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Impacts of multi-scenario land use change on ecosystem services and ecological security pattern: A case study of the Yellow River Delta

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Research in Cold and Arid Regions 2024年第1期16卷 30-44页

作者： XueHua cen Hua Zhang College of Geography and Environmental Science Northwest Normal UniversityLanzhouGansu 730070China

The Yellow River Delta(YRD), a critical economic zone along China's eastern coast, also functions as a vital ecological reserve in the lower Yellow River. Amidst rapid industrialization and urbanization, the region has witnessed significant land use/cover changes(LUCC), impacting ecosystem services(ES) and ecological security patterns(ESP). Investigating LUCC's effects on ES and ESP in the YRD is crucial for ecological security and sustainable development. This study utilized the PLUS model to simulate 2030 land use scenarios, including natural development(NDS), economic development(EDS), and ecological protection scenarios(EPS). Subsequently, the InVEST model and circuit theory were applied to assess ES and ESP under varying LUCC scenarios from 2010 to 2030. Findings indicate:(1) Notable LUCC from 2010 to 2030, marked by decreasing cropland and increasing construction land and water bodies.(2) From 2010 to 2020, improvements were observed in carbon storage,water yield, soil retention, and habitat quality, whereas 2020–2030 saw increases in water yield and soil retention but declines in habitat quality and carbon storage. Among the scenarios, EPS showed superior performance in all four ES.(3) Between 2010 and 2030, ecological sources, corridors, and pinchpoints expanded, displaying significant spatial heterogeneity. The EPS scenario yielded the most substantial increases in ecological sources,corridors, and pinchpoints, totaling 582.89 km^(2), 645.03 km^(2),and 64.43 km^(2), respectively. This study highlights the importance of EPS, offering insightful scientific guidance for the YRD's sustainable development.

关键词： Land use change Multi-scenario simulation Ecosystem services Ecological security pattern The Yellow River Delta Circuit theory

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Multi-Smart Meter Data Encryption Scheme Basedon Distributed Differential Privacy

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Big Data Mining and Analytics 2024年第1期7卷 131-141页

作者： Renwu Yan Yang Zheng Ning Yu cen Liang School of Electronic Electrical Engineering and PhysicsFujian University of TechnologyFuzhou 350118China Department of Computing Sciences at The College at Brockport State University of New YorkNew YorkNY 14420USA

Under the general trend of the rapid development of smart grids,data security and privacy are facing serious challenges;protecting the privacy data of single users under the premise of obtaining user-aggregated data has attracted widespread attention.In this study,we propose an encryption scheme on the basis of differential privacy for the problem of user privacy leakage when aggregating data from multiple smart meters.First,we use an improved homomorphic encryption method to realize the encryption aggregation of users’data.Second,we propose a double-blind noise addition protocol to generate distributed noise through interaction between users and a cloud platform to prevent semi-honest participants from stealing data by colluding with one another.Finally,the simulation results show that the proposed scheme can encrypt the transmission of multi-intelligent meter data under the premise of satisfying the differential privacy mechanism.Even if an attacker has enough background knowledge,the security of the electricity information of one another can be ensured.

关键词： smart grid homomorphic encryption data aggregation differential privacy cloud computing

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Deep genome skimming reveals the hybrid origin of Pseudosasa gracilis (Poaceae: Bambusoideae)

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Plant Diversity 2024年第3期46卷 344-352页

作者： Xiang-Zhou Hu cen Guo Sheng-Yuan Qin De-Zhu Li Zhen-Hua Guo Germplasm Bank of Wild Species Kunming Institute of BotanyChinese Academy of SciencesKunmingYunnan 650201China University of Chinese Academy of Sciences Beijing 100049China

Pseudosasa gracilis(Poaceae:Bambusoideae)is a temperate woody bamboo species endemic to Southcentral China with a narrow distribution.Previous phylogenetic studies revealed an unexpected,isolated phylogenetic position of Ps.gracilis.Here we conducted phylogenomic analysis by sampling populations of Ps.gracilis and its sympatric species Ps.nanunica and Sinosasa polytricha reflecting different genomic signals,by deep genome skimming.Integrating molecular evidence from chloroplast genes and genome-wide SNPs,we deciphered the phylogenetic relationships of Ps.gracilis.Both plastid and nuclear data indicate that Ps.gracilis is more closely related to Sinosasa,which is discordant with the taxonomic treatment.To further explore this molecular-morphological conflict,we screened 411“perfect-copy”syntenic genes to reconstruct phylogenies using both the concatenation and coalescent methods.We observed extensive discordance between gene trees and the putative species tree.A significant hybridization event was detected based on 411 genes from the D subgenome,showing Ps.gracilis was a hybrid descendant between Sinosasa longiligulata and Ps.nanunica,with 63.56%and 36.44%inheritance probabilities of each parent.Moreover,introgression events were detected in the C subgenome between Ps.gracilis and S.polytricha in the same distribution region.Our findings suggest that sympatric hybridization and introgression play a crucial role in the origin of Ps.gracilis.By providing an empirical example of bamboo of hybrid origin using comprehensive analyses based on genomic data from different inheritance systems and morphological characters,our study represents a step forward in understanding of reticulate evolution of bamboos.

关键词： Phylogenomics Hybridization Introgression Pseudosasa gracilis Pseudosasa Sinosasa

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Ecological impacts of the expansion of offshore wind farms on trophic level species of marine food chain

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Journal of Environmental Sciences 2024年第5期139卷 226-244页

作者： Lijing Wang Bangguo Wang Wenxi cen Rui Xu Yuwei Huang Xin Zhang Yinghui Han Yuanxun Zhang College of Resources and Environment University of Chinese Academy of SciencesBeijing 101408China Yanshan Earth Critical Zone and Surface Fluxes Research Station University of Chinese Academy of SciencesBeijing 101408China Chinese Research Academy of Environmental Sciences Beijing 100012China National Joint Research center for Yangtze River Conservation Beijing 100012China College of Environmental Engineering Beijing University of TechnologyBeijing 100124China Institute of Eco-Environmental Forensics Shandong UniversityQingdao 266237China

The global demand for renewable energy has resulted in a rapid expansion of offshore wind farms(OWFs)and increased attention to the ecological impacts of OWFs on the marine ecosystem.Previous reviews mainly focused on the OWFs’impacts on individual species like birds,bats,or mammals.This review collected numerous field-measured data and simulated results to summarize the ecological impacts on phytoplankton,zooplankton,zoobenthos,fishes,and mammals from each trophic level and also analyze their interactions in the marine food chain.Phytoplankton and zooplankton are positively or adversely affected by the‘wave effect’,‘shading effect’,oxygen depletion and predation pressure,leading to a ±10% fluctuation of primary production.Although zoobenthos are threatened transiently by habitat destruction with a reduction of around 60% in biomass in the construction stage,their abundance exhibited an over 90% increase,dominated by sessile species,due to the‘reef effect’in the operation stage.Marine fishes and mammals are to endure the interferences of noise and electromagnetic,but they are also aggregated around OWFs by the‘reef effect’and‘reserve effect’.Furthermore,the complexity of marine ecosystem would increase with a promotion of the total system biomass by 40% through trophic cascade effects strengthen and resource partitioning alternation triggered by the proliferation of filter-feeders.The suitable site selection,long-term monitoring,and life-cycle-assessment of ecological impacts of OWFs that are lacking in current literature have been described in this review,as well as the carbon emission and deposition.

关键词： Offshore wind farms(OWFs) Ecological impacts Plankton Benthos Fish Mammal

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Structured Multi-Head Attention Stock Index Prediction Method Based Adaptive Public Opinion Sentiment Vector

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Computers, Materials & Continua 2024年第1期78卷 1503-1523页

作者： Cheng Zhao Zhe Peng Xuefeng Lan Yuefeng cen Zuxin Wang School of Economics Zhejiang University of TechnologyHangzhouChina College of Management Zhejiang University of TechnologyHangzhouChina Informatization Office Zhejiang University of TechnologyHangzhouChina School of Information and Electronic Engineering Zhejiang University of Science&TechnologyHangzhouChina School of Economics and Management Universiti Putra MalaysiaDaerah PetalingNegeri SelangorMalaysia

The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk.The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research.In this paper,a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed.The proposedmethod utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments.It then analyzes the continuous impact of these vectors on the market through the use of aggregating techniques and public opinion data via a structured multi-head attention mechanism.The experimental results demonstrate that the public opinion sentiment vector can provide more comprehensive feedback on market sentiment than traditional sentiment polarity analysis.Furthermore,the multi-head attention mechanism is shown to improve prediction accuracy through attention convergence on each type of input information separately.Themean absolute percentage error(MAPE)of the proposedmethod is 0.463%,a reduction of 0.294% compared to the benchmark attention algorithm.Additionally,the market backtesting results indicate that the return was 24.560%,an improvement of 8.202% compared to the benchmark algorithm.These results suggest that themarket trading strategy based on thismethod has the potential to improve trading profits.

关键词： Public opinion sentiment structured multi-head attention stock index prediction deep learning

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Performance and mechanism of CO_(2) absorption during the simultaneous removal of SO_(2) and NO_(x) by wet scrubbing process

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Journal of Environmental Sciences 2024年第1期135卷 534-545页

作者： Peng Lu Xianhui Yan Lyumeng Ye Dingsheng Chen Dongyao Chen Jianhang Huang Chaoping cen Guangdong Province Engineering Laboratory for Air Pollution Control South China Institute of Environmental SciencesMinistry of Ecology and EnvironmentGuangzhou 510655China Guangdong Provincial Key Laboratory of Water and Air Pollution Control Guangzhou 510655China School of Environmental Science and Engineering Sun Yat-sen UniversityGuangzhou 510275China

The co-removal of CO_(2)while removing SO_(2)and NOxfrom industrial flue gas has great potential of carbon emission reduction but related research is lacking.In this study,a wet scrubbing process with various urea solutions for desulfurization and denitrification was explored for the possibility of CO_(2)absorption.The results showed that the urea-additive solutions were efficient for NOxand SO_(2)abatement,but deliveredze,which covered the unreacted Ca(OH)_(2)surface after the reaction.Kinetic analysis showed that the CO_(2)absorption in urea-NaClO_(2)-Ca(OH)_(2)absorbent was controlled by chemical reaction in early stage,then by ash layer diffusion in later stage.

关键词： CO_(2)absorption Synergistic desulfurization and denitrification Wet scrubbing process Urea-NaClO_(2)-Ca(OH)_(2)absorbent Carbonation reaction

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HEN:a novel hybrid explainable neural network based framework for robust network intrusion detection

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Science China(Information Sciences) 2024年第7期67卷 72-90页

作者： Wei WEI Sijin CHEN cen CHEN Heshi WANG Jing LIU Zhongyao CHENG Xiaofeng ZOU School of Computer Science and Engineering Xi'an University of TechnologyShaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Technology Wuhan University of Science and Technology School of Future Technology South China University of Technology Shenzhen Research Institute of Hunan University School of Computer Science Hunan University of Technology and Business Institute for Infocomm Research (I2R) Agency for Science Technology and Reseach (A*STAR)

With the rapid development of network technology and the automation process for 5G, cyberattacks have become increasingly complex and threatening. In response to these threats, researchers have developed various network intrusion detection systems(NIDS) to monitor network traffic. However, the incessant emergence of new attack techniques and the lack of system interpretability pose challenges to improving the detection performance of NIDS. To address these issues, this paper proposes a hybrid explainable neural network-based framework that improves both the interpretability of our model and the performance in detecting new attacks through the innovative application of the explainable artificial intelligence(XAI)method. We effectively introduce the Shapley additive explanations(SHAP) method to explain a light gradient boosting machine(Light GBM) model. Additionally, we propose an autoencoder long-term short-term memory(AE-LSTM) network to reconstruct SHAP values previously generated. Furthermore, we define a threshold based on reconstruction errors observed during the training phase. Any network flow that surpasses the specified threshold is classified as an attack flow. This approach enhances the framework's ability to accurately identify attacks. We achieve an accuracy of 92.65%, a recall of 95.26%, a precision of 92.57%,and an F1-score of 93.90% on the dataset NSL-KDD. Experimental results demonstrate that our approach generates detection performance on par with state-of-the-art methods.

关键词： explainable artificial intelligence light gradient boosting machine machine learning network intrusion detection Shapley additive explanation hybrid explainable neural network (HEN)

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A Normalizing Flow-Based Bidirectional Mapping Residual Network for Unsupervised Defect Detection

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Computers, Materials & Continua 2024年第2期78卷 1631-1648页

作者： Lanyao Zhang Shichao Kan Yigang cen Xiaoling Chen Linna Zhang Yansen Huang School of Mechanical Engineering Guizhou UniversityGuiyang550025China School of Computer Science and Engineering Central South UniversityChangsha410083China School of Computer and Information Technology Beijing Jiaotong UniversityBeijing100044China College of Civil Engineering Guizhou UniversityGuiyang550025China Guizhou Lianjian Civil Engineering Quality Inspection Monitoring center Co. Ltd.Guiyang550025China

Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately evaluate sample distributions,mapping normal features to the normal distribution and anomalous features outside it.Consequently,this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network(NF-BMR).It utilizes pre-trained Convolutional Neural Networks(CNN)and normalizing flows to construct discriminative source and target domain feature spaces.Additionally,to better learn feature information in both domain spaces,we propose the Bidirectional Mapping Residual Network(BMR),which maps sample features to these two spaces for anomaly detection.The two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives,which leads to the improvement of detection performance.Comparative experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network(B-PFM)and other state-of-the-art methods demonstrate that the proposed approach achieves superior performance.On the MVTec AD dataset,NF-BMR achieves an average AUROC of 98.7%for all 15 categories.Especially,it achieves 100%optimal detection performance in five categories.On the DAGM dataset,the average AUROC across ten categories is 98.7%,which is very close to supervised methods.

关键词： Anomaly detection normalizing flow source domain feature space target domain feature space bidirectional mapping residual network

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Unveiling inverse vulcanized polymers as metal-free,visible-light-driven photocatalysts for cross-coupling reactions

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Chinese Chemical Letters 2024年第4期35卷 142-146页

作者： Lang Gao cen Zhou Rui Wang Feng Lan Bohang An Xiaozhou Huang Xiao Zhang Fujian Key Laboratory of Polymer Science Fujian Provincial Key Laboratory of Advanced Materials Oriented Chemical EngineeringCollege of Chemistry and Materials ScienceFujian Normal UniversityFuzhou 350007China Fujian Engineering and Research center of New Chinese Lacquer Materials College of Materials and Chemical EngineeringMinjiang UniversityFuzhou 350108China

Inverse vulcanized polymers(IVPs) that generated from elemental sulfur and smaller amounts of alkenes have found broad promising applications such as cathode materials for Li-S batteries, dynamic and repairable materials, optics applications, and metal sorption. However, their exploration in organic synthesis is still unprecedented. Here we first report the application of inverse vulcanized polymers in catalysis for organic transformations. A biomass-derived inverse vulcanized polymer(IVP-EAE) is found to be capable of catalyzing cross-coupling reactions in a transition-metal-free fashion under visible light.This method allows the direct C–H functionalization of pyrroles and N-arylacrylamides with(hetero)aryl halides, respectively, leading to the formation of two sets of structurally important scaffolds including pyrrole-containing biaryls and 3,3-disubstituted oxindoles with high selectivity. We anticipate this study will not only unveil the new potential of IVPs, but also offer a distinct type of catalysts for organic transformations.

关键词： Cross coupling Inverse vulcanized polymers Metal free Photocatalyst Visible light

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In-situ construction of abundant active centers on hierarchically porous carbon electrode toward high-performance phosphate electrosorption: Synergistic effect of electric field and capture sites

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Green Energy & Environment 2024年第1期9卷 126-137页

作者： Peng Zhang Fukuan Li Mingming He Silu Huo Xueli Zhang Benqiang cen Dezhi Fang Kexun Li Hao Wang College of Environmental Science and Engineering Nankai UniversityTianjin300350China MOE Key Laboratory of Pollution Processes and Environmental Criteria Tianjin Key Laboratory of Environmental Remediation and Pollution ControlTianjin Key Laboratory of Environmental Technology for Complex Trans-Media PollutionNankai UniversityTianjin300350China State Key Laboratory of Simulation and Regulation of Water Cycles in River Basins China Institute of Water Resources and Hydropower ResearchBeijing100038China

Phosphate removal is crucial for eutrophication control and water quality improvement.Electro-assisted adsorption,an eco-friendly elec-trosorption process,exhibited a promising potential for wastewater treatment.However,there are few works focused on phosphate electro-sorption,and reported electrodes cannot attach satisfactory removal capacities and rates.Herein,electro-assisted adsorption of phosphate via in-situ construction of La active centers on hierarchically porous carbon(LaPC)has been originally demonstrated.The resulted LaPC composite not only possessed a hierarchically porous structure with uniformly dispersed La active sites,but also provided good conductivity for interfacial electron transfer.The LaPC electrode achieved an ultrahigh phosphate electrosorption capability of 462.01 mg g^(-1) at 1 V,outperforming most existing electrodes.The superior phosphate removal performance originates from abundant active centers formed by the coupling of electricﬁeld and capture sites.Besides,the stability and selectivity toward phosphate capture were maintained well even under comprehensive conditions.Moreover,a series of kinetics and isotherms models were employed to validate the electrosorption process.This work demonstrates a deep understanding and promotes a new level of phosphate electrosorption.

关键词： Electro-assisted adsorption Electrosorption Phosphate removal Active centers MOF-derived carbon

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