle Sizer into two-component three-component or multi-component system according to mathematic model, and then put them into computer. Whereafter use the computer results to adjust particle distributing. computer system can synchronously print graph of adjusted frequency distributing and cumulating distribution, which accompanied with respective data. If cooperate with sampling online and analysis of milling system, we can control particle distributing of production and control manufacture on time to reach requirement of *** system has been operated on thousand-ton’s level product line of high-pure ultra-fine silica with designed by Tsinghua university for Quartz Produce Company of Xinhai Electric Power Co., Ltd., lianyungang, Jiangsu Province. It has been certified to be efficient method and has obtained favorable effect.

关键词： laser diffraction instrument, mathematical model frequency distributing cumulating distribution

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The Bluetooth Body Centre Network Channel Character For Wear...

Astrometric Observations of NEA 1998 HH49 Using the Daocheng 50 cm Telescope

IEEE 2.07 International Symposium on Microwave,Antenna,Propagation and EMC Technologies for Wireless Communications(ISMAPE-2.07)

作者： CHEN jin-rong~(1,4),SONG jun-de~1,HAN {2.-nan~(2.3),zhang Yong~1 (1.The school of electronic {2.,beijinguniversity of posts and telecommunications, beijing,100876 China 2.The First System Design Department of the First Research Academy, Aerospace science and technology Group,beijing 100076 China 3.The school of telecommunication and network technology, beijing university of posts and telecommunications,beijing 100876 China 4.beijing Zhongchuang telecom test Co.LTD.beijing 100081 China)

The narrow band body centre netork based on Bluetooth technology has been constructed by integrated planar inverted-F antenna and the communication channel character has been studied by measurement for wearable computer *** Bluetooth system,the path loss of different antenna positions on body for measurement can range from-35dB *** the two antenna located on the arms and moved the two arms,the path loss can change from -40.2.B *** deep breath can affect the path loss within 5dB and metal ornament such as metal ring can affect about *** specific absorption rate distribution can also be studied by finite-difference time-domain(FDTD)method.

关键词：

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Research in Astronomy and Astrophysics 2024年第7期24卷 71-78页

作者： Huan Xu Xiang-Ming Cheng Yi-Gong zhang Teng-Fei Song Zhen-jun zhang Qing-yu Peng yunnan Observatories Chinese Academy of SciencesKunming 650216China university of Chinese Academy of sciences Beijing 100049China Key Laboratory of the Structure and Evolution of Celestial Objects Chinese Academy of SciencesKunming 650216China engineering Laboratory on Applied Astronomical technology of {2.nnan Kunming 650216China school of Information engineering Kunming UniversityKunming 650214China school of computer {2. and Information engineering Anyang Institute of TechnologyAnyang 455000China Department of computer science Jinan UniversityGuangzhou 510632China Sino-French Joint Laboratory for Astrometry Dynamics and Space ScienceJinan UniversityGuangzhou 510632China

This study details an astrometric observation campaign of the Near-Earth Asteroid 1998 HH49,conducted with the aim of refining our understanding of its physical *** the 50 cm telescope located at the Wumingshan Mountain in Daocheng,Sichuan,images were obtained over four nights,from 2.2. October 19 to October *** observations were processed using Astrometrica software,facilitating the precise determination of the asteroid's *** observational results were compared with the ephemerides from three distinct sources to verify accuracy:the Jet Propulsion Laboratory(JPL)Horizons System,the Institut de Mécanique Céleste et de Calcul deséphémérides(IMCCE)Miriade,and the Near-Earth Objects Dynamic Site(NEODyS-2..When compared with the JPL ephemeris,a mean observed-minus-calculated(O-C)result of 0.″07 in the *** and-0.″35 in the *** was ***,the comparison with the IMCCE ephemeris yielded mean O-C results of 0.″08 in the *** and-0.″06 in the *** comparison with the NEODyS-2.ephemeris yielded the mean O-C results of 0.″06 in ***-0.″49 in *** study's findings demonstrate a general consistency between the observed data and the ephemeris predictions,with minor discrepancies observed across the data ***,both the JPL and NEODyS-2.ephemerides show that the residuals in the *** exceed those in the *** disparities may result from atmospheric differential color refraction,ephemeris discrepancies,observational errors,and other ***,it is worth noting that further investigation is required due to the potential influence of additional ***,the Daocheng 50 cm Telescope exhibits the ability to conduct high-precision positional measurements.

关键词： astrometry ephemerides time

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Frontiers in high entropy alloys and high entropy functional materials

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Rare Metals 2024年第10期43卷 4639-4776页

作者： Wen-Tao zhang Wen Huang Fu-Jie yu Qi Zhao Xin-Dong Zhu Tao Feng Yao Jiang Xiao-Ming li Xue-Fei Miao He Zhu Si Lan Xue-Qian Wang Bao-Long Shen Zhe Jia Feng-Qi zhang Wei Wang Qi liu Yang Ren Xun-li Wang Xiao-Ya Cui Xiao-Yang Wang Ya-Nan Chen Bing-Bing Fan Meng-Ru li Qi-Long Gao Jia-Ming Guo Zeng-Bao Jiao Zhi-Min Guo yu-Ping Zhao Qian yu Rui Huang ling-Kun zhang Yi-Ping Lu Xu-Bo li Huai-jun lin Yan Ma Wei-Ming Yang Zhi-Hua Shen Hai-Yan He Yong-Gang Sun Yong-Jiang Huang De-Zhuang Wang Qing-Feng liu Fei-Yang Wang Hui liu Hong-Hui Wu li-Qiang Wang Yang Lu Nan Wang Tian-li Wang Wei-Bing liao Yi-Han Wang yuan Wu yu-Zhen Yin Jie Pan Yi zhang Qing Wang Jian-Yang zhang Tao Yang Yasir Sohail jin-yu zhang Hua Ji Qiang li Wei-Di liu Zhi-Gang Chen Xiong-jun liu Wen Ma liang Mao Zhen-Duo Wu school of Materials science and {2. Nanjing University of Science and Technology Jiangsu Key Laboratory for Advanced Metallic Materials School of Materials Science and Engineering Southeast University Department of Physics City University of Hong Kong school of Materials science and {2. Tianjin University school of Physics and Microelectronics Zhengzhou University Department of Mechanical engineering Research Institute for Advanced Manufacturing The Hong Kong Polytechnic University Center of Electron Microscopy and State Key Laboratory of Silicon Materials School of Materials Science and Engineering Zhejiang University Key Laboratory of Solidification Control and Digital Preparation technology (liaoning Province) School of Materials Science and Engineering Dalian University of Technology institute of Advanced Wear & Corrosion Resistance and Functional Materials Jinan University school of Mechanics and civil {2. China University of Mining and Technology school of Physical sciences Great Bay University school of Materials science and{2. Harbin Institute of Technology State Key Laboratory of Materials-Oriented Chemical engineering College of Chemical Engineering Nanjing Tech University beijing Advanced Innovation Center for Materials Genome engineering Research Institute for Carbon Neutrality University of Science and Technology Beijing Department of Mechanical engineering The University of Hong Kong school of Computing Macquarie University Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic Engineering Shenzhen University State Key Laboratory for Advanced Metals and Materials University of Science and Technology Beijing State Key Laboratory of Materials Processing and Die & Mould technology School of Materials Science and Engineering Huazhong University of Science and Technology institute of materials Shanghai University Department of Materials science and engineering C

Owing to their exceptional properties, high-entropy alloys(HEAs) and high-entropy materials have emerged as promising research areas and shown diverse applications. Here,the recent advances in the field are comprehensively reviewed,organized into five sections. The first section introduces the background of HEAs, covering their definition, significance,application prospects, basic properties, design principles, and microstructure. The subsequent section focuses on cutting-edge high-entropy structural materials, highlighting developments such as nanostructured alloys, grain boundary engineering,eutectic systems, cryogenic alloys, thin films, micro-nano-lattice structures, additive manufacturing, high entropy metallic glasses, nano-precipitate strengthened alloys, composition modulation, alloy fibers, and refractory systems. In the following section, the emphasis shifts to functional materials, exploring HEAs as catalysts, magneto-caloric materials, corrosion-resistant alloys, radiation-resistant alloys, hydrogen storage systems, and materials for biomedicine. Additionally, the review encompasses functional high-entropy materials outside the realm of alloys, including thermoelectric, quantum dots, nanooxide catalysts, energy storage materials, negative thermal expansion ceramics,and high-entropy wave absorption materials. The paper concludes with an outlook, discussing future directions and potential growth areas in the field. Through this comprehensive review,researchers, engineers, and scientists may gain valuable insights into the recent progress and opportunities for further exploration in the exciting domains of high-entropy alloys and functional materials.

关键词： Cutting-edge structural materials Cutting-edgefunctional materials High entropy alloys

Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying liu jin {2. Long yu Fei Wang Yong Xie jun li Fang Wang Sabine Grunwald Bryan MWong Fan {2. Zhen Qian Yongjun Xu Chengqing yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai {2. Husi Letu Bing {2. li Wang Lei Luo Chong Shi Hongjun Su Hongsheng {2. Shuai Yin Ni Huang Wei Zhao Nan li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi lin Tangtang {2. Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao {2. Jiancheng Shi lizhe Wang Aerospace Information Research institute Chinese Academy of SciencesBeijing 100094China school of computer {2. China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China institute of Computing technology Chinese Academy of SciencesBeijing 100190China school of Geographical sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials science engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China school of Environmental science and {2. Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

Knowledge-guided machine learning reveals pivotal drivers for gasto-particle conversion of atmospheric nitrate

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Environmental science and Ecotechnology 2024年第3期19卷 100-108页

作者： Bo Xu Haofei yu Zongbo Shi jinxing liu yuting Wei Zhongcheng zhang Yanqi Huangfu Han Xu yue li linlin zhang Yinchang Feng Guoliang Shi State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control Tianjin Key Laboratory of Urban Transport Emission ResearchCollege of Environmental Science and EngineeringNankai UniversityTianjin300350China CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research(CLAER) College of Environmental Science and EngineeringNankai UniversityTianjin300350China Department of civil Environmentaland Construction EngineeringUniversity of Central FloridaOrlandoFLUSA school of Geography Earth and Environment sciences University of BirminghamBirminghamB152TTUK State Key Laboratory of Precision Measuring technology and Instruments Tianjin Key Laboratory of air Pollutants Monitoring TechnologySchool of Precision Instrument and Opto-electronics EngineeringTianjin UniversityTianjin300072China Gigantic technology(Tianjin)Co. LtdTianjin300072China College of computer science Nankai UniversityTianjin300350China China National Environmental Monitoring Centre Beijing100012China

Particulate nitrate,a key component of fine particles,forms through the intricate gas-to-particle conversion *** process is regulated by the gas-to-particle conversion coefficient of nitrate(ε(NO_(3)^(-))).The mechanism betweenε(NO_(3)^(-))and its drivers is highly complex and nonlinear,and can be characterized by machine learning ***,conventional machine learning often yields results that lack clear physical meaning and may even contradict established physical/chemical mechanisms due to the influence of ambient *** urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact ofε(NO_(3)^(-)).Here we introduce a supervised machine learning approachdthe multilevel nested random forest guided by theory *** approach robustly identifies NH4 t,SO_(4)^(2.),and temperature as pivotal drivers forε(NO_(3)^(-)).Notably,substantial disparities exist between the outcomes of traditional random forest analysis and the anticipated actual ***,our approach underscores the significance of NH4 t during both daytime(30%)and nighttime(40%)periods,while appropriately downplaying the influence of some less relevant drivers in comparison to conventional random forest *** research underscores the transformative potential of integrating domain knowledge with machine learning in atmospheric studies.

关键词： Machine learning Data driven Theoretical approach Domain knowledge Guide

Radical innovation breakthroughs of biodegradation of plastics by insects:history,present and future perspectives

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Frontiers of Environmental science & engineering 2024年第6期18卷 127-165页

作者： Shan-Shan Yang Wei-Min Wu Federica Bertocchini Mark Eric Benbow Suja P.Devipriya Hyung Joon Cha Bo-yu Peng Meng-Qi Ding Lei He Mei-Xi li Chen-Hao Cui Shao-Nan Shi Han-{2. Sun Ji-Wei Pang Defu He Yalei zhang {2. Yang Deyi Hou De-Feng Xing Nan-Qi Ren Jie Ding Craig S.Criddle State Key Laboratory of Urban Water Resource and Environment School of EnvironmentHarbin Institute of TechnologyHarbin 150090China Department of civil and Environmental engineering William&Cloy Codiga Resource Recovery CenterStanford UniversityStanfordCA 94305USA Plasticentropy rue Thiers 28Reims 51100France Department of Entomology and Department of Osteopathic Medical Specialties Michigan State UniversityEast LansingMI 48824USA school of Environmental Studies Cochin University of Science and TechnologyKochi 682022India Department of Chemical engineering Pohang University of Science and TechnologyPohang 37673Republic of Korea school of Environmental science and {2. Tongji UniversityShanghai 200092China China Energy Conservation and Environmental Protection Group CECEP Talroad Technology Co.Ltd.Beijing 100096China school of Ecological and Environmental sciences East China Normal UniversityShanghai 200241China Research institute for Frontier science Beihang UniversityBeijing 100191China school of Environment Tsinghua UniversityBeijing 100084China

Insects damaging and penetrating plastic packaged materials has been reported since the *** innovation breakthroughs of plastic biodegradation have been initiated since the discovery of biodegradation of plastics by Tenebrio molitor larvae in 2.15 followed by Galleria mellonella in *** we review updated studies on the insect-mediated biodegradation of *** biodegradation by insect larvae,mainly by some species of darkling beetles(Tenebrionidae)and pyralid moths(Pyralidae)is currently a highly active and potentially transformative area of *** the past eight years,publications have increased explosively,including discoveries of the ability of different insect species to biodegrade plastics,biodegradation performance,and the contribution of host and microbiomes,impacts of polymer types and their physic-chemical properties,and responsible enzymes secreted by the host and gut *** date,almost all major plastics including polyethylene(PE),polypropylene(PP),polyvinyl chloride(PVC),polyethylene terephthalate(PET),polyurethane(PUR),and polystyrene(PS)can be biodegraded by *** and ten other insect species representing the Tenebrionidae and Pyralidae *** biodegradation processes are symbiotic reactions or performed by synergistic efforts of both host and gut-microbes to rapidly depolymerize and biodegrade plastics with hourly *** digestive ezymens and bioreagents screted by the insects play an essential role in plasatic biodegradation in certain species of Tenebrionidae and Pyralidae *** research on the insect itself,gut microbiomes,transcriptomes,proteomes and metabolomes has evaluated the mechanisms of plastic biodegradation in *** conclude this review by discussing future research perspectives on insect-mediated biodegradation of plastics.

关键词： Plastics Biodegradation Insects Gut microbiomes Enzymes