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Interannual evolution of the chemical composition,sources and processes of PM_(2.5)in Chengdu,China:Insights from observations in four winters

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Journal of Environmental sciences 2024年第4期138卷 32-45页

作者： Junke Zhang Jiaqi Li Yunfei Su Chunying Chen Luyao Chen Xiaojuan Huang Fangzheng Wang Yawen Huang Gehui Wang Faculty of Geosciences and Environmental Engineering Southwest Jiaotong UniversityChengdu 611756China department of Environmental science&Engineering Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention(LAP3)Fudan UniversityShanghai 200438China Key Lab of geographic information science of the Ministry of Education School of Geographic SciencesEast China Normal UniversityShanghai 200241China

The air quality in China has improved significantly in the last decade and,correspondingly,the characteristics of PM_(2.5)have also changed.We studied the interannual variation of PM_(2.5)in Chengdu,one of the most heavily polluted megacities in southwest China,during the most polluted season(winter).Our results show that the mass concentrations of PM_(2.5)decreased significantly year-by-year,from 195.8±91.0μg/m~3in winter 2016 to 96.1±39.3μg/m^(3)in winter 2020.The mass concentrations of organic matter(OM),SO_()4^(2-),NH_(4)^(+)and NO_(3)^(-)decreased by 49.6%,57.1%,49.7% and 28.7%,respectively.The differential reduction in the concentrations of chemical components increased the contributions from secondary organic carbon and NO_(3)^(-)and there was a larger contribution from mobile sources.The contribution of OM and NO_(3)^(-)not only increased with increasing levels of pollution,but also increased year-by-year at the same level of pollution.Four sources of PM_(2.5)were identified:combustion sources,vehicular emissions,dust and secondary aerosols.Secondary aerosols made the highest contribution and increased year-by-year,from 40.6%in winter 2016 to 46.3% in winter 2020.By contrast,the contribution from combustion sources decreased from 14.4% to 8.7%.Our results show the effectiveness of earlier pollution reduction policies and emphasizes that priority should be given to key pollutants(e.g.,OM and NO_(3)^(-))and sources(secondary aerosols and vehicular emissions)in future policies for the reduction of pollution in Chengdu during the winter months.

关键词： PM_(2.5) Chemical composition Sources Interannual evolution Winter Chengdu

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Spatiotemporal changes of gross primary productivity and its response to drought in the Mongolian Plateau under climate change

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Journal of Arid Land 2024年第1期16卷 46-70页

作者： ZHAO Xuqin LUO Min MENG Fanhao SA Chula BAO Shanhu BAO Yuhai College of geographical science Inner Mongolia Normal UniversityHohhot 010022China Inner Mongolia Key Laboratory of Remote Sensing and geographic information Systems Hohhot 010022China

Gross primary productivity(GPP)of vegetation is an important constituent of the terrestrial carbon sinks and is significantly influenced by drought.Understanding the impact of droughts on different types of vegetation GPP provides insight into the spatiotemporal variation of terrestrial carbon sinks,aiding efforts to mitigate the detrimental effects of climate change.In this study,we utilized the precipitation and temperature data from the Climatic Research Unit,the standardized precipitation evapotranspiration index(SPEI),the standardized precipitation index(SPI),and the simulated vegetation GPP using the eddy covariance-light use efficiency(EC-LUE)model to analyze the spatiotemporal change of GPP and its response to different drought indices in the Mongolian Plateau during 1982-2018.The main findings indicated that vegetation GPP decreased in 50.53% of the plateau,mainly in its northern and northeastern parts,while it increased in the remaining 49.47%area.Specifically,meadow steppe(78.92%)and deciduous forest(79.46%)witnessed a significant decrease in vegetation GPP,while alpine steppe(75.08%),cropland(76.27%),and sandy vegetation(87.88%)recovered well.Warming aridification areas accounted for 71.39% of the affected areas,while 28.53% of the areas underwent severe aridification,mainly located in the south and central regions.Notably,the warming aridification areas of desert steppe(92.68%)and sandy vegetation(90.24%)were significant.Climate warming was found to amplify the sensitivity of coniferous forest,deciduous forest,meadow steppe,and alpine steppe GPP to drought.Additionally,the drought sensitivity of vegetation GPP in the Mongolian Plateau gradually decreased as altitude increased.The cumulative effect of drought on vegetation GPP persisted for 3.00-8.00 months.The findings of this study will improve the understanding of how drought influences vegetation in arid and semi-arid areas.

关键词： gross primary productivity(GPP) climate change warming aridification areas drought sensitivity cumulative effect duration(CED) Mongolian Plateau

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Categorizing methods for integrating machine learning with executable specifications

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science China(information sciences) 2024年第1期67卷 5-19页

作者： David HAREL Raz YERUSHALMI Assaf MARRON Achiya ELYASAF department of Computer science and Applied Mathematics Weizmann Institute of Science department of Software and information Systems Engineering Ben-Gurion University of the Negev

Deep learning(DL),which includes deep reinforcement learning(DRL),holds great promise for carrying out real-world tasks that human minds seem to cope with quite readily.That promise is already delivering extremely impressive results in a variety of areas.However,while DL-enabled systems achieve excellent performance,they are far from perfect.It has been demonstrated,in several domains,that DL systems can err when they encounter cases they had not hitherto encountered.Furthermore,the opacity of the produced agents makes it difficult to explain their behavior and ensure that they adhere to various requirements posed by human engineers.At the other end of the software development spectrum of methods,behavioral programming(BP) facilitates orderly system development using self-standing executable modules aligned with how humans intuitively describe desired system behavior.In this paper,we elaborate on different approaches for combining DRL with BP and,more generally,machine learning(ML) with executable specifications(ES).We begin by defining a framework for studying the various approaches,which can also be used to study new emerging approaches not covered here.We then briefly review state-of-the-art approaches to integrating ML with ES,continue with a focus on DRL,and then present the merits of integrating ML with BP.We conclude with guidelines on how this categorization can be used in decision making in system development,and outline future research challenges.

关键词： machine learning artificial intelligence grey box learning domain knowledge rules behavioral programming deep reinforcement learning survey

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Social Media-Based Surveillance Systems for Health Informatics Using Machine and Deep Learning Techniques:A Comprehensive Review and Open Challenges

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Computer Modeling in Engineering & sciences 2024年第5期139卷 1167-1202页

作者： Samina Amin Muhammad Ali Zeb Hani Alshahrani Mohammed Hamdi Mohammad Alsulami Asadullah Shaikh Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan department of Computer science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia department of information System College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance.Since,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by SM.This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic outbreaks.DL has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation results.In recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM analysis.This paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM analysis.Finally,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.

关键词： Social media epidemic machine learning deep learning health informatics pandemic

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Automated Algorithms for Detecting and Classifying X-Ray Images of Spine Fractures

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Computers, Materials & Continua 2024年第4期79卷 1539-1560页

作者： Fayez Alfayez department of Computer science and information College of ScienceMajmaah UniversityAL-Majmaah11952Saudi Arabia

This paper emphasizes a faster digital processing time while presenting an accurate method for identifying spinefractures in X-ray pictures. The study focuses on efficiency by utilizing many methods that include picturesegmentation, feature reduction, and image classification. Two important elements are investigated to reducethe classification time: Using feature reduction software and leveraging the capabilities of sophisticated digitalprocessing hardware. The researchers use different algorithms for picture enhancement, including theWiener andKalman filters, and they look into two background correction techniques. The article presents a technique forextracting textural features and evaluates three picture segmentation algorithms and three fractured spine detectionalgorithms using transformdomain, PowerDensity Spectrum(PDS), andHigher-Order Statistics (HOS) for featureextraction.With an emphasis on reducing digital processing time, this all-encompassing method helps to create asimplified system for classifying fractured spine fractures. A feature reduction program code has been built toimprove the processing speed for picture classification. Overall, the proposed approach shows great potential forsignificantly reducing classification time in clinical settings where time is critical. In comparison to other transformdomains, the texture features’ discrete cosine transform (DCT) yielded an exceptional classification rate, and theprocess of extracting features from the transform domain took less time. More capable hardware can also result inquicker execution times for the feature extraction algorithms.

关键词： Feature reduction image classification X-ray images

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Monitoring Surface Water Change in Northeast China in 1999–2020:Evidence from Satellite Observation and Refined Classification

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Chinese geographical science 2024年第1期34卷 106-117页

作者： LIU Kai ZHANG Dapeng CHEN Tan CUI Peipei FAN Chenyu SONG Chunqiao Key Laboratory of Watershed geographic sciences Nanjing Institute of Geography and LimnologyChinese Academy of SciencesNanjing210008China University of Chinese Academy of sciences Nanjing(UCASNJ)Nanjing211135China geographic information and Tourism College Chuzhou UniversityChuzhou239000China

As a typical region with high water demand for agricultural production,understanding the spatiotemporal surface water changes in Northeast China is critical for water resources management and sustainable development.However,the long-term variation characteristics of surface water of different water body types in Northeast China remain rarely explored.This study investigated how surface water bodies of different types(e.g.,lake,reservoir,river,coastal aquaculture,marsh wetland,ephemeral water) changed during1999–2020 in Northeast China based on various remote sensing-based datasets.The results showed that surface water in Northeast China grew dramatically in the past two decades,with an equivalent area increasing from 24 394 km^(2) in 1999 to 34 595 km^(2) in 2020.The surge of ephemeral water is the primary driver of surface water expansion,which could ascribe to shifted precipitation pattern.Marsh wetlands,rivers,and reservoirs experienced a similar trend,with an approximate 20% increase at the interdecadal scale.By contrast,coastal aquacultures and natural lakes remain relatively stable.This study is expected to provide a more comprehensive investigation of the surface water variability in Northeast China and has important practical significance for the scientific management of different types of surface water.

关键词： surface water spatiotemporal variation water body classification remote sensing Northeast China

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CToMP: a cycle-task-oriented memory protection scheme for unmanned systems

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science China(information sciences) 2024年第6期67卷 399-417页

作者： Chengyan MA Ning XI Di LU Yebo FENG Jianfeng MA School of Cyber Engineering Xidian University School of Computer science and Technology Xidian University department of Computer and information science University of Oregon

Memory corruption attacks(MCAs) refer to malicious behaviors of system intruders that modify the contents of a memory location to disrupt the normal operation of computing systems, causing leakage of sensitive data or perturbations to ongoing processes. Unlike general-purpose systems, unmanned systems cannot deploy complete security protection schemes, due to their limitations in size, cost and performance.MCAs in unmanned systems are particularly difficult to defend against. Furthermore, MCAs have diverse and unpredictable attack interfaces in unmanned systems, severely impacting digital and physical sectors. In this paper, we first generalize, model and taxonomize MCAs found in unmanned systems currently, laying the foundation for designing a portable and general defense approach. According to different attack mechanisms,we found that MCAs are mainly categorized into two types — return2libc and return2shellcode. To tackle return2libc attacks, we model the erratic operation of unmanned systems with cycles and then propose a cycle-task-oriented memory protection(CToMP) approach to protect control flows from tampering. To defend against return2shellcode attacks, we introduce a secure process stack with a randomized memory address by leveraging the memory pool to prevent Shellcode from being executed. Moreover, we discuss the mechanism by which CTo MP resists the return-oriented programming(ROP) attack, a novel variant of return2libc attacks. Finally, we implement CTo MP on CUAV V5+ with Ardupilot and Crazyflie. The evaluation and security analysis results demonstrate that the proposed approach CTo MP is resilient to various MCAs in unmanned systems with low footprints and system overhead.

关键词： unmanned system memory corruption attack memory protection system security randomized memory address

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Near-field wideband beam training for ELAA with uniform circular array

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science China(information sciences) 2024年第6期67卷 370-383页

作者： Yuhao CHEN Linglong DAI department of Electronic Engineering Tsinghua UniversityBeijing National Research Center for Information Science and Technology (BNRist)

Extremely large-scale antenna array(ELAA) at millimeter wave(mmWave) and Terahertz(THz)band has been considered a key technology for combating high attenuation in high-frequency bands in future6G communications. Uniform circular arrays(UCAs) have attracted much attention because of their ability to provide flat beamforming gain at all angles. To realize efficient beamforming, beam training is widely used to acquire channel state information. However, with a large antenna number, the beam training overhead in ELAA systems becomes overwhelming. Moreover, with a large bandwidth, the beam defocus effect severely degrades beam training accuracy. To address these issues, this paper proposes a frequency-dependent focusing(FDF)-based beam training scheme to realize effective beam training in near-field wideband ELAA systems with UCA. Specifically, we first analyze the FDF property of UCA, where signals at different subcarriers can simultaneously focus on different distances. Then, by exploiting the FDF property to search different distances using different subcarriers simultaneously, we design a hierarchical codebook and propose an FDFbased beam training scheme. To reveal the effectiveness of the proposed scheme, we compare its necessary beam training overhead with that of existing schemes. Finally, the simulation results demonstrate that the proposed scheme can achieve accurate beam training in near-field wideband UCA systems with a low beam training overhead.

关键词： extremely large-scale antenna array (ELAA) uniform circular array (UCA) near-field wideband beam training

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Emission and optical characteristics of brown carbon in size-segregated particles from three types of Chinese ships

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Journal of Environmental sciences 2024年第8期142卷 248-258页

作者： Fengqin Yang Fan Zhang Zeyu Liu Yingjun Chen Yan Zhang Can Wu Yali Lei Shijie Liu Binyu Xiao Xinyi Wan Yubao Chen Yong Han Min Cui Cheng Huang Gehui Wang Key Lab of geographic information science of the Ministry of Education School of Geographic SciencesEast China Normal UniversityShanghai 200241China Institute of Eco-Chongming 20 Cuiniao Rd.ChongmingShanghai 202150China State Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Pollution Complex Shanghai Academy of Environmental SciencesShanghai 200233China Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention(LAP3) Department of Environmental Science and EngineeringFudan UniversityShanghai 200438China Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092China department of Civil and Environmental Engineering and State Key Laboratory of Marine Pollution The Hong Kong Polytechnic UniversityKowloon 100872Hong KongChina College of Environmental science and Engineering Yangzhou UniversityYangzhou 225009China

Brown carbon(BrC)is one of the important light absorption substances that have high light absorption ability under short wavelength light.However,limit studies have focused on the BrC emission from ships.In this study,size-segregated particulate matters(PM)were collected from three different types of ships,light absorption characteristics and size distribution of methanol-soluble BrC and water-soluble BrC in PM from ship exhausts were investigated.Results showed that four-stroke low-power diesel fishing boat(4-LDF)had the highest mass concentrations of methanol-soluble organic carbon(MSOC)and water-soluble organic carbon(WSOC),followed by 2-stroke high-power heavy-fuel-oil vessel(2-HHV),and fourstroke high-power marine-diesel vessel(4-HMV).While 2-HHV had obviously higher light absorption coefficients of methanol-soluble BrC(Abs365,M)and water-soluble BrC(Abs365,W)in unit weight of PM than the other two types of ships.The tested ships presented comparable or higher absorption efficiency of BrC in water extracts(MAE365,W)compared with other BrC emission sources.Majority of BrC was concentrated in fine particles,and the particle size distributions of both Abs365,Mand Abs365,Wshowed bimodal patterns,peaking at0.43–0.65μm and 4.7–5.8μm,respectively.However,different particle size distributions were found for MAE365,Mbetween diesel and heavy fuel oil ships.Besides,different wavelength dependence in particles with different size were also detected.Ship exhaust could be confirmed as a non-ignorable BrC emission source,and complex influencing factor could affect the light absorption characteristics of ship emissions.Particle size should also be considered when light absorption ability of BrC was evaluated.

关键词： Ship emission Brown carbon Light absorption Size distribution

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Quantum self-attention neural networks for text classification

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science China(information sciences) 2024年第4期67卷 301-313页

作者： Guangxi LI Xuanqiang ZHAO Xin WANG Institute for Quantum Computing Baidu Research Centre for Quantum Software and information University of Technology Sydney QICI Quantum information and Computation Initiative Department of Computer ScienceThe University of Hong Kong Thrust of Artificial Intelligence Information HubHong Kong University of Science and Technology(Guangzhou)

An emerging direction of quantum computing is to establish meaningful quantum applications in various fields of artificial intelligence, including natural language processing(NLP). Although some efforts based on syntactic analysis have opened the door to research in quantum NLP(QNLP), limitations such as heavy syntactic preprocessing and syntax-dependent network architecture make them impracticable on larger and real-world data sets. In this paper, we propose a new simple network architecture, called the quantum selfattention neural network(QSANN), which can compensate for these limitations. Specifically, we introduce the self-attention mechanism into quantum neural networks and then utilize a Gaussian projected quantum self-attention serving as a sensible quantum version of self-attention. As a result, QSANN is effective and scalable on larger data sets and has the desirable property of being implementable on near-term quantum devices. In particular, our QSANN outperforms the best existing QNLP model based on syntactic analysis as well as a simple classical self-attention neural network in numerical experiments of text classification tasks on public data sets. We further show that our method exhibits robustness to low-level quantum noises and showcases resilience to quantum neural network architectures.

关键词： quantum neural networks self-attention natural language processing text classification parameterized quantum circuits

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